Earth system stability relies on functioning ecosystems , providing, e.g., carbon sequestration, moisture recycling, and resilience to disturbances and disruptions . The global status and functioning of ecosystems is being evaluated using a range of approaches, including assessments of human footprint (e.g., ), indicators based on empirically collected biodiversity data (e.g., ), and ecological computer models (e.g., ).

Ecosystems depend on photosynthesis as major energy source, producing the primary biomass that is the foundation of almost all food webs. During the Neolithic Revolution, humanity decoupled its biomass demand from the natural cycle, which led to large-scale modification of Earth's surface . Today more than 75 % of the ice-free land area of the Earth is affected by human use . However, the level of management and appropriation differs widely from extensive (e.g., occasionally livestock-grazed steppes or extensively used forests) to intensive (e.g., machine-aided agriculture with high mineral fertilizer use and irrigation). The aggregate effect of land use on net primary production (NPP – for a list of all abbreviations see Appendix ), i.e., altered productivity and removed biomass due to agricultural and forestry harvest, is often referred to as the human appropriation of NPP (HANPP) . Utilizing this concept, it was shown that humanity has doubled its impact during the 20th century and thereby substantially decreased the NPP remaining in ecosystems .

As one consequence of land use, only 40 % of remaining forests are still characterized by high ecosystem integrity . Globally, only 18.6 % of highly intact habitats are currently protected , while modeling pressure–impact relationships suggest an increasing mean species abundance loss until 2050, even under optimistic scenarios with decreasing land use . Over the course of this century, the dilemma of negative effects from either climate change or climate change mitigation via large-scale biomass plantation highlights the need to also integrate biodiversity in planning for negative emission technologies .

To prevent further degradation of the biosphere and to reverse the current loss of nature, land use scenarios should take into account the regional risk for ecosystem destabilization . We define ecosystem destabilization as a severe change in ecosystem functioning, resulting in, e.g., a decline in carbon sequestration, species composition, or water provisioning. Shifts in biogeochemical conditions can act as a proxy for this risk based on the argument that substantial changes in either basic biogeochemical properties or vegetation composition are likely to imply far-reaching, potentially self-amplifying transformations in the underlying system characteristics, food chains, and species composition . The Γ metric (Gamma) proposed by represents such a metric, has been used to separate the historical drivers of ecosystem change , compares the effects of climate warming and land use under future climate scenarios , and finds temperature thresholds above which severe change is to be expected with high probability . Additionally, it has been applied component-wise to model outputs from the ISIMIP fast track ensemble, indicating an increasing area under risk of severe ecosystem change with a rise in global mean temperature .

The original definition of Γ, however, did not include nitrogen variables, and the code to compute it was hardly accessible to the scientific public. HANPP, on the other hand, has so far been based on census statistics and inventory data and has not been calculated purely from vegetation model outputs.

We therefore propose an easy-to-apply R package with two complementary biosphere metrics, BioCol and EcoRisk, building on the existing indicators HANPP and Γ , which are here exemplarily calculated and evaluated based on simulations with the global vegetation model LPJmL5 .

BioCol quantifies the human colonization pressure on the biosphere through extraction of biomass and prevention of natural NPP by photosynthesis. The metric basically follows the HANPP approach by , who spatially explicitly sum extracted and inhibited biomass amounts based on biomass inventory data and compare them to potential NPP in a counterfactual world without human land use. We replace biomass inventory data with the corresponding LPJmL5 model outputs and thereby add the possibility of also computing BioCol from deep historical and future simulations.

EcoRisk illustrates state shifts in ecosystems as a result of land, water, and fertilizer use, as well as climate change based on the Γ metric . It quantifies, on a scale from 0 (no change) to 1 (very strong change), the dissimilarity of an ecosystem state from a reference condition and comprises four subcomponents (vegetation structure, local change, global importance, ecosystem balance) that are aggregated as a multidimensional proxy for the risk of biosphere destabilization. We follow the original publications but (in addition to water and carbon) now include nitrogen fluxes and pools. The addition of nitrogen variables fills a major gap because nitrogen limitation or surplus is a key determinant for plant growth and the overall ecosystem status.

