Next Article in Journal
Changes in the Microbiological Properties of Soils along the Gradient of the Altitude Zone of Mount Kivaka in Eastern Fennoscandia, Russia
Previous Article in Journal
Abandoned Fishpond Reversal to Mangrove Forest: Will the Carbon Storage Potential Match the Natural Stand 30 Years after Reforestation?
Previous Article in Special Issue
Availability and Environmental Performance of Wood for a Second-Generation Biorefinery
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Vegetation Characteristics Based Climate Change Vulnerability Assessment of Temperate Forests of Western Himalaya

1
Indian Council of Forestry Research Education (ICFRE), Dehradun 248006, India
2
Forest Research Institute, Dehradun 248006, India
3
GB Pant National Institute of Himalayan Environment and Development, Almora 175126, India
4
Department of Biological and Environmental Sciences and Technologies, University of Salento, Via Monteroni, 73100 Lecce, Italy
*
Authors to whom correspondence should be addressed.
Forests 2022, 13(6), 848; https://doi.org/10.3390/f13060848
Submission received: 25 March 2022 / Revised: 19 May 2022 / Accepted: 26 May 2022 / Published: 29 May 2022
(This article belongs to the Collection Sustainable Forest Management: Past, Present, Future)

Abstract

:
Forests are under stress due to variety of climatic and non-climatic factors. Therefore for suitably managing the forests, vulnerability of the forests needs to be understood. The present paper attempts to estimate the vulnerability of various temperate forests of Western Himalaya due to climate change by analyzing the patterns of different taxonomical indices, based on primary data i.e., vegetation data. The paper presents a novel approach for climate change vulnerability assessment based on field data through a bottom-up approach. The vulnerability of the forests was assessed through the IPCC framework by suitably selecting indicators (taxonomy indices and climatic parameters) for the three dimensions of vulnerability i.e., exposure, sensitivity and adaptive capacity. The field data were collected from 17 different temperate forests distributed at the elevation “1600 to 3500 m” in Uttarakhand and Himachal Pradesh, India. Abundance and richness for each forest were collected by randomly laying ten quadrats of size 0.1 ha each. The analysis resulted into identifying the most and the least vulnerable temperate forests of the western Himalaya to climate change. The analysis showed that the Neoza Pine; Moist Deodar; Ban Oak and Dry Broadleaved and Coniferous forest were the most vulnerable forests in the Himalayan temperate forests due to climate change. Moreover, the variation in the levels of the vulnerability status of the selected forests was insignificant with elevational range as well as exposure to climate. The proposed method will serve for vulnerability estimation of forests due to climate change based on the actual realization of the species in the field.

1. Introduction

Temperate forests are considered very prone to fragmentation as well as degradation [1] thereby vulnerable to the climate and anthropogenic factors. The climate of the Himalaya is changing as temperature is increased by 0.2–0.4 °C per decade in western Himalayan region [2], 0.6 °C per decade in central Himalayan region [3] and 1.6 °C in last century in northwest Himalayan region [4]. The change in climate makes the forest vulnerable and shifts the ecological niches of biological diversity [5] such as the upward movement of the dominant plant species due to increased winter temperature [6,7]; shift of temperate tree species as Pinus wallichiana [8] and Rhododendron campanulatum along an altitudinal transect [9]. Precisely, climate change influences species composition, productivity and biodiversity of temperate ecosystems [10] i.e., shapes the vegetation pattern [9] and also leads to susceptibility. The variations in vegetation structure, richness, diversity and distribution are directly correlated with functioning and ecosystem services provided by the forest [11,12,13]. For example, biodiversity, structural diversity, forest disturbances and environmental variables affect above ground tree carbon storage [14]; depletion of soil organic carbon (SOC) leads to a declining trend in productivity [15]; soil microbial biomass carbon decreases due to forest fire in Himalayan temperate oak forest [16]. Inevitably, change in the forest community (distribution, density and species composition) due to climatic and non-climatic factors immensely influence the functioning of the forests [17] and make the forest vulnerable. Therefore, effective current and future management of the temperate forest ecosystems under the changing factors should be based on scientifically generated information about the current status of the forests [18,19].
Vulnerability assessments of forest system provide important information for understanding the potential risks, challenges, and opportunities of climate change [20]. Therefore, vulnerability assessment of forests is important not only for identifying the factors causing vulnerability at the local level, but also to facilitate forest managers to develop the most recognizable choice for forest management [21,22]. A top-down approach using global and regional climate and vegetation models has been applied to assess the forest vulnerability at global scale [23]; national scale [24]; and regional scale [25,26]. However, very few studies have attempted the bottom-up approach for vulnerability assessment [27] thereby failing to propose the measures for the resilience of the forests at local level [28]. The adaptation measures for the forests cannot be decided on large scale evaluation of the vulnerability through environmental parameters rather must be based on vulnerability assessment based on ecological parameters i.e., plant diversity, richness, at local scale [29]. Evaluation of vegetation and biodiversity is important to understand the status of forest disturbances of forest [30,31]; forest succession [32,33,34], and flow of various ecosystem services [35,36] thereby also provides inputs to the development of effective conservation and management plans for the forest [37]. Therefore, the present study aims to analyze the vulnerability of forests due to climate change by developing an index using various diversity indices based on the current vegetation structure and composition of woody species of temperate forests of Uttarakhand and Himachal Pradesh, India. The study hypothesizes that the vulnerability of the various sub-types of temperate forests of the western Himalayan region varies with climate change. The other hypothesis predicts that an association exists between the vulnerability status of the temperate forests of the region and the topographical dimension i.e., elevation ranges of the occurrences of the forest as well as the exposure to climate. The study based on field observation generates the information about the current status of the structure and composition of the forests by collecting primary information from 17 forests. The synthesis of the evaluation would facilitate the current status of forests along with their vulnerability status, thereby supporting the development of a protocol for conservation and management of the forests.

