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Cattle Hill Wind Farm Eagle Utilisation Assessment, Collision Risk Modelling and Population Viability Analysis

E204165.EUA.REP1

May 2010

Cattle Hill Wind Farm: Eagle Utilisation Assessment Revision No: 1 E204165.EUA.REP1 May 2010

The concepts and information contained in this document are the property of Hydro Tasmania Consulting. This document may only be used for the purposes for which, and upon the conditions, the report is supplied. Use or copying of this document in whole or in part for any other purpose without the written permission of Hydro Tasmania Consulting constitutes an infringement of copyright.

Document information

Title Cattle Hill Wind Farm

Eagle Utilisation Assessment

Client organisation NP Power Pty Ltd

Client contact Shane Bartel

Document number E204165.EUA.REP

Project manager Raymond Brereton

Project reference P207834

Revision history Revision 0

Revision description Revision No. 1

Prepared by

Raymond Brereton(HTC), Stuart Muir (Symbolix), Simon Plowright (Wildspot Consulting), and Ian Smales (Biosis Research)

20 May 2010

Reviewed by

Raymond Brereton, Stuart Muir, Simon Plowright, Ian Smales and Fiona Keserue-Ponte (SEMF)

20 May 2010

Approved by Scott Lobdale 20 May 2010

Distributed to Shane Bartel NP Power Pty Ltd 11 Feb 2010

(name) (organisation) (date)

Acknowledgements

The authors would like to thank Shane Bartel and NP Power for their assistance and valuable feedback

in preparing this report. We would also like to thank Roaring 40s for providing wedge-tailed eagle

mortality data for the Studland Bay and Bluff Point Wind Farms.


i

Executive summary

NP Power Pty Ltd is proposing to construct a wind farm on a 4,000 hectare area of land at Cattle Hill,

on the eastern shores of Lake Echo in the central highlands of Tasmania. An initial survey of the site

for eagle nests confirmed that there were two Tasmanian wedge-tailed eagle (Aquila audax fleayi)

nests within the wind farm site; one in the north and one in the south. The northern nest was active in

the 2008-9 breeding season, whilst the southern-most nest was in a state of disrepair. As a result of the

initial site assessment NP Power commissioned an eagle utilisation study to measure the eagle activity

at the site to assess the potential level of risk to the eagle population.

NP Power commissioned Wildspot Consulting Pty Ltd to carry out a field survey of eagle activity

across the Cattle Hill Wind Farm site. Hydro Tasmania Consulting in partnership with Symbolix (a

specialist data analysis consultancy) was contracted by NP Power to carry out an analysis and

interpretation of the eagle utilisation data from the site. The aim of the analysis was to gain an

understanding of use of the site by eagles and to assess the level of risk that the wind farm

development might pose to the local eagle population.

The objective of the study was to survey eagle utilisation at the proposed wind farm site at different

eagle activity periods over twelve consecutive calendar months to cover one full wedge-tailed and

white-bellied sea eagle’s breeding cycle.

The eagle utilisation study commenced in November 2008. The study collected and analysed eagle

flight data to:

• determine patterns of use across the site for each eagle activity period, and for a full calendar

year

• identify any within-year variation in eagle activity levels and patterns of use at the site

• identify any seasonal differences in flight behaviour (e.g. do eagles at the site fly high all year

round)

• determine if there are any significant changes in the pattern of use of the site

These data have been used to prepare maps to show the level of site utilisation within the wind farm

area.

The eagle utilisation maps provided input data for collision risk modelling to assist with predicting the

potential number of eagles at risk of collision. The model was run for a range of utilisations and the


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output values were used along with the results of the Population Viability Analysis to evaluate the

overall risk of the wind farm to the Tasmanian wedge-tailed eagle population.

This report describes the methods that were used to:

• measure and map eagle activity on the site

• model the potential collision risk posed by the wind farm

• assess the impact of the proposed wind farm layout on the Tasmanian wedge-tailed-eagle

population.

Eagle observations

Four surveys have been carried out, one in November and December 2008, February 2009, May 2009

and August 2009. The field survey results indicated that there are three wedge-tailed eagle pairs

occupying the proposed Cattle Hill Wind Farm site; a pair occupying a territory in the north east of the

site (the “Mushroom” territory), a pair with larger territory extending along the eastern shore of Lake

Echo (the “Macclesfield” territory), and pair occupying the forested areas in the south of site (the

“Lakeside” territory).

The analysis of the 363 eagle flights recorded during the surveys has found that overall there was a

consistent pattern of eagle utilisation within the wind farm site. There was high eagle activity in the

north east of the site corresponding to an active nest, relatively high activity in the north west of the

site corresponding to the “Macclesfield” territory and, in the south west of the site, associated with the

“Lakeside” territory. There was an area of lower observed activity in the central part of the site.

The analysis did find that there were differences in the level of activity between the November,

December, February, May and August surveys, in particular there were more flights observed in

December, suggesting that there are seasonal differences in the level of eagle activity.

The data analysis of flight data showed that 75% of flights in November, December and February

were at heights greater than turbine height1 (above 125 m). However, there was a greater incidence of

lower flights (31% of flights below 125 m) in the May survey period and an associated smaller

proportion of observed flights above 125 m (18%). Most flights in May were in the mixed flights

category2. In August, there were more observed flights above 125 m (60%) compared to May. There

were fewer mixed flights in August (4.5%) and the proportion of flights below 125 m (35%) was

similar to May.

1 Based on a turbine with a rotor of 90 m diameter mounted on an 80 m high tower 2 Mixed flights are where the observed flight was in more than one height class, i.e. for some of the recorded flight the bird was below 125 m, and for some of time it was above 125 m.


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There is evidence from the flight displacement data that the lower flights in May were more direct.

This is evidenced by the shorter flight distances (end to end) recorded even though the ground

distances covered were the same.

The analysis of flight data found no relationship between wind direction and flight activity. That is

eagles at the Cattle Hill Wind Farm site showed no preference for flights in a particular wind direction.

Nor was there any correlation between flight height and wind direction or flight behaviour and wind

direction.

Overall, the utilisation maps produced from the eagle flight data showed that there are areas within the

wind farm site that have higher levels of utilisation. Conversely, there are areas of the site with

consistently relatively lower levels of eagle activity.

Collision risk modelling

The predictions from the collision risk modelling of wedge-tailed eagles at Cattle Hill Wind Farm

ranged from an average of 0.1 to 0.5 collisions per year using 99%, 98%, 95% and 90% avoidance

rates. Taking a precautionary approach and using an avoidance rate for the wedge-tailed eagle of 90%

the predicted annual average mortality rate for a layout using 144 Vestas V90 turbines (maximum

possible without any constraints applied) is 0.5 eagles per year.

Population Viability Analysis

A population viability analysis was carried out using the VORTEX (v9.96) software to model the

state-wide Tasmanian wedge-tailed eagle population. The life-history data that was used in the model

was derived from published sources namely Bekessy et. al. (2009). The carrying capacity that was

used in the model was derived from figures provided in the Threatened Tasmanian Eagles Recovery

Plan: 2006-2010 (Threatened Species Section, 2006). The model included a total of 426 available

territories, with a maximum 90% occupation rate (Threatened Species Section, 2006) which results in

a carrying capacity of 766 breeding adults. The model begins with 466 breeding adults (taken from the

54.75% occupancy of the 426 available territories) and a floating (non-breeding) population of 554.

The PVA found that the floating population contributed 53% of the total population, and the carrying

capacity ran at about 80% of the available territories. The projected total population that the model

arrived at from the population and demographic data that was input into the PVA was 1,280

individuals, which is in the middle of the population estimate provided in the Recovery Plan of 1,000

to 1,500 birds (Threatened Species Section, 2006).


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Current background and human induced mortality data was incorporated into the PVA this includes

recorded deaths from collisions with vehicles and wind turbines and deaths from electrocution on

power lines.

To assess the potential impact of the proposed Cattle Hill Wind Farm a series of additional mortality

scenarios were introduced into the PVA. These scenarios investigated the level of mortality that would

increase the extinction risk for the wedge-tailed eagle population (the ‘tipping point’) and compared

this to the expected mortality from the proposed development. Twenty mortality scenarios were used

in the model ranging from zero to a sustained average of 40 additional mortalities per annum. The

PVA indicated that the Tasmanian wedge-tailed eagle population is capable of sustaining an additional

annual average mortality of 22 birds.

Collision risk zones

The results from the CRM and the utilisation maps were used to identify areas on the wind farm site

that were most at risk of collisions. This was done by attributing the annual mortality from the CRM

across all wind turbines and weighting it such that the total remained constant, yet a turbine in an area

with twice the utilisation is twice as likely to be involved in an incident. The distribution of the

contribution of each of the turbines to the collision risk was then evaluated and natural breaks in the

distribution were identified. These breaks were used to identify boundary points between high and

medium risk, and medium and lower risk.

The results of the likelihood calculations for flights at all heights where used to generate a spatial map

of the higher and lower relative risk zones. These were mapped as ‘Higher’, ‘Medium’, and ‘Lower’

relative risk zones.

Development of an eagle sensitive wind turbine layout

A revised wind farm layout was produced using the eagle risk zone mapping and other constraints that

were identified within the wind farm site which included restrictions on the number of turbines that

could be placed in the Private Reserve, a 1 km buffer around the eagle nest sites, and the avoidance of

Aboriginal and historic heritage sites. This resulted in a revised wind farm layout comprised of 100

wind turbines.

The effect of the revised layout on the collision risk was calculated. The revised layout presents 62%

of the collision risk of the original 144 turbine layout. This is a reduction in collision risk of 38%

which results in the predicted annual average eagle mortality rate dropping from 0.5 birds per annum

to 0.3 birds per annum for a 90% avoidance rate.


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Collision risk modelling for a larger turbine

Following the development of the 100 Vestas V90 wind turbine layout a larger turbine was being

considered for use at the Cattle Hill Wind Farm site. This larger turbine was a Vestas V112 which has

a rotor diameter of 112 m diameter, mounted on an 84 m high tower. The CRM for the Vestas V112

turbines uses three height categories of flight data ‘below 125 m’, ‘mixed flights’ and ‘above 125’

(which are the flights between 125 and 300 m) whereas the Vestas V90 CRM used two height

categories ‘below 125 m’ and ‘mixed flights’. The predictions from the collision risk modelling of

wedge-tailed eagles at Cattle Hill Wind Farm for the Vestas V112 turbine ranged from an annual

average of to 2.1 to 0.5 collisions per year using 99%, 98%, 95%, and 90% avoidance rates. The

collision risk value for the 90% avoidance rate for the Vestas V112 turbine at the Cattle Hill Wind

Farm results in an annual average predicted mortality rate of 2.1 eagles.

Implications of the eagle studies for the Cattle Hill Wind Farm

An assessment of the impact of State-wide wind farm mortality on the Tasmanian eagle population

was also carried out. This analysis included the annual mortalities recorded at Bluff Point and

Studland Bay combined with the modelled prediction from the V90 100 wind turbine layout and the

V112 wind turbine layout for the Cattle Hill wind farm. The 100 V90 wind turbine layout resulted in a

total State-wide eagle mortality at wind farms of 3.8 birds per annum. The 100 V112 wind turbine

layout resulted in a total State-wide eagle mortality at wind farms of 5.6 birds per annum. The PVA

modelling indicated that for a long term average eagle mortality of either 3.8 birds (100 turbine V90

layout) or 5.6 birds (100 turbine V112 layout), there is no effect on the extinction risk of the

Tasmanian wedge-tailed eagle within the 160 years of the simulation run.

The results of the eagle utilisation studies, the collision risk modelling and the PVA indicate that

neither the revised 100 V90 wind turbine layout nor the 100 V112 wind turbine layout for the Cattle

Hill Wind Farm development is likely to impact on the long term sustainability of the state-wide

population of the Tasmanian wedge-tailed eagle, based on the current eagle flight data set from the

site, and the current published understanding of the population dynamics and behaviours.


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Contents 1. Introduction 9

2. Methods 12 2.1 Eagle nest survey 12 2.2 Eagle activity survey 12

2.2.1 Survey periods 12 2.2.2 Survey points 13 2.2.3 Observation period 13 2.2.4 Data recording 14

2.3 Data analysis 19 2.3.1 Database development 19 2.3.2 Data quality 19 2.3.3 Data review 20

2.4 Collision risk modelling (CRM) 21 2.4.1 Eagle utilisation data 21 2.4.2 Turbine data 22 2.4.3 Avoidance Rates 23 2.4.4 Verification 24

2.5 Eagle collision risk zones 25 2.6 Population Viability Analysis 25 2.7 Development of an eagle sensitive wind turbine layout 26

3. Results 27 3.1 Eagle nest surveys 27 3.2 Survey effort 27 3.3 Eagle observations 28 3.4 Data analysis 29

3.4.1 Data quality 29 3.4.2 Data summary 31

3.5 Collision risk modelling (CRM) 50 3.6 Population Viability Analysis 51

3.6.1 Development of the metapopulation model 51 3.6.2 Life history assumptions and model development 52 3.6.3 The PVA for the Tasmanian eagle population 54 3.6.4 Additional mortality 55 3.6.5 Modelling the mortality scenarios 56

3.7 Development of an eagle sensitive wind turbine layout 59 3.7.1 Determining the individual contribution to utilisation 59 3.7.2 Estimating the individual collision risk 59 3.7.3 Visualising areas of high, medium and low risk 60 3.7.4 Development of the revised layout 66 3.7.5 Collision risk modelling for a larger turbine 70 3.7.6 Implications of the eagle studies for the Cattle Hill Wind Farm 73


vii

4. References 75

List of figures

Figure 1-1 Outline of the process for developing an eagle sensitive wind turbine layout Cattle Hill Wind Farm 11 Figure 2-1 Site map showing survey points and known eagle nests 16 Figure 2-2 Observer field of view from observation points (bare earth model) 17 Figure 2-3 Example of a flight record track 18 Figure 3-1 Map showing approximate location and extent of wedge-tailed eagle territories 30 Figure 3-2 Spaghetti diagram illustrating the relationship between observed flights (dark blue lines) and the utilisation maps (shaded contours) 35 Figure 3-3 Site utilisation map for the November 2008 Survey period (Figure 1 from Appendix D) 36 Figure 3-4 Site utilisation map for the December 2008 survey period (Figure 2 from Appendix D) 37 Figure 3-5 Site utilisation map for February 2009 survey period (Figure 3 from Appendix D) 38 Figure 3-6 Site utilisation map for the May 2009 survey period (Figure 4 from Appendix D) 39 Figure 3-7 Site utilisation map for the August 2009 survey period (Figure 5 from Appendix D) 40 Figure 3-8 Site utilisation map for all surveys combined - November 2008 to August 2009 (Figure 6 from Appendix D) 41 Figure 3-9 All flights observed by wind direction* 45 Figure 3-10 All flights at all heights combined 46 Figure 3-11 Flights below 125 m and mixed heights 47 Figure 3-12 Flights above 125 m 48 Figure 3-13 Wedge-tailed eagle life cycle adapted from Bekessy et. al. (2009), 53 Figure 3-14 Evolution of the mean population size 55 Figure 3-15 Mortality scenarios for the PVA 56 Figure 3-16 Stochastic value of r for scenarios of increasing average mortality as a function of time 58 Figure 3-17 The individual and cumulative contribution to utilisation, using all flights and the maximum unconstrained 144 turbine layout. 61 Figure 3-18 Likelihood of involvement in one or more incidents over the lifetime of the wind farm. 62 Figure 3-19 Risk Zones and the 144 turbine layout for all flight heights. 63 Figure 3-20 Eagle risk zones at the Cattle Hill Wind Farm site 64 Figure 3-21 Risk Zones and the 144 turbine layout for flights at risk. 65 Figure 3-22 The revised 100 turbine layout and constraints 67 Figure 3-23 Wind turbine layout and eagle risk zones for all flights – the original 144 wind turbine layout is on the left and the revised 100 wind turbine layout is on the right 68 Figure 3-24 Wind turbine layout and eagle risk zones for flights at risk (flights less than 125 m and mixed flights) – the original 144 wind turbine layout is on the left and the revised 100 wind turbine layout is on the right 69 Figure 3-25 Outline of the Collision Risk Modelling process 72 Figure 3-26 Probability of survival of the meta-population 74


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List of tables

Table 2-1 Field survey dates 12 Table 2-2 Turbine data that was input in the Biosis Research collision risk model 23 Table 3-1 Survey effort (hours) 28 Table 3-2 Eagle survey periods summary 31 Table 3-3 The number of flights observed per hour by observer by location* 33 Table 3-4 Flight activity per hour for each survey month* 42 Table 3-5 Observed flights by behavioural classification* 42 Table 3-6 Proportion of flights in each height class for each survey period* 43 Table 3-7 Summary of flight length data* 49 Table 3-8 Summary of flight displacement data* 50 Table 3-9 Median flight length to displacement ratio* 50 Table 3-10 Predicted annual average numbers of wedge-tailed eagle collisions with 144 V90 turbines at Cattle Hill Wind Farm. 51 Table 3-11 Predicted annual average numbers of wedge-tailed eagle collisions with 144 V90 turbines at Cattle Hill Wind Farm. 71 Table 3-12 Currently known additional wind farm mortalities 74

.Cattle Hill Wind Farm: Eagle Utilisation Assessment Revision No: 1 E204165.EUA.REP1 May 2010

9

1. Introduction

NP Power Pty Ltd is proposing to construct a wind farm on a 4,000 hectare area of land at Cattle Hill,

on the eastern shores of Lake Echo in the Central Highlands of Tasmania.

An initial survey of the site for eagle nests confirmed that there were two Tasmanian wedge-tailed

eagle (Aquila audax fleayi) nests within the wind farm site, one in the north (#1723) and one in the

south (#1751) (SEMF 2008), (Figure 2-1). There is another nest (#1724) just off the southern

boundary of the wind farm site, on adjacent private land.

A third nest in the south-western corner of the site (#490), which is recorded on the Natural Values

Atlas (an on-line database of flora and fauna records managed by DPIW), is no longer present and has

been declared a ‘nest absent’ by the Threatened Species Section of the Department of Primary

Industries, Parks, Water and Environment (SEMF 2008).

SEMF (2008) noted that eagle searches of the area by others have located a white-bellied sea eagle

(Haliaeetus leucogaster) nest on or near the property “Macclesfield”, to the north-west of the

development site.

SEMF (2008) reported that nest #1723 was active in the 2008-9 breeding season, whilst nest #1724

was in a state of disrepair. Nest #1751 is in relatively good condition (large and intact) and may have

been used within the last three to five breeding seasons.

Due to the presence of eagle nests on site and a number of known nests in the vicinity of the site the

wind farm development, SEMF (2008) concluded that there needed to be a more comprehensive

impact assessment made for threatened eagles. As a result, NP Power commissioned an eagle

utilisation study to measure the eagle activity at the site to assess the potential level of risk to the eagle

population.

NP Power contracted Wildspot Consulting Pty Ltd to carry out a field survey of eagle activity across

the Cattle Hill Wind Farm site. Hydro Tasmania Consulting (HTC) in partnership with Symbolix (a

specialist data analysis consultancy) was contracted by NP Power to carry out an analysis of the eagle

utilisation data provided by Wildspot Consulting Pty Ltd. The aim of the analysis was to gain an

understanding of use of the site by eagles and to assess the level of risk that the wind farm

development might pose to the local eagle population.

The objective of the eagle utilisation study was to survey eagle utilisation at the proposed wind farm

site during peak eagle activity periods over twelve months to cover one year of the wedge-tailed and


10

white-bellied sea eagle’s breeding cycles commencing in November 2008. This would allow the

collection and analysis of flight data to assess whether there:

• is within year variation in eagle activity levels at the site

• are changes in flight behaviour (i.e. do eagles at the site fly high all year round)

• are any significant changes in the pattern of use of the site

This data was also used to prepare maps which show the level of eagle utilisation within the wind farm

site. The eagle utilisation information provided input data for collision risk modelling. Collision risk

models assess the potential for bird interactions with wind turbines based on inputs such as turbine

swept area, tower size, bird size, flight speed and number of bird movements. The output of the

collision risk model is the potential number of birds at risk of collision.

A Population Viability Analysis was also carried out to evaluate the overall risk of the wind farm to

the Tasmanian wedge-tailed eagle population. Population Viability Analysis is a method of identifying

the threats to a species and evaluating the extinction risk for that species. It can also be used to assess

the sensitivity of the eagle population to the impact of particular threats such as increased mortality

due to collisions with wind turbines.

The collision risk modelling and the eagle utilisation maps were used to produce a collision risk zone

map for the wind farm which was used to develop a balanced turbine layout which takes advantage of

the available wind at the site while minimising the risk of collisions to wedge-tailed eagles.

The focus of the eagle utilisation and risk assessment studies was the wedge-tailed eagle because no

white-bellied sea eagle nests were located on site and no sea eagles were recorded on site during the

eagle field surveys.


11

Figure 1-1

Outline of the process for developing an eagle sensitive wind turbine layout Cattle Hill Wind Farm

Eagle field observations

Identification mapping of patterns of eagle

utilisation

Analysis of eagle data (flight heights, flights and wind direction)

Collision Risk Modelling


Development of an eagle sensitive wind

turbine layout

Published wedge-tailed eagle demographic data

Development of an eagle collision risk zone

map

Assessment of the cumulative impact of the development of the Cattle Hill Wind

Farm predicted collision risk on the Tasmanian wedge-tailed-eagle

population using the predicted collision risk from the eagle sensitive wind turbine

layout


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2. Methods

2.1 Eagle nest survey

Eagle nest surveys were carried out to verify the location and breeding status of the currently known

nests on site and to locate any new nests. SEMF (2008) identified suitable nest habitat within the wind

farm site and carried out an eagle nest search in 2008. In addition, Wildspot Consulting checked all

known nests within the wind farm site to assess if they were active in November 2009 and carried out

additional nest surveys within the site and within 2 km from the wind farm boundary.

2.2 Eagle activity survey

2.2.1 Survey periods

An eagle utilisation survey program was developed which aimed to sample eagle activity periods

including:

• the breeding period in November and December

• the post breeding period in early March when juvenile eagles are fledging

• the period following fledging and prior to the next breeding season in mid May when birds are

least likely to be active

• the display period in late July and early August when birds are likely to be most active and to

exhibit a range of flight behaviours

The dates of the field surveys are shown in Table 2-1. Table 2-1

Field survey dates

Survey period Survey start date Survey end date No. of survey days

19th November 2008 26th November 2008 8 Breeding season

10th December 2008 12th December 2008 3

Post breeding 24th February 2009 27th February 2009 4

Non breeding 4th May 2009 8th May 2009 5

Displaying 10th August 2009 15th August 2009 6

Breeding season3 4th November 2009 8th November 2009 5

Total 31

3 The data from this survey has been included in the utilisation maps used for the development of the turbine layout but has not been included in the analysis of eagle flights but will be incorporated in later reports.


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2.2.2 Survey points

Three survey points were initially established (S1, S2 and S3) which gave an overview of eagle

activity across the site. Points were located in positions that had good visibility across the site. The

survey points in the north of the site had good visibility across the site because of the open terrain,

however it was more difficult to find observation points with good visibility in the south because of

the paucity of high points in the landscape and there was a greater forest cover. The distance between

the initial observation points ranged from 2.6 and 2.9 km which was recognized as exceeding the

distance that flight paths could be recorded accurately across the site. However, they were deemed

adequate to obtain an initial understanding of eagle utilisation at the site.

After four days of initial observations it was determined that additional survey points were required to

improve coverage of the site. Subsequently four additional observation points were added on day five.

Survey point S3 was removed at this time because the field of view from this point was poor and the

area it covered could be observed from observation points S5 and S6. This resulted in a total of five

observation points; S1, S2, S4, S5, and S6. Figure 2-1 shows the location of these observation points

and Figure 2-2 their view field. The resulting layout of the observation point meant that observers in

adjacent sectors could continue recording a flight as it passed from one observation sector to the next.

Another observation point was added in the May 2009 survey (S7) to cover the expansion of the

proposed wind farm area to the south east (Figure 2-1 and Figure 2-2).

Note that Figure 2-2 is a representation of the observers field of view based on topography only and

does not include trees which will limit the view field. The south west of the site is the most affected by

tree cover particularly observations to the west and south of observation point (S6).

2.2.3 Observation period

The establishment of six observation points led to the site being divided into North and South areas, so

that all points could be covered by the number of observers available on site, either five or six. The

North division included observation points S1, S2, and S4, and the South included points S2, S5 and

S6. The North and South divisions were divided into separate shifts, a morning shift and an afternoon

shift for the first period of survey. Point S2 is included in both divisions because it is in the centre of

the study area and was therefore used twice per shift. Site S7 was added for the May survey and

located in the South division. Observations in each division were carried out on alternate days, i.e. on

day one observations would be carried out in the North division in the morning and in the South

division in the afternoon. On the next day the shifts would change with observations in South division

carried out in the morning and in the North in the afternoon.


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Initially observations were carried out between 6 am and 8 pm to cover the eagle activity period and

typically an observer would spend between 6am and 1pm at a point and be relieved by another

observer from 1pm to 8pm in the evening. An assessment of eagle behaviour and frequency of activity

from the November survey concluded that there was minimal eagle activity before 8 am and again late

in the afternoon. These observations were validated by the analysis of flights recorded by time of day

which found that nearly all flights were between 8:00 am and 4:00 pm. Consequently during the

December survey, the observation period was changed to occur between 8 am and 4.30 pm each day.

This allowed for an eight hour observation period with 30 minutes available for travelling through the

site.

Six observers were used for the November 2008 survey and five observers were used in the December

2008 survey period and they were positioned at points S1, S2, S4, S5 and S6 on each survey day for an

eight hour observation period. During the survey period in February 2009, five observers were used at

the same five observation points (S1, S2, S4, S5, and S6) over the same observation period (8am to

4.30pm). In the May 2009 and August 2009 survey periods, six observers were used at the six

observation points (S1, S2, S4, S5, S6, and S7) over the same observation period (8am to 4.30pm).

2.2.4 Data recording

The observer recorded the following data for each observed flight to a data sheet:

• species of eagle (either wedge-tailed eagle or white-bellied sea-eagle)

• age class of eagle (e.g. immature, juvenile, adult) where possible, sometimes it is not possible to

identify the age of an eagle particularly when birds are backlit against the sky

• time eagle was first observed;

• the height category within which the birds’ flight was observed (i.e. above 125 m, below 125 m,

mixed height, and above 300 m);

• the category of behaviour that the eagle was exhibiting (i.e. soaring, displaying, flying, conflict)

• the sector in which the behaviour was observed

• the time at which the eagle disappeared from the observer’s field of view

The observed flight was also later transcribed from the field sheet as a GIS ground track. The wind

speed and direction in degrees was recorded at three hourly intervals throughout the observation

period.

This information was recorded for a 30 ha segment as well as the flight path which was transcribed to

a photo map of the site which was overlaid with 30 ha hexagonal grid cells. An example of a flight


15

track is presented in Figure 2-3. The 30 ha segment was chosen because given the size of the site they

were seen as being the smallest manageable unit for recording data in the field. Behaviours were also

recorded at a finer scale (for each 8 ha segment) because some behaviour such as displaying can be

performed repeatedly at one location.

Flight height

The nominated flight height categories that were used were (i) below 125 m, (ii) above 125 m, (ii)

mixed height, and (iv) above 300 m. These categories are based on the observers experience with

recording flight heights at an operating wind farm. These height classes relate to the height of a turbine

with a 90 m diameter blade mounted on an 80 m tower. The observers were familiar with recording

the flight heights of birds above and below a turbine height of 125 m.

Flights below 125 m are potentially within the swept area of the turbine, flights above 125 m are

above turbine height and flights above 300 m are flights that are significantly above turbine height and

could be regarded as not at risk flights. Mixed flights are where the observed flight was in more than

one height class, i.e. for some of the recorded flight the bird was below 125 m, and for some of time it

was above 125 m.

Behaviour

Eagle behaviours were allocated to one of four categories:

• soaring – where birds are riding thermals and updrafts and not flapping their wings

• flying – birds direct flight with wing flapping

• displaying – where birds are exhibiting flight behaviours associated with displaying such as

mutual soaring, rolling, talon-grappling and undulating displays (e.g. pot-hook display),

(Marchant & Higgins 1993)

• conflict – chasing and diving at intruders, and fighting

Eagle territories

Observations from the field survey provided information on the number of eagles that used the wind

farm site and also enabled the approximate boundaries of eagle territories to be identified.


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Figure 2-1 Site map showing survey points and known eagle nests


17

Figure 2-2 Observer field of view from observation points (bare earth model)


18

*Prepared by S. Plowright Figure 2-3

Example of a flight record track


19

2.3 Data analysis

2.3.1 Database development

A relational database was built to hold the eagle observation records that were collected during the

field surveys.

The database has three tables:

(i) data sheet records - sheet ID, date, observer, start time, end time, observer location,

wind speed and direction, precipitation

(ii) flight records - flight reference, sheet ID, species, age, time first observed, wind

speed, wind direction and time last observed

(iii) flight details - sheet ID, flight ID, bird location, height classification, behaviour

type and location of behaviour type

There is also a GIS data table in the database, which is an appended table that holds the details of the

GIS flights which are used to produce site utilisation maps. The field data has been entered into the

database from the four eagle utilisation surveys.

2.3.2 Data quality

Once the data was reviewed a data quality check was carried out and any corrections or adjustments

were made prior to the data analysis being carried out.

The review checked the data for:

• logical consistency

• typographical errors

• null fields - the percentage of records which are incomplete is noted (e.g. a field has not been

filled in)

• null value distributions - some variables in the database are ‘paired’ to facilitate future analysis

(e.g. behaviour is related to the location ID)


20

2.3.3 Data review

The data review looked at all aspects of the eagle observation data to see if there were any discernable

patterns in the use of the site by eagles, including an analysis of:

• the number of flights by observer by location to identify any effects that are dependant on the

observer contribution and account for them in the interpretations of the analysis if required

• observer location to assess whether the location from which the observer was recording could

influence the number of observed flights (i.e. to assess if bird activity was spatially dependant)

• the flight records including:

o flights observed by month

o flights observed by week day

o flights observed by time of day

o flights observed by wind direction

o flights observed by behaviour type

o flights observed by flight height including:

− flights observed by flight height by wind direction

− flights observed by flight height by behaviour type

o flight length and displacement

Eagle utilisation maps

The GIS ground tracks were used to produce eagle utilisation maps for the site. The eagle utilisation

maps show eagle activity as determined from a kernel interpolation of the recorded flights (Symbolix

2009a). The utilisation maps do not show a rate of usage, but indicate a density of usage, which is

dependent upon the amount of observation time. There is an implicit assumption of uncertainty in the

density of usage measure, as a result of employing a smoothing factor of 150 metres. The smoothing

treatment effectively smears out all recorded flight points to be 65% within +/- 150 metres of the

actual recorded grid point, and 95% certainty within +/- 300 metres (Symbolix 2009a).

