Environmental Research Letters

Nature-based climate solutions in Earths forests could strengthen the land carbon sink and contribute to climate mitigation, but must adequately account for climate risks to the durability of carbon storage. Forest carbon offset protocols use a buffer pool to insure against disturbance risks that may compromise durability. However, current buffer pool tools and allocations are not based on existing scientific data or models. Here, we use a tropical forest stand biomass model and an extensive set of long-term tropical forest plots to test whether current buffer pools are adequate to insure against observed disturbance regimes. We find that forest age and disturbance regime both influence necessary buffer pool sizes. In the vast majority of disturbance scenarios, current buffer pools are substantially smaller than required by carbon cycle science. Buffer pool estimates urgently need to be updated based on rigorous, open scientific datasets for nature-based climate solutions to succeed. Plain Language SummaryForests could contribute to climate mitigation through conservation and restoration activities. Carbon offsets are a widespread pathway to fund these nature-based climate solutions in forests, but must account for the risks to durability that forests face in a changing climate. Current carbon offset protocols have a buffer pool to insure against risk in different disturbance regimes, but the buffer pool contributions have not been tested with observed disturbance regimes and rigorous models. We tested these contributions using widespread tropical forest plot data and a carbon cycle model and find that the current buffer pool contributions are generally not adequate for most disturbance regimes. Our results highlight that better datasets, models, and tools are urgently needed in forest carbon offset protocols. Key pointsO_LINature-based climate solutions in forests face substantial and rising climate risks to durability C_LIO_LICarbon offsets use a buffer pool to insure against disturbance, which is not currently based on rigorous evidence C_LIO_LIOur results reveal current carbon offset protocols do not have an adequate buffer pool for most tropical forest disturbance regimes C_LI

Globally, forests play an important role in climate change mitigation. However, land-use impacts the ability of forests to sequester and store carbon. Here we quantify the impacts of five divergent future land-use scenarios on aboveground forest carbon stocks and fluxes throughout New England. These scenarios, four co-designed with stakeholders from throughout the region and the fifth a continuation of recent trends in land use, were simulated by coupling a land cover change model with a mechanistic forest growth model to produce estimates of aboveground carbon over 50 years. Future carbon removed through harvesting and development was tracked using a standard carbon accounting methodology, modified to fit our modeling framework. Of the simulated changes in land use, changes in harvesting had the most profound and immediate impacts on carbon stocks and fluxes. In one of the future land-use scenarios including a rapid expansion of harvesting for biomass energy, this changed New Englands forests from a net carbon sink to a net carbon source in 2060. Also in these simulations, relatively small reductions in harvest intensities (e.g., 10% reduction), coupled with an increased percent of wood going into longer-term storage, led to substantial reductions in net carbon emissions (909 MMtCO2eq) as compared to a continuation of recent trends in land use. However, these projected gains in carbon storage and reduction in emissions from less intense harvesting regimes can only be realized if it is paired with a reduction in the consumption of the timber products, and their replacements, that otherwise would result in additional emissions from leakage and substitution.

There is a substantial interest in the potential of carbon credits generated by Reducing Emissions from tropical Deforestation and Degradation (REDD+) and traded on the voluntary carbon market for generating the finance needed to slow forest loss. However, such credits have become marred in controversy. Recent global-scale analysis using a range of methods for estimating the counterfactual rate of deforestation ex post suggest that many REDD+ projects have overestimated their effectiveness at reducing deforestation and consequently issued more credits than can be justified. All such methods include potentially arbitrary choices which can affect the estimate of the treatment effect. In addition, using pixels as the sampling unit, as some of the studies do, can introduce biases. One study which has been widely cited in the debate (Guizar-Coutino et al. 2022) estimated avoided deforestation using statistical matching of pixels and a single set of matching options. We estimate avoided deforestation from the same set of projects using 7-hectare plots rather than pixels to sample deforestation and explore the sensitivity of the results to matching choices (exploring 120 matched sets in total). We filtered the results on three criteria: 1) post-matching covariate balance, 2) proportion of REDD+ samples that were successfully matched, and 3) similarity of trends in deforestation rates prior to REDD+ implementation (parallel trends). While one of the 44 REDD+ projects failed these quality control process, we estimate treatment effects for the remaining 43 projects. There was a substantial correlation between our new estimates and those published in Guizar-Coutino et al. 2022 (0.72 measured in annual percent change, and 0.9 measured in total area change) and our headline estimate of 0.22% per yr (95% CI: 0.13 to 0.34) is essentially unchanged. At a time when confidence in the voluntary carbon markets is low, we hope these results provide reassurance that ex-post counterfactual estimates of avoided deforestation are consistent, helping accelerate their widespread adoption and rebuild trust in nature-based climate solutions.

