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The density of wood is a key indicator of the carbon investment strategies of trees, impacting productivity and carbon storage. Despite its importance, the global variation in wood density and its environmental controls remain poorly understood, preventing accurate predictions of global forest carbon stocks. Here we analyse information from 1.1 million forest inventory plots alongside wood density data from 10,703 tree species to create a spatially explicit understanding of the global wood density distribution and its drivers. Our findings reveal a pronounced latitudinal gradient, with wood in tropical forests being up to 30% denser than that in boreal forests. In both angiosperms and gymnosperms, hydrothermal conditions represented by annual mean temperature and soil moisture emerged as the primary factors influencing the variation in wood density globally. This indicates similar environmental filters and evolutionary adaptations among distinct plant groups, underscoring the essential role of abiotic factors in determining wood density in forest ecosystems. Additionally, our study highlights the prominent role of disturbance, such as human modification and fire risk, in influencing wood density at more local scales. Factoring in the spatial variation of wood density notably changes the estimates of forest carbon stocks, leading to differences of up to 21% within biomes. Therefore, our research contributes to a deeper understanding of terrestrial biomass distribution and how environmental changes and disturbances impact forest ecosystems., (© 2024. The Author(s).)

Countries within the tropics face ongoing challenges in completing or updating their national forest inventories (NFIs), critical for estimating aboveground biomass (AGB) and for forest-related greenhouse gas (GHG) accounting. While previous studies have explored the integration of map information with local reference data to fill in data gaps, limited attention has been given to the specific challenges presented by the clustered plot designs frequently employed by NFIs when combined with remote sensing-based biomass map units. This research addresses these complexities by conducting four country case-studies, encompassing a variety of NFI characteristics within a range of AGB densities. Examining four country case-studies (Peru, Guyana, Tanzania, Mozambique), we assess the potential of European Space Agency's Climate Change Initiative (CCI) global biomass maps to increase precision in (sub)national AGB estimates. We compare a baseline approach using NFI field-based data with a model-assisted scenario incorporating a locally calibrated CCI biomass map as auxiliary information. The original CCI biomass maps systematically underestimate AGB in three of the four countries at both the country and stratum level, with particularly weak agreement at finer map resolution. However, after calibration with country-specific NFI data, stratum and country-level AGB estimates from the model-assisted scenario align well with those obtained solely from field-based data and official country reports. Introducing maps as a source of auxiliary information fairly increased the precision of stratum and country-wise AGB estimates, offering greater confidence in estimating AGB for GHG reporting purposes. Considering the challenges tropical countries face with implementing their NFIs, it is sensible to explore the potential benefits of biomass maps for climate change reporting mechanisms across biomes. While country-specific NFI design assumptions guided our model-assisted inference strategies, this study also uncovers transferable insights from the application of global biomass maps with NFI data, providing valuable lessons for climate research and policy communities., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Aboveground biomass density (AGBD) estimates from Earth Observation (EO) can be presented with the consistency standards mandated by United Nations Framework Convention on Climate Change (UNFCCC). This article delivers AGBD estimates, in the format of Intergovernmental Panel on Climate Change (IPCC) Tier 1 values for natural forests, sourced from National Aeronautics and Space Administration's (NASA's) Global Ecosystem Dynamics Investigation (GEDI) and Ice, Cloud and land Elevation Satellite (ICESat-2), and European Space Agency's (ESA's) Climate Change Initiative (CCI). It also provides the underlying classification used by the IPCC as geospatial layers, delineating global forests by ecozones, continents and status (primary, young (≤20 years) and old secondary (>20 years)). The approaches leverage complementary strengths of various EO-derived datasets that are compiled in an open-science framework through the Multi-mission Algorithm and Analysis Platform (MAAP). This transparency and flexibility enables the adoption of any new incoming datasets in the framework in the future. The EO-based AGBD estimates are expected to be an independent contribution to the IPCC Emission Factors Database in support of UNFCCC processes, and the forest classification expected to support the generation of other policy-relevant datasets while reflecting ongoing shifts in global forests with climate change., (© 2024. The Author(s).)

The article presents results of using remote sensing images and machine learning to map and assess land potential based on time-series of potential Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) composites. Land potential here refers to the potential vegetation productivity in the hypothetical absence of short-term anthropogenic influence, such as intensive agriculture and urbanization. Knowledge on this ecological land potential could support the assessment of levels of land degradation as well as restoration potentials. Monthly aggregated FAPAR time-series of three percentiles (0.05, 0.50 and 0.95 probability) at 250 m spatial resolution were derived from the 8-day GLASS FAPAR V6 product for 2000-2021 and used to determine long-term trends in FAPAR, as well as to model potential FAPAR in the absence of human pressure. CCa 3 million training points sampled from 12,500 locations across the globe were overlaid with 68 bio-physical variables representing climate, terrain, landform, and vegetation cover, as well as several variables representing human pressure including: population count, cropland intensity, nightlights and a human footprint index. The training points were used in an ensemble machine learning model that stacks three base learners (extremely randomized trees, gradient descended trees and artificial neural network) using a linear regressor as meta-learner. The potential FAPAR was then projected by removing the impact of urbanization and intensive agriculture in the covariate layers. The results of strict cross-validation show that the global distribution of FAPAR can be explained with an R 2 of 0.89, with the most important covariates being growing season length, forest cover indicator and annual precipitation. From this model, a global map of potential monthly FAPAR for the recent year (2021) was produced, and used to predict gaps in actual vs . potential FAPAR. The produced global maps of actual vs . potential FAPAR and long-term trends were each spatially matched with stable and transitional land cover classes. The assessment showed large negative FAPAR gaps (actual lower than potential) for classes: urban, needle-leave deciduous trees, and flooded shrub or herbaceous cover, while strong negative FAPAR trends were found for classes: urban, sparse vegetation and rainfed cropland. On the other hand, classes: irrigated or post-flooded cropland, tree cover mixed leaf type, and broad-leave deciduous showed largely positive trends. The framework allows land managers to assess potential land degradation from two aspects: as an actual declining trend in observed FAPAR and as a difference between actual and potential vegetation FAPAR., Competing Interests: Julia Hackländer, Leandro Parente, Yu-Feng Ho, Tomislav Hengl, Rolf Simoes, Davide Consoli, Murat Şahin, Xuemeng Tian & Ichsani Wheeler are employed by OpenGeoHub., (© 2024 Hackländer et al.)

African forest are increasingly in decline as a result of land-use conversion due to human activities. However, a consistent and detailed characterization and mapping of land-use change that results in forest loss is not available at the spatial-temporal resolution and thematic levels suitable for decision-making at the local and regional scales; so far they have only been provided on coarser scales and restricted to humid forests. Here we present the first high-resolution (5 m) and continental-scale mapping of land use following deforestation in Africa, which covers an estimated 13.85% of the global forest area, including humid and dry forests. We use reference data for 15 different land-use types from 30 countries and implement an active learning framework to train a deep learning model for predicting land-use following deforestation with an F1-score of [Formula: see text] for the whole of Africa. Our results show that the causes of forest loss vary by region. In general, small-scale cropland is the dominant driver of forest loss in Africa, with hotspots in Madagascar and DRC. In addition, commodity crops such as cacao, oil palm, and rubber are the dominant drivers of forest loss in the humid forests of western and central Africa, forming an "arc of commodity crops" in that region. At the same time, the hotspots for cashew are found to increasingly dominate in the dry forests of both western and south-eastern Africa, while larger hotspots for large-scale croplands were found in Nigeria and Zambia. The increased expansion of cacao, cashew, oil palm, rubber, and large-scale croplands observed in humid and dry forests of western and south-eastern Africa suggests they are vulnerable to future land-use changes by commodity crops, thus creating challenges for achieving the zero deforestation supply chains, support REDD+ initiatives, and towards sustainable development goals., (© 2024. The Author(s).)

Forests are a substantial terrestrial carbon sink, but anthropogenic changes in land use and climate have considerably reduced the scale of this system 1 . Remote-sensing estimates to quantify carbon losses from global forests 2-5 are characterized by considerable uncertainty and we lack a comprehensive ground-sourced evaluation to benchmark these estimates. Here we combine several ground-sourced 6 and satellite-derived approaches 2,7,8 to evaluate the scale of the global forest carbon potential outside agricultural and urban lands. Despite regional variation, the predictions demonstrated remarkable consistency at a global scale, with only a 12% difference between the ground-sourced and satellite-derived estimates. At present, global forest carbon storage is markedly under the natural potential, with a total deficit of 226 Gt (model range = 151-363 Gt) in areas with low human footprint. Most (61%, 139 Gt C) of this potential is in areas with existing forests, in which ecosystem protection can allow forests to recover to maturity. The remaining 39% (87 Gt C) of potential lies in regions in which forests have been removed or fragmented. Although forests cannot be a substitute for emissions reductions, our results support the idea 2,3,9 that the conservation, restoration and sustainable management of diverse forests offer valuable contributions to meeting global climate and biodiversity targets., (© 2023. The Author(s).)

