Web Ecology
Web Ecology
WE
Articles | Volume 24, issue 2
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.
https://doi.org/10.5194/we-24-81-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 24, issue 2
Comment/reply
 | 
21 Nov 2024
Comment/reply |  | 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, Angel Giménez-García, Ainhoa Magrach, Javier Galeano, Ana María Tarquis, and Ignasi Bartomeus

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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.
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