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Climate change and its cascading impacts are being increasingly recognized as a major challenge across the globe. Climate is one of the most critical factors affecting biomes and their distribution. The present study assessed shifts in biome types of India using the conceptual framework of Holdridge life zone (HLZ)model, minimum distance classifier...
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... r = number of classes (biomes) in the error matrix; x ii = number of observations in row i and column i (on the major diagonal); x i + = total observation in row i ; x + i = total observation in column i . The geographical analysis shows that following the definition of the HLZ model, there are 19 life zones (sub-biomes) in India. The LZs (sub-biomes) have been grouped in the seven biomes based on climatic factors and ecological zonation. The seven biomes mapped in this study are; (i) Tropical rain forest (TRF), (ii) Tropical wet forest (TWF), (iii) Tropical moist forest (TMF), (iv) Tropical dry forest (TDF), (v) Tropical thorn woodland (TTW), (vi) Tropical desert (TDr), and (vii) Himalayan temperature tundra (HTT). The area distribution of biome and sub-biomes (life zones) are given in Table 1. Fig. 3 shows distribution of the classes mapped in the present study. TDF is the most dominant class and is distributed through the country followed by distribution of TMF in selected zones. TTW and HTT are precisely confined to their own climatic zones in the northwestern and northern part of the country, respectively. TD, TWF and TRF are small in size and are confined to few geographic locations. This illustrates the wide variability in physical features and climatic conditions that have led to diversified ecological life zones found in the country. It is said that true tropical rain forests ( TRF ) does not exist in India (Roy et al., 2006), yet a few pockets in Western Ghats and northeast Himalayas do represent near rainforest-like vegetation. The present method helps in identifying these regions. These are further classified based on the altitudinal belts as: (i) tropical rain forests ( lower montane ); and, (ii) tropical rain forests ( plains ). The forests of Andaman and Nicobar Islands come mostly under the equatorial belt of tropical wet forests ( TWF ). Other than this region, they are found all along the Western Ghats, and in the northeastern region. The tropical moist forest ( TMF ) have been mapped in north-eastern Himalaya, western Himalaya, parts of central and eastern India, all along the Western Ghats, and in very few patches in Andaman and Nicobar Islands. On the basis of the altitudinal belts, they have been further classified as: (i) tropical moist forest ( plains ) which is mapped in patches along the Western Ghats, north eastern region and Andaman and Nicobar Islands; (ii) tropical moist forest ( lower montane ) that covers maximum area among these four life zones, including regions in central, eastern and southern India, central part of north-eastern India, and along the lower western Himalayan belt; (iii) tropical moist forest ( montane ), majorly in the north-eastern region; and, (iv) tropical moist forest ( sub-alpine ) extending along from Kashmir to Arunachal Pradesh. The tropical dry forest ( TDF ) is found throughout the country and occupies the maximum area. It has been mapped for entire northern, central and southern India. This biome is further classified into five life zones: (i) tropical dry forest ( plains ) found in the dry regions of Gujarat, Maharashtra, Uttar Pradesh, West Bengal, Chhattisgarh, Jharkhand, Andhra Pradesh, Orissa, and Tamil Nadu; (ii) tropical dry scrub ( plains ) found in western India; (iii) tropical very dry forest ( plains ) found in southern and western India; (iv) tropical dry forest ( lower montane ) covering the maximum area among these five life zones, found throughout the extent of India, except the northeastern region; and, (v) tropical dry forest ( montane ) found mainly in lower northern and eastern Himalayas. The tropical thorn woodland ( TTW ) has been mapped in the western part of the country and few patches have also been mapped in the Deccan Plateau. It has been sub-classified into: (i) tropical thorn woodland ( plains ) near the Thar Desert and Kutch Desert; and, (ii) tropical thorn woodland ( lower montane ) which spread across western India and in patches in southern India. The tropical desert ( TDr ) of the country is found mainly across states of Rajasthan and Gujarat. This is further catego- rized as: (i) tropical desert ( plains ) found in Rajasthan only; and, (ii) tropical desert scrub ( lower montane ) is found mainly in Rajasthan and patches in Gujarat. The Himalayan temperate tundra ( HTT ) is considered to be one of