Background: The application of different approaches calculating the anthropogenic carbon net flux from land, leads to estimates that vary considerably. One reason for these variations is the extent to which approaches consider forest land to be "managed" by humans, and thus contributing to the net anthropogenic flux. Global Earth Observation (EO) datasets characterising spatio-temporal changes in land cover and carbon stocks provide an independent and consistent approach to estimate forest carbon fluxes. These can be compared against results reported in National Greenhouse Gas Inventories (NGHGIs) to support accurate and timely measuring, reporting and verification (MRV). Using Brazil as a primary case study, with additional analysis in Indonesia and Malaysia, we compare a Global EO-based dataset of forest carbon fluxes to results reported in NGHGIs., Results: Between 2001 and 2020, the EO-derived estimates of all forest-related emissions and removals indicate that Brazil was a net sink of carbon (- 0.2 GtCO 2 yr -1 ), while Brazil's NGHGI reported a net carbon source (+ 0.8 GtCO 2 yr -1 ). After adjusting the EO estimate to use the Brazilian NGHGI definition of managed forest and other assumptions used in the inventory's methodology, the EO net flux became a source of + 0.6 GtCO 2 yr -1 , comparable to the NGHGI. Remaining discrepancies are due largely to differing carbon removal factors and forest types applied in the two datasets. In Indonesia, the EO and NGHGI net flux estimates were similar (+ 0.6 GtCO 2 yr -1 ), but in Malaysia, they differed in both magnitude and sign (NGHGI: -0.2 GtCO 2 yr -1 ; Global EO: + 0.2 GtCO 2 yr -1 ). Spatially explicit datasets on forest types were not publicly available for analysis from either NGHGI, limiting the possibility of detailed adjustments., Conclusions: By adjusting the EO dataset to improve comparability with carbon fluxes estimated for managed forests in the Brazilian NGHGI, initially diverging estimates were largely reconciled and remaining differences can be explained. Despite limited spatial data available for Indonesia and Malaysia, our comparison indicated specific aspects where differing approaches may explain divergence, including uncertainties and inaccuracies. Our study highlights the importance of enhanced transparency, as set out by the Paris Agreement, to enable alignment between different approaches for independent measuring and verification., (© 2023. The Author(s).)

Understanding what controls global leaf type variation in trees is crucial for comprehending their role in terrestrial ecosystems, including carbon, water and nutrient dynamics. Yet our understanding of the factors influencing forest leaf types remains incomplete, leaving us uncertain about the global proportions of needle-leaved, broadleaved, evergreen and deciduous trees. To address these gaps, we conducted a global, ground-sourced assessment of forest leaf-type variation by integrating forest inventory data with comprehensive leaf form (broadleaf vs needle-leaf) and habit (evergreen vs deciduous) records. We found that global variation in leaf habit is primarily driven by isothermality and soil characteristics, while leaf form is predominantly driven by temperature. Given these relationships, we estimate that 38% of global tree individuals are needle-leaved evergreen, 29% are broadleaved evergreen, 27% are broadleaved deciduous and 5% are needle-leaved deciduous. The aboveground biomass distribution among these tree types is approximately 21% (126.4 Gt), 54% (335.7 Gt), 22% (136.2 Gt) and 3% (18.7 Gt), respectively. We further project that, depending on future emissions pathways, 17-34% of forested areas will experience climate conditions by the end of the century that currently support a different forest type, highlighting the intensification of climatic stress on existing forests. By quantifying the distribution of tree leaf types and their corresponding biomass, and identifying regions where climate change will exert greatest pressure on current leaf types, our results can help improve predictions of future terrestrial ecosystem functioning and carbon cycling., (© 2023. The Author(s).)

Climate change and increasing human activities are impacting ecosystems and their biodiversity. Quantitative measurements of essential biodiversity variables (EBV) and essential climate variables are used to monitor biodiversity and carbon dynamics and evaluate policy and management interventions. Ecosystem structure is at the core of EBVs and carbon stock estimation and can help to inform assessments of species and species diversity. Ecosystem structure is also used as an indirect indicator of habitat quality and expected species richness or species community composition. Spaceborne measurements can provide large-scale insight into monitoring the structural dynamics of ecosystems, but they generally lack consistent, robust, timely and detailed information regarding their full three-dimensional vegetation structure at local scales. Here we demonstrate the potential of high-frequency ground-based laser scanning to systematically monitor structural changes in vegetation. We present a proof-of-concept high-temporal ecosystem structure time series of 5 years in a temperate forest using terrestrial laser scanning (TLS). We also present data from automated high-temporal laser scanning that can allow upscaling of vegetation structure scanning, overcoming the limitations of a typically opportunistic TLS measurement approach. Automated monitoring will be a critical component to build a network of field monitoring sites that can provide the required calibration data for satellite missions to effectively monitor the structural dynamics of vegetation over large areas. Within this perspective, we reflect on how this network could be designed and discuss implementation pathways., Competing Interests: The authors declare no known conflicts of interest. DC is employed by Environmental Sensing Systems and developed the LEAF sensors. The manuscript discloses that essentially any instrument that fits the operation and data criteria can be used in StrucNet., (© 2023 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London.)

Determining the drivers of non-native plant invasions is critical for managing native ecosystems and limiting the spread of invasive species 1,2 . Tree invasions in particular have been relatively overlooked, even though they have the potential to transform ecosystems and economies 3,4 . Here, leveraging global tree databases 5-7 , we explore how the phylogenetic and functional diversity of native tree communities, human pressure and the environment influence the establishment of non-native tree species and the subsequent invasion severity. We find that anthropogenic factors are key to predicting whether a location is invaded, but that invasion severity is underpinned by native diversity, with higher diversity predicting lower invasion severity. Temperature and precipitation emerge as strong predictors of invasion strategy, with non-native species invading successfully when they are similar to the native community in cold or dry extremes. Yet, despite the influence of these ecological forces in determining invasion strategy, we find evidence that these patterns can be obscured by human activity, with lower ecological signal in areas with higher proximity to shipping ports. Our global perspective of non-native tree invasion highlights that human drivers influence non-native tree presence, and that native phylogenetic and functional diversity have a critical role in the establishment and spread of subsequent invasions., (© 2023. The Author(s).)

Countries have pledged to different national and international environmental agreements, most prominently the climate change mitigation targets of the Paris Agreement. Accounting for carbon stocks and flows (fluxes) is essential for countries that have recently adopted the United Nations System of Environmental-Economic Accounting - ecosystem accounting framework (UNSEEA) as a global statistical standard. In this paper, we analyze how spatial carbon fluxes can be used in support of the UNSEEA carbon accounts in five case countries with available in-situ data. Using global multi-date biomass map products and other remotely sensed data, we mapped the 2010-2018 carbon fluxes in Brazil, the Netherlands, the Philippines, Sweden and the USA using National Forest Inventory (NFI) and local biomass maps from airborne LiDAR as reference data. We identified areas that are unsupported by the reference data within environmental feature space (6-47% of vegetated country area); cross-validated an ensemble machine learning (RMSE=9-39 Mg C [Formula: see text] and [Formula: see text]=0.16-0.71) used to map carbon fluxes with prediction intervals; and assessed spatially correlated residuals (<5 km) before aggregating carbon fluxes from 1-ha pixels to UNSEEA forest classes. The resulting carbon accounting tables revealed the net carbon sequestration in natural broadleaved forests. Both in plantations and in other woody vegetation ecosystems, emissions exceeded sequestration. Overall, our estimates align with FAO-Forest Resource Assessment and national studies with the largest deviations in Brazil and USA. These two countries used highly clustered reference data, where clustering caused uncertainty given the need to extrapolate to under-sampled areas. We finally provide recommendations to mitigate the effect of under-sampling and to better account for the uncertainties once carbon stocks and flows need to be aggregated in relatively smaller countries. These actions are timely given the global initiatives that aim to upscale UNSEEA carbon accounting., (© 2023. Springer Nature Limited.)