The analysis includes all recorded points at all locations, therefore it is not meaningful to interpret

‘low counts’ as similar to a statistical analysis. The utilisation maps show relative usage only and are

informative in indicating differences in local activity by eagles at the site (Symbolix 2009a).


21

The following is an explanation provided by Stuart Muir of the process of generating a utilisation map

if it was done by hand.

“Take an overhead transparency and draw the observed eagle flight ground track on it in a water-based

felt pen. A flight ground track is the eagle flight path as recorded by the observer on the ground. The

ground track will be a dark, sharp edged line. Then take a camel hair paint brush, dip it in water, and

trace along the line. The sharp line becomes a smear which has gentle shading at the edges, graduating

to a heavier, more defined centreline of colour. This process is repeated on a new transparency for

each recorded eagle flight. Then if all the transparencies with smeared ground tracks are stacked on

each other and placed on an overhead projector the resulting projected image will be the utilisation

map.”

2.4 Collision risk modelling (CRM)

Collision risk modelling is used to obtain an indication of the potential for birds to collide with wind

turbines at a wind farm development. Modelled collision rates can be used to assess the level of risk to

birds that use a wind farm. The Collision Risk Modelling was undertaken by Biosis Research using the

Biosis Research deterministic collision risk model. This CRM uses data collected on the measured

frequency and heights of bird flights and the specifications and configurations of turbines (e.g. tower

height, rotor swept area) and information on bird size, flight speed, population size and avoidance

rates to estimate the potential annual rate of collisions between birds and turbines. The collision risk

model incorporates rotor speed and a measure of the clustering versus the geographic linearity of the

turbine array.

The model combines input data for the number of eagle flights with the number of individual eagles

likely to use the site in order to provide predictions of the average number of eagle collisions per

annum. In the assessment of the impacts of collisions on the eagle population, all collisions are

assumed to be fatal.

2.4.1 Eagle utilisation data

The eagle utilisation data was collected to identify patterns of utilisation across the wind farm site and

was not specifically designed for collision risk modelling. Usually bird utilisation data for input into

the Biosis CRM is collected using a point count method. However, the eagle utilisation database was

designed so that it could be sampled as if a point count was carried out. An eagle utilisation data set

was extracted from the Eagle Utilisation Study Database according to the Biosis protocol by which the

data is obtained in the field using a point count survey method. The eagle utilisation data set was

extracted from site locations plotted by Biosis Research without field knowledge of the site. Therefore


22

the wedge-tailed eagle count locations are effectively random relative to wedge-tailed eagle activity on

site. This is the same method as observation sites for point counts are located which also results in

point count locations being effectively random relative to wedge-tailed eagle activity on site.

The eagle utilisation data set recorded flight height in four categories (see Section 2.2.4: ‘below

125m’; ‘above 125m’; ‘mixed flights’ (for flights where the flight was above and below 125 m over

the length of the observed flight), and ‘above 300m’. The point count method normally records flight

data as a continuous variable to the nearest metre or 10 metres. When using categorical data in the

CRM all flights within a category must be included or excluded for use in the model even if some

flights are outside the height range of the turbine being modelled because it is not possible to

subdivide flight data from the records collected in a single height category.

2.4.2 Turbine data

The other main inputs into the CRM are the number of turbines, their dimensions and the ‘effective’

area of the turbine presented to a bird in flight, averaged over all potential directions of approach. The

turbine area is comprised of two parts - stationary and dynamic. The entire turbine is included in the

stationary component (as it poses a risk whether the rotors are not turning or not), while the dynamic

component is the area swept during the motion of the three rotors in the time taken for an eagle to pass

through the rotor-swept zone. Calculation of the dynamic component thus incorporates rotor speed and

the bird's size and flight speed.

Initial collision risk modelling was carried out for a Vestas V90 wind turbine with a blade diameter of

90 m mounted on a tower 80 m high (turbine specifications are provided in Table 2-2). The number of

turbines used for the initial CRM for the V90 turbine was 144 wind turbines, which is the theoretical

maximum number of turbines of this size that can be placed on the wind farm site taking into account

the available wind speed and the minimum turbine spacing required to avoid wake effects. The output

of the CRM from the 144 V90 turbine layout was combined with other constraints such as the

requirements of the private reserve areas, Aboriginal and historic heritage and eagle collision risk, to

revise the layout, resulting in a layout with 100 V90 turbines.

A larger wind turbine model is also being considered for the site and CRM modelling was done for

this larger turbine using the revised 100 turbine layout developed for the smaller V90 turbines. The

larger turbine is a Vestas V112 wind turbine with a blade diameter of 112 m mounted on a tower

84.6 m high (Table 2-2).


23

Table 2-2 Turbine data that was input in the Biosis Research collision risk model

Make & model of turbine Vestas V90 Vestas V112

Number of turbines 144 100

Tower height 80 metres 84.6 metres

Rotor diameter 90 metres 112 metres

Highest point of rotor blade 125 metres 140 metres

Lowest point of rotor blade 35 metres 28 metres

Expected annual percentage turbine downtime

15% 3%

2.4.3 Avoidance Rates

‘Avoidance’ is the rate at which birds of particular species do not collide with objects within their

airspace. All species of birds are known to have a capacity to avoid structures they see (the avoidance

rate), and therefore collisions with structures, including wind turbines, presumably occur because birds

do not observe the structure in time to avoid it. Avoidance includes cognitive behaviours in which a

bird becomes aware of a turbine and, at any given distance, actively changes course to avert a

collision. It may also include involuntary behaviours such as flight characteristics whereby some

species are more aerobatic, and may thus be innately less likely to collide with objects, than are other

species.

Avoidance is provided as a rate so that, for example, 90% avoidance rate means that in nine of every

ten flights toward a turbine a bird would actively avoid it. Most birds have better avoidance capacity

than this and there is evidence that most bird species avoid collisions with wind generators on most

occasions. However, different species of birds have different capacities to avoid collisions. Measured

avoidance rates for a variety of bird species from overseas studies range from 100% (Percival, 1998)

to 98% (Winkelman, 1992, Still et al. 1995). Whitfield (2009) in a report on the collision avoidance of

golden eagles (Aquila chrysaetos) at wind farms using the “Band” collision risk model reported

avoidance rates of between 98.64 % and 99.89 %. However, indications from eagle mortality studies

at the Studland Bay and Bluff Point Wind Farms are that the avoidance rate for the Tasmanian wedge-

tailed eagle may be lower than these rates.


24

A range of avoidance rates was used in the CRM because of the uncertainty over actual avoidance

rates for the wedge-tailed eagle. Collision risk modelling was done with 90%, 95%, 98% and 99%

avoidance rates for the area swept by moving rotors.

Birds are considered to have a better ability to avoid collisions with a stationary turbine and the model

used a 99% avoidance rate for the static components of a turbine, such as the tower and stationary

rotors.

Note that avoidance rates are likely to vary throughout the year and will be higher at some times and

lower at others depending on the levels of flight activity at the wind farm site. Avoidances rates may

be lower or higher according to particular activities or behaviours in the birds’ annual cycle. Other risk

factors which may affect the avoidance rate include poor visibility due to weather conditions.

Predictions of risk modelling are expressed as annual average numbers of Tasmanian wedge-tailed

eagle collisions. Since eagle utilisation data have been obtained throughout the annual cycle for the

birds at Cattle Hill, the predictions according to various avoidance rates account for seasonal

variability that may occur in the birds ‘avoidance ability’.

2.4.4 Verification

The extraction of the eagle utilisation data from the GIS flight path database in a form that is suitable

for input into a CRM is a new approach. This approach has potentially two problems, a failure to

generate appropriate input data to the CRM, and a potential weakness in observer bias. Each of these

has been accounted for separately.

Previously bird utilisation data for collision risk modelling has been collected using a point count

method. To confirm that the data extracted from the eagle utilisation database for use in the CRM was

valid, a verification process was carried out. This involved the generation of a single metric of eagle

activity (as described by Buckland et al., 2001; Buckland et al., 2004) from the original data collected

by Wildspot Consulting. The same metric is generated from the database, using alternate, random

observer locations. Similarity between these metrics was validated and supported the use of the

database extraction to generate utilisation data to input into the CRM. A detailed explanation of the

data manipulation that was done and an assessment of the ‘robustness’ of the technique is provided in

Appendix E.

In addition, a field validation exercise was carried out whereby observers that were not used in the

initial field surveys are used to obtain a set of wedge-tailed eagle utilisation data according to the point

count protocol. The data from this point count survey was used to check the validity of the inputs to

the database. The same metric of eagle activity generated from this independent source was used to


25

confirm that the method, from the field observations, through the data manipulation and the input into

the CRM modelling, is valid, and returns similar values independent of the observer.

Due to logistical issues, the two sets of observations did not take place at exactly the same time even

though they were in the same month (November 2009). As a result, the sets of observations are not

directly comparable; however the differences can be explained by seasonal variations in activity.

2.5 Eagle collision risk zones

The results from the 144 V90 CRM and the utilisation maps were used to identify areas on the wind

farm site that were most at risk of collisions. This was done by attributing the annual mortality from

the 144 V90 CRM across all wind turbines and weighting it such that the total remained constant, yet a

turbine in an area with twice the utilisation is twice as likely to be involved in an incident. The

distribution of the contribution of each of the turbines to the collision risk was then assessed to see

where the natural breaks in the distribution were. These breaks were used to identify boundary points

between higher and medium risk, and medium and lower risk. The clusters of turbines at risk were

then used to generate spatial maps of zones of Higher, Medium, and Lower relative risk.

The underlying assumption in attributing collision risk using the eagle utilisation measures is that

collisions are random events, related to the level of eagle use in the local region surrounding the wind

turbine. That is, there is a relationship between utilisation and collision risk and the higher the

utilisation the higher the risk of a collision. Note that there is currently no conclusive evidence to

indicate the relative importance of utilisation as a risk factor, or the linear relationship between risk

and utilisation (as opposed to logarithmic, exponential or other dependence). However, it is considered

that the approach to identify relative collision risk zones based on collision risk and utilisation is

reasonable in the absence of other techniques. It should be noted that the risk zones are based on

observed patterns of eagle use within the wind farm site.

2.6 Population Viability Analysis

A Population Viability Analysis (PVA) was carried out to evaluate the overall risk posed to the

Tasmanian wedge-tailed eagle population by collision mortality at Cattle Hill. Population Viability

Analysis is a method of identifying the threats faced by a species and evaluating the extinction risk for

that species. A PVA can be used to determine the level of impact on the population from particular

potential threats that would significantly increase the extinction risk for the population.

The PVA was used to investigate how the number of potential eagle deaths from collisions with

turbines impacts on the extinction risk to gain an understanding of what mortality rate due to the wind


26

farm is ecologically sustainable at the population level. The results of the PVA were used to assist in

the determination of an acceptable impact for the wedge-tailed eagle at the Cattle Hill Wind Farm site.

The PVA model that was developed for this study was written within the VORTEX (v9.96) software

package. This is a stochastic modelling platform that uses an agent based technique. Agent based

models create individual, computational entities, that live out their lives and deaths within the

computer simulation.

The PVA for the Tasmanian wedge-tailed eagle that was developed for this study followed closely

previous work by Bekessy et al. (2009) and Smales & Muir (2005). The Bekessy et al. (2009) PVA

models, which this study relied upon, were performed using a similar agent based philosophy,

although they included explicit landscape connectivity through RAMAS Landscape, under the

connected design of Wintle et al. (2005). The Bekessy et al. (2009) study was concerned with the

effects of different levels of native forest harvesting on the eagle population. Thus, the connectivity

element employed in that study are not as relevant for this study which focuses on the impacts on the

Tasmanian wedge-tailed eagle from wind farm mortality. Instead this study used a carrying capacity to

place an upper cap on the number of breeding pairs, and defined a nested population to describe the

relationship between the breeding and non-breeding groups. To validate the PVA model and the

results, this study replicated the ‘no harvest’ scenario of Bekessy et al. (2009).

The development of a PVA model requires inputs including life history data, for example survival,

fecundity and unnatural mortality associated with particular other existing threatening processes.

These were obtained from existing data sources, predominantly Bekessy et al. (2009), Smales and

Muir (2005) and Threatened Species Section (2006). All assumptions used in the development of the

PVA model are reported in Appendix F.

The results of the PVA were also used to assess the cumulative impacts of wind farm developments in

Tasmania and followed the approach for resident species described by Smales (2006) and Smales and

Muir (2005).

2.7 Development of an eagle sensitive wind turbine layout

The identification of collision risk zones was used to inform the wind turbine layout for the wind farm

which takes advantage of the available wind at the site while minimising the collision risk to wedge-

tailed eagles. The impact of the estimated mortality from the revised layout on the wedge-tailed eagle

population was assessed using the results of the Population Viability Analysis. The collision risk zones

were used to produce a wind turbine layout that did not increase the risk of extinction of the

Tasmanian wedge-tailed eagle.


27

3. Results

3.1 Eagle nest surveys

SEMF (2008) located two Tasmanian wedge-tailed eagle (Aquila audax fleayi) nests within the

proposed wind farm area, one in the north (RND #1723) and one in the south (RND #1751) (Figure

2-1). There is another nest just off the southern boundary of the wind farm site (RND #1724).

Wildspot Consulting checked all known wedge-tailed eagle and white-bellied sea-eagle nests within

the wind farm site in November 2009 and found that nest RND #1318 was active with a pair of sea-

eagles occupying the nest and nest RND #1723 was active with a pair of wedge-tailed eagles. They

also surveyed the wind farm site and the area within 2 km of the wind farm site for other eagle nests.

One new wedge-tailed eagle nest was located within the wind farm site (RND #1812). It was not

active and was considered not to have been used for a number of years but was still in reasonable

condition (Figure 2-1). A new nest RND #1829 was also discovered to the north east of the wind farm

site (Figure 2-1). This nest was not active in the 2009 breeding season.

There are two white-bellied sea-eagle nest sites to the north west of the wind farm site, RND #1703

and #1318 (Figure 2-1). These were both checked by Wildspot Consulting in November 2009 and nest

#1318 was found to be active in the 2009 breeding season.

3.2 Survey effort

Over the four survey periods there have been 148 observer sessions resulting in 1131.75 hours of

observing time (Table 3-1, and Table 3-2). Site 3 (observer location 78) was removed as an

observation site after the first round of surveys in November 2008 because it had limited visibility and

was also considered no longer necessary as the area it covered could be observed from Sites 5 and 6

(see Section 2.1). Site 7 (observer location 371) was added prior to the May 2009 surveys to cover the

additional area added to the proposed wind farm site (Figure 2-1).


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Table 3-1 Survey effort (hours)

Observer location

Survey period S6 (53)4 S3 (78) S5 (113) S2 (164) S1 (200) S4 (249) S7 (371) Total

19/11/08 to 12/12/08 47.25 49 47 125.5 128.5 54.5 0 451.75

24/2/09 to 27/2/09 34 0 34 34 34 34 0 170

4/5/2009 to 8/9/2009 42.5 0 42.5 42.5 42.5 42.5 42.5 255

10/8/2009 to 15/8/2009 42.5 0 42.5 42.5 42.5 42.5 42.5 255

Total (hours) 166.25 49 166 244.5 247.5 173.5 85 1131.75

3.3 Eagle observations

During the November/December 2008 survey period, a total of 10 wedge-tailed eagles were observed

using the study area during the field survey, including three adult pairs, two immature birds and at

least two juveniles, a third juvenile was in nest #1723 and not far off fledging. Subsequent

observations from the February, May and August 2009 surveys have indicated that there are three

territories that cover most of the site. They are:

1. The “Mushroom” territory in the north east of the site which is centred on nest #1723 (Figure 2-1,

Figure 3-1) in the gully formed by the Bashan Plains Rivulet adjacent to Mushroom Hill. This

nest produced a chick in the 2008/09 and the 2009/10 breeding season. The territory occupies the

north east section of the site and extends to the south east over Mushroom Hill.

2. The “Macclesfield” territory which encompassed a large area mainly along the eastern shore line

of Lake Echo and includes Macclesfield Hill. The nest location for this pair is unknown although

it is thought to be further north on the adjacent “Macclesfield” property where nest #1704 is

located. The observed behaviour of this pair combined with the presence of a well grown juvenile

bird in November/December suggested they were not breeding during 2008-09 season. This pair

was rarely observed during the 2009-10 breeding season.

3. The “Lakeside” territory covering the forested areas in the south of the site around Five Mile

Marsh and borders Lake Echo. This territory includes nest sites #1812, #1751 and #1724 which

were not active in the 2008/09 or the 2009/10 breeding season. The territory of this pair is thought

to extend further south where the active nest may be located. Nest searches in the 2km buffer

south of the operating area of the wind farm failed to locate another nest in this territory.

Whilst undertaking bird utilisation surveys in the north east of the operating area during October 2009

it was discovered that an additional pair of wedge-tailed eagles use the site. These birds are thought to

use nest #1320 located in the Ouse River valley, 3.5 km north of the boundary of the wind farm site.

4The numbers in brackets are Observation Site ID numbers that are used in the analysis of the eagle utilisation data


29

Their territory (the “Ouse River” territory) also most likely encompasses nests #1829 (Figure 2-1) and

#4895. The birds were observed on several occasions approaching from the north onto the site in the

Doves Creek area. They were never observed to go any further south than this although they did range

up to 3 km west at times and often displayed in that area.

The “Ouse River” birds were not observed during the initial four eagle utilisation surveys

(November/December 2008, February, May and August 2009) and therefore they are not included in

the utilisation data analysis in this report. The eagle utilisation analysis and collision risk assessment is

currently based on the three pairs that have been observed on site over the initial 12 months of survey.

However, as more data is collected during the ongoing eagle utilisation surveys and as the utilisation

report is updated, the use of the site by the “Ouse River” territory birds will be accounted for.

3.4 Data analysis

3.4.1 Data quality

The data quality check found that overall the eagle observational data for all surveys was of high

quality with minimal data entry errors (Appendices A-D). In the November-December 2008 survey

there were some missing null values for five fields, the main ones being bird location (30 ha),

behaviour location – soaring (8ha) and behaviour location – flying (8ha). However, these missing data

presented no major issues and these missing data are covered by the GIS data tracks.

In the February 2009 survey the 8 ha location for the different behavioural classifications was not

recorded for most flights however it was recorded for the 30 ha location for each flight point

(Appendix B). The consequence of not recording the flight behaviour for the 8 ha location is a lower

spatial resolution of the data. In May 2009 there were only a small number of minor absent records

from the ‘flight details’ table with two flights having no height recorded and one occurrence of the

30 ha sector not being recorded (Appendix C). These absences will not significantly impact on the

data analysis. In August 2009 the data sheets were entered correctly with the exception of the failure to

record five individual flight point ‘height’ classifications (Appendix D). This minor amount of missing

data (less than 1.5% of the records for this attribute) is not expected to be a problem for the analysis of

this data.

5 Nest #489 is not shown on the map because it is off site and further to the north


30

*Prepared by S. Plowright Figure 3-1

Map showing approximate location and extent of wedge-tailed eagle territories


31

3.4.2 Data summary

The eagle utilisation data set represents a total of 1131.75 hours of observing time during which 363

individual eagle flights were recorded during the November-December 2008, February, May and

August 2009 survey periods (Table 3-2). Table 3-2 Eagle survey periods summary

Survey period Observation sessions

Observing time (hours)

Wedge-tailed eagle flights

November-December 2008 62 451.75 181

February 2009 20 170 24

May 2009 30 30 84

August 2009 36 255 74

Totals 148 906.75 363

3.4.2.1 Observer summary

The analysis of observer effort in the February, May and August 2009 survey periods showed that all

observers used all locations equally and no observer worked at the same location for consecutive

sessions (Appendices B and C). This contrasts with the November/December surveys when the survey

sites and protocols were being established (Appendix A). In this survey:

• not all observers used every observation site

• not all sites were utilised by time equally

• not all observers contributed for the same amount of time

The analysis of the observer effort and the number of birds that were observed indicated that there

were differences in the levels of bird activity observed by each of the observers (Table 3-3). This is

not an indication of an observer’s performance because it does not take into account the time of day,

the location, or the environmental factors which will influence the eagle activity. What the analysis of

observer effort does show is that there is localised variation at the site; both spatial and temporal

(Table 3-3).

3.4.2.2 Observer location overview

The analysis of the observer location data which have been standardised to the number of flights

observed per hour indicated that overall more flights were observed in the November – December

2008 survey (Table 3-3). However, observer location S1 (200) has a uniform high level of activity


32

across all survey periods compared to the other four sites which have a lower level of flight activity

apart from observer location S6 (53) which had a similar high level of activity in November-December

(Table 3-3). The site S7 (371) which was added in May also had a higher flight activity than the other

sites apart from observer location S1 (200) (Table 3-2). The levels of eagle utilisation across the site

are explored further in the utilisation maps (see Section 3.2.2.3).

.Cattle Hill Wind Farm: Eagle Utilisation Assessment Revision No: DRAFT E204165.EUA.REP1 July 2009

33

Table 3-3 The number of flights observed per hour by observer by location*

Observer S6 (53) S5 (113) S2 (164) S1 (200) S4 (249) S7 (371)

Nov-Dec

Feb May Aug Nov-Dec

Feb May Aug Nov-Dec

Feb May Aug Nov-Dec

Feb May Aug Nov-Dec

Feb Aug May May Aug

A 0.35 0.24 0.47 0.00 0.79 0.00 _ 0.00 0.07 _ 0.12 0.24 0.67 0.35 0.59 0.00 0.82 0.00 _ 0.00 0.24 0.35

B 0.71 0.00 0.47 1.06 0.24 0.12 0.00 1.06 0.24 0.00 0.47 0.00 1.17 _ _ _ 1.03 0.35 1.54 0.24 0.71 0.00

C 1 0.00 0.24 _ 0.35 _ 0.00 0.00 0.24 0.24 0.12 0.62 0.46 0.71 1.06 0.47 0 0.00 0.00 0.00 _ 0.59

D - _ _ 0.00 0 _ 0.00 0.00 0 _ 0.00 _ 0.25 _ 0.71 1.53 _ _ 0.24 0.47 0.94 0.00

E - _ 0.12 0.00 - _ 0.35 0.35 0 _ 0.12 0.00 0.5 _ 0.47 0.46 0 _ 0.00 _ 0.35 _

F - _ _ _ - 0.00 _ _ 0.24 0.00 _ _ - 0.35 _ _ 0.47 0.00 _ _ _ _

G 0.54 0.24 0 _ 0.82 0.00 0.71 _ 0.49 0.00 _ _ 0.71 0.24 0.35 _ 0.71 0.12 _ 0.35 0.24 _

H 0.47 _ 0.12 0.19 0.00 0.00

Average 0.65 0.12 0.26 0.31 0.44 0.03 0.21 0.28 0.18 0.06 0.16 0.19 0.63 0.41 0.64 0.53 0.51 0.09 0.35 0.21 0.49 0.19

* data from Appendices A-D


34

3.4.2.3 Site utilisation by eagles

The GIS ground tracks were used to produce site utilisation maps for each survey period and for all surveys combined (Figure 3-3 to Figure 3-8). Figure 3-2 illustrates the relationship between observed eagle flights and the utilisation maps. The areas with a high number of flights, for example the area in the east which corresponds to nest #1723 is shaded bright green on the map and grades through to blue for low utilisation. The white areas correspond to minimal eagle flight activity. For example a single crossing flight distance has been recorded at that location (Figure 3-2). The hashing corresponds to no observed activity, that is, there were no observed flights in these areas during the survey periods (Figure 3-2). Note that the utilisation charts show the relative patterns of eagle utilisation based on an absolute number of flights and are used to highlight differences in utilisation within the site.

Figure 3-3 and Figure 3-4 indicate that within the November and December 2008 survey period there was an area of high utilisation in the north east of the site associated with the active nest in the “Mushroom” territory (Figure 3-1). There were also relatively higher levels of activity in the north west of the site in the area associated with the “Macclesfield” territory (Figure 3-1, Figure 3-3 and Figure 3-4). There were also smaller patches of high utilisation in the south west of the site corresponding to the “Lakeside” territory (Figure 3-1, Figure 3-3 and Figure 3-4). In the November and December surveys the areas of low utilisation were primarily within the central part of the site.

The apparently lower eagle utilisation for the February 2009 survey period shown in Figure 3-5 is a result of the small number of flights recorded in this survey (Table 3-2). However, despite there being fewer observed flights there is still a consistent pattern of high utilisation in the north east of the site around observer location 200 (S1) as was seen in the November/December 2008 survey period (Figure 3-5). The dominant feature of the area of high utilisation in the north east of the site is an elongated north south contour pattern which is indicative of a north south flyway (Figure 3-5).

The high level of flight activity around observer location 200 is again evident in the May 2009 utilisation data as shown in Figure 3-6. The addition of observer location 371 (S7) for the May surveys has identified a new area of activity extending north west and south east from the survey point (Figure 3-6).

The August 2009 utilisation map is consistent with the previous utilisation map in showing the area of high activity around observer location 200 (S1), (Figure 3-7). The area of high activity which was seen in the May survey around observation point 371 (S7) is also evident (Figure 3-7).

Figure 3-8 is a compilation of the utilisation data from all of the surveys. Figure 3-8 shows that the

underlying pattern of site utilisation is that:

• The areas of highest eagle activity are around observer location 200 (S1) and observation point

371 (S7).

• There is an area of high activity located west of observer location 249 (S4).


35

• There are also several peaks and ridges of high activity in the south west of the site around

observer locations 53 (S6) and 78 (S3).

Figure 3-2 Spaghetti diagram illustrating the relationship between observed flights (dark blue lines) and the

utilisation maps (shaded contours)


36

Figure 3-3 Site utilisation map for the November 2008 Survey period (Figure 1 from Appendix D)


37

Figure 3-4 Site utilisation map for the December 2008 survey period (Figure 2 from Appendix D)


38

Figure 3-5 Site utilisation map for February 2009 survey period (Figure 3 from Appendix D)


39

Figure 3-6 Site utilisation map for the May 2009 survey period (Figure 4 from Appendix D)


40

Figure 3-7 Site utilisation map for the August 2009 survey period (Figure 5 from Appendix D)


41

Figure 3-8 Site utilisation map for all surveys combined - November 2008 to August 2009 (Figure 6 from

Appendix D)


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3.4.2.4 Flight records overview

Flights observed by month

The analysis of flights observed by month indicate that there is a significantly increased level of

activity in December 2008 over that observed in November 2008, and February, May and August

2009 indicating that the site does experience seasonal differences in the level of eagle activity (Table

3-4). Note that the low levels of activity recorded in February may have been affected by disturbances

that were occurring on site at the time. The February survey was carried out during the deer hunting

season on the Central Plateau and there were shooters on site during the observation periods which

may have affected eagle behaviour. Table 3-4

Flight activity per hour for each survey month*

Nov 2008 Dec 2008 Feb 2009 May 2009 Aug 2009

Flights per Hour 0.31 0.58 0.14 0.33 0.29

SE² 0.02 0.04 0.04 0.02 0.03


Flights Observed by behaviour type

The analysis of flights observed by behaviour type showed that dominant behaviour type recorded at

the site over all survey periods was Soaring, and Flying (Table 3-5). Displaying flights made up a

small proportion of observed flights, with the highest proportion, 6% being observed in the November

–December 2008 survey (Table 3-5).

Table 3-5 Observed flights by behavioural classification*

Behaviour Soaring Displaying Flying In conflict

Nov/Dec 2008 73% 6% 47% 0%

Feb 2009 60% 1% 46% 0%

May 2009 68% 4% 28% 0%

August 2009 74% 1% 29% 0%


Flights observed by flight height

The data analysis of flights observed by flight height is presented as flight height utilisation maps

(Figure 3-10, and Figure 3-11). The analysis found that for the November and December 2008 survey


43

period most observed flights were at the higher flight classes, over 125 m and over 300 m, with these

two classes accounting for 75% of the flight observations (Appendix A). The analysis of the February

2009 data found a similar result with 75% of observed flights in the higher flight classes, that is over

125 m and over 300 m (Appendix B). Although, there was a change in the proportion of flights in

these two classes with more flights being recorded in the over 125 m range (52.1%) and fewer flights

recorded in the over 300 m range (22.5%), (Table 3-6).

Flights below 125 m and mixed flights comprise potential flights at risk based on a nominal wind

turbine with a 90 m blade diameter mounted on an 80 m tower (see Section 2.1). In both the

November/December 2008 and the February 2009 survey period 25% of observed flights were below

125 m or mixed (Table 3-6,). However, the May 2009 flight data showed a significantly different

flight height pattern with a greater proportion of lower flights, 50% of observed flights were below

125 m and 82% of observed flights were either completely below 125 m or partially below 125 m

(Table 3-6). The August 2009 observations showed an even distribution of flights across the height

classes (Table 3-6). However, there were still a greater proportion of lower flights, with 35% of flights

below 125 m compared to the November/December and the February survey periods.

For three of the survey periods, November/December 2008, February 2009 and August 2009 most

observed flights were above the turbine swept area, between 60% and 75% of flights (Table 3-6).

Compared with May 2009 when the majority of observed flights (82%) where either wholly or partly

below maximum turbine height. Table 3-6

Proportion of flights in each height class for each survey period*

<125 m Mixed >125 m >300 m

Nov/Dec 08 11.9% 12.8% 37.8% 37.5%

February 09 16.9% 8.5% 52.1% 22.5%

May 09 31.2% 50.7% 2.5% 15.6%

August 09 35.5% 4.5% 30.1% 29.8%

* data from Appendix D

The flight height data are also presented as utilisation site maps in Figure 3-10, Figure 3-11, and

Figure 3-12. Figure 3-10 shows site utilisation for all flight heights and are the same data as shown in

Figure 3-8. Figure 3-11 shows site utilisation for flights below 125 m and mixed flights (potential

flights at risk) which highlights that low flights are concentrated around observer location 200 (S1)

and observation point 371 (S7). Flights above 125 m also occur around these two points and contribute

to the high utilisation observed west of observer location 249 (S4) and in the south west of the site

around observer locations 53 (S6) and 78 (S3), (Figure 3-12).