Agroforestry is a cornerstone of Natural Climate Solutions, yet the hierarchical importance of its soil organic carbon (SOC) drivers remains poorly resolved across heterogeneous tropical landscapes. Current global assessments predominantly rely on categorical system typologies that mask the continuous influence of biophysical drivers, leaving the reliability of mitigation estimates unclear. Here, we synthesize 643 observations from 54 field studies in Latin America and the Caribbean to decouple the determinants of SOC sequestration using a machine-learning framework. We show that baseline soil carbon stocks and temporal kinetics override management design, collectively explaining [~]85% of sequestration variability, whereas system typology and species richness contribute marginally (R2<0.10). While the median SOC storage rate was 0.26 Mg C ha{superscript 1} yr{superscript 1}, accumulation followed a distinct non-linear trajectory: sequestration intensity peaked early before decelerating sharply after a critical inflection at year 7. Critically, sequestration is governed by a robust negative feedback from initial SOC stocks, which cross a zero-net-gain threshold at [~]80 Mg C ha{superscript 1}. Depth-resolved analyses reveal that subsoil layers (up to 55-75 cm) exhibit a cumulative relative response up to fourfold greater than surface horizons, indicating that conventional shallow monitoring could systematically underestimates long-term stabilization potential. Our findings demonstrate that current carbon accounting frameworks, rooted in generic system averages (IPCC Tier 1), are structurally limited by their inability to account for baseline-dependent saturation feedbacks and non-linear effects. Transitioning toward Tier 3 context-aware, depth-explicit modeling is therefore essential to transform agroforestry from a broad practice into a precision-based, high-integrity Natural Climate Solution. HighlightsO_LISoil carbon sequestration in tropical agroforestry is primarily controlled by baseline soil conditions and temporal dynamics rather than system typology. C_LIO_LIDepth-resolved analyses reveal long-term carbon stabilization processes overlooked by surface-based assessments. C_LIO_LICarbon accumulation is strongly front-loaded, declining sharply after system establishment. C_LIO_LIContext-dependent responses challenge generic carbon accounting frameworks and highlight the need for predictive, site-specific deployment of agroforestry. C_LI

Land-use change is currently the greatest driver of biodiversity change, with climate change predicted to match or surpass its impacts by mid-century. The global food system is a key driver of both these anthropogenic pressures, thus the development of sustainable food systems will be critical to halting and reversing biodiversity loss. Previous studies of the biodiversity footprint of food tend to focus on land use alone. We use the multi-regional input-output model EXIOBASE to estimate the impacts of biodiversity embedded within the global food system. We build on prior analyses, calculating the impacts of both agricultural land-use and greenhouse gas (GHG) emission footprints for the same two metrics of biodiversity: local species richness and rarity-weighted species richness. Our biodiversity models capture regional variation in the sensitivity of biodiversity both to land-use differences and to climate change. We find that the footprint of land area does not capture the biodiversity impact embedded within trade that is provided by our metric of land-driven species richness change, and that our metric of rarity-weighted richness places a greater emphasis on the biodiversity costs in Central and South America. We find that methane emissions are responsible for 70% of the overall GHG-driven biodiversity footprint and that, in several regions, emissions from a single years food production cause biodiversity loss equivalent to 2% or more of that regions total historic land use. The measures we present are simple to calculate and could be incorporated into decision making and environmental impact assessments by governments and businesses.

