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The intricate oceanic climate interactions with terrestrial primary production of Asian ecosystems exert crucial social-economical-environmental repercussions. Yet, a holistic understanding of tropical sea surface temperature (SST) anomalies associated with the gross primary productivity (GPP) variations of monsoon-Asia remains constrained. This study provides a statistical framework demonstrating how SST perturbations in the tropics influence GPP fluctuations in monsoon-Asia by modulating hydrothermal conditions of different climate system components. Observation evidence explicitly illustrated the characteristic anomalous SST signatures of positive and negative GPP anomalies in South and Southeast Asia during June-August. The SST anomalies of the central-eastern tropical Pacific showed a robust negative impact on the GPP variability of South-Asia. The GPP alterations in maritime-Southeast-Asia exhibited strong connections with SST anomalies of the western Pacific (positive) and eastern equatorial Pacific (negative). The oceanic signals in the GPP variability of South-Asia and maritime-Southeast-Asia mirrored canonical El Niño and La Niña patterns. The detected SST-GPP link is feasible through large-scale atmospheric circulation variability and the consequent regional modulation of heat and moisture fluxes. The anomalous strengthening (weakening) of Walker cell enhances (reduces) water availability to plants for photosynthesis during the La Niña (El Niño) phase of the ENSO cycle and thus elevates (lowers) GPP in South-Asia and Maritime-southeast-Asia. In contrast, the enhanced GPP anomaly in mainland-Southeast-Asia depicts signs of canonical La Niña and Indian Ocean subtropical dipole (IOSD) teleconnections. The positive impact of IOSD was through the modulation of the Mascarene High and the consequent impact on the monsoon. Meanwhile, decreased GPP bears the imprint of El Niño Modoki and warm tropical Indian Ocean SSTs. The atmospheric teleconnections demonstrated the delayed impact of El Niño Modoki on GPP variability through the Indian Ocean capacitor effect. Our findings could be instrumental in forecasting the probable effects on vegetation growth in monsoon-Asia associated with high-frequency tropical oceanic changes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Objective: To describe the incidence, risk factors, and outcomes associated with serious infections in patients with Takayasu arteritis (TA)., Methods: Serious infections, defined as infections resulting in hospitalization or death or unusual infections like tuberculosis, were identified from a cohort of patients with TA. Corticosteroid and disease-modifying antirheumatic drug (DMARD) use at the time of serious infection was noted. Demographic characteristics, clinical presentation, angiography, and disease activity at presentation, and the use of DMARDs during follow-up were compared between patients with TA with or without serious infections. Mortality in patients with TA who developed serious infections was compared to those who did not using hazard ratios (HR; with 95% CI)., Results: Of 238 patients with TA, 38 (16%) had developed serious infections (50 episodes, multiple episodes in 8; 3 episodes resulted in death). Among the 38 initial episodes, 11/38 occurred in those not on corticosteroids and 14/38 in those not on DMARDs. Pneumonia (n = 19) was the most common infection, followed by tuberculosis (n = 12). Patients with TA who developed serious infections vs those who did not had higher disease activity at presentation (active disease 97.4% vs 69.5%, mean Indian Takayasu Arteritis Activity Score 2010 12.7 (SD 7.3) vs 10.2 (SD 7.0), mean Disease Extent Index in Takayasu Arteritis 11.2 (SD 6.1) vs 8.8 (SD 6.1) and were more frequently initiated on corticosteroids or DMARDs. HRs calculated using exponential parametric regression survival-time model revealed increased mortality rate in patients with TA who developed serious infections (HR 5.52, 95% CI 1.75-17.39)., Conclusion: Serious infections, which occurred in the absence of immunosuppressive treatment in approximately one-fifth of patients with TA, were associated with increased mortality in patients with TA.

This study provides a comprehensive analysis of the hydrological effects and flood risks of the Hirakud Reservoir, considering different CMIP6 climate change scenarios. Using the HEC-HMS and HEC-RAS models, the study evaluates future flow patterns and the potential repercussions of dam breaches. The following summary of the work: firstly, the HEC-HMS model is calibrated and validated using daily stage-discharge observations from the Basantpur station. With coefficient of determination (R 2 ) values of 0.764 and 0.858 for calibration and validation, respectively, the model demonstrates satisfactory performance. Secondly, The HEC-HMS model predicts future flow for the Hirakud Reservoir under three climate change scenarios (SSP2-4.5, SSP3-7.0 and SSP5-8.5) and for three future periods (near future, mid future and far future). Thirdly, by analyzing time-series hydrographs, the study identifies peak flooding events. In addition, the HEC-RAS model is used to assess the effects of dam breaches. Downstream of the Hirakud Dam, the analysis highlights potential inundation areas and depth variations. The study determines the following inundation areas for the worst flood scenarios: 3651.52 km 2 , 2931.46 km 2 and 4207.6 km 2 for the near-future, mid-future and far-future periods, respectively. In addition, the utmost flood depths for these scenarios are determined to be 31 m, 29 m and 39 m for the respective future periods. The study area identifies 105 vulnerable villages and several towns. This study emphasizes the importance of contemplating climate change scenarios and implementing proactive measures to mitigate the peak flooding events in the Hirakud reservoir region., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

