Examining the occupancy–density relationship for a low‐density carnivore. Issue 6 (11th March 2017)

• Record Type: Journal Article
• Authors: Linden, Daniel W.; Fuller, Angela K.; Royle, J. Andrew; Hare, Matthew P.
• Editors: Frair, Jacqueline
• Is Part Of: Journal of applied ecology. Volume 54:Issue 6(2017:Dec.)
• Page Start: 2043
• Page End: 2052
• Publication Date: 2017-03-11
• Subjects: carnivore -- density estimation -- detection–non‐detection data -- fisher -- hair snares -- non‐invasive sampling -- occupancy -- Pekania pennanti -- remote cameras -- spatial capture–recapture; Agriculture -- Periodicals; Biology, Economic -- Periodicals; Agricultural ecology -- Periodicals; Applied ecology -- Periodicals; 577
• DOI: 10.1111/1365-2664.12883
• Languages: English
• ISSNs: 0021-8901

Abstract

Summary: The challenges associated with monitoring low‐density carnivores across large landscapes have limited the ability to implement and evaluate conservation and management strategies for such species. Non‐invasive sampling techniques and advanced statistical approaches have alleviated some of these challenges and can even allow for spatially explicit estimates of density, one of the most valuable wildlife monitoring tools. For some species, individual identification comes at no cost when unique attributes (e.g. pelage patterns) can be discerned with remote cameras, while other species require viable genetic material and expensive laboratory processing for individual assignment. Prohibitive costs may still force monitoring efforts to use species distribution or occupancy as a surrogate for density, which may not be appropriate under many conditions. Here, we used a large‐scale monitoring study of fisher Pekania pennanti to evaluate the effectiveness of occupancy as an approximation to density, particularly for informing harvest management decisions. We combined remote cameras with baited hair snares during 2013–2015 to sample across a 70 096‐km 2 region of western New York, USA. We fit occupancy and Royle–Nichols models to species detection–non‐detection data collected by cameras, and spatial capture–recapture (SCR) models to individual encounter data obtained by genotyped hair samples. Variation in the state variables within 15‐km 2 grid cells was modelled as a function of landscape attributes known to influence fisher distribution. We found a close relationship between grid cell estimates of fisher state variables from the models using detection–non‐detection data and those from the SCR model, likely due to informative spatial covariates across a large landscape extent and a grid cell resolution that worked well with the movement ecology of the species. Fisher occupancy and density were both positively associated with the proportion of coniferous‐mixed forest and negatively associated with road density. As a result, spatially explicit management recommendations for fisher were similar across models, though relative variation was dampened for the detection–non‐detection data. Synthesis and applications . Our work provides empirical evidence that models using detection–non‐detection data can make similar inferences regarding relative spatial variation of the focal population to models using more expensive individual encounters when the selected spatial grain approximates or is marginally smaller than home range size. When occupancy alone is chosen as a cost‐effective state variable for monitoring, simulation and sensitivity analyses should be used to understand how inferences from detection–non‐detection data will be affected by aspects of study design and species ecology. Abstract : Our work provides empirical evidence that models using detection–non‐detection data can make similar inferences regarding relative spatial variation of the focal population to models using more expensive individual encounters when the selected spatial grain approximates or is marginally smaller than home range size. When occupancy alone is chosen as a cost‐effective state variable for monitoring, simulation and sensitivity analyses should be used to understand how inferences from detection–non‐detection data will be affected by aspects of study design and species ecology.