This dataset includes modeled ecological systems that occur within South America. Five input data layers - elevation (SRTM, 2000), landform (SRTM, 2000 and WWF-Hydrosheds, 2005), geology (TNC and GDS, 2005), landcover (GLC, 2000) and bioclimate (WorldClim, 2004 and TNC, 2005) - were combined to produce a map of unique ecological system footprint gridcodes. These gridcodes were then evaluated and attributed to one of NatureServe's Latin America and Caribbean Ecological Systems.
This dataset was orignally completed in 2005. Since then, TNC staff have been updating this dataset with data that is considered of better quality and of much better temporal and spatial resolutions. The following areas have been upgraded with new datasets: Colombia, Ecuador, Peruvian Andes, Chilean Matorral, Brazilian Caatinga, and Brazilian Cerrado. The updates were done after a comparison with the existing ecosystems dataset and evaluation of its resolutions (temporal and spatial).
Some people would argue that GIS data is only as accurate as the worst (smallest scale, least accurate) input data layer, which in this project would be the bioclimate data. But bioclimate was the least important dataset in attributing the ecological systems. It was used as a general guide for confirming the location of different ecological systems, but was understood to be continuous data with poorly-defined transitional zones. The three most important datasets in attributing the ecological systems were landcover, landform and elevation. Landcover was used as the primary criteria for identifying most ecological systems, the selection was then modified by landform and elevation, and then, of lesser significance, by geology and bioclimate. So the spatial resolution of the final ecological systems data based on the most significant input data layer (landcover) would be limited to 1 km (if you believe the argument that limiting scale in GIS is defined by the least accurate dataset). But the nominal working scale of the final ecological system linework was 'improved' by sub-dividing the landcover data (1 km) with the more detailed landform and elevation data (450 meters). But how exactly does 450 meters grid resolution translate into a working scale ratio? Some studies suggest that if the smallest unit of measurement on a map is 10 meters the scale would be 1:20,000 "Generally, a line cannot be drawn much narrower than about 1/2 a millimetre. Therefore, on a 1:20,000 scale paper map, the minimum distance which can be represented (resolution) is about 10 metres" (Scale, Accuracy, and Resolution in a GIS. 1999). Using this guideline, the smallest unit of measurement on the ecological systems map is 450 meters, so the scale would be approximately 1:900,000. A simple visual comparison of the ecological systems linework against Landsat imagery suggests the working scale is somewhere from 1:500,000 to 1:1,000,000. The linework was evaluated against visibly distinct features in the Landsat imagery. By progressively zooming in until the linework started to move off of a feature it was possible to get a rough sense of the accuracy of the data at different scales.
In five areas, Southern Chile, Peruvian Yungas, Bolivian Yungas, Equatorial Pacific Forest, and Chaco, existing ecological system data, produced by in-country programs at expert-derived ecological system mapping workshops, was unioned with the modeled unique gridcode data. Each polygon on the map still possessed a 'modeled' unique gridcode value but the final ecological system linework and attribution was based on the expert-derived map information, instead of the gridcode. The CES codes and ecological system names from these five datasets were used to populate the Ecode and Econame fields.
Identifying non-vegetated and degraded vegetation was important in this project because it focused on identifying the extent of existing ecological systems, rather than potential ecological systems. Therefore the general GLC class 50 (intensive agriculture, mosaic agriculture/degraded vegetation, forest plantations, permanent snow/ice and urban) and 52 (mosaic agriculture/degraded forests) were selected from the unqiue gridcodes across South America and their ecode values respectively coded as converted and degraded. GLC class 83 (water bodies) was also selected and coded as water. In the five special areas noted above that were mapped using existing ecological system data, any information about the ecological system code or name was preserved in the comment field before the polygons were recoded as converted, degraded or water.