Analysis of local habitat selection and large-scale attraction/avoidance based on animal tracking data: is there a single best method?

Movement Ecology

Moritz Mercker¹  Philipp Schwemmer²  Leonie Enners²  Stefan Garthe²  Verena Peschko² 

[1] Bionum GmbH - Consultants in Biostatistics, Hamburg, Finkenwerder Norderdeich 15 A, Hamburg, Germany;Institute of Applied Mathematics (IAM) Heidelberg University, Im Neuenheimer Feld 205, 69120, Heidelberg, Germany;Research and Technology Centre (FTZ) Kiel University, Hafentörn 1, 25761, Büsum, Germany;

关键词: Animal movement;    Autocorrelation;    Bio-logging;    Habitat selection;    Point process;    Resource selection;    Species distribution;    Telemetry;    Avoidance;   

DOI  :  10.1186/s40462-021-00260-y

来源: Springer

【 摘 要 】

BackgroundNew wildlife telemetry and tracking technologies have become available in the last decade, leading to a large increase in the volume and resolution of animal tracking data. These technical developments have been accompanied by various statistical tools aimed at analysing the data obtained by these methods.MethodsWe used simulated habitat and tracking data to compare some of the different statistical methods frequently used to infer local resource selection and large-scale attraction/avoidance from tracking data. Notably, we compared spatial logistic regression models (SLRMs), spatio-temporal point process models (ST-PPMs), step selection models (SSMs), and integrated step selection models (iSSMs) and their interplay with habitat and animal movement properties in terms of statistical hypothesis testing.ResultsWe demonstrated that only iSSMs and ST-PPMs showed nominal type I error rates in all studied cases, whereas SSMs may slightly and SLRMs may frequently and strongly exceed these levels. iSSMs appeared to have on average a more robust and higher statistical power than ST-PPMs.ConclusionsBased on our results, we recommend the use of iSSMs to infer habitat selection or large-scale attraction/avoidance from animal tracking data. Further advantages over other approaches include short computation times, predictive capacity, and the possibility of deriving mechanistic movement models.

【 授权许可】

CC BY