Above- and belowground tree biomass models for three mangrove species in Tanzania: a nonlinear mixed effects modelling approach

International audienceAbstractKey messageTested on data from Tanzania, both existing species-specific and common biomass models developed elsewhere revealed statistically significant large prediction errors. Species-specific and common above- and belowground biomass models for three mangrove species were therefore developed. The species-specific models fitted better to data than the common models. The former models are recommended for accurate estimation of biomass stored in mangrove forests of Tanzania.ContextMangroves are essential for climate change mitigation through carbon storage and sequestration. Biomass models are important tools for quantifying biomass and carbon stock. While numerous aboveground biomass models exist, very few studies have focused on belowground biomass, and among these, mangroves of Africa are hardly or not represented.AimsThe aims of the study were to develop above- and belowground biomass models and to evaluate the predictive accuracy of existing aboveground biomass models developed for mangroves in other regions and neighboring countries when applied on data from Tanzania.MethodsData was collected through destructive sampling of 120 trees (aboveground biomass), among these 30 trees were sampled for belowground biomass. The data originated from four sites along the Tanzanian coastline covering three dominant species: Avicennia marina (Forssk.) Vierh, Sonneratia alba J. Smith, and Rhizophora mucronata Lam. The biomass models were developed through mixed modelling leading to fixed effects/common models and random effects/species-specific models.ResultsBoth the above- and belowground biomass models improved when random effects (species) were considered. Inclusion of total tree height as predictor variable, in addition to diameter at breast height alone, further improved the model predictive accuracy. The tests of existing models from other regions on our data generally showed large and significant prediction errors for aboveground tree biomass.ConclusionInclusion of random effects resulted into improved goodness of fit for both above- and belowground biomass models. Species-specific models therefore are recommended for accurate biomass estimation of mangrove forests in Tanzania for both management and ecological applications. For belowground biomass (S. alba) however, the fixed effects/common model is recommended

Infective mitral valve myxoma with coronary artery embolization: Surgical intervention followed by prolonged survival

Mauyaetal.CarbonBalanceandManagement (2015) 10:10 DOI 10.1186/s13021-015-0021-x © 2015 Mauya et al.; licensee Springer. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.Background: Airborne laser scanning (ALS) has recently emerged as a promising tool to acquire auxiliary information for improving aboveground biomass (AGB) estimation in sample-based forest inventories. Under design-based and model-assisted inferential frameworks, the estimation relies on a model that relates the auxiliary ALS metrics to AGB estimated on ground plots. The size of the field plots has been identified as one source of model uncertainty because of the so-called boundary effects which increases with decreasing plot size. Recent re- search in tropical forests has aimed to quantify the boundary effects on model prediction accuracy, but evidence of the consequences for the final AGB estimates is lacking. In this study we analyzed the effect of field plot size on model prediction accuracy and its implication when used in a model-assisted inferential framework. Results: The results showed that the prediction accuracy of the model improved as the plot size increased. The adjusted R 2 increased from 0.35 to 0.74 while the relative root mean square error decreased from 63.6 to 29.2%. Indicators of boundary effects were identified and confirmed to have significant effects on the model residuals. Variance estimates of model-assisted mean AGB relative to corresponding variance estimates of pure field-based AGB, decreased with increasing plot size in the range from 200 to 3000 m 2 . The variance ratio of field-based esti- mates relative to model-assisted variance ranged from 1.7 to 7.7. Conclusions: This study showed that the relative improvement in precision of AGB estimation when increasing field-plot size, was greater for an ALS-assisted inventory compared to that of a pure field-based inventory

Scenarios of land use and land cover change and their multiple impacts on natural capital in Tanzania

REDD+ (reducing emissions from deforestation, and forest degradation, plus the conservation of forest carbon stocks, sustainable management of forests, and enhancement of forest carbon stocks, in developing countries) requires information on land use and land cover changes (LULCC) and carbon emissions trends from the past to the present and into the future. Here we use the results of participatory scenario development in Tanzania, to assess the potential interacting impacts on carbon stock, biodiversity and water yield of alternative scenarios where REDD+ is effectively implemented or not by 2025, the green economy (GE) and the business as usual (BAU) respectively. Under the BAU scenario, land use and land cover changes causes 296 MtC national stock loss by 2025, reduces the extent of suitable habitats for endemic and rare species, mainly in encroached protected mountain forests, and produce changes of water yields. In the GE scenario, national stock loss decreases to 133 MtC. In this scenario, consistent LULCC impacts occur within small forest patches with high carbon density, water catchment capacity and biodiversity richness. Opportunities for maximising carbon emissions reductions nationally are largely related to sustainable woodland management but also contain trade-offs with biodiversity conservation and changes in water availability