44

The analysis of flights observed by flight height was also analysed by behaviour type to assess

whether flight height was associated with a particular flying behaviour. The analysis indicated that no

particular behaviour was associated with a height class (Appendices A to D). In summary, the eagles

were observed behaving in a similar way at all heights and were flying at a height of 125 m or more in

all survey periods with the exception of the May 2009 survey when there was a greater proportion of

flights below 125 m and ‘mixed flights’6 (82%).

Flights observed by wind direction

When the data for flights observed by wind direction are combined for all surveys, there was no

apparent preference for flights in a particular wind direction, and the minor differences between

surveys for flights observed by wind direction were likely to be the result of short term variability in

flight behaviour or observation conditions (Figure 3-9). For example, it is evident from Figure 3-9 that

in the August surveys, flights were observed more frequently in westerly winds. However, this appears

to have been due to the weather conditions that were experienced in the August surveys. There was a

period on the 11/8/2009 when the cloud base was at ground level and visibility was reduced to less

than 50 m and the winds were from the east. Consequently, there were 720 minutes of observations in

easterly winds where visibility was poor and no flights were observed even if they had occurred,

which is likely to account for an increased frequency of flights in westerly winds.

The analysis of the flights observed by flight height by wind direction and flight behaviour by wind

direction also found that there was no correlation between flight height, behaviour and wind direction

(Appendices A-D).

6 Mixed flights are where the observed flight was for some of the recorded flight below 125 m, and for some of time it was above 125 m.


45

*From Appendix D

Figure 3-9 All flights observed by wind direction*


46

Figure 3-10 All flights at all heights combined


47

Figure 3-11

Flights below 125 m and mixed heights


48

Figure 3-12 Flights above 125 m


49

Flight lengths and displacements

The flight data was also analysed to see if the observed flights were directional flights or birds were

soaring overhead. The air-distance a bird flies (the flight length) can be substantially different to the

ground distance (the flight displacement) between the start and end of a flight. The flight length and

flight displacement were calculated from the GIS data recorded in the field surveys (see Appendix B

for description of method). The flight length (distance) was calculated from the number of GIS points

on a recorded flight path. There is a 20 m spatial resolution between points. The flight displacement

(ground coverage) is the distance between the first and last recorded GIS points for a flight path.

The information on flight length and displacement provides additional risk information for the siting

of wind turbines. Direct flights that result in shorter flight lengths may present a lower risk than

soaring flights that have a greater flight length where birds are potentially at risk of greater exposure to

the turbine swept area.

The flight distance and displacement data indicate that flights were more direct in February, May and

August 2009 and there were fewer soaring flights because flight lengths were shorter (Table 3-7) but

the ground distance covered was approximately the same (Table 3-8). The median flight length of

displacement ratios shown in Table 3-9 demonstrate that more direct flights were observed in the

months of February, May and August. This compares to November and December 2008 when flight

length was observed to be greater but flight displacement was similar to February and May (Table 3-7

and Table 3-8) indicating that flights were less direct, i.e. they were soaring or circling flights as

evidenced by the flight length to displacement ratio (Table 3-9). Table 3-7

Summary of flight length data*

Nov 08 Dec 08 Feb 09 May09 Aug 09

Min (m) 140 220 260 200 80

Max (m) 28920 1350 840 8560 18020

Median (m) 3010 3020 2690 1800 1910

S.E. 625 456 630 317 432

No. of flights 96 84 24 80 74

*data from Appendix D


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Table 3-8 Summary of flight displacement data*


Min (m) 32 69 39 125 19

Max (m) 3173 3203 2924 2694 2379

Median (m) 746 776 949 851 422

S.E. 101 128 168 96 93

No. of flights 96 84 24 80 74


Table 3-9 Median flight length to displacement ratio*


4.00 3.01 2.64 2.18 2.15


3.5 Collision risk modelling (CRM)

The input into to the collision risk model is the number of bird movements per unit time and space,

which was derived from the eagle utilisation data base as described in the methods. However, the

output is the number of movements at risk which is converted to the estimated number of bird deaths

based on the number of individuals that might interact with turbines at the site in a given year. For the

Cattle Hill Wind Farm site collision risk modelling the population of eagles at the site was assumed to

be the three resident pairs and associated flying immature and juveniles, a total of ten birds (Section

3.3). The inputs used in the Vestas V90 wind turbine collision risk modelling by Biosis were:

• 144 Vestas V90 turbines with a rotor hub centreline height of 80 metres

• 15% annual turbine downtime (rotors not turning)

• a wedge-tailed eagle population on the site of 10 birds per year

• a total of 7,900 minutes of count observations

• all wedge-tailed eagle flights within 500 m horizontal radius of observers

• the wedge-tailed eagle utilisation study flight data in three height categories ‘below 125m’; and

‘mixed’. Flights ‘above 125m’ and ‘above 300m’ were excluded.

The results of the collision risk modelling for the Vestas V90 turbine for a range of avoidance rates are

shown inTable 3-10. The predictions from the collision risk modelling of wedge-tailed eagles at Cattle


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Hill Wind Farm ranged from an average of 0.5 to 0.1 collisions per year using 90%, 95%, 98% and

99% avoidance rates. Taking a precautionary approach and using an avoidance rate for the wedge-

tailed eagle of 90% (see Section 2.4.3) the predicted annual average mortality rate for the Vestas V90

144 turbine layout is 0.5 eagles per year. Table 3-10

Predicted annual average numbers of wedge-tailed eagle collisions with 144 V90 turbines at Cattle Hill Wind Farm.

Vestas V90 (144 turbines)

Static avoidance rate 99% 99% 99% 99%

Avoidance rate 90% 95% 98% 99%

Predicted annual average mortalities

0.5 0.3 0.2 0.1

3.6 Population Viability Analysis

3.6.1 Development of the metapopulation model

A PVA was carried out that assessed the impact of wind farms on the Tasmanian population of wedge-

tailed eagles. This necessitated considering the metapopulation as two, nested sub-populations,

‘breeders’ and ‘floaters’. Paired breeding adults reside in their home ranges year round, whilst the

‘floating’ non-breeding population are not constrained by territories but range across them (Marchant

and Higgins 1993). This indicates that there is a natural cap on the size of the breeding sub-population,

which is the availability of suitable home territory.

The description of the meta-population that was used in the PVA is:

• A breeding pair is one of requisite age and that owns a suitable territory

• having produced a fledgling, this fledging migrates immediately to the ‘floating’

population

• the floating sub-population may not breed itself

• the breeding sub-population is static, until death creates a vacancy

• given a vacancy in the breeding sub-population (an available territory and mate) a

floater of breeding age may disperse and become a breeder.


52

Figure 3-13 illustrates the modelled life cycle of the wedge-tailed eagle used in the PVA. The

indicated survival rates (f and s1 through s8) are taken from Table 1 in Bekessy et al. (2009), and were

used as inputs into the VORTEX model.

3.6.2 Life history assumptions and model development

The model has a number of other assumptions which describe the population including:

• There is no cost of dispersal between the two sub-populations. The ability to disperse between

sub-population classification (from floater to breeder and vice versa) is controlled by a rule that

specifies that all surviving chicks fledge to the floating sub-population, and may only enter the

breeding population if there is capacity within the breeding territories of the breeding sub-

population.

• The species is treated as a long term monogamous species and all breeding adults that occupy a

territory, are assumed to be fit, healthy and socially suitable for breeding attempts. However,

only one half of these will successfully breed in any year (Bekessy et al. 2009).

• A carrying capacity has been placed on the breeding population that reflects the limit of

geographic territories.

• The floaters have not been constrained by territory. However, this does not mean that the

number of floaters is not limited. The size of the floater sub-population can only increase in size

through the breeding success of the breeders. The floating population is limited in the model by

the complex interaction of the population dynamics, including the fecundity of the breeders and

the senescence of the floaters. This level does not need to be defined prior to running the model,

because the model will find the supportable ratio within some time related to the senescence of

the floating sub-population. This (unrepresentative) relaxation period will have an impact on

short range forecasts of the model, but has minimal impact in the long term models that were

produced.

The model was run using the above assumptions to see if it could reproduce the results of the Bekessy

et al (2009) study and to see if it agreed with the statement in the Recovery Plan (Threatened Species

Section 2006) that there is estimated to be a non-breeding floating population of 50% of the total

population. The model was found to be able to reproduce the results of Bekessy et al (2009). It also

quickly found that an optimum ratio between the two sub-populations (breeders and floaters) was a

50% contribution of floaters to the total population which is in agreement with Recovery Plan

(Threatened Species Section 2006).


53

Figure 3-13 Wedge-tailed eagle life cycle adapted from Bekessy et. al. (2009),


54

3.6.3 The PVA for the Tasmanian eagle population

To correctly calculate cumulative and individual impacts of developments, the model requires that

there is migration within the Tasmanian wide population. That is, the geographic extent of Tasmania is

used to define the population compared to the Bekessy et al. (2009) model which was constrained to

north west Tasmania. The development of a Tasmanian wide PVA is consistent with the Recovery

Plan (Threatened Species Section 2006) which considers that the Tasmanian wedge-tailed eagle is in a

single population.

The VORTEX model has been built so that the state-wide population is modelled by increasing the

carrying capacity of the breeder sub-population to reflect the Tasmanian capacity.

The carrying capacity that was used in the model was derived from figures provided in the Recovery

Plan (Threatened Species Section, 2006). The model included a total of 426 available territories, with

a maximum 90% occupation rate (Threatened Species Section, 2006) which results in a carrying

capacity of 766 breeding adults. The floating capacity is unconstrained as described above. The model

begins with 466 breeding adults (taken from the 54.75% occupancy of the 426 available territories)

and a floating population of 554. The model is not sensitive to this value of the floating population, as

noted above. The model was then allowed to relax to a stable solution before taking results.

Figure 3-14 shows the evolution of the model under a ‘no harvest’ scenario. This forms the baseline

model to which the mortality scenarios are compared to. Other than the relaxation period at the

beginning of the simulation, the baseline population model rapidly runs to equilibrium.

The floating population contributes 53% of the total population, and the carrying capacity runs at

about 80% of the available territories (Figure 3-14). These figures are not programmed into the model.

Rather, they arise as dynamics of the population, determined by the model based on the input

parameters. The values for the breeding population, the floating population, and the metapopulation as

shown in Figure 3-14 are consistent with those provided in the Recovery Plan (Threatened Species

Section, 2006). The projected total population under these modelled conditions is 1280 individuals,

which is in the middle of the population estimate provided in the Recovery Plan of 1,000 to 1,500

birds (Threatened Species Section, 2006).

The initial spike at the beginning of the simulation is the stabilisation between the floaters and the

available breeding sites. Then there is a period of growth of the floating sub-population, which

indicates the iteration of the model towards a stable equilibrium. After reaching the equilibrium at

about 40 years, the population proceeds at these values for the chosen model run length of 160 years.

The model indicates that the Tasmanian population is stable under the current situation, without


55

additional population pressure, irrespective of the specification and knowledge of the availability of

breeding territories.

Figure 3-14 Evolution of the mean population size

3.6.4 Additional mortality

To assess the potential impact of the proposed Cattle Hill Wind Farm a series of additional mortality

scenarios were introduced into the PVA. These scenarios investigated the level of mortality that would

increase the extinction risk for the wedge-tailed eagle population eagle population (the ‘tipping point’)

and compared this to the expected mortality from the proposed development. Twenty mortality

scenarios were used in the model. The baseline model (scenario 0) was based on mortality rates from

the no harvest baseline model of Bekessy et al. (2009). The twenty mortality scenarios ranged from

zero to a sustained average of 40 additional mortalities per annum. Figure 3-15 shows the average

mortalities per annum and 95% range for each scenario. The expected additional collision rate from

the proposed Cattle Hill development is an average of 0.5 eagles per annum for the 144 turbine layout

as indicated by the red arrow.


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Figure 3-15

Mortality scenarios for the PVA

The model assumes that there is an equal likelihood of male and females from both sub-populations

being involved in a mortality event. This seems to be a valid assumption given that floaters appear to

contribute equally to the overall population when they become breeding birds.

The extra mortalities are not introduced into the model until 20 years into the simulation to allow the

two populations (breeding birds and floaters) to stabilise as indicated by Figure 3-14.

The model allows the additional mortality to persist as a ‘press’ impact. That is, it continues

perpetually. It should be noted that the planned life of a wind farm is generally 20 to 25 years after

which it may be decommissioned or repowered with new technologies that would make this

assumption possibly false or overly pessimistic. However, if a population is not threatened by a press

impact, then a ‘pulse’ impact lasting for only the lifetime of a farm will not affect it adversely either.

A precautionary approach has been taken for this PVA model where all additional impacts are

assumed to be long lasting press events.

3.6.5 Modelling the mortality scenarios

The exponential growth (or decline) rate of the population ‘r’ was used to demonstrate the long term

impact of the press impact scenarios described above. If the growth rate ‘r’ is positive, the population

is growing. If ‘r’ is negative the population shrinks. If ‘r’ continues to be negative, the population will

tend towards extinction.

Figure 3-16 shows the ‘r’ values for the range of morality scenarios that were modelled. The

continued negative ‘r’ value curves are in bold. The number of years of simulation is on the x-axis and

‘r’ parameter is on the y-axis.

‘Tipping point’


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The thin dark blue line on the graph (Figure 3-16) that generally sits above the other scenarios for the

first 40 years is the baseline scenario (no additional mortality introduced). The tendency for the growth

rate ‘r’, to hover around zero is an interaction with the carrying capacity, and denotes a stable

population. Figure 3-16 shows that up to and including 22 average additional mortalities a year, the

variation of the growth rate ‘r’ is no more than for the baseline scenario. This equates to a sustainable

press event. It is only when 26 additional mortalities (bold dark red line) each year are added to the

baseline scenario that there is a consistent decline in the growth rate ‘r’ in the long term (a period of

160 years).

Analysis of the model outputs that feed into Figure 3-15 and Figure 3-16 indicates that if additional

mortalities go beyond a sustained average of 22 birds per annum, the Tasmanian wedge-tailed eagle

population is likely to go extinct. At the 95% confidence levels shown in Figure 3-15 this means an

annual mortality of between 14 – 33 adults per annum. Note that as described above the model is

assuming a press impact, in which the 22 additional mortalities are assumed to be sustained in

perpetuity. The population is likely to be able to sustain pulse (short lived) impacts of similar

magnitude accordingly, although this scenario has not been explicitly modelled.

Cattle Hill Wind Farm: Eagle Utilisation Assessment Revision No: DRAFT E204165.EUA.REP1 November 2009

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Figure 3-16

Stochastic value of r for scenarios of increasing average mortality as a function of time


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3.7 Development of an eagle sensitive wind turbine layout

Maps of relative risk for the Cattle Hill wind farm project were generated for the 144 Vestas V90 wind

turbine layout using:

• The utilisation maps shown in Section 3.4.2.3 which were updated to include observations from

November 2009

• The Collision Risk Model projection of long term average mortality 0.5 birds per annum.

3.7.1 Determining the individual contribution to utilisation

Using a long term prediction of 0.5 annual mortalities across a homogenous site of 144 wind turbines

with an operational lifespan of 25 year, results in each turbine having an 8.3% chance of being

involved in a collision incident, over the life of the wind farm. This baseline figure is then weighted

such that the total remains constant, yet a turbine in an area with twice the utilisation is twice as likely

to be involved in an incident. The results of this approach are shown in Figure 3-17. Note that

utilisation data used in this analysis is for all flights observed at all heights across the wind farm and

not just flights at risk below 125 m and at ‘mixed’ heights.

The dashed, vertical lines in Figure 3-17 indicate where the cumulative risk crosses 20%, 50% and

80% values. These values have been selected to indicate where the levels of risk are and it can be seen

that 20% of the total risk comes from just seven wind turbines, (numbers 109, 132, 126, 85, 100, 86

and 127). The scale on the y-axis is a relative measure; the numbers represent program scaling.

3.7.2 Estimating the individual collision risk

The contributory risk can then be converted to a measure of likelihood of a turbine being involved in

one or more strike incidents. This is achieved by taking the results of the (homogeneous) CRM and

applying the assumption that twice the utilisation is likely to result in twice the risk of incident (even

though the incidents remain ostensibly random, unrelated events). This results in a plot of the

likelihood of each individual turbine being involved in one or more collisions over the operating life of

the wind farm (Figure 3-18).

There are two breaks suggested in the distribution of the collision likelihood in Figure 3-18, which

have been selected as break points between higher and medium risk, and medium and lower risk.

These are indicated by red dotted lines on the chart (Figure 3-18).


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There are three heights that are important in the interpretation of Figure 3-18. Using turbine 109 (far

left) as an example, the blue bar shows the likelihood of being involved in one collision incident

(about 30%). The top of the white bar is the likelihood of being involved in any number of incidents

(about 38%) with the length of the yellow bar (8%) indicating the chance of more than a single event.

This chart is based on the assumption of a linear relationship between utilisation and risk of collision.

Thus Figure 3-18 should be viewed as a visualisation of the collision risk within the wind farm site

and not a predictive tool.

Should there be any other relationship between utilisation, such as double the activity results in four

times the risk, this will naturally change. However, the relativity of the relationships will not change.

The areas of high risk will occur in the same places, and the order of the turbines that present risk will

remain the same. The likelihood calculations have been used to make decisions about where to make

the distinction between ‘higher’ and ‘lower’ risk.

Caveat

The relationships used to map the homogenous projection generated by the CRM into a risk zone map

are ‘one-way’, that is they are non-linear and non-commutative. This means that, although two

incidents are attributed to a single turbine in the heterogeneous model, removing that single turbine

will not result in the CRM reducing by the equivalent amount. There is currently no available

technique to perform this task that we are aware of.

3.7.3 Visualising areas of high, medium and low risk

The results of the likelihood calculations for flights at all heights have been used to generate a spatial

map of the higher and lower relative risk zones. These have been mapped as ‘Higher’, ‘Medium’, and

‘Lower’ relative risk zones in Figure 3-19. Dark green indicates areas of relatively high collision risk.

Light green are areas of medium risk and purple are areas of low risk. These risk zones were used to

develop a revised turbine layout for the Cattle Hill Wind Farm. The spatial relationship of the risk

zones to the wind farm site are shown in Figure 3-20.

The same likelihood calculations were done for flights at risk (flights at less than 125 m) and used to

generate the same map showing ‘Higher’, ‘Medium’, and ‘Lower’ risk zones (Figure 3-21). This risk

zone map has not been used in the development of a layout for the wind farm as it was decided that a

conservative approach would be taken which would use all flights.


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Figure 3-17 The individual and cumulative contribution to utilisation, using all flights and the maximum unconstrained 144 turbine layout.


62

Figure 3-18 Likelihood of involvement in one or more incidents over the lifetime of the wind farm.


63

Figure 3-19 Risk Zones and the 144 turbine layout for all flight heights.

Cattle Hill Wind Farm: Eagle Utilisation AssessmentE204165.EUA.REP1

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Figure 3-20 Eagle risk zones at the Cattle Hill Wind Farm site


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Figure 3-21 Risk Zones and the 144 turbine layout for flights at risk.

Cattle Hill Wind Farm: Eagle Utilisation AssessmentE204165.EUA.REP1

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3.7.4 Development of the revised layout

A revised wind farm layout was produced using the eagle risk zone mapping and other constraints that

were identified within the wind farm site which included restrictions on the number of turbines that

could be placed in the Private Reserve, a 1 km buffer around the eagle nest sites7, and the avoidance of

Aboriginal and historic heritage sites. At this stage of the wind farm development it was decided that

no wind turbines would be placed in the ‘higher’ risk zones within the wind farm site. The revised

wind farm layout as shown in Figure 3-22 is comprised of 100 wind turbines.

The effect of the revised layout on the collision risk was calculated. If it is assumed that all flights at

all heights are at equal risk to each other, the revised layout is expected to present 53% of the collision

risk of the original maximum unconstrained 144 turbine layout because of the removal of 44 wind

turbines and the relocation of other turbines as illustrated by comparing the two layouts (Figure 3-23).

This is a reduction in collision risk of 47% which results in the predicted annual average mortality rate

for this wind farm dropping from 0.5 birds per annum for a 144 wind turbine layout to 0.3 birds per

annum for a 100 turbine layout when using a 90% avoidance rate.

If it is assumed that only flights classified as less than 125 m and ‘mixed’ flights are at risk of collision

with wind turbines, then the revised layout is expected to present the same reduction in risk. That is

47% of the collision risk of the original maximum unconstrained 144 turbine layout as indicated when

comparing the two layouts in Figure 3-24. This results in the predicted annual average mortality rate

dropping from 0.5 birds per annum to 0.3 birds per annum, which is the same as the all flights model.

The revised collision risk values are a result of both the reduction in numbers of turbines, and the

changed locations of turbines across the wind farm8.

As noted previously the risk zone mapping assumes a correlation between utilisation and risk. The

CRM does not have this assumption explicitly within it. Consequently, re-running the CRM on the

new 100 wind turbine layout will not produce a commensurate reduction in risk identified above. This

is a direct result of attributing spatial risk from the assumed site homogeneity of the CRM. The

reduction in risk we have deduced comes from moving and removing turbines of perceived higher

risk. A CRM calculation of reduced risk assumes only the reduction in risk due to a reduction in total

turbines on the site. This study has used the eagle utilisation to inform the collision risk at the site.

7 Nest buffers have been included for eagle nests that are outside the wind farm site boundary but the 1 km buffer extends into the site 8 Note that two of the turbines in the revised layout are just outside the originally modelled footprint (turbines numbers 45 and 52). They are assumed to present a zero risk as there is no flight data from that area. They are immediately adjacent the modelled low risk zone and it is likely that they would be included in this zone if the model was extended to cover this area.


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Figure 3-22

The revised 100 turbine layout and constraints


68

Figure 3-23 Wind turbine layout and eagle risk zones for all flights – the original 144 wind turbine layout is on the left and the revised 100 wind turbine layout is on the

right


69

Figure 3-24 Wind turbine layout and eagle risk zones for flights at risk (flights less than 125 m and mixed flights) – the original 144 wind turbine layout is on the left and

the revised 100 wind turbine layout is on the right


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3.7.5 Collision risk modelling for a larger turbine

Following the development of the 100 Vestas V90 wind turbine layout a larger turbine is being

considered for use at the Cattle Hill Wind Farm site. This larger turbine is a Vestas V112 which has a

rotor diameter of 112 m diameter, mounted on an 84 m high tower (see Section 2.4.2). Collision risk

modelling was carried out for this turbine by Biosis using the following inputs:

• 100 Vestas V112 turbines with a rotor hub centreline height of 84 metres

• 3% annual turbine downtime (rotors not turning)

• a wedge-tailed eagle population on the site of 10 birds per year

• total of 7,900 minutes of count observations

• all wedge-tailed eagle flights within 500 m horizontal radius of observers

• the wedge-tailed eagle utilisation study flight data in three height categories ‘below 125m’;

‘mixed’; and, ‘above 125m’. Flights ‘above 300m’ are excluded.

The inputs are the same as for the V90 collision risk modelling apart from the turbine dimensions and

the downtimes (3% as compared to 15%). The results of the collision risk modelling for the Vestas

(V90 and V112) for the range of avoidance rates are shown in Table 3-10. Figure 3-25 illustrates the

collision risk modelling process for the Vestas V90 and V112 turbines.

The CRM for the Vestas V112 turbines uses three height categories of flight data ‘below 125 m’,

‘mixed flights’ and ‘above 125’ (which includes flights between 125 and 300 m) whereas the Vestas

V90 CRM used two height categories ‘below 125 m’ and ‘mixed flights’. The addition of the ‘above

125 m category for the V112 CRM includes flights that are above the highest point of the V112

turbine at 140 m. These flights between 140 m and 300 m are flights that are above the swept area

and not necessarily flights at risk of collision therefore the modelled collision rate will be an

overestimate.

Note that flight heights were recorded in the field using four broad height categories because the data

set was designed to identify patterns of eagle utilisation and flights at risk in terms of above and below

the height of a Vestas V90 turbine rather than as a direct input to the Biosis Research Collision Risk

Model. Biosis Research were engaged at a later date to provide collision risk modelling services

following a request by the Environment Protection Authority to undertake collision risk modelling.

Given that the flight height data was recorded categorically it is not possible to subdivide the data for a

given height category.

The predictions from the collision risk modelling of wedge-tailed eagles at the Cattle Hill Wind Farm

for the Vestas V112 turbine ranged from an annual average of 0.5 to 2.1 collisions per year using 99%,


71

98%, 95% and 90% avoidance rates. The collision risk value for the 90% avoidance rate for the Vestas

V112 turbine at the Cattle Hill Wind Farm results in an annual average predicted mortality rate of 2.1

eagles.

The main factor in the increased annual average mortality rate for the larger V112 wind turbine is the

addition of the flight height data ‘above 125 m. However, the change to a large turbine with a greater

swept area (112 m compared to 90 m) and a reduced downtime (3% instead of 15%) will also

contribute to an increased mortality. Significantly more eagle flights were recorded above 125 m than

below 125 m as shown in Table 3-6. In the November/December 2008, February 2009 and August

2009 surveys, between 60% and 75% were in the ‘above 125 m’ and ‘above 300 m’ categories. Only

in the May 2009 survey were there more flights in the ‘below 125 m’ and ‘mixed categories’ (82%). Table 3-11

Predicted annual average numbers of wedge-tailed eagle collisions with 144 V90 turbines at Cattle Hill Wind Farm.

Vestas V90 (144 turbines) Vestas V112 (100 turbines)

Static avoidance rate 99% 99% 99% 99% 99% 99% 99% 99%

Avoidance rate 90% 95% 98% 99% 90% 95% 98% 99%

Flight height categories modelled

Below 125m and mixed Below 125m, mixed and above 125m*.

Predicted annual average mortalities

0.5 0.3 0.2 0.1 2.1 1.3 0.7 0.5

*note that the above 125m category includes flights between 140-300m, which are not at risk


72

Figure 3-25 Outline of the Collision Risk Modelling process

CRM on Vestas V90 144 wind turbine layout using flights less

than 125 m and mixed flights (as appropriate to smaller turbine). Predicted collision rate of 0.5

eagles per year at 90% avoidance.

Biosis eagle point count data collected on site (short

baseline).

Wildspot eagle survey data (long baseline in hotspots makes it biased for CRM).

Point count data extracted from utilisation data set

(long baseline).

Revised Vestas V90 100 wind turbine layout using flights less than 125 m

and ‘mixed’ flights... Revised predicted collision rate of 0.3 eagles

per year at 90% avoidance.

Redesign turbine layout taking into account site constraints including

eagle high risk zones.

CRM on Vestas V112 100 wind turbines using the revised turbine

layout and three flight classes up to 300 m. (as appropriate for a larger turbine). Predicted collision rate of

2.1 eagles per year at 90% avoidance.

Validation of use of point count data from eagle

utilisation data set


73

3.7.6 Implications of the eagle studies for the Cattle Hill Wind Farm

It is noted that from the eagle mortality data supplied by the Threatened Species Section of the

Department of Primary Industries, Parks, Water and Environment (B. Brown unpub. data) that

mortality caused by wind farms in operation at the time was included in the mortality assessments of

the Bekessy et al. (2009) work. Therefore, the sustainable mortality rate of 22 birds per annum

calculated in the PVA based on the existing population and demographic data is above and beyond the

effects of current wind farm developments. As the data set supplied by the Threatened Species Section

remains unpublished, the different mortality causes were unable to be separated. Therefore, adding the

current known wind farm mortality as outlined below in Table 3-12 below is a conservative approach

to the estimate of the sustainable mortality, as there is double counting of some wind farm mortalities.

Nevertheless, the mortalities are provided in Table 3-12 to enable an assessment of the State-wide

wind farm mortality on the Tasmanian eagle population.

The annual mortalities recorded at Bluff Point and Studland Bay combined with the modelled

prediction from the 100 V90 turbine layout for Cattle Hill results in an additional eagle mortality of

3.8 birds per annum. This corresponds to Scenario 2 in Figure 3-26 (the thick pink line) which

indicates that for a long term average of 3.8 mortalities, there is no effect on the extinction risk of the

species within the 160 years of the simulation run.

When the annual mortalities recorded at Bluff Point and Studland Bay are combined with the

modelled prediction from the 100 V112 turbine layout for Cattle Hill this results in an additional eagle

mortality of 5.6 birds per annum. This average annual mortality is within the range of Scenario 2 in

Figure 3-26 (the thick pink line) which indicates that for a long term average of 5.6 mortalities, there is

no effect on the extinction risk of the species within the 160 years of the simulation run.

As described in Section 3.6.5 the number of mortalities per annum at which the Tasmanian eagle

population is at risk of extinction is above 22 mortalities and the first level of additional mortalities

that deviates from complete survivability, is the scenario of 24 additional mortalities per annum.

Above this mortality rate, the model indicates that the ability of the species to persist progressively

declines. Below this mortality rate, there is no discernible impact.

The results of the eagle utilisation studies, the collision risk modelling and the PVA indicate that

neither the revised 100 V90 wind turbine layout nor the 100 V112 wind turbine layout for the Cattle

Hill wind farm development are likely to impact on the long term sustainability of the State-wide

population of the Tasmanian wedge-tailed eagle, based on the current eagle flight data set from the

site, and the current published understanding of the population dynamics and behaviours.


74

Table 3-12 Currently known additional wind farm mortalities

Development Mortality (pa) Confidence interval

Percentage of sustainable press

Bluff Point9 2.03 1.04 - 3.55 9.2% (4.7%-16.1%)

Studland Bay9 1.51 0.41 - 3.87 6.9% (1.8%-17.6%)

Combined 3.49 1.99 - 5.66 15.9% (9.0%-25.7%)

Cattle Hill V9010 0.3111 1.4%

Cattle Hill V11211 2.1412 9.5%

Figure 3-26 Probability of survival of the meta-population

9 Wedge-tailed eagle mortality rates for Bluff Point and Studland Bay Wind Farms have been provided Roaring 40s and are correct to the 15th of December 2009. 10 Note that the mortality for Cattle Hill is the CRM output for the 144 V90 wind turbine layout and the 100 V112 wind turbine layout using a 90% avoidance rate compared with the actual mortalities recorded at the other two wind farm sites. In addition, the predicted mortalities from other wind farms (e.g. Mussleroe Wind Farm) have not been included as this wind farm has not substantially commenced. 11 The predicted collision mortality from the revised 100 V90 turbine layout 12 The predicted collision mortality from the 100 V112 turbine layout


75

4. References

Bekessy, S.A., Wintle, B.A., Gordon, A., Fox, J.C., Chisholm, R., Brown, B., Regan, T., Mooney, N.,

Read, S.M. and Burgman, M.A. (2009). Modelling human impacts on the Tasmanian wedge-

tailed eagle (Aquila audax fleayi). Biological Conservation. 142: 2438-2448

Marchant, S. and Higgins, P.J. (Eds). (1993). Handbook of Australia, New Zealand and Antarctic

birds, Volume 2: Raptors to Lapwings. Oxford University Press.

