Long-Term Impact of White-tailed Deer on Community Structure and. Biodiversity in Mississippi: Year 5. Research Associate.

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1 Long-Term Impact of White-tailed Deer on Community Structure and Biodiversity in Mississippi: Year 5 Research Associate Phillip Hanberry Principal Investigators Stephen Demarais and Bruce D. Leopold Department of Wildlife and Fisheries Mississippi State University Mississippi State, MS June 1, 2006 Federal Aid in Wildlife Restoration Project W-48, Study 64 Final Report

2 2 ACKNOWLEDGEMENTS Major funding for this project was provided by Mississippi Department of Wildlife, Fisheries, and Parks with Federal Aid for Wildlife Restoration Project W-48, Study 64. The U. S. Forest Service, Weyerhaeuser Company, and Anderson-Tulley Company provided additional funding. U.S. Forest Service staff provided assistance for exclosure maintenance, particularly Stephen Lee and Ed Moody at Desoto National Forest, Larry Moore and Stephanie Allison at Delta National Forest, and John Baswell and Joel Okula at Tombigbee National Forest Additional support on wildlife management areas was provided by John Taylor at Choctaw Wildlife Management Area, Dwight Morrow at Leaf River Wildlife Management Area, and Bobby Hodnett at Sunflower Wildlife Management Area. We would also like to thank Scott Edwards, Jarrod Fogarty, Phil Jones, and Aaron Pearse for their aid.

3 3 INTRODUCTION White-tailed deer (Odocoileus virgianus) densities have increased during the past century, due to harvest restrictions and improved habitat resulting from modified land use (Russell et al. 2001). High deer densities, in combination with their large body mass, generalized diet, mobility, and high rate of increase, create the potential to influence not only plant species composition and abundance, but long-term successional and ecosystem processes as well (Coté et al. 2004). Nevertheless, vegetation types may respond idiosyncratically to similar herbivorous pressure depending on community composition, competition, life history traits, root reserves, seed germination and dispersal, available deer forage, seasonal timing of herbivory, isolation, historical and current land use, disturbance, region, climate, topography, and gradients of moisture, soil fertility, and light availability (Mladenoff and Stearns 1993). Alterations in vegetative structure or species composition furthermore may affect birds, small mammals, and invertebrates through habitat loss and reduced foraging opportunities. Studies have documented that heavy deer browsing decreased plant growth, number of reproductive structures, and species richness (Tilghman 1989, Balgooyen and Waller 1995, Shelton and Inouye 1995, Augustine and Frelich 1998, Anderson et al. 2001, Fletcher et al. 2001, Horsley et al. 2003). Herbivory likewise can alter vegetation structure and composition. After 5 years of deer exclusion in the Virginia Piedmont, Rossell et al. (2005) detected reduced forb cover, vertical plant cover, and selective browsing of tree seedlings. There is strong evidence that browsing reduces survival and recruitment of palatable hardwoods and slow growing conifers in northern forests, particularly eastern hemlock (Tsuga canadensis), Canada yew (Taxus candensis), and

4 4 northern white cedar (Thuja occidentalis) (Frelich and Lorimer 1985, Tilgham 1989, Balgooyen and Waller 1995, Waller and Alverson 1997, Horsley et al. 2003). However, at regional levels, a combination of factors influences long-term tree recruitment failure (Mladenoff and Stearns 1993). Selective foraging may affect competitive relationships and reduce relative plant abundance (Balgooyen and Waller 1995, Anderson et al. 2001, Horsley et al. 2003). Intensive deer browsing can shift the understory toward grasses, ferns, sedges, and browse-tolerant or nonpalatable herbs and shrubs (Tilghman 1989, Anderson et al. 2001, Horsley et al. 2003). Plants that are rare or belong to the Liliaceae and Orchidaceae families can be more negatively affected by overbrowsing than abundant species (Miller et al. 1992, Anderson et al. 2001, Fletcher et al. 2001), and in fact they may be potential indicators of overbrowsing. Selective foraging of legumes is a mechanism which potentially may lead to changes in nitrogen pools and cycling, as well as primary productivity (Ritchie et al. 1998). Deer may affect plant composition enough to disturb rate or trajectory of successional pathways (Stromayer and Warren 1997, Horsley et al. 2003). Heavy browsing of early seral plants may allow quicker establishment of later successional species. Conversely, deer browsing may prevent woody plant invasion of clearings, or select against certain understory tree species. Forests may not regenerate after disturbance in the presence of overbrowsing (Stromayer and Warren 1997). In Pennsylvania, hardwoods are not regenerating after harvest or are being succeeded by black cherry (Prunus serotina) and American beech (Fagus grandifolia), while in the Great Lakes Region mixed forests, conifers may be replaced by sugar maple (Acer

5 5 saccharum) and other hardwoods (Frelich and Lorimer 1985, Tilghman 1989, Stromayer and Warren 1997, Horsley et al. 2003). When basic plant biology requirements are met, then combinations of land context, plant community and site characteristics, in conjunction with time length and intensity of deer density impacts, may most influence whether browsing pressure is evident (Coté et al. 2004). Many studies with detectable differences have occurred in small, older forests within a matrix of young stands (Alverson et al. 1988). Effects of browsing pressure may be less evident in young stands than in mature forests, due to plentiful forage, greater plant species turnover and growth, and increased resiliency to browsing. Early successional sites may increase deer densities to levels greater than adjoining mature forest can sustain without loss of biodiversity. Severe vegetational changes caused by deer may preclude use of the altered habitat by some bird species. Proximate factors, such as characteristic vegetative configuration and floristic cues, induce a settling response in birds (Hildén 1965). If the set of proximate factors is missing, birds will not select the habitat. In contrast, small mammals may be less sensitive because they can adapt to changing conditions (Bourlière 1975) and may not have specific habitat needs beyond broad vegetation type and successional stage. Deer-induced effects on some animal species can be subtle. Deer may compete directly with herbivorous mammals and birds for food resources (McShea 2000). Additionally, insect densities may decrease if vegetative structure is simplified, reducing prey for birds and insectivorous mammals (Coté et al. 2004). Structural changes may result in loss of cover from predators for birds and small mammals, fewer available

6 6 nesting sites, or greater nest exposure to predators (Fuller 2001). Furthermore, deer may trample bird nests or consume fledglings (Pietz and Granfors 2000). Prior research noted a correlation between high white-tailed deer densities and reduced bird species richness and abundance (Casey and Hein 1983, McShea et al. 1995). DeGraaf et al. (1991) documented negative impacts from high deer density on several species of birds, even though management practices (e.g. thinning) and avian species absence on treatments complicated analysis. Currently, experimental evidence for whitetailed deer impacts on birds appears to be limited to two studies (Fuller 2001). A study of Pennsylvania enclosures of 13 or 26 ha after 10 years, detected decreases in species abundance and richness of intermediate canopy nesters (nest at m) (decalesta 1994). After monitoring 4 ha exclosures for 9 years in Virginia, McShea and Rappole (2000), found avian abundance changes due to increased density and diversity of woody shrubs. Most species had greater bird numbers in exclosures, with the exception of some non-migrant birds. White-tailed deer effects on small mammals have been examined only in the context of acorn availability (Ostfield et al. 1996, McShea 2000). Following low-mast years, small mammal species (e.g. Tamias striatus and Peromyscus leucopus) increased in deer exclosures relative to reference sites (McShea 2000). Despite extensive research and management focused on deer, limited investigations assessing deer impacts on plant and animal communities have occurred in the southeastern United States (Fuller 2001, Russell et al. 2001). To meet the need for long-term monitoring of response by vegetation and wildlife to high deer densities, we compared vegetation characteristics in Mississippi s Lower Coastal Plain, Upper Coastal

7 7 Plain, and Delta physiographic regions in exclosures and paired controls five years after exclosure construction (Demarais et al. 2003). This study provides information needed to unite deer management with ecosystem management. METHODS In the Delta physiographic region, three study sites were located in bottomland hardwoods and forested wetlands on Sunflower Wildlife Management Area within the Delta National Forest. In the Lower Coastal Plain, three study sites were located in upland pine on Leaf River Wildlife Management Area within the DeSoto National Forest. In the Upper Coastal Plain, three study sites were located in upland pine on Choctaw Wildlife Management Area within the Tombigbee National Forest. Study areas are managed by Mississippi Department of Wildlife, Fisheries, and Parks and U.S. Forest Service personnel. Cooperators agreed that timber stand management activities, such as thinning and prescribed burning, would be conducted as scheduled and would be applied to each exclosure and control within the same time period. Physiographic regions were delineated by Pettry (1977). Three exclosures were constructed at each of the three selected physiographic regions in Mississippi during spring Exclosures ranged from 5.8 to 7.4 acres, and were made from 8 ft Solid Lock High Tensile wildlife fencing (Demarais et al. 2003). Vegetation Sampling Understory vegetation coverage was recorded using a modification of the lineintercept method (Canfield 1941) during June Ten, 30-m transects were randomly placed within each exclosure and control. Plants were identified by species and growth

8 8 form. Each species was recorded as being either the top layer of vegetation or total understory coverage. Deer preference ratings were obtained from Warren and Hurst (1981). We sampled overstory and midstory vegetation during June We identified overstory tree species within 10, 0.1-acre plots with a diameter at breast height (DBH) of 4.5 inches. Midstory tree species with a DBH between inches were recorded within the 10, 0.01-acre plots. We used spherical densiometer readings to estimate overstory canopy coverage. Readings were taken at plot center of the 10, 0.1-acre plots, in each cardinal direction. We used a Nudds Density Board to estimate vegetative structure during June 2005 (Nudds 1977). We measured the percentage of visual obstruction of each section of the Nudds Board in each cardinal direction from a distance of 37 feet from plot center. We estimated growing season production by randomly placing 15, 1-m 2 hoops within each exclosure and control during July Vegetation within the hoop 6 ft tall was clipped and sorted by species as current or residual growth. Samples were dried in a forced-air oven at 60ºC for 72 hours and weighed to determine dry matter weight. To obtain relative amount of new growth, we divided weight of the current year s growth by total plant biomass. Breeding Bird Sampling We conducted avian point counts during May We used a 10-minute point count and identified birds to species by sound and sight. Point count stations were located at the center of each exclosure and control, and each site was sampled on 4 different days during optimal weather conditions (i.e., < 40% cloud cover and calm wind

9 9 conditions). Identified birds were placed in distance categories of < 25 meters, meters, and >50 meters. Analysis was conducted only on recorded birds < 50 meters to ensure birds were not in the edge of the exclosure. Partners in Flight created a system to assess the conservation status of North American bird species (Panjabi et al. 2005). Six vulnerability categories are scored from 1 for low vulnerability to 5 for high vulnerability. The 6 vulnerability factors are: population size, breeding distribution, non-breeding distribution, threats to breeding, threats to non-breeding, and population trend. Summation of the scores generates priority species pools for physiographic regions. Species are of regional concern if they scored > 13, had a moderate regional threat, and a declining population trend. We calculated the conservation score by multiplying the mean abundance of each species by its Partners in Flight score and summing all scores within the exclosures and controls. Calculation of the priority score was similar, but included only the species of regional concern within the exclosures and controls. Small Mammal Sampling We trapped small mammals during late winter 2005 using Sherman live traps (3 x 3 x 11 inches) baited with peanut butter. We placed traps on a 5 x 5 grid at plot center of each exclosure and control with 65.6 feet between traps. Traps were checked daily, and all exclosures and controls in a forest were sampled simultaneously for five days. Each captured small mammal was identified to species, weighed, sexed, and toe-clipped for individual identification. Capture and handling procedures were approved by the Mississippi State University Institutional Animal Care and Use Committee (protocol #05-008).

10 10 Deer Camera Survey We surveyed deer populations surrounding the exclosures and controls of each region during February We placed 6 infrared-triggered cameras at a rate of one per 80 acres around the exclosures and controls. We pre-baited camera stations for 5 days using corn, and surveyed the next 5 days. We identified number of individual bucks and used an extrapolation factor from McKinley (2002) and equation from McDonald (2003) to estimate the total population around the exclosures and controls. We used the extrapolation factor of 0.71 because McKinley (2002) found that 71 % of the population had been surveyed at day 5 with 1 camera / 100 acres. Statistical Analysis We used a mixed model analysis of variance to test for regional, treatment, and regional x treatment interactions for vegetative differences in species richness, Shannon- Wiener and Simpson s indices, top and total understory coverage by growth form, relative new growth, and understory, midstory, and overstory species that occurred in 2 exclosures and controls in all regions. We used the same analysis for bird species richness, conservation score, priority score, and abundance, as well as small mammal species richness and abundance. We also tested for treatment effects within regions. We conducted analyses in SAS Proc MIXED (SAS Institute 2003). We treated paired exclosures and controls (i.e., blocks) as a random effect and used data from establishment 5-years ago as covariates (Demarais et al. 2003). We considered differences significant if P < Prior to analysis, we tested normality and equal variance assumptions. When equal variance assumptions were not met, we arcsine square-root transformed percentage variables and square-root transformed all other variables (Zar 1999). We presented back-

11 11 transformed means of transformed percentage variables and untransformed means for square-root transformed variables. RESULTS Vegetation Sampling Baseline sampling conducted during 1999 and 2000 indicated that vegetative characteristics on each respective pair of exclosures and controls were similar prior to and during construction of exclosures (Demarais et al. 2003). These baseline measures were used as covariates for each respective variable in the current analysis. We recorded 153 plant species on line-intercepts among all three regions. There were 74 species detected in the Delta, 75 species in the Lower Coastal Plain, and 95 species in the Upper Coastal Plain (Tables 1-6). The Delta region was dominated by vines, primarily Campsis radicans, Pueria lobata, and Toxicodendron radicans (Table 1). The Lower Coastal Plain was mostly a mix of woody vegetation (Ilex glabra) and grass (Andropogon virginicus; Table 2). The Upper Coastal Plain was primarily a mix of woody vegetation (Rhus copallina and Liquidambar styraciflua) and vines (Vitis rotundifolia; Table 3). Across regions there were no canopy coverage differences between exclosures and controls of common individual species, growth form, Shannon-Wiener, and Simpson s indices (Tables 7-12). There were 5 common species that occurred in at least 2 exclosures and 2 controls of each region, Diospyros virginiana, Liquidambar styraciflua, Parthenocissus quinquefolia, Smilax glauca, and Toxicodendron radicans (Table 9).

12 12 Within regions, several species were affected by the exclusion of deer. Each of the Delta and Lower Coastal plain regions had one species that differed between exclosures and controls in percent coverage of the top layer of vegetation. Cyperus ovularis (F 1,2 = 62.93, P = 0.016) was greatest in Delta region controls (Table 1). In the Lower Coastal Plain, Acer rubra (F 1,1 = , P = 0.019) was greatest in exclosures (Table 2). Multiple layered transects also had two species that differed in percent coverage. Aster pilosus (F 1,2 = 36.17, P = 0.027) was greatest in the Lower Coastal Plain exclosures (Table 5), and Acer rubra (F 1,2 = 56.03, P = 0.017) was greatest in the Upper Coastal Plain controls (Table 6). We recorded 28 overstory species and 48 midstory species among all three regions. There were 15 overstory and 22 midstory species recorded in the Delta, 9 overstory and 15 midstory species in the Lower Coastal Plain, and 14 overstory and 23 midstory species in the Upper Coastal Plain (Tables 13-19). The Delta region overstory was dominated by several oak species (Quercus lyrata, Q. pagoda, and Q. phellos) (Table 13). The Lower Coastal Plain overstory was primarily composed of Pinus palustris (Table 15). The Upper Coastal Plain overstory was mostly comprised of Pinus taeda (Table 17). Within all of the regions there were no species that dominated the midstory. Overstory and midstory basal area did not differ among or within regions. No treatment effects or interactions were seen for the Nudds density board (Table 20), and densiometer readings (Table 21). We recorded 130 species in biomass plots among all three regions. There were 52 species detected in the Delta, 69 species in the Lower Coastal Plain, and 76 species in the Upper Coastal Plain (Tables 22-26). Delta region biomass was mainly composed of

13 13 vines (Campsis radicans, Pueria lobata, Rubus Flagellaris, and Toxicodendron radicans) (Table 22). Lower Coastal Plain biomass was primarily grasses (Andropogon virginicus and Aristida spp.; Table 23). Upper Coastal Plain biomass was mainly a mixture of grass (Andropogon virginicus), vine (Lonicera japonica), and legume (Desmodium ciliare). Relative amount of new growth had differences within regions, but not among regions and had no interaction between region and treatment. Relative amount of new growth was greater in Delta controls for Celtis laevigata (F 1,1 = , P = 0.003)and Vitis aestivalis (F 1,1 = 170.3, P = 0.049) (Table 22). Breeding Bird Sampling We recorded 35 bird species among all 3 regions with 26 species in the Delta, 23 species in Lower Coastal Plain, and 23 species in Upper Coastal Plain. Species richness, conservation score, priority score, and abundance of birds that occurred in at least 2 exclosures and 2 controls of each region showed no differences between treatments or interactions of region and treatment. Similarly, no species differences were observed between exclosures and controls within each region (Tables 27-31). Small Mammal Sampling We captured a total of four small mammal species: house mouse (Mus musculus), golden mouse (Ochrotomys nuttalli), white-footed mouse (Peromyscus leucopus), and hispid cotton rat (Sigmodon hispidus). Within the Delta and Upper Coastal Plain only the golden mouse, white-footed mouse, and hispid cotton rat were captured (Tables 32 & 34). All four species were captured in the Lower Coastal Plain (Table 33). Species richness and abundance of species that occurred in at least 2 exclosures and 2 controls in each region did not differ between treatments or interactions of region and treatment (Tables

14 ). Also, there were no differences within each region between exclosures and controls. Deer Camera Survey We estimated a total population around the exclosures and controls of 40 individuals (1 deer/12 acres) within the Lower Coastal Plain, and 42 individuals (1 deer/11 acres) in the Upper Coastal Plain. Deer within the Delta region were not attracted to the bait. Therefore, we were unable to obtain a reliable estimate within the Delta region. DISCUSSION Little is known about recovery rates of understory vegetation released from browsing. However, there are examples of both immediate differences (Rossell et al. 2005) and lack of response (Kraft 2004, Webster 2005). In our study, herbivorous pressure may not have been sufficiently intense in the controls, or herbivory may not affect plant communities relative to other factors, such as competition. The magnitude of effects may not be sufficient to be detected by the sample size. Or conversely, deer had browsed the study sites before exclosure construction to the extent that rare and highly preferred plants were already absent, leaving only plants tolerant to intense browsing. Effects can not be detected because they occurred prior to study commencement, a sort of ghost of herbivory past (Connell 1980). If this is the case, it will take more than 5 years for the exclosure sites to reach a state resembling the community prior to heavy browsing. The re-establishment process of browse sensitive plants and seed bank replenishment will depend on location and dispersal rates of source plants.

15 15 Understory species differences may be explained by changes in deer browsing intensity. Cyperus ovularis is a sedge, and therefore greater abundance in controls reflects the shift in plant composition toward less palatable species due to deer browsing preferences. In contrast, Aster pilosus, a species that is highly preferred (Warren and Hurst 1981), increased within exclosures. Celtis laevigata and Vitis aestivalis had more current growth relative to older growth in the controls, which may reflect compensatory growth in response to browsing (Russell et al. 2001). In the future, significant differences might arise from plants that were only detected in exclosures, such as Ceanothus americanus and Hydrangea quercifolia. Deer preference for any one plant species must be interpreted relative to abundance of other more or less preferred species within an area. Relative preference for a species may vary as plant communities vary among regions. Such a relationship is evident in the effects of deer browsing on Acer rubra. In the Lower Coastal Plain, Acer rubra increases within the exclosures indicate that it is a relatively preferred forage within that region, possibly due to the lack of other more highly preferred species. In contrast, in the Upper Coastal Plain, Acer rubra increases within the controls indicate that it is less preferred. This effect in the Upper Coastal Plain represents a shift toward less preferred browse species within areas with deer foraging activity. Although Acer rubra is not likely to become an overstory tree in pine-dominated habitats managed with prescribed burns, it may represent an important ecological impact in other habitats. A greater length of time may be required before we are able to observe more differences, particularly in the midstory and overstory. Jones et al. (1997), in a similar study examining an 18-year-old exclosure in Mississippi s Lower Coastal Plain,

16 16 identified great plant community divergence. A suite of preferred deer browse plants were either more abundant or only present within the exclosure, and vertical structure density was greater. Interestingly, Horsley et al. (2003) found in Pennsylvania that time length before browsing differences became detectable increased with amount of overstory. Regenerating stands showed significant divergence after 3 to 10 years, thinned stands needed 10 years, and uncut stands demonstrated little change after 10 years. Therefore, despite increased food resources in clearings, they were more dynamic in response to herbivory, at least initially. Older stands were more stable, with greater thresholds before harmful browsing disturbance. This study was not able to detect treatment effects after 5 years of deer exclusion on bird and small mammal abundance and species richness. Plant community characteristics did not vary substantially between exclosures and controls and deer are not likely to impact avian habitat selection until vegetation exhibits measurable differences. We did not measure total food availability, but if there were differential resources due to deer foraging, there were apparently no negative effects upon birds and small mammals. A longer time length will be required to assess if deer browsing produces vegetation dissimilarities and, consequently, impacts avian and small mammal species richness and abundance. Further research, either through long-term deer exclusion or inclusion or repeatedly censused individual plants, is needed to provide greater evidence of the extent to which deer browsing decreases plant survival and reproduction relative to other factors (Russell et al. 2001). Most studies have been localized where deer densities are high and sites have been limited in geographic region (Russell et al. 2001). Thus, there is no

17 17 evidence that deer impacts are widespread (Mladenoff and Stearns 1993). For example, some studies strongly suggest that failed recruitment of eastern hemlock is due to the presence of high deer densities (Alverson et al. 1988). These studies, however, may have unclear experimental design and analysis, and do not provide an explanation for lack of hemlock seedlings in nearby areas with low deer densities (Mladenoff and Stearns 1993). Rather, hemlock germination requirements of a disturbed soilbed and moist conditions for several years have not been met (Mladenoff and Stearns 1993). Long-term studies also need to confirm shifts in successional trajectories due compositional changes from preferential browsing. Research should identify regional-specific browsing levels that are detrimental to biodiversity. Plants preferred by deer will reach this threshold at lower deer densities, and identification of specific plants for use as indicator species seems practical. Presence of flowers or seeds in lilies may be simple indicators (Fletcher et al. 2001). Characteristics of highly preferred plants, as rated by Warren and Hurst (1981), should be explored as potential indices as well. There is an obvious lack in research that investigates white-tailed deer browsing impacts on avian and small mammal species. Birds should respond to vegetation changes created by deer, whereas mammals may suffer from resource competition. Therefore, experiments need to be established in places where there are concerns about deer overabundance throughout white-tailed deer range. Studies should seek to identify a regional-specific threshold of browsing that impacts birds and small mammals. Management that has focused successfully on augmenting deer populations must reverse now to consider limiting deer abundance. Overharvesting, to near extirpation in

18 18 some locations by the end of the nineteenth century, created a philosophy of protection and limited harvest of does (Russell et al. 2001). Managers need to overturn this paradigm and emphasize the benefits of maintaining deer densities compatible with ecosystem management. Otherwise, deer abundance and consequent negative impacts will continue to escalate. Land managers should avoid supplementing overabundant deer populations. Alverson et al. (1988) recommended establishing interior forest areas that can support only limited deer densities, to protect animals sensitive to overbrowsing. We propose that future research in this study incorporate morphological measurements. Population and community metrics, such as percentage cover and richness, may require more time to exhibit differences than individual plant measurements, such as height, leaf number, and reproductive structures (Kraft et al. 2004). Selected plant species should be present in both exclosure and control, and be preferred deer browse. This project was designed as a long-term research and educational project involving wildlife management areas operated by Mississippi Department of Wildlife, Fisheries, and Parks on United States Forest Service land. We documented changes in vegetation community characteristics after 5 years of deer foraging exclusion in three different regions. We will update deer exclusion effects on vegetative communities after 10 years (2010) and 15 years (2015). LITERATURE CITED Alverson, W.S., D.M. Waller, and S.L. Solheim Forests too deer: edge effects in northern Wisconsin. Conservation Biology. 2: Anderson, R. C., E. A. Corbett, M. R. Anderson, G. A. Corbett, and T. M. Kelley High white-tailed deer density has negative impact on tallgrass prairie forbs. Journal of the Torrey Botanical Society 128:

19 19 Augustine, D. J., and L. E. Frelich Effects of white-tailed deer on populations of an understory forb in fragmented deciduous forests. Conservation Biology 12: Balgooyen, C. P., and D. M. Waller The use of Clintonia borealis and other indicators to gauge impacts of white-tailed deer on plant communities in northern Wisconsin, USA. Natural Areas Journal 15: Bourlière, F Mammals, small and large: the ecological implications of size. Pages 1-8 in F. B. Golley, K. Petrusewicz, and L. Ryszkowski. Small mammals: their productivity and population dynamics. Cambridge University Press, New York, New York. Canfield, R Application of the line interception method in sampling of range vegetation. Journal of Forestry 39: Casey, D., and D. Hein Effects of heavy browsing on a bird community in deciduous forest. Journal of Wildlife Management 47: Connell, J. H Diversity and the coevolution of competitors, or the ghosts of competition past. Oikos 35: Coté, S. D., T. P. Rooney, J. Tremblay, C. Dussault, and D. M. Waller Ecological impacts of deer overabundance. Annual Review of Ecology, Evolution, and Systematics 35: decalesta, D. S Effects of white-tailed deer on songbirds within managed forests in Pennsylvania. Journal of Wildlife Management 58: DeGraaf, R. M., W. M. Healy, and R. T. Brooks Effects of thinning and deer browsing on breeding birds in New England oak woodlands. Forest Ecology and Management 41: Demarais, S., B. D. Leopold, J. Phillips, and B. K. Strickland Long-term impact of white-tailed deer on community structure and biodiversity in Mississippi: Final Report. Mississippi State University, Mississippi State, Mississippi, USA.. Fletcher, J. D., W. J. McShea, L. A. Shipley, and D. Shumway Use of common forest forbs to measure browsing pressure by white-tailed deer (Odocoileus virginianus) in Virginia, USA. Natural Areas Journal 21: Frelich, L.E. and C.J. Lorimer Current and predicted long-term impacts of deer browsing in hemlock forests in Michigan, USA. Biological Conservation. 32:

20 20 Fuller, R. J Responses of woodland birds to increasing numbers of deer: a review of evidence and mechanisms. Forestry 74: Hildén, O Habitat selection in birds: a review. Ann. Zool. Fenn. 2: Horsley, S. B., S. L. Stout, and D. S. decalesta White-tailed deer impact on the vegetation dynamics of a northern hardwood forest. Ecological Applications 13: Jones, J.C., H.A. Jacobson, and D.A. Arner Plant community characteristics within an 18-year-old deer exclosure in Southern Mississippi. Proc. Annu. Conf. Southeast. Assoc. Fish and Wildl. Agencies. 51: Kraft, L. S., T. R. Crow, D. S. Buckley, E. A. Nauertz, J. C. Zasada Effects of harvesting and deer browsing on attributes of understory plants in northern hardwood forests, Upper Michigan, USA. Forest Ecology and Management 199: McDonald, C. G Estimating white-tailed deer population characteristics on wildlife management areas in Mississippi. Thesis, Mississippi State University, Mississippi State, Mississippi, USA. McKinley, W. T Evaluating infrared camera and other census techniques for white-tailed deer in Mississippi. Thesis, Mississippi State University, Mississippi State, Mississippi, USA.. McShea, W. J The influence of acorn crops on annual variation in rodent and bird populations. Ecology 81: McShea, W. J., M. V. McDonald, G. E. Morton, R. Meier, and J. H. Rappole Long-term monitoring of Kentucky Warbler habitat selection. Auk 112: McShea, W. J., and J. H. Rappole Managing the abundance and diversity of breeding bird populations through manipulation of deer densities. Conservation Biology 14: Miller, S.G., S.P. Bratton, and J. Hadidian Impacts of white-tailed deer on endangered plants. Natural Areas Journal. 12: Mladenoff, D. J., and F. Stearns Eastern hemlock regeneration and deer browsing in the Northern Great Lakes region: a re-examination and model simulation. Conservation Biology 7: Nudds, T. D Quantifying the vegetative structure of wildlife cover. Wildlife Society Bulletin 5:

21 21 Ostfield, R. S., C. G. Jones, and J. O. Wolff Of mice and mast: ecological connections in eastern deciduous forests. Bioscience 46: Panjabi, A. O., E. H. Dunn, P. J. Blancher, W. C. Hunter, B. Altman, J. Bart, C. J. Beardmore, H. Berlanga, G. S. Butcher, S. K. Davis, D. W. Demarest, R. Dettmers, W. Easton, H. Gomez de Silva Garza, E. E. Iñigo-Elias, D. N. Pashley, C. J. Ralph, T. D. Rich, K. V. Rosenberg, C. M. Rustay, J. M. Ruth, J. S. Wendt, and T. C. Will The Partners in Flight handbook on species assessment. Version Partners in Flight Technical Series No. 3. Rocky Mountain Bird Observatory website: Pettry, D. E Soil resource areas of Mississippi. Mississippi Agricultural and Forestry Experiment Station Information Sheet Pietz, P. J., and D. A. Granfors White-tailed deer (Odocoileus virginianus) predation on grassland songbird nestlings. American Midland Naturalist 144: Ritchie, M. E., D. Tilman, and J. M. H. Knops Herbivore effects on plant and nitrogen dynamics in oak savanna. Ecology 79: Rossell, C. R., B. Gorsira, and S. Patch Effects of white-tailed deer on vegetation structure and woody seedling composition in three forest types in the Piedmont Plateau. Forest Ecology and Management 210: Russell, F. L., D. P. Zippin, and N. L. Fowler Effects of white-tailed deer (Odocoileus virginianus) on plants, plant populations and communities: a review. American Midland Naturalist 146: SAS Institute SAS User s Guide, Version 9.1. SAS Institute, Cary, North Carolina, USA. Shelton, A. L., and R. S. Inouye Effects of browsing by deer on the growth and reproductive success of Lactuca canadensis (Asteraceae). American Midland Naturalist 134: Stromayer, K. A., and R. J. Warren Are overabundant deer herds in the eastern United States creating alternate stable states in forest plant communities? Wildlife Society Bulletin 25: Tilghman, N.F Impacts of white-tailed deer on forest regeneration in northwestern Pennsylvania. Journal of Wildlife Management 53: Waller, D.M. and W.S. Alverson The white-tailed deer: a keystone herbivore. Wildlife Society Bulletin. 25:

22 22 Warren, R. C., and G. A. Hurst Ratings of plants in pine plantations as whitetailed deer food. Mississippi Agricultural Forest Experiment Station, Information Bulletin 18. Webster, C. R., M. A. Jenkins, and J. H. Rock Long-term response of spring flora to chronic herbivory and deer exclusion in Great Smoky Mountains National Park, USA. Biological Conservation 125: Zar, J. H Biostatistical analysis. Fourth edition. Prentiss Hall, Upper Saddle River, New Jersey, USA.

23 Figure 1. Locations of exclosure study sites on U.S. Forest Service land in Mississippi (Demarais et al. 2003). 23

24 Figure 2. Exclosure and control locations on Delta National Forest/Sunflower WMA in Mississippi (Demarais et al. 2003). 24

25 Figure 3. Exclosure and control locations on Desoto National Forest/Leaf River WMA in Mississippi (Demarais et al. 2003).. 25

26 Figure 4. Exclosure and control locations on Tombigbee National Forest/Choctaw WMA in Mississippi (Demarais et al. 2003). 26

27 27 Table 1. Mean canopy coverage a (%) of the top layer of understory vegetation by growth form and species in deer exclosures and controls 5-years post-treatment in the Delta physiographic region, June Exclosure Control Species 0 LCL UCL 0 LCL UCL P Forb Ambrosia artemisiifolia Boehmeria cylindrica Commelina communis Conyza canadensis Eupatorium album Eupatorium serotinum Euphorbia corollata Hibiscus aculeatus Lactuca canadensis Phytolacca americana Pluchea camphorata Polygonum punctatum Sanicula canadensis Solanum americanum Solidago canadensis Trepocarpus aethusae Viola papilionacea Grass Andropogon virginicus Dicanthelium aciculare Grasslike Carex lurida Cyperus ovularis 0.1 A B Rhynchospora rariflora Legume Desmodium ciliare Vine Ampelopsis arborea Berchemia scandens Brunnichia ovata Campsis radicans Clematis crispa Gelsemium sempervirens Hedera helix Mikania scandens Parthenocissus quinquefolia Pueria lobata Rubus argutus Rubus flagellaris Rubus trivialis Smilax bona-nox Smilax glauca Smilax rotundifolia Toxicodendron radicans Trachelospermum difforme Vitis aestivalis Vitis rotundifolia

28 28 Exclosure Control Species 0 LCL UCL 0 LCL UCL P Woody Acer negundo Acer rubra Amelanchier canadensis Baccharis halimifolia Callicarpa americana Carya aquatica Carya cordiformus Celtis laevigata Cephalanthus occidentalis Cornus amomum Crataegus viridis Diospyros virginiana Fraxinus pennslyvanica Gleditsia triacanthos Ilex decidua Liquidambar styraciflua Lonicera japonica Morus rubra Prunus angustifolia Quercus lyrata Quercus pagoda Quercus phellos Rhus copallina Taxodium distichum Ulmus alata Ulmus rubra Vaccinium arboreum Debris a Values were arcsin square-root transformed for statistical analyses and back-transformed for presentation.

29 29 Table 2. Mean canopy coverage a (%) of the top layer of understory vegetation by growth form and species in deer exclosures and controls 5-years post-treatment in the Lower Coastal Plain, June Exclosure Control Species 0 LCL UCL 0 LCL UCL P Fern Pteridium aquilinum Forb Acalypha gracilens Ambrosia artemisiifolia Aster dumosus Aster pilosus Carduus spinosissimus Conyza canadensis Coreopsis major Elephantopus tomentosus Eupatorium album Eupatorium capillifolium Eupatorium serotinum Euphorbia corollata Hibiscus aculeatus Lactuca canadensis Mimosa quadrivalvis Pityopsis graminifolia Polygala nana Rhexia virginica Solidago canadensis Grass Andropogon virginicus Aristida spp Chasmanthium laxum Dicanthelium aciculare Dicanthelium acuminatum Dicanthelium commutatum Saccharum giganteum Grasslike Rhynchospora inexpansa Legume All Desmodium spp All Lespedeza spp All Other Legumes Desmodium rotundifolium Lespedeza repens Stylosanthes biflora Vicia sativa Vine Gelsemium sempervirens Mikania scandens Parthenocissus quinquefolia Rubus argutus Rubus trivialis Smilax bona-nox Smilax glauca Smilax pumila

30 30 Exclosure Control Species 0 LCL UCL 0 LCL UCL P Vine Toxicodendron radicans Vitis rotundifolia Woody Acer rubra 0.3 A B Cornus florida Diospyros virginiana Geobalanus oblongifoliuus Ilex glabra Ilex opaca Ilex vomitoria Liquidambar styraciflua Liriodendron tulipifera Myrica cerifera Nyssa sylvatica Pinus palustris Pinus taeda Prunus angustifolia Prunus serotina Quercus falcata Quercus incana Quercus marilandica Quercus nigra Quercus stellata Rhus copallina Symplocos tinctoria Ulmus alata Vaccinium arboreum Vaccinium darrowii Vaccinium elliottii Vaccinium staminium Debris a Values were arcsin square-root transformed for statistical analyses and back-transformed for presentation.

31 31 Table 3. Mean canopy coverage a (%) of the top layer of understory vegetation by growth form and species in deer exclosures and controls 5-years post-treatment in the Upper Coastal Plain, June Exclosure Control Species 0 LCL UCL 0 LCL UCL P Fern Pteridium aquilinum Forb Acalypha gracilens Ambrosia artemisiifolia Asclepias variegata Aster dumosus Aster pilosus Commelina communis Conyza canadensis Dioscorea villosa Elephantopus tomentosus Erechtites heiracifolia Eupatorium album Eupatorium serotinum Euphorbia corollata Euthamia tenuifolia Helianthus heterophyllus Lactuca canadensis Oxalis stricta Phytolacca americana Pityopsis graminifolia Solidago canadensis Vernonia gigantea Grass Andropogon virginicus Chasmanthium latifolium Chasmanthium laxum Dicanthelium aciculare Dicanthelium acuminatum Dicanthelium commutatum Saccharum giganteum Legume All Desmodium spp All Lespedeza spp All Other Legumes Centrosema virginianum Desmodium ciliare Desmodium laevigatum Desmodium obtusum Desmodium rotundifolium Desmodium strictum Galactia regularis Lespedeza bicolor Lespedeza procumbens Lespedeza repens Lespedeza virginica Stylosanthes biflora Tephrosia virginica

32

33 33 Table 4. Mean canopy coverage a (%) of the total vegetation in the understory by growth form and species in deer exclosures and controls 5-years post-treatment in the Delta physiographic region, June Exclosure Control Species 0 SE 0 SE P Forb Ambrosia artemisiifolia Boehmeria cylindrica Commelina communis Conyza canadensis Eupatorium album Eupatorium serotinum Euphorbia corollata Hibiscus aculeatus Lactuca canadensis Phytolacca americana Pluchea camphorata Polygonum punctatum Sanicula canadensis Solanum americanum Solidago canadensis Trepocarpus aethusae Viola papilionacea Grass Andropogon virginicus Chasmanthium laxum Dicanthelium aciculare Grasslike Carex lurida Cyperus ovularis Rhynchospora rariflora Legume Desmodium ciliare Vine Ampelopsis arborea Berchemia scandens Brunnichia ovata Campsis radicans Clematis crispa Gelsemium sempervirens Hedera helix Mikania scandens Parthenocissus quinquefolia Pueria lobata Rubus argutus Rubus flagellaris Rubus trivialis Smilax bona-nox Smilax glauca Smilax pumila

34 34 Exclosure Control Species 0 SE 0 SE P Vine Smilax rotundifolia Toxicodendron radicans Trachelospermum difforme Vitis aestivalis Vitis rotundifolia Woody Acer negundo Acer rubra Amelanchier canadensis Baccharis halimifolia Callicarpa americana Carya aquatica Carya cordiformus Carya tomentosa Celtis laevigata Cephalanthus occidentalis Cornus drummondii Crataegus viridis Diospyros virginiana Fraxinus pennslyvanica Gleditsia triacanthos Ilex decidua Liquidambar styraciflua Lonicera japonica Morus rubra Prunus angustifolia Quercus lyrata Quercus pagoda Quercus phellos Rhus copallina Taxodium distichum Ulmus alata Ulmus rubra Vaccinium arboreum Vaccinium staminium Debris a Values were square-root transformed for statistical analyses, but actual means are presented.