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

Integrated modelling for economic valuation of the role of forests and woodlands in drinking water provision to two African cities

Rapidly growing economies often have high population growth, resulting in agricultural expansion in rural areas and increased water demand in urban areas. Conversion of forests and woodlands to agriculture may threaten safe and reliable water supply in cities. This study assesses the regulating functions and economic values of forests and woodlands in meeting the water needs of two major cities in Tanzania and proposes an integrated modelling approach with a scenario-based analysis to estimate costs of water supply avoided by forest conservation. We use the process-based hydrological Soil and Water Assessment Tool (SWAT) to simulate the role of woody habitats in the regulation of hydrological flow and sediment control. We find that the forests and woodlands play a significant role in regulating sediment load in rivers and reducing peak flows, with implications for the water supply from the Ruvu River to Dar es Salaam and Morogoro. A cost-based value assessment under water treatment works conditions up to 2016 suggests that water supply failure due to deforestation would cost Dar es Salaam USD 4.6-17.6 million per year and Morogoro USD 308 thousand per year. Stronger enforcement of forest and woodland protection in Tanzania must balance water policy objectives and food security

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

Biomass estimation and carbon storage in Mangrove forests of Tanzania

This study aimed to develop tools for biomass estimation and quantify carbon stored in mangrove forests of Tanzania mainland. The study was carried out in four sites along the Tanzanian coastline; Pangani, Bagamoyo, Rufiji and Lindi-Mtwara. A total of 120 plots were measured along transects running perpendicular to sea/rivers. From each plot, one tree was destructively sampled for aboveground biomass. Thirty among 120 trees were sampled for belowground biomass. Data analysis was carried out in R software. Procedures for quantification of belowground biomass for Avicennia marina (Forssk.) Vierh, Sonneratia alba J. Smith and Rhizophora mucronata Lam. were documented in detail. Root sampling is recommended for A. marina and S. alba while for R. mucronata, total root excavation method may be applied. The methods are more comprehensive than previously reported methods, therefore they should be applied in quantification of BGB. The study found an overall mean tree aboveground basic density of 0.60±0.00 (SE) g cm -3 , 0.54 ± 0.01 (SE) g cm -3 and 0.69 ± 0.01 (SE) g cm -3 for A. marina, S. alba and R. mucronata, respectively. Similarly, the overall mean tree belowground basic density was 0.57 ± 0.02 (SE) g cm -3 , 0.32 ± 0.01 (SE) g cm -3 and 0.53 ± 0.02 (SE) g cm -3 for A. marina, S. alba and R. mucronata, respectively. The study also showed that basic density varied between species, tree sizes and tree components. Accordingly, if properly determined and applied, basic density may be useful as a conversion factor and yield accurate biomass estimates. Otherwise they are likely to be a source of uncertainties in biomass estimation. Common (multi-species) and species-specific above- and belowground biomass models for the three mangrove species were developed.ii Species-specific models had better fit than common models. Evaluation of existing biomass models on data from this study generally showed large and significant prediction errors. Possibly this may be due to application of the models beyond data size ranges, geographical locations, and differences in forest structure and tree architecture. Species-specific models from this study are therefore recommended. The use models to unrepresented species is not recommended, where necessary however a conservativeness principle (i.e. when accuracy of estimates cannot be achieved, the risk of over- or under-estimation should be minimised) need to be applied. Using biomass models from this study and forest inventory data collected by National Forest Resources Monitoring and Assessment (NAFORMA) of Tanzania, the study quantified aboveground carbon (AGC), belowground carbon (BGC) and total carbon (TC) stored in mangrove forests of Tanzania mainland. Results showed that, AGC, BGC and TC were 33.5 ± 5.8 Mg C ha -1 (53% of TC), 30.0 ± 4.5 Mg C ha -1 (47% of TC) and 63.5 ± 8.4 Mg C ha -1 respectively. Given that, mangroves of Tanzania mainland cover approximately 158, 100 ha, a total of 10.0 millions Mg C (i.e. 37.2 millions Mg CO 2 e) is stored in mangrove forests of Tanzania. Results from this study are essential for REDD+ initiatives and provides useful input in management of mangrove forests in the country.Climate Change Impacts and Mitigation Programme (CCIAM)and the Kingdom of Norwa

