2012 NORTHEASTERN NATURALIST 19(1):67–76 Survival of White-tailed Deer in Wisconsin’s Chronic Wasting Disease Zone Seth B. Magle1,*, Jeromy C. Chamberlin2, and Nancy E. Mathews2 Abstract - We analyzed the survival rates of 160 Odocoileus virginianus (White-tailed Deer) over 6 years in the area of Wisconsin’s highest prevalence of chronic wasting disease. Survival was very high for all age/sex classes and varied by season but not by year. When we omitted hunting mortality, yearlings and adults had similar annual survival, with survival of males (0.83–0.89) slightly lower than survival of females (0.91). However, including hunting mortality reduced survival of yearling and adult males to 0.72 and 0.41, and survival of yearling and adult females to 0.88 and 0.83, respectively. We also observed seasonal patterns, characterized by reduced survival across all sex and age classes during the rut season (10 October to 31 December), which includes the period of maximum hunting. Six mortalities (8.3%) were associated with chronic wasting disease, including one deer that died from the disease. We find no evidence that CWD was substantially increasing mortality rates during the duration of our study from 2003 to 2007, though the disease is relatively new to this area. Our results can serve as a baseline by which to compare future mortality rates in this area to assess the virulence of CWD over time. Introduction Survival is an essential part of population dynamics (Sibley and Hone 2003). As such, measuring the survival rates of individuals within populations is a fundamental task of wildlife biologists and managers. Understanding these dynamics is especially important when population reduction is prescribed to manage a disease outbreak. Chronic wasting disease (CWD) is a fatal transmissible spongiform encephalopathy of Odocoileus virginianus (Zimmermann) (White-tailed Deer), which models predict may post substantial risk to deer populations (Gross and Miller 2001, Joly et al. 2003, Miller et al. 2000), and which has had signifi- cant economic impacts through reduction in hunter participation (Bishop 2004, Needham et al. 2004, Vaske et al. 2004). To determine whether a disease such as CWD has increased the mortality rate in a given area, it is essential to know pre-existing or background survival rates. In February 2002, wildlife biologists detected CWD in 3 White-tailed Deer in south-central Wisconsin (Joly et al. 2003). The Wisconsin Department of Natural Resources’ (WDNR) management actions for CWD included relaxing harvest regulations to reduce deer density, increasing harvest quotas, and engaging in agency sharp-shooting of deer (Blanchong et al. 2006). From 2003 to 2008, we used radio-collars to track White-tailed Deer in the area of highest CWD 1Department of Conservation and Science, Lincoln Park Zoo, Chicago, IL 60614. 2Nelson Institute for Environmental Studies, University of Wisconsin-Madison, Madison, WI 53706. *Corresponding author - smagle@lpzoo.org. 68 Northeastern Naturalist Vol. 19, No. 1 prevalence in Wisconsin (Oyer et al. 2007, Skuldt et al. 2008). Our objective was to estimate survival of White-tailed Deer, including the effects of life-history stage, year, and season, during the first 5 years of CWD management. Methods We conducted our research in south-central Wisconsin (Fig. 1) from 2002– 2007 within the CWD disease eradication zone (DEZ), as designated by the WDNR (Bartelt et al. 2003). We captured White-tailed Deer from December– April during 2003–2005 using modified Clover (1956) and Stephenson box traps (Rongstad and McCabe 1984), rocket nets (Hawkins et al. 1968), drop-nets (Ramsey 1968), and darting (Skuldt 2005). We determined the sex of each deer, and aged deer as fawns (<1 year), yearlings (≥1 year, <2 years), and adults (≥2 years) by tooth wear and replacement (Severinghaus 1949). We immobilized captured deer with xylazine (4.0 mg/kg) and ketamine (4.0 mg/kg) or Telazol® (tiletamine hydrochloride and zolaxepam, 4.0 mg/kg) and xylazine (2.2 mg/kg) administered intramuscularly (Skuldt 2005). All deer were fitted with a 480-g VHF radio transmitter (Advanced Telemetry Systems Inc., Isanti, MN) with a mortality sensor (Skuldt 2005). We took a tonsil biopsy from each deer to test for CWD (Schreuder et al. 1996, Schuler et al. 2005, Wild et al. 2002, Wolfe et al. 2002). Antemortum sampling Figure 1. Study landscapes 1 and 2 within the 2002 chronic wasting disease eradication zone (outlined in black) in south-central Wisconsin. 