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Based on an extensive model intercomparison, we assessed trends in biodiversity and ecosystem services from historical reconstructions and future scenarios of land-use and climate change. During the 20th century, biodiversity declined globally by 2 to 11%, as estimated by a range of indicators. Provisioning ecosystem services increased several fold, and regulating services decreased moderately. Going forward, policies toward sustainability have the potential to slow biodiversity loss resulting from land-use change and the demand for provisioning services while reducing or reversing declines in regulating services. However, negative impacts on biodiversity due to climate change appear poised to increase, particularly in the higher-emissions scenarios. Our assessment identifies remaining modeling uncertainties but also robustly shows that renewed policy efforts are needed to meet the goals of the Convention on Biological Diversity.

Forest carbon is a large and uncertain component of the global carbon cycle. An important source of complexity is the spatial heterogeneity of vegetation vertical structure and extent, which results from variations in climate, soils, and disturbances and influences both contemporary carbon stocks and fluxes. Recent advances in remote sensing and ecosystem modeling have the potential to significantly improve the characterization of vegetation structure and its resulting influence on carbon. Here, we used novel remote sensing observations of tree canopy height collected by two NASA spaceborne lidar missions, Global Ecosystem Dynamics Investigation and ICE, Cloud, and Land Elevation Satellite 2, together with a newly developed global Ecosystem Demography model (v3.0) to characterize the spatial heterogeneity of global forest structure and quantify the corresponding implications for forest carbon stocks and fluxes. Multiple-scale evaluations suggested favorable results relative to other estimates including field inventory, remote sensing-based products, and national statistics. However, this approach utilized several orders of magnitude more data (3.77 billion lidar samples) on vegetation structure than used previously and enabled a qualitative increase in the spatial resolution of model estimates achievable (0.25° to 0.01°). At this resolution, process-based models are now able to capture detailed spatial patterns of forest structure previously unattainable, including patterns of natural and anthropogenic disturbance and recovery. Through the novel integration of new remote sensing data and ecosystem modeling, this study bridges the gap between existing empirically based remote sensing approaches and process-based modeling approaches. This study more generally demonstrates the promising value of spaceborne lidar observations for advancing carbon modeling at a global scale., (© 2023 The Authors. Global Change Biology published by John Wiley & Sons Ltd.)

Atmospheric methane (CH 4 ) concentrations have shown a puzzling resumption in growth since 2007 following a period of stabilization from 2000 to 2006. Multiple hypotheses have been proposed to explain the temporal variations in CH 4 growth, and attribute the rise of atmospheric CH 4 either to increases in emissions from fossil fuel activities, agriculture and natural wetlands, or to a decrease in the atmospheric chemical sink. Here, we use a comprehensive ensemble of CH 4 source estimates and isotopic δ 13 C-CH 4 source signature data to show that the resumption of CH 4 growth is most likely due to increased anthropogenic emissions. Our emission scenarios that have the fewest biases with respect to isotopic composition suggest that the agriculture, landfill and waste sectors were responsible for 53 ± 13% of the renewed growth over the period 2007-2017 compared to 2000-2006; industrial fossil fuel sources explained an additional 34 ± 24%, and wetland sources contributed the least at 13 ± 9%. The hypothesis that a large increase in emissions from natural wetlands drove the decrease in atmospheric δ 13 C-CH 4 values cannot be reconciled with current process-based wetland CH 4 models. This finding suggests the need for increased wetland measurements to better understand the contemporary and future role of wetlands in the rise of atmospheric methane and climate feedback. Our findings highlight the predominant role of anthropogenic activities in driving the growth of atmospheric CH 4 concentrations., (© The Author(s) 2021. Published by Oxford University Press on behalf of China Science Publishing & Media Ltd.)

Wetlands are thought to be the major contributor to interannual variability in the growth rate of atmospheric methane (CH 4 ) with anomalies driven by the influence of the El Niño-Southern Oscillation (ENSO). Yet it remains unclear whether (i) the increase in total global CH 4 emissions during El Niño versus La Niña events is from wetlands and (ii) how large the contribution of wetland CH 4 emissions is to the interannual variability of atmospheric CH 4 . We used a terrestrial ecosystem model that includes permafrost and wetland dynamics to estimate CH 4 emissions, forced by three separate meteorological reanalyses and one gridded observational climate dataset, to simulate the spatio-temporal dynamics of wetland CH 4 emissions from 1980-2016. The simulations show that while wetland CH 4 responds with negative annual anomalies during the El Niño events, the instantaneous growth rate of wetland CH 4 emissions exhibits complex phase dynamics. We find that wetland CH 4 instantaneous growth rates were declined at the onset of the 2015-2016 El Niño event but then increased to a record-high at later stages of the El Niño event (January through May 2016). We also find evidence for a step increase of CH 4 emissions by 7.8±1.6 Tg CH 4 yr -1 during 2007-2014 compared to the average of 2000-2006 from simulations using meteorological reanalyses, which is equivalent to a ~3.5 ppb yr -1 rise in CH 4 concentrations. The step increase is mainly caused by the expansion of wetland area in the tropics (30°S-30°N) due to an enhancement of tropical precipitation as indicated by the suite of the meteorological reanalyses. Our study highlights the role of wetlands, and the complex temporal phasing with ENSO, in driving the variability and trends of atmospheric CH 4 concentrations. In addition, the need to account for uncertainty in meteorological forcings is highlighted in addressing the interannual variability and decadal-scale trends of wetland CH 4 fluxes.

