Forest Ecosystem

Implications of Anthropogenic Disturbances for Species Diversity, Recruitment and Carbon Density in the Mid-hills Forests of Nepal

  • Hari Prasad PANDEY , *
  • Ministry of Forests and Environment, Kathmandu 44600, Nepal
*Hari Prasad PANDEY, E-mail:

Received date: 2020-07-18

  Accepted date: 2020-09-07

  Online published: 2021-03-30


Almost three-fourths of forests are experiencing anthropogenic disturbances globally, and more than two-thirds of the forests in Nepal receive different types of disturbances. In community forests (CFs), local communities are dependent on the ecosystem services provided by the forests for various aspects of their livelihoods, which disturb the forests’ natural conditions and ecosystem functioning in a variety of ways. This study tested the major disturbance factors that had influential roles on plant species diversity, recruitment (seedlings and saplings), biomass, soil organic carbon (SOC) and total carbon density in two community-managed forests in the Mid-hills of Nepal. The stump number, cut-off seedlings and saplings, lopping, dropping, and grazing/trampling were used as measures of the major anthropogenic disturbances. The necessary data were collected from 89 randomly selected sample plots, each with an area of 250 m2. The responses to anthropogenic disturbances were analyzed using Generalized Linear Models (GLM). The results showed that forest lopping was the most significant anthropogenic disturbance for biomass and total carbon density balance. A higher degree of lopping in the forests resulted in a lowering of the forests' carbon stock in the study area. SOC showed no significant response to any of the tested anthropogenic disturbances. Woody species richness and number of saplings increased with an increasing number of stumps, which signifies that intermediate disturbance was beneficial. However, a higher intensity of lopping reduced the sapling density. Grazing/trampling was the most significant disturbance for inhibiting seedling growth. Areas in the forests with a higher intensity of trampling showed lower numbers of seedlings and saplings. These results will be a guide for managing anthropogenic disturbances in multiple-use forests in Nepal, as well as those in similar socio-economic environments worldwide.

Cite this article

Hari Prasad PANDEY . Implications of Anthropogenic Disturbances for Species Diversity, Recruitment and Carbon Density in the Mid-hills Forests of Nepal[J]. Journal of Resources and Ecology, 2021 , 12(1) : 1 -10 . DOI: 10.5814/j.issn.1674-764x.2021.01.001

1 Introduction

The world has a total forest area of 4.06 billion ha, which is 31% of the total land area (FAO, 2020). More than four-fifths of the area of Nepal consists of sloped terrain, where 44.74% of the total landmass is covered with forests (DFRS, 2015a). To manage the vast forested area, Nepal has been practising a variety of different forest management models. The community-based forest management system is one of the showcase systems of management throughout the world. Community Forests (CFs) provide various ecosystem services, including timber, firewood, fresh water, carbon sequestration, water regulation, soil protection, and landscape beauty, as well as biodiversity (Paudyal et al., 2015). In a mountainous country like Nepal, forests play key roles in watershed protection, soil conservation and biodiversity maintenance (Acharya et al., 2011) regardless of the mode of forest management. These services may be influenced by any number of anthropogenic causes, either directly or indirectly.
Globally, out of the total forest area of 4.06 billion ha, about 2.95 billion ha is being influenced by human activities (FAO, 2020). In Nepal, more than two-thirds of the forest area receives some sort of disturbance. The Department of Forest Research and Survey (2015a) has identified 15 types of natural and anthropogenic disturbances in Nepalese forests (DFRS, 2015a). Among them, stump cutting, lopping, and grazing are the major anthropogenic disturbances. Forest disturbances can be loosely divided into the categories of anthropogenic and natural, although there is no clear demarcation between these two. Natural causes would be considered exogenous and uncontrollable, so policy instruments would not help to control them (Acharya et al., 2011); but anthropogenic causes can be regulated and controlled. Communities use forest products for their daily livelihoods in various ways. As a result, forests experience different forms and intensities of anthropogenic disturbances. For example, tree felling, bush cutting, lathra (seedlings and saplings) cutting, lopping and forest fires occur throughout Nepal (DFRS, 2015a), whereas forests in the Middle Mountains region are mostly affected by disturbances in the forms of grazing, lathra cutting, tree felling and lopping (DFRS, 2015b). These anthropogenic factors also cause forest degradation, deforestation, fragmentation and other problems (Acharya et al., 2011). However, the disturbance intensities are site-specific and need to be explored separately in the individual forests for targeted conservation and management purposes (Sodhi et al., 2009; Poudyal et al., 2019).
Although the Hindu-Kush Himalayan (HKH) region is rich in biodiversity, geographic variation and climatic diversity, it is one of the most understudied regions of the world in many aspects (Sharma and Chhettri, 2005; Paudyal et al., 2015; Soni et al., 2019). Moreover, the Intergovernmental Panel on Climate Change (IPCC) has recognized the HKH region as a ‘data deficient area’ (IPCC, 2007). Consequently, very few studies have been carried out which consider disturbance factors, although some work has been done with respect to species diversity (Baral and Katzensteiner, 2009; Shrestha et al., 2013) and species focused disturbances, especially the response to lopping (Sapkota et al., 2010), in addition to primary productivity and carbon dynamics in the eastern region (Gautam and Mandal, 2016). These studies are limited because they are site- and case-specific. However, disturbance analysis can help to contribute to the sustainable livelihoods of the people who depend on local forest resource use and to the conservation of plant species diversity (Shrestha et al., 2013). Moreover, a critical analysis of disturbances has direct evidence-based conservation implications for management in areas which are connected to anthropogenic disturbances (Poudyal et al., 2019) and the elements that are sensitive to disturbance (Sodhi et al., 2009). Realizing these facts, this study analyzes the anthropogenic disturbances and the responses indicated by the diversity of plant species, seedling and sapling frequencies, biomass density, SOC density and total carbon density in community-managed forests in Nepal. The results of this study give insight and knowledge for the effective co-management of carbon, forest resources and balancing forest ecosystem services for the livelihoods of local communities by focusing on the sustainability attributes.

