Gray wolves were nearly eradicated from the state of Washington in the early 1900s . Though classified as an endangered species in the state in 1980, it wasn’t until 2008* that a breeding pair were observed in Washington . Conservation efforts are ongoing – yet, you might not imagine that these efforts would involve layered vegetation maps built in GIS. Comparing vegetation height and distribution over time is one technique used by Chris Bachman, Director of the Wildlife Program at the Lands Council, and team, in their efforts to protect gray wolves in the state.
The Lands Council is an Inland NW organization working on an amazing multitude of projects to fight for the preservation and health of the land, water, and wildlife in the region. Projects range from restoration, to education, to green urban infrastructure, to public land use – and of course – wildlife conservation. The Council often employs innovative solutions to address problems. For example, they curtail water pollution by catching contaminated stormwater runoff in urban “storm gardens” before it can reach the Spokane River.
When it comes to gray wolf conservation, encounters between wolves and livestock are a key obstacle to the livelihood of both groups. Cattle and other livestock have been grazing public lands, since the early days of land use policy . This means that, should you find yourself in the Colville National Forest in eastern Washington, you may be accompanied by cattle feasting on the undergrowth. Naturally, livestock are sometimes preyed upon by wolves living in the area. When livestock are hunted by wolves, an entire bureaucratic proceeding ensues that often leads to monetary compensation from the state, and sometimes lethal removal of the wolf or wolves involved [3, 4, 5].
Interestingly, forest structure in the Colville may be partially to blame for encounters between wolves and cattle. Predominantly a ponderosa pine forest, these trees typically grow relatively sparsely (one factor affording them some protection against naturally occurring wildfires ). According to Bachman, cattle grazing has been one reason for the change in density of the forest over time. Cattle graze grass in the understory, but don’t consume tree saplings. This removes competition for nutrients and sun, allowing saplings to grow that would not have survived otherwise. Over time, this has created a denser forest, which making it more dangerous for cattle; Cattle that are separated from their herd and in difficult forest terrain are easier targets for wolves.
Bachman aims to facilitate a return to historical vegetation conditions using the power of GIS. GIS (Geographical Information Systems), provides methods for studying and analyzing data in map format. By layering current vegetation levels with historical ones, Bachman can show where the forest currently differs from the historical state, thus providing an evidence-based method for returning the forest to a healthier, historical level of tree cover.
Though it sounds straightforward, the approach is not simple. Much of the data must be painstakingly acquired. In his attempts to get the needed data sets, Bachman makes Freedom of Information Act requests to acquire historical vegetation measurements from the state. These requests must be very specific , and therefore take a long time to generate, and can have a long response time. Once the data are received, they may or may not be in a format that is easy to understand and analyze. For anyone who has excitedly obtained a data set from a colleague, only to find it is an unlabeled table inside a PDF, this situation will be very familiar.
Informing forest management is just one way GIS can inform gray wolf conservation. As part of her PhD research, wildlife ecologist Dr. Zoë Hanley used GIS to model areas of high interaction risk between wolves and cattle . Hanley and Bachman would like to expand her original model to factor in predictors they think are important in determining these risk levels, such as the abundance and location of cattle, abundance and location of other prey (elk, moose, deer, etc.), location of wolf den sites, location of salt blocks, and presence or absence of human supervision over livestock herds. While these data may be difficult to gather, especially on a fine enough geographic scale to give good predictions, the resulting model would show high risk areas for cattle and potentially help reduce conflicts further. And there is hope that the model could come to fruition sooner than expected. Hanley and Bachman are currently writing a grant to support data gathering for this project.