evaluating animal space use: new developments to estimate animal movements, home range, and habitat...
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![Page 1: Evaluating Animal Space Use: New Developments to Estimate Animal Movements, Home Range, and Habitat Selection Dr. Jon S. Horne, University of Idaho](https://reader035.vdocuments.site/reader035/viewer/2022070412/56649d4c5503460f94a2b17d/html5/thumbnails/1.jpg)
Evaluating Animal Space Use:
New Developments to Estimate Animal Movements, Home Range, and Habitat Selection
Dr. Jon S. Horne, University of Idaho
![Page 2: Evaluating Animal Space Use: New Developments to Estimate Animal Movements, Home Range, and Habitat Selection Dr. Jon S. Horne, University of Idaho](https://reader035.vdocuments.site/reader035/viewer/2022070412/56649d4c5503460f94a2b17d/html5/thumbnails/2.jpg)
Central question:“Where do animals live… and why?”
• Space Use is Non-uniform
• Quantitative Description of Space Use
“Utilization Distribution”
The relative amount of time spent in an area
![Page 3: Evaluating Animal Space Use: New Developments to Estimate Animal Movements, Home Range, and Habitat Selection Dr. Jon S. Horne, University of Idaho](https://reader035.vdocuments.site/reader035/viewer/2022070412/56649d4c5503460f94a2b17d/html5/thumbnails/3.jpg)
Biotelemetry • Cannot Monitor Animals Continuously
– Have locations at discrete intervals (e.g., telemetry)
![Page 4: Evaluating Animal Space Use: New Developments to Estimate Animal Movements, Home Range, and Habitat Selection Dr. Jon S. Horne, University of Idaho](https://reader035.vdocuments.site/reader035/viewer/2022070412/56649d4c5503460f94a2b17d/html5/thumbnails/4.jpg)
• Describing and Understanding Animal Space Use is Important
• We Want an Estimate of the Utilization Distribution
• Obtain This Estimate Using Discrete Locations
![Page 5: Evaluating Animal Space Use: New Developments to Estimate Animal Movements, Home Range, and Habitat Selection Dr. Jon S. Horne, University of Idaho](https://reader035.vdocuments.site/reader035/viewer/2022070412/56649d4c5503460f94a2b17d/html5/thumbnails/5.jpg)
New Developments
1) Objective Method for Choosing Among Home Range Models
2) Improve the Kernel – Likelihood-based smoothing parameter
3) Correct Home Range Models for Observation Bias4) The Brownian Bridge Movement Model 5) Incorporate Other Variables (e.g., habitat, other
organisms) Into Home Range Models
![Page 6: Evaluating Animal Space Use: New Developments to Estimate Animal Movements, Home Range, and Habitat Selection Dr. Jon S. Horne, University of Idaho](https://reader035.vdocuments.site/reader035/viewer/2022070412/56649d4c5503460f94a2b17d/html5/thumbnails/6.jpg)
1) Selecting the Best Home Range Model
• How do we choose the best home range model?– Popularity– Evaluate using simulated data
• Shouldn’t we “Let the data speak”?– Information-theoretic model selection
![Page 7: Evaluating Animal Space Use: New Developments to Estimate Animal Movements, Home Range, and Habitat Selection Dr. Jon S. Horne, University of Idaho](https://reader035.vdocuments.site/reader035/viewer/2022070412/56649d4c5503460f94a2b17d/html5/thumbnails/7.jpg)
Selecting the Best Model
• Selection Criteria– Akaike’s Information Criteria (AIC)
• Adjusts model likelihood for overfitting
– Likelihood Cross-validation Criteria (CVC)• Evaluates the predictive ability
• Stone (1977) Showed Asymptotic Equivalence
![Page 8: Evaluating Animal Space Use: New Developments to Estimate Animal Movements, Home Range, and Habitat Selection Dr. Jon S. Horne, University of Idaho](https://reader035.vdocuments.site/reader035/viewer/2022070412/56649d4c5503460f94a2b17d/html5/thumbnails/8.jpg)
Applying Selection Criteria to Home Range Models
• Can we calculate likelihood?– Yes, if home range models estimate the
utilization distribution– No for minimum convex polygon (MCP)
• New model based on uniform distribution
![Page 9: Evaluating Animal Space Use: New Developments to Estimate Animal Movements, Home Range, and Habitat Selection Dr. Jon S. Horne, University of Idaho](https://reader035.vdocuments.site/reader035/viewer/2022070412/56649d4c5503460f94a2b17d/html5/thumbnails/9.jpg)
Ecologically Based Shapesfor Territories (from Covich 1976)
![Page 10: Evaluating Animal Space Use: New Developments to Estimate Animal Movements, Home Range, and Habitat Selection Dr. Jon S. Horne, University of Idaho](https://reader035.vdocuments.site/reader035/viewer/2022070412/56649d4c5503460f94a2b17d/html5/thumbnails/10.jpg)
Exponential Power Model
0
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Location (x)
Pro
bab
