Articles | Volume 24, issue 2
https://doi.org/10.5194/we-24-81-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/we-24-81-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Comment/reply
| 
21 Nov 2024
Comment/reply |
| 21 Nov 2024
| 21 Nov 2024
Comment on “Pollination supply models from a local to global scale”: convolutional neural networks can improve pollination supply models at a global scale
Alfonso Allen-Perkins
CORRESPONDING AUTHOR
Grupo de Sistemas Complejos (GSC), Universidad Politécnica de Madrid, Madrid, Spain
Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid, Spain
Angel Giménez-García
Basque Centre for Climate Change – BC3, Leioa, Spain
Ainhoa Magrach
Basque Centre for Climate Change – BC3, Leioa, Spain
Ikerbasque, Basque Foundation for Science, Bilbao, Spain
Javier Galeano
Grupo de Sistemas Complejos (GSC), Universidad Politécnica de Madrid, Madrid, Spain
Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid, Spain
Ana María Tarquis
Grupo de Sistemas Complejos (GSC), Universidad Politécnica de Madrid, Madrid, Spain
Research Centre for the Management of Agricultural and Environmental Risks (CEIGRAM), Universidad Politécnica de Madrid, Madrid, Spain
Ignasi Bartomeus
Departamento de Ecología Integrativa, Estación Biológica de Doñana, EBD-CSIC, Seville, Spain
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Modelling tools may provide a method of measuring pollination supply and promote the use of ecological intensification techniques among farmers and decision-makers. This study benchmarks different modelling approaches to provide clear guidance on which pollination supply models perform best at different spatial scales. These findings are an important step in bridging the gap between academia and stakeholders in modelling ecosystem service delivery under ecological intensification.
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Manuscript not accepted for further review
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We found that crowded neighborhoods reduced individual seed production via plant–plant competition, but they also made individual plants more attractive for some pollinator guilds, increasing visitation rates and, therefore, plant fitness. The balance between these two forces varied depending on the species identity and the spatial scale considered. Our results indicate that plant spatial aggregation plays an important role in defining the net effect of mutualistic and antagonistic interactions.
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Short summary
Machine learning models outperform simple mechanistic models in predicting pollinator visitation rates. We use deep learning to infer rules from land cover maps to estimate pollination services globally. Results suggest deep learning can improve predictions by identifying complex patterns in landscape composition, especially in data-rich but knowledge-poor areas. The challenge is to make deep learning algorithms more interpretable so that experts can validate prediction rules for pollination.
Machine learning models outperform simple mechanistic models in predicting pollinator visitation...