2 Study area

This study was carried out in Gorkha district which extends between 27°15°-28°45°N and 84°27°-84°58°E, in the Mid-hills and High Mountains of Gandaki Province, Nepal (Fig. 1). This district has an area of 3614.70 km2, with an elevation range from 228 m to 8163 m above sea level (asl). Gorkha possesses five distinct types of vegetation belts according to the altitudinal range, namely tropical, subtropical, temperate, sub-alpine, and alpine. The district receives an average annual rainfall of 1776 mm and has average annual maximum and minimum temperatures of 26.1 ℃ and 15.9 ℃, respectively (DDC, 2011).
Fig. 1 Map showing the study area

Note: Numbers in the Nepal national map indicate the names of the seven Provinces; 1=Province 1; 2=Province 2; 3=Bagmati Province; 4=Gandaki Province; 5=Lumbini Province; 6=Karnali Province; and 7=Sudurpachhim Province.

The study was carried out in two Community Forests (CFs), namely: Ghaledanda Ranakhola Community Forest (GRCF) and Ludi Damgade Community Forest (LDCF). In total, about 269 ha of forest were studied using a sampling intensity of 0.83%, and 2.23 ha of the forest area was sampled (Fig. 1). The Gorkha district with two community forests was selected for the data collection for two reasons. First, the data were easy to collect in kind without any funding support. Second, these two community forests are in the Middle Mountains of Nepal and have received the carbon pilot projects funds which allow us to examine the disturbances with carbon conservation scenarios (ANSAB, 2011). Brief descriptions of the community forests are presented here.

2.1 Ghaledanda Ranakhola Community Forest

In 1998, Ghaledanda Ranakhola Community Forest (GRCF) was formally handed over to the Ghaledanda Ranakhola Community Forest Users’ Group (GRCFUG), which had 459 households (HHs). This CFUG consists mainly of indigenous people (CFUG, 2008). GRCF covers an area of 194.2 ha, but this study considered only the area which falls under the Ludikhola sub-watershed (181.7 ha). The forest has a sub-tropical climate and characteristics, and faces south-east, south and south-west, with an elevation ranging from approximately 700 m asl to 1100 m asl. The main dominant species was Shorea robusta (>80% crown dominated) and the major associated species were Schima wallichii and Castanopsis indica. Some mature but unexploited Schima wallichii trees were also common in this forest.

2.2 Ludi Damgade Community Forest

Ludi Damgade Community Forest (LDCF) was handed over in 1993 to the Ludi Damgade Community Forest Users Group (LDCFUG), which consisted of 503 HHs of different ethnic groups and castes. The total forest area was 270.7 ha and the elevation extended between 650 m asl and 1050 m asl (CFUG, 2008). This forest mainly consisted of four species, namely Shorea robusta, Schima wallichii and Castanopsis indica as naturally regenerated stands with Pinus roxburghii plantations in small patches. Other associated common species were Clistocalyx species, Syzygium cumini, Lyonia ovalifolia, Wendlandia coriacea, and Engelhardtia spicata. Within this forest, the study considered 86.9 ha area which falls under the Ludikhola sub-watershed.

3 Materials and methods

3.1 Sampling design

Using the geographic information system (GIS), random sample plots were identified and field locations were determined using Global Positioning System (GPS) devices. Concentric circular sample plots (CCSP, see Fig. 2) of size 250 m2 were laid out throughout the forest as prescribed by ANSAB (2010). Altogether, 89 plots were sampled in a similar manner, covering the total sample area of 2.23 ha.
Fig. 2 Concentric sample plot layout in the forests

Note: AGTB= Above-ground tree biomass; AGSB= Above-ground sapling biomass; SOC= Soil organic carbon; LHG= Leaf-litter, herbs and grass; and DBH= Diameter at breast height.

