Abundance estimation for line transect sampling: A comparison of distance sampling and spatial capture-recapture models

Abstract

Accurate and precise abundance estimation is vital for informed wildlife conservation and management decision-making. Line transect surveys are a common sampling approach for abundance estimation. Distance sampling is often used to estimate abundance from line transect survey data; however, search encounter spatial capture-recapture can also be used when individuals in the population of interest are identifiable. The search encounter spatial capture-recapture model has rarely been applied, and its performance has not been compared to that of distance sampling. We analyzed simulated datasets to compare the performance of distance sampling and spatial capture-recapture abundance estimators. Additionally, we estimated the abundance of North Atlantic right whales in the southeastern United States with two formulations of each model and compared the estimates. Spatial capture-recapture abundance estimates had lower root mean squared error than distance sampling estimates. Spatial capture-recapture 95% credible intervals for abundance had nominal coverage, i.e., contained the simulating value for abundance in 95% of simulations, whereas distance sampling credible intervals had below nominal coverage. Moreover, North Atlantic right whale abundance estimates from distance sampling models were more sensitive to model specification compared to spatial capture-recapture estimates. When estimating abundance from line transect data, researchers should consider using search encounter spatial capture-recapture when individuals in the population of interest are identifiable, when line transects are surveyed over multiple occasions, when there is imperfect detection of individuals located on the line transect, and when it is safe to assume the population of interest is closed demographically. When line transects are surveyed over multiple occasions, researchers should be aware that individual space use may induce spatial autocorrelation in counts across transects. This is not accounted for in common distance sampling estimators and leads to overly precise abundance estimates.

Fig 1. Simulated datasets.

Examples of simulated datasets following a spatial capture-recapture model where density was uniform across the state space (top row) or a function of a spatial covariate in the state space (bottom row). Each individual was associated with an activity center, depicted by the points in the left column. Individuals were located around their respective activity centers on each sampling occasion, represented for one individual by the open circles in the middle column. The probability of an individual moving to a particular location during an occasion was a function of the distance from its activity center, with the individual being more likely to be located close to rather than far from the activity center. These locations were also distributed as a function of the spatial covariate in the spatial covariate scenario (bottom row). Line transect surveys, represented by the horizontal lines, were conducted during each sampling occasion. The probability that an individual was detected on an occasion was a function of the distance between its location on that occasion and the closest point on a transect line, with the probability being higher the closer an individual was to a line (right column). The locations of detections are depicted by the crossed-out circles in the right column.

Fig 2. North Atlantic right whale density.

Expected North Atlantic right whale (NARW) density per 400 km². NARW density was estimated as a quadratic function of depth within the state space using distance sampling (DS; bottom left panel and red in top panel) and spatial capture-recapture (SCR; bottom right panel and blue in top panel). Posterior mean expected density and 95% credible intervals across the range of depth in the state space is illustrated in the top panel for both DS (red) and SCR (blue) models. Bottom panels illustrate posterior mean expected density across the state space for DS (left) and SCR (right) models that included depth and depth² as covariates on density.