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

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. • Citizen science data integrated with MaxEnt models identify key drivers of jellyfish distribution. • Early spring stratification and productivity drive summer jellyfish occurrence. • High salinity may enhance jellyfish survival and food availability. • Spring marine currents facilitate jellyfish transport and coastal aggregations.

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Publication Details

Journal
Ocean & Coastal Management
Published
2025-04-15
DOI
https://doi.org/10.1016/j.ocecoaman.2025.107694
Primary Topic
Marine Invertebrate Physiology and Ecology
Type
article
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article

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

Juan Jesús Bellido López, José Carlos Báez, Juan Carlos Gutiérrez‐Estrada, Jairo Castro‐Gutiérrez
Ocean & Coastal Management
Marine Invertebrate Physiology and Ecology
article

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

Juan Jesús Bellido López, José Carlos Báez, Juan Carlos Gutiérrez‐Estrada, Jairo Castro‐Gutiérrez
article en

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. • Citizen science data integrated with MaxEnt models identify key drivers of jellyfish distribution. • Early spring stratification and productivity drive summer jellyfish occurrence. • High salinity may enhance jellyfish survival and food availability. • Spring marine currents facilitate jellyfish transport and coastal aggregations.

Ocean & Coastal ManagementVol. 266
Universidad Autónoma de Chile (CL), Instituto Español de Oceanografía (ES), Mediterranean Center for Environmental Studies (ES), Universidad de Huelva (ES), Universidad de Málaga (ES)
Life below water
Openalex Percentile: Top 96%
Marine Invertebrate Physiology and Ecology
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