Estimating non-indigenous species establishment and their impact on biodiversity, using the Relative Suitability Richness model.
Abstract
Key questions in invasion biology include where will a non-indigenous species (NIS) establish, and how will it affect biodiversity? Scientists have addressed the first question, largely through correlating species' distributions with environmental factors (i.e. species distribution models, SDM). Conceptually, SDMs reflect a species' abiotic constraints, but in reality, they measure the realized rather than fundamental niche. This is a limitation of SDMs, but analysed correctly, it may also be a strength since SDMs already incorporate the outcome of species interactions. We postulate that we can use the relative predicted probabilities of occurrences in each location from SDMs to predict the outcome of interactions between species. Based on this idea, we develop the Relative Suitability Richness (RSR) model, and generate two protocols to (i) assess theoretically whether we can predict the probability of establishment, and (ii) by conducting counterfactual analyses, the extent to which we can infer the impact of NIS on biodiversity. Species distribution models based solely on abiotic factors have the potential to predict species establishment well, even when biotic interactions are strong. However, predictions improved with inclusion of biotic data, even with only partial information. Even with weak environmental predictors and no community data, macro-scale predictions could still be strong and one can estimate loss of native populations, as long as the fitting environment was representative of the prediction environment. If environmental conditions change, predictions were still possible so long as either good environmental predictors and/or biotic information was available. Policy implications. Risk analyses are often recommended to guide management practices; they depend upon forecasting the likelihood and severity of an invasion. The Relative Suitability Richness protocols developed here predict non-indigenous species occurrences and impact, using even partial biotic data on native species occurrences, to provide key information needed to estimate invasion risk, prioritize management effort and advance invasive species policy.