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c = 1
c = 0.5
c = 0.1
Circular Uniform is a Particular Case3 parameters: location, scale, shape ( c )
![Page 11: Evaluating Animal Space Use: New Developments to Estimate Animal Movements, Home Range, and Habitat Selection Dr. Jon S. Horne, University of Idaho](https://reader035.vdocuments.site/reader035/viewer/2022070412/56649d4c5503460f94a2b17d/html5/thumbnails/11.jpg)
Application: Home Range Model Selection
• Used location data from a variety of species
• Evaluated 6 home range models:– Bivariate normal– Exponential power– 2-mode circular normal mix– 2-mode bivariate normal mix– Fixed and adaptive kernels
• Calculated AIC and CVC
![Page 12: Evaluating Animal Space Use: New Developments to Estimate Animal Movements, Home Range, and Habitat Selection Dr. Jon S. Horne, University of Idaho](https://reader035.vdocuments.site/reader035/viewer/2022070412/56649d4c5503460f94a2b17d/html5/thumbnails/12.jpg)
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Adaptive kernel 2-mode circular normal mix
2-mode bivariate mix Bivariate normal
![Page 13: Evaluating Animal Space Use: New Developments to Estimate Animal Movements, Home Range, and Habitat Selection Dr. Jon S. Horne, University of Idaho](https://reader035.vdocuments.site/reader035/viewer/2022070412/56649d4c5503460f94a2b17d/html5/thumbnails/13.jpg)
Conclusions• AIC or CVC
– No strong arguments favoring one over the other– Must use CVC with kernel models
• No Single Model Was Always Best– Kernels performed quite well
• Goal of Model Selection– Find model closest to truth– “Test” hypotheses
Use model selection to understand home range
![Page 14: Evaluating Animal Space Use: New Developments to Estimate Animal Movements, Home Range, and Habitat Selection Dr. Jon S. Horne, University of Idaho](https://reader035.vdocuments.site/reader035/viewer/2022070412/56649d4c5503460f94a2b17d/html5/thumbnails/14.jpg)
New Approaches
1) Develop Objective Method for Choosing Among Home Range Models
2) Improve the Kernel – Likelihood-based smoothing parameter
3) Correct Home Range Models for Observation Bias 4) The Brownian Bridge Movement Model 5) Incorporate Other Variables (e.g., habitat, other
organisms) Into Home Range Models
![Page 15: Evaluating Animal Space Use: New Developments to Estimate Animal Movements, Home Range, and Habitat Selection Dr. Jon S. Horne, University of Idaho](https://reader035.vdocuments.site/reader035/viewer/2022070412/56649d4c5503460f94a2b17d/html5/thumbnails/15.jpg)
Influence of smoothing parameter (h) on kernel density
Smoothing parameter (h)
kernels Kernel estimate
![Page 16: Evaluating Animal Space Use: New Developments to Estimate Animal Movements, Home Range, and Habitat Selection Dr. Jon S. Horne, University of Idaho](https://reader035.vdocuments.site/reader035/viewer/2022070412/56649d4c5503460f94a2b17d/html5/thumbnails/16.jpg)
Influence of smoothing parameter (h) on kernel density
![Page 17: Evaluating Animal Space Use: New Developments to Estimate Animal Movements, Home Range, and Habitat Selection Dr. Jon S. Horne, University of Idaho](https://reader035.vdocuments.site/reader035/viewer/2022070412/56649d4c5503460f94a2b17d/html5/thumbnails/17.jpg)
Previously Recommended for Home Range Estimation
• Fixed Kernel Density– Least Squares Cross-validation (LSCVh)
• Drawbacks to LSCVh– High variablility– Tendency to undersmooth– Multiple minima in the LSCVh function
![Page 18: Evaluating Animal Space Use: New Developments to Estimate Animal Movements, Home Range, and Habitat Selection Dr. Jon S. Horne, University of Idaho](https://reader035.vdocuments.site/reader035/viewer/2022070412/56649d4c5503460f94a2b17d/html5/thumbnails/18.jpg)
An Alternative
• Likelihood cross-validation (CVh)• Minimizes Kullback-Leibler Distance
• CVh outperforms LSCVh– Especially at smaller sample sizes (i.e., <~50)– Especially if you enjoy Kullback-Leibler
![Page 19: Evaluating Animal Space Use: New Developments to Estimate Animal Movements, Home Range, and Habitat Selection Dr. Jon S. Horne, University of Idaho](https://reader035.vdocuments.site/reader035/viewer/2022070412/56649d4c5503460f94a2b17d/html5/thumbnails/19.jpg)
Influence of Smoothing Parameter
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CVh LSCVh
![Page 20: Evaluating Animal Space Use: New Developments to Estimate Animal Movements, Home Range, and Habitat Selection Dr. Jon S. Horne, University of Idaho](https://reader035.vdocuments.site/reader035/viewer/2022070412/56649d4c5503460f94a2b17d/html5/thumbnails/20.jpg)
Beware of Your Home Range Program4 Programs All with LSCVh?
