Modelling of spatial variability of foraging efficiency in Lauwers Lake

A remote sensing and GIS Approach

Sam Varghese March, 2008

Course Title: Geo-Information Science and Earth Observation

for Environmental Modelling and Management Level: Master of Science (Msc) Course Duration: September 2006 - March 2008 Consortium partners: University of Southampton (UK)

Lund University (Sweden) University of Warsaw (Poland) International Institute for Geo-Information Science and Earth Observation (ITC) (The Netherlands)

GEM thesis number: 2006-25

Modelling of spatial variability in foraging efficiency in Lauwers Lake - A remote sensing and GIS perspective

by

Sam Varghese

Thesis submitted to the International Institute for Geo-information Science and Earth Observation in partial fulfilment of the requirements for the degree of Master of Science in Geo-information Science and Earth Observation for Environmental Modelling and Management Thesis Assessment Board Chairman: Prof. Dr. Andrew Skidmore External Examiner: Dr. Kasia Dabrowska-Zielinska Member: Dr. Ir. Thomas Groen Primary Supervisor: Dr. Jan de Leeuw Secondary Supervisor: Mr. Bas Wesselman

International Institute for Geo-Information Science and Earth Observation Enschede, The Netherlands

Disclaimer This document describes work undertaken as part of a programme of study at the International Institute for Geo-information Science and Earth Observation. All views and opinions expressed therein remain the sole responsibility of the author, and do not necessarily represent those of the institute.

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Abstract

One of the reasons for the decrease in population of migratory birds is the loss of stopover sites. As the stopover sites of migratory birds are declining there is a need to increase the capacity of the existing stop over sites by proper management. The provisioning of food resources for the migratory birds forms objective of an increasing number of protected areas. In this context we attempted to study the possibility of increasing the food availability for Bewick’s Swans visiting the Lauwers Lake during October and November months. They forage on below ground biomass of Potamogeton pectinatus. After a certain period they switch towards foraging on sugarbeets in the near agricultural fields. This is because swans give-up foraging when the net energy intake rate from an area is less than 65 Js-1. We developed a spatial model which estimates the net energy intake rate of while foraging. Model uses spatial data of tuber biomass density, sediment texture, water depth, and water level in the Lake. We sampled the below biomass and mapped the food density using NDVI derived from ASTER data and depth as predictive variables using a Gaussian model type II model. Point data of bathymetry and sediment texture were interpolated for generating spatial maps. Historical data of 15 years were used to know the water level pattern of the lake. Our foraging efficiency model predicted the net energy intake rate in Lauwers Lake at different water levels. The areas having net energy intake greater than 65Js-1 were mapped. The study revealed that the maximum foraging efficiency in Lauwers Lake can achieve by manipulating maintaining water level below -1.18 m from NAP. An increase in 44% of area was estimated when water level is manipulated from average to optimum level. And an increase of 5.3 % was estimated when water level is manipulated from the target level to optimal level. Our research in brief, presents a model to predict the impact of water management on the carrying capacity of wetland to overwintering migratory birds.

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Acknowledgements

All glory and honour be to Jesus Christ my lord and saviour for showering his abundant grace up on me to make this project a success.

I am extremely thankful to Dr. Jan de Leeuw my supervisor, for his invaluable guidance right from the conception stage and thus imparting a great amount of knowledge in me in the subject. Thanks are also to him for being kind enough to grant permission for doing the project under his guidance. My heartfelt gratitude to Dr. Bart A. Nolet for his valuable guidance and constant support during my research.

I am deeply indebted to Abel Gyimesi for his support, guidance and friendship throughout the project. Without that it was impossible to finish this work.

I further extend my appreciation to all my friends in Department of Plant-Animal Interactions, Netherlands Institute of Ecology (NIOO-KNAW) who assisted me in the field work. Also I am thankful to Nichole, Charlie and Pauline who joined me in for the field work.

I thank god for giving me brilliant friends from different parts of the world as my class mates. I am grateful to all my classmates for the academic and social support I got during my study.

I thank to all the teaching faculties and other supporting staff members in the Erasmus Mundus consortium.

I am thankful to my friend Vijai, who was with me from the beginning of this course as good friend and brother. Also I thank Sekhar for his help and friendship during my stay in ITC.

I am grateful to Kunju aunty for her encouragement and prayer support during my stay in Europe. I express my heartfelt gratitude to Surekha, my best friend for her constant encouragement, prayer and advices in during the course period. Also I am deeply indebted to my church at Kakkanad for their constant prayer support. My heartfelt gratitude and thanks to my beloved parents and sister without their extended support, I could not have successfully completed my course. Finally, I would like to express my gratitude to the European Union, for providing me the opportunity and the fellowship to pursue this MSc.

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Table of contents

1. Introducion ............................................................................................. 7 1.1. Research Problem ......................................................................... 9 1.2. Research objective ...................................................................... 10 1.3. Research questions...................................................................... 10

2. Materials and Methods ......................................................................... 11 2.1. Study area ................................................................................... 11 2.2. Materials ..................................................................................... 12

2.2.1. Software Products................................................................... 12 2.2.2. Equipments ............................................................................. 12

2.3. Available data ............................................................................. 12 2.4. Field data collected ..................................................................... 13

2.4.1. Tuber sampling....................................................................... 13 2.4.2. Sediment sampling ................................................................. 14 2.4.3. Bird count ............................................................................... 14

2.5. Foraging efficiency model .......................................................... 15 2.6. Generation of spatial dataset for the model ................................ 17

2.6.1. Mapping of tuber biomass density.......................................... 17 2.6.2. Mapping of bathymetry and sediment texture ........................ 18 2.6.3. Analysis of water level variation ............................................ 20

2.7. Simulation of the foraging efficiency model .............................. 20 2.8. Estimation of optimum water level............................................ 21 2.9. Test of foraging efficiency model............................................... 21

2.9.1. Overlaying of bird count on PNEI map.................................. 21 2.9.2. Comparison of foraging efficient area and bird count............ 21 2.9.3. Comparison of PNEI density and bird count.......................... 22

2.10. Sensitivity analysis...................................................................... 22 3. Results .................................................................................................. 23

3.1. Prediction of Tuber Biomass ...................................................... 23 3.2. Validation of the biomass prediction .......................................... 25 3.3. Interpolation of Bathymetry Data ............................................... 25

3.3.1. Kriging.................................................................................... 25 3.3.2. Inverse distance weighted interpolation ................................. 26 3.3.3. Selection of interpolation technique ....................................... 26

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3.4. Interpolation of Sediment Texture .............................................. 28 3.4.1. Kriging.................................................................................... 28 3.4.2. Inverse Distance Weighted..................................................... 28 3.4.3. Selection of interpolation technique ....................................... 28

3.5. Variation of water level ............................................................ 30 3.6. Potential net energy intake.......................................................... 32 3.7. Estimation of optimum water level............................................ 33 3.8. Testing of the foraging efficiency model .................................... 35

