Hierarchical mark‐recapture distance sampling to estimate moose abundance

Abstract

Estimating the abundance of wide‐ranging wildlife, difficult under any circumstances, is particularly challenging when detection is low and affected by factors that also influence density and distribution. In northeastern Washington, moose (Alces alces) have evidently increased since the 1970s but spend most of their time under coniferous cover that makes detection from the air difficult. We used a Bayesian hierarchical approach to incorporate habitat use (in the form of availability as a function of canopy closure) into a detection model within a mark‐recapture distance sampling framework to estimate moose density. Our model of availability used a latent density surface employing habitat use data obtained from 17 adult female moose wearing global positioning system (GPS) collars. Distance sampling data, obtained from helicopter surveys in winters 2014, 2015, and 2016, consisted of double‐observer detections of 166 moose groups along 2,241 km of systematically placed line transects within 29 survey blocks selected using a stratified‐random design. We estimated moose density over the entire survey area as 0.49/km2 (95% credible interval = 0.33–0.67/km2). Extrapolated to the 10,513‐km2 survey area, we estimated 5,169 moose (95% credible interval = 3,510–7,034). Our methodology allowed us to adjust for availability bias and produce an estimate even where detection was difficult but required many hours of helicopter flights, acceptable weather conditions, and the availability of GPS collared‐moose. © 2018 The Wildlife Society.

Moose calf detection probabilities: quantification and evaluation of a ground-based survey technique

“…Distance sampling approaches model and correct for the decay in detection probability as distances between observers and animals increase (Buckland et al 2008). While less common, distance sampling has also been used for moose (Oyster et al 2018) and white-tailed deer Odocoileus virgianus (Anderson et al 2013). More commonly, aerial sightability models quantify the underestimation of raw count data due to habitat type, animal behavior, seasonal conditions and observer platform, and provide a numerical inflation to correct for bias.…”

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

“…Ten horizontal line transects were surveyed each occasion, although each line need not be surveyed every occasion in practice (Fig 1). Line transects were defined as y � (11,13,15,17,19,21,23,25,27,29), with endpoints at x = 10 and 30. The probability that individual i was detected on occasion j, denoted as y i,j = 1, was a function of g(0) and the distance between the individual and the closest transect on occasion j, d detection,i,j , with a scale parameter, σ d , of 1/3.…”

“…The proportion of detections made by one observer to detections made by both observers is used to estimate the probability of detection given an individual is located on the transect line [6,11]. Alternatively, a separate study may be conducted to estimate detection probability on the transect line for the species of interest [12][13][14][15]. SCR, unlike DS, requires that individuals in the population of interest be individually identifiable.…”

Assessment and novel application ofN‐mixture models for aerial surveys of wildlife

“…One widely used method for characterizing imperfect detection for transect surveys is distance sampling, a method that uses data on the distance of animals from a survey line for estimating detection probability and ultimately density and abundance (Buckland et al 2001(Buckland et al , 2004. Distance sampling has been applied to aerial surveys of terrestrial species as a precise survey method in many studies and environments and has substantial literature on its use (Buckland et al 2001, 2004, Fewster and Pople 2008, Schmidt et al 2012, Peters et al 2014, Oyster et al 2018. Recently, hierarchical distance sampling (HDS) was developed to estimate heterogeneity in detection and abundance using spatially referenced data and covariates (Royle 2004, Sollmann et al 2015, Kéry and Royle 2016.…”

“…2014, Oyster et al. 2018). Recently, hierarchical distance sampling (HDS) was developed to estimate heterogeneity in detection and abundance using spatially referenced data and covariates (Royle 2004, Sollmann et al.…”