The success of a species in future climate change scenarios depends on its morphological, physiological, and demographic adaptive responses to changing climate. The existence of threatened species against climate adversaries is constrained due to their small population size, narrow genetic base, and narrow niche breadth. We examined if ecological niche model (ENM)-based distribution predictions of species align with their morpho-physiological and demographic responses to future climate change scenarios. We studied three threatened Ilex species, viz., Ilex khasiana Purkay., I. venulosa Hook. f., and I. embelioides Hook. F, with restricted distribution in Indo-Burma biodiversity hotspot. Demographic analysis of the natural populations of each species in Meghalaya, India revealed an upright pyramid suggesting a stable population under the present climate scenario. I. khasiana was confined to higher elevations only while I. venulosa and I. embelioides had wider altitudinal distribution ranges. The bio-climatic niche of I. khasiana was narrow, while the other two species had relatively broader niches. The ENM-predicted potential distribution areas under the current (2022) and future (2050) climatic scenarios (General Circulation Models (GCMs): IPSL-CM5A-LR and NIMR-HADGEM2-AO) revealed that the distribution of highly suitable areas for the most climate-sensitive I. khasiana got drastically reduced. In I. venulosa and I. embelioides, there was an increase in highly suitable areas under the future scenarios. The eco-physiological studies showed marked variation among the species, sites, and treatments (p < 0.05), indicating the differential responses of the three species to varied climate scenarios, but followed a similar trend in species performance aligning with the model predictions., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Tropical forests sequester six times higher carbon than that released by humans annually into the atmosphere. These biodiversity-rich tropical forests have high net primary productivity (NPP), which differs among constituent plant communities. Tropical moist deciduous forests occupy 179,335 km 2 of India's geographical area and constitute 44% of the country's total protected area (PA) forests. The productivity of these forests has neither been estimated specifically nor precisely. We measured the annual NPP of three predominant distinct community types, viz., mixed (DM), sal (SM), and teak (TP), in a tropical moist deciduous forest in northern India. The NPP was estimated from tree biomass data collected from nine long-term ecological research (LTER) plots of 1 ha each representing the above three community types. The estimated annual NPP were 10.28, 6.25, and 9.79 Mg ha -1  year -1 in DM; 8.93, 7.09, and 10.59 Mg ha -1  year -1 in SM; and 14.57, 7.14, and 13.56 Mg ha -1  year -1 in TP for the years 2010, 2011, and 2012, respectively. The NPP was correlated with tree density, height and DBH, species richness, diversity, microclimatic and edaphic variables, and leaf area index (LAI) using principal component analysis (PCA) and generalized linear modeling (GLM). Air temperature and humidity were strongly related to NPP in all the community types, while "complementarity" and "selection effects" contributed to the NPP in both the sal and mixed forest communities with equal importance, and the NPP in teak plantation ould point to "dominance effect.", (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Cyclonic storms and extreme precipitation lead to loss of lives and significant damage to land and property, crop productivity, etc. The "Gulab" cyclonic storm formed on the 24 th of September 2021 in the Bay of Bengal (BoB), hit the eastern Indian coasts on the 26 th of September and caused massive damage and water inundation. This study used Integrated Multi-satellite Retrievals for GPM (IMERG) satellite precipitation data for daily to monthly scale assessments focusing on the "Gulab" cyclonic event. The Otsu's thresholding approach was applied to Sentinel-1 data to map water inundation. Standardized Precipitation Index (SPI) was employed to analyze the precipitation deviation compared to the 20 years mean climatology across India from June to November 2021 on a monthly scale. The water-inundated areas were overlaid on a recent publicly available high-resolution land use land cover (LULC) map to demarcate crop area damage in four eastern Indian states such as Andhra Pradesh, Chhattisgarh, Odisha, and Telangana. The maximum water inundation and crop area damages were observed in Andhra Pradesh (~2700 km 2 ), followed by Telangana (~2040 km 2 ) and Odisha (~1132 km 2 ), and the least in Chhattisgarh (~93.75 km 2 ). This study has potential implications for an emergency response to extreme weather events, such as cyclones, extreme precipitation, and flood. The spatio-temporal data layers and rapid assessment methodology can be helpful to various users such as disaster management authorities, mitigation and response teams, and crop insurance scheme development. The relevant satellite data, products, and cloud-computing facility could operationalize systematic disaster monitoring under the rising threats of extreme weather events in the coming years., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Recently, ecotourism has been identified as an adaptation strategy for mitigating climate change impacts, as it can optimize carbon sequestration, biodiversity recovery, and livelihood benefits and generate new opportunities for the sustenance of the economy, environment, and society of the area endowed with natural resources and cultural values. With the growing responsibility at the global level, ecotourism resource management (ERM) becomes inevitable for its sustainable requirements. The integration of ecological and socio-economic factors is vital for ERM, as has been demonstrated by developing an Ecotourism Sustainability Maximization Model for an area under study, that is the Yuksam-Dzongri corridor (also known as Kangchendzonga Base Camp Trek), in the Khangchendzonga Biosphere Reserve (KBR), Sikkim, India. This model is based on the earlier developed ecotourism sustainability assessment (ESA) framework by the authors, which is based on the hierarchical relationship among ecotourism principles, criteria, indicators, and verifiers. Employing such relationships, this paper attempts to maximize ecotourism sustainability (ES) as a function of its sustainability principles, criteria, indicators, and verifiers, subject to the constraints identified through the safe minimum standard (SMS) approach by employing linear programming. Using 58 indicators as decision variables and 114 constraints, the model resulted in a maximum level of achievable ES with a score of 84.6%, allowing the resultant optimum values of the indicators to be maintained at the operational level. A central tenet of the model is the collective responsibility and adoption of a holistic approach involving the government, tourists, tourism enterprises, and local people., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

It is imperative to understand the climate change impact on the forest ecosystem to develop appropriate mitigation and management strategies. We have employed a process-based dynamic vegetation modeling (MAPSS-CENTURY: MC) approach to project change in vegetation life forms under projected climate conditions that attained 81% overall accuracy. The present and projected climate conditions suggested highly resilient/stable forest covers in wet climate regimes and moderately resilient in dry semi-arid regions. Several forested grids in the seasonally dry tropical forest in the Eastern Ghats and dry Deccan peninsula regions are estimated to be less resilient, which may experience a regime shift toward scrub and grassland. The future prediction demonstrated an upward temperature shift in the Western Himalayas and trans-Himalaya, which may facilitate forest spread at higher elevations. Although the forest cover resilience may increase in future climate conditions, the disturbances in several regions in the Deccan Peninsula and the Eastern Ghats may trigger forest to scrub and grassland transition. The inaccuracy in model simulation in the Western Himalayas could be attributed to coarse resolution grids (0.5°) failing to resolve the narrow climate niches. The spatially explicit model simulation provides opportunities to develop long-term climate change adaptation and conservation strategies., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

