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

Moritz Mercker,Leonie Enners,Verena Peschko,Stefan Garthe,Philipp Schwemmer

doi:10.1186/s40462-021-00260-y

Moritz Mercker, Leonie Enners + Show 3 more

Open Access

https://doi.org/10.1186/s40462-021-00260-y

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Journal: Movement Ecology	Publication Date: Apr 23, 2021
Citations: 6	License type: open-access

Affiliation: Kiel University, Heidelberg University

Abstract

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.

Highlights

The identification of factors that influence species distribution and resource selection is an important ecological issue [1] that has traditionally been addressed using appropriate regression methods based on presence-absence or count data [2,3,4]
The model s_SLRM showed the smallest type I error rates here, because spatial autocorrelation was reduced by the use of a spatial 2D-regression spline in the predictors
The analysis of multiple trips with respect to local habitat selection (Fig. 2B) was much more consistent, with all methods showing type I error rates at or Interplay between method and tracking-data properties To analyse the interplay between the different statistical methods and variables related to environmental and movement properties, we first restricted the final data frame for the power evaluation to the spatio-temporal point process models (ST-point process models (PPMs)) and integrated step selection models (iSSMs) methods, because only these methods showed type I error rates at or below the nominal level (c.f., previous subsection and Fig. 2)

Summary

Introduction

The identification of factors that influence species distribution and resource selection is an important ecological issue [1] that has traditionally been addressed using appropriate regression methods based on presence-absence or count data [2,3,4]. Frequent ecological questions associated with animal tracks concern either the selection/avoidance of a certain resource/habitat or structure, or alternatively changes (2021) 9:20 in behaviour related to such covariates. Both questions can again be applied at different spatial scales. New 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

Methods

Results

Discussion

Conclusion