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1. The literature is awash with studies purporting to show how various species and data characteristics affect the performances of Species Distribution Models (SDMs). Many of these studies follow a similar template: they fit SDMs for several species, or the same species using different datasets; assess the accuracy of those SDMs using skill statistics; and then identify correlates thereof. Interpreting the findings of these studies is challenging because skill statistics can reflect species and data characteristics rather than model accuracy, and correlates of model performance are not necessarily causes. 2. Here, we took a different approach to identifying causes of variation in SDM performance. We fitted models for 535 species across 5 invertebrate groups and 1 plant group in the United Kingdom (UK), using a fairly typical SDM workflow. We measured two components of SDM performance: the variance among replicate fits and accuracy. Rather than using skill statistics, accuracy was assessed by taxon experts. We constructed Directed Acyclic Graphs (DAGs) depicting plausible effects of explanatory variables (e.g. species’ prevalence, sample size) on SDM performance, then quantified those effects using multilevel piecewise path models. 3. We found that the degree to which the available data covered species’ environmental niches was the only explanatory variable to affect SDM accuracy. We suggest that previously reported associations between sample size and SDM accuracy reflect improved coverage of species environmental niches at higher sample sizes; that is to say, niche completeness confounds the effect of sample size on SDM accuracy. We also report that the completeness of species’ environmental niches, sample size, species’ prevalence and the degree to which the available data cover species’ geographic ranges affect SDM variance. 4. Our results demonstrate the challenges associated with the high-throughput approach to modelling species’ distributions. There is no guarantee that accurate and precise SDMs can be constructed for large numbers of species unless their ranges and niches have been sampled comprehensively. Decisions about whether modelling is worthwhile should not be based on simple criteria like sample size.

In recent years, a spate of dangerous invasive alien species (IAS) have become established in the Caribbean, and many others threaten the region. Some established species continue to spread, causing economic and environmental damage. This paper reviews the status and significance of IAS in the Caribbean. Factors contributing to the upsurge of new problems and pathways are discussed from a perspective of prevention. Steps recently taken to prevent or mitigate the impact of these species are also discussed. On the basis of these experiences, suggestions are made for the future direction of efforts to prevent or manage such invasive species within the Caribbean context.