3.8.1. Test by overlaying of bird count on PNEI map...................... 35 3.8.2. Comparison of foraging efficient area and bird count............ 36 3.8.1. Comparison of PNEI density and bird count.......................... 36 3.8.2. Discussion on test results........................................................ 37

3.9. Sensitivity Analysis of the model ............................................... 37 4. Summary of Results ............................................................................. 38 5. Conclusion............................................................................................ 39 6. References ............................................................................................ 40 7. Appendix 1. Photographs from the field ............................................. 42 8. Appendix.2. Model build in Arc GIS to predict tuber biomass............ 43 9. Appendix.3 Model build in Arc GIS to estimate PNEI........................ 45

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List of figures

Figure 2-1. Location of Lauwers Lake (National Park), The Netherlands... 11 Figure 2-2 Sampling points and the area selected for the study in the lake . 13 Figure 2-3 Triangulation method used for the bird density mapping........... 15 Figure 2-5. A typical semivariogram as an example.................................... 19 Figure 2-6 Schematic diagram of the model ................................................ 21 Figure 3-1 Tuber biomass density map (year 2007) of Lauwers Lake......... 24 Figure 3-2 Validation of predicted biomass using a linear regression ......... 25 Figure 3-3 Isotropic variogram for bathymetry data .................................... 26 Figure 3-4. Digital Bathymetry Model developed by IDW interpolation .... 27 Figure 3-5. Isotropic variogram plot for sediment texture data.................... 28 Figure 3-6. Sediment texture map developed by IDW interpolation ........... 29 Figure serious 3-7. Variation in water level from 1993-2006 ...................... 31 Figure 3-8. Water level and foraging bird count during 2007 in Lauwers Lake.............................................................................................................. 31 Figure 3-9 Efficient foraging area in each creek .......................................... 32 Figure 3-10. Potential Net Energy Intake of the year 2007 in Lauwers Lake...................................................................................................................... 33 Figure 3-11. Variation in PNEI density at different water level .................. 34 Figure 3-12. Variation in foraging efficient area from the optimum............ 34 Figure 3-13. Spatial variation in NEI and the number of foraging birds...... 35 Figure 3-14. Comparison of area having high NEI and foraging birds in each creek ............................................................................................................. 36 Figure 3-15 Comparison of PNEI density and bird count ............................ 36 Figure 3-16. Sensitivity analysis of the model ............................................. 37

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List of tables

Table 2-1 Parameters and their values from literatures................................ 16 Table 2-2 Parameters which are function of sediment type ......................... 17 Table 2-3. ASTER data specifications for first three bands......................... 18 Table 3-1 Regression statistics for the full model described in equation 3-123 Table 3-2 Regression statistics for the full model described in equation 3-2...................................................................................................................... 24 Table 3-3 Regression statistics ..................................................................... 25 Table 3-4 Kriging parameters generated from the variogram ...................... 26 Table 3-5 Cross validation and goodness ranking of interpolations ............ 26 Table 3-6. Kriging parameters from the variogram...................................... 28 Table 3-7 Cross validation and goodness ranking of interpolations ............ 29 Table 3-8. NEI distribution in the creeks ..................................................... 32

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1. Introducion

Increasingly, migratory birds are under pressure. It has been shown that their populations continue to decline, not withstanding several decades of conservation effort (US Fish and Wild Life Service 2002). One of the reasons for this decline might be the loss of stopover sites along their flyway. The migration of birds consists of several flight episodes interrupted by residence at stopover sites for resting and refuelling. These stop over sites are selected by the birds based on the availability of food resources and roasting facility (Rees 1990; Zhang 1998; Nolet, Oscar Langevoord et al. 2001; Santamaria and Rodriguez-Girones 2002; Valta, Partamen et al. 2002; Pinnel, Heege et al. 2004; Bauer, Madsen et al. 2006; Salazar 2007; Salewski and Schaub 2007). The Edinburg declaration (2005) called for more concentrated effort to protect the stopover sites along the flyways of migratory bird species. The provision of food resources for the migratory birds forms objective of an increasing number of protected areas. Most of these stopovers were identified and are conserved as Important Bird Area (IBA). Many of them are located within conservation areas like Ramsar Sites, National Parks, Wild Life Sanctuaries, etc. According to (Melanie and Michael 2006) there are 3,404 IBAs in the European Union. The management of such protected areas requires knowledge of their carrying capacity, in order to be able to accommodate the provisioning of food resources to migratory birds. The residence time of migrating species in a stopover depends on the relation between food requirements and the availability of the forage resource at population level (Rees 1990; Bauer, Madsen et al. 2006; Nolet, Gyimesi et al. 2006; Salewski and Schaub 2007). Increasingly there is knowledge and understanding of food requirements for a variety of migratory birds. For example, many studies reported the food requirement of Bewick’s swan in the Lauwers Lake in the North of the Netherlands (Lovvorn and Gillingham 1996; Nolet, Langevoord et al. 2001; Nolet, Oscar Langevoord et al. 2001; Nolet, Bevan et al. 2002; Nolet, Gyimesi et al. 2006). With such information and knowing the size of a migrant population, it should be possible to asses the food requirements at population level. When linked to spatial information on distribution of food resources, this could be used to calculate carrying capacity.

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Estimation of carrying capacity has frequently been applied in terrestrial ecosystems. More recently it has been introduced to migratory birds in aquatic ecosystems. In the case of migratory birds the availability of food resource in a given area is used to calculate for the number of bird days (Nolet, Gyimesi et al. 2006). The technique has so far not been applied to bird species foraging on below ground aquatic vegetation. Development of remote sensing has improved the possibility to assess the geographical distribution of food resources. Many researchers used visual interpretation technique of aerial photographs for the mapping of submerged aquatic vegetation (Valta, Partamen et al. 2002). Zhang (1998) used Landsat thematic mapper (TM) in an empirical assessment of submerged vegetation biomass in Honghu Lake along the Yangtse River, China. In the regression analysis with principal components of TM bands as variables and sampled biomass as dependent, a linear relationship was observed. In another study IKNOS data was used to map Potamogeton pectinatus (tubers of this plant is a food source for Bewick's swans) in the Lauwers Lake (Agbor Delphine 2006). The author estimated the biomass by using a simple linear quadratic equation between the band ratio indices and biomass samples from the field. A similar work has been done by Salazar (2007) using ASTER data and the prediction of submerged biomass of Potamogeton pectinatus was improved by the addition of bathymetry data. The above approach would be straight forward, if animals deplete all available food resources. However animals strive to optimize their intake of food while reducing the costs of foraging (Santamaria and Rodriguez-Girones 2002). They give up foraging when its cost exceed the benefits of intake (Nolet, Gyimesi et al. 2006). They may even give up foraging at a specific food item, when greater net intake rates can be achieved on alternative food items (Nolet, Bevan et al. 2002). The net energy intake is defined as the difference between metabolizable energy and energy expenditure (Nolet, Bevan et al. 2002). Resource density influences the net energy gain in two ways. Higher resource densities allow an animal to increase its intake rate. Resource density also influences the costs of foraging. Below a threshold of resource density, animals have to increase its effort in searching, and the cost of acquiring food may increase. The costs of foraging are also influenced by factors other than resource density. The study on foraging profitability on submerged vegetation by Canvasback ducks, in the field level by Lovvorn and Gillingham (1996) shows that foraging profitability (net intake energy) is determined by the size of the food and the locomotive costs of diving. They also observed that cost is

