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Tree diversity in Asia's tropical and subtropical forests is central to nature-based solutions. Species vulnerability to multiple threats, which affect provision of ecosystem services, is poorly understood. We conducted a region-wide, spatially explicit assessment of the vulnerability of 63 socioeconomically important tree species to overexploitation, fire, overgrazing, habitat conversion, and climate change. Trees were selected for assessment from national priority lists, and selections were validated by an expert network representing 20 countries. We used Maxent suitability modeling to predict species distribution ranges, freely accessible spatial data sets to map threat exposures, and functional traits to estimate threat sensitivities. Species-specific vulnerability maps were created as the product of exposure maps and sensitivity estimates. Based on vulnerability to current threats and climate change, we identified priority areas for conservation and restoration. Overall, 74% of the most important areas for conservation of these trees fell outside protected areas, and all species were severely threatened across an average of 47% of their native ranges. The most imminent threats were overexploitation and habitat conversion; populations were severely threatened by these factors in an average of 24% and 16% of their ranges, respectively. Our model predicted limited overall climate change impacts, although some study species were likely to lose over 15% of their habitat by 2050 due to climate change. We pinpointed specific natural areas in Borneo rain forests as hotspots for in situ conservation of forest genetic resources, more than 82% of which fell outside designated protected areas. We also identified degraded areas in Western Ghats, Indochina dry forests, and Sumatran rain forests as hotspots for restoration, where planting or assisted natural regeneration will help conserve these species, and croplands in southern India and Thailand as potentially important agroforestry options. Our results highlight the need for regionally coordinated action for effective conservation and restoration.Especies de Árboles Valoradas y Amenazadas de Asia Tropical y Subtropical Resumen La diversidad de árboles en los bosques tropicales y subtropicales de Asia es un eje central para las soluciones basadas en la naturaleza. La vulnerabilidad de las especies ante las múltiples amenazas, las cuales afectan el suministro de servicios ambientales, es un tema poco comprendido. Realizamos una evaluación regional espacialmente explícita de la vulnerabilidad de 63 especies de árboles de importancia socioeconómica ante la sobreexplotación, incendios, sobrepastoreo, conversión del hábitat y cambio climático. Los árboles se seleccionaron para su evaluación a partir de listas nacionales de prioridades, y las selecciones fueron validadas por una red de expertos de 20 países. Usamos el modelado de idoneidad Maxent para predecir el rango de distribución de las especies, conjuntos de datos espaciales de libre acceso para mapear la exposición a las amenazas y rasgos funcionales para estimar la susceptibilidad a las amenazas. Con base en la vulnerabilidad a las amenazas actuales y al cambio climático, identificamos las áreas prioritarias para su conservación y restauración. En general, el 74% de las áreas más importantes para la conservación de estos árboles quedó fuera de las áreas protegidas y todas las especies estaban seriamente amenazadas en promedio en el 47% de su distribución nativa. Las amenazas más inminentes fueron la sobreexplotación y la conversión del hábitat; las poblaciones estuvieron seriamente amenazadas por estos factores en promedio en el 24% y 16% de su distribución, respectivamente. Nuestro modelo predijo un impacto general limitado del cambio climático, aunque algunas especies estudiadas tuvieron la probabilidad de perder más del 15% de su hábitat para el 2050 debido a este factor. Identificamos áreas naturales específicas en las selvas de Borneo como puntos calientes para la conservación in situ de los recursos genéticos forestales, más del 82% de los cuales estaban fuera de las áreas protegidas designadas. También identificamos áreas degradadas en los Ghats Occidentales, los bosques secos de Indochina y las selvas de Sumatra como puntos calientes para la restauración, en donde la siembra o la regeneración natural asistida ayudarán a conservar estas especies. Además, identificamos campos de cultivo al sur de India y Tailandia como potenciales opciones importantes de agrosilvicultura. Nuestros resultados resaltan la necesidad de acciones regionales coordinadas para la conservación y restauración efectivas.【摘要】保护亚洲热带和亚热带森林的树木多样性是基于自然的解决方案的关注重点。物种面对多种威胁的脆弱性会影响生态系统服务的供给, 但目前人们对此仍知之甚少。我们对63种具有社会经济意义的树种面对过度开发、火灾、过度放牧、栖息地转换和气候变化的脆弱性进行了全区域空间显式评估。我们进行评估的树种来自国家优先保护名录, 树种的选择得到代表 20个国家的专家网络的认可。研究使用Maxent适宜性模型预测了物种分布范围, 并用可获取的空间数据集绘制了威胁暴露情况, 用功能特征估计了威胁敏感性。我们利用暴露地图和敏感性估计结果得到了物种特异的脆弱性地图, 并根据物种对当前威胁和气候变化的脆弱性, 确定了保护和恢复的优先区域。总的来说, 对这些树种的保护最重要的区域有 74%在保护区之外, 所有树种平均在其原生分布区 47%的范围内受到严重威胁。最紧迫的威胁是过度开发和栖息地转换;树木种群平均在其 24%和16%的范围内受到这些因素的严重威胁。我们的模型预测得到的气候变化整体影响有限, 尽管一些物种到2050年可能会因气候变化而失去超过15%的栖息地。我们发现婆罗洲雨林中特定的自然区域应作为森林遗传资源原地保护的热点地区, 该区域82%以上的面积位于已有保护区之外。我们还确定了西高止山、中南半岛旱林和苏门答腊雨林的退化地区为恢复热点地区, 在这些地区种植树木或辅助自然再生将有助于保护树种, 而印度南部和泰国的耕地则是潜在的重要农林地。我们的结果强调了为有效保护和恢复而采取区域性协调行动的必要性。【翻译: 胡怡思;审校: 聂永刚】.