2. Materials and Methods

2.1. Study Area

The study was focused on the temperate forests of Uttarakhand and Himachal Pradesh of India (Figure 1). Uttarakhand, predominantly a Himalayan mountainous state spreads in 53,483 km2 and lies between 28°43′ N to 31°28′ N latitude and 77°34′ E to 81°03′ E longitude with varied climate and vegetation along with altitude. The average annual rainfall is 1500 mm with the annual temperature varies from 0 °C in alpine region to 43 °C in plain region. The population density of the State is 189-person per km2. Himachal Pradesh is a western Himalayan mountainous state with a geographical area of 55,673 sq km and distributed between 30°22′ N to 33°12′ N latitude and 75°45′ E to 79°04′ E longitude. The average annual rainfall is about 1800 mm with varied temperature from sub-zero in alpine region to 35 °C in plain region. The population density of the State is 123 per km2. Topographically both of these states have all the three major ranges of Himalayas resulting in the formation of alpine pastures in higher altitudes to sub-tropical forests in the lower altitudes. Himalayan moist temperate forest (Group 12) covers 31.64% (Uttarakhand) and 40.24% (Himachal Pradesh) of geographical area and Himalayan dry temperate forest (Group 13) covers 1.01% (Uttarakhand) and 3.32% (Himachal Pradesh) [35] (Supplementary Materials Tables S1 and S2).

2.2. Sampling

Temperate forest in India covers 12.84% of Himalayan region [35] and provides various ecosystem services ranging from consumer goods to durables to the neighboring communities for their subsistence [38]. These forests are vulnerable to varied climatic and non-climatic factors such as temperature [5]. These factors attributed to changes in the composition and structure of the forests and therefore changes in the distribution of areas under various forest density classes from the year 2005 to 2019 [35]. The contribution of the forests to the adjoining communities for their subsistence [38] and their vulnerable status to the climate [25,26] were the contenders for selecting these forests for evaluation. The selection of forest sites was made through stratified random sampling with Forest Type Group as a stratification criterion. Temperate forests of the region have two major group types as Himalayan moist temperate forests and Himalayan dry temperate forests and further classified into two sub-groups each as Lower Western Himalayan Temperate Forests (eight subgroup types) and Upper West Himalayan Temperate Forests (six subgroup types) and Western Types (eight subgroup types) and Eastern Types (one subgroup type) (Supplementary Materials Table S1). These were further classified into subgroup type [39]. Out of these subgroup types, many subgroups have a low geographical area (proportion) and therefore were not considered. A total of seventeen sites (eleven from Uttarakhand and six from Himachal Pradesh) representing the various subgroups of the temperate forest type Groups 12 and 13 were selected at random from both the states (Figure 1) (Supplementary Materials Tables S1 and S2). Climatic details such as precipitation and maximum and minimum temperature of the study area from 2010 to 2018 were obtained from WorldClim data to evaluate climatic changes during the last decades (Supplementary Materials Table S3).
A detailed survey and vegetation analysis were conducted in the selected sites of the forests of both the states. Ten quadrats of 0.1 ha were randomly laid in each of the 17 selected sites following the methodology of forest inventory [35] from September 2020 to March 2021. Tree species were identified with the support of literature and taxonomists of the Forest Research Institute, Dehradun, India. Latitude, longitude, elevation, aspect, and slope were recorded for all the selected forests (Supplementary Materials Table S2). Various taxonomical indices such as Shannon-Wiener Index [36], Simpson’s Index [37], Pielou’s Equitability Index [40], Menhinick’s Index of Richness [41], Degree of Maturity [42] and the ratio of abundance to frequency were calculated using the standard procedures.

2.3. Vulnerability Analysis

In this study, vulnerability is considered as the susceptibility of forest ecosystem in terms of species composition and diversity under the exposure to the climate change. Moreover, temperate forests of Uttarakhand and Himachal Pradesh are vulnerable to the precipitation and number of wet days’ frequency [25]. The impact of warming on temperate forests has huge implications for changing vegetation i.e., composition, net primary productivity [24]. Temperature is responsible for changes in the structure and composition of Ridge Top forests of Uttarakhand and Himachal Pradesh [22]. Moreover, evaluation of statistics showed that areas under different selected temperate forest types of Uttarakhand and Himachal Pradesh are already changed (Supplementary Materials Tables S4 and S5) with changes in shifting to lower density classes [35].
The vulnerability of the forest ecosystem was assessed through the IPCC framework considering the three dimensions of vulnerability i.e., exposure, sensitivity and adaptive capacity using bottom-up approaches [43]. Exposure in the present case is degree to which a system is exposed to climatic variations and determined based on of temperature and precipitation. Sensitivity is the degree to which the forest ecosystem is affected by climate-related stimuli and determined through the composition and structure parameters of the forests [24] i.e., abundance, number of species, basal area, Simpson’s Index and Menhinick’s Index of Richness Index. Adaptive capacity is the ability of a forest ecosystem to adjust to climate change to moderate potential damages, and or to cope with the consequences and therefore determined using Equitability Index, Shannon-Weiner Diversity Index and Degree of maturity. Indicators for each dimension of vulnerability were considered based on the conceptualization of vulnerability. The indices have different scales and therefore, normalization was used to make them comparable and to facilitate aggregation. All indicators except the maturity index which was considered adversely related to adaptive capacity due to facilitation of succession were normalized to values between 0 and 1 based on the following formula [44].
N o r m a l i z e d   V a l u e ,   Xij   = Xi   MinXj   MaxXj     MinXj  
where Xij is the normalized value of indicator (j) with respect to forest type (i), Xi is the actual value of the indicator with respect to forest type (i), and MinXj and MaxXj are the minimum and maximum values, respectively, of indicator (j) among all forests type.
Exposure index (EI) was calculated based on the mean of minimum (Tmin), mean of maximum temperature (Tmax) and mean of precipitation (Pr). Sensitivity Index (SI) was calculated based on number of individuals (NI), number of species (NS), basal area (BA), Simpson’s Index (Sim) and Menhinick’s Index of Richness Index (Men). Adaptive Capacity Index (AI) was measured using Equitability Index (EqI), Shannon-Weiner Diversity Index (SW) and Degree of maturity (DM). Following formula were used for calculating the EI, SI and AI for each selected forest subtypes based on relevant data collected from the subtypes:
EI   = Tmin + Tmax + Pr   3
SI   = NI + NS + BA + Sim + Men   5
AI   = EqI + SW + DM   4
The limits of the three indices were 0 ≤ EI, SI, AI ≤ 1. The three dimensions were aggregated to obtain a composite vulnerability index (VI) using Manush approach. The approach is based on a Displaced Ideal method and was applied to assess the vulnerability with the premise that a better system should have less distance from the ideal. In this approach, the ideal system was considered as a most vulnerable system. The ideal vulnerable system would have no adaptive capacity i.e., value zero and highly sensitive i.e., value one under highly exposed system i.e., value one [43,44]. The Euclidean distance from the ideal was used to assess the vulnerability based on the premise that the impact realized in the system due to exposure (climate) on sensitive systems was countered by the adaptive capacity of the system. The following formula was applied to assess the vulnerability:
Vulnerability index (VI) = [(1 − EI) (1 − SI) − AI]
The value of the VI ranges from 1 (most vulnerable) to −1 (least vulnerable) i.e., where a high value of VI for an ecosystem indicates closeness to the most vulnerable state signifies a most vulnerable state i.e., the positive value shows that sensitivity dominates adaptive capacity; and a high departure from most vulnerable state signifies least vulnerable state i.e., the negative value shows that adaptive capacity overshadows sensitivity [44]. The vulnerability status (class) was estimated based on quartile measures as least vulnerable (up to first quartile), low vulnerable (between first to second quartile), moderate vulnerable (between second and third quartile) and most vulnerable (after the third quartile).
The association between vulnerability status with elevation range and exposure level (measured as exposure index above on the basis of climatic parameters) was also tested through Fisher’s Exact test.