Planted forests are critical to climate change mitigation and constitute a major supplier of timber/non-timber products and other ecosystem services. Globally, approximately 36% of planted forest area is located in East Asia. However, reliable records of the geographic distribution and tree species composition of these planted forests remain very limited. Here, based on extensive in situ and remote sensing data, as well as an ensemble modeling approach, we present the first spatial database of planted forests for East Asia, which consists of maps of the geographic distribution of planted forests and associated dominant tree genera. Of the predicted planted forest areas in East Asia (948,863 km 2 ), China contributed 87%, most of which is located in the lowland tropical/subtropical regions, and Sichuan Basin. With 95% accuracy and an F1 score of 0.77, our spatially-continuous maps of planted forests enable accurate quantification of the role of planted forests in climate change mitigation. Our findings inform effective decision-making in forest conservation, management, and global restoration projects., (© 2023. The Author(s).)

Amazonian forests function as biomass and biodiversity reservoirs, contributing to climate change mitigation. While they continuously experience disturbance, the effect that disturbances have on biomass and biodiversity over time has not yet been assessed at a large scale. Here, we evaluate the degree of recent forest disturbance in Peruvian Amazonia and the effects that disturbance, environmental conditions and human use have on biomass and biodiversity in disturbed forests. We integrate tree-level data on aboveground biomass (AGB) and species richness from 1840 forest plots from Peru's National Forest Inventory with remotely sensed monitoring of forest change dynamics, based on disturbances detected from Landsat-derived Normalized Difference Moisture Index time series. Our results show a clear negative effect of disturbance intensity tree species richness. This effect was also observed on AGB and species richness recovery values towards undisturbed levels, as well as on the recovery of species composition towards undisturbed levels. Time since disturbance had a larger effect on AGB than on species richness. While time since disturbance has a positive effect on AGB, unexpectedly we found a small negative effect of time since disturbance on species richness. We estimate that roughly 15% of Peruvian Amazonian forests have experienced disturbance at least once since 1984, and that, following disturbance, have been increasing in AGB at a rate of 4.7 Mg ha -1  year -1 during the first 20 years. Furthermore, the positive effect of surrounding forest cover was evident for both AGB and its recovery towards undisturbed levels, as well as for species richness. There was a negative effect of forest accessibility on the recovery of species composition towards undisturbed levels. Moving forward, we recommend that forest-based climate change mitigation endeavours consider forest disturbance through the integration of forest inventory data with remote sensing methods., (© 2023 The Authors. Global Change Biology published by John Wiley & Sons Ltd.)

Multiple sclerosis (MS) is a focal inflammatory and demyelinating disease. The inflammatory infiltrates consist of macrophages/microglia, T and B cells. Remyelination (RM) is an endogenous repair process which frequently fails in MS patients. In earlier studies, T cells either promoted or impaired RM. Here, we used the combined cuprizone/MOG-EAE model to further dissect the functional role of T cells for RM. The combination of MOG immunization with cuprizone feeding targeted T cells to the corpus callosum and increased the extent of axonal injury. Global gene expression analyses demonstrated significant changes in the inflammatory environment; however, additional MOG immunization did not alter the course of RM. Our results suggest that the inflammatory environment in the combined model affects axons and oligodendrocytes differently and that oligodendroglial lineage cells might be less susceptible to T cell mediated injury., (© 2022 The Authors. GLIA published by Wiley Periodicals LLC.)

Space-based remote sensing can make an important contribution toward monitoring greenhouse gas emissions and removals from the agriculture, forestry, and other land use (AFOLU) sector, and to understanding and addressing human-caused climate change through the UNFCCC Paris Agreement. Space agencies have begun to coordinate their efforts to identify needs, collect and harmonize available data and efforts, and plan and maintain a long-term roadmap for observations. International cooperation is crucial in developing and realizing the roadmap, and the Committee on Earth Observation Satellites (CEOS) is a key coordinating driver of this effort. Here, we first identify the data and information that will be useful to support the global stocktake (GST) of the Paris Agreement. Then, the paper explains how existing and planned space-based capabilities and products can be used and combined, particularly in the land use sector, and provides a workflow for their harmonization and contribution to greenhouse gas inventories and assessments at the national and global level., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Author(s).)

Forests contribute to climate change mitigation through carbon storage and uptake, but the extent to which this carbon pool varies in space and time is still poorly known. Several Earth Observation missions have been specifically designed to address this issue, for example, NASA's GEDI, NASA-ISRO's NISAR and ESA's BIOMASS. Yet, all these missions' products require independent and consistent validation. A permanent, global, in situ, site-based forest biomass reference measurement system relying on ground data of the highest possible quality is therefore needed. Here, we have assembled a list of almost 200 high-quality sites through an in-depth review of the literature and expert knowledge. In this study, we explore how representative these sites are in terms of their coverage of environmental conditions, geographical space and biomass-related forest structure, compared to those experienced by forests worldwide. This work also aims at identifying which sites are the most representative, and where to invest to improve the representativeness of the proposed system. We show that the environmental coverage of the system does not seem to improve after at least the 175 most representative sites are included, but geographical and structural coverages continue to improve as more sites are added. We highlight the areas of poor environmental, geographical, or structural coverage, including, but not limited to, Canada, the western half of the USA, Mexico, Patagonia, Angola, Zambia, eastern Russia, and tropical and subtropical highlands (e.g. in Colombia, the Himalayas, Borneo, Papua). For the proposed system to succeed, we stress that (1) data must be collected and processed applying the same standards across all countries and continents; (2) system establishment and management must be inclusive and equitable, with careful consideration of working conditions; and (3) training and site partner involvement in downstream activities should be mandatory., (© 2022 The Authors. Global Change Biology published by John Wiley & Sons Ltd.)

Background: Atrial fibrillation (AF) affects 46.3 million people; its prevalence has tripled over the last 50 years. AF leads to formation of blood clots increasing four-fold the risk of a stroke. Preventive anticoagulant therapy with warfarin has been well established for over 50 years but has efficacy and safety limitations. New anticoagulants do not require laboratory monitoring of prothrombin time, have low risk of adverse events, yet are more costly., Methods: This non-interventional (Act 378/2007 Coll.) retrospective-prospective single-arm cohort study consisted of 3 visits. The primary objective was to compare the total direct cost of treatment with warfarin and apixaban. Patients with non-valvular AF were enrolled at the time of discontinuation of warfarin and switching to apixaban. Costs were derived from the care provided and the list of medical procedures (Decrees 268/ 2019 Coll.). Satisfaction was assessed using SAFUCA® questionnaire., Results: Between February 2017 and June 2019, 499 patients were enrolled in 29 Czech internal medicine clinics. The mean age of the patients was 73.6 ± 10.2 years, 36.5% were at high risk of bleeding (HAS-BLED score). Previous warfarin treatment lasted 5.9 ± 2.7 months, 63% were unable to achieve target prothrombin time, 18% switched due to adverse reactions. New apixaban treatment was followed for the first 6 months. Treatment with warfarin was associated with higher rates of major bleeding and adverse events (22 vs. 2), stroke (17 vs. 0), ischemic heart attack (11 vs. 0), and minor bleeding (173 vs. 2). The average daily cost following the switch to apixaban decreased from CZK 65.2 to CZK 4.8 (p.

National forest inventories (NFIs) are a reliable source for national forest measurements. However, they are usually not developed for linking with remotely sensed (RS) biomass information. There are increasing needs and opportunities to facilitate this link towards better global and national biomass estimation. Thus, it is important to study and understand NFI characteristics relating to their integration with space-based products; in particular for the tropics where NFIs are quite recent, less frequent, and partially incomplete in several countries. Here, we (1) assessed NFIs in terms of their availability, temporal distribution, and extent in 236 countries from FAO's Global Forest Resources Assessment (FRA) 2020; (2) compared national forest biomass estimates in 2018 from FRA and global space-based Climate Change Initiative (CCI) product in 182 countries considering NFI availability and temporality; and (3) analyzed the latest NFI design characteristics in 46 tropical countries relating to their integration with space-based biomass datasets. We observed significant NFI availability globally and multiple NFIs were mostly found in temperate and boreal countries while most of the single NFI countries (94 %) were in the tropics. The latest NFIs were more recent in the tropics and many countries (35) implemented NFIs from 2016 onwards. The increasing availability and update of NFIs create new opportunities for integration with space-based data at the national level. This is supported by the agreement we found between country biomass estimates for 2018 from FRA and CCI product, with a significantly higher correlation in countries with recent NFIs. We observed that NFI designs varied greatly in tropical countries. For example, the size of the plots ranged from 0.01 to 1 ha and more than three-quarters of the countries had smaller plots of ≤0.25 ha. The existing NFI designs could pose specific challenges for statistical integration with RS data in the tropics. Future NFI and space-based efforts should aim towards a more integrated approach taking advantage of both data streams to improve national estimates and help future data harmonization efforts. Regular NFI efforts can be expanded with the inclusion of some super-site plots to enhance data integration with currently available space-based applications. Issues related to cost implications versus improvements in the accuracy, timeliness, and sustainability of national forest biomass estimation should be further explored., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