As energy demands continue to rise, energy production from sources including natural gas is expected to rapidly accelerate in the coming decades, potentially leading to substantial land-use changes. In the Appalachian region of the United States, natural gas development often occurs in forested areas, which can cause high levels of forest disturbance and loss. Here, we use nationwide forest inventory and remotely sensed data in a Bayesian model to quantify the impacts of natural gas development at fine spatial resolutions between 2008-2021. Based on well permit locations in the states of Ohio, Pennsylvania, and West Virginia, the analysis quantifies disturbance area, forest carbon loss, and opportunity cost with associated levels of uncertainty at the pixel-level. Overall, we estimate 10,854 ha of forest land were disturbed, resulting in 542,675 Mg ({+/-} 4,275) of forest carbon loss. The opportunity cost associated with these disturbances is estimated to be 575,246 Mg ({+/-} 30,774). Pixel-level estimates are generated for individual well sites, which can be aggregated to the county-level to highlight regional patterns. Specifically, we observe greater levels of disturbance in Northern West Virginia, while opportunity costs are greatest for large forested counties in Northeastern Pennsylvania. This study demonstrates the importance of quantifying balances and tradeoffs between energy production and forest ecosystem services, and provides important insights into the impacts of energy development on forest carbon.

Forests are central to the EUs climate neutrality strategy, currently offsetting [~]9% of total greenhouse gas emissions and offering further mitigation potential through harvested wood products and the substitution effect. However, the climate benefit of the forest sector is influenced by multiple interacting factors, including forest management, climate change, wood-use strategies, and assumptions about substitution benefits, as well as the timing and fate of carbon across the forest ecosystem and technosphere. To evaluate these drivers, we used a coupled forest growth and a wood products model to simulate five different silvicultural strategies under three climate change scenarios, four wood use schemes, and five displacement factor decay pathways over a 285-year period (2015-2300), applied to a Pinus nigra forest in Italy, questioning the impact of these factors on climate mitigation potential of the forest sector. We assessed forest sector balance (FSB, net carbon exchange between forest system and atmosphere), radiative forcing from biogenic CO2 (RFbio), and mitigation efficiency (ME) - the proportion of sequestered carbon contributing to net climate benefit. Results showed that FSB and RFbio were "broadly" aligned, but ME varied with the magnitude and duration of biogenic emissions. The scenarios BIOE (bioenergy) and TM (modular cutting) achieved high FSB but showed lower ME due to concentrated or sustained emissions. WOOD (promotion of long-lived wood) and ADAPT (adaptation management) yielded higher ME under SSP1-2.6, while several strategies (WOOD, ADAPT, TRANS) became net sources under SSP5-8.5 after 2200. Substitution benefits declined under degressive assumptions, reducing mitigation by up to 53% especially for high-harvest scenarios. FSB was primarily shaped by climate and management, secondly by substitution, however; wood-use strategies had no significant long-term effect provided they did not impact resource availability. Together, these findings underscore that effective forest-sector mitigation requires not only maximizing cumulative carbon stocks, but also minimizing the magnitude, timing, and atmospheric residence time of emissions while carefully considering the role of substitution benefits. HighlightsO_LIThe long-term forest sector carbon balance is mainly governed by active management, climate conditions, and wood substitution pathways. C_LIO_LIForest Sector Balance as a metric is "broadly" aligned with the radiative forcing from biogenic emissions C_LIO_LIThe mitigation efficiency of forest sector options depends on emissions timing, duration, and amplitude C_LIO_LISubstitution factors also known as displacement factors need to be considered with greater caution C_LI

Process-based cropping systems models (CSMs) are key components of measurement, monitoring, reporting, and verification (MMRV) frameworks of carbon markets, but their application suffers from model-specific differences that keep any one model from working well across all combinations of soils, climates, crops, and agronomic practices at varying scales. Multi-model ensemble (MME), successfully used to quantify soil, management and climate impact on crop productivity, provide an opportunity to better estimate changes in soil organic carbon (SOC) outcomes for agronomic practices that have the potential to mitigate SOC loss at scale. We used an MME across 46 million hectares of US Midwest cropland at a resolution of 4- km2 to assess the aggregate ability of different regenerative practices to sequester SOC at this scale compared to their dynamic baselines. MME was validated with long-term experimental data and compared to its constituent CSMs, showing greater accuracy and lower uncertainty. The results show that adopting no-till combined with cover crops increased SOC stocks by 0.36 {+/-} 0.12 Mg ha-1 yr-1 aggregated across the entire U.S. Midwest cropland. At the regional scale, this corresponds to a net SOC gain of 16.4 Tg C yr-1 compared to business-as-usual baselines. These benefits are approximately halved when each management change is practiced individually, and the modest gains are only fully realized when continued over the long-term in soils with low initial carbon stock. Results demonstrate the power of MMEs run at high resolution for providing robust estimates of environmental outcomes following agricultural practice change, and for pinpointing locations for most effective intervention. This approach can alleviate many producer carbon market participation barriers and help address market issues while ultimately supporting large-scale regenerative agriculture initiatives.

