Flexible density surface estimation for spatially explicit capture-recapture surveys

1. Existing spatially explicit capture-recapture (SECR) software does not have the ability to fit flexible nonparametric models of animal density. 2. We describe and implement in the R package secrgam, a flexible method for estimating density surfaces from SECR data, using regression splines. 3. Package secrgam is an extension of package secr to implement some models available in the generalised additive model package mvcv. It accommodates density models that are arbitrarily flexible functions of spatially- and temporally-referenced variables. This includes one-dimensional and multi-dimensional smooths of covariates and smooths with interactions. The shape and smoothness of the fitted density surfaces is data-driven and can be determined using AIC or similar criteria. We illustrate use of the package by estimating the density surface from a simulated camera trap survey of leopards. 4. Package secrgam provides a flexible tool for species distribution modelling using SECR data.Postprin

Capture-Recapture Abundance Estimation using a Semi-complete Data Likelihood Approach

Capture–recapture data are often collected when abundance estimation is of interest. In this manuscript we focus on abundance estimation of closed populations. In the presence of unobserved individual heterogeneity, specified on a continuous scale for the capture probabilities, the likelihood is not generally available in closed form, but expressible only as an analytically intractable integral. Model-fitting algorithms to estimate abundance most notably include a numerical approximation for the likelihood or use of a Bayesian data augmentation technique considering the complete data likelihood. We consider a Bayesian hybrid approach, defining a “semi-complete” data likelihood, composed of the product of a complete data likelihood component for individuals seen at least once within the study and a marginal data likelihood component for the individuals not seen within the study, approximated using numerical integration. This approach combines the advantages of the two different approaches, with the semi-complete likelihood component specified as a single integral (over the dimension of the individual heterogeneity component). In addition, the models can be fitted within BUGS/JAGS (commonly used for the Bayesian complete data likelihood approach) but with significantly improved computational efficiency compared to the commonly used superpopulation data augmentation approaches (between about 10 and 77 times more efficient in the two examples we consider). The semi-complete likelihood approach is flexible and applicable to a range of models, including spatially explicit capture–recapture models. The model-fitting approach is applied to two different data sets: the first relates to snowshoe hares where model Mh is applied and the second to gibbons where a spatially explicit capture–recapture model is applied.Publisher PDFPeer reviewe

Minke whale abundance estimation from the NASS 1987 and 2001 aerial cue–counting surveys taking appropriate account of distance estimation errors

We estimate the abundance of minke whales (Balaenoptera acutorostrata) from the Icelandic coastal shelf aerial surveys carried out as part of the 1987 and 2001 North Atlantic Sightings Surveys (NASS). In the case of the 1987 survey, the probability of detecting animals at distance zero (g(0)) is very close to 1 but there is substantial random measurement error in estimating distances. To estimate abundance from these data, we use methods which assume g(0)=1 but which includea distance measurement error model. In the case of the 2001 survey, measurement errors were sufficiently small to be negligible, and we use double platform methods which estimate g(0) and assume no measurement error to estimate abundance. From the 1987 survey, we estimate abundance to be 24,532 animals, with 95% CI (13,399; 44,916). From the 2001 NASS survey data, minke whale abundance is estimated to be 43,633 animals, with 95% CI (30,148; 63,149).Publisher PDFPeer reviewe

A short review of the distribution of short-beaked common dolphins (Delphinus delphis) in the central and eastern North Atlantic with an abundance estimate for part of this area

This paper uses data from 3 programmes: (1) the North Atlantic Sightings Surveys (NASS) surveys undertaken throughout much of the central and eastern North Atlantic north of about 40° N in 1987, 1989, 1995 and 2001; (2) the MICA-93 programme; and (3) the north eastern Atlantic segment of the Small Cetacean Abundance in the North Sea (SCANS) survey in 1994. The data from all surveys were used to examine the distribution of common dolphins in the NE Atlantic. No sightings were made north of 57° N. An initial attempt to examine distribution against 4 potential non biological explanatory variables was made. A simple interpretation of the preliminary analyses presented here is that the primary areas for groups of common dolphins were in waters over 15° C and depths of 400-1,000 m (there does appear a link with shelf features), between around 49°-55° N especially between 20°-30°W. An illustrative example of spatial modelling is presented. Only for 1 year (and part of the total survey area) were there sufficient data to attempt to estimate abundance: 1995. The estimated abundance in the W Block of the NASS-95 Faroese survey was 273,159 (cv=0.26; 95% CI=153,392-435,104) short-beaked common dolphins. This estimate is corrected for animals missed on the trackline (g(0)) and for responsive movement.Publisher PDFPeer reviewe

