Forest Ecology and Management xxx (xxxx) xxxx

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Forest Ecology and Management

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Indicator based integrated vulnerability assessment of community forests in Indian west Himalaya ⁎ Shinny Thakur, Vikram S. Negi , Ravi Pathak, Rupesh Dhyani, Kamini Durgapal, ⁎ Ranbeer S. Rawal

G.B. Pant National Institute of Himalayan Environment and Sustainable Development, Kosi-Katarmal, Almora, Uttarakhand, India

ARTICLE INFO ABSTRACT

Keywords: The Himalaya is often referred to as forested landscape, which provides a range of ecosystem services vital for Community Forest sustaining life of billions of people. The region is recognized amongst the 35 global biodiversity hotspots for its Vulnerability assessment unique and rich biodiversity. Also, the region is highly vulnerable to perturbations due to anthropogenic dis- Indicators turbances and climate change. Especially the forests in the region are subject to stress from such perturbations. Climate change The higher dependency of communities on forests and changing climate has impacts on structure and function of Anthropogenic pressure forest ecosystems. This has severe implications for forest dependent communities. Therefore, vulnerability as- Altitudinal gradient Western Himalaya sessment of forests is urgently needed to understand the likely consequences of these changes and responses. Such information would help in developing better management and conservation planning. Earlier studies on vulnerability assessment of forests and forest-dependent people have failed to acknowledge the importance of spatial and temporal aspects of vulnerability investigated through field based observations. Realizing this, present study focuses on forest vulnerability assessment through field based observations along an altitudinal gradient (700–3400 m) in the Indian west Himalaya. This study, for the first time, provides vulnerability as- sessment of community forests at local scale following integrated approach of multiple indicators across diverse domains. The vulnerability indicators have been identified through a systematic analysis and extensive review of the available literature. A total of 14 indicators in six domains (viz. forest, climate, anthropogenic, topographic, soil and management practices) were identified to assess inherent vulnerability of Community Forests (Van Panchayats) in the target region. Furthermore, Forest Vulnerability Index (FVI) was calculated by integrating the selected indicators across domains. The results revealed high vulnerability at low altitude (< 1200 m) forests. Disturbance index, expansion of invasive species and people dependency has emerged as the major factors responsible for forest vulnerability in the region. The value of FVI declined significantly (R2 = 0.51, p < 0.001) with increasing altitude range. The study also analysed perceptions of inhabitant community regarding de- pendency on forest resources, management practices and status of community forest in the studied area. The outcomes of this study would help in developing management interventions and strategies to ensure sustainable management of forest resources in the targeted landscape in particular and Indian Himalaya in general.

1. Introduction 2014; Negi et al., 2018a). However, the Himalayan ecosystem is re- cognized one amongst the most vulnerable ecosystems to consequences Forest ecosystems, which roughly cover one third of the global land of climate change and anthropogenic disturbances (IPCC, 2014; area, are among the most biologically rich and genetically diverse Ravindranath et al., 2011; Upgupta et al., 2015; Chakraborty et al., ecosystems on the earth (Köhl et al., 2015). About 410 million people 2018a, 2018b). For instance, the increase in average temperature is are reported to be highly dependent on forests for subsistence and in- expected to rise higher in the Himalayan region as compared to global come, and 1.6 billion people depend on forest goods and services for average temperature (Shrestha et al., 2012; IPCC, 2013). Further, cli- some part of their livelihoods (Munang et al., 2011). The Himalaya, a mate change modelling studies for India exhibit that the Indian sub- forested landscape, is provider of a wide range of ecosystem services continent is likely to experience a warming of over 3–5°C and goods to its inhabitants and downstream communities (Rasul, (Ravindranath et al., 2011). The Indian Network for Climate Change

⁎ Corresponding authors. E-mail addresses: [email protected] (V.S. Negi), [email protected] (R.S. Rawal). https://doi.org/10.1016/j.foreco.2019.117674 Received 15 May 2019; Received in revised form 30 September 2019; Accepted 5 October 2019 0378-1127/ © 2019 Elsevier B.V. All rights reserved.

Please cite this article as: Shinny Thakur, et al., Forest Ecology and Management, https://doi.org/10.1016/j.foreco.2019.117674 S. Thakur, et al. Forest Ecology and Management xxx (xxxx) xxxx