Land cover (LC) is an essential variable for environmental monitoring in many application domains. The detection of changes in LC can support the understanding of environmental dynamics. However, LC legends present a high degree of inconsistencies that significantly reduce their usability. This study investigates the effectiveness of ISO standard 19144-2, better known as Land Cover Meta-Language (LCML), to improve the standardization and harmonization of different LC taxonomies and maps. LCML vocabulary and syntactic rules facilitate the integration of natural resources information. LC classes are represented by a sequence of “Basic Elements” and attributes defined as “Properties” and “Characteristics.” Such elements are formalized in a Unified Modeling Language class diagram. This study presents first, a method to evaluate and score the “similarity” of different LCML legends, second, an application of the similarity assessment criteria to an area located in Bangladesh for translating its specific LCML legend into a different taxonomy, i.e., the System of Environmental Economic Accounting, and third, a Python implementation to be incorporated in new or already existing tools. The results obtained show that when class similarity assessment is carried out by Basic Elements only, the process performs well for simple classes. When classes are characterized by similar basic elements (e.g., biotic elements) structure, the introduction of class properties is needed to disambiguate complex situations. The findings indicate that the proposed methodology can exploit LCML land feature semantic representation. Moreover, it can be used for translating LCML classes into different taxonomies, for facilitating class comparison and change detection.

Allometric models are commonly used to estimate biomass, nutrients and carbon stocks in trees, and contribute to an understanding of forest status and resource dynamics. The selection of appropriate and robust models, therefore, have considerable influence on the accuracy of estimates obtained. Allometric models can be developed for individual species or to represent a community or bioregion. In Bangladesh, the nation forest inventory classifies tree and forest resources into five zones (Sal, Hill, Coastal, Sundarbans and Village), based on their floristic composition and soil type. This study has developed allometric biomass models for multi-species of the Sal zone. The forest of Sal zone is dominated by Shorea robusta Roth. The study also investigates the concentrations of Nitrogen, Phosphorus, Potassium and Carbon in different tree components. A total of 161 individual trees from 20 different species were harvested across a range of tree size classes. Diameter at breast height (DBH), total height (H) and wood density (WD) were considered as predictor variables, while total above-ground biomass (TAGB), stem, bark, branch and leaf biomass were the output variables of the allometric models. The best fit allometric biomass model for TAGB, stem, bark, branch and leaf were: ln (TAGB) = -2.460 + 2.171 ln (DBH) + 0.367 ln (H) + 0.161 ln (WD); ln (Stem) = -3.373 + 1.934 ln (DBH) + 0.833 ln (H) + 0.452 ln (WD); ln (Bark) = -5.87 + 2.103 ln (DBH) + 0.926 ln (H) + 0.587 ln (WD); ln (Branch) = -3.154 + 2.798 ln (DBH) - 0.729 ln (H) - 0.355 ln (WD); and ln (Leaf) = -4.713 + 2.066 ln (DBH), respectively. Nutrients and carbon concentration in tree components varied according to tree species and component. A comparison to frequently used regional and pan-tropical biomass models showed a wide range of model prediction error (35.48 to 85.51%) when the observed TAGB of sampled trees were compared with the estimated TAGB of the models developed in this study. The improved accuracy of the best fit model obtained in this study can therefore be used for more accurate estimation of TAGB and carbon and nutrients in TAGB for the Sal zone of Bangladesh.