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KERNELHR(Seaman)
HOMERANGE(Carr)My Program(Horne)
AnimalMovement(Hooge)
![Page 21: Evaluating Animal Space Use: New Developments to Estimate Animal Movements, Home Range, and Habitat Selection Dr. Jon S. Horne, University of Idaho](https://reader035.vdocuments.site/reader035/viewer/2022070412/56649d4c5503460f94a2b17d/html5/thumbnails/21.jpg)
New Approaches
1) Develop Objective Method for Choosing Among Home Range Models
2) Improve the Kernel – Likelihood-based smoothing parameter
3) Correct Home Range Models for Observation Bias 4) The Brownian Bridge Movement Model 5) Incorporate Other Variables (e.g., habitat, other
organisms) Into Home Range Models
![Page 22: Evaluating Animal Space Use: New Developments to Estimate Animal Movements, Home Range, and Habitat Selection Dr. Jon S. Horne, University of Idaho](https://reader035.vdocuments.site/reader035/viewer/2022070412/56649d4c5503460f94a2b17d/html5/thumbnails/22.jpg)
3) Observation Bias of Locations• Home range models traditionally assumed
locations were obtained with equal probability
• Documented Unequal Observation Rates– Mostly for satellite telemetry
– Can be modeled across a study site
• Corrections Based on Probability Sampling– Weight locations by 1/probability of inclusion
![Page 23: Evaluating Animal Space Use: New Developments to Estimate Animal Movements, Home Range, and Habitat Selection Dr. Jon S. Horne, University of Idaho](https://reader035.vdocuments.site/reader035/viewer/2022070412/56649d4c5503460f94a2b17d/html5/thumbnails/23.jpg)
Difference between corrected and uncorrected models
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Fixed Kernel Bias
-0.184 - -0.145
-0.145 - -0.106
-0.106 - -0.067
-0.067 - -0.028
-0.028 - 0.011
0.011 - 0.051
0.051 - 0.09
0.09 - 0.129
0.129 - 0.168
2-mode Bivariate Mix Bias
-0.142 - -0.105
-0.105 - -0.068
-0.068 - -0.031
-0.031 - 0.006
0.006 - 0.043
0.043 - 0.08
0.08 - 0.118
0.118 - 0.155
0.155 - 0.192
1.34 - 1.46#
1.46 - 1.58#
1.58 - 1.76#
1.76 - 1.98#
1.98 - 2.34#
Location Weights
Bivariate Normal Bias
-0.025 - -0.011
-0.011 - 0.003
0.003 - 0.017
0.017 - 0.031
0.031 - 0.044
0.044 - 0.058
0.058 - 0.072
0.072 - 0.086
0.086 - 0.1
Bivariate normal
2-mode BVN Mix
Locationweights
Fixed Kernel
Underestimate ~18%
Overestimate ~19%
![Page 24: Evaluating Animal Space Use: New Developments to Estimate Animal Movements, Home Range, and Habitat Selection Dr. Jon S. Horne, University of Idaho](https://reader035.vdocuments.site/reader035/viewer/2022070412/56649d4c5503460f94a2b17d/html5/thumbnails/24.jpg)
Contributors to Magnitude of Bias
• Magnitude of differences in observation rates
• Aggregation of observation rates
• Home range model
• Sample size
• Intentional Differences (i.e., sampling design) – Diurnal vs. nocturnal locations
![Page 25: Evaluating Animal Space Use: New Developments to Estimate Animal Movements, Home Range, and Habitat Selection Dr. Jon S. Horne, University of Idaho](https://reader035.vdocuments.site/reader035/viewer/2022070412/56649d4c5503460f94a2b17d/html5/thumbnails/25.jpg)
New Approaches
1) Develop Objective Method for Choosing Among Home Range Models
2) Improve the Kernel – Likelihood-based smoothing parameter
3) Correct Home Range Models for Observation Bias 4) The Brownian Bridge Movement Model5) Incorporate Other Variables (e.g., habitat, other
organisms) Into Home Range Models
![Page 26: Evaluating Animal Space Use: New Developments to Estimate Animal Movements, Home Range, and Habitat Selection Dr. Jon S. Horne, University of Idaho](https://reader035.vdocuments.site/reader035/viewer/2022070412/56649d4c5503460f94a2b17d/html5/thumbnails/26.jpg)
0.5 Kilometer0.5 Kilometer
![Page 27: Evaluating Animal Space Use: New Developments to Estimate Animal Movements, Home Range, and Habitat Selection Dr. Jon S. Horne, University of Idaho](https://reader035.vdocuments.site/reader035/viewer/2022070412/56649d4c5503460f94a2b17d/html5/thumbnails/27.jpg)
From: Stokes, D. L., P. D. Boersma, and L. S. Davis. 1998. Satellite tracking of Megellanic Penguin migration. Condor 100:376-381.