The latitudinal diversity gradient (LDG) is one of the most recognized global patterns of species richness exhibited across a wide range of taxa. Numerous hypotheses have been proposed in the past two centuries to explain LDG, but rigorous tests of the drivers of LDGs have been limited by a lack of high-quality global species richness data. Here we produce a high-resolution (0.025° × 0.025°) map of local tree species richness using a global forest inventory database with individual tree information and local biophysical characteristics from ~1.3 million sample plots. We then quantify drivers of local tree species richness patterns across latitudes. Generally, annual mean temperature was a dominant predictor of tree species richness, which is most consistent with the metabolic theory of biodiversity (MTB). However, MTB underestimated LDG in the tropics, where high species richness was also moderated by topographic, soil and anthropogenic factors operating at local scales. Given that local landscape variables operate synergistically with bioclimatic factors in shaping the global LDG pattern, we suggest that MTB be extended to account for co-limitation by subordinate drivers., (© 2022. The Author(s), under exclusive licence to Springer Nature Limited.)

Tropical deforestation continues at alarming rates with profound impacts on ecosystems, climate, and livelihoods, prompting renewed commitments to halt its continuation. Although it is well established that agriculture is a dominant driver of deforestation, rates and mechanisms remain disputed and often lack a clear evidence base. We synthesize the best available pantropical evidence to provide clarity on how agriculture drives deforestation. Although most (90 to 99%) deforestation across the tropics 2011 to 2015 was driven by agriculture, only 45 to 65% of deforested land became productive agriculture within a few years. Therefore, ending deforestation likely requires combining measures to create deforestation-free supply chains with landscape governance interventions. We highlight key remaining evidence gaps including deforestation trends, commodity-specific land-use dynamics, and data from tropical dry forests and forests across Africa.

This article describes a data-driven framework based on spatiotemporal machine learning to produce distribution maps for 16 tree species ( Abies alba Mill., Castanea sativa Mill., Corylus avellana L., Fagus sylvatica L., Olea europaea L., Picea abies L. H. Karst., Pinus halepensis Mill., Pinus nigra J. F. Arnold, Pinus pinea L., Pinus sylvestris L., Prunus avium L., Quercus cerris L., Quercus ilex L., Quercus robur L., Quercus suber L. and Salix caprea L.) at high spatial resolution (30 m). Tree occurrence data for a total of three million of points was used to train different algorithms: random forest, gradient-boosted trees, generalized linear models, k-nearest neighbors, CART and an artificial neural network. A stack of 305 coarse and high resolution covariates representing spectral reflectance, different biophysical conditions and biotic competition was used as predictors for realized distributions, while potential distribution was modelled with environmental predictors only. Logloss and computing time were used to select the three best algorithms to tune and train an ensemble model based on stacking with a logistic regressor as a meta-learner. An ensemble model was trained for each species: probability and model uncertainty maps of realized distribution were produced for each species using a time window of 4 years for a total of six distribution maps per species, while for potential distributions only one map per species was produced. Results of spatial cross validation show that the ensemble model consistently outperformed or performed as good as the best individual model in both potential and realized distribution tasks, with potential distribution models achieving higher predictive performances (TSS = 0.898, R 2 logloss = 0.857) than realized distribution ones on average (TSS = 0.874, R 2 logloss = 0.839). Ensemble models for Q. suber achieved the best performances in both potential (TSS = 0.968, R 2 logloss = 0.952) and realized (TSS = 0.959, R 2 logloss = 0.949) distribution, while P. sylvestris (TSS = 0.731, 0.785, R 2 logloss = 0.585, 0.670, respectively, for potential and realized distribution) and P. nigra (TSS = 0.658, 0.686, R 2 logloss = 0.623, 0.664) achieved the worst. Importance of predictor variables differed across species and models, with the green band for summer and the Normalized Difference Vegetation Index (NDVI) for fall for realized distribution and the diffuse irradiation and precipitation of the driest quarter (BIO17) being the most frequent and important for potential distribution. On average, fine-resolution models outperformed coarse resolution models (250 m) for realized distribution (TSS = +6.5%, R 2 logloss = +7.5%). The framework shows how combining continuous and consistent Earth Observation time series data with state of the art machine learning can be used to derive dynamic distribution maps. The produced predictions can be used to quantify temporal trends of potential forest degradation and species composition change., Competing Interests: The authors declare that they have no competing interests. Carmelo Bonannella, Tomislav Hengl and Leandro Parente declare that they are officially employed by OpenGeoHub., (© 2022 Bonannella et al.)

One of the most fundamental questions in ecology is how many species inhabit the Earth. However, due to massive logistical and financial challenges and taxonomic difficulties connected to the species concept definition, the global numbers of species, including those of important and well-studied life forms such as trees, still remain largely unknown. Here, based on global ground-sourced data, we estimate the total tree species richness at global, continental, and biome levels. Our results indicate that there are ∼73,000 tree species globally, among which ∼9,000 tree species are yet to be discovered. Roughly 40% of undiscovered tree species are in South America. Moreover, almost one-third of all tree species to be discovered may be rare, with very low populations and limited spatial distribution (likely in remote tropical lowlands and mountains). These findings highlight the vulnerability of global forest biodiversity to anthropogenic changes in land use and climate, which disproportionately threaten rare species and thus, global tree richness., Competing Interests: Competing interest statement: M.L.B. is an employee of PNAS., (Copyright © 2022 the Author(s). Published by PNAS.)

Tropical forests store 40-50 per cent of terrestrial vegetation carbon 1 . However, spatial variations in aboveground live tree biomass carbon (AGC) stocks remain poorly understood, in particular in tropical montane forests 2 . Owing to climatic and soil changes with increasing elevation 3 , AGC stocks are lower in tropical montane forests compared with lowland forests 2 . Here we assemble and analyse a dataset of structurally intact old-growth forests (AfriMont) spanning 44 montane sites in 12 African countries. We find that montane sites in the AfriMont plot network have a mean AGC stock of 149.4 megagrams of carbon per hectare (95% confidence interval 137.1-164.2), which is comparable to lowland forests in the African Tropical Rainforest Observation Network 4 and about 70 per cent and 32 per cent higher than averages from plot networks in montane 2,5,6 and lowland 7 forests in the Neotropics, respectively. Notably, our results are two-thirds higher than the Intergovernmental Panel on Climate Change default values for these forests in Africa 8 . We find that the low stem density and high abundance of large trees of African lowland forests 4 is mirrored in the montane forests sampled. This carbon store is endangered: we estimate that 0.8 million hectares of old-growth African montane forest have been lost since 2000. We provide country-specific montane forest AGC stock estimates modelled from our plot network to help to guide forest conservation and reforestation interventions. Our findings highlight the need for conserving these biodiverse 9,10 and carbon-rich ecosystems., (© 2021. The Author(s), under exclusive licence to Springer Nature Limited.)

Quantifying the dynamics of land use change is critical in tackling global societal challenges such as food security, climate change, and biodiversity loss. Here we analyse the dynamics of global land use change at an unprecedented spatial resolution by combining multiple open data streams (remote sensing, reconstructions and statistics) to create the HIstoric Land Dynamics Assessment + (HILDA +). We estimate that land use change has affected almost a third (32%) of the global land area in just six decades (1960-2019) and, thus, is around four times greater in extent than previously estimated from long-term land change assessments. We also identify geographically diverging land use change processes, with afforestation and cropland abandonment in the Global North and deforestation and agricultural expansion in the South. Here, we show that observed phases of accelerating (~1960-2005) and decelerating (2006-2019) land use change can be explained by the effects of global trade on agricultural production.

