![]() ![]() Informed management decisions rely on accurate estimates which can be hard to achieve but are critical as the conservation status of any species is dependent on its population size, which is inversely correlated with extinction risk 2. Our method can potentially be adapted for other species with individually distinctive vocalisations, representing an advanced tool for individual-level monitoring.Īccurate population estimates are a critical part of wildlife biology, conservation and inform management strategies 1. This has potential for studies of wolves’ territory use, pack composition, and reproductive behaviour. The model can reduce bias in population estimations using Capture-Mark-Recapture and track individual wolves non-invasively by their howls. We tested our model’s predictive power using 20 novel howls from a further four individuals (test dataset) and resulted in 75% accuracy in classifying howls to individuals. We trained a supervised Agglomerative Nesting hierarchical clustering (AGNES) model using 49 howls from five Indian wolves and achieved 98% individual identification accuracy. Here, we explore the use of a supervised classifier to identify unknown individuals. Like other wolves, Indian wolves produce howls that can be detected over distances of more than 6 km, making them ideal candidates for acoustic surveys. The cryptic behaviour of Indian wolf ( Canis lupus pallipes) poses serious challenges to survey efforts, and thus, there is no reliable estimate of their population despite a prominent role in the ecosystem. However, decreasing the bias in population estimations, such as by using Capture–Mark–Recapture, requires the identification of individuals using supervised classification methods, especially for sparsely populated species like the wolf which may otherwise be counted repeatedly. Previous studies have posited the use of acoustics-based surveys to monitor population size and estimate their density. ![]()
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