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having less influence on bud metabolization energy, water temperature, bud dispersion and search and handling time coefficients. The profitability of foraging has a direct relation with the food density, food item size and the accessibility (Earnst 2006). In the case of tuber feeding birds foraging is influenced not only by the biomass density of Potamogeton pectinatus, but also the depth and the soil characteristics at which the tuber is present (Nolet, Oscar Langevoord et al. 2001; Nolet, Bevan et al. 2002). The studies on Bewick’s swan reveals that they can reach up to 0.51 m below the water by head dipping and a depth of 0.86 m by up ending positions (Nolet, Langevoord et al. 2001). Costs of foraging differ between these two foraging methods (Nolet, Langevoord et al. 2001). Deeper water limits Swan’s access to the tuber. It has also been reported by many researchers that the given up density of tuber biomass is higher in clayey and loamy areas than in the sandy lakes (Nolet, Langevoord et al. 2001; Nolet, Bevan et al. 2002; Santamaria and Rodriguez-Girones 2002). Costs of foraging may also increase by social interaction. People increase costs of foraging, while animals abstain from foraging due to disturbances. Ultimately, animals may decide to abstain from foraging in areas where the risk of disturbance is too high. The above mentioned environmental factors influence the geographical variation in potential net energy intake (PNEI). The above reviewed literature states that energy intake and foraging costs vary with resource availability and environmental conditions. These factors vary geographically and hence it is likely that net energy gain would vary spatially as well. Potentially one could thus model the spatial variability in net energy gain of foraging animals. So far to our knowledge no such spatial models have been developed for tuber feeding bird species.

1.1. Research Problem

Bewick’s swans visit Lauwers Lake (National Park) during October and November months. They forage on the tubers of Potamogeton pectinatus. After a certain period they switch towards foraging on sugarbeet (remaining of after harvest) in the near agricultural fields. This is because after a certain period of time the energy returns by foraging on the biomass in the lake become lesser than that from the sugarbeets. It was estimated that the swans give-up foraging when the net energy intake rate is less than 65 Js-1 (Nolet and Klaassen, submitted).

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The sugarbeets are not a permanent food source for the swans visiting the Lauwers Lake. The cultivation of the sugarbeets is dependent on the subsidies that the EU gives to the farmers. The subsidies can be stopped or reduced at any time resulting, in a reduction in the production of sugarbeets. This can cause a loss of food availability in this stopover site. This may decrease the number of Bewick’s swans visiting the Lauwers Lake. It is important to attract these birds into the nature resource by maintaining the National Park with sufficient food resource. PNEI depends on biomass density, particle size of the sediment and water level. Water level is the only factor that can be controlled by the National Park authorities. The maximum PNEI can be attained by optimising the water level. The water level of the lake is influenced by two factors. One is the runoff (due to the precipitation in the catchment area) from the rivers discharged into the lake and other is the sea level (due to the high tide and the low tide). If the water level of the sea is higher than that of the lake (due to high tide or westerly winds), sluicing is not possible and water level will increase by accumulating the water from the rivers that flow into the lake. The authorities maintain a target water level of -0.93 m by opening the sluice of the lake whenever the sea level becomes lesser than the water level in lake.

By modelling the spatial variability of foraging efficiency in Lauwers Lake, it is possible to determine the optimum water level required to maximise the foraging efficient area in the lake. The foraging efficient area is the area where the PNEI is higher than 65Js-1 (Nolet and Klaassen, submitted).

1.2. Research objective

Objective of the study is to develop a spatial model (foraging efficiency model) to assess the net energy balance of Bewick’s swan foraging in the lake Lauwers

1.3. Research questions

1. How do the PNEI of Bewick’s swans vary spatially?

2. To what extent does the water level vary in the Lauwers Lake during the residence of Bewick’s Swan?

3. What is the optimum water level to maintain maximum foraging efficient area?

4. How much is the foraging efficient area in the Lake at different water levels?

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2. Materials and Methods

2.1. Study area

The Lauwers Lake is a freshwater resource situated in the northern part of The Netherlands. It is located at 06º 13’E and 53º 22’N and covers an area of about 1970 ha. The study area is shown in figure 2-1. About 750 ha of the lake consist of water less than 70 cm deep. The Lauwers Lake was created in 1969 when the Lauwers Sea was closed off from the Wadden Sea. The Lauwers Lake is a protected area and therefore hunting is forbidden by law, but the deeper parts of the lake are open for boat traffic. The northern parts of the lake are sandier and the southern part is more clayey. The lake serves as a reservoir for superfluous polder water. The water levels may rise over one meter when strong north westerly winds prevail and superfluous water cannot be discharged into the Wadden Sea. The shallow areas of the lake are dominated by the submerged macrophyte Potamogeton pectinatus

Figure 2-1. Location of Lauwers Lake (National Park), The Netherlands

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2.2. Materials

2.2.1. Software Products

1. Erdas Imagine 9.1: • Generation of Normalised Differential Vegetation Index • Generation of Normalised Differential Water Index for delineating

boundary of the lake 2. ArcGIS 9.2:

• Spatial analyst in ArcGIS was used for Interpolation of point data. • Model builder was used for building the biomass predictive model (shown

in appendix 2) • Model builder was used for building spatial model for foraging efficiency

(shown in appendix 3) 3. SPSS 15.0:

• Used for generating the coefficients for biomass prediction variables • For testing Significance testing significance of predictions.

4. GS+ 7: • The point data set was checked for duplicates • Used for developing variograms for generating kriging parameters

2.2.2. Equipments

1. 1. Differential Global Positioning System (DGPS) Z-Max from Thales surveying system is used for locating the random sampling points.

2. Core Samplers 1.4 m and 0.5m for sediment sampling to collect tubers and sediment texture. (shown in appendix.1)

2.3. Available data

The available data are: 1. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)

data of 02nd June 2007 2. Water depth data for 4250 points from previous studies conducted by

Netherlands Institute of Ecology (NIOO). 3. Water level of the Lake and its time of measurement, acquired from

Waterschap Noorderzijlvest, Zoutkamp. 4. Sediment particle size data of 260 point from NIOO. 5. Foraging characteristics of Bewick’s swans from field observation and

experiments (Source: NIOO and other published literatures)

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2.4. Field data collected

The field work was organized from 15th of October to 16th of November 2007. The duration of the field work was divided into 3 periods. 1. Pre-sampling: Sampling of tuber biomass and sediment texture was conducted

before the arrival of the swans. 2. Bird counting: Bird observation was conducted for the estimation of the bird

density. 3. Post-sampling: Tuber biomass was sampled after the departure of the Swans

(Post-sampling data has not been used in this study because of time constrains).

