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Documents for the Meeting

1. Draft Course Syllabus
2. Required data for students (for those bringing their own data sets to the workshop)

Draft Course Syllabus



1. Biodiversity and Biogeography
   Here, the general scenario of biodiversity is reviewed. We will cover topics such as the dimensions of biological diversity (how many species?), and its general global distribution. We will also discuss some of the studies that have been carried out to date, speaking to what has been learned and what remains to be learned. We begin to treat ideas related to the intersection of biodiversity science with geography. That is, we speak to issues of why species are found where they are found, and why they are not found where they are not found. The answer to these questions is key in understanding global patterns of biodiversity.
   • Dimensions of biological diversity
   • How many species are there?
   • Where are species distributed?
   • Which general factors stand out in the distribution of biological diversity?
   • Theory of the ecological niche
   • Niche applied to the areas of distribution
2. Informatics of biodiversity
   To learn more about biodiversity, we must marshal the information resources that we have available. This task leads not only to improving the storehouse of information available, but also to more educated efforts to improve and build further that storehouse without duplication of effort. As such, the emerging field of biodiversity informatics is introduced here.
   • Computerization of data
   • Data integration
   • Data analysis and interpretation
3. Sources of Data
   Here, a brief review is provided of existing biodiversity data resources. This will include demos of most of the existing distributed biodiversity information networks, as well as review of sources of geospatial data and resources for geo-referencing biodiversity data.
   • Occurrence data sources (GBIF, REMIB, MaNIS, HerpNet, FishNet, ORNIS)
   • Sources of environmental data (IPCC, Hydro-1K, AVHRR, MODIS, WorldClim)
   • Hands-on practice
   • Quality control for data sets
4. Inferential Modeling
   Here, we proceed to the question of how one can fill in gaps in existing knowledge of biodiversity. The idea is the use of ecological niche modeling, which can be carried out via numerous different inferential algorithms.
   • Basics of ecological niche modeling
   • Predictive model options (GLMs, GAMs, GARP, MAXENT, etc.) pros and cons
   • Validating models and selecting 'best' models
5. GARP and MAXENT
   We present 2 inferential algorithms, GARP and MAXENT, in greater detail…
6. Case studies:
   Here, we present a series of applications to which ecological niche modeling has been used. The idea is to illustrate a fairly broad swath of potential applications … from terrestrial to aquatic, across space and time, etc.
7. DesktopGARP and MAXENT details
   Review of the details of use of these two programs, which will be the principal foci of class exercises: Installation requirements, installation, use, results, output, interpretation, etc.
8. Hands-on work: Participants may bring their own data for analysis and exploration, alternatively data that is available on-line can be used.

Participants will be encouraged to bring their own 'challenges' and develop their own products based on skills learned in class. By consulting in advance with participants, we hope to make these analyses possible (please also see section IV of the application form).

Students are required to bring the following data for the modeling course:

1. Geographic data.
   • A series of GIS layers of environmental variables of the area of interest, such as climate (e.g. min and max temperature, precipitation, Bioclim variables, solar radiation, etc.), topography (elevation, slope, aspect, etc.), vegetation and/or land use / land cover, satellite images, soils, and other data that might be important in determining the distribution of the species of interest. As a general rule, the more layers you bring the better, although not necessarily all of them will be used in the analysis. Environmental data may come from different sources, but all of them have to be in the same geographic projection, and preferably (but not mandatory) in raster format. Pixel size does not need to be the same for all layers.
   • Optional: A second set of parallel GIS layers (same thematic maps) for a different region or time period. This set of layers will be used for projecting produced niche models in the original area onto a different region (e.g., for invasiveness analysis) and/or time (e.g. climate change analysis).
   It is very important that all geographic information comes with metadata (source, description of the information, date, etc.) and geographic reference (projection, datum, spheroid, reference parallels, etc.).
2. Biological Data.
   A spreadsheet or database of biological entities records (genes, subspecies, species, etc.) georeferenced in the same projection as the geographic data. Important fields are three: entity, x-coord, and y-coord. Different entities DO NOT have to be in different files, they should be in the same spreadsheet sorted alphabetically. Double-check for spelling errors! Format can be in Plain text, MSExcel, MSAccess, or any compatible format.
   Students are urged to consult with course instructors Town Peterson and Enrique Martinez (town@ku.edu, emm@ibiologia.unam.mx) regarding the design of specific projects. Email contact with Town and Enrique can likely avoid problems and loss of class time, so we hope that each student will send a summary of particular data to be brought to the class.