Habitat modeling used to predict relative abundance of bobcats in Iowa

Abstract

We combined observations of bobcats (Lynx rufus) from bowhunters with remotely‐sensed data to build models that describe habitat and relative abundance of this species in the agricultural landscape of Iowa, USA. We calculated landscape composition and configuration from publicly available land cover, census, road, hydrologic, and elevation data. We used multiple regression models to examine county‐level associations between several explanatory variables and relative abundance of bobcats reported by surveyed bowhunters in each county. The most influential explanatory variables in the models were metrics associated with the presence of grassland, including Conservation Reserve, along with configuration of this perennial habitat with forests, although human population density and abundance of eastern cottontails (Sylvilagus floridanus) also correlated with abundance of bobcats. Validation of predictions against 3 years of independent data provided confidence in the models, with 66% of predictions within 1 bobcat/1,000 hunter‐hours and 95% within 5 bobcats/1,000 hunter‐hours of observed values. Once we accounted for landscape differences, no residual spatial trend was evident, despite relatively recent bobcat recolonization of Iowa. Models suggested that future range expansion of the bobcat population may be possible in some northern Iowa counties where habitat composition is similar to counties in southern Iowa where bobcats are abundant. Results from the county‐level model have been useful to the Iowa Department of Natural Resources in evaluating the expansion of this once rare species and for delineating harvest opportunities. © 2011 The Wildlife Society.

Estimating density and detection of bobcats in fragmented midwestern landscapes using spatial capture–recapture data from camera traps

“…In particular, we navigated to survey unit centroids and selected fine‐scale camera station (2 cameras situated on opposite sides of known trails [vehicle, game, human]) locations based on topographic or vegetation features typical of suitable habitat or travel routes (Thornton and Pekins , Alexander and Gese ). In cases when centroid locations were not located in potential habitat (i.e., forested cover; Kolowski and Woolf , Nielsen and Woolf , Tucker et al , Linde et al ) or along travel routes, we adjusted them by placing cameras in or along the edge of the nearest forested habitat. In instances where survey units consisting primarily of row crop agriculture were selected, we systematically selected the nearest survey units that contained sufficient forest cover to maximize the likelihood that bobcats had a nonzero probability of being captured and spatially recaptured.…”

“…Bobcats ( Lynx rufus ) are widespread across North America and population status across most of the current geographic range is stable or increasing (Roberts and Crimmins , Linde et al ). Nevertheless, population status of bobcats in other regions is unknown or of priority management concern (Soule and Terborgh , Riley et al , Litvaitis et al ).…”

Predicting bobcat abundance at a landscape scale and evaluating occupancy as a density index in central Wisconsin

“…The bobcat (Lynx rufus) is widespread across North America but exists at low density at the edges of its distribution in the northern United States (Anderson and Lovallo 2003) where it appears to be repopulating historically occupied regions, and managers are considering expanding harvest Crimmins 2010, Linde et al 2012). Although estimates of population size would be the most useful information for revising harvest delineations, contemporary bobcat management has been guided by less intensive efforts to predict habitat suitability, distribution, or relative abundance , Preuss and Gehring 2007, Long et al 2011, Linde et al 2012, Broman et al 2014). An implicit assumption of these efforts and many other carnivore occurrence surveys (e.g., Karanth et al 2011, Zielinksi et al 2013 has been that the probability of species presence is a useful index for density.…”

An evaluation of hunter surveys to monitor relative abundance of bobcats

“…Using O B instead of the number of bobcats observed was expected to reduce bias due to chance encounters of bobcat family groups and from repeated observations of the same individual during 1 outing (Mahard ). Although hunters reported the number of hours they spent during each outing, we felt that the number of outings was a better parameter for effort (contrary to Kindberg et al , Cooper et al , and Linde et al ). This was based on the notion that bobcats have large home ranges relative to the area we expected most hunters to cover in 1 outing and many short outings in different locations would likely produce more bobcat detections than a small number of longer outings.…”