Combining watershed models and knowledge-based models to predict local-scale impacts of climate change on endangered wildlife. (October 2016)

• Record Type: Journal Article
• Authors: Htun, Hla; Gray, Steven A.; Lepczyk, Christopher A.; Titmus, Andrew; Adams, Keenan
• Is Part Of: Environmental modelling & software. Volume 84(2016:Oct.)
• Page Start: 440
• Page End: 457
• Publication Date: 2016-10
• Subjects: Climate change -- Decision-support -- Endangered species -- Fuzzy cognitive mapping -- Hawaii -- Watershed modeling; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2016.07.009
• Languages: English
• ISSNs: 1364-8152

Abstract

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, Kauaʻi. 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. Highlights: 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.