Hierarchical mark-recapture distance sampling to estimate moose abundance

2018 ◽  
Vol 82 (8) ◽  
pp. 1668-1679 ◽  
Author(s):  
Jared H. Oyster ◽  
Ilai N. Keren ◽  
Sara J.K. Hansen ◽  
Richard B. Harris
Keyword(s):  
Distance Sampling ◽  
Mark Recapture

scholarly journals Using mark-recapture distance sampling methods on line transect surveys

2014 ◽  
Vol 5 (11) ◽  
pp. 1180-1191 ◽  
Author(s):  
Mary Louise Burt ◽  
David L. Borchers ◽  
Kurt J. Jenkins ◽  
Tiago A. Marques
Keyword(s):  
Sampling Methods ◽  
Distance Sampling ◽  
Line Transect ◽  
Mark Recapture ◽  
On Line

scholarly journals Mark-recapture vs. line-transect abundance estimates of a coastal dolphin population: a case study of Tursiops truncatus from Laguna, southern Brazil

2017 ◽  
Vol 11 (1-2) ◽  
pp. 133-143 ◽  
Author(s):  
Fábio G. Daura-Jorge ◽  
Paulo César Simões-Lopes
Keyword(s):  
Anthropogenic Impacts ◽  
Bottlenose Dolphins ◽  
A Priori ◽  
Distance Sampling ◽  
Estimation Methods ◽  
Line Transect ◽  
Southern Brazil ◽  
Mark Recapture ◽  
Pilot Studies ◽  
Robust Estimates

Cetacean populations in coastal habitats are increasingly threatened by multiple anthropogenic impacts. Monitoring these populations to obtain robust estimates of abundance and detect trends over time is critical to achieve conservation goals. Here, we conducted a pilot study to evaluate the effectiveness of two commonly used abundance estimation methods: mark-recapture and distance sampling line-transect. Surveys were conducted to estimate the abundance of bottlenose dolphins in Laguna, southern Brazil. We implemented power-analysis models and compared both techniques in terms of cost, time and effectiveness to detect trends over a five-year period. Mark-recapture models were analyzed in MARK and resulted in an abundance of 50 individuals (CI = 39-64) with a coefficient of variation (CV) of 0.13. The line-transect models were implemented using the program DISTANCE and resulted in an estimate of 62 individuals (CI = 38-103), with a CV of 0.24. Comparing both approaches, mark-recapture resulted 1.30 time more expensive than line-transect for a single season of effort, but was twice as effective in terms of precision. As a consequence, the probability of detecting a 5% trend during a five-year period is 2.08 times higher with mark recapture. Conversely, the final cost to detect a trend with distance sampling is 1.19 time higher but considering six more years of effort. These results highlight the importance of selecting a-priori sampling design techniques that include developing pilot studies that evaluate the bias, precision and accuracy of estimates while considering costs involved. Considering the small population size estimated herein, the sensitivity of both approaches for detecting trends is not sufficient because the original population would be markedly reduced by the time a declining trend was detected. Thus, a precautionary approach is still imperative, even when robust estimates are obtained.

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

2017 ◽  
Vol 9 (2) ◽  
pp. 354-362 ◽  
Author(s):  
Olivia N. P. Hamilton ◽  
Sophie E. Kincaid ◽  
Rochelle Constantine ◽  
Lily Kozmian‐Ledward ◽  
Cameron G. Walker ◽  
...  
Keyword(s):  
Group Size ◽  
Distance Sampling ◽  
Mark Recapture

scholarly journals Simultaneous Modelling of Movement, Measurement Error, and Observer Dependence in Double-Observer Distance Sampling Surveys

2017 ◽  
Author(s):  
By Paul B. Conn ◽  
Ray T. Alisauskas
Keyword(s):  
Measurement Error ◽  
Detection Probability ◽  
Marginal Likelihood ◽  
Distance Sampling ◽  
Mark Recapture ◽  
Animal Groups ◽  
Animal Abundance ◽  
Helicopter Survey ◽  
Using Data ◽  
Double Observer

