HomeMy WebLinkAboutSchmits, O Documentation DEIS.pdf Journal of Wildlife Management 74(6):1257-1263;2010;DO]; 10.2193/2009-068 1
Management and Conservation Note
Regional-Scale Assessment of Deer
Impacts on Vegetation Within Western
Connecticut, USA
ANGELA C.RUTHERFORD,School of Forestry and Environmental Studies, Yale University,370 Prospect Street, New Hawn, CT 06511, USA
OSWALD J. SCHMITZ,r School of Forestry and Environmental Studies, Yale University,370 Prospect Street, New Haven, CT 06511, USA
ABSTRACT High densities of white-tailed deer(Odoraileus virginumm)are believed to cause broad-scale forest regeneration failure and
loss of plant diversity.But,the empirical basis for such presumptions is limited.We,therefore,conducted a survey in western Connecticut,
USA,woodlots to examine how spatial variation in deer densities influences variation in impacts on plant species abundance, identity and
diversity,and tree regeneration.We also used a Geographic Information System to quantify trends between land-cover type and deer density.
Deer density was not correlated with any vegetation or land use variable.This suggests that deer density is not a leading factor determining
variation in vegetation impacts across western Connecticut.
KEY WORDS Connecticut, deer damage, deer—human interactions, deer overabundance, deer population control, habitat
fragmentation, Odocoileus virginianus, plant diversity, white-tailed deer,woodlot regeneration.
White-tailed deer(0docoilew virginianus)populations in the settlements (Fig. 1) and in which there was a perceived deer
northeastern United States have increased from near- overabundance problem (Kilpatrick and LeBonte 2007).
extirpation around the turn of the 20th century to >25 We used 2 complementary approaches to assess whether
deer/km2 today (DeCalesta and Stout 1997, Russell et al. deer abundance is a leading factor determining environ-
2001, Horsley et al. 2003). Such dense populations, and mental impacts across this region. We tested for potential
inevitable interactions between deer and humans, have deer impacts on woodlot plant diversity and regeneration by
precipitated numerous calls for management to address what conducting an intensive field survey across western Con-
is now widely viewed as a problem of deer overabundance necticut(Fig. ld).We also evaluated whether deer densities
(McShea and Rappole 2000, Russell et al. 2001, Cote et al. varied with the nature of land use, especially in relation to
2004, Levy 2006). important determinants of deer presence like proportion of
Calls to manage the overabundance problem stem from developed habitat and the degree of habitat fragmentation
observations that where deer are highly abundant there are (Cote et al. 2004, Brownstein et al. 2005).
also high incidences of several environmental impacts such If deer density is the leading factor driving environmental
as forest regeneration failure,loss of biodiversity,invasion by impacts, then across western Connecticut, variation in deer
exotic species, Lyme disease risk,and deer—vehicle collisions density should explain a high amount of variation in the
(DeCalesta 1994, McShea and Rappole 2000, Levy 2006, magnitude of environmental impacts. Nevertheless, deer
Kilpatrick and LaBonte 2007). Yet, such observations of abundances in this region (Fig. lb) are below minimum
environmental impact may be conflated by contemporane- levels(on the order of 14-15 deer/kM2)for which significant
ous human alteration of landscapes because rising human damaging impacts on plant species abundance and diversity
populations have changed local land-use regimes through- have been reported (Russell et al. 2001). We, therefore, did
out a region(Bashore et al. 1985,Hubbard et al.2000,Allan not expect to see a strong relationship between variation in
et al.2003,Brownstein et al.2005).Thus,conclusions about deer density and variation in.environmental impact across
overabundance are often based on human values in relation western Connecticut. We hypothesized further that if
to preferences for particular environmental states (Sinclair transformation of the landscape by human land-use patterns
1997, Russell et al. 2001). determines deer density, then across this geographic region,
Because local values and deer impacts may be conflated, a variation in land use should explain variation in deer
general policy to institute density reductions can only be densities.
