Simultaneous vegetation classification and mapping at large spatial scales

Abstract

AbstractAimMultivariate mixture models offer the ability to streamline the typically multi‐stage process of vegetation classification and mapping into a single, simultaneous analytical step. Our aim is to demonstrate the model's utility over large and diverse areas, with comparison to existing classification and mapping conventions.LocationNew South Wales, Australia.MethodsWe demonstrate a statistical model that uses both multivariate species data and environmental covariate data to simultaneously classify observations into groups and predict those groups out into environmental and geographical space. We used two large data sets to demonstrate the method: an ~810,000 km2 area with 4,715 sites and 488 species and a ~220,000 km2 area with 5,183 sites and 446 species. A range of topographic, terrain, climate and soil predictors were used as environmental variables, including future projected climate variables. Models can be fit with the R package “RCPmod.”ResultsThe method results in probabilistic memberships to vegetation assemblages, for both the classification and map. There was general agreement between our approach and existing vegetation classification conventions, but we explore some notable differences. We were able to examine the environmental gradients that define the predicted vegetation distributions, as well as make predictions about how the distribution of species assemblages might shift as a result of climate change.Main conclusionsOur approach tightens the link between description of biodiversity patterns and their depiction in space, by considering both biotic and spatially explicit abiotic information simultaneously. The method allows uncertainty to be quantified objectively across a consistent set of groups for both vegetation classification and mapping, which is rarely the case in traditional multi‐stage approaches. A simultaneous modelling approach increases capacity to make predictions into varying spatial, temporal and environmental dimensions, providing new ecological insights and increasing capacity for evidence‐based decision making.