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

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‐match...

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Published in:Methods in ecology and evolution Vol. 11; no. 2; pp. 281 - 293
Main Authors: Pichler, Maximilian, Boreux, Virginie, Klein, Alexandra‐Maria, Schleuning, Matthias, Hartig, Florian, Carvalheiro, Luisa
Format: Journal Article
Language:English
Published: London John Wiley & Sons, Inc 01.02.2020
Subjects:
Algorithms
Artificial intelligence
Artificial neural networks
Bayesian analysis
bipartite networks
causal inference
Computer simulation
deep learning
Ecological effects
Ecology
Generalized linear models
hummingbirds
insect pollinators
Learning algorithms
Machine learning
Matching
Mathematical models
Neural networks
Object recognition
Pattern recognition
Pollination
pollination syndromes
Pollinators
predictive modelling
Regression analysis
Regression models
Species
Statistical analysis
Statistical models
Support vector machines
ISSN:2041-210X, 2041-210X
Online Access:Get full text
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Summary: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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ISSN:2041-210X
2041-210X
DOI:10.1111/2041-210X.13329