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Does wildlife resource selection accurately inform corridor conservation?
Author(s) -
Abrahms Briana,
Sawyer Sarah C.,
Jordan Neil R.,
McNutt J. Weldon,
Wilson Alan M.,
Brashares Justin S.
Publication year - 2017
Publication title -
journal of applied ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.503
H-Index - 181
eISSN - 1365-2664
pISSN - 0021-8901
DOI - 10.1111/1365-2664.12714
Subject(s) - wildlife , resource (disambiguation) , selection (genetic algorithm) , landscape connectivity , foraging , biological dispersal , wildlife conservation , environmental resource management , ecology , geography , computer science , biology , environmental science , population , computer network , demography , artificial intelligence , sociology
Summary Evaluating landscape connectivity and identifying and protecting corridors for animal movement have become central challenges in applied ecology and conservation. Currently, resource selection analyses are widely used to focus corridor planning where animal movement is predicted to occur. An animal's behavioural state (e.g. foraging, dispersing) is a significant determinant of resource selection patterns, yet has largely been ignored in connectivity assessments. We review 16 years of connectivity studies employing resource selection analysis to evaluate how researchers have incorporated animal behaviour into corridor planning, and highlight promising new approaches for identifying wildlife corridors. To illustrate the importance of behavioural information in such analyses, we present an empirical case study to test behaviour‐specific predictions of connectivity with long‐distance dispersal movements of African wild dogs Lycaon pictus . We conclude by recommending strategies for developing more realistic connectivity models for future conservation efforts. Our review indicates that most connectivity studies conflate resource selection with connectivity requirements, which may result in misleading estimates of landscape resistance, and lack validation of proposed connectivity models with movement data. Our case study shows that including only directed movement behaviour when measuring resource selection reveals markedly different, and more accurate, connectivity estimates than a model measuring resource selection independent of behavioural state. Synthesis and applications . Our results, using African wild dogs as a case study, suggest that resource selection analyses that fail to consider an animal's behavioural state may be insufficient in targeting movement pathways and corridors for protection. This failure may result in misidentification of wildlife corridors and misallocation of limited conservation resources. Our findings underscore the need for considering patterns of animal movement in appropriate behavioural contexts to ensure the effective application of resource selection analyses for corridor planning.