Percival, S. (1998). Beinn Churlaich, Islay, Proposed Wind Cluster – On Geese and Turbines.

Supplementary Precognition to Board of Inquiry.

SEMF (2008) Proposed Cattle Hill Wind Farm – Ecological Assessment. Report to NP Power Pty Ltd,

October 2008. SEMF, Hobart

Smales, I. (2006). Impacts of avian collisions with wind power turbines: an overview of the modelling

of cumulative risks posed by multiple wind farms. Biosis Research Pty. Ltd (for Australian

Department of the Environment and Heritage)

Smales, I. and Muir, S. (2005). Modelled cumulative impacts on the Tasmanian wedge-ailed eagle of

wind farms across the species’ range. Report for the Department of Environment and Heritage.

Accessed 2nd February 2010. http://www.environment.gov.au/epbc/pblications/pubs/wind-

farm-bird-risktasmanianwedgetailedeagle.pdf

Still, D., Little, B. & Lawrence, S. (1996). The effect of wind turbines on the Bird Population at Blyth

Harbour, Northumberland. ETSU W/13/00394/REP, Energy Technology Support, UK

Department of Trade and Industry.

Threatened Species Section. (2006). Threatened Tasmanian Eagles Recovery Plan: 2002-2010.

Department of Primary Industries and Water, Hobart.

Whitfield, D.P. (2009). Collision Avoidance of Golden Eagles at Wind Farms under the “Band”

Collision Risk Model. Natural Research Ltd, Banchory, UK. Report to Scottish Natural

Heritage.

Winkelman, J.E. (1992). The impact of the Sep wind Park near Oosterbierum (Fr.), The Netherlands

on birds. 1. Collision victims. RIN-Rapport 92/2. Rijksinstitut voor Natuurbeheer, Arnhem, The

Netherlands.


76

Wintle, B.A, Bekessy, S.A., Venier, L.A., Pearce, J.L., Chisholm, R.A. (2005). Utility of dynamic-

landscape metapopulation model for sustainable forest management. Conservation Biology 19:

1930-1943


77

The information contained in this document has been carefully compiled but Hydro Tasmania

Consulting takes no responsibility for any loss or liability of any kind suffered by any party,

not being the intended recipient of this document, in reliance upon its contents whether

arising from any error or inaccuracy in the information or any default, negligence or lack of

care in relation to the preparation of the information in this document.

Cattle Hill Wind Farm Eagle Utilisation Assessment, Collision Risk Modelling and Population Viability Analysis:

Appendices

E204165.EUA.REP1

May 2010


Contents

Appendix A Eagle Flight Data Analysis and Interpretation

Appendix B Eagle Flight Data Analysis and Interpretation: February 2009 Observations.

Appendix C Eagle Flight Data Analysis and Interpretation: May 2009 Observations.

Appendix D Eagle Flight Data Analysis and Interpretation: August 2009.

Appendix E Cattle Hill Wind Farm: Robustness of Data Techniques

Appendix F Population Viability Analysis for the Tasmania Wedge-tailed Eagle


Appendix A Eagle Flight Data Analysis and Interpretation

www.symbolix.com.au

1/14 Akuna Drive

Williamstown North

VIC 3016

Eagle Flight Data Analysis and

Interpretation Proposed Cattle Hill Windfarm

Issue 1

10th of February, 2009

Report to

Hydro Tasmania Consulting

- i -

Acknowledgements Symbolix would like to acknowledge and thank the following people for their valuable insights, contributions and feedback in the preparation of this report and its contained analyses.

Simon Plowright – Wild Spot Consulting

Raymond Brereton – Hydro Tasmania Consulting

Version Control

Version Edited By Changes/Notes

0 MPR Initial Draft

0.1 SCM Review

Draft EMS Draft

Issue 1 MPR Additional Information Added – Flight height details

Final Version Approved :

10/2/09

Signed Date

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Table of Contents 1 Data Quality Assurance............................................................................................1

1.1 Database Structure ............................................................................................1 1.1.1 Database Schema ......................................................................................1

1.2 Data Vetting......................................................................................................2 1.2.1 Errors .......................................................................................................3

1.3 Null Fields.........................................................................................................3 1.3.1 Null Value Distributions...............................................................................3

2 Data Review...........................................................................................................5 2.1 Observer Overview ............................................................................................5

2.1.1 Total Observer Time...................................................................................5 2.1.2 Flight Count – By Observer .........................................................................5 2.1.3 Birds per Hour – By Observer ......................................................................6

2.2 Observer-Location Overview ...............................................................................7 2.2.1 Observed Minutes against Location ..............................................................8 2.2.2 Observed Flights by Location.......................................................................8 2.2.3 Raw Utilisation Measures ..........................................................................10 2.2.4 Corrected Utilisation Map ..........................................................................11 2.2.5 Interpretation of the utilisation maps..........................................................13

2.3 Flight Records Overview ...................................................................................15 2.3.1 Flights observed by Month ........................................................................15 2.3.2 Flights observed by Weekday ....................................................................16 2.3.3 Flights observed by Time of Day................................................................17 2.3.4 Flights observed by Wind Direction ............................................................18 2.3.5 Flights observed by Behaviour Type ...........................................................19 2.3.6 Flights Observed by Flight Height...............................................................20 2.3.7 Flights Observed By Flight Height by Wind Direction ....................................22

3 Final Comments....................................................................................................23

- iii -

Table of Figures Figure 1: Referential Database Structure..........................................................................2 Figure 2: Observer Location Map.....................................................................................7 Figure 3: Site Utilisation Map (raw flights)......................................................................10 Figure 4: Site Utilisation Map (corrected weights)............................................................12 Figure 5: Orthonormal Corrected-Utilisation Overlay........................................................13 Figure 6: Utilisation By Observer Location ......................................................................14 Figure 7: Bird Activity by Weekday ................................................................................17 Figure 8: Proportion of Bird Flights and Sessions By Flight Start Hour................................18 Figure 9: Activity and Observation Proportions By Wind Direction .....................................19 Figure 10: Flight Height Utilisation Maps ........................................................................21

List of Tables

Table 1: Null Values employed........................................................................................3 Table 2: Null Records - Behavioural Type.........................................................................4 Table 3: Null Record Rankings ........................................................................................4 Table 4: Total Observer Sessions ....................................................................................5 Table 5: Total Observer Minutes .....................................................................................5 Table 6: Total Observed Flights ......................................................................................5 Table 7: Observed Birds Per Hour ...................................................................................6 Table 8: Observer Minutes by Location ............................................................................8 Table 9: Flight Count by Observer by Location..................................................................8 Table 10: Flights per Hour by Observer By Location ..........................................................9 Table 11: Flight Count by Month ...................................................................................15 Table 12: Observed Minutes by Month...........................................................................15 Table 13: Bird Activity by Month ...................................................................................15 Table 14: Flight Count by Weekday...............................................................................16 Table 15: Observed Minutes by Weekday.......................................................................16 Table 16: Bird Activity By Weekday ...............................................................................16 Table 17 : Proportion of Flight Behaviours......................................................................19 Table 18: Flight Count By Flight Height..........................................................................20 Table 19: Proportion of Flight-Points by Height and Wind Direction...................................22 Table 20: Normalised Proportion of Flight-Points by Height and Wind Direction ..................22

Eagle Flight Data Analysis and Interpretation - Proposed Cattle Hill Windfarm

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Summary This report details the contents of the database of Eagle Observation records to date. Contained within the database are the records gathered from Wedge-Tailed Eagle observations at the proposed site of the Cattle Hill Windfarm. The data was collected from 19/11/2008 to 12/12/2008 (inclusive) and recordss individual bird movements along with their corresponding environmental measurements.

Wildspot Consulting was contracted both to perform the observations and the data entry.

Their field measurements also include an orthonormal photographic map which allows the observers to graphically record the flight path (through an approximate ground track) of the bird. This flight path was then entered into a GIS software package from which the Easting and Northing pairs were extracted at 20m resolution for each flight path.

Using the Observations Database it is possible to extract the individual records for any individual flight, or group of flights which meet a set of criteria. This work will facilitate future queries for risk assessments and mitigations.

The work also includes a visualisation of the record set that highlights regions of higher relative utilisation. This visualisation is capable of being filtered through the usual database features and so can be used to generate images of behavioural differences or dependencies on weather patterns and drivers. The visualisation assumes that the actual location of the birds is accurate to within 300 metres on the ground (95% confidence) and can be corrected for identified observer effects.

Even at this early stage of development, it is possible to assert that there are regions of high utilisation appearing, with distinct variations of utilisation both the geographically and temporally.

1 Data Quality Assurance Upon receiving any new data, in any format, and before any preliminary analysis can be performed, the data must be subjected to quality assurance checks to provide a “trusted” dataset. It is only once a trusted dataset has been established that any further work can meaningfully take place.

A trusted dataset allows for informed decisions to be made as the information forming the foundation of future analyses is of a known, quantified quality.

1.1 Database Structure The structural integrity of the database and the relationships between the tables provides the ability to extract complex data sub-sets with relative ease and speed. It is essential that the data types are strictly adhered to in order to maintain the links between fields and tables. The database was checked and found to be structurally sound with all key pairs maintaining integrity and functionality.

1.1.1 Database Schema The database schema is supplied below to facilitate third party queries across the set. In order to maximise data handling ability, while minimising the time required to filter, search, sort and extract individual records a relational database with full bi-directional referential integrity has been constructed.

For data other than GIS flight paths, the database uses three tables: Sheet Records, Flight Records and Flight Details.


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Figure 1: Referential Database Structure

These cascading structures hold, respectively: The header details from the observation sheets, identifiers for the flights recorded on the relevant sheet and, finally, the individual behaviours and locations for an individual flight. The GISData table is an appended table that allows details of GIS flights to be extracted and used in the utilisation maps. It is also possible to take a single flight reference, or list of flight references, and to extract all of the relevant information relating to the observation session(s) from which they came (date, observer ID, observer location, etc.…)

The GIS information is not entered into the tables in a traditional fashion. Due to its nature it undergoes an “import and stitch” procedure. It therefore undergoes two quality checks: the first being a check of the data itself, and the second ensures that the flight paths are connected to the correct data sheets.

This keyed tabular structure allows for information to be extracted based on any combination of criteria. It also allows for future expansion should new information sources become available.

1.2 Data Vetting The structural integrity has been tested and found to be sound. To generate confidence in future analysis attention is turned to the contents of the database. The data is checked to ensure that logical values with consistent formatting are used for each data field. Failure to do so weakens any analytical outcomes in that they may not have access all relevant information correctly. For example; if an analytical tool looks for all records with a wind direction between 60 and 105 degrees from North, and some entries have been given the value “East” as opposed to 90, the tool will not include these records in the report.

The supplied data was found to be of a high quality requiring minimal corrections to conform to standard.


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1.2.1 Errors Obvious typographical errors (where the correct value is readily apparent) are generally easy to find and rectify. In the case of this database there was only one flight record which needed to be amended (the Observer ID had been entered incorrectly).

It is possible for errors to exist in the database which cannot be uncovered unless every record is checked against the field notes from which it was originally entered. These errors typically come from mistyping or recording a variable. While all attempts have been made to ensure that the database contains the correct values, there is a possibility that some values are erroneous.

However, the overall consistency of the records implies any issues are well within design limits and will not affect analysis.

The data entry Quality Assurance appears to be functioning correctly, with only two values requiring reference back to the original hard copy records. These values were corrected manually from the original paper records.

1.3 Null Fields The final check establishes a note of the percentage of records which are incomplete sets. The occurrence of null values in a data set can weaken an analysis by reducing the baseline reported against. Once a measure of the incidence of null values has been established a true indication of the usefulness of the data becomes apparent; the fewer null values, the more useful the dataset becomes.

A default value has been automatically applied where data was not been entered for any individual field, whether it did not apply or was mistakenly omitted. The exact format of the default value varies across data types. However they are all clearly marked as being different from valid data which makes their identification a rapid process.

Data type Null record Times Midnight (0:00) Date Australia Day, 1788 Numeric -9999 Text Null (“”) or XX Table 1: Null Values employed

Use of null cases in such a way allows better and more stable access and querying across the database. It does necessitate a little care be taken, particularly in numerical fields, when averages or summing is performed. One should filter out the nulls first and keep note of them as a measure of the reliability of the individual records.

Weather details were to be recorded at several times throughout the day. It is possible that the observer was not on site for the entire day and so will record a legitimate null in the record set. Records where this is the case have been excluded from the following counts of null values. This excision is justified as any analysis using time based parameters normally exposed to the legitimate nulls, will be protected from their effects through the “Start” and “End Time” variables used in the query.

1.3.1 Null Value Distributions Some variables in the record keeping design are “paired” to facilitate future analysis. One such example of this is the behaviour binaries coupled to the location ID. For example, whether a given behaviour is occurring is recorded as a simple yes/no. Associated with this variable is a geographical identifier for an 8 ha sector. This is independent to the GIS applications that are also being employed.


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Soaring Displaying Flying Conflict Total Records 378 36 265 0 Paired Location Recorded 0 36 0 0 Geographical Gap 100% 0% 100% 0% Table 2: Null Records - Behavioural Type

The Database design requires that, for each of the points along the flight paths, the location of each behaviour type be recorded. This was only completed for Displaying behaviours. The dominant behaviours in the database, Soaring and Flying (see Table 2), were not recorded.

Technically, this data is redundant, as similar information is available in the GIS record. The primary intent of the sector identifier allows for reverse querying, that is not well supported by the GIS tracks. Reverse querying allows one to ask for the weather conditions that were associated with all flights in a given sector. The GIS allows us to see which locations flights were associated with given an environmental variable of interest. Due to the GIS data being a high-resolution locator, the number of flights which share a precise co-ordinate pair is very low. Subsequently, the GIS data is unable to effectively tell us variables given a location (such as a WTG pad) of interest. It can only perform ranged requests, where the flight paths’ coordinates are within a certain values.

For the time being such analysis is of little import for the planning phase. However, in the future it is foreseeable that dynamic management of Wind Turbine Generators may be instituted. The generation of such plans will be greatly assisted by this data, which can certainly be added later if the need is identified.

As well as needing to record the location of each of the behaviour types (with an 8ha resolution) there was a coarser location for general bird location which was to be recorded at each point (with 30ha) resolution. Of the 602 flight points recorded, there were 98 times where this value was left unrecorded. This 16% loss of location data weakens the ability to associate flight types with certain locations, but it is not insurmountable.

For the entire database as outlined in Figure 1 there are only five fields which contained illegitimate null values. These are presented in Table 3 which indicates the proportion of the total number of possible records that are null. The higher the percentage of null values for each field the lower the usability of the field in future analyses.

Variable % Not Recorded

Behaviour Location – Soaring (8ha) 100.0%Behaviour Location – Flying (8ha) 100.0%Bird Location (30ha) 16.3%Precipitation 8.5%Flight Heights 1.2%Table 3: Null Record Rankings

Overall, the missing data offers no major issues, and the data base is considered an excellent quality dataset. The consistently missing details are adequately covered by the redundancy within the GIS tracks.

Future applications based upon this data set, such as Bayesian clustering or pattern recognition, will be affected by the inability to distinguish dry weather from legitimate nulls caused by no-one being on site (8.5% of records). At this stage in data collection, no serious impact is expected.


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2 Data Review Contained within the database are the digitised observation records which cover 62 observing sessions, 92 observing sheets, 181 observed Wedge Tailed Eagle flights and span dates from 19/11/2008 to 12/12/2008. There is a total of 451 ½ hours of observing time.

The following overview provides a snapshot of the characteristics of the data indicating activity levels on-site and the areas that warrant further investigation.

They identify some of the information contained within the analysis and will highlight where patterns are above or beyond the expectations of noise. Failure to detect a pattern is not necessarily evidence of a lack of pattern. It is naturally limited by its short baseline, being less than two months of a yearly cycle. With longer baseline studies, the individual behaviours necessary to mitigate risk will stabilise and the ability to detect finer scale patterns will increase.

2.1 Observer Overview These records were produced by 7 individual observers and were recorded from 6 separate observing locations. This is not an investigation into the efficacy of the individual observers, so each of the 7 members of the observing team has been assigned a simple single character identifier, enabling comparisons to be made while providing a level of anonymity. This allows us to identify any effects that are dependent upon observer contribution and so eliminate them from our interpretations if need be.

2.1.1 Total Observer Time When comparing values between observers it is important to keep in mind that not all observers spent an equal amount of time out in the field.

Observer A B C D E F G Number of Sessions 10 11 13 8 7 3 10% 16% 18% 21% 13% 11% 5% 16%Table 4: Total Observer Sessions

Observer A B C D E F G Contributed Minutes 4500 4500 6000 3450 3015 1530 4095% 17% 17% 22% 13% 11% 6% 15%Table 5: Total Observer Minutes

2.1.2 Flight Count – By Observer It would seem logical that the observers who spent a greater length of time observing would see more birds than those who spent very little time observing. The pattern in Table 6 suggests a significant deviation from this assumption amongst observers. However, there are several competing factors which may be proxies to the numbers of flights observed. These factors include time of day, wind strength, location and others.

Observer A B C D E F G Flights observed 39 47 30 11 7 8 38% 22% 26% 17% 6% 4% 4% 21%Table 6: Total Observed Flights


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2.1.3 Birds per Hour – By Observer The number of flights recorded by each observer should be viewed in the context of their contribution of time. A comparative measure which takes into account the proportion of observing time performed by each observer is the number of birds per hour they each observed

Observer A B C D E F G Birds per hour 0.520 0.627 0.300 0.191 0.139 0.314 0.557Table 7: Observed Birds Per Hour

Whilst a first glance would suggest that observers A, B and G are seeing more activity than the other observers this figure does not take into account the time of day, the location or the environmental factors which will all influence the result. It should not be treated as a performance metric, but an indicator of localised variation, both spatial and temporal, upon the site.

This deviation suggests that there might be significant variation in activity across the site which we will explore further.


- 7 -

2.2 Observer-Location Overview As mentioned previously the exact location from which the observer was recording could influence the number of flights observed. To suggest otherwise would require the assumption that bird activity is not spatially dependant.

Figure 2: Observer Location Map

In Figure 2 the red markers denote the location of each of the utilised observing sites, the green area indicates that birds were observed in the area and stripes indicate that no birds were observed in the area.


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Whilst highlighting the difference in observations between the different locations (identified in Figure 2) it is seemly to re-iterate the statements made accompanying Table 7. It is prudent to highlight the following:

Not all observers used every observation site

Not all sites were utilised by time equally

Not all observers contributed for the same amount of time

Not all observation sessions yielded an observed flight

Each of these will affect the interpretation of utilisation.

2.2.1 Observed Minutes against Location By showing directly the number of minutes each observer spent at each location (Table 8) the differences in observation location usage become readily apparent. Only four out of the seven observers undertook observations from all six locations, while only one of the observation locations was utilised by all seven observers.

Location Observer 53 78 113 164 200 249

A 510 420 840 870 1350 510B 510 420 510 1260 870 930C 480 1260 510 1515 1815 420D - 420 450 420 2160 -E - - - 1695 840 480F - - - 1020 - 510G 1335 420 510 735 675 420

TOTAL 2835 2940 2820 7515 7710 3270Table 8: Observer Minutes by Location

This distribution of observational minutes by location shows a rather large inequality in the observing effort across the survey area (under the assumption that all observers are equally proficient), with a clear majority (68.3%) of the observing time being spent in the northern half of the proposed windfarm site.

2.2.2 Observed Flights by Location Under the caveat that different observing sessions were subject to different prevailing environmental and temporal conditions yet remained representative surveys, it is possible to start to form a measure of the activity levels for each location. In order to form this measure a count of the number of bird flights by each observer at each location is required.

Observer Location 53 78 113 164 200 249A 3 2 11 1 15 7B 6 1 2 5 17 16C 8 0 3 6 14 0D 0 2 0 0 9 0E 0 0 0 0 7 0F 0 0 0 4 0 4G 12 0 7 6 8 5Total 29 5 23 22 70 32

Table 9: Flight Count by Observer by Location


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When considering Table 9 it is important to reference Table 8 as these tables together provide an indication of the utilisation of the survey region. Individually these tables provide an overview of the data contained in the dataset. When combined as in Table 10 they provide information about the bird activity level for different regions of the proposed windfarm site.

Observer Location 53 78 113 164 200 249A 0.35 0.29 0.79 0.07 0.67 0.82B 0.71 0.14 0.24 0.24 1.17 1.03C 1.00 0.00 0.35 0.24 0.46 0.00D - 0.29 0.00 0.00 0.25 - E - - - 0.00 0.50 0.00F - - - 0.24 - 0.47G 0.54 0.00 0.82 0.49 0.71 0.71Average 0.65 0.14 0.44 0.18 0.63 0.51

Table 10: Flights per Hour by Observer By Location

The blank cells in Table 10 occur when that particular observer spent no time observing at the corresponding location. This null value is different to the seven observer/location combinations which resulted in 0.00 flights per hour being observed. These are the result of observations at these locations which did not yield any flights.

The important difference between Table 9 and Table 10 is for Location 53, in the first column. Once we correct for the observational effort disparity (which does not initially appear too great an imbalance), we can see that the activity in this sector of the site rivals that around Location 200.

The presented risk may be significantly different between these two regions, depending upon other drivers of behaviour. Yet on the simplistic utilisation driver that indicates the preference of the birds to be in the vicinity, the two areas of the site present similar risk.


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2.2.3 Raw Utilisation Measures With the GIS data stitched into the database via the flight record keys, it is possible to generate utilisation maps for the proposed site of the windfarm. The maps that follow present an interpretation of the preference to be in various regions of the site. They are a more intuitive and expressive method of visualisation for the activity levels recorded by the observers, as they compress all the records into a single image and so exhibit longer term tendencies and patterns.

Figure 3: Site Utilisation Map (raw flights)


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Bright green corresponds to high utilisation. This fades to cyan for low utilisation with white and hashing corresponding to minimal and none respectively. Minimal utilisation is defined as being only a single crossing flight distance being recorded at that location. The hashing areas constitute regions with not enough records to combine to a single passing event.

The maps work from a metric of “Metres of flight per square metre.” Using such a metric, one cannot distinguish between an area (sq. metre) that has many single, direct flights over it, or a single flight of complex, looping patterns. Both will generate a large number of ground tracks per square metre.

The method used to generate the utilisation map involves the effective diffusion of all GIS tracks over all locations on the site. As such, all locations have an equal number of points (being the number of flight points in the database), but their combined weights are modified according to the distance the location is from the original recorded GIS track. For the sake of this analysis, we have set the dispersion to be 66% weighting within 150 metres of the original track, 95% weighting within 300 metres.

The utilisation charts are relative usage only, and serve merely to highlight differences in local utilisation. They are not designed to assess an absolute measure of risk, which might involve a wide variety of variables above and beyond the birds’ propensity to use the location.

As such, care should be used when comparing maps. The colouring of the contours is a relative measure and may not necessarily be consistent between two maps.

2.2.4 Corrected Utilisation Map As can be seen in Table 8 the observing effort was not divided equally amongst the 6 observing locations, this then necessitates the implementation of scaling to retrospectively equalise the efforts. Each record is then weighted by the inverse of the proportional observing effort corresponding to that observation location. This produces a utilisation map corrected for observational effort, provided each record is representative.


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Figure 4: Site Utilisation Map (corrected weights)

Once the observation effort has been normalised across the site, it becomes apparent that there are areas other than the top right quadrant which ought to be classified as high utilisation. This supports the distribution within Table 10, which show observer locations 53 and 164 (refer to Figure 2) to have similar levels of activity associated with them.

Figure 4 highlights a double-pronged region in the south west of the site with higher local utilisation. To try to identify reasons for this apparent preference, we have superimposed Figure 4 on an orthonormal photograph of the site.


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Figure 5: Orthonormal Corrected-Utilisation Overlay

2.2.5 Interpretation of the utilisation maps We have presented two versions of the flight locality information, in Figure 3 and Figure 4. This was driven by the identification of a potential bias introduced by the various contributing observational efforts. The contribution from each Observer Location are shown in Figure 6.


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Figure 6: Utilisation By Observer Location

To aid in the clarity of Figure 6 the lowest level of utilisation, previously denoted as hashing, has been removed.

Whilst Figure 3 and Figure 4 appear significantly different from one another, the underlying characteristics are the same. The peaks in utilisation occur in the same place on both, it is only the height of the contours which change and this is due to the difference in observational effort between sites.

To arrive at Figure 4 it is assumed that all observers have equal acuity. This is necessary as not all observers used each site equally and not all observers were used equally. It is also assumed that each observing session is representative of the general site conditions at that


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locale. For example if all of the observation sessions for a particular location were ALL performed in a Southerly wind this could affect the results.

The northern section of the site shows a good and clear distinction between the observer and their regions of responsibility. In the south-western corner these regional responsibilities overlap (most likely in time). There is no meaningful way of correcting for this numerically. The observing team have performed correctly in the record keeping, it is merely a surveying artefact that stops us from quantifying precisely the effect of the competing observations.

Allowing for these overlaps, the true utilisation map will be somewhere between Figure 3 and Figure 4. As the observational effort inequalities are assumed to have the greatest influence over the amplitude of the utilisation contours, one would expect the true utilisation to be closer to Figure 4 than Figure 3.

2.3 Flight Records Overview As well as the differences in the activity levels between different observation locations, other possible combinations of flights-observed by [variable] can give rise to some important information.

2.3.1 Flights observed by Month By examining the flight activity split by month seasonal patterns can emerge. To date the observations only span two calendar months, which enables only preliminary indications as to the bird activity over longer periods. Obviously, as future observations are incorporated into the data set it will be possible to determine bird activity levels through out the entire year. Currently it is only possible to generate activity numbers for November and December. It is still instructive to do so.

Nov DecCount 96 84% 53% 47%Table 11: Flight Count by Month

As with weekly data one might begin by expecting that the more time spent looking, the more birds you would expect to see. As such the total number of observed minutes must be considered.

Nov DecMinutes 18390 8700% 68% 32%Table 12: Observed Minutes by Month

This now allows us to calculate activity levels again using Flights per Hour as the metric.

Nov Dec

Flights per Hour 0.31 0.58Standard Error 0.02 0.04Table 13: Bird Activity by Month

The errors presented in Table 13 arise not from inconsistencies in the dataset itself but from the interaction of the different fields assuming that the recording of a flight is represented by a binomial distribution.

The errors in Table 13 suggest a significantly increased level of activity in December over that observed in November. This implies the site may experience significant seasonal differences.


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2.3.2 Flights observed by Weekday Though not often immediately apparent as to why this split can be important, checking to see if there are strong weekly cycles can provide some insights into how the surrounding region impacts the activity levels on-site.

Table 14, Table 15 and Table 16 show the flight count, observed minutes per day and the relationship between the two.

M T W T F S S Flights 26 5 44 27 48 4 26% 14% 3% 24% 15% 27% 2% 14%Table 14: Flight Count by Weekday

Under the assumption that environmental conditions a) do not follow a weekly cycle, and, b) do not impact on the activity levels, it would be logical to assume that the number of flights observed on any given day (Table 14) is proportional to the number of minutes observed on that day (Table 15).

M T W T F S S Minutes 3120 2700 5595 5550 5070 2550 2505% 12% 10% 21% 20% 19% 9% 9%Table 15: Observed Minutes by Weekday

Having the measure of both the number of flights and the number of minutes observed on each day of the week, a measure of activity can be generated. In this case Bird Flights Per Hour for each day of the week has been calculated and shown in Table 16 and Figure 7 below.

M T W T F S S Flights per Hour 0.50 0.11 0.47 0.29 0.57 0.09 0.62Standard Error 0.090 0.051 0.061 0.052 0.070 0.044 0.11Table 16: Bird Activity By Weekday


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0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

M T W T F S S

Weekday

Activ

ity L

evel

(BPH

)

Figure 7: Bird Activity by Weekday

The errors presented in Table 16 and Figure 7 arise not from estimates of variances within the data set.

Tuesday and Saturday both exhibit slightly reduced activity levels, with Thursday also presenting with reduced activity level, which may warrant further study. This could be due to external effects on the farm location (change in off-site activities). From this collected data the cause is not readily apparent.

This particular example presents an opportunity to understand the difference between a statistical significant finding, such as the identification of a reduction in activity on Tuesdays, and a substantively different result, being one that makes a difference to management policies.

2.3.3 Flights observed by Time of Day Whilst understanding the monthly, and daily patterns of activity level are important, having an understanding of the activity level throughout the day allows for the tailoring of management protocols to minimise any potential risks which would present themselves due to periods of high utilisation throughout the day.

This knowledge also allows for tailoring of surveys to generate the greatest return by surveying during peak period of activity.

It is accepted that Wedge Tailed Eagles only fly during daylight hours, which means that activity levels should approach 0 Flights around dawn and dusk. The recorded activity is represented graphically in Figure 8.


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0%

5%

10%

15%

20%

25%

6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

Recorded Flight Start Hour

Pro

port

ion

Figure 8: Proportion of Bird Flights and Sessions By Flight Start Hour

The proportion of total flights recorded is represented by the blue line. To provide a context in which to view the bird flights, the proportion of sessions which were active at that particular time have been plotted in pink. This pink curve indicates an excellent observing pattern that is likely to have detected changes in behaviour across all hours of the day with good representation.

The peak in activity at 9AM is within the bounds of noise, while the peak at 11AM is significantly higher than either the 10AM or the 12PM activity levels. This could be representative of a disturbance which regularly occurs at this time (either on-site or off-site) or a natural behavioural pattern of the birds. In general, it is fair to say that there is an increase in activity in the late morning on this site.

2.3.4 Flights observed by Wind Direction There could be sections of the observation area which are more highly used than others. Any correlations detected in the following section do not necessarily indicate a causal relationship, but might suggest that factors such as air-currents, the direction of the wind, and the topography of the land might be used as behavioural predictors.