3. Results

Total number of species was more in West Himalayan Upper Oak/Fir forest, Maid; Ban Oak forest, Binog; Moru Oak forest, Deoriatal and Moist Temperate Deciduous forest, Dunagiri; however, the number of individuals was more in Ban Oak forest, Mcleodganj (Table 1). The total basal area was highest in Kharsu Oak forest, Kunjkharak whereas minimum in Dry Broadleaved and Coniferous forest, Kalpa and Neoza Pine Forest, Akpa. Simpson’s Index was highest in Ban Oak forest, Pangot whereas minimum in Neoza Pine Forest, Akpa. Shannon-Weiner Index was highest in West Himalayan Upper Oak/Fir forest, Maid, whereas minimum in Ban Oak forest, Pangot. Pielou’s Index of Equitability was highest in West Himalayan High Level Blue Pine Forest, Narkanda whereas minimum in Ban Oak forest, Pangot. Menhinick’s Index of species richness was highest in West Himalayan Upper Oak/Fir Forest, Maid whereas minimum for West Himalayan High Level Blue Pine Forest, Narkanda. Degree of maturity was highest in Moru Oak forest, Deoriatal whereas minimum for Ban Oak forest, Pangot (Table 1).

Vulnerability Status

Exposure level was high in Ban Oak forest, Mcleodganj forest followed by Ban Oak forest, Binog and lowest in Dry Deodar forest, Harshil, Low-Level Blue Pine forest, Manali; Neoza Pine Forest, Akpa. The high exposure level (index) was due to heavy to moderate snow fall with heavy rains in the region, however the low exposure was primarily due to consistency in the weather patterns in the region as the climate of Harshil; Manali; Akpa and Kalpa were more or less consistent and cold with heavy snowfalls during winters (Supplementary Materials Table S3). Sensitivity index was high in Neoza Pine forest, Akpa; High Level Blue Pine, Narkanda; Low Level Blue Pine, Manali and Dry Broad-Leaves Coniferous forests and lowest in West Himalayan Upper Oak/Fir forest, Maid; Kharsu Oak forest, Kunjkharak; Moist temperate Deciduous forests, Dunagiri and Ban Oak forest, Binog. The high sensitivity was attributed to various stand characteristics such as low species diversity along with low species richness and total basal area (Table 1). The adaptive capacity index was low in Moist Deodar forest, Dalhousie; Ban Oak forest, Pangot; Moist Temperate Deciduous forest, Sutol and Neoza Pine forest, Akpa (Table 2) due to their low diversity and equitability (Table 1). The result showed that the four most vulnerable forests were Neoza Pine forest, Akpa; Moist Deodar forest, Dalhousie; Ban Oak forest, Pangot; and Dry Broadleaved and Coniferous forest, Kalpa and the least vulnerable forests were Moru Oak forest, Kilbury; West Himalayan Upper Oak/Fir forest, Maid; Kharsu Oak forest, Kunjkharak; Moist Temperate Deciduous, Dunagiriand Moru Oak forest, Deorital (Table 2 and Figure 2).
Out of 17 sub-forest types, five were assessed as least vulnerable whereas the remaining 12 were uniformly distributed against the three higher vulnerability classes i.e., low, moderate and most as per the vulnerability classification approach mentioned in the method section. Though a varied distribution of vulnerability status of the forests with respect to elevation range was observed, Fisher’s Exact test results point towards non-association between the two i.e., elevation and vulnerability status were independent to each other (Table 3).

