The increasing availability of high quality occurrence and abundance data at the species level accompanied by many environmental data layers has boosted the developments in species distribution modeling (SDM). As a result, there are nowadays numerous data-driven modeling tools available for enhancing understanding of ecological systems or generating predictions, which has however not made life easier for most young researchers.
This course aims at introducing the students to the fundamental research questions, to important model assumptions and to several basic data analysis steps. In addition, the most important techniques are introduced to make geo-ecological data accessible in a database and analyze it with appropriate modeling procedures.
This course uses the book 'Mapping species distributions - Spatial inference and prediction' by Janet Franklin (2009, Cambridge university press) as a theoretical basis.
The focus is on understanding data processing and data analysis steps for diverse taxa and work-flows for interpreting model results, rather than computer skills. Notwithstanding this emphasis, a number of software tools are used and explained, such as GIS software to process environmental and species occurrence data and the R environment for SDM.
The course combines short lectures with participant discussions and predominantly hands-on (computer) work. Also data and problems brought in by participants will be analyzed. Participants working with their own data are recommended, and will be supported, to publish their data online through the infrastructure of the Global Biodiversity Information Facility (GBIF).
The course is coordinated by Emiel van Loon (Computational Geo-Ecology, UvA) and Cees Hof (the coordinator of NLBIF, the Dutch GBIF-node) and brings in international expertise in different fields.