Mark-recapture Distance Sampling Research Articles

Re-assessing abundance of Southern Hudson Bay polar bears by aerial survey: effects of climate change at the southern edge of the range

The Southern Hudson Bay polar bear (Ursus maritimus Phipps, 1774) subpopulation is considered stable, but conflicting evidence lends uncertainty to that designation. Capture–recapture studies conducted in 1984–1986 and 2003–2005 and an aerial survey conducted in 2011/2012 suggested that abundance was likely unchanged since the mid-1980s. However, body condition and body size declined since then, and duration of sea ice decreased by about 30 days. Due to the conflicting information on subpopulation status and ongoing changes in sea ice, we conducted another aerial survey in 2016 to determine whether abundance had changed. We collected data via mark–recapture distance sampling and double-observer protocols. Results suggest that abundance declined 17% from 943 bears (95% CI: 658–1350) in 2011/2012 to 780 (95% CI: 590–1029) in 2016. The proportion of yearlings declined from 12% of the population in 2011 to 5% in 2016, whereas the proportion of cubs remained similar (16% in 2011 vs. 19% in 2016) suggesting low survival of the 2015 cohort. In a warming Arctic, duration of sea ice is predicted to continue to decline in Hudson Bay affecting all ice-dependent wildlife; therefore, further monitoring of this subpopulation is warranted. We recommend a conservative approach to harvest management and repeating the aerial survey in 2021.

Simultaneous modelling of movement, measurement error, and observer dependence in mark-recapture distance sampling: An application to Arctic bird surveys

Mark-recapture distance sampling is a promising method for surveying bird populations from aircraft in open landscapes. However, commonly available distance sampling estimators require that distances to target animals are made without error and that animals are stationary while sampling is being conducted. Motivated by a recent bird survey where these requirements were routinely violated, we describe a marginal likelihood framework for estimating abundance from double-observer data that can accommodate movement and measurement error when observations are made consecutively (as with front and rear observers), when animals are uniformly distributed during detection by the first observer, and when detections consist of both moving and stationary animals. Assuming that all animals are subject to measurement error and that some animals can move between detections, we integrate over unknown animal locations to construct a marginal likelihood for detection, movement, and measurement error parameters. Estimates of animal abundance are then obtained using a modified Horvitz–Thompson-like estimator. In addition, unmodelled heterogeneity in detection probability can be accommodated through observer dependence parameters. Using simulation, we show that our approach yields low bias compared to approaches that ignore movement and/or measurement error, including in cases where there is considerable detection heterogeneity. Applying our approach to data from a double-observer waterfowl helicopter survey in northern Canada, we are able to estimate bird density accounting for movement and measurement error and corrected for observer heterogeneity. Our approach appears promising for generating unbiased estimates of bird abundance necessary for reliable conservation and management.

Accounting for uncertainty in duplicate identification and group size judgements in mark–recapture distance sampling

AbstractMark–recapture distance sampling (MRDS) surveys with two independent observers are widely used to estimate wildlife population abundance. The analysis relies on accurate identification of duplicate sightings common to both observers, and correct judgements of group size, both of which are hard to achieve for species that exhibit complex grouping patterns.In this paper, we examine the impact of these sources of uncertainty on bias and precision of abundance estimates, using a case study of 22 aerial surveys of common dolphins (Delphinus delphis) in the Hauraki Gulf, New Zealand. We develop a novel probabilistic method to identify duplicate observations, and account for various sources of uncertainty using a simulation‐intensive approach.For our case study, identifying duplicates using reasonable but arbitrary thresholds of time and angle discrepancies created a range of abundance estimates differing by up to 20%, whereas our novel threshold‐free probabilistic analysis returned an estimate roughly central to this range. Uncertainty in group size made a smaller impact of up to 5% on abundance estimates. All analysis choices returned similar values for the coefficient of variation, from 20 to 23%.Generating robust estimates of abundance, and accounting for all associated sources of uncertainty, is critical for informing conservation management. Our novel approach provides a way to eliminate arbitrary decisions associated with MRDS, and account for a wider range of uncertainties. Our method allows for the reliable application of MRDS to a wider range of terrestrial and marine species, and will be a useful tool for producing robust abundance estimates for species that exhibit complex grouping patterns.

Improving inference for aerial surveys of bears: The importance of assumptions and the cost of unnecessary complexity.

