Open Advanced Search
Get 20M+ Full-Text Papers For Less Than $1.50/day.
Start a 14-Day Trial for You or Your Team.
Learn More →
Analyzing the Relationship, Distribution of Tree Species Diversity, and Above-Ground Biomass on the Chitwan-Annapurna Landscape in Nepal
Analyzing the Relationship, Distribution of Tree Species Diversity, and Above-Ground Biomass on...
Pokhrel, Shiva;Sherpa, Chungla
Hindawi International Journal of Forestry Research Volume 2020, Article ID 2789753, 10 pages https://doi.org/10.1155/2020/2789753 Research Article AnalyzingtheRelationship,DistributionofTreeSpeciesDiversity, and Above-Ground Biomass on the Chitwan-Annapurna Landscape in Nepal 1 2 Shiva Pokhrel and Chungla Sherpa International Centre for Integrated Mountain Development, Kathmandu, Nepal Forest Research and Training Center, Ministry of Industry, Tourism, Forest and Environment, Gandaki Province, Pokhara, Nepal Correspondence should be addressed to Shiva Pokhrel; email@example.com Received 31 December 2019; Revised 22 October 2020; Accepted 4 November 2020; Published 21 November 2020 Academic Editor: Ahmad A. Omar Copyright © 2020 Shiva Pokhrel and Chungla Sherpa. )is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Forests provide numerous ecosystem goods and services. )eir roles are considered as important for both climate mitigation and adaptation program. In Nepal, there are signiﬁcant forest resources which are distributed in diﬀerent regions; however, the studies on the spatial tree species distribution and the above-ground biomass and their relationship at the landscape level have not been well studied. )is study aims to analyze the relationship, distribution of tree species diversity, and above-ground biomass at a landscape level. )e data used for this study were obtained from the Forest Research and Training Center of Nepal, International Centre for Integrated Mountain Development (ICIMOD), and Worldwide Wildlife Fund (WWF-Nepal). )e landscape has a −1 mean of 191.89 tons ha of the above-ground biomass. )e highest amount of the above-ground biomass measured was 650 −1 −1 tons ha with 96 individual trees, and the least was 3.428 tons ha . )e measured mean height of the tree was 11.77 m, and diameter at breast height (DBH) was 18.59 cm. In the case of the spatial distribution of the above-ground biomass, plots distributed at the middle altitude range greater than 900 meters above sea level (m. a. s. l) to 3000 meters above sea level taking more amount of the above-ground biomass (AGB). Similarly, the highest plot-level Shannon diversity index (H’) was 2.75 with an average of 0.96 at the middle altitude region followed by the lower region with an average of 0.89 and least 0.87 at a higher 2 2 elevation. Above-ground biomass (R � 0.48) and tree height (R � 0.506) signiﬁcantly increased with increasing elevation up to a certain level increased of elevation. Diameter at breast height (DBH) showed signiﬁcance (R � 0.364) but small increase with increasing elevation, while the relationship among tree species diversity index, above-ground biomass, and elevation showed a 2 2 weak and very weak positive relationship with R � 0.018 and R � 0.002, respectively. Based on the overall results, it is concluded that elevation has some level of inﬂuence on the forest tree diversity and above-ground biomass. )e ﬁnding of this study could be useful for landscape-level resource management and planning under various changes. other terrestrial ecosystem and store more carbon than the 1. Introduction world’s oil reserves; they also continually remove carbon Forests have a signiﬁcant function in the world climate from the atmosphere through photosynthesis that alters system by changing the concentration of carbon dioxide in atmospheric carbon to organic matter due to which the the atmosphere . As regards important services, forests relationship between biodiversity and carbon cycle has and trees play a vital role in the preservation of ecosystems become an important consideration in an international by sustaining the quality of goods and services they produce eﬀort for conserving the natural ecosystem . )e sig- during their functioning. Forests are the most important niﬁcant involvement of forest ecosystem in carbon con- natural stores of biomass and sink and store carbon than any servation and sequestration has been playing a vital role in 2 International Journal of Forestry Research occupies an area of 32,057 square kilometers . )e the present global climate change context, and therefore, forest is kept in the center of climate change mitigation physiographic regions of the landscape range from sub- tropical in lowland to alpine in highland. )e huge variation strategies [3, 5]. Pan et al.  stated that 40% of global terrestrial carbon is contained in the living forest biomass. in elevation, climate, and topography has supported dif- )e quantity of above-ground biomass in a forest governs ferent types of forest in the landscape which range from the prospective volume of carbon that can be added to the tropical mixed deciduous forest dominated by Shorea ro- atmosphere or sequestered on the land when forests are busta in Siwaliks to Schima-Castanopsis forests in the accomplished for achieving emission goals . )e amount midhills and subalpine-alpine