The main reasons for selecting circular plots were that they were easy to layout, and they cover a greater area with less perimeter which reduces the bias that might arise due to border trees (ANSAB, 2011).

3.2 Measurements in the plots

Sample plots were laid out using a standardized-length of rope which was stretched from the centre of the sampling plot to its periphery. All the trees within the inscribed periphery around the centre were assessed, starting measurements from the north and heading in a clockwise direction. Each tree was recorded, together with its species name. Trees on the border were included if >50% of their basal area fell within the plot, otherwise they were excluded. The diameter at breast height (DBH) was measured for all trees of size greater than 5 cm at 130 cm above the ground-level from the uphill-side. The total height of each tree was measured by using Vertex IV and Transponder. Woody species having a diameter at breast height (DBH) < 5 cm were regarded as regeneration (saplings). The total number of individuals was counted within a 5.64 m radius for saplings and within a 1 m radius for seedlings in CCSP. Woody species with a height less than 1.30 m were considered as seedlings. Soil organic carbon was determined by collecting samples from the depth of 30 cm as prescribed by the IPCC (IPCC, 2006). Within a radius of 0.56 m, soil samples from three depths, 0-10 cm, 10-20 cm and 20-30 cm, were collected from the centre of the plots and four additional samples were taken from the periphery in all four directions.

3.3 Disturbance accounting

The Department of Forest Research and Survey (DFRS) has identified 15 major categories of natural and anthropogenic disturbances in Nepalese forests (DFRS, 2015a). Among these disturbances, stump cutting, lopping, and grazing are the major anthropogenic disturbances. For the purpose of this study, these three anthropogenic disturbances were considered. These anthropogenic disturbance variables were recorded as numbers of cut stumps (trees felled), degree of lopping (low to high: 0, 1, 2, 3 scores), frequency of cutting (low to high: 0, 1, 2, 3 scores), and trampling and droppings of domestic animals (low to high: 0, 1, 2, 3 scores). These values were estimated in a participatory way, in consultation with local people engaged in field surveys while standing at the centre of the plots. If no disturbance sign was seen, then the score assigned was zero (no disturbance). When the disturbance level was very low (defined as plots with disturbance of up to one-third of the area) then that plot score was assigned as 1. Similarly, if the recorded disturbance level was between 34% to 66% within the sample plots, then the score assigned was 2 (medium disturbance); and disturbance beyond 66% was considered as 3 (the highest disturbance score) for each of the disturbance-related variables.

3.4 Data management, calculation and analysis

Data were analyzed by using the guidelines for measuring carbon stocks in community-managed forests (ANSAB, 2010). The allometric equation for wet forest types developed by Chave et al., (2005) was used to estimate forest biomass density. The biomass stock density of each sampling plot was converted into carbon stock density using the IPCC (2006) default carbon fraction of 0.47 (IPCC, 2006). Sapling (DBH<5cm) biomass was calculated by using national allometric biomass tables (Tamrakar, 2000). Soil organic carbon (SOC) was calculated using the methods of Pearson et al. (2007). Measurements of root biomass are indeed highly uncertain, and the lack of empirical values for this type of biomass has been a major weakness in ecosystem studies for decades (Geider et al., 2001). Therefore, to simplify the process for estimating below-ground biomass, the MacDicken (1997) root-to-shoot ratio of 1:5 was used. The biomasses of leaf litter, grasses, dead wood, and stumps in Nepalese forests are less than 1% (DFRS, 2015a), so they were excluded from the analysis. The aggregate value of the components described above gave the total biomass for each of the forests. The SOC was analyzed in the lab for samples taken from the field using core samplers. Five samples from each sample plot were collected and weighed on-site. These five samples were mixed well, sealed in airtight polypropylene zipper bags and carried to the laboratory for further analysis. Lab work was done in the Nepal Agriculture Research Council (NARC) Soil Laboratory, Lalitpur, Nepal. In the laboratory, SOC was analysed using Walkley-Black’s Wet Oxidation method (Walkley and Black, 1934).
Woody species were identified based on the authors’ previous experience with familiar vegetation and herbarium samples were also collected from the field for both known and unknown species. Herbarium samples were pressed and framed in a standardized wooden frame with all required standard labelling. For identification, all of the collected herbarium specimens were brought to the Central Department of Botany, Tribhuvan University, and the species of each was identified.
The following models were tested for each of the response variables, with combinations of predictor disturbances using GLM:
Y1 = a + a1X1 + a2X2 + a3X3 + a4X4 + a5X5 + ei(Model 1)
Y2 = b + b1X1 + b2X2 + b3X3 + b4X4 + b5X5 + ei (Model 2)
Y3 = c + c1X1 + c2X2 + c3X3 + c4X4 + c5X5 + ei(Model 3)
Y4 = d + d1X1 + d2X2 + d3X3 + d4X4 + d5X5 + ei (Model 4)
Y5 = f + f1X1 + f2X2 + f3X3 + f4X4 + f5X5 + ei(Model 5)
Y6 = g + g1X1 + g2X2 + g3X3 + g4X4 + g5X5 + ei (Model 6)
where, Y1 = species richness; Y2 = saplings; Y3 = seedlings; Y4 = biomass density; Y5 = SOC density; and Y6 = total carbon density; a, b, c, d, f, g are intercept for corresponding models; ai, bi, ci, di, fi, and gi are coefficients for respective variables in corresponding models, i=1, 2, 3, 4, 5; X1 = number of stumps; X2 = intensity of lopping; X3 = intensity of cutting; X4 = intensity of trampling; X5 = number of droppings; and ei are the error terms of corresponding models.

