Landscape connectivity predicts chronic wasting disease risk in Canada

Transcription

1 Journal of Applied Ecology 2016, 53, doi: / Landscape connectivity predicts chronic wasting disease risk in Canada Barry R. Nobert 1 *, Evelyn H. Merrill 1, Margo J. Pybus 1,2, Trent K. Bollinger 3 and Yeen Ten Hwang 4 1 Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada; 2 Alberta Fish and Wildlife Division, Government of Alberta, Edmonton, AB T6H 4P2, Canada; 3 Canadian Cooperative Wildlife Health Centre, Western College of Veterinary Medicine, University of Saskatchewan, Saskatoon, SK S7N 5B4, Canada; and 4 Saskatchewan Ministry of Environment, Government of Saskatchewan, Regina, SK S4S 5W6, Canada Summary 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. Key-words: circuit theory, Euclidian distance, movement modelling, mule deer, Odocoileus hemionus, Odocoileus virginianus, resistance, step selection function, white-tailed deer Introduction Landscape connectivity is a fundamental component of many ecological processes. High connectivity between populations decreases the chances of localized extinction because populations can be rescued by immigration (Brown & Kodric-Brown 1977; Wilcox & Murphy 1985). At the same time, if connectivity facilitates pathogen movement, high connectivity may lead to declines in populations (Hess 1994, 1996). As a result, connectivity between disease sources is commonly incorporated into *Correspondence author. brnobert@ualberta.ca models of disease dynamics (Hess et al. 2002). For example, for human diseases, such as severe acute respiratory syndrome (SARS), connectivity among individuals can be based on interviews to establish social networks and used to explore outbreak potential (Meyers et al. 2005). Similarly, for livestock diseases such as bovine tuberculosis, connectivity between farms has been quantified by tracking the human-mediated exchange of animals to inform models for predicting disease occurrence (Gilbert et al. 2005). In contrast, for many wild animal populations, what directs host movement is typically unclear. As a result, Euclidean distance to a disease source is used as a 2016 The Authors. Journal of Applied Ecology 2016 British Ecological Society

2 Connectivity and chronic wasting disease 1451 surrogate for predicting disease risk (Joly et al. 2006; Rees et al. 2012). However, evidence is accumulating that landscape features can bias host movements (Fortin et al. 2005; Coulon et al. 2008), which may direct disease spread (Smith et al. 2002; Blanchong et al. 2008). To date, only a few studies have used information on animal movements to predict disease occurrence (Bar-David, Lloyd-Smith & Getz 2006; Garlick et al. 2014). In none of these cases were predictions based on animal movements compared to simple Euclidian distance to determine whether landscape connectivity improved predictions of disease occurrence. In this paper, we address the spatial risk of chronic wasting disease (CWD) to better understand the potential value of using connectivity metrics based on animal movement to predict disease occurrence. CWD is a fatal prion disease of cervids that is transmitted by animal-to-animal contact and through the environment (Williams et al. 2002; Miller et al. 2004). Since its detection in Colorado in the late 1960s, it has spread or was transported to 23 US states and three Canadian provinces. Emerging patterns of CWD prevalence in wild populations appear to be influenced by landscape heterogeneity (Rees et al. 2012; O Hara Ruiz et al. 2013; Robinson et al. 2013). We used CWD surveillance data collected in Saskatchewan and Alberta since 2000, and a model selection approach to identify the top risk model predicting the probability that an individual deer harvested from an area is CWD positive (CWD+). We used the top individualdeer risk model to map CWD risk within seven wildlife management units (WMUs) where CWD had not been detected as of The risk predictions were weighted by the proportion of deer species and sex in the population of each WMU. We then evaluated the predictions using independent surveillance data from 2011 to We considered deer species and sex as variables in the risk model because prevalence is highest in males across regions and at least initially higher in mule deer Odocoileus hemionus Rafinesque, 1817 than white-tailed deer Odocoileus virginianus Zimmermann, 1780 where they cooccur (Miller et al. 2000; Conner et al. 2007), but not age because it was not available for deer