The leaf area index (LAI) has been traditionally used as a photosynthetic variable. LAI plays an essential role in forest cover monitoring and has been identified as one of the important climate variables. However, due to challenges in field sampling, complex topography, and availability of cloud-free optical satellite data, LAI assessment on larger scale is still unexplored in the Sikkim Himalayan area. We used two optical instruments, digital hemispherical photography (DHP) and LAI-2200C, to assess the LAI across four different forests following 20 × 20 m 2 elementary sampling units (ESUs) in the Himalayan state of Sikkim, India. The use of Sentinel-2 derived vegetation indices (VIs) demonstrated a better correlation with the DHP based LAI estimates than using LAI-2200C. Further, the combination of both reflectance bands and VIs were integrated to predict the LAI maps using random forest model. The temperate evergreen forests demonstrated the highest LAI value, while the predicted maps exhibited LAI maxima of 3.4. The estimated vs predicted LAI for DHP and LAI-2200C based estimation demonstrated reasonably good (R 2  = 0.63 and R 2  = 0.68, respectively) agreement. Further, improvements on the LAI prediction can be attempted by minimizing errors from the inherent field protocols, optimizing the density of field measurements, and representing heterogeneity. The recent rise of frequent forest fires in Sikkim Himalaya prompts for better understanding of fuel load in terms of surface fuel or canopy fuel that can be linked to LAI. The high-resolution LAI map could serve as input to forest fuel bed characterization, especially in seasonal forests with significant variations in green leaves and litter, thereby offering inputs for forest management in changing climate., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Citrus is an important horticultural crop of India and is often prone to diseases, particularly under increased temperature scenarios. For developing disease-resistant Citrus varieties, conservation of wild relatives is extremely important. However, our knowledge on temperature tolerance of these wild relatives of Citrus to varied climate change scenarios is extremely limited. Therefore, we determined the climatic niche of six wild relatives of cultivated Citrus species (C. indica Tanaka, C. karna Rafin., C. latipes (Swingle) Tanaka, C. macroptera Montrouz., C. medica L., and C. sinensis (L.) Osbeck.) and identified the geographical areas in India that would remain climatically stable in future through ecological niche modeling (ENM). Raster data on 19 bioclimatic variables with a resolution of 0.04° were used to generate niche models for each Citrus species that delineated their potential distribution areas. Future species distribution predictions for the year 2050 were made using the climate change scenarios from the most appropriate climate models, i.e., IPSL-CM5A-LR and NIMR-HADGEM2-AO with four Representative Concentration Pathways (RCPs). Ensemble of current and future projections was used to identify climatically stable areas for each species. Precipitation-related bioclimatic variables were the key climatic determinants for the modeled distribution pattern. The consensus of current and future projections suggests that most areas with stable climates for the species in the future would be available in the northeastern states of Arunachal Pradesh, Meghalaya, Mizoram, and Tripura. Efforts for in situ conservation and establishment of germplasm banks and citrus orchards may be encouraged in these identified areas., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

The spectral information derived from satellite data provides important inputs for assessing plant diversity. If a suitable satellite-derived biophysical proxy is applicable to assess and monitor plant diversity of different biogeographic regions will be of interest to policy makers and conservationists. We selected four biogeographic regions of India, i.e., semi-arid, Eastern Ghats, Western Ghats, and Northeast as the test sites on the basis of variations in moisture availability. The flora data collected for the study sites are the extract of the national biodiversity project 'Biodiversity Characterization at Landscape Level'. The available Moderate Resolution Imaging Spectroradiometer (MODIS)-derived biophysical proxies at high temporal frequencies was considered to compare the biophysical proxies: surface reflectance-red and near-infrared, normalized difference vegetation index-NDVI, enhanced vegetation index-EVI, leaf area index-LAI, and fraction of absorbed photosynthetically active radiation-FAPAR at different temporal scales (monthly, post-monsoon, seasonal, annual) in each selected biogeographic regions of India. Generalized linear model (GLM) and multivariate adaptive regression spline (MARS) were utilized to evaluate the relationship between plant diversity and MODIS-derived biophysical proxies. MARS summarized the suitable biophysical proxies at monthly scale in descending order for the total forest area in semi-arid was red, NDVI, and FAPAR; for Eastern Ghats was EVI, FAPAR, and LAI; for Western Ghats was EVI, LAI, and FAPAR; and for Northeast was NDVI, near-infrared, and red. Furthermore, monthly FAPAR commonly found to be the suitable proxy to large scale monitoring of plant diversity in the moisture-varied biogeographic regions of India, except Northeast. Using artificial neural network, the relationship of plant diversity and monthly FAPAR/NDVI were modeled. The correlation between the predicted and reference plant diversity was found to be r = 0.56 for semi-arid, r = 0.52 for Eastern Ghats, r = 0.52 for Western Ghats and r = 0.61 for Northeast at p-value < 0.001. The study affirms that FAPAR is potentially an essential biodiversity variable (EBV) for carrying out rapid/indicative assessment of plant diversity in different biogeographic regions, and thereby, meeting various international commitments dealing with conservation and management measures for biodiversity., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Mangroves can play a crucial part in climate change mitigation policies due to their high carbon-storing capacity. However, the carbon sequestration potential of Indian mangroves generally remained unexplored to date. In this study, multi-temporal Sentinel-1 and 2 data-derived variables were used to estimate the AGB of a tropical carbon-rich mangrove forest of India. Ensemble prediction of multiple machine learning algorithms, including Random Forest (RF), Gradient Boosted Model (GBM), and Extreme Gradient Boosting (XGB), were used for AGB prediction. The multi-temporal dataset was used in two different ways to find the most suitable method of using them. The results of the analysis showed that the modeling field measured AGB with individual date data values results in estimates with root mean square errors (RMSE) ranging from 149.242 t/ha for XGB to 151.149 t/ha for the RF. Modeling AGB with the average and percentile metrics of the multi-temporal image stack improves the prediction accuracy of AGB, with RMSE ranging from 81.882 t/ha for the XGB to 74.493 t/ha for the RF. The AGB modeling using ensemble prediction showed further improvement in accuracy with an RMSE of 72.864 t/ha and normalized RMSE of 11.38%. In this study, the intra-seasonal variation of Sentinel-1 and 2 data for mangrove ecosystems was explored for the first time. The variations in remotely sensed variables could be attributed mainly to soil moisture availability and rainfall in the mangrove ecosystem. The efficiency of Sentinel-1 and 2 data-derived variables and ensemble prediction of machine learning models for Indian mangroves were also explored for the first time. The methodologies established in this study can be used in the future for accurate prediction and repeated monitoring of AGB for mangrove ecosystems., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