3.5 Model selection procedure

The diversity (species richness), seedlings, saplings, biomass density and SOC were considered as response variables and tested against the individual predictors as described above. Firstly, the data were plotted and the skewness in its distribution was found. Secondly, the data were fitted with linear regression and the constant variance and normality of the residuals was tested using the Shapiro-Wilk Normality test. This test showed the overdispersion of the data. Thirdly, the data were fitted in the Generalized Linear Model with a Poisson distribution and tested using Chi-square tests. However, due to the overdispersion of the data, these analyses found a high degree of standard errors. Finally, to determine the safest model, the aforementioned models were each tested by considering the Quasi-Poisson distribution with log-linked functions. The final models were tested for each response variable against every predictor variable individually, but the result did not cause very much change in the standard errors. Also, the respective residuals were plotted as histograms (Fig. 3) and a normal Q-Q plot for each model to determine whether the residuals were normally distributed. Thus, the final models were selected and tested for each response variable against a combination of disturbance variables as explained in the aforementioned models (Models 1-6). All these data were analyzed using R (R Core Team, 2018) and MS Excel.
Fig. 3 The histograms of residuals of the final models based on the GLM as described in data analysis section
Residuals of the final models were plotted in the form of histograms (Fig. 3). The resulting diagrams showed an almost uni-model residual distribution of the final model connections to species richness (diversity), seedlings and saplings, SOC density and total carbon density of the study area. Such a uni-model residual distribution signifies a well explained and justified model for response variables to predictors, in which the residuals are normally distributed.

4 Results and discussion

4.1 Characteristics of the forests

The overall characteristics of each community-managed forest are briefly described in the study area section above. Here, a synopsis of the forests is given, taking into account of the response variables under analysis (Table 1).
Table 1 Basic characteristics of the forests surveyed in this study
S.N. Variables Unit Quantity Remarks
1 Species richness number of
26.00 Woody habit only
2 Tree density number ha-1 1468.80
3 Sapling density number ha-1 2695.80 Recruitments or regeneration
4 Seedling density number ha-1 32522.00
5 Biomass density t ha-1 151.15
6 SOC density t ha-1 46.76
7 Total carbon density t ha-1 117.80
The tree density in this study (Table 1) is far higher than either the national average (430 trees ha-1) of Nepal (DFRS, 2015a) or that found in a study of CFs in Dadeldhura district (Pandey et al., 2019). Very high density for recruitment (seedlings and saplings) signifies the very good regeneration state of these forests (Table 1). The findings of biomass density, SOC and total carbon are less than the national means (DFRS, 2015a) and those from a study of CFs in Dadeldhura district (Pandey et al., 2019), but relatively similar to the Middle Mountains forests of Nepal (DFRS, 2015b). The limited variations (smaller) in the amounts of biomass, SOC and carbon density may be due to the exclusion of leaf litter, grasses and branches from consideration in this study. Also, given the high density of pole-sized trees in the study areas (Table 1), the corresponding biomass and carbon constituted lesser amounts. This type of young forest structure indicates that there is a high potential for sequestering carbon from the atmosphere.

4.2 Species diversity and disturbance

Woody species richness was weakly and positively correlated to the number of stumps (felled trees) in the forests. Pearson’s product-moment correlation test (t = 2.7431, DF = 87, P-value = 0.007389) showed a positive correlation (R = 0.282147) between species richness and the number of cut stumps (Fig. 4). This result relates the stumps (disturbance) with diversity (species richness) in the community-managed forests of Nepal (Table 2). A similar result was found in a Quercus forest of Nepal, where lopping was considered the main disturbance factor. In such a situation, the conservation policy may accept small-scale human impacts as part of the forest landscape for maintaining the plant diversity in the forests. In the Phulchoki and Annapurna regions of Quercus species dominated forests, the lopping response showed a linear relationship between beta diversities (βSD and βA) and the disturbance gradient, which indicated that plant species diversity measures increased up to the level where forest disturbance was intermediate (Shrestha et al., 2013). An additional study in Sikkim, India, found that open-canopy forest showed greater plant diversity than closed-canopy forest (Chhettri et al., 2002). Moreover, recent levels of human disturbance were associated with higher species diversity in a biosphere reserve in India (Sahu et al., 2008). These results indicate that the intermediate disturbed forests had enhanced species richness of vascular plants as found in this study.
Fig. 4 Relationships between response variables and predictor variables, showing only the significant results among the variables

