DOI: 10.5061/dryad.d3q4q
title: Data from: Examining the occupancy-density relationship for a low-density carnivore

description: 1. 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.
 2. 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
 lab 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.
 3. 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 km2 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 models to individual encounter data
 obtained by genotyped hair samples. Variation in the state variables
 within 15-km2 grid cells was modeled as a function of landscape attributes
 known to influence fisher distribution. 4. 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 spatial
 capture-recapture 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. 5. 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.
description: Fisher
 detection–non-detection dataThis text file contains
 an R-formatted list of data objects for fitting occupancy models in the
 "unmarked" package.fisher_occ_data.txtFisher individual encounter dataThis text file contains an R-formatted list of data objects for fitting spatial capture-recapture models in the "oSCR" package.fisher_scr_data.txtFisher statespace gridThis csv file contains the original UTMs (Zone 18) and relative XY coordinates for the statespace grid used to predict fisher occupancy and density, as well as standardized pixel values for coniferous-mixed forest and road density.fisher_statespace.csvScript for model runningThis text file contains R script for reading in the fisher data and fitting occupancy and SCR models.fisher_Rcode.txt