National‐scale predictions of plant assemblages via community distribution models: Leveraging published data to guide future surveys

Abstract

Abstract Species distribution models (SDMs) have been widely used to create maps of expected species incidence, often using citizen science (CS) occurrence data as inputs. Environmental policy is informed by knowledge of community distributions, but there have been fewer attempts to utilise the potential of community distribution models (CDMs) to predict these. Many countries have vegetation community classification systems which include phytosociological information on individual species. Within Great Britain, the National Vegetation Classification (NVC) is the primary standard for vegetation communities, and while maps have been produced at regional scales, cost‐effective techniques are required for national scales. Published NVC occurrence records of 22 upland NVC communities in England and Wales were used as observed occurrences (presence‐only data). Predictors for the CDMs were enhanced vegetation index (EVI), elevation, slope, aspect, temperature and rainfall. Five modelling methods were investigated: generalised linear models (GLMs), support vector machines (SVMs), random forests (RFs), maximum entropy (MaxEnt) and maximum likelihood (MaxLike). Model quality was assessed via bootstrapping via area under the curve (AUC), true skill statistic (TSS) and Kappa index. There were only small differences in the accuracy of the models (median TSS model accuracy 0.742; range 0.280 to 0.873) with RF models the best overall CDM method. Across all NVC communities, summer and winter maximum temperatures and annual rainfall were the most important predictor variables. NVCs with spatially disjunct distributions in both lowlands and uplands, or that responded to localised management or environmental conditions, were poorly predicted. Synthesis and applications. Vegetation communities can be reliably predicted at large spatial scales using CDMs from extant datasets. Management practitioners can use community‐level predictions to design targeted field surveys for individual species typically associated with specific communities. Most existing CS survey schemes focus on species rather than communities. Hence future development of new CS schemes similar to the National Plant Monitoring Scheme (NPMS), that aligns with the NVC, will enable CS data to generate up‐to‐date maps of both communities and species.