Cushions serve as conservation refuges for the Himalayan alpine plant diversity: Implications for nature-based environmental management.

Globally, rapid climate and land-use changes in alpine environments are posing severe risks to their bountiful biodiversity and ecosystem services. Currently, nature-based solutions are fast-emerging as the preferred approach to address the challenges of environmental sustainability. In alpine environments, cushion plants owing to their unique architecture and adaptability offer a potential nature-based system to plan biodiversity conservation and habitat restoration strategies. Here, we employed an analytical framework to test whether and how the cushion plants facilitate the sustenance of alpine plant diversity in Kashmir Himalaya. We specifically aimed to answer: what are the effects of the cushion plants on the patterns of alpine species richness and phylogenetic diversity, and whether these effects vary across spatial scales (local versus landscape), cushion types, and changing elevation. We randomly selected pairs of cushion and neighbouring non-cushion plots (size 100 m2) across 34 different alpine sites in the study region. Within each plot, we randomly laid three 5 m2 quadrats for vegetation sampling, and sampled a total of 204 quadrats in 68 plots with seven cushion types along elevation ranging from 3100 to 3850 m. Our results revealed positive effects of the cushions by supporting a higher community species richness (SR) and phylogenetic diversity (PD). The effects were consistent both at the local (i.e., quadrat) and landscape (i.e., plot) scales, but varied significantly with the cushion type. Interestingly, SR and PD showed an increasing trend with increase in elevation in cushion communities, thereby supporting stress gradient hypothesis. Along the elevational gradient, the cushion communities showed phylogenetic overdispersion, but clustering by non-cushions. Overall, our study provides empirical evidence to reinforce the role of the cushions as conservation refugia for an imperilled alpine plant diversity in the Himalaya. Looking ahead, we highlight the far-reaching implications of our findings in guiding the nature-based environmental management of alpine ecosystems worldwide.

Seasonal dynamics and impact factors of atmospheric CO2 concentration over subtropical forest canopies: observation from eddy covariance tower and OCO-2 satellite in Northwest Himalaya, India.

Carbon dioxide (CO2) is the key atmospheric gas that controls the earth's greenhouse effect, and forests play a major role in abating the atmospheric CO2 by storing carbon as biomass. Therefore, it is vital to understand the role of different forests in regulating the spatiotemporal dynamics of atmospheric CO2 concentration. In this study, we have used eddy covariance (EC) tower-based atmospheric CO2 concentration measurements and satellite-retrieved column average CO2 concentration of 2018 to understand the diurnal and seasonal dynamics of atmospheric CO2 concentration over the sub-tropical forest in the foothills of northwest Himalaya, Uttarakhand, India. EC study revealed that the CO2 concentration over the forest canopy peaks during mid-night to early morning and drop to a minimum during the afternoon. On a monthly scale, peak atmospheric CO2 concentration was observed during July in both the sites, which was a result of more release of CO2 by the forest ecosystem through ecosystem respiration and microbial decomposition. Enhanced photosynthetic activities during the late monsoon and post-monsoon resulted in the decrease of atmospheric CO2 concentration over the forest ecosystem. Among the meteorological variables, rainfall was found to have the highest control over the seasonal variability of the atmospheric CO2 concentration. Orbiting Carbon Observatory-2 (OCO-2) satellite-retrieved column average CO2 (XCO2) was also examined to comprehend its reliability on an ecosystem scale. The OCO-2 retrieved XCO2 value was higher than the EC carbon flux tower-measured atmospheric CO2 concentration, which might be due to differences in the vertical resolution of the CO2 column and scale difference. However, the monthly atmospheric XCO2 retrieved from OCO-2 strongly adheres with the ground-measured monthly pattern. Our study highlights that forests with varying functional traits within the same climatic conditions show variability in the regulation of atmospheric CO2 concentration.

Assessment of historical forest cover loss and fragmentation in Asian elephant ranges in India.