In this section, we detail the calculation of both BioCol and EcoRisk, our biome classification for spatial aggregation of results, and the relevant specifics of the vegetation model LPJmL5.

The results of BioCol are (except for forest harvest) strictly model based and are in general well in line with the existing data-based assessments . The global sum of HANPP, based on LPJmL5 simulated outputs, increases from below 2 [2.5] GtCyr-1 before 1700 to more than 13 [15] GtCyr-1 today, depending on whether the absolute values are summed up for cells with negative values or not (Fig. a – left y axis). Over the course of the 20th century, relative values of BioCol thus approximately doubled from about 0.1 to over 0.2 (10 % to over 20 %) compared to the potential preindustrial NPPref of the 16th century (Fig. a – right y axis). Since 2000 the values increased further. When taking the absolute for negative cells, BioCol is approaching 0.25 (25 %) in 2016 – meaning that almost a quarter of the total terrestrial preindustrial biomass production on Earth is rerouted to human use or inhibited compared to a world without humans.

The global relative BioCol pattern for the year 2000 (1995–2005 average) shows high values for areas of intense agricultural use (Fig. b). The abundance of cells with negative BioCol values (higher productivity in the simulation with land use than in the similar run with only potential natural vegetation) might be explained by the explicit simulation of agricultural management, especially in regions with low natural NPP (e.g., irrigation in the Middle East) and potential legacy effects from earlier land use on now natural areas included in our land use dataset (e.g., in the Amazon, NPP upon regrowth of natural vegetation on abandoned agricultural areas is simulated to be higher than potential NPP in the equilibrium state; see Fig. ).

Isolated cells with very high absolute BioCol values (e.g., in the boreal zone) are locations of reservoirs established before the year 2005, with associated decline in NPP and thus high values of NPPluc.

EcoRisk calculated between 1550–1579 and 1985–2016 shows large areas with values above 0.5 (Fig. ), mostly in regions with high land use intensity today. Land use change is reflected in the vegetation structural change component (vs), which quite closely resembles the current land use extent (see inset in Fig. ). However, the components building on changes in biochemical variables (lc, gi, eb) indicate a much larger extent for regions with values >0.9. Figures b and  show that in many regions changes in nitrogen fluxes are responsible for these strong changes.

Climatic changes are also reflected in EcoRisk. They are most visible in the components ecosystem balance (eb) and vs as bands of higher values along the frontier of boreal vegetation onset to the North in Canada. In Eurasia (and other land-use-free regions of the Arctic) this vegetation shift appears to be more patchy. The local change component (lc) indicates high values among especially vulnerable ecosystems with low absolute values (Arctic, deserts) that do not show up in the global importance (gi). Values of gi are high in regions with strong absolute changes, e.g., with intense land use or loss of tropical rainforests. provides a more detailed discussion of separate and combined effects of land use and climate change (albeit for a somewhat differently defined metric using an earlier model version and different input database).

For the original Γ-metric, a threshold of 0.3 had been established, which indicates the transition from moderate to high risk of ecosystem destabilization. To determine whether this threshold is still valid for EcoRisk (given the adaptations to the computation, particularly the inclusion of nitrogen flows and pools that show more variability), we performed two sets of synthetic simulations. We assume that a meaningful threshold should meet the following two criteria: (i) it should be higher than internal variability within biomes but (ii) lower than the variability between distinct biomes, such that a simulated EcoRisk above this threshold is indicative of changes equivalent to a shift in biome. For (i) we checked the homogeneity within forest biomes by computing EcoRisk with values from 1550–1579 between each cell of a biome and the average cell of this biome. In all forest biomes, internal biome variability for at least half the cells is higher than 0.3 compared to the average biome cell (Fig. ). Thus, values <0.5 could better describe most of the internal variation.

