Skip to main content
NCBI home page
AoB Plants logoLink to AoB Plants
. 2019 Sep 23;11(5):plz059. doi: 10.1093/aobpla/plz059

Effects of white-tailed deer (Odocoileus virginianus) exclusion on plant recovery in overwash fans after a severe coastal storm

Chellby R Kilheffer 1,✉, H Brian Underwood 2, Lindsay Ries 3, Jordan Raphael 3, Donald J Leopold 1
Editor: Whigham Dennis
PMCID: PMC6804481  PMID: 33014323

Abstract

We documented the impacts of an abundant deer population on dune vegetation recovering from severe storm surge on a barrier island through use of permanent plots and a repeated measures analysis. Three years after landfall of the storm, vegetation cover was dominated by American beachgrass, Ammophila breviligulata, though we observed 12 plant species among plots surveyed. We documented significantly greater vegetation cover in fenced than unfenced plots in overwash fans in two consecutive years. The difference in species richness between fenced and unfenced plots was significant, though richness was consistently low (≤4 species per plot) and we did not detect a statistically significant difference between years. Both deer trampling and foraging effects were captured in this study, though separation between these effects was not possible. Because trampling effects are often exacerbated on sandy soils, trampling and foraging effects should be isolated and investigated in future assessments of deer impacts on coastal vegetation. Managing deer to lower abundance may enhance coastal resilience if vegetation is allowed to recover unimpeded by foraging and trampling, though a better understanding of the precise nature of deer impacts on dune vegetation is necessary.

Keywords: Barrier island, coastal disturbance, exclosure, Hurricane Sandy, resilience, species richness, vegetation community recovery, vegetation cover, white-tailed deer

Fire Island National Seashore is a barrier island off the southern coast of Long Island, New York where a population of white-tailed deer has increased in the last few decades. After Hurricane Sandy hit New York in 2012, we used fences to prevent deer from eating and stepping on plants in some areas that were affected by the storm. We found that fenced areas had more plant cover than unfenced areas and most cover was beachgrass. Beachgrass is responsible for building dunes after storm events, so a better understanding of how much deer limit grass growth is necessary.

Introduction

Foredunes of barrier islands erode during storm events and serve as natural defences against disruption of inland ecosystems (Sallenger 2000; Durán and Moore 2013; Hapke et al. 2013). Dune overwashing occurs when wave run-up overtops a foredune, deposits large volumes of sand inland and removes the foredune (Sallenger 2000). After a disruptive storm event, vegetation recovery is essential to foredune re-establishment (Olson 1958) as dominant dune-building plants influence island vulnerability to future disturbance (Pendleton et al. 2004; Durán and Moore 2013; Brantley et al. 2014; Vinent and Moore 2015). Characteristic of dune-building grasses, growth of roots and shoots increases as sand accumulates at the base (Stuckey and Gould 2000; Kent et al. 2001; Maun 2009), forming a new dune over time in the absence of continued disturbance (Vinent and Moore 2015). Burial-tolerant shrubs and herbs grow on the leeward side of forming dunes and further stabilize sand into a stronger, more resilient foredune (Ehrenfeld 1990; Tilman 1990; Stuckey and Gould 2000). Greater cover of dune-stabilizing plants increases the likelihood of dune reformation before the next disturbance (Durán and Moore 2013; Vinent and Moore 2015). In addition, succession leads to more diverse, heterogeneous plant communities under the protection of a foredune (Ehrenfeld 1990). Factors that hinder vegetation recovery rates, including herbivory, can reduce resilience to future disturbances through increased coastal vulnerability (Houser et al. 2015; Vinent and Moore 2015).

Ungulate herbivores are characterized along a spectrum from browsers to grazers based on their foraging behaviours and digestive physiology (McNaughton et al. 1988). Browsing typically describes selective foraging of highly nutritious plant parts, while grazing describes bulk consumption of monocot vegetation (Shipley 1999). Ungulate herbivores affect plant dispersal (Maun 2009) and fitness (Waller and Alverson 1997; Ruhren and Handel 2000; Rooney and Waller 2003), alter system nutrient cycles (Augustine et al. 2003), change plant community composition (Tilghman 1988; Kittredge and Ashton 1995; Proulx and Mazumder 1998; Long 1999; Blumenthal and Augustine 2009; Raphael 2014), decrease plant community diversity (Urbanek et al. 2012; Pendergast et al. 2015) and affect growing conditions by altering the physical surroundings (Sabo et al. 2017). In addition, ungulate hooves act as chisels in sandy substrates and often damage soils and plant communities (Duncan and Holdaway 1989).

Where browsers and grazers co-occur, effects on vegetation are often specific (Valdés-Correcher et al. 2018). On Assateague Island in Maryland, USA, grazing by feral horses (Equus caballus) changed dune vegetation structure by reducing plant diversity and cover (Seliskar 2003). Where horses and white-tailed deer (Odocoileus virginianus) were sympatric, deer minimally browsed dune vegetation (Keiper 1990; Porter et al. 2014) and used dunes less frequently than available (Sherrill et al. 2010). Little is known about effects of deer foraging on dune vegetation in the absence of horses (Carruthers et al. 2013). Though deer are typically classified as browsers, they consume grass regularly (Bryant et al. 1981; Hobbs et al. 1983; Waller and Alverson 1997) and may, in high abundance, pose a threat to barrier island stability through effects on dune-stabilizing grasses and forbs (Durán and Moore 2013; Vinent and Moore 2015).