Note: A: Biomass density and lopping intensity; B: Carbon density and lopping intensity; C: Species richness and number of stumps; D: Saplings and lopping intensity; E: Seedlings and trampling intensity; F: Saplings and number of stumps.

Table 2 Statistical test outputs on response variables against predictors
Attributes Disturbances DF Deviance Resid. DF Resid. Deviance F-value P-value Sig.
Species richness Number of stumps 1 5.796 87 83.82 6.5418 0.0125 Yes
Degree of lopping 3 0.408 84 83.41 0.1537 0.9270 No
Degree of cutting 3 4.774 81 78.64 1.7964 0.1549 No
Dropping count 1 0.219 80 78.42 0.2471 0.6205 No
Degree of trampling 3 5.944 77 72.48 2.2365 0.0907 No
Number of saplings Number of stumps 1 148.035 87 1554.60 8.7686 0.0041 Yes
Degree of lopping 3 168.849 84 1385.70 3.3338 0.0237 Yes
Degree of cutting 3 11.427 81 1374.30 0.2256 0.8783 No
Dropping count 1 31.542 80 1342.80 1.8687 0.1756 No
Degree of trampling 3 64.271 77 1278.50 1.2690 0.2901 No
Number of seedlings Number of stumps 1 2.348 87 345.69 0.6518 0.4220 No
Degree of lopping 3 3.197 84 342.49 0.2959 0.8283 No
Degree of cutting 3 15.074 81 327.42 1.3950 0.2507 No
Dropping count 1 2.807 80 324.61 0.7792 0.3801 No
Degree of trampling 3 42.268 77 282.34 3.9116 0.0118 Yes
SOC density Number of stumps 1 0.576 87 267.18 0.1863 0.6672 No
Degree of lopping 3 19.920 84 247.26 2.1470 0.1011 No
Degree of cutting 3 4.196 81 243.07 0.4523 0.7164 No
Dropping count 1 1.272 80 241.80 0.4111 0.5233 No
Degree of trampling 3 9.642 77 232.15 1.0392 0.3801 No
Total biomass density Number of stumps 1 0.240 87 9885.60 0.0022 0.9624 No
Degree of lopping 3 1048.380 84 8837.20 3.1911 0.0282 Yes
Degree of cutting 3 380.290 81 8456.90 1.1575 0.3315 No
Dropping count 1 57.820 80 8399.10 0.5280 0.4697 No
Degree of trampling 3 488.810 77 7910.30 1.4879 0.2245 No
Total carbon density Number of stumps 1 0.050 87 2843.70 0.0014 0.9699 No
Degree of lopping 3 367.040 84 2476.70 3.7827 0.0138 Yes
Degree of cutting 3 85.480 81 2391.20 0.8810 0.4548 No
Dropping count 1 21.050 80 2370.20 0.6508 0.4223 No
Degree of trampling 3 132.510 77 2237.60 1.3657 0.2596 No

Note: Significant level = 5%; Sig.= Significant; DF= Degree of freedom; Resid.= Residuals.

Contrary to this result, a forest in Bardia National Park of Nepal which was subjected to resource extraction had a lower species richness and diversity (Thapa and Chapman, 2010). A similar finding was seen in a Shorea robusta dominated forest, where diversity declines with an increasing magnitude of disturbance, which in turn favours a higher dominance of Shorea robusta. Thus, where a single-species dominated, alpha diversity measures declined linearly along a disturbance gradient in a Shorea robusta dominated forest of Nepal (Sapkota et al., 2010). Forest operations carried out in community forests have altered plant community composition, species richness and distribution, the age class distribution of trees and vegetation structure. As a result, the CFs are being transformed into increasingly less diverse regular forests, although the overall vascular plant diversity is retained with sufficient niches within the understory vegetation (Baral and Katzensteiner, 2009). In Brazilian tropical forests, landscape and within- forest disturbances were found to contribute to biodiversity loss (Barlow et al., 2016). It is also very important to maintain a relatively high plant diversity that offers a relatively high level of goods and services to the local communities for their livelihood options (Rana et al., 2017).

