Prioritizing Archaeological Inventory and Protection with Predictive Probability Models at Glen Canyon National Recreation Area, USA

KIVA ◽  
2019 ◽  
Vol 86 (1) ◽  
pp. 1-23
Author(s):  
Jered Hansen ◽  
Mark Nebel
2011 ◽  
Vol 5 (1) ◽  
pp. 20-70
Author(s):  
John. R. Spence ◽  
Charles T. LaRue ◽  
John D. Grahame
Keyword(s):  

Author(s):  
Gwendolyn Waring

The plant community along the shoreline of Lake Powell in Glen Canyon National Recreation Area is resilient and dynamic. It is surviving the fluctuations in water level that characterize such reservoirs, and native plants are becoming established. Although the diversity of animals associated with the exotic dominant, Tamarix ramosissima, is lower than that of native riparian species, a surprising number of species are associated with tamarisk in this harsh country.


1999 ◽  
Author(s):  
Gordon Mueller ◽  
Lewis Boobar ◽  
Richard Wydoski ◽  
K.M. Cornella ◽  
R.A. Fridell ◽  
...  
Keyword(s):  

2008 ◽  
Vol 65 (7) ◽  
pp. 1093-1101 ◽  
Author(s):  
Trine Bekkby ◽  
Eli Rinde ◽  
Lars Erikstad ◽  
Vegar Bakkestuen ◽  
Oddvar Longva ◽  
...  

Abstract Bekkby, T., Rinde, E., Erikstad, L., Bakkestuen, V., Longva, O., Christensen, O., Isæus, M., and Isachsen, P. E. 2008. Spatial probability modelling of eelgrass (Zostera marina) distribution on the west coast of Norway. – ICES Journal of Marine Science, 65: 1093–1101. Based on modelled and measured geophysical variables and presence/absence data of eelgrass Zostera marina, we developed a spatial predictive probability model for Z. marina. Our analyses confirm previous reports and show that the probability of finding Z. marina is at its highest in shallow, gently sloping, and sheltered areas. We integrated the empirical knowledge from field samples in GIS and developed a model-based map of the probability of finding Z. marina using the model-selection approach Akaike Information Criterion (AIC) and the spatial probability modelling extension GRASP in S-Plus. Spatial predictive probability models contribute to a better understanding of the factors and processes structuring the distribution of marine habitats. Additionally, such models provide a useful tool for management and research, because they are quantitative and defined objectively, extrapolate knowledge from sampled to unsurveyed areas, and result in a probability map that is easy to understand and disseminate to stakeholders.


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