Arizona Missing Linkages

Northern Arizona University, 2005-2006

The Arizona Missing Linkages project aimed to design wildlife linkages which reconnect important blocks of habitat throughout Arizona. Based on a similar effort in California, our approach created individual corridor models for 5-15 focal species per study area. These corridors weree combined into a multi-species linkage design which—if conserved and integrated with underpasses or overpasses across potential barriers—will best maintain the ability of wildlife to move between protected wildlands even after the remaining land has been converted to uses incompatible with wildlife movement.

In 2005-2006, we created 8 linkage designs throughout Arizona. Because this was the first year of the project, I had many challenges to complete before I could begin analysis, including data acquisition, parameterizing species models, programming analysis tools, and developing a framework to rapidly conduct corridor analyses and create reports. Once a framework was in place, I had to create one 75-150 page report per month on average, so every aspect of the project—modeling, data management, cartography, and writing—had to be optimized for efficiency.

Workflow optimization

One of the most challenging aspects of this project was ensuring consistency in modeling procedures across all study areas. To enforce consistency, I wrote a suite of tools in Python (which eventually became CorridorDesigner) to perform multiple analyses including habitat suitability, patch configuration, and corridor analyses for a species simultaneously. This enabled me to batch process all analyses by supplying just a few key parameters. Instead of taking days to construct models by hand, I was able to consistently create them off-hours, allowing me to spend work time concentrating on other tasks.

Field database storage

We conducted field investigations to document crossing structures and recent changes in the landscape. To store our field investigation data for every study area, I created a hybrid geodatabase-MS Access database. Using Python, the geodatabase imported the coordinates of GPS waypoints, detected all photographs taken at each waypoint, and populated a table linked to each waypoint with metadata such as the date of observation, names of observers, and name of study area. When the database is then opened in MS Access, a user enters information relevant to each waypoint and photo into two different forms. Finally, a report could be generated which shows spatial information, an overview map, and photo notes.

crossing structures map

crossing structures map field database