Non‐circular home ranges and the estimation of population density

Abstract

Spatially explicit capture–recapture (SECR) models have emerged as one solution to the problem of estimating the population density of mobile and cryptic animals. Spatial models embody assumptions regarding the spatial distribution of individuals and the spatial detection process. The detection process is modeled in SECR as a radial decline in detection probability with distance from the activity center of each individual. This would seem to require that home ranges are circular. The robustness of SECR when home ranges are not circular has been the subject of conflicting statements. Ivan et al. previously compared the SECR density estimator to a telemetry‐scaled non‐spatial estimator. I suggest that the apparent non‐robustness of SECR in their study was a simulation artefact. New simulations of elliptical home ranges establish that the SECR density estimator is largely robust to non‐circularity when detectors are spread in two dimensions, but may be very biased if the detector array is linear and home ranges align with the array. Transformation to isotropy reduces bias from designs of intermediate dimension, such as hollow square arrays. Possible alignment of home ranges should be considered when designing detector arrays.

Detecting social media users based on pedestrian networks and neighborhood attributes: an observational study

“…In order to obtain a model with different assumptions, a different distance measure must be tested. In future research, the ecological distance proposed in (Royle et al 2013;Efford 2019) could be used instead. This type of distance requires using cost surface based on some theoretically relevant -but still unknown-spatial covariant.…”

Investigating effects of soil chemicals on density of small mammal bioindicators using spatial capture-recapture models

“…This detection function has two parameters that are estimated, the probability of capture at the activity center of an individual ( g 0 ) and the spatial scale of detection (σ), the latter of which reflects how rapidly capture probability declines with distance from a trap [ 32 , 33 ]. A default assumption of SCR models is that animal home ranges are approximately circular, but this is often violated in natural populations due to a myriad of factors, such as territoriality, heterogeneous distribution of resources, or movement-restricting landscape features [ 72 , 73 ]. If home ranges are elongated in a particular direction (e.g., because of landscape features that restrict movement, such as canyons or rivers) and traps are deployed primarily along the major axis of animal movement such that the trapping array aligns with the directionality of home ranges, estimated density may be severely biased under the circularity assumption [ 72 ].…”

“…A default assumption of SCR models is that animal home ranges are approximately circular, but this is often violated in natural populations due to a myriad of factors, such as territoriality, heterogeneous distribution of resources, or movement-restricting landscape features [ 72 , 73 ]. If home ranges are elongated in a particular direction (e.g., because of landscape features that restrict movement, such as canyons or rivers) and traps are deployed primarily along the major axis of animal movement such that the trapping array aligns with the directionality of home ranges, estimated density may be severely biased under the circularity assumption [ 72 ]. Our trapping grids were collectively oriented along the direction of Los Alamos Canyon (the x -axis), with minimal trap coverage along the y -axis, and exploratory analyses suggested that animal movement may have been predominantly aligned with the canyon.…”

“…Our trapping grids were collectively oriented along the direction of Los Alamos Canyon (the x -axis), with minimal trap coverage along the y -axis, and exploratory analyses suggested that animal movement may have been predominantly aligned with the canyon. Therefore, we applied an anisotropic transformation to the detection function, which has been shown to mitigate density estimate bias in such scenarios [ 72 , 74 ]. We implemented the anisotropic transformation using the R package geoR [ 75 , 76 ], specified the anisotropy angle parameter (Φ A ) as 105° (1.83 radians) based on the orientation of the canyon, and estimated the anisotropy ratio parameter (Φ R ) via maximum likelihood [ 74 ].…”

Fast, flexible alternatives to regular grid designs for spatial capture–recapture

“…For wildlife surveys, the focus is often animal density or abundance, and survey designs ideally minimize the mean square error of density (or abundance) estimators, equal to the square of the bias plus the variance. SCR estimators have been shown to be unbiased under a wide range of detector arrangements (Efford, 2019a; Efford & Boulanger, 2019; Sun et al., 2014) so that designs that maximize the precision of density (or abundance) estimators—or equivalently, minimize the coefficient of variation of the density estimator —could reasonably be considered optimal (Efford & Boulanger, 2019; Royle et al., 2014).…”