harvested in Alberta. We considered landscape variables because high CWD prevalence has been associated with variables like forest cover in Wisconsin (Joly et al. 2006; O Hara Ruiz et al. 2013), urban areas in Colorado (Farnsworth et al. 2005) and agricultural land adjacent to the river valleys in Saskatchewan (Rees et al. 2012), which may reflect high deer densities and contact rates (Kjaer, Schauber & Nielsen 2008; Habib et al. 2011; Silbernagel et al. 2011). We hypothesized that landscape connectivity to known sources of CWD may be based on movement responses of deer to landscape features, because spread of CWD outward from a core area of infection can be impeded by large rivers (Lang & Blanchong 2012), major highways (Blanchong et al. 2008; Robinson et al. 2013) and agriculture (Kelly et al. 2014). Therefore, we compared the utility of a landscape connectivity metric based on deer movements to a simpler, and more commonly used Euclidian distance metric for predicting CWD occurrence. Materials and methods STUDY AREA The study area includes c km 2 of rolling hills located near Wainwright, Alberta, along the Alberta Saskatchewan border (Fig. 1). The area is largely within the aspen parkland ecosystem with small portions of boreal transition in the north. The Battle River, North Saskatchewan River and Ribstone Creek are the major drainages with a few large lakes and numerous wetlands throughout (47% of land cover). Land use is dominated by agriculture (675%), oil and gas development (03 wells km 2 ) and roads (09 kmkm 2 ). Native land cover types include the following: deciduous forest (65%) primarily around rugged terrain like river valleys; coniferous and mixed forest (05%) in the north, with deciduous shrublands (74%) and grasslands (12%) found throughout. CWD surveillance commenced in Saskatchewan in 1997 and Alberta in 1998 with the first CWD+ case detected in Saskatchewan in 2000 and Alberta in As of 2012, 100 mule deer and 12 white-tailed deer CWD+ cases were detected within the study area. Elk Cervus canadensis Erxleben, 1777 and moose Alces alces Gray, 1821 are possible CWD hosts, but the disease has not been detected in these species in this area. INDIVIDUAL-DEER CWD RISK MODEL Deer samples and disease testing Samples for disease testing were obtained from the heads of deer harvested either by hunters or in a government sharpshooter programme. Hunter-harvested deer were killed from September to December. Hunters provided the kill site location as either GPS coordinates or legal land description (quarter section km or section km). However, prior to 2005 in Saskatchewan and 2006 in Alberta, only approximate locations were recorded (i.e. WMU), expect among CWD+ cases which were followed up on to determine specific locations. Deer collected during the government sharpshooter programme in Alberta were killed from the ground (2006) or helicopters ( ) during February March within a 10 km radius of previous CWD+ cases. CWD testing was completed using standard immunohistochemical procedures (see Appendix S1, Supporting information). To develop the risk model, we compared deer characteristics, features of kill locations and connectivity of kill locations to known CWD cases between CWD-positive (CWD+) and CWDnegative (CWD ) deer harvested in the surveillance programmes from 2005 to 2010 in Saskatchewan and in Alberta (see Table S1). We restricted the model construction to this time period because of the low accuracy of CWD locations prior to these years, but the CWD+ cases detected in prior years were used for calculating distance and connectivity to previous CWD+ cases (see Table S2). Independent data from the surveillance programme in were used to evaluate the risk predictions. Because of the processing time associated with a large number of locations in developing connectivity metrics (see below), we randomly reduced the number of CWD deer from to 4863

3 River 1452 B. R. Nobert et al. Alberta Saskatchewan CWD-positive CWD-negative Community AB-SK border Major Drainage study area extent Wildlife Management Units (WMU) WMU Risk Prediction WMU Kilometres 258 North 500 Saskatchewan Lloydminster 238 River Wainwright North Battleford 234 Battle Creek 203 Ribstone Fig. 1. Overview of the study area in east-central Alberta and west-central Saskatchewan, Canada. Disease surveillance data used for the chronic wasting disease (CWD) risk model construction are displayed including CWD-positive deer ( ) and CWD-negative deer in Alberta ( ) and Saskatchewan ( ).