Assessment indicated for Indian Himalayan Region (IHR) an increase in Gopalakrishnan et al., 2011; Shukla et al., 2017; Sharma et al., 2017; annual mean surface air temperature from 0.9 ± 0.6 °C to Kumar et al., 2018) and regional level (Ravindranath et al., 2011; 2.6 ± 0.7 °C in the 2030s (I.N.C.C.A. Indian, 2010). Expected changes Pandey and Jha 2012; Sharmaet al., 2013; Upgupta et al., 2015; Pandey in the temperature and associated impacts on other aspects of en- et al., 2016) have been attempted. These studies have indicated an- vironment will have a profound effect on the future distribution, pro- thropogenic pressure and environmental changes as major stressors/ ductivity and health of forests (Chaturvedi et al., 2011; Gottfried et al., shocks responsible for determining forest vulnerability. Forests are 2012; Corlett and Westcott, 2013; IPCC 2014; Gómez et al., 2015; subjected to stress from climatic and non-climatic sources (Sharma Chakraborty et al., 2018a, 2018b). Also the anthropogenic pressures et al., 2015, 2017; Kumar et al., 2018). Most of the vulnerability studies have emerged as a major contributing factor for increased vulnerability address through spatio-temporal assessment, which is lacking at local of the Himalayan forests (Ravindranath et al., 2011; Rawal et al., 2012; scale. However, studies at local level using field based assessment are Malik et al., 2016; Negi et al., 2018a; Chakraborty et al., 2018a, expected to help in addressing impact of different stressors on forest 2018b). Ever increasing demands on forest ecosystem goods and ser- health. Often the stressor/ shocks, defined as environmental (climate, vices are putting pressure on the natural resources these forests contain, soil, slope), biological (low biological diversity) and anthropogenic and making them more vulnerable. Increasing human population factors (i.e. over-exploitation of forest resources, proliferation of in- combined with unsustainable resource use, poor management and vasive species, fire incidences etc.), are responsible for decrease in the limited investment in conservation further contributes to their vulner- viability and productivity of the forest community (Brandt et al., 2016). ability (Sharma et al., 2009; Tse-ring et al., 2010). Therefore, forest Literature review suggests that knowledge of forest vulnerability that vulnerability assessment has emerged as a critical pre-requisite to en- identifies drivers of vulnerability and vulnerable areas at local level, sure forest resource management, conservation and long-term adapta- especially under anthropogenic activities, are meager in IHR and tion and/or mitigation under increasing perturbations (Murthy et al., mountainous regions globally. Most of the forest vulnerability studies 2011; Ribot, 2011). focused on climate change are generally based on use of gridded data The Indian National Action Plan on Climate Change (NAPCC) and model based predictions. None of the study considers both, climatic identifies IHR vital for ecological security of the country. However, the (observed climate data) and non-climatic sources (i.e anthropogenic action plan also underlines the intense vulnerability of the region to pressure) of vulnerability, and simplify the discussion on forest vul- changing climate. Further, people's higher dependency on goods and nerability notably due to lack of necessary understanding on its causal services emanating from the Himalayan forests is well recognized as a factors and processes (Polsky et al., 2007; Sharma et al., 2015, 2017). major factor causing significant changes in forest structure and function Even though there is no well-accepted set of indicators for forest vul- (Negi et al., 2011; Birch et al., 2014; Chakraborty et al., 2016; Negi and nerability studies, which could reflect the actual state of the forest Maikhuri, 2017; Negi et al., 2019). Such changes are bound to affect the health. Assessments at a larger spatial scale would assist in the identi- livelihoods of millions of people living in the Himalaya and billions of fication of vulnerable forest areas for prioritization and efficient re- downstream inhabitant drawing benefits of ecosystems services ema- source allocations (Naess et al., 2006), while at local scale such in- nating from the regional forests. formation can be utilized for designing enhanced site specific forest Community forestry in the Indian Himalaya is one of the most cited resilience and management measures (Upgupta et al., 2015). Further, it examples of participatory natural resources management globally. This is not feasible to implement adaptation measures at national and in- type of management refers to a kind of social movement that ensures ternational level based on the results of forest vulnerability assessments greater control over surrounding natural resources and access to their done at a larger scale using the model based approach only. According benefits (Baker and Kusel, 2003; Armitage, 2005). Community forest to Rijal and Meilby (2012) development of sustainable management management largely targets: (i) improving the livelihood options for plans, taking into account both ecological and socio-economic issues, is forest dependent communities, (ii) ensuring community participation often limited by lack of knowledge of forest structure and of awareness and cooperation in forest resource management, and (iii) conserving about human impact on the ecosystem. Therefore, assessment of in- natural forest systems and biodiversity (Baker and Kusel, 2003; herent vulnerability of forest ecosystems under current climate in- Germain et al., 2018). In IHR, among others, the state of Uttarakhand tegrated with other factors such as anthropogenic pressure, biological has provided an example of sustained community forestry. Under this richness, soil conditions and management practices is a reliable and initiative, almost for nearly 100 years, this exclusive forest management practical option to forest managers and conservations (Sharma et al., system has worked as successful model in the form of Community 2013, 2015, 2017; Metzger et al., 2006). Forests (commonly known as Van Panchayats – VPs). Currently there In this context, to define and implement adaptation measures at are over 13,000 VPs in Uttarakhand that manage a total area of 5,220 local scale, it becomes imperative to understand and analyze ground km2 under forests (Chauhan, 2010). However, Van Panchayat (VP) realities by way of including major socio-ecological parameters. With system, with a very glorious past as a vibrant forest management in- this realization, present study followed an integrated approach of using stitution, is now facing a serious decline. Most of the forests under VPs ecological and socio-economic indicators for assessing the inherent are now considered extremely vulnerable to environmental perturba- vulnerability of selected community forests in western Himalayan tions, anthropogenic pressure and declining interest of younger gen- province of the IHR. Also, the study attempted to understand current eration (Ballabh et al., 2002). Although, the extent of forest vulner- status of the selected VPs under climate change and anthropogenic ability varies considerably across forest types and the region. pressure. In general, the assessment of forest vulnerability is made using global and regional climate and vegetation models (Gonzalez et al., 2. Material and methods 2010; Seidl et al., 2011; Allen et al., 2015; Yongxiang et al., 2015; Abrams and Nowacki, 2016). Review of literature reveals, most of the 2.1. Study area forest vulnerability assessments have been carried out through model based projections using gridded meteorological data set with limited Present study focuses on vulnerability of community forests (Van ground information. Surprisingly most of the studies have predicted Panchayats) considering their significant contribution in the partici- forest vulnerability at larger scale ignoring vulnerability at local level to patory natural resources management. Van Panchayat forests are a particular forest type. Therefore, these studies have failed in sug- managed by the local villagers themselves. The objective behind se- gesting effective ground based planning for forest resource manage- lection of VPs was to provide deeper understanding of current status of ment and biodiversity conservation. these forests under changing climate and anthropogenic pressure re- Vulnerability assessments at national (Chaturvedi et al., 2011; gime. This study was conducted in Pithoragarh district (Fig. 1), which