The present study reports pollution evaluation indices employed to assess the intensity of metal pollution in water systems affected by acid mine drainage from rat-hole coal mines prevalent in North-east India. The concentration of seven eco-toxic metals was evaluated from coal mine waters which showed concentration order of Iron (Fe) > Manganese (Mn) > Zinc (Zn) > Chromium (Cr) > Lead (Pb) > Copper (Cu) > Cadmium (Cd). The water samples were acidic with mean pH 2.67 and burdened with dissolved solids (924.8 mg/L). The heavy metal pollution index (HPI) and heavy metal evaluation index (HEI) displayed high and medium range of pollution level in majority of the water samples. Statistical correlation suggested strong positive correlation between metals such as Cr with Mn ( r  = 0.780), Mn with Fe (r = 0.576), Cr with Fe ( r  = 0.680), Pb with Mn ( r  = 0.579) and Cr with Pb ( r  = 0.606), indicating Mn, Pb, Fe and Cr to be major metal contaminants; an unequivocal affirmation of degradation in water quality. The sampled waters had lower heavy metal concentration during monsoon and post-monsoon seasons. The commonly occurring bacterial species Bacillus pseudomycoides and Bacillus siamensis were chosen to understand their behavioral responses toward metal contamination. Findings demonstrated that Bacillus spp. from control environment had low tolerance to metals stress as evident from their MTC, MIC and growth curve studies. The survival of the native isolates across varying pH, salinity and temperature in the coal mine areas suggest these isolates as promising candidates for reclamation of rat-hole coal mining sites., Competing Interests: Conflict of interestThe authors have no conflict of interest to declare that are relevant to this article., (© King Abdulaziz City for Science and Technology 2021.)

The land, oceans, and atmosphere are tightly linked and form the most dynamic component of the climate system. Studies on terrestrial and ocean science enhance the understanding on the impacts of climate change. Across India and the world over, human-driven land use and climate changes are altering the structure, function, and extent of natural terrestrial ecosystems and in turn regional biogeochemical feedbacks. In this special issue, we present 29 manuscripts; those discuss wide-ranging aspects of terrestrial and oceanic characterization and dynamics. These contributions are based on selected presentations made at the 2nd International Workshop on Biodiversity and Climate Change (BDCC-2018) held on 24-27 February 2018 at the Indian Institute of Technology Kharagpur, India. The manuscripts are arranged in five sections such as Ecological Assessment, Plant Invasion, Carbon Dynamics, Ecosystem Characterization, and Ocean Dynamics. We realized that the utility of satellite remote sensing data has been emerging as a dominant trend in environmental monitoring and assessment studies in India.

Plant-disperser relationship is a mutual approach that regulates the species composition and habitat diversity. Here, we unfold the dispersal profile of India and provide comprehensive information on plant-disperser relationships, emphasising on plant longevities (annual, biennial, and perennial), plant life forms (tree, shrub, herb, liana), and vegetation types. The floral data were collected from a national database, and the dispersal information of 3301 geo-tagged plant species was gathered. The plant dispersal types were mainly (1) abiotic (hydrochory-water, anemochory-wind) and (2) biotic (endozoochory-internal gut, epizoochory-adherence to external surface, anthropochory-human, ornithochory-bird, myrmecochory-insect, and chirepterochory-bat) that included five dispersal modes, i.e. monochory (single), dichory (double), trichory (triple), quadrichory (four), and quintuchory (five). The generalised linear model was utilised to evaluate plant-disperser relationships. Monochory could explain variances of 56.8%, 51.2%, and 45.1% in perennials, annuals, and biennials, and 45.3%, 46.3%, 39.4%, and 47.7% for trees, shrubs, herbs, and lianas, respectively. Monochory has more significant influence on all major vegetation types, with at least 40% variance explanation. Anemochory, the dispersal by wind factor, was found to exercise by most plants. The life form wise analytics revealed inclination of multiple modes of dispersal for herbs with abiotic factors might be due to lighter weight, followed by trees with biotic dispersers could be owing to large size seeds. The same trend was reported from herb-dominant grassland where abiotic factors mostly contribute to dispersal, whereas the tree-dominant vegetation types exhibit dispersal primarily due to biotic means. This study provides a synoptic diagnosis to understand the dispersal profile of India, which has been an understudied domain.

Investigating the impact of climate variables on net primary productivity is crucial to evaluate the ecosystem health and the status of forest type response to climate change. The objective of this paper is (1) to estimate spatio-temporal patterns of net primary productivity (NPP) during 2001 to 2010 in a tropical deciduous forest based on the input variable dataset (i.e.meteorological and biophysical) derived from the remote sensing and other sources and (2) to investigate the effects of climate variables on NPP during 2001 to 2010. The study was carried out in Katerniaghat Wildlife Sanctuary that forms a part of a tropical forest and is situated in Uttar Pradesh, India, along the Indo-Nepal border. Mean annual NPP was observed to be highest during 2007 with a value of 878 g C m -2  year -1 and 781.25 g C m -2  year -1 for sal and teak respectively. A decline in mean NPP during 2002-2003, 2005 and 2008-2010 could be attributed to drought, increased temperature and vapour pressure deficit (VPD). The time lag correlation analysis revealed precipitation as the major variables affecting NPP, whereas combination of temperature and VPD showed dominant effect on NPP as revealed by generalized linear modelling. The carbon gain in NPP in sal forest was observed to be marginal higher than that of teak plantation throughout the study period. The decrease in NPP was observed during 2010, pertaining to increased VPD. Contribution of different climatic variables through some link process was revealed in statistical analysis and clearly indicated the co-dominance of all the variables in explaining NPP.

