Insights into fish-anthropogenic pressures relationships using machine learning techniques: the case of Castilla-La Mancha (Spain)

Since 1970 the number of freshwater species has suffered a decline of 83% worldwide and anthropic activities are considered to be major drivers of ecosystems degradation. Linking the ecological response to the multiple anthropogenic stressors acting in the system is essential to effectively design policy measures to restore riverine ecosystems. However, obtaining quantitative links between stressors and ecological status is still challenging, given the non-linearity of the ecosystem response and the need to consider multiple factors at play. This study applies machine learning techniques to explore the relationships between anthropogenic pressures and the composition of fish communities in the river basins of Castilla-La Mancha, a region covering nearly 79 500 km² in central Spain. During the past two decades, this region has experienced an alarming decline of the conservation status of native fish species. The starting point for the analysis is a 10x10 km grid that defines for each cell the presence or absence of several fish species before and after 2001. This database was used to characterize the evolution of several metrics of fish species richness over time, accounting for the species origin (native or alien), species features (e.g. pollution tolerance) and habitat preferences. Random Forest and Gradient Boosted Regression Trees algorithms were used to relate the resulting metrics to the stressor variables describing the anthropogenic pressures acting in the rivers, such as urban wastewater discharges, land use cover, hydro-morphological degradation and the alteration of the river flow regime. The study provides new, quantitative insights into pressures-ecosystem relationships in rivers and reveals the main factors that lead to the decline of fish richness in Castilla-La Mancha, which could help inform environmental policy initiatives.