Sediment Tuber biomass

Sample points

Sediment Tuber biomass

Sample points

Sampling points

i Creek name

Area Selected for the study

Area deeper than -3 m

Island

Lake boundary

´

Sediment Tuber biomass

Sample points

Sediment Tuber biomass

Sample points

Sampling points

i Creek name

Area Selected for the study

Area deeper than -3 m

Island

Lake boundary

´

Figure 2-2 Sampling points and the area selected for the study in the lake

2.4.1. Tuber sampling

Tuber sampling was conducted for the estimation of the tuber biomass density before the arrival of the Swans (pre-sampling) and after the arrival of the Swans (post-sampling). 90 sampling plots of 12x12 m were selected randomly. The 12x12 plot area was selected to have a higher chance for the plots to fall within the pixel size of ASTER image. A DGPS was used to locate the sampling plots. Each

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plot was divided into four equal quadrates of 4x4 m. A core sampler of 1.4 m length and 0.1m diameter was inserted into the sediments up to at least 0.35 m to collect the sediment core. From each quadrate 4 cores were collected. Thus 16 cores from one plot can minimise the sampling effort with achieving reliable estimates of the tuber biomass (Nolet et al. 2001). The sediment was sieved using a mesh size of 3 mm diameter. The tubers were separated from the sediments and their dry mass was measured after drying for 48 hour at 70o C (Nolet et al. 2001). The tuber biomass density per plot was calculated. The location of the sampling points in the lake and sampling plot design is given in the figure 2-3.

2.4.2. Sediment sampling

Sediment samples were collected from 90 points during the field work for improving the available data set of 260 points. They were collected from the centre point of each plot used for tuber sampling. A core sampler of 50 cm length and 5 cm diameter was used for taking out the sediment from the lake bed. Sediment layers from 5 to 10 cm and from 20 to 25 cm of the core sampler were separated for the Malvern analyses of particle size. A principle components analysis (PCA) was used to calculate the sediment granulometric composition as described in Nolet et al. (2001). The PCA 1 values were used as sediment texture index

2.4.3. Bird count

During residence of birds in the lake, daily bird count was conducted for foraging Swans in each creek. Clusters of the foraging birds were selected and the position (xn, yn) of the birds at the corners (marked as black in the figure 2-3.) of the cluster were calculated using triangulation (explained in figure 2-3) method. Two theodolites (T1 and T2 in the figure 2-4) were used to measure the angels α and β from the shore. The theodolite observers communicated with each other using walky-talkies. This helped observes to explain the behaviour of the bird under observation, so that both could make sure they are focusing on the same bird at a time. A GPS was used to measure the positions (x1, y1) and (x2, y2) of the theodolites T1 and T2 respectively. The positions (xn, yn) of the observed birds were estimated by solving the equations of two straight lines given as equation 2-1 and 2-2.

) x- (x tany - y 1n 1n α= ………...Equation 2-1.

)x-(x tan y - y 2n2n β= …………Equation 2-2. The average of the coordinates was used to mark the location of the foraging cluster in the map. The number of the foraging birds inside the cluster was counted.

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Nor

thin

g

Easting

Nor

thin

g

Easting

The locations of the foraging cluster and its size were mapped and used to test the PNEI predicted by foraging efficiency model.

Figure 2-3 Triangulation method used for the bird density mapping

2.5. Foraging efficiency model

The foraging efficiency model of Bewick’s Swans was developed using the concepts adopted from (Nolet, Langevoord et al. 2001; Nolet, Bevan et al. 2002; Nolet, Gyimesi et al. 2006). The model considers the metabolizable energy intake (MEI) and energy expenditure of Bewick’s Swans, foraging on the tuber biomass. The net energy intake (NEI) is calculated by subtracting the energy expenditure from the MEI. Foraging consists of trampling, underwater feeding and recovery at the surface. The mode of feeding in shallow water is head-dipping and that in deep water is up-ending. The energy expense for these two foraging modes is different. In the up-ending mode more energy is required compared to the head dipping. Also the energy expenditure is more as the particle size of the sediment increases. Metabolizable energy intake rate (J s-1) while foraging can be explained as,

))(.).(1()()(..

dDtsadDsaeqMEI

h+= ϕ …………Equation 2-3.

Where φ is the proportion of foraging time spent on feeding q is the tuber assimilation efficiency. This value explains how much the animal can effectively use from the ingested food. e is the energy density of the tuber

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th is the handling time. This is the time required for a forager to extract the encountered food item from the substrate and place it in its mouth. a(s) is the attack area. This is the speed of the animals searching through the area for food and it's proportional to the amount of food in the patches. Attack area is a function of sediment type (based on particle size) D(d) is the accessible tuber biomass. The accessible tuber biomass is a function of water depth (d), the maximum reachable length of swan (L) and tuber burial depth (b(s)=L-d). That is D(d)=0.00082*( L – d) 2*D. The value 0.00082 is biomass proportion to the burial depth (Nolet and Klaassen, submitted).

Net energy intake rate (J s-1) is the difference between the mei and foraging costs, and can be expressed as NEI = MEI - c(d,s), where the cost of foraging is a function of water depth (d) and sediment type (s). The values for each parameter are given in the table 2.1.

Table 2-1 Parameters and their values from literatures Parameter Value (mean ± SE) Reference

φ 0.80 ± 0.01 (Nolet, Langevoord et al. 2001)

q 0.90 ± 0.02 (Nolet, Bevan et al. 2002) e 16866 ± 296 (Nolet, Bevan et al. 2002) th 1.82 ± 0.48 S (Nolet, Bevan et al. 2002)

a(s) s is sand: 0.00102 ± 0.00011

s is clay: 0.6 × 0.00102

(Nolet, Bevan et al. 2002), (Nolet, Langevoord et al. 2001)

L 0.86 ± 0.3 (Nolet, Fuld et al. 2006)

b(s) s is sand: 0.00077 ± 0.00003

s is clay: 0.00090 ± 0.00005

(Nolet and Klaassen, submitted)

s is sand, d ≤0.51m: 45.1 ± 1.3

s is clay, d ≤0.51m: 1.3 ± 0.1 × 45.1

(Nolet, Fuld et al. 2006) c(d,s)

s is sand, d >0.51m: 56.7 ± 1.2

s is clay, d >0.51m: 1.3 ± 0.1× 56.7

(Nolet, Langevoord et al. 2001)

In the equation 2-3, a(s), c(d,s) and b(s) are functions of sediment texture. Their values are given table 2-1. These values from the literatures are discrete. When the sediment type is sand corresponding sediment texture index value -1.662 and that for clay is 2.5. In the spatial model design sediment texture index is a raster data. Hence it is required to make continuous.

