ORNITHOLOGY MONITORING AT OFFSHORE WIND FARMS: SURVEY DESIGN & INFERENCE

Mark Trinder MacArthur Green

MacArthur Green • Specialist services in marine ornithology for Developers,

Regulators and Statutory Advisory Bodies

• Impact assessment & monitoring work including:

• Beatrice,

• Hornsea,

• Dogger Bank,

• EA1, EA3

• Guidance & Research: • Seabird sensitivity scoring and mapping, CIA methods for PFOW,

Biologically Defined Minimum Population Scales, Compensation options for seabird SPAs, Offshore wind farm collision risk database, NERC PhD studentship

• Experience and background: • Population modelling and analysis, impact assessment, seabird ecology

Ornithology Monitoring: Talk Overview • Why and what we monitor for birds and OWFs

• Survey design & power analysis

• Power analysis and simulation illustration

• Summary

• Recommendations

Ornithology Monitoring • What is the purpose?

• Tick a box,

• Validate site specific assumptions in assessment,

• Reduce uncertainties for future developments?

• All of the above – need not be mutually exclusive

• How to maximise value

• So what? – detection of an impact doesn’t have

to mean there is an effect on the population.

What are the key bird issues? • Collisions & displacement (precautionary estimates)

• Monitoring can help to reduce uncertainties

• There is a need to focus on:

• Phrasing of questions and

• Design of surveys

• To ensure results are useful (not wasted

opportunity)

How to ensure monitoring is useful • Identify key assumptions made in assessment

(e.g. flight height, displacement rate, SPA

connectivity)

• Clearly state purpose of monitoring:

• Were the flight heights used in CRM appropriate?

• Is species x displaced by 50%?

• What proportion of birds seen are from particular colony?

• What are consequences of answers (‘so what?’)

• Avoid vagueness – ‘monitor bird usage of the wind

farm’

Survey design

• More data (usually) means greater confidence in

results

• But, particularly infrequent events such as

collisions would need very large scale (spatial and

temporal) monitoring to reliably estimate

• Even if aim is to measure more common events

such as displacement still important to understand

what is feasible:

• How much data is enough to detect an effect?

• Power analysis is a tool for answering this

question

Power Analysis

• Power analysis is a means for:

determine the sample size required to detect an

effect of a given size with a given degree of

confidence.

• Or put another way it allows us to:

determine the probability of detecting an effect

of a given size with a given level of confidence,

under sample size constraints

• So, what does that actually mean?

Statistical significance

• Statistical tests typically define a threshold of significance

- p value (e.g. 0.05)

• This is used to minimise risk that an observation is simply

chance

• So, at p = 0.05 the result is only expected 5% of the time,

which is considered sufficiently unlikely to be termed

‘significant’

• So, one way to increase the likelihood (power) of

detecting an effect is by increasing the p threshold, but:

• This increases the risk of false positives (identifying an effect when

it was just chance; Type I error)

• Conversely lower p threshold values risk false negatives (real

effects being dismissed; Type II error)

Power analysis – trade off

• By convention:

• Maximum p value for significance is 5%, • Risk of a false negative is 5% (wrongly identifying a chance effect)

• Minimum probability of detecting an effect is 80% • Risk of a false positive is 20% (expect to detect an effect, if present,

80% of the time)

• This is the trade-off:

• 1 in 20 chance of false negative

• 1 in 5 chance of false positive,

Power analysis – offshore wind determine the sample size required to detect an

effect of a given size with a given degree of

confidence

• So, we have 2 of the 3 values above: • given degree of confidence = 5% (i.e. a significant result)

• probability of detecting an effect = 80%

• And can estimate the third: • determine the sample size required

• Use existing data to develop a simulation of the

bird distributions and the survey design

Power analysis – illustration

• Survey area – random bird

distributions

• Based on Sandwich tern at

2 birds/km2

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Power analysis – illustration

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fore

• Survey area – random bird

distributions

• Based on Sandwich tern at

2 birds/km2

• Add a wind farm

(Dudgeon)