Population growth rate indicates the proportional rate of settlement expansion and landscape modification in any river basin. The Mahanadi River basin (MRB), which is a densely populated, cropland and forest-dominated landscape, is selected as a case study area for studying the nature of built-up expansion and the corresponding land cover modifications. Satellite data-derived land use/land cover (LU/LC) maps for the years 1995, 2005, and 2015 were used for identification of landscape changes during the past three decades. One of the major LU/LC changes are observed in terms of increase in the water, which may be attributed to construction of new dams at the cost of the croplands and forest areas. Conversion of forest to cropland and expansion and densification of built-up areas in and around the existing built-up areas are also identified as a major LU/LC change. The geostatistical analysis was performed to identify the relationship between LU/LC classes with drivers, which showed that built-up areas were more in topographically flat terrain with higher soil depth, and expanded more around the existing built-up areas; cropland areas were more at lower elevation and less sloppy terrain, and forest areas were more at higher elevation. The LU/LC scenario of 2025 was projected using a spatially explicit dynamic conversion of land use and its effects (Dyna-CLUE) modeling platform with the LU/LC change trends of past 10 years (2005-2015) and 20 years (1995-2015). The major LU/LC changes observed during 2005-2015 were built-up expansion by 36.53% and deciduous forest and cropland reduction by 0.35% and 0.45%, respectively. Thus, the corresponding predicted change during 2015-2025 estimated built-up expansion by 25.70% and deciduous forest and croplands loss by 0.43% and 0.35%, respectively. On the other hand, during 1995 to 2015, the total built-up expansion and deciduous forest and cropland reduction were observed 50.79%, 0.45%, and 0.73%, respectively. Thus, the predicted changes during 2015-2025 were estimated as 18.48% built-up expansion and 0.22% and 0.21% deciduous forest and cropland loss. However, with the conditions of restricted deforestation and less landscape modification, the LU/LC projections show less built-up area expansion, reducing the cropland, fallow land, plantation, and waste land. The reduced numbers of land cover conversions types during 2005-2015 compared with 1995-2005 indicate more stabilized landscape. The input LU/LC maps and statistical analysis demonstrated the landscape modifications and causes observed in the basin. The model projected LU/LC maps are giving insights to possible changes under multiple pathways, which will help the agriculture, forest, urban, and water resource planners and managers in improved policy-making processes.

Harvesting surface runoff during monsoon season for further utilization in crop production during the post-monsoon season is now becoming an effective solution to mitigate water scarcity problems. In this study, multi-criteria analysis-analytic hierarchy process (MCA-AHP)-based approach was envisaged for rainwater harvesting (RWH) zoning for a case study area, i.e., two districts of Odisha state situated in Eastern India. In spite of having a large irrigation network in the study area, major portion of these two densely populated and agriculture dominated districts remains fallow during dry seasons. Suitable locations for RWH structures such as farm pond, check dam, and percolation tanks were identified through Boolean conditions. RWH potential map was generated using different thematic layers namely land use/land cover (LU/LC), geomorphology, slope, stream density, soil type, and surface runoff. AHP-based MCA technique was used to integrate these thematic layers by assigning weights to the thematic layers and ranks to the individual theme features on 1-9 AHP Saaty's scale, considering their relative importance on RWH potential of the study area. The Natural Resources Conservation Service-Curve Number method was used to derive surface runoff using Climate Hazards Group Infra-Red Precipitation with Station rainfall data, satellite-derived LU/LC and FAO soil maps. In comparison to single cropped areas in 48% of the total study area, only 4% area was under double and triple cropped areas during 2016-2017. Moderate runoff was observed in > 50% of the study area dominated by agricultural landscape. Nearly 40%, 25.11%, and 32.45% of the study area indicated very high, high, and moderate RWH potentials, respectively. Particularly, very high RWH potential is observed in the eastern and central portion of the study area. The use of appropriate RWH structures in less irrigated areas will facilitate multiple cropping and will substitute the use of sub-surface water harvesting practices. In these two districts, 73 check dams and 153 percolation tanks are prescribed along the 2nd- and 3rd-order streams. In coarser textured soil, nearly 306 km 2 and 608 km 2 areas are identified as moderate and highly suitable zones for percolation tank construction on ground, while in fine soil, around 786 km 2 area is identified as suitable for farm pond construction. Majority of the suitable zones for percolation tanks is found in Jajpur district, while suitability for adoption of farm pond and check dam is more in Bhadrak district. It is expected that implementation of the prescribed RWH structures can mitigate the threats of flood, drought, soil erosion, and enhance the soil moisture and cropping intensity significantly. The use of GIS platform with the spatial layers and the methodology adopted can be updated and replicated in larger regions in a shorter time. The spatially explicit maps are offering insights to different themes, providing useful information to the water resource managers, and may improve the decision-making process.

Land degradation is a long-term loss of ecosystem function and productivity which takes place due to a wide variety of land processes, namely soil erosion, soil sodification, green-cover loss, and soil conditions such as soil infertility that leads to productivity loss. About 41% of the land in India is under different forms of land degradation in which a major part lies in the Indian Ganga River Basin (IGRB). In this work, we evaluated the evidence of land degradation in the IGRB by analyzing (i) the changes in the forest cover and land use (FCLU) between 1975 and 2010, (ii) forest fragmentation status for the same time period, and (iii) decline in rain-use efficiency (RUE) during 2000-2010. The FCLU-type mapping for the year 1975 and 2010 was carried out using 216 Landsat satellite scenes that derived 40 vegetation and 7 non-vegetation classes. The highest negative change (loss) was observed in the dry deciduous forest of mixed forest formation (4699.9 km 2 ) and gregarious formation (1337.6 km 2 ), and a major gain in settlement (5396.3 km 2 ) and managed lands (3408.4 km 2 ). An increase in forest fragmentation was observed in all the forest classes with the highest rise in the deciduous forest of the central basin. A consistent decline in RUE was observed highest in the South-Western semi-arid IGRB (0.02-0.15) that stretched up to the central basin. All the three analyses showed evidence of active land degradation in the form of green-cover loss, fragmented forests, and declined primary productivity with visual evidence for some of the severely degraded areas. The use of Geoinformatics to analyze land degradation using surface indicators is promising and provide possibilities of further improvements using better resolution data.

