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Spatially explicit information on forest management at a global scale is critical for understanding the status of forests, for planning sustainable forest management and restoration, and conservation activities. Here, we produce the first reference data set and a prototype of a globally consistent forest management map with high spatial detail on the most prevalent forest management classes such as intact forests, managed forests with natural regeneration, planted forests, plantation forest (rotation up to 15 years), oil palm plantations, and agroforestry. We developed the reference dataset of 226 K unique locations through a series of expert and crowdsourcing campaigns using Geo-Wiki (https://www.geo-wiki.org/). We then combined the reference samples with time series from PROBA-V satellite imagery to create a global wall-to-wall map of forest management at a 100 m resolution for the year 2015, with forest management class accuracies ranging from 58% to 80%. The reference data set and the map present the status of forest ecosystems and can be used for investigating the value of forests for species, ecosystems and their services.

While the scientific community documents environmental degradation and develops scenarios to identify the operational margins of system Earth, less attention is given to how decisions are made that steer the system in one direction or the other. We propose to use strategy games for this purpose, increasing the representation of human agency in scenario development and creating spaces for deliberation between different worldviews. Played by the right people, strategy games could help break free from established norms and support more transparent democratic dialogues, responding to the human and social limitations of current decision-making. The question is, who gets to play?

We provide four data records: 1.The reference data set as a comma-separated file ("reference_data_set.csv") with the following attributes: “ID” is a unique location identifier “Latitude, Longitude” are centroid coordinates of a 100m x 100m pixel. “Land_use_ID “is a land use class: 11 - Naturally regenerating forest without any signs of human activities, e.g., primary forests. 20 - Naturally regenerating forest with signs of human activities, e.g., logging, clear cuts etc. 31 - Planted forest. 32 - Short rotation plantations for timber. 40 - Oil palm plantations. 53 - Agroforestry. “Flag” identifies a data origin: 1- the crowdsourced locations, 2- the control data set, 0 – the additional experts' classifications following the opportunistic approach. 2. The 100 m forest management map in a geoTiff format with the classes presented - "FML_v3.2.tif ". 3. The predicted class probability from the Random Forest classification in a geoTiff format - "ProbaV_LC100_epoch2015_global_v2.0.3_forest-management--layer-proba_EPSG-4326.tif" 4. Validation data set as a comma-separated file ("validation_data_set.csv) with the following attributes: “ID” is a unique location identifier “pixel_center_x” , “pixel_center_y ” are centroid coordinates of a 100m x 100m pixel in lat/lon projection “first_landuse_class “is a land use class, as in (1). “second_landuse_class “is a second possible land use class, as in (1), identified in case it was difficult to assign one class with high confidence.

Global vegetation and land-surface models embody interdisciplinary scientific understanding of the behaviour of plants and ecosystems, and are indispensable to project the impacts of environmental change on vegetation and the interactions between vegetation and climate. However, systematic errors and persistently large differences among carbon and water cycle projections by different models highlight the limitations of current process formulations. In this review, focusing on core plant functions in the terrestrial carbon and water cycles, we show how unifying hypotheses derived from eco-evolutionary optimality (EEO) principles can provide novel, parameter-sparse representations of plant and vegetation processes. We present case studies that demonstrate how EEO generate parsimonious representations of core, leaf-level processes that are individually testable and supported by evidence. EEO approaches to photosynthesis and primary production, dark respiration, and stomatal behaviour are ripe for implementation in global models. EEO approaches to other important traits, including the leaf economics spectrum and applications of EEO at the community level are active research areas. Independently tested modules emerging from EEO studies could profitably be integrated into modelling frameworks that account for the multiple time scales on which plants and plant communities adjust to environmental change.