4. Discussion

The proposed vulnerability analysis method depicted the vulnerability status of the various sub-groups of temperate forest types on relative basis. The indices were based on the various contextual taxonomical indices, representing species richness, abundances and thereby the species composition. “Species richness of the forests signifies resource availability and increase with spatial heterogeneity and conservation practices [45,46]. However, causes of biodiversity loss are mainly anthropogenic involving over-harvesting, pollution, habitat change and climate change [47]”. The study region has huge anthropogenic disturbances [48,49,50,51], moreover, changes in climate as temperature and rainfall patterns are also observed in the region [2,4].
The lower values of basal area in Dry Broadleaved and Coniferous forest, Kalpa might be due to trees with low diameter classes as well as anthropogenic disturbances as forest vegetation of Himalayas is severely affected by anthropogenic disturbances such as logging, uncontrolled grazing, lopping for firewood and fodder, and litter removal [52]. Tree with high basal area signifies the high performance of the species in specific environmental conditions, and reduced basal area suggests either the chance occurrence of a species or the occurrence of biotic disturbances in the past [53]. Accumulation of biomass and total basal area might decrease with altitude because of several factors such as low air and soil temperatures, short growing season, increased exposure to wind, and reduced nutrient supply [54]. Low species richness in the forests indicated that these sites have low resources and low spatial heterogeneity thereby restricting the establishment of other species. In contrast, the sites with more number of species and high density have high richness and in general high basal area i.e., the resource availability and heterogeneity within the forests were high therefore facilitating the establishment of other species. Forest community diversity is generally influenced by the stability or evolution time of the forest resulting in heterogeneity of micro and macro environment which affects the diversity of the community [55].
The least vulnerability in the forests of the Moru Oak forest, Deorital; West Himalayan Upper Oak/Fir forest, Maid; Kharsu Oak forest, Kunjkharak; Moist Temperate Deciduous forest, Dunagiri and Moru Oak forest, Kilbury were attributed to high diversity with high density and more number of species. More number of species and high diversity may be strengthened and made the internal ecosystem stable leading to a resilient ecosystem to the changes. The low vulnerability of the forests of Ban Oak forest, Mcleodganj; Moist Deodar forest, Jageshwar; Ban Oak forests, Binog; Western Mixed Coniferous forest, Chakrata were primarily due to low degradation and low anthropogenic disturbances besides high density along with more number of species with the high basal area. Forests with high basal area signify the high performance of the species of the forests in specific environmental conditions thereby attributing better adaptation of the species [53]. The moderate vulnerability to the forests of the Dry Deodar forest, Harshil; Low-Level Blue Pine forest, Manali; West Himalayan High Level Blue Pine forest, Narkanda and Moist Temperate Deciduous forest, Sutol was attributed to high anthropogenic pressure with relatively low density and less number of species i.e., dominated by one species on those forest patches. The most vulnerability in the forests of Dry Broadleaved and Coniferous forest, Kalpa; Ban Oak forests, Pangot; Moist Deodar forest, Dalhousie and Neoza Pine forest, Akpa were attributed to low diversity with low density i.e., sparsely distributed of single dominated species with low maturity thus low adaptability to change [56]. Moreover, the fragmentation patches also contribute to increased vulnerability as the movement of species and genes within a landscape are restricted [57], thereby the ability of forests ecosystems to maintain identity and function as the climate shifts are restricted [20]. The low basal area indicates a low occurrence of a species or biotic disturbances or both [53] thereby making the forests more vulnerable. The resilience of vulnerable forests may be increased by promoting the species and genetic diversity through the plantation of multiple use trees in a changing climate [58]. The diversity of forest community indicates the stability or evolution of the forest under the micro and macro environment heterogeneity of the forests [59] thereby facilitating for adaptation. Moreover, the attributable causes of the changes in the forests in Himalayan region are due to high dependency of the people on the forests [60]. The low vulnerability in Moru Oak forest, Deoriatal and Ban Oak forest, Binog was also due to the low external pressure in these forests being protected areas (Figure 2).
Changes in the temperature and rainfall pattern in the Himalayan temperate forests (Supplementary Materials Table S3) may be the factors, suggesting the changing pattern of species composition, distribution and structure of the forest. Moreover, shifting from a very dense forest to a moderate and open forest (Supplementary Materials Table S4) and decreasing forest area (Supplementary Materials Table S5) reflects the degradation as well as deforestation in Uttarakhand and Himachal Pradesh. Ban oak forest was designated as one of the most degraded deforested and vulnerable forests in the present study, comparable to that of the FSI report (Supplementary Materials Table S5). Forest degradation changes species composition, physico-chemical properties of soil and micro-climate of the forests community [61,62]. The finding of the present paper supports that climatic as well as anthropogenic factors are making the internal ecosystem weak and causing irreversible damage to Himalaya temperate forests and also making them vulnerable to the different stressors.
The selected temperate forests occur in a wide range of local physiographic landforms of Himalaya i.e., from rocky slopes to rolling planes. The present evaluation noted differential vulnerabilities among the selected forests. Moreover, the variability within each vulnerability status of the temperate forests was not governed by the various levels of elevation i.e., altitude of the occurrences of the forests and by the various level of exposure to climate. This might be due to the fact that forests are biological units, and various climatic and biological factors, interact in a complex way, and may counter the impacts of the altitude and exposure level i.e., species adjustments, nativeness of species, maturity and plasticity of the species may be coping with the changes and adjusting themselves for their survival. Besides, the forests adapted to the respective sites as per the availability of the resources matching with the growth of the individuals, composition and structure of the forests. The understanding of variation in habitat conditions of trees in temperate forests and their adaptation may support improved management strategies and conservation of forests over diverse habitats.
The applied vulnerability index may be used for assessing the vulnerability of forests against the changes in climate using taxonomical indices. The estimation of taxonomical indices is easy and based on actual field data about species in the forest. The actual data about the species abundance are capable of portraying the actual performance of the species occurrences, which forms the basis of the comparison of vulnerability.