A close interaction between gut immune responses and distant organ-specific autoimmunity including the CNS in multiple sclerosis has been established in recent years. This so-called gut-CNS axis can be shaped by dietary factors, either directly or via indirect modulation of the gut microbiome and its metabolites. Here, we report that dietary supplementation with conjugated linoleic acid, a mixture of linoleic acid isomers, ameliorates CNS autoimmunity in a spontaneous mouse model of multiple sclerosis, accompanied by an attenuation of intestinal barrier dysfunction and inflammation as well as an increase in intestinal myeloid-derived suppressor-like cells. Protective effects of dietary supplementation with conjugated linoleic acid were not abrogated upon microbiota eradication, indicating that the microbiome is dispensable for these conjugated linoleic acid-mediated effects. Instead, we observed a range of direct anti-inflammatory effects of conjugated linoleic acid on murine myeloid cells including an enhanced IL10 production and the capacity to suppress T-cell proliferation. Finally, in a human pilot study in patients with multiple sclerosis (n = 15, under first-line disease-modifying treatment), dietary conjugated linoleic acid-supplementation for 6 months significantly enhanced the anti-inflammatory profiles as well as functional signatures of circulating myeloid cells. Together, our results identify conjugated linoleic acid as a potent modulator of the gut-CNS axis by targeting myeloid cells in the intestine, which in turn control encephalitogenic T-cell responses., (© The Author(s) (2021). Published by Oxford University Press on behalf of the Guarantors of Brain.)

Multiple sclerosis (MS) is the most frequent demyelinating disease in young adults and despite significant advances in immunotherapy, disease progression still cannot be prevented. Promotion of remyelination, an endogenous repair mechanism resulting in the formation of new myelin sheaths around demyelinated axons, represents a promising new treatment approach. However, remyelination frequently fails in MS lesions, which can in part be attributed to impaired differentiation of oligodendroglial progenitor cells into mature, myelinating oligodendrocytes. The reasons for impaired oligodendroglial differentiation and defective remyelination in MS are currently unknown. To determine whether intrinsic oligodendroglial factors contribute to impaired remyelination in relapsing-remitting MS (RRMS), we compared induced pluripotent stem cell-derived oligodendrocytes (hiOL) from RRMS patients and controls, among them two monozygous twin pairs discordant for MS. We found that hiOL from RRMS patients and controls were virtually indistinguishable with respect to remyelination-associated functions and proteomic composition. However, while analyzing the effect of extrinsic factors we discovered that supernatants of activated peripheral blood mononuclear cells (PBMCs) significantly inhibit oligodendroglial differentiation. In particular, we identified CD4 + T cells as mediators of impaired oligodendroglial differentiation; at least partly due to interferon-gamma secretion. Additionally, we observed that blocked oligodendroglial differentiation induced by PBMC supernatants could not be restored by application of oligodendroglial differentiation promoting drugs, whereas treatment of PBMCs with the immunomodulatory drug teriflunomide prior to supernatant collection partly rescued oligodendroglial differentiation. In summary, these data indicate that the oligodendroglial differentiation block is not due to intrinsic oligodendroglial factors but rather caused by the inflammatory environment in RRMS lesions which underlines the need for drug screening approaches taking the inflammatory environment into account. Combined, these findings may contribute to the development of new remyelination promoting strategies.

The continued increase of anthropogenic pressure on the Earth's ecosystems is degrading the natural environment and then decreasing the services it provides to humans. The type, quantity, and quality of many of those services are directly connected to land cover, yet competing demands for land continue to drive rapid land cover change, affecting ecosystem services. Accurate and updated land cover information is thus more important than ever, however, despite its importance, the needs of many users remain only partially attended. A key underlying reason for this is that user needs vary widely, since most current products - and there are many available - are produced for a specific type of end user, for example the climate modelling community. With this in mind we focus on the need for flexible, automated processing approaches that support on-demand, customized land cover products at various scales. Although land cover processing systems are gradually evolving in this direction there is much more to do and several important challenges must be addressed, including high quality reference data for training and validation and even better access to satellite data. Here, we 1) present a generic system architecture that we suggest land cover production systems evolve towards, 2) discuss the challenges involved, and 3) propose a step forward. Flexible systems that can generate on-demand products that match users' specific needs would fundamentally change the relationship between users and land cover products - requiring more government support to make these systems a reality., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2020 The Authors.)

The European Space Agency (ESA)'s Sentinel-2A (S2A) mission is providing time series that allow the characterisation of dynamic vegetation, especially when combined with the National Aeronautics and Space Administration (NASA)/United States Geological Survey (USGS) Landsat 7 (L7) and Landsat 8 (L8) missions. Hybrid retrieval workflows combining non-parametric Machine Learning Regression Algorithms (MLRAs) and vegetation Radiative Transfer Models (RTMs) were proposed as fast and accurate methods to infer biophysical parameters such as Leaf Area Index (LAI) from these data streams. However, the exact design of optimal retrieval workflows is rarely discussed. In this study, the impact of five retrieval workflow features on LAI prediction performance of MultiSpectral Instrument (MSI), Enhanced Thematic Mapper Plus (ETM+) and Operational Land Imager (OLI) observations was analysed over a Dutch beech forest site for a one-year period. The retrieval workflow features were the (1) addition of prior knowledge of leaf chemistry (two alternatives), (2) the choice of RTM (two alternatives), (3) the addition of Gaussian noise to RTM produced training data (four and five alternatives), (4) possibility of using Sun Zenith Angle (SZA) as an additional MLRA training feature (two alternatives), and (5) the choice of MLRA (six alternatives). The features were varied in a full grid resulting in 960 inversion models in order to find the overall impact on performance as well as possible interactions among the features. A combination of a Terrestrial Laser Scanning (TLS) time series with litter-trap derived LAI served as independent validation. The addition of absolute noise had the most significant impact on prediction performance. It improved the median prediction Root Mean Square Error (RMSE) by 1.08 m 2 m -2 when 5 % noise was added compared to inversions with 0 % absolute noise. The choice of the MLRA was second most important in terms of median prediction performance, which differed by 0.52 m 2 m -2 between the best and worst model. The best inversion model achieved an RMSE of 0.91 m 2 m -2 and explained 84.9% of the variance of the reference time series. The results underline the need to explicitly describe the used noise model in future studies. Similar studies should be conducted in other study areas, both forest and crop systems, in order to test the noise model as an integral part of hybrid retrieval workflows., Competing Interests: Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Sub-Saharan Africa (SSA) could face food shortages in the future because of its growing population. Agricultural expansion causes forest degradation in SSA through livestock grazing, reducing forest carbon (C) sinks and increasing greenhouse gas (GHG) emissions. Therefore, intensification should produce more food while reducing pressure on forests. This study assessed the potential for the dairy sector in Kenya to contribute to low-emissions development by exploring three feeding scenarios. The analyses used empirical spatially explicit data, and a simulation model to quantify milk production, agricultural emissions and forest C loss due to grazing. The scenarios explored improvements in forage quality (Fo), feed conservation (Fe) and concentrate supplementation (Co): FoCo fed high-quality Napier grass (Pennisetum purpureum), FeCo supplemented maize silage and FoFeCo a combination of Napier, silage and concentrates. Land shortages and forest C loss due to grazing were quantified with land requirements and feed availability around forests. All scenarios increased milk yields by 44%-51%, FoCo reduced GHG emission intensity from 2.4 ± 0.1 to 1.6 ± 0.1 kg CO 2 eq per kg milk, FeCo reduced it to 2.2 ± 0.1, whereas FoFeCo increased it to 2.7 ± 0.2 kg CO 2 eq per kg milk because of land use change emissions. Closing the yield gap of maize by increasing N fertilizer use reduced emission intensities by 17% due to reduced emissions from conversion of grazing land. FoCo was the only scenario that mitigated agricultural and forest emissions by reducing emission intensity by 33% and overall emissions by 2.5% showing that intensification of dairy in a low-income country can increase milk yields without increasing emissions. There are, however, risks of C leakage if agricultural and forest policies are not aligned leading to loss of forest to produce concentrates. This approach will aid the assessment of the climate-smartness of livestock production practices at the national level in East Africa., (© 2019 The Authors. Global Change Biology published by John Wiley & Sons Ltd.)

