Is scat marking a reliable tool for otter census and surveys at the landscape scale?

Lutra lutra Machine learning algorithms Occupancy models Population monitoring tool Scent marking Species distribution models

Journal

Journal of environmental management
ISSN: 1095-8630
Titre abrégé: J Environ Manage
Pays: England
ID NLM: 0401664

Informations de publication

Date de publication:
01 Aug 2022
Historique:
received: 31 08 2021
revised: 06 04 2022
accepted: 16 04 2022
pubmed: 4 5 2022
medline: 25 5 2022
entrez: 3 5 2022
Statut: ppublish

Résumé

Biological significance of scat marking by otters has been a controversial subject among scientists. Using multiyear (2014-2017) data of otter spraint counts in South Korea, this study aimed to test whether the observed pattern of spraint presence/absence is driven by detection error and if/how scat counts can be a proxy for otter abundance at the landscape scale. To test the first hypothesis, spraint presence/absence was analyzed through occupancy models, which relied on environmental variables related to otter detectability and presence. Spraint count models were used to test the second hypothesis against resource-related covariates in combination with landscape, anthropogenic, and climate variables through machine learning algorithms (MLAs). The detection probability has specifically decreased in areas characterized by high rainfall and human population densities, whereas the probability has increased near food-rich sites, characterized by high marking frequencies. The temporal trends of spraint count predictions were in line with changes in the diversity of fish communities in 2014-2017 instead of fish biomass, suggesting that the availability of feeding resources is higher where fish communities are more diverse. Because diverse fish communities can attract otters, fish diversity conservation is critical for preserving this mammal's populations. This fine scale four-year monitoring has contributed to the disentanglement of the role of spraint presence/absence and spraint counts in detectability and population trends. This will assist in identifying key resource areas and planning strategies to promote otter conservation and dispersal dynamics.

Identifiants

pubmed: 35504183
pii: S0301-4797(22)00671-5
doi: 10.1016/j.jenvman.2022.115098
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

115098

Informations de copyright

Copyright © 2022 Elsevier Ltd. All rights reserved.

Auteurs

Sungwon Hong (S)

Department of Horse/Companion, And Wild Animal Science, Kyungpook National University, Sangju, 37224, Republic of Korea; Department of Animal Science and Biotechnology, Kyungpook National University, Sangju, 37224, Republic of Korea. Electronic address: shong@knu.ac.kr.

Mirko Di Febbraro (M)

Environmetrics Lab, Department of Biosciences and Territory, University of Molise, Contrada Fonte Lappone 86090, Pesche, Italy.

Hyo Gyeom Kim (HG)

Fisheries Science Institute, Chonnam National University, Yeosu, 59626, Republic of Korea.

Anna Loy (A)

Environmetrics Lab, Department of Biosciences and Territory, University of Molise, Contrada Fonte Lappone 86090, Pesche, Italy.

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Classifications MeSH