Many studies have shown that the impact exerted on a vegetation community by herbivores is proportional to their abundance (Anderson 1994; Augustine et al. 1998; Augustine and Frelich 1998; Reimoser et al. 2009). The white-tailed deer population on Fire Island, New York, USA irrupted from low to high (~80 deer per km2) density (Underwood 2005) in the 1980s, exposing plant communities to a novel biotic stressor. In October 2012, storm surge from Hurricane Sandy overwashed many large areas of Fire Island and deposited large volumes of sand inland where herbs, grasses and shrubs once dominated (Hapke et al. 2013). Overwashed areas provided a unique opportunity to better understand the potential effects of white-tailed deer on recovering coastal vegetation after dune disturbance.

White-tailed deer are the largest and most impactful herbivore inhabiting Fire Island (O’Connell and Sayre 1989; Forrester et al. 2006) and, unlike populations on Assateague Island, deer are not hunted on Fire Island (Underwood 2005). However, the degree to which deer impact the composition and structure of recovering vegetation in overwash fans after storms like Hurricane Sandy remains speculative. The purpose of this paper is to elucidate the nature of white-tailed deer impacts on vegetation recovering from severe storm surge. Our specific objective is to assess the impact of deer on plant community composition and percent cover (i.e. a measure of dominance) among a series of overwash fans created by storm surge in a protected natural area. We hypothesize that in the presence of an abundant deer herd, the effect of deer exclusion will manifest in higher species richness and more vegetation cover over time.

Methods

Study area

We conducted this study in the Otis Pike Fire Island High Dune Wilderness Area (OPWA), New York, USA (40.714382 N, 72.928964 W). The OPWA, ~450 ha, is located on Fire Island, a barrier island located ~6 km from the south shore of Long Island (Fig. 1), and is managed as a National Seashore by the National Park Service. The OPWA was established in 1980 as the only federally designated wilderness area in the state of New York (FIIS 2015). At that time, the deer population in the OPWA was irrupting after about a decade of protected status. Based on aerial and ground surveys, deer density across the OPWA has ranged from 0.25 to 0.40 deer per ha since the mid-1980s (O’Connell and Sayre 1989; Underwood 2005).

Figure 1.

Figure 1.

Open in a new tab

The Otis Pike Fire Island High Dune Wilderness Area (OPWA) is located on Fire Island, a barrier island off the southern coast of Long Island, New York, USA. Hurricane Sandy left behind nine overwash fans (shown in black) where most vegetation and foredunes were eliminated, and breached the island at Old Inlet, which remains open from ocean to bay to this day. Trail cameras in the OPWA captured photographs of white-tailed deer appearing to forage senescent Ammophila breviligulata in winter 2015.

In October 2012, the OPWA was heavily impacted by Hurricane Sandy, a post-tropical cyclone with a massive wind radius (>185 km). Before Hurricane Sandy, foredunes in the OPWA were 4–15 m high (Hapke et al. 2010). During Hurricane Sandy, many of these dunes were overwashed by significant storm surge (Blake et al. 2013; Hapke et al. 2013), leaving behind overwash fans varying in size from 0.60 to 3.24 ha. Deer utilization of recovering overwash fans during this study was high, with an average local density of 27 deer per ha (SE = 8.2), two orders of magnitude larger than area-wide density estimates (Kilheffer et al. 2019).

Study design

We designed an exclosure experiment to assess the effects of deer on plant recovery in overwash fans created by Hurricane Sandy. We anticipated substantial variability in vegetation cover and species richness among overwash fans for several reasons: (i) sand accretion occurs at different rates at each overwash fan (Brenner et al. 2018; Kilheffer 2018), (ii) overwash fan size and configuration differ along the island and (iii) storm impacts from Hurricane Sandy varied in intensity for each overwash fan (Hapke et al. 2013). To control for variation in plant species richness and cover, we incorporated overwash fan designation as a blocking variable (i.e. whole plot or Block) in a split-plot-in-time experimental design (Littell et al. 1998).

We used permanent plots as treatments (i.e. A) with two randomly allocated levels (i.e. unfenced vs. fenced) applied to whole plots, and designated year of study as the split-plot factor (i.e. B). A conventional analysis of a 2-factor split-plot-in-time experiment involves the construction of a whole-plot error term to test treatment A (i.e. A * Block), a pooled residual error (i.e. B * Block and A * B * Block) to test year of study, B, and its interaction with treatment (i.e. A * B; Littell et al. 2002). Because they share the same linear model (Quinn and Keough 2002), repeated measures analysis is the contemporary solution to the split-plot-in-time experimental design (Meredith and Stehman 1991; Littell et al. 1998).

Vegetation surveys

Sixty-one permanent plots (1 m2) were deployed randomly in post-Hurricane Sandy overwash fans during spring 2013 (Fig. 2) and marked in the southeast corner with fibreglass posts. The number of permanent plots placed in each of nine overwash fans (range: 4–10) was roughly proportional to the size of the overwash fan. Each overwash fan received at least two of each treatments (i.e. two fenced, two unfenced), but more unfenced plots were established due to logistical constraints of transporting fencing materials and people to remote locations. Fences were constructed from 1.5-m high woven wire fence with 5 × 10 cm openings. Each fence was buried ~30 cm into the sand to prevent deer access from beneath.

Figure 2.

Figure 2.

Open in a new tab

Maps of permanent plot locations in each of nine post-Hurricane Sandy overwash fans in the Otis Pike Fire Island High Dune Wilderness Area, New York, USA.

In September 2015 and 2016, 2 and 3 years after deployment, we assessed vegetation cover in all permanent plots using a digital point sampling method (Kilheffer 2018). We used a Canon T3i digital single-lens reflex camera with 18–55 mm zoom lens mounted 2 m above the ground on an adjustable aluminium frame with a 1-m2 quadrat base (Booth et al. 2004). We captured a nadir photograph using a Bluetooth remote and digitally created 100 equally spaced points (Geospatial Modelling Environment version 0.7.3.0, Spatial Ecology LLC, Queensland, Australia) inside the 1-m2 quadrat to quantify percent frequency of bare sand and each plant species. An analysis of species frequency was facilitated with PointSampler (Gobbett and Zerger 2014), an ArcGIS extension for tallying species frequency at user-defined locations in an image. We used species frequency (%) as a proxy for percent foliar cover (Elzinga et al. 1998) in subsequent analyses and discussion.

