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• W2912850119 endingPage "e0211445" @default.
• W2912850119 startingPage "e0211445" @default.
• W2912850119 abstract "Sensitivity analysis applied to Artificial Neural Networks (ANNs) as well as to other types of empirical ecological models allows assessing the importance of environmental predictive variables in affecting species distribution or other target variables. However, approaches that only consider values of the environmental variables that are likely to be observed in real-world conditions, given the underlying ecological relationships with other variables, have not yet been proposed. Here, a constrained sensitivity analysis procedure is presented, which evaluates the importance of the environmental variables considering only their plausible changes, thereby exploring only ecological meaningful scenarios. To demonstrate the procedure, we applied it to an ANN model predicting fish species richness, as identifying relationships between environmental variables and fish species occurrence in river ecosystems is a recurring topic in freshwater ecology. Results showed that several environmental variables played a less relevant role in driving the model output when that sensitivity analysis allowed them to vary only within an ecologically meaningful range of values, i.e. avoiding values that the model would never handle in its practical applications. By comparing percent changes in MSE between constrained and unconstrained sensitivity analysis, the relative importance of environmental variables was found to be different, with habitat descriptors and urbanization factors that played a more relevant role according to the constrained procedure. The ecologically constrained procedure can be applied to any sensitivity analysis method for ANNs, but obviously it can also be applied to other types of empirical ecological models." @default.
• W2912850119 created "2019-02-21" @default.
• W2912850119 creator A5005545237 @default.
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• W2912850119 creator A5050801153 @default.
• W2912850119 creator A5052120543 @default.
• W2912850119 creator A5063716595 @default.
• W2912850119 date "2019-01-30" @default.
• W2912850119 modified "2023-09-30" @default.
• W2912850119 title "An ecologically constrained procedure for sensitivity analysis of Artificial Neural Networks and other empirical models" @default.
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• W2912850119 doi "https://doi.org/10.1371/journal.pone.0211445" @default.
• W2912850119 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6353184" @default.
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• W2912850119 hasPublicationYear "2019" @default.
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