2012 S.B. Magle, J.C. Chamberlin, and N.E. Mathews 69 for CWD is challenging, and it is possible that some infected individuals were undetected. The University of Wisconsin-Madison (UW-Madison), College of Agriculture and Life Sciences’ Animal Care and Use Committee (Permit A-3368- 01), UW-Madison Research Animal Resources Center (protocol A01088309-02), and the WDNR (Scientific collector’s permit SCP-SCR-018-0202), provided oversight of our capture and handling methods. We located radio-collared deer using triangulation and checked them for mortality events at least once per week (Oyer et al. 2007). When we detected a mortality signal, we located the collar and determined the cause of death through either gross pathology or necropsy at the University of Wisconsin Veterinary Diagnostics Laboratory. We estimated survival using known-fate models in program MARK (White and Burnham 1999). Deer data were divided into 5 lifehistory stages (STAGE: fawn, yearling male, yearling female, adult male, and adult female), 5 years (YEAR: 2002–2006), and 4 seasons (SEASON: gestation [1 Jan–9 May], parturition [10 May–30 Jun], pre-rut [1 July–9 Oct)] and rut [10 Oct–31 Dec]; Skuldt 2005). We used a model-selection process to relate predictors to survival rates based on a priori hypotheses about deer survival in this study system. For this process, we created 7 models comprised of combinations of variables (Table 1). Typically, mortality varies by age and sex for deer, particularly when subject to hunting (e.g., Nixon et al. 2001, Patterson et al. 2002, Van Deelen et al. 1997), and as such, STAGE was among our tested covariates. Similarly, SEASON was included because mortality can vary seasonally due to energetic constraints associated with reproduction (Ditchkoff et al. 2001, Lopez et al. 2003), as well as changes in weather and available food (DelGuidice et al. 2002). Finally, landowner participation and satisfaction with CWD management changed during the 5 years of our study, as did weather and hunting regulations. To capture the potential effects of these yearly sources of variation, we included YEAR as a predictor variable. We included each combination of the three above predictors (STAGE, SEASON, and YEAR) in our modeling, creating 7 total candidate models (Table 1). Table 1. Models used to explain variation in the survival (Sx) of radio-collared White-tailed Deer in Wisconsin’s chronic wasting disease outbreak area (2003–2006). Model Interpretation STAGEA Sx varies with life-history stage SEASONB Sx varies with reproductive phenology YEARC Sx varies with year of the study STAGE+SEASON Sx varies with life-history stage and reproductive pehnology STAGE+YEAR Sx varies with life-history stage and year of the study SEASON+YEAR Sx varies with reproductive phenology, and year of the study STAGE+SEASON+YEAR Sx varies with life-history stage, reproductive phenology, and year of the study ASTAGE = fawn, yearling male, yearling female, adult male, or adult female. BSEASON = pre-rut (July 1–October 9), rut (October 10–December 31), gestation (January 1–May 9), or parturition (May 10–June 30). CYEAR = 2002, 2003, 2004, 2005, or 2006. 70 Northeastern Naturalist Vol. 19, No. 1 To maintain a relatively simple model set, we used additive model combinations throughout rather than including interactive terms (Burnham and Anderson 2002). Thus, the model containing both STAGE and SEASON omits any potential interaction between the two. When there is no a priori reason to expect interactions between explanatory variables, testing them arbitrarily can lead to spurious selection of uninformative models. Based on reviewer comments, we conducted post-hoc evaluation of models including interactive terms, which revealed that the model selection process would have been unaffected by their inclusion. We evaluated models using Akaike’s information criterion (AIC; Burnham and Anderson 2002). All models were fit using the log-link function in program MARK (White and Burnham 1999). Because WDNR encouraged harvest in this area, which was likely a factor in increasing hunting mortality, and because we were interested in both hunting and natural sources of mortality, we analyzed 2 data sets: 1) the full telemetry data set including hunting mortalities (HUNT) and 2) the same dataset, but with huntercaused mortalities censored on the date that the deer was killed (HUNTCENS; VanGilder and Sheriff 1990). This approach allowed estimation of survival including and without hunting mortality (Webb et al. 2007). We split out the dataset in this way because our landscape contained a complex mosaic of management strategies, ranging from compliance with WDNR-sponsored disease control efforts, to harvest only during traditional hunting seasons, to no deer harvest, and as such, deer were subject to differing levels of potential hunting mortality based on spatial location (Skuldt 2005). Evaluating total mortality and non-hunting motality separately allowed us to explore the varying impacts of harvest on deer