As countries advance in greenhouse gas (GHG) accounting for climate change mitigation, consistent estimates of aboveground net biomass change (∆AGB) are needed. Countries with limited forest monitoring capabilities in the tropics and subtropics rely on IPCC 2006 default ∆AGB rates, which are values per ecological zone, per continent. Similarly, research into forest biomass change at a large scale also makes use of these rates. IPCC 2006 default rates come from a handful of studies, provide no uncertainty indications and do not distinguish between older secondary forests and old-growth forests. As part of the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories, we incorporate ∆AGB data available from 2006 onwards, comprising 176 chronosequences in secondary forests and 536 permanent plots in old-growth and managed/logged forests located in 42 countries in Africa, North and South America and Asia. We generated ∆AGB rate estimates for younger secondary forests (≤20 years), older secondary forests (>20 years and up to 100 years) and old-growth forests, and accounted for uncertainties in our estimates. In tropical rainforests, for which data availability was the highest, our ∆AGB rate estimates ranged from 3.4 (Asia) to 7.6 (Africa) Mg ha -1  year -1 in younger secondary forests, from 2.3 (North and South America) to 3.5 (Africa) Mg ha -1  year -1 in older secondary forests, and 0.7 (Asia) to 1.3 (Africa) Mg ha -1  year -1 in old-growth forests. We provide a rigorous and traceable refinement of the IPCC 2006 default rates in tropical and subtropical ecological zones, and identify which areas require more research on ∆AGB. In this respect, this study should be considered as an important step towards quantifying the role of tropical and subtropical forests as carbon sinks with higher accuracy; our new rates can be used for large-scale GHG accounting by governmental bodies, nongovernmental organizations and in scientific research., (© 2019 The Authors. Global Change Biology Published by John Wiley & Sons Ltd.)

Interference with immune cell proliferation represents a successful treatment strategy in T cell-mediated autoimmune diseases such as rheumatoid arthritis and multiple sclerosis (MS). One prominent example is pharmacological inhibition of dihydroorotate dehydrogenase (DHODH), which mediates de novo pyrimidine synthesis in actively proliferating T and B lymphocytes. Within the TERIDYNAMIC clinical study, we observed that the DHODH inhibitor teriflunomide caused selective changes in T cell subset composition and T cell receptor repertoire diversity in patients with relapsing-remitting MS (RRMS). In a preclinical antigen-specific setup, DHODH inhibition preferentially suppressed the proliferation of high-affinity T cells. Mechanistically, DHODH inhibition interferes with oxidative phosphorylation (OXPHOS) and aerobic glycolysis in activated T cells via functional inhibition of complex III of the respiratory chain. The affinity-dependent effects of DHODH inhibition were closely linked to differences in T cell metabolism. High-affinity T cells preferentially use OXPHOS during early activation, which explains their increased susceptibility toward DHODH inhibition. In a mouse model of MS, DHODH inhibitory treatment resulted in preferential inhibition of high-affinity autoreactive T cell clones. Compared to T cells from healthy controls, T cells from patients with RRMS exhibited increased OXPHOS and glycolysis, which were reduced with teriflunomide treatment. Together, these data point to a mechanism of action where DHODH inhibition corrects metabolic disturbances in T cells, which primarily affects profoundly metabolically active high-affinity T cell clones. Hence, DHODH inhibition may promote recovery of an altered T cell receptor repertoire in autoimmunity., (Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

Forests play a crucial role in the global carbon (C) cycle by storing and sequestering a substantial amount of C in the terrestrial biosphere. Due to temporal dynamics in climate and vegetation activity, there are significant regional variations in carbon dioxide (CO2) fluxes between the biosphere and atmosphere in forests that are affecting the global C cycle. Current forest CO2 flux dynamics are controlled by instantaneous climate, soil, and vegetation conditions, which carry legacy effects from disturbances and extreme climate events. Our level of understanding from the legacies of these processes on net CO2 fluxes is still limited due to their complexities and their long-term effects. Here, we combined remote sensing, climate, and eddy-covariance flux data to study net ecosystem CO2 exchange (NEE) at 185 forest sites globally. Instead of commonly used non-dynamic statistical methods, we employed a type of recurrent neural network (RNN), called Long Short-Term Memory network (LSTM) that captures information from the vegetation and climate's temporal dynamics. The resulting data-driven model integrates interannual and seasonal variations of climate and vegetation by using Landsat and climate data at each site. The presented LSTM algorithm was able to effectively describe the overall seasonal variability (Nash-Sutcliffe efficiency, NSE = 0.66) and across-site (NSE = 0.42) variations in NEE, while it had less success in predicting specific seasonal and interannual anomalies (NSE = 0.07). This analysis demonstrated that an LSTM approach with embedded climate and vegetation memory effects outperformed a non-dynamic statistical model (i.e. Random Forest) for estimating NEE. Additionally, it is shown that the vegetation mean seasonal cycle embeds most of the information content to realistically explain the spatial and seasonal variations in NEE. These findings show the relevance of capturing memory effects from both climate and vegetation in quantifying spatio-temporal variations in forest NEE., Competing Interests: The authors have declared that no competing interests exist.

Terrestrial laser scanning (TLS) opens up the possibility of describing the three-dimensional structures of trees in natural environments with unprecedented detail and accuracy. It is already being extensively applied to describe how ecosystem biomass and structure vary between sites, but can also facilitate major advances in developing and testing mechanistic theories of tree form and forest structure, thereby enabling us to understand why trees and forests have the biomass and three-dimensional structure they do. Here we focus on the ecological challenges and benefits of understanding tree form, and highlight some advances related to capturing and describing tree shape that are becoming possible with the advent of TLS. We present examples of ongoing work that applies, or could potentially apply, new TLS measurements to better understand the constraints on optimization of tree form. Theories of resource distribution networks, such as metabolic scaling theory, can be tested and further refined. TLS can also provide new approaches to the scaling of woody surface area and crown area, and thereby better quantify the metabolism of trees. Finally, we demonstrate how we can develop a more mechanistic understanding of the effects of avoidance of wind risk on tree form and maximum size. Over the next few years, TLS promises to deliver both major empirical and conceptual advances in the quantitative understanding of trees and tree-dominated ecosystems, leading to advances in understanding the ecology of why trees and ecosystems look and grow the way they do., Competing Interests: We declare we have no competing interests.

Terrestrial laser scanning (TLS) and unmanned aerial vehicles (UAVs) equipped with digital cameras have attracted much attention from the forestry community as potential tools for forest inventories and forest monitoring. This research fills a knowledge gap about the viability and dissimilarities of using these technologies for measuring the top of canopy structure in tropical forests. In an empirical study with data acquired in a Guyanese tropical forest, we assessed the differences between top of canopy models (TCMs) derived from TLS measurements and from UAV imagery, processed using structure from motion. Firstly, canopy gaps lead to differences in TCMs derived from TLS and UAVs. UAV TCMs overestimate canopy height in gap areas and often fail to represent smaller gaps altogether. Secondly, it was demonstrated that forest change caused by logging can be detected by both TLS and UAV TCMs, although it is better depicted by the TLS. Thirdly, this research shows that both TLS and UAV TCMs are sensitive to the small variations in sensor positions during data collection. TCMs rendered from UAV data acquired over the same area at different moments are more similar (RMSE 0.11-0.63 m for tree height, and 0.14-3.05 m for gap areas) than those rendered from TLS data (RMSE 0.21-1.21 m for trees, and 1.02-2.48 m for gaps). This study provides support for a more informed decision for choosing between TLS and UAV TCMs to assess top of canopy in a tropical forest by advancing our understanding on: (i) how these technologies capture the top of the canopy, (ii) why their ability to reproduce the same model varies over repeated surveying sessions and (iii) general considerations such as the area coverage, costs, fieldwork time and processing requirements needed., Competing Interests: The authors declare that they have no competing interests.

As the applications of Earth system models (ESMs) move from general climate projections toward questions of mitigation and adaptation, the inclusion of land management practices in these models becomes crucial. We carried out a survey among modeling groups to show an evolution from models able only to deal with land-cover change to more sophisticated approaches that allow also for the partial integration of land management changes. For the longer term a comprehensive land management representation can be anticipated for all major models. To guide the prioritization of implementation, we evaluate ten land management practices-forestry harvest, tree species selection, grazing and mowing harvest, crop harvest, crop species selection, irrigation, wetland drainage, fertilization, tillage, and fire-for (1) their importance on the Earth system, (2) the possibility of implementing them in state-of-the-art ESMs, and (3) availability of required input data. Matching these criteria, we identify "low-hanging fruits" for the inclusion in ESMs, such as basic implementations of crop and forestry harvest and fertilization. We also identify research requirements for specific communities to address the remaining land management practices. Data availability severely hampers modeling the most extensive land management practice, grazing and mowing harvest, and is a limiting factor for a comprehensive implementation of most other practices. Inadequate process understanding hampers even a basic assessment of crop species selection and tillage effects. The need for multiple advanced model structures will be the challenge for a comprehensive implementation of most practices but considerable synergy can be gained using the same structures for different practices. A continuous and closer collaboration of the modeling, Earth observation, and land system science communities is thus required to achieve the inclusion of land management in ESMs., (© 2017 John Wiley & Sons Ltd.)

