activity : nca4-projected-expansion-of-russian-olive-habitat-panel-2-process



• In July, 2010, we used an opportunistic field data collection method to obtain presence points of E. angustifolia trees using a handheld Global Positioning System (GPS) (Garmin- Oregon 450 Olathe, KS, USA).
• Riparian and floodplain areas were roughly delineated from ArcMap (ESRI 2009 ArcGIS Desktop: Release 9.3.1. Redlands, CA, USA. Environmental Systems Research Institute), and we used a 1.61 to 2.0 km buffer zone from the center of each river to focus collection points.
• These data were uploaded from a handheld GPS, converted to a comma delimited format, and uploaded to the National Institute of Invasive Species Science website (
• We used a maximum entropy predictive model (MaxEnt) (Phillips, S. J., R. P. Anderson, and R. E. Schapire. 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling 190:231-259.) in a web- based system through the National Institute of Invasive Species Science (NIISS) to map current and predict potential distribution of the invasive E. angustifolia along the Little Bighorn and Bighorn River systems within the Crow Indian Reservation, Montana, USA.
• We used predictive variables such as precipitation and mean annual temperature from the BioClim dataset ( in our MaxEnt models.
• MaxEnt uses presence points and associated environmental and background factors to distinguish suitable habitat from non-suitable habitat.
• Predictions of the model are defined by values for area under the curve (AUC), which are then compared to a random distribution.
• An AUC ranges from 0 to 1, where a score of 1 indicates predictive discrimination, a score of 0.5 suggests that the discrimination is no different than prediction at random, and a score below the 0.5 suggests that the model fit the data, yet did not accurately predict distribution.

• We visualized the data using the Google Maps JavaScript API (v3) and then used Adobe Fireworks software for image editing to create the PNG file. • In partnership among Colorado State University (CSU), the United States Geological Survey (USGS), the National Aeronautics and Space Administration (NASA), and other organizations, the Global Organism Detection and Monitoring (GODM) cyber infrastructure was added to the National Invasive Species Institute Science website. • Available via the World-Wide-web, GODM provides open-access, and user- friendly invasive species modeling capabilities.

Methodology Citation: PrettyPaint-Small, V. 2013. Linking Culture, Ecology and Policy: The Invasion of Russian-Olive (Elaeagnus angustifolia L.) on the Crow Indian Reservation, South-Central Montana, USA. Ph.D. thesis. Colorado State University, CO.

Methodology Contact: Valerie Small, University of Arizona


The duration of this activity was 8 hours.

Interim artifacts generated by this activity :
Crow Reservation Russian olive project
Output artifacts generated by this activity :
Maxent model for Species_1 (;;

Computing environment : • Windows Server 2012 R2

Software used : The global organism detection & modeling (GODM) software (Graham, J., G. Newman, C. Jarnevich, R. Shory, and T. J. Stohlgren. 2007. A global organism detection and monitoring system for non-native species. Ecological Informatics 2:177-183.) that in turn leveraged software from the R open source software foundation and the MaxEnt software (Phillips et al., 2006).

Visualization software used : • We visualized the data using the Google Maps JavaScript API (v3) and then used Adobe Fireworks software for image editing to create the PNG file.

Data Bounding

The input object was time bounded starting from May 01, 2010 (00:00 AM)

The input object was time bounded ending at July 21, 2010 (00:00 AM)

The input object was bounded spatially:

This activity resulted in the following :

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