Statistical analysis

In exclosure studies, a split-plot design guards against pseudo-replication by explicitly identifying the proper degrees of freedom associated with the whole-plot and split-plot factors (Forrester et al. 2006). We used SAS® software to conduct a repeated measures analysis of the split-plot design for both percent cover and species richness (PROC MIXED, SAS 2017). The PROC MIXED procedure automatically incorporates the correct error terms associated with the split-plot design into test statistics (Wolfinger et al. 1992; Littell et al. 2002). To enhance normality of the residuals, we transformed (i.e. natural logarithm) the observed species richness after adding the value of one to plot counts to include zero as a valid observation. Diagnostics revealed that percent cover required no transformation prior to analysis (Ahrens et al. 1990).

With just 2 years of data, testing different within-subjects covariance structures was not possible. However, by assuming a first-order autoregressive covariance structure for the repeated measures, a clear distinction can be made of within- and between-subjects effects (Littell et al. 2002; Liu et al. 2007). Using information criteria (i.e. Akaike Information Criterion corrected for small sample sizes (AICc) and Bayesian Information Criterion (BIC); Burnham and Anderson 2001; Littell et al. 2002), we compared the fit of a random coefficients model (Laird and Ware 1982) with the full linear mixed model containing both random coefficients and repeated measures, and a model with repeated measures only (i.e. marginal model; West et al. 2014). Random coefficients are used to test between-subjects effects (i.e. among overwash fans) and the repeated measures are used to test within-subjects effects (i.e. treatment effect). Finally, we charted least-squares means for all fixed effects in the most parsimonious models. Because SAS uses restricted maximum likelihood to estimate mixed-model parameters (Littell et al. 2002), we adjusted the denominator degrees of freedom and standard errors around least-squares means due to missing observations and small, unequal sample sizes (Kenward and Roger 1997).

Results

We surveyed at least one fenced and two unfenced plots in each overwash fan in 2015 and 2016 (Table 1). Two fenced plots in overwash 1 were impacted by a storm event in spring 2016 and were removed from further consideration. We observed a total of 12 plant species among permanent plots (Table 2). In samples pooled across overwash fans and by year, plots were dominated by Ammophila breviligulata, a dune-building grass. Of the total vegetation cover present among plots (range: 33.2–66.0 %), from 87 to 96 % was A. breviligulata.

Table 1.

Number of plots surveyed in nine overwash fans (OW) in the Otis Pike Fire Island High Dune Wilderness Area, New York, USA in 2015 and 2016. Surveys include permanent fenced and unfenced plots used to assess effects of white-tailed deer on percent vegetation cover and species richness.

OW Number of plots
Unfenced Fenced
1 6 3
2 8 2
3 5 3
4 4 2
5 2 2
6 6 2
7 2 2
8 5 3
9 2 2
Open in a new tab

Table 2.

Plant species encountered and their mean percent covers in fenced and unfenced plots established within overwash fans of the Otis Pike Fire Island High Dune Wilderness Area, New York, USA. *Non-native species.

2015 2016
Scientific name Common name Fenced Unfenced Fenced Unfenced
Ammophila breviligulata American beachgrass 51.7 31.8 57.6 46.2
Hudsonia tomentosa Beach heather 4.6 0.2 0.0 0.1
Lathyrus japonicus Beach pea 0.1 0.1 0.5 0.2
Morella pensylvanica Northern bayberry 0.2 0.0 0.8 0.0
Prunus maritima Beach plum 0.1 0.5 0.1 0.4
Polygonella articulata Sand jointweed 0.0 0.0 0.0 0.1
Pseudognaphalium obtusifolium Sweet everlasting 0.0 0.0 0.2 0.3
Rosa multiflora* Multiflora rose 1.2 0.0 2.7 0.0
Smilax glauca Cat greenbrier 0.3 0.0 0.5 0.2
Symphyotrichum pilosum Hairy white aster 0.0 0.0 0.6 0.1
Toxicodendron radicans Poison ivy 0.0 0.6 3.0 0.9
Vaccinium corymbosum Highbush blueberry 0.0 0.0 0.0 0.1
Total vegetation cover 58.2 33.2 66.0 48.6
Open in a new tab

Two plant species were found inside fenced plots, but not in unfenced plots, in both 2015 and 2016: a native shrub (Morella pensylvanica) and a non-native shrub (Rosa multiflora). Smilax glauca, a native liana, was only found in fenced plots in 2015. Though S. glauca was observed in both fenced and unfenced plots in 2016, much lower cover was observed in unfenced plots. Hudsonia tomentosa, a native shrub, exhibited over an order of magnitude greater cover in fenced than unfenced plots in 2015, but was virtually absent in all permanent plots in 2016. Finally, three perennial herbs, Lathyrus japonicus, Symphyotrichum pilosum and Toxicodendron radicans, increased in cover between years, but each with much greater cover in fenced than unfenced plots in 2016.

Percent cover

Based on AICc and BIC, the most parsimonious model for percent cover was the random coefficients model (Table 3). The random coefficients model, which only modelled overwash-level effects and not treatment-level effects, revealed substantial variation in percent cover within and among overwash fans (Fig. 3). Analysis of the fixed effects demonstrated significant contributions from both treatment (i.e. fenced vs. unfenced; F = 8.5; df = 1, 8.25; P = 0.0187) and year of study (F = 6.3; df = 1, 94; P = 0.0142) to the analysis of variance. However, the interaction between treatment and year of study was not significant (F = 0.24; df = 1, 94; P = 0.6287). Fenced plots had greater cover (+20.7 percentage points) than unfenced plots, and all plots in 2016 had greater cover (+12.1 percentage points) than plots in 2015 (Fig. 4).