5. Conclusions

The present paper highlights the vulnerability status of different temperate forests of Uttarakhand and Himachal Pradesh, India based on field measurements on the composition and distribution of species in the forests. The analysis confirms that the vulnerability of the temperate forests varies with respect to the climatic condition of the sites and variations in vulnerability status remain similar across the altitude and exposure level i.e., changes within the vulnerability levels were insignificant with elevation and climate.
The paper proposes a method for evaluation of vulnerability based on primary data on the species abundance and occurrences thereby providing support for vulnerability assessment as per the actual realisation of the exposure to the forest ecosystem.
Based on vulnerability scale, Neoza Pine forest; Akpa; Moist Deodar Forest; Dalhousie; Ban Oak Forest, Pangot and Dry Broadleaved and Coniferous Forest, Kalpa was prone to change in species composition and hence affect ecosystem functioning and services of these forest communities in the future if appropriate measures would not be implemented.
The information from the analysis raises an urgent call to stakeholders for sustainable conservation and management of the forest. Overall the changes may be addressed by managing the forests through adequate protection by reducing anthropogenic pressures and supplementing supportive programs for forest improvement in terms of species density and composition.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f13060848/s1. Supplementary Table S1: Details of selected Forest Type Group in Uttarakhand and Himachal Pradesh; Supplementary Table S2: Site description of selected Forest Type Group in Uttarakhand and Himachal Pradesh; Supplementary Table S3: Spatio-temporal details of climatic parameters of study sites; Supplementary Table S4: Forest cover of Uttarakhand and Himachal Pradesh (at 1000–3000 m amsl elevation); and Supplementary Table S5: Change in forest area under different temperate forest types of Uttarakhand and Himachal Pradesh.

Author Contributions

Conceptualization, R.P. and M.R.; data collection, J.S., H.S., R.S. and P.K.T.; methodology, R.P., M.R. and R.S.; formal analysis, R.P., J.S., L.T. and R.S., and J.S.; investigation, M.R., R.P., J.S., H.S. and R.S.; data curation, R.S., J.S. and H.S.; resources, L.T.; writing—original draft preparation, J.S., R.P. and R.S.; writing—review and editing, R.P., V.P.P., M.R., I.D.B., T.S., T.C. and N.B.; visualization, R.P., L.T. and V.P.P.; supervision, R.P.; project administration, R.P., T.C., V.P.P. and I.D.B.; funding acquisition, R.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by National Mission on Himalayan Studies (NMHS), of Ministry of Environment, Forest and Climate Change under the, GOI, New Delhi vide sanction order no. GBPNI/NMHS-2020-21/MG/ dated 19.06.2020 and NMSHE TF 3 (phase II), DST, Govt. of India.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data may be obtained from Corresponding author upon reasonable request.