Accurate inventories of terrestrial carbon pools and fluxes are crucial for understanding ecosystem processes, tracking climate change impacts, and meeting the monitoring, reporting, and verification (MRV) requirements in international treaties and voluntary carbon markets. In meeting this need, the fusion of process-based modeling, field data, and remote sensing observations has the potential to provide more accurate and precise estimates than each alone. However, as the number of data constraints on a system increases, different sources of information can interact with each other in complex ways across space, time, and processes. In this study, we undertake a value-of-information analysis to assess the contribution of different observations to reducing carbon cycle uncertainties across pools, fluxes, and spatial domains within the PEcAn carbon cycle data assimilation system. We used a novel block-based Tobit Gamma Ensemble Filter to assimilate four synergistic data constraints, MODIS leaf area index, Landtrendr aboveground biomass, SMAP soil moisture, and SoilGrids soil organic C, into a process-based ecosystem model (SIPNET) at 39 National Ecological Observatory Network sites across the contiguous U.S. from 2012 to 2021. Results showed that not only did we greatly reduce uncertainty among the directly constrained pools but many observations were able to share information across variables and space. These indirect constraints helped identify synergies and conflicts among data streams and across space, which provides insights for further constraining carbon inventories. Overall, soil carbon remains the largest source of uncertainty in the overall carbon budget due to both its large size and limited observational constraints.

Reliable freshwater access drives terrestrial wildlife movements and habitat use globally. The small, rain-fed seasonal pools critical for dryland wildlife persistence are vulnerable to rising temperatures and unstable precipitation regimes projected under climate change. In southern Africa, which is expected to warm rapidly by 2100, the drying and disappearance of surface water may cause a breakdown in seasonal migrations of large, area-sensitive, and water-dependent wildlife species. Furthermore, the disappearance of ephemeral water may concentrate wildlife around remaining surface water, increasing resource competition and human-wildlife conflict. An accurate understanding of the dynamics and drivers of seasonal surface water will therefore be critical to wildlife and human health as climate change intensifies. Here, we present a framework and empirical analysis of fine-scale surface water mapping in the 520,000km2 Kavango Zambezi Transfrontier Conservation Area (KAZA), the worlds largest terrestrial conservation area. From 2019-2025, we implemented Otsu thresholding on median Automated Water Extraction Index imagery from 10m Sentinel-2 MSI, leveraging high wet season contrast between vegetation and water as a dry season positive mask. We created >35 quasi-monthly KAZA-wide Ephemeral Surface Water (ESW) rasters (mean classification accuracy 87%, compared to 50% accuracy for existing water products), and found wet season precipitation drivers of non-riparian water fill levels did not extend into the dry season. Then, using GPS data from 27 African savanna elephants (Loxodonta africana), which typically visit water every 48 hours, we compared elephant water visitation rates based on ESW to existing 30m Global Surface Water (GSW) maps. Models using ESW estimated 99% of elephant data came within a 48-hour window, compared to 42% for GSW, suggesting that ESW is a better proxy for actual wildlife water use in animal movement modeling. As aridification threatens to diminish surface water resources, we must model the drivers of wildlife movements at the scale of wildlife needs. With ESW, we provide fine scale accessible surface water data and a straightforward coding architecture for applications beyond KAZA.