4.3 Saplings and disturbance

Saplings are the established form of regeneration in a forest. The test results showed that stump number and lopping had significant influences on the number of saplings in the forests (Table 2). Cutting, dropping and trampling have no significant effect on saplings in the study area. Pearson’s product-moment correlation test showed that a significant (t = 3.4016, DF = 87, P-value = 0.001014) positive correlation (R = 0.3426144) between number of stumps and number of saplings was observed (Table 2). Lopping negatively impacted the number of saplings in the forests (Table 2, Fig. 4).
A similar result was found in Shorea robusta dominated forest, where moderately disturbed forests contained the highest advanced regeneration (sapling) and pole densities (Sapkota et al., 2009). In contrast, forest disturbance in Bardia National Park of Nepal led to a lower density of trees and smaller DBH plants (Thapa and Chapman, 2010). Because of the space occupancy in the forests, this result indicates competition for resources, mainly sunlight. It had been observed that making more open space available by felling more trees (stumps), increased the space available for light to reach on the ground surface, which fosters the germination of seedlings that then grow up as saplings. The reverse cause-effect relationship of lopping on saplings indicated that the intensity of lopping severely hampered the growth of saplings in the study area (Fig. 4). This indicates that saplings had only limited tolerance to the lopping and tree felling (stumps), as these activities may cause damage during tree felling and conversion.

4.4 Seedlings and disturbance

Seedlings are the primary form of regeneration in a forest. The results showed that only the trampling by domestic cattle significantly influenced seedling counts in the forests (Fig. 4). Other predictors had no significant effect on seedling density in the study area. The data show that a higher degree of trampling in the forests was associated with more hindered growth of the seedlings in the Mid-hill forests of Nepal (Table 2).
A similar result was found in Bardia National Park of Nepal, where the forest subjected to disturbance had a lower density of smaller DBH trees (Thapa and Chapman, 2010). Both short- and long-term trampling reduced plant cover, plant height and species density, though long-term effects were more pronounced than short-term effects (Marion- Kissling et al., 2009). Seedlings in the juvenile stage could be hampered by trampling effects in two different ways. The first would be destroying the seeds by the hoofs of the livestock, which could damage the embryo, leading to permanent loss of dormancy. The other could be damage to the sprouting seedlings or sprouted seedlings, or permanent damage at the post-germination stage. The ease with which these damages can occur indicates that the promotion of regeneration in an area requires a strict ban on grazing and browsing. These results suggest that stall feeding of the domestic cattle would be the better option for newly developing forests.

4.5 Soil organic carbon and disturbance

Forest soil is a major source of organic carbon in the forests. It represents the second-largest storage of carbon in the forest, after biomass (DFRS, 2015a). None of the anthropogenic disturbances considered in this study were found to have any significant influence on soil organic carbon density. In short, anthropogenic disturbance had very little influence on the soil organic carbon density in the forests (Table 2). However, SOC increased with increasing trampling intensity. This is possibly due to the addition of cattle dung into the soil during the open grazing and browsing period. Other soil characteristics, including soil moisture, total soil organic matter content and total organic nitrogen content, were either not affected or only marginally affected by short- or long-term trampling (Marion-Kissling et al., 2009). In addition, a study in eastern Nepal found that several anthropogenic disturbance activities resulted in significantly higher carbon emissions from the soil (Gautam and Mandal, 2016). But in this study area, the disturbances showed neither loss nor gain in SOC, which signifies that the simple anthropogenic disturbances had little if any influence on the SOC in the forest ecosystem. The lack of an effect may have been because the forests were not made completely bare or clear-felled (Table 1), but they still consisted of significant densities of all size-classes of trees which allowed them to retain the SOC in the soil.

4.6 Biomass, carbon density and disturbance

Biomass is the most important measurement to consider in a forest ecosystem because of its multiple benefits. The results showed that the degree of lopping had a significant influence on biomass density in the forests (Table 2, Fig. 4). This means that a higher degree of lopping led to a more significant loss of biomass in the forests (Table 2). The sum of the soil organic carbon density and biomass carbon density is the total carbon density of a forest. The result of this calculation showed that only lopping has a significant effect on the total carbon density of the forests in the study area (Table 2).
A higher degree of lopping in the forests resulted in lower amounts of total carbon density, especially biomass or biomass carbon density, in the community-managed forests in the study area (Table 2). Similar findings were observed in several other studies of Nepalese forests at other sites. The total carbon storage across the sites ranged from 55.125 to 98.548 t ha-1, and the value was higher for the least disturbed site and lowest for the medium disturbed site (Pawar et al., 2014). Moreover, several disturbance activities resulted in the significant loss of carbon sequestration capacity of tropical forests in eastern Nepal (Gautam and Mandal, 2016). This result indicates that CFUGs largely depend on the CFs for fodder collection. It was also likely that livestock was a major source of livelihoods in the rural communities of Nepal. Other variables seemed to have no effect on biomass density in the study area, and similar findings have been seen in many studies. For example, several disturbance activities resulted in the significant losses of stand biomass (53%) and net primary production (NPP, 44%) of tropical forests in eastern Nepal (Gautam and Mandal, 2016). In addition, the intermediate level of disturbance has been affirmed for biodiversity enrichment in community forests (Poudyal et al., 2019). This was probably the reason that the community forests users’ groups were largely dependent on the forests for fodder collection in Middle Hills of Nepal.
Anthropogenic disturbances are prominent in almost all parts of the world. However, these disturbances are severe in terms of their impacts on forest ecological functioning, as they tend to change the land use system (Allnut et al., 2013; Barlow et al., 2016; Moreno-Mateos et al., 2017). However, in Nepal, especially in community-managed forests, the disturbance is more likely to be linked to the livelihood options of the residents (Paudyal et al., 2015; Poudyal et al., 2019). These disturbances may have less severe impacts nationally and globally in terms of land use changes and ecosystem functioning, but due to the global climatic problem and local forest ecosystem health, the localized actions are equally important to address for reducing climate change through ecological solutions.