Many places of the earth support high plant species richness, but emphasis is given to biodiversity hotspots with rich endemic species under threats of destruction by anthropogenic interventions. This definitely underplays species conservation at several places significant for optimisation of preserving natural ecosystems. Here we explore influences of climate, physiography and disturbance on plant species richness of the Eastern Ghats. We focus on the implications of water-energy dynamics and climatic heterogeneity on community distribution. Initially, 26-environmental variables were considered for the study, but eight least correlated variables viz., aspect, human appropriation of net primary productivity, global human footprint, mean annual temperature, mean annual precipitation, precipitation of driest quarter, terrain ruggedness index and temperature seasonality were utilised for further analysis. A total of 1670 species from 2274 sampling locations of 22564 records were examined using canonical correspondence analysis (CCA) and decision trees. Water-energy dynamics broadly regulates plant richness, with significant influence of mean annual precipitation and temperature. Precipitation of the driest quarter is the most significant factor in describing plant richness, indicating the availability of water during the dry period is crucial. The rise in temperature is likely to deteriorate further, where temperature seasonality is significant. Temperature seasonality determines thermal variability and assesses the intensity of climate change impacts on plant richness. The study offers ecological insights for successful conservation and management planning for the sustenance of the Eastern Ghats' rich biodiversity.

Ecotourism is the greener variant of tourism which advocates conservation of biodiversity and acts as a development strategy to build a self-sustainable system to help protect and further enhance the ecosystem through the income generated by ecotourism activities. There is a strong linkage between biodiversity conservation and ecotourism which has also been recognized by the UN and finds its place under the technical note on "Biodiversity and the 2030 Agenda for Sustainable Development". But, are the stakeholders of the ecotourism destinations religiously following practices that will ensure biodiversity conservation at all times is something that needs continuous evaluation and validation. The authors have worked in the past on developing multi-stage methodology (ESA Framework, ESM Model and their validation) using Qualitative and Quantitative techniques and successfully developed Ecotourism Sustainability Assessment Method (ESAM) for such an appraisal. The present paper aims at devising a process which will show the applicability of ESM model in identifying the biodiversity related and other environmental factors, adversely impacting the ecotourism destination at present, or may impact it in times to come. In addition, it also offers the prescription to solve these issues and achieve the goal of ecotourism sustainability at the operational level. This proposed process initially will be defined with the help of a site-level case study of Mt. Kangchendzonga Base Camp Trek, Sikkim. Later, this would be extended to other ecologies and geographies so that in the future, a robust and useful model, applicable for most of the ecotourism destinations, can be developed.

We assess the invasive potential of Ageratum conyzoides, Hevea brasiliensis, Urena lobata and Imperata cylindrica differing in habit and biogeographic origin through ecological niche modelling in the context of the 2000 and 2050 climates of North-East (NE) India. Out of these four species, Ageratum conyzoides, Urena lobata and Imperata cylindrica are naturally occurring weed species and Hevea brasiliensis is a cultivated tree species. This study tries to address a basic question whether species with similarity in biogeographic origin may have some uniform strategy to succeed in invasion process. Ecological niche models predicted that Ageratum conyzoides (a shrub) and Hevea brasiliensis (a tree) of South American origin have greater potential to invade/distribute in NE region of India by 2050 than two other species, Urena lobata and Imperata cylindrica, of South-Asian origin. The latter two species show lower potential to invade in NE India in 2050 compared with their extent of distribution in 2000. A set of major contributing bioclimatic factors responsible for distribution of two South-Asian species (Urena and Imperata sp.) remain more or less constant between 2000 and 2050 climates. However, the distribution of Ageratum sp. and Hevea sp. with respect to two climate scenarios is attributed by two different sets of major bioclimatic factors. This indicates the robustness of the species to get adapted to different set of climatic variables over time.

Gross primary productivity (GPP) is a vital ecosystem variable that is used as a proxy to study the functional behaviour of a terrestrial ecosystem and its ability to regulate atmospheric CO 2 by working as a carbon pool. India, having the potential terrestrial ecosystem dynamics to absorb the atmospheric carbon dioxide to some extent, is one of the least-explored regions in terms of carbon monitoring studies. The current study evaluates the applicability of a newly developed, quantum yield-based, remote sensing data-driven diagnostic model called the Southampton Carbon Flux (SCARF). This model was used to estimate the annual and seasonal variability of the terrestrial GPP over the Indian region with a spatial resolution of 1 km during 2008. This modified version of the conventional production efficiency model successfully predicted GPP using meteorological variables (PAR, air temperature and dew point temperature), the fraction of photosynthetically active radiation and quantum yield of C3 and C4 plants as the key input parameters. The annual GPP values were in the range from 0 to 4147.55 g C m -2 year -1 , with a mean value of 1507.32 g C m -2 year -1 . The maximum and minimum GPP were during the summer monsoon and pre-monsoon, respectively. The seasonal and annual distributions of GPP over the study area obtained using the SCARF model, and the MODIS GPP product (MOD17A2H) were similar. However, MODIS was found to underestimate the GPP in all regions and an overestimation in eastern Himalaya region. The study reveals that environmental scalars, specifically water stress, are the pivotal controlling variables responsible for the variation of GPP in India. The estimates of the GPP in different regions of the study area were made using SCARF, and an eddy covariance technique was similar. The SCARF model can be used to estimate GPP on a global scale. SCARF appears to be a better model in terms of the simplicity of the algorithm, performance and resolution. Thus, it may give higher accuracy in carbon monitoring studies.

