Machine Learning for Biodiversity Conservation: Species Identification, Habitat Monitoring, and Wildlife Protection

Abstract

Biodiversity monitoring traditionally depends on labour-intensive fieldwork, including manual review of camera trap imagery, acoustic recordings, and field survey data, creating a persistent bottleneck between the volume of ecological data that can be collected and the capacity to analyse it in support of timely conservation decisions. Machine learning has increasingly been applied to close this gap, automating species identification from images and audio, monitoring habitat change from satellite imagery, and supporting population assessment and anti-poaching efforts. This paper surveys machine learning applications in biodiversity conservation, organising methods into a taxonomy spanning camera trap image classification, bioacoustic species identification, remote sensing-based habitat monitoring, individual animal re-identification, species distribution modelling, and environmental DNA analysis. We review applications, including large-scale camera trap networks, anti-poaching systems, deforestation monitoring, marine biodiversity assessment, and citizen science platforms; summarise benchmarks and datasets used in this domain; and present a case study illustrating an integrated camera trap and anti-poaching alert pipeline. We discuss challenges, including geographic and taxonomic bias in training data, the long-tailed distribution of species observations, and the difficulty of generalising models across ecosystems, and outline future directions connecting conservation machine learning with multimodal monitoring systems and community-based conservation partnerships.