A flexible framework for spatial capture-recapture with unknown identities

Funding: We acknowledge the funding support of the Natural Sciences and Engineering Research Council of Canada (NSERC) and the University of St Andrews. The fisher study was funded by InnoTech Alberta grants, Government of Alberta (Environment and Parks), The Beaver Hills Initiative, Alberta Conservation Association, NSERC, Royal Canadian Geographic Society, TD Friends of the Environment Foundation, and the Fur Institute of Canada scholarships.Camera traps or acoustic recorders are often used to sample wildlife populations. When animals can be individually identified, these data can be used with spatial capture-recapture (SCR) methods to assess populations. However, obtaining animal identities is often labor-intensive and not always possible for all detected animals. To address this problem, we formulate SCR, including acoustic SCR, as a marked Poisson process, comprising a single counting process for the detections of all animals and a mark distribution for what is observed (eg, animal identity, detector location). The counting process applies equally when it is animals appearing in front of camera traps and when vocalizations are captured by microphones, although the definition of a mark changes. When animals cannot be uniquely identified, the observed marks arise from a mixture of mark distributions defined by the animal activity centers and additional characteristics. Our method generalizes existing latent identity SCR models and provides an integrated framework that includes acoustic SCR. We apply our method to estimate density from a camera trap study of fisher (Pekania pennanti) and an acoustic survey of Cape Peninsula moss frog (Arthroleptella lightfooti). We also test it through simulation. We find latent identity SCR with additional marks such as sex or time of arrival to be a reliable method for estimating animal density.Peer reviewe

Model-based distance sampling

CSO was part-funded by EPSRC/NERC Grant EP/1000917/1.Conventional distance sampling adopts a mixed approach, using model-based methods for the detection process, and design-based methods to estimate animal abundance in the study region, given estimated probabilities of detection. In recent years, there has been increasing interest in fully model-based methods. Model-based methods are less robust for estimating animal abundance than conventional methods, but offer several advantages: they allow the analyst to explore how animal density varies by habitat or topography; abundance can be estimated for any sub-region of interest; they provide tools for analysing data from designed distance sampling experiments, to assess treatment effects. We develop a common framework for model-based distance sampling, and show how the various model-based methods that have been proposed fit within this framework.Publisher PDFPeer reviewe

Spatially Explicit Maximum Likelihood Methods for Capture-Recapture Studies

Live-trapping capture-recapture studies of animal populations with fixed trap locations inevitably have a spatial component: animals close to traps are more likely to be caught than those far away. This is not addressed in conventional closed-population estimates of abundance and without the spatial component, rigorous estimates of density cannot be obtained. We propose new, flexible capture-recapture models that use the capture locations to estimate animal locations and spatially referenced capture probability. The models are likelihood-based and hence allow use of Akaike's information criterion or other likelihood-based methods of model selection. Density is an explicit parameter, and the evaluation of its dependence on spatial or temporal covariates is therefore straightforward. Additional (nonspatial) variation in capture probability may be modeled as in conventional capture-recapture. The method is tested by simulation, using a model in which capture probability depends only on location relative to traps. Point estimators are found to be unbiased and standard error estimators almost unbiased. The method is used to estimate the density of Red-eyed Vireos (Vireo olivaceus) from mist-netting data from the Patuxent Research Refuge, Maryland, U.S.A. Estimates agree well with those from an existing spatially explicit method based on inverse prediction. A variety of additional spatially explicit models are fitted; these include models with temporal stratification, behavioral response, and heterogeneous animal home ranges.</p

Flexible density surface estimation for spatially explicit capture-recapture surveys

1. Existing spatially explicit capture-recapture (SECR) software does not have the ability to fit flexible nonparametric models of animal density. 2. We describe and implement in the R package secrgam, a flexible method for estimating density surfaces from SECR data, using regression splines. 3. Package secrgam is an extension of package secr to implement some models available in the generalised additive model package mvcv. It accommodates density models that are arbitrarily flexible functions of spatially- and temporally-referenced variables. This includes one-dimensional and multi-dimensional smooths of covariates and smooths with interactions. The shape and smoothness of the fitted density surfaces is data-driven and can be determined using AIC or similar criteria. We illustrate use of the package by estimating the density surface from a simulated camera trap survey of leopards.4. Package secrgam provides a flexible tool for species distribution modelling using SECR data

Workshop on new developments in cetacean survey methods

This report contains the slides from a workshop on New Developments in Cetacean Survey Methods held on 27th November 2011 at the 19th Biennial Conference on the Biology of Marine Mammals, Tampa, Florida. Review talks were given on Passive Acoustic Density Estimation (Len Thomas); Dealing with g(0)<1: Perception Bias (Stephen Buckland); Dealing with g(0)<1: Availability Bias (Hans Skaug); Dealing with Measurement Error (David Borchers); and Density Surface Modelling (Jay Barlow). The sessions were followed by a discussion, and this is summarized at the end of the report