An ecologically constrained procedure for sensitivity analysis of Artificial Neural Networks and other empirical models.

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 richnes

SUBMITTER: Franceschini S 

PROVIDER: S-EPMC6353184 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature