Expert elicitation of seasonal abundance of North Atlantic right whales Eubalaena glacialis in the mid-Atlantic

This work was supported in part by US Office of Naval Research (ONR) grants to E.F.: N00014-09-1-0896 at University of California, Santa Barbara and N00014-12-1-0274 at University of California, Davis. This work was also supported by ONR grant N000141210286 to the University of St Andrews. In addition, we gratefully acknowledge funding for this work from The Marine Alliance for Science and Technology for Scotland (MASTS). MASTS is funded by the Scottish Funding Council (grant reference HR09011) and contributing institutions.North Atlantic right whales (Eubalaena glacialis; henceforth right whales) are among the most endangered large whales. Although protected since 1935, their abundance has remained low. Right whales occupy the Atlantic Ocean from southern Greenland and the Gulf of St. Lawrence south to Florida. The highly industrialized mid-Atlantic region is part of the species’ migratory corridor. Gaps in knowledge of the species’ movements through the mid-Atlantic limit informed management of stressors to the species. To contribute to filling of these gaps, we elicited estimates of the relative abundance of adult right whales in the mid-Atlantic during four months, representing each season, from ten experts. We elicited the minimum, maximum, and mode as the number of individuals in a hypothetical population of 100 right whales, and confidence estimates as percentages. For each month-sex combination, we merged the ten experts’ answers into one distribution. The estimated modes of relative abundances of both sexes were highest in January and April (females, 29 and 59; males, 22 and 23) and lowest in July and October (females, five and nine; males, three and five). In some cases, our elicitation results were consistent with the results of studies based on sightings data. However, these studies generally did not adjust for sampling effort, which was low and likely variable. Our results supplement the results of these studies and will increase the accuracy of priors in complementary Bayesian models of right whale abundances and movements through the mid-Atlantic.Publisher PDFPeer reviewe

A comparison of three methods for estimating call densities of migrating bowhead whales using passive acoustic monitoring

TAM thanks partial support by Centro de Estatistica e Aplicações, Universidade de Lisboa (funded by FCT—Fundação para a Ciência e a Tecnologia, Portugal, through the project UID/MAT/00006/2013).Various methods for estimating animal density from visual data, including distance sampling (DS) and spatially explicit capture-recapture (SECR), have recently been adapted for estimating call density using passive acoustic monitoring (PAM) data, e.g., recordings of animal calls. Here we summarize three methods available for passive acoustic density estimation: plot sampling, DS, and SECR. The first two require distances from the sensors to calling animals (which are obtained by triangulating calls matched among sensors), but SECR only requires matching (not localizing) calls among sensors. We compare via simulation what biases can arise when assumptions underlying these methods are violated. We use insights gleaned from the simulation to compare the performance of the methods when applied to a case study: bowhead whale call data collected from arrays of directional acoustic sensors at five sites in the Beaufort Sea during the fall migration 2007–2014. Call detections were manually extracted from the recordings by human observers simultaneously scanning spectrograms of recordings from a given site. The large discrepancies between estimates derived using SECR and the other two methods were likely caused primarily by the manual detection procedure leading to non-independent detections among sensors, while errors in estimated distances between detected calls and sensors also contributed to the observed patterns. Our study is among the first to provide a direct comparison of the three methods applied to PAM data and highlights the importance that all assumptions of an analysis method need to be met for correct inference.Publisher PDFPeer reviewe

Mixed effect models in distance sampling

Recently, much effort has been expended for improving conventional distance sampling methods, e.g. by replacing the design-based approach with a model-based approach where observed counts are related to environmental covariates (Hedley and Buckland, 2004) or by incorporating covariates in the detection function model (Marques and Buckland, 2003). While these models have generally been limited to include fixed effects, we propose four different methods for analysing distance sampling data using mixed effects models. These include an extension of the two-stage approach (Buckland et al., 2009), where we include site random effects in the second-stage count model to account for correlated counts at the same sites. We also present two integrated approaches which include site random effects in the count model. These approaches combine the analysis stages for the detection and count models and allow simultaneous estimation of all parameters. Furthermore, we develop a detection function model that incorporates random effects. We also propose a novel Bayesian approach to analysing distance sampling data which uses a Metropolis-Hastings algorithm for updating model parameters and a reversible jump Markov chain Monte Carlo (RJMCMC) algorithm for assessing model uncertainty. Lastly, we propose using hierarchical centering as a novel technique for improving model mixing and hence facilitating an RJMCMC algorithm for mixed models. We analyse two case studies, both large-scale point transect surveys, where the interest lies in establishing the effects of conservation buffers on agricultural fields. For each case study, we compare the results from one integrated approach to those from the extended two-stage approach. We find that these may differ in parameter estimates for covariates that were both in the detection and the count model and in model probabilities when model uncertainty was included in inference. The performance of the random effects based detection function is assessed via simulation and when heterogeneity in the data is present, one of the new estimators yields improved results compared to conventional distance sampling estimators

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

Mark recapture distance sampling : using acoustics to estimate the fraction of dolphins missed by observers during shipboard line-transect surveys

Funding: U.S. Navy’s N45 Program and NOAA Southwest Fisheries Science Center.Cetacean abundance estimation often relies on distance sampling methods using shipboard visual line-transect surveys, which assumes that all animals on the trackline are detected and that the detection of animals decreases with increasing distance from the trackline. Mark–Recapture Distance Sampling (MRDS) typically employs a secondary visual observation team and may be used to identify the fraction of animals detected on the trackline when it is suspected that animals may have been missed. For species that are difficult to detect using visual observation methods, such as deep-diving species or those with cryptic surfacing behavior, this secondary team may be prone to the same limitations in detection as the primary observation team and alternative modes of detection may improve estimates. Here we examine the potential use of passive acoustic detection as a secondary platform for MRDS of rough-toothed dolphins (Steno bredanensis) during a combined visual and acoustic shipboard line-transect survey. The average trackline detection probability for rough-toothed dolphins was less than one for both the trial configuration (average p(0)=0.45 for the visual team) and independent observer configuration (average p(0)=0.37 for the visual, p(0)=0.77 for the acoustic and p(0)=0.84 for both teams combined). This study, while limited in scope, strongly suggests that passive acoustic methods may be an effective alternative for estimating p(0) for some cetaceans species.PostprintPeer reviewe

Model-based distance sampling

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

Distance sampling:methods and applications

In this book, the authors cover the basic methods and advances within distance sampling that are most valuable to practitioners and in ecology more broadly. This is the fourth book dedicated to distance sampling. In the decade since the last book published, there have been a number of new developments. The intervening years have also shown which advances are of most use. This self-contained book covers topics from the previous publications, while also including recent developments in method, software and application. Distance sampling refers to a suite of methods, including line and point transect sampling, in which animal density or abundance is estimated from a sample of distances to detected individuals. The book illustrates these methods through case studies; data sets and computer code are supplied to readers through the book’s accompanying website.  Some of the case studies use the software Distance, while others use R code. The book is in three parts.  The first part addresses basic methods, the design of surveys, distance sampling experiments, field methods and data issues.  The second part develops a range of modelling approaches for distance sampling data.  The third part describes variations in the basic method; discusses special issues that arise when sampling different taxa (songbirds, seabirds, cetaceans, primates, ungulates, butterflies, and plants); considers advances to deal with failures of the key assumptions; and provides a check-list for those conducting surveys.