Biodiversity monitoring through AI approaches is essential, as it enables the efficient analysis of vast amounts of data, providing comprehensive insights into species distribution and ecosystem health and aiding in informed conservation decisions. Species identification based on images and sounds, in particular, is invaluable for facilitating biodiversity monitoring efforts and enabling prompt conservation actions to protect threatened and endangered species. The LifeCLEF virtual lab has been promoting and evaluating advances in this domain since 2011. The 2023 edition proposes five data-oriented challenges related to the identification and prediction of biodiversity: (i) BirdCLEF: bird species recognition in long-term audio recordings (soundscapes), (ii) SnakeCLEF: snake identification in medically important scenarios, (iii) PlantCLEF: very large-scale plant identification, (iv) FungiCLEF: fungi recognition beyond 0–1 cost, and (v) GeoLifeCLEF: remote sensing-based prediction of species. This paper overviews the motivation, methodology, and main outcomes of that five challenges.