BioMapper, and Maxent). We used synthetic surfaces to create visualizations of the various models in environmental space and used modified Area Under the Curve (AUC) statistic and Akaike Information Criterion (AIC) as measures of model performance. We show that HEMI produced slightly better AUC values, except for Maxent and better AIC values overall. HEMI created a model with only 10 parameters while Maxent produced a model with over 100 and BioClim used only 8. Additionally, HEMI allowed visualization and editing of the model in environmental space to develop alternative potential habitat scenarios. The use of Bezier surfaces can provide simple models that match our expectations of biological niche models and, at least in some cases, out-performs more complex approaches. Keywords: habitat suitability modeling, species distribution modeling, Tamarix, tamarisk, species niche, Bezier curves Introduction Habitat suitability modeling, which is closely related to species distribution modeling, is used to understand and predict the potential distribution of a species and is used in many areas of conservation and restoration ecology (). Habitat suitability modeling can provide critical insight into the potential spatial and temporal distribution of a population (; ), how species respond to novel environments species’ (), locating rare and endangered species (), exploring the distribution of invasive species (; ; ), and defining conservation priority areas (). In most cases, habitat suitability models integrate data on the observed location (i.e., presence points) of a species with environmental data (e.g., temperature, precipitation, slope, soil type) to create a model of the niche in environmental space and then convert the results to geographic space as a species’ raster (or grid), which can be used to generate a final suitability map (). This modeling approach assumes that the variables chosen for the environmental data are the most influential ones.
BioMapper, and Maxent). We used synthetic surfaces to create visualizations of the various models in environmental space and used modified Area Under the Curve (AUC) statistic and Akaike Information Criterion (AIC) as measures of model performance. We show that HEMI produced slightly better AUC values, except for Maxent and better AIC values overall. HEMI created a model with only 10 parameters while Maxent produced a model with over 100 and BioClim used only 8. Additionally, HEMI allowed visualization and editing of the model in environmental space to develop alternative potential habitat scenarios. The use of Bezier surfaces can provide simple models that match our expectations of biological niche models and, at least in some cases, out-performs more complex approaches. Keywords: habitat suitability modeling, species distribution modeling, Tamarix, tamarisk, species niche, Bezier curves Introduction Habitat suitability modeling, which is closely related to species distribution modeling, is used to understand and predict the potential distribution of a species and is used in many areas of conservation and restoration ecology (). Habitat suitability modeling can provide critical insight into the potential spatial and temporal distribution of a species’ population (; ), how species respond to novel environments (), locating rare and endangered species (), exploring the distribution of invasive species (; ; ), and defining conservation priority areas (). In most cases, habitat suitability models integrate data on the observed location (i.e., presence points) of a species with environmental data (e.g., temperature, precipitation, slope, soil type) to create a model of the species’ niche in environmental space and then convert the results to geographic space as a raster (or grid), which can be used to generate a final suitability map (). This modeling approach assumes that the variables chosen for the environmental data are the most influential ones.
TamarixEnvelope_final_all.docx