Assessing Integrity Using Vegetation Structure and Composition

Abstract

ContextThe draft post-2020 Global Biodiversity Framework aims to achieve a 15% net gain in the area, connectivity and integrity of natural systems by 2050. ObjectivesFirst, we analyse the complexity (foliage cover) and composition (native species richness) of 6 plant functional groups relative to their empirically defined benchmark. Second, we extrapolate the spatial patterns in foliage cover and species richness to predict where different plant functional groups are above or below benchmark as spatially-explicit, continuous characteristics across the landscape.MethodsWe assess the integrity of vegetation relative to a numerical benchmark using the log of the response ratio (LRR) to reflect the proportional change in the response variable. We use ensembles of artificial neural networks to build spatially-explicit, continuous, landscape-scale models of cover and species richness to assess locations where functional groups meet or exceed benchmarks.ResultsModels of vegetation cover LRR performed well (R2 0.79 – 0.88), whereas models of the vegetation richness LRR were more variable (R2 0.57 – 0.80). Predicted patterns show that across the landscape (11.5 million ha), there is a larger area that meets or exceeds the cover benchmarks (approximately 112 000 ha or 1%), and an order of magnitude lower (approximately 10 000 ha or 0.1%) for richness benchmarks. ConclusionsSpatially explicit maps of vegetation integrity can provide important information to complement assessments of area and connectivity. Our results highlight that net gains in the area, connectivity and integrity of ecosystems will require significant investment in restoration.