4 Connectivity and chronic wasting disease 1453 (75% reduction) for modelling risk. In contrast, we used all observations of CWD+ deer from 2005 through 2010 (n = 94). Kill site characteristics Landscape features were measured within circular buffers around the kill site (3, 6 and 12 km 2 ) that reflected the variation in deer home range sizes in the study area ( km 2 ). In each buffer, we quantified percentage of three land cover types (agriculture, forest/shrub cover and native grasslands) as well as terrain ruggedness and stream density (km km 2 ). Terrain ruggedness was considered as the standard deviation in elevation (m) within a kill site buffer. We also calculated the distance (km) of kill sites to the nearest major river and human development (roads, well sites, towns). Spatial data were provided by Spatial Data Warehouse, Agriculture Canada and Saskatchewan Environment (see Appendix S2), and spatial analyses were done in ARCGIS 9 (ESRI, Redlands, CA, USA). For each harvested deer, we calculated landscape connectivity from the kill site to previously detected CWD+ cases in the region since For example, for a deer harvested in 2007, connectivity was calculated to all known CWD+ cases from 2000 to We quantified connectivity as the resistance to the flow of electrical current across a hypothetical electronic circuit representing the landscape using circuit theory (McRae et al. 2008). Resistance values along the circuit were derived as the inverse of a value predicted from a step selection function (SSF) (Fortin et al. 2005) based on movements of GPS-collared deer (see below). We summarized connectivity with two metrics: (i) the mean resistance distance to all previous CWD+ cases and (ii) the minimum resistance distance among all previous CWD+ cases. We tested whether connectivity was an improvement over Euclidian distance by considering two additional metrics, the mean straight line distance (km) to all previous CWD+ cases and distance to the nearest previous CWD+ case. Statistical Analyses We used rare events logistic regression in R (R Development Core Team 2014) within the package ZELIG (Imai, King & Lau 2008) to model the probability of a deer being CWD+. This approach, similar to the penalized likelihood, utilizes maximum likelihood to estimate model parameters while correcting for bias in parameter estimation caused by a low number of samples in one binary category (i.e. CWD+). We included species, sex and time in years since first detection (t) in the study area because prevalence rates are likely to increase over time (Miller & Conner 2005; Heisey et al. 2010). The province (Saskatchewan or Alberta) was included to account for potential differences in the surveillance programmes or disease history not attributed to time since first detection. Harvest type (hunter or government sharpshooter) was included because government removal of deer in Alberta occurred where positives had been previously detected and therefore a CWD+ deer was more likely to be detected. All covariates were tested for correlation and were not placed in the same model if their Spearman s rank correlation was high (r s 050, P < 005). We used Akaike information criterion (AIC, Anderson 2008) to rank and choose among competing models (see Table S3). We assessed the ability of the model to accurately predict the disease status of a deer using area under the receiver operating curve (AUC, Fielding & Bell 1997) in the package ROCR (Sing et al. 2005). To determine the relative importance of a covariate to influence disease status, we estimated standardized coefficients (Menard 2004). We assessed the spatial autocorrelation in model residuals by fitting a spline to the plotted correlogram with bootstrap confidence intervals (Rhodes et al. 2009). MOVEMENT-BASED LANDSCAPE CONNECTIVITY We quantified connectivity using a three-step process that combines SSF modelling (Fortin et al. 2005) with circuit theory (McRae et al. 2008). First, we constructed an SSF based on GPS-collared deer movements, which reflect the propensity for a deer to move