Power analysis – illustration

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fore

• Survey area – random bird

distributions

• Based on Sandwich tern at

2 birds/km2

• Add a wind farm

(Dudgeon)

• Add potential survey

design – aerial transects

• Birds observed (in

transect) generate survey

data

Power analysis – illustration

• Run again for post-

construction

• Apply a displacement

effect from the wind

farm (reduce number

inside wind farm in

‘after’ period)

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Power analysis – illustration

• Run survey model multiple times (e.g. 50+)

• Analyse simulated before / after data using spatial

models (MRSea; CREEM, St. Andrews University)

• Vary survey design: • Survey frequency,

• Survey coverage,

• Vary biology:

• Bird distribution (uniform or concentrated), and

• Displacement effect size.

• Compare before (no effect) with after (displacement

from wind farm) - how often is effect detected?

(target 80%)

Power analysis – illustration Simulation A B C D

Percentage redistribution

(effect size) 25 50 50 50

Transect separation (km) 2 2 1.5 1

No. surveys per

month (April – July)

1 39.4 46.2 31.0 45.5

2 28.1 47.4 46.3 55.3

3 33.3 50.0 56.8 50.0

4 43.3 58.3 65.2 77.3

5 24.1 65.8 68.9 73.8

• Large effects – higher probability of detection

• More surveys – increase probability of detection

Power analysis – illustration Simulation A B C D

Percentage redistribution

(effect size) 25 50 50 50

Transect separation (km) 2 2 1.5 1

No. surveys per

month (April – July)

1 39.4 46.2 31.0 45.5

2 28.1 47.4 46.3 55.3

3 33.3 50.0 56.8 50.0

4 43.3 58.3 65.2 77.3

5 24.1 65.8 68.9 73.8

• Key message – only with 4-5 surveys/month for 4

months at 2x typical coverage and large effect do

results get close to achieving 80% target

Power analysis - distributions

• In this example - need very high intensity of

surveying effort to detect even large effect

• Even then – result based on low modelled level of

inter-annual variability

• This won’t always be the case (e.g. more

numerous species)

• But - can any effect detected be definitely

attributed to wind farm?

• Are there alternatives which may be more

suitable addressing key questions?

One option - tagging

• GPS tags:

• Track individuals

• Collect detailed spatial and behavioural data (identify

foraging areas, behavioural responses, flight

characteristics)

• Estimate connectivity with breeding populations

• Simulation can have a role in study design

• Use to estimate sample sizes on basis of

previous studies (e.g. trip characteristics)

Simulate tagging data

Simulate tagging data

• Generate bird tracks

for population (yellow)

• Identify all grid cells

visited (red)

• Randomly select a

subset ‘tagged’ (green)

• Identify cells visited by tracked birds (blue)

• 15 tagged birds from population of 4,000

• 60% of total area identified from tagged data

Simulate tagging data

• Generate bird tracks

for population (yellow)

• Identify all grid cells

visited (red)

• Randomly select a

subset ‘tagged’ (green)

• Identify cells visited by tracked birds (blue)

• 15 tagged birds from population of 4,000

• 60% of total area identified from tagged data

• Estimate area with different sample sizes

Monitoring conclusions

• Need to carefully define monitoring goals and questions (set up Advisory groups)

• Link monitoring aims to survey design (iterative process)

• Power analysis and simulation have roles to play in the decision making process – reduce risks of undertaking monitoring which may be of limited value

• And so what – detection of effect need not mean there is a population impact.

Monitoring recommendations

• If evidence suggests that detecting site specific effects using current approaches is not possible - why do it?

• Key consenting concerns are cumulative impacts on SPA populations so monitoring should be population centred not focussed on wind farm sites

• Strategic monitoring offers potential to assess population level impacts to benefit of industry

• Consent conditions should allow for flexibility in approach to make most of opportunities

Acknowledgements:

• Chris Nunn at Statkraft for permission to present data from Dudgeon simulations