Dan Warren

Dan Warren is a Senior Research Fellow at the Gulbali Research Institute, Charles Sturt University, where he conducts interdisciplinary research in population biology, ecology, evolution, and conservation. His work centers on developing and refining species distribution models and environmental niche models to understand biodiversity and support conservation under global change.

• PhD in Population Biology, University of California, Davis

Dan Warren's research interests lie in the development and application of quantitative tools for ecology and evolution. He focuses on species distribution models (SDMs), environmental niche models (ENMs), and their use in understanding biodiversity patterns, evolutionary processes, and conservation planning. His methodological innovations, such as those in the ENMTools R package, are widely adopted. His work spans animal behavior, climate change impacts, and conservation management, with strong relevance to UN Sustainable Development Goals on climate action and life on land.

His recent publications reflect a consistent focus on improving the accuracy, interpretation, and application of species distribution models. Trends include addressing bias in model outputs, enhancing methodological standards, and developing robust tools for conservation under climate uncertainty. He frequently publishes in top ecological journals such as Ecography and Methods in Ecology and Evolution, and his work integrates software development with theoretical ecology.

Dan Warren has served in key scientific roles, including as Associate Editor for Ecography and Systematic Biology, and as a reviewer for the IPBES global assessment. These contributions highlight his leadership in advancing scientific rigor and policy relevance in biodiversity science.

He is actively involved in mentoring and collaborative research, contributing to datasets and methodological frameworks used by the broader ecological community. His work supports both academic inquiry and practical conservation, emphasizing robust, data-driven decision-making.