The measurements recorded were listed as degree values for the direction of the wind. These were then binned according to their respective cardinal classes. This acts to increase the strength of any comparisons which may be performed using this data, whilst leaving the original records untouched preserving the resolution of the original measurements should they be required in further analyses.


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0%

5%

10%

15%

20%

25%N

NE

E

SE

S

SW

W

NW

Bird Flights

Observed Wind Directions

Figure 9: Activity and Observation Proportions By Wind Direction

The pink line plotted in Figure 9 represents the proportion of observation time spent monitoring with wind coming from each of the cardinal points. It ought to represent the region’s long term average windrose for the relevant months. The blue line indicates the proportion of total flights which were observed. Dashed lines indicate the range of confidence (66%) that we can employ over the flight records.

There are two interesting sectors in this figure. Firstly, in spite of not many observation minutes in northerly winds, there is a large proportion of flights recorded during this time. Secondly, there are less birds present than expected given the large amount of time spent observing in south-easterly winds. Other than these two sectors, there are no distinguishable patterns of preference for wind directions.

2.3.5 Flights observed by Behaviour Type The behavioural type for each flight was recorded in order to build up a behavioural profile for the Wedge Tailed Eagle population which inhabits the area. At its most basic level this information shows the proportion of all flights which either do or do not exhibit each of the behavioural classifications, irrespective of other factors.

Behaviour Soaring Displaying Flying In Conflict

Percentage of flights 73% 6% 47% 0%

Standard Error 1.8% 1.0% 2.0% NA

Table 17 : Proportion of Flight Behaviours

These proportions are not based on the number of flights recorded, but the number of way points recorded. Many flights recorded multiple way points as the bird moved around the landscape. In several instances the behaviour from one location differs from another for the same bird in the same flight. It is also possible for a bird at any given time to have more than one behavioural classification apply to it. For instance, it is possible to be both Soaring and Displaying at the same location in the same flight record. This allows that the total


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number of records used in the preceding analysis (602) exceeds the total number of individual flights (180).

Performing the analysis in such as way allows a more accurate representation of the bird’s behaviours, as it is more closely related to the amount of time spent in motion, than other methods. It is because of this last point that the total for all behaviours sums to be greater than the number of points recorded.

By far the dominant behaviour type is Soaring with 73% of flight points recorded. There were a low proportion of flight points which were classified as Displaying, and none which were counted as Conflict.

Future analysis, with a larger data set will allow the behaviours to be connected to risk factors.

2.3.6 Flights Observed by Flight Height When considering the height at which the birds fly it is important to remember that the only height at which there is zero chance of interacting with the turbine blades is BELOW the rotor swept height (RSH). A bird soaring at many hundreds of meters in the air, at some point, must have passed through the rotor swept height and accordingly has a higher risk associated with it than the flights which fail to reach RSH.

There were four different height classifications used throughout the observation period: Under 125m, Mixed, Above 125m and Above 300m. As many of the flights had more than one flight point recorded they also recorded many flight heights per flight. This has lead to not having 180 flight-height records, but rather having 602, of which 7 were unrecorded.

Height <125m mixed >125m >300m Count 71 76 225 223

% 11.9% 12.8% 37.8% 37.5%Table 18: Flight Count By Flight Height

Utilisation maps illustrate the locations used most heavily by the birds at different flight-heights. As it is not possible to isolate individual segments of flights based on their height, if a flight was recorded with multiple flight-heights it is represented in each of the utilisation maps. e.g. If flight record 99A has one point at >125m and one at <125m the entire flight path has been used in the generation of each of the corresponding utilisation maps.


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Figure 10: Flight Height Utilisation Maps

The images presented in Figure 10: Flight Height Utilisation Maps are as follows;

Top Left: Flights Under 125m

Top Right: Flights with Mixed Flight Heights

Bottom Left: Flights Over 125m

Bottom Right: Flights Over 300m


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The contouring levels used indicate HALF the value of the same colours used in Figure 4, this is done as the reduced number of flights in each map leads to comparatively reduced utilisation rates. The contour levels are identical between all four charts of Figure 10, allowing direct comparison of relative utilisation levels.

The top Left and top right charts are the flights recording at least some time at RSH. One might expect the “Mixed” classifier to represent a different behaviour to other flight height classifications. It may be indicative of topological effects.

It is apparent that many of the key features of Figure 4 are also present in the bottom left section of Figure 10, which corresponds to flights over 125m.

2.3.7 Flights Observed By Flight Height by Wind Direction Soaring birds may rely on updraughts in order to hold their altitude. The ground-track location of these updraughts depends on many factors, an important one being wind-direction. The prevailing wind interacts with the local topology to create zones which may become more attractive to a bird in flight than others.

There are four height classifications combined with eight different wind classes giving rise to 32 different combinations. The individual utilisation maps have not been created for each of these possible combinations as many of them have flight counts too low to enable the generation of a meaningful map.

Height <125m mixed >125m >300m TOTAL N 5% 3% 8% 14% 30%

NE 2% 1% 2% 4% 8% E 0% 1% 4% 4% 9%

SE 1% 0% 5% 0% 6% S 0% 2% 4% 3% 10%

SW 2% 1% 5% 5% 13% W 0% 2% 2% 3% 8% W

ind

Dire

ctio

n

NW 3% 2% 7% 5% 16% TOTAL 12% 13% 38% 37%

Table 19: Proportion of Flight-Points by Height and Wind Direction

As seen in Figure 10 there is a preference for the higher flight classes (being over 125m or over 300m), with these two classes accounting for three quarters of the flight way point observations. As in Figure 9, there is a clear preference for Northerly winds.

When the above values are normalised to correct for the different proportions of time spent observing in different wind directions, the distribution across the flight heights only shifts a small amount, whilst the distribution across wind directions changes drastically.

This would suggest that flight heights are driven by a behaviour that is independent of wind direction. And that overall flight preference is for Northerly winds.

Height <125m mixed >125m >300m TOTAL N 10% 7% 17% 28% 61%

NE 1% 1% 1% 2% 4% E 0% 1% 2% 2% 5%

SE 0% 0% 1% 0% 2% S 0% 1% 1% 1% 3%

SW 2% 1% 4% 4% 11% W 0% 1% 2% 2% 5% W

ind

Dire

ctio

n

NW 1% 1% 4% 3% 9% TOTAL 14% 12% 32% 42%

Table 20: Normalised Proportion of Flight-Points by Height and Wind Direction


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Table 20 normalises the observations portrayed in Table 19 to the amount of time spent observing under each prevailing wind direction. This presents the relative preference for moving in each wind direction, as opposed to sheer volume of movement in each wind direction. Either representation complements the findings in Figure 9.

Any differences between these two representations (Figure 9 and Table 20) are due to one relying on records of flights, and the other on way points. Fundamentally, they both express the same concept of a preference for activity in Northerly winds, overlying a basic activity rate that is independent of wind direction.

A significant amount of recorded activity upon this site occurs at above 125 metres.

3 Final Comments The database and its associated observations, data entry and quality assurance process have shown themselves to function well. The database is positioned to facilitate advanced analytics and predictive assessments as soon as it reaches an appropriate depth of records.

In the event of future observations being recorded, we suggest that only the minor points of location sectors be modified in their entry.

Allowing for only two months of records of a perhaps seasonally dependent species, patterns are detectable for a preference for Northerly winds, and the potential for large scale changes in bird utilisation and behaviour rates across the year. On a daily timescale there is an increase in activity in late mornings.

The total numbers of records are generally too low to detect any patterns with significant confidence.

The utilisation charts, and their supporting tables of observers and location dependent observation rates, imply significant variation in eagle utilisation across the site. This manifests itself near the nest in the North-Eastern sector, and a unique pattern of behaviours in the southern sector.

It would be our recommendation at this time to consider continuing observations and record keeping at some level. There is enough information within the current database to adequately design surveying responses, although seasonal changes may affect these projections. These continued observations would expand the current data set to provide meaningful information on seasonal changes to behaviour, and metrics that might be predictors of risk.

For risk projections to hold any meaning, the baseline studies will need to be statistically representative of at least one year’s behaviour. To be representative necessitates an understanding of inter year variation. This can be difficult to obtain, particularly with complex creatures that are high up a food chain. A combination of domain expert inputs, and good, representative data collection can aid in overcoming this issue. Based on the current level of quality, we foresee the observation routine ably supporting quite high level and complex risk analyses and mitigation support measures.

About Symbolix Symbolix specialise in industrial process modelling, forecasting and support. We use your existing knowledge to audit, improve and expose your current business systems, or to design new approaches. Symbolix consultants will work alongside you and your team, providing you with analysis in conjunction with communication and project management services, Symbolix can provide a complete solution package, or expert consultants to assist at any stage of your project development.

Find out more at www.symbolix.com.au.

1/14 Akuna Dr Williamstown Nth VIC 3016 Telephone: +61 3 9397 2520 [email protected]


Appendix B Eagle Flight Data Analysis and Interpretation: February 2009 Observations.

www.symbolix.com.au

1/14 Akuna Drive

Williamstown North

VIC 3016


Interpretation Feb 2009 Observations

Issue 1

8th of April, 2009

Report to


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Simon Plowright – Wild Spot Consulting


Version Control


0 MPR Data Tables and initial findings

0.1 EMS Draft report and findings

0.2 SCM Technical Review and finalising

1.0 EMS Issue 1 release


16/6/09

Signed Date

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Table of Contents 1 Introduction ...........................................................................................................1

1.1 Observations – Round 2 .....................................................................................1 1.2 Observation Protocols ........................................................................................1

2 Data Overview........................................................................................................1 2.1 Data Summary ..................................................................................................1 2.2 Observer Summary ............................................................................................2

3 Data Review...........................................................................................................3 3.1 Utilisation Map ..................................................................................................3 3.2 Observed Flights Summary .................................................................................6

3.2.1 Observations by Wind Direction ...................................................................6 3.2.2 Observed Flights by Height Classification ......................................................9 3.2.3 Observed Flights by Behaviour Classification .................................................9 3.2.4 Flights per hour by month.........................................................................10 3.2.5 Behaviour and wind direction versus height ................................................10 3.2.6 Flight Lengths and Displacements ..............................................................10

4 Final Comments....................................................................................................11

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List of Tables Table 1: Percentage Unrecorded Behavioural Classification. ...............................................2 Table 2: Observation Sessions by Observer and Location ...................................................2 Table 3: Observation Minutes by Observer and Location ....................................................2 Table 4: Observed Flights by Observer and Location .........................................................2 Table 5: Raw count of all the flights in each month by wind direction..................................8 Table 6: Total minutes spent observing in each month by wind direction .............................8 Table 7: Distribution of flight heights for each observing period..........................................9 Table 8: Observed Flights by Behavioural Classification......................................................9 Table 9 : Flight Activity per Hour...................................................................................10 Table 10: Distribution of Flight Heights by Wind Direction................................................10 Table 11: Distribution of Behaviours According to Flight Height ........................................10 Table 12: Flight Length Summary..................................................................................11 Table 13: Flight Displacement Summary ........................................................................11

Table of Figures Figure 1: February 2009 Observed Utilisation Map ............................................................4 Figure 2: Complete Observation Set Utilisation Map...........................................................5 Figure 3: Observations by Wind Direction For February......................................................6 Figure 4 : Flight directions for Nov / Dec 2008..................................................................7 Figure 5: Bird Flights per wind direction for both surveys aggregated..................................8

Eagle Flight Data Analysis and Interpretation - Feb 2009 Observations

Issue 1 - 1 - 8th of April, 2009

Summary The addition of surveys from February, 2009 supports previously determined local activity preferences. The flights detected correspond to peaks in the previously determined utilisation areas of the site and so offer no suggestion that preference for local areas of the site changes. Flight distributions above Rotor Swept Height remain the same proportion of activity, although the distribution of flights within that classification does alter.

Although concern may be raised over the total amount of activity, that which was observed does not change the findings of previous studies, and suggests that the current assessment of localised utilisation and behaviour is robust.

1 Introduction

1.1 Observations – Round 2 In addition to the raptor observations undertaken in November and December of 2008 a further observation period, from 24 – 27 February (inclusive) was commissioned. As with the initial observations, this data was collected by Wildspot Consulting.

The data collection was cut short by one day, due to shooting onsite. Anecdotal reports from the consultants cite poor weather during observations, and an increase in available carrion onsite. This may have lead to the lower number of flight counts, and should be noted.

In this report we review the February observations separately from the previously acquired data, before assessing the combined dataset on a number of key parameters.

1.2 Observation Protocols In order to maximise the usefulness of the data collected in this second round of observations, the same observation protocols as were used previously were adhered to. This has allowed meaningful comparisons to be made between the 2008 data and the 2009 data as they were collected, collated and recorded consistently.

2 Data Overview This second round of observations spanned from 24th February 2009 to 27th February 2009 inclusive. Over this time there were 20 different observing sessions, amounting to a total of 170 man-hours of observation. During this time 24 individual wedge-tailed eagle (WTE) flights were observed and recorded.

Each of the sessions lasted from 8:00AM to 4:30PM, being a total of 510 minutes. These 24 flights were both recorded as a ground-track, later converted to GIS co-ordinates, and as individual flight points which results in a total of 142 flight-point records.

Only five of the previously used six observing locations were employed during this round of observations, leading to two observing locations in the southern half of the proposed wind-farm site and three in the northern half.

Prior to any analysis being performed on the newly collected data, it was subjected to the same validation as the initial analysis of the November/December data.

2.1 Data Summary The sheet header information and flight record information were recorded in their respective entireties, with only a few simple omissions from the flight details information.



In one instance the ‘BirdLocation’ was either not recorded or not entered for a single point, mid-flight, this can be accounted for in future analyses by using the available GIS data for the flight, which is fully intact.

As with the data collected in the first round of observations, the 8ha location for the different behavioural classifications was either not recorded or not entered in the majority of cases.

Soaring Displaying Flying Conflict Proportion Unrecorded 100% 98.6% 100% 100%

Table 1: Percentage Unrecorded Behavioural Classification.

This is unlikely to represent a problem for future analysis as the 30ha sector location for each flight point is provided which, although providing a lower spatial resolution, does provide a location for each flight point and the associated behavioural classification.

2.2 Observer Summary We provide here a brief summary of the observer configuration for these observations. As previously the identity of the observer has been masked, as this analysis is not about comparing one observer to another, it is merely conducted in order to show the location and duration of each of the observer’s sessions. For this round of observations, no observer worked at the same location for any two sessions, and all locations were utilised (by the observers) equally.

Location Observer 53 113 164 200 249 TOTALA 1 1 0 1 1 4B 1 1 1 0 1 4C 1 0 1 1 1 4F 0 1 1 1 1 4G 1 1 1 1 0 4TOTAL 4 4 4 4 4 Table 2: Observation Sessions by Observer and Location

Location Observer 53 113 164 200 249 TOTALA 510 510 0 510 510 2040B 510 510 510 0 510 2040C 510 0 510 510 510 2040F 0 510 510 510 510 2040G 510 510 510 510 0 2040TOTAL 2040 2040 2040 2040 2040 Table 3: Observation Minutes by Observer and Location

As can be seen the distribution of effort is the same as Table 2, which is due to the fact that all observation sessions were the same length.

Location Observer 53 113 164 200 249 TOTALA 2 0 3 0 5B 0 1 0 3 4C 0 2 6 0 8F 0 0 3 0 3G 2 0 0 2 4Total 4 1 2 14 3 Table 4: Observed Flights by Observer and Location



Table 4 shows the distribution of observations per observer per site. The shaded squares indicate location/observer combinations for which no observations took place. Figure 1 and Table 4 suggest Location 200 has observed the highest level of activity while the other four sites have an even distribution of flights, but we will analyse the utilisation more fully below.

We do not perform any detailed comparison between these tables and previous observing sessions, as the counts are too low to allow meaningful statistical analysis.

Instead these tables are presented to allow understanding of the uniform manner of the observations, and to eliminate observer bias as cause of any deviations we might find below.

3 Data Review

3.1 Utilisation Map To allow a direct comparison between the utilisation map presented here and the one previously supplied1 the same contouring levels have been used, unless otherwise indicated. The data has also been normalised to account for any differences that may exist in that number of observing hours at each location.

The apparently lower utilisation in Figure 1 is an artefact of the small number of flights recorded in February. Despite there being fewer flights it is readily apparent that there is a section of high utilisation in the north-eastern quadrant, as found in the November/December observation round.

When the February data is mapped in conjunction with the previously acquired data it produces an utilisation map (Figure 2) that does not appear to deviate from the previous supplied map in its general characteristics.

However, there is one new area of observed utilisation worth noting, as highlighted by the yellow circle on Figure 2. This feature is as a result of the north-south flyway, which is the dominant feature of Figure 1.

Aside from this there is still a dominant peak in activity located near observer location “200”, with several smaller peaks and ridges in the South West centred on the (unused in February) observer location “78”.

1 Eagle Flight Data- Analysis & Interpretation : Issue 2 13th of February, 2009 – Hydro Tasmaina Consulting internal report



Figure 1: February 2009 Observed Utilisation Map

The peaks of activity recorded correspond generally to those witnessed previously. The north-south fly way is a slightly different usage pattern, albeit in a previously identified area.

Figure 2 shows the combined data set, which does not appear to deviate substantially from the previously suppled charts.



Figure 2: Complete Observation Set Utilisation Map

The yellow circle highlights the only tangible change in flight preference exhibited in the February flights over the observations collected in the previous year. It merely pushes the edge of the zone of recorded activity slightly outwards from that observed before. There is no other significant deviation in site preference, with the February flights aligning to the peaks of the previously recorded site usage.



3.2 Observed Flights Summary

3.2.1 Observations by Wind Direction One of the aims of this study is to assess the possibility of risk behaviours as a function of wind direction. We wish to understand both the prevailing connection between flight counts and wind direction and behaviour and wind direction.

Figure 3 shows the number of bird flights versus the observed wind directions for the February observing run. When compared to Figure 9 of Report 1 (reproduced in Figure 4), some noteworthy differences come to light.

0%5%

10%

15%

20%25%

30%

35%

40%45%

N

NE

E

SE

S

SW

W

NW

Bird Flights


Figure 3: Observations by Wind Direction For February

The most obvious difference between earlier observations and the current round is the apparent inversion of flight preference in the north and south-easterly directions.



0%

5%

10%

15%

20%

25%N

NE

E

SE

S

SW

W

NW

Bird Flights


Figure 4 : Flight directions for Nov / Dec 2008

The February data shows an over-representation of the number of observed flights that take place in a south-easterly wind, and an under-representation in northerlies. This is the opposite of that seen previously.

To understand the possible implications of this we look at the distribution of flights per wind direction for each month observed. We also consider the flights per wind direction on the aggregate data.



0

0.05

0.1

0.15

0.2

0.25N

NE

E

SE

S

SW

W

NW

Bird Flights

Observed Minutes

Figure 5: Bird Flights per wind direction for both surveys aggregated

Figure 5 shows that when we consider flights per direction over a longer baseline, we begin to see less preference for a particular direction. There is still a slight over-representation of flights in the south-east direction, but much smaller than has been seen previously.

The indication is that, although preferences may be seen on short timescales, wind direction does not seem to be a driver for bird flight activity when considered over longer baselines. At the very least this is a relationship worth testing over the course of future surveys.

In order to be well placed to gain further understanding as more data becomes available, we consider the time variation in the relationship between bird flights and wind direction. To do this we must consider the count of flights per direction (Table 5) normalised by the total number of minutes spent observing in each wind direction (Table 6). 2

N NE E SE S SW W NW Nov08 4 9 11 22 29 6 9 6 Dec08 23 6 10 3 8 12 6 16 Feb09 0 0 4 10 5 1 2 2

Table 5: Raw count of all the flights in each month by wind direction

N NE E SE S SW W NW Nov 08 960 1755 3360 3420 5010 1650 1470 660 Dec 08 2100 1650 0 900 450 900 0 2700 Feb 09 1650 0 1650 720 2730 1350 1200 900

Table 6: Total minutes spent observing in each month by wind direction

Firstly we assess the similarity of the normalised distribution of the first survey (November and December) against that of February. Because of the low number of counts involved, a

2 Because wind direction was noted every three hours per observing session, and also noted separately on individual flight records it is possible to have the situation where flights were recorded against a particular wind direction even though no observing time was recorded in that direction (e.g. easterly winds in December). This is just an artefact of the survey design and should not affect the underlying distribution. These instances have been precluded from the above analysis.



non-parametric test (the Spearman Rank Correlation Test) was more appropriate tool than a standard chi-squared.

We find (as hinted at by Figure 3 through Figure 5) that the first survey shows a strong inverse correlation to the recent one, with a Spearman Rank Coefficient (Rs) of -0.893. However, when we repeat this test to compare November and February, we find a small positive correlation (Rs = 0.321).

The lack of observing hours in each wind direction with correlated flight records in December means that it is not possible to directly run this test on December and February, but from the results above and the tables above, we see that the inverse correlation is driven by different wind-flight behaviour in December.

Whether this is indicative of a deeper seasonal pattern or just an example of short-term fluctuations that even out over a longer baseline remains to be seen.

3.2.2 Observed Flights by Height Classification As expounded in previous reports the observed instances of flights at above rotor height might be an important indicator of possible risk. Table 7 shows the distribution of flight heights. Note that, because the height was recorded at multiple points throughout each flight, the sum of the count will add to the number of flight-points rather than the number of flights.

Raw Flight Count % <125m Mixed >125m >300m <125m Mixed >125m >300m

Nov/Dec 71 76 225 223 11.9% 12.8% 37.8% 37.5% Feb 24 12 74 32 16.9% 8.5% 52.1% 22.5% Table 7: Distribution of flight heights for each observing period

Performing a Chi-squared test on the above results3 indicates that the observed heights in February obey a different distribution to that seen previously. However, inspection of the above table reveals that both observing efforts record approximately 75% of flight points as higher than rotor height (nominally 125m). The difference in distribution appears to be driven by a decrease in the proportion of flights in the >300m range, which have relocated into the >125 metres range.

3.2.3 Observed Flights by Behaviour Classification Behaviour Soaring Displaying Flying In ConflictPercentage of flights 60% 1% 46% 0%Standard Error 4.1% 1.0% 4.2% NATable 8: Observed Flights by Behavioural Classification

NOTE: The Behavioural types are analysed separately, they are reported in a single table for convenience. As such it is permissible for the sum of “Percentage of flights” to be greater than 100%.

Comparing Table 8 to the values reported previously, we see that the behaviour is again dominated by soaring and flying. However there is a decrease in both the proportion of soaring flights (from 73% to 60%) and in Displaying activity (from 6% to 1%). Because of the small number of counts in February we are not able to make any statistically meaningful statements about these changes at this stage.

3 χ2 = 17.16



3.2.4 Flights per hour by month As we obtain more survey results and are able to quantify any seasonal effects, it will be useful to have a measure of base activity per month. We form the measure ‘Birds per hour’ from the total number of flights recorded per month normalised by the total time available for observation (i.e. the sum time spent observing in minutes).

As with the bird activity versus wind direction, we see that December is a standout, with the highest activity rate so far. No analysis is advisable at this stage, but it is hoped that further observations will reveal seasonal trends.

Nov 2008 Dec 2008 Feb 2009

Flights per Hour 0.31 0.58 0.14SE 0.02 0.04 0.04Table 9 : Flight Activity per Hour

3.2.5 Behaviour and wind direction versus height Table 10 and Table 11 show the distribution of height versus wind direction and behaviour respectively. Flight counts were simply too low in February to garner any statistically significant insight into possible changes in these distributions. We present them, and the updated aggregated observations for completeness.

February Survey Aggregated Observations Height <125m mixed >125m >300m <125m mixed >125m >300m

N 0.0% 0.0% 0.0% 0.0% 31% 22% 16% 32% NE 0.0% 0.0% 0.0% 0.0% 9% 9% 4% 8% E 16.7% 0.0% 5.4% 15.6% 4% 8% 10% 10% SE 29.2% 66.7% 74.3% 21.9% 11% 11% 28% 3% S 16.7% 0.0% 10.8% 21.9% 6% 14% 11% 11% SW 4.2% 8.3% 8.1% 0.0% 14% 9% 12% 11% W 8.3% 8.3% 1.4% 0.0% 3% 15% 5% 8% NW 25.0% 16.7% 0.0% 40.6% 22% 13% 14% 17%

Table 10: Distribution of Flight Heights by Wind Direction

February Survey Combined Survey Height <125m mixed >125m >300m <125m mixed >125m >300mSoaring 17.6% 11.8% 34.1% 36.5% 11.9% 13.7% 36.0% 38.3%

Displaying 0.0% 0.0% 50.0% 50.0% 13.9% 27.8% 38.9% 19.4% Flying 13.8% 3.1% 78.5% 4.6% 9.4% 12.6% 47.5% 30.5%

Conflict 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% Table 11: Distribution of Behaviours According to Flight Height

3.2.6 Flight Lengths and Displacements The air-distance that a bird flies (the flight length) can be substantially different to the ground distance between the start and end point of the flight path (the flight displacement). It is possible that this may, over time, give us some insight into the way in which the birds are using the site, i.e. are more transit flights or circling flights indicated?

Both the absolute air distance (meaning the total length covered in flight) and the displacement (meaning the difference between start and end points of the flight have been calculated directly from the GIS data provided. The flight length (distance) was calculated



based on the number of GIS points for that flight path, with there being 20m spatial resolution between points. The flight displacement was determined based on the first and last GIS co-ordinates for each flight path.

Nov 08 Dec 08 Feb 09 Min 140 220 260Max 28920 13500 8400Median 3010 3020 2690S.E. 625 456 630Count 96 84 24

Table 12: Flight Length Summary

Nov 08 Dec 08 Feb 09 Min 32 69 39Max 3173 3203 2924Median 746 776 949S.E. 101 128 168Count 96 84 24

Table 13: Flight Displacement Summary

Due to the low counts, it is uncertain the degree that the flight behaviour has altered. There is some limited support for the behaviour to have become more “efficient,” with the air distance (Table 12) reducing, and the displacement increasing (Table 13) during the February observations.

This could be an effect of either the season with its related activity, or the contamination of the data by third party activities during the observation collection.

The important issue relating to risk is that during this period, there is some support for more direct, less soaring behaviours. For geographical areas of risk that might be employed for micro-siting of WTGs, this possible change in activity occurs in the same localised geography as previously determined.

4 Final Comments The results from the latest surveying are made with the obvious caution inherent to low activity count. The issue is not merely the low count (which would be robust enough due to the surveying activity and effort) but also the observers’ notes regarding unusual activity in and around the site.

Overall, there appears no evidence that accepted risk activity has changed substantially. The flight behaviour is lower, but remains above Rotor Swept height. The detected flights correspond to peaks of utilisation detected in the November/December surveys. This has implications for micro-siting of turbines, as there appears little support for suggesting that the areas of the proposed site used by eagles changes greatly with seasons or weather.

Of significance to the potential development of risk mitigation strategies is that, as the baseline of the survey increases, support for behavioural differences to be driven by the wind direction is dissipating.


Appendix C Eagle Flight Data Analysis and Interpretation: May 2009 Observations.

www.symbolix.com.au

1/14 Akuna Drive

Williamstown North

VIC 3016


Interpretation May 2009 Observations

Issue 1.0

30th of June, 2009

Report to


- i -


Simon Plowright – Wildspot Consulting


Version Control


0 MPR Data Tables and initial findings

0.9 SCM/EMS Complete – for Review

0.9a MPR Reassigned ObsLoc 3 Data, and updated

Issue 1 SCM Corrected for miskeyed observer locations


30/06/09

Signed Date

- ii -

Table of Contents 1 Introduction........................................................................................1

1.1 Observations – Round 3 ...............................................................1

1.2 Observation Protocols ..................................................................1

2 Data Overview ....................................................................................1

2.1 Data Summary ............................................................................2

2.2 Observer Summary ......................................................................3

3 Data Review .......................................................................................5

3.1 Utilisation Map.............................................................................5

3.2 Observed Flights Summary ...........................................................8

3.2.1 Observations by Wind Direction .................................................8

3.2.2 Observed Flights by Height Classification ..................................11

3.2.3 Observed Flights by Behaviour Classification .............................12

3.2.4 Flights per hour by month .......................................................12

3.2.5 Flight Lengths and Displacements ............................................13

4 Final Comments ................................................................................15

- iii -

List of Tables Table 1: Percentage of Recorded Behaviour Location on the 8 ha grid...........2

Table 2: Observation Sessions by Observer and Location .............................3

Table 3: Observation Minutes by Observer and Location ..............................3

Table 4: Observed Flights by Observer and Location....................................3

Table 5: Raw count of all the flights in each month by wind direction..........10

Table 6: Total minutes spent observing in each month by wind direction.....11

Table 7: Distribution of flight heights for each observing period..................11

Table 8: May 09 Observed Flights by Behavioural Classification ..................12

Table 9 : Flight Activity per Hour..............................................................12

Table 10: Distribution of Flight Heights by Wind Direction ..........................13

Table 11: Distribution of Behaviours According to Flight Height ..................13

Table 12: Flight Length Summary.............................................................13

Table 13: Flight Displacement Summary ...................................................14

Table of Figures Figure 1: May 2009 Observed Utilisation Map ..............................................6

Figure 2: Complete Observation Set Utilisation Map .....................................7

Figure 3: Observations by Wind Direction for May........................................9

Figure 4: Bird Flights per wind direction for all surveys aggregated .............10

Eagle Flight Data Analysis and Interpretation - May 2009 Observations

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Summary This report details the latest incarnation of Hydro Tasmania Consulting’s knowledge base of Eagle Behaviour at the Cattle Hill proposed Wind Farm site. The database is stabilising as one would expect, and it is generating significant support for a number of questions that might be asked about risk behaviour by wedge tailed eagles.

The inclusion of May’s observations adds support that December is an anomalous month for activity. Behaviours observed in May suggest an increase in purposeful, efficient flights that are at lower heights than previously observed on the site. Baseline activity appears to be stabilising at around 0.3 flights per hour. May observations were dominated by south westerly winds.

The addition of a new observing location (371) has resulted in the identification of a new cluster of activity in the east of the site. It cannot be concluded whether this is a seasonal change, or activity previously not observed due to obfuscation of the other observing locations. Discussions with the observation team have lead to the latter interpretation being employed in the analysis. Other than this pocket, the general site preference and utilisation remains constant over the survey periods.