Table 3.

Selection criteria for three models exploring the effects of deer exclusion on percent vegetation cover and species richness in nine overwash fans in the Otis Pike Fire Island High Dune Wilderness Area, New York, USA in 2015 and 2016. Smaller AICc and BIC values indicate more robust model fit. Bold-face type indicates the most parsimonious model indicated by information criterion. 1RC = random coefficients; RM = repeated measures. 2Akaike Information Criterion (corrected for small sample sizes). 3Bayesian Information Criterion.

Response variable Model1 AICc2 BIC3
Percent vegetation cover RC 1084.4 1084.7
RC + RM 1086.2 1086.6
RM 1084.8 1086.5
Species richness RC 76.9 77.3
RC + RM 76.9 77.2
RM 75.1 76.8
Open in a new tab

Figure 3.

Figure 3.

Open in a new tab

Box plots of percent vegetation cover among overwash fans from the most parsimonious model (i.e. random effects model; only overwash-level effects) of vegetation cover in fenced and unfenced plots in the Otis Pike Fire Island High Dune Wilderness Area, New York, USA.

Figure 4.

Figure 4.

Open in a new tab

Least-squares means (±95 % CI) of fixed effects of treatment (A) and year (B) in a mixed-model analysis of variance of vegetation cover sampled in fenced and unfenced plots in nine overwash fans of the Otis Pike Fire Island High Dune Wilderness Area, New York, USA.

Number of species

A repeated measures or marginal model was most parsimonious for species richness (Table 3), which varied from zero to four. The fixed effect of treatment was significant (F = 4.6; df = 1, 37.7; P = 0.0395), but year of study (F = 2.2; df = 1, 75.1; P = 0.1460) and its interaction with treatment (F = 0.02; df = 1, 75.1; P = 0.8996) were not. Fenced plots contained from one to three additional species than unfenced plots (Fig. 5).

Figure 5.

Figure 5.

Open in a new tab

Back-transformed least-squares means (±95 % CI) of fixed effects of treatment (A) and year (B) in a mixed-model analysis of variance of species richness in fenced and unfenced plots in nine overwash fans of the Otis Pike Fire Island High Dune Wilderness Area, New York, USA. Open bars indicate lack of a statistically significant (P > 0.05) difference between least-squares means.

Discussion

We documented impacts of deer on both vegetation cover and species richness in overwash fans created by the catastrophic storm surge associated with Hurricane Sandy. Our study was initiated on bare sand, which permitted us to measure the effects of deer herbivory on plant community assembly following a major disturbance. In addition, we explicitly dealt with spatial variation in biophysical processes, unrelated to deer exclusion, that impinge on plant community development through experimental design. In spite of logistical issues preventing full deployment of treatments, we documented compelling effects between fenced and unfenced plots. For understanding various responses of plant communities to deer exclusion, repeated measures analysis through linear mixed modelling is a powerful tool (Averill et al. 2017). The linear mixed model allowed us to interpret different responses of vegetation community to deer exclusion depending on which experimental factors were relevant. For example, the random effect (i.e. between-subjects) of overwash fan was an important source of variation for percent vegetation cover, but did not appear in the best model for species richness in permanent plots. Instead, the best model for species richness contained only repeated measures (i.e. within-subjects). We interpret these findings as indicating that the variation associated with plant demographic processes is larger than the environmental variation induced by overwash fan in the analysis of variance. This interpretation is supported by the contrasting presence and cover of H. tomentosa in permanent plots from 1 year to the next, and by other studies documenting high year-to-year variation in plant colonization and survival rates of coastal dune plant species (Miller et al. 2010).

While effects of deer herbivory in old-growth maritime forests (Art 1987; Forrester et al. 2006; Forrester et al. 2007; Raphael 2014) have been well-documented, identifying clear impacts of deer on vegetation in dune and swale habitats has been challenging. A previous exclosure study attempted to assess effects of deer herbivory in dune and swale habitats of OPWA with limited success (Art 1990). However, that study was initiated in established vegetation after the deer population had irrupted (Underwood 2005) and impacts incurred before the establishment of plots would have been captured in both fenced and unfenced plots. In addition, the previous exclosure study included only two paired plots (i.e. fenced and unfenced)―a sample size too small to distinguish meaningful effect sizes.

Dense swards of A. breviligulata formed during the first two growing seasons after Hurricane Sandy throughout most of the overwash fans (Kilheffer et al. 2019). Average cover for this species was nearly twice that recorded by Art (1976; page 31: 25.1 %) on Fire Island and from 10 to 20 % higher than reported in Seliskar (2003) and de Stoppelaire et al. (2004) on nearby Assateague Island. Ammophila breviligulata is consumed yearlong by horses on all Atlantic barrier islands where horses and white-tailed deer co-occur (Seliskar 2003). Marram grass (Ammophila arenaria), a close relative to A. breviligulata, has been used as fodder for domestic animals for centuries on the sand dunes of Europe and elsewhere, especially during times when other forages are unavailable (Gadgil 2002). Ammophila species are cool season grasses that produce new tillers mostly during spring and late summer (Briske and Noy-Meir 1998). Deer foraging on new tillers could induce a reduction in A. breviligulata cover by diminishing carbohydrate stores in rhizomes (Trlica 1990; Trlica and Rittenhouse 1993), and we believe that this is the effect we captured in differences between fenced and unfenced plots. We observed abundant deer sign in overwash fans (Kilheffer et al. 2019) and recorded deer foraging on dormant A. breviligulata during winter in trail camera photographs (Fig. 1), but observed scarce evidence of deer foraging on A. breviligulata during our field sampling. Very little is known about the value of A. breviligulata as winter forage for white-tailed deer, though it likely represents a good source of dietary energy (Trlica 1999). Of the remaining species exhibiting reductions in cover or occurrence between fenced and unfenced plots, all except S. pilosum are known to be readily consumed by white-tailed deer on Fire Island (O’Connell and Sayre 1988).