India is home of the largest remaining population of the Asian elephant (Elephas maximus L.) in the South and Southeast Asia. The forest loss and fragmentation is the main threat to the long-term survival of Asian elephants. In the present study, we assessed forest loss and fragmentation in the major elephant ranging provinces in India, viz., north-eastern, north-western, central, and southern since the 1930s. We quantified forest cover changes by generating and analyzing forest cover maps of 1930, 1975, and 2013, whereas fragmentation of contiguous forest areas was quantified by applying landscape metrics on the temporal forest cover maps. A total of 21.49% of the original forest cover was lost from 1930 to 1975, while another 3.19% forest cover was lost from 1975 to 2013 in the elephant ranges in India. The maximum forest loss occurred in the southern range (13,084 km2) followed by north-eastern (10,188 km2), central (5614 km2), and north-western (4030 km2) elephant ranges in the past eight decades. The forests in the central range were the most fragmented followed by southern, north-eastern, and north-western elephant ranges. The forest fragmentation in the southern range occurred at the fastest rate than central, north-eastern, and north-western ranges. The core forest areas shrunk by 39.6% from 1930 to 2013. The causative factors of forest change and situation of elephant-human conflict have been discussed. Study outcomes would be helpful in planning effective conservation strategies for Asian elephants in India.

Evaluating sensitivity of hyperspectral indices for estimating mangrove chlorophyll in Middle Andaman Island, India.

Mangroves are the highly productive and extensive ecosystem in the tropical coasts. Chlorophyll is the key foliar determinant of mangrove productivity. Optical characteristics of mangrove markedly differ from land vegetation; hence, defining narrowband spectral indices most sensitive to mangrove chlorophyll is crucial, in view of their importance to the coastal environment and mounting biotic pressures. We assessed the sensitivity of a set of satellite hyperspectral remote sensing indices to mangrove canopy chlorophyll in Middle Andaman Island, India, and propose most robust spectral indices for mangrove chlorophyll estimation. We generated simple, modified simple, normalized difference vegetation, and non-linear indices from all possible two band combinations of EO-1 Hyperion bands in the 500-900 nm spectral range. The strength of correlation between each pair of spectral indices to mangrove chlorophyll was analyzed in 2D correlograms and validated using k-fold cross-validation technique. Results show that 549 nm, 559 nm (green) and 702 nm, 722 nm, 742 nm, and 763 nm (red-edge) wavelengths are the most sensitive to mangrove chlorophyll. We report performance of traditional chlorophyll indices and new indices with higher predictive capability for mangrove chlorophyll prediction. Simple ratio (559 nm/885 nm) offered the strongest correlation with mangrove chlorophyll (R2-0.75, RMSE-0.60, p < 0.05). Study findings will help researchers in deciding suitable chlorophyll indices for mangrove productivity and stress assessment. The best calibrated index was used to prepare mangrove chlorophyll spatial variability map of the study area.

Does spatial heterogeneity of landscape explain the process of plant invasion? A case study of Hyptis suaveolens from Indian Western Himalaya.

Biological invasion is probably one of the most serious threats to biodiversity after climate change. Landscape distinguished by the heterogeneity of structure, forms, human interferences, and environmental settings plays an important role in the establishment and spread of invasive species. We investigated the effect of the spatial heterogeneity for a selected landscape upon the invasion process through a case study of Hyptis (Hyptis suaveolens) in the Indian Western Himalayan region. The selected study site constitutes a heterogeneous landscape of 32,300 ha in the state of Uttarakhand, placed at the lower elevation of the Indian Himalaya. The landscape has varying levels and patterns of Hyptis invasion. We quantified the spatial heterogeneity in terms of elevation; distance from the canal, river, road, and settlement; and 18 landscape metrics (at the patch and land use class level) to investigate their influence on the invasion; for this purpose, a logistic regression model was developed. The invasion of Hyptis was found to be governed by spatial heterogeneity. The highest probability of invasion was found in the areas adjacent to rivers and roads. The analysis at patch level revealed that the invasion is largely governed by the perimeter-area ratio of patches and is positively correlated. This suggests for greater invasion chances in smaller patches as compared with larger ones. The analysis for the land use class metrics indicated a higher influence of edge density expressed as total edge length of patches per unit area, followed by patch density expresses as a total number of patches per unit area. Hence, the landscapes with larger edges and more number of patches are supposed to be more prone to invasion risks. The results of the study can be used by forest managers in designing a landscape-level system to control invasion.

