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Biological nitrogen fixation (BNF) by symbiotic and free-living bacteria is an important source of plant-available nitrogen (N) in terrestrial ecosystems supporting carbon (C) sequestration and food production worldwide. Dynamic global vegetation models (DGVMs) are frequently used to assess the N and C cycles under dynamic land use and climate. BNF plays an important role in the components of both these cycles, making a robust representation of the processes and variables that BNF depends on important to reduce uncertainty within the C and N cycles and improve the ability of DGVMs to project future ecosystem productivity, vegetation patterns or the land C sink. Still, BNF is often modelled as a function of net primary productivity or evapotranspiration, and the actual drivers are neglected. We implemented plant-functional-type-specific limitations for BNF dependent on soil temperature and soil water content, as well as a cost of BNF, in the Lund–Potsdam–Jena managed Land (LPJmL) DGVM and compared the new ("C-costly") against the previous ("Original") approach and data from the scientific literature. For our comparison, we simulated a potential natural vegetation scenario and one including anthropogenic land use for the period from 1901 to 2016 for which we evaluate BNF and legume crop yields. Our results show stronger agreement with BNF observations for the C-costly than the Original approach for natural vegetation and agricultural areas. The C-costly approach reduced the overestimation of BNF, especially in hot spots of legume crop production. Despite the reduced BNF in the C-costly approach, yields of legume crops were similar to the Original approach. While the net C and N balances were similar between the two approaches, the reduced BNF in the C-costly approach results in a slight underestimation of N losses from leaching, emissions and harvest compared to the values in the literature, supporting further investigation of the underlying reasons, such as processes represented in DGVMs and scenario assumptions. While we see the potential for further model development, for example, to separate symbiotic and free-living BNF, the C-costly approach is a major improvement over the simple Original approach because of the separate representation of important drivers and limiting factors of BNF, and the C-costly approach also improves the ability of LPJmL to project future C and N cycle dynamics. [ABSTRACT FROM AUTHOR]

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. The BioCol metric that quantifies the human colonization pressure exerted on the biosphere through alteration and extraction (appropriation) of net primary productivity. 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. [ABSTRACT FROM AUTHOR]