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Fig. 1. Map of the study area with sampling sites. forms Indian part of globally recognized Kailash Sacred landscape (KSL) to be under community management or not?. Interview based in- in Uttarakhand. The district has a total of 1621 VPs, that is 12% of total formation was further augmented through reviews and direct ob- VPs in Uttarakhand (13158 VPs), and covers about 42% of total forest servations during field visits. area in the district (UFD, 2017). These VPs are distributed along a wide altitude range (700–3400 m asl), which form the basis for selection of the area for present study. Based on altitudinal distribution and cor- 2.3. Selection of indicators for forest vulnerability responding changes in climate conditions, the VPs in the study area were grouped into three altitudinal zones: (i) lower zone with warm Realizing that vulnerability of a system mainly depends on the subtropical climate (altitude below 1200 m), (ii) middle zone with mild stressors/shock, which increases susceptibility of that system temperate climate (1200–1700 m), and (iii) upper zone with cool (Eigenbrod et al., 2015; IPCC, 2007), the concept of inherent vulner- temperature climate (altitude above 1700 m). The study area shows ability was used (Sharma et al., 2013, 2017), wherein it is considered as different climate gradients attitudinally (Fig. 2). According to records of a pre-existing state independent of exposure (Allen, 2003; Eriksen and UKFD (2017), Gubrauli Van Panchayat established in 1919 is the oldest Kelly, 2007). According to Sharma et al. (2013), inherent vulnerability ‘ VP (100 year old) in the landscape. in the climate change context is a system property that determines the capacity of a system to resist a disturbance and adjust to it. It is in- dependent of exposure, i.e. any system is always vulnerable to some 2.2. Household survey stressors/shocks and these stressors can be determined. Indicator-based methodologies are widely used and reported (Lexer and Seidl, 2009; Preliminary surveys were conducted to understand the inhabitant’s Seidl et al., 2011; Upgupta et al., 2015; Sharma et al., 2013, 2017)tobe dependency on the forests in the study area. A total of twenty house- the approach for vulnerability assessment at local level. In the present holds in each village (n = 620) were selected and individually inter- study, an extensive literature review was done for the identification of viewed. In addition, key informants such as village leaders (head of the indicators for vulnerability assessment of community forests (Table 1). village-Paradhan) and president of community forest (Sarpanch) were Towards developing index of vulnerability, an integrated approach by also interviewed (n = 62). Group discussions (n = 31) were organized taking six different domains (i.e., forest, climate, management, an- to elaborate and validate the information documented through in- thropogenic, topographic and soil) were taken together (Fig. 3). Major dividual’s interviews. Six major questions, asked during interview and indicators such as biological richness (here plant diversity and species group discussion, included (i) what is the significance of VP forests for richness) within a particular forest area, disturbance index, slope and you?, (ii) what resources do you collect from your VP forest? (iii) what aspect following earlier studies were selected (Sharma et al., 2015, are the existing management practices in your VP forest?, (iv) does 2017; Upgupta et al., 2015; Ravindranath et al., 2011; Lexer and Seidl, population of the village have any effect on VP forests?, (v) what is your 2009; Seidl et al., 2011). Management practices i.e. people involved in opinion about future of VP forests?, and (vi) should these forests remain management of forest and number of devoted forests (sacred forests)

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Fig. 2. Temperature zones in Pithoragarh district (average of 1960–2000). were considered keeping in mind that the study is focusing on com- represent the major characteristics of forests, as well as the biophysical munity forests. Among selected indicators, species richness and plant and anthropogenic environment in which forests exist in the region. diversity is a composite indicator of ecosystem uniqueness and biolo- Management of forest resources and forests through various activities gical value, and accounts for the level of stress on biodiversity of a such as plantation, rotational collection of resources, promotion of particular landscape; disturbance index, accounts for forest structure sacred forests and strictly following the rules of village forest councils and contiguity, is a composite indicator that includes stress and biotic are important indicator of forest health and thus their susceptibility. disturbance on forest resources; invasion by alien species is important This approach by and large follows the understanding that vulnerability to assess the status of native/endemic diversity of a particular area, and assessment is usually done to allow comparisons between different also as indicator of climate change (Tilman, 1999; Walther et al., 2009). places, social groups, or sectors whose vulnerabilities are not static but Slope and aspects become important as the study was carried out along responds to changes in physical, economic, social, political, or institu- a wide elevation gradient (700–3400 m) in mountainous area. Tem- tional conditions over time (Smit and Wandel, 2006). perature and precipitation influence ecosystem structure and functions, is based on gridded data. Change in soil moisture and carbon influences 2.4. Data generation for forest indicators growth of the trees and community structure (McKenney et al., 2007), which increases the rate of vulnerability towards pest attack and fire The quantitative information on forest based indicators (i.e., di- (Portier et al., 2016; Boulanger et al., 2017). The selected indicators versity and species richness) was generated by conducting systematic

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Table 1 Indicators selected for development of forest vulnerability index.

Component Indicator (unit) Functional Indicator description Reference relationship

Forest Diversity (−) It represent forest structure and composition, major proxy of biodiversity Lexer and Seidl (2009), Seidl et al. (2011) Richness (−) Species richness denotes status of biodiversity and potential to host biodiversity in an area Eigenbrod et al. (2015), Upgupta et al. (2015), Sharma et al. (2017) Forest area (hectare) (−) Loss of forest area has been considered significant owing to expansion of disturbance zone into the Sharma et al. (2013) forest and the consequent impact on species habitat Climate Temperature (oC) (+) Alterations of temperature might affect forest type distributions and carbon-related functions Cui et al. (2016), Seidl et al. (2011), Upgupta et al. (2015), Tepley et al. (2017), Zhang et al. (2017) Rainfall (mm) (+) Alterations of precipitation might affect forest type distributions and carbon-related functions Cui et al. (2016), Seidl et al. (2011), Upgupta et al. (2015), Tepley et al. (2017), Zhang et al. (2017)

5 Disturbance Index (+) Disturbance Index accounts for the change in spatial and structural attributes of forests arising from Upgupta et al. (2015); Lexer and Seidl (2009) anthropogenic factors and or a combination of anthropogenic and natural factors Invasive species density (Ind/ (+) Proliferation of invasive exotic species has implications on composition and species richness and thus Sharma et al. (2013); Pathak et al. (2019) hac) functions of ecosystem Dependent population (+) Removal of leaf, fuelwood and other biomass impacts productivity and health of forests; seed removal Upgupta et al. (2015), Sharma et al. (2013) impacts regeneration status; disturbance facilitates proliferation of invasive species and thereby adds to ‘inherent vulnerability’ of forests Topographic Slope (Degree) (−) Propensity to landslides, soil disturbance and erosion due to higher ground slope enhances ‘inherent Upgupta et al. (2015), Périé and De Blois (2016), Sharma vulnerability’ of forests et al. (2017) Aspect (Degree) (+) It is a causative parameter that are responsible for forest fire Périé and De Blois (2016), Sharma et al. (2017) Soil Soil moisture (%) (−) Tree growth and establishment are limited by soil moisture Buotte et al. (2018) Soil carbon (%) (+) Reduction in soil carbon is related to soil respiration, a complex process that is affected by numerous Cui et al. (2016), factors Management People involvement (−) Involvement of people in management activities/practices enhance ecological integrity Negi (2010); Negi et al. (2018b) (number)

