The Mexican Central Pacific (MCP) has complex oceanographic dynamics, with a well-defined seasonal pattern and influence of interannual sea surface temperature anomalies. The present study investigated the relationship between the distribution/abundance of spotted dolphins and the environmental conditions in the MCP. Dolphin sighting data were collected during January 2010–December 2015; distance to coast, sea surface temperature, and chlorophyll a (Chla) were obtained for each sighting location through georeferenced maps and satellite images. A total of 746 spotted dolphin sightings were obtained with a survey effort of 31,695 km. Spotted dolphins in the MCP showed a predominantly coastal distribution regardless of environmental conditions. Significant differences were detected concerning regional seasonal variation, with higher mean sighting rate during the stratified periods and higher density/abundance estimates during the mixed periods. Spotted dolphins showed preference for warmer conditions, with higher mean sighting rate, abundance, and distance to coast during the neutral and warm interannual periods. During the coolest conditions (cold periods), mean sighting rate and abundance decreased and spotted dolphins were located closer to the coast, where Chla concentration increased. These results suggest that changes in the distribution/abundance of these dolphins could be associated with habitat quality related to tropical (El Niño/Southern Oscillation) and extratropical (The Blob) oceanographic phenomena, which could be modulating their foraging activities.

Coastal nurseries are critical for the early stages of many elasmobranchs, and understanding spatial ecology during these periods is essential for effective population management. Here, we investigated the environmental drivers shaping shark presence and spatial distribution in an open coastal nursery used by young-of-the-year Sphyrna lewini along the southern Pacific Coast of Mexico. Using acoustic telemetry data collected over three consecutive seasons, we combined Random Forest models with an interpretable machine learning framework, including permutation-based variable importance, accumulated local effects, and a Rashomon set approach. Shark presence was primarily driven by seasonal patterns and lunar illumination, whereas spatial distribution within the nursery area was structured by tide level, shark length, accumulated precipitation, and sea surface temperature. Tide level emerged as the most influential and stable predictor of spatial preference, while size-dependent responses revealed ontogenetic spatial segregation among zones. These results demonstrate that open-coast nurseries can operate through dynamic environmental processes rather than static habitat features, with river-influenced areas playing a key role for smaller individuals. By integrating telemetry data with interpretable machine learning methods, this study provides a mechanistic understanding of nursery habitat use and offers a transferable framework for assessing spatial ecology and conservation priorities in threatened coastal shark populations.