Tree allometric equations that are used to predict the biomass of a tree from its dendrometrical characteristics are key elements for the estimation of forest carbon stocks. Few equations compared to the diversity of forest ecosystems are currently available for the tropical forests of the Congo Basin. Pantropical equations are available but a pending question is whether they remain valid for all types of forest ecosystems in central Africa. The Plateau Bateke that covers a large area across Congo, Gabon and the Democratic Republic of Congo is dominated by savannahs that contrast with the dense rain forest often found under this tropical wet climate. Four 1-ha plots were inventoried in a savannah of the Plateau Bateke in Congo. Ninety trees from the five most abundant species were destructively sampled for aboveground biomass, while belowground biomass was measured for a sub-sample of 36 of these trees. The relationship between height ( H in m) and diameter at breast height (dbh, D in cm) was modelled by a Mitscherlich model: H = 6.755–5.570 exp(−0.0946 D ). The relationship between biomass ( B in kg) and D , H and wood density (ρ in g cm −3 ) was modelled as: B = 0.6229 × (ρ D 2 H ) 0.7031 . This locally fitted allometric model had a bias of −2%, while a commonly used pantropical equation had a bias of −38%. Nevertheless, the bias of the pantropical equation at the Bateke site was of the same order as the bias of this pantropical equation at other sites used to establish it. The shoot-root ratio was 0.272. The savannah of the Plateau Bateke had a basal area of 6.32 m 2 ha −1 and an aboveground woody biomass of 11.7 Mg ha −1 while belowground woody biomass accounted for an additional 3.4 Mg ha −1 . The estimate of the aboveground woody biomass dropped by 50% when using the pantropical allometric equation. Insofar as site differences in biomass allometry are negligible in the pantropical equation, so is the difference between the pantropical equation and the locally fitted biomass equation. Nevertheless, the difference between the estimate of aboveground biomass using the locally fitted equation and the pantropical equation is large enough to call for a further examination of site differences in pantropical approaches.

The food system is a significant source of rising greenhouse gas emissions in Bangladesh due to increasing food demand, and ongoing economic growth. The country needs to develop low carbon pathways for the food system that strengthen food security, reduce vulnerability to climate change, and help to achieve the United Nations Sustainable Development Goals. In this paper we analyse the historical trajectory of greenhouse gas emissions of the food system in Bangladesh, and identify various strategies for reducing those emissions. Our analysis reveals that greenhouse gas emissions from Bangladesh's food system doubled from 1990 to 2017, and from the processes included in this study are projected to rise up to 250% by 2050 under a business as usual scenario. Compared with that scenario, a ‘National Policy-Based Circular Economy’ scenario could achieve a 75% reduction in greenhouse gas emissions in the same period for same level of food supply and production. Major reductions in greenhouse gas emissions could be achieved through activities such as alternative wetting and drying in rice cultivation, and the valorisation of organic waste generated by the food system.

