Conservation efforts have traditionally focused on biodiversity hotspots, overlooking the essential ecological roles and ecosystem services provided by cold spots, the regions that harbour relatively low species diversity. In this study, we used a novel plant species database aggregated at 1˚ grid resolution to predict present and future plant species distribution in major cold spot biogeographic zones of India: Desert, Semi-Arid, Deccan Peninsula, and Gangetic Plain. We employed multiple models: Generalized Linear Model, Generalized Boosted Model, Random Forest, Support Vector Machine, and their ensemble. The results demonstrated reasonable predictive ability, with water and energy variables dominating in all the zones, showing a strong agreement with the field based data. Temperature annual range, annual precipitation, and precipitation of the driest month significantly influenced (r > 0.4) plant species patterns in the Desert and Semi-Arid zone. The ensemble model output improved predictive ability, with reduced root mean square error and enhanced correlation (r = 0.8). Other environmental variables (topography: elevation, and Human Influence Index) showed high correlation in combination with water and energy variables in the Deccan Peninsula. Continuous species loss is anticipated under future climate scenarios across all the zones. Semi-Arid is expected to see the most significant increase, with 69

Despite substantial progress in understanding global biodiversity loss, major taxonomic and geographic knowledge gaps remain. Decision makers often rely on expert judgement to fill knowledge gaps, but are rarely able to engage with sufficiently large and diverse groups of specialists. To improve understanding of the perspectives of thousands of biodiversity experts worldwide, we conducted a survey and asked experts to focus on the taxa and freshwater, terrestrial, or marine ecosystem with which they are most familiar. We found several points of overwhelming consensus (for instance, multiple drivers of biodiversity loss interact synergistically) and important demographic and geographic differences in specialists’ perspectives and estimates. Experts from groups that are underrepresented in biodiversity science, including women and those from the Global South, recommended different priorities for conservation solutions, with less emphasis on acquiring new protected areas, and provided higher estimates of biodiversity loss and its impacts. This may in part be because they disproportionately study the most highly threatened taxa and habitats. Front Ecol Environ 2022;

Abstract Introduction Conservation efforts have traditionally focused on biodiversity hotspots, overlooking the essential ecological roles and ecosystem services provided by cold spots. Cold spots are areas outside biodiversity hotspots, characterized by low species diversity and harboring rare species living in threatened habitats. Aim This study aims to predict the present and future plant species distribution in cold spots across India, considering various environmental and non-environmental variables. Location India Methods The Indian national-level plant species database generated through the project ‘ Biodiversity Characterization at Landscape Level’ was used . The species modelling (70% randomly selected training data) was carried out for four major biogeographic zones of India namely Arid and semi-arid zone, Deccan peninsula, and Gangetic plain. Generalized Linear Model (GLM), Generalized Boosted Model (GBM), Random Forest (RF), Support Vector Machine (SVM), and ensemble modeling were compared to predict species distribution. Future representative concentration pathways (RCP4.5 & RCP8.6) were used to forecast species distribution. Results The study demonstrated good predictive ability with water and energy variables dominating in all zones, showing a strong agreement with the observed data (30% subset of the original data). Temperature annual range, annual precipitation, and precipitation of the driest month (bio7, bio12, and bio14) significantly influenced (r > 0.4) plant species patterns in the arid and semi-arid zone. Ensemble modeling showed improved results when validated with observed data, exhibiting a significant reduction in the RMSE and an improved correlation (r=0.8). Non-environmental variables (elevation and human influence index) showed significant influence in combination with water and energy variables in the Deccan peninsula zone. We observed continuous species loss in both future climate scenarios. Among biogeographic zones, the semi-arid and arid zones showed the maximum probable increase in species, with 69% and 52.5% of grids gaining species in 2050 (RCP4.5) and 69% and 84.7% of grids gaining species in 2070 (RCP8.6) respectively. Conclusion The study provides insights into the species richness distribution of cold spots in major Indian biogeographic zones, supporting their climate-derived patterns at a macro-scale. Ensemble modeling proves to be more accurate than individual models, emphasizing its potential for conservation efforts. The study calls for a performance-based conservation approach, prioritizing criteria to safeguard valuable ecosystems and prevent species loss.

