Habitat Intelligence: How Machine Learning Reveals Species Preferences for Ecological Planning and Conservation

The emerging confluence between artificial intelligence and ecology has generated a new research frontier, which we refer to as habitat intelligence, aiming to unveil species environment relationships through data-driven approaches. This SLR aims to summarise the pass to the current year (2025) of the research on the use of ML and DL models to represent species preferences, habitat suitability and ecological niches. Based on 365 peer-reviewed studies extracted from SCOPUS, Web of Science and OpenAlex, we identify four main areas of innovation which encompass: automated species identification and ecological monitoring; AI-enhanced species distribution models (SDMs); advanced data collection and processing for ecological research; and conservation-oriented decision support systems. Our review shows that AI has the potential for a more precise and scalable approach to biodiversity investigations in the age of integrated remote sensing, acoustics, citizen science, and environmental data. But we also point out pressing challenges such as data paucity, model interpretability and computational limitations. We suggest that future advancements in this branch of the food web could come from interdisciplinary cooperation using explainable AI (xAI) and the construction of bridging hybrid models between prediction and ecological interpretability. In the end, this review offers a conceptual and methodological ‘roadmap’ to other researchers and conservation practitioners who wish to apply AI to the service of global biodiversity aims.

@article{Ennakri2025,
  title     = {Habitat Intelligence: How Machine Learning Reveals Species Preferences for Ecological Planning and Conservation},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {6},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Meryem Ennakri and Soumia Ziti and Mohamed Dakki},
  doi       = {10.14569/IJACSA.2025.0160698},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160698}
}