GIS Modeling and Marginal Analysis

Physiologically based demographic models (PBDMs) are driven by daily weather, and hence weather data with the same time step is the major data requirement. Multiple sources of weather data are used to drive PBDMs (see Fig. 1) including weather station records, satellite remote sensing, reanalyses of weather observations, and climate model data. Other variables (data layers) may also be used to run the simulation (Fig. 2). Typically weather data and other variables are found in a wide variety of formats that need to be accessed and processed for extracting the information needed. To this end, we mainly use Perl, a programming language that borrows features from other programming languages to provide powerful data processing facilities. The observed or simulated data may be mapped, and marginal analysis using econometric methods used to estimate the effects of different biotic and abiotic factors on the mapped variable.

GIS PBDM Methodology
PBDM ecosystem analysis using site-specific weather, GIS maps, and marginal analysis (Gutierrez et al., 2010).

Geographic Information Systems (GIS) are a key data management component for CASAS. GIS have the capacity to integrate panoply digital data into joint databases, and to provide data analysis and visualization techniques (Neteler et al. 2012). At CASAS, we use a cutting-edge, free and open source software GIS called GRASS because of the following main advantages that are particularly important in a research setting (see Neteler et al. 2012):

  • Algorithms are assessed via public peer review.
  • Customization is easy.
  • Support is good and fast thanks to a lively community.

See for example how the invasiveness of exotic species can be assessed using PBDMs and GRASS GIS: http://grasswiki.osgeo.org/wiki/GlobalChangeBiology.

Figure 2. Factors that may affect species distribution and abundance (Mediterranean Basin) may be integrated into ecosystem modeling as digital data using joint geo-referenced databases in a GIS for data analysis and visualization (Gutierrez 1996).

Another important component in our data management system is R, a free software programming language and a software environment that offers a wide variety of statistical and graphical techniques, high extensibility, and a two-way interface to GRASS (Bivand 2007). In addition to R, GRASS may be interfaced to a number of open source geospatial technologies (i.e., a software stack) that are developed under the umbrella of the Open Source Geospatial Foundation (OSGeo) and go under the name of OSGeo Stack (Fig. 3).

Figure 3. Open source geospatial technologies form an interacting stack of software developed collaboratively around the world under the umbrella of the Open Source Geospatial Foundation (OSGeo).

Source: http://gis.cri.fmach.it/gis-development/

References

Bivand, R., 2007. Using the R–Grass interface: current status. OSGeo Journal, 1:36-38.

Gutierrez, A.P., 1996. Applied population ecology: a supply-demand approach. John Wiley and Sons, New York, USA, 300 p.

Gutierrez, A.P., Ponti, L. and Gilioli, G., 2010. Climate change effects on plant-pest-natural enemy interactions. In: D. Hillel and C. Rosenzweig (Editor), Handbook of climate change and agroecosystems: impacts, adaptation, and mitigation. Imperial College Press, London, UK, pp. 209-237.

Neteler, M., Bowman, M.H., Landa, M. and Metz, M., 2012. GRASS GIS: a multi-purpose Open Source GIS. Environmental Modelling & Software, 31: 124-130.