scientifically justified if deer abundance is shown to be the STUDY AREA
leading factor determining environmental impacts among
localities within a region (Schmitz and Sinclair 1997, Our study focused on an 8,261-km2 area of western
Russell et al. 2001, Cote et al. 2004). Such regional-scale Connecticut, USA, between Long Island Sound in the
determinations of deer impact have, however, rarely been south and the state of Massachusetts in the north (Fig. 1).
done (Russell et al. 2001, Cote et al. 2004). We, therefore, Developed land was concentrated within an urban belt along
conducted an assessment of deer impacts across western the south and eastern side of the region and became more
Connecticut, USA, a geographic region that encompassed a rural and forested toward the northwest corner of the state
broad range of deer densities, land-use types, and human (Fig. la). Deer population densities varied from an average
of 11.5 deer/km2 in urbanized southeastern areas to 1.4
'E-mail. oswald.schmitzOyale.eda deer/km' in rural parts (Fig. lb). The degree of land
Rutherford and Schmitz•Deer and Regional Vegetation Impacts 1257
nonlandscaped woodlot, defined as a small(<5 ha)forested
patch of land within an urban or agricultural land matrix,to
accommodate a 20 X 20-m sampling plot. Our final choice
of 10 sampling locations was determined by property owner
permission to access the location.
We conducted vegetation surveys within each of the 60
plot locations between 7 June and 11 July 2007. At each
sampling location we flagged a 20 X 20-m plot. This plot
size was small enough to fit into neighborhood study
locations, yet large enough to include a representative
number of plant species (Condit et al. 1996, Healy 1997,
n Ladd et al. 2005, Goslee 2006). We then located 1 X 1-m
subplots in each corner and in the center of the 20 X 20-m
plot. In each of the 5 subplots, we identified each plant to
species, designated it as woody or herbaceous and native,
invasive, or ornamental.We estimated the percent of the 1-
m2 subplot area covered by each herbaceous and woody
seedling species that was within 1 in above ground, and we
counted the number of trees (saplings and canopy trees)
taller than 1 in. We estimated plant species diversity within
a R 1 in above the ground in each subplot using richness and
s evenness metrics because deer can affect both the number of
P plant species in a community (richness) and the relative
dominance of particular plant species(evenness)via selective
M foraging (Russell et al. 2001, Cote et al. 2004). We
R calculated evenness using the standard Shannon index
(Krebs 1998). An evenness value of zero would indicate
that one or a few species dominate the plot; an evenness
Figure 1. Study region of western Connecticut, USA, separated by value approaching 1 would indicate that all species in a plot
Connecticut Department of Environmental Protection Hunting Manage- have similar relative abundance. We averaged the subplot
ment Zone.The region presents spatial variability in land use,deer density, abundance and diversity data to obtain an independent
and human population size to assess regional deer impacts on vegetation.(a)
land use patterns(red = developed,brown=field,green = forest,blue = estimate of each variable for each 20 X 20-m sampling Plot.
water);(b)deer density(deer/km')■= 11.5,1=8.8,■= 7.9,®=5.6,0= Deer impacts vary with plant species composition and
2.8,0=2.0,0= 1.4,0= 1.1;(c)human population density(persons/km2) palatability among sites (Russell et al. 2001, Cote et al.
=60.9, =59.1,■=54.5,■=54.1,1=53.0, =36.0,0= 19.31 0= 2004). We categorized all plant species as palatable or
9.3; (d) sampling sites: Woodbridge W, Monroe (M), Salisbury (S),
Ridgefield (R), Norfolk (N), and Portland (P). Maps are based on data unpalatable based on lists assembled by official wildlife
current to 2007. extension services (Burroughs and Dudek 2008, Bridgen
2009, Morton Arboretum 2009, Rutgers University 2009).
development corresponded to human population density, If a species did not make any list, we categorized it as
which was highest within the urban belt at approximately 23 unknown. We calculated the proportion of species within
persons/km' and decreased toward the northwestern corner each plot that was palatable. We also estimated the degree
to <10 persons/kM2 (Fig. lc). Western Connecticut thus to which plant species composition was common among all
offered a range of environmental conditions needed to assess town sites using pair-wise comparisons. We divided the
how variation in deer density,human land-use patterns, and number of species that 2 towns shared by the total number
environmental impacts might be related. of species in each of the 2 towns.