Introduction: Knowledge of species richness patterns and their relation with climate is required to develop various forest management actions including habitat management, biodiversity and risk assessment, restoration and ecosystem modelling. In practice, the pattern of the data might not be spatially constant and cannot be well addressed by ordinary least square (OLS) regression. This study uses GWR to deal with spatial non-stationarity and to identify the spatial correlation between the plant richness distribution and the climate variables (i.e., the temperature and precipitation) in a 1° grid in different biogeographic zones of India., Methodology: We utilized the species richness data collected using 0.04 ha nested quadrats in an Indian study. The data from this national study, titled 'Biodiversity Characterization at Landscape Level', were aggregated at the 1° grid level and adjudged for sampling sufficiency. The performances of OLS and GWR models were compared in terms of the coefficient of determination (R2) and the corrected Akaike Information Criterion (AICc)., Results and Discussion: A comparative study of the R2 and AICc values of the models showed that all the GWR models performed better compared with the analogous OLS models. The climate variables were found to significantly influence the distribution of plant richness in India. The minimum precipitation (Pmin) consistently dominated individually (R2 = 0.69; AICc = 2608) and in combinations. Among the shared models, the one with a combination of Pmin and Tmin had the best model fits (R2 = 0.72 and AICc = 2619), and variation partitioning revealed that the influence of these parameters on the species richness distribution was dominant in the arid and the semi-arid zones and in the Deccan peninsula zone., Conclusion: The shift in climate variables and their power to explain the species richness of biogeographic zones suggests that the climate-diversity relationships of plants species vary spatially. In particular, the dominant influence of Tmin and Pmin could be closely linked to the climate tolerance hypothesis (CTH). We found that the climate variables had a significant influence in defining species richness patterns in India; however, various other environmental and non-environmental (edaphic, topographic and anthropogenic) variables need to be integrated in the models to understand climate-species richness relationships better at a finer scale., Competing Interests: The authors have declared that no competing interests exist.

With the availability of satellite data from free data domain, remote sensing has increasingly become a fast-hand tool for monitoring of land and water resources development activities with minimal cost and time. Here, we verified construction of check dams and implementation of plantation activities in two districts of Tripura state using Landsat and Sentinel-2 images for the years 2008 and 2016-2017, respectively. We applied spectral reflectance curves and index-based proxies to quantify these activities for two time periods. A subset of the total check dams and plantation sites was chosen on the basis of site condition, nature of check dams, and planted species for identification on satellite images, and another subset was randomly chosen to validate identification procedure. The normalized difference water index (NDWI) derived from Landsat and Senitnel-2 were used to quantify water area evolved, qualify the water quality, and influence of associated tree shadows. Three types of check dams were observed, i.e., full, partial, and fully soil exposed on the basis of the presence of grass or scrub on the check dams. Based on the nature of check dam and site characteristics, we classified the water bodies under 11-categories using six interpretation keys (size, shape, water depth, quality, shadow of associated trees, catchment area). The check dams constructed on existing narrow gullies totally covered by branches or associated plants were not identified without field verification. Further, use of EVI enabled us to approve the plantation activities and adjudge the corresponding increase in vegetation vigor. The plantation activities were established based on the presence and absence of existing vegetation. Clearing on the plantation sites for plantation shows differential increase in EVI values during the initial years. The 403 plantation sites were categorized into 12 major groups on the basis of presence of dominant species and site conditions. The dominant species were Areca catechu, Musa paradisiaca, Ananas comosus, Bambusa sp., and mix plantation of A. catechu and M. paradisiaca. However, the highest maximum increase in average EVI was observed for the pine apple plantation sites (0.11), followed by Bambussa sp. (0.10). These sites were fully covered with plantation without any exposed soil. The present study successfully demonstrates a satellite-based survey supplemented with ground information evaluating the changes in vegetation profile due to plantation activities, locations of check dams, extent of water bodies, downstream irrigation, and catchment area of water bodies.

Understanding the impact of climate change on species invasion is crucial for sustainable biodiversity conservation. Through this study, we try to answer how species differing in phenological cycles, specifically Cassia tora and Lantana camara, differ in the manner in which they invade new regions in India in the future climate. Since both species occupy identical niches, exploring their invasive potential in different climate change scenarios will offer critical insights into invasion and inform ecosystem management. We use three modelling protocols (i.e., maximum entropy, generalised linear model and generalised additive model) to predict the current distribution. Projections are made for both moderate (A1B) and extreme (A2) IPCC (Intergovernmental Panel on Climate Change) scenarios for the year 2050 and 2100. The study reveals that the distributions of C. tora (annual) and L. camara (perennial) would depend on the precipitation of the warmest quarter and moisture availability. C. tora may demonstrate physiological tolerance to the mean diurnal temperature range and L. camara to the solar radiation. C. tora may invade central India, while L. camara may invade the Western Himalaya, parts of the Eastern Himalaya and the Western Ghats. The distribution ranges of both species could shift in the northern and north-eastern directions in India, owing to changes in moisture availability. The possible alterations in precipitation regimes could lead to water stress, which might have cascading effects on species invasion. L. camara might adapt to climate change better compared with C. tora. This comparative analysis of the future distributions of two invasive plants with contrasting habits demonstrates that temporal complementarity would prevail over the competition., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Land use and land cover (LULC) change has been recognized as a key driver of global climate change by influencing land surface processes. Being in constant change, river basins are always subjected to LULC changes, especially decline in forest cover to give way for agricultural expansion, urbanization, industrialization etc. We used on-screen digital interpretation technique to derive LULC maps from Landsat images at three decadal intervals i.e., 1985, 1995 and 2005 of two major river basins of India. Rain-fed, Mahanadi river basin (MRB) attributed to 55% agricultural area wherein glacier-fed, Brahmaputra river basin (BRB) had only 16% area under agricultural land. Though conversion of forest land for agricultural activities was the major LULC changes in both the basins, the rate was higher for BRB than MRB. While water body increased in MRB could be primarily attributed to creation of reservoirs and aquaculture farms; snow and ice melting attributed to creation of more water bodies in BRB. Scrub land acted as an intermediate class for forest conversion to barren land in BRB, while direct conversion of scrub land to waste land and crop land was seen in MRB. While habitation contributed primarily to LULC changes in BRB, the proximity zones around habitat and other socio-economic drivers contributed to LULC change in MRB. Comparing the predicted result with actual LULC of 2005, we obtained >97% modelling accuracy; therefore it is expected that the Dyna-CLUE model has very well predicted the LULC for the year 2025. The predicted LULC of 2025 and corresponding LULC changes in these two basins acting as early warning, and with the past 2-decadal change analysis this study is believed to help the land use planners for improved regional planning to create balanced ecosystem, especially in a changing climate., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Several factors describe the broad pattern of diversity in plant species distribution. We explore these determinants of species richness in Western Himalayas using high-resolution species data available for the area to energy, water, physiography and anthropogenic disturbance. The floral data involves 1279 species from 1178 spatial locations and 738 sample plots of a national database. We evaluated their correlation with 8-environmental variables, selected on the basis of correlation coefficients and principal component loadings, using both linear (structural equation model) and nonlinear (generalised additive model) techniques. There were 645 genera and 176 families including 815 herbs, 213 shrubs, 190 trees, and 61 lianas. The nonlinear model explained the maximum deviance of 67.4% and showed the dominant contribution of climate on species richness with a 59% share. Energy variables (potential evapotranspiration and temperature seasonality) explained the deviance better than did water variables (aridity index and precipitation of the driest quarter). Temperature seasonality had the maximum impact on the species richness. The structural equation model confirmed the results of the nonlinear model but less efficiently. The mutual influences of the climatic variables were found to affect the predictions of the model significantly. To our knowledge, the 67.4% deviance found in the species richness pattern is one of the highest values reported in mountain studies. Broadly, climate described by water-energy dynamics provides the best explanation for the species richness pattern. Both modeling approaches supported the same conclusion that energy is the best predictor of species richness. The dry and cold conditions of the region account for the dominant contribution of energy on species richness.

