Data from: Landscape connectivity predicts chronic wasting disease risk in Canada

Predicting the spatial pattern of disease risk in wild animal populations is important for implementing effective control programmes. We developed a risk model predicting the probability that a deer harvested in a wild population was chronic wasting disease positive (CWD+) and evaluated the importance of landscape connectivity based on deer movements. We quantified landscape connectivity from deer 'resistance' to move across the landscape similar to the flow of electrical current across a hypothetical electronic circuit. Resistance values to deer movement were derived as the inverse of step selection function values constructed using movement data from GPS-collared deer. The top CWD risk model indicated risk increased over time was higher among mule deer Odocoileus hemionus than white-tailed deer Odocoileus virginianus, males than females, and was greater in areas with high stream density and abundant agriculture. A metric of connectivity derived from mule deer movements outperformed models including Euclidean distance, with high connectivity being associated with high CWD risk. The CWD risk model was a good predictor of CWD occurrence among an independent set of surveillance data collected in subsequent years. Synthesis and applications. We found that landscape connectivity was a major contributor to the spatial pattern of chronic wasting disease (CWD) risk on a heterogeneous landscape. For this reason, future disease surveillance programmes and models of disease spread should consider landscape connectivity. In the aspen parkland ecosystem, we recommend managers focus surveillance and control efforts along river valleys surrounded by agriculture where mule deer abound, because of the high risk of CWD infection.Mule Deer and White-tailed Deer GPS Collar LocationsMule deer and white-tailed deer were captured and fit with GPS-collars within East-Central Alberta. The collars recorded the location of the deer every two hours. These sequential locations were used to construct a step selection function (SSF) model to quantify the habitat selection of deer movements.SSF_deer_GPS_collar_locations.csvStep Selection Function Regression DataThe SSF regression data was used to build a Step Selection Function (SSF) model for mule deer and white-tailed deer (two separate models). We used conditional logistic regression to do so. Each line in the database corresponds to a spatial line segment connecting two consecutive GPS-collar locations (aka "step").SSF_regression_data.csvCWD Risk Model Regression DataThe data set used to build the chronic wasting disease (CWD) risk model. The basis for the data set are mule deer and white-tailed deer harvested by hunters or government sharp-shooters in East-Central Alberta and West-Central Saskatchewan. These deer were tested for CWD. Landscape covariates describe the features surrounding each deer harvested with covariates corresponding to features previously shown to be important for CWD risk and/or deer habitat selection.CWD_risk_model_regression_data.csvCWD Surveillance Data from 2000 to 2010This chronic wasting disease (CWD) surveillance data is the basis for the construction of the CWD risk model. Mule deer and white-tailed deer were harvested by hunters and government sharp-shooters in East-Central Alberta and West-Central Saskatchewan from 2000 to 2010. Deer heads were submitted to a laboratory for testing for the presence of CWD.CWD_Surveillance_data_2000_2010.csvCWD Surveillance Data in Alberta from 2011 to 2013 for validationThis data was used for validating/evaluating the CWD risk model predictions. Mule deer and white-tailed deer were harvested by hunters in East-Central Alberta from 2000 to 2010. Deer heads were submitted to a laboratory for testing for the presence of CWD.CWD_surveillance_validation_data_2011_2013.cs