Obtaining useful estimates of wildlife abundance or density requires thoughtful attention to potential sources of bias and precision, and it is widely understood that addressing incomplete detection is critical to appropriate inference. When the underlying assumptions of sampling approaches are violated, both increased bias and reduced precision of the population estimator may result. Bear (Ursus spp.) populations can be difficult to sample and are often monitored using mark‐recapture distance sampling (MRDS) methods, although obtaining adequate sample sizes can be cost prohibitive. With the goal of improving inference, we examined the underlying methodological assumptions and estimator efficiency of three datasets collected under an MRDS protocol designed specifically for bears. We analyzed these data using MRDS, conventional distance sampling (CDS), and open‐distance sampling approaches to evaluate the apparent bias‐precision tradeoff relative to the assumptions inherent under each approach. We also evaluated the incorporation of informative priors on detection parameters within a Bayesian context. We found that the CDS estimator had low apparent bias and was more efficient than the more complex MRDS estimator. When combined with informative priors on the detection process, precision was increased by >50% compared to the MRDS approach with little apparent bias. In addition, open‐distance sampling models revealed a serious violation of the assumption that all bears were available to be sampled. Inference is directly related to the underlying assumptions of the survey design and the analytical tools employed. We show that for aerial surveys of bears, avoidance of unnecessary model complexity, use of prior information, and the application of open population models can be used to greatly improve estimator performance and simplify field protocols. Although we focused on distance sampling‐based aerial surveys for bears, the general concepts we addressed apply to a variety of wildlife survey contexts.

Large numbers of marine mammals winter in the North Water polynya

The importance of the North Water polynya in Smith Sound as an overwintering area for marine mammals has been questioned. One way to address the issue is to assess the abundance of selected marine mammals that are present during winter in the North Water. Visual aerial surveys involving double observer platforms were conducted over the eastern part of the North Water polynya in April 2014. Four species of marine mammals were included in strip-census estimation of abundance. Perception bias was addressed using a double-platform survey protocol, a Chapman mark–recapture estimator for whales, seals and walruses (Odobenus rosmarus) on ice and a mark–recapture distance sampling estimation technique for walruses in water. Availability bias was addressed by correcting abundance estimates by the percentage of time animals detected in water that were available for detection at the surface. The resulting estimates suggested that 2544 walruses (95 % CI 1513–4279), 6005 bearded seals (Erignathus barbatus, 95 % CI 4070–8858), 2324 belugas (Delphinapterus leucas, 95 % CI 968–5575) and 3059 narwhals (Monodon monoceros, 95 % CI 1760–5316) wintered in the eastern part of the North Water polynya in April 2014. The walrus estimate is larger than previous summer estimates, and it emphasizes the importance of the habitat along the Greenland coast as a walrus wintering ground. The estimate of belugas is likely negatively biased due to the partial coverage of the potential habitat. The estimate of narwhals is large compared to the few previous observations of narwhals in winter in the North Water, and it demonstrates that large numbers of narwhals winter there. The overall conclusion is that the North Water is indeed an important wintering area for at least walruses, belugas, narwhals and bearded seals.

Double-observer evaluation of pronghorn aerial line-transect surveys

Context Distance sampling is used to estimate abundance for several taxa, including pronghorn (Antilocapra americana). Comparisons between population estimates derived from quadrat sampling and distance sampling suggest that distance sampling underestimates pronghorn density, likely owing to violations of the critical assumption of distance sampling that all pronghorn within the innermost distance band (A band; nearest to the aircraft) are detected. Aims We sought to rigorously test the assumption that all pronghorn clusters are detected within the innermost distance band by applying a double-observer approach to an established pronghorn aerial-survey protocol. Additionally, we evaluated potential effects of cluster size, landscape composition and seat position (front seat versus rear) on the probability of detection. Methods We conducted aerial line-transect distance-sampling surveys using independent, paired observers and modelled the probability of detection with mark–recapture distance-sampling (MRDS) analysis techniques that explicitly estimate the probability of detection for pronghorn clusters in the innermost distance band. We compared density estimates produced by the MRDS analysis with those produced by multiple-covariate distance sampling (MCDS), a method that assumes complete detection for clusters on the transect line. Key results We identified violations of the assumption that all clusters within the innermost distance band were detected, which would contribute to proportional biases in density estimates for analysis techniques that assume complete detection. The frequency of missed clusters was modest from the front-seat position, with 45 of the 47 (96%) clusters in the A band detected. In contrast, the frequency of missed clusters was more substantial for the rear position, from which 37 of 47 (79%) clusters in the A band were detected. Further, our analysis showed that cluster size and landscape composition were important factors for pronghorn sightability. Conclusions When implementing standard survey methodologies, pronghorn aerial-line transect surveys underestimated population densities. A double-observer survey configuration allowed us to quantify and correct for the bias caused by the failure of observers to detect all pronghorn clusters within the innermost distance band. Implications Population monitoring programs should incorporate double-observer validation trials to quantify the extent of bias owing to undetected clusters within the innermost distance band realised under typical survey conditions. Wildlife managers can improve the precision of pronghorn aerial line-transect surveys by incorporating cluster size and measures of landscape composition and complexity into detection models without incurring additional survey costs.

