Harmonising distributed tree inventory datasets across India can fill critical gaps in tropical ecology

Abstract

AO_SCPLOWBSTRACTC_SCPLOWO_LIGlobal analyses of tree diversity and function are strongly biased geographically, with poor representation from forests of the Indian subcontinent. Even though data from India - representing two-thirds of the subcontinent and spanning a wide range of tree-based biomes - exists, a barrier to syntheses is the absence of accessible and standardised data. Further, with increasing human footprint across ecosystems, data from Indian landscapes, with their long history of human-nature interactions is a key link to understand the future of tropical forested landscapes. Given the long history of human-nature interactions and high human footprint in the region, accessible and standardized data from the subcontinent can enable understanding the future of tropical landscapes under increasing human footprints globally. C_LIO_LICombining literature searches with manual data retrieval, we assembled the INdia Tree Inventory dataset, INvenTree. INvenTree is the largest meta-dataset of peer-reviewed publications (n = 465) from 1991-2023 on geolocated plot-based tree inventories of multispecies communities from Indian ecosystems, in aggregate covering 4653.64 ha and all of its woody biomes. C_LIO_LIUsing INvenTree, we show extensive sampling across tropical moist and dry forests, the dominant ecosystem types in the country. However, most studies have low sampling effort (median sampled area = 2 ha) and data across studies is not openly accessible (73.33 % of studies representing 83.43% of the sampled area), potentially hindering inclusion into regional or global syntheses. C_LIO_LISignificantly, we show majority authorship from within the country; 82.8% of corresponding authors were from India and 73.33% of the studies had all authors affiliated with Indian institutions. We also identify ecological and conservation sampling priority regions based on forest cover and forest loss and set a blueprint for future sampling efforts in the country. C_LIO_LIBased on extensive Indian scholarship in forest ecology showcased through the INvenTree dataset, we see opportunity for regional collaboration to create scientific inferences that are larger and scalable, while prioritising data and knowledge equity. Harmonising these existing datasets, and synthesising historic and grey literature, will contribute enormously to understanding the human dimension of tropical ecology as well as informing regional management and conservation. C_LI