Are miombo woodlands vital to livelihoods of rural households? Evidence from Urumwa and surrounding communities, Tabora, Tanzania

Forests, trees and livelihoods 2013; 22(2):124-140This study investigated contribution of miombo woodland resources accrued from Urumwa Forest Reserve (UFR) to income of rural households. Data and conclusions are based on 84 randomly surveyed households in four villages adjacent to UFR. Using descriptive statistics, the analysis was guided by the sustainable livelihood framework conceptual model. Results show that the miombo woodlands of the UFR account for 42% of total household income. Further analysis reveals that woodlands contribute 28% and 59% of non-monetary and monetary income, respectively. This demonstrates a significant role played by miombo woodlands. Woodland resources contribute to household income through various livelihood activities. Accordingly the woodland resources accrued from the UFR cover human basic needs. Results from this study empirically demonstrate the vital role played by miombo woodlands in either supporting current consumption or serving as safety net. It is, therefore, recommended that current and future management strategies in the forest sector emphasize forest and livelihood dimensions for sustainability of both livelihood and forest and woodland resources

Are miombo woodlands vital to livelihoods of rural households? Evidence from Urumwa and surrounding communities, Tabora, Tanzania

Forests, trees and livelihoods 2013; 22(2):124-140This study investigated contribution of miombo woodland resources accrued from Urumwa Forest Reserve (UFR) to income of rural households. Data and conclusions are based on 84 randomly surveyed households in four villages adjacent to UFR. Using descriptive statistics, the analysis was guided by the sustainable livelihood framework conceptual model. Results show that the miombo woodlands of the UFR account for 42% of total household income. Further analysis reveals that woodlands contribute 28% and 59% of non-monetary and monetary income, respectively. This demonstrates a significant role played by miombo woodlands. Woodland resources contribute to household income through various livelihood activities. Accordingly the woodland resources accrued from the UFR cover human basic needs. Results from this study empirically demonstrate the vital role played by miombo woodlands in either supporting current consumption or serving as safety net. It is, therefore, recommended that current and future management strategies in the forest sector emphasize forest and livelihood dimensions for sustainability of both livelihood and forest and woodland resources

Modelling above ground biomass in Tanzanian miombo woodlands using TanDEM-X WorldDEM and field data

The use of Interferometric Synthetic Aperture Radar (InSAR) data has great potential for monitoring large scale forest above ground biomass (AGB) in the tropics due to the increased ability to retrieve 3D information even under cloud cover. To date; results in tropical forests have been inconsistent and further knowledge on the accuracy of models linking AGB and InSAR height data is crucial for the development of large scale forest monitoring programs. This study provides an example of the use of TanDEM-X WorldDEM data to model AGB in Tanzanian woodlands. The primary objective was to assess the accuracy of a model linking AGB with InSAR height from WorldDEM after the subtraction of ground heights. The secondary objective was to assess the possibility of obtaining InSAR height for field plots when the terrain heights were derived from global navigation satellite systems (GNSS); i.e., as an alternative to using airborne laser scanning (ALS). The results revealed that the AGB model using InSAR height had a predictive accuracy of RMSE = 24.1 t·ha−1 ; or 38.8% of the mean AGB when terrain heights were derived from ALS. The results were similar when using terrain heights from GNSS. The accuracy of the predicted AGB was improved when compared to a previous study using TanDEM-X for a sub-area of the area of interest and was of similar magnitude to what was achieved in the same sub-area using ALS data. Overall; this study sheds new light on the opportunities that arise from the use of InSAR data for large scale AGB modelling in tropical woodlands.Modelling above ground biomass in Tanzanian miombo woodlands using TanDEM-X WorldDEM and field datapublishedVersio