through an area dependent on the landscape features (i.e. terrain ruggedness). Secondly, we constructed a gridbased map (raster) of our study area with 30-m pixels assigned SSF values based on landscape attributes. Because an SSF is proportional to the probability of a deer selecting to move through an area then if the landscape is modelled as an electronic circuit, the inverse of SSF values equate to the resistance with which electrical current flows through a circuit. Thirdly, the SSF raster was modelled as an electronic circuit within the program CIR- CUITSCAPE (v.3.5; McRae & Shah 2011) where the resistance distance between points on the landscape is calculated with Kirchoff s laws in matrix form (Shah & McRae 2008). We used resistance distance as our measure of connectivity because it considers all possible paths between locations rather than a single optimal path as in least cost path analysis (McRae & Beier 2007), and it has been shown to be a good predictor of movement between populations (McRae et al. 2008). Deer movement Movements of 20 GPS-collared mule deer (8 male, 12 female) and 18 GPS-collared white-tailed deer (5 male, 13 female) monitored from 2006 to 2008 were used to develop a SSF. Deer were captured using Clover traps and helicopter net-gunning. Adults were fitted with GPS collars (3300S/4400S; Lotek Wireless, Newmarket, ON, Canada). All capture and handling procedures were approved by the University of Alberta s Animal Care Committee (#494701). We concentrated collaring in four areas in an effort to represent the variation in forest/shrub cover and proximity to major drainages within the study area (see Fig. S1). Deer movements were monitored at a 2-h frequency. The average location error was m (SD) with a >99% fix success rate (n = 1148, see Appendix S3). Step selection function We used values predicted from a SSF to build the resistance raster for input into CIRCUITSCAPE. The SSF compares landscape characteristics of steps along a movement path, represented by a straight line connecting two consecutive GPS locations, to a set of available steps not taken by the animal (see Appendix S4 for model details). We considered landscape features shown to influence deer movements (Lingle & Pellis 2002; Fortin et al. 2005; Coulon et al. 2008; Sawyer et al. 2009) including terrain ruggedness, roads, oil and gas well sites, forest/shrub cover, lakes, ponds, rivers and streams. We made separate SSF models for mule deer and white-tailed deer. The SSF rasters were smoothed

5 1454 B. R. Nobert et al. to km cells to keep computation time manageable and extended 50 km beyond the study area to avoid bias near map edges (see Appendix S5). To evaluate whether the smoothed 1- km raster captured the variation in SSF values at the fine scale (30 m), we quantified the Pearson s correlation (r p ) between the two rasters at 5000 random points across the study area. POPULATION-WEIGHTED RISK PREDICTIONS We assessed the risk of CWD being detected in deer harvested from seven WMUs in AB where CWD had not been detected as of 2010 (Fig. 1) based on the individual-deer risk model weighted by species and sex composition of deer in the WMU. First, we used the risk model to derive four spatial risk maps ( m pixels) of predicted risk (P): one for each sex of mule deer and white-tailed deer. Secondly, using information on deer species abundance and sex composition in each WMU (see below), we derived a population-weighted measure of CWD risk for any deer harvested in a 30-m pixel within a WMU as: WR ¼ X D ij P ij eqn 1 where D ij is the probability that a randomly chosen deer in a WMU is of species i (mule deer, white-tailed deer) and sex j (male, female) and P ij is the probability that a harvested deer of species i and sex j from a pixel, in year 2011, is CWD+ given the landscape characteristics of the 30-m pixel and connectivity to known CWD+ cases from 2000 to Possible values ranged from zero to one. D ij was based on 2011 pre-hunting season deer population data collected by Alberta Fish and Wildlife in a WMU based on winter