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Table 2-2 Parameters which are function of sediment type Sediment texture index

a(s) c(d,s) (d≤0.51m)

c(d,s) (d >0.51m).

b(s)

Sand -1.662 0.00102 45.1 56.7 0.00102

Clay 2.5 0.0000612 58.63 73.71 0.00090 The continious values can be estimated for a(s), c(d,s)-shallow, c(d,s)-deep and b(s) from the equations 2-4, 2-5, 2-6 and 2-7 respectively.

0.0009 index) texture(sediment 0.0001- a(s) +×= ….Equation.2-4

45.1 1.1198 index) texture(sediment 0.0721- s)c(d, ×+×=

..……Equation.2-5

7.65 1.1198 index) texture(sediment 0.0721- s)c(d, ×+×=

……..Equation 2-6

0.0008 index) texture(sediment 0.00003 b(s) +×= …….Equation 2-7

2.6. Generation of spatial dataset for the model

The spatial data required for the model are tuber biomass density map, bathymetry map and sediment texture map. The entire spatial database was projected in the Dutch R D coordinate system with a cell size of 15m. The water level of the lake during the swan days was also used as the model input.

2.6.1. Mapping of tuber biomass density

The decision to investigate the relation between remotely sensed data and tuber biomass was chosen deliberately, although it is known that remote sensing is not capable to pick the reflectance of tubers buried in the sediment. But it has been proved that remote sensing can be used for mapping the submerged vegetation (Valta, Partamen et al. 2002). Hence, spectral reflectance of above ground biomass was tried to relate with the below biomass. It is assumed that below ground tuber biomass is related to the above ground biomass (Aerts, Boot et all 2004). Because of time constrain no above ground biomass was able to collect, hence satellite data was used for the generation of main predictive variable.

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ASTER data was used for the prediction of tuber biomass. The maximum reflectance observed for Potamogeton pectinatus is at 0.55 and 0.665 µm (Pinnel, Heege et al. 2004). The first 3 bands of the ASTER data were used for the analysis because they are of higher spatial resolution than the other bands and the maximum reflectance of the Potamogeton pectinatus is fond in the first 3 bands. The specifications of first three bands of ASTER data is given in the table 2.2.

Table 2-3. ASTER data specifications for first three bands

Band Wavelength (µm) Spatial Resolution (m)

1. Green 0.52 - 0.60 15

2. Red 0.63 - 0.69 15

3. Near Infra red 0.76 - 0.86 15 Tuber biomass was predicted using a nonlinear Gaussian model type II (Huisman, J., H. Olff, et al. 1993) shown in equation 2-8.

)XX Xh )(X )(X f )(X e )(X d )(X c )(X b (a 3212

332

222

1111

++++++++=

geMy ……Equation 2-8

Where y is the predicted biomass; M is the maximum biomass value observed during sampling; x1, x2, x3 are the prediction variables.; a, b, c, d, e, f, g and h are the coefficients of the prediction variables (determined using SPSS 15.0 statistical software); The variables for the prediction of biomass were selected from NDVI, depth, sediment texture index. The inclusion of the variables was based on their significance determined by t-value and corresponding p-value.

2.6.2. Mapping of bathymetry and sediment texture

Water depths of the 5056 points were used for the interpolation of the shallow (0.15 to 2.0 m deep) region of the lake. Water level of the lake during the depth measurement was acquired from Waterschap Noorderzijlvest, who monitors the water level every 15 minutes. Bathymetry data was calculated by subtracting the water depth from the water level. The water level measurements in this study were in Normal Amsterdam’s Point (NAP).

The PCA1 values of the Malvern grain size analysis of 350 sediment samples were considered as sediment texture index which range from -4.6 (sandy) to 8.2 (clayey). Nolet et al. (2001) used PCA1 values as to represent sediment texture for analysing the energy requirement of foraging Bewick’s Swans in sandy and clayey sediment. Interpolation of texture index was done for the generation of sediment texture map.

19

70% of bathymetry and texture indices were randomly selected for interpolation and rest (30%) were selected for validation of the interpolation. The most commonly used interpolation methods such as Kriging and Inverse Distance Weighted Average (IDWA) (Burrough and Mcdonnell 1988) were used to generate a digital bathymetry model and sediment texture map. Kriging assumes that the spatial variation of a variable is the sum of three major components:

1. a structural component, having a constant mean or trend called range, 2. a random and locally varying but spatially correlated component (the

variation of the regionalized variable) known as sill, 3. and a spatially uncorrelated random noise or residual error termed as

nugget. These components were estimated by plotting semivariogram of the data (is explained using figure 2-6).

Sem

i var

ianc

e

Separation distance

Sem

i var

ianc

e

Separation distance Figure 2-4. A typical semivariogram as an example

IDWA is based on the assumption that the value of an unknown point is a distance weighted average of data points occurring within a neighbourhood. The difficulties associated with IDWA are determination of the weight associated with the distance and determination of the number of known points that should be used for predicting the unknown value. In addition, there could be an edge effect in the surface made by IDW interpolations and the estimated value can never be lower than the lowest known data value and higher than the highest known data value (Burrough and Mcdonnell 1998).

20

The average number of sample points in the creeks as the number of known points around the unknown and the average radius of the creeks as the search radius were selected for IDWA interpolation. Interpolation was carried out using different weigh values. The maps of bathymetry and sediment texture generated using different interpolation techniques were cross-validated and the Root Mean Square Error (RMSE) and the Mean Error were calculated. The difference between the maximum values in the measured and predicted (Dmax) and the difference between the minimum values in the measured and predicted (Dmin) were also calculated. A ranking was given to the interpolation techniques based on the error and the calculated differences. Higher rank was given to the technique that is comparably error less and having low differences. Sum of the ranking for RMSE, ME, Dmax and Dmin was calculated as goodness rank. The generated map with high goodness rank was selected as the model input.

2.6.3. Analysis of water level variation

The target water level during the period when Swans normally reside in the lake Lauwers (October- November months) is -0.93m. The actual water level differs from this water level, depending on the sea level and discharge from the rivers. Hence the water level in the lake is not same always. So there is a need to analyse the historical water level of the Lake. Daily average water level from 1993 to 2007 was analysed to estimate the maximum, minimum and average water level during the months of October and November.

2.7. Simulation of the foraging efficiency model

The spatial data of tuber biomass density, digital bathymetry model, sediment texture and the average water level of the swan days are used in the model along with the other input data related to swan’s foraging characteristics (in the table 2.1.) The expected out come of the model was a spatial variation of PNEI in the Lauwers Lake. The schematic diagram of the model is given in the figure 2-5. Foraging efficient area is determined by masking the resultant PNEI map with a critical PNEI critical value of 65 Js-1. Bewick’s Swans give-up foraging below this value (Nolet and Klaassen, submitted)

21

Satellite data

Field data

Sediment texture

Bathymetry

Water Level

Tuber density

Water depth

Bird foraging data from LiteraturesAttack area

Handling timeMetabolisable energyEtc from the literature

MODELThe net energy balance of Bewick’s swan foraging in the lake Lauwers

Figure 2-5 Schematic diagram of the model

2.8. Estimation of optimum water level

An optimum water level was calculated that would maintain the maximum foraging efficient area in the lake. The model was simulated with varying water levels (-1.7 to -0.5m) keeping other inputs constant. The foraging efficient area in the lake was estimated for each water level. A scatter plot was developed by plotting water level and foraging efficient area. The water level at which maximum foraging efficient area attained was selected as the optimal water level.