Growing water scarcity and increasing Nitrogen (N) fertiliser prices in South Africa require more prudent N fertiliser and irrigation water use approaches. If the goal of sustainable agricultural intensification is to be realised, it is vital to develop location-specific agricultural land management strategies that promote increased crop productivity while minimising negative environmental impacts. In this study, a calibrated and validated Environmental Policy Integrated Climate (EPIC) model was used to simulate a range of N fertiliser and irrigation water levels on maize (Zea mays L.) yield in the Eastern Cape of South Africa. Results showed that a fertiliser and irrigation water management schedule combining approximately 200 kg N ha-1 and 580 mm irrigation water per maize growing season provided the highest average maize yield of 12.2 Mg ha-1 (an increase of +69% above farmers’ current maize yield levels). Nitrogen fertiliser application levels greater than 160 kg N ha-1 resulted in potential N fertiliser leaching losses of more than 35 kg N ha-1. The EPIC model can be considered a valuable tool to aid decision-makers in identifying optimal, site-specific irrigation water and N fertiliser application levels that contribute to increased maize crop productivity while maximising water use.

The metabolic response to endotoxemia closely mimics those seen in sepsis. Here, we show that the urinary excretion of the metabolite 2-hydroxyglutarate (2HG) is dramatically suppressed following lipopolysaccharide (LPS) administration in vivo, and in human septic patients. We further show that enhanced activation of the enzymes responsible for 2-HG degradation, D- and L-2-HGDH, underlie this effect. To determine the role of supplementation with 2HG, we carried out co-administration of LPS and 2HG. This co-administration in mice modulates a number of aspects of physiological responses to LPS, and in particular, protects against LPS-induced hypothermia. Our results identify a novel role for 2HG in endotoxemia pathophysiology, and suggest that this metabolite may be a critical diagnostic and therapeutic target for sepsis. © Copyright © 2020 Fitzpatrick, Lambden, Macias, Puthucheary, Pietsch, Mendil, McPhail and Johnson.

The first ever global map of forest management was generated based on remote sensing data. To collect training data, we launched a series of Geo-Wiki (https://www.geo-wiki.org/) campaigns involving forest experts from different world regions, to explore which information related to forest management could be collected by visual interpretation of very high-resolution images from Google Maps and Microsoft Bing, Sentinel time series and normalized difference vegetation index (NDVI) profiles derived from Google Earth Engine. A machine learning technique was then used with the visually interpreted sample (280K locations) as a training dataset to classify PROBA-V satellite imagery. Finally, we obtained a global wall-to-wall map of forest management at a 100m resolution for the year 2015. The map includes classes such as intact forests; forests with signs of management, including logging; planted forests; woody plantations with a rotation period up to 15 years; oil palm plantations; and agroforestry. The map can be used to deliver further information about forest ecosystems, protected and observed forest status changes, biodiversity assessments, and other ecosystem-related aspects.

The plantation forestry sector in Indonesia has seen a change in species from Acacia mangium to Eucalyptus pellita. This change, forced by diseases spreads, has affected more than 90% of the plantation area in Indonesia and it is unprecedented in its scale in the history of plantation forestry (Nambiar et al., 2018). It is also a change of tree species with very different eco-physiological patterns: Acacias – in contrast to eucalypts - are leguminous trees and therefore self-sufficient in nitrogen supply and capable of building significant stocks of nitrogen and carbon in the soil, two main determinants of plantation productivity. The large-scale species shift therefore raises questions about the sustainability of the pulp and paper sector in Indonesia in the coming decades as well as the role of past land use and site quality in Indonesia’s forest restoration pledge. The proposed contribution therefore aims at analyzing the sustainability in terms of productivity of the Indonesian plantation forestry sector under the new eucalypts regime. To that end, we will deploy the BioGeoChemistry Management Model (BGCMAN; Pietsch, 2014) which is capable of representing the carbon, water and nitrogen cycles in great detail. Expected results will include a reconstruction of the rise and fall of A. mangium and shift to E. pellita as well as forecasts of plantation productivity and soil fertility over the next decades; it will notably answer the question for how long soil N-stock accumulated by acacias will be able to feed eucalypts.