Probabilistic model of the movement path?
“Brownian Bridge”
![Page 28: Evaluating Animal Space Use: New Developments to Estimate Animal Movements, Home Range, and Habitat Selection Dr. Jon S. Horne, University of Idaho](https://reader035.vdocuments.site/reader035/viewer/2022070412/56649d4c5503460f94a2b17d/html5/thumbnails/28.jpg)
Brownian Bridge Movement Model
• Can we model the probability of occurrence?
• Given:– Known locations– Time interval between locations
![Page 29: Evaluating Animal Space Use: New Developments to Estimate Animal Movements, Home Range, and Habitat Selection Dr. Jon S. Horne, University of Idaho](https://reader035.vdocuments.site/reader035/viewer/2022070412/56649d4c5503460f94a2b17d/html5/thumbnails/29.jpg)
2-location BrownianBridge
Shape dependent on:
1. Distance2. Time interval3. Animal mobility
![Page 30: Evaluating Animal Space Use: New Developments to Estimate Animal Movements, Home Range, and Habitat Selection Dr. Jon S. Horne, University of Idaho](https://reader035.vdocuments.site/reader035/viewer/2022070412/56649d4c5503460f94a2b17d/html5/thumbnails/30.jpg)
Brownian Bridge Applications
• Estimate movement paths– Home range– Migration routes– Resource utilization/selection
![Page 31: Evaluating Animal Space Use: New Developments to Estimate Animal Movements, Home Range, and Habitat Selection Dr. Jon S. Horne, University of Idaho](https://reader035.vdocuments.site/reader035/viewer/2022070412/56649d4c5503460f94a2b17d/html5/thumbnails/31.jpg)
Kilometer
• Black bear
• Satellite telemetry
• 1470 locations
• 20-min. intervals
• ~1 month
![Page 32: Evaluating Animal Space Use: New Developments to Estimate Animal Movements, Home Range, and Habitat Selection Dr. Jon S. Horne, University of Idaho](https://reader035.vdocuments.site/reader035/viewer/2022070412/56649d4c5503460f94a2b17d/html5/thumbnails/32.jpg)
Home range of a male black bear
Brownian bridge
Probability
low
high
Fixed Kernel
Probability
low
high
![Page 33: Evaluating Animal Space Use: New Developments to Estimate Animal Movements, Home Range, and Habitat Selection Dr. Jon S. Horne, University of Idaho](https://reader035.vdocuments.site/reader035/viewer/2022070412/56649d4c5503460f94a2b17d/html5/thumbnails/33.jpg)
Advantages of Brownian Bridge Home Range
• ASSUMES serially correlated data
• Models the movement path
• Location error explicitly incorporated
![Page 34: Evaluating Animal Space Use: New Developments to Estimate Animal Movements, Home Range, and Habitat Selection Dr. Jon S. Horne, University of Idaho](https://reader035.vdocuments.site/reader035/viewer/2022070412/56649d4c5503460f94a2b17d/html5/thumbnails/34.jpg)
Caribou Migration
• Fall migration in southwestern Alaska
• 11 female caribou with GPS collars
• Locations every 7 hours
![Page 35: Evaluating Animal Space Use: New Developments to Estimate Animal Movements, Home Range, and Habitat Selection Dr. Jon S. Horne, University of Idaho](https://reader035.vdocuments.site/reader035/viewer/2022070412/56649d4c5503460f94a2b17d/html5/thumbnails/35.jpg)
Probability
Low
High
![Page 36: Evaluating Animal Space Use: New Developments to Estimate Animal Movements, Home Range, and Habitat Selection Dr. Jon S. Horne, University of Idaho](https://reader035.vdocuments.site/reader035/viewer/2022070412/56649d4c5503460f94a2b17d/html5/thumbnails/36.jpg)
Fine-scale Resource Selection
• Why did the bear cross the road…
where it did?
