Cave and Karst Data Access
  
  
  
    
      
      by
      Rosanne Hessmiller
      
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      last modified
    
    Jul 05, 2017 02:15 PM
  
  
  
  
  
  
                   The cave and karst dataset from this research is available through our Conservation Planning Atlas.
                
            
            
        
                             
                             These  GIS data represent the input and results of a spatial statistical model  used to examine the hypothesis that the presence of major faunal groups  of cave obligate species could be predicted based on features of the  Earth surface.
Georeferenced records of cave obligate amphipods,  crayfish, fish, isopods, beetles, millipedes, pseudoscorpions, spiders,  and springtails within the area of Appalachian Landscape Conservation  Cooperative (LCC) in the eastern United States (Illinois to Virginia,  and New York to Alabama) were assigned to 20 x 20 km grid cells. Habitat  suitability for these faunal groups was modeled using logistic  regression with twenty predictor variables within each grid cell, such  as percent karst, soil features, temperature, precipitation, and  elevation. The models successfully predicted the presence of a group  greater than 65 percent of the time (mean=88 percent) for the presence  of single grid cell endemics, and for all faunal groups except  pseudoscorpions. The most common predictor variables were latitude,  percent karst, and the standard deviation of the Topographic Position  Index (TPI), a measure of landscape rugosity within each grid cell. The  overall success of these models points to a number of important  connections between the surface and cave environments, and some of  these, especially soil features and topographic variability, suggest new  research directions. These models should prove to be useful tools in  predicting the presence of species in understudied areas.
            
        
 
    
    
   
    
   
    
    
    
  
  
  
 
  
  
 
 
    
    
   
 
   
 
  
   










 
 