scrub vegetation comprising of above-ground biomass produced diﬀers from places to Juniperus species and Rhododendron species in the high mountains and high Himal areas . )e spatial ﬁeld- places, and the relationship between above-ground biomass and species diversity has been discoursed for many years measured forest-tree diameter at breast height and tree height data from the forest plot area could be utilized to [7–9]. In this regard, regular inventory and monitoring are considered as an important means to understand the calculate the forest above-ground biomass, and later, this could supplement for the evaluation of the amount of carbon structure, diversity, above-ground biomass of diﬀerent habitats, and vegetation type support for achieving inter- that a particular forest ecosystem stored [28, 29]. Similarly, national agreements . In tropical forests, variation may at the same time, information collected during the plot-level occur due to regional diﬀerences in climate, species diver- survey also supplements the particular species which exist in sity, stem density, canopy height, stem size distribution, that plot, and this can be utilized for the tree’s species di- edaphic circumstances, geography, and disturbance history versity and forest structure assessment. )is study uses data [1, 10–16]. Likewise, changes in several environmental collected from various plots of equal size that is a spatially distributed thought study area. )e relation between above- variables along with a change in altitude could also inﬂuence the amount of above-ground biomass of that particular ground biomass, species diversity, elevation, and stand parameters such as DBH and height was assessed using species that is found in a particular area. )erefore, climate variables are considered as the greatest importance where standard statistical tools and methods. altitudinal gradient and species richness are concerned . Furthermore, there is a signiﬁcant relationship between 1.1. Study Area. Chitwan-Annapurna Landscape is the part species richness and ecosystem productivity [18–20]. Gen- of high biodiversity-rich landscapes of the greater Hima- erally, the higher productivity is associated with the greater ° ° layan landscape, located at 27 35″ and 29 33″ N latitude and number of individual and/or more total biomass of tree ° ° 82 88″ and 85 80″ E longitude in Nepal, which envisaged species in a forest ecosystem . Climatic variables, mainly during the development of biodiversity vision in Nepal . temperature and precipitation, having inﬂuencing ability on )e landscape covered 32,057 square kilometers of the 19 primary productivity resemble species richness [22, 23]. districts of central Nepal. Eight major river systems namely: Hence, altitudinal gradients and species richness are among Kali Gandaki, Seti, and Madi, Marsyangdi, Daraundi, Budi the most weighted prevailing natural trials for testing en- Gandaki, Trishuli, Rapti and their tributaries. )e landscape vironmental and evolutionary retorts of biota to environ- also covers the full or partial part of six protected areas and mental changes. Spatial variability information on forest their buﬀer zones. )is landscape links the ecologically above-ground biomass, species diversity distribution, and its unique ecoregion between north and south of Nepal through allotment along the altitudinal gradients could provide terrestrial connectivity via exiting diﬀerent forest regimes: better spatial forest management plan considering the government-managed, community, protection, leasehold, multiple beneﬁts of the forest ecosystem for climate miti- and buﬀer zone community forest. Geographically, 11.4 gation and forest resource management. percentage is located in the Siwalik region, 37.8 percentage Globally, around 9% of the total number of tree species in the midhills, and 50.8 percentage in the mountain region accounting around 8000 individual species are under the with 200 m to 8,091 m altitudinal variation with forest area threats of extension due to declining the forest and threats of distributed on less than 4000 m (Figure 1). climate change. Tropic which is rich in biodiversity has been )e landscape has varied climate which ranges from facing double impacts from climate change and anthropo- subtropical humid in the lowlands at Siwaliks to cold alpine genic pressure . )e interrelation between climate semidesert in the trans-Himalayan zone . change, biodiversity, and decreasing forest area has been observed in a diﬀerent spatial scale ranging from local, regional, and at a global level. Nepal, a small South Asian 2. Materials and Methods nation, covers only 0.1% of the global land surface but homes to 136 ecosystem types with about 2% of ﬂowering plants, )e main materials used for this study were ﬁeld-collected 6% of the bryophytes of world ﬂora, and 3% of the pteri- data, geospatial data layer, and sets of software that are dophytes. Eight types of species are assumed to be extinct, required for the statistical and spatial analysis. 