Effects of deer exclusion on recovering vegetation in overwash fans include both foraging and trampling components (Kilheffer et al. 2019), though trampling of vegetation by ungulates has been largely ignored (but see Heggenes et al. 2017; Sabo et al. 2017). Some plant species are more resistant to trampling than others (Dale and Weaver 1974; Davidson and Fox 1974; Pellerin et al. 2006), but sandy soils exacerbate effects of trampling on barrier islands (Andersen 1995; Santoro et al. 2012). Ammophila breviligulata is vulnerable to trampling when underground rhizomes are compromised (McDonnell 1981; Maun 2009). In the OPWA, legacy deer trails, where no vegetation grows due to highly localized trampling, are ubiquitous and so stark that they are visible in aerial and satellite imagery (Kilheffer et al. 2019). A different experimental approach, in addition to exclusion, will be required to better understand effects of trampling by deer on recovering vegetation in overwash fans and to distinguish them from foraging.

Species richness is inherently low in dune and swale habitat due to harsh environmental conditions (Enrenfeld 1990; Maun 2009), limiting the ability to detect significant differences between fenced and unfenced plots. In time, the value of a repeated measures analysis will increase as specific response curves are developed and analysed (Meredith and Stehman 1991). Future monitoring could aim to identify specific effects of herbivory to growth of A. breviligulata and species typical of swale habitat (Ehrenfeld 1990; Tilman 1990) as vegetation communities transition from grass- to shrub-dominated communities (Ehrenfeld 1990).

In an era of sea level rise, the ability of coastal barrier island vegetation to recover quickly after storm surge is a critical feature of resilience with important implications for ecosystem services provided to millions of people (Houser et al. 2015). The addition of a biotic stressor like an abundant deer population impinges on decisions about how to sustainably manage natural resources after major storm events now and into the future (Carruthers et al. 2013). Managing deer to lower abundance may enhance coastal resilience if vegetation is allowed to recover unimpeded by foraging and trampling. However, management of native ungulates in protected areas is challenging, expensive and often controversial (Porter 1992; Demarais et al. 2012; Plumb et al. 2014). Thus, understanding the precise nature of the impacts of deer on dune vegetation recovery is key to successful remediation.

Conflict of Interest

None declared.

Sources of Funding

This research was funded through Hurricane Sandy disaster relief appropriations to the National Park Service (agreement: P14AC00469). C.R.K. was partially supported by a Virginia Sea Grant Fellowship (R/71858J).

Contribution by the Authors

C.R.K., H.B.U., and D.J.L. conceived the ideas and defined the methodology; C.R.K., H.B.U., J.R. and L.R. collected the data; C.R.K. and H.B.U. analyzed the data; C.R.K. and H.B.U. led the writing of the manuscript. All authors contributed fundamentally to the manuscript drafts and gave final approval for its publication.

Acknowledgements

We thank the Student Conservation Association interns and National Park Service staff for field and logistical support: Thomas Alexander, Michael Bilecki, Michelle Blydenburgh, Courtney Buckley, Jill Peters, Theresa Schaffner, Kelsey Taylor, Matthew Zampariello, Gina Zanarini. We thank the SUNY Research Foundation and the Department of Environmental and Forest Biology at SUNY ESF for their administrative assistance. Any use of trade, product or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