2.9. Test of foraging efficiency model

Simple tests were conducted to assess the capability of the model to predict PNEI. The methods used are explained below.

2.9.1. Overlaying of bird count on PNEI map

This test was carried out by overlaying the positions of the foraging bird cluster (estimated using triangulation method) over a PNEI map. The spatial variation of PNEI and the distribution of the foraging birds were visually assessed. It was expected to see the location of the foraging clusters falling over the areas where high PNEI values predicted.

2.9.2. Comparison of foraging efficient area and bird count

This is a test for spatial distribution of predicted PNEI. In this method comparison of bird count data and estimated foraging efficient area in all creeks were tabulated. It was expected to have large number of foraging birds in the creeks with larger

22

foraging efficient area and less number of birds where foraging efficient area is low.

2.9.3. Comparison of PNEI density and bird count

This is a temporal test with daily PNEI density in the whole lake and with daily bird count. PNEI density (Jm-2s-1) was compared with the number of foraging birds in all residence day (13th October to 7th November 2007) of Swans. It was expected to have larger number of foraging birds in the days where PNEI density was high.

2.10. Sensitivity analysis

The relation between the input and the output of a model is explained by sensitivity analysis. The variation in the input leads to changed output quantities. A first order second moment method was used for the sensitivity analysis of the model (Ang and Tang 1984). The sensitivity of PNEI towards biomass, bathymetry, water level and sediment texture were analysed by simulating the model keeping other input variables as constant and by only varying the values of the parameter under test. Each parameter is tested in separate simulations with its average value and average + standard deviation.

23

3. Results

3.1. Prediction of Tuber Biomass

The variables for the prediction of biomass were selected from NDVI, depth and sediment texture index.

))(X )(X f )(X e )(X d )(X c )(X b (a 233

222

2111

1ge

My+++++++

= ……Equation 3-1

Where y is the predicted biomass; M is the maximum biomass value observed during sampling = 40.1g; the prediction variables, x1 = NDVI; x2 = depth and x3

= sediment texture index. The selection for the inclusion of variables in the model was based on their significance determined by t-value and corresponding p-value. The estimated coefficients of the prediction variables and their t-values are given in the table 3-1.

Table 3-1 Regression statistics for the full model described in equation 3-1

Parameter Estimate Std. Error student t b -5.471 1.322 -4.138 c 29.438 8.450 3.484 d -8.574 4.083 -2.100 e 8.252 3.797 2.173 f -0.052 0.049 -1.054 g -0.022 0.016 -1.348 p-value at α0.05 = +/-1.98

A backward selection was carried out to remove non-significant variables one by one based on their t-value. As the t-value for the coefficients of sediment texture (f and g in table 3-1) was less than the p-value, sediment texture index was removed from the prediction equation. The new equation 3-2 was used for the estimation of coefficients of prediction variables. Table 3-2 shows the estimated coefficient of the variables selected for the final model after removing non-significant variable (sediment). The final model, with NDVI and depth as the prediction variable of biomass is shown in the equation 3-2.

depth) NDVI f (depth) e depth d (NDVI) c NDVIb (a 22

11

××+×+×+×+×++=

eMy …Equation 3-2

24

Table 3-2 Regression statistics for the full model described in equation 3-2

Parameter Estimate Std. Error student t a 2.00 0.671 2.986 b -14.06 3.290 -4.27 c 37.49 6.039 6.208 d -10.11 2.800 -3.611 e 10.925 2.828 3.863 f 18.555 6.546 2.834 Critical value at α0.05 = +/-1.98

The tuber density map (year 2007) of the Lauwers Lake was generated using the equation 3-2 and the coefficients used are given in the table 3-2. The tuber biomass map generated is shown in the figure 3-1.

Figure 3-1 Tuber biomass density map (year 2007) of Lauwers Lake

25

3.2. Validation of the biomass prediction

The predicted biomass was validated using the biomass measured from the field. The validation of the predicted values by the measured data is shown in the figure3-2. .

y = 0.5359x + 2.5441R2 = 0.5049

0

5

10

15

20

25

30

0 5 10 15 20 25 30 35 40

Measured

Pre

dict

ed

Figure 3-2 Validation of predicted biomass using a linear regression

Table 3-3 Regression statistics

β Coefficients Std. Error t Constant 2.544 0.584 4.354 Measured 0.536 0.048 11.244 Critical value at α0.05 = +/-1.98

The R2 value for the linear regression relationship (figure 3-3) between measured biomass and predicted biomass is 0.50. The regression coefficient (shown in table 3-5) of the relation is significant at 5% level of significance. Hence the biomass estimated for the year 2007 was used as the input variable of the foraging efficiency model.

3.3. Interpolation of Bathymetry Data

3.3.1. Kriging

A semiveriogram was created using all the data points. A strong anisotropy in the data set along the creeks was observed. The point data was limited to isotropic situations by avoiding those points in the deeper regions of the lake. A reasonable fit in the variogram (shown in the figure 3.1) was observed when the bathymetry points were limited to 0.93 to 3m using a spherical model. The parameter generated from the variogram for kriging is given in the table 3.1.

26

0.000

0.028

0.057

0.085

0.114

0.00 1376.97 2753.95 4130.92

Sem

ivar

ianc

e

Separation Distance (h)

Spherical model (Co = 0.04030; Co + C = 0.10560; Ao = 1707.00; r2 = 0.925; RSS = 3.464E-04)

Figure 3-3 Isotropic variogram for bathymetry data

Table 3-4 Kriging parameters generated from the variogram

Kriging parameters Values Nugget 0.04 Sill 0.105 Range 1707 Lag size 641

3.3.2. Inverse distance weighted interpolation

IDW interpolation was conducted with different inverse weight values using 15 known points within a radius of 150m around the unknown. This values were used, because the average transect length of sampling was 150m and average number of samples collected from one transect was 15.

3.3.3. Selection of interpolation technique

Based on the given ranking (R) the suitable interpolation technique was selected. The results of the validation of the bathymetry data are reported in the table 3.2.

Table 3-5 Cross validation and goodness ranking of interpolations Method Weight RMSE Rank ME Rank Dmax Rank Dmin Rank Goodness

rank IDW 1 0.1566 3 0.0012 5 0.1 2 0.23 1 11 IDW 1.5 0.1489 4 -0.0001 6 0.1 2 0.23 1 13 IDW 2 0.1459 5 -0.003 4 0.1 2 0.23 1 12 IDW 2.5 0.144 6 -0.0001 6 0.1 2 0.23 1 15 IDW 3 0.144 6 -0.007 3 0.1 2 0.23 1 12 Kriging - 0.1715 2 -1 6 0.2 1 0.1 2 11

27

Based on the goodness rank calculated, IDW method with weight 2.5 was selected. The bathymetry map generated by this method is shown in figure3.2.