aerial surveys (see Appendix S6, Table S4). We assumed a constant proportion of 070 females among mule deer and 075 females among white-tailed deer because aerial surveys were typically done in late winter when antlers were shed making sex determination difficult. These ratios were based on an average among seven aerial surveys within the study area between 2005 and 2011 (see Table S5). In Alberta from 2011 to 2013, an independent set of hunterharvested deer were tested for CWD (see Fig. S2, Table S6). We evaluated the weighted risk map based on 1718 of these deer (CWD+ =14, CWD = 1704) sampled within the seven WMUs in Alberta where CWD had not been detected as of We compared the weighted risk values predicted for these known CWD+ and CWD deer using AUC (Sing et al. 2005) to determine whether CWD+ deer are harvested from areas of higher weighted risk compared to CWD, as would be expected. Results STEP SELECTION FUNCTIONS The top SSF for mule deer (AIC c weight [w i ] = 074) and white-tailed deer (w i = 099) included distance to forest/ shrub cover, distance to water and terrain ruggedness (see Table S7). Both species selected to move through areas close to forest/shrub cover and far from water (see Table S8). Mule deer selected to move through more rugged terrain, while white-tailed deer selected gentle terrain. The smoothed 1-km resolution rasters of SSF values, used for quantifying connectivity, were significantly correlated with the finer scale 30-m SSF rasters, for both mule deer (r p = 070, P < 001) and white-tailed deer (r p = 063, P < 001). INDIVIDUAL-DEER RISK MODEL Of the deer tested for CWD within the study area, which were the basis of the CWD risk model, the majority were mule deer (61%), with 21% of deer harvested through the government sharpshooter programme in Alberta (see Table S1). CWD prevalence estimates, calculated using a Bayesian posterior binomial distribution with diffuse priors (Dorai-Raj 2014), indicated that when hunter-harvest data were pooled across provinces and years CWD prevalence was higher among mule deer (069%, % [95% confidence interval]) than white-tailed deer (013%, %). Males had a prevalence of 058% ( %), slightly higher than females at 046% ( %). Prevalence appeared to increase over time especially in Saskatchewan, which also tended to have higher prevalence (Fig. 2, see Fig. S3). There was strong support (w i = 085) for the top risk model containing deer species and sex, time since CWD was first detected, province, harvest type, two environmental covariates and mean resistance distance to previously detected CWD+ deer based on the mule deer SSF. There was little support for other models that included Euclidian distance (w i < 001) or resistance distance based on the white-tailed deer SSF (w i < 001). The Euclidian distance model had a significantly lower AIC score (DAIC = 173) than the null model (DAIC = 335) that included no landscape covariates (see Table S9). According to the risk model parameters (b), the odds (e b ) of a mule deer being infected were 57 times higher than a white-tailed deer, while the odds of male deer being CWD+ were 21 times higher than females (Table 1). The odds of a deer harvested by a hunter being CWD+ was 039 times lower than those harvested by a sharpshooter, while a deer from Alberta had 023 the infection odds compared to Saskatchewan. From 1 year to the next, the odds of infection increase by 14 times. A harvested deer was more likely to be infected in areas with abundant agriculture and high stream density. There was no significant spatial autocorrelation in the model based on visual inspection of the spline fitted to the correlogram of model residuals (see Fig. S4). POPULATION-WEIGHTED RISK The proportion of mule deer ranged between 012 and 066 in the seven WMUs where CWD had not been detected as of Where mule deer comprised a high proportion of the population (i.e. WMU 730 and 203), mean risk values were times that of the other WMUs. The spatial patterns in CWD risk within a WMU emerged largely due to connectivity (Fig. 3, see Fig. S5).