Accounting for Lack of Independence and Partial Overlap of Observation Zones in Line-Transect Mark-Recapture Distance Sampling

Line-transect mark-recapture distance sampling methods can be used to estimate abundance when at least two observers sight and record distances to detected groups of individuals within the survey area. However, a lack of independence between the observer’s detections will cause biased abundance estimates. Studies are also typically designed such that there is complete overlap of the regions searched by the two observers, but that may not always be possible. Here we detail an intuitive approach for line-transect distance sampling applications based upon logistic regression to account for a potential lack of independence by using the detections of one observer as a covariate in the detection function of the second observer. Partial overlap of the observer survey regions can be addressed by constraining detection probability to equal 0 for the respective observer outside of the overlap zone. We show via simulation that the method provides reliable estimates of abundance and is not affected by random unmodeled heterogeneity in detection probability. The method is illustrated by estimating abundance within the covered region of an aerial line-transect survey for New Zealand’s endemic Hector’s dolphin (Cephalorhynchus hectori hectori) conducted in the austral summer of 2013, the motivating application for this work. Supplementary materials accompanying this paper appear on-line.

Estimating the abundance of the Southern Hudson Bay polar bear subpopulation with aerial surveys

The Southern Hudson Bay (SH) polar bear subpopulation occurs at the southern extent of the species’ range. Although capture–recapture studies indicate abundance was likely unchanged between 1986 and 2005, declines in body condition and survival occurred during the period, possibly foreshadowing a future decrease in abundance. To obtain a current estimate of abundance, we conducted a comprehensive line transect aerial survey of SH during 2011–2012. We stratified the study site by anticipated densities and flew coastal contour transects and systematically spaced inland transects in Ontario and on Akimiski Island and large offshore islands in 2011. Data were collected with double-observer and distance sampling protocols. We surveyed small islands in James Bay and eastern Hudson Bay and flew a comprehensive transect along the Quebec coastline in 2012. We observed 667 bears in Ontario and on Akimiski Island and nearby islands in 2011, and we sighted 80 bears on offshore islands during 2012. Mark–recapture distance sampling and sight–resight models yielded an estimate of 860 (SE = 174) for the 2011 study area. Our estimate of abundance for the entire SH subpopulation (943; SE = 174) suggests that abundance is unlikely to have changed significantly since 1986. However, this result should be interpreted cautiously because of the methodological differences between historical studies (physical capture–recapture) and this survey. A conservative management approach is warranted given previous increases in duration of the ice-free season, which are predicted to continue in the future, and previously documented declines in body condition and vital rates.

A Unifying Model for Capture–Recapture and Distance Sampling Surveys of Wildlife Populations

A fundamental problem in wildlife ecology and management is estimation of population size or density. The two dominant methods in this area are capture–recapture (CR) and distance sampling (DS), each with its own largely separate literature. We develop a class of models that synthesizes them. It accommodates a spectrum of models ranging from nonspatial CR models (with no information on animal locations) through to DS and mark-recapture distance sampling (MRDS) models, in which animal locations are observed without error. Between these lie spatially explicit capture–recapture (SECR) models that include only capture locations, and a variety of models with less location data than are typical of DS surveys but more than are normally used on SECR surveys. In addition to unifying CR and DS models, the class provides a means of improving inference from SECR models by adding supplementary location data, and a means of incorporating measurement error into DS and MRDS models. We illustrate their utility by comparing inference on acoustic surveys of gibbons and frogs using only capture locations, using estimated angles (gibbons) and combinations of received signal strength and time-of-arrival data (frogs), and on a visual MRDS survey of whales, comparing estimates with exact and estimated distances. Supplementary materials for this article are available online.

