Examining speed versus selection in connectivity models using elk migration as an example

Angela Brennan,Paul C Cross,Alyson B Courtemanch,Jerod A Merkle,Sarah R Dewey,Ephraim M Hanks,Eric K Cole

doi:10.1007/s10980-018-0642-z

Abstract

ContextLandscape resistance is vital to connectivity modeling and frequently derived from resource selection functions (RSFs). RSFs estimate relative probability of use and tend to focus on understanding habitat preferences during slow, routine animal movements (e.g., foraging). Dispersal and migration, however, can produce rarer, faster movements, in which case models of movement speed rather than resource selection may be more realistic for identifying habitats that facilitate connectivity.ObjectiveTo compare two connectivity modeling approaches applied to resistance estimated from models of movement rate and resource selection.MethodsUsing movement data from migrating elk, we evaluated continuous time Markov chain (CTMC) and movement-based RSF models (i.e., step selection functions [SSFs]). We applied circuit theory and shortest random path (SRP) algorithms to CTMC, SSF and null (i.e., flat) resistance surfaces to predict corridors between elk seasonal ranges. We evaluated prediction accuracy by comparing model predictions to empirical elk movements.ResultsAll connectivity models predicted elk movements well, but models applied to CTMC resistance were more accurate than models applied to SSF and null resistance. Circuit theory models were more accurate on average than SRP models.ConclusionsCTMC can be more realistic than SSFs for estimating resistance for fast movements, though SSFs may demonstrate some predictive ability when animals also move slowly through corridors (e.g., stopover use during migration). High null model accuracy suggests seasonal range data may also be critical for predicting direct migration routes. For animals that migrate or disperse across large landscapes, we recommend incorporating CTMC into the connectivity modeling toolkit.

Highlights

Connectivity modeling and corridor conservation are aimed at predicting and preserving space for animal movement, gene flow, and ecological processes to occur across landscapes affected by habitat loss and fragmentation (Chetkiewicz et al 2006; Hilty et al 2006)
We evaluated continuous time Markov chain model (CTMC) as an alternative to resource selection functions (RSFs) for estimating resistance and modeling connectivity, but rather than use traditional RSFs for this comparison, we used a movement-based RSF to estimate resource selection along the movement pathway, conditional on local availability estimated from empirical step lengths and angles (Fortin et al 2005)
We examined continuous time Markov chain models (CTMC) as an alternative to step selection functions (SSFs) for understanding drivers of elk movement during the spring migration, estimating landscape resistance and predicting migration corridors

Summary

Introduction

Connectivity modeling and corridor conservation are aimed at predicting and preserving space for animal movement, gene flow, and ecological processes to occur across landscapes affected by habitat loss and fragmentation (Chetkiewicz et al 2006; Hilty et al 2006). That corridors could fail to protect rare, fast and directed movements that can be important to ecological processes (e.g., dispersal and migration), because popular connectivity modeling methods rely on weak relationships to these types of movement behaviors (e.g., Elliot et al 2014; Zeller et al 2014; Abrahms et al 2016; Keeley et al 2017). Existing methods for estimating resistance could result in conservative corridor predictions that exclude areas important for connectivity

Methods

Results

Conclusion