30 m one species is threatened, seven types of species are vul- SRTM digital elevation model developed by NASA, nerable, and 31 species tumble under the IUCN rare species geodatabase consisting of study area shapeﬁle, and 20210 groups [25, 26]. )e Chitwan-Annapurna Landscape forest cover from the ICIMOD were used for the elevation (CHAL), a part of the Sacred Himalayan Landscape, is lo- determination and identiﬁcation of the forest cover area cated in central Nepal with a rich biodiversity landscape and in the landscape. )e lowest plot elevation from the forest International Journal of Forestry Research 3 area was 237 m, and higher elevation was 3484 m from relationship established by Sharma  for a solitary species mean sea level (MSL). Forest inventory data at the plot of analogous forest types of Nepal which was later imple- level were collected from FRTC-Nepal, ICIMOD, and mented by Shrestha and Singh . )e biomasses of WWF-Nepal representing this entire stratum. )ese in- branches and leaves (foliage) were estimated to be 42% and stitutes have collected those data by using a stratiﬁed 8% of the stem biomass, respectively , to calculate the random sampling method with a circular plot of 500 m total biomass of trees. and a radius of 12.62 m following IPCC standard guide- Total AGB � stem biomass + branch biomass + foliage biomass, lines . Generally, out of various sampling techniques, equation (3) − AGB. stratiﬁed random sampling has been extensively used for forest biomass inventory as it gives more accurate esti- (3) mation . During the ﬁeldwork, they have collected diﬀerent information such as tree height, diameter at breast height (DBH) measured at 1.3 meters above 10 cm, 3.2. Measuring Forest Tree Species Diversity. )is study re- tree species (local name and scientiﬁc name), and plot lates the Shannon diversity index (H’) as a compute of di- spatial information. A total of 384 plot data collected from versity among the trees in diﬀerent measured plots in the 2010 to 2015 in diﬀerent physiographic regions were CHAL area to measure the diversity of the tree species across processed and analyzed for measuring the spatial distri- the landscape. )is index grabs jointly species wealth and bution of tree diversity and above-ground biomass; fur- species lushness on interpretation: ther data from 90 plots representing diﬀerent elevations were used for statistical analysis along the Chitwan- ′ p ln p , (4) H � − i i Annapurna Landscape. i�1 where S equals number of species, p equals the ratio of 3. Biomass Stock Mapping and Modeling individual of species I divided by all individuals N of all species, and ln is the natural logarithm to the base. )e 3.1. Biomass Stock Calculation. Generally, above-ground Shannon diversity index ranges usually from 1.5 to 3.5 and biomass is assessed from volumetric and structural di- infrequently reaches 4.58 . mensions of the trees for which diameter at breast height (DBH) and height of the tree are taken as major variables. In 3.3. Mapping the Spatial Distribution of Above-Ground Bio- lack of species-speciﬁc biomass equation of the trees, spe- mass and Tree Diversity. Spatial variations are common in cies-speciﬁc volume equations established by Sharma and many ecological variables and have common charac- Pukkala  were used to measure the above-ground bio- teristics and properties in their occurrence. For this mass of standing tress. )e overall stem volume of single reason, topographical position of examination should be trees was derived from ﬁeld-measured DBH and tree height stated for reﬂection on concurrently in totaling a be- using the relationship in the following form : longing value to describe patterns of quantitative dis- ln(V) � a + b∗ ln(DBH) + c∗ ln(Ht), tribution of such ecological parameters . In such (1) circumstances, the geostatistical technique could con- equation(1) − allometric volume equation, tribute to analyze and estimate the variation of the where ln is the natural logarithm to the base 2.71828, V is spatially dependent variable based on the properties. the total stem volume with bark in m , to get the volume in Based on the principle of nearer observation, sample cubic meters, the estimate is to be divided by 1000, DBH is values are more likely to be nearer than the samples of at the diameter at breast height in centimeter (cm), Ht is the a distance one. tree height in meter, and a, b, and c are model parameters. Many earlier studies on metrology, mining, geology, and )e estimated parameter value of a, b, and c for diﬀerent diﬀerent branches of ecology have used this approach for species and wood density of the foremost tree species are identifying spatial variations/distribution [38–42]. For this speciﬁed in Table 1. study, a geostatistical approach was used to extrapolate the )e obtained volume was multiplied with dry wood spatial distribution of the Shannon diversity index and density (speciﬁc gravity) of the species to obtain an air-dry above-ground measure biomass that is measured at the plot bulk of trunk biomass  by applying equation (2). Species level [43–46]. )is technique was presented by Matheron in initiate in the landscape, and correspondingly, other species 1963 and commonly applied to determine the consequence and their values were also kept, and volume was derived of spatial conglomeration in forestry sectors and environ- accordingly as shown in equation (1). mental variable analysis also. Equation (5) was deployed for this calculation: Stem biomass � stem volume∗ wood density, (2) N equation(2) − calculation