Accurate tree biomass estimation is critical and crucial for calculating carbon stocking as well as for studying climate change, forest health, productivity, nutrient cycling and budget etc. A total of 50 individuals of Shorea robusta Gaertn. f. were harvested to assess the biomass of tree components (leaf, branch, bark and stem). Carbon and nutrients (N, P and K) content in the tree components were also measured. This study adopted component biomass models with cross-validation technique. Additive biomass models were developed following the modified Gaussian maximum likelihood aggregated approach using open source software R (version 4.0.1). Component and additive biomass model used D (Diameter at Breast Height) as a sole predictor performed satisfactorily, the inclusion of total tree height (H) in Da*Hb form showed its supremacy over all the models. The best fitted additive model (AGB = 0.002056*D2.923998*H−0.69278 + 0.00848*D2.3896*H0.29648 + 0.04224*D2.06986*H0.65549 + 0.00552*D2.06723*H0.70536) and conventional model (Ln (AGB) = -2.7977 + 2.1829*ln(D) + 0.5073*ln(H)) took the lowest AIC, MPE and MAE and the highest model efficiency values. The derived species-specific additive and non-additive model showed its superiority over the frequently used pan-tropical models and suggested using this model for estimating aboveground biomass of S. robusta in Bangladesh.

Against a backdrop of growing concerns over REDD+ implications for tenure of forest-dependent communities, very few scientific studies have attempted to assess the linkages between the two. This paper offers empirical evidences for impacts of REDD+ intervention based on a case study of Thailand, where de jure state property and de facto tenure apparently co-exist. In contrast to the existing literature proposing positive tenure impacts, the findings suggest that REDD+ would not be sufficient to incentivize the government to embark on tenure reforms or any improvement of customary tenure and challenge REDD+ about its incentives for countries to accelerate tenure reforms. The findings also reveal that risks associated with tenure insecurity discouraged REDD+ investment leading to the withdrawal of the fund altogether. The findings enrich the on-going debates on the tenure impacts, tenure reform and competing agendas and provide insights to support the development of future national strategies and frameworks. The paper moreover proposes measures to create enabling conditions for REDD+.

The implementation of forest-based projects to mitigate greenhouse gas emissions requires the estimation of emission factors (here the difference in biomass stocks between two forest types). The estimation of these quantities using forest inventory data and allometric models implies different sources of errors that need to be prioritized to improve the precision of estimation. Using data from permanent sample plots in a tropical moist forest in central Africa and considering four allometric models with equal likelihood, the largest source of error in the estimate of the difference of biomass between intact and logged-over forest was that due to the model choice (40 % of the sum of squares). The error due to the model choice did not cancel out in the difference due to an interaction between the model’s prediction and the diameter structure of the forest. The variability in biomass between plots was the second largest source of error, but was underestimated because of post-stratification. The error due to the model choice could be reduced by weighting the models’ predictions.

Deforestation and forest degradation represent an important part of global CO2emissions. The identification of the multiple drivers of land-use change, past and present forest cover change and associated carbon budget, and the presence of locally adapted systems to allow for proper monitoring are particularly lacking in sub-Saharan Africa (SSA). Any incentive system to reduce emissions from deforestation and forest degradation (REDD+) will have to overcome those limits. This paper reviews the main challenges to implementing effective REDD+ mitigation activities in SSA. We estimate that SSA is currently a net carbon sink of approximately 319 TgCO2yr−1. Forest degradation and deforestation put the forest carbon stock at risk (mean forest carbon stock is 57,679 TgC). Our results highlight the importance of looking beyond the forest sector to ensure that REDD+ efforts are aligned with agricultural and land-use policies.

The most of the global organic carbon is stored in the forest soil. The large quantity of CO2 emitted from soil is a consequence of land use change, both through human activities (e.g. agriculture) or natural hazards (e.g. flooding, landslides, erosion). A net carbon loss from soils adds to the increase in the atmospheric CO2 concentration, probably leading to higher global temperatures. On the other hand, a net soil CO2 sequestration could help to mitigate the greenhouse effect and to improve soil quality. In this purpose, identifying areas suitable for projects aimed to reduce emissions through SOC sequestration, is of the most importance for both developed and developing countries. This report describes the sources and procedures used to develop a global database for SOC content. The FAO approaches and methodologies provided by using Geographic Information Systems (GIS) software, play a crucial role in this process. Global Soil Organic Carbon Database (at 30 arcsec). Available from: https://www.researchgate.net/publication/317958234_Global_Soil_Organic_Carbon_Database_at_30_arcsec [accessed Sep 5, 2017].