populations. Each dataset was analyzed using identical model sets and modelselection procedures. Results Between January 2003 and April 2007, we captured 179 individual deer, (110 females, 69 males), of which 4 tested CWD-positive (3 females, 1 male), and were found and euthanized shortly thereafter. At capture, 91 deer were fawns, 10 were yearling females, 8 were yearling males, 56 were adult females, and 14 were adult males. Nineteen animals were eliminated from analysis because they died during handling (n = 14), or soon after capture (n = 5), or because they shed their radio-collars (n = 4). We documented 71 mortalities including 6 that were associated with CWD (Table 2). In addition to the 4 euthanized animals, 1 adult female died of end-stage CWD and 1 yearling female was mistaken for a CWD positive animal and killed. Two adult males whose source of mortality was hunter harvest later tested positive for CWD as well. The majority of our mortality events were associated with hunting during the rut period. Model selection from each dataset had a similar result, suggesting that survival varied with STAGE and SEASON, but not with YEAR (Table 3). The top model for both datasets included only STAGE and SEASON (wi = 0.99 for HUNT, 0.99 for HUNTCENS), with the other 6 models receiving virtually no 2012 S.B. Magle, J.C. Chamberlin, and N.E. Mathews 71 support. Survival of fawns prior to the parturition season following their first winter was not estimated, because deer were captured during late winter or early spring and we could not estimate mortality for deer that died before surviving one full season. Seasonal survival rates were relatively high for all stages during all seasons, with few exceptions (Table 4). For the data set with hunting mortalities censored, survival rates ranged from 0.92 to 0.99. Similarly, survival rates ranged from 0.79 to 0.99 for the full data set, except for adult males during rut (0.54). Annual survival rates for yearlings and adult female deer were high, and consistent when hunting mortalities were censored. However, annual survival of males was substantially lower when hunting mortality was included (Table 4). Table 2. Mortalities of radio-collared White-tailed Deer in Wisconsin’s chronic wasting disease outbreak area (Dane and Iowa counties) during 2003–2007. Stage Season Hunting Vehicle Predation CWDA OtherB UnknownC Fawn Gestation 1 2 Parturition 2 Yearling male Gestation 1 Parturition Pre-rut 1 3 Rut 3 Yearling female Gestation 2 2 Parturition Pre-rut Rut 1 Adult male Gestation Parturition Pre-rut Rut 22 Adult female Gestation 1 1 1 4 1 3 Parturition 2 Pre-rut Rut 15 3 Total 43 1 2 6 3 16 ATwo adult males tested positive for chronic wasting disease (CWD) after being harvested (not included), 4 deer tested positive for chronic wasting disease during initial capture and were killed shortly after, 1 yearling female was confused for a CWD-positive deer and killed despite being uninfected with CWD, and 1 adult female died of end-stage CWD. BThese mortalities chiefly appeared to be related to pneumonia or other non-specific pathogens. CCause of death indeterminate due to the poor condition of the carcass. One of the yearling males and 3 adult females could not be tested for disease, and CWD cannot be ruled out as a potential cause of death. 72 Northeastern Naturalist Vol. 19, No. 1 Discussion We found that survival of White-tailed Deer in Wisconsin’s area of high prevalence of CWD varied by both life-history stage and reproductive season. With the exception of males during the rut (when hunting is included), estimates of seasonal rates of survival were quite high (>0.78; Table 4). Survival of deer in our study area did not vary by year. Our observation of very low impacts of hunting on survival of female deer, as well as an evaluation of previous studies of Midwestern deer populations (e.g., Brinkman et al. 2004; DelGuidice et al. 2002, 2006; DePerno et al. 2000; Nixon et al. 2001; Sitar 1996) suggest that the magnitude of hunting of female deer that we observed was not typical. We suspect low levels of harvest occurred because we asked landowners and hunters to refrain from shooting radio-collared deer after the first year of the study (Jacques et al. 2011), though their compliance may have been restricted primarily to female deer. The magnitude of hunting of male deer we observed is representative of that in most hunted populations (reviewed by Ricca et al. 2002). Non-hunting mortality was remarkably low for all age classes in all seasons. Despite predictions that the prevalence of CWD will continue to increase and that this disease may have a significant impact on the population of White-tailed Deer in Wisconsin (Grear et al. 2006), we recorded low levels of