Figure 3-4. Digital Bathymetry Model developed by IDW interpolation

28

3.4. Interpolation of Sediment Texture

3.4.1. Kriging

A reasonable fit of the variogram of the sediment texture indices was observed when a Gaussian model was used to fit the curve. The isotropic variogram is shown in figure 3-5. The parameters generated for the kriging are given in table 3-6.

0.00

1.64

3.27

4.91

6.54

0.00 80.25 160.50 240.75

Sem

ivar

ianc

e

Separation Distance (h)

Gaussian model (Co = 0.61000; Co + C = 6.73900; Ao = 129.90; r2 = 0.961; RSS = 2.07)

Figure 3-5. Isotropic variogram plot for sediment texture data

Table 3-6. Kriging parameters from the variogram Parameters Value Nugget 0.62 Sill 6.72 Range 225 Lag size 240

3.4.2. Inverse Distance Weighted

IDW interpolation was conducted with different inverse weight value using a 35 number of known points with in a radius of 700 m around the unknown. This values were used because 30 is the average number of sampling points and 700 m is the average radius of the creeks.

3.4.3. Selection of interpolation technique

Based on the given ranking (R), the suitable interpolation technique was selected. The results of the validation of the sediment maps are reported in table 3-7.

29

Table 3-7 Cross validation and goodness ranking of interpolations

Method Weight RMSE Rank ME Rank DMax Rank DMin RankGoodness rank

IDW 1 1.693 4 -0.112 1 0.09 2 0.05 2 9 IDW 1.5 1.667 5 -0.01 4 0.09 2 0.05 2 13 IDW 2 1.671 4 -0.096 2 0.09 2 0.05 2 10 IDW 3 1.711 2 -0.092 3 0.09 2 0.05 2 9 Kriging -- 1.68 1 -0.025 5 3.79 1 -1.11 1 8

Based on the goodness rank calculated, IDW method with weight 1.5 was selected. The sediment texture map generated by this method is shown in figure 3-7.

Figure 3-6. Sediment texture map developed by IDW interpolation

It is observed in the figure 3.4, that more area in the lake is sandy. This is an expected result as it is known that the lake initially was the part of sea. From moving North to South the clay content in the sediment is increasing. This is justified that near to the river mouths the clay is more and towards the sea it is sandy.

30

3.5. Variation of water level

Variation of water level in Lauwers Lake in October and November months from 1993 to 2006 were analysed. The results are shown in the charts given in figure3-7.

LEGEND

Target water level

Daily average

Annual average

1993

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

1 5 10 15 20 25 1 5 10 15 20 25 30

October November

1994

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

1 5 10 15 20 25 1 5 10 15 20 25 30

October November

1995

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

1 5 10 15 20 25 1 5 10 15 20 25 30

October November

1996

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

1 5 10 15 20 25 1 5 10 15 20 25 30

October November

1997

-1

-0.9

-0.8

-0.7

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

1 5 10 15 20 25 1 5 10 15 20 25 30

October November

1998

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

1 5 10 15 20 25 1 5 10 15 20 25 30

October November

1999

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

1 5 10 15 20 25 1 5 10 15 20 25 30

October November

2000

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

1 5 10 15 20 25 1 5 10 15 20 25 30

October November

2001

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

1 5 10 15 20 25 1 5 10 15 20 25 30

October November

2002

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

1 5 10 15 20 25 1 5 10 15 20 25 30

October November

2003

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

1 5 10 15 20 25 1 5 10 15 20 25 30

October November

Figure serious 3.7. Variation in water level from 1993-2006

31

2004

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

1 5 10 15 20 25 1 5 10 15 20 25 30

October November

2005

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

1 5 10 15 20 25 1 5 10 15 20 25 30

October November

2006

-1

-0.9

-0.8

-0.7

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

01 5 10 15 20 25 1 5 10 15 20 25 30

October November

2007

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

1 5 10 15 20 25 1 5 10 15 20 25 30

October November

Figure serious 3-7. Variation in water level from 1993-2006 (…continuation)

Highest water level (0.082 m) was recorded on 29th October 1998 and the lowest water level of -1.12 m on 8th October 1993. The average water level was -0.84m in the years from 1993 to 2007. Comparison of water level and number of foraging birds in the year 2007 is given in the figure 3-8.

0

50

100

150

200

250

300

350

13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 1 2 3 4 5 6 7

Oct. 2007 Nov. 2007

Bird

cou

nt

-1

-0.9

-0.8

-0.7

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

Wat

er L

evel

from

NA

P (m

)

Average number of foragingbirds

Highest water Level

Average Water Level

Figure 3-8. Water level and foraging bird count during 2007 in Lauwers Lake

The birds started to arrive to the lake from 13th of October. As the water level kept on increasing from 16th onwards, the number of foraging birds were found decreased. A sudden rose of water level to -0.1m on 18th of October, decreased the number of foraging birds to zero. It can be interpreted from the results (figure 3.8),

32

that the increase in the water level in the initial stages of the visiting period made the Swans to quit the lake upon the arrival staying in the lake for the refuelling.

3.6. Potential net energy intake

The spatial data of tuber biomass (year 2007), sediment texture, bathymetry and average water level of the lake during the residence of Swans in the year 2007 were used to estimate the PNEI. The spatial variation of PNEI is shown in figure 3-9. The area of the lake (creek-wise) having the PNEI higher than 1 Js-1 and 65 Js-1 is shown in the table 3-8. The efficient foraging area in the year 2007 was estimated as 81.35 ha.

Table 3-8. NEI distribution in the creeks Area (ha) of Lake having PNEI

Creek >1 Js-1 >65 Js-1

Achter de Zwarten (ADZ) 66.69 24.83 Babelaar (BBL) 4.00 0.46 Bilkplaat (BPG) 15.34 4.06 De Rug (DR) 5.92 1.59 Jaap Deepsgat (JDG) 32.37 7.41 Newe Robbenghat (NRG) 30.23 17.03 Oude Robbengat (ORG) 38.12 12.58 Simonsgat (SG) 9.04 1.37 Vinderbalg (VLB) 20.18 10.27 Zouthkamperplaat (ZKR) 4.19 1.76 Total 226.07 81.35

0

5

10

15

20

25

30

ADZ NRG ORG VLB JDG BPG ZKR DR SG BBL

Creek

Are

a (h

a)

Figure 3-9 Efficient foraging area in each creek

33

Figure 3-10. Potential Net Energy Intake of the year 2007 in Lauwers Lake

3.7. Estimation of optimum water level

The model was simulated with varying water level. The foraging efficient area in the lake was calculated by masking the areas having PNEI less than 65Js-1. A scatter plot was generated with water level and estimated foraging efficient area (shown in figure 3-11). The highest foraging efficient area was obtained when the

34

water level was at -1.18 m from NAP. This water level was considered as the optimal water level.

