Comment on “Pollination supply models from a local to global scale”: convolutional neural networks can improve pollination supply models at a global scale Article Swipe
Alfonso Allen‐Perkins
,
Á. Giménez-García
,
Ainhoa Magrach
,
Javier Galeano
,
Ana M. Tarquís
,
Ígnasi Bartomeus
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.5194/we-24-81-2024
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.5194/we-24-81-2024
Tools to predict pollinator activity at regional scales generally rely on land cover maps, combined with human-inferred mechanistic rules and/or expert knowledge. Recently, Giménez-García et al. (2023) showed that, using large pollinator datasets, different environmental variables, and machine learning models, those predictions can be enhanced but at the cost of losing model interpretability. Here, we complement this work by exploring the potential of using advanced machine learning techniques to directly infer wild-bee visitation rates across different biomes only from land cover maps and available pollinator data while maintaining a mechanistic interpretation. In particular, we assess the ability of convolutional neural networks (CNNs), which are deep learning models, to infer mechanistic rules able to predict pollinator habitat use. At a global scale, our CNNs achieved a rank correlation coefficient of 0.44 between predictions and observations of pollinator visitation rates, doubling that of the previous human-inferred mechanistic models presented in Giménez-García et al. (2023) (0.17). Most interestingly, we show that the predictions depend on both landscape composition and configuration variables, with prediction rules being more complex than those of traditional mechanistic processes. We also demonstrate how CNNs can improve the predictions of our previous data-driven models that did not use land cover maps by creating a new model that combined the predictions of our CNN with those of our best regression model based on environmental variables, a Bayesian ridge regressor. This new ensemble model improved the overall rank correlation from 0.56 to 0.64.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5194/we-24-81-2024
- OA Status
- diamond
- References
- 30
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404597873
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4404597873Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5194/we-24-81-2024Digital Object Identifier
- Title
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Comment on “Pollination supply models from a local to global scale”: convolutional neural networks can improve pollination supply models at a global scaleWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-11-21Full publication date if available
- Authors
-
Alfonso Allen‐Perkins, Á. Giménez-García, Ainhoa Magrach, Javier Galeano, Ana M. Tarquís, Ígnasi BartomeusList of authors in order
- Landing page
-
https://doi.org/10.5194/we-24-81-2024Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.5194/we-24-81-2024Direct OA link when available
- Concepts
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Interpretability, Machine learning, Land cover, Pollinator, Convolutional neural network, Artificial intelligence, Computer science, Pollination, Scale (ratio), Artificial neural network, Ecology, Land use, Cartography, Geography, Biology, PollenTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
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30Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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