of stem biomass. S � λ Z S , (5) 0 i i i�1 Due to the nonappearance of conventional biomass associations of diverse tree components of separate tree where Z(S ) indicates the calculated value of the i location; species of sample forest types, this analysis used the λ indicates the indeﬁnite value-weight for the measured i 4 International Journal of Forestry Research .967295 .560754 .154213 .747672 .341131 82 83 84 84 85 W E CHAL boundary Nepal boundary Elevation (M) 50 25 0 50 Kilometers Data sources: DoS-Nepal, FRTC-Nepal,ICIMOD, WWF-Nepal, NASASRTM .967295 .560754 .154213 .747672 .341131 82 83 84 84 85 Field sample plots CHAL forest boundary CHAL boundary Figure 1: Map showing the study area with the ﬁeld sample plot location. value on the i location; S indicates the predication location; study, it was observed that above-ground biomass at the and N indicates the number of calculated values. middle altitudinal zone ranging from 902 m amsl to 3000 m MASL takes the largest amount. Altogether, 160 diﬀerent tree species were recorded from 4. Results the plot-level survey data from diﬀerent altitudinal ranges in 4.1. Above-Ground Biomass and Forest Tree Species Diversity. the CHAL area. )e highest number of diﬀerent individual tree species recorded is 29 in a plot, and the least is 1 species Above-ground biomass stock of separate trees was calculated species-wise in kilograms; formerly, total biomass of trees which is only one species that exists in that plot. Regarding calculated in the sampling plots was transformed into ton, Shannon diversity index (H'), the highest calculated index and biomass stock ton per hectare was then estimated by value for the study area is 2.75, and the least is 0, where there extrapolating the biomass stock from the sample plot of is no diversity. In the case of altitudinal variation and tree 500 m to hectare (ha). )e average above-ground biomass species diversity, an average Shannon diversity index for −1 measured was 191.89 tons ha for the Chitwan-Annapurna diﬀerent altitudinal zones was calculated based on the plot Landscape. )e largest amount of tree above-ground bio- elevation location. )e calculation shows the middle region mass measured was 650 tons per hectare with 96 individual expanding from 1000 to 2500 m has the largest average index value of 0.96 followed by a lower region below 1000 m, which trees in a plot of broadleaved forest dominated by Shorea robusta, while the least amount of biomass measured was has 0.89, and least in the upper region 2500–3500 m with 0.87 Shannon diversity index value. Figure 2 shows the 3.428 tons per hectare in a forest dominated by Pinus species and Schima wallichii with 13 individuals. Mean diameter at spatial distribution of tree species calculated through the breast height (DBH) and height of stems in the study were Shannon diversity index (H'). Shorea robusta seems to be the 18.59 cm (SD � 6.21) and 11.77 m (SD � 4.45). )e highest most dominant tree species in the lowland forest area. amount of biomass was found within a tall tree greater than Likewise, Pinus species, Schima wallichii, Castanopsis indica, 15 m height accounting for approximately 90% of the bio- Alnus nepalensis, Quercus species, Lyonia ovalifolia, Eurya mass across all of the plots. Similarly, for diameter at breast acuminata, Engelhardia spicata, Daphniphyllum himalense, height (DBH), the greatest biomass was observed on a tree and Rhododendron species dominancy have been observed having DBH greater than 45 cm in all plots. Similarly, in this with the change in certain elevations in the CHAL region. .643120 .058315 .473510 .888705 .303900 .719095 .134290 .549485 26 27 27 27 28 28 29 29 .643120 .058315 .473510 .888705 .303900 .719095 .134290 .549485 26 27 27 27 28 28 29 29 <1000 1000 – 2000 2000 – 3500 3500 – 4500 >4500 International Journal of Forestry Research 5 Table 1: Model parameters and wood density of major trees. Species a b c Wood density (kg/m ) Shorea robusta −2.4554 1.9026 0.8352 880 Syzygium cumini −2.5693 1.8816 0.8498 770 Schima wallichii −2.7385 1.8155 1.0072 690 Adina cordifolia −2.5626 1.8598 0.8783 670 Albizia sps. −2.4284 1.7609 0.9662 425 Alnus nepalensis −2.7761 1.9006 0.9428 440 Castanopsis indica −2.3204 1.8507 0.8223 740 Quercus −2.3601 1.968 0.7469 594 Rhododendron −2.3204 1.8507 0.8223 640 .967295 .560754 .154213 .747672 .341131 82 83 84 84 85 W E Forest distribution in CHAL Elevation (M) Shannon diversity index (H′) High: 2.75 80 40 080 Low: 0 Kilometers .967295 .560754 .154213 .747672 .341131 82 83 84 84 85 CHAL boundary Figure 2: Tree species richness (Shannon diversity index (H')) distribution in CHAL. 4.2. Spatial Distribution of Above-Ground Biomass and Tree 4.3. Relationship between AGB, Elevation, Species Diversity, Species Diversity in CHAL. For mapping the above-ground and Stand Parameters. Figure 4(a) shows the relationship of biomass and diversity index value, geospatial technique- above-ground biomass with elevations. )e maximum volume calculated values were classiﬁed into diﬀerent classes for of the above-ground biomass remained detected in elevation AGB (four classes) representing the amount of biomass in between 900 m and 2500 m amsl. )e analysis from the linear −1 −1 ton·ha . i.e., forest area having less than 10 ton ha , 10–200 regression model y � 2.630x + 762.1 was established to be the −1 −1 −1 2 ton ha , 200–500 ton ha , and greater than 500 ton ha , line of preeminent ﬁt. A positive correlation (R � 0.48) was respectively. Similarly, for tree species diversity, the diversity found between elevation and above-ground biomass, showing index was classiﬁed into color pith values, namely, area increases of above-ground biomass up to certain level of in- having a high index value, medium index value area, and low crease in elevation in the CHAL area. Ampere numeral of index value area; resulting outputs are shown in Figures 2 regression comparisons remained tried to infer the relationship and 3. among elevation and tree height; the best regression model .643120 .058315 .473510 .888705 .303900 .719095 .134290 .549485 26 27 27 27 28 28 29 29 .643120 .058315 .473510 .888705 .303900 .719095 .134290 .549485 26 27 27 27 28 28 29 29 <1000 1000 – 2000 2000 – 3500 3500 – 4500 >4500 6 International Journal of Forestry Research .967295 .560754 .154213 .747672 .341131 82 83 84 84 85 W E CHAL forest distribution Elevation (M) Kilometres .967295 .560754 .154213 .747672 .341131 82 83 84 84 85 Forest above-ground biomass (T/Ha) <10 200 – 500 CHAL boundary 10 – 200 >500 Figure 3: Map showing the spatial distribution of above-ground biomass in the CHAL area. which found to be ﬁtting was y � 148.9x − 411.0 with a con- tree biomass in a forest ecosystem that may be because of vincingly good correlation (R � 0.506) indicating the existence elevation variation species composition, pattern of rainfall, of a relationship between tree height and elevation as shown in forest types, age and structure of the forest stand, local site Figure 4(b). Likewise, the linear regression model observed factors, and trees size [47, 48]. In this present study, some between average tree diameter and elevation also shows similar plots having high tree species diversity had comparatively results with y � 0.005x + 11.56 and R � 0.364 (Figure 4(c)). In low biomass, and some plots with a minimum number of species had high above-ground biomass indicating near-no the case of species diversity index and above-ground biomass, a very weak relationship has been observed as shown in substantial relationship amid tree spices diversity and above- Figure 4(d). A linear regression model y � 0.020x + 7.874 was ground biomass. Some studies which were carried out in found to be the best line of ﬁt among various regression types, some parts of Nepal also found a very weak relationship with (R � 0.506) indicating the increase in tree height which between carbon stock and biodiversity [49–51]. means increasing above-ground biomass (Figure 4(e)). Like- However, in a few survey plots, a little positive corre- wise, the linear regression model between elevation and di- lation between tree species diversity and above-ground versity index y � 193.7x + 991.5 with R � 0.018 was ﬁt with biomass was observed. Such a positive relationship is im- indication of the conﬁdent feeble association amongst plot-tree portant in policy implementation for a program like REED+ species diversity and elevation. We relate a single-factor which could co-beneﬁt for biodiversity conservation as well ANOVA for analyzing the eﬀect of elevation on diﬀerent as biodiversity hotspot identiﬁcation for policymakers of local, provincial, and central level to implement a policy for variables, where elevation had some signiﬁcant eﬀects on various parameters, as shown in Table 2. resources management and planning development activities around their areas. )e above-ground biomass that is es- timated in this study is within the ranges that are found in a 5. Discussion study carried out in diﬀerent parts of Nepal [5, 49, 51–54]. )e results of this study show a multifarious and variable )e present biomass estimate demonstrates the high spatial association among elevation, tree species diversity, and variability of biomass storage within diﬀerent forest areas. forest above-ground biomass in CHAL forest areas. As )e survey plot with the highest above-ground biomass numbers of factors play a signiﬁcant role for variability of calculated contained more than 186 times the biomass of the .058315 .473510 .888705 .303900 .719095 .134290 27 27 27 28 28 29 .058315 .473510 .888705 .303900 .719095 .134290 27 27 27 28 28 29 >4000 3000 – 4000 1000 – 3000 <1000 International Journal of Forestry Research 7 3000 y = 2.6303x + 762.19 4000 y = 148.9x – 411.0 R = 0.48 R = 0.506 1500 2000 0 0 0 200 400 600 800 010 20 30 –1 AGB tons (ha ) Average tree height (m) (a) (b) 45 3 y = 4E – 05x + 0.924 y = 0.0051x + 11.561 R = 0.02 R = 0.3649 2.5 30 2 1.5 15 1 0.5 0 0 0 1000 2000 3000 0 200 400 600 800 –1 Elevation (m) AGB tons (ha ) (c) (d) 25 4000 y = 193.7x + 991.51 y = 0.0201x + 7.8745 2 R = 0.0188 R = 0.5068 0 0 0 200 400 600 0.00 1.00 2.00 3.00 –1 AGB tons (ha ) Shannon diversity index (H′) (e) (f) Figure 4: Relationship between AGB, elevation, species diversity, and stand parameters. −1 lowest calculated plot. Stand parameters such as tree height (ton·ha ), respectively. )e higher altitudinal ranges take and DBH show positive correlation; a tree having larger the lowest portion of the above-ground biomass. Based on DBH and bigger height has contributed more