Fig 1.
Basin of the Usumacinta River in Mexico. The study area consists of the complete area of the Usumacinta River Basin located in Mexican territory. While significant portions of the basin are located in Guatemala, the Mexican portion (study area) includes a marked physiographic and environmental gradient that divides the region into upper, middle, and lower basins.
Table 1.
Raster coverages representing drivers, and the layers and GRASS functions used in their calculation.
Table 2.
Results from generalised additive models.
Percentage of the deviance explained by models built using single variables separately and partial deviances in the presence of all competing variables. The last column shows the deviance explained by each univariate model expressed as a percentage of the total deviance explained by the most complex multivariate model.
Table 3.
Results from generalised additive models.
GAM models including the most relevant variables as defined by partial deviance.
Fig 2.
Response of vegetation cover in the upper basin.
Response of vegetation cover in the upper basin to each term in a GAM model including local relative accessibility (Access100), accessibility to regional markets (Access10000), slope, and population density (PopDens). The response is on the scale of the link function. Bands show two standard errors around the response.
Fig 3.
Response of vegetation cover in the middle basin.
Response of vegetation cover in the middle basin to each term in a GAM model including local relative accessibility (Access100), population density (PopDens), and hydroperiod. The response is on the scale of the link function. Bands show two standard errors around the response.
Fig 4.
Response of vegetation cover in the lower basin.
Response of vegetation cover in the lower basin to each term in a GAM model including local relative accessibility (Access100), accessibility to regional markets (Access10000), direct beam radiation (Beam), population density (PopDens), and hydroperiod. The response is on the scale of the link function. Bands show two standard errors around the response.
Fig 5.
Results from recursive partitioning models.
Recursive partitioning decision tree based on all predictor variables: a) upper basin; b) middle basin; and c) lower basin. The probability that a given pixel is forested can be found as a series of binary decisions. The values used are relative indices. Direct beam radiation remains an important factor in addition to slope per se as is accessibility.