Devoted forest (year) (−) Devoting forests to local deity is an approach of people in the region to ensure protection of forests. This Negi (2010); Negi et al. (2018b) Forest EcologyandManagementxxx(xxxx)xxxx is a unique practice that suggests religious belief and customs contribute for enhanced ecological integrity and conservation S. Thakur, et al. Forest Ecology and Management xxx (xxxx) xxxx

Fig. 3. Methodology used for developing forest vulnerability index. vegetation sampling of forests falling under 31 VPs during 2015–2017. (https://bhuvan-app3.nrsc.gov.in). Afterword, slope (in degrees) and In each selected VP forest, three random plots (50 × 50m) were laid. aspect layer were generated using spatial analyst tools of ArcGIS 10.1 Within each plot, ten (10 × 10 m) random quadrats were placed for software called slope and aspect. We have overlaid geo-coordinates of enumerating tree species. Each 10 × 10m quadrat was further sub- the study plots and extracted slope and aspect values using Extract divided into two sub-quadrats of 5 × 5 m for enumeration of shrubs and Multi Values to Points tool of ArcGIS 10.1. as a baseline climate and saplings (20 quadrats in each plot), and 5 sub-quadrats of 1 × 1 m for link with vegetation data (Moura et al., 2016; Qian, 2016; Jingfang enumerating seedlings and herbs (a total 50 quadrats in each plot). et al., 2018; Marchi et al., 2019). Demographic data for villages asso- Quadrat data was pooled by plots to estimate plant species richness, ciated with each sampled community forest were obtained from the density and diversity following the standard phyto-sociological ap- Census of India (2011). Forest area under each VPs were taken from the proaches (Misra, 1968; Muller Dombois and Ellenberg, 1974). The Uttarakhand Forest Department (UKFD, 2017). The number of depen- density of all the species is then summed up to get total density for each dent population on per hectare of forest area was used as an indicator of plot. For a particular VP forest average density of all the three plots was anthropogenic domain, and calculated by dividing total area of VP considered for use as an indicator. Species diversity (H́) was calculated forest with the total number of dependent population. following Shannon Weiner diversity Index (Shannon and Weiner, 1963). For determining disturbance index, lopping intensity (LI) and cut 2.5. Development of forest vulnerability index stump intensity (CSI) was used as a measure of disturbance. Cut stump intensity was determined following Murali et al. (1996), and lopping Many vulnerability methods have been developed for vulnerability intensity was calculated following Rawal et al. (2012). Both the dis- assessment various purposes such as climate vulnerability index turbance measures were calculated for each plot and then averaged to (Pandey and Jha 2012), socio-environmental Vulnerability Index make it representative for a particular VP forest. (Gupta et al., 2019), Adaptive capacity index (Pandey et al., 2016) and forest vulnerability index (Ravindranath et al., 2011). Various index No. of cut stumps in the plot Cut stump intensity =×100 based methods, were used for vulnerability assessment of forests in the Total No. of standing stems in the plot Indian Himalayan Region are presented (Table 2). In the present study, the entropy method was adopted to determine the weights for each Total no. of lopped trees in a plot Lopping intensity(%) =×100 indicator following Li et al. (2011) and Ye et al. (2011). The entropy Total No. of trees in the plot method is an unbiased and accurate quantitative weight-assigning After calculation of CSI and LI, disturbance index was calculated method compared to others such as the, analytic hierarchy process and following Rawat (2013). The intensities of cut-stump and lopping were subjective techniques (Zhi-hong et al., 2002; Lotfi and Fallahnejad, given weightage values so that index can be developed. The average of 2010; Guo, 2017). The first step is normalization of selected indicators the weightage value of CSI and LI formed disturbance index (DI) for a following methodology developed for Human Development Index (HDI) particular VP forest. DI for all the VP forests was calculated and used as in 1990. Since indicators possess a different level of contribution to an indicator (anthropogenic disturbance) for vulnerability assessment. vulnerability; finding an appropriate weight for each indicator is one of Invasion status of non-native species was determined following Khuroo the precarious points in vulnerability assessment. Each indicator has its et al. (2012). own positive and negative impacts. It can act as stressors as well as In selected forest plots, soil samples were collected in three re- factor to improve adaptive capability of the forest. The normalization of plicates with the soil depths (0–15 cm) for analysis of soil carbon and indicators was done on the basis of functional relationship of indicator moisture. Soil carbon and moisture was calculated following Walkley with the vulnerability, as indicators having positive (e.g. vulnerability (1947) and Zobel et al. (1987) method. The ISRO’ Cartosat-2 Digital increases with increase in invasion) and negative functional relation- Elevation Model (DEM) data was downloaded from Bhuvan website ship (e.g. vulnerability decreases with increase in diversity) with

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Table 2 Most common methods used for forest vulnerability assessments in the Indian Himalayan Region.