Sustainable forest management practices can potentially reverse loss of forest cover due to deforestation, while concomitantly preserving and maintaining biodiversity, and stimulating jobs, income, and forest services. Recent studies found that significant logging residues (i.e., leaves, branches, and buttress roots) suitable for bioenergy production were often left in the felling area, triggering risks of forest fires and increased CO2 emissions due to wildfires or decomposition processes. For impact assessment of forest management practices, we collected primary harvesting data and estimated net primary productivity (NPP) and net ecosystem exchange (NEE) for 13 forest plots in the Brazilian Amazon. We applied a process-based forestry growth model (BGC-Man) to analyze the impacts on forest dynamics of selective logging and removal of logging residues, subject to landscape, soil texture, and daily weather. We explored the following selective logging scenarios: the Legal Reserve (i.e., reference) scenario, a scenario with one cutting cycle over the whole period, and a scenario with three timber rotation periods of 30 years. Two of the later scenarios were complemented with harvesting of the woody logging residues (LR; Ø≥10 cm) for charcoal production. For each scenario, we computed forest NPP and NEE over a 120-year time horizon. Results suggest that using woody logging residues (i.e., 77% of total LR) for charcoal production would result in an economic gain equivalent to 24-46% of the timber price. Our findings indicate that under scenarios where LR were removed, forest NPP recovered to the reference level and even higher, while income and jobs from harvesting LR for charcoal production were generated. We conclude that sustainable forest management could enhance forest productivity and deliver economic benefit from otherwise unexploited logging residues.

Forests across the world stand at a crossroads where climate and land-use changes are shaping their future. Despite demonstrations of political will and global efforts, forest loss, fragmentation, and degradation continue unabated. No clear evidence exists to suggest that these initiatives are working. A key reason for this apparent ineffectiveness could lie in the failure to recognize the agency of all stakeholders involved. Landscapes do not happen. We shape them. Forest transitions are social and behavioral before they are ecological. Decision makers need to integrate better representations of people’s agency in their mental models. A possible pathway to overcome this barrier involves eliciting mental models behind policy decisions to allow better representation of human agency, changing perspectives to better understand divergent points of view, and refining strategies through explicit theories of change. Games can help decision makers in all of these tasks.

Forest biomass is an essential indicator for monitoring the Earth’s ecosystems and climate. It is a critical input to greenhouse gas accounting, estimation of carbon losses and forest degradation, assessment of renewable energy potential, and for developing climate change mitigation policies such as REDD+, among others. Wall-to-wall mapping of aboveground biomass (AGB) is now possible with satellite remote sensing (RS). However, RS methods require extant, up-to-date, reliable, representative and comparable in situ data for calibration and validation. Here, we present the Forest Observation System (FOS) initiative, an international cooperation to establish and maintain a global in situ forest biomass database. AGB and canopy height estimates with their associated uncertainties are derived at a 0.25ha scale from field measurements made in permanent research plots across the world's forests. All plot estimates are geolocated and have a size that allows for direct comparison with many RS measurements. The FOS offers the potential to improve the accuracy of RS-based biomass products while developing new synergies between the RS and ground-based ecosystem research communities. Live, most up-to-date dataset is available at https://forest-observation-system.net

Forest biomass is an essential indicator for monitoring the Earth’s ecosystems and climate. It is a critical input to greenhouse gas accounting, estimation of carbon losses and forest degradation, assessment of renewable energy potential, and for developing climate change mitigation policies such as REDD+, among others. Wall-to-wall mapping of aboveground biomass (AGB) is now possible with satellite remote sensing (RS). However, RS methods require extant, up-to-date, reliable, representative and comparable in situ data for calibration and validation. Here, we present the Forest Observation System (FOS) initiative, an international cooperation to establish and maintain a global in situ forest biomass database. AGB and canopy height estimates with their associated uncertainties are derived at a 0.25 ha scale from field measurements made in permanent research plots across the world’s forests. All plot estimates are geolocated and have a size that allows for direct comparison with many RS measurements. The FOS offers the potential to improve the accuracy of RS-based biomass products while developing new synergies between the RS and ground-based ecosystem research communities.

