December 2012 Number 1 - Utah Native Plant Society

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Utah Native Plant Society Figure 1: Colorado Plateau Study Area Map quirement data and climate envelope constraints that may not be available (Stockwell and Peterson 2002). Environmental data listed in Table 1 are from www. worldclim.org and the USGS Hydro1K digital elevation model data. These variables include precipitation, temperature, aspect and slope. Geologic layers are from the USGS 1995 soils data. The geologic layers were converted from polygon to raster and downscaled to 1 km to match the climate and elevation layers. The modeling technique used in this study was Maximum Entropy or MaxEnt. MaxEnt is a general-purpose machine learning method of ecological niche modeling (Phillips, Anderson and Schapire 2006). MaxEnt estimates a species probability distribution by finding the probability distribution of maximum entropy, subject to a set of constraints that represent the information about the species distribution (Phillips, Anderson and Schapire 2006). An important factor for choosing MaxEnt was that it allows for the use of presence-only data and categorical variables. In addition, MaxEnt has been shown to perform better than other algorithms for modeling distributions with limited data points (Elith et al 2006 and Pear- son et al 2007). MaxEnt provides mechanisms to assess the relative importance of each independent variable, which provides a better understanding of range shifts due to climate change. To calibrate the models all location points were randomly divided into training (70 percent) and testing (30 percent) datasets. To evaluate the accuracy of the model and each variable’s predictive power, the Receiver Operating Characteristic (ROC) curve (Hanley and McNeil 1982) was used for both training and test data. The ROC curve represents the relationship between the percentage of presences correctly predicted (sensitivity) and one minus the percentage of the absences correctly predicted (specificity). The Area Under the Curve (AUC) measures the ability of the model to classify correctly a species as present or absent. AUC values can be interpreted as the probability that when a site with the species present and a site with the species absent are drawn at random, the former will have a higher predicted value than the latter. Following Araujo and Guisan (2006), a rough guide for classifying the model accuracy is: 0.5- 0.6=insufficient, 0.6-0.7=poor, 0.7-0.8=average, 0.8- 0.9=good and 0.9-1=excellent. 64
Calochortiana December 2012 Number 1 Table 1. Environmental Variables BIO 1-Annual Mean Temperature BIO 2-Mean Diurnal Range BIO 3-Isothermality BIO 4-Temperature Seasonality BIO 5-Maximum Temperature of Warmest Month BIO 6-Minimum Temperature of Coldest Month BIO 7-Temperature Annual Range BIO 8-Mean Temperature of Wettest Quarter BIO 9-Mean Temperature of Driest Quarter BIO 10-Mean Temperature of Warmest Quarter BIO 11-Mean Temperature of Coldest Quarter BIO 12-Annual Precipitation BIO 13-Precipitation of Wettest Month BIO 14-Precipitation of Driest Month BIO 15-Precipitation Seasonality BIO 16-Precipitation of Wettest Quarter BIO 17-Precipitation of Driest Quarter BIO 18-Precipitation of Warmest Quarter BIO 19-Precipitation of Coldest Quarter DEM-Digital Elevation Model 1km Slope Angle-Derived from DEM Slope Aspect-Derived from DEM Geology-Landform Description Alternative Suitability Models Three final models were built for each species: 1) full model, 2) pruned model, and 3) topo model. The full model contained all variables. The pruned model was based on findings from a jackknife analysis, used to evaluate individual variable importance in model development. The jackknife method evaluates variable predictive strength by excluding each variable and creating a tentative model with the remaining variables (Phillips, Anderson and Schapire 2006). Then tentative models are created using each variable in isolation (Phillips, Anderson and Schapire 2006). Tentative models are then compared to the full model. The pruned model is then produced with only important predictive variables found during the jackknife analysis. The goal of the pruned model was to remove redundant variables and provide a better fit to the most important environmental predictors, when compared with the full model. The topo model used only elevation, slope angle and slope aspect as variables. There is a known correlation between elevation and climate (precipitation and temperature). The topo model was used to see how predictions of current species distributions based on topography alone (elevation, slope and aspect) compared to models with climate data (temperature, precipitation, slope and aspect). All three models are evaluated for accuracy with an AUC score. Spatial Comparison of Model Output The habitat suitability maps produced by the three models were then compared spatially to identify places of predictive agreement between models (consistently predicted present or absent), and places where predicted area of suitable habitat were in disagreement. A spatially-explicit (by pixel) comparison was performed following the methods proposed by Parolo and others (2008), which produced two output maps. The first map identifies areas of maximum agreement between the models and the second map identifies areas of minimum agreement between models. RESULTS Two hundred and eleven endemic plants were identified to have 10 or more representative location points for accurate modeling. The least number of points used for training data were seven with three test points, and the largest was 168 training points with 71 testing points. Model accuracy varied across species with AUC values ranging from 0.6423 to 1. The jackknife analysis demonstrated that slope aspect was the least predictive variable for the most species and Precipitation Seasonality (BIO15) was the highest predictive variable for the most species (Table 2). Example of ENM for Sclerocactus mesae-verdae One example of a rare endemic plant modeled is the Mesa Verde cactus (Sclerocactus mesae-verdae). Mesa Verde cactus is listed as Threatened by the US Fish and Wildlife Service and a recovery plan was written in 1984 (Heil 1984). The recovery plan suggested that populations should be monitored to determine their stability (Ladyman 2004). This cactus is restricted to populations in the Four Corners region of New Mexico and Colorado. Mesa Verde cactus occurs in salt-desert scrub communities, typically in the Fruitland and Mancos shale formations, but has also been found to grow in the Menefee Formation overlaying Mancos shale (Roth 2001). It is most frequently found on the tops of hills or benches and along slopes and at elevation ranging from 4900 to 5500 ft (Roth 2001). Annual precipitation varies from approximately 8 to 20 cm. Average temperatures 65
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December 2012 Number 1 - Utah Native Plant Society

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