Abstract Introduction Conservation efforts have traditionally focused on biodiversity hotspots, overlooking the essential ecological roles and ecosystem services provided by cold spots. Cold spots are areas outside biodiversity hotspots, characterized by low species diversity and harboring rare species living in threatened habitats. Aim This study aims to predict the present and future plant species distribution in cold spots across India, considering various environmental and non-environmental variables. Location India Methods The Indian national-level plant species database generated through the project ‘Biodiversity Characterization at Landscape Level’ was used. The species modelling (70% randomly selected training data) was carried out for four major biogeographic zones of India namely Arid and semi-arid zone, Deccan peninsula, and Gangetic plain. Generalized Linear Model (GLM), Generalized Boosted Model (GBM), Random Forest (RF), Support Vector Machine (SVM), and ensemble modeling were compared to predict species distribution. Future representative concentration pathways (RCP4.5 & RCP8.6) were used to forecast species distribution. Results The study demonstrated good predictive ability with water and energy variables dominating in all zones, showing a strong agreement with the observed data (30% subset of the original data). Temperature annual range, annual precipitation, and precipitation of the driest month (bio7, bio12, and bio14) significantly influenced (r > 0.4) plant species patterns in the arid and semi-arid zone. Ensemble modeling showed improved results when validated with observed data, exhibiting a significant reduction in the RMSE and an improved correlation (r=0.8). Non-environmental variables (elevation and human influence index) showed significant influence in combination with water and energy variables in the Deccan peninsula zone. We observed continuous species loss in both future climate scenarios. Among biogeographic zones, the semi-arid and arid zones showed the maximum probable increase in species, with 69% and 52.5% of grids gaining species in 2050 (RCP4.5) and 69% and 84.7% of grids gaining species in 2070 (RCP8.6) respectively. Conclusion The study provides insights into the species richness distribution of cold spots in major Indian biogeographic zones, supporting their climate-derived patterns at a macro-scale. Ensemble modeling proves to be more accurate than individual models, emphasizing its potential for conservation efforts. The study calls for a performance-based conservation approach, prioritizing criteria to safeguard valuable ecosystems and prevent species loss.

This manual has been prepared for use by the disaster management community. It introduces remote sensing and geospatial concepts, ICIMOD’s science applications and their applications in disaster preparedness. The manual’s contents were used in training sessions on using Earth observation and geospatial applications for disaster preparedness in Nepal. It provides a step-by-step guide to using free and open-source geospatial software, remote sensing data, and ICIMOD’s science applications for preparedness, management, and risk reduction of disasters. It uses examples and sample datasets from Nepal.

Automated long-term mapping and climate niche modeling are important for developing adaptation and management strategies for rubber plantations (RP). Landsat imageries at the defoliation and refoliation stages were employed for RP mapping in the Indian state of Tripura. A decision tree classifier was applied to Landsat image-derived vegetation indices (Normalized Difference Vegetation Index and Difference Vegetation Index) for mapping RPs at two-three years intervals from 1990 to 2017. A comparison with actual plantation data indicated more than 91% mapping accuracy, with most RPs able to be identified within six years of plantation, while several patches were detected after six years of plantations. The RP patches identified in 1990 and before 2000 were used for training the Maxent species distribution model, wherein bioclimatic variables for 1960–1990 and 1970–2000 were used as predictor variables, respectively. The model-estimated suitability maps were validated using the successive plantation sites. Moreover, the RPs identified before 2017 and the Shared Socioeconomic Pathways (SSP) climate projections (SSP126 and SSP245) were used to predict the habitat suitability for 2041–2060. The past climatic changes (decrease in temperature and a minor reduction in precipitation) and identified RP patches indicated an eastward expansion in the Indian state of Tripura. The projected increase in temperature and a minor reduction in the driest quarter precipitation will contribute to more energy and sufficient water availability, which may facilitate the further eastward expansion of RPs. Systematic multi-temporal stand age mapping would help to identify less productive RP patches, and accurate monitoring could help to develop improved management practices. In addition, the existing RP patches, their expansion, and the projected habitat suitability maps could benefit resource managers in adapting climate change measures and better landscape management.

