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Tytuł pozycji:

Multi-scale habitat selection and impacts of climate change on the distribution of four sympatric meso-carnivores using random forest algorithm

Tytuł:
Multi-scale habitat selection and impacts of climate change on the distribution of four sympatric meso-carnivores using random forest algorithm
Autorzy:
Tahir Ali Rather
Sharad Kumar
Jamal Ahmad Khan
Temat:
Multiple-scale
Multi-species
Sympatric carnivores
Species distribution modeling
Bandhavgarh
Climate change
Ecology
QH540-549.5
Źródło:
Ecological Processes, Vol 9, Iss 1, Pp 1-17 (2020)
Wydawca:
SpringerOpen, 2020.
Rok publikacji:
2020
Kolekcja:
LCC:Ecology
Typ dokumentu:
article
Opis pliku:
electronic resource
Język:
English
ISSN:
2192-1709
Relacje:
http://link.springer.com/article/10.1186/s13717-020-00265-2; https://doaj.org/toc/2192-1709
DOI:
10.1186/s13717-020-00265-2
Dostęp URL:
https://doaj.org/article/4c826deae99247738662a342551560e9  Link otwiera się w nowym oknie
Numer akcesji:
edsdoj.4c826deae99247738662a342551560e9
Czasopismo naukowe
Abstract Background The habitat resources are structured across different spatial scales in the environment, and thus animals perceive and select habitat resources at different spatial scales. Failure to adopt the scale-dependent framework in species habitat relationships may lead to biased inferences. Multi-scale species distribution models (SDMs) can thus improve the predictive ability as compared to single-scale approaches. This study outlines the importance of multi-scale modeling in assessing the species habitat relationships and may provide a methodological framework using a robust algorithm to model and predict habitat suitability maps (HSMs) for similar multi-species and multi-scale studies. Results We used a supervised machine learning algorithm, random forest (RF), to assess the habitat relationships of Asiatic wildcat (Felis lybica ornata), jungle cat (Felis chaus), Indian fox (Vulpes bengalensis), and golden-jackal (Canis aureus) at ten spatial scales (500–5000 m) in human-dominated landscapes. We calculated out-of-bag (OOB) error rates of each predictor variable across ten scales to select the most influential spatial scale variables. The scale optimization (OOB rates) indicated that model performance was associated with variables at multiple spatial scales. The species occurrence tended to be related strongest to predictor variables at broader scales (5000 m). Multivariate RF models indicated landscape composition to be strong predictors of the Asiatic wildcat, jungle cat, and Indian fox occurrences. At the same time, topographic and climatic variables were the most important predictors determining the golden jackal distribution. Our models predicted range expansion in all four species under future climatic scenarios. Conclusions Our results highlight the importance of using multiscale distribution models when predicting the distribution and species habitat relationships. The wide adaptability of meso-carnivores allows them to persist in human-dominated regions and may even thrive in disturbed habitats. These meso-carnivores are among the few species that may benefit from climate change.

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