5 Conclusions and policy implication

Forest lopping was found to be the most and significant anthropogenic disturbance for influencing biomass and total carbon density balance in the community-managed forests in the study sites. A higher degree of lopping in the forests led to lower forest biomass/carbon in the study area. SOC had no significant response to the tested anthropogenic disturbances. Woody species richness and number of saplings increased with an increasing number of stumps, which signifies that intermediate disturbance favoured regeneration. A higher intensity of lopping hampered the sapling counts, whereas grazing/trampling was found to be the most significant disturbance that hampered the seedlings. A higher intensity of trampling was found to lower the recruitments in Mid-hills community-managed forests of Nepal.
Of the anthropogenic disturbance factors, lopping is the one most directly associated with the livelihood options of the forest-dependent community - i.e., livestock husbandry. The results indicate that the livelihoods of the community forest user groups largely depend on the forests for fodder collection and for rearing livestock. To conserve the forest carbon at an optimum level, the results suggest it is necessary to consider alternative livelihood options for the forest-dependent communities; otherwise, a different mode of livestock husbandry should be practised. Several options could be the promotion of private fodder trees in the private and marginal, bare and public lands, or the strengthening of agroforestry practices could be another possible alternative to preserve the carbon in the community forests. The results of this study offer a direction for managing anthropogenic disturbances for multiple-use forests services in Nepal, or in countries worldwide with similar forest-dependent socio- economic characteristics.
Acharya K P, Dagi R B, Acharya M. 2011. Understanding forest degradation in Nepal. Unasylva, 62: 31-38.

Allnutt T F, Asner G P, Golden C D, et al. 2013. Mapping recent deforestation and forest disturbance in northeastern Madagascar. Tropical Conservation Science, 6(1):1-15.


ANSAB. 2010. Forest carbon stock measurement: Guidelines for measuring carbon stocks in the community-managed forest. Asia Network for Sustainable Agriculture and Bio-resources (ANSAB), Federation of Community Forest Users, Nepal (FECOFUN), International Centre for Integrated Mountain Development (ICIMOD), Norwegian Agency for Development Cooperation (NORAD), 76.

ANSAB. 2011. Forest carbon stock in community forests in three watersheds (Ludikhola, Kayarkhola and Charnawati). Asia Network for Sustainable Agriculture and Bio-resources (ANSAB), Federation of Community Forest Users, Nepal (FECOFUN), International Centre for Integrated Mountain Development (ICIMOD).

Baral S K, Katzensteiner K. 2009. Diversity of vascular plant communities along a disturbance gradient in a central mid-hill community forest of Nepal. Banko Janakari, 19(1): 3-10.

Barlow J, Lennox G, Ferreira J, et al. 2016. Anthropogenic disturbance in tropical forests can double biodiversity loss from deforestation. Nature, 535: 144-147.


CFUG. 2008. Constitution and operational plan of community forest user groups. Gorkha: Community Forest User Groups (Ghaledanda Ranakhola CFUG and Ludi Damgade CFUG).

Chave J, Andalo C, Brown S. 2005. Tree allometry and estimation of carbon stocks. Oecologia, 145(1): 87-99.


Chhettri N, Sharma E, Deb D C. 2002. Impact of firewood extraction on tree structure, regeneration and woody biomass productivity in a trekking corridor of the Sikkim Himalaya. Mountain Research and Development, 22(2): 150-158.


DDC. 2011. District profile of Gorkha. Gorkha: District Development Committee.

DFRS. 2015a. State of Nepal’s forests. Forest Resource Assessment (FRA) Nepal, Department of Forest Research and Survey (DFRS). Kathmandu, Nepal.

DFRS. 2015b. Middle mountains forests of Nepal. Forest Resource Assessment (FRA) Nepal, Department of Forest Research and Survey (DFRS). Kathmandu, Nepal.

FAO. 2020. Global forest resources assessment 2020—Key Findings. Rome.

