Global climate change is causing unprecedented impacts on biodiversity. In India, there is little information available regarding how climate change affects biodiversity at the taxon/group level, and large-scale ecological analyses have been lacking. In this study, we demonstrated the applicability of eBird and GBIF (Global Biodiversity Information Facility), and produced national-scale forecasts to examine the possible impacts of climate change on terrestrial avifauna in India. Using data collected by citizen scientists, we developed fine-tuned Species Distribution Models (SDMs) and predicted 1091 terrestrial bird species that would be distributed in India by 2070 on two climatic surfaces (RCP 4.5 and 8.5), using Maximum Entropy-based species distribution algorithms. Of the 1091 species modelled, our findings indicate that 66–73% of bird species in India will shift to higher elevations or shift northward, and 58–59% of bird species (RCP 4.5 and 8.5) would lose a portion of their distribution ranges. Furthermore, distribution ranges of 41–40% of bird species would increase. Under both RCP scenarios (RCP 4.5 and 8.5), bird species diversity will significantly increase in regions above 2500 m in elevation. Both RCP scenarios predict extensive changes in the species richness of the western Himalayas, Sikkim, northeast India, and the western Ghats regions by 2070. This study has resulted in novel, high-resolution maps of terrestrial bird species richness across India, and we predict predominantly northward shifts in species ranges, similar to predictions made for avifauna in other regions, such as Europe and the USA.

Birds and their habitats are threatened with extinction around the world. Regional assessments of the “State of Birds” are a vital means to prioritize data‐driven conservation action by informing national and global policy. Such evaluations have traditionally relied on data derived from extensive, long‐term, standardized surveys that require significant resources, limiting their feasibility to a few regions in the world. In the absence of such “structured” long‐term datasets, “semi‐structured” datasets have recently emerged as a promising alternative in other regions around the world. Semi‐structured data are generated and uploaded by birdwatchers to citizen science platforms such as eBird. Such data contain inherent biases because birdwatchers are not required to adhere to a fixed protocol. An evaluation of the status of birds from semi‐structured data is therefore a difficult task that requires careful curation of data and the use of robust statistical methods to reduce errors and biases. In this article, we present a methodology that was developed for this purpose and was applied to produce the comprehensive State of India's Birds (SoIB) 2023 report. SoIB 2023 assessed the status of 942 bird species in India by evaluating each species based on three metrics: (1) long‐term change, (2) current annual trend, and (3) distribution range size. We found evidence that 204 species have declined in the long term and that 142 species are in current decline. Birds that have vertebrate or invertebrate diets have declined most rapidly in the long term, whereas those that feed on fruits and nectar have been stable. Birds that require grasslands have declined more rapidly than those that require other habitats, indicating that grasslands are an important ecosystem to prioritize conservation in India. We classify 178 species as high conservation priority and present and discuss important insights about India's birds that can guide research and conservation action in the region. We hope that the detailed methodology described here can act as a blueprint to produce State of Birds assessments from semi‐structured citizen science datasets and springboard conservation action in many other regions where structured data are lacking but strong communities of birdwatchers exist.

Conservation management heavily relies on accurate species distribution data. However, distributional information for most species is limited to distributional range maps, which could not have enough resolution to take conservation action and know current distribution status. In many cases, distribution maps are difficult to access in proper data formats for analysis and conservation planning of species. In this study, we addressed this issue by developing Species Distribution Models (SDMs) that integrate species presence data from various citizen science initiatives. This allowed us to systematically construct current distribution maps for 1091 bird species across India. To create these SDMs, we used MaxEnt 3.4.4 (Maximum Entropy) as the base for species distribution modelling and combined it with multiple citizen science datasets containing information on species occurrence and 29 environmental variables. Using this method, we were able to estimate species distribution maps at both a national scale and a high spatial resolution of 1 km². Thus, the results of our study provide species current species distribution maps for 968 bird species found in India. These maps significantly improve our knowledge of the geographic distribution of about 75% of India’s bird species and are essential for addressing spatial knowledge gaps for conservation issues. Additionally, by superimposing the distribution maps of different species, we can locate hotspots for bird diversity and align conservation action.