Literature Cited

  1. Ahrens WH, Cox DJ, Budhwar G. 1990. Use of the arcsine and square root transformations for subjectively determined percentage data. Weed Science 38:452–458. [Google Scholar]
  2. Anderson RC. 1994. Height of white-flowered trillium (Trillium grandiflorum) as an index of deer browsing intensity. Ecological Applications 4:104–109. [Google Scholar]
  3. Andersen UV. 1995. Resistance of Danish coastal vegetation types to human trampling. Biological Conservation 71:223–230. [Google Scholar]
  4. Art HW. 1976. Ecological studies of the Sunken Forest, Fire Island National Seashore, New York. Scientific Monograph 7:1-237. [Google Scholar]
  5. Art HW. 1987. Patterns of community dynamics in the Sunken Forest: 1967 to 1985 and 1985 to 1986. Boston, MA: North Atlantic Regional Office, National Park Service. [Google Scholar]
  6. Art HW. 1990. The impacts of deer on the Sunken Forest and Fire Island National Seashore, Fire Island, NY 1967-89. Boston, MA: North Atlantic Regional Office, National Park Service. [Google Scholar]
  7. Augustine DJ, Frelich LE. 1998. Effects of white-tailed deer on populations of an understory forb in fragmented deciduous forests. Conservation Biology 12:995–1004. [Google Scholar]
  8. Augustine DJ, Frelich LE, Jordan PA. 1998. Evidence for two alternative stable states in an ungulate grazing system. Ecological Applications 8:1260–1269. [Google Scholar]
  9. Augustine DJ, McNaughton SJ, Frank DA. 2003. Feedbacks between soil nutrients and large herbivores in a managed savanna ecosystem. Ecological Applications 13:1325–1337. [Google Scholar]
  10. Averill KM, Mortensen DA, Smithwick EAH, Kalisz S, McShea WJ, Bourg NA, Parker JD, Royo AA, Abrams MD, Apsley DK, Blossey B, Boucher DH, Caraher KL, DiTommaso A, Johnson SE, Masson R, Nuzzo VA. 2017. A regional assessment of white-tailed deer effects on plant invasion. AoB Plants 10:plx047; doi: 10.1093/aobpla/plx047. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Blake ES, Kimberlain TB, Berg RJ, Cangialosi JP, Beven JL II. 2013. Tropical cyclone report Hurricane Sandy, AL182012. Miami, FL: National Hurricane Center. [Google Scholar]
  12. Blumenthal D, Augustine D. 2009. Plant interactions with herbivores. In: Encyclopedia of Life Sciences. Chichester: John Wiley & Sons, Ltd. doi:10.1002/9780470015902.a0003203.pub2 [Google Scholar]
  13. Booth DT, Cox SE, Louhaichi M, Johnson DE. 2004. Technical note: lightweight camera stand for close-to-Earth remote sensing. Journal of Range Management 57:675–678. [Google Scholar]
  14. Brantley ST, Bissett SN, Young DR, Wolner CW, Moore LJ. 2014. Barrier island morphology and sediment characteristics affect the recovery of dune building grasses following storm-induced overwash. PLoS One 9:e104747. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Brenner OT, Lentz EE, Hapke CJ, Henderson RE, Wilson KE, Nelson TR. 2018. Characterizing storm response and recovery using the beach change envelope: Fire Island, New York. Geomorphology 300:189–202. [Google Scholar]
  16. Briske DD, Noy-Meir I. 1998. Plant responses to grazing: a comparative evaluation of annual and perennial grasses. In: Papanastasis VP, Peter D, eds. Ecological basis of livestock grazing in Mediterranean ecosystems. Luxembourg: European Commission, Science, Research and Development, 13–26. [Google Scholar]
  17. Bryant FC, Taylor CA, Merrill LB. 1981. White-tailed deer diets from pastures in excellent and poor range condition. Journal of Range Management 34:193–200. [Google Scholar]
  18. Burnham KP, Anderson DR. 2001. Kullback-Leibler information as a basis for strong inference in ecological studies. Wildlife Research 28:111–119. [Google Scholar]
  19. Carruthers TJB, Beckert K, Schupp CA, Saxby T, Kumer JP, Thomas J, Sturgis B, Dennison WC, Williams M, Fisher T, Zimmerman CS. 2013. Improving management of a mid-Atlantic coastal barrier island through assessment of habitat condition. Estuarine, Coastal and Shelf Science 116:74–86. [Google Scholar]
  20. Dale D, Weaver T. 1974. Trampling effects on vegetation of the trail corridors of North Rocky Mountain Forests. Journal of Applied Ecology 11:767–772. [Google Scholar]
  21. Davidson E, Fox M. 1974. Effects of off-road motorcycle activity on Mojave Desert vegetation and soil. Madrono 22:381–390. [Google Scholar]
  22. Demarais S, Cornicelli L, Kahn R, Merrill E, Miller C, Peek JM, Porter WF, Sargeant GA. 2012. Ungulate management in national parks of the United States and Canada. Bethesda, MD: The Wildlife Society. [Google Scholar]
  23. de Stoppelaire GH, Gillespie TW, Brock JC, Tobin GA. 2004. Use of remote sensing techniques to determine the effects of grazing on vegetation cover and dune elevation at Assateague Island National Seashore: impact of horses. Environmental Management 34:642–649. [DOI] [PubMed] [Google Scholar]
  24. Duncan K, Holdaway R. 1989. Footprint pressures and locomotion of moas and ungulates and their effects on the New Zealand indigenous biota through trampling. New Zealand Journal of Ecology 12:97–101. [Google Scholar]
  25. Durán O, Moore LJ. 2013. Vegetation controls on the maximum size of coastal dunes. Proceedings of the National Academy of Sciences of the United States of America 110:17217–17222. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Ehrenfeld JG. 1990. Dynamics and processes of barrier-island vegetation. Reviews in Aquatic Sciences 2:437–480. [Google Scholar]
  27. Elzinga CL, Salzer DW, Willoughby JW. 1998. Measuring and monitoring plant populations. Technical Reference 1730-1. Denver, CO: Bureau of Land Management. [Google Scholar]
  28. Fire Island National Seashore [FIIS]. 2015. Draft general management plan environmental impact statement. Patchogue, NY: National Park Service, 491. [Google Scholar]
  29. Forrester JA, Leopold DJ, Art HW. 2007. Disturbance history and mortality patterns in a rare Atlantic barrier island maritime holly forest. Natural Areas Journal 27:169–182. [Google Scholar]
  30. Forrester JA, Leopold DJ, Underwood HB. 2006. Isolating the effects of white-tailed deer on the vegetation dynamics of a rare maritime American holly forest. The American Midland Naturalist 156:135–150. [Google Scholar]
  31. Gadgil RL. 2002. Marram grass (Ammophila arenaria) and coastal sand stability in New Zealand. New Zealand Journal of Forest Science 32:165–180. [Google Scholar]
  32. Gobbett DL, Zerger A. 2014. PointSampler: a GIS tool for point intercept sampling of digital images. Biodiversity Informatics 9:13–17. [Google Scholar]
  33. Hapke CJ, Brenner O, Hehre R, Reynolds BJ. 2013. Coastal change from Hurricane Sandy and the 2012-13 winter storm season—Fire Island, New York. Open-file report 2013-1231. Woods Hole, MA: US Geological Survey. [Google Scholar]
  34. Hapke CJ, Lentz EE, Gayes PT, McCoy CA, Hehre R, Schwab WC, Williams SJ. 2010. A review of sediment budget imbalances along Fire Island, New York: can nearshore geologic framework and patterns of shoreline change explain the deficit? Journal of Coastal Research 263:510–522. [Google Scholar]
  35. Heggenes J, Odland A, Chevalier T, Ahlberg J, Berg A, Larsson H, Bjerketvedt DK. 2017. Herbivore grazing-or trampling? Trampling effects by a large ungulate in cold high-latitude ecosystems. Ecology and Evolution 7:6423–6431. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Hobbs NT, Baker DL, Gill RB. 1983. Comparative nutritional ecology of montane ungulates during winter. The Journal of Wildlife Management 47:1–16. [Google Scholar]
  37. Houser C, Wernette P, Rentschlar E, Jones H, Hammond B, Trimble S. 2015. Poststorm beach and dune recovery: implications for barrier island resilience. Geomorphology 234:54–63. [Google Scholar]
  38. Keiper RR. 1990. Biology of large grazing mammals on the Virginia barrier islands. Virginia Journal of Science 41:352–363. [Google Scholar]
  39. Kent M, Owen NW, Dale P, Newnham RM, Giles TM. 2001. Studies of vegetation burial: a focus for biogeography and biogeomorphology? Progress in Physical Geography 25:455–482. [Google Scholar]
  40. Kenward MG, Roger JH. 1997. Small sample inference for fixed effects from restricted maximum likelihood. Biometrics 53:983–997. [PubMed] [Google Scholar]
  41. Kilheffer CR. 2018. Plant community development in storm-induced overwash fans of the Otis Pike Fire Island High Dune Wilderness Area, New York. PhD Thesis, State University of New York College of Environmental Science and Forestry, Syracuse, NY. [Google Scholar]
  42. Kilheffer CR, Underwood HB, Raphael J, Ries L, Farrell S, Leopold DJ. 2019. Deer do not affect short-term rates of vegetation recovery in overwash fans on Fire Island after Hurricane Sandy. Ecology and Evolution in press. doi:10.1002/ece3.5674 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Kittredge DB, Ashton PMS. 1995. Impact of deer browsing on regeneration in mixed stands in southern New England. Northern Journal of Applied Forestry 12:115–120. [Google Scholar]
  44. Laird NM, Ware JH. 1982. Random-effects models for longitudinal data. Biometrics 38:963–974. [PubMed] [Google Scholar]
  45. Littell RC, Henry PR, Ammerman CB. 1998. Statistical analysis of repeated measures data using SAS procedures. Journal of Animal Science 76:1216–1231. [DOI] [PubMed] [Google Scholar]
  46. Littell RC, Stroup WW, Freund RJ. 2002. SAS for linear models. Cary, NC: SAS Institute. [Google Scholar]
  47. Liu C, Cao D, Chen P, Zagar T. 2007. RANDOM and REPEATED statements – how to use them to model the covariance structure in Proc Mixed. Paper S02-2007. Cary, NC: Proceedings of the SAS® Users Group International. [Google Scholar]
  48. Long ZT. 1999. The effect of herbivory on metrics of shade tolerance of understory trees in an old-growth beech-maple forest. PhD Thesis, University of Pittsburgh, Pittsburgh, PA. [Google Scholar]
  49. Maun A. 2009. The biology of coastal sand dunes. New York: Oxford University Press. [Google Scholar]
  50. McDonnell MJ. 1981. Trampling effects on coastal dune vegetation in the Parker River National Wildlife Refuge, Massachusetts, USA. Biological Conservation 21:289–301. [Google Scholar]
  51. McNaughton SJ, Ruess RW, Seagle SW. 1988. Large mammals and process dynamics in African ecosystems. BioScience 38:794–800. [Google Scholar]
  52. Meredith MP, Stehman SV. 1991. Repeated measures experiments in forestry: focus on analysis of response curves. Canadian Journal of Forest Research 21:957–965. [Google Scholar]
  53. Miller TE, Gornish ES, Buckley HL. 2010. Climate and coastal dune vegetation: disturbance, recovery, and succession. Plant Ecology 206:97. [Google Scholar]
  54. O’Connell AF, Sayre MW. 1988. Physical characteristics of Fire Island’s deer population and an evaluation of sport hunting within Fire Island National Seashore. Cooperative Agreement # 1600-4-0005, Amendment # 35. Patchogue, NY: National Park Service. [Google Scholar]
  55. O’Connell AF, Sayre MW. 1989. White-tailed deer management study. NatureBIB #34036. Amherst, MA: National Park Service. [Google Scholar]
  56. Olson JS. 1958. Lake Michigan dune development 2. Plants as agents and tools in geomorphology. The Journal of Geology 66:345–351. [Google Scholar]
  57. Pellerin S, Huot J, Côté SD. 2006. Long term effects of deer browsing and trampling on the vegetation of peatlands. Biological Conservation 128:316–326. [Google Scholar]
  58. Pendergast TH, Hanlon SM, Long ZM, Royo A, Carson WP. 2015. The legacy of deer overabundance: long-term delays in herbaceous understory recovery. Canadian Journal of Forest Research 46:362–369. [Google Scholar]
