Abstract
In this work a fish species distribution model (SDM) was developed, by merging species occurrence data with environmental layers, with the scope to produce high resolution habitability maps for the whole Mediterranean Sea. The final model is capable to predict the probability of occurrence of each fish species at any location in the Mediterranean Sea. Eight pelagic, commercial fish species were selected for this study namely Engraulis encrasicolus, Sardina pilchardus, Sardinella aurita, Scomber colias, Scomber scombrus, Spicara smaris, Thunnus thynnus and Xiphias gladius. The SDM environmental predictors were obtained from the databases of Copernicus Marine Environmental Service (CMEMS) and the European Marine Observation and Data Network (EMODnet). The probabilities of fish occurrence data in low resolution and with several gaps were obtained from Aquamaps (FAO Fishbase). Data pre-processing involved feature engineering to construct 6830 features, representing the distribution of several mean-monthly environmental variables, covering a time-span of 10 years. Feature selection with the ensemble Reciprocal Ranking method was used to rank the features according to their relative importance. This technique increased model’s performance by 34%. Ten machine learning algorithms were then applied and tested based on their overall performance per species. The XGBoost algorithm performed better and was used as the final model. Feature categories were explored, with neighbor-based, extreme values, monthly and surface ones contributing most to the model. Environmental variables like salinity, temperature, distance to coast, dissolved oxygen and nitrate were found the strongest ones in predicting the probability of occurrence for the above eight species.
Highlights
The marine world is rapidly changing as humans perform a number of activities, such as fish stocking, shipping, aquaculture, pollution and habitat modification, which result in ecological and economic damage
Feature engineering is performed, and after that, feature selection to reduce the number of variables to the optimum
Throughout the present study we have presented a comprehensive Species Distribution Model (SDM) for eight commercial pelagic fish species in the Mediterranean Sea
Summary
The marine world is rapidly changing as humans perform a number of activities, such as fish stocking, shipping, aquaculture, pollution and habitat modification, which result in ecological and economic damage. Species distribution models (SDMs) provide a measure of species occupancy in response to the local/regional oceanographic and environmental conditions and habitat [1]. Such models combine occurrence locations of known species with a series of environmental layers, by developing a statistical inference system which unveils the impact of environmental parameters on specific species distribution patterns and by expanding the species distribution layer towards. Distribution models for marine organisms and habitat mapping are essential tools in understanding the links between the ecology of marine fishes and the factors that affect species presence/absence patterns [2]
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