6 Connectivity and chronic wasting disease 1455 Fig. 2. Chronic wasting disease (CWD) prevalence among hunter-harvested mule deer and white-tailed deer from 2005 to 2010 in west-central Saskatchewan (SK) and from 2006 to 2010 in east-central Alberta (AB), including 95% confidence intervals (CI). Table 1. Parameters (b) for the chronic wasting disease (CWD) risk model, which predicts the probability that a deer harvested on the landscape is infected with CWD. In addition to associated standard errors (SE) and standardized regression coefficients (SRC) Covariate b SE SRC Intercept Agri 12 km * Stream 3km MUDE_Resist mean Province (Alberta = 1) Harvest (hunter = 1) Time Species (mule deer = 1) Sex (male = 1) * Proportion of agriculture within a 12-km 2 circular buffer. Stream density (km 1000 km 2 ) within a 3-km 2 circular buffer. Mean resistance distance, a connectivity metric based on the mule deer step selection function to previously detected CWD+ cases. WMUs with high connectivity throughout still had spatial variation in risk because of other landscape features, including streams and agriculture (Fig. 4). Of the deer sampled in , the predicted weighted risk averaged among known CWD+ deer was (95% confidence interval, n = 14) and for known CWD deer only (95% CI, n = 1704). AUC indicated a 075 probability of a CWD+ deer being assigned a higher weighted risk value compared to a CWD deer. Discussion In the prairie parkland of Alberta and Saskatchewan, we found the highest CWD risk in areas of high stream density when associated with abundant agriculture. Riparian habitat within agriculture regions corresponds with high deer densities (Compton, Mackie & Dusek 1988; Walter et al. 2011a). Even if deer grouping behaviour precludes homogenous mixing at high densities leading to frequency-dependent transmission (Grear et al. 2010; Storm et al. 2013; Jennelle et al. 2014), density likely still plays a role because group sizes increase with density and therefore potentially increase within-group contact rates (Habib et al. 2011). Further, extensive agriculture results in small isolated patches of woody cover where deer may concentrate, increasing spatial overlap and between-group contact rates (Kjaer, Schauber & Nielsen 2008; Habib et al. 2011; Silbernagel et al. 2011). Deer aggregation in these areas may initially promote direct transmission, but over time these areas also may serve as environmental reservoirs of CWD prions (Walter et al. 2011b; Saunders, Bartz & Bartelt-Hunt 2012), in particular where soil clay content is high and modifies the infectivity or longevity of soil bound prions (Johnson et al. 2006). Disease dynamics may change as reservoirs build up in the environment (Almberg et al. 2011) and subsequent risk models may be improved by including soil information. We also found evidence that CWD+ animals on the landscape were associated with nearer Euclidian distance to previous cases of hunter-killed CWD+ deer, as found elsewhere (Joly et al. 2006; Rees et al. 2012). The use of Euclidian distance is a practical approach to relating

7 River 1456 B. R. Nobert et al. (a) (b) North Sask Battle River 730 Ribstone Cr. 232 Surveillance CWD-negative 203 CWD-positive CWD Risk Kilometres Resistance Fig. 3. For seven wildlife management units: (a) predicted chronic wasting disease (CWD) risk weighted by species and sex composition and (b) resistance distance (MUDE_Resist mean ), the inverse of connectivity, to previously detected CWD+ cases. CWD risk value bins are based on frequency distribution breaks (see Fig. S5). patterns of prevalence on the landscape because CWD generally clusters within localized foci with diminishing risk as you move outward (Joly et al. 2006; Robinson et al. 2013). However, based on studies of genetic relatedness among deer, it appears some landscape features like agriculture areas (Kelly et al. 2014) and rivers (Blanchong et al. 2008) may impede or facilitate dispersal outward from disease foci (Oyer, Mathews & Skuldt 2007), and Euclidian distance ignores these effects. We found that incorporating host movement responses to landscape features via habitat selection improved our ability to predict the occurrence of infected hosts. Our results suggest that the effects of landscape connectivity should be considered among more complex, temporal models to predict CWD spread. We found that CWD prevalence was generally higher in Saskatchewan and increasing at a greater rate, compared to Alberta. One hypothesis for this trend is earlier