1 IntroductionGiven the pressing need to quantify carbon fluxes associatedwith terrestrial vegetation dynamics, an increasing number ofresearchers have sought to improve estimates of tree volume,biomass, and carbon stocks. Tree allometric equations arecritical tools for such purpose and have the potential toimprove our understanding about carbon sequestration inwoody vegetation, to support the implementation of policiesand mechanisms designed to mitigate climate change (e.g.CDM and REDD+; Agrawal et al. 2011), to calculate costsand benefits associated with forest carbon projects, and toimprove bioenergy systems and sustainable forest manage-ment (Henry et al. 2013).

International audience; This paper, developed under the framework of the RECCAP initiative, aims at providing improved estimates of the carbon and GHG (CO 2 , CH 4 and N 2 O) balance of continental Africa. The various components and processes of the African carbon and GHG budget are considered, existing data reviewed, and new data from different methodolo-gies (inventories, ecosystem flux measurements, models, and atmospheric inversions) presented. Uncertainties are quantified and current gaps and weaknesses in knowledge and monitoring systems described in order to guide future requirements. The majority of results agree that Africa is a small sink of carbon on an annual scale, with an average value of Published by Copernicus Publications on behalf of the European Geosciences Union. 382 R. Valentini et al.: A full greenhouse gases budget of Africa −0.61 ± 0.58 Pg C yr −1. Nevertheless, the emissions of CH 4 and N 2 O may turn Africa into a net source of radiative forcing in CO 2 equivalent terms. At sub-regional level, there is significant spatial variability in both sources and sinks, due to the diversity of biomes represented and differences in the degree of anthropic impacts. Southern Africa is the main source region; while central Africa, with its evergreen tropical forests, is the main sink. Emissions from land-use change in Africa are significant (around 0.32 ± 0.05 Pg C yr −1), even higher than the fossil fuel emissions: this is a unique feature among all the continents. There could be significant carbon losses from forest land even without deforestation, resulting from the impact of selective logging. Fires play a significant role in the African carbon cycle, with 1.03 ± 0.22 Pg C yr −1 of carbon emissions, and 90 % originating in savannas and dry woodlands. A large portion of the wild fire emissions are compensated by CO 2 uptake during the growing season, but an uncertain fraction of the emission from wood harvested for domestic use is not. Most of these fluxes have large in-terannual variability, on the order of ±0.5 Pg C yr −1 in standard deviation, accounting for around 25 % of the year-to-year variation in the global carbon budget. Despite the high uncertainty, the estimates provided in this paper show the important role that Africa plays in the global carbon cycle, both in terms of absolute contribution, and as a key source of interannual variability.

GlobAllomeTree is an international platform for tree allometric equations. It is the first worldwide web platform designed to facilitate the access of the tree allometric equation and to facilitate the assessment of the tree biometric characteristics for commercial volume, bio-energy or carbon cycling. The webplatform presents a database containing tree allometric equations, a software called Fantallomatrik, to facilitate the comparison and selection of the equations, and documentation to facilitate the development of new tree allometric models, improve the evaluation of tree and forest resources and improve knowledge on tree allometric equations. In the Fantallometrik software, equations can be selected by country, ecological zones, input parameters, tree species, statistic parameters and outputs. The continuously updated database currently contains over 5000 tree allometric equations classified according to 73 fields. The software Fantallometrik can be also used to compare equations, insert new data and estimate the selected output variables using field inventory. The GlobAllomeTree products are freely available at the URL: http://globallometree.org for a range of users including foresters, project developers, scientist, student and government staff.

International audience; Tropical forests in Africa store large amounts of carbon and there is an urgent need for accurate methods to estimate this important carbon stock. One method to achieve this is by allometric equations but in many forest ecosystems in Africa these have not been developed. This study combined biomass data of 896 trees from five tropical countries in Africa and eight different sources to develop mixed-species regression equations for estimation of total biomass and height in Dry, Moist and Wet forest types. For estimation of total biomass, allometric equations combining diameter, height and wood density provided the best estimators in the three forest types. Because adding wood density to diameter improved height estimation, we recommend using allometric equations that combine diameter and wood density for height estimations in mixed and diverse tropical forests. Comparing ecosystem-specific (Dry, Moist and Wet) allometric equations to general allometric equations developed with combined data, and also to pan-tropical equations, showed that ecosystem-specific equations provided better estimators. The results highlight the importance of considering wood density in tree allometry for biomass as well as for tree height estimations. Although general allometric equations can be useful, this study recommends when they are available, the use of existing site-specific or ecosystem-specific allometric equations which provide better estimates.

