Salt Tolerance of <i>Xylocarpus mekongensis</i>Pierre: Survival and Growth of Seedlings, Nutrients and Carbon Distribution in Seedling Parts

Xylocarpus mekongensis Pierre is the important tree species of the Sundarbans. The present study was conducted to evaluate the effect of salinity on the survival and growth of X. mekongensis seedlings. The distributional patterns of nitrogen, phosphorus, potassium, sodium, and carbon in seedling parts were also examined in relation to salinity. Comparatively higher survival (95%) of seedlings was observed at non-saline to slightly saline conditions (0-5 PSU) and it was decreased to 78% at 35 PSU salinity. The relative growth rate (RGR) was higher at non-saline to slightly saline (0 to 5 PSU) conditions compared to higher salinity. Nutrients, sodium, and carbon concentration are found to vary significantly (p < 0.05) among the parts of seedlings. Comparatively (p < 0.05) highest concentration of nitrogen (20 to 34 mg/g), phosphorus (0.50 to 0.75 mg/g), potassium (9 to 27 mg/g) and sodium (7 to 36 mg/g) were found in leaves, while the highest concentration of carbon (42% to 45%) was detected in the stem. However, nitrogen, potassium, and carbon concentration in different parts of seedlings showed significant (p < 0.05) negative correlations with salinity levels. It can be concluded that X. mekongensis has the capacity to tolerate higher saline condition but they grow well in non-saline to less saline conditions.

Stem and Total Above-Ground Biomass Models for the Tree Species of Freshwater Wetlands Forest, Coastal Areas and Dry Areas of Bangladesh: Using a Non-Destructive Approach

Biomass and carbon stock in a forested areas are now prime important indicators of forest management and climate change mitigation measures. But the accurate estimation of biomass and carbon in trees of forests is now a challenging issue. In most cases, pantropical and regional biomass models are used frequently to estimate biomass and carbon stock in trees, but these estimations have some uncertainty compared to the species-specific allometric biomass model. Acacia nilotica, Casuarina equisetifolia and Melia azedarach have been planted in different areas of Bangladesh considering the species-specific site requirements. While Barringtonia acutangula and Pongamia pinnata are the dominant tree species of the freshwater swamp forest of Bangladesh. This study was aimed to develop species-specific allometric biomass models for estimating stem and above ground biomass (TAGB) of these species using the non-destructive method and to compare the efficiency of the derived biomass models with the frequently used regional and pantropical biomass models. Four Ln-based models with diameter at breast height (DBH) and total height (H) were tested to derive the best fit allometric model. Among the tested models, Ln (biomass) = a + b Ln (D) + c Ln (H) was the best-fit model for A. nilotica, M. azedarach, B. acutangula and P. pinnata and Ln (biomass) = a + b Ln (D2H) was best-fit for C. equisetifolia. Finally, the derived best-fit species-specific TAGB models have shown superiority over the other frequently used pantropical and regional biomass models in relation to model efficiency and model prediction error.

Nutrient dynamics associated with leaf litter decomposition of three agroforestry tree species (Azadirachta indica, Dalbergia sissoo, and Melia azedarach) of Bangladesh

Azadirachta indica A. Juss, Dalbergia, sissoo Roxb., and Melia azedarach L. are little studied species in nutrient return capabilities from leaf litter decomposition to maintenance of the soil fertility despite their importance in agroforestry practices of Bangladesh. A leaf litter decomposition experiment was conducted using a litterbag teeh7 nique to assess the nutrient reaun efficiency of these species. The de- composition rate of leaf litter was highest for M. azedarach and lowest for D. sissoo. Rainfall and temperature of study sites showed a significant （p〈0.05） positive relationship with the rate of leaf litter decomposition. The highest decay constant was observed for M. azedarach （6.67）. Nitrogen and Phosphorus concentration in leaf litter showed a decreased trend sharply at the end of the first month, whereas rapid decrease of Potassium concentration was reported within 10 days. Conversely, higher concentration of nutrient was observed at the later stages of decomposition. All three species showed a similar pattern of nutrient release （K 〉 N 〉 P） during the decomposition process of leaf litter. Among the studied species, D. sissoo was best in terms of N and P return and A. indica was best in terms of K return.

