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• W4319303707 abstract "Abstract Identifying suitable habitats for endangered species is critical in order to promote their recovery. However, conventional species distribution models (SDMs) need large amounts of labeled sample data to learn the relationship between species and environmental conditions, and are difficult to fully detangle the role of the environment in the distribution of the endangered species, which are very sparsely distributed and have environmental heterogeneity. This study’s first innovation used the semi-supervised model to accurately simulate the suitable habitats for endangered species with a small sample size. The model performance was compared with three conventional SDMs, namely Maxent, the generalized linear model, and a support vector machine. Applying the model to the endangered species Populus euphratica (P. euphratica) in the lower Tarim River basin (TRB), Northwest China. The results showed that the semi-supervised model exhibited better performance than conventional SDMs with an accuracy of 85% when only using 443 P. euphratica samples. All models developed using smaller sample sizes exhibit worse performance in the prediction of habitat suitability areas for endangered species while the semi-supervised model is still excellent. The results showed that the suitable habitat for P. euphratica is mainly near the river channel of the lower TRB, accounting for 13.49% of the study area. The lower Tarim River still has enormous land potential for the restoration of endangered P. euphratica . The model developed here can be used to evaluate a suitable habitat for endangered species with only a small sample size, and provide a basis for the conservation of endangered species." @default.
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• W4319303707 date "2023-02-24" @default.
• W4319303707 modified "2023-09-27" @default.
• W4319303707 title "Simulation of potential endangered species distribution in drylands with small sample size based on semi-supervised models" @default.
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• W4319303707 doi "https://doi.org/10.1088/1748-9326/acb95b" @default.
• W4319303707 hasPublicationYear "2023" @default.
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