Over the next couple of decades, jobs across the globe will increasingly require digital literacy and information and communications technology skills, and likely involve the use of geospatial technologies such as Earth observation (EO) and geospatial information technology (GIT). Despite the growth of EO and GIT, the number of women professionals in this sector remains low across the world. To bridge the technology and gender gap and to promote a gender-balanced workforce in the Hindu Kush Himalaya (HKH), our SERVIR-HKH Initiative has been training young women STEM professionals through its “Empowering women in GIT” (WoGIT) training series, initiated in 2018. This publication records our WoGIT journey – its milestones, impacts, and efforts to help build a skilled, gender-balanced workforce that can keep up with the rapid innovations in EO and GIT. To date, we have organized nine WoGIT trainings (two in-person, and seven virtual) in five HKH countries, benefitting 410 women.

Earth and Space Science Open Archive Presented WorkOpen AccessYou are viewing the latest version by default [v1]Capacity building in EO and GIT - bridging the gender and capacity gap in the HKH regionAuthorsPoonamTripathiiDRajeshBahadur ThapaiDSee all authors Poonam TripathiiDCorresponding Author• Submitting AuthorICIMODiDhttps://orcid.org/0000-0003-1534-4650view email addressThe email was not providedcopy email addressRajesh Bahadur ThapaiDICIMODiDhttps://orcid.org/0000-0003-4467-0148view email addressThe email was not providedcopy email address

Land use and land cover (LULC) change has one of the key modes of human modifications and has raised several queries related to its impact on hydrology and climate. Since the LULC plays a vital role in partitioning energy and water fluxes at the land surface into different components, the changes in LULC can impact the water and energy cycles to a significant level. However, at a basin scale, the severity of such consequences depends on the scale, type, and heterogeneity of LULC changes. An assessment of the impacts of LULC change on hydrology in three major river basins—Brahmaputra, Ganga, and Mahanadi of India is performed in this study using the variable infiltration capacity (VIC) model, a physically-based distributed hydrological model. The assessment reveals an important compensating effect in the hydrologic changes resulted from LULC transformations. The negative consequences (e.g., increased surface runoff due to urbanization) at one place are compensated by opposite positive changes (e.g., decreased surface runoff due to forest plantation) at other places at the basin scale. Such compensation leads to insignificant hydrologic changes at the basin scale; however, the consequences are significant at the local and sub-basin scales.

AbstractThe innovative transformation in geospatial information technology (GIT) and Earth observation (EO) data provides a significant opportunity to study the Earth’s environment and enables an advanced understanding of natural and anthropogenic impacts on ecosystems at the local, regional, and global levels (Thapa et al. in Carbon Balance Manag 10(23):1–13, 2015; Flores et al. in SAR handbook: comprehensive methodologies for forest monitoring and biomass estimation. NASA Publication, 2019; Leibrand et al. in Front Environ Sci 7:123, 2019; Chap. 1). The major advantages of these technologies can be briefly categorized into five broad areas: multidisciplinary; innovative and emerging; providing platforms for analysis, modelling, and visualization; capability to support decision-making; and impact on policies.

The growth of population has created a need for better and economical vehicular operation which requires good highways, proper geometric design and pavement condition maintenance. Road transportation is undoubtedly the lifeline of the nation and its development is crucial concern. The process of soil stabilization helps to achieve the required properties in a soil needed for the pavement construction work. One of the main reason for the failure of pavement is due to lack of strength. Strength can be increased by adding additive materials to the sub-grade in different proportions. The work presented in this paper deals with the strength properties of natural and stabilized subgrade. In this research, Silica fume, Recron 3-S fibre and Terrasil are used as stabilizers in improving engineering properties of soil. The aim of this study is to evaluates the effect of different percentages of Silica fume, Recron 3-S fibre and Terrasil are used separately and combination as stabilizers to improve the sub-grade characteristics of locally available soil. Mainly we have focused on increasing the CBR of the soil because on increasing the CBR value it helps in reducing the thickness of the pavement and it is also beneficial to use economically.

