Online live sessions from Monday, April 12th t0 Friday, April 16th, 2021; 14:00 to 17:00 (GMT+1, Madrid time zone).
Species distribution data are always incomplete snapshots of actual species distributions, which are dynamic and thus constantly changing. Predicting species distributions and how they may evolve is crucial for ecosystem management and conservation planning, but the intrinsic unpredictability that is inherent to living organisms prevents accurate categorical forecasts of species’ occurrence sites, or accurate delimitations of their distribution ranges.
In this course we will analyse the limitations and uncertainties associated with species distribution records, and provide methods for dealing with them, including species distribution models treated with fuzzy logic and quantum mechanical concepts.
The course will include theoretical and practical lessons. We will use particular R packages, such as fuzzySim and modEvA, for 1) modelling species distributions based on available occurrence records; 2) evaluating the predictive capacity of these models using a varied set of measures that address different facets of model performance, such as discrimination, classification, explanatory power and calibration; and 3) incorporating natural uncertainty in analyses of species distributions, namely using fuzzy logic and formulas from quantum mechanics to assess similarity, diversity and change in biogeographical communities.
We will provide sample data for most of the course, but students will also have a chance to apply what they have learned to their own data if they wish.
All participants must have a personal laptop with R installed, webcam if possible, and a good internet connection. Basic knowledge of R is required.