Acknowledgments

We would like to extend our acknowledgement to Arun Singh Rawat, ICFRE, Dehradun; Late Ranbeer Singh Rawal and Kireet Kumar, Govind Ballabh Pant National Institute of Himalayan Environment (NIHE), Almora, India for their kind support. The authors are also thankful to the two reviewers for their generous support in terms of quality inputs. The support for taxonomical inputs by Pravin Verma of Forest Research Institute, Dehradun is duly acknowledged.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. FAO and UNEP. The State of the World’s Forests 2020: Forests, Biodiversity and People; FAO: Rome, Italy, 2020. [Google Scholar]
  2. Dimri, A.P.; Dash, S.K. Wintertime climatic trends in the western Himalayas. Clim. Chang. 2012, 111, 775–800. [Google Scholar] [CrossRef]
  3. Shrestha, A.B.; Aryal, R. Climate change in Nepal and its impact on Himalayan glaciers. Reg. Environ. Change 2011, 11, 65–77. [Google Scholar] [CrossRef]
  4. Bhutiyani, M.R.; Kale, V.S.; Pawar, N.J. Long-term trends in maximum, minimum and mean annual air temperatures across the Northwestern Himalaya during the twentieth century. Clim. Change 2007, 85, 159–177. [Google Scholar] [CrossRef]
  5. Chakraborty, A.; Joshi, P.K.; Sachdeva, K. Predicting distribution of major forest tree species to potential impacts of climate change in the central Himalayan region. Ecol. Eng. 2016, 97, 593–609. [Google Scholar] [CrossRef]
  6. Kelly, A.E.; Goulden, M.L. Rapid shifts in plant distribution with recent climate change. Proc. Natl. Acad. Sci. USA 2008, 105, 11823–11826. [Google Scholar] [CrossRef] [Green Version]
  7. Yadava, A.K.; Sharma, Y.K.; Dubey, B.; Singh, J.; Singh, V.; Bhutiyani, M.R.; Yadav, R.R.; Misra, K.G. Altitudinal treeline dynamics of Himalayan pine in western Himalaya, India. Quat. Int. 2017, 444, 44–52. [Google Scholar] [CrossRef]
  8. Dubey, B.; Yadav, R.R.; Singh, J.; Chaturvedi, R. Upward shift of Himalayan pine in Western Himalaya, India. Curr. Sci. 2003, 85, 1135–1136. [Google Scholar]
  9. Singh, P.; Arya, V.; Negi, G.C.S.; Singh, S.P. Expansion of Rhododendron campanulatum krummholz in the treeline ecotone in Tungnath, Garhwal Himalaya. Trop. Ecol. 2018, 59, 287–295. [Google Scholar]
  10. Leemans, R.; Eickhout, B. Another reason for concern: Regional and global impacts on ecosystems for different levels of climate change. Glob. Environ. Chang. Benefits Clim. Policy 2004, 14, 219–228. [Google Scholar] [CrossRef]
  11. Woodward, F.I. Temperature and the distribution of plant species. Symp. Soc. Exp. Biol. 1988, 42, 59–75. [Google Scholar]
  12. Eriksson, O. Regional Dynamics of Plants: A Review of Evidence for Remnant, Source-Sink and Metapopulations. Oikos 1996, 77, 248–258. [Google Scholar] [CrossRef]
  13. Criddle, R.S.; Church, J.N.; Smith, B.N.; Hansen, L.D. Fundamental Causes of the Global Patterns of Species Range and Richness. Russ. J. Plant Physiol. 2003, 50, 192–199. [Google Scholar] [CrossRef]
  14. Måren, I.E.; Sharma, L.N. Seeing the wood for the trees: Carbon storage and conservation in temperate forests of the Himalayas. For. Ecol. Manag. 2021, 487, 119010. [Google Scholar] [CrossRef]
  15. Martin, D.; Lal, T.; Sachdev, C.B.; Sharma, J.P. Soil organic carbon storage changes with climate change, landform and land use conditions in Garhwal hills of the Indian Himalayan mountains. Agric. Ecosyst. Environ. 2010, 138, 64–73. [Google Scholar] [CrossRef]
  16. Singh, D.; Sharma, P.; Kumar, U.; Daverey, A.; Arunachalam, K. Effect of forest fire on soil microbial biomass and enzymatic activity in oak and pine forests of Uttarakhand Himalaya, India. Ecol. Processes 2021, 10, 29. [Google Scholar] [CrossRef]
  17. Rawat, M.; Arunachalam, K.; Arunachalam, A.; Alatalo, J.M.; Kumar, U.; Simon, B.; Hufnagel, L.; Micheli, E.; Pandey, R. Relative contribution of plant traits and soil properties to the functioning of a temperate forest ecosystem in the Indian Himalayas. Catena 2020, 194, 104671. [Google Scholar] [CrossRef]
  18. Adams, M.B.; Kelly, C.; Kabrick, J.; Schuler, J. Temperate forests and soils [Chapter 6]. In Global Change and Forest Soils: Cultivating Stewardship of a Finite Natural Resource. Developments in Soil Science; Busse, M., Giardina, C.P., Morris, D.M., Page-Dumroese, D.S., Eds.; Elsevier: Amsterdam, The Netherlands, 2019; Volume 36, pp. 83–108. [Google Scholar] [CrossRef]
  19. Monson, R.K. Ecology of Temperate Forests. In Ecology and the Environment, the Plant Sciences; Monson, R.K., Ed.; Springer: New York, NY, USA, 2014; pp. 273–296. [Google Scholar] [CrossRef]
  20. Swanston, C.; Brandt, L.A.; Janowiak, M.K.; Handler, S.D.; Butler-Leopold, P.; Iverson, L.; Thompson, F.R., III; Ontl, T.A.; Shannon, P.D. Vulnerability of forests of the Midwest and Northeast United States to climate change. Clim. Change 2018, 146, 103–116. [Google Scholar] [CrossRef]
  21. Varughese, G.; Ostrom, E. The contested role of heterogeneity in collective action: Some evidence from community forestry in Nepal. World Dev. 2001, 29, 747–765. [Google Scholar] [CrossRef]
  22. Upgupta, S.; Sharma, J.; Jayaraman, M.; Kumar, V.; Ravindranath, N.H. Climate change impact and vulnerability assessment of forests in the Indian Western Himalayan region: A case study of Himachal Pradesh, India. Clim. Risk Manag. 2015, 10, 63–76. [Google Scholar] [CrossRef] [Green Version]
  23. Abrams, M.D.; Nowacki, G.J. An interdisciplinary approach to better assess global change impacts and drought vulnerability on forest dynamics. Tree Physiol. 2016, 36, 421–427. [Google Scholar] [CrossRef]
  24. Chaturvedi, R.K.; Gopalakrishnan, R.; Jayaraman, M.; Bala, G.; Joshi, N.V.; Sukumar, R.; Ravindranath, N.H. Impact of climate change on Indian forests: A dynamic vegetation modeling approach. Mitig. Adapt. Strat. Gl. 2011, 16, 119–142. [Google Scholar] [CrossRef]