The nuclear receptor Liver X Receptor (LXR) is a ligand-activated transcription factor that has been implicated in control of chronic inflammation by downregulating pro-inflammatory T cell responses. An impaired function of regulatory T cells, a subset of CD4+ T cells with a crucial role in maintaining lymphocytes homeostasis and immune regulation, is frequently observed in chronic inflammatory diseases. We observed that pharmacological activation of LXR in T cells not only resulted in a thorough suppression of Th1 and Th17 polarization in vitro, but also significantly induced regulatory T cells (Treg) cell differentiation in a receptor-specific fashion. In line with this, systemic LXR activation by oral treatment of mice with the LXR agonist GW3965 induced gut-associated regulatory T cells in vivo. Importantly, such LXR-activated Tregs had a higher suppressive capacity in functional in vitro coculture assays with effector T cells. Our data thus point towards a dual role of LXR-mediated control of inflammation by suppression of pro-inflammatory T cells and reciprocal induction of regulatory T cells.

Wetlands are important providers of ecosystem services and key regulators of climate change. They positively contribute to global warming through their greenhouse gas emissions, and negatively through the accumulation of organic material in histosols, particularly in peatlands. Our understanding of wetlands' services is currently constrained by limited knowledge on their distribution, extent, volume, interannual flood variability and disturbance levels. We present an expert system approach to estimate wetland and peatland areas, depths and volumes, which relies on three biophysical indices related to wetland and peat formation: (1) long-term water supply exceeding atmospheric water demand; (2) annually or seasonally water-logged soils; and (3) a geomorphological position where water is supplied and retained. Tropical and subtropical wetlands estimates reach 4.7 million km 2 (Mkm 2 ). In line with current understanding, the American continent is the major contributor (45%), and Brazil, with its Amazonian interfluvial region, contains the largest tropical wetland area (800,720 km 2 ). Our model suggests, however, unprecedented extents and volumes of peatland in the tropics (1.7 Mkm 2 and 7,268 (6,076-7,368) km 3 ), which more than threefold current estimates. Unlike current understanding, our estimates suggest that South America and not Asia contributes the most to tropical peatland area and volume (ca. 44% for both) partly related to some yet unaccounted extended deep deposits but mainly to extended but shallow peat in the Amazon Basin. Brazil leads the peatland area and volume contribution. Asia hosts 38% of both tropical peat area and volume with Indonesia as the main regional contributor and still the holder of the deepest and most extended peat areas in the tropics. Africa hosts more peat than previously reported but climatic and topographic contexts leave it as the least peat-forming continent. Our results suggest large biases in our current understanding of the distribution, area and volumes of tropical peat and their continental contributions., (© 2017 The Authors. Global Change Biology Published by John Wiley & Sons Ltd.)

Understandably, given the fast pace of biodiversity loss, there is much interest in using Earth observation technology to track biodiversity, ecosystem functions and ecosystem services. However, because most biodiversity is invisible to Earth observation, indicators based on Earth observation could be misleading and reduce the effectiveness of nature conservation and even unintentionally decrease conservation effort. We describe an approach that combines automated recording devices, high-throughput DNA sequencing and modern ecological modelling to extract much more of the information available in Earth observation data. This approach is achievable now, offering efficient and near-real-time monitoring of management impacts on biodiversity and its functions and services.

The studies in this PLOS ONE collection investigated the feasibility of community participation in Measuring, Reporting and Verifying (Participatory MRV-PMRV) initiatives in the context of national programs to reduce emissions from deforestation and forest degradation (REDD+). While such participation is desirable, its feasibility has been uncertain. This collection builds the empirical foundations for putting PMRV into practice. The authors of this article identified five crucial considerations: (1) clarify the stakeholders, (2) understand their motivation to participate, (3) integrate knowledge and information from multiple disciplines and sources, (4) convey knowledge and information across multiple levels of governance, and (5) clarify and enable the links to REDD+ safeguards. We conclude that local communities and other local actors can play a major role in achieving REDD+ MRV, however, this requires attention to their needs and motivations. Future activities should include assessment of past PMRV experiences, costs and benefits, operationalization of reporting and verification, formalization of PMRV and full scale testing on the ground.

Background and Purpose: Peripheral immune cell infiltration contributes to neural injury after ischemic stroke. However, in contrast to lymphocytes and neutrophils, the role of different monocyte/macrophage subsets remains to be clarified. Therefore, we evaluated the effects of selective and unselective monocyte/macrophage depletion and proinflammatory (M1-) and anti-inflammatory (M2-) macrophage transfer on the outcome after experimental cerebral ischemia., Methods: To assess short-term effects of monocytes/macrophages in acute ischemic stroke, mice underwent transient middle cerebral artery occlusion and received either clodronate liposomes for unselective macrophage depletion, MC-21-antibody for selective depletion of proinflammatory Ly-6C high monocytes, or proinflammatory (M1-) or anti-inflammatory (M2-) macrophage transfer. In addition, the impact of MC-21-antibody administration and M2-macrophage transfer on long-term neural recovery was investigated after photothrombotic stroke. Neurobehavioral tests were used to analyze functional outcomes, infarct volumes were determined, and immunohistochemical analyses were performed to characterize the postischemic inflammatory reaction., Results: Selective and unselective monocyte/macrophage depletion and M1- and M2-macrophage transfer did not influence tissue damage and neurobehavioral outcomes in the acute phase after middle cerebral artery occlusion. Beyond, selective depletion of Ly-6C high monocytes and M2-macrophage transfer did not have an impact on neural recovery after photothrombotic stroke., Conclusions: Targeting different monocyte/macrophage subsets has no impact on outcome after ischemic stroke in mice. Altogether, our study could not identify monocytes/macrophages as relevant therapeutic targets in acute ischemic stroke., (© 2017 American Heart Association, Inc.)

In the light of daunting global sustainability challenges such as climate change, biodiversity loss and food security, improving our understanding of the complex dynamics of the Earth system is crucial. However, large knowledge gaps related to the effects of land management persist, in particular those human-induced changes in terrestrial ecosystems that do not result in land-cover conversions. Here, we review the current state of knowledge of ten common land management activities for their biogeochemical and biophysical impacts, the level of process understanding and data availability. Our review shows that ca. one-tenth of the ice-free land surface is under intense human management, half under medium and one-fifth under extensive management. Based on our review, we cluster these ten management activities into three groups: (i) management activities for which data sets are available, and for which a good knowledge base exists (cropland harvest and irrigation); (ii) management activities for which sufficient knowledge on biogeochemical and biophysical effects exists but robust global data sets are lacking (forest harvest, tree species selection, grazing and mowing harvest, N fertilization); and (iii) land management practices with severe data gaps concomitant with an unsatisfactory level of process understanding (crop species selection, artificial wetland drainage, tillage and fire management and crop residue management, an element of crop harvest). Although we identify multiple impediments to progress, we conclude that the current status of process understanding and data availability is sufficient to advance with incorporating management in, for example, Earth system or dynamic vegetation models in order to provide a systematic assessment of their role in the Earth system. This review contributes to a strategic prioritization of research efforts across multiple disciplines, including land system research, ecological research and Earth system modelling., (© 2016 John Wiley & Sons Ltd.)

More than 100 countries pledged to reduce agricultural greenhouse gas (GHG) emissions in the 2015 Paris Agreement of the United Nations Framework Convention on Climate Change. Yet technical information about how much mitigation is needed in the sector vs. how much is feasible remains poor. We identify a preliminary global target for reducing emissions from agriculture of ~1 GtCO 2 e yr -1 by 2030 to limit warming in 2100 to 2 °C above pre-industrial levels. Yet plausible agricultural development pathways with mitigation cobenefits deliver only 21-40% of needed mitigation. The target indicates that more transformative technical and policy options will be needed, such as methane inhibitors and finance for new practices. A more comprehensive target for the 2 °C limit should be developed to include soil carbon and agriculture-related mitigation options. Excluding agricultural emissions from mitigation targets and plans will increase the cost of mitigation in other sectors or reduce the feasibility of meeting the 2 °C limit., (© 2016 The Authors Global Change Biology Published by John Wiley & Sons Ltd.)

