Landscape connectivity predicts chronic wasting disease risk in Canada

Abstract

1. 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. 2. 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. 3. 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. 4. The CWD risk model was a good predictor of CWD occurrence among an independent set of surveillance data collected in subsequent years. 5. 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. Fig. 4. (a) Chronic wasting disease (CWD) risk weighted by species and sex composition within wildlife management unit 730 in Alberta, and (b) forest/shrub cover, streams and agriculture within 730, where streams and agriculture are covariates in the CWD risk model.

Assessment of spatial genetic structure to identify populations at risk for infection of an emerging epizootic disease

“…Disease diffusion models also suggest the axial spread of chronic wasting disease along landscape features resistant to deer movement in other regions (Hefley et al, 2017). The proximity of connectivity corridors to forested riparian cover also supports previous predictions that these features are likely to play an important role in deer movement and may affect transmission dynamics of chronic wasting disease in heterogeneous environments (Nobert, Merrill, Pybus, Bollinger, & Hwang, 2016; Walter et al, 2011). Based on our results, we also predict that disease transmission may be more diffusive in homogeneous landscapes, such as those in the Allegheny Plateau, due to the undirected nature of deer movement.…”

The ecology of chronic wasting disease in wildlife

“…). Similar patterns are observed in modelling wild populations (Joly et al ., ), as disease prevalence typically declines with distance from heavily affected areas and landscape connectivity plays a larger role in the spread of disease (Conner & Miller, ; Joly et al ., ; Blanchong et al ., ; Nobert et al ., ). It is evident that CWD does not occur randomly in the geography, and geomorphology seems to play a role shaping its distribution (Fig.…”

“…Studies at the community, landscape, and biogeographic levels underlying CWD occurrence remain neglected and are critically needed. Blanchong et al, 2008;Nobert et al, 2016). It is evident that CWD does not occur randomly in the geography, and geomorphology seems to play a role shaping its distribution (Fig.…”

“…fights with other males, allogrooming courtship, copulation, scent verification). In North American elk and deer species, males disperse (permanent movement away from a natal range) more frequently than females (Skuldt, Mathews & Oyer, 2008;Clements et al, 2011;Miller & Conner, 2013;Nobert et al, 2016) (Fig. 2).…”

Challenges in managing the risks of chronic wasting disease

“…• Applied Research Programs: For example, modelling to assess changes in spatial distribution and prevalence of disease (for assessing the effectiveness of management actions), and improved disinfection and decontamination protocols (Nobert et al, 2016;Potapov et al, 2016;Uehlinger et al, 2016). …”