AbstractThe ability to accurately assess ecosystem C budgets across scales from individual sites to continents is essential for C accounting, management, and ultimately mitigating climate change. State data assimilation (SDA) provides a framework for harmonizing observations with models, while robustly accounting for and reducing multiple sources of uncertainty. In this study, we employed a hybrid SDA framework that combines process-based terrestrial biosphere modeling, hierarchical Bayesian inference, and machine learning to harmonize bottom-up and remotely-sensed data streams for 8,000 pre-selected 1km2 locations across North America within a hybrid structure. Combining bottom-up soils data (SoilGrids) with spectral (MODIS and Landsat) and microwave (SMAP) remote sensing helps constrain the major C and water stocks through space and time. Machine learning is used both to identify and correct systematic errors in the process model (SIPNET) and to interpolate the pre-selected locations onto a 1km grid, making it computationally feasible to generate annual ensemble maps of the North American carbon budget. Furthermore, the uncertainties for each variable were reduced compared to those from observations or models alone. Spatiotemporal analysis showed a slight decrease in aboveground biomass (AGB) across the western US, a loss of leaf area across the boreal, and a slight greening of the Alaskan tundra. The uncertainty trends suggest a significant reduction in the uncertainty about soil organic carbon (SOC), the largest C reservoir. Validation results show that we accurately estimate C pools, compared to the assimilated data streams and held-out observations of AGB from GEDI, ICESat-2, and the US FIA, and SOC from the ISCN network. Our ML-debiasing algorithm further improved the accuracy of major C pools (AGB, SOC). In general, our continental SDA framework will facilitate global C MRV (monitoring, reporting, and verification) by providing accurate and precise C-cycle estimates, along with their corresponding spatiotemporal uncertainties.

Kelp forests are one of the earths most productive ecosystems and are at the greatest risk from climate change, yet little is known regarding their future threats and current conservation status. By combining a global remote sensing dataset of floating kelp forests with climate data and projections, we find that exposure to projected marine heatwaves will increase [~]8 times compared to contemporary (2001-2020) exposure for intermediate climate scenarios. While exposure will intensify for all forests, climate refugia emerge for some southern hemisphere kelp forests, which have lower exposure to contemporary and projected marine heatwaves. Under these escalating threats, less than 3% of global kelp forests are currently within highly restrictive marine protected areas, the most effective conservation measure for providing climate resilience. Our findings emphasize the urgent need to increase the global protection of kelp forests and set bolder climate adaptation goals.

Climate-smart conservation addresses the vulnerability of biodiversity to climate change impacts but may require transboundary considerations. Here, we adapt and refine 16 biophysical guidelines for climate-smart marine reserves for the transboundary California Bight ecoregion. We link several climate-adaptation strategies (e.g., maintaining connectivity, representing climate refugia, and forecasting effectiveness of protection) by focusing on kelp forests and associated species. We quantify transboundary larval connectivity along [~]800 km of coast and find that the number of connections and the average density of larvae dispersing through the network under future climate scenarios could decrease by [~]50%, highlighting the need to protect critical steppingstone nodes. We also find that although focal species will generally recover with 30% protection, marine heatwaves could hinder subsequent recovery in the following 50 years, suggesting that protecting climate refugia and expanding the coverage of marine reserves is a priority. Together, these findings provide a first comprehensive framework for integrating climate resilience for networks of marine reserves and highlight the need for a coordinated approach in the California Bight ecoregion.

Peatlands provide one of the largest terrestrial carbon stocks in the UK. However, a large proportion of peatlands are drained for peat extraction, agriculture and other uses, turning them into a major source of the UKs land use greenhouse gas (GHG) emissions. Successful restoration can ultimately return peatlands into carbon sinks. However, rewetting - the primary step in peatland restoration - can reduce CO2 emissions while increasing CH4 emissions. This may result in little overall climate benefit, or even increased warming for several years post peatland restoration, as CH4 is a short-lived but strong GHG, and may overpower the reduction in CO2. Such consequences are rarely explored in detail, since most studies are based on comparing total CO2-equivalent emissions pre- and post-restoration using the 100-year Global Warming Potential (GWP100), which can fail to reveal the full dynamics. We evaluated the emissions and resultant climate impacts from peatland restoration using data from The Wildlife Trusts, a federation of UK-based conservation charities, as a case-study. The total emissions of each restoration stage were estimated by multiplying peatland areas under restoration with up-to-date UK emission factors (EF), then compared under multiple pulse emission metrics (GWP100, GWP20, GTP100) to indicate the impacts over a range of time-horizons, and GWP* to reveal the varying warming impacts over time. We also used Monte-Carlo Simulation to investigate the uncertainties in total emissions drawing from EF ranges. We found that the restoration so far has provided large emission reductions under all metrics, even considering the uncertainties. Increased CH4 is unlikely to cause extra warming in the extremely near-term (<20 years), and if the peatlands are maintained in their rewetted states, they can contribute to net-cooling in the long term. There is less certainty over the climate benefits of further restoration, from rewetted to "near-natural" states, especially in the shorter term, but we argue that any risks are low, while this continued restoration will provide further ecological benefits and support biodiversity. Our study lends further support for peatland restoration in the UK and other regions with similar habitats, and provides insight into the climate roles of peatlands more broadly.

