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.

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

2020 ◽  
Vol 22 ◽  
pp. e00903 ◽  
Author(s):  
Timothy R. Frasier ◽  
Stephen D. Petersen ◽  
Lianne Postma ◽  
Lucy Johnson ◽  
Mads Peter Heide-Jørgensen ◽  
...  
Keyword(s):  
Bowhead Whale ◽  
Abundance Estimation ◽  
Mark Recapture

scholarly journals Identifying unknown Indian wolves by their distinctive howls: its potential as a non-invasive survey method

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sougata Sadhukhan ◽  
Holly Root-Gutteridge ◽  
Bilal Habib
Keyword(s):  
Reproductive Behaviour ◽  
Identification Accuracy ◽  
Individual Identification ◽  
Survey Method ◽  
Reliable Estimate ◽  
Mark Recapture ◽  
Test Dataset ◽  
Individual Level ◽  
Non Invasive ◽  
Supervised Classification Methods

AbstractPrevious studies have posited the use of acoustics-based surveys to monitor population size and estimate their density. However, decreasing the bias in population estimations, such as by using Capture–Mark–Recapture, requires the identification of individuals using supervised classification methods, especially for sparsely populated species like the wolf which may otherwise be counted repeatedly. The cryptic behaviour of Indian wolf (Canis lupus pallipes) poses serious challenges to survey efforts, and thus, there is no reliable estimate of their population despite a prominent role in the ecosystem. Like other wolves, Indian wolves produce howls that can be detected over distances of more than 6 km, making them ideal candidates for acoustic surveys. Here, we explore the use of a supervised classifier to identify unknown individuals. We trained a supervised Agglomerative Nesting hierarchical clustering (AGNES) model using 49 howls from five Indian wolves and achieved 98% individual identification accuracy. We tested our model’s predictive power using 20 novel howls from a further four individuals (test dataset) and resulted in 75% accuracy in classifying howls to individuals. The model can reduce bias in population estimations using Capture-Mark-Recapture and track individual wolves non-invasively by their howls. This has potential for studies of wolves’ territory use, pack composition, and reproductive behaviour. Our method can potentially be adapted for other species with individually distinctive vocalisations, representing an advanced tool for individual-level monitoring.

scholarly journals Estimating brown bear abundance and harvest rate on the southern Alaska Peninsula

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245367
Author(s):  
Earl F. Becker ◽  
David W. Crowley
Keyword(s):  
Sampling Method ◽  
Distance Sampling ◽  
Brown Bear ◽  
Aerial Survey ◽  
Abundance Estimation ◽  
Game Management ◽  
Geographic Scale ◽  
Detection Function ◽  
Harvest Rate ◽  
Harvest Data

Abundance estimation of hunted brown bear populations should occur on the same geographic scale as harvest data analyses for estimation of harvest rate. Estimated harvest rates are an important statistic for managing hunted bear populations. In Alaska, harvest data is collected over large geographic units, called Game Management Units (GMUs) and sub-GMUs. These sub GMUs often exceed 10,000 km2. In the spring of 2002, we conducted an aerial survey of GMU 9D (12,600 km2) and GMU 10 (4,070 km2) using distance sampling with mark-resight data. We used a mark-resight distance sampling method with a two-piece normal detection function to estimate brown bear abundance as 1,682.9 (SE = 174.29) and 316.9 (SE = 48.25) for GMU 9D and GMU 10, respectively. We used reported hunter harvest to estimate harvest rates of 4.35% (SE = 0.45%) and 3.06% (SE = 0.47%) for GMU 9D and GMU 10, respectively. Management objective for these units support sustained, high quality hunting opportunity which harvest data indicate are met with an annual harvest rate of approximately 5–6% or less.

scholarly journals Tiger (Panthera tigris) scent DNA: a valuable conservation tool for individual identification and population monitoring

2015 ◽  
Vol 7 (3) ◽  
pp. 681-683 ◽  
Author(s):  
Anthony Caragiulo ◽  
Rob Stuart Alexander Pickles ◽  
Joseph Alexander Smith ◽  
Olutolani Smith ◽  
John Goodrich ◽  
...  
Keyword(s):  
Population Monitoring ◽  
Individual Identification ◽  
Panthera Tigris

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 Environmental DNA facilitates accurate, indigenous-led, inexpensive, and multi-year population estimates of millions of Eulachon fish

2019 ◽  
Author(s):  
Meredith Pochardt ◽  
Jennifer M. Allen ◽  
Ted Hart ◽  
Sophie D. L. Miller ◽  
Douglas W. Yu ◽  
...  
Keyword(s):  
Area Under The Curve ◽  
Cost Savings ◽  
Environmental Dna ◽  
Population Monitoring ◽  
Population Estimates ◽  
Water Sampling ◽  
Mark Recapture ◽  
Temporal Sampling ◽  
The Relationship

AbstractAlthough environmental DNA shed from an organism is now widely used for species detection in a wide variety of contexts, mobilizing environmental DNA for management requires estimation of population size and trends rather than simply assessing presence or absence. However, the efficacy of environmental-DNA-based indices of abundance for long-term population monitoring have not yet been assessed. Here we report on the relationship between six years of mark-recapture population estimates for eulachon (Thaleichthys pacificus) and ‘eDNA rates,’ which are calculated from the product of stream flow and DNA concentration. Eulachon are a culturally and biologically important anadromous fish that have significantly declined in the southern part of their range but were historically rendered into oil and traded. Both the peak eDNA rate and the area under the curve of the daily eDNA rate were highly predictive of the mark-recapture population estimate, explaining 84.96% and 92.53% of the deviance respectively. Even in the absence of flow correction, the peak of the daily eDNA concentration explained an astonishing 89.53% while the area under the curve explained 90.74% of the deviance. These results support the use of eDNA to monitor eulachon population trends and represent a >80% cost savings over mark-recapture, which could be further increased with automated water sampling, reduced replication, and focused temporal sampling. Due to its logistical ease and affordability, eDNA sampling can facilitate monitoring a larger number of rivers and in remote locations where mark-recapture is infeasible.

Sign in / Sign up

Export Citation Format

Share Document