Evaluation and application of statistical methods
The project is an accessory study of the revision of the red list of fern and vascular plants and aims to evaluate the application of statistical methods as a tool for revisions and quality control of red lists. It is realized by the Swiss Federal Institute WSL and the University of Lausanne (Prof. Antoine Guisan).
2011 - 2014
The project consists of three parts:
The aim of Prospective Sampling is to find unknown occurrences of species by using species distribution models (SDMs) which are illustrated in a potential vegetation map and verified in the field. SDMs correlate occurrence information of a species with local environment data and calculate the likelihood of occurrence within a raster map. This approach is used to detect rare and threatened species in terms of the revision of the red list for plants.
Assessment of SDMs in terms of an additional IUCN criterion
The IUCN criteria “area of occupancy“ (AOO) and “extend of occurence” (EOO) are standardized methods to estimate the distribution of species. SDMs might be suited better as they present the actual distribution more accurate and are supposed to be more robust against changes of species occurrences. However, both methods have never been compared to each other as far as we know. Hence, scenarios of extinction and immigration should be tested and evaluated in detail. Finally, the utility of SDMs in terms of revisions of red lists should be assessed to local agencies.
Testing plausibility of the categorization of plant species in the red list with the help of trait analysis
Using trait analyses the categorization of the current ret list of fern- and vascular plants is aimed to be evaluated (quality assurance). Threatening patterns of species characteristics (traits) are hypothesized to identify differing species which could be misclassified in the red list. The Red List could then be rechecked for these species. Information about species characteristics can be used from trait data bases e.g. Flora indicativa or BioFlor.