biomass in a the above-ground biomass calculation, the total above- plot. A similar ﬁnding was obtained by an earlier study ground biomass rank was ordered as middle altitude zone [55–57]. (1000–2500 m) > lower altitude (less than 1000 m) Besides storing a large amount of biomass, the trees > higher altitude (2500–3500 m). In terms of tree species having bigger in both DBH and height size also provide distribution, results of this study are important, both for greater ecosystem resilience and better sustainable dog- nature conservation and planning for any land trans- gedness of forest and places; therefore for carbon con- formation activities/forest resource management plan, as servation/sequestration and such stand need better the tree species is manifested diﬀerently in diﬀerent protection for climate change mitigation . In the case forests along various measured plots from diﬀerent ele- of elevation-wise biomass distribution, it was observed vations; higher diversity index is observed in the plot area that the elevations from the middle altitudinal zone take which shares the physiographic zones or could have the the larger portion of the above-ground biomass ecotone or edge eﬀects. Elevation (m) Average diameter (cm) Tree height (m) Elevation (m) Elevation (m) Diversity index (H′) 8 International Journal of Forestry Research Table 2: Analysis of variance (ANOVA) of elevation with respect to diﬀerent variables. Above-ground biomass source of variation SS df MS F P value F crit. Between groups 64068370.77 1 64068371 191.0602 5.4E − 30 3.89423 Within groups 59688894.86 178 335330.9 Total 123757265.6 179 Shannon diversity index source of variation Between groups 93774717 1 93774717 298.7432 6.26E − 40 3.894232 Within groups 55873746 178 313897.5 Total 1.5E + 08 179 DBH source of variation Between groups 90709048 1 90709048 288.1358 4.67E − 39 3.89423 Within groups 56036815 178 314813.6 Total 1.47E+08 179 Tree height source of variation Between groups 92269591 1 92269591 293.9372 1.55E − 39 3.89423 Within groups 55875837 178 313909.2 Total 1.48E + 08 179 concerning the delimitation of its frontiers or boundaries or 6. Conclusion the endorsement of any product. Identifying tree species distribution and the amount of biomass that a particular forest stores at the landscape Conflicts of Interest level are crucial to show forest ecosystem potential to climate change adaptation and mitigation program, as )e authors declare no conﬂicts of interest. well as for biodiversity conservation. Our analysis reveals that some degree of variability subsists between elevation, Acknowledgments above-ground biomass, and tree species diversity in the CHAL area. However, more research is obligatory to the ICIMOD gratefully acknowledges the support of its core causes of this variation with more spatial and temporal donors: the governments of Afghanistan, Australia, Austria, evidence information. Forest trees’ above-ground biomass Bangladesh, Bhutan, China, India, Myanmar, Nepal, Nor- can be inﬂuenced by numbers of natural as well as an- way, Pakistan, Sweden, and Switzerland. thropogenic factors. As some national-level assessment shows there is no signiﬁcant change in forest cover in the References CHAL area, especially in conservation areas over last few decades, however, as per informal reports/news, many  UNFCCC, Report on a Workshop on Reducing Emissions from parts of the landscape have been facing anthropogenic Deforestation in Developing Countries: Note by the Secretariat, United Nations Framework Convention on Climate Change, pressure via unscientiﬁc expansion of rural road, reset- Rio de Janeiro, Brazil, 2006. tlements, urban expansion, and other land transformation  FAO, 6e State of the World’s Forests 2020: Forest- activities which would directly inﬂuence the tree above- s—Biodiversity and People, FAO, Rome, Italy, 2020. ground biomass and species distribution. Looking at  J. Kishwan, R. Pandey, and V. K. Dadhwal, India’s Forest and present global climate change issues and the role of the Tree Cover: Contribution as a Carbon Sink, Indian Council of forest ecosystem, the above-ground biomass and tree Forestry Research and Education, Dehradun, India, 2009. diversity in the landscape could play signiﬁcant positive  Y. Pan, R. A. Birdsey, J. Fang et al., “A large and persistent roles in the long-term stability and resilience of both carbon sink in the world’s forests,” Science, vol. 333, no. 6045, humans and the ecology of this landscape. pp. 988–993, 2011.  S. Pokhre, “Assessment of above ground biomass and ﬁre risk zonation in selected forest areas of ludhikhola watershed, Data Availability gorkha Nepal,” Remote Sensing of Land, vol. 2, no. 1, pp. 47–64, 2018. )e datasets generated and/or analyzed during the current  S. L. Brown, P. Schroeder, and J. S. Kern, “Spatial distribution study are available from the corresponding author upon of biomass in forests of the eastern USA,” Forest Ecology and reasonable request. Management, vol. 123, no. 1, pp. 81–90, 1999.  