Structural diversity is a key driver of above-ground biomass in tropical forests

A gamut of abiotic and biotic factors is related to the amount of above-ground biomass (AGB) produced in ecosystems. Some factors have direct and others indirect relationships with AGB. Detailed analyses in tropical forests are few but much needed for better understanding the potential impacts of global change drivers and for mitigating impacts. Here, we examined the relationship between AGB and different predictor variables and quantitatively evaluated their relative importance in lowland to lower montane deciduous and lower montane – montane evergreen forest types. We hypothesised that the relationship between AGB and climate, topography, structural diversity, species diversity (alpha and beta) and phylogenetic diversity would differ between the two forest types. We inventoried trees from 114 plots (each 0.1 ha) and used partial least square structural equation modelling to test the direct and indirect relationship between AGB and the predictor variables. We found that structural diversity variables, stem density and tree girth, were significantly and positively related to AGB in both forest types, displaying a stronger relationship in montane evergreen forests (w = 0.65 for density and 0.89 for tree girth). In the deciduous forest, alpha and phylogenetic diversity were also important factors, whereas beta and phylogenetic diversity were important in the evergreen forest. The effects of topography and climate varied between forest types, with elevation and precipitation being related to AGB directly and indirectly through their relationship with structural diversity. Our results suggest that structural diversity is a key driver of tropical forest biomass, both directly and indirectly. This fundamental understanding can aid in the predictive efforts of biodiversity conservation and forest management.</p

Remote sensing based characterisation of community level phenological variations in a regional forest landscape of Western Ghats, India

The use of remote sensing for examining phenological variation in tropical forests is scarce. The major objectives of the study were to characterize the intra-annual variability of phenological cycle of the Biligiri Ranganathaswamy Temple Tiger Reserve (BRT) and the potentiality of these phenological metrices in defining species assemblages by classifying the forest. Sentinel-2 derived temporal Normalized Difference Vegetation Index (NDVI) data of 2019 was used to extract the vegetation trends and to derive phenological metrics using CropPhenology R package. Seasonal trends revealed that the highest greenness was associated with high NDVI values in September and October. We identified seven vegetation classes in the region and used Random Forest classifier to prepare a community level classification map with an overall classification accuracy of 68.9%. Our results revealed that incorporating the field sampling data and NDVI data can be effectively used for identifying, mapping and monitoring phenology of the BRT landscape.