biospheremetrics v1.0.2: an R package to calculate two complementary terrestrial biosphere integrity indicators – human colonization of the biosphere (BioCol) and risk of ecosystem destabilization (EcoRisk)

Abstract

Abstract. Ecosystems are under multiple stressors, and impacts can be measured with multiple variables. Humans have altered mass and energy flows of basically all ecosystems on Earth towards dangerous levels. However, integrating the data and synthesizing conclusions is becoming more and more complicated. Here we present an automated and easy-to-apply R package to assess terrestrial biosphere integrity that combines two complementary metrics. (i) The BioCol metric that quantifies the human colonization pressure exerted on the biosphere through alteration and extraction (appropriation) of net primary productivity.(ii) The EcoRisk metric that quantifies biogeochemical and vegetation structural changes as a proxy for the risk of ecosystem destabilization. Applied to simulations with the dynamic global vegetation model LPJmL5 for 1500–2016, we find that large regions presently (period 2007–2016) show modification and extraction of >20 % of the preindustrial potential net primary production. The modification (degradation) of net primary production (NPP) as a result of land use change and extraction in terms of biomass removal (e.g., from harvest) leads to drastic alterations in key ecosystem properties, which suggests a high risk of ecosystem destabilization. As a consequence of these dynamics, EcoRisk shows particularly high values in regions with intense land use and deforestation and in regions prone to impacts of climate change, such as the Arctic and boreal zone. The metrics presented here enable spatially explicit global-scale evaluation of historical and future states of the biosphere and are designed for use by the wider scientific community, being applicable not only to assessing biosphere integrity but also to benchmarking model performance. The package will be maintained on GitHub and through that we encourage its future application to other models and data sets.