C. M. Sharma, A. K. Mishra, R. Krishan, O. P. Tiwari, and Disclosure Y. S. Rana, “Variation in vegetation composition, biomass production, and carbon storage in ridge top forests of high )e views and interpretations in this publication are those of mountains of garhwal himalaya,” Journal of Sustainable the authors. )ey are not necessarily attributable to ICI- Forestry, vol. 35, no. 2, pp. 119–132, 2016. MOD and FRTC-Pokhara and do not imply the expression  J. Szwagrzyk and A. Gazda, “Above-ground standing biomass of any opinion by the ICIMOD concerning the legal status of and tree species diversity in natural stands of central Europe,” any country, territory, city, or area of its authority or Journal of Vegetation Science, vol. 18, no. 4, pp. 555–562, 2007. International Journal of Forestry Research 9  R. J. Whittaker, “Meta-analyses and mega-mistakes: calling  NBSAP, National Biodiversity Strategy and Action Plan, time on meta-analysis of the species richness-productivity Ministry of Forests and Soil Conservation, Kathmandu, relationship,” Ecology, vol. 91, no. 9, pp. 2522–2533, 2010. Nepal, 2014.  UNEP/CBD, Global Strategy for Plant Conservation for a  WWF Nepal, Hariyo Ban Program: Learning Strategy, Sep- World Flora Online by 2020 United Nations Environmental tember 2013, WWF Nepal, Kathmandu, Nepal, 2013. Program/Convention for Biological Diversity, United Nations  J. Chave, C. Andalo, S. Brown et al., “Tree allometry and improved estimation of carbon stocks and balance in tropical Environment Programme, Nairobi, Kenya, 2012.  T. V. Con, N. T. )ang, D. T. T. Ha et al., “Relationship forests,” Oecologia, vol. 145, no. 1, pp. 87–99, 2005.  Q. M. Ketterings, R. Coe, M. Van Noordwijk, Y. Ambagau, between aboveground biomass and measures of structure and species diversity in tropical forests of Vietnam,” Forest Ecology and C. A. Palm, “Reducing uncertainty in the use of allometric and Management, vol. 310, pp. 213–218, 2013. biomass equations for predicting above-ground tree biomass  T. V. Do, A. Osawa, and N. T. )ang, “Recovery process of a in mixed secondary forests,” Forest Ecology and Management, mountain forest after shifting cultivation in northwestern vol. 146, no. 1–3, pp. 199–209, 2001. Vietnam,” Forest Ecology and Management, vol. 259,  IPCC, Good Practice Guidance for Land Use, Land Use Change pp. 1650–1659, 2010. and Forestry, IPCC, Hayama, Japan, 2013.  D. Mohandass, A. C. Hughes, B. Mackay, P. Davidar, and  K. G. MacDicken, A Guide to Monitoring Carbon Storage in T. Chhabra, “Floristic species composition and structure of a Forestry and Agroforestry Projects, Winrock International Institute for Agricultural Development, Morrilton, AR, USA, mid-elevation tropical montane evergreen forests (sholas) of the western ghats, southern India,” Tropical Ecology, vol. 57, 1997.  E. R. Sharma and T. Pukkala, Volume Equations and Biomass pp. 533–543, 2016.  M. S. R. Murthy, S. Wesselman, and H. Gilani, Multi-Scale Prediction of Forest Trees of Nepal, Forest Survey and Statistics Forest Biomass Assessment and Monitoring in the Hindu Kush Divison, Kathmandu, Nepal, 1990. Himalayan Region: A Geospatial Perspective, International  A. N. Chaturvedi and L. Khanna, Forest Mensuration, 9, Centre for Integrated Mountain Development (ICIMOD), International Book Distributors, Dehra Dun, India, 1982.  R. P. Sharma, “Allometric models for total-tree and com- Kathmandu, Nepal, 2015.  K. M. Ngo, B. L. Turner, H. C. Muller-Landau et al., “Carbon ponent-tree biomass of alnusnepalensis d. don in Nepal,” 6e Indian Forester, vol. 137, no. 12, pp. 1386–1390, 2011. stocks in primary and secondary tropical forests in Singa- pore,” Forest Ecology and Management, vol. 296, pp. 81–89,  B. M. Shrestha and B. R. Singh, “Soil and vegetation carbon pools in a mountainous watershed of Nepal,” Nutrient Cycling  T. Urquiza-Haas, P. M. Dolman, and C. A. Peres, “Regional in Agroecosystems, vol. 81, no. 2, pp. 179–191, 2008.  W. L. Gaines, J. R. Harrod, and J. F. Lehmkuhl, Monitoring scale variation in forest structure and biomass in the yucatan peninsula, Mexico: eﬀects of forest disturbance,” Forest Biodiversity: Quantiﬁcation and Interpretation, USDA Forest Ecology and Management, vol. 247, no. 1–3, pp. 80–90, 2007. Service, Washington, DC, USA, 1999.  V. Boucher-Lalonde, A. Morin, and D. J. Currie, “How are  J. Bouma, H. W. G. Booltink, and P. A. Finke, “Use of soil tree species distributed in climatic space? a simple and general survey data for modeling solute transport in the vadose zone,” Journal of Environmental Quality, vol. 25, no. 3, pp. 519–526, pattern,” Global Ecology and Biogeography, vol. 21, no. 12, pp. 1157–1166, 2012. 1996.  J. C. Fox, P. K. Ades, and H. Bi, “Stochastic structure and  R. T. Belote and G. H. Aplet, “Land protection and timber harvesting along productivity and diversity gradients in the individual-tree growth models,” Forest Ecology and Man- northern rocky mountains,” Ecosphere, vol. 5, no. 2, p. 17, agement, vol. 154, no. 1, pp. 261–276, 2001. 2014.  E. A. Freeman and G. G. Moisen, “Evaluating kriging as a tool  R. A. Chisholm, H. C. Muller-Landau, K. Abdul Rahman to improve moderate resolution maps of forest biomass,” Environmental Monitoring and Assessment, vol. 128, no. 1–3, et al., “Scale-dependent relationships between tree species richness and ecosystem function in forests,” Journal of pp. 395–410, 2007.  S. L. King, A. J. Lister, and M. Hoppus, “A compare of kriging Ecology, vol. 101, no. 5, pp. 1214–1224, 2013.  