Allometric relationship for estimating above-ground biomass of Aegialitis rotundifolia Roxb. of Sundarbans mangrove forest, in Bangladesh

Tree biomass plays a key role in sustainable management by providing different aspects of ecosystem. Estimation of above ground biomass by non-destructive means requires the dex;elopment of allometric equations. Most researchers used DBH （diameter at breast height） and TH （total height） to develop allometric equation for a tree. Very few spe- cies-specific allometric equations are currently available for shrubs to estimate of biomass from measured plant attributes. Therefore, we used some of readily measurable variables to develop allometric equations such as girth at collar-height （GcH） and height of girth measuring point （GMH） with total height （TH） for A. rotundifolia, a mangrove species of Sundarbans of Bangladesh, as it is too dwarf to take DBH and too ir- regular in base to take Girth at a fixed height. Linear, non-linear and logarithmic regression techniques were tried to determine the best re- gression model to estimate the above-ground biomass of stem, branch and leaf. A total of 186 regression equations were generated from the combination of independent variables. Best fit regression equations were determined by examining co-efficient of determination （R：）, co-efficient of variation （Cv）, mean-square of the error （Ms^r）, residual mean error （Rmax）, and F-value. Multiple linear regression models showed more efficient over other types of regression equation. The performance of regression equations was increased by inclusion of GMn as an independ- ent variable along with total height and GCH.

Applicability of semi-destructive method to derive allometric model for estimating aboveground biomass and carbon stock in the Hill zone of Bangladesh

Biomass estimation using allometric models is a nondestructive and popular method.Selection of an allometric model can influence the accuracy of biomass estimation.Bangladesh Forest Department initiated a nationwide forest inventory to assess biomass and carbon stocks in trees and forests.The relationship between carbon storage and sequestration in a forest has implications for climate change mitigation in terms of the carbon sink in Bangladesh.As part of the national forest inventory,we aimed to derive multi-species biomass models for the hill zone of Bangladesh and to determine the carbon concentration in tree components(leaves,branches,bark and stem).In total,175 trees of 14 species were sampled and a semi-destructive method was used to develop a biomass model,which included development of smaller branch(base dia<7 cm)biomass allometry and volume estimation of bigger branches and stems.The best model of leaf,branches,and bark showed lower values for adjusted R2(0.3152-0.8043)and model efficiency(0.436-0.643),hence these models were not recommended to estimate biomass.The best fit model of stem and total aboveground biomass(TAGB)showed higher model efficiency 0.948 and 0.837,respectively,and this model was recommended for estimation of tree biomass for the hill zone of Bangladesh.The best fit allometric biomass model for stem was Ln(Stem)=-10.7248+1.6094*Ln(D)+1.323*Ln(H)+1.1469*Ln(W);the best fit model for TAGB was Ln(TAGB)=-6.6937+0.809*Ln(D^2*H*W),where DBH=Diameter at Breast Height,H=Total Height,W=Wood density.The two most frequently used pan-tropical biomass models showed lower model efficiency(0.667 to 0.697)compared to our derived TAGB model.The best fit TAGB model proved applicable for accurate estimation of TAGB for the hill zone of Bangladesh.Carbon concentration varied significantly(p<0.05)by species and tree components.Higher concentration(48-49%)of carbon was recorded in the tree stem.

Development and Evaluation of Species-Specific Biomass Models for Most Common Timber and Fuelwood Species of Bangladesh

Allometric biomass models are efficient tools to estimate biomass of trees and forest stands in a non-destructive way. Development of species-specific allometric biomass models requires extensive fieldwork and time. Our study aimed to generate species-specific allometric biomass models for the most common fuelwood and timber species of Bangladesh. We also wanted to evaluate the performances of our models relative to the performances of regional and commonly used pan-tropical biomass models. We used semi-destructive method that incorporates tree-level volume, species-specific biomass expansion factor (BEF), and wood density. We considered four base models, 1) Ln (biomass) = a + bLn (D);2) Ln (biomass) = a + bLn (H);3) Ln (Biomass) = a + bLn (D^2H);4) Ln (Biomass) = a + bLn (D) + cLn (H) to develop species-specific best-fitted models for Total Above-Ground Biomass (TAGB) and stem biomass. The best-fitted model for each species was selected by the lowest value of Akaike Information Criterion (AIC), Residual Standard Error (RSE) and Root Mean Square Error (RMSE). The derived best-fitted models were then evaluated with respect to regional and pan-tropical models using a separate set of observed data. This evaluation was conducted by computing ME (Model Efficiency) and MPE (Model Prediction Error). The best-fitted allometric biomass models have shown higher model efficiency (0.85 to 0.99 at scale 1) and the lowest model prediction error (-8.94% to 5.27%) compared to the regional and pan-tropical models. All the examined regional and pan-tropical biomass models showed different magnitude of ME and MPE. Some models showed higher level (>0.90 at scale 1) of ME compared to the best-fitted specific species biomass model.