1 Introduction

1.1 Observations – Round 3 Building upon the previous data collected, Nov/Dec 2008 and Feb 2009, was another survey conducted by Wildspot Consulting in May 2009. This additional data supplements the original dataset which will allow for stronger conclusions to be made based on analysis of the data.

The key objective of this report is to analyse the latest data, then compare it to the previous surveys and the dataset in its entirety.

1.2 Observation Protocols In order to maximise the usefulness of the data collected in this second round of observations, the same observation protocols as were used previously were adhered to. This has allowed meaningful comparisons to be made between the 2008 data and the 2009 data as they were collected, collated and recorded consistently.

2 Data Overview The data was recorded from 4/5/2009 to 8/9/2009 inclusive. There were 30 individual observing sessions giving a total of 255 observer-hours and 84 distinct, wedge tailed eagle (WTE) flights were observed.


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Each flight was recorded as a series of observation points, complete with environmental data as well as a GIS ground-track record. Four flights were missing GIS records, giving a total of 80 complete GIS tracks.

Prior to any analysis being performed on the newly collected data, it was subjected to the same validation as previously collected data1.

It has been noted that there was one new observer location (ID# 371) used in this round of surveys. Observer location 371 takes its location coordinates from the 8ha sectors, which is a different approach to that used for the previous observer locations that were all taken from the 30 ha grid.

Future analysts need to be aware of this when comparing observer sites, but there is otherwise no effect caused by this change.

2.1 Data Summary There were no absent values from the sheet header information fields, and all flight records were correctly recorded. There were a few relatively minor omissions from the “Flight Details” table however, with two separate flight points having no height recorded, and one instance of the 30ha sector being unrecorded.

The failure to record the 30ha sector is unlikely to impact upon any analyses as this information can be determined from the GIS data. The absence of the flight height data is likewise unlikely to impact on any analyses as these two points represent 0.58% of the total number of flight points.

Soaring Displaying Flying Conflict Proportion Recorded 0% 4.4% 0% 0% Table 1: Percentage of Recorded Behaviour Location on the 8 ha grid.

This is unlikely to represent a problem for future analysis as the 30ha sector location for each flight point is provided which, although providing a lower spatial resolution, does provide a location for each flight point and the associated behavioural classification.

Table 1 shows how, for the records with a given flight behaviour, the number of times that the location was recorded. This is consistent with previous observation runs. It tells us that only displaying behaviours have any location data for the behaviour, and of these, only 4.4% of flight positions are located upon the 8 ha sector grid.

1 See Eagle Flight Data – Analysis and Interpretation: Iss 2 13th Feb 2009 – report to Hydro Tasmania Consulting


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This means we cannot resolve where within a flight displaying behaviour occurs, but we can identify entire flights which had some displaying behaviour within them.

2.2 Observer Summary Table 2 and Table 3 show the amount of sessions and minutes spent observing by each observer employed. The identity of the observers has been suppressed.

Location Observer 53 113 164 200 249 371 TOTAL A 1 0 1 1 1 1 5 B 1 1 1 0 1 1 5 C 1 1 1 1 1 0 5 D 0 1 1 1 1 1 5 E 1 1 1 1 0 1 5 G 1 1 0 1 1 1 5 TOTAL 5 5 5 5 5 5

Table 2: Observation Sessions by Observer and Location

Location Observer 53 113 164 200 249 371 TOTAL A 510 0 510 510 510 510 2550 B 510 510 510 0 510 510 2550 C 510 510 510 510 510 0 2550 D 0 510 510 510 510 510 2550 E 510 510 510 510 0 510 2550 G 510 510 0 510 510 510 2550 TOTAL 2550 2550 2550 2550 2550 2550

Table 3: Observation Minutes by Observer and Location

Location Observer 53 113 164 200 249 371 TOTAL A 4 - 1 5 0 2 12 B 4 0 4 - 2 6 16 C 2 0 1 9 0 - 12 D - 0 0 6 4 8 18 E 1 3 1 4 - 3 12 G 0 6 - 3 3 2 14 TOTAL 11 9 7 27 9 21

Table 4: Observed Flights by Observer and Location


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Table 4 shows the distribution of observations per observer per site. The shaded squares indicate location/observer combinations for which no observations took place.


These tables are provided as a means of outlining the observational effort performed at each location, as well as the effort contributed by each individual. The shared load across each of the observers is equal, as outlined in the total column in Table 3. The observational effort spread across locations is also equal which follows the observing protocols used for the February sessions.

There is no reason to suspect any significant variation in observer performance, which is equally dispersed across sites. Again, site 200 shows itself to have a different baseline activity, and the new location 371 is returning higher activity than the majority of sites.


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3 Data Review

3.1 Utilisation Map To allow a direct comparison between the utilisation map presented here and the previously supplied maps,2 unless otherwise indicated the same contouring levels have been used. The data has also been normalised to account for any differences that may exist in that number of observing hours at each location.

The apparently lower utilisation in Figure 1 is an artefact of the relative number of flights recorded in May and the use of a constant contouring level.

When the May data is mapped in conjunction with the previously acquired data it produces the utilisation map shown in Figure 2.

2 Eagle Flight Data- Analysis & Interpretation : Issue 2 13th of February, 2009 – Hydro Tasmania Consulting internal report


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Figure 1: May 2009 Observed Utilisation Map

The previously observed flight activity around Observer Location ‘200’ is again present, however subsequent to the introduction of Observer Location ‘371’ there is a new area of activity which has been identified immediately south of the existing peak in activity. Future site surveys should ensure to use ‘317’ in order to collect more data on this region.


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Figure 2 shows the combined data set, which does not appear to deviate substantially from the previously suppled charts. We assume that the new area identified by Observers at Location ‘371’ is present all year round, and not a seasonal artefact. The May observations are then weighted according to this assumption.

Figure 2: Complete Observation Set Utilisation Map


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The most obvious change in feature in this chart is the new area of utilisation which has been recorded as a result of the observations from ‘371’. This region displays similar characteristics to the nest region around ‘200’. It may be a region that is frequented by the birds seasonally or it might be an area which is used year round, further observations of this area should be undertaken to determine the nature of the activity present.

To assert to relevance of this behaviour, one might ask whether the region/airspace can be seen from the previous locations of 200, 164 or 113. If it can be clearly surveyed, then Figure 2 as presented shows too strong an activity level for this local sector. We have presumed that the region was previously obscured in generating the weights for Figure 23.


3.2.1 Observations by Wind Direction

3.2.1.1 May Data One of the aims of this study is to assess the possibility of risk behaviours as a function of wind direction. We wish to understand both the connection between flight counts and wind direction and behaviour and prevailing wind direction.

Figure 3 shows the number of bird flights versus the observed wind directions for the May observing run. When compared to Figure 4, some noteworthy differences come to light.

The most obvious difference is that no wind directions in the eastern half were observed. There were only four wind directions observed, and only two of these have flights attributed to them.

3 Discussion with S. Plowright suggests that this is the appropriate interpretation. Pers. Comm.


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0%10%20%30%40%50%60%70%80%

N

NE

E

SE

S

SW

W

NW

Bird FlightsObserved Wind Directions

Figure 3: Observations by Wind Direction for May

This observation period was clearly dominated by winds from the South-Westerly quadrant, and as such this is also the wind conditions where the birds were observed to be flying.

3.2.1.2 Entire Data Set Figure 4 was constructed using data for wind direction observed and bird flight counts, as given in Table 5 and Table 6. Figure 4 shows that when we consider flights per direction over a longer baseline (the entire dataset), we begin to see less preference for a particular direction.

This long-baseline data appears to support the assertion made in Report Two that the direction of the wind does not appear to reduce/increase the activity of the birds significantly.


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0%

5%

10%

15%

20%

25%

30%N

NE

E

SE

S

SW

W

NW

Bird Flights


Figure 4: Bird Flights per wind direction for all surveys aggregated

Figure 4 suggests that there is no correlation or preference for flights in wind direction. Table 6 can be used to cross check the observation effort against the site’s wind rose pattern for each month to quantify how representative the survey effort was. The relative observation efforts for a given month ought to match the region’s wind pattern.

N NE E SE S SW W NW Nov 08 4 9 11 22 29 6 9 6 Dec 08 23 6 10 3 8 12 6 16 Feb 09 0 0 4 10 5 1 2 2 May 09 0 0 0 0 0 65 19 0 Total 27 15 25 35 42 84 36 24 Table 5: Raw count of all the flights in each month by wind direction


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N NE E SE S SW W NW Nov 08 960 1755 3360 3420 5010 1650 1470 660 Dec 08 2100 1650 0 900 450 900 0 2700 Feb 09 1650 0 1650 720 2730 1350 1200 900 May 09 0 0 0 0 1080 10080 2700 1440 Total 4710 3405 5010 5040 9270 13980 5370 5700Table 6: Total minutes spent observing in each month by wind direction

3.2.2 Observed Flights by Height Classification As in previous reports the observed instances of flights at above rotor height might be an important indicator of possible risk. Table 7 shows the distribution of flight heights. Note that, because the height was recorded at multiple points throughout each flight, the counts will sum to the number of flight waypoints rather than the number of flights.

Raw Flight Count % <125m Mixed >125m >300m <125m Mixed >125m >300m

Nov/Dec08 71 76 225 223 11.9% 12.8% 37.5% 37.5% Feb 09 24 12 74 32 16.9% 8.5% 52.1% 22.5% May 09 126 205 10 63 31.2% 50.7% 2.5% 15.6% Table 7: Distribution of flight heights for each observing period

Table 7 indicates that there was no change in risk behaviours from Nov/Dec 2008 to Feb 2009. Although the overall shape of the flight height distribution of these surveys is different4, both of them had around 75% of flight time spent above the Rotor Swept Height.

The May data suggests activity at a lower height, with 31.2% below this safe height, and 80% of flights below or crossing this threshold. A χ2 correlation test reveals that the May flight height data conforms to a significantly different distribution than either February or Nov/Dec5.

4 2 17.17 (3 degrees of freedom, p<0.001)

5 2 336.57 for Nov/Dec and May comparison and 2 226.27 for Feb and May comparison.


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3.2.3 Observed Flights by Behaviour Classification Behaviour Soaring Displaying Flying In ConflictMay - % of flights 68% 4% 28% 0%May - Standard Error 2.3% 0.9% 2.2% 0%Nov/Dec 08 73% 6% 47% 0%Feb 09 60% 1% 46% 0%

Table 8: May 09 Observed Flights by Behavioural Classification


Comparing Table 8 to the values reported previously, we see that the behaviour is again dominated by soaring and flying, with no evidence for a large change in proportion of behaviours.

Two points of note are that it appears that the amount of time flying was diminished in May, and that the flights showed a greater propensity to exhibit only a single behaviour type.

3.2.4 Flights per hour by month

Nov 2008

Dec 2008

Feb 2009

May 2009

Flights per Hour 0.31 0.58 0.14 0.33 Standard Error 0.02 0.04 0.04 0.02 Table 9 : Flight Activity per Hour

The trend suggested is that December is the outlier with regards to activity. February exhibits low activity but it should be noted that site disturbances were occurring, which may have impacted on behaviour.6

Table 10 and Table 11 show the distribution of height versus wind direction and behaviour respectively.

6 Symbolix 2009b. Eagle Flight Data Analysis and Interpretation – Feb 2009 Observations. Report to HydroTasmania Consulting.


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May 2009 Survey Combined Observations Height <125m mixed >125m >300m <125m mixed >125m >300mN 0.0% 0.0% 0.0% 0.0% 9.7% 19.4% 11.5% 25.8%NE 0.0% 0.0% 0.0% 0.0% 3.0% 8.2% 2.8% 6.6%E 0.0% 0.0% 0.0% 0.0% 1.3% 7.1% 7.1% 8.2%SE 0.0% 0.0% 0.0% 0.0% 3.3% 10.2% 20.0% 2.2%S 0.0% 0.0% 0.0% 0.0% 2.0% 12.2% 7.5% 8.5%SW 70.2% 90.0% 93.7% 44.4% 52.3% 17.3% 36.0% 17.9%W 29.8% 10.0% 6.3% 55.6% 21.3% 14.3% 5.4% 12.3%NW 0.0% 0.0% 0.0% 0.0% 7.0% 11.2% 9.6% 13.5%

Table 10: Distribution of Flight Heights by Wind Direction

Due to a dearth of alternate wind directions in the May period, it is difficult to draw any conclusions from the above table that are not more readily identified in previous tables. We present these figures for completeness only.

May 2009 Survey Combined Survey Height <125m mixed >125m >300m <125m mixed >125m >300mSoaring 34.4% 3.3% 39.9% 22.5% 19.7% 10.1% 37.4% 32.8%

Displaying 35.7% 7.1% 35.7% 21.4% 20.0% 22.0% 38.0% 20.0%Flying 86.2% 0.8% 13.1% 0.0% 30.6% 9.3% 38.0% 22.1%

Conflict 0% 0% 0% 0% 0% 0% 0% 0% Table 11: Distribution of Behaviours According to Flight Height

The clearest differentiator exhibited in Table 11 is the overall boost in activity at or below Rotor Swept Height during his period. The leading behaviour in this space is the increase in low level Flying, followed by Soaring.

3.2.5 Flight Lengths and Displacements Nov 08 Dec 08 Feb 09 May 09Min 140 220 260 200 Max 28920 1350 840 8560 Median 3010 3020 2690 1800 S.E.7 625 456 630 317 Count 96 84 24 80

Table 12: Flight Length Summary

There is some indication that the amount of time spent in the air was reduced in May, as both the median flight length and variance are decreased, but the evidence is not strong enough to make a statistically confident statement.

7 Given by 1.253 the standard error of the sampling mean, under the assumption of normality


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The ground displacement covered in flight, which has been identified as an indicator of WTG risk, is shown in Table 13 to be constant (within standard error) across the survey periods to date. Combined with the potential reduction in median air distance, this indicates more direct flight activity.

Nov 08 Dec 08 Feb 09 May 09Min 32 69 39 125 Max 3173 3203 2924 2694 Median 746 776 949 851 S.E. 101 128 168 96 Count 96 84 24 80

Table 13: Flight Displacement Summary


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4 Final Comments The Hydro Tasmania Consulting digital knowledge base of Eagle activity at the proposed Cattle Hill wind farm site is showing itself to be stabilising, and providing valuable insight into the potential risk behaviours on site.

In particular, there is no suggestion of a preference for movement in particular wind directions (Figure 4) and that behaviours are generally constant (Table 8). This is said with the note that for the May observations, overall Flying was diminished, yet low-level flying was significantly increased (Table 11).

The ground track distances of flights has remained constant across the survey periods (Table 13), although there is some evidence that the air-distance of flights may have decreased during this period (Table 12).

This is consistent with the notion suggested by the increase in low level flights, that the behaviours during May are more “directed” and purposeful flights that are more efficient overall. This type of behaviour may counter the increased risk due to the increased number of flights at a risk height through those same flights exhibiting more attentive flying behaviours.

The overall site preference behaviour is suggestive of a clear pattern, with the now usual fingerprint previously detected again appearing in Figure 1. This combines into a cumulative picture that remains very similar to that seen previously, bar the inclusion of a tight cluster of activity in the mid-east of the site (Figure 2).

This is observed from the new location of 371, yet may not be entirely an artefact of this location’s insertion into the observing sessions. Only if the region was not seen from any of the previous vantage points does it affect our understanding of the site. If it was able to be seen beforehand, then the pocket of activity likely has some seasonal driver associated with why the birds are seen there. If it was hidden from view before, by terrain or otherwise, then nothing can be said about the activity being a general one, or a local season effect. We have assumed the latter case in our analysis.


Appendix D Eagle Flight Data Analysis and Interpretation: August 2009.

www.symbolix.com.au

1/14 Akuna Drive

Williamstown North

VIC 3016


Interpretation August 2009

Issue 1

Version 1.0

6th of October, 2009

Report to


- i -




Version Control

Version Status Date Approved for release Issued to Copies Comments

0.1 Draft 26/8/09 Internal Internal e For comment

0.2 Draft 10/9/09 Internal Internal e Revised Draft

0.9 For review 23/9/09 E Stark Ray Brereton e For review

1.0 Issue 1 6/10/09 S. Muiir R. Brereton e

Approved for Release:

6/10/09

Signed Date

- ii -

Table of Contents 1 Introduction........................................................................................2

1.1 Observations – Round 4 ...............................................................2

1.2 Observation Protocols ..................................................................2

2 Data Overview ....................................................................................2

2.1 Data Summary ............................................................................2

2.2 Observer Summary ......................................................................3

3 Data Review .......................................................................................5

3.1 Utilisation Map.............................................................................5

3.2 Observed Flights Summary .........................................................12

3.2.1 Observations by Wind Direction ...............................................12

3.2.2 Observed wind and Bureau of Meteorology observations ...........13

3.2.3 Observed Flights by Height Classification ..................................15

3.2.4 Observed Flights by Behaviour Classification .............................17

3.2.5 Flights per Hour by Month.......................................................17

3.2.6 Flight Lengths and Displacements ............................................18

4 Seasonality .......................................................................................19

4.1 Flight heights ............................................................................19

4.1.1 Kilometres spent at each height...............................................19

4.1.2 Proportion of flights at each height ..........................................20

4.2 Efficiency of flights.....................................................................22

5 Final Comments ................................................................................24

6 References & Further Reading ............................................................25

- iii -

List of Figures Figure 1: November 2008 Utilisation Map (New Contours) ............................6

Figure 2: December 2008 Utilisation Map (New Contours) ............................7

Figure 3: February 2009 Utilisation Map (New Contours) ..............................8

Figure 4: May 2009 Utilisation Map (New Contours).....................................9

Figure 5: August 2009 Utilisation Map.......................................................10

Figure 6: All Flights Utilisation Map...........................................................11

Figure 7: Observation by Wind Direction for August 2009...........................12

Figure 8: Observation by Wind Direction for All Observation Periods............13

Figure 9: BOM Wind Rose Data for the nearest automated stations. The blue wind-rose corresponds to the twice daily record of wind direction for the period 1/9/2008 to 27/8/2009. The pink wind-rose corresponds to the data from the dates during which there were observers active at the proposed windfarm site. ........................................................................................14

Figure 10: Ground track distance flown at each height classification for each survey period (kilometres) .......................................................................19

Figure 11: Flight total displacement at each height classification for each survey period (kilometres) .......................................................................20

Figure 12: Proportion of ground track distance flown at each height category by survey period .....................................................................................21

Figure 13: Proportion of total displacement at each height category by survey period....................................................................................................21

Figure 14: Flight length, displacement and efficiency for each survey period. Flight length and displacement is reported in meters, while the relative flight inefficiency is given as the ratio of the two, with 1 being a perfect direct flight. A score of two indicates that the bird covered twice as many meters as it needed to get from point A to point B....................................................22

Figure 15: Average (mean) flight efficiencies for each month, plotted against the median values. The error bars on the averages indicate a measure of the variation within the underlying flight data. ................................................23

- iv -

List of Tables Table 1: Observation Sessions by Observer and Location .............................3

Table 2: Observed Minutes by Observer and Location ..................................4

Table 3: Observed Flights by Observer by Location......................................4

Table 4: Raw count of flights observed by wind direction by month ............15

Table 5: Total minutes spent observing in each wind direction by month.....15

Table 6: Distribution of flight height by month ..........................................16

Table 7: Proportion of Flight Points by Wind direction by Flight Height ........16

Table 8: Behavioural Classification by Flight Height....................................17

Table 9: Observed Flights by behavioural classification...............................17

Table 10: Flight activity per hour..............................................................17

Table 11: Flight Length (total ground track) Summary (in metres) ..............18

Table 12: Flight Displacement (difference between flight end points) Summary.............................................................................................................18

Table 13: Median Flight length to displacement ratio (the higher the number the more inefficient the flight is) ..............................................................18

Eagle Flight Data Analysis and Interpretation - August 2009

For Release Page 1 Hyd0809CatA, Version 1.0

Summary This report summarises the additional observation reports from the August survey of Eagle activity at the proposed Cattle Hill Wind Farm development.

It includes seasonal comparisons, as the baseline now extends for one year. The featured utilisation measurements have been altered from previous reports, with the contouring levels adjusted to better represent the variations detected. This is simply a matter of the longer baseline of the study.

Key findings regarding the August Survey alone are:

There is a discrepancy between flights and dominant wind conditions, possibly due to low cloud and poor visibility effects. However, when viewed in context of the full set of observations, there remains no clear preference.

The flight height behaviour is evenly spread across the classifiers, and sits between the November/December pattern and the May pattern.

As with previous studies, the overall site utilisation is suggestive of a clear pattern, with the now usual fingerprint previously detected again appearing. This combines into a cumulative picture that remains very similar to that seen previously, bar the inclusion of a tight cluster of activity in the mid-east of the site.

Analysis of seasonal indicators suggest:

The increase of flight directness and purpose detected in the May observations has continued into August, although this behaviour appears to plateau, possibly indicative of a seasonal variation

The November/ December records appear different to the other survey periods. It is not yet established that this represents true seasonality

The hunting effect that arose during February can now be resolved as resulting in a small number of highly complex flights, and not as an increase in the overall complex flying behaviours.

There is a possible seasonal trend in flight heights, with May data showing the highest number of flights at less than 125m high.



1 Introduction

1.1 Observations – Round 4 This latest series of observations at the proposed Cattle Hill Windfarm (CHWF) site were conducted by Wildspot Consulting in August 2009. They supply additional data which was used to further strengthen the analytical tests already performed on wedge-tailed eagle (WTE) observation data collected in November/December 2008, February and May 2009.

1.2 Observation Protocols The observing teams have utilised the same observing protocols and recording protocols as the previous rounds of data collection for CHWF, making it possible to directly compare the latest round of observations to the pre-existing dataset. This allows for investigations into seasonal trends, and environmentally dependent utilisation.

2 Data Overview Observations were carried out on all days from 10/08/2009 to 15/08/2009 inclusive.

However on 11/08/2009, due to the cloud-base being at zero metres above ground level, visibility was reduced to less than 50m and observations were terminated at 10am. Under these conditions two birds were encountered. As these were not during an active observation session these sightings were not formally recorded (in accordance with protocol).

There were 36 unique observing sessions, totalling 15,300 minutes of observation. During this time 74 WTE flights were recorded. Each flight was recorded as a series of observation points, complete with environmental data as well as a GIS-compatible ground-track.

Prior to any analysis being performed on the newly collected data, it was subjected to the same validation as previously collected data (Symbolix, 2009b).

Once again, observer location #371 on the east of the site has been used. This location was introduced in the May 09 round of observations and will be included in future observations as the boundaries of the proposed windfarm development have been expanded to include this area.

2.1 Data Summary The required sheet header information and flight record information was recorded completely. The only omission located was the failure to record 5 individual flight point “height” classifications. This represents less than a 1.5% error in records for this single attribute and is not expected to pose any problem in the analysis of this data.

As with previous observation runs the 30ha sector location of the behavioural classifications has only been partially recorded. However, through the



redundancy of the accompanying GIS ground-track data, this does not pose a problem in the analysis of the data.

2.2 Observer Summary Table 1 and Table 2 show the amount of sessions and minutes spent observing by each observer employed. The identity of the observers has been suppressed.

Location Observer 53 113 164 200 249 371 Total

A 2* 1 1 1 0 1 6 B 1 1 2* 0 1 1 6 C 0 2* 1 1 1 1 6 D 1 1 0 1 1 2* 6 E 1 1 1 1 2* 0 6 H 1 0 1 2* 1 1 6

Total 6 6 6 6 6 6 36 Table 1: Observation Sessions by Observer and Location

N.B: * these double sessions occurred after one of the days observations were terminated after only 2 hours due to 50m visibility. The observers then returned and completed a full day of observations from this location the following day. This will not affect the final analysis.




A 630 390 510 510 0 510 2550 B 510 510 630 0 390 510 2550 C 0 630 390 510 510 510 2550 D 390 510 0 510 510 630 2550 E 510 510 510 390 630 0 2550 H 510 0 510 630 510 390 2550

Total 2550 2550 2550 2550 2550 2550 15300 Table 2: Observed Minutes by Observer and Location

One can be confident that the balance of observations, observers and locations has been adequately incorporated. The Latin Square design indicated in Table 2 controls for variation in both differences in location and observer ability.


A 0 0 2 0 - 3 5 B 9 9 0 - 10 0 28 C - 0 4 4 0 5 13 D 0 0 - 13 2 0 15 E 0 3 0 3 0 - 6 H 4 - 1 2 0 0 7

Total 13 12 7 22 12 8 74 Table 3: Observed Flights by Observer by Location

Table 3 shows the distribution of the number of flights seen by each observer at each location. Shaded cells indicate Observer/Location combinations which were not utilised. This allows the distinction to be made between these null records and a record of zero flights observed (indicated by a zero).


These tables are provided as a means of outlining the observational effort performed at each location, as well as the effort contributed by each individual. The shared load across each of the observers is equal, as outlined in the total column in Table 2. The observational effort spread across locations is also equal which follows the observing protocols used for the February sessions.

There is no reason to suspect any significant variation in observer performance, which is equally dispersed across sites. Again, site 200 shows itself to have a different baseline.



3 Data Review

3.1 Utilisation Map As the total number of observed flights contained in the dataset has grown over time the original utilisation contours no longer provide a usable scale. This round of observations has been mapped using a new set of utilisation contours which have been derived from the total database.

This means that the images which appear in this report can no longer be directly compared to the images in previous reports. It is for this reason that the past utilisation maps have been re-constructed to make use of the new contouring intervals, thus enabling a comparison to be made between them.

The new utilisation contours were developed using all the available GIS flight paths weighted according to the amount of observational time attributed to each observing location. This amounts to some 358 flights.

As each of the observational periods contains significantly less than this number of flights, the contouring interval used for the “Global” map has been halved for the individual maps. This ensures a meaningful level of information is conveyed by these charts, and their contours are directly comparable to each other, yet are only half of the “global” contours.



Figure 1: November 2008 Utilisation Map (New Contours)



Figure 2: December 2008 Utilisation Map (New Contours)



Figure 3: February 2009 Utilisation Map (New Contours)



Figure 4: May 2009 Utilisation Map (New Contours)



Figure 5: August 2009 Utilisation Map



Figure 6: All Flights Utilisation Map



The addition of the newly observed flights, to the previously published utilisation map (Figure 5) does not alter the structure of the map significantly (see Symbolix, 2009a for comparison).

There are a few small new features which have arisen to the south west of observer location 371. The same internal characteristics are present in the south west quadrant of the map. There is a small area of elevated utilisation due west of observer location 371 (as can be seen in both Figure 4 and Figure 5).


3.2.1 Observations by Wind Direction

3.2.1.1 August 2009 Data

0.00%

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50.00%N

NE

E

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Bird FlightsObserved Wind Directions

Figure 7: Observation by Wind Direction for August 2009

Unlike the previous observation periods there is a large apparent over-representation of observed flights in westerly winds. It is unclear as to the exact reason behind this.

As previously mentioned, there was a period of observation conducted in extremely poor visibility. The available records show that these periods of very poor visibility, often less than 50m (reported as being the second day) were comprised entirely of winds from the East. Thus there were 720 minutes of observational effort in an Easterly wind where visibility made any flight observations impossible. It is for this reason that observations were



terminated prematurely on the second day. This may account for some of the apparent discrepancy.

There are a small number of flights which were recorded in wind directions which are not listed under ‘Observed Wind Directions’. This is due to the nature of the data collection, i.e. observed wind directions are recorded every three hours, whilst the wind direction for each bird flight is recorded at the time of flight. If the wind changes direction between observational readings, it is possible for a bird to be observed during an unrecorded wind direction. This does not affect any analysis of the data.

3.2.1.2 Combined Data

0.00%

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30.00%N

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Bird Flights


Figure 8: Observation by Wind Direction for All Observation Periods

The effect of adding the data from the latest observation period is quite evident when compared to the previously published chart (Symbolix, 2009a).

The apparent over representation of flights in a westerly wind and under-representation of the flights in an easterly wind is due solely to the observation records from this latest observation period. It does not amount to a significant deviation between the two curves.

3.2.2 Observed wind and Bureau of Meteorology observations One way we can understand the validity of the observational periods as a true representation of on site conditions, is to compare the full set of observed wind data to the data recorded by the Bureau of Meteorology. This comparison is shown in Figure 9. The three nearest recording stations are selected and presented.



For each of the three automated weather stations the blue wind-rose corresponds to the twice daily record of wind direction for the period 1/9/2008 to 27/8/2009. The pink wind-rose corresponds to the data from the dates during which there were observers active at the proposed windfarm site.

Figure 9: BOM Wind Rose Data for the nearest automated stations. The blue wind-rose corresponds to the twice daily record of wind direction for the period 1/9/2008 to 27/8/2009. The pink wind-rose corresponds to the data from the dates during which there were observers active at the proposed windfarm site.

The data from the Tarraleah (approximately 25km SW of the windfarm site) station appears to contain some rather unexpected patterns; this data cannot be reconciled with any other data source and should be treated with caution.

The data from Butlers Gorge (approximately 40km WSW from the windfarm site) and Ouse (approximately 40km S from the windfarm site) appear to be



similar in nature over the entire year. The geographical profile unique to each of these sites causes the differences between each of the three, and also explains the discrepancy between the wind rose from the observer data (Figure 8) and the BOM sites (Figure 9).

There is no reason to suspect, from comparison of these figures, that the observation periods lack representation. If needed, it could be confirmed with an on-site meteorological mast.

Table 4 and Table 5 are included to allow future checking of the representativeness of the observations only. The wind directions associated with the full set of flights reflect the wind-rose for the site more than any preferential behaviour or risk effects.

N NE E SE S SW W NW Nov08 4 9 11 22 29 6 9 6 Dec08 23 6 10 3 8 12 6 16 Feb09 0 0 4 10 5 1 2 2 May09 0 0 0 0 0 65 19 0 Aug09 2 0 2 1 0 16 36 17 Total 29 15 31 36 42 100 72 41

Table 4: Raw count of flights observed by wind direction by month

N NE E SE S SW W NW Nov08 960 1755 3360 3420 5010 1650 1470 660 Dec08 2100 1650 0 900 450 900 0 2700 Feb09 1650 0 1650 720 2730 1350 1200 900 May09 0 0 0 0 1080 10080 2700 1440 Aug09 0 0 4860 3420 0 1620 3060 2340 Total 4710 3405 9870 8460 9270 15600 8430 8040

Table 5: Total minutes spent observing in each wind direction by month

3.2.3 Observed Flights by Height Classification As discussed in previous reports, the observed instances of flights above rotor height might be an important indicator of possible risk. Table 6 shows the distribution of flight heights for each recording period.