the driest and coldest life zones on earth; the Trans-Himalaya shows the climatic conditions for these life zones. Based on the climatic regimes and the altitudinal belts, it has been further classified into three life zones; (i) very dry tundra ( alvar ) found in Trans-Himalaya; (ii) dry tundra ( alpine ) in northwestern Himalaya; and, (iii) moist tundra ( alpine ) found in both northeast and northwestern Himalayas. The main characteristic of the biome distribution throughout the country is that it varies according to altitudinal, latitudinal, and the climatic zones. For accuracy assessment of biome map, vegetation type map produced using IRS WiFS (Joshi et al., 2006) was used as reference. The resulting distribution pattern of the simulated Holdridge life zones with the current climate data is in good agreement with the mapped distribution of actual vegetation complexes, except where intensive agriculture has obliterated the natural patterns of ecosystems. The forest areas and natural landscapes except the land use area give an overall accuracy of 82.73% and the kappa coefficient, 0.75. Fig. 4 shows the distribution pattern of the producer’s accuracies for the current biome map; this measure conveys the error of omission. The map indicates high producer accuracies for biomes, HTT, TDr and TMF; and low producer accuracies for biomes, TDF, TTW, TWF and TRF. The biome maps generated using the projected and ...
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... in this study are; (i) Tropical rain forest (TRF), (ii) Tropical wet for- est (TWF), (iii) Tropical moist forest (TMF), (iv) Tropical dry forest (TDF), (v) Tropical thorn woodland (TTW), (vi) Tropical desert (TDr), and (vii) Himalayan temperature tundra (HTT). The area distribu- tion of biome and sub-biomes (life zones) are given in Table 1. Fig. 3 shows distribution of the classes mapped in the present study. TDF is the most dominant class and is distributed through the country followed by distribution of TMF in selected zones. TTW and HTT are precisely confined to their own climatic zones in the northwestern and northern part of the country, respectively. TD, TWF and TRF are ...
Citations
... Numerous regional studies in India (Kothawale and Kumar 2005;Singh et al. 2015;Srivastava et al. 2017;Jeganathan et al. 2018;Praveen et al. 2020;Rai et al. 2020) focused on the patterns and variability of temperature and precipitation, showing significant precipitation variability and consistent warming. However, because climatic projections and their associated impacts are diverse and highly heterogeneous at regional levels, there are still a number of uncertainties regarding climatic variability and its effects at those levels (Chakraborty et al. 2013;Romshoo et al. 2020) owing to large topographical unevenness. ...
Himalaya – one of the pristine and ecologically fragile mountain ecosystem is highly vulnerable to any small changes in climatic system. Under changing climate conditions, assessing regional trends become more important owing to dependence of more than 1 billion people on Himalayas. To analyze the climatic trends and magnitude, this study utilized the long term meteorological data (1980–2022) for temperature and precipitation. Investigations were carried out for 11 meteorological stations located in different topographical zones of Jammu, Kashmir and Ladakh region. The non-parametric Mann–Kendall test was used for significance of trends in precipitation and temperature data on monthly, seasonal, and annual scales, while Sen’s non-parametric estimator of the slope was used to estimate the magnitude of trend. For TMax, except Jammu plains (-0.018oC a− 1) all regions experienced increasing trend with annual rate of increase 0.018oC a− 1, 0.032oC a− 1 and 0.051oC a− 1 in Pir Panjal region, Kashmir valley and Ladakh region respectively. For annual TMin, all four geographical regions and individual stations have observed an increase. Ladakh region observed highest rate of increase (0.070oC a− 1) which was significant followed by Pir Panjal region (0.048oC a− 1), Kashmir valley (0.013oC a− 1) and the lowest rate was observed in Jammu Plains (0.006oC a− 1). Precipitation revealed a general decreasing trend with large inter annual variability. Seasonally, TMin has seen most significant changes across all topographical regions. Our results indicate that influence of Indian Summer Monsoon (ISM) was more towards the Jammu plains and its impact reduced towards Pir Panjal and Kashmir valley with increasing influence of Western Disturbances (WDs). Jammu plains received 75.8% precipitation from ISM while Kashmir valley received 72.4% precipitation from WDs. Shift in climatic variables could have serious environmental and socio-economic implications which can alter the regional ecological stability.