Method Description Limitation Reference

Equal weight Spatial forest vulnerability assessment of the Limited by the availability of biophysical as well as Ravindranath et al. (2011) India socio-economic data Unequal weight (pairwise comparison Spatial forest vulnerability Himachal Pradesh, -do- Upgupta et al. (2015) method) Unequal weight (pairwise comparison Spatial forest vulnerability assessment of the -do- Sharma et al. (2017) method) India Sensitivity and adaptive capacity approach Spatial based forest vulnerability assessment of Could not prioritize for the local scale adaptation Kumar et al. (2018) Uttarakhand planning vulnerability. These positive and negative relationships were ensured 3. Results based on field observations as well as theoretical assumptions. The normalized values fall between 0 and 1. Following mathematical 3.1. Forest biodiversity patterns equation was used when indicator has positive functional relationship with vulnerability The details of major tree species and associated dominant shrubs and herbs in selected VP forests are presented in (Table 3). In lower NMinN ij− () ij zone (subtropical conditions) most of the community forests (72.7%) Nij = Max() Nij− Min () N ij are dominated by Pinus roxburghii (IVI-184–300). The middle zone (mild temperate condition) VP forests exhibited dominance of Pinus where Nij ∉ [0, 1] is the normalized value. roxburghii (IVI-111–300) in 50% VPs, Q. leucotrichophora (IVI-163–196) However, if indicator is negative functional relationship with vul- in 30% VPs, and Q. glauca (IVI-151–196) in 20% VPs. The VPs in upper nerability then normalization of indicator was done by following zone (temperate condition) were quite diverse in tree species dom- equation inance. P. wallichiana (IVI-186–204) and Q. floribunda (IVI-109–131) each were dominant in two VPs, Q. leucotrichophora (IVI-228), Alnus Max() Nij− N ij Nij = nepalensis (IVI-230), Abies pindrow (IVI-126), A. spectabilis (IVI-226) and Max() Nij− Min () N ij Betula utilis (IVI-286) dominated one VP each. The lower zone VP for- − ests revealed density range between 67 and 800 trees ha 1 (average Now all Nij values are dimensionless. Hence it can be used for fur- − − 324 trees ha 1) and basal area range from 2.77 to 40.45 m2 ha 1 ther assessment. The steps of entropy method are as follows: (i) − (average 20.24 m2 ha 1). In mid zone density ranged from 120 to Normalized Nij matrix using the following equation: −1 Nij 800 ha (average 481 individuals), and basal area from 12.75 to zij = k wherezij is the normalized index value for each indicator; 2 −1 2 −1 ∑i Nij 45.35 m ha (average-26.93 m ha ). The high altitude VPs ex- fi m −1 (ii) de ned the entropy value for the index Ej as: Ej =−Kz.,∑i ij hibited tree density range from 193 to 693 trees ha (average-367 −1 2 −1 where K > 0, ln is the natural logarithm, K = 1∕ln(m) then 0 ≤ Ej ≤ 1. trees ha ) and basal area between 6.1–68.8 m ha (average 2 −1 In the next step, the difference coefficient of the index Gj is calcu- 26.21 m ha ). In general, tree species richness remained relatively lated using the following equation: Gj =−1Ej; (iii) estimating the low (average-2.27) in lower zone VP forests followed by mid zone weights for each indicator using the following equation: (average 3.3) and high zone (average 4.9 spp.). Maximum tree species Gj j € richness (9) was found in VP at 2250 m zone. Infestation of invasive Wj = n , ∑1 Wj = 1 where Wj (0,1) is the weight of j the in- ∑1 Gj dicator; and (iv) calculating the forest vulnerability index (FVI) using Ageratina adenophora, Ageratum conyzoides and Lantana camara was the following equation: observed at low and mid altitude zone VP forests.

J FVI= ∑ W.ji N j 3.2. Forest vulnerability index I

Beta distribution approach (Iyengar and Sudarshan, 1982) followed Following the entropy method, weight of vulnerability indicators for ranking of FVI into different vulnerability classes using assumption were calculated (Table 4). For the beta distribution, the estimated of skewness of FVI. The probability density of the Beta distribution is parameter were a = 11.35, and b = 25.80. The estimated 20% cut-off defined as: points were as z1 = 0.045, z2 = 0.077, z3 = 0.096 and z4 = 0.102. Disturbance index (0.103) scored maximum weightage value followed z(1z)dx(a−− 1)− b 1 f(z) = ,0< 0 by invasive species density (0.101) and population dependency (0.094). B(a, b) People’s engagement in forest management was the fourth contributing

1 indicator with weight value (0.091). The indicators with the lowest where B(a, b)=− xa1−− (1 x) b1 dx. ∫0 contribution included forest area (0.0565), slope (0.0524), aspect The parameter a and b in Beta distribution was estimated by max- (0.0514), soil moisture (0.0473) and rainfall (0.0392). The categor- ‘fi ’ imum likelihood method using the R package tdistrplus (R Core ization of VPs under different Forest Vulnerability Index (FVI) revealed Team, 2018). 45.16% of VP forests fall in high to very high vulnerability range, Finally, the intervals (0, z1), (z1,z2), (z2,z3), (z3,z4), (z4,z5) was 29.03% in vulnerable, and 25.80% moderate to low vulnerability range fi fi ff used as meaningful vulnerability classi cation into ve di erent cate- (Fig. 4). More than half (55%) of VP forests in low zone exhibited very gories with probability weight of 20% of each interval. The vulner- high FVI, remaining VPs (45%) were however having FVI in vulnerable ability categories are as follows: to highly vulnerable range. In middle zone, 50% of VP forests showed Less vulnerable if 0 < FVI < z1 high and 40% very high FVI. In higher zone, 50% forests have moderate Moderate vulnerable if z1 < FVI < z2 level of vulnerability index. The FVI, in general, showed a significantly Vulnerable if z2 < FVI < z3 declining trend (R2 = 0.51; p < 0.001) from lower to higher altitude High vulnerable if z3 < FVI < z4 zone (Fig. 5). Very high vulnerable if z4 < FVI < 1.

7 .Tau,e al. et Thakur, S. Table 3 Major tree species of studied Van Panchayat forests in Pithoragarh (Uttarakhand).

Altitude Tree species Dominant species (IVI) Associated species Tree Density (Ind/ Total Basal area Remarks richness ha) (m2/ha)