Models to assist management of lowland forests in Tanzania are in most cases lacking. Using a sample of 60 trees which were destructively harvested from both dry and wet lowland forests of Dindili in Morogoro Region (30 trees) and Rondo in Lindi Region (30 trees), respectively, this study developed site specific and general models for estimating total tree volume and aboveground biomass. Specifically the study developed (i) height-diameter (ht-dbh) models for trees found in the two sites, (ii) total, merchantable, and branches volume models, and (iii) total and sectional aboveground biomass models of trees found in the two study sites. The findings show that site specific ht-dbh model appears to be suitable in estimating treeheightsince the tree allometry was found to differ significantly between studied forests. The developed general volume models yielded unbiased mean prediction error and hence can adequately be applied to estimate tree volume in dry and wet lowland forests in Tanzania. General aboveground biomass model appears to yield biased estimates; hence, it is not suitable when accurate results are required. In this case, site specific biomass allometric models are recommended. Biomass allometric models which include basic wood density are highly recommended for improved estimates accuracy when such information is available.

With the growing interest in estimating carbon stocks in forests, available allometric equations have been compiled. These compilations often reveal contrasting models for the same species and site. Rather than choosing a model with the risk of not selecting the best available one, Bayesian model averaging (BMA) offers a way to combine different allometric equations into a single predictive model. In the deterministic version of BMA, existing models with known coefficients are combined. In the statistical version, competing models are at the same time fitted and combined. Using the BMA of deterministic models, we combined three existing multispecies pan-tropical biomass equations for tropical moist forests. The resulting model brought a relatively minor although consistent improvement of the predictions of the aboveground dry biomass of trees. These three models were particular cases of a family of models that were subsequently combined using the BMA of statistical models. Again, the resulting model was able to capture features in the biomass response to diameter that no single model was able to fit. BMA thus is an alternative to model selection that allows integrating the biomass response from different models. (Resume d'auteur)

The African continent contributes one of the largest uncertainties to the global CO 2 budget, because very few long-term measurements are carried out in this region. The contribution of Africa to the global carbon cycle is characterized by its low fossil fuel emissions, a rapidly increasing population causing cropland expansion, and degradation and deforestation risk to extensive dryland and savannah ecosystems and to tropical forests in Central Africa. A synthesis of the carbon balance of African ecosystems is provided at different scales, including observations of land–atmosphere CO 2 flux and soil carbon and biomass carbon stocks. A review of the most recent estimates of the net long-term carbon balance of African ecosystems is provided, including losses from fire disturbance, based upon observations, giving a sink of the order of 0.2 Pg C yr −1 with a large uncertainty around this number. By comparison, fossil fuel emissions are only of the order of 0.2 Pg C yr −1 and land-use emissions are of the order of 0.24 Pg C yr −1 . The sources of year-to-year variations in the ecosystem carbon-balance are also discussed. Recommendations for the deployment of a coordinated carbon-monitoring system for African ecosystems are given.