... It is situated in the South Asia Continent and separated from mainland Asia by the Himalayas. The country is situated between 8°4 0 N to 37°6 0 N and 68°7 0 E to 97°25 0 E (Chakraborty et al. 2013). There are a total of 28 states and 8 union territories. ...
... While West India is bordered to the north by the Vindhya Range and to the northwest by the Thar Desert. It is important to note that the climate of the country is influenced by the Himalayas and Thar Desert (Chakraborty et al. 2013). Besides, there are four states and one union territory in the region. ...
Good health and well-being require safe drinking water and improved sanitation facilities. Lack of access to drinking water and sanitation facilities poses serious health risks. There are very few studies based on the fifth round of National Family Health Survey (NFHS-5) data in the literature. Therefore, the present study used the NFHS-5 data to investigate the spatial distribution of limited access to drinking water and sanitation facilities in India. The Stata version 14.1 software was used for statistical analysis, and the Arc Map 10.4 was used for spatial analysis. A binary logistic regression model was applied to investigate the association between dependent and independent variables. The prevalence of limited access to drinking water and sanitation facilities was higher in Madhya Pradesh (12.28%) and Manipur (22.65%), respectively. Besides, the spatial distribution of limited access to drinking water and sanitation facilities was spatially clustered among a few central and western Indian states. In the binary logistic regression model, education, wealth index, and place of residence were significantly associated with limited drinking water and sanitation services. There is regional heterogeneity in drinking water and sanitation services. With this in mind, we suggest spatially optimized target-oriented policy measures in unprivileged areas. Improving the water distribution networks and construction of sanitation facilities is also recommended.
... We focused on simple and readily available measures of compositional and structural diversity, as used by D'Amato et al. (2011) and Chakraborty et al. (2013). We used our plot-level inventory data for this purpose. ...
Whether environmental conditions under exotic tree plantations abandoned in the Western Ghats of India can facilitate the natural regeneration of tropical montane forest (Shola forest) tree species is being debated. In many cases, the exotic tree plantations are being cleared to allow for the restoration of native ecosystems. In this paper, we examined whether exotic tree plantations have indeed a negative effect on the regeneration of Shola forest tree species. For this, we assessed the abundance, diversity, and composition of the regeneration of Shola forest tree species in plantations, each with different dominant tree species (Acacia mearnsii, Pinus sp., and Eucalyptus sp.). We tested the abundance of regenerating native tree species against the main plantation canopy species (plantation type) as well as other environmental factors (aspect, distance to nearest Shola forest, structural diversity, slope, elevation, presence of herbivores, and canopy closure). We found that the number of native tree species regenerating in all plantation types was at an acceptable level: 1960, 1773, and 462 individuals ha−1 for Acacia, Eucalyptus, and Pinus plantations, respectively. A rare fraction analysis showed that the highest number of Shola tree species were regenerating under Acacia mearnsii (25) followed by Eucalyptus (19) and Pinus (8) plantations. The density and diversity of regenerating Shola trees was greatest under Acacia plantations and northern aspects but declined with increasing elevation. The presence of herbivores also reduced the density and diversity of Shola tree regeneration. We concluded that the restoration of Shola forest in the Western Ghats is possible in existing stands of exotic tree species and this process can be accelerated with appropriate silvicultural methods. We additionally recommend that studies involving long-term exclosures can provide valuable insights into the effects of browsing on regeneration and species composition.