The likely mechanism for implementing REDD policy in Tanzania

Kyoto: Think Global Act Local, Research Project Sokoine University of Agriculture, Morogoro, Tanzania.Till 2012, establishing new forest is the only eligible practice for forest carbon trading under the Clean Development Mechanism (CDM) of the Kyoto Protocol of the United Nations Framework Convention on Climate Change (UNFCCC). Management of natural forest is not credited at present. Reduced Emissions from Deforestation and forest Degradation (REDD) policy, is an alternative mechanism that is still discussed for the post 2012 regime. Under REDD, countries would, on a voluntary basis, aim to reduce the rate at which their forests are being lost, and receive compensation in proportion to the carbon emissions saved compared to a baseline reference scenario which represent the ‘without intervention’ case. The REDD policy is therefore likely to be undertaken nationally, the country deforestation baseline would be determined by depicting historical land use changes from satellite imagery and typical carbon stock data for different types of forests to calculate the changes in terms of tons of carbon. After developing national level reference scenarios for the entire country, a system of ‘nested baselines’ i.e. an interlocking set of baselines that covers the whole country and sums to the national baseline is needed. ‘Nested baselines’ are necessary to operationalize REDD internally for the different geographic regions and to account for different forest regimes e.g. national parks, forest reserves, community forests, and private forests. This system is needed in order to provide incentives to stakeholders who are responsible for reductions in carbon losses within the country. In line with the current forest policy, the government is urged to consider Participatory Forest Management (PFM) as part of their approach under REDD. The established village framework in the Tanzanian Government offers the opportunity for implementing the REDD policy nationally. This can be achieved through developing and implementing land use plan for each village. From the start of the project, monitoring is done to determine the standing stock in both protective forests and productive forests. For a village to be rewarded carbon credits at any accounting time there must be evidence of forest enhancement or reduced deforestation/degradation. Since there are no data on carbon stocks, studies on forest inventories using methodology such as that developed by the Kyoto: Think Global Act Local research project are recommended. Possible strategy for the scaling up of the participatory inventory methodology is to train villagers and their local supporting forest staff to carry-out forest inventories on their own in the entire country

Stump diameter: potential predictor of removed biomass through tree cutting

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The likely mechanism for implementing REDD policy in Tanzania

Kyoto: Think Global Act Local, Research Project Sokoine University of Agriculture, Morogoro, Tanzania.Till 2012, establishing new forest is the only eligible practice for forest carbon trading under the Clean Development Mechanism (CDM) of the Kyoto Protocol of the United Nations Framework Convention on Climate Change (UNFCCC). Management of natural forest is not credited at present. Reduced Emissions from Deforestation and forest Degradation (REDD) policy, is an alternative mechanism that is still discussed for the post 2012 regime. Under REDD, countries would, on a voluntary basis, aim to reduce the rate at which their forests are being lost, and receive compensation in proportion to the carbon emissions saved compared to a baseline reference scenario which represent the ‘without intervention’ case. The REDD policy is therefore likely to be undertaken nationally, the country deforestation baseline would be determined by depicting historical land use changes from satellite imagery and typical carbon stock data for different types of forests to calculate the changes in terms of tons of carbon. After developing national level reference scenarios for the entire country, a system of ‘nested baselines’ i.e. an interlocking set of baselines that covers the whole country and sums to the national baseline is needed. ‘Nested baselines’ are necessary to operationalize REDD internally for the different geographic regions and to account for different forest regimes e.g. national parks, forest reserves, community forests, and private forests. This system is needed in order to provide incentives to stakeholders who are responsible for reductions in carbon losses within the country. In line with the current forest policy, the government is urged to consider Participatory Forest Management (PFM) as part of their approach under REDD. The established village framework in the Tanzanian Government offers the opportunity for implementing the REDD policy nationally. This can be achieved through developing and implementing land use plan for each village. From the start of the project, monitoring is done to determine the standing stock in both protective forests and productive forests. For a village to be rewarded carbon credits at any accounting time there must be evidence of forest enhancement or reduced deforestation/degradation. Since there are no data on carbon stocks, studies on forest inventories using methodology such as that developed by the Kyoto: Think Global Act Local research project are recommended. Possible strategy for the scaling up of the participatory inventory methodology is to train villagers and their local supporting forest staff to carry-out forest inventories on their own in the entire country