Mark-recapture distance sampling uses detections, non-detections and recorded distances of animals encountered in transect surveys to estimate abundance. 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. In practice these requirements are often violated. In this paper, 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) and when animals are randomly distributed when detected by the first observer. Our framework requires that two observers independently detect and record binned distances to observed animal groups, as we well as a binary indicator for whether animals were moving or not. We then assume that stationary animals are subject to measurement error whereas moving animals are subject to both movement and measurement error. Integrating over unknown animal locations, we 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. We demonstrate our approach using data from a double-observer waterfowl helicopter survey.

Point-Based Mark-Recapture Distance Sampling

2011 ◽  
Vol 16 (3) ◽  
pp. 389-408 ◽  
Author(s):  
J. L. Laake ◽  
B. A. Collier ◽  
M. L. Morrison ◽  
R. N. Wilkins
Keyword(s):  
Distance Sampling ◽  
Mark Recapture

scholarly journals Bayesian abundance estimation from genetic mark-recapture data when not all sites are sampled: an example with the bowhead whale

2019 ◽  
Author(s):  
Timothy R. Frasier ◽  
Stephen D. Petersen ◽  
Lianne Postma ◽  
Lucy Johnson ◽  
Mads Peter Heide-Jørgensen ◽  
...  
Keyword(s):  
Population Biology ◽  
Distance Sampling ◽  
Population Monitoring ◽  
Population Based ◽  
Bowhead Whale ◽  
Individual Identification ◽  
Abundance Estimation ◽  
Eastern Canada ◽  
Mark Recapture ◽  
Analytical Technique

AbstractEstimating abundance is one of the most fundamental and important aspects of population biology, with major implications on how the status of a population is perceived and thus on conservation and management efforts. Although typically based on one of two methods (distance sampling or mark-recapture), there are many individual identification methods that can be used for mark-recapture purposes. In recent years, the use of genetic data for individual identification and abundance estimation through mark-recapture analyses have increased, and in some situations such genetic identifications are more efficient than their field-based counterparts for population monitoring. One issue with mark-recapture analyses, regardless of which method of individual identification is used, is that the study area must provide adequate opportunities for “capturing” all individuals within a population. However, many populations are unevenly and widely distributed, making it unfeasible to adequately sample all necessary areas. Here we develop an analytical technique that accounts for unsampled locations, and provides a means to infer “missing” individuals from unsampled locations, and therefore obtain more accurate abundance estimates when it is not possible to sample all sites. This method is validated using simulations, and is used to estimate abundance of the Eastern Canada-West Greenland (EC-WG) bowhead whale population. Based on these analyses, the estimated size of this population is 9,089 individuals, with a 95% highest density interval of 5,107–17,079.

Correcting wildlife counts using detection probabilities

2005 ◽  
Vol 32 (3) ◽  
pp. 211 ◽  
Author(s):  
Gary C. White
Keyword(s):  
Detection Probability ◽  
Distance Sampling ◽  
Probability Of Detection ◽  
Mark Recapture ◽  
Additional Information ◽  
Release Studies ◽  
Detection Probabilities ◽  
Minimum Number ◽  
Capture And Release ◽  
Wildlife Populations

One of the most pervasive uses of indices of wildlife populations is uncorrected counts of animals. Two examples are the minimum number known alive from capture and release studies, and aerial surveys where the detection probability is not estimated from a sightability model, marked animals, or distance sampling. Both the mark–recapture and distance-sampling estimators are techniques to estimate the probability of detection of an individual animal (or cluster of animals), which is then used to correct a count of animals. However, often the number of animals in a survey is inadequate to compute an estimate of the detection probability and hence correct the count. Modern methods allow sophisticated modelling to estimate the detection probability, including incorporating covariates to provide additional information about the detection probability. Examples from both distance and mark–recapture sampling are presented to demonstrate the approach.

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