... Although these definitions point chronologically to the teenage years of an individual, the cultural and social experiences associated with this phase may start earlier or later. Physical, emotional, social, and intellectual developments can be used to classify adolescence into the following three categories: early adolescence (ages [11][12][13][14], mid adolescence (ages [15][16][17], and late adolescence (ages [18][19][20][21] [2]. ...
... The study area included the entire territory of India, located in the southwestern section of the Asian continent. The nation has a total land area of 3,287,263 square kilometers [11]. ...
Adolescence is a crucial phase marked by significant physical, psychological, emotional, and social changes. India has the world’s largest adolescent population. Understanding and addressing their health needs is vital for the nation’s social, political, and economic progress. The primary aim of this study was to evaluate the main adolescent health policies and strategies implemented from 2006 to 2020 and analyze the outcomes for adolescent health in India. To achieve this objective, the research adopted a mixed-method approach, combining qualitative and quantitative analyses of health policies, strategies, and programs implemented since 2005. Additionally, data from the most recent three Demographic Health Surveys (DHSs) were analyzed and compared to assess changes in adolescent health indicators after implementing these policies/strategies. The findings focused on India’s major adolescent health policies, namely the Adolescent Reproductive and Sexual Health (ARSH) Strategy2005, Rashtriya Kishor Swasthya Karyakram (RKSK) 2014, and the School Health Program 2020. All the strategies and programs aim to provide a comprehensive framework for sexual and reproductive health services, expand the scope of adolescent health programming, and address various health aspects. The analysis highlighted strengths in targeted interventions, monitoring, and promotion but weaknesses in awareness, societal barriers, and healthcare worker participation. Opportunities include female-friendly clinics and education about early pregnancy, while addressing substance abuse and training volunteers remain challenges. Family planning has improved with higher contraception usage and a decline in unmet needs. The incidence of violence decreased, and positive health behaviors increased, such as condom use. However, challenges remain, including limited access to health services, concerns about female providers, and low health insurance coverage. Nutrition indicators showed a slight increase in overweight/obesity and anemia rates. In conclusion, progress has been made, but certain adolescent health aspects still require attention. Further efforts are needed to achieve universal health coverage and improve adolescent health outcomes. Conducting targeted awareness campaigns, strengthening health worker and NGO engagement, and combating the increasing prevalence of overweight and obesity among adolescents are recommended.
... Inputs leached from the surface will likely decrease. However, changes in precipitation may alter biome types (Salazar et al. 2007, Chakraborty et al. 2013, with a potential to impact deep SOC by changing rooting depths. In general, rooting depth tends to increase with evaporative demand (Fan et al. 2017, Tumber-Dávila et al. 2022. ...
Over 70% of soil organic carbon (SOC) is stored at a depth greater than 20 cm belowground. A portion of this deep SOC actively cycles on annual to decadal timescales and is sensitive to global change. However, deep SOC responses to global change likely differ from surface SOC responses because biotic controls on SOC cycling become weaker as mineral controls predominate with depth. Here, we synthesize the current information on deep SOC responses to the global change drivers of warming, shifting precipitation, elevated CO 2 , and land use and land cover change. Most deep SOC responses can only be hypothesized because few global change studies measure deep soils, and even fewer global change experiments manipulate deep soils. We call on scientists to incorporate deep soils into their manipulations, measurements, and models so that the response of deep SOC can be accounted for in projections of nature-based climate solutions and terrestrial feedbacks to climate change.
Expected final online publication date for the Annual Review of Ecology, Evolution, and Systematics, Volume 54 is November 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
... India has a 7517 km long coastline, which is bordered by the Indian Ocean on the south, the Arabian Sea on the southwest, and the Bay of Bengal on the southeast. [10] 3 With 253 million teenagers, India has the biggest adolescent population in the world, with one in five citizens being between the ages of 10 and 19. If this enormous population of teenagers is secure, healthy, educated, and provided with knowledge and life skills to support the nation's future development, India will benefit socially, politically, and economically [11]. ...
