Causal inference and large-scale expert validation shed light on the drivers of SDM accuracy and variance

• Published in: Diversity & distributions Vol. 29; no. 6; pp. 774 - 784
• Main Authors: Boyd, Robin J., Harvey, Martin, Roy, David B., Barber, Tony, Haysom, Karen A., Macadam, Craig R., Morris, Roger K. A., Palmer, Carolyn, Palmer, Stephen, Preston, Chris D., Taylor, Pam, Ward, Robert, Ball, Stuart G., Pescott, Oliver L.
• Format: Journal Article
• Language: English
• Published: Oxford Wiley 01-06-2023; John Wiley & Sons, Inc
• Subjects: Accuracy; Algorithms; causal inference; Completeness; directed acyclic graph; expert elicitation; Geographical distribution; Graph theory; Habitats; Model accuracy; Niches; Regression analysis; RESEARCH ARTICLE; Species; species distribution modelling; Statistics; Structural equation modeling; structural equation modelling; Variables; Variance analysis; United Kingdom > UK
• ISSN: 1366-9516; 1472-4642
• DOI: 10.1111/ddi.13698

Aim
To develop a causal understanding of the drivers of Species distribution model (SDM) performance.
Location
United Kingdom (UK).
Methods
We measured the accuracy and variance of SDMs fitted for 518 species of invertebrate and plant in the UK. Our measure of variance reflects variation among replicate model fits, and taxon experts assessed model accuracy. Using directed acyclic graphs, we developed a causal model depicting plausible effects of explanatory variables (e.g. species' prevalence, sample size) on SDM accuracy and variance and quantified those effects using a multilevel piecewise path model.
Results
According to our model, sample size and niche completeness (proportion of a species' niche covered by sampling) directly affect SDM accuracy and variance. Prevalence and range completeness have indirect effects mediated by sample size. Challenging conventional wisdom, we found that the effect of prevalence on SDM accuracy is positive. This reflects the facts that sample size has a positive effect on accuracy and larger sample sizes are possible for widespread species. It is possible, however, that the omission of an unobserved confounder biased this effect. Previous studies, which reported negative correlations between prevalence and SDM accuracy, conditioned on sample size.
Main conclusions
Our model explicates the causal basis of previously reported correlations between SDM performance and species/data characteristics. It also suggests that niche completeness has similarly large effects on SDM accuracy and variance as sample size. Analysts should consider niche completeness, or proxies thereof, in addition to sample size when deciding whether modelling is worthwhile.