Biotic interactions in species distribution modelling: 10 questions to guide interpretation and avoid false conclusions
Abstract
Aim: Recent studies increasingly use statistical methods to infer biotic interactions from co‐occurrence information at a large spatial scale. However, disentangling biotic interactions from other factors that can affect co‐occurrence patterns at the macroscale is a major challenge. Approach: We present a set of questions that analysts and reviewers should ask to avoid erroneously attributing species association patterns to biotic interactions. Our questions relate to the appropriateness of data and models, the causality behind a correlative signal, and the problems associated with static data from dynamic systems. We summarize caveats reported by macroecological studies of biotic interactions and examine whether conclusions on the presence of biotic interactions are supported by the modelling approaches used. Findings: Irrespective of the method used, studies that set out to test for biotic interactions find statistical associations in species’ co‐occurrences. Yet, when compared with our list of questions, few purported interpretations of such associations as biotic interactions hold up to scrutiny. This does not dismiss the presence or importance of biotic interactions, but it highlights the risk of too lenient interpretation of the data. Combining model results with information from experiments and functional traits that are relevant for the biotic interaction of interest might strengthen conclusions. Main conclusions: Moving from species‐ to community‐level models, including biotic interactions among species, is of great importance for process‐based understanding and forecasting ecological responses. We hope that our questions will help to improve these models and facilitate the interpretation of their results. In essence, we conclude that ecologists have to recognize that a species association pattern in joint species distribution models will be driven not only by real biotic interactions, but also by shared habitat preferences, common migration history, phylogenetic history and shared response to missing environmental drivers, which specifically need to be discussed and, if possible, integrated into models.
What the paper shows and why it matters
(AI-generated)
Species ranges reflect the environment, dispersal, history and interactions with other organisms, and there is growing interest in detecting the last from large-scale data on which species occur together. Joint species distribution models (jSDMs) set each species’ baseline from the abiotic environment and look for interactions in the residuals, the associations left over once the environment is accounted for (Pollock et al. 2014). In principle this should be more robust, because it can rule out some explanations based on shared environmental requirements. The assumption the paper takes on is that such a leftover association can be read as a biotic interaction.
The paper argues that species associations found in large-scale co-occurrence data, including those left over in joint species distribution models, cannot be read as biotic interactions without further evidence, because other causes can produce the same pattern. The difficulty lies less in the statistics than in how their results are interpreted. Such causes and complications include missing environmental predictors, interactions that depend on the environment, shared phylogenetic history, dispersal limitation, temporal dynamics and food-web context. The paper condenses this into ten questions for analysts and reviewers, on data and models, confounding with the environment, and snapshots of changing populations. One simulated example illustrates the point: two shrub species suppress each other yet often occur together, which would normally suggest facilitation, because a tree species left out of the analysis confines both to the same habitats. The paper also scanned published studies on biotic interactions in species distribution models and reports that associations turned up whatever the method, but that few interpretations of them as biotic interactions held up against the questions.
The paper does not dismiss biotic interactions, and it calls the move from species-level to community-level models important for understanding processes and forecasting. It advises careful, sceptical interpretation: at present, it sees model results as most promising for generating hypotheses, and it holds that the burden of proof lies with whoever claims an interaction. The paper suggests that claims might be strengthened by comparison with alternative hypotheses and independent data sets and by support from experiments and from species traits relevant to the interaction. The questions turn caveats raised in earlier work into concrete suggestions for interpreting and reporting results.
References
- Pollock et al. (2014). Understanding co‐occurrence by modelling species simultaneously with a Joint Species Distribution Model ( JSDM ). Methods in Ecology and Evolution 5, 397–406. doi:10.1111/2041-210x.12180