Distance sampling surveys: using components of detection and total error to select among approaches

Abstract

Wildlife population estimators often require formal adjustment for imperfect detection of individuals during surveys. Conventional distance sampling (CDS) and its extensions (mark‐recapture distance sampling [MRDS], temporary emigration distance sampling [TEDS]) are popular approaches for producing unbiased estimators of wildlife abundance. However, despite extensive discussion and development of distance sampling theory in the literature, deciding which of these alternatives is most appropriate for a particular scenario can be confusing. Some of this confusion may stem from an incomplete understanding of how each approach addresses the components of the detection process. Here we describe the proper application of CDS, MRDS, and TEDS approaches and use applied examples to help clarify their differing assumptions with respect to the components of the detection process. To further aid the practitioner, we summarize the differences in a decision tree that can be used to identify cases where a more complex alternative (e.g., MRDS or TEDS) may be appropriate for a given survey application. Although the more complex approaches can account for additional sources of bias, in practical applications one also must consider estimator precision. Therefore, we also review the concept of total estimator error in the context of comparing competing methods for a given application to aid in the selection of the most appropriate distance sampling approach. Finally, we detail how the use of more advanced techniques (i.e., informed priors, open‐population distance sampling models, and integrated modeling approaches) can further reduce total estimator error by leveraging information from existing and ongoing data collection. By synthesizing the existing literature on CDS, MRDS, TEDS and their extensions, in conjunction with the concepts of total estimator error and the components of the detection process, we provide a comprehensive guide that can be used by the practitioner to more efficiently, effectively, and appropriately apply distance sampling in a variety of settings.

Detection, occupancy, and abundance of the alligator snapping turtle in Texas

“…The assumption of no immigration and emigration across surveys was reasonably met by the limited timeframe over which repeat surveys occurred, and variation in detection probability was accounted for to the extent possible with site‐ and survey‐specific covariates. Assessing abundance through a mark‐recapture distance sampling framework was precluded on the basis of our sampling design, in which detection was contingent on responsive movement of alligator snapping turtles into traps (Schmidt et al 2022).…”

“…Assessing abundance through a mark-recapture distance sampling framework was precluded on the basis of our sampling design, in which detection was contingent on responsive movement of alligator snapping turtles into traps (Schmidt et al 2022).…”

An easily implemented single‐visit survey method for intermittently available and imperfectly detectable wildlife applied to the Florida east coast diamondback terrapin (Malaclemys terrapin tequesta)

“…Several methods compensate for one or both sources of observation bias. Distance sampling is a proven method for coping with detection bias during single‐visit sampling (Schmidt et al., 2022 ; Thomas et al., 2014 ). Double observer methods are an alternative approach that utilizes replicate counts across observers to correct for detection bias (Nichols et al., 2000 ).…”

A spatially explicit model for density that accounts for availability: a case study withMojavedesert tortoises

“…Another important advance enabled estimation of temporal trends in addition to density and detection parameters within a unified Bayesian framework (Moore & Barlow, 2011). Finally, strategies have been developed to estimate density reliably for species that might not always be available for detection during surveys, such as marine mammals when they are below the water surface or terrestrial animals that spend significant time below ground (Schmidt et al, 2022; Ver Hoef et al, 2014). Estimating density for these species is especially challenging because in addition to accounting for detection probability, researchers must also account for the proportion of individuals that are available to observers, a quantity that likely varies in response to a multitude of factors, including local environmental conditions, animal behavior, and physiological demands.…”

“…Although this approach provides estimates of critical population parameters, it is relatively inefficient and labor‐intensive, and requires multiple, complex analyses. Moreover, piecemeal approaches do not properly account for multiple sources of uncertainty associated with the estimation process (e.g., uncertainty about relationships between density or availability and environmental covariates) and unmodeled heterogeneity in ecological and detection parameters (Calder et al, 2003; Moore & Barlow, 2011; Schmidt et al, 2022).…”