Biomaterial Database

Accounting for animal movement in estimation of resource selection functions: sampling and data analysis. 2009

James D Forester, and Hae Kyung Im, and Paul J Rathouz

Center for Integrating Statistics and Environmental Science, University of Chicago, 5734 South Ellis Avenue, Chicago, Illinois 60637, USA. jdforester@gmail.com

Patterns of resource selection by animal populations emerge as a result of the behavior of many individuals. Statistical models that describe these population-level patterns of habitat use can miss important interactions between individual animals and characteristics of their local environment; however, identifying these interactions is difficult. One approach to this problem is to incorporate models of individual movement into resource selection models. To do this, we propose a model for step selection functions (SSF) that is composed of a resource-independent movement kernel and a resource selection function (RSF). We show that standard case-control logistic regression may be used to fit the SSF; however, the sampling scheme used to generate control points (i.e., the definition of availability) must be accommodated. We used three sampling schemes to analyze simulated movement data and found that ignoring sampling and the resource-independent movement kernel yielded biased estimates of selection. The level of bias depended on the method used to generate control locations, the strength of selection, and the spatial scale of the resource map. Using empirical or parametric methods to sample control locations produced biased estimates under stronger selection; however, we show that the addition of a distance function to the analysis substantially reduced that bias. Assuming a uniform availability within a fixed buffer yielded strongly biased selection estimates that could be corrected by including the distance function but remained inefficient relative to the empirical and parametric sampling methods. As a case study, we used location data collected from elk in Yellowstone National Park, USA, to show that selection and bias may be temporally variable. Because under constant selection the amount of bias depends on the scale at which a resource is distributed in the landscape, we suggest that distance always be included as a covariate in SSF analyses. This approach to modeling resource selection is easily implemented using common statistical tools and promises to provide deeper insight into the movement ecology of animals.

UI	MeSH Term	Description	Entries
D008954	Models, Biological	Theoretical representations that simulate the behavior or activity of biological processes or diseases. For disease models in living animals, DISEASE MODELS, ANIMAL is available. Biological models include the use of mathematical equations, computers, and other electronic equipment.	Biological Model,Biological Models,Model, Biological,Models, Biologic,Biologic Model,Biologic Models,Model, Biologic
D009068	Movement	The act, process, or result of passing from one place or position to another. It differs from LOCOMOTION in that locomotion is restricted to the passing of the whole body from one place to another, while movement encompasses both locomotion but also a change of the position of the whole body or any of its parts. Movement may be used with reference to humans, vertebrate and invertebrate animals, and microorganisms. Differentiate also from MOTOR ACTIVITY, movement associated with behavior.	Movements
D003627	Data Interpretation, Statistical	Application of statistical procedures to analyze specific observed or assumed facts from a particular study.	Data Analysis, Statistical,Data Interpretations, Statistical,Interpretation, Statistical Data,Statistical Data Analysis,Statistical Data Interpretation,Analyses, Statistical Data,Analysis, Statistical Data,Data Analyses, Statistical,Interpretations, Statistical Data,Statistical Data Analyses,Statistical Data Interpretations
D003670	Deer	The family Cervidae of 17 genera and 45 species occurring nearly throughout North America, South America, and Eurasia, on most associated continental islands, and in northern Africa. Wild populations of deer have been established through introduction by people in Cuba, New Guinea, Australia, New Zealand, and other places where the family does not naturally occur. They are slim, long-legged and best characterized by the presence of antlers. Their habitat is forests, swamps, brush country, deserts, and arctic tundra. They are usually good swimmers; some migrate seasonally. (Walker's Mammals of the World, 5th ed, p1362)	Deers
D005247	Feeding Behavior	Behavioral responses or sequences associated with eating including modes of feeding, rhythmic patterns of eating, and time intervals.	Dietary Habits,Eating Behavior,Faith-based Dietary Restrictions,Feeding Patterns,Feeding-Related Behavior,Food Habits,Diet Habits,Eating Habits,Behavior, Eating,Behavior, Feeding,Behavior, Feeding-Related,Behaviors, Eating,Behaviors, Feeding,Behaviors, Feeding-Related,Diet Habit,Dietary Habit,Dietary Restriction, Faith-based,Dietary Restrictions, Faith-based,Eating Behaviors,Eating Habit,Faith based Dietary Restrictions,Faith-based Dietary Restriction,Feeding Behaviors,Feeding Pattern,Feeding Related Behavior,Feeding-Related Behaviors,Food Habit,Habit, Diet,Habit, Dietary,Habit, Eating,Habit, Food,Habits, Diet,Pattern, Feeding,Patterns, Feeding,Restrictions, Faith-based Dietary
D000818	Animals	Unicellular or multicellular, heterotrophic organisms, that have sensation and the power of voluntary movement. Under the older five kingdom paradigm, Animalia was one of the kingdoms. Under the modern three domain model, Animalia represents one of the many groups in the domain EUKARYOTA.	Animal,Metazoa,Animalia
D001522	Behavior, Animal	The observable response an animal makes to any situation.	Autotomy Animal,Animal Behavior,Animal Behaviors
D013037	Spatial Behavior	Reactions of an individual or groups of individuals with relation to the immediate surrounding area including the animate or inanimate objects within that area.	Behavior, Spatial,Behaviors, Spatial,Spatial Behaviors
D015233	Models, Statistical	Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.	Probabilistic Models,Statistical Models,Two-Parameter Models,Model, Statistical,Models, Binomial,Models, Polynomial,Statistical Model,Binomial Model,Binomial Models,Model, Binomial,Model, Polynomial,Model, Probabilistic,Model, Two-Parameter,Models, Probabilistic,Models, Two-Parameter,Polynomial Model,Polynomial Models,Probabilistic Model,Two Parameter Models,Two-Parameter Model
D016013	Likelihood Functions	Functions constructed from a statistical model and a set of observed data which give the probability of that data for various values of the unknown model parameters. Those parameter values that maximize the probability are the maximum likelihood estimates of the parameters.	Likelihood Ratio Test,Maximum Likelihood Estimates,Estimate, Maximum Likelihood,Estimates, Maximum Likelihood,Function, Likelihood,Functions, Likelihood,Likelihood Function,Maximum Likelihood Estimate,Test, Likelihood Ratio