The mid-latitude ecotone (MLE)—a transition zone between boreal and temperate forests, which includes the regions of Northeast Asia around 30°–60° N latitudes—delivers different ecosystem functions depending on different management activities. In this study, we assessed forest volume and net primary productivity changes in the MLE of Northeast Asia under different ecological characteristics, as well as various current management activities, using the BioGeoChemistry Management Model (BGC-MAN). We selected five pilot sites for pine (Scots pine and Korean red pine; Pinus sylvestris and P. densiflora), oak (Quercus spp.), and larch forests (Dahurian larch and Siberian larch; Larix gmelinii and L. sibirica), respectively, which covered the transition zone across the MLE from Lake Baikal, Russia to Kyushu, Japan, including Mongolia, Northeast China, and the Korean Peninsula. With site-specific information, soil characteristics, and management descriptions by forest species, we established their management characteristics as natural preserved forests, degraded forests, sandy and cold forest stands, and forests exposed to fires. We simulated forest volume (m3) and net primary productivity (Mg C ha−1) during 1960–2005 and compared the results with published literature. They were in the range of those specified in previous studies, with some site-levels under or over estimation, but unbiased estimates in their mean values for pine, oak, and larch forests. Annual rates of change in volume and net primary productivity differed by latitude, site conditions, and climatic characteristics. For larch forests, we identified a high mountain ecotype which warrants a separate model parameterization. We detected changes in forest ecosystems, explaining ecological transition in the Northeast Asian MLE. Under the transition, we need to resolve expected problems through appropriate forest management and social efforts

Global wood demand is projected to increase with accompanying intensification in forest management practices. There are concerns that intensive management practices such as whole-tree harvest (WTH) and shortened rotation lengths could risk the long-term productivity and carbon sink capacity of forest ecosystems. The historical (1915-2005) and future (2005-2095) development of five Scots pine (Pinus sylvestris) and five Norway spruce (Picea abies) stands were simulated across a long latitudinal gradient in Europe. The responses of above- and belowground carbon and nutrient cycles to changing forest management and climate were simulated using a biogeochemical ecosystem model and a dynamic litter and soil carbon model. The uncertainty deriving from the inter-annual climate variability was quantified by Monte Carlo simulations. The biogeochemical model estimated the historical stand development similarly to measurement-based estimates derived from growth and yield tables, supporting the validity of the modelling framework. Stand productivity increased drastically in 2005-2095 as a result of climate change. The litter and soil carbon and nitrogen stocks decreased as a result of WTH while its effect on the biomass carbon stock was positive. This indicates that the microbial controls of post-harvest on stand productivity require further research. Shortened rotation length reduced the carbon stock of biomass more than that of litter and soil. The response of the litter and soil carbon stock to forest management was very similar irrelevant of the model used demonstrating the pattern to be robust. Forest management dominated over the impacts of climate change in the short term.

Monitoring large forest areas is presently feasible with satellite remote sensing as opposed to time-consuming and expensive ground surveys as alternative. This study evaluated, for the first time, the potential of using freely available medium resolution (30 m) Landsat time series data for deforestation monitoring in tropical rainforests of Kalimantan, Indonesia, at sub-annual time scales. A simple, generic, data-driven algorithm for deforestation detection based on a consecutive anomalies criterion was proposed. An accuracy assessment in the spatial and the temporal domain was carried out using high-confidence reference sample pixels interpreted with the aid of multi-temporal very high spatial resolution image series. Results showed a promising spatial accuracy, when three consecutive anomalies were required to confirm a deforestation event. Recommendations in tuning the algorithm for different operational use cases were provided within the context of satisfying REDD+ requirements, depending on whether spatial accuracy or temporal accuracy need to be optimized.

The current state of the art in modeling forest cover change is to combine a detailed representation of biophysical processes with economic decision-making principles. Yet, there is an increasing consensus that the quality of political institutions is another relevant component in determining forest cover change patterns. In this paper, the Global Forest Model is used to analyze whether including an index, measuring the capacity of political institutions to guarantee sustainable natural resource management, allows to improve the precision of the modeled forest cover trend. The analysis shows that incorporating the index indeed allows reducing the gap between the estimated and observed forest cover trends for the 2000 to 2010 calibration period.