Lower zone Van Panchayat Forests 700 1 Pinus roxburghii (300) Lantana camara, Murraya koengii, Fragaria indica, Woodfordia fruticosa, Reinwardtia 67 2.77 Forest infested with Lantana camara indica, etc. 870 7 Erythrina variegata (126.84) Mallotus philippensis, Celtis australis, Asparagus racemosus, Solanum spp., Ageratum 220 17.3 Open forest conyzoides, Ageratina adenophora, etc. 950 3 Pinus roxburghii (263.32) Pyrus pashia, Ficus spp., Innula cappa, Rubus ellipticus, Ageratina adenophora, Ageratum 220 21.6 Infestation of Ageratina adenophora conyzoides, etc. 960 2 Shorea robusta (207.98) Pinus roxburghii, Innula cappa, Rhus parviflora, Woodfordia indica, Flemingia strobilifera, 393 16.45 Degraded slopes Anaphalis spp. 1000 1 Pinus roxburghii (300) Rubus ellipticus, Murraya koenzii, Woodfordia fruticosa, Ageratum conyzoides, Ageratina 367 23.25 Infestation of Ageratum houstonianum adenophora, Bidens pilosa, etc. 1020 2 Pinus roxburghii (288.16) Terminalia chebula, Woodfordia fruticosa, Berberis asiatica, Thymus linearis, Leucas 160 21.5 Open forest lanata, Flemingia strobilifera, etc. 1040 1 Pinus roxburghii (300) Oxalis corniculata, Thymus linearis, Reinwardtia indica, Leucas lanata, Senecio nudicaulis, 340 26.05 Open forest etc. 1150 1 Pinus roxburghii (300) Innula cappa, Woodfordia fruticosa, Thymus linearis, Flemingia strobilifera, Oxalis 327 40.45 Open forest corniculata, etc. 1190 3 Pinus roxburghii (183.59) Myrica esculenta, Pirus pashia, Pyracantha crenulata, Rubus ellipticus, Ageratina 800 19.15 Dense forest adenophora, Flemingia strobilifera, etc. 1200 1 Pinus roxburghii (300) Pyracantha crenulata, Woodfordia fruticosa, Rhus parviflora, Ageratina adenophora, 233 19.45 Open forest Flemingia strobilifera, Anaphalis spp., etc. 1200 2 Quercus leucotrichophora (286.44) Quercus glauca, Pyracantha crenulata, Rubus ellipticus, Leucas lanata, Thymus linearis, 440 14.7 Degraded slopes etc. Middle zone Van Panchayat Forests 8 1300 3 Quercus leucotrichophora (179.03) Myrica esculenta, Pinus roxburghii, Pyracantha crenulata, Rubus ellipticus, Ageratina 520 12.75 Dense forest adenophora, Anaphalis spp., etc. 1340 5 Quercus leucotrichophora (162.74) Myrica esculenta, Pinus roxburghii, Pyracantha crenulata, Berberis asiatica, Ageratina 447 17.65 Degraded slopes adenophora, Ajuga brachystemon, etc. 1380 2 Quercus glauca (195.86) Quercus leucotrichophora, Rubus ellipticus, Innula cappa, Asparagus racemosus, Ageratina 480 45.35 Dense forest, less undercanopy adenophora, Reinwardtia indica, etc. 1440 5 Pinus roxburghii (110.82) Quercus leucotrichophora, Alnus nepalensis, Rubus ellipticus, Woodfordia fruticosa, 120 36.6 Open forest and degraded slope Ageratina adenophora, Thymus linearis, etc. 1460 2 Quercus leucotrichophora (195.61) Pinus roxburghii, Lantana camara, Cotoneaster spp. Rhus parviflora, Ageratina 787 14.5 Dense forest with infestation of lantana adenophora, Reinwardtia indica, etc. camara 1500 1 Pinus roxburghii (300) Rhus parviflora, Rubus ellipticus, Berberis asiatica, Reinwardtia indica, Flemingia 307 28.15 Open forest strobilifera, Leucas lanata, etc. 1530 5 Quercus glauca (150.51) Myrica esculenta, Quercus leucotrichophora, Randia tetrasperma, Pyracantha crenulata, 387 14 Dense forest with dense shrub layer Origanum vulgare, Bidens pilosa, etc.

1650 4 Pinus roxburghii (201.78) Quercus leucotrichophora, Syzygium cumunii, Rubus ellipticus, Woodfordia fruticosa, 800 40.65 Dense forest Forest EcologyandManagementxxx(xxxx)xxxx Ageratina adenophora, Eregeron acris, etc. 1650 6 Pinus roxburghii (142.68) Myrica esculenta, Quercus leucotrichophora, Innula cappa, Rubus ellipticus, Reinwardtia 460 42.4 Dense forest indica, Ageratina adenophora, etc. 1700 1 Pinus roxburghii (300) Innula cappa, Rhus parviflora, Thymus linearis, Reinwardtia indica, Berberis asiatica, etc. 500 17.25 Degraded slope Upper zone Van Panchayat Forests 1780 3 Pinus roxburghii (242.37) Myrica esculenta, Quercus leucotrichophora, Innula cappa, Cotoneaster spp., Thymus 434 29.6 Degraded forest linearis, Flemingia strobilifera, etc. 1900 5 Quercus leucotrichophora (227.87) Aesculus indica, Quercus floribunda, Beberis asiatica, Asparagus racemosus, Hedychium 340 12.85 Dense canopy spicatum, Origanum vulgare, etc. 2050 4 Alnus nepalensis (230.22) Cupressus torulosa, Lyonia ovalifolia, Pyracantha crenulata, Principia utilis, Origanum 193 19.1 Good under canopy vulgare, Anaphalis contorta, etc. 2241 9 Quercus floribunda (130.75) Aesculus indica, Ilex dipyrena, Berberis chitrea, Vibernum spp. , Fragaria indica, Viola 347 26.85 Dense forest biflora, etc. (continued on next page) S. Thakur, et al. Forest Ecology and Management xxx (xxxx) xxxx

Table 4 Indicators as Estimated weight values of indicators reflecting the contribution of each variable that defines the level of vulnerability of forest.