For (ii), we compared the average biome cells against each other by computing EcoRisk between the current states of each of them. Most combinations show EcoRisk values >0.3 when compared against each other (Fig. ), with some exceptions: Boreal Needleleaved Evergreen Forest is classified as being relatively similar to Temperate Broadleaved Deciduous Forest (0.26), a fact that we cannot explain and that is also complicated by the reverse direction showing a high value of 0.62. Tropical Rainforest is classified as being similar to Tropical Deciduous Forest. This can be partially explained through their similar locations and the overlap in species. Arctic Tundra and Montane Grassland are classified as being similar. They are in fact the same biome but at different elevations. Comparably, Temperate Savanna, Temperate Woodland, and Warm Grassland are classified as similar biomes that are only differentiated by total tree cover fraction and C3/C4 grass shares. There are further biomes that show intermediate values of 0.3<EcoRisk<0.5 when compared against each other, all of which are partially explainable through compositional similarity or similar average conditions.

However, Fig.  also highlights that EcoRisk is not symmetric, mainly because of the normalization to different reference conditions for local change and ecosystem balance. Strong directional discrepancies can, for example, be observed for the difference between the three biomes Temperate Needleleaved Evergreen Forest, Temperate Broadleaved Deciduous Forest, and Boreal Needleleaved Evergreen Forest.

Aggregating EcoRisk grid cell values to the biome level according to the biome classification yields three classes (Fig. ): (i) those with a median EcoRisk<0.3, i.e., Tropical Rainforest, Tropical Deciduous Forest, Boreal Broadleaved Deciduous Forest, Boreal Needleleaved Deciduous Forest (only few cells), and Arctic Tundra; (ii) those with 0.3<median EcoRisk<0.65, i.e., Boreal Needleleaved Evergreen Forest, Warm Woodland, Warm Savanna, Warm Grassland, Temperate Savanna, Temperate Grassland, Montane Grassland, and Desert; and (iii) those with a median EcoRisk>0.65, i.e., Temperate Broadleaved Deciduous Forest, Temperate Needleleaved Evergreen Forest, and Temperate Woodland. The subcomponent local change is generally the one with highest values (except for TrRF, TrDF, and BoND), and nitrogen fluxes show stronger changes than those for carbon and water in all cases.

Comparison of EcoRisk and BioCol with other biosphere integrity indicators (Table ) shows similar trends despite high scattering (Fig. k and m). The maps highlight that the pattern of BioCol is more similar to that of other transgressed indicators (Fig. a–j); however, this might stem from most of them being directly affected by land use. The additional benefit of EcoRisk is that it also captures effects due to climate and deposition changes.

We present a model-based indicator set that allows for the assessment of the state of the biosphere. We show that large regions presently (period 2007–2016) show modification and extraction of >20 % of the preindustrial potential net primary production according to the indicator BioCol, along with climatic changes leading to drastic alterations in key ecosystem properties and suggesting a high risk of ecosystem destabilization according to the indicator EcoRisk.

Generally, the indicators presented in this package can serve both as an analytic tool to assess biosphere integrity from model simulations or as a means of benchmarking model performance (e.g., after new development). Therefore, depending on the context, the performance of the vegetation model is important. This paper primarily describes the methodology behind the biospheremetrics package, with the application to model results only being secondary. LPJmL5 is currently in a recalibration and validation phase, with major changes to code and key processes, following the implementation of tillage and the nitrogen cycle. Further model development may particularly focus on an improved distribution of PFTs and thus biomes (see for comparison) and the explicit simulation of multi-cropping, which has been neglected here. A better representation of human-induced fire emissions and including process-based forest management will also be important, since the effects on NPP are currently not considered.

The next step for us is thus to extend the biospheremetrics package to be compatible with other vegetation models (e.g., utilizing outputs from the ISIMIP3 ensemble) and thus also allow for intermodel comparison.

As presented in the previous section, the addition of nitrogen fluxes leads to a strong increase in values for EcoRisk, when compared to earlier results of the Γ-Metric (Figs.  and  and ). Relative changes in nitrogen fluxes (Fig. ) as a result of nitrogen fertilizer and manure application are much stronger than those for carbon and water fluxes and pools (Figs.  and ). This is intuitively plausible; however, the question remains, whether the associated high values for EcoRisk mainly resulting from the relative changes in nitrogen fluxes really correspond to a strongly increased risk for ecosphere destabilization. Generally, a theory of how changes in different components (vs, lc, gi, eb) or classes of state variables (e.g., water pools or nitrogen fluxes) can be translated to risk of ecosphere destabilization is lacking (and could be different among components and classes). Theoretically, a weighted downscaling of any component would be possible; however, the literature base for such changes is currently lacking. We thus refrained from any weighting until further research is done on this end.

