Trap Configuration and Spacing Influences Parameter Estimates in Spatial Capture-Recapture Models

Abstract

An increasing number of studies employ spatial capture-recapture models to estimate population size, but there has been limited research on how different spatial sampling designs and trap configurations influence parameter estimators. Spatial capture-recapture models provide an advantage over non-spatial models by explicitly accounting for heterogeneous detection probabilities among individuals that arise due to the spatial organization of individuals relative to sampling devices. We simulated black bear (Ursus americanus) populations and spatial capture-recapture data to evaluate the influence of trap configuration and trap spacing on estimates of population size and a spatial scale parameter, sigma, that relates to home range size. We varied detection probability and home range size, and considered three trap configurations common to large-mammal mark-recapture studies: regular spacing, clustered, and a temporal sequence of different cluster configurations (i.e., trap relocation). We explored trap spacing and number of traps per cluster by varying the number of traps. The clustered arrangement performed well when detection rates were low, and provides for easier field implementation than the sequential trap arrangement. However, performance differences between trap configurations diminished as home range size increased. Our simulations suggest it is important to consider trap spacing relative to home range sizes, with traps ideally spaced no more than twice the spatial scale parameter. While spatial capture-recapture models can accommodate different sampling designs and still estimate parameters with accuracy and precision, our simulations demonstrate that aspects of sampling design, namely trap configuration and spacing, must consider study area size, ranges of individual movement, and home range sizes in the study population.

Identifying individual cougars (Puma concolor) in remote camera images – implications for population estimates

“…However, these factors should not have biased our results, because our trap array was larger than individual cougar movements (Sollmann et al 2012). Although the full-survey SECR parameter estimates were biased by the misidentification errors, we note that three of the four estimates of s were greater than half the trap spacing, and consistent with trap spacing recommendations by Sollmann et al (2012) and Sun et al (2014). The only other study we are aware of that attempted to measure the level of agreement between independent observers identifying cougars in camera trapping photos was Kelly et al (2008).…”

“…This concern is upheld by our four full-survey SECR densities, which varied widely (Table 1). SECR can be prone to other sources of bias, notably trap array size and spacing (Sollmann et al 2012;Sun et al 2014). However, these factors should not have biased our results, because our trap array was larger than individual cougar movements (Sollmann et al 2012).…”

Unifying population and landscape ecology with spatial capture–recapture

“…density of sampling in order to obtain spatial recaptures, and spatial extent of sampling to obtain a sufficient sample of unique individuals , chapter 10). Sampling design studies have been considered by several authors including Efford and Fewster (2013), Sollmann et al (2013) and Sun et al (2014), with general guidance suggesting a trap spacing of about 2s (s here of the half-normal encounter probability model) in order to provide the optimal sample of spatial recaptures and unique individuals for a given fixed population size. When the geographic area is large relative to the amount of sampling effort that can be expended, cluster designs have been shown to be efficient.…”

“…While typical applications involve a relatively simple homogeneous point process model in which activity centers are distributed independently and uniformly over the state-space S, the SCR framework accommodates inhomogeneous point process models in which the density of activity centers varies as a function of explicit covariates or flexible spatial response surface models (Borchers and Kidney 2014) that affect density. Inhomogeneous point process models show great promise for testing explicit hypotheses about 2nd order selection, understanding mechanisms that influence species density distribution, and developing conservation and management strategies with explicit abundance-or density-based objectives (Sun et al 2014, Proffitt et al 2015, Kendall et al 2016, Linden et al 2017a.…”

Untitled

“…150 Camera stations were distributed 311 ± 91 m (mean ± 1 standard deviation) apart with a 151 minimum goal of four camera stations accessible to each female (Sun et al 2014). Each station 152 consisted of attractants (~250g chicken, ~100 g strawberry jam; olfactory lure), and a remotely 153 triggered camera (Fig.…”