Gautam T P, Mandal T N. 2016. Effect of disturbance on biomass, production and carbon dynamics in the moist tropical forest of eastern Nepal. Forest Ecosystems, 3:11. DOI: 10.1186/s40663-016-0070-y.


Geider R J, Delucia E H, Falkowski P G. 2001. Primary productivity of planet earth: Biological determinants and physical constraints in terrestrial and aquatic habitats. Global Change Biology, 7: 849-882.


IPCC. 2006. IPCC guidelines for national greenhouse gas inventories: Intergovernmental panel on climate change, national greenhouse inventory program. United Nations Environment Program (UNEP).

IPCC. 2007. Climate change: Impacts, adaptation and vulnerability. Contribution of Working Group II to the fourth assessment report of the Intergovernmental Panel on Climate Change. Cambridge, UK: Cambridge University Press.

Marion-Kissling K, Hegetschweiler T, Rusterholz H P. 2009. Short-term and long-term effects of human trampling on above-ground vegetation, soil density, soil organic matter and soil microbial processes in suburban beech forests. Applied Soil Ecology, 42(3): 303-314.


MacDicken K. 1997. A guide to monitoring carbon storage in forestry and agroforestry projects arlington (VA). Forest Carbon Monitoring Programme, Winrock International Institute for Agriculture Development.

Moreno-Mateos D, Barbier E, Jones P, et al. 2017. Anthropogenic ecosystem disturbance and the recovery debt. Nature Communications, 8:14163. DOI: 10.1038/ncomms14163.


Pandey H, Pandey P, Pokhrel S, et al. 2019. Relationship between soil properties and forests carbon: Case of three community forests from Far Western Nepal. Banko Janakari, 29(1): 43-52.


Paudyal K, Baral H, Burkhard B. 2015. Participatory assessment and mapping of ecosystem services in a data-poor region: A case study of community-managed forests in central Nepal. Ecosystem Services, 13: 81-92.


Poudyal B H, Maraseni T, Cockfield G. 2019. Impacts of forest management on tree species richness and composition: Assessment of forest management regimes in Tarai landscape Nepal. Applied Geography, 111: 1-11.

Pawar G V, Singh L, Jhariya M K, et al. 2014. Effect of anthropogenic disturbances on biomass and carbon storage potential of a dry tropical forest in India. Journal of Applied and Natural Science, 6(2): 383-392.


Pearson T R, Brown S L, Birdsey R A. 2007. Measurement guidelines for the sequestration of forest carbon. Northern Research Station, Department of Agriculture, USA.

Rana E, Thwaites R, Luck G. 2017. Trade-offs and synergies between carbons, forest diversity and forest products in Nepal community forests. Environmental Conservation, 44(1): 5-13.


R Core Team. 2018. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.

Sahu P K, Sagar R, Singh J S. 2008. Tropical forest structure and diversity in relation to altitude and disturbance in a Biosphere Reserve in central India. Applied Vegetation Science, 11(4): 461-470.


Sapkota I P, Tigabu M, Odén P C. 2009. Spatial distribution, advanced regeneration and stand structure of Nepalese Sal(Shorea robusta) forest subject to disturbances of different intensities. Forest Ecology and Management, 257(9): 1966-1975.


Sapkota I P, Tigabu M, Odén P C. 2010. Changes in tree species diversity and dominance across a disturbance gradient in Nepalese Sal (Shorea robusta Gaertn.) forests. Journal of Forest Research, 21: 25-32.

Sharma E, Chhettri N. 2005. ICIMOD’s transboundary biodiversity management initiative in the Hindu Kush-Himalayas. Mountain Research and Development, 25(3): 278-281.


Shrestha K B, Maren I E, Arneberg E, et al. 2013. Effect of anthropogenic disturbance on plant species diversity in oak forests in Nepal, Central Himalaya. International Journal of Biodiversity Science, Ecosystem Services & Management, 9: 21-29.

Sodhi S, Lee T M, Koh L P, et al. 2009. A meta-analysis of the impact of anthropogenic forest disturbance on southeast Asia’s biotas. Biotropica, 41(1): 103-109.


Soni A, Decesari S, Shridhar V, et al. 2019. Investigation of potential source regions of atmospheric Black Carbon in the data deficit region of the Western Himalayas and its foothills. Atmospheric Pollution Research, 10(6): 1832-1842.


Tamrakar P R. 2000. Biomass and volume tables with species. Description for Community Forest Management, Ministry of Forest and Soil Conservation, Nepal.

Thapa S, Chapman D S. 2010. Impacts of resource extraction on forest structure and diversity in Bardia National Park, Nepal. Forest Ecology and Management, 259(3): 641-649.


Walkley A, Black I A. 1934. An examination of the method for determining soil organic matter, and a proposed modification of the chromic acid titration method. Soil Science, 37(1): 29-38.