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.

Policy makers require high-level summaries of biodiversity change. However, deriving such summaries from raw biodiversity data is a complex process involving several intermediary stages. In this paper, we describe a workflow for generating annual estimates of species’ occupancy at national scales from raw species occurrence data, which can be used to construct a range of policy-relevant biodiversity indicators. We describe the workflow in detail: from data acquisition, data assessment and data manipulation, through modelling, model evaluation, application and dissemination. At each stage, we draw on our experience developing and applying the workflow for almost a decade to outline the challenges that analysts might face. These challenges span many areas of ecology, taxonomy, data science, computing and statistics. In our case, a key output of the workflow is annual estimates of occupancy, with measures of uncertainty, for over 5,000 species in each of several defined “regions” (e.g., countries, protected areas, etc.) of the United Kingdom from 1970-2019. This product corresponds closely to the notion of a species distribution “Essential Biodiversity Variable” (EBV). Throughout the paper, we note where the workflow can be adapted to other situations (e.g., geographic regions or data types). We also highlight areas where the workflow can be improved; in particular, we suggest incorporation of methods to diagnose biases in the species occurrence data, to understand whether and to what extent these bias downstream products, and to mitigate them if needed. Finally, we compare the data products generated using our workflow to the first generation of species distribution EBVs and the “idealized” product as defined by others. Going forward, we hope that this paper can act as a template for research groups around the world seeking to develop similar data products.

Born in Belfast, Robin Boyd studied there and in Dublin, Edinburgh, and Basel. Ordained in the Irish Presbyterian Church, he served the Student Christian Movement and in 1954 went to India (Gujarat), becoming a presbyter of the nascent (1970) Church of North India. In 1974 he moved to Melbourne, Australia, and ministered in a parish of the Uniting Church (1977). From 1980–87 he was director of the Irish School of Ecumenics (Dublin). His writing includes An Introduction to Indian Christian Theology (1969), and recent work on the eschatology of interfaith relations. Twice happily married, he lives in Edinburgh.

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