Faculty Opinions recommendation of On the Estimation of Dispersal Kernels from Individual Mark-Recapture Data.

Author(s):  
Otso Ovaskainen
2006 ◽  
Vol 13 (2) ◽  
pp. 183-197 ◽  
Author(s):  
Masami Fujiwara ◽  
Kurt E. Anderson ◽  
Michael G. Neubert ◽  
Hal Caswell

2019 ◽  
Author(s):  
Akira Terui

AbstractDispersal is a fundamental ecological process that links populations, communities and food webs in space. However, dispersal is tremendously difficult to study in the wild because we must track individuals dispersing in a landscape. One conventional method to measure animal dispersal is a mark-recapture technique. Despite its usefulness, this approach has been recurrently criticized because it is virtually impossible to survey all possible ranges of dispersal in nature. Here, I propose a novel Bayesian model to better estimate dispersal parameters from incomplete mark-recapture data. The dispersal-observation coupled model, DOCM, can extract information from both recaptured and unrecaptured individuals, providing less biased estimates of dispersal parameters. Simulations demonstrated the usefulness of DOCM under various sampling designs. I also suggest extensions of the DOCM to accommodate more realistic scenarios. Application of the DOCM may, therefore, provide valuable insights into how individuals disperse in the wild.


1999 ◽  
Vol 249 (4) ◽  
pp. 455-461
Author(s):  
El Hassan El Mouden ◽  
Mohammed Znari ◽  
Richard P. Brown

2018 ◽  
Vol 589 ◽  
pp. 263-268 ◽  
Author(s):  
B Calmanovici ◽  
D Waayers ◽  
J Reisser ◽  
J Clifton ◽  
M Proietti

2019 ◽  
Vol 26 (1) ◽  
pp. 63
Author(s):  
Yan LIU ◽  
Changping YANG ◽  
Binbin SHAN ◽  
Dianrong SUN ◽  
Shengnan LIU ◽  
...  

2008 ◽  
Vol 43 (3) ◽  
pp. 409-419 ◽  
Author(s):  
Norio Arakaki ◽  
Atsushi Nagayama ◽  
Aya Kobayashi ◽  
Kazuhiko Tarora ◽  
Mitsunobu Kishita ◽  
...  

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sougata Sadhukhan ◽  
Holly Root-Gutteridge ◽  
Bilal Habib

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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