Estimating Abundance from Mark-Recapture Data

2014 ◽  
pp. 159-168
Author(s):  
K. B. Newman ◽  
S. T. Buckland ◽  
B. J. T. Morgan ◽  
R. King ◽  
D. L. Borchers ◽  
...  
Keyword(s):  
Mark Recapture

Skeletochronology and mark–recapture assessments of growth in the North African agamid lizard (Agama impalearis)

1999 ◽  
Vol 249 (4) ◽  
pp. 455-461
Author(s):  
El Hassan El Mouden ◽  
Mohammed Znari ◽  
Richard P. Brown
Keyword(s):  
North African ◽  
Mark Recapture ◽  
Agamid Lizard ◽  
The North

I3S Pattern as a mark-recapture tool to identify captured and free-swimming sea turtles: an assessment

2018 ◽  
Vol 589 ◽  
pp. 263-268 ◽  
Author(s):  
B Calmanovici ◽  
D Waayers ◽  
J Reisser ◽  
J Clifton ◽  
M Proietti
Keyword(s):  
Sea Turtles ◽  
Free Swimming ◽  
Mark Recapture

Investigation of a mark-recapture method of black porgy, Acanthopagrus schlegelii, in Daya Bay using plastic oval tags

2019 ◽  
Vol 26 (1) ◽  
pp. 63
Author(s):  
Yan LIU ◽  
Changping YANG ◽  
Binbin SHAN ◽  
Dianrong SUN ◽  
Shengnan LIU ◽  
...  
Keyword(s):  
Daya Bay ◽  
Mark Recapture ◽  
Acanthopagrus Schlegelii ◽  
Black Porgy

scholarly journals Estimation of abundance and dispersal distance of the sugarcane click beetle Melanotus sakishimensis Ohira (Coleoptera: Elateridae) on Kurima Island, Okinawa, by mark-recapture experiments

2008 ◽  
Vol 43 (3) ◽  
pp. 409-419 ◽  
Author(s):  
Norio Arakaki ◽  
Atsushi Nagayama ◽  
Aya Kobayashi ◽  
Kazuhiko Tarora ◽  
Mitsunobu Kishita ◽  
...  
Keyword(s):  
Dispersal Distance ◽  
Mark Recapture ◽  
Click Beetle

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.

The closed-subpopulation method and estimation of population size from mark-recapture and ancillary data

2000 ◽  
Vol 78 (2) ◽  
pp. 320-326 ◽  
Author(s):  
Frank AM Tuyttens
Keyword(s):  
Population Size ◽  
Ancillary Data ◽  
Mark Recapture ◽  
Animal Populations ◽  
Accuracy And Precision ◽  
Population Size Estimates ◽  
Minimum Number ◽  
Flexible Framework ◽  
And Performance ◽  
Better Than

The algebraic relationships, underlying assumptions, and performance of the recently proposed closed-subpopulation method are compared with those of other commonly used methods for estimating the size of animal populations from mark-recapture records. In its basic format the closed-subpopulation method is similar to the Manly-Parr method and less restrictive than the Jolly-Seber method. Computer simulations indicate that the accuracy and precision of the population estimators generated by the basic closed-subpopulation method are almost comparable to those generated by the Jolly-Seber method, and generally better than those of the minimum-number-alive method. The performance of all these methods depends on the capture probability, the number of previous and subsequent trapping occasions, and whether the population is demographically closed or open. Violation of the assumption of equal catchability causes a negative bias that is more pronounced for the closed-subpopulation and Jolly-Seber estimators than for the minimum-number-alive. The closed-subpopulation method provides a simple and flexible framework for illustrating that the precision and accuracy of population-size estimates can be improved by incorporating evidence, other than mark-recapture data, of the presence of recognisable individuals in the population (from radiotelemetry, mortality records, or sightings, for example) and by exploiting specific characteristics of the population concerned.

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