The results for EcoRisk show a strong increase in the overall values. A new threshold between moderate and high risk (replacing 0.3 in ) would be as high as 0.48, when picking the “optimal” EcoRisk threshold, balancing between forest biome internal homogeneity on the one hand and inter-biome dissimilarity on the other hand (Fig. ).

Our way to calculate BioCol differs substantially from previous approaches for HANPP. First, our calculation is exclusively based on model output, with the aim to also simulate future scenarios and historical periods outside of available biomass inventory data. However, we also use a different baseline when comparing it to the static preindustrial NPP and taking the absolute of cells with negative HANPP values. Both processes are done deliberately in order to (i) not let ourselves get distracted from the fact that global NPP is mainly rising because the biosphere is in a resilience response phase to increasing atmospheric CO2 levels, meaning that this additional NPP is not ours to use, and (ii) acknowledge that management-driven NPP increases beyond the potential natural values (negative NPPluc) usually go together with modification of the water, carbon, or nitrogen cycles stressing local ecosystems.

The underlying principle of BioCol, NPP appropriation, is a function of human demand, i.e., land use change and biomass harvesting for food, fiber, fodder, bioenergy, and bioeconomy. It is therefore closely associated with issues not just of Earth system stability but also of justice, access, and sustainable management of resources (see ). The challenge is to maintain the productivity of the biosphere, i.e., the sustainably available NPP, while ensuring the stability of the biosphere and also including climate and the ecosphere, i.e., the Earth system as used by humans.

Complementarily, EcoRisk indicates where and when critical transitions in ecosystems occur (as a result of NPP appropriation, but also from climate change, or other environmental pollution). It is thus a very useful indicator to assess the risk of ecosystem destabilization today and also going forward, depending on which pathway humanity will take in the future. We deliberately call it a risk metric because the mathematical property of measuring a non-directional change for us directly translates to a proxy for the risk of ecosystem destabilization. Of course there could be regions where ecosystems can benefit from changing biogeochemical properties; however, we would argue that when comparing to a long-term stable, land-use-free preindustrial reference situation (like the Holocene), most ecosystems would be at the risk of being thrown out of this equilibrium when presented with changing conditions. This choice of reference might need to be changed for application of EcoRisk to future climate stabilization scenarios with “Earth-System Stewardship” .

Humanity faces the challenge of stabilizing the Earth system in the Anthropocene. This requires stabilizing the climate and maintaining a resilient biosphere, which represents, according to , a key pillar of the Earth system. Here we present two computable biosphere integrity indicators that allow for the assessment of the historical and future risk of biosphere destabilization. These are designed to complement other biosphere integrity indicators.

Human appropriation of NPP is a direct result of land use changes, modulated by climatic changes, irrigation, fertilization, and other management, leading to biosphere integrity loss that can be measured by EcoRisk. On the other hand, natural NPP is also modified by changes in climate, water availability, biosphere integrity, and human land use. Thus, BioCol and EcoRisk are metrics that integrate multiple terrestrial planetary boundaries and human drivers into two numbers, comparable to what global mean temperature does for the underlying more complex climate processes. Therefore, BioCol and EcoRisk have the potential to be included as indicators in an updated planetary boundaries framework. While well established as a concept, so far there has been no simulation model for assessing and projecting HANPP on the global scale for example to analyze future scenarios not accessible through inventory data.

Both BioCol and EcoRisk are spatially explicit, process-based, and computable metrics that can be aggregated over space and time. They can therefore serve as integrative meta-level proxies for biosphere integrity and Earth system stability and their dynamic changes. The code used here for their calculation is distributed as an open-source R package, and we invite external contributions. With the next update, we plan to equip it to deal with netcdf files, a common data exchange format for spatial data, which allows for future application to different models. Thereby, we hope to encourage others to join our quest to better understand the role of ecosystems for Earth system stability and find ways to preserve it.