Summary 1. Regional above-ground biomass estimates for tropical moist forests remain highly inaccurate mostly because they are based on extrapolations from a few plots scattered across a limited range of soils and other environmental conditions. When such conditions impact biomass, the estimation is biased. The effect of soil types on biomass has especially yielded controversial results. 2. We investigated the relationship between above-ground biomass and soil type in undisturbed moist forests in the Central African Republic. We tested the effects of soil texture, as a surrogate for soil resources availability and physical constraints (soil depth and hydromorphy) on biomass. Forest inventory data were collected for trees ‡20 cm stem diameter in 2754 0.5 ha plots scattered over 4888 km 2 . The plots contained 224 taxons, of which 209 were identified to species. Soil types were characterized from a 1:1 000 000 scale soil map. Species-specific values for wood density were extracted from the CIRAD’s data base of wood technological properties. 3. We found that basal area and biomass differ in their responses to soil type, ranging from 17.8 m 2 ha )1 (217.5 t ha )1 )t o 22.3 m 2 ha )1 (273.3 t ha )1 ). While shallow and hydromorphic soils support forests with both low stem basal area and low biomass, forests on deep resource-poor soils are typically low in basal area but as high in biomass as forests on deep resource-rich soils. We demonstrated that the environmental filtering of slow growing dense-wooded species on resource-poor soils compensates for the low basal area, and we discuss whether this filtering effect is due to low fertility or to low water reserve. 4. Synthesis. We showed that soil physical conditions constrained the amount of biomass stored in tropical moist forests. Contrary to previous reports, our results suggest that biomass is similar on resource-poor and resource-rich soils. This finding highlights both the importance of taking into account soil characteristics and species wood density when trying to predict regional patterns of biomass. Our findings have implications for the evaluation of biomass stocks in tropical forests, in the context of the international negotiations on climate change.

This study gives an outlook on the carbon balance of Sub-Saharan Africa (SSA) by presenting a summary of currently available results from the project CarboAfrica (namely net ecosystem productivity and emissions from fires, deforestation and forest degradation, by field and model estimates) supplemented by bibliographic data and compared with a new synthesis of the data from national communications to UNFCCC. According to these preliminary estimates the biogenic carbon balance of SSA varies from 0.16 Pg C y−1 to a much higher sink of 1.00 Pg C y−1 (depending on the source data). Models estimates would give an unrealistic sink of 3.23 Pg C y−1, confirming their current inadequacy when applied to Africa. The carbon uptake by forests and savannas (0.34 and 1.89 Pg C y−1, respectively,) are the main contributors to the resulting sink. Fires (0.72 Pg C y−1) and deforestation (0.25 Pg C y−1) are the main contributors to the SSA carbon emissions, while the agricultural sector and forest degradation contributes only with 0.12 and 0.08 Pg C y−1, respectively. Savannas play a major role in shaping the SSA carbon balance, due to their large extension, their fire regime, and their strong interannual NEP variability, but they are also a major uncertainty in the overall budget. Even if fossil fuel emissions from SSA are relative low, they can be crucial in defining the sign of the overall SSA carbon balance by reducing the natural sink potential, especially in the future. This paper shows that Africa plays a key role in the global carbon cycle system and probably could have a potential for carbon sequestration higher than expected, even if still highly uncertain. Further investigations are needed, particularly to better address the role of savannas and tropical forests and to improve biogeochemical models. The CarboAfrica network of carbon measurements could provide future unique data sets for better estimating the African carbon balance.

Accurately monitoring tropical forest carbon stocks is an outstanding challenge. Allometric models that consider tree diameter, height and wood density as predictors are currently used in most tropical forest carbon studies. In particular, a pantropical biomass model has been widely used for approximately a decade, and its most recent version will certainly constitute a reference in the coming years. However, this reference model shows a systematic bias for the largest trees. Because large trees are key drivers of forest carbon stocks and dynamics, understanding the origin and the consequences of this bias is of utmost concern. In this study, we compiled a unique tree mass dataset on 673 trees measured in five tropical countries (101 trees > 100 cm in diameter) and an original dataset of 130 forest plots (1 ha) from central Africa to quantify the error of biomass allometric models at the individual and plot levels when explicitly accounting or not accounting for crown mass variations. We first showed that the proportion of crown to total tree aboveground biomass is highly variable among trees, ranging from 3 to 88 %. This proportion was constant on average for trees < 10 Mg (mean of 34 %) but, above this threshold, increased sharply with tree mass and exceeded 50 % on average for trees ≥ 45 Mg. This increase coincided with a progressive deviation between the pantropical biomass model estimations and actual tree mass. Accounting for a crown mass proxy in a newly developed model consistently removed the bias observed for large trees (> 1 Mg) and reduced the range of plot-level error from −23–16 to 0–10 %. The disproportionally higher allocation of large trees to crown mass may thus explain the bias observed recently in the reference pantropical model. This bias leads to far-from-negligible, but often overlooked, systematic errors at the plot level and may be easily corrected by accounting for a crown mass proxy for the largest trees in a stand, thus suggesting that the accuracy of forest carbon estimates can be significantly improved at a minimal cost.