Mosaic landscapes under shifting cultivation, with their dynamic mix of managed and natural land covers, often fall through the cracks in remote sensing-based land cover and land use classifications, as these are unable to adequately capture such landscapes' dynamic nature and complex spectral and spatial signatures. But information about such landscapes is urgently needed to improve the outcomes of global earth system modelling and large-scale carbon and greenhouse gas accounting. This study combines existing global Landsat-based deforestation data covering the years 2000 to 2014 with very high-resolution satellite imagery to visually detect the specific spatio-temporal pattern of shifting cultivation at a one-degree cell resolution worldwide. The accuracy levels of our classification were high with an overall accuracy above 87%. We estimate the current global extent of shifting cultivation and compare it to other current global mapping endeavors as well as results of literature searches. Based on an expert survey, we make a first attempt at estimating past trends as well as possible future trends in the global distribution of shifting cultivation until the end of the 21st century. With 62% of the investigated one-degree cells in the humid and sub-humid tropics currently showing signs of shifting cultivation-the majority in the Americas (41%) and Africa (37%)-this form of cultivation remains widespread, and it would be wrong to speak of its general global demise in the last decades. We estimate that shifting cultivation landscapes currently cover roughly 280 million hectares worldwide, including both cultivated fields and fallows. While only an approximation, this estimate is clearly smaller than the areas mentioned in the literature which range up to 1,000 million hectares. Based on our expert survey and historical trends we estimate a possible strong decrease in shifting cultivation over the next decades, raising issues of livelihood security and resilience among people currently depending on shifting cultivation.

Background: Continental-scale aboveground biomass maps are increasingly available, but their estimates vary widely, particularly at high resolution. A comprehensive understanding of map discrepancies is required to improve their effectiveness in carbon accounting and local decision-making. To this end, we compare four continental-scale maps with a recent high-resolution lidar-derived biomass map over Maryland, USA. We conduct detailed comparisons at pixel-, county-, and state-level., Results: Spatial patterns of biomass are broadly consistent in all maps, but there are large differences at fine scales (RMSD 48.5-92.7 Mg ha -1 ). Discrepancies reduce with aggregation and the agreement among products improves at the county level. However, continental scale maps exhibit residual negative biases in mean (33.0-54.6 Mg ha -1 ) and total biomass (3.5-5.8 Tg) when compared to the high-resolution lidar biomass map. Three of the four continental scale maps reach near-perfect agreement at ~4 km and onward but do not converge with the high-resolution biomass map even at county scale. At the State level, these maps underestimate biomass by 30-80 Tg in forested and 40-50 Tg in non-forested areas., Conclusions: Local discrepancies in continental scale biomass maps are caused by factors including data inputs, modeling approaches, forest/non-forest definitions and time lags. There is a net underestimation over high biomass forests and non-forested areas that could impact carbon accounting at all levels. Local, high-resolution lidar-derived biomass maps provide a valuable bottom-up reference to improve the analysis and interpretation of large-scale maps produced in carbon monitoring systems.

Land-use change to meet 21st-century demands for food, fuel, and fiber will depend on many interactive factors, including global policies limiting anthropogenic climate change and realized improvements in agricultural productivity. Climate-change mitigation policies will alter the decision-making environment for land management, and changes in agricultural productivity will influence cultivated land expansion. We explore to what extent future increases in agricultural productivity might offset conversion of tropical forest lands to crop lands under a climate mitigation policy and a contrasting no-policy scenario in a global integrated assessment model. The Global Change Assessment Model is applied here to simulate a mitigation policy that stabilizes radiative forcing at 4.5 W m(-2) (approximately 526 ppm CO(2)) in the year 2100 by introducing a price for all greenhouse gas emissions, including those from land use. These scenarios are simulated with several cases of future agricultural productivity growth rates and the results downscaled to produce gridded maps of potential land-use change. We find that tropical forests are preserved near their present-day extent, and bioenergy crops emerge as an effective mitigation option, only in cases in which a climate mitigation policy that includes an economic price for land-use emissions is in place, and in which agricultural productivity growth continues throughout the century. We find that idealized land-use emissions price assumptions are most effective at limiting deforestation, even when cropland area must increase to meet future food demand. These findings emphasize the importance of accounting for feedbacks from land-use change emissions in global climate change mitigation strategies.