Modelling space-use and habitat preference from wildlife telemetry data

Spatial behavior in animals--Mathematical models;Spatial behavior in animals--Statistical methods;Habitat selection--Mathematical models;Habitat selection--Statistical methods;Home range (Animal geography)--Mathematical methods;Home range (Animal geography)--Statistical methods;Biotelemetry;Gray seal--Scotland--Geographical distribution;Northern gannet--Scotland--Bass Rock--Geographical distribution

Aarts, Geert ; Matthiopoulos, Jason ; Matthiopoulos, Jason

University:University of St Andrews

Department:Biology (School of)

关键词: Spatial behavior in animals--Mathematical models;    Spatial behavior in animals--Statistical methods;    Habitat selection--Mathematical models;    Habitat selection--Statistical methods;    Home range (Animal geography)--Mathematical methods;    Home range (Animal geography)--Statistical methods;    Biotelemetry;    Gray seal--Scotland--Geographical distribution;    Northern gannet--Scotland--Bass Rock--Geographical distribution;   

Others  :  https://research-repository.st-andrews.ac.uk/bitstream/handle/10023/327/GeertAartsPhDThesis.pdf?sequence=6&isAllowed=y

来源: DR-NTU

【 摘 要 】

Management and conservation of populations of animals requires information on where they are, why they are there, and where else theycould be. These objectives are typically approached by collecting data on theanimals’ use of space, relating these to prevailing environmental conditionsand employing these relations to predict usage at other geographical regions.Technical advances in wildlife telemetry have accomplished manifoldincreases in the amount and quality of available data, creating the need for astatistical framework that can use them to make population-level inferencesfor habitat preference and space-use. This has been slow-in-coming becausewildlife telemetry data are, by definition, spatio-temporally autocorrelated,unbalanced, presence-only observations of behaviorally complex animals,responding to a multitude of cross-correlated environmental variables.I review the evolution of techniques for the analysis of space-use andhabitat preference, from simple hypothesis tests to modern modelingtechniques and outline the essential features of a framework that emergesnaturally from these foundations. Within this framework, I discuss eightchallenges, inherent in the spatial analysis of telemetry data and, for each, Ipropose solutions that can work in tandem. Specifically, I propose a logistic,mixed-effects approach that uses generalized additive transformations of theenvironmental covariates and is fitted to a response data-set comprising thetelemetry and simulated observations, under a case-control design.I apply this framework to non-trivial case-studies using data fromsatellite-tagged grey seals (Halichoerus grypus) foraging off the east andwest coast of Scotland, and northern gannets (Morus Bassanus) from BassRock. I find that sea bottom depth and sediment type explain little of thevariation in gannet usage, but grey seals from different regions stronglyprefer coarse sediment types, the ideal burrowing habitat of sandeels, theirpreferred prey. The results also suggest that prey aggregation within thewater column might be as important as horizontal heterogeneity. Moreimportantly, I conclude that, despite the complex behavior of the studyspecies, flexible empirical models can capture the environmentalrelationships that shape population distributions.