Shaping up model transferability and generality of species distribution modeling for predicting invasions: implications from a study on Bythotrephes longimanus

Abstract

When using species distribution models to predict distributions of invasive species, we are faced with the trade-off between model realism, generality, and precision. Models are most applicable to specific conditions on which they are developed, but typically not readily transferred to other situations. To better assist management of biological invasions, it is critical to know how to validate and improve model generality while maintaining good model precision and realism. We examined this issue with Bythotrephes longimanus, to determine the importance of different models and datasets in providing insights into understanding and predicting invasions. We developed models (linear discriminant analysis, multiple logistic regression, random forests, and artificial neural networks) on datasets with different sample sizes (315 or 179 lakes) and predictor information (environmental with or without fish data), and evaluated them by cross-validation and several independent datasets. In cross-validation, models developed on 315-lake environmental dataset performed better than those developed on 179-lake environmental and fish dataset. The advantage of a larger dataset disappeared when models were tested on independent datasets. Predictions of the models were more diverse when developed on environmental conditions alone, whereas they were more consistent when including fish (especially diversity) data. Random forests had relatively good and more stable performance than the other approaches when tested on independent datasets. Given the improvement of model transferability in this study by including relevant species occurrence or diversity index, incorporating biotic information in addition to environmental predictors, may help develop more reliable models with better realism, generality, and precision.