  25. Kumar, M.; Savita; Singh, H.; Pandey, R.; Singh, M.P.; Kalra, N.; Ravindranath, N.H. Assessing vulnerability of forest ecosystem in the Indian Western Himalayan region using trends of net primary productivity. Biodivers. Conserv. 2018, 28, 2163–2182. [Google Scholar] [CrossRef] [Green Version]
  26. Pokhriyal, P.; Rehman, S.; Areendran, G.; Raj, K.; Pandey, R.; Kumar, M.; Sahana, M.; Sajjad, H. Assessing forest cover vulnerability in Uttarakhand, India using analytical hierarchy process. Model. Earth Syst. Environ. 2020, 6, 821–831. [Google Scholar] [CrossRef]
  27. Thakur, S.; Negi, V.S.; Pathak, R.; Dhyani, R.; Durgapal, K.; Rawal, R.S. Indicator based integrated vulnerability assessment of community forests in Indian west Himalaya. For. Ecol. Manag. 2020, 457, 117674. [Google Scholar] [CrossRef]
  28. Hooper, D.U.; Chapin, F.S.; Ewel, J.J.; Hector, A.; Inchausti, P.; Lavorel, S.; Lawton, J.H.; Lodge, D.M.; Loreau, M.; Naeem, S.; et al. Effects of Biodiversity on Ecosystem Functioning: A Consensus of Current Knowledge. Ecol. Monogr. 2005, 75, 3–35. [Google Scholar] [CrossRef]
  29. Mccann, K. The diversity–stability debate. Nature 2000, 405, 228–233. [Google Scholar] [CrossRef]
  30. Caspersen, J.P.; Pacala, S.W. Successional diversity and forest ecosystem function. Ecol. Res. 2001, 16, 895–903. [Google Scholar] [CrossRef]
  31. Thompson, I.D. (Ed.) Forest Resilience, Biodiversity, and Climate Change: A Synthesis of the Biodiversity/Resiliende/Stability Relationship in Forest Ecosystems, CBD Technical Series. Secretariat of the Convention on Biological Diversity: Montreal, QC, Canada, 2009. [Google Scholar]
  32. Tilman, D. The Influence of Functional Diversity and Composition on Ecosystem Processes. Science 1997, 277, 1300–1302. [Google Scholar] [CrossRef] [Green Version]
  33. Cardinale, B.J.; Palmer, M.A.; Collins, S.L. Species diversity enhances ecosystem functioning through interspecific facilitation. Nature 2002, 415, 426–429. [Google Scholar] [CrossRef]
  34. Duffy, J.E. Why biodiversity is important to the functioning of real-world ecosystems. Front. Ecol. Environ. 2009, 7, 437–444. [Google Scholar] [CrossRef] [Green Version]
  35. FSI. India State of Forest Report; Forest Survey of India: Dehradun, India, 2019. [Google Scholar]
  36. Shannon, C.E. A Mathematical Theory of Communication. Bell Syst. Tech. J. 1948, 27, 379–423. [Google Scholar] [CrossRef] [Green Version]
  37. Simpson, E.H. Measurement of Diversity. Nature 1949, 163, 688. [Google Scholar] [CrossRef]
  38. Pandey, R. Forest resource utilisation by tribal communities of Jaunsar, Uttrakhand. Indian For. 2009, 135, 655–662. [Google Scholar] [CrossRef]
  39. Champion, H.G.; Seth, S.K. A Revised Forest Types of India; Manager of Publications, Government of India: Delhi, India, 1968. [Google Scholar]
  40. Pielou, E.C. An introduction to mathematical ecology. In Wiley Interscience; John Wiley & Sons: Hoboken, NJ, USA, 1969. [Google Scholar]
  41. Menhinick, E. A comparison of some species diversity indices applied to samples of field insects. Ecology 1964, 45, 859–861. [Google Scholar] [CrossRef]
  42. Pichi-Sermolli, R. An index for establishing the degree of maturity in plant communities. J. Ecol. 1948, 36, 85–90. [Google Scholar] [CrossRef]
  43. IPCC. Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change: Chapter 10 Asia; Cambridge University Press: Cambridge, UK, 2007. [Google Scholar]
  44. Omerkhil, N.; Kumar, P.; Mallick, M.; Meru, L.B.; Chand, T.; Rawat, P.S.; Pandey, R. Micro-level adaptation strategies by small-holders to adapt climate change in the least developed countries (LDCs): Insights from Afghanistan. Ecol. Indic. 2020, 11, 106781. [Google Scholar] [CrossRef]
  45. Gotelli, N.; Colwell, R. Quantifying biodiversity: Procedures and pitfalls in the measurement and comparison of species richness. Ecol. Lett. 2001, 4, 379–391. [Google Scholar] [CrossRef] [Green Version]
  46. Tilman, D. Resource competition and community structure. Monogr. Popul. Biol. 1982, 17, 1–296. [Google Scholar]
  47. Wood, A.; Stedman-Edwards, P.; Mang, J. The Root Causes of Biodiversity Loss; Routledge: London, UK, 2013. [Google Scholar] [CrossRef]
  48. Gilliam, F.S. The Ecological Significance of the Herbaceous Layer in Temperate Forest Ecosystems. BioScience 2007, 57, 845–858. [Google Scholar] [CrossRef]
  49. International Union for Conservation of Nature Annual Report 2019. Available online: https://www.iucn.org/about/programme-work-and-reporting/annual-reports (accessed on 4 June 2021).
  50. International Union for Conservation of Nature Annual Report 2020. Available online: https://www.iucnredlist.org/statistics (accessed on 8 June 2021).
  51. FAO. Biodiversity for Food and Agriculture and Ecosystem Services: Thematic Study for The State of the World’s Biodiversity for Food and Agriculture; FAO: Rome, Italy, 2020. [Google Scholar] [CrossRef]
  52. Malik, Z.A.; Bhat, J.A.; Ballabha, R.; Bussmann, R.W.; Bhatt, A.B. Ethnomedicinal plants traditionally used in health care practices by inhabitants of Western Himalaya. J. Ethnopharmacol. 2015, 172, 133–144. [Google Scholar] [CrossRef]
  53. Saxena, A.K.; Pandey, U.; Singh, J.S. On the Ecology of Oak Forest in Nainital Hills, Kumoun Himalaya. Glimpses of Ecology: Prof. R. Misra Commemoration Volume; Jaipur International Scientific Publication: Jaipur, India, 1978; pp. 167–180. [Google Scholar]
  54. Coomes, D.A.; Allen, R.B. Effects of size, competition and altitude on tree growth. J. Ecol. 2007, 95, 1084–1097. [Google Scholar] [CrossRef]
  55. Verma, R.K.; Kapoor, K.S.; Subramani, S.P.; Rawat, R.S. Evaluation of plant diversity and soil quality under plantation raised in surface mined areas. Indian J. For. 2004, 27, 227–233. [Google Scholar]
  56. Shaheen, H.; Ullah, Z.; Khan, S.M.; Harper, D.M. Species composition and community structure of western Himalayan moist temperate forests in Kashmir. For. Ecol. Manag. 2012, 278, 138–145. [Google Scholar] [CrossRef]
