Distribution modelling of jellyfish in Spanish coastal areas: An approach based on the maximum entropy principle

Abstract

Jellyfish blooms are natural events with significant ecological and socio-economic impacts, particularly in regions like the Andalusian Mediterranean coast, where tourism is an important industry. This study employs Maximum Entropy (MaxEnt) species distribution model to predict jellyfish presence using citizen science data from the Infomedusa app and environmental variables derived from the Copernicus platform. In particular, while users reports do not enforce species-level identification, Pelagia noctiluca likely dominated the sightings in our study region. Presence-only data from summer 2019 were analyzed alongside key environmental factors from March to September. The models consistently identified the mixed layer depth in April as the most influential variable, contributing between 72.7 % and 93.9 % to the probability distribution, highlighting the role of early spring conditions in shaping summer jellyfish dynamics. The results revealed spatial and temporal patterns in jellyfish presence, providing valuable insights for coastal management and early-warning systems. To validate the MaxEnt predictions, observed absence data were used to compare areas of low predicted probability of jellyfish presence with high densities of observed absences, revealing a moderate level of agreement, with 22.58 %–37.13 % coincidence between low-probability MaxEnt predictions and areas of high absence density. While the study is limited to a single summer season due to data availability, it highlights the potential of integrating advanced modeling techniques with crowd-sourced data, this study underscores the value of citizen science for marine ecology and highlights the importance of proactive strategies to mitigate the socio-economic impacts of jellyfish blooms on local communities and ecosystems. These findings can inform regional planning and contribute to global efforts to address gelatinous zooplankton proliferation.