Peatlands are an important terrestrial carbon sink which, when drained, can produce substantial CO2 efflux. Low productivity forestry planted on drained peatlands can become a net carbon source if losses from drained soils exceed sequestration by the trees. Decision support tools which assist resource allocation and intervention planning in forest-to-bog restoration are needed to mediate this substantial environmental harm. Predicting carbon mitigation benefits associated with forest-to-bog restoration is a major challenge, however, due to the lack of long-term monitoring programs and the fact that mitigation times depend on processes distant from the intervention. Here we introduce the PEATREST life cycle assessment (LCA) which predicts carbon fluxes associated with forest-to-bog restoration, including due to processes far from restored sites. The LCA estimates mitigation timescales defined as the time following intervention at which the restored peatland is predicted to sequester or store more carbon than the forestry would have if retained. HighlightsO_LIHere we develop a novel forest-to-bog Life cycle assessment (LCA) tool C_LIO_LIThe LCA predicts carbon mitigation times following peatland restoration C_LIO_LIThe model combines a variety of process-based and empirical sub-models C_LIO_LIExample implementations for two different restoration scenarios are explored C_LIO_LISensitivity analysis highlights the model inputs that most impact outcomes C_LI Graphical abstract(A single, concise figure that serves as a visual summary of the main research findings described in your manuscript.) O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/715261v1_ufig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@f243f5org.highwire.dtl.DTLVardef@14bc4c7org.highwire.dtl.DTLVardef@164261borg.highwire.dtl.DTLVardef@1db3b_HPS_FORMAT_FIGEXP M_FIG The PEATREST Life cycle assessment (LCA) generates compound time series of carbon sequestration and carbon storage for two scenarios: the forest-to-bog peatland restoration (PR) and a counterfactual (CF) of forestry retention. By comparing the two scenarios, the LCA predicts the carbon mitigation timescales (vertical dashed lines). These are defined as the time following harvesting at which the peatland is predicted to sequester more (emit less), or to have stored more (lost less) carbon, than the forestry would have if retained. C_FIG

Projections of future conditions from Earth systems models (ESMs) are necessary to understand and predict effects of changing environmental conditions on biological systems. Such projections suffer from biases, or mismatches between model output and observations. While adjusting or bias-correcting model output is common, many methods exist with little understanding of their effects on forecasts of biological change. Here, we explore the bias-correction process and its effects on downstream predictive biological models. As an example, we use the Bering 10K, a downscaled ESM for a productive and economically important subarctic ecosystem. We first characterize existing biases for three categories of variables exhibiting different scales and challenges: bottom temperature, sea ice, and net primary production. We then apply eight bias-correction approaches to six indices generated from the three categories and quantify sources of uncertainty in the trajectories of these ecosystem variables. Finally, we demonstrate how different bias-correction approaches affect downstream biological models using three case studies: 1) fish thermal spawning habitat suitability, (2) predicted zooplankton abundance, and (3) match-mismatch of phytoplankton and zooplankton bloom timing. We find that biases manifest in absolute values over time and in the timing of seasonal events. Time series of all six indices differed depending on bias-correction method, differences that were propagated to downstream biological models. For a given year and simulation, depending on method, thermal spawning habitat suitability and zooplankton abundance differed up to 149% and 151%, and match-mismatch increased or did not change. Our work highlights that bias correction reduces mismatches between observations and model output but choosing an approach requires careful consideration as to not amplify and propagate bias in downstream biological models. To that end, we identify best practices for bias correcting global or regional ESMs, including a decision tree to help improve forecasts of the effects of climate change on biological systems.