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.

The present work investigates the applicability of a widespread bio-geochemical model (Biome BGC) to simulate monthly net primary productivity (NPP) and leaf area index (LAI) of Indian tropical deciduous forests. We simulated the monthly NPP and LAI of three plant functional types (PFTs) [dry mixed (DM), sal mixed (SM) and teak plantation (TP)] having distinct tree species compositions, canopy structure, different carbon assimilation rates and microclimate within a broad tropical deciduous forest during 2011–2012. The parameterization of 11 major eco-physiological parameters of Biome BGC was performed from in-situ physiological measurements gathered from 9 long-term ecological research plots in above three PFTs and PFT specific indices were developed. Bimodal trends, with highest peak in September during autumn and second peak in January during winter were observed for simulated monthly NPP in all three PFTs. Simulated NPP (gC/m2/year) values were 408.8 and 414.6; 376.8 and 392.9; and 327.5 and 338.2 during 2011 and 2012 in DM, SM and TP PFTs respectively. Observed NPP (gC/m2/year) values ranged between 463.4 and 493.1; 498.0 and 529.5; and 542.1 and 677.9 in 2012 in DM, SM and TP PFTs respectively. Biome BGC simulated NPP were in positive agreement with observed NPP in all PFTs (R2 = 0.92, 0.83 and 0.72 in DM, SM and TP respectively). In all PFTs Biome BGC led to an underestimation of LAI. The current investigation evaluated the operational application of Biome BGC in Indian tropical deciduous forest and opens scope for further improvement for LAI algorithms for better in-situ LAI simulation.

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.

The Hindu Kush Himalaya (HKH) region is among the most discrete and diverse region facing various ecological, environmental and socio-economic threats in terms of increasing demands for natural resources and its consequences in the form of overexploitation, disaster, droughts, extreme weather, and climate change etc. Geospatial information technology (GIT) with Earth observation (EO) data are effectively supporting the implementation of development agendas in HKH by providing extensive solutions to above-pressing issues by not only addressing them but also providing services in daily life. These technologies have effectively bolstered in time via innovation, creating jobs and confidence in people that supports filling the data and knowledge gaps in the region. However, the involvement and participation of women in GIT is mere in the region despite their vital role in environmental management and decision making. Realizing the issue, we acknowledged and implemented the twin challenges i.e. capacity building and gender equality for building the pathways to sustainable development via innovative steps and processes to bridge the gender imbalance in GIT workforce in HKH. For the purpose, we organized various capacity building trainings and workshops with a broad focus towards GIT applications in forest, agriculture, water management, drought and climate change along with the hands-on exercises. In addition, specific women focused training programs i.e. Empowering Nepali Women through Technology Training and Women in GIT were organized during 2017 and 2018 respectively. These efforts delivered optimistic results in terms of building confidence, decision making and more women participation showing an increment of ~5% participation by women in 2017–2018 fiscal year with respect to 2016–2017 fiscal year. In HKH nations with less social parity, the information delivered by this gender mainstreaming effort will have life-changing implications to achieve workforce parity.

