W. Daniel Kissling

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I am a quantitative biodiversity scientist with research interests in (macro)ecology, biodiversity monitoring, and global change. I am working as an Associate Professor at the Institute for Biodiversity and Ecosystem Dynamics (IBED) of the University of Amsterdam (UvA), The Netherlands, where I lead the Biogeography & Macroecology (BIOMAC) lab embedded in the Department Theoretical and Computational Ecology (TCE). Additionally, I am leading several tasks and work packages in various national and international, multi-institutional and multi-disciplinary projects, including the biodiversity monitoring demonstration sites in the Netherlands (ARISE), the co-design of the future EU-wide biodiversity observation network (EuropaBON), the automation of workflows for generating LiDAR metrics for habitat condition indicators in Natura2000 sites (MAMBO), the modelling of important Arctic bird areas under climate change (NPP project from NWO), and the mobilization, integration and FAIRification of data for Digital Twins of ecosystems (LTER-LIFE).

My work often bridges from compiled large ecological and environmental datasets to study species distributions, trophic interactions, functional traits, ecosystem structure and animal habitats across space and time. I often take advantage of recent advances in computing, big data availability, digital sensors, remote sensing and statistical modelling to (1) better understand the broad-scale distribution of life on Earth, and (2) address how biodiversity is changing under past, present and future global change. I am interested in the use of remote sensing and digital sensors for measuring and monitoring biodiversity change, and in the development of automated data streams, high-throughput processing workflows and artificial intelligence (AI) models for biodiversity research. Please see my research pages, my publication list, or the BIOMAC lab homepage for more details.



Call for papers:

Special Issue on "LiDAR Metrics for Habitat Condition Indicators" in the journal Remote Sensing. We are seeking contributions which address how ecological remote sensing research with LiDAR can be used to quantify habitat structure and condition.