Given the pressing need to quantify carbon fluxes associated with terrestrial vegetation dynamics, an increasing number of researchers have sought to improve estimates of tree volume, biomass, and carbon stocks. Tree allometric equations are critical tools for such purpose and have the potential to improve our understanding about carbon sequestration in woody vegetation, to support the implementation of policies and mechanisms designed to mitigate climate change (e.g. CDM and REDD+; Agrawal et al. 2011), to calculate costs and benefits associated with forest carbon projects, and to improve bioenergy systems and sustainable forest management (Henry et al. 2013).

Three options are proposed to improve the accuracy of national forest biomass estimates and decrease the uncertainty related to tree model selection depending on available data and national contexts. Introduction: Different tree volume and biomass equations result in different estimates. At national scale, differences of estimates can be important while they constitute the basis to guide policies and measures, particularly in the context of climate change mitigation. Method: Few countries have developed national tree volume and biomass equation databases and have explored its potential to decrease uncertainty of volume and biomasttags estimates. With the launch of the GlobAllomeTree webplatform, most countries in the world could have access to country-specific databases. The aim of this article is to recommend approaches for assessing tree and forest volume and biomass at national level with the lowest uncertainty. The article highlights the crucial need to link allometric equation development with national forest inventory planning efforts. Results: Models must represent the tree population considered. Data availability; technical, financial, and human capacities; and biophysical context, among other factors, will influence the calculation process. Conclusion: Three options are proposed to improve accuracy of national forest assessment depending on identified contexts. Further improvements could be obtained through improved forest stratification and additional non-destructive field campaigns. © 2015, The Author(s).

Advances in ecology face the complexity of ecosystems with dynamics longer than a single scientist’s career. In forestry and REDD+ practice, in particular, our ability to understand forest ecosystem dynamics and to manage them for mitigation and adaptation strongly relies on the combination of long-term research efforts and on data sharing. However, data collected by many measurement campaigns are regularly lost because of a lack of capacity to archive and maintain such information. Much progress would be achieved by encouraging researchers to provide access to primary data or publish “data-papers” (Chavan and Penev 2011; Cifuentes Jara et al. 2013; Fady et al. 2014).

The main goal of national forest programs is to lead and steer forest policy development and implementation processes in an inter-sectoral way (FAO 2006). National forest monitoring systems contribute to forest programs through monitoring forest changes and forest services over time (FAO 2013). To do so, they generally collect and analyze forest-related data and provide knowledge and recommendations at regular intervals. The collection of forest-related data and their analyses have continually evolved with technological and computational advances (Kleinn 2002). For instance, ground measurements, such as diameter or height measurements, which were typically measured with measuring tape or forest compasses and relascopes, are now enhanced with new technologies, such as laser range finders. Furthermore, remote sensing is being increasingly used to improve ground sampling strategies (Maniatis and Mollicone 2010), to calculate forested land area and area changes (INPE 2006; INPE 2008; Hansen et al. 2013), and to detect many variables of interest such as forest fires, pest outbreaks, or trees outside forests (Barducci et al. 2002). The use of remotely sensed data together with ground-based observations has gained a lot of attention for estimating greenhouse gas emissions and removals associated with forests, particularly in the context of REDD+ (GOFC-GOLD 2010; GFOI 2014). During the last decades, the amount of information collected during forest inventories has thus grown rapidly and has, in turn, improved our ability to survey and manage many services such as biodiversity, carbon sequestration, or recreation. However, national forest monitoring approaches remain very heterogeneous from one country to another, and many national systems have still not taken the full advantage of newly operational technologies, despite an increasing availability of free, or at least less costly, data. This is probably because the use of these technologies to assess forest structural properties is, for the most part, used by only a few specialists and is largely confined to the research sector. The objective of this paper is to raise awareness by presenting, in a comprehensible way, some existing and promising technologies for supporting national forest monitoring.