Dhanda, A. | bioRxiv Science

bioRxiv2026

A large-scale crowd-sourced annotated acoustic dataset of Indian fauna

Global rates of biodiversity loss warrant conservation action and monitoring at large geographic scales. Conservation technologies such as acoustic monitoring in conjunction with deep learning now enable us to monitor wildlife simultaneously across space and time. However, for a significant proportion of biodiversity in tropical regions, we cannot yet rely on automated recognition approaches because we lack acoustic templates to robustly train deep learning algorithms. In this paper, we relied on a novel participatory approach, enlisting researchers, conservation practitioners, and nature enthusiasts to create a unique crowd-sourced, open-access dataset of acoustic annotations across taxonomic groups for biodiversity in India. Our dataset comprises 3311 minutes of strongly labelled data (bounding boxes or annotations for a species vocalization) and 2504 minutes of weakly labelled data (indicating the presence of a species within an audio file but lacking bounding boxes) for 518 species across India, spanning 25 of 36 states and union territories. We present metadata and code for data processing and highlight the strengths of a participatory approach to biodiversity monitoring.

ecology↗

bioRxiv2023

Ecology and not phylogeny influences sensitivity to climate change in Muscicapidae Flycatchers in Eastern Himalayan and Indo-Burman hotspots

AimsSpecies distributional responses to climate change can depend on ecology and phylogeny. The degree of habitat specialism is potentially important because habitat generalists with wider distributions are assumed to be less sensitive to environmental changes compared to narrowly-distributed habitat specialists. Additionally, predicting which aspects of the changing climate impacts species with different habitat associations may be particularly challenging in complex environments such as those of tropical mountains. We explore the effects of climate change on potential distributions of Muscicapidae Flycatchers in parts of the Eastern Himalayan Mountains and the Indo-Burman range, and relate predictions to the level of ecological habitat specialism, while accounting for phylogenetic relatedness. LocationBhutan and Northeast India MethodsWe used Maxent to develop ecological niche models of 60 Muscicapidae Flycatchers under different climatic scenarios by collating presences from Global Biodiversity Information Facility, Bioclimatic variables from WorldClim, and elevation from ASTER. Species were scored as habitat specialists or generalists using the Species Specialism Index. Variables with high contributions to Maxent models were extracted to explore sensitivity to climate change based on habitat specialism while testing for phylogenetic signal using Phylogenetic Generalised Least Squares. ResultsMaxent models had the highest contributions from variable bio8 (mean temperature of wettest quarter) under present climate, and tmax (maximum temperature) under future climatic scenarios. Phylogenetic Generalised Least Squares revealed that habitat generalists had higher sensitivity to climate change than specialists. We did not detect strong phylogenetic signal in sensitivity to abiotic variables under all climate scenarios. Main conclusionsPotential distributions of Muscicapidae Flycatchers were sensitive to temperature variable in the month of the highest precipitation, and to maximum temperature. Potential distributions of habitat generalists were particularly sensitive to these abiotic variables, and those of habitat specialists less so. Sensitivity to abiotic variables did not show a pattern of phylogenetic niche conservatism. climate change, Muscicapidae Flycatchers, phylogenetic niche conservatism, habitat specialism, tropical mountains, Maxent

ecology↗