M. Jacob, C. Leuschner, and F. M. )omas, “Productivity of and cokriging for mapping forest volume in Connecticut,” in Proceedings of the 12th Southern Forestry and Natural Re- temperate broad-leaved forest stands diﬀering in tree species diversity,” Annals of Forest Science, vol. 67, no. 5, p. 503, 2010. source Management GIS Conference, W. G. Hubbard and  H. C. Keeling and O. L. Phillips, “)e global relationship J. B. Jorden, Eds., , Athens, GA, USA, October 2000. between forest productivity and biomass,” Global Ecology and  C. S. A. Wallace, J. M. Watts, and S. R. Yool, “Characterizing Biogeography, vol. 16, no. 5, pp. 618–631, 2007. the spatial structure of vegetation communities in the mojave  C. M. McCain and J. A. Grytnes, “Elevational gradients in desert using geostatistical techniques,” Computers & Geo- species richness,” Encyclopedia of Life Sciences, John Wiley & sciences, vol. 26, no. 4, pp. 397–410, 2000.  R. Webster and M. A. Oliver, “Optimal interpolation and Sons, Hoboken, NJ, USA, 2010.  D. Nogues-Bravo, ´ M. B. Araujo, ´ T. Romdal, and C. Rahbek, isarithmic mapping of soil properties: VI disjunctive kriging and mapping the conditional porbability,” Journal of Soil “Scale eﬀects and human impact on the elevational species richness gradients,” Nature, vol. 453, no. 7192, pp. 216–219, Science, vol. 40, no. 3, pp. 497–512, 1989.  S. E. Ahmed and R. M. Ewers, “Spatial pattern of standing  FAO, Global Forest Resources Assessment 2010 Main Report, timber value across the Brazilian amazon,” PLoS One, vol. 7, FAO Food and Agriculture Organization of the United Na- no. 5, Article ID e36099, 2012. tions, Rome, Italy, 2010.  P. E. Barni and P. M. Graça, “Simulating deforestation and  MFSC, National Biodiversity Action Plan, Ministry of Forests carbon loss in amazonia: impacts in Brazil’s roraima state from reconstructing highway BR-319 (manaus-porto velho),” and Soil Conservation, HMG of Nepal, United Nations De- velopment Programme, New York, NY, USA, 2000. Environmental Management, vol. 55, no. 2, pp. 259–278, 2015. 10 International Journal of Forestry Research  F. L. Ben´ıtez, L. O. Anderson, and A. R. Formaggio, “Eval- uation of geostatistical techniques to estimate the spatial distribution of aboveground biomass in the amazon rainforest using high-resolution remote sensing data,” Acta Amazonica, vol. 46, no. 2, pp. 151–160, 2016.  A. L. Pelissari, A. Figueiredo Filho, S. P´ellico Netto, A. A. Ebling, M. Roveda, and C. R. Sanquetta, “Geostatistical modeling applied to spatiotemporal dynamics of successional tree species groups in a natural mixed tropical forest,” Eco- logical Indicators, vol. 78, pp. 1–7, 2017.  W. Xiang, J. Zhou, S. Ouyang et al., “Species-speciﬁc and general allometric equations for estimating tree biomass components of subtropical forests in southern China,” Eu- ropean Journal of Forest Research, vol. 135, no. 5, pp. 963–979,  Y. Zhao, Y. Ding, X. Hou, F. Y. Li, W. Han, and X. Yun, “Eﬀects of temperature and grazing on soil organic carbon storage in grasslands along the eurasian steppe eastern transect,” PLoS One, vol. 12, no. 10, pp. 1–16, 2017.  R. A. Mandal, I. C. Dutta, P. K. Jha, and S. Karmacharya, “Relationship between carbon stock and plant biodiversity in collaborative forests in Terai, Nepal,” International Scholarly Research Notices, vol. 2013, Article ID 625767, 7 pages, 2013.  WWF Nepal Program, Ecoregion-based conservation in the eastern himalaya: Identifying Important Areas for Biodiversity Conservation, WWF Nepal Program, Kathmandu, Nepal,  Y. K. Karna, “Mapping above ground carbon using world view satellite image and lidar data in relationship with tree diversity and forests,” M.S. thesis, University of Twente, Enschede, Netherlands, 2012.  DFRS, Tarai forests of Nepal: forests resource assessment Nepal project, Department of Forest Research and Survey, Kath- mandu, Nepal, 2014.  DFRS, State of Nepal’s Forests, Department of Forest Research and Survey Centre, Kathmandu, Nepal, 2015.  Forest Resource Assessment, Terai Forests of Nepal, Forest Resource Assessment, Kathmandu, Nepal, 2014.  R. Baishya, S. K. Barik, and K. Upadhaya, “Distribution pattern of aboveground biomass in natural and plantation forests of humid tropics in northeast India,” Tropical Ecology, vol. 50, pp. 295–304, 2009.  M. N. K. Djuikouo, J.-L. Doucet, C. K. Nguembou, S. L. Lewis, and B. Sonke, ´ “Diversity and aboveground biomass in three tropical forest types in the dja biosphere reserve, Cameroon,” African Journal of Ecology, vol. 48, no. 4, pp. 1053–1063, 2010.  K. R. Kirby and C. Potvin, “Variation in carbon storage among tree species: implications for the management of a small-scale carbon sink project,” Forest Ecology and Man- agement, vol. 246, no. 2, pp. 208–221, 2007.  I. )ompson, B. Mackey, S. McNulty, and A. Mosseler, Forest Resilience, Biodiversity and Climate Change: A Synthesis of the Biodiversity/Resilience/Stability Relationship in Forest Eco- systems, United Nations Environment Programme, Montreal, Canada, 2009.
International Journal of Forestry Research
Hindawi Publishing Corporation
Analyzing the Relationship, Distribution of Tree Species Diversity, and Above-Ground Biomass on the Chitwan-Annapurna Landscape in Nepal
International Journal of Forestry Research
, Volume 2020 –
Nov 21, 2020
Share Full Text for Free
Add to Folder
Web of Science