  57. Ibáñez, I.; Clark, J.S.; Dietze, M.; Feeley, K.J.; Hersh, M.; LaDeau, S.; McBride, A.C.; Welch, N.E.; Wolosin, M.S. Predicting biodiversity change: Outside the climate envelope, beyond the species-area curve. Ecology 2006, 87, 1896–1906. [Google Scholar] [CrossRef]
  58. Halofsky, J.E.; Peterson, D.L. Climate change vulnerabilities and adaptation options for forest vegetation management in the northwestern USA. Atmosphere 2016, 7, 46. [Google Scholar] [CrossRef] [Green Version]
  59. Verma, A.; Garkoti, S. Population structure, soil characteristics and carbon stock of the regenerating banj oak forests in Almora, Central Himalaya. For. Sci. Technol. 2019, 15, 117–127. [Google Scholar] [CrossRef] [Green Version]
  60. Malik, J.; Bhatt, A.B.; Pandey, R. Anthropogenic Disturbances and Their Impact on Vegetation in Western Himalaya, India. J. Mt. Sci. 2016, 13, 69–82. [Google Scholar] [CrossRef]
  61. Ranjan, R. What drives forest degradation in the central Himalayas? Understanding the feedback dynamics between participatory forest management institutions and the species composition of forests. For. Policy Econ. 2018, 95, 85–101. [Google Scholar] [CrossRef]
  62. Manral, V.; Bargali, K.; Bargali, S.; Shahi, C. Changes in soil biochemical properties following replacement of Banj oak forest with Chir pine in Central Himalaya, India. Ecol. Processes 2020, 9, 30. [Google Scholar] [CrossRef]
Figure 1. Study area of the selected forests.
Figure 1. Study area of the selected forests.
Forests 13 00848 g001
Figure 2. Vulnerability status of the selected forests.
Figure 2. Vulnerability status of the selected forests.
Forests 13 00848 g002
Table 1. Community attributes of different Himalayan temperate forests.
Table 1. Community attributes of different Himalayan temperate forests.
Forest TypeNo. of IndividualTotal SpeciesTBA (m2/ha)Simpson’s IndexEquitability IndexMenhinick’s Species Richness Index Shannon-Weiner IndexDegree of Maturity
BOF-I4091222.000.270.690.591.701.25
BOF-II381435.050.960.080.200.110.37
BOF-III930831.240.490.430.260.900.42
DBCF313318.630.540.750.170.830.89
DDF336353.220.530.710.160.770.57
KOF4386212.650.390.680.291.220.64
LLBPF471324.300.470.730.140.800.57
MDF-I708641.140.320.760.231.370.72
MDF-II279576.920.170.240.300.390.75
MOF-I5111347.170.220.700.581.781.31
MOF-II705939.820.270.710.341.560.62
MTDF-I237787.530.520.440.450.861.14
MTDF-II5651235.190.230.700.501.731.10
NPF182218.840.160.430.150.300.82
WHHLBPF387230.020.420.880.100.610.52
WHUO/FF4041370.590.190.730.651.861.14
WMCF400930.490.280.660.451.441.03
BOF-I—Ban Oak Forest, Binog; BOF-II—Ban Oak Forest, Pangot; BOF-III—Ban Oak Forest, Mcleodganj; DBCF—Dry Broadleaved and Coniferous Forest, Kalpa; DDF—Dry Deodar Forest, Harshil; KOF—Kharsu Oak Forest, Kunjkharak; LLBPF—Low-Level Blue Pine Forest, Manali; MDF-I—Moist Deodar Forest, Jageshwar; MDF-II—Moist Deodar Forest, Dalhuosie; MOF-I—Moru Oak Forest, Deoria Tal; MOF-II—Moru Oak Forest, Kilbury; MTDF-I—Moist Temperate Deciduous Forest, Sutol; MTDF-II—Moist Temperate Deciduous Forest, Dunagiri; NPF—Neoza Pine Forest, Akpa; WHHLBPF –West Himalayan High Level Blue Pine Forest, Narkanda; WHUO/FF—West Himalayan Upper Oak/fir Forest, Maid; WMC—Western Mixed Coniferous Forest, Deoban.
Table 2. Vulnerability status of different Himalayan temperate forests.
Table 2. Vulnerability status of different Himalayan temperate forests.
Forest TypeExposure IndexSensitivity IndexAdaptive Capacity IndexVulnerabilityVulnerability Status
BOF-I0.850.550.58−0.03Low
BOF-II0.710.660.330.32Most
BOF-III1.000.540.61−0.07Low
DBCF0.250.830.570.26Most
DDF0.10.790.650.14Moderate
KOF0.730.530.7−0.17Least
LLBPF0.140.810.660.14Moderate
MDF-I0.710.680.73−0.06Low
MDF-II0.540.780.320.47Most
MOF-I0.430.490.58−0.08Least
MOF-II0.710.600.78−0.19Least
MTDF-I0.560.610.350.25Moderate
MTDF-II0.760.540.64−0.11Least
NPF0.160.980.360.63Most
WHHLBPF0.340.870.710.16Moderate
WHUO/FF0.540.480.66−0.18Least
WMCF0.560.640.590.05Low
Vulnerable status was estimated based on quartile and the classes was as Least vulnerable (upto first quartile i.e., −0.08), Low vulnerable (between first (−0.08) to second quartile (0.05)), Moderate vulnerable (between second (0.05) and third quartile (0.25)) and Most vulnerable (above the third quartile (above 0.25)).
Table 3. Association between vulnerability status with elevation range and exposure level.
Table 3. Association between vulnerability status with elevation range and exposure level.
CategoryVulnerability StatusTotal
Elevation RangeLeastLowModerateMost
High (above 2500 m)11114
Middle (between 2000–2500 m)30328
Lower (below 2000 m)13015
Total544417
Fisher’s Exact Test = 6.74 (0.33)
Exposure LevelLeastLowModerateMostTotal
Least00325
Low20114
Moderate12014
High22004
Total544417
Fisher’s Exact Test = 11.59 (0.14)
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Pandey, R.; Sharma, J.; Singh, R.; Rawat, M.; Saklani, H.; Tomar, P.K.; Tiwari, L.; Bhatt, I.D.; Chand, T.; Bala, N.; et al. Vegetation Characteristics Based Climate Change Vulnerability Assessment of Temperate Forests of Western Himalaya. Forests 2022, 13, 848. https://doi.org/10.3390/f13060848

AMA Style

Pandey R, Sharma J, Singh R, Rawat M, Saklani H, Tomar PK, Tiwari L, Bhatt ID, Chand T, Bala N, et al. Vegetation Characteristics Based Climate Change Vulnerability Assessment of Temperate Forests of Western Himalaya. Forests. 2022; 13(6):848. https://doi.org/10.3390/f13060848

Chicago/Turabian Style

Pandey, Rajiv, Jyoti Sharma, Rajat Singh, Monika Rawat, Himani Saklani, Pankaj Kumar Tomar, Laxmikant Tiwari, Indra Dutt Bhatt, Tara Chand, Nirmalya Bala, and et al. 2022. "Vegetation Characteristics Based Climate Change Vulnerability Assessment of Temperate Forests of Western Himalaya" Forests 13, no. 6: 848. https://doi.org/10.3390/f13060848

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop