469 S.A.B. Campbell and T.C. Wood 22001133 NORNToHrEthAeSaTstEeRrnN N NaAtuTrUaRlisAtLIST 20V(o3l). :2406,9 N–4o7. 73 Influences of Precipitation, Temperature, and Acorn Mast on White-Tailed Deer Body Weight in the Northern Piedmont of Virginia Sean A.B. Campbell1,* and Thomas C. Wood2,3 Abstract - Management strategies for Odocoileus virginianus (White-tailed Deer) often use changes in body weight as an indicator of population health and density. Annual fluctuations in White-tailed Deer age-sex classes are influenced by numerous environmental variables. We analyzed 1989–2009 deer harvest data from Virginia’s Deer Management Assistance Program (DMAP), consisting of 15,622 deer in the Northern Piedmont of Virginia. We used Pearson’s correlation and t-tests to examine impacts of annual fluctuations in precipitation, days with snow accumulation ≥1 inch, temperature, and acorn mast on average weights (kg) of current-year, 1-year-lag, and 2-year-lag fawn, and yearling age-sex classes. Of the variables we examined, seasonal precipitation appears to be the most significant environmental factor influencing White-tailed Deer body weight in the Northern Piedmont of Virginia. When using deer weight fluctuations as an indicator in management plans, managers should consider the influence of seasonal precipitation specific to their geographic region or management unit. Managers in the Northern Piedmont of Virginia should continue to use changes in average deer weights as an indicator of population density and habitat quality due to the stability of weights in response to regional environmental stochastic events. Introduction Annual trends in Odocoileus virginianus Zimmerman (White-tailed Deer) body weight variation are often used as an indicator of population health and density, which then becomes the basis for management decisions involving harvest limits (Cypher and Cypher 1988, Keyser et al. 2005a). However, temporal changes in deer body weight are hypothesized to be directly correlated to environmental variables (Osborne 1976). Wildlife managers often try to influence deer population body weight by addressing density-dependent factors through harvest limits and increasing food availability. Variables that can influence average body weight of deer populations, but which managers cannot control, include abiotic, density-independent climatic variables such as temperature, precipitation, and snow accumulation (French et al. 1960, Mech et al. 1987, Silver et al. 1971). Understanding the influence of abiotic variables on deer body weight will enable wildlife managers to forecast potential regional body weight changes outside their control, especially if observed with a time lag effect. Climatic variables are known to influence 1256 Stevens Avenue, Portland, ME 04103. 2New Century College, George Mason University, 4400 University Drive MSN 5D3, Fairfax, VA 22030-4444. 3Environmental Studies on the Piedmont, 6712 Blantyre Road, Warrenton, VA 20187. *Corresponding author - sean.abcampbell@yahoo.com. S.A.B. Campbell and T.C. Wood 2013 Northeastern Naturalist Vol. 20, No. 3 470 White-tailed Deer behavior such as movement patterns, habitat selection, and feeding rates (Beier and McCullough 1990, Moen 1976, Ozoga and Gysel 1972). Because climatic variables also influence deer body weight and certain physiological processes, understanding how these variables interact with regionally specific biotic factors to impact deer body weight is essential for effective management (Bauer 1993, Hoffman and Robbinson 1966, Mautz et al. 1992, Messier 1995, Patterson and Power 2002, Strickland et al. 2005, Taillon et al. 2006). Quercus spp. (Oak) mast (acorns) is a widely studied biotic variable known to influence White-tailed Deer movement patterns, body condition, and feeding behaviors (Harlow et al. 1975, McCullough 1985, McShea and Healy 2002, McShea and Schwede 1993, Wentworth et al. 1989). During the fall and winter, acorns are a staple food for White-tailed Deer and can compose up to 76–90% of their diet (Harlow et al., 1975, McCullough, 1985). In Front Royal, VA, deer increased their annual home ranges during the fall to include areas where acorns were abundant (McShea and Schwede 1993). Feldhammer et al. (1989) and Wentworth et al. (1989) stated that birth weights and fawn survival in deciduous hardwood forests were positively correlated with the abundance of the previous year’s hard-mast crops. However, oak-mast availability varies both spatially and temporally (McShea and Healy 2002). The significance of oak mast in the diet of a White-tailed Deer population varies considerably depending on oak forest abundance and species, land cover types, and availability of alternative food sources. White-tailed Deer may compensate for oak-mast scarcity or decreased forage quality by altering their movement patterns, foraging time, and consumption rates (Taillon et al. 2006). The influence of annual fluctuation in climatic and acorn-mast variables on deer weights may not become evident until one or two years later. Current deer body condition and population productivity, including recruitment and fecundity, may be a reflection of past food abundance and quality, rainfall, and population density, suggesting the importance of understanding time-lag effects of environmental variables on deer body weight (Fryxell et al. 1991, Keyser et al. 2005a, b; Monteith et al. 2009; Patterson and Power 2002). Time lags have been identified in relationships between rainfall, vegetation, and deer recruitment (Teer et al. 1965), as well as in correlations between deer body weight and current and previous year oak-mast index (Feldhamer et al. 1989, Wentworth et al. 1992). The objective of our study is to determine the influence of climatic and oakmast variables on annual White-tailed Deer average body weights in the Northern Piedmont of Virginia. Specifically, we correlated seasonal fluctuations in temperature, precipitation, snow accumulation, and acorn mast with current-year, and subsequent one- and two-year average body weights of White-tailed Deer. The results of our study will provide managers with insight into how environmental factors influence changes in deer population body weight within the Northern Piedmont of Virginia. 471 S.A.B. Campbell and T.C. Wood 2013 Northeastern Naturalist Vol. 20, No. 3 Study Area Our study area included the Virginia counties of Fauquier, Culpeper, Rappahannock, Loudoun, Madison, and Orange, which together encompasses 3698 km2 and represents the physiographic region of the Northern Piedmont in Virginia (Fig. 1). The 2006 National Land Cover Database shows that land-cover types in the study area were a mix of pasture/hay (36%), deciduous forest (32%), cultivated crops (7%), and developed landscapes (15%) (Fry et al. 20 11). In the Northern Piedmont, composition of mature hardwood forest communities varies regionally with soils and topography. Xeric, acidic soils support oak/ heath forests composed of Quercus alba L. (White Oak), Q. prinus L. (Chestnut Oak), Q. rubra L. (Northern Red Oak), and common Ericaceous (heath) plants including Kalmia latifolia L. (Mountain Laurel) and Rhododendron periclymenoides (Michaux.) Shinners (Wild Azalea). More basic (higher pH) upland soils usually support oak-hickory (Quercus-Carya) forests. White Oak is a ubiquitous dominant in both groups. Mixed forests of Fagus grandifolia Ehrhart (American Beech), oaks, and Liriodendron tulipifera L. (Tulip-Poplar) are common in mesic, acidic ravines throughout the Piedmont (VADCR 2010). Methods White-tailed Deer data White-tailed Deer body-weight data from deer harvested by hunters between 1989–2009 were compiled by Virginia Department of Game and Inland Fisheries (VDGIF) District Biologists and Department personnel using the VDGIF Deer Management Assistance Program (DMAP). White-tailed Deer were harvested during the Virginia hunting season from September through January. Growth rates of fawns and yearlings during the hunting season were assumed to be consistent between years of the study and have minimal impact on average weights Figure 1. Virginia counties within our study area (in dark): Fauquier, Culpeper, Rappahannock, Loudoun, Madison, and Orange. S.A.B. Campbell and T.C. Wood 2013 Northeastern Naturalist Vol. 20, No. 3 472 for each deer age-sex class. The Virginia DMAP requires participants to record sex, eviscerated body weight (to nearest 0.45 kg), lactation, age (Severinghaus 1949), antler points, outside antler spread, and diameter at antler base from every deer harvested. Fawn (0.5 years), yearling (1.5 years), and mature (≥ 2.5 yearold) deer are most accurately aged through tooth development and wear (Gee et al. 2002, Severinghaus 1949). We limited our analysis to fawns and yearlings because these age groups represented >50% of our dataset. More importantly, these age classes exhibit the greatest growth rate and, therefore, would be more likely to exhibit the influences of environmental variables on body weight. The number of fawn and yearling deer harvested in our study area from 1989–2009 was 57–1326 per year, with a total of 15,622 individuals harvested. Offspring per female and fawn date of birth are two potential variables that may impact end-ofyear weights. These two variables were not included in the analysis due to lack of available data. Oak-mast data Oak-mast data was obtained from surveys conducted by foresters working with the Virginia Department of Forestry (G. Norman, VDGIF , Verona, VA, 2009, unpubl. data). Annual canopy surveys of oak tree-mast crop were conducted between 18 August and 18 September from 1993 through 2009. Surveys were conducted in various locations throughout the Northern Piedmont of Virginia. Oak mast is surveyed and classified by Red Oak group, White Oak group, and Chestnut Oak group. Oak-mast abundance was visually surveyed on individual trees and given an average mast-index ranking on a scale from 1 to 4 (1 = light, 2 = moderate, 3 = heavy, 4 = no opinion). Mast index rankings were averaged by county and region. The Northern Piedmont mast-index rankings were used in this analysis. Weather data Precipitation, snow accumulation, and temperature data from 1989–2009 were obtained from the National Climatic Data Center, Climate Data Online System (NCDC CDO System). The annual climatic summary of monthly surface data for the weather station listed as Warrenton 3 SE, VA (448888/99999) was used for precipitation and temperature data because it is centrally located in the study area. For this study, we used the weather variables: departure from normal monthly precipitation (DPNP), days with snow accumulation ≥1 in (DSNW), and departure from normal monthly temperature (DPNT). The DPNP measures the difference in monthly precipitation from the 30-year average for total monthly precipitation. The DPNT data, measured in degrees Fahrenheit, is the monthly average of daily deviations from the 30-year average monthly temperature. The DSNW dataset was obtained from three weather stations (Warrenton 3SE, The Plains, and Sperryville) located centrally in the study area. These data sets were averaged to generate a mean snow accumulation data set to be correlated with the DMAP data. 473 S.A.B. Campbell and T.C. Wood 2013 Northeastern Naturalist Vol. 20, No. 3 Data analysis Pearson’s correlation coefficient was used to examine the relationships between the environmental variables and deer weight (kg). Annual oak-mast index, DPNP, DSNW, and DPNT and were treated as the independent variables. Annual average deer weights by age-sex class were treated as dependent variables. Using t-tests, correlations were considered significant at P < 0.05. The NCDC CDO system records weather data by month. Therefore, to analyze precipitation and temperature by season, we designated January–March as winter, April–June as spring, July–September as summer, and October–December as fall. DPNT and DPNP for each month were correlated to yearly average deer weights for each age-sex class. Pearson correlations were performed between the seasonal (three-month) weather periods and average deer wei ghts of sex-age classes to determine the influence of seasonal weather variables on deer body weight. Total numbers of days per year with snow accumulation were correlated to average yearly body weight of each deer age-sex class. Annual oak-mast rankings for each oak group were correlated to annual average body weight for each age-sex group. To test for time-lag effects, we correlated environmental variables to same year and subsequent 1- and 2-year average deer body wei ghts. Results Significant relationships were observed between winter precipitation and current-year body weight (1” above average = 0.17–0.32 kg) in fawn females (r = 0.114, P = 0.026) and yearling males (r = 0.121, P = 0.028). Significant relationships were seen between winter precipitation and deer weights (each 1” above average = 0.17–0.56 kg) with a 1-year time lag for fawn females (r = 0.11, P = 0.033), fawn males (r = 0.127, P = 0.022), yearling females (r = 0.164, P = 0.005), and yearling males (r = 0.211, P = 0.004). Significant relationships were seen between winter precipitation from two-years-prior and current-year deer body weight (each 1” above average = 0.23–0.26 kg) for yearling females (r = 0.124, P = 0.025) and yearling males (r = 0.099, P = 0.053). We found no correlation between DSNW data and current-year, 1-year-lag, and 2-year-lag body weight of any age-sex class (P > 0.05), nor between spring precipitation and current-year body weight of any age-sex class (P > 0.05). There was a significant correlation between spring precipitation and deer body weight (each 1” above average = -0.28 kg) under a 1-year time lag in yearling males (r = -0.112, P = 0.022). Significant effects from spring precipitation were seen on deer body weights (each 1” above average = 0.17–0.25 kg) under a 2-year lag in fawn females (r = 0.126, P = 0.015), fawn males (r = 0.17, P = 0.002), and yearling males (r = 0.142, P = 0.007). The relationship between summer precipitation and current-year body weight in fawn males (each 1” above average = 0.1 kg) was significant (r = 0.095, P = 0.048). Significant negative relationships were seen between fall precipitation and current-year body weight (each 1” above average S.A.B. Campbell and T.C. Wood 2013 Northeastern Naturalist Vol. 20, No. 3 474 = -0.17-0.3 kg) in fawn females (r = -0.134, P = 0.011) and yearling males (r = -0.115, P = 0.029). Fall precipitation had a significant effect on deer body weight (each 1” above average = 0.14-0.28 kg) with a 1-year time lag for fawn females (r = 0.099, P = 0.053), fawn males (r = 0.103, P = 0.045), and yearling males (r = 0.112, P = 0.034). Significant relationships (each 1 °F above average = 0.08 kg) were observed between winter temperature and male fawn weights with a 2-year time lag (r = 0.118, P = 0.023). Spring temperature had a significant relationship (each 1 °F above average = -0.24 kg) for yearling males after a 2-year time lag (r = -0.115, P = 0.026). There were no other effects of seasonal temperature on deer weights for current-year or 1- or 2-year lags (P > 0.05). White Oak, Chestnut Oak, and Red Oak groups had no effect on current-year or 1- or 2-year-time-lag White-tailed Deer body weight for any age-sex class (P > 0.05). Discussion Precipitation correlations The results of our study suggest that, of the variables we tested, seasonal precipitation had the most significant effect on White-tailed Deer body weight in the Northern Piedmont of Virginia. Above normal precipitation in winter correlated with increased deer weights in current-year fawn female, yearling male; 1-year lags in fawn female, fawn male, yearling female, and yearling male; and 2-year lag in yearling female and yearling male age-sex classes. Maternal condition during gestation can impact weights of fawns and have lifelong consequences for offspring that may become apparent in subsequent years (Monteith et al. 2009). Increased winter precipitation may improve vegetation and habitat quality, likely resulting in improved maternal condition and greater body weights of offspring (Halls 1984). In the Northern Piedmont of Virginia, snow depths and winter weather are not as harsh as those encountered in more northern latitudes. Snow in this region does not generally reach depths severe enough to restrict movements and substantially reduce food sources (Halls 1984). Indeed, no effect was seen between days with snow accumulation (ranging from 5–31 days) and current-year, 1-yeartime- lag or 2-year-time-lag deer weights. Oak correlations Prior studies indicate the importance of oak mast as a staple food for Whitetailed Deer during the fall and winter (Harlow et al. 1975, McCullough 1985, McShea and Schwede 1993). The results of our study indicate that annual fluctuations in oak mast had no influence on current-year, 1-year-lag, or 2-year-lag body weights of fawn and yearling White-tailed Deer in the Northern Piedmont of Virginia. These results may be explained by the ability of White-tailed Deer to utilize alternate food sources, and alter their feeding behaviors during years of oak-mast scarcity (Anderson 1997, Harlow et al. 1975, McShea and Schwede 1993, Taillon et al. 2006). Human development and habitat alteration 475 S.A.B. Campbell and T.C. Wood 2013 Northeastern Naturalist Vol. 20, No. 3 in the region have created a diversity of habitats and an abundance of available food sources that may mitigate the adverse impact of oak-mast failures on deer body weight, especially as compared to other study areas composed primarily of oak-dominated forests (Harlow et al. 1975, McShea and Schwede 1993). The Northern Piedmont of Virginia has a high biological carrying capacity, and most of the region’s deer population remains under this threshold (VDGIF 2011). Considering the findings of these studies, regional best management practices for White-tailed Deer management should incorporate habitat diversity and increase availability of alternate food sources to optimize deer health in forest-dominated landscapes. Management implications White-tailed Deer have developed numerous adaptations to endure both shortterm (i.e., weather events) and long-term (i.e., season and climate) changes in their environment. Impacts from seasonal variations in normal temperature and days with snow accumulation, when looked at as density-independent, exogenous influences on deer health, appear to be relatively minimal to cumulative health as observed in body weights of harvested White-tailed Deer in the Northern Piedmont of Virginia. The ability of deer to acclimatize and flourish in the region’s temperate ecosystem is enhanced by the evolutionary adaptations of White-tailed Deer to cope with fluctuating temperature, precipitation, and fo od availability. Changes in deer body weights are used as an indicator of density and habitat quality (Keyser et al. 2005a, Smith et al 1975, Strickland and Demarias 2000). The results of our study suggest that deer weights were generally stable in response to fluctuating environmental variables and environmental stochastic events over a 16-year period in the Northern Piedmont of Virginia. Wildlife managers in the Northern Piedmont of Virginia should continue to use changes in deer body weight as an indicator of density and habitat quality. Acknowledgments We would like to thank Environmental Studies on the Piedmont for financial support of this study. We would like to thank all the staff and research scientists who contributed to our datasets and helped to make this analysis possible, and for their timely and informative responses to all requests with our study. We especially are grateful to the staff from the Virginia Department of Game and Inland Fisheries, Virginia Department of Forestry, National Oceanic and Atmospheric Administration, and the US Geologic Survey. Literature Cited Anderson, R.C. 1997. Native pests: The impact of deer in highly fragmented habitats. Pp. 117–134, In M.W. Schwartz (Ed.). Conservation in Highly Fragmented Landscapes. Chapman and Hall, New York, NY. 436 pp. Bauer, E.A. 1993. Whitetails: Behavior, Ecology, Conservation. Voyageur Press, Stillwater, MN. 157 pp. Beier, P., and D.R. McCullough. 1990. Factors influencing White-tailed Deer activity patterns and habitat use. Journal of Wildlife Management 109:3–51. S.A.B. Campbell and T.C. Wood 2013 Northeastern Naturalist Vol. 20, No. 3 476 Cypher, B.L., and E.A. Cypher. 1988. Ecology and management of White-tailed Deer in northeastern coastal habitats: A synthesis of the literature pertinent to National Wildlife Refuges from Maine to Virginia. USFWS Biological Report 88(15). Feldhammer, G.A., T.P. Kilbane, and D.W. Sharp. 1989. Cumulative effect of winter on acorn yield and deer body weight. The Journal of Wildlife Management 53:292–295. French, C.E., L.C. McEwen, N.D. Magruder, T. Radar, T.A. Long, and R.W. Swift. 1960. Response of White-tailed bucks to added artificial light. Journal of Mammology 41(1):23–29. Fry, J., G. Xian, S. Jin, J. Dewitz, C. Homer, L. Yang, C. Barnes, N. Herold, and J. Wickham. 2011. Completion of the 2006 National Land Cover Database for the conterminous United States. Photogrammetric Engineering and Remote Sensing 77(9):858–864. Fryxell, J.M., D.J.T. Hussel, A.B. Lambert, and P.C. Smith. 1991. Time lags and population fluctuations in White-tailed Deer. The Journal of Wildlife Management 55(3):377–385. Gee, K.L., J.H. Holman, M.K. Causey, A.N. Rossi, and J.B. Armstrong. 2002. Aging White-tailed Deer by tooth replacement and wear: A critical evaluation of a timehonored technique. Wildlife Society Bulletin 30(2):387–393. Halls, L.K. 1984. White-tailed Deer: Ecology and Management. Stackpole Books, Harrisburg, PA. 632 pp. Harlow, R.F., J.B. Whelan, H.S. Crawford, and J.E. Skeen. 1975. Deer foods during years of oak-mast abundance and scarcity. The Journal of Wildlife Management 39:330–336. Hoffman, R.A., and P.F. Robinson. 1966. Changes in some endocrine glands of Whitetailed Deer as affected by season, sex, and age. Journal of Mammalogy 47(2):266–280. Keyser, P.D., D.C. Guynn, Jr., and H.S. Hill, Jr. 2005a. Population density-physical condition relationships in White-tailed Deer. Journal of Wildlife Management 69(1):356–365. Keyser, P.D., D.C. Guynn Jr., and H.S. Hill Jr. 2005b. Density-dependent recruitment patterns in White-tailed Deer. Wildlife Society Bulletin 33(1):222–232. Mautz, W.W., J. Kanter, and P.J. Perkins. 1992. Seasonal metabolic rhythms of captive female White-tailed Deer: A reexamination. Journal of Wildlife Management 56:656–661. McCullough, D.R. 1985. Variables influencing food habits of White-tailed Deer on George Reserve. Journal of Mammalogy 63:682–692. McShea, W.J., and W.M. Healy. 2002. Oak Forest Ecosystems: Ecology and Management for Wildlife. The John Hopkins University Press, Maryland. 432 pp. McShea, W.J., and G. Schwede. 1993. Variable acorn crops: Responses of White-tailed Deer and other mast consumers. Journal of Mammology 74(4):999–1 006. Mech, L.D., R.E. McRoberts, and R.O. Peterson. 1987. Relationships of deer and Moose populations to previous winter ’s snow. Journal Animal Ecology 56:615–627. Messier, F. 1995. Is there evidence for a cumulative effect of snow on Moose and deer populations? Journal of Animal Ecology 64:136–140. Moen, A.N. 1976. Energy conservation by White-tailed Deer in the winter. Ecology 57(1):192–198. Monteith, K.L., L.E. Schmitz, J.A. Jenks, J.A. Delger, and R.T. Bowyer. 2009. Growth of male White-tailed Deer: Consequences of maternal effects. Journal of Mammalogy 90(3):651–660. 477 S.A.B. Campbell and T.C. Wood 2013 Northeastern Naturalist Vol. 20, No. 3 Ofcarcik, R.P., E.E. Burns. 1971. Chemical and physical properties of select acorns. Journal of Food Science 36:576–578. Osborne, J.S. 1976. Population dynamics of the Blackbeard Island White-tailed Deer. M.Sc. Thesis. University of Georgia, Athens, GA.117 pp. Ozoga, J.J., and L.W. Gysel. 1972. Response of White-tailed Deer to winter weather. Journal of Wildlife Management 36:892–896. Patterson, B.R., and V.A. Power. 2002. Contributions of forage competition, harvest, and climate fluctuations to changes in population growth of northern White-tailed Deer. Oecologia 130(1):62–71. Severinghaus, C.W. 1949. Tooth development and wear as criteria of age in White-tailed Deer. The Journal of Wildlife Management 13:195–216. Silver, H., N.F. Colovos, and H.H. Hayes. 1971. Effects of falling temperature on heat production and fasting in White-tailed Deer. Journal of Wildlife Management 35:37–46. Smith, C.M., E.D. Michael, and H.V. Wiant JR. 1975. Size of West Virginia deer as related to soil fertility. West Virginia Agriculture and Forestry 6:12–13. Strickland, B. K., and S. Demarais. 2000. Age and regional differences in antlers and mass of White-tailed Deer. Journal of Wildlife Management 64(4):903–911. Strickland, B. K., D.G. Hewitt, C.A. DeYoung, and R.L. Bingham. 2005. Digestible energy requirements for maintenance of body mass of White-tailed Deer in southern Texas. Journal of Mammalogy 86(1):56–60. Taillon, J., D.G. Sauve, and S.D. Cote. 2006. The effects of decreasing winter diet quality on foraging behavior and life-history traits of White-tailed Deer fawns. The Journal of Wildlife Management 70(5):1445–1454. Teer, S.G., J.W. Thomas, and E.A. Walker. 1965. Ecology and management of Whitetailed Deer in the Llano Basin of Texas. Wildlife Monographs 15. Virginia Department of Game and Inland Fisheries (VDGIF). 2011. The buck stops here: History, biology, and management of White-tailed Deer in Virginia. Available online at http://www.dgif.virginia.gov/wildlife/deer/factsheet/pdf. Accessed 10 January 2011. Virginia Department of Conservation and Recreation (VADCR). 2010. Natural Heritage. The natural communities of Virginia: Classification of ecological community groups. Second Approximation (Version 2.3). Available online at http://www.dcr.virginia.gov/ natural_heritage/ncoverview.shtml. Accessed 10 January 2011. Wentworth, J.M., S.A. Johnson, and P.E. Hale. 1989. Influence of acorn abundance on White-tailed Deer in the southern Appalachians. In C.E. McGee (Ed.). Proceedings of the workshop on southern Appalachian mast management. University of Tennessee, Knoxville, TN. 85pp. Wentworth, J.M., S.A. Johnson, P.E. Hale, and K.E. Kammermeyer. 1992. Relationships of acorn abundance and deer herd characteristics in the southern Appalachians. Southern Journal of Applied Forestry 16:5–8.