Domain Indicator Indicator weight

Forest Diversity 0.0647 Richness 0.0776 Forest area 0.0565 Climate Temperature 0.0861 Rainfall 0.0392 Anthropogenic Disturbance Index 0.1028 Invasive species density 0.1015 Remarks Dependent population 0.0943 Topographic Slope 0.0524 Aspect 0.0514 Soil Soil moisture 0.0473 Soil carbon 0.0606 Management People involvement 0.0916 /ha) 2 Devoted forest 0.0740

Total Basal area (m Sum of weights 1

3.3. People’s dependency and community forest management

The inhabitants of the studied villages depend on VP forests for ha) 373 30.95 Dense forest 400 68.8 Dense forest 273 8.3 Dense forest 693 38.95 Dense forest 373 20.55 Degraded slopes 247 6.1 Good herb layer fuelwood, leaf-fodder, leaf-litter, green grasses, timber and non-timber forest products (e.g. medicinal plants, wild edibles, resin, etc.). Forest species were used as fuelwood (67–78%) and fodder (30–60%) in the , region. In the study area the dependency of local people is high in oak

spp. dominated forests than the pine forests. The perception study revealed most of the villagers (79%) consider VP forests significantly valuable , Viola pilosa,

, Thallictrum minus, and they use resources from these community forests. However, ma- spp.

spp. jority of them (89%) agreed their VP forests are unable to meet the biomass demands of the villagers. The villagers (84%) accepted that most of the community forests are not being managed properly. The younger generation revealed that none of the VP is making efforts to improve the situation. However, 85% of respondents still wanted to maintain the community forests and they expect provisions for the fi-

etc. nancial support from governments for boundary wall/fencing, security guard, fire management, plantation activity, etc. etc.

etc. 4. Discussion etc.

Village forest councils with nearly nine decades of existence in the state of Uttarakhand have witnessed a glorious past as strong and un- etc. ique community institution for effective management of forests. However, over the years, with series of amendments to the Forest Panchayat Act of 1931, the essence of this institution has gradually Rhododendron arboreum, Quercus leucotrichophora, Berberis jeshkiana, Rosa Anaphalis contorta, Roscea procera, Betula alnoides, Taxus wallichiana,Carum Berberis carvi, jeshkiana, Cotoneaster Pinus wallichiana, Betula utilis, BerberisPolygonum jeshkiana, peniculatum, Rosa sericea, Persicaria amplexicaulis, Abies pindrow, Rosa macrophylla, Juniperusetc. indica, Polygonatum Quercus leucotrichophora, Lyonia ovalifolia,indica, Pricipia Oxalis utilis, corniculata, Berberis jeshkiana, Fragaria Alnus nepalensis, Lyonia ovalifolia,Araesema Berberis tortuosum, chitrea, Pyracantha Anaphalis crenulata, contorta, Rubus nevis, weakened (Isha and Geetanjoy, 2018). The forests falling under Van Panchayats (VPs) are often considered no more in position to meet the need of villagers (Singh and Sundriyal, 2009; Germain et al., 2018). The present study also provides evidences and supports to such arguments of earlier workers. The study also highlights the critical state of health (109.17) (186.05) (203.67) and vulnerability of community forests particularly in the study area (225.59)

(126.10) (western Himalaya). (286.38)

oribunda Dominance of Chir-Pine (Pinus roxburgii) in most of the lower zone fl community forests is a challenge. These forests often fail to provide sufficient fuelwood and fodder to the villagers. However, being most Dominant species (IVI) Associated species Tree Density (Ind/ Abies spectabilis Pinus wallichiana Abies pindrow Betula utilis Pinus wallichiana Quercus easily available forests in the vicinity, they face tremendous pressure of villagers and livestock grazing (Rawal et al. 2012; Joshi et al., 2018). − These forests with lower mean tree density (324 tree ha 1) and basal − area (20.24 m2 ha 1) are largely open canopy forests that invite in- ) festation of invasive weeds in presence of high anthropogenic pressure. The mid and higher zone forests are relatively in better state of health richness

continued with multiple species dominance, which possess more value for meeting ( sustenance needs of villagers. In general, high proportion (45.2%) of community forests in high or very high vulnerability state calls for an Altitude Tree species 2523 5 2548 6 2915 5 3400 2 2285 4 2264 6

Table 3 urgent attention. However, heterogeneity in forest vulnerability along

9 S. Thakur, et al. Forest Ecology and Management xxx (xxxx) xxxx

Fig. 4. Categorization of VPs in vulnerability classes along altitudinal gradient. altitude zones suggests zone specific intensification of management globally vulnerable. Long-term resilience and the productivity (Cramer interventions. Greater vulnerability of community forests in low and et al., 2004) of tropical forests becomes uncertain, which is compro- mid (700–1700) altitude zone would require more frequent and intense mising the flow of ecosystem services (Thompson et al., 2009) and interventions. Increased vulnerability in these zones can be attributed carbon sequestration capacity of forests (Fischlin et al., 2009). The to higher number of dependent local population on respective forests climate change driven vulnerability projections provide valuable in- for diverse goods and services. This is consistent with reported high formation about the likely impacts. However, they do not cover miti- biophysical vulnerability coinciding with intensified agriculture land gation and adaptation strategies, hence such predictions are not suffi- and absence of dense forests (Shukla et al., 2016). In general, low al- cient to guide forest management at ground level. Therefore, titude zone of Himalaya is referred to have higher environmental vul- comprehending complex ecosystem processes is important to plan and nerability due to various human activities such as agriculture, horti- implement different adaptation strategies. The results of present study culture, hydro-electric projects, road networking and settlements, etc., did not follow the findings of regional studies that indicated high forest (Nandy et al., 2015). Earlier studies have also indicated that the forests vulnerability to climate change in high altitude ranges (Sharma et al., in lower altitudes with high accessibility are more exposed to vulner- 2017; Kumar et al., 2018). The study, therefore, establishes that the ability (Reddy et al., 2016; Lonn et al., 2018). The people during in- local level adaptation interventions cannot be designed on regional or terviews opined that continuous extraction of resources from commu- global level models. Study also revealed that earlier studies have nity forests all through the year has increased the threat to these forests. identified only a few high inherent vulnerability grid points in northern Climate change is now inevitable (IPCC, 2013), and forests are Himalayan region for Himalayan moist temperate and subtropical pine

Fig. 5. Trend of VP forest vulnerability index (FVI) along altitudinal gradient.