Note that, because the height was recorded at multiple points throughout each flight, the counts will sum to the number of flight waypoints rather than the number of flights.



Raw Flight Count Percentage of Flights per Month

<125m Mixed >125m >300m <125m Mixed >125m >300mNov/Dec08 71 76 225 223 11.9% 12.8% 37.5% 37.5%

Feb09 24 12 74 32 16.9% 8.5% 52.1% 22.5%May09 205 10 126 63 50.7% 2.5% 31.2% 15.6%Aug09 125 16 106 105 35.5% 4.5% 30.1% 29.8%

Table 6: Distribution of flight height by month

There appears to be a change in the distribution of the four height classifications over the study period.

The August observations show an even spread across the range of flight heights. Previously, one or two classifications have dominated (though not always the same ones).

The percentage of activity performed in the upper two bands does not appear to have changed greatly across the observing periods. Rather there is a significant reduction in the recording of flights that cross the risk band (the “Mixed” flights) after the initial observation.

The winter observations (May and August) also show a slight increase in lower level activity compared to summer. It is conceivable that the August observations indicate a gradual return to the November/December activity level, though this cannot be verified statistically at present.

3.2.3.1 Flight heights and wind direction

August 2009 Survey Combined Surveys <125m Mixed >125m >300m <125m Mixed >125m >300m

N 0% 0% 0% 100% 16% 10% 27% 46% NE --- --- --- --- 18% 16% 24% 42% E 67% 0% 33% 0% 9% 10% 44% 37%

SE 100% 0% 0% 0% 10% 9% 75% 6% S --- --- --- --- 8% 16% 42% 35%

SW 21% 12% 28% 39% 38% 6% 38% 19% W 43% 4% 37% 17% 42% 6% 27% 25%

NW 31% 0% 17% 52% 23% 6% 28% 43% Table 7: Proportion of Flight Points by Wind direction by Flight Height

Note: Each row in Table 7 sums to 100%, for each of the time periods. The proportions are calculated based on only the flights for each wind direction for each time period e.g. of all the flight way points recorded in August in a westerly wind, 37% were above 125m. Interpretation of this table should be performed in conjunction with the raw flight records exhibited in Table 4.

Using a χ2 test on the raw numbers behind the “combined surveys” section of Table 7 we are able to show1 that there is some association between wind

1 with a Yates corrected χ 2 value of 346



direction and flight height displayed within the table. However a Cramer-V score of only 0.277 indicates that while the association may be present, it is not particularly strong.

This means there are no immediately available risk mitigation strategies available that rely upon relations between flight height and wind directions.

3.2.3.2 Flight heights and behavioural classification

August 2009 Survey Combined Surveys

<125m Mixed >125m >300m <125m Mixed >125m >300m

Soaring 19% 6% 35% 40% 20% 9% 37% 35% Displaying 20% 0% 20% 60% 20% 20% 36% 24%

Flying 76% 1% 19% 4% 39% 8% 35% 19% Table 8: Behavioural Classification by Flight Height

Note: Each row (for each survey) adds to 100%, the proportions are calculated based on only the flights for each specific behavioural class for each survey length. That is, for all of the flights in August 2009 which were Soaring, 40% of them were above 300m.

As with other studies, it would appear that there is no real correlation between flight type and preference for height.

3.2.4 Observed Flights by Behaviour Classification Soaring Displaying Flying In Conflict

Aug09 - % of flights 74% 1% 29% 0% Aug09 – Standard Error 0.022 0.005 0.024 0

Nov/Dec08 73% 6% 47% 0% Feb09 60% 1% 46% 0% May09 68% 4% 28% 0%

Table 9: Observed Flights by behavioural classification


3.2.5 Flights per Hour by Month Nov08 Dec08 Feb09 May09 Aug09

Flights Per Hour 0.31 0.58 0.14 0.33 0.29 Standard Error2 0.03 0.04 0.03 0.03 0.03

Table 10: Flight activity per hour

The modal value for the surveys is around 0.3 flights per hour, with December exhibiting a higher value, and February being lower. February has

2 The Standard errors have been revised to previous reports, incorporating a more accurate Taylor Series approximation of errors in all contributing components, rather than the previously employed relative approximation



been explained by hunting activity on the site, leaving December as an activity driven increase in risk.

3.2.6 Flight Lengths and Displacements Nov08 Dec08 Feb09 May09 Aug09

Min 140 220 260 200 80 Max 28920 13500 8400 8560 18020

Median 3010 3020 2690 1800 1910 S.E. 625 456 630 317 432

Count 96 84 24 80 74 Table 11: Flight Length (total ground track) Summary (in metres)

Nov08 Dec08 Feb09 May09 Aug09 Min 32 69 39 125 19 Max 3173 3203 2924 2694 2379

Median 746 776 949 851 422 S.E. 101 128 168 96 93

Count 96 84 24 80 74 Table 12: Flight Displacement (difference between flight end points) Summary

The August data suggests that, for the first time, the actual displacement (ground coverage) of the flight behaviour may have decreased. The flight distance is also less than in previous studies, barring the May collection.

Nov08 Dec08 Feb09 May09 Aug09 4.00 3.01 2.64 2.18 2.15

Table 13: Median Flight length to displacement ratio (the higher the number the more inefficient the flight is)

The values presented in Table 13 are indicators of how inefficient the paths of the birds are during that month. For example in the latest data collected, the median air track of the birds was just over two times more than the most direct route from their departure to destination.

This is the lowest recorded value, indicating simpler flight behaviours during this period. The latest survey shows much shorter flight displacements than previously seen. We can compare this to November, where the relative amount of air distance is considerably higher, suggesting more complex, locally confined flights during this period of observation than current.



4 Seasonality As the data set grows in size, and the findings cover a longer and longer temporal base line, it is possible to start looking for seasonal patterns in the data. This is the first time that we have presented this type of material from the studies.

4.1 Flight heights

4.1.1 Kilometres spent at each height The GIS information provides a spatial resolution of 20m which allows an accurate determination of total flight length as well as the total displacement. This GIS data does not have any height information associated directly with it.

However using the flight details provided it is possible to determine the proportion of each flight spent in each of the different height classes. The measurements reported below are based on this calculated proportion of either the total flight length or the total displacement.

Figure 10 and Figure 11 show the total number of meters flown (either as distance or displacement) at each of the four different height classifications for each of the observational periods. The ‘Nov09’ data is, at this stage, taken as a direct copy of the November 2008 data, as an aid in the visualisation of seasonal trends.

Height Classifications by Month (base Length)

0

50

100

150

200

250

Nov-08 Dec-08 Feb-09 May-09 Aug-09 Nov09(sim)

Kilo

met

res <125

mixed>125>300

Figure 10: Ground track distance flown at each height classification for each survey period (kilometres)



Height Classifications by Month (base Displacement)

0

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60

Nov-08 Dec-08 Feb-09 May-09 Aug-09 Nov09 (sim)

Kilo

met

res <125

mixed>125>300

Figure 11: Flight total displacement at each height classification for each survey period (kilometres)

From a purely data analytical point of view, very little can be said about either Figure 10 or Figure 11. They consist of a presentation of only one year’s worth of data, with a projected value for the next observing period superimposed to aid the eye.

They are included for completeness, that readers skilled in the unique biology and behaviour of these creatures might feel confident in that they can identify trends and behavioural patterns that reflect current understanding of seasonal variation.

4.1.2 Proportion of flights at each height Although Figure 10 and Figure 11 give an indication of trend, they are skewed by the change in total flights recorded at each session. To remove this effect, we consider the proportion of flights recorded at each height classification, and display the results in Figure 12 and Figure 13.

These show, for each month, the proportion of distance flown for each of the height categories. For example, in Figure 12 for the May 2009 survey period 50% of the distance flown was at a height of less than 125m.



Height Classifications by Month (base Length)

0%

10%

20%

30%

40%

50%

60%


<125mixed>125>300

Figure 12: Proportion of ground track distance flown at each height category by survey period

Flights recorded as less than 125m or mixed may have a higher risk profile than other heights.

Figure 12 suggests the amount of time spent at less than 125 metres in height is centred upon the May period.

Mixed height flights show a strong propensity to occur around November, diametrically opposite (6 months opposed) to the peak in “less than 125 metre” flights. However, some concern should be raised about the use of the “Mixed” classifier here, as the spike also corresponds to the earliest observations, when observers are least familiar with the landscape. However, there is also possible that the spike is linked to a true risk behaviour that dominates the November month. This cannot be verified at this stage.

Height Classifications by Month (base Displacement)

0%

10%

20%

30%

40%

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<125mixed>125>300

Figure 13: Proportion of total displacement at each height category by survey period



4.2 Efficiency of flights Here we define the inefficiency of a flight as the ratio between flight length and flight displacement (e.g. a straight flight path from one on ground point to another is classed as more efficient that a meandering path).

Table 11 through to Table 13 suggest a strong pattern of efficiencies in flight paths, which can be seen in Figure 14. There is a marked drop in the overall flight length and displacement over the study period (from November 2008 to August 2009), with flights becoming more efficient over this time

0

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Median Flight Length

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Relative Flight Inefficiency

Figure 14: Flight length, displacement and efficiency for each survey period. Flight length and displacement is reported in meters, while the relative flight inefficiency is given as the ratio of the two, with 1 being a perfect direct flight. A score of two indicates that the bird covered twice as many meters as it needed to get from point A to point B.

The yellow trend line in Figure 14 shows the overall, median inefficiency score. The pink line relates to the total air track (measured from the GIS ground track representation), which we can see has been decreasing up until the latest survey.

The Blue line shows a relatively flat displacement curve, that represents the usage of the site through the distance between the two end points of the flight. The dip in the displacement curve during August counters the increase in the ground track (pink curve).

The combination of the trends in the distance and displacement measures produces a flattening of the overall inefficiency measure. It is possible (though not yet verifiable) that this represents a turning point in the seasonal behaviour.

If this is the case then we would expect to see a return to less efficient, more complex flight patterns throughout summer.



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Mean relative inefficiencyMedian relative inefficiency

Figure 15: Average (mean) flight efficiencies for each month, plotted against the median values. The error bars on the averages indicate a measure of the variation within the underlying flight data.

There is a lot of behavioural information shown in Figure 15. Firstly, the inefficiency metric diminishes towards the latest survey period, suggesting that the flights become more clustered, with less variation. This extends the results shown in Figure 14.

The second feature is the spike in the mean for February, relative to the median. The median curve indicates that half the flights have efficiency metrics greater than 2.4 (and half less). The February average diverges from this general trend, indicating that the high average (in)efficiency of flights is caused by relatively few flights that are highly complex in their pattern, as opposed to a general increase in complexity of all flights. We also note that the February data exhibits a large error (due to low counts). It is likely that this feature can be attributed to the nearby hunting activities that caused interference with the February round of observations.



5 Final Comments In concluding this study, we draw attention to the stabilising behaviour of all the major reported metrics. We begin to resolve a “general” behaviour with identified anomalies which aid us in describing the site overall.

This general behaviour shows a clear tendency to quash many concepts of simple relationships between risk drivers and environmental conditions.

In particular

There remains no clear discriminator in preference for movement in particular wind directions.

The increase of flight directness and purpose detected in the May observations has continued into August, although this behaviour appears to plateau, possibly indicative of a seasonal variation.

As with previous studies, the overall site utilisation is suggestive of a clear pattern, with the now usual fingerprint previously detected again appearing. This combines into a cumulative picture that remains very similar to that seen previously, bar the inclusion of a tight cluster of activity in the mid-east of the site.

The only concern remaining is the apparent discrepancy with flight heights of the very first observing session with all subsequent sessions. The Mixed classifier, and the 300 metre plus classifiers appear to be at odds with subsequent surveys.

This survey period (November/ December) also produced the least efficient flights, and the greatest number of multiple flight behaviours per flight. These two circumstantial metrics could support the genuine difference of avian behaviour during this period, but remain inconclusive. This is due to the simple fact that this pattern may also appear if observers were lacking confidence, as might be readily and justifiably expected on their first tour of the site. Further study periods would be required to allude to which of these two options is the more likely.



6 References & Further Reading

Symbolix. 2009a. Eagle flight data analysis and interpretation - May 2009 Observations. Report to Hydro Tasmania Consulting. Jun 30.

———. 2009b. Eagle flight data analysis and interpretation - Proposed Cattle Hill Windfarm. Report to Hydro Tasmania Consulting. Feb 1.


Appendix E Cattle Hill Wind Farm: Robustness of Data Techniques

www.symbolix.com.au

1/14 Akuna Drive

Williamstown North

VIC 3016

Cattle Hill Wind Farm : Robustness of Data

Techniques

Issue

Version 1.0

30th of April, 2010

Submitted to

Tasmania Hydro Consulting

Cattle Hill Wind Farm : Robustness of Data Techniques

For Release - i - Hyd1109CatA, Version 1.0



Ian Smales – Biosis Research


Version Control Doc ID: Hyd1109CatA

Main Author: Stuart Muir

Path:

C:\SVNStuff\Clients\HTC_HydroTasmaniaConsulting_Tas\Cattle Hill\Reports\HTC_CHWFR_REP_Robustness_20100430.doc


0.1 Draft 16/11/09 internal Internal 1e Initial draft

0.9 Draft 10/12/09 E. Stark Ray Brereton e For Review

0.95 Draft 16/12/09 S. Muir R. Brereton e Inserted Golden Table

1.0 Issue 30/4/10 S. Muir R. Brereton e Extended Golden Table to 25 years


Limitation: This report has been prepared in accordance with the scope of services described in the contract or agreement between Symbolix Pty Ltd and the Client. Any findings, conclusions or recommendations only apply to the aforementioned circumstances and no greater reliance should be assumed or drawn by the Client. Furthermore, the report has been prepared solely for use by the Client and Symbolix Pty Ltd.

Copyright: The concepts and information contained in this document are the property of Symbolix Pty Ltd. Use or copying of this document in whole or in part without the written permission of Symbolix Pty Ltd constitutes an infringement of copyright.

The Symbolix logo is a registered trade mark of Symbolix Pty Ltd.

30/04/2010

Signed Date


For Release - ii - Hyd1109CatA, Version 1.0

Table of Contents 1 The Utilisation Charts......................................................................................2

1.1 Assessing robustness through the coefficient of variation..........................2 1.2 Results ..................................................................................................2 1.3 Validity of the utilisation maps ................................................................4

2 Collision Risk Modelling ...................................................................................5 2.1 Validating the consistency between utilisation counts and pseudo-point counts (test 1)...................................................................................................5

2.1.1 Basic outline of validation technique ....................................................5 2.1.2 Logical expectations ...........................................................................6 2.1.3 Pseudo-Observer Locations .................................................................7 2.1.4 A simplistic cue density .......................................................................8 2.1.5 An observed cue density .....................................................................8 2.1.6 A pseudo point count cue density ........................................................9 2.1.7 Comparison of metrics ........................................................................9

2.2 Verifying the consistency between the observed utilisation data and third party point counts (test 2)................................................................................10

2.2.1 Comparing Observers .......................................................................10 2.3 Implications for the CRM ......................................................................11

2.3.1 Seasonal Variation: ...........................................................................11 2.3.2 Discretisation ...................................................................................11

3 Summary .....................................................................................................14 4 References & Further Reading .......................................................................15

List of Figures

Figure 1 : Observer Locations (Red Circles) and Pseudo points (Blue) ......................7 Figure 2: Showing the population distribution of radial distance from observer to point of detection .................................................................................................8 Figure 3 : Detectability rate (half-normal with second order cosine correction term used on the original data set. ................................................................................9


For Release - 1 - Hyd1109CatA, Version 1.0

Introduction To generate inputs for collision risk modelling, the standard approach is to undertake a physical point count survey of the site. In the case of Cattle Hill Wind Farm we have bird utilisation studies (but not point counts) covering an entire year.

To incorporate this data as an input to the CRM (thus ensuring collision risk is assessed using the most comprehensive data) we must extract point count information from the utilisation data.

To do this, a pseudo-point count is constructed. This metric obeys all the required protocols for inclusion in the CRM, whilst using the full set of observations.

The data techniques applied here are not new, but represent a unique and innovative application to this industry. For that reason, a full validation of the methodology and resulting metrics was carried out.

The purpose of this report is to outline the technical validation and to present, in one place, an explanation of the “robustness” of the general metrics. It will explain how they have been compared to each other, and so provide evidential support and confidence in the studies that rely upon their findings.

Fundamentally we aim to quantify the validity of using pseudo point counts, generated from GIS utilisation observations, as a proxy for on ground point count data.

There are two “new,” techniques employed at this site: the use of GIS observations and flight tracks, and the utilisation metrics based upon them.

These are tackled independently.

Firstly we present a measure of the common “Coefficient of Variance” measure to indicate the robustness of the utilisation metric and its implications for on-site preference.

We then consider the validity of creating pseudo-point counts from the ground track data collected in the utilisation study. This is done through two separate evaluations:

1. The first is to validate consistency between the pseudo-point count data set and the underlying observed utilisation data set.

2. The second uses independent, third party point count observations to verify the consistency between the observed utilisation data and third party point counts.



1 The Utilisation Charts The utilisation maps as presented in Symbolix 2009a, b, c, d project the recorded flights into an interpretable visualisation, and highlight relative levels of utilisation.

This is done through a kernel integration which ensures that it does not introduce spurious extra activity. It functions by diffusing what is essentially a one dimensional flight record (i.e. a line) into a two dimensional probability distribution.

Naturally, then, we raise the question about how stable such a generation is to the underlying data. We seek to understand how the underlying map representation is sensitive to slight changes in recorded flights. That is, should an individual flight or flights have been undetected, or mis-recorded, we are interested in how the underlying map representation may alter.

1.1 Assessing robustness through the coefficient of variation

The natural, and commonly employed and understood metric for this is the “coefficient of variation”. This is simply the standard deviation ( ) of the utilisation levels, divided by the expected utilisation level, ( )E x . That is, at a point, x, on the map, we have

. .( )( )XC V x

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If the maps are a robust representation of the relative utilisation levels, we would expect a low constant of variation.

For the expectation values, we have the measured utilisation levels (as presented in Symbolix 2009a, b, c, d).

As we only have one data set available (albeit consisting of a multitude of individual flights) we have no direct measure of the variance of the set at any one point. We therefore employ a “bootstrap” technique to calculate it (see, for example, Sheskin, D, 2004).

In this instance, if we have 800 flights in the original data set, we generate a new set of 800 flights, selected randomly with replacement from the original set. This allows us to generate a second utilisation map.

Repeating this process generates a measure of the standard deviation inherent within the individual maps due to the individual component flight records.

1.2 Results Below is a figure showing the C.V. (Coefficient of Variation) of the utilisation maps.

The centre of the site, including the regions of typically higher activity, exhibit CVs of less than 5%. Typically, they are much less than this value.

Larger uncertainty is evident at the edges of the site, where the C.V. reaches a maximum of 12 %. This is to be expected, considering there is less observer overlap in these areas.

The lower reaches of the utilisation charts have CV’s of 15% on typical values of 0.1. The upper contour was set at an utilisation parameter of 2.5, with CV of <5%.



1.3 Validity of the utilisation maps The key implication of this exercise is that the noise within the system is less then the contouring levels. In other words, the general shape of the utilisation patterns is unchanged by minor changes in flight observations.

The outcome from this is that the utilisation maps are relatively impervious to errors from the odd missed observation, or drop in attention from the observers. We can consider them as safe and representative maps of the utilisation variation across the site.



2 Collision Risk Modelling To accomplish collision risk modelling with meaningful and robust results, the input data must adhere to a number of specified protocols and standards. One of these conditions is that it be representative of the actual site usage.

To obtain a representative sample requires a “long enough” baseline. In the field of ecology, “long enough” is hard to quantify. When we add complications such as climatic or seasonal variation, this issue is exacerbated.

As such, we wish to use our existing data sets to support inputs into the CRM. As the data collection protocols of the existing data set do not necessarily comply with those established for this particular CRM (Ian Smales, Biosis Research, pers comm.), we will need to generate a pseudo dataset that is compliant.

This is an extra processing layer that decision makers will wish to understand and feel confident with.

The pseudo-point count data set is generated from the original utilisation database by recording all flights that a superimposed observer might see given the constraints and specifications of a supplied protocol.

As such we expect the pseudo dataset to exhibit a couple of key properties (which we can test):

It does not suffer the effects of detectability that affect all genuine observations.

It is limited to only be able to recognise, and so be heavily aligned to, the original records.

We have proposed two tests to establish that the approach is robust and stable:

1. The first is to validate consistency between the pseudo-point count data set and the underlying observed utilisation data set.

2. The second uses independent, third party point count observations to verify the consistency between the observed utilisation data and third party point counts.

2.1 Validating the consistency between utilisation counts and pseudo-point counts (test 1)

2.1.1 Basic outline of validation technique To validate the use of pseudo point counts (points 1 and 2 above) we need to construct a comparison metric. For this purpose we will use the expected rate of bird cues1 per hectare per minute (the cue density).

This analysis draws heavily on common distance sampling techniques (Buckland, 2001, Buckland 2004) to estimate and compare this cue rate.

Distance sampling is built on the simple premise that an observer’s ability to detect wildlife will diminish with distance. The detectability function (see Buckland, 2001

1 The term ‘Cue’ is used here as in Buckland, 2001 and other ecological texts. It refers to a recording of a single sighting (or other cue) of an animal, rather than the number of animals.



and Figure 3) relates detectability to distance and can be used to construct a correction to the observed cue rate to produce an ‘actual’ cue density2.

The detectability function is used to “correct” the observations to generate a site-wide cue density measure.

2.1.2 Logical expectations In the following sections, we will determine a simplistic site-wide cue density – based solely on the number of observations, the size of the site and the number of hours spent observing. Because this metric contains no detectability effects or utilisation, we expect it to represent a lower bound on the cue density.

Secondly, we use the original utilisation data set to generate an observed site-wide cue density. Because our observers prefer higher observing sites, we expect the ‘fall-off’ of detections to be effected by both detectability effects and an actual decrease in utilisation. Therefore we expect the observed cue density to represent a higher bound on the actual cue density

This will be compared to that determined from the pseudo data set, which should lie in between (as it contains information about utilisation but no observer effects).

That is we expect

Simplistic cue density < Pseudo point count cue density < Observed cue density

If this logic is validated, then we can assert that the pseudo data set generation process has added no unqualifiable effects to the study, and is a valid input to the CRM.

2 It should be noted the original observation protocols were not set up according to strict distance sampling practice so the estimate of expected cue rate should be treated with caution as a stand alone calculation. For example, as the eagle cues are likely to be associated with topological forms, as are the choices of observer location, the cue density will contain a consistent bias.

For this reason, it should not be used for any independent inherent risk calculation. However, it remains suitable for the purposes of validation, as used here.



2.1.3 Pseudo-Observer Locations

Figure 1 : Observer Locations (Red Circles) and Pseudo points (Blue)



2.1.4 A simplistic cue density We are working with the concept of a cue, as it makes no assertions regarding the individual density. It is movements that place an individual at risk, not an individual’s existence alone. Therefore, we consider the observations as records of cues, not individuals.

If we examine all observations recorded and the radial distance from the observer, have a data set consisting of 358 wedge tailed eagle flights that have associated complete and correct GIS details.

The area of Cattle Hill observed (considered as all land within 420 metres of an existing observation waypoint) is 43 km2 and time resource (1131.5 Hours) spent generates a lower bound simplistic cue density of 1.20 x 10-6 Cues/ha/min.

2.1.5 An observed cue density As discussed above, and shown in Figure 2, there is an obvious decay in detections with distance exhibited by the utilisation observations.

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The behaviour portrayed in Figure 2 suggests that we might be missing some observed movements at larger distances from the observer’s location. This would usually be corrected for through distance sampling techniques.

However, these rely upon an assumption that our transects are randomised with respect to the stochastic drivers of the animals’ behaviour. Due to the need to see properly, the observer locations are not random. In fact, typically they are related in some way to the higher points of the observation site, factors that are implied in assumptions about how eagles use the topography.

Therefore, the decay curve in Figure 2 is too aggressive by some amount.



Using DISTANCE (Thomas, et al, 2009), we generate an observed cue density of 1.92 x 10-5 cues/ha/min. We know that, due to the decay rate being confounded by both the observer detectability and the site specifics that this rate of 1.92 x 10-5 is biased high.

2.1.6 A pseudo point count cue density We then take our pseudo set of observations, which have no such detection bias, but do take into account the variation in utilisation across the site (as they are drawn form the observed data).

This generates a pseudo point count cue density of 8.5 x 10-6 cues/ha/min.

2.1.7 Comparison of metrics This pseudo point count cue density value falls midway between our “too low” simplistic assumption, and our “too high” distance sampling regime, as expected.

As a further validation, we examine the detectability curve derived for the original observations, and note that the closest pseudo point to each physical observing point is around 850 metres. Using standard distance sampling (Buckland, 2001) we can see that we expect a correction term of about 42%.

If we apply this correction term to the pseudo point count cue rate, it lifts our pseudo count rate from 8.5 x 10-6 cues/ha/min to 2.02 x 10-5 cues/ha/min. This is the same (within noise) cue rate as the observed cue density, 1.92 x 10-5 cues/ha/min.

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This serves as a further validation of the consistency between the pseudo-point count data set and the underlying observed utilisation data set. We can therefore



conclude that the pseudo set is returning a comparable cue rate to the genuine observer set (so is a comparable yet unique set).

There is therefore no dependence on the outcome from the use of pseudo observer protocols.

2.2 Verifying the consistency between the observed utilisation data and third party point counts (test 2)

The original data set relies upon certain protocols that are inconsistent with the Biosis Research proprietary modelling techniques used in the Collision risk model (Ian Smales, pers comm).

In addition to validating the pseudo point counts as we require assurance against the possibility that two different observers, using different protocols, will see a different view of the activity on the site.

To test this we compare the results of two teams, one using the original utilisation observation protocols, and one using the CRM specific point count protocols.

2.2.1 Comparing Observers The November 2009 utilisation studies (as per the protocols outlined in Symbolix 2009a) were performed on the 4th through to the 8th November 2009 inclusive. There was nearly two weeks between these and the point count verification set (18th to the 22nd November 2009).

Again using the program DISTANCE to generate a cue density, we can show that the detectability corrected cue count based on the utilisation protocol is 6.6 x 10-5 cues per ha per minute3.

The subsequent point count survey returns a cue density of 17.8 x 10-5 cues per ha per min4.

We are confident that the difference is a genuine increase in activity as the Effective Detection Radius (EDR) is identical between the two sets. The utilisation studies return an EDR of 669 metres5, whereas the point count protocols operate with an EDR of 676 metres6.

The increase of a factor of about threefold between the first week of November and the later weeks was also seen in the 2008 data (Symbolix 2009a). Here we saw a doubling in the (non-detection corrected) metrics of movements per hour. From November to the first week in December the recorded movements per hour rose from 0.31 to 0.58 with similar CV values of 6.4 to 6.8 %.

3 95% confidence interval of (5.3 x 10-5, 8.4x 10-5) 4 95% confidence interval of (12.8 x 10-5, 24.7 x 10-5) 5 95% Confidence interval of (595, 751) 6 95% Confidence interval of (573,797)



2.3 Implications for the CRM Collision Risk Modelling (CRM) uses measures of recorded movements to generate a prediction of the potential for direct impact upon the bird population. It is important to note that the CRM assumes the observed activity is representative of a period being modelled. This representativeness is a requirement for a valid and meaningful outcome. Any bias in the sampling time will reflect directly in the CRM predictions.

CRM should rely on the longest baseline data it can gather, to smooth out any seasonal or sporadic, transient effects. Done correctly, this will result in the CRM prediction being an average mortality, which will reflect long term trends.

As such, there are two forms of variability that will manifest in variable on-site mortalities: Seasonal and Discretisation effects.

2.3.1 Seasonal Variation: The steep increase in activity from November into December has implications for the risk values derived from the CRM. A short baseline study is potentially subject to bias.

The case in point, if we did not realise that there is a significant increase in activity during late November-early December, and used a survey from this period, we would generate an upwardly biased prediction.

However, having the capacity to use validated pseudo point counts in this case ensures that the CRM output is a closer representation of the annual level of risk. It is worth noting that both the utilisation studies (see Symbolix 2009d for seasonal comparisons) and the validation observations in November 2009 indicate heightened activity in this period.

We should also not that, when comparing predictions of the CRM, we must be aware of its baseline, and whether it is reporting a long term average, or a peak prediction. The use of a long baseline study mitigates this risk.

2.3.2 Discretisation Discretisation effects are the realisation that the long term average is unrestricted in its allowable value, yet mortalities can only occur as discrete integers. i.e. one cannot strike ½ a bird.

To explain this further, we use Table 1 as a tool to translate predicted (fractional) mortalities into counts that are a more practical tool for risk management.

The table describes all of the possible combinations that mortalities might arise in a system described by 0.5 mortalities per annum.



