Mark-Recapture and Mark-Resight Methods for Estimating Abundance with Remote Cameras: A Carnivore Case Study

<div><p>Abundance estimation of carnivore populations is difficult and has prompted the use of non-invasive detection methods, such as remotely-triggered cameras, to collect data. To analyze photo data, studies focusing on carnivores with unique pelage patterns have utilized a mark-recapture framework and studies of carnivores without unique pelage patterns have used a mark-resight framework. We compared mark-resight and mark-recapture estimation methods to estimate bobcat (<i>Lynx rufus</i>) population sizes, which motivated the development of a new "hybrid" mark-resight model as an alternative to traditional methods. We deployed a sampling grid of 30 cameras throughout the urban southern California study area. Additionally, we physically captured and marked a subset of the bobcat population with GPS telemetry collars. Since we could identify individual bobcats with photos of unique pelage patterns and a subset of the population was physically marked, we were able to use traditional mark-recapture and mark-resight methods, as well as the new “hybrid” mark-resight model we developed to estimate bobcat abundance. We recorded 109 bobcat photos during 4,669 camera nights and physically marked 27 bobcats with GPS telemetry collars. Abundance estimates produced by the traditional mark-recapture, traditional mark-resight, and “hybrid” mark-resight methods were similar, however precision differed depending on the models used. Traditional mark-recapture and mark-resight estimates were relatively imprecise with percent confidence interval lengths exceeding 100% of point estimates. Hybrid mark-resight models produced better precision with percent confidence intervals not exceeding 57%. The increased precision of the hybrid mark-resight method stems from utilizing the complete encounter histories of physically marked individuals (including those never detected by a camera trap) and the encounter histories of naturally marked individuals detected at camera traps. This new estimator may be particularly useful for estimating abundance of uniquely identifiable species that are difficult to sample using camera traps alone.</p></div

Variable importance weights (<i>ω</i>) for predictors of occupancy for bobcats, coyotes, and gray foxes.

<p>Variable importance weights (<i>ω</i>) for predictors of occupancy for bobcats, coyotes, and gray foxes.</p

Orchard on hillslope.

<p>Typical landscape pattern of steep hills with orchards surrounded by wildlands.</p

Camera survey model-averaged mark-recapture and mark-resight bobcat <i>Lynx rufus</i> abundance (N^) and density/km² (D^) estimates in the San Joaquin Hills study area, Orange County, California.

<p>Right-side (RS) and left-side (LS) analyses were conducted for the mark-recapture and hybrid mark-resight estimators. Separate density estimates were derived from the estimated radius (</p><p></p><p></p><p></p><p></p><p><mi>D</mi><mo>^</mo></p><mi>r</mi><p></p><p></p><p></p><p></p>) and diameter (<p></p><p></p><p></p><p></p><p><mi>D</mi><mo>^</mo></p><mi>d</mi><p></p><p></p><p></p><p></p>) of average home range size. % CIL denotes the 95% confidence (or highest posterior density) interval length relative to <p></p><p></p><p><mi>N</mi><mo>^</mo></p><p></p><p></p> and <p></p><p></p><p><mi>D</mi><mo>^</mo></p><p></p><p></p>.<p></p><p>Camera survey model-averaged mark-recapture and mark-resight bobcat <i>Lynx rufus</i> abundance (</p><p></p><p></p><p><mi>N</mi><mo>^</mo></p><p></p><p></p>) and density/km² (<p></p><p></p><p><mi>D</mi><mo>^</mo></p><p></p><p></p>) estimates in the San Joaquin Hills study area, Orange County, California.<p></p

The San Joaquin Hills study area, Orange County, California.

<p>The sampling unit grid (dashed lines) was used to determine the locations of remote camera stations. Map figure base layers from SCAG [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0123032#pone.0123032.ref035" target="_blank">35</a>] and StreetMap USA for ESRI ArcGIS 9.3, for representational purposes only.</p

Top-ranked models of site occupancy (ψ) and detection rate (p) for gray fox.

<p>Footnote: All models with <b>Δ</b>AICc <2.0, plus the intercept-only models, are reported. K is the number of parameters, <b>Δ</b>AICc is the difference between the AICc of the model and the lowest-AICc model, <i>ω</i> is the AICc model weight (summed for the averaged model), ψ is the predicted occupancy at a site and <i>p</i> is the probability of detecting the species at a given site. Covariate abbreviations: distwild is distance to continuous wildland, land cover is land cover (avocado orchard, near orchard, or wildland) at the camera site, and woodland, avocado orchard, shrub and disturbed refer to the area of that land cover in the neighborhood of the camera site.</p

Study area in southern California.

<p>Study sites within Santa Barbara and Ventura counties.</p

Top-ranked models of site occupancy (ψ) and detection rate (p) for bobcat.

<p>Footnote: All models with <b>Δ</b>AICc <2.0, plus the intercept-only models, are reported. K is the number of parameters, <b>Δ</b>AICc is the difference between the AICc of the model and the lowest-AICc model, <i>ω</i> is the AICc model weight (summed for the averaged model), ψ is the predicted occupancy at a site and <i>p</i> is the probability of detecting the species at a given site. Covariate abbreviations: distwild is distance to continuous wildland, land cover is land cover (avocado orchard, near orchard, or wildland) at the camera site, and woodland, grass/herbaceous, shrub, avocado orchard and disturbed refer to the area of that land cover in the neighborhood of the camera site.</p

Influence of cold-air pooling on simulations of regional snow water equivalents.

<p>Monthly snow water equivalent (SWE) spatially averaged over the entire Sierra Nevada Ecoregion for the early-century period (2011–2040) using GFDL-A2, showing increases in SWE related to the use of a temperature correction factor (−1.6°C) to adjust minimum air temperatures and simulate cold-air pooling (CAP).</p

Historic changes in April 1^st snow water equivalents.

<p>A spatially distributed estimate of the change in April 1^st snow water equivalent (SWE) from historic (1951–1980) to current (1981–2010) climatic conditions.</p