Reforestation is an important strategy for nature-based climate solutions and identifying carbon storage potential of different locations is critical to its success. Applying average carbon values from forest inventories ignores the spatial heterogeneity in forest carbon and the effects of forest edges on carbon storage degradation. Here we show how spatially-explicit, predictive carbon modeling, that leverages satellite, social and biogeophysical datasets, can be used to identify more efficient restoration opportunities for climate mitigation than area-based carbon stock averages. Accounting for regeneration of forest edges, in addition to reforestation, boosts estimates of potential carbon gains by more than 20%. The total potential carbon gain that could be achieved through reforestation at the level indicated by the Bonn Challenge (350Mha) is 51 Gt CO2-eq, but the "missing carbon" in our current forests accounts for 64.6 Gt CO2-eq globally; the greatest potential carbon gains are found in areas of high fragmentation.

Forests are currently a substantial carbon sink globally. Many climate change mitigation strategies rely on forest preservation and expansion, but the effectiveness of these approaches hinges on forests sequestering carbon for centuries despite anthropogenic climate change. Yet climate-driven disturbances pose critical risks to the long-term stability of forest carbon. We quantify the key climate drivers that fuel wildfire, drought, and insects, for the United States over 1984-2018 and project future disturbance risks over the 21st century. We find that current risks are widespread and projected to increase across different emission scenarios by a factor of 4-14 for fire and 1.3-1.8 for drought and insects. Our results provide insights for carbon cycle modeling, conservation, and climate policy, underscoring the escalating climate risks facing forests and the need for emissions reductions to mitigate climate change.

Lions (Panthera leo) in Africa have lost nearly half their population in just the last two decades, and effective management of the protected areas (PAs) where lions live will cost an estimated USD >$1 B/year in new funding. We explore the potential for launching a fire management and habitat restoration carbon-financing program to help fill this PA management funding gap. We demonstrate how introducing early dry season fire management programs could produce potential carbon revenues (PCR) from either a single carbon-financing method (avoided emissions) or from multiple sequestration methods of USD $59.6-$655.9 M/year (at USD $5/ton) or USD $155.0 M-$1.7 B/year (at USD $13/ton). We highlight variable but significant PCR for PAs with the greatest potential for restoring lion numbers between USD $1.5-$44.4 M per PA. We suggest investing in lion-centric fire management programs to jump-start the United Nations Decade of Ecological Restoration and help preserve African lions across their range. SCIENCE FOR SOCIETYThe United Nations recently launched the Decade of Ecological Restoration in response to planet-wide land degradation. This study analyses the potential for savanna fire management programs to restore fire regimes that can generate new sources of revenue from carbon financing for chronically under-funded protected areas in Africa with lions, as lions are a key indicator of savanna ecosystem health. We estimated the amount of carbon saved by shifting fires that normally burn in the late dry season (and emit more carbon) to the early dry season (that accrue more carbon in the soil and woody vegetation). Based on current carbon market values we found substantial potential to eliminate or significantly reduce the $>1B annual funding gap needed to save the lion from extinction. Given additional benefits for nature and people from new savanna fire programs, we recommend integrated conservation and development projects direct more funding to some of the least developed countries with high biodiversity and support fire management programs in Africa.

The impacts of invasive pests and diseases are routinely estimated and measured in the context of agriculture, but less so in the context of biodiversity and ecosystem services. In this study, we estimate the potential reduction of carbon sequestration in Australia due to exotic strains of myrtle rust (Austropuccinia psidii, also known as Puccinia psidii, guava rust, or [o]hia rust). We model the contribution of susceptible plants to carbon sequestration and use previously known myrtle rust damage estimates to susceptible plant species and the valuation of carbon sequestration in Australia to estimate the potential monetary impact. This method can be systematically extended to other pests impacting plant growth as well as other ecosystem services. In the case of myrtle rust, we estimate that it could cause up to a 1.6% (95% CI: 1.3-2.0%) annual reduction in national carbon sequestration if it were to spread across all climatically suitable areas in Australia, resulting in an estimated value loss of over $340 million AUD (over $220 million USD) per year. Compared with contemporary syntheses of known cost estimates, our results show that the potential consequences of invasive species can be substantially larger than reported, and may be currently undervalued. Our work shows the need to systematically compile the potential impacts and costs to the environment and ecosystem services globally, to support both biosecurity decision-making and climate-change related initiatives such as net-zero emissions targets and reforestation efforts.