CWD-related mortality (only 1 animal died from CWD, though 4 more were euthanized after testing positive) and relatively low rates of infection (4 of 179 animals, or 2.2% tested positive upon initial capture), in a study area located at the center of Wisconsin’s CWD outbreak zone. It should be noted, however, that the disease was discovered in Wisconsin fairly recently (2002), and it may take much longer for population-level effects Table 3. Model selection statistics (Burnham and Anderson 2002) for explanatory models of survival for radio-collared White-tailed Deer in Wisconsin’s chronic wasting disease outbreak zone (2003–2007). Models were fit using known-fate models in program MARK (White and Burnham 1999). Data set Rank Model ΔAICc wi Full data set with hunting mortalities included 1 STAGE+SEASON 0.00 0.99 2 SEASON 15.05 <0.01 3 YEAR+SEASON 43.82 <0.01 4 STAGE 55.15 <0.01 5 YEAR 85.63 <0.01 6 STAGE+YEAR 94.76 <0.01 7 STAGE+YEAR+SEASON 155.79 <0.01 Data set with hunting mortalities censored 1 STAGE+SEASON 0.00 0.99 2 SEASON 8.75 0.01 3 YEAR+SEASON 12.58 <0.01 4 STAGE 17.01 <0.01 5 YEAR 22.61 <0.01 6 STAGE+YEAR 57.34 <0.01 7 STAGE+YEAR+SEASON 176.95 <0.01 2012 S.B. Magle, J.C. Chamberlin, and N.E. Mathews 73 to become evident. In addition, since infected animals were euthanized shortly after capture, the disease was not permitted to progress into the population from these deer, which perhaps very slightly reduces the potential rates of incidence and spread in this system. As prevalence of the disease is currently very low, it seems most likely that any impact of the disease on the White-tailed Deer population is minor, and compensatory in nature. If the prevalence of CWD increases over time in this system, eventually a threshold would be expected to be reached where CWD-related mortality becomes additive (Gross and Miller 2001), but details regarding such a threshold are currently unknown. In this area, deer are not typically long-lived, due largely to hunting, which represented the most prevalent mortality source in our study. Assuming that hunting pressure is not dependent on CWD status (Grear et al. 2006), areas with low rates of harvest pressure will be more likely characterized by dense populations of deer with a more mature age structure, and disease mortality will be more likely to remain compensatory. Similarly, where hunting pressure is high, deer populations are likely to be younger and more sparse, and disease mortality, if present, may be expected to eventually become additive. However, as deer harvest is also the primary tool for both deer and CWD management, the resultant dynamics on a landscape with varying levels of hunter participation are complex and can be predicted only with detailed mathematical Table 4. Seasonal survival rates of White-tailed Deer in Wisconsin’s CWD outbreak area (2002–2007). Hunting mortalities included Hunting mortalities censored Stage Season N Sx 95% C. I. Sx 95% C. I. Fawn Parturition 36 0.93 0.86–0.97 0.93 0.86–0.97 Yearling male Pre-rut 14 0.98 0.96–0.99 0.98 0.93–0.99 Rut 11 0.81 0.65–0.90 0.93 0.82–0.97 Gestation 5 0.92 0.83–0.96 0.92 0.83–0.96 Parturition 15 0.99 0.97–1.00 0.99 0.97–1.00 Yearling female Pre-rut 22 0.99 0.98–1.00 0.99 0.98–1.00 Rut 21 0.93 0.83–0.97 0.96 0.89–0.99 Gestation 12 0.97 0.92–0.99 0.97 0.92–0.99 Parturition 21 0.99 0.98–1.00 0.99 0.98–1.00 Adult male Pre-rut 11 0.97 0.90–0.99 0.99 0.95–1.00 Rut 8 0.54 0.39–0.68 0.96 0.84–0.99 Gestation 7 0.79 0.64–0.88 0.95 0.80–0.99 Parturition 11 0.98 0.94–0.99 0.99 0.97–1.00 Adult female Pre-rut 69 0.99 0.98–1.00 0.99 0.98–1.00 Rut 67 0.89 0.85–0.93 0.97 0.93–0.98 Gestation 56 0.96 0.93–0.98 0.96 0.93–0.98 Parturition 69 0.99 0.99–1.00 0.99 0.99–1.00 74 Northeastern Naturalist Vol. 19, No. 1 modeling (Osnas et al. 2009). Any impact on the population will reduce hunting opportunities, though hunter participation is already negatively impacted by the presence of the disease (Vaske et al. 2004). Our study provides one of the earliest assessments of White-tailed Deer survival in Wisconsin’s Chronic Wasting Disease Eradication Zone. We report extremely low rates of non-hunting mortality, finding no evidence that CWD was substantially increasing mortality rates during the duration of our study from 2002 to 2007. Our results can serve as a baseline by which to compare future mortality rates in this area to assess the virulence of CWD over time. Acknowledgments We thank A. Oyer and L. Skuldt, V. St.-Louis, T. Sickley, R. Rolley, and G. Bartelt for contributions to study design, and data collection during 2002–2005. R. MacLean, D. Grove, W. Delanis, and T. Hoffman provided veterinary and project support. We thank V. Greene, J. Isabelle, M. 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