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Simultaneous vegetation classification and mapping at large spatial scales
Author(s) -
Lyons Mitchell B.,
Foster Scott D.,
Keith David A.
Publication year - 2017
Publication title -
journal of biogeography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.7
H-Index - 158
eISSN - 1365-2699
pISSN - 0305-0270
DOI - 10.1111/jbi.13088
Subject(s) - vegetation (pathology) , multivariate statistics , range (aeronautics) , abiotic component , covariate , biodiversity , probabilistic logic , terrain , scale (ratio) , elevation (ballistics) , species distribution , physical geography , geography , ecology , cartography , habitat , computer science , statistics , mathematics , machine learning , medicine , materials science , geometry , pathology , composite material , biology
Abstract Aim Multivariate 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. Location New South Wales, Australia. Methods We 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 km 2 area with 4,715 sites and 488 species and a ~220,000 km 2 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 “ RCP mod.” Results The 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 conclusions Our 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.