Using mark–recapture distance sampling methods on line transect surveys

SummaryMark–recapture distance sampling (MRDS) methods are widely used for density and abundance estimation when the conventionalDSassumption of certain detection at distance zero fails, as they allow detection at distance zero to be estimated and incorporated into the overall probability of detection to better estimate density and abundance. However, incorporatingMRdata inDSmodels raises survey and analysis issues not present in conventionalDS. Conversely, incorporatingDSassumptions inMRmodels raises issues not present in conventionalMR. As a result, being familiar with either conventionalDSmethods or conventionalMRmethods does not on its own put practitioners in good a position to applyMRDSmethods appropriately. This study explains the sometimes subtly different varieties ofMRDSsurvey methods and the associated concepts underlyingMRDSmodels. This is done as far as possible without giving mathematical details – in the hope that this will make the key concepts underlying the methods accessible to a wider audience than if we were to present the concepts via equations.We illustrate use of the two main types ofMRDSmodel by using data collected on two different types of survey: a survey of ungulate faecal pellets where two observers searched independently of each other; and a cetacean survey that used a search protocol that could accommodate responsive movement, with only one observer searching independently and the other being aware of all detections.Synthesis and applications. Mark–recaptureDSis a widely used method for estimating animal density and abundance when detection of animals at distance zero is not certain. Two observer configurations and three statistical models are described, and it is important to choose the most appropriate model for the observer configuration and target species in question. By way of making the methods more accessible to practicing ecologists, we describe the key ideas underlyingMRDSmethods, the sometimes subtle differences between them, and we illustrate these by applying different kinds ofMRDSmethod to surveys of two different target species using different survey configurations.

Abundance of harbour porpoises (Phocoena phocoena) in the western Baltic, Belt Seas and Kattegat

In July 2012, a ship-board double-platform line-transect survey was conducted to assess harbour porpoise (Phocoena phocoena) abundance in the Kattegat, Belt Seas and the Western Baltic. A total of 826 km of track lines were surveyed between the 2nd and 21st of July 2012, and 169 observations were made by the primary observers, comprising a total of 230 porpoises. Fifty-seven observations were identified as duplicate sightings observed by both tracker and primary observers and were used to correct for availability and perception bias of the primary detections. Using Mark–Recapture Distance Sampling analysis, we produced a model using the half-normal key function, including sightability as the only covariate to estimate the density and abundance of harbour porpoise within the 51,511 km2 survey area. Estimated detection probability on the transect line, known as g(0), was at 0.571 (±0.074; CV = 0.130). Using a point independence model of the detection function, the abundance of harbour porpoises within the survey area was estimated at 40,475 animals (95 % CI 25,614–65,041, CV = 0.235) with an associated density of 0.786 animals km−2 (95 % CI 0.498–1.242, CV = 0.235) and an average group size of 1.488 animals. These results reflect densities obtained during the SCANS surveys in 1994 and 2005, indicating no significant population trend in the area. However, it should be noted that the survey area covers more than one population and that results are therefore not necessarily reflecting local population trends. Until proper population borders are obtained, the abundance estimate provides baseline data for future monitoring and is an important input to the assessment of the conservation status of harbour porpoises in the area.

Modeling spatially explicit densities of endangered avian species in a heterogeneous landscape

ABSTRACT. Relating population density to spatially explicit habitat characteristics can inform management by directing efforts to areas with lower densities or focusing conservation and land protection on high-density areas. We conducted point-transect surveys for the endangered Golden-cheeked Warbler (Setophaga chrysoparia) and Black-capped Vireo (Vireo atricapilla) in the live-fire region of Fort Hood, Texas. We used mark—recapture distance sampling and combined a Horvitz-Thompson estimator with a habitat-based, resourceselection gradient to estimate spatially explicit density for both species. We detected Golden-cheeked Warblers at 120 locations (202, 197, and 89 detected by primary, secondary, and both observers, respectively) and Black-capped Vireos at 173 locations (241, 255, and 107 detected by primary, secondary, and both observers, respectively). For Golden-cheeked Warblers, the average (± SE) composite detection probability estimate within a 100-m point-sample radius was 0.57 ± 0.14, and for vir...

Spatial trends in abundance of long-finned pilot whales, white-beaked dolphins and harbour porpoises in West Greenland

Data from an aerial line transect survey conducted off West Greenland during August–September 2007 were used to estimate the abundance of long-finned pilot whales (Globicephala melas), white-beaked dolphins (Lagenorhynchus albirostris) and harbour porpoises (Phocoena phocoena). The abundance of each species was estimated using mark-recapture distance sampling techniques to correct for perception bias, and correction factors for time spent at the surface were applied. The fully corrected abundance estimates were 8,133 long-finned pilot whales, 11,984 white-beaked dolphins and 33,271 harbour porpoises. Based on density surface modelling methods, a count model with a generalised additive model formulation was used to relate abundance to spatial variables. Response curves indicated that the preferred habitats were deep offshore areas in Midwest Greenland for pilot whales, deep water over steep seabed slopes in South Greenland for white-beaked dolphins and relatively shallow inshore waters in Midwest–South Greenland for harbour porpoises. The abundance estimates and spatial trends for the three species are the first obtained from Greenland.