... In conclusion, efforts have been made in India to implement initiatives addressing the health needs of adolescents requires strategic focus on nutrition, sexual and reproductive health, noncommunicable diseases, substance abuse, injuries and violence, and mental health. While progress has been made in certain areas, challenges remain in terms of inadequate infrastructure, societal 10 resistance, and health insurance coverage for adolescents. Engaging NGOs and communities is crucial in fostering acceptance and addressing sensitive issues. ...
Adolescence is a crucial phase marked by significant physical, psychological, emotional and social changes. India having the world's largest adolescent population, understanding and addressing their health needs are vital for the nation's social, political, and economic progress. The primary aim of this study is to examine the impact of key health policies on adolescent health outcomes in India. To achieve this objective, the research adopts a mixed-method approach, combining qualitative and quantitative analysis of health policies, strategies, and programs implemented since 2005 was conducted. Additionally, data from the most recent three Demographic Health Surveys (DHS) were analyzed and compared to assess changes in adolescent health indicators after the implementation of these policies/strategies. Major adolescent health policies in India were assessed, namely the Adolescent Reproductive and Sexual Health Strategy (ARSH 2005), Rashtriya Kishor Swasthya Karyakram (RKSK 2014), and School Health Programme 2020. All the strategies and programs aim to provide a comprehensive framework for sexual and reproductive health services, expand the scope of adolescent health programming, and address various health aspects. The SWOT analysis findings, highlighted strengths in targeted interventions, monitoring, and promotion, but weaknesses in awareness, societal barriers, and healthcare worker participation. Opportunities include female-friendly clinics and education about early pregnancy, while addressing substance abuse and training volunteers remain challenges. Family planning has improved, with higher contraception usage and a decrease in unmet needs. Violence reduced, and positive health behaviors increased, such as condom use. However, challenges remain, including limited access to health services, concerns about female providers, and low health insurance coverage. Nutrition indicators show a slight increase in overweight/obesity and anemia rates. Overall, progress has been made, but certain health aspects still require attention. Therefore, conducting targeted awareness campaigns, strengthening health worker and NGOs engagement, combating the increasing prevalence of overweight and obesity among adolescents are highly recommended. Further efforts are needed to achieve universal health coverage and improve adolescent health outcomes globally.
... Evidences show that global warming has drastically affected vegetation coverage in recent decades [5][6][7], and caused shifts in biome types. In India, for example, climate change has led to a decrease in the area cover for tropical desert scrubs, tropical deserts, tropical wet forests, and tropical moist forests [8]. ...
Climate change affects plant dynamics and functioning of terrestrial ecosystems. This study aims to investigate temporal changes in global vegetation coverage and biomes during the past three decades. We compared historic annual NDVI time series (1982, 1983, 1984 and 1985) with recent ones (2015, 2016, 2017 and 2018), captured from NOAA-AVHRR satellite observations. To correct the NDVI time series for missing data and outliers, we applied the Harmonic Analysis of Time Series (HANTS) algorithm. The NDVI time series were decomposed in their significant amplitude and phase given their periodic fluctuation, except for ever green vegetation. Our findings show that the average NDVI values in most biomes have increased significantly (F-value
... Correa Ayram et al., 2017;Golicher et al., 2008;Ponce-Reyes et al., 2017, 2013. Similar outcomes have been forecast for other montane regions, including Costa Rica (Colwell et al., 2008), the tropical Andes (Ledo, Montes & Condes, 2009;Tejedor-Garavito et al., 2015;Godoy-Bürki, 2016), the 'campos rupestres' (Bitencourt et al., 2016) and the Atlantic forest biomes in Brazil (Castro et al., 2020), the Neotropical realm as a whole (Helmer et al., 2019), the Western Ghats region in India (Chakraborty et al., 2013), China's Abies forests (Liao et al., 2020), Myanmar's natural protected areas (Nwe, Zomer & Corlett, 2020), and the Australian Wet Tropics (Costion et al., 2015). ...
