Species distribution modeling and machine learning in assessing the potential distribution of freshwater zooplankton in Northern Italy

Abstract

Species distribution models (SDM's) are powerful tools used to describe species suitable habitats and spatial occurrences and many statistical methods and algorithms are available to model the spatial distribution of a target species. Here we explore a species distribution model framework combined with machine learning algorithms to describe the distribution of two freshwater zooplankton species Daphnia longispina (Cladocera) and Eucyclops serrulatus (Copepods) in a system of 283 shallow and ephemeral freshwater habitats in the Northern Italian Appennines. For each species, we model the habitat suitability by comparing one regression-based model, one generalized linear model (GLM) and two machine learning algorithms: random forest (RF) and artificial neural network (ANN) with one hidden layer. We used a total of 27 predictor variables. The modeling framework was used considering a scenario of future climate change in order to evaluate potential shifts in spatial distribution of the zooplankton species. For both species, the supervised machine learning algorthn (ANN) produced the highest mean values for all the performance metrics. For D. longispina and E. serrulatus, the two most important variables ranked by the shap analysis and global sensitivity and uncertainty analysis (GSUA) were temperature seasonality and precipitation of the warmest quarter. Both species, in a future climatic change scenario, are expected to shift their distribution mainly toward lower northern altitudes with an overall expansion of 7% with respect to the past/present climatic conditions. However, the spatial expansion of D. longispina and E. serrulatus was qualitatively different. In agricultural and natural areas, the expansion of E. serrulatus was greater than that of D. longispina but, in natural areas, the expansion of E. serrulatus was counterbalanced by a greater spatial contraction than that of D. longispina. As hypothesized, direct and indirect anthropogenic pressures may affect the predicted potential shift and expansion of the zooplankton species.