A multi-purpose National Forest Inventory in Bangladesh:design,operationalisation and key results

Background:National forest inventory and forest monitoring systems are more important than ever considering continued global degradation of trees and forests.These systems are especially important in a country like Bangladesh,which is characterised by a large population density,climate change vulnerability and dependence on natural resources.With the aim of supporting the Government’s actions towards sustainable forest management through reliable information,the Bangladesh Forest Inventory(BFI)was designed and implemented through three components:biophysical inventory,socio-economic survey and remote sensing-based land cover mapping.This article documents the approach undertaken by the Forest Department under the Ministry of Environment,Forests and Climate Change to establish the BFI as a multipurpose,efficient,accurate and replicable national forest assessment.The design,operationalization and some key results of the process are presented.Methods:The BFI takes advantage of the latest and most well-accepted technological and methodological approaches.Importantly,it was designed through a collaborative process which drew from the experience and knowledge of multiple national and international entities.Overall,1781 field plots were visited,6400 households were surveyed,and a national land cover map for the year 2015 was produced.Innovative technological enhancements include a semi-automated segmentation approach for developing the wall-to-wall land cover map,an object-based national land characterisation system,consistent estimates between sample-based and mapped land cover areas,use of mobile apps for tree species identification and data collection,and use of differential global positioning system for referencing plot centres.Results:Seven criteria,and multiple associated indicators,were developed for monitoring progress towards sustainable forest management goals,informing management decisions,and national and international reporting needs.A wide range of biophysical and socioeconomic data were collected,and in some cases integrated,for estimating the indicators.Conclusions:The BFI is a new information source tool for helping guide Bangladesh towards a sustainable future.Reliable information on the status of tree and forest resources,as well as land use,empowers evidence-based decision making across multiple stakeholders and at different levels for protecting natural resources.The integrated socioeconomic data collected provides information about the interactions between people and their tree and forest resources,and the valuation of ecosystem services.The BFI is designed to be a permanent assessment of these resources,and future data collection will enable monitoring of trends against the current baseline.However,additional institutional support as well as continuation of collaboration among national partners is crucial for sustaining the BFI process in future.

Above-Ground Biomass, Nutrients and Carbon in Aegiceras corniculatum of the Sundarbans

Aegiceras corniculatum grows as single-stemmed evergreen shrub or small tree in the Sundarbans mangrove forest of Bangladesh. The objectives of this study were to derive the allometric models for estimating above-ground biomass, nutrients (N, P and K) and carbon stock in A. corniclatum. A total of 8 linear models (y = aX + b, , y = aLogX + b, Logy = aX + b, Logy = aLogX + b, y = alnX + b, Lny = aX + b and Lny = alnX + b) with 64 regression equations were tested to derive the allometric model for biomass of each plant part;and nutrients and carbon stock in total aboveground biomass. The best fit allometric models were selected by considering the values of R2, CV, Rmse, MSerror, Sa, Sb, F value, AICc and Furnival Index. The selected allometric models were Logbiomass = 0.76LogDBH2 - 1.39;Biomass = 0.07DBH2 - 0.49;Logbiomass = 1.04LogDBH2 - 1.80;Logbiomass = 1.04LogDBH2 - 0.99;= 0.48DBH - 0.13 for leaves, branches, bark, stem without bark and total above-ground biomass respectively. The selected allometric models for Nitrogen, Phosphorous, Potassium and Carbon stock in total above-ground biomass were = 0.67DBH + 0.11;= 0.94DBH + 0.08;= 1.06DBH - 0.18;= 0.33DBH - 0.09 respectively.