![Page 37: Evaluating Animal Space Use: New Developments to Estimate Animal Movements, Home Range, and Habitat Selection Dr. Jon S. Horne, University of Idaho](https://reader035.vdocuments.site/reader035/viewer/2022070412/56649d4c5503460f94a2b17d/html5/thumbnails/37.jpg)
Probability of crossing
![Page 38: Evaluating Animal Space Use: New Developments to Estimate Animal Movements, Home Range, and Habitat Selection Dr. Jon S. Horne, University of Idaho](https://reader035.vdocuments.site/reader035/viewer/2022070412/56649d4c5503460f94a2b17d/html5/thumbnails/38.jpg)
Probability of Crossing
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0 0.5 1 1.5 2 2.5 3 3.5
Road kilometer
Prob
abil
ity
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New Approaches
1) Develop Objective Method for Choosing Among Home Range Models
2) Improve the Kernel – Likelihood-based smoothing parameter
3) Correct Home Range Models for Sampling Bias 4) The Brownian Bridge Movement Model5) Incorporate Other Variables (e.g., habitat, other
organisms) Into Home Range Models
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Ecological Factors Affecting Space Use
– Site Fidelity (i.e., home range)
– Habitat Selection
– Interrelations with Other Organisms
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5) A Synoptic Model of Space Use
• Space Use Generally Estimated Using Discrete Locations (x-y coordinates)
• Can we get a better model and learn more by incorporating additional variables?
– Distribution of…• Important resources
• Avoided areas
• Other animals
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Synoptic Approach… Multiple Competing Models
Initial/Null Model(site fidelity)
Habitat Inter/Intraspecific Relationships
Model Assessment
“Best” Model(s)
Prediction and Inference
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Example: Space Use of Male White Rhinos
• Location Data: 3 Adult Males, Matobo National Park, Zimbabwe
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Candidate Models… Covariates
• Park boundary
• 3 Environmental Covariates H(x)
0 2 Kilometers
Percent slopeGrassland/openwoodland Female Density
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Candidate Models… Hypotheses
• Null model: no environmental covariates– Exponential Power + Park boundary
• Habitat model:– Null + open covertype + percent slope
• Social model:– Null + female density
• Combined model:– Null + habitat + social
AIC Model Selection
Best Model
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slope
0 2 Kilometers
OPEN femaleslope
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Interpretation of Best Model
• Best Model Can Be Used to:– Estimate space use– Define home range– Determine factors affecting space use– Infer importance of these factors
• Answers not only “Where?” but “Why?”
M05 was 3 times more likely to be in an area: 2% slope, 0.5 relative female density, and open covertype10% slope, 0.7 relative female density, and not open
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To Summarize
• Information theoretic criteria for choosing among home range models
• Use likelihood cross-validation choice of smoothing parameter
• Home range models can be corrected for observation bias
• Brownian bridge for serially correlated data• Synoptic models answer “where?” and “why?”
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Publications
Horne, J. S. and E. O. Garton. 2006. Likelihood Cross-validation vs. Least SquaresCross-validation for Choosing the Smoothing Parameter in Kernel Home Range Analysis. Journal of Wildlife Management 70:641-648
Horne, J. S., E. O. Garton, and K. A. Sager. 2007. Correcting Home Range ModelsFor Sample Bias. Journal of Wildlife Management 71:996-1001
Horne, J. S. and E. O. Garton. 2006. Selecting the Best Home Range Model: AnInformation Theoretic Approach. Ecology 87:1146-1152
Horne, J. S., E. O. Garton, and J. L. Rachlow. 2008. A Synoptic Model of AnimalSpace Use: Simultaneous Analysis of Home Range, Habitat Selection, and Inter/Intra-specific Relationships. Ecological Modelling 214:338-348.
Horne, J. S., E. O. Garton, S. M. Krone, and J. S. Lewis. 2008. Analyzing AnimalMovements using Brownian Bridges. Ecology 88:2351-2363