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The general construction protocol that was used for models has been previously published (Sarkar et al. 2010, ; Labay et al. 2011), so the description here will be cursory. A wide variety of machine-learning algorithms have been used for SDM construction (reviewed in Elith et al. 2006). This project used a maximum entropy algorithm incorporated in the Maxent software package (Phillips & Dudik 2008, Phillips et al. 2006; Phillips & Dudik 2008) because it directly provides probabilistic output (unlike the genetic algorithm of GARP (Stockwell 1999)) that can be used without further treatment for subsequent analyses, and because a variety of recent studies have concluded that its performance is superior to those of other methods (Elith et al. 2006, ; Wisz et al. 2008). Maxent was parameterized following published recommendations (Phillips et al. 2006), with models replicated 100 times withholding randomly in each replicate 40% of localities as "test" records, with the remaining 60% serving as model "training" records. Model performance was evaluated using a (threshold-independent) receiver operating characteristic (ROC) analysis and 11 internal binomial analyses of "raining" and "˜test" occurrence omission. The ROC analysis characterizes model performance at all possible thresholds using the area under the curve (AUC), a measure of model performance independent of any threshold (Hanley & McNeil 1982). An optimal model with perfect discrimination would have an AUC of 1 while a model that predicted species occurrences at random would have an AUC of 0.5 (Hanley & McNeil 1982).
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Occurrence data input consists of FoTX records. Records with > one km potential georeferencing error (radius, see Georeferencing and Geographic Units) were excluded to assure input occurrences closely corresponded in spatial resolution to environmental layers used in modeling. This spatial error threshold of one km approximately matches the grid cell resolution of 30 arc-seconds (which approximates one km at the Equator), but is slightly larger than the longitudinal boundary of the average cell size (0.73 km2) due to geographic projection at the latitude of Texas. However, the maximum entropy algorithm used for analysis (see Model Construction above) has been shown not to be affected by spatial errors in occurrence datasets with standard deviations up to five km (Hernandez et al. 2006, Wisz et al. 2008). Occurrence records before 1950 were similarly excluded so that occurrence data were temporally congruent with climatic variables used (see Table 1 below). Finally, since model performance stabilizes with respect to accuracy of prediction at about 10 records when using the maximum entropy model construction algorithm (Phillips & Dudik 2008, Phillips et al. 2006; Phillips & Dudik 2008), models were produced only for those species for which we had a minimum of 10 occurrences corresponding to at least 10 unique cells on the environmental layer grids.
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| Layer Category | Layer Type | Description | Source |
|---|---|---|---|
| Topological | Continuous | aspect | 1km DEM |
| Topological | Continuous | slope | 1km DEM |
| Topological | Continuous | compound topological index (ln(acc.flow/tan[slope])) | 1km DEM |
| Topological | Continuous | altitude | 1km DEM |
| Climate | Continuous | annual mean temperature | Wordclim variable 1 |
| Climate | Continuous | mean diurnal range (mean of monthly (max temp - min temp)) | Wordclim variable 2 |
| Climate | Continuous | isothermality (P2/P7)(*100) | Wordclim variable 3 |
| Climate | Continuous | (temperature seasonality (sd *100) | Wordclim variable 4 |
| Climate | Continuous | max temperature of warmest month | Wordclim variable 5 |
| Climate | Continuous | min temperature of coldest month | Wordclim variable 6 |
| Climate | Continuous | temperature annual range (P5-P6) | Wordclim variable 7 |
| Climate | Continuous | annual precipitation | Wordclim variable 12 |
| Climate | Continuous | precipitation of wettest month | Wordclim variable 13 |
| Climate | Continuous | precipitation of driest month | Wordclim variable 14 |
| Climate | Continuous | precipitation seasonality (coefficient of variation) | Wordclim variable 15 |
| Climate | Continuous | precipitation of wettest quarter | Wordclim variable 16 |
| Climate | Continuous | precipitation of driest quarter | Wordclim variable 17 |
| Climate | Continuous | precipitation of warmest quarter | Wordclim variable 18 |
| Climate | Continuous | precipitation of coldest quarter | Wordclim variable 19 |
| Geographic | Categorical | major river basins | Texas Water Development Board |
| Geographic | Categorical | 8-digit hydrologic unit code (HUC) | Texas Water Development Board |
| Hydrologic | Continuous | cumulative drainage | National Hydrology Dataset plus |
| Hydrologic | Continuous | mean annual flow | National Hydrology Dataset plus |
| Hydrologic | Continuous | mean annual velocity | National Hydrology Dataset plus |
References
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