Plant invasion is highly responsive to rising temperature, altered precipitation and various anthropogenic disturbances. Therefore, climate anomalies might provide opportunities to identify the relationship of past climate in deriving the distribution of invasive species and to detect their probable future distribution. In this work, we studied the correlation of climate anomaly i.e. temperature and precipitation with an indicative map of plant invasive species (1° grid) of India. The indicative map was generated through the plant data available from the project ‘Biodiversity Characterization at Landscape Level’. Climate anomaly was calculated and represented by average temperature and precipitation using ‘Climate Research Unit’ data for the period of 1901 to 2000. A comparison of local geographically weighted regression (GWR) model and a global ordinary least square regression (OLS) model was carried out for statistical analysis to depict the correlation at 1° spatial grids. Overall, 20,501 records with a total of 9112 unique plots and 161 unique invasive species were recorded in the database that shows a maximum of 15 invasive species in a 0.04 ha nested quadrat. Cumulative analysis showed a maximum of 53 invasive species at 1° grid. Individually, GWR could reveal a significant correlation with invasive species distribution for temperature anomaly (r2 = 0.73, AIC= 2206) and precipitation anomaly (r2 = 0.74, AIC= 2221), while OLS model did not offer a good correlation (r2 < 0.001, AIC > 2400) compared to GWR. Combination of temperature and precipitation anomaly (shared model) showed an improved spatial correlation (r2 > 0.75) using GWR. Variation partitioning revealed the dominant influence (> 0.40 of variation) of temperature anomaly over Deccan Peninsula, Himalaya and North East zone. Influence of precipitation anomaly was more prominent over arid and semi-arid zone explaining > 0.35 of variation. Results revealed the strength of GWR to see the interaction of invasive plant species w.r.t. climate anomalies that explain the influence of spatial variation due to heterogeneity at varying neighbour distances. The significant correlation of invasive species with both the anomalies revealed the affinity of invasive species towards warmer, drier and wet places. This gives an indication that the distribution of invasive species could be triggered by climate anomaly. The use of other predictor variables (i.e. edaphic and anthropogenic) could be an inclusive input in a future perspective.

As a catchment phenomenon, land use and land cover change (LULCC) has a great role in influencing the hydrological cycle. In this study, decadal LULC maps of 1985, 1995, 2005 and predicted-2025 of the Subarnarekha, Brahmani, Baitarani, Mahanadi and Nagavali River basins of eastern India were analyzed in the framework of the variable infiltration capacity (VIC) macro scale hydrologic model to estimate their relative consequences. The model simulation showed a decrease in ET with 0.0276% during 1985–1995, but a slight increase with 0.0097% during 1995–2005. Conversely, runoff and base flow showed an overall increasing trend with 0.0319 and 0.0041% respectively during 1985–1995. In response to the predicted LULC in 2025, the VIC model simulation estimated reduction of ET with 0.0851% with an increase of runoff by 0.051%. Among the vegetation parameters, leaf area index (LAI) emerged as the most sensitive one to alter the simulated water balance. LULC alterations via deforestation, urbanization, cropland expansions led to reduced canopy cover for interception and transpiration that in turn contributed to overall decrease in ET and increase in runoff and base flow. This study reiterates changes in the hydrology due to LULCC, thereby providing useful inputs for integrated water resources management in the principle of sustained ecology.

Abstract Net Primary Productivity (NPP) is a significant biophysical vegetation variable to understand the spatio-temporal distribution of carbon and source-sink nature of the ecosystem. This study was carried out in a forest plantation area and aimed to (i) estimate the spatio-temporal patterns of NPP during 2009 and 2010 using Carnegie-Ames-Stanford Approach [CASA] model and (ii) study the effects of climate variables on the NPP using generalized linear modelling (GLM) approach. The total annual NPP varied from 157.21 to 1030.89 gC m−2 yr−1 for the year 2009 and from 154.36 to 1124.85 g C m−2 yr−1 for the year 2010. The annual NPP was assessed across four major plantation types, where maximum NPP gain (106 and 139 g C m−2 yr−1 ) in October was noticed in teak (Tectona grandis) and minimum (77 and 109 g C m−2 yr−1 ) in eucalyptus (Eucalyptus hybrid) during 2009 and 2010.The validation, using field-estimated NPP, showed under-estimation of modelled NPP, with maximum MAPE of 34% for eucalyptus and minimum of 13% for teak. The dominant influence of precipitation on the NPP was revealed by GLM explaining more than 20% of variation. CASA model efficiently estimated the annual NPP of plantations. The accuracy could be improved further with inclusion of higher resolution data.