Molecular mechanisms that determine lesion localization or phenotype variation in multiple sclerosis are mostly unidentified. Although transmigration of activated encephalitogenic T cells across the blood-brain barrier (BBB) is a crucial step in the disease pathogenesis of CNS autoimmunity, the consequences on brain endothelial barrier integrity upon interaction with such T cells and subsequent lesion formation and distribution are largely unknown. We made use of a transgenic spontaneous mouse model of CNS autoimmunity characterized by inflammatory demyelinating lesions confined to optic nerves and spinal cord (OSE mice). Genetic ablation of a single immune-regulatory molecule in this model [i.e., B7-homolog 1 (B7-H1, PD-L1)] not only significantly increased incidence of spontaneous CNS autoimmunity and aggravated disease course, especially in the later stages of disease, but also importantly resulted in encephalitogenic T-cell infiltration and lesion formation in normally unaffected brain regions, such as the cerebrum and cerebellum. Interestingly, B7-H1 ablation on myelin oligodendrocyte glycoprotein-specific CD4 + T cells, but not on antigen-presenting cells, amplified T-cell effector functions, such as IFN-γ and granzyme B production. Therefore, these T cells were rendered more capable of eliciting cell contact-dependent brain endothelial cell dysfunction and increased barrier permeability in an in vitro model of the BBB. Our findings suggest that a single immune-regulatory molecule on T cells can be ultimately responsible for localized BBB breakdown, and thus substantial changes in lesion topography in the context of CNS autoimmunity., Competing Interests: L.K. received compensation for serving on Scientific Advisory Boards for Genzyme and Novartis; received speaker honoraria and travel support from Novartis, Merck Sorono, and CSL Behring; and receives research support from Novartis and Biogen Idec. C.C.G. received speaker honoraria and travel expenses for attending meetings from Genzyme (2014), Novartis Pharma GmbH (2011), and Bayer Health Care (2014). N.S. received speaking honoraria from Novartis and Biogen and travel expenses from Biogen. S.G.M. received honoraria for lecturing and travel expenses for attending meetings and financial research support from Bayer, Bayer Schering, Biogen Idec, Genzyme, Merck Serono, Novartis, Omniamed, Novo Nordisk, Sanofi-Aventis, and Teva. T.K. received honoraria for lectures from Novartis and Biogen Idec Canada and served as a consultant for Genzyme Corporation. H.W. received compensation for serving on Scientific Advisory Boards/Steering Committees for Bayer Healthcare, Biogen Idec, Genzyme, Merck Serono, Novartis, and Sanofi Aventis; received speaker honoraria and travel support from Bayer Vital GmbH, Bayer Schering AG, Biogen Idec, CSL Behring, Fresenius Medical Care, Genzyme, Glaxo Smith Kline, GW Pharmaceuticals, Lundbeck, Merck Serono, Omniamed, Novartis, and Sanofi Aventis; received compensation as a consultant from Biogen Idec, Merck Serono, Novartis, and Sanofi Aventis; and received research support from Bayer Vital, Biogen Idec, Genzyme, Merck Serono, Novartis, Sanofi Aventis Germany, and Sanofi US.

Innate immune responses by myeloid cells decisively contribute to perpetuation of central nervous system (CNS) autoimmunity and their pharmacologic modulation represents a promising strategy to prevent disease progression in Multiple Sclerosis (MS). Based on our observation that peripheral immune cells from relapsing-remitting and primary progressive MS patients exhibited strongly decreased levels of the bile acid receptor FXR (farnesoid-X-receptor, NR1H4), we evaluated its potential relevance as therapeutic target for control of established CNS autoimmunity. Pharmacological FXR activation promoted generation of anti-inflammatory macrophages characterized by arginase-1, increased IL-10 production, and suppression of T cell responses. In mice, FXR activation ameliorated CNS autoimmunity in an IL-10-dependent fashion and even suppressed advanced clinical disease upon therapeutic administration. In analogy to rodents, pharmacological FXR activation in human monocytes from healthy controls and MS patients induced an anti-inflammatory phenotype with suppressive properties including control of effector T cell proliferation. We therefore, propose an important role of FXR in control of T cell-mediated autoimmunity by promoting anti-inflammatory macrophage responses.

Legacy effects of land cover/use on carbon fluxes require considering both present and past land cover/use change dynamics. To assess past land use dynamics, model-based reconstructions of historic land cover/use are needed. Most historic reconstructions consider only the net area difference between two time steps (net changes) instead of accounting for all area gains and losses (gross changes). Studies about the impact of gross and net land change accounting methods on the carbon balance are still lacking. In this study, we assessed historic changes in carbon in soils for five land cover/use types and of carbon in above-ground biomass of forests. The assessment focused on Europe for the period 1950 to 2010 with decadal time steps at 1-km spatial resolution using a bookkeeping approach. To assess the implications of gross land change data, we also used net land changes for comparison. Main contributors to carbon sequestration between 1950 and 2010 were afforestation and cropland abandonment leading to 14.6 PgC sequestered carbon (of which 7.6 PgC was in forest biomass). Sequestration was highest for old-growth forest areas. A sequestration dip was reached during the 1970s due to changes in forest management practices. Main contributors to carbon emissions were deforestation (1.7 PgC) and stable cropland areas on peaty soils (0.8 PgC). In total, net fluxes summed up to 203 TgC yr(-1) (98 TgC yr(-1) in forest biomass and 105 TgC yr(-1) in soils). For areas that were subject to land changes in both reconstructions (35% of total area), the differences in carbon fluxes were about 68%. Overall for Europe the difference between accounting for either gross or net land changes led to 7% difference (up to 11% per decade) in carbon fluxes with systematically higher fluxes for gross land change data., (© 2015 John Wiley & Sons Ltd.)

Aberrant immune responses represent the underlying cause of central nervous system (CNS) autoimmunity, including multiple sclerosis (MS). Recent evidence implicated the crosstalk between coagulation and immunity in CNS autoimmunity. Here we identify coagulation factor XII (FXII), the initiator of the intrinsic coagulation cascade and the kallikrein-kinin system, as a specific immune cell modulator. High levels of FXII activity are present in the plasma of MS patients during relapse. Deficiency or pharmacologic blockade of FXII renders mice less susceptible to experimental autoimmune encephalomyelitis (a model of MS) and is accompanied by reduced numbers of interleukin-17A-producing T cells. Immune activation by FXII is mediated by dendritic cells in a CD87-dependent manner and involves alterations in intracellular cyclic AMP formation. Our study demonstrates that a member of the plasmatic coagulation cascade is a key mediator of autoimmunity. FXII inhibition may provide a strategy to combat MS and other immune-related disorders.

We combined two existing datasets of vegetation aboveground biomass (AGB) (Proceedings of the National Academy of Sciences of the United States of America, 108, 2011, 9899; Nature Climate Change, 2, 2012, 182) into a pan-tropical AGB map at 1-km resolution using an independent reference dataset of field observations and locally calibrated high-resolution biomass maps, harmonized and upscaled to 14 477 1-km AGB estimates. Our data fusion approach uses bias removal and weighted linear averaging that incorporates and spatializes the biomass patterns indicated by the reference data. The method was applied independently in areas (strata) with homogeneous error patterns of the input (Saatchi and Baccini) maps, which were estimated from the reference data and additional covariates. Based on the fused map, we estimated AGB stock for the tropics (23.4 N-23.4 S) of 375 Pg dry mass, 9-18% lower than the Saatchi and Baccini estimates. The fused map also showed differing spatial patterns of AGB over large areas, with higher AGB density in the dense forest areas in the Congo basin, Eastern Amazon and South-East Asia, and lower values in Central America and in most dry vegetation areas of Africa than either of the input maps. The validation exercise, based on 2118 estimates from the reference dataset not used in the fusion process, showed that the fused map had a RMSE 15-21% lower than that of the input maps and, most importantly, nearly unbiased estimates (mean bias 5 Mg dry mass ha(-1) vs. 21 and 28 Mg ha(-1) for the input maps). The fusion method can be applied at any scale including the policy-relevant national level, where it can provide improved biomass estimates by integrating existing regional biomass maps as input maps and additional, country-specific reference datasets., (© 2015 John Wiley & Sons Ltd.)

This paper describes an interactive web-based near real-time (NRT) forest monitoring system using four levels of geographic information services: 1) the acquisition of continuous data streams from satellite and community-based monitoring using mobile devices, 2) NRT forest disturbance detection based on satellite time-series, 3) presentation of forest disturbance data through a web-based application and social media and 4) interaction of the satellite based disturbance alerts with the end-user communities to enhance the collection of ground data. The system is developed using open source technologies and has been implemented together with local experts in the UNESCO Kafa Biosphere Reserve, Ethiopia. The results show that the system is able to provide easy access to information on forest change and considerably improves the collection and storage of ground observation by local experts. Social media leads to higher levels of user interaction and noticeably improves communication among stakeholders. Finally, an evaluation of the system confirms the usability of the system in Ethiopia. The implemented system can provide a foundation for an operational forest monitoring system at the national level for REDD+ MRV applications.