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W. Daniel Kissling

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I am an ecologist specializing in biodiversity and ecosystem science. I currently serve as Associate Professor of Quantitative Biodiversity at the Institute for Biodiversity and Ecosystem Dynamics (IBED) of the University of Amsterdam (UvA), The Netherlands, where I lead the Biodiversity & Macroecology (BIOMAC) lab within the Department Theoretical and Computational Ecology (TCE).

My research focuses on the quantitative analysis, modelling, and monitoring of biodiversity and ecosystems. I lead several tasks and work packages in national and international, multi-institutional, and multidisciplinary projects, including:

• TABMON – analysis of bioacoustic monitoring data
• MAMBO – development of workflows to derive habitat condition indicators from airborne LiDAR data
• LTER-LIFE – mobilization, integration, and FAIRification of geospatial and ecological data for Digital Twins of ecosystems

I have also contributed to a range of EU-funded projects (e.g. EuropaBON, GLOBIS-B, Biodiversa+ pilot ABMS) and national initiatives supported by the Dutch Research Council (eEcoLiDAR, Frugivory & palm diversification, ARISE, Arctic migratory birds).

My work bridges ecology and biodiversity science with remote sensing, informatics, geospatial data science, computer vision, conservation management, and environmental policy. I compile and analyze large ecological, environmental, and geospatial datasets to study species distributions, trophic interactions, functional traits, ecosystem structure and function, and animal habitats across spatial and temporal scales.

By integrating advanced computational methods, big data analytics, and remote sensing technologies, I aim to improve our understanding of the spatial distribution of life on Earth and how biodiversity and ecosystems respond to global change. I am particularly interested in the use of terrestrial, airborne, and satellite remote sensing and ground-based digital sensor networks to monitor biodiversity and ecosystem dynamics, as well as in developing automated data pipelines, high-throughput workflows, and machine learning approaches for biodiversity research.

For more details, please visit my research pages, my publication list, or the BIOMAC lab homepage.