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Cited by 149Plant and animal studiesEcology and Vegetation Dynamics StudiesInsect and Arachnid Ecology and Behavior

The DOI matches its Crossref record; 78 of the 78 references it cites were found in a registry or an index.

Machine learning algorithms to infer trait‐matching and predict species interactions in ecological networks

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Pichler, Maximilian, Boreux, Virginie, Klein, Alexandra‐Maria, Schleuning, Matthias, Hartig, Florian·2020·Methods in Ecology and Evolution
DOI 10.1111/2041-210x.13329
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Abstract

Ecologists have long suspected that species are more likely to interact if their traits match in a particular way. For example, a pollination interaction may be more likely if the proportions of a bee's tongue fit a plant's flower shape. Empirical estimates of the importance of trait‐matching for determining species interactions, however, vary significantly among different types of ecological networks. Here, we show that ambiguity among empirical trait‐matching studies may have arisen at least in parts from using overly simple statistical models. Using simulated and real data, we contrast conventional generalized linear models (GLM) with more flexible Machine Learning (ML) models (Random Forest, Boosted Regression Trees, Deep Neural Networks, Convolutional Neural Networks, Support Vector Machines, naïve Bayes, and k‐Nearest‐Neighbor), testing their ability to predict species interactions based on traits, and infer trait combinations causally responsible for species interactions. We found that the best ML models can successfully predict species interactions in plant–pollinator networks, outperforming GLMs by a substantial margin. Our results also demonstrate that ML models can better identify the causally responsible trait‐matching combinations than GLMs. In two case studies, the best ML models successfully predicted species interactions in a global plant–pollinator database and inferred ecologically plausible trait‐matching rules for a plant–hummingbird network from Costa Rica, without any prior assumptions about the system. We conclude that flexible ML models offer many advantages over traditional regression models for understanding interaction networks. We anticipate that these results extrapolate to other ecological network types. More generally, our results highlight the potential of machine learning and artificial intelligence for inference in ecology, beyond standard tasks such as image or pattern recognition.

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Research works printed but not linked: 4

  1. Cristianini N., 2000, An introduction to support vector machines
  2. Faegri K., 1979, The principles of pollination ecology
  3. Pichler M., 2019, Zenodo
  4. R Core Team, 2019, R: A language and environment for statistical computing

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Metadata

Authors
Pichler, M., Boreux, V., Klein, A., Schleuning, M., & Hartig, F.
Affiliations
  • University of Regensburg (DE)
  • University of Freiburg (DE)
  • Senckenberg Biodiversity and Climate Research Centre (DE)
Corresponding author
Pichler, Maximilian
ORCID
Venue
Methods in Ecology and Evolution
Publisher
Wiley
Published
2019-11-19
Volume
11
Issue
2
Pages
281-293
ISSN
2041-210X (ISSN-L)
DOI
10.1111/2041-210x.13329
OpenAlex
W2970584612
Cited by
149
References
78
Citations by year
2026: 20 · 2025: 29 · 2024: 24 · 2023: 21 · 2022: 23 · 2021: 19 · 2020: 13
FWCI
10.77
Type
journal article
Language
en
Open access
Licence
Locations
  • Methods in Ecology and Evolution (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Life Sciences › Agricultural and Biological Sciences › Ecology, Evolution, Behavior and Systematics › Plant and animal studies
  • Physical Sciences › Environmental Science › Nature and Landscape Conservation › Ecology and Vegetation Dynamics Studies
  • Life Sciences › Agricultural and Biological Sciences › Insect Science › Insect and Arachnid Ecology and Behavior
Keywords
trait matching, species interactions, ecological networks, plant-pollinator networks, machine learning, generalized linear models, random forest, boosted regression trees, deep neural network, convolutional neural networks, support vector machine, naive Bayes, k-nearest neighbors, trait combinations, plant-hummingbird interactions, interaction prediction

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