Abstract
Ecologists often use multiple observer transect surveys to census animal populations. In addition to animal counts, these surveys produce sequences of detections and non-detections for each observer. When combined with additional data (i.e. covariates such as distance from the transect line), these sequences provide the additional information to estimate absolute abundance when detectability on the transect line is less than one. Although existing analysis approaches for such data have proven extremely useful, they have some limitations. For instance, it is difficult to extrapolate from observed areas to unobserved areas unless a rigorous sampling design is adhered to; it is also difficult to share information across spatial and temporal domains or to accommodate habitat-abundance relationships. In this paper, we introduce a hierarchical modeling framework for multiple observer line transects that removes these limitations. In particular, abundance intensities can be modeled as a function of habitat covariates, making it easier to extrapolate to unsampled areas. Our approach relies on a complete data representation of the state space, where unobserved animals and their covariates are modeled using a reversible jump Markov chain Monte Carlo algorithm. Observer detections are modeled via a bivariate normal distribution on the probit scale, with dependence induced by a distance-dependent correlation parameter. We illustrate performance of our approach with simulated data and on a known population of golf tees. In both cases, we show that our hierarchical modeling approach yields accurate inference about abundance and related parameters. In addition, we obtain accurate inference about population-level covariates (e.g. group size). We recommend that ecologists consider using hierarchical models when analyzing multiple-observer transect data, especially when it is difficult to rigorously follow pre-specified sampling designs. We provide a new R package, hierarchicalDS, to facilitate the building and fitting of these models.
Highlights
Transect surveys are often used to sample animal populations and are a central component of many inventory and monitoring programs
In its canonical form (e.g. [2,3]), distance sampling is a simple extension of quadrat sampling, whereby an observer notes the perpendicular distance of animals or groups of animals from the centerline
Previously available estimators have proven remarkably versatile for estimating abundance from such data, they are of limited utility in making inference about the effects of ecological covariates on abundance, estimating the distribution of individual covariates in a population, and in making predictions about abundance in unsampled areas
Summary
Transect surveys are often used to sample animal populations and are a central component of many inventory and monitoring programs. In such surveys, an observer travels along a set of lines or visits a finite collection of points, recording all animals they encounter within a fixed distance of the line (or point). In: Buckland S, Anderson D, Burnham K, Laake J, Borchers D, et al, editors, Advanced Distance Sampling, Oxford, U.K.: Oxford University Press. 8. Borchers DL, Laake JL, Southwell C, Paxton CGM (2006) Accomodating unmodeled heterogeneity in double-observer distance sampling surveys. 9. Buckland ST, Laake JL, Borchers DL (2010) Double-observer line transect methods: Levels of independence.
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