Spatially explicit capture-recapture
We used a spatially explicit capture-recapture (SECR) framework to estimate bobcat density (Efford and Fewster 2013). Because we performed the sampling on pre-determined transects revisited three times during the study area, we created “detectors” by splitting the study areas into 1 km x 1 km grid cells. Only grid cells that overlapped the transects were retained and we defined ‘detectors’ as the centroid of each grid cell. We then assigned all bobcat-identified scats collected on the transect/s in a given cell the unique code of that cell (or detector) (Royle et al. 2014). Bobcats can move large distances (several km in a day) and have large home ranges averaging 15.83 to 39.70 km2 (Ferguson et al. 2009); the distance between the center of each cell and locations of scats were therefore negligible from a bobcat movement and space use perspective and assigning the scats location to the cell centroid facilitated the development of capture history data and data analysis.
The following modeling framework and workflow used package secr(Efford 2022) implemented in the program R (R Core Team 2022). We used ArcGIS (ESRI, Redlands CA) to create the habitat mask used as an effective sampling area in our analysis. To model detection, we calculated the sigma (σ) model parameter using a root pooled variance function as a measure of 2D dispersion of the centroids, pooled over individuals (Efford 2022). We found that a buffer width of 5 × σ around our detector array reduced the probability of capturing a bobcat outside this buffer to zero and increasing buffer width beyond this value had no discernable effect on the estimated density (Figure 2). This area is thus typically used as the effective sampling area in spatial capture-recapture models (Borchers and Efford 2008). The value of σ was 1230 m. To investigate differences between the 2 study areas, AEP and Vinton-Zaleski, we built a habitat mask by creating buffers around the detectors equal to 5 × σ (6152 m) in ArcGIS; the resulting mask had two different polygons, corresponding to the two study areas, and they had an area of 543 km2 (AEP) and 580 km2(Vinton-Zaleski).
We tested several detection functions and selected a ‘cumulative lognormal ’ detection function to use in subsequent analyses, as this function performed better than other detection functions based on Akaike Information Criterion corrected for small sample size (AICc) (Akaike 1998) comparisons of SECR models fit with half-normal, compound half-normal, cumulative gamma, and cumulative lognormal (Table 3). We also compared several predictor variables for detection including length of transect per grid cell (t_length ), and various habitat variables (proportion of developed, forest, open, and wetland habitat) against a constant detection (null) model. We found that the constant detection model performed the best, but several other models were <2 ΔAICc from this model (Table 4). The model that included detection as a function of the length of transect per grid cell failed some variance calculations and thus was not included in model comparison.
We fit a SECR state (observation) model using a spatial Poisson process for animal activity centers (Borchers and Efford 2008) and included a categorical predictor (study area: AEP or Vinton- Zaleski), as we expected differences in density between the two areas based on preliminary studies (Prange and Rose 2020; Popescu et al. 2021). We compared this model to a constant density model (null) using AICc. Lastly, because the data were collected within a single year (July 2018 to April 2019), it included a single birth pulse and each survey was conducted over the course of several months, we did not investigate potential differences in density between the three surveys. Instead, we quantified the overall bobcat density and abundance during the study period and differences between the two focal areas.