Estimation of leaf area index using PROSAIL based LUT inversion, MLRA-GPR and empirical models: Case study of tropical deciduous forest plantation, North India.

Forests play a vital role in biological cycles and environmental regulation. To understand the key processes of forest canopies (e.g., photosynthesis, respiration and transpiration), reliable and accurate information on spatial variability of Leaf Area Index (LAI), and its seasonal dynamics is essential. In the present study, we assessed the performance of biophysical parameter (LAI) retrieval methods viz. Look-Up Table (LUT)-inversion, MLRA-GPR (Machine Learning Regression Algorithm-Gaussian Processes Regression) and empirical models, for estimating the LAI of tropical deciduous plantation using ARTMO (Automated Radiative Transfer Models Operator) tool and Sentinel-2 satellite images. The study was conducted in Central Tarai Forest Division, Haldwani, located in the Uttarakhand state, India. A total of 49 ESUs (Elementary Sampling Unit) of 30m×30m size were established based on variability in composition and age of plantation stands. In-situ LAI was recorded using plant canopy imager during the leaf growing, peak and senescence seasons. The PROSAIL model was calibrated with site-specific biophysical and biochemical parameters before used to the predicted LAI. The plantation LAI was also predicted by an empirical approach using optimally chosen Sentinel-2 vegetation indices. In addition, Sentinel-2 and MODIS LAI products were evaluated with respect to LAI measurements. MLRA-GPR offered best results for predicting LAI of leaf growing (R2 = 0.9, RMSE = 0.14), peak (R2 = 0.87, RMSE = 0.21) and senescence (R2 = 0.86, RMSE = 0.31) seasons while LUT inverted model outperformed VI's based parametric regression model. Vegetation indices (VIs) derived from 740 nm, 783 nm and 2190 nm band combinations of Sentinel-2 offered the best prediction of LAI.

How climate change might influence the potential distribution of weed, bushmint (Hyptis suaveolens)?

Invasive species and climate change are considered as the most serious global environmental threats. In this study, we investigated the influence of projected global climate change on the potential distribution of one of the world's most successful invader weed, bushmint (Hyptis suaveolens (L.) Poit.). We used spatial data on 20 environmental variables at a grid resolution of 5 km, and 564 presence records of bushmint from its native and introduced range. The climatic profiles of the native and invaded sites were analyzed in a multi-variate space in order to examine the differences in the position of climatic niches. Maximum Entropy (MaxEnt) model was used to predict the potential distribution of bushmint using presence records from entire range (invaded and native) along with 14 eco-physiologically relevant predictor variables. Subsequently, the trained MaxEnt model was fed with Hadley Centre Coupled Model (HadCM3) climate projections to predict potential distribution of bushmint by the year 2050 under A2a and B2a emission scenarios. MaxEnt predictions were very accurate with an Area Under Curve (AUC) value of 0.95. The results of Principal Component Analysis (PCA) indicated that climatic niche of bushmint on the invaded sites is not entirely similar to its climatic niche in the native range. A vast area spread between 34 ° 02' north and 28 ° 18' south latitudes in tropics was predicted climatically suitable for bushmint. West and middle Africa, tropical southeast Asia, and northern Australia were predicted at high invasion risk. Study indicates enlargement, retreat, or shift across bushmint's invasion range under the influence of climate change. Globally, bushmint's potential distribution might shrink in future with more shrinkage for A2a scenario than B2a. The study outcome has immense potential for undertaking effective preventive/control measures and long-term management strategies for regions/countries, which are at higher risk of bushmint's invasion.