scholarly journals Bayesian mark–recapture–resight–recovery models: increasing user flexibility in the BUGS language

Ecosphere ◽  
2021 ◽  
Vol 12 (12) ◽  
Author(s):  
Thomas V. Riecke ◽  
Dan Gibson ◽  
Alan G. Leach ◽  
Mark S. Lindberg ◽  
Michael Schaub ◽  
...  
Keyword(s):  
Mark Recapture ◽  
Recovery Models

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.

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