0.5 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 181 60.7% 30.3% 7.6% 1.3% 0.2% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%2 36.8% 36.8% 18.4% 6.1% 1.5% 0.3% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%3 22.3% 33.5% 25.1% 12.6% 4.7% 1.4% 0.4% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%4 13.5% 27.1% 27.1% 18.0% 9.0% 3.6% 1.2% 0.3% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%5 8.2% 20.5% 25.7% 21.4% 13.4% 6.7% 2.8% 1.0% 0.3% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%6 5.0% 14.9% 22.4% 22.4% 16.8% 10.1% 5.0% 2.2% 0.8% 0.3% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%7 3.0% 10.6% 18.5% 21.6% 18.9% 13.2% 7.7% 3.9% 1.7% 0.7% 0.2% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%8 1.8% 7.3% 14.7% 19.5% 19.5% 15.6% 10.4% 6.0% 3.0% 1.3% 0.5% 0.2% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%9 1.1% 5.0% 11.2% 16.9% 19.0% 17.1% 12.8% 8.2% 4.6% 2.3% 1.0% 0.4% 0.2% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0%

10 0.7% 3.4% 8.4% 14.0% 17.5% 17.5% 14.6% 10.4% 6.5% 3.6% 1.8% 0.8% 0.3% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0%11 0.4% 2.2% 6.2% 11.3% 15.6% 17.1% 15.7% 12.3% 8.5% 5.2% 2.9% 1.4% 0.7% 0.3% 0.1% 0.0% 0.0% 0.0% 0.0%12 0.2% 1.5% 4.5% 8.9% 13.4% 16.1% 16.1% 13.8% 10.3% 6.9% 4.1% 2.3% 1.1% 0.5% 0.2% 0.1% 0.0% 0.0% 0.0%13 0.2% 1.0% 3.2% 6.9% 11.2% 14.5% 15.7% 14.6% 11.9% 8.6% 5.6% 3.3% 1.8% 0.9% 0.4% 0.2% 0.1% 0.0% 0.0%14 0.1% 0.6% 2.2% 5.2% 9.1% 12.8% 14.9% 14.9% 13.0% 10.1% 7.1% 4.5% 2.6% 1.4% 0.7% 0.3% 0.1% 0.1% 0.0%15 0.1% 0.4% 1.6% 3.9% 7.3% 10.9% 13.7% 14.6% 13.7% 11.4% 8.6% 5.9% 3.7% 2.1% 1.1% 0.6% 0.3% 0.1% 0.0%16 0.0% 0.3% 1.1% 2.9% 5.7% 9.2% 12.2% 14.0% 14.0% 12.4% 9.9% 7.2% 4.8% 3.0% 1.7% 0.9% 0.5% 0.2% 0.1%17 0.0% 0.2% 0.7% 2.1% 4.4% 7.5% 10.7% 12.9% 13.8% 13.0% 11.0% 8.5% 6.0% 4.0% 2.4% 1.4% 0.7% 0.4% 0.2%18 0.0% 0.1% 0.5% 1.5% 3.4% 6.1% 9.1% 11.7% 13.2% 13.2% 11.9% 9.7% 7.3% 5.0% 3.2% 1.9% 1.1% 0.6% 0.3%19 0.0% 0.1% 0.3% 1.1% 2.5% 4.8% 7.6% 10.4% 12.3% 13.0% 12.4% 10.7% 8.4% 6.2% 4.2% 2.7% 1.6% 0.9% 0.5%20 0.0% 0.0% 0.2% 0.8% 1.9% 3.8% 6.3% 9.0% 11.3% 12.5% 12.5% 11.4% 9.5% 7.3% 5.2% 3.5% 2.2% 1.3% 0.7%

21 0.0% 0.0% 0.2% 0.5% 1.4% 2.9% 5.1% 7.7% 10.1% 11.8% 12.4% 11.8% 10.3% 8.3% 6.3% 4.4% 2.9% 1.8% 1.0%22 0.0% 0.0% 0.1% 0.4% 1.0% 2.2% 4.1% 6.5% 8.9% 10.9% 11.9% 11.9% 10.9% 9.3% 7.3% 5.3% 3.7% 2.4% 1.5%23 0.0% 0.0% 0.1% 0.3% 0.7% 1.7% 3.3% 5.3% 7.7% 9.8% 11.3% 11.8% 11.3% 10.0% 8.2% 6.3% 4.5% 3.1% 2.0%24 0.0% 0.0% 0.0% 0.2% 0.5% 1.3% 2.5% 4.4% 6.6% 8.7% 10.5% 11.4% 11.4% 10.6% 9.0% 7.2% 5.4% 3.8% 2.6%25 0.0% 0.0% 0.0% 0.1% 0.4% 0.9% 2.0% 3.5% 5.5% 7.7% 9.6% 10.9% 11.3% 10.9% 9.7% 8.1% 6.3% 4.7% 3.2%

Physical MortalityYe

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Table 1: Discretised Mortality Effects for a predicted average annual mortality of 0.5 per annum. The left hand side describes a time period length, with potential mortality counts along the horizontal axis. The green values are expected in the given time period, hashed values are unexpected, and yellow values are possible but not probable.

Although it is complete with individual probability assessments, Table 1 shows a green band through its core. This shows the range of mortality counts that are statistically plausible for each period.

Note how two strikes in a given year (the first row) is well within possibility, despite being eight times the long term average.

The hashing indicates values that are beyond our expectation for a twenty year project life. The Yellow indicate values that would cause us concern, but are not beyond belief. The left hand side describes a time period length, with possible mortalities along the horizontal axis. The case of a long term average of 0.5 per annum is shown.

A table like this allows us to translate predicted mortalities into management indicators, for example:

Every additional year is independent, and looks like the first row, appended to the current count

The track should enter the yellow zone no more than once during the life of the farm

The path should not enter the hashed region during the life of the farm.

For example, let us assume in the first year, there are two mortalities. The most likely outcome in the second year is that there are no more mortalities. Yet, there is a one in twelve likelihood of having another two strikes. This would place us in a yellow cell. To still believe that we are witnessing a system with a long term average of 0.5, we can only allow a zero result in the third year. Anything else would place us out of bounds.



Similarly, if we went seven years without mortality, we would become suspicious.

The key here is that individual years have tremendous freedom to resolve their fractional expectation values. However, long term, the patterns become locked in.



3 Summary The study and Risk Analysis of the Cattle Hill project involves a significant amount of data and associated analysis. As such, it bears a high risk of both confusion, and potential for misrepresentations and misinterpretation.

We have presented here a number of points regarding both the validity of the underlying data, and the robustness of any conclusions drawn from it.

They are:

A coefficient of variation of typically less than 5% for the utilisation charts. Confidence ranges at this level mean that the contour levels are unlikely to be incorrect by more than the contour spacing as presented

The program DISTANCE (Thomas, 2009) has been used to generate detectability curves for the observations, which yield a corrected rate of 1.9 x 10-5 cues/ha/minute

The pseudo point count observers do not have a corrected rate, as their detectability is “perfect”. This set yields a rate of 8.5 x10-6 cues/ha/minute.

This can be corrected using the original detectability function to generate a rate of 20.2 x 10-6 cues/ha/minute

The utilisation charts have a complete range of rates that encompass this rate.

The utilisation chart rates are less than the detectability rates as the utilisation metric is specifically disallowed from generating false or unobserved corrections.

From these points we can conclude that the analyses and modelling are consistent with Wildspot’s observations, and that the observations taken in the test week between Biosis and Wildspot are of comparable efficacy.



4 References & Further Reading

Buckland, S., Anderson, D., Burnham, K., Laake, J., Borchers, D. and Thomas, L. 2001. Introduction to distance sampling: estimating abundance of biological populations. 1st edition. USA: Oxford University Press.

Buckland, S., Anderson, D., Burnham, K., Laake, J., Borchers, D. and Thomas, L. 2004. Advanced distance sampling: estimating abundance of biological populations. 1st edition. USA: Oxford University Press.

Sheskin, D. 2004. Handbook of parametric and nonparametric statistical procedures. 3rd edition. USA: CRC Press.

Symbolix. 2009a. Eagle Flight Data Analysis and Interpretation. Report to Hydro Tasmania Consulting. Symbolix, Williamstown North.

Symbolix. 2009b. Eagle Flight Data Analysis and Interpretation: February 2009 Observations. Report to Hydro Tasmania Consulting. Symbolix, Williamstown North.

Symbolix. 2009c. Eagle Flight Data Analysis and Interpretation: May 2009 Observations. Report to Hydro Tasmania Consulting. Symbolix, Williamstown North.

Symbolix. 2009d. Eagle Flight Data Analysis and Interpretation: August 2009. Report to Hydro Tasmania Consulting. Symbolix, Williamstown North.

Thomas, L., S.T. Buckland, E.A. Rexstad, J. L. Laake, S. Strindberg, S. L. Hedley, J. R.B. Bishop, T. A. Marques, and K. P. Burnham. 2009. Distance software: design and analysis of distance sampling surveys for estimating population size. In press. Journal of Applied Ecology. Software version 6. Release 2. http://www.ruwpa.st-and.ac.uk/distance/


Appendix F Population Viability Analysis for the Tasmania Wedge-tailed Eagle

www.symbolix.com.au

1/14 Akuna Drive

Williamstown North

VIC 3016


Tasmanian Wedge Tailed Eagle

ISSUE

Version 1.0

1st May, 2010

Submitted to



For Release - i - HTCPVAM20100119, Version 1.0


Ray Brereton – Hydro Tasmania Consulting

Ian Smales – Biosis Research Pty Ltd

Version Control Doc ID: HTCPVAM20100119

Main Author: Stuart Muir

Path: C:\SVNStuff\Clients\HTC_HydroTasmaniaConsulting_Tas\Cattle Hill\PVA\HTC_PVAMR_PVAModelDescriptionAndFindings_20100202.doc


0.8 Draft 19/1/10 internal Internal 1e For comment

0.95 2/2/10 E.Stark Ray Brereton e Background info added.

Sent for review

1.0 Issue 1/5/10 E.Stark Ray Brereton e Minor copy editing


Limitation: This report has been prepared in accordance with the scope of services described in communications between Symbolix Pty Ltd and Hydro Tasmania Consulting. Any findings, conclusions or recommendations only apply to the aforementioned circumstances and no greater reliance should be assumed or drawn by Hydro Tasmania Consulting. Furthermore, the report has been prepared solely for use by Hydro Tasmania Consulting and Symbolix Pty Ltd.

Copyright: The concepts and information contained in this document are the property of Symbolix Pty Ltd. Use or copying of this document in whole or in part without the written permission of Symbolix Pty Ltd constitutes an infringement of copyright.

The Symbolix logo is a registered trade mark of Symbolix Pty Ltd.

1/5/2010

Signed Date


For Release - ii - HTCPVAM20100119, Version 1.0

Table of Contents 1 Underlying Model............................................................................................2

1.1 Description of the PVA............................................................................2 1.1.1 Nested populations .............................................................................2 1.1.2 Connectivity and dispersion.................................................................3 1.1.3 Reproduction details ...........................................................................4 1.1.4 Carrying Capacity and contributory participation ...................................4

1.2 Comparison with previously published data..............................................4 2 Expanding the Model – A statewide PVA...........................................................6

2.1.1 Carrying Capacity ...............................................................................6 2.2 Results – no additional losses..................................................................6

3 Introducing additional mortality .......................................................................8 3.1 The harvest function ..............................................................................8

3.1.1 Contributing sub-populations...............................................................9 3.1.2 Timescales .........................................................................................9

3.2 Results ..................................................................................................9 3.2.1 Establishing a population tipping-point.................................................9 3.2.2 The tipping point ..............................................................................12

4 Conclusions for Cattle Hill Wind Farm.............................................................13 5 References ...................................................................................................15


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List of Figures Figure 1 : Life cycle, adapted from Bekessy et al.(2009) .........................................3 Figure 2 : reproduction of Bekessy et al. 2009 Scenario One, showing capping by Carrying Capacity..................................................................................................5 Figure 3 : Evolution of the Mean population sizes ...................................................7 Figure 4 : Hypothetical Mortality scenarios for input into the PVA, showing the average mortalities per annum, and 95% range for each scenario. The red arrow indicates the Expected (average) mortality for the Cattle Hill Wind Farm, for comparison. .........................................................................................................8 Figure 5 : Stochastic value of r for scenarios of increasing average mortality as a function of time ..................................................................................................10 Figure 6 : Likelihood of extinction after 100, 120 and 140 years under various press events................................................................................................................11 Figure 7 : The Probability of Survival of the meta-population.................................14

List of Tables Table 1: Meta-population dispersion rules ..............................................................3 Table 2 : Known additional mortalities .................................................................13


For Release - 1 - HTCPVAM20100119, Version 1.0

Summary NP Power is proposing a windfarm at Cattle Hill, located on the eastern side of Lake Echo, in the Tasmanian highlands. As part of this proposal, a series of studies have been undertaken to assess and quantify the risk posed to the Tasmanian wedge-tailed eagle (Aquila audax fleayi).

To date, a series of bird utilisation studies have been carried out by Wildspot Consulting Pty Ltd, with surveys designed and analysed by Symbolix Pty Ltd. These studies enabled the assessment of onsite activity over an annual cycle, and were used as inputs to an assessment of whole site collision risk modelling and a micro-site relative risk assessment.

This report aims to assess the addition of the proposed development to the cumulative effects of Tasmanian windfarms on the statewide population. A population viability analysis (PVA) is carried out, following closely on previous work by Bekessy et al. (2009) and Smales & Muir (2005).

The model employed is an agent based stochastic simulation built upon the VORTEX v 9.96 software platform. Heavy reliance is made upon Bekessy et al. (2009) to inform the inputs required to generate this model. This is particularly so for life parameters, such as mortality and fecundity rates. Where appropriate, other values are taken from The Threatened Tasmanian Eagles Recovery Plan 2006-2010 (DPIPWE, 2006) and from Smales & Muir (2005).

The purpose of the model is to represent the ability of the Tasmanian wedge-tailed eagle population to resist a number of imposed, long term additional mortality impacts (“press” impacts) representing the cumulative windfarm effects.

The VORTEX model finds that the population should have around 50% contribution from a floating population and is currently stable, with size dictated by the carrying capacity of available and suitable breeding territories.

It also indicates that the population is capable of surviving a number of significant, long term “press” impacts, including up to 22 additional mortalities per annum indefinitely. This is shown through both the stochastic variability of the population growth rate, r, and through a scenario analysis of calculated likelihood of extinction.

We show that, under this model, the additional expected long term average mortality rate posed by the cumulative effects of the Cattle Hill Windfarm and existing wind farm developments is not likely to pose a significant impact to the state’s wedge-tailed eagle population. This report is in three sections:

Section One deals with the actual population viability analysis (PVA) modelling, and establishes its credentials through replicating the findings of Bekessy et al. (2009), for the Bass District in north-west Tasmania.

Section Two outlines the expansion of the PVA to model the entire statewide population, and explores the long term viability of this population, under a ‘no additional losses’ scenario.

Finally Section Three looks at some scenarios for various potential mortality effects and the implications for the population.



1 Underlying Model

1.1 Description of the PVA The model employed is written within the VORTEX (v9.96) software package. This is a stochastic platform that uses an agent based technique. Agent based models create individual, computational entities, that live out their lives and deaths within the computer simulation.

The previous models of Bekessy et al. (2009), which we rely heavily upon, were performed using a similar agent based philosophy, although they deployed explicit landscape connectivity through RAMAS Landscape, under the connected design of Wintle et al. (2005).

This connectivity is not as crucial for this assessment, as we are not concerned with the specific effects of native forest harvesting on the PVA. Instead we proscribe a carrying capacity (see section 1.1.4) to place an upper cap on the number of breeding pairs, and define a nested population to relate the breeding and non-breeding groups (section 1.1.1).

To validate the model and the findings, we replicate the ‘no harvest’ scenario of Bekkesy et al. (2009) in section 1.2.

1.1.1 Nested populations To adequately assess the statewide impact of windfarms, we must consider the statewide wedge-tailed eagle population. To do this, we consider the meta-population as two, nested sub-populations, “breeders” and a “floaters” (c.f. Bekessy et al. (2005), who employ a single population assumption).

Paired breeding adults reside in their home ranges year round, whilst the floating sub-population range through different territory. This implies that there is a natural cap on the size of the breeder sub-population, enforced by the availability of suitable home territory.

The dynamics of the meta-population then are simple:

A breeding pair is one of requisite age and that owns a suitable territory

Having produced a fledgling, this fledging migrates immediately to the “floating” population

The floating sub-population may not breed of itself

The breeding sub-population are static, until death creates a vacancy

Given a vacancy in the breeding sub-population (an available territory and mate) a floater of breeding age may disperse and become a breeder.

This process is shown in Figure 1. The indicated rates (f and s1 through s8) are taken directly from Bekessy et al (2009), Table 1 and used accordingly as inputs to the VORTEX model.

In defining such a nested population, we can replicate the landscape requirements of the species, without an explicit modelling platform doing so. This is valid provided that the dispersal/ migration time for a floater to become a breeder is less than one modelling time-step, which for this work is one year.



Figure 1 : Life cycle, adapted from Bekessy et al.(2009)

1.1.2 Connectivity and dispersion To correctly account for this population configuration, we ensure that there is no cost for dispersal between the two sub-populations, and that the two sub-populations have a perfect correlation between their Environmental Variabilities.

The ability to disperse between sub-population classification (Breeder to Floater and vice versa) is controlled through the following rules:

To Population Breeder Floater

Breeder If age ≥ 6, 100% else 0%

If age < 6 100%, else 0%

From

P

opu

lati

on

Floater If capacity exists (i.e. a vacancy in the breeder

population) If no capacity exists

Table 1: Meta-population dispersion rules



This specifies that all surviving chicks fledge to the general floating sub-population, and may only return if there is capacity within the breeding territory allocation of the breeding sub-population.

1.1.3 Reproduction details The species is deemed a long term monogamous, that allows for the static nature of the breeding sub-population.

We have employed a definition that all breeding adults, occupying a home territory, are fit, healthy and socially suitable for breeding attempts. However, only one half of these will produce a brood per annum. This is our interpretation of Bekessy et al. (2009), Section 2.2.2, paragraph 4.

1.1.4 Carrying Capacity and contributory participation To model the dynamics caused through the territories, we place a carrying capacity upon the breeders that reflects the limit of geographic territories.

The floaters, however, are unconstrained by territory. This does not mean that the number of floaters in unbounded. As we saw in the previous section, the size of the floater sub-population can only increase in size through the actions of the breeders. It is dynamically capped by the complex interaction of various population dynamics and relationships of the species, including the fecundity of the breeders and the senescence of the floaters.

We do not need to determine this level beforehand, and the model will relax to the correct, supportable ratio within some time related to the senescence of the floating sub-population. This (unrepresentative) relaxation period will have an impact on any short range forecasts of the model, but is of minimal impact in the long term models we produce in this work.

1.2 Comparison with previously published data To highlight that the deployment of VORTEX in such as way as described in the preceding section is valid, it is important to (within capability) reproduce the findings of previous works.

The ‘no harvest’ model of Bekessy et al. is the base line assumption that indicates the population without further interference. It can be seen to align with the carrying capacity of the Bass Region (an initial value of 142 breeding adults). Under this scenario, the population is found to be relatively stable over 160 years, fluctuating about a mean population of 125-130 breeding adults (Bekessy et al (2009) Figure 5 top).

A second comparison comes from DPIPWE (2006) which states a theoretical contribution of 50% floaters to the total population. As we determine the size of the floater sub-population dynamically, it can be assessed against this published value.

To assess the model employed herein, we employ identical life table parameters as Bekessy et al. (2009) Figure 1, along with the two sub-population model and dispersion rules specified above. We also cap the carrying capacity to 142 breeding adults.

Figure 2 below shows the VORTEX model predicting a long term stable population, fluctuating around an average of about 115 breeding eagles (about 80% of carrying capacity).



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Figure 2 : reproduction of Bekessy et al. 2009 Scenario One, showing capping by Carrying Capacity

The important feature of Figure 2 is that it rapidly stabilises across the two sub- populations, dominated by the carrying capacity of the breeding population. The first twenty years or so of the simulation can be seen to reflect the “relaxation” of the model, as the correct ratios stabilise and assert themselves.

The model rapidly finds an optimum ratio between the two sub-populations, being a 50% contribution to the entire population from the floaters. This value is in agreement with the theoretically determined value to be found in the Recovery Plan (DPIPWE, 2006).



2 Expanding the Model – A statewide PVA

To correctly calculate cumulative and individual impacts of developments, the model requires that migration be free amongst all available population. This simply means that the model requires the entire state of Tasmania to adequately define the correct population. Unlike the Bekessy model, we are not able to arbitrarily define a pocket of the state and place an osmotic boundary to accommodate an immigration/ emigration equilibrium

The VORTEX model has been built in such a way as to allow us to model a statewide population by simply increasing the carrying capacity of the breeder sub-population, to better reflect the statewide capacity.

2.1.1 Carrying Capacity From the Recovery Plan data (DPIPWE, 2006), we have a total of 426 available territories, with a maximum 90% occupation rate. This leads us to the simple carrying capacity of 766 breeding adults. The floating capacity is unconstrained, for reasons outline previously (see Section 1.1.4).

The model is initialised with 466 breeding adults (taken from the 54.75% occupancy of the 426 available territories) and a floating population of 554. The model is not sensitive to this value of the floating population, as noted beforehand. We allow the model to relax to a stable solution before taking results.

2.2 Results – no additional losses Figure 3 shows the evolution of the statewide model under a ‘no harvest’ assumption. This forms our baseline comparison model.

Other than the obvious relaxation period at the beginning of the simulation, we see that such a population model rapidly runs to an equilibrium as in Figure 2 (for the Bass Region only).

The floating population contributes 53% of the total population, and the carrying capacity runs at about 80% of the available territories. These figures are not programmed into the model, but rather manifest themselves as the dynamics of the population. As in section 1.2, these values align with that published in DPIPWE (2006). In addition, the projected total population under these conditions is 1280 individuals, which placed it squarely in the middle of the population estimate of the Recovery Project’s projections.



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0 20 40 60 80 100 120 140 160Time (yrs)

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Figure 3 : Evolution of the Mean population sizes

The initial spike at the beginning of the simulation is the stabilisation between the floaters and the available breeding sites. We then have a period of growth of the floating sub-population, which indicates the iteration of the model towards a stable equilibrium. After reaching the desired equilibrium, it is evident that the population will proceed at these values for the chosen run length of 160 years.

Comparing this to Bekessy et al. (2009), we can say that, irrespective of our specification and knowledge of the availability of breeding territories, the Tasmanian population is stable under the current situation, without additional population pressure.



3 Introducing additional mortality In order to assess the potential impact of future development in general, and the proposed NP Power windfarm at Cattle Hill specifically, we impose a series of additional mortality scenarios into the PVA.

These allow us to establish a potential “tipping-point” for the statewide wedge-tailed eagle population and compare this with the expected mortality from the proposed development.

3.1 The harvest function To understand the impacts on the population of introducing mortalities above and beyond the background mortality rate, we invoke a “Harvest” function.

Bekessy et al. (2009) have incorporated the current understanding of background and human induced mortality into their life stage matrix. We are interested in additional mortality due to future developments, and so add additional mortalities as a series of hypothetical scenarios.

The model uses a zero inflated normalised approximation to the Poisson distribution. This results in the following additional mortality and ranges. This is the only option we have within the VORTEX framework.

The scenario name is the modelling variable we use, that corresponds to the mortality shown in Figure 4. The bars indicate the 95% window for the actual physical mortality that occurs during the simulation runs.

It can be seen that we use 20 scenarios to explore the outcomes. Our baseline model (scenario 0) is based on mortality rates from the no harvest baseline model of Bekessy et al.. The most severe scenario considered is a sustained average of 40 extra mortalities per annum. The expected additional collision rate from the proposed Cattle Hill development is around 0.5 per annum.

Figure 4 : Hypothetical Mortality scenarios for input into the PVA, showing the average mortalities per annum, and 95% range for each scenario. The red arrow indicates the Expected (average) mortality for the Cattle Hill Wind Farm, for comparison.

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3.1.1 Contributing sub-populations At the moment, the model is taking an equal likelihood (although the actual ratios vary stochastically) of male and females from both sub-populations. Given that floaters seem to naturally contribute about equal proportion to the overall population as breeders, this seems a valid assumption.

It may be that interlopers are more at risk from wind turbine strike, in particular. However, this is a population model that is modelling all 426 available nesting territories and only a few will be exposed to this extra risk, and we have free migration between the populations.

It may be possible to attribute different population risks presented by this extra mortality. At the present, we have no understanding of how this relative risk may present itself, so assume a “blind” hand of fate in the application of extra mortality effects.

3.1.2 Timescales Due to the relaxation noticed earlier, we do not start the extra mortalities from occurring until twenty years into the simulation, to allow the two populations to stabilise.

We also allow any increased mortality to persist as a “press” impact. That is, it continues perpetually. For the specific cases of wind turbines, there is repowering and decommissioning and new technologies that would make such an assumption false and overly pessimistic.

However, if a population is not threatened by a press impact, then a pulse impact lasting for only the lifetime of a farm will not affect it adversely either. It therefore seems valid to employ the precautionary assumption that all additional impacts are long lasting press events.

3.2 Results

3.2.1 Establishing a population tipping-point

3.2.1.1 Growth rate In order to visualise the long term impact of the press impact scenarios introduced above, we use the exponential growth (or decline) rate of the population, r.

If the growth rate, r, is positive, the population is growing,

If r is negative the population is shrinking, and

If r is consistently negative, the population tends toward extinction.

Figure 5 shows the r value for the range of harvest scenarios considered. We have made the consistently negative curves bold to highlight them. Along the x-axis are the years of the simulation, with the ordinate being the r parameter.



-0.1

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0.040 8 16 24 32 40 48 56 64 72 80 88 96 104

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Scenario 1Additional Mortality -> 20Additional Mortality -> 22Additional Mortality -> 24Additional Mortality -> 26Additional Mortality -> 28Additional Mortality -> 32Additional Mortality -> 36Additional Mortality -> 40

Figure 5 : Stochastic value of r for scenarios of increasing average mortality as a function of time



The points to note in Figure 5 are that the period of relaxation is evident, as is the beginning of the additional mortalities at twenty years. The thin blue line that sits above the other scenarios for the first 40 years is the baseline scenario (no additional mortality introduced). The propensity for the growth rate, r, to hover around zero is an interaction with the carrying capacity, and denotes a stable population (in agreement with Figure 3).

For up to and including 22 average additional mortalities a year, one can see that the variation of the growth rate is no more than for the base line scenario. This translates to a sustainable press event.

It is only after we introduce an average of 26 additional mortalities each year that we see a consistent decline in the growth rate over a period of 160 years.

3.2.1.2 Likelihood of extinction An alternative, and potentially more accessible, visualisation of the population tipping point is to plot the likelihood of extinction after n years.

Figure 6 plots the likelihood of extinction as a function of the number of additional mortalities introduced. We have presented these curves after 100, 120 and 140 years. 1

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

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0 5 10 15 20 25 30 35 40 45

Additional Mean Mortality pa

Like

lihoo

d of

ext

inct

ion

100120140

Figure 6 : Likelihood of extinction after 100, 120 and 140 years under various press events

1 Note that the press of the additional mortality does not begin until twenty years, making these chart representative of the ability of the population to resist the press for 80, 100 and 120 years respectively.



As in Figure 5, it is immediately apparent that there is no effect upon the population’s sustainability (over a 160 year period) until we have increased the mean additional mortality to around 25 per annum.

3.2.2 The tipping point Analysis of the model outputs that feed into Figure 4 and Figure 5 allow us to conclude that the true tipping point for the Tasmanian wedge-tailed eagle population is greater than a sustained average of 22 additional mortalities per annum.2

It does need to be re-iterated that this model is assuming a press impact, in which the 22 additional mortalities are assumed to be sustained in perpetuity. The population is able to sustain pulse (short lived) impacts of similar magnitude accordingly, although this is not explicitly modelled here.

2 From Figure 4, this translates to an annual mortality between 14 – 33 adults per annum, at the 95% confidence level.



4 Conclusions for Cattle Hill Wind Farm

It is noted in the supplied mortality data (B. Brown unpub.) that background mortality due directly to current windfarm projects is included in the mortality assessments of the original Bekessy et al. (2009) work.

Therefore this additional, sustainable mortality rates calculated in this work would appear to be above and beyond the effects of current developments. We are unable to separate the differing mortality rates as this data set remains unpublished. As such, adding additional mortality as we will below amounts to conservatism in the estimate of sustainable load, as we are in effect double counting certain effects.

We however provide the following chart to enable assessment of various impacts.

Development Mortality (pa)

Confidence interval

Percentage of sustainable press

Bluff Point 2.03 (1.04,3.55) 9.2% (4.7%-16.1%) Studland Bay 1.51 (0.41,3.87) 6.9% (1.8%-17.6%)

Woolnorth Combined 3.493 (1.99,5.66) 15.9% (9.0%-25.7%) Cattle Hill 0.54 2.2%

Table 2 : Known additional mortalities

The combined Woolnorth development plus the Cattle Hill projection, amounts to an additional mortality of 3.99 per annum.

Figure 7 indicates that for a long term average of 4, there is no effect on the survivability of the species within 160 years. Scenario 2 (mortality rate of 4.2) is a stable population that exhibits no likelihood of extinction within the 160 years simulated.

As alluded to previously, the tipping point is above 22 mortalities. The first level of additional mortalities that deviates from complete survivability is the scenario of 24 additional mortalities per annum. Above this rate, the ability of the species to survive is affected progressively more, below it there is no discernible impact.

3 C.Hull Pers Comm. 3rd Feb 2010 4 Hydro Tasmania Consulting, Cattle Hill Wind Farm: Eagle Utilisation Assessment, Table 3-10. Draft, November 2009. The rate of 0.5 Mortalities per annum is the predicted figure for 144 turbines and a 90% avoidance rate.



0%

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P[S

urvi

val]

Additional Mortality --> 4.3 pa

Additional Mortality --> 24 pa





Figure 7 : The Probability of Survival of the meta-population

We therefore conclude that the Proposed Cattle Hill windfarm development is unlikely to impact the long term sustainability of the statewide population of the Tasmanian wedge-tailed eagle, based on current published understanding of the population dynamics and behaviours.



5 References

Bekessy, S.A., Wintle, B.A., Gordon, A., Fox, J.C., Chisholm, R., Brown, B., Regan, T., Mooney, N., Read, S.M. and Burgman, M.A. 2009, ‘Modelling human impacts on the Tasmanian wedge-tailed eagle (Aqula audax fleayi)’, Biological Conservation, vol. 142 (2009), pp. 2438-2448.

DPIPWE 2006, Threatened Tasmanian eagles recovery plan: 2002-2010. Threatened Species Section, Departmet of Primary Industries and Water, Hobart. Accessed 2nd February 2010, <http://www.dpiw.tas.gov.au/internnsf/Attachments/LJEM-7389QG/$FILE/Threatened%20Tasmanian%20Eagles%20RP.pdf>.

Smales, I. and Muir, S 2005, Modelled cumulative impacts on the Tasmanian wedge-tailed eagle of wind farms across the species’ range, Report for the Department of Environment and Heritage, Accessed 2nd February 2010, <http://www.environment.gov.au/epbc/pblications/pubs/wind-farm-bird-risk-tasmanianwedgetailedeagle.pdf>.

Wintle, B.A, Bekessy, S.A., Venier, L.A., Pearce, J.L., Chisholm, R.A. 2005, ‘Utility of dynamic-landscape metapopulation model for sustainable forest management’, Conservation Biology, vol. 19, pp. 1930-1943.


9

The information contained in this document has been carefully compiled but Hydro Tasmania

Consulting takes no responsibility for any loss or liability of any kind suffered by any party, not being

the intended recipient of this document, in reliance upon its contents whether arising from any error or

inaccuracy in the information or any default, negligence or lack of care in relation to the preparation of

the information in this document.

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