8 Connectivity and chronic wasting disease 1457 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. disease arrival in Saskatchewan, which is consistent with the idea that CWD was first introduced into the study area through spillover from infected game farms in Saskatchewan (Bollinger et al. 2004). Alternatively, the government sharpshooter programme, implemented only in Alberta, could have reduced the prevalence and rate of increase relative to Saskatchewan (Mateus-Pinilla et al. 2013; Manjerovic et al. 2014). Although the clustering of deer samples around previously detected CWD+ cases, characteristic of the sharpshooter programme, could potentially bias the CWD risk model through spatial autocorrelation (K uhn & Dormann 2012), we found little support for autocorrelation in risk. In addition, the risk model was a relatively good predictor of new disease cases on the edge of our study area in subsequent years. Mule deer connectivity best predicted CWD risk. This is not surprising given prevalence in this region is approximately five times higher in mule deer than white-tailed deer, which is consistent with other jurisdictions where these two deer species overlap and CWD is recently detected (Miller et al. 2000; Rees et al. 2012). As a result, mule deer movements should have a disproportionally higher impact on CWD dynamics, consistent with our finding. Mule deer selected to move through rugged terrain and woody cover, consistent with general mule deer habitat selection in this region (Lingle & Pellis 2002; Silbernagel et al. 2011; Habib, Moore & Merrill 2012). We used within home range movements in our movement modelling because dispersal events are hard to observe (Spear et al. 2010), and we had too few (<5%) of our collared deer that actually dispersed. Future studies may improve connectivity based models if they consider using dispersal movements exclusively, but whether animals use different (Selonen & Hanski 2006) or similar habitat (La Morgia et al. 2011) as they disperse beyond their home ranges is often uncertain. Eradication of CWD where the disease is long established and geographically widespread has proven difficult (Williams et al. 2002; Manjerovic et al. 2014). As a result in jurisdictions where CWD is encroaching, the primary goal is early detection of the disease to improve the ability of managers to remove infected animals and limit spread (Samuel et al. 2003). The efficacy and affordability of disease surveillance can be improved by focusing on the area with the highest risk of infection (Hadorn & St ark 2008). For CWD, infection risk tends to be based on proximity to known cases and the abundance of susceptible hosts (Russell et al. 2015). Our results suggest landscape connectivity is an additional component of CWD risk that could improve surveillance programmes and the likelihood of early detection. In jurisdictions where CWD is established, there is emerging evidence that hunter-harvest and sharpshooter programmes can reduce CWD prevalence (Mateus-Pinilla et al. 2013; Manjerovic et al. 2014). We suggest focusing disease control on mule deer, particularly males, because they are likely to contribute to disease spread due to their high prevalence and mobility (Miller et al. 2000; Green et al. 2014). However, to further improve management efforts, we need to better understand the spatial patterns of CWD prevalence and risk, specifically the underlying dynamics driving these patterns. Acknowledgements We thank David Coltman, Tom Habib, Eric Brownrigg, Chris Garrett, Mark Lewis, Scott Nielsen, Erin Rees and many field technicians who helped with field work and study design. Mark Ball and Rob Corrigan provided data. Funding was provided by the Alberta Prion Research Institute, Alberta Conservation Association, Alberta Cooperative Conservation Research Unit, Natural Sciences and Engineering Research Council of Canada (NSERC), Alberta Professional Outfitter Society, Alberta Fish and Wildlife and Saskatchewan Environment. Personal funding for B.R.N. provided by NSERC and Sucker Creek First Nation. Data accessibility The data used in this study have been deposited in the Dryad data repository: doi: /dryad.521ng (Nobert et al. 2016).

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Appendix S5. SSF raster construction information. Appendix S6. Deer population data details. Fig. S1. Collared deer home ranges. Fig. S2. Surveillance locations Fig. S3. Species-sex prevalence estimates. Fig. S4. Risk model spatial autocorrelation. Fig. S5. Risk values frequency distribution. Table S1. Surveillance data risk model. Table S2. Past surveillance data. Table S3. Alternative CWD risk models. Table S4. Deer population estimates. Table S5. Deer population sex ratios. Table S6. Surveillance data model evaluation. Table S7. SSF model rankings. Table S8. SSF model coefficients. Table S9. CWD Risk model rankings.