Abundance of narwhals (Monodon monoceros) on the hunting grounds in Greenland

Narwhals (Monodon monoceros L.) occur in the Atlantic sector of the Arctic where for centuries they have been subject to subsistence hunting by Inuit in Greenland and Canada. Scientific advice on the sustainable levels of removals from narwhal populations provides the basis for quotas implemented in both Greenland and Canada. The scientific advice relies heavily on extensive aerial surveys that are the only feasible way to acquire data on narwhal densities and abundance throughout their range. In some areas lack of information on abundance, in combination with high exploitation levels, has caused conservation concerns leading to restrictions on the international trade in narwhal tusks. Narwhals also are regarded as highly sensitive to habitat disturbance caused by global warming. This study analyzed data from aerial sighting surveys covering four major narwhal hunting grounds in Greenland. The surveys were conducted as double observer experiments with 2 independent observation platforms, 1 at the front and 1 at the rear of the survey plane. The sighting data were analyzed using mark–recapture distance sampling techniques that allow for correction for whales that were missed by the observers. The surveys also were corrected for animals that were submerged during the passage of the survey plane, using diving and submergence data from satellite-linked time–depth recorders deployed on 2 free-ranging narwhals. The abundance of narwhals on the wintering ground in West Greenland in 2006 was 7,819 (95% confidence interval [CI]: 4,358–14,029). The abundances of narwhals in Inglefield Bredning and Melville Bay, northwest Greenland in 2007 were 8,368 (95% CI: 5,209–13,442) and 6,024 (95% CI: 1,403–25,860), respectively. The abundance of narwhals in East Greenland in 2008 was 6,444 (95% CI: 2,505–16,575). These surveys provide the first estimates of narwhal abundance from important hunting areas in East and West Greenland and provide larger and more complete estimates from previously surveyed hunting grounds in Inglefield Bredning. The estimates can be used for setting catch limits for the narwhal harvest in West and East Greenland and as a baseline for examining the effects of climate change on narwhal abundance. DOI: 10.1644/ 09-MAMM-A-198.1.

A non-technical overview of spatially explicit capture–recapture models

Most capture–recapture studies are inherently spatial in nature, with capture probabilities depending on the location of traps relative to animals. The spatial component of the studies has until recently, however, not been incorporated in statistical capture–recapture models. This paper reviews capture–recapture models that do include an explicit spatial component. This is done in a non-technical way, omitting much of the algebraic detail and focussing on the model formulation rather than on the estimation methods (which include inverse prediction, maximum likelihood and Bayesian methods). One can view spatially explicit capture–recapture (SECR) models as an endpoint of a series of spatial sampling models, starting with circular plot survey models and moving through conventional distance sampling models, with and without measurement errors, through mark–recapture distance sampling (MRDS) models. This paper attempts a synthesis of these models in what I hope is a style accessible to non-specialists, placing SECR models in the context of other spatial sampling models.

Distance software: design and analysis of distance sampling surveys for estimating population size

Summary 1. Distance sampling is a widely used technique for estimating the size or density of biological populations. Many distance sampling designs and most analyses use the software Distance. 2. We briefly review distance sampling and its assumptions, outline the history, structure and capabilities of Distance, and provide hints on its use. 3. Good survey design is a crucial prerequisite for obtaining reliable results. Distance has a survey design engine, with a built‐in geographic information system, that allows properties of different proposed designs to be examined via simulation, and survey plans to be generated. 4. A first step in analysis of distance sampling data is modelling the probability of detection. Distance contains three increasingly sophisticated analysis engines for this: conventional distance sampling, which models detection probability as a function of distance from the transect and assumes all objects at zero distance are detected; multiple‐covariate distance sampling, which allows covariates in addition to distance; and mark–recapture distance sampling, which relaxes the assumption of certain detection at zero distance. 5. All three engines allow estimation of density or abundance, stratified if required, with associated measures of precision calculated either analytically or via the bootstrap. 6. Advanced analysis topics covered include the use of multipliers to allow analysis of indirect surveys (such as dung or nest surveys), the density surface modelling analysis engine for spatial and habitat modelling, and information about accessing the analysis engines directly from other software. 7. Synthesis and applications. Distance sampling is a key method for producing abundance and density estimates in challenging field conditions. The theory underlying the methods continues to expand to cope with realistic estimation situations. In step with theoretical developments, state‐of‐the‐art software that implements these methods is described that makes the methods accessible to practising ecologists.