Biomass estimation and carbon storage in Mangrove forests of Tanzania

This study aimed to develop tools for biomass estimation and quantify carbon stored in mangrove forests of Tanzania mainland. The study was carried out in four sites along the Tanzanian coastline; Pangani, Bagamoyo, Rufiji and Lindi-Mtwara. A total of 120 plots were measured along transects running perpendicular to sea/rivers. From each plot, one tree was destructively sampled for aboveground biomass. Thirty among 120 trees were sampled for belowground biomass. Data analysis was carried out in R software. Procedures for quantification of belowground biomass for Avicennia marina (Forssk.) Vierh, Sonneratia alba J. Smith and Rhizophora mucronata Lam. were documented in detail. Root sampling is recommended for A. marina and S. alba while for R. mucronata, total root excavation method may be applied. The methods are more comprehensive than previously reported methods, therefore they should be applied in quantification of BGB. The study found an overall mean tree aboveground basic density of 0.60±0.00 (SE) g cm -3 , 0.54 ± 0.01 (SE) g cm -3 and 0.69 ± 0.01 (SE) g cm -3 for A. marina, S. alba and R. mucronata, respectively. Similarly, the overall mean tree belowground basic density was 0.57 ± 0.02 (SE) g cm -3 , 0.32 ± 0.01 (SE) g cm -3 and 0.53 ± 0.02 (SE) g cm -3 for A. marina, S. alba and R. mucronata, respectively. The study also showed that basic density varied between species, tree sizes and tree components. Accordingly, if properly determined and applied, basic density may be useful as a conversion factor and yield accurate biomass estimates. Otherwise they are likely to be a source of uncertainties in biomass estimation. Common (multi-species) and species-specific above- and belowground biomass models for the three mangrove species were developed.ii Species-specific models had better fit than common models. Evaluation of existing biomass models on data from this study generally showed large and significant prediction errors. Possibly this may be due to application of the models beyond data size ranges, geographical locations, and differences in forest structure and tree architecture. Species-specific models from this study are therefore recommended. The use models to unrepresented species is not recommended, where necessary however a conservativeness principle (i.e. when accuracy of estimates cannot be achieved, the risk of over- or under-estimation should be minimised) need to be applied. Using biomass models from this study and forest inventory data collected by National Forest Resources Monitoring and Assessment (NAFORMA) of Tanzania, the study quantified aboveground carbon (AGC), belowground carbon (BGC) and total carbon (TC) stored in mangrove forests of Tanzania mainland. Results showed that, AGC, BGC and TC were 33.5 ± 5.8 Mg C ha -1 (53% of TC), 30.0 ± 4.5 Mg C ha -1 (47% of TC) and 63.5 ± 8.4 Mg C ha -1 respectively. Given that, mangroves of Tanzania mainland cover approximately 158, 100 ha, a total of 10.0 millions Mg C (i.e. 37.2 millions Mg CO 2 e) is stored in mangrove forests of Tanzania. Results from this study are essential for REDD+ initiatives and provides useful input in management of mangrove forests in the country.Climate Change Impacts and Mitigation Programme (CCIAM)and the Kingdom of Norwa

Carbon dynamics and sequestration by urban mangrove forests of Dar es Salaam, Tanzania

This study intended to 1) determine spatial and temporal changes of mangrove forests, 2) identify drivers of mangrove deforestation and forest degradation, 3) determine historical carbon storage, sequestration and deforestation emissions by mangrove forests, and 4) determine whether mangrove forests are a source or sink of CO2 in Dar es Salaam, Tanzania. Mangrove forests have decreased from 4,813 hectares in 1986 to 1961 hectares in 2016. The following were prominent drivers of deforestation in descending order: clearing mangrove forests for salt pans; hotel construction; settlement; and charcoal making. Tree removals for firewood and building poles were also prominent drivers of mangrove forest degradation. Similarly, carbon stored in mangrove forests has decreased from 1,131,055 tonnes CO2e in 1986 to 460,835 tonnes CO2e in 2016. Sequestration of CO2 by mangrove forests is estimated at 133,516 (1986-1995); 106,110 (1995-2006) and 69,616 (2006-2016) tonnes CO2e year-1. Conversely, mangrove deforestation has resulted in emissions of about 27,400, 16,500 and 24,000 tonnes CO2e year-1 in 1986-1995, 1995- 2006 and 2006-2016, respectively. Urban mangrove forests play an important environmental role in mitigating climate change and amelioration of local weather through the large carbon stocks they store and sequester. Mangrove forests in the study area remain a net carbon sink, however, the sink role played by mangrove forests in the study area is decreasing rapidly. The declining spatial and temporal trends of urban mangrove forest cover has resulted in a systematic decrease in the total carbon stored and sequestered by mangrove forests. In the absence of timely measures of preserving and rehabilitating degraded mangrove areas, the mangrove forests of Dar es Salaam may become the source of CO2. The study recommends effective urban land use planning and effective law enforcement to ensure a win-win situation through sustained ecosystem services offered by urban mangrove forests to support economic growth

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

Carbon stocks for different land cover types in Mainland Tanzania

Abstract Background Developing countries participating in the mitigation mechanism of reducing emissions from deforestation and forest degradation (REDD+), are required to establish a forest reference emission level (FREL), if they wish to seek financial support to reduce carbon emissions from deforestation and forest degradation. However, establishment of FREL relies heavily on the accurate estimates of carbon stock as one of the input variable for computation of the emission factors (EFs). The product of an EF and activity data, such as the area of deforestation, results in the total emissions needed for establishment of FREL. This study presents the carbon stock estimates for different land cover classes based on an analysis of Tanzania’s national forest inventory data generated through the National Forest Resources Monitoring and Assessment (NAFORMA). Results Carbon stocks were estimated in three carbon pools, namely aboveground, belowground, and deadwood for each of the three land cover classes (i.e. Forest, non-forest, and wetland). The weighted average carbon stock was 33.35 t C ha−1 for forest land, 4.28 t ha−1 for wetland and 5.81 t ha−1 for non-forest land. The uncertainty values were 0.9% for forest land, 11.3% for wetland and 1.8% for non-forest land. Average carbon stocks for land cover sub-classes, which make up the above mentioned major land cover classes, are also presented in our study. Conclusions The values presented in this paper correspond to IPCC tier 3 and can be used for carbon estimation at the national scale for the respective major primary vegetation type for various purposes including REDD+. However, if local based estimates values are needed, the use of auxiliary data to enhance the precision of the area of interest is recommended