Predicting Potential Spawning Habitat by Ensemble Species Distribution Models: The Case Study of European Anchovy (<i>Engraulis encrasicolus</i>) in the Strait of Sicily

Abstract

Species distribution models (SDMs) are important tools for exploring the complex association between species and habitats. Here, we applied six SDMs combining 1946 pieces of presence/absence data regarding European anchovy eggs with environmental parameters from surveys conducted in the Strait of Sicily from 1998 to 2016. We aimed to investigate the mechanisms influencing spawning habitat suitability for anchovy (Engraulis encrasicolus). The dataset was split into a training subset (75%) and a test subset (25%) for evaluating the predictive performance of the models. The results suggested the role of environmental parameters in explaining egg occurrence, model accuracy and spatial predictions. Bottom depth consistently had the highest importance, followed by absolute dynamic topography, which gives insights about local mesoscale oceanographic features. Each modelling method, except the linear model, produced successful performance for both the training and the test datasets. The spatial predictions were estimated as weighted averages of single-model predictions, with weights based on discriminatory power measured by the area under the receiver operating characteristic curve (AUC). This ensemble approach often provided more robust predictions than a single model. The coastal waters were identified as the most favorable for anchovy spawning, especially the south-central sector and the area around the southern-most tip of Sicily.

Keywords

• ensemble distribution modelling
• regression models
• machine-learning methods
• spawning habitat
• anchovy
• Strait of Sicily