  59. Pendleton EA, Williams SJ, Thieler ER. 2004. Coastal vulnerability and assessment of Fire Island National Seashore to sea-level rise. Open-File Report 03-439. Woods Hole, MA: US Geological Survey. [Google Scholar]
  60. Plumb G, Monello R, Resnik J, Kahn R, Leong K, Decker D, Clarke M. 2014. A comprehensive review of ungulate management by the National Park Service: second century challenges, opportunities, and coherence. Natural Resource Report NPA/NRSS/BRMD/NRR-2014/898. Fort Collins, CO: National Park Service. [Google Scholar]
  61. Porter WF. 1992. Burgeoning ungulate populations in national parks: is intervention warranted? In: McCullough DR, Barrett RH, eds. Wildlife 2001: populations. Dordrecht, The Netherlands: Springer, 304–312. [Google Scholar]
  62. Porter KM, DePerno CS, Krings A, Krachey M, Braham R. 2014. Vegetative impact of feral horses, feral pigs, and white-tailed deer on the Currituck National Wildlife Refuge, North Carolina. Southern Appalachian Botanical Society 79:8–17. [Google Scholar]
  63. Proulx M, Mazumder A. 1998. Reversal of grazing impact on plant species richness in nutrient-poor vs, nutrient-rich ecosystems. Ecology 79:2581–2592. [Google Scholar]
  64. Quinn GP, Keough MJ. 2002. Experimental design and data analysis for biologists. Cambridge, UK: Cambridge University Press. [Google Scholar]
  65. Raphael J. 2014. 50 years of vegetation change in a holly maritime forest. Master’s Thesis, Hofstra University, New York. [Google Scholar]
  66. Reimoser S, Partl E, Reimoser F, Vospernik S. 2009. Roe-deer habitat suitability and predisposition of forest to browsing damage in its dependence on forest growth – model sensitivity in an alpine forest region. Ecological Modelling 220:2231–2243. [Google Scholar]
  67. Rooney TP, Waller DM. 2003. Direct and indirect effects of white-tailed deer in forest ecosystems. Forest Ecology and Management 181:165–176. [Google Scholar]
  68. Ruhren S, Handel SN. 2000. Considering herbivory, reproduction, and gender when monitoring plants: a case study of Jack-in-the-pulpit (Arisaema triphyllum [L.] Schott). Natural Areas Journal 20:261–266. [Google Scholar]
  69. Sabo A, Frerker KL, Waller DM, Kruger EL. 2017. Deer‐mediated changes in environment compound the direct impacts of herbivory on understorey plant communities. Journal of Ecology 105:1386–1398. [Google Scholar]
  70. Sallenger AH. 2000. Storm impact scale for barrier islands. Journal of Coastal Research 16:890–895. [Google Scholar]
  71. Santoro R, Jucker T, Prisco I, Carboni M, Battisti C, Acosta AT. 2012. Effects of trampling limitation on coastal dune plant communities. Environmental Management 49:534–542. [DOI] [PubMed] [Google Scholar]
  72. SAS Institute Inc. 2017. SAS/STAT® 14.3 user’s guide. Cary, NC: SAS Institute. [Google Scholar]
  73. Seliskar DM. 2003. The response of Ammophila breviligulata and Spartina patens (Poaceae) to grazing by feral horses on a dynamic mid-Atlantic barrier island. American Journal of Botany 90:1038–1044. [DOI] [PubMed] [Google Scholar]
  74. Sherrill BL, Snider AG, DePerno CS. 2010. White-tailed deer on a barrier island: implications for preserving an ecologically important maritime forest. Proceedings from the Annual Conference of the Southeast Association of Fish and Wildlife 64:38–43. [Google Scholar]
  75. Shipley LA. 1999. Grazers and browsers: how digestive morphology affects diet selection. Grazing Behavior of Livestock and Wildlife 70:20–27. [Google Scholar]
  76. Stuckey IH, Gould LL. 2000. Coastal plants from Cape Cod to Cape Canaveral. Chapel Hill, NC: University of North Carolina Press. [Google Scholar]
  77. Tilghman NG. 1988. Impacts of white-tailed deer on forest regeneration in Northwestern Pennsylvania. Journal of Wildlife Management 53:524–532. [Google Scholar]
  78. Tilman D. 1990. Constraints and tradeoffs: toward a predictive theory of competition and succession. Oikos 58:3–15. [Google Scholar]
  79. Trlica MJ. 1990. Constraints and tradeoffs: toward a predictive theory of competition and succession. Oikos 58:3–15. [Google Scholar]
  80. Trlica MJ. 1999. Grass growth and response to grazing. Range Natural Resource 6.108. Fort Collins, CO: Colorado State University Cooperative Extension. [Google Scholar]
  81. Trlica MJ, Rittenhouse LR. 1993. Grazing and plant performance. Ecological Applications 3:21–23. [DOI] [PubMed] [Google Scholar]
  82. Underwood HB. 2005. White-tailed deer ecology and management on fire Island National Seashore (Fire Island National Seashore Science Synthesis Paper). Technical report NPS/NER/NRTR—2005/022. Boston, MA: National Park Service. [Google Scholar]
  83. Urbanek RE, Nielsen CK, Glowacki GA, Preuss TS. 2012. White-tailed deer (Odocoileus virginianus Zimm.) herbivory in herbaceous plant communities in northeastern Illinois. Natural Areas Journal 32:6–14. [Google Scholar]
  84. Valdés-Correcher E, Rodriguez E, Kemp YJM, Wassen MJ, Cromsigt JPGM. 2018. Comparing the impact of a grazing regime with European bison versus one with free-ranging cattle on coastal dune vegetation in the Netherlands. Mammal Research. 63:1–12. [Google Scholar]
  85. Vinent OD, Moore LJ. 2015. Barrier island bistability induced by biophysical interactions. Nature Climate Change 5:158–162. [Google Scholar]
  86. Waller DM, Alverson WS. 1997. The white-tailed deer: a keystone herbivore. Wildlife Society Bulletin 25:217–226. [Google Scholar]
  87. West BT, Welch KB, Galecki AT. 2014. Linear mixed models: a practical guide using statistical software, 2nd edn. New York: Chapman and Hall/CRC. [Google Scholar]
  88. Wolfinger R, Miles-McDermott N, Kendall J. 1992. Analyzing split-plot and repeated measures designs using mixed models. Conference on Applied Statistics in Agriculture. doi:10.4148/2475-7772.1405 [Google Scholar]

Articles from AoB Plants are provided here courtesy of Oxford University Press

RESOURCES