Data on the distribution of plant species at spatial (grid) scales are required as input for integrative analysis along with related climate, environment, topography and soil data. Although the world's scientific community is increasingly generating data on plant species at various spatial grids and statistically interpolating and extrapolating the available information, data on plant diversity from the Asian continent are scant. Such data are unavailable for India, the mainland of which has part of three of the world's 36 biodiversity hotspots. Although sufficient field sampling is always impossible and impractical, it is essential to utilize fully any available database by adjudging the sampling sufficiency at a given scale. In this work, we used an exhaustive database of the plant species of the Indian mainland that was sufficient in terms of sampling vegetation types. We transformed the data, obtained the distribution at the 1° and 2° spatial grid levels and evaluated the sampling sufficiency at acceptable threshold limits (60% to 80%). The greatest species richness values recorded in the 0.04 ha quadrant, 1° grid and 2° grid were 59, 623 and 1244, respectively. Clench model was significantly (p value < 0.001) fitted using the plant species data at both the grid levels with a very high coefficient of determination (>0.95). At an acceptable threshold limit of 70%, almost all the grids at the 2° level and more than 80% of the grids at the 1° level were found to be sufficiently sampled. Sampling sufficiency was observed to be highly scale-dependent as a greater number of 2° grids attained asymptotic behaviour following the species-area curve. Grid-level sampling insufficiency was attributed to lower numbers of sampling quadrats in forests with poor approachability, which coincided with the world biodiversity hotspots', suggesting that additional sampling was required. We prescribe the use of the 1° and 2° spatial grids with sufficient sampling for any ecological analysis in conjunction with other data and thereby offer grid-level plant species richness data for the Indian mainland for the first time.

India is one of the 12 mega biodiversity countries of the world, which represents 11% of world's flora in about 2.4% of global land mass. Approximately 28% of the total Indian flora and 33% of angiosperms occurring in India are endemic. Higher human population density in biodiversity hotspots in India puts undue pressure on these sensitive eco-regions. In the present study, we predict the future distribution of 637 endemic plant species from three biodiversity hotspots in India; Himalaya, Western Ghats, Indo-Burma, based on A1B scenario for year 2050 and 2080. We develop individual variable based models as well as mixed models in MaxEnt by combining ten least co-related bioclimatic variables, two disturbance variables and one physiography variable as predictor variables. The projected changes suggest that the endemic flora will be adversely impacted, even under such a moderate climate scenario. The future distribution is predicted to shift in northern and north-eastern direction in Himalaya and Indo-Burma, while in southern and south-western direction in Western Ghats, due to cooler climatic conditions in these regions. In the future distribution of endemic plants, we observe a significant shift and reduction in the distribution range compared to the present distribution. The model predicts a 23.99% range reduction and a 7.70% range expansion in future distribution by 2050, while a 41.34% range reduction and a 24.10% range expansion by 2080. Integration of disturbance and physiography variables along with bioclimatic variables in the models improved the prediction accuracy. Mixed models provide most accurate results for most of the combinations of climatic and non-climatic variables as compared to individual variable based models. We conclude that a) regions with cooler climates and higher moisture availability could serve as refugia for endemic plants in future climatic conditions; b) mixed models provide more accurate results, compared to single variable based models.

Background: Frequent relapses in opioid addiction may be a result of abstinentemergent craving. Exposure to various stimuli associated with drug use (drug cues) may trigger craving as a conditioned response to 'drug cues'., Aims: The present study explored the effects of imagery cue exposure on psychophysiological mechanisms of craving, viz. autonomic arousal, in detoxified opiate addicts., Methodology: Opiate dependent subjects (N=38) following detoxification underwent imagery cue reactivity trials.The subjects were asked to describe verbally and then imagine their craving experiences., Results: Craving was measured subjectively by using Visual Analogue Scale and autonomic parameters of galvanic skin resistance (GSR), pulse rate (PR), and skin temperature (ST) was taken during cue imagery. Spearman's r and Wilcoxon signed ranks test were employed in analysis. Multivariate repeated measurement analysis (wilk's Lambda) was employed wherever appropriate. Subjective measures of craving, GSR and PR increased significantly whereas ST decreased significantly during drug related cue imagery as compared to neutral cues., Conclusions: The results support that cue imagery is a powerful tool in eliciting craving. Hence, it can be used as a screening manoeuvre for detecting individuals with high cue reactivity, as well as for extinction of craving.