Abstract
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.
Highlights
The understanding and analysis of species interactions in ecological networks has become a central building block of modern ecology
In our analysis of predictive performance, we found that Machine Learning (ML) models such as Random forest (RF), BRT and Deep neural networks (DNN) exceeded generalized linear models (GLM) performance for predicting plant–pollinator interactions from trait-matching data
Our study demonstrates that RF, BRT, and DNN exceeded GLM performance in predicting plant–pollinator interactions from trait information
Summary
The understanding and analysis of species interactions in ecological networks has become a central building block of modern ecology. Research in this field, has concentrated in particular on analyzing observed network structures Galiana et al, 2018; González, Dalsgaard, & Olesen, 2010; Mora, Gravel, Gilarranz, Poisot, & Stouffer, 2018; Poisot, Stouffer, & Gravel, 2015). A key hypothesis regarding this question is that species interact when their functional properties (traits) make an interaction possible (e.g. Eklöf et al, 2013; Jordano, Bascompte, & Olesen, 2003). The idea that interactions will occur when traits are compatible is known as trait-matching (e.g. Schleuning, Fründ, & García, 2015, see Figure 1)
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