METHODS We assessed regeneration by measuring woody vegetation
(tree seedling and sapling) density (% cover) up a 2-m
We sampled in 6 replicate towns across the western vertical profile within each subplot location. We estimated
Connecticut study area (Woodbridge, Monroe, Ridgefield, percent cover at 4 heights (0-0.5 in, 0.5-1.0 in, 1.0-1.5 in,
Salisbury,Norfolk,and Portland),which ranged from low to 1.5-2.0 m) using a vertical cover-board technique (Nudds
high deer densities based on the Connecticut Department of 1977, McShea and Rappole 2000). We quantified an index
Environmental Protection (CT DEP) aerial survey data of woody vegetation regeneration in a plot as foliage height
(Fig. lb). Within each of these 6 towns, we randomly diversity (FHD) where FHD = —E p; In (pi), and pi is
identified 20 potential sampling locations using Geographic average vegetation density (% cover) of the 5 subplots at
Information Systems(GIS)and then sampled a subset of 10, height i, and the summation was over the 4 height
giving 60 sampling plots statewide (Fig. ld). Given our categories. Our rationale was that if an area is regenerating
focus on woodlot plant diversity and regeneration, we in all age classes uniformly, then woody vegetation density
deemed a sampling site as suitable if it had sufficient should be equal at all heights along the vertical cover-board
1259 The Journal of Wildlife Management•74(6)
gradient.Lack of consistent regeneration would be indicated Table 1. Mean percentage similarity in woody and herbaceous plant
by little or no woody vegetation density along the gradient species among 6 towns across a range of deer densities in western
(Mc$hea and Rappole 2000). Accordingly, a high FHD Connecticut,USA.Estimates are based on plant species samplingwithin 10
plots in each town during summer 2007.
value indicates that habitat was regenerating throughout the
2-m understory; a low FHD indicates that regeneration was Town
uneven. We averaged the subplot FHD data to obtain an Town Monroe Ridgefield Salisbury Norfolk Portland
independent estimate for each 20 X 20-m sampling plot. Woodbridge 38.1 37.8 25 24 27
We estimated relative deer density at each sampling Monroe 38.4 30.5 31.1 41.3
location along 4 2-m-wide transects beginning at the center Ridgefield 34.1 39.6 34.4
of each 20 X 20-m lot and radiating 50 m outward from Salisbury 39.6 33.4
p g Norfolk 33.7
the 4 sides of the plot. We measured 3 indices of deer
density. We counted the number of woody stems browsed
by deer relative to available stems up to 2 m high within 1 X graphic-scale relationship for all towns combined. At this
1-m plots spaced every 10 to along the transect. Stems could scale, the appropriate regression model includes all the deer
be browsed either by rabbits(Sylvilagis floridanus)or by deer. density data as an independent variable,but also treats town
We attributed browsing to rabbits wherever there was an as a random effect (block) to account for potential
obvious 45" sharp angular clip. We attributed it to deer correlation structure of the town-level sampling data owing
wherever there was an obvious sign of twig removal (no to multiple plots in each town. We also conducted a
apical bud and remnants of bark strips) along an erect stem. correlation analysis between the proportion of palatable
We counted deer pellet groups (fresh and old) comprised of species within a plot and vegetation variables in a plot using
>13 pellets and normalized counts by the sampling area data for all 60 plots. We normalized all proportion data
they covered (400 mz over all 4 transects) to yield an using a square-root transformation.