10 S. Thakur, et al. Forest Ecology and Management xxx (xxxx) xxxx forests (Sharma et al., 2017). Therefore, we strongly argue outcomes of resource collection from forests in prevalent, however, they often do modeling at regional to national level, which would require intense not find space in VPs or any decision making on forest resource man- ground verification at local scale. agement. While under Van Panchayat rules, it is mandatory to include Following the results, it is prudent to assign extremely vulnerable two women members in the VP council. This contradiction requires to areas to high priority forest management plans and practices to reduce be carefully adjusted in new thinking of VPs governance. We experi- their vulnerabilities and facilitate early response strategies to climate enced to meet pressing protection needs, the VPs often have maintained change. Analysis of factors influencing forest vulnerability is mainly a symbiotic relationship between natural resources and cultural belief determined by climate variables, i.e., temperature and rainfall. Critical systems. For instance, the Bhotiya tribes in the region have promoted role of climate in distribution of plant species is well known (Currie the concept of sacred forests in their VPs (Negi et al., 2018b). This et al., 2004). Similar is true for determining patterns of species richness practice has contributed for low vulnerability of forests in the high al- with the altitude (Day and Monk, 1974). Therefore, under predicted titude zone. Besides sacred forests, there are number of small patches of rate of warming in the Himalaya, which is much higher than global VP forests that have been devoted to local deity, and harvesting from average, one may easily predict increasing vulnerability of community such areas is strictly banned resulting in complete protection of such forests, especially in low to mid hills where VP forests are likely to patches of VPs. experience greater changes. The indicators of disturbance domain fur- At local level, assessment of inherent vulnerability of forests for ther contributes for increased FVI. Reports indicate disturbances to identifying the sources of vulnerability and to sensitize forest man- forests can change forest composition and simplify forest structure, agement for action on ground emerges as most recognizable choice which have implications for functionality of forests and their capacity (Varughese and Ostrom, 2001; Sharma et al., 2015). There is a need to to resist and or adapt (Noss, 1999). In general, grazing, lopping, cutting involve people who are engaged with the forest management and de- (including soil erosion/forest clearing activities), and fire are con- pendent on forest resources for their daily requirements. For example, sidered as a major cause of forest loss in the mountains (Rawal et al. as mentioned earlier, women in the region are a critical part of com- 2012; Chakraborty et al., 2018a, 2018b). It is also reported that dis- munity forestry but however, they have been given a passive role in turbance can enhance regeneration to some extent, but increase in decision-making. It was also found that the election of Sarpanch (head disturbance from certain limit have negative impact on the forest of VP) and committee organizational setup is not a regular process, and community structure (Malik et al., 2016). Present study, further es- not being updated on regular basis. Also, the appointment of forest tablishes the prevalence of invasive species in studied VPs, which is guards often remains irregular due to paucity of funds. As per VP rules, another major cause for forest degradation. Invariably low tree species one member from Forest Department should be part of community richness is yet another factor that contributes to vulnerability of a forest council. However, villagers perceived that the Forest Department particular forest. Studies suggest forest type and altitude have lesser was meeting its obligations to the communities only regarding resource influence on tree richness, while shrub and herb richness may be more harvesting; similar has also been indicated in the previous study sensitive to disturbance (Kumar and Ram, 2005). Therefore, in highly (Germain et al., 2018). Villagers revealed that the initial concept of the vulnerable community forests, promotion of species and genetic di- VPs was to maintain the resources for lean period and harvest only once versity through plantation of multiple use trees is likely to increase or twice on rotational basis in a year, which fulfilled the demand on resilience of forests to changing climate (Halofsky and Peterson, 2016). sustainable basis. However, with increasing population and declining Among anthropogenic parameters, number of forest dependent interest of younger generation, the status of VPs has deteriorated in people contributed high weight to the forest vulnerability. It is well recent decades. In many community forests in low and mid-hills, the established that the unsystematic harvest without adequate knowledge overgrazed and fire prone areas were infested by alien invasive species creates disproportionate impact on forest resources and plant diversity such as Lantana camara, Ageratina adenophora, etc. All these factors are (Rawal et al. 2012; Sharma et al., 2015; Negi et al., 2018a; Rawal et al., indicative of increasingly pathetic state of community forests in the 2018). The severity of forest vulnerability is determined by the inter- Indian western Himalaya The study strongly suggests, better resource actions of several biological, physical and social (i.e. management) management planning and conservation can potentially reduce forest factors. Community forestry is increasingly recognized as important vulnerability under climate change scenario. This study further pro- form of management, where local community at the village level con- vides the ground realities of VPs, which might be useful for diverse tributes for management. The result of present study however reveal group of stakeholders, especially policy planner for execution of rules declining trends of interest of local inhabitants in forest management. and regulations under changing scenario. This was evident from the fact that many of the target forests were unable to meet the fuelwood and fodder needs of the respective villa- 5. Conclusion gers in lower altitude zone. Therefore, income diversification with re- spect to alternate livelihood sources, institutional reforms, and infra- Community forests in Indian Himalaya are known to be well man- structure facilities is needed to urgently reduce forest dependency, aged forests, which contribute for (i) uninterrupted supply of ecosystem thereby, allowing sustainable forest management (Chakraborty et al., goods and services to the dependent communities, and (ii) biodiversity 2018a, 2018b). While the villagers are aware of the changing climate conservation in general. However, observed high vulnerability of and its impacts, role of forests in climate change mitigation is rarely community forests in the mountain region of Indian western Himalaya known. Hence systematic awareness and capacity building of in- reflects their poor health, which hampers the forest based need of de- habitants is required. The plan of management thus essentially requires pendent communities. In the region, besides climate change as major to incorporate people’s needs and conservation aspects together. Under driving force for forest degradation and vulnerability, anthropogenic changing climate and human needs these community forests may not disturbance emerges as a dominant indicator of forest vulnerability. survive in need of scientific management interventions. In particular, Our results strongly discard the findings from model based regional awareness among the villagers especially women is needed for effective studies, and establish that the adaptation interventions at ground (local management of VPs management and climate mitigation. level) require much more intense information than the one enshrined in As the forest vulnerability index varied significantly along the alti- regional or global level models. tudinal zones, adaptation planning eff orts for community forests Community forests of Uttarakhand require urgent attention by the needed to respond effectively to emerging needs under changing sce- natural resource managers and concerned government department to nario. In general, the forest resource utilization in the region is con- ensure long-term goals of the community forests, and also promoting sidered to be not dependent on the education level, age, caste and land their role in climate change mitigation. Furthermore, un-willingness of area holdings (Chhetri et al., 2013). The participation of women for younger generation, lack of technical knowledge, lack of financial

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