0

20

40

60

80

-1.7 -1.5 -1.3 -1.1 -0.9 -0.7 -0.5Water level m

Fora

ging

effi

cien

t are

a (h

a)

Figure 3-11. Variation in PNEI density at different water level

The target water level the authority trying to maintain in the lake during the residence time of Swans is -0.93m. The average water level is -0.84 m. The deviation from the optimum level to the target water level is 0.12m and that to the average water level is 0.16m. By manipulate water level from target to optimal water level, an increase of 12.9 ha foraging and efficient area was fond. When level was reduced from average water level to the optimum water level an increase of 32.4 ha of foraging and efficient area was estimated. This increase in the efficient foraging area is shows in figure 3-12.

0

10

20

30

40

50

60

70

80

90

Optimum (-1.18) Target (-.93) Average (-84)

Water level (m)

Are

a (h

a)

Achievable

Foraging effiecent

Figure 3-12. Variation in foraging efficient area from the optimum

35

An increase in 44% of efficient foraging area was estimated when water level is manipulated from average to optimum level. And an increase of 5.3 % was estimated when water level is manipulated from the target level to optimal level.

3.8. Testing of the foraging efficiency model

3.8.1. Test by overlaying of bird count on PNEI map

In the first test, positions of the foraging birds and their population (as bar diagram) were over laid on the PNEI map. The overlay map is shown in figure 3-13. It was expected to see the foraging clusters to be over the area where PNEI is high. The positions of 70 % of the foraging cluster fell on the places where PNEI is high.

Figure 3-13. Spatial variation in NEI and the number of foraging birds

36

3.8.2. Comparison of foraging efficient area and bird count

In the second test (explained using figure 3-14), it was expected to have more number of foraging birds in the creeks where efficient area is high and less number of birds in the creeks where efficient foraging area is less. It has been observed that the number of foraging birds were zero in the creeks with less PNEI comparatively more where the PNEI is high.

0

10

20

30

40

50

60

70

ADZ NRG ORG VLB JDG BPG ZKR DR SG BBL

Creek

Area

(ha)

0

100

200

300

400

500

600

700

Area (ha) of Lake having PNEI rate >1 J/sArea (ha) of Lake having PNEI rate >65 J/sNumber of foraging birds

Figure 3-14. Comparison of area having high NEI and foraging birds in each creek

3.8.1. Comparison of PNEI density and bird count

The result of this test is explained using the figure 3-15.

0

50

100

150

200

250

300

350

13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 1 2 3 4 5 6 7

Oct. 2007 Nov. 2007

Bird

cou

nt

0

2

4

6

8

10

12

14

16

18

20

PN

EI d

ensi

ty J

m-2s-1

Average number of foragingbirds

PNEI density

Figure 3-15 Comparison of PNEI density and bird count

It can be observed in the figure 3-15 the foraging birds are less in the days (18th of October) where PNEI is below zero. The decrease in the PNEI density in the initial

37

stages of the visiting period, made the Swans to quit the lake upon the arrival to the Lake.

3.8.2. Discussion on test results

From the three test results given above, it is proved that the results of the foraging efficiency model are reliable. In these three tests the disturbance factor was not considered and hence the bird clusters may not be visible in those areas with high PNEI values. The disturbance can be due to the boats, motor vehicles and visitors.

3.9. Sensitivity Analysis of the model

The input variables (bathymetry, water level, sediment and biomass) used in the foraging efficiency model was analysed and the result are given in the figure 21.

-100

-50

0

50

100

150

200

Ave

rage

+ S

td d

ev

Ave

rage

Ave

rage

-Std

dev

Ave

rage

+ S

td d

ev

Ave

rage

Ave

rage

-Std

dev

Ave

rage

+ S

td d

ev

Ave

rage

Ave

rage

-Std

dev

Ave

rage

+ S

td d

ev

Ave

rage

Ave

rage

-Std

dev

Bathymetry Water Level Sediment texture Biomass

PNE

I rat

e (J

/s)

Figure 3-16. Sensitivity analysis of the model

The sensitivity of the out put PNEI is to the input parameters are in the order 1. Bathymetry data, 2.water level, 3. Biomass and 4. Sediment texture.

38

4. Summary of Results

1. PNEI varies with biomass, sediment texture and depth as the result of this there is a considerable spatial variation in PNEI in a Lake system

2. The average water level of the Lauwers Lake is -0.84 m with a standard

deviation of 0.15, the maximum is -0.49m and the minimum is -1.12 m. 3. Optimum water level in the Lauwers Lake is -1.18m 4. The foraging efficient area in the Lake at different water levels are :

Optimum water level (-1.18m) : 88.9 ha Target water level (-0.93m) : 76.0 ha Average water level (-0.84 m) : 56.4 ha

5. An increase in 44% of area was estimated when water level is manipulated from average to optimum level.

6. A increase of 5.3 % was estimated when water level is manipulated from the

target level to optimal level

39

5. Conclusion

In this research, we presented a model predicting the net energy intake of Bewick’s Swans in the Lauwers Lake. The model revealed that the area with tubers above give-up density increased while reducing water level below the current average and the target water level. The area of the lake with NEI above give up density which was optimal at -1.18 m below NAP, equalled 88.9 ha. At target water level and at the actual water level this area was much lower, 76.0 ha and 56.4 ha respectively. These results indicate that Lauwers Lake could accommodate significantly more swan days, if a lower water level was maintained. Hence the carrying capacity of the Lake for Bewick’s swan could be enlarged while reducing water level. In the current research we did not have sufficient time to predict the impact of reduced water level on number of swan days. It would be interesting to further investigate the effect of water level on carrying capacity. Our research in brief, presents a model to predict the impact of water management on the carrying capacity of wetland to overwintering migratory birds. To our knowledge this is the first model ever attempt to predict this relation in a spatial level. Such models are urgently needed, because wetland environment and migratory bird species are under increasing pressure. With fewer stopover sites remaining along the migratory path ways of many birds, there is a need to optimize carrying capacity of these systems for the migrating species.

40

6. References

1. Aerts, R., Boot, R.G.A., & van der Aart, P.J.M. (2004). The relation

between above- and belowground biomass allocation patterns and competitive ability Oecologia, 87

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3. Agbor Delphine, T. E. (2006). Remote sensing and GIS in mapping Potamogeton Pectinatus as food source for Bewick's Swans : case study of lake Lauwersmeer, The Netherlands. Enschede, ITC. Master of Science: 43.

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7. Appendix 1. Photographs from the field

Figure 7-1 Positioning the sample plot using a DGPS before sampling

Figure 7-2. Tuber sampling at a shallow

Figure 7-3 Sediment sampling

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8. Appendix.2. Model build in Arc GIS to predict tuber biomass

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9. Appendix.3 Model build in Arc GIS to estimate PNEI