estimate of pellet group density (Grund et al. 2002, Using GIS, we quantified land-use attributes within a 1-
Thompson and Sharpe 2005). We counted the number of km-radius buffered circle around each sampling location.
independent deer tracks crossing each transect. Because the We used 2008 land cover data from the University of
ground in the woodlots was typically moist,we could readily Connecticut's Center for Land use Education And Research
observe hoof impressions on the soil surface.We conducted (CLEAR 2009) land-use cover ArcGIS polygon shapefile.
correlation analysis among the 3 indices of density for each Because deer thrive in fragmented landscapes,we estimated
plot to determine whether those indices were congruent. degree of habitat fragmentation around each sampling
We also verified whether our original designation of a location by calculating the perimeter to area ratio of each
south-to-north deer density gradient across Connecticut, polygon within the 1-km-radius buffered circle (Cote et al.
based on CT DEP aerial survey data,was indeed reflected in 2004, Brownstein et al. 2005). We also calculated the
our sampling by correlating our measures with the CT DEP proportion of land within the buffered-circle classified by
data (Gregonis 2007). The CT DEP data provide one CLEAR as developed, field, and forested. Here, the
estimate for each management zone (Fig. lb), whereas our developed class represents the urban built environment;
sampling provided several estimates within each zone field includes barren lands,nonforested wetlands,turf,grass,
(Fig. ld). We, therefore, used a randomization procedure and agriculture; and forested includes coniferous forest,
to generate single, independent town-level data for deciduous forest, and forested wetland. We conducted
comparison with management zone data. We took 5 regression analyses to examine effects of local habitat
random deer density estimates from the 10 density estimates fragmentation and proportion of land use of developed,
within each of the 6 town sites and averaged them for each field, or forest on our estimates of local deer density.
town.We then conducted a correlation analysis between our RESULTS
average town-level estimate and the respective CT DEP
hunting management zone estimate. We repeated this We sampled 157 plant species (119 herbaceous, 38 woody)
algorithm 10 times for each of the 3 indices of deer density across the entire geographic region. In any one town, there
(browse, tracks, and pellets). were 15-28 woody species (40-74% of total woody species)
We used a general linear model procedure to examine and 34-51 herbaceous species (28-43% of total herbaceous
relationships between our sampled indices of deer density species). Typically, there was 33% overlap in species
(independent variable) and vegetation variables (i.e.,average composition among towns (Table 1). Density of saplings
vegetative cover, plant species richness and evenness, FHD, and trees that were part of the canopy (dbh > 10 cm) and
abundance of ornamental and native species). Broad-scale seedlings (woody stems growing within 1 m above ground)
geographic analyses run the risk of obscuring important local were similar among towns except for Salisbury, which had
scale trends if qualitative differences in the strength of the double the typical sapling density (Table 2).
relationship between deer density and impact exist among We found significant correlations between our sampled
locations. We, therefore, conducted our analyses of deer indices of deer density among the 60 sampling sites (all P<
impact at 2 scales.We examined relationships between plot- 0.01). Deer track counts correlated poorly with the pellet
level deer density and plot-level impact for each of the 6 group index and browse index(both r<0.45). Pellet counts
towns individually. We then examined the overall geo- and browse indices were better correlated (r = 0.68).
Rutherford and Schmitz•Deer and Regional Vegetation Impacts 1259
Table 2. Density(stems/400-m2 plot)of trees(saplings and canopy trees
>10 cm dbh)and woody seedlings within sampling sites in 6 towns across a = r o o 0 0 0 0 0 0 0 0 0
range of deer densities in western Connecticut,USA,in summer 2007. ,o
Trees Woody seedlings .o :C '7 c, .'OD N t1�1 o N N N v r �
> o 0 0 0 0 0 o b o d o
Town i SE z SE rz u o ti
Woodbridge 24.9 6.6 46.1 8.6 10 3 o rn e N
Monroe 28.7 2.8 44.4 6.8 10
Ridgefield 24.2 3.9 49.1 15.4 10 0 0 0 0 0 o 0 0 0 0 0
0
Salisbury 23.5 4.8 93.1 23.4 10 n
Norfolk 24.8 2.2 42.9 6.9 10 a o0000000000
Portland 20.4 2.3 58.5 11.8 10 0
u
a o '� .moo aornnncc � n n
t 2, o 0 0 0 0 o b o 0 0 0
(Rabbit browsing accounted for <1% of impact on tree v
seedlings and saplings.) Randomization test comparisons of 5
S NCO Vt '+ NNg OOHO
our sampled density indices with CT DEP density estimates g
revealed that each of the 10 replications produced
statistically significant (P < 0.05) results, indicating that t o 0 0 0 0 0 0 0 0 0 0
statistical power of our conclusion was high. Our conclusion a d
was that there was a correlation between each of our indices
rC a M M t` N N M O; N C W NR
of deer density (based on browse impact,pellet groups, and a ry> d d o 0 0 0 0 o d d o
track counts) and those reported by the CT DEP (all r >_ 3
0.659),thus affirming that we did indeed sample along a deer
0 0 0 0 0 0 0 0 0 0 0
density gradient. We found the highest average correlation E
between browse impact data and CT DEP data (mean r = e o 0 0 0 0 0 0 0 0 0 0
0.776, mean P < 0.05). We, therefore, used average browse 3
impact for each sampling plot as an independent measure of n N M ry c ,�„
deer density in our subsequent analyses. 4 0 0 0 o O o 0 0 0 0 0
Our index of deer density and values of plant variables N = a a N
varied considerablywithin towns and across the stud re ion a o 0 0 0 0 0
}' g o 0 o c c o b o 6 0 6
(Table 3). For example, all towns had similar FHD (index
of forest regeneration) values (0.2-0.6) but had highly o 0 0 0 0 0 0 0 0 0 0
variable indices of deer abundance (Fig. 2). In general, we
I 'y Vl H M •O D) M .. In IN M
found no significant town (block) effect on any of the M In M
regressions (all P > 0.35, df = 5, 53). There was also no z h o 0 0 o b o 0 0 0 0 0
correlation among deer density and any of the plant variables
within specific locations and across the entire geographic x o 0 0 0 0 0 0 0 0 0 0
region (Table 3).
The proportion of palatable plants ranged from an average 8 r o 0 0 0 0 0 0 0 0 0 0
5% in Woodbridge to 8% in Monroe. Because of small A Z y o
within-and among-site variation in palatability there was no
�`. h, oob00000000
correlation between plant palatability and deer density 5ti
among locations (r = 0.07, P > 0.60). Accordingly, there
v
was no correlation (all r < 0.02, P > 0.18) betweena
c
proportion of palatable plants among sampling locations and p o 0 0 0 0 0 0 0 0 0 0
�, m o o .o o o •o.a .o .o
either of plant percent cover, plant species richness, plant o
species evenness, FHD, percentage ornamental plants, or
percentage native plants. Given our sampling effo N�+ �l h OJ OJ M P OJ M DJ h V,
rt and > o 0 0 0 0 do 0 0 o o v
error structure of the data, we estimated that statistical E °
power of our results ranged from 75% to 90% (i.e., the `E C o o I o 0 0 0 0 0 0 0
likelihood of falsely concluding that there is no relationship o 0 0 6 6 ,566666
e
between any 2 variables ranged between 10% and 25%).
