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• W2493683913 abstract "Climate change is expected to have significant impacts on native, threatened and endangered wildlife. Understanding and modeling these impacts useful for wildlife managers, however, remain difficult due to complex climate change, and costly and high data requirements. Consequently, we proposed an easily-interpretable and data-efficient decision support approach to understand climate change impacts on the abundance of three endangered wetland birds (Hawaiian Stilt, Hawaiian Coot and Hawaiian Moorhen). We coupled a watershed model, AnnAGNPS, and ecological models using fuzzy-cognitive mapping software, Mental Modeler, in Hanalei watershed, Kauaźi. Results suggested that increased precipitation would increase Stilt abundance, but decrease Coot and Moorhen abundance. Decreasing precipitation might have negative effects for all three species. Moreover, decision-makers should pay equal attention to controlling components (water depth, food availability and disease) with system-wide influence. Finally, besides being adaptable to similar environmental contexts, our approach captured both direct and indirect climate change impacts through ecological connectivity. Proposed modeling framework is a potential decision-support tool for understanding local-scale climate change impacts.IPCC AR4 based climate change scenarios drove empirical watershed model, AnnAGNPS, simulations.Watershed dynamics functioned as scenarios for knowledge-driven ecological models of three wetland birds in Mental Modeler.Water depth, food availability and disease with system-wide influence would have profound impacts on bird abundance.Wildlife managers can use the tool in similar ecological contexts with even poor data, and easily interpret the results." @default.
• W2493683913 created "2016-08-23" @default.
• W2493683913 creator A5001925056 @default.
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• W2493683913 creator A5090249368 @default.
• W2493683913 date "2016-10-01" @default.
• W2493683913 modified "2023-10-01" @default.
• W2493683913 title "Combining watershed models and knowledge-based models to predict local-scale impacts of climate change on endangered wildlife" @default.
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• W2493683913 doi "https://doi.org/10.1016/j.envsoft.2016.07.009" @default.
• W2493683913 hasPublicationYear "2016" @default.
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