In spite of their small global area and restricted distributions, tropical montane forests (TMFs) are biodiversity hotspots and important ecosystem services providers, but are also highly vulnerable to climate change. To protect and preserve these ecosystems better, it is crucial to inform the design and implementation of conservation policies with the best available scientific evidence, and to identify knowledge gaps and future research needs. We conducted a systematic review and an appraisal of evidence quality to assess the impacts of climate change on TMFs. We identified several skews and shortcomings. Experimental study designs with controls and long-term (≥10 years) data sets provide the most reliable evidence, but were rare and gave an incomplete understanding of climate change impacts on TMFs. Most studies were based on predictive modelling approaches, short-term (<10 years) and cross-sectional study designs. Although these methods provide moderate to circumstantial evidence, they can advance our understanding on climate change effects. Current evidence suggests that increasing temperatures and rising cloud levels have caused distributional shifts (mainly upslope) of montane biota, leading to alterations in biodiversity and ecological functions. Neotropical TMFs were the best studied, thus the knowledge derived there can serve as a proxy for climate change responses in under-studied regions elsewhere. Most studies focused on vascular plants, birds, amphibians and insects, with other taxonomic groups poorly represented. Most ecological studies were conducted at species or community levels, with a marked paucity of genetic studies, limiting understanding of the adaptive capacity of TMF biota. We thus highlight the long-term need to widen the methodological, thematic and geographical scope of studies on TMFs under climate change to address these uncertainties. In the short term, however, in-depth research in well-studied regions and advances in computer modelling approaches offer the most reliable sources of information for expeditious conservation action for these threatened forests.
... Evidences show that global warming has drastically affected vegetation coverage in recent decades [5][6][7], and caused shifts in biome types. In India, for example, climate change has led to a decrease in the area cover for tropical desert scrubs, tropical deserts, tropical wet forests, and tropical moist forests [8]. ...
... Although several studies on climate change impacts and variability in rainfall patterns at the country level are available (Gurung et al. 2021;Ray et al. 2021;Singh et al. 2020Singh et al. , 2015Shekhar et al. 2010;Dash et al. 2009), yet there are reservations in understanding the uncertainties in the region-specific future behavior of rainfall in Himalaya. The projection of climate and its effect on specific regions of the Himalayas are diverse and extremely heterogeneous (Chakraborty et al. 2013;Romshoo et al. 2020;Basistha et al. 2009;Batool et al. 2019;Kumar and Jain 2010;Bhutiyani et al. 2010;Giri et al. 2008), indicating toward the complexity in modeling the inconsistency of rainfall events and the local-scale projections. The multifaceted approach needs to be considered and simulated with the help of sophisticated mathematical models for combating the extreme effects of climate change in the Himalayan mountains. ...
The high Himalayas in northern India are an essential source of climate generation and maintenance over the entire northern belt of the Indian subcontinent. It also affects extreme weather phenomena such as western disturbances in the region during winter. The work presented here describes the trends in 117-year precipitation changes and their impact on the western Himalayas and suggests some possible explanations in the context of changing rainfall patterns. Under the investigation, the forecasting efficiency and the prediction pattern of artificial neural network (ANN) and seasonal autoregressive integrated moving average (SARIMA) models for rainfall series in the western Himalayan states of India have been assessed. The results revealed significant changes in the monthly, seasonal, and annual rainfall series data for the three states of the Western Himalayan regions from the years 1900 to 2017. The study also concludes that the nonlinear autoregressive neural network (NARNN) models can be used to forecast the western Himalayan region data series well. According to the result interpretation, the highest rainfall may be estimated in August, 1632.63 mm (2023), whereas the lowest rainfall can be obtained in October (0.43 mm) during 2023. The model predicted a gradual decrease in annual rainfall trends in Uttarakhand and Himachal Pradesh from 2018 to 2023 despite heavy rainfall prediction in the monsoon season, whereas Jammu and Kashmir increase in annual rainfall has been predicted from 2018 to 2023. Possible explanations for the change in precipitation over the western Himalayas have also been proposed and explained.
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