Neither deer density and degree of habitat fragmentation, o,
nor deer density and land use allocation, were correlated
(Table 3). o ® v s a
DISCUSSION a C 6 C C O
Our analysis of the relationship between deer abundance
and impact across western Connecticut tested whether deer F .E
1260 The Journal of Wildlife Management•74(6)
,
ne salm"Y Qe R'=0.013 P�IOrfdk 0.8 Fbrda d
Z 66 ♦ SEL6 P=0.749 ♦ =Ob •
u. 0.4 ♦ R°=0A57 Y. 0.4 • • • LL a4 • W=0.032
O ♦ ♦ P=0.505 0♦ ♦ ♦ ♦ 0.2 ♦ P=0.624
0 02 0.4 QB 08 0 O.02 Opt 0.05 op8 O 0.2 n4 0.6 0.8
Irdecd dear dalalY Index of deer derdly U[lardcles dermity
, 1
All tovalsibm
Qe
0
n6
x
U. R2=0.022
0.4
P=0.255
Q2
0
0 Q1 Q2 Q3 Q4 05 OB Q7 Ob
Indeer d cber drstY
OA F§ck~ Q8 R40FV B 0.8
= CIS ♦ • = 0.6 ♦ = 0.6
LL 04 • W=0.032 Q. 64 Pr=0.010 u. Q4 ♦ ♦♦ R+=0.000
O • + M ♦♦ P=0.619♦ 0.2 • P=0782 0.2 • • P=0.963
0 O
0 02 Qa a6 09 0 0.2 0.4 0A Q8 Q 0.2 Q4 Q6 0.8
4skrd deer daWry Index d deer dWWW Index of deer dWWly
Figure 2. Correlation of foliage height diversity(FHD;an indicator of forest regeneration)and deer density index for 6 town sites in western Connecticut,
USA,in which deer impacts were sampled during summer 2007.High FHD values indicate high forest regeneration and high deer index values indicate high
deer densities.The data are presented for each town individually and for all town sites collectively.Values for individual town sites are the mean subplot value
for each of 10 sampling plots.Values for all town sites are mean of the plot values± 1 standard error.The patterns observed in this figure are representative of
all other associations of deer density index and vegetation variables measured in our study.
density was a leading factor determining loss of plant comparatively low abundance as we observed in western
identity, plant diversity, and plant regeneration across this Connecticut corroborate an outcome of no correlation
geographic region. Our index of deer abundance was between deer density and impact on seedling density
unrelated to any measure of plant species abundance or (Tilghman 1989).
diversity, and deer abundance did not seem to impact By treating deer as an independent variable in our
woodlot tree regeneration, measured either as FHD or total statistical analyses, we explicitly assumed that deer are the
abundance of canopy trees(Table 2;Fig. 2).We believe that direct determinant of environmental impacts. However, it
a lack of deer impact arises from 2 factors. First, only a may be that deer impacts are an indirect consequence of
minor fraction of plant species within each of the sites was other factors that encourage local deer effects (Russell et al.
palatable. Second,although data indicate deer in this region 2001, Cote et al. 2004). For example, the nature and extent
have erupted in abundance over the last 50 years, deer of land management and land development such as creation
abundance still tends to range between low and modest of edge via habitat fragmentation can create habitat that is
levels compared to deer densities in other locations attractive to deer, leading to local impacts (Chang et al.
throughout the United States (Russell et al. 2001, Cote et 1995, Russell et al. 2001, Kie et al. 2002, Cote et al. 2004,
al. 2004, Kilpatrick and LeBonte 2007). Moreover the Tremblay et al. 2007). That is, deer would be a proximate
highest average densities reported for our study region (11.5 cause of damage, not the ultimate cause, which is attractive
deer/km2) remain below minimum levels (approx. 14-15 habitat created by human land use. However, we did not
deer/kM2) for which significant damaging impacts on plant find a correlation between deer density and degree of habitat
species abundance and diversity have been reported(Russell fragmentation or between deer density and land use
et al. 2001). Indeed,deer enclosure treatments in unthinned allocation (Table 3). We do not deny that deer impacts
forests for which deer densities varied over the same range of can be quite strong within each of the towns throughout our
Rutherford and Schmitz•Deer and Regional Vegetation Impacts 1261
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