Journal of Ecology 2011, 99, 764–776
doi: 10.1111/j.1365-2745.2011.01807.x
The roles of environmental filtering and colonization
in the fine-scale spatial patterning of ground-layer
plant communities in north temperate deciduous
forests
Julia I. Burton1*†, David J. Mladenoff1, Murray K. Clayton2,3 and Jodi A. Forrester1
1
Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Dr., Madison, WI 53706,
USA; 2Department of Plant Pathology, University of Wisconsin-Madison, 1630 Linden Dr., Madison, WI 53706, USA;
and 3Department of Statistics, University of Wisconsin-Madison, 1210 W. Dayton St., Madison, WI 53706, USA
Summary
1. The majority of plant species in northern temperate deciduous forests are restricted to the
ground layer, but the importance of colonization processes relative to environmental filtering in
structuring spatial variation in ground-layer plant communities is poorly understood.
2. Using multivariate analyses, structural equation modelling and geostatistics, we examined interactions among ground-layer plant communities, the live overstorey and environmental gradients
across a 70- to 90-year-old northern hardwood forest in Wisconsin (USA). We hypothesized that (i)
fine-scale variation is related to environmental filtering rather than dispersal limitation and colonization processes; and (ii) exogenous ‘site’ filters exert more control than the composition and structure of the overstorey.
3. A transition from communities of spring ephemerals to communities of evergreen-dimorphic
species is related to a hierarchy of controls driven by elevation, soil texture and associated effects on
soil moisture, overstorey composition, and O-horizon and soil properties. An orthogonal axis distinguished among sparse communities associated with high levels of soil moisture early in the growing season and rich communities of early summer forbs associated with increasing O-horizon N:P
and %Ca, and short-distance dispersal mechanisms. Indirect effects of tree species are significant,
but cumulatively less important than exogenous site filters.
4. Synthesis. The spatial patterning of ground-layer plant communities is related to both environmental filtering and colonization. These patterns were related to species’ functional and dispersal
characteristics, suggesting that processes structuring ground-layer plant communities are not
merely neutral. Loose regulation of environmental and resource gradients resulting in a coarsegrained spatial patterning of plant communities observed in second-growth forests may therefore
be related to a simplification in overstorey composition and the absence of heterogeneity accumulating through gap dynamics.
Key-words: determinants of plant community diversity and structure, dispersal, environmental filtering, functional groups, herbaceous layer, soil resources, spatial patterning, structural
equation modelling, understorey vegetation, Wisconsin
Introduction
Understanding the relative importance of historical and environmental filters that drive variation in plant communities is a
central goal for ecologists (Ricklefs 1987; Keddy 1992; Lam*Correspondence author. E-mail: julia.burton@oregonstate.edu
†Present address: 321 Richardson Hall, Oregon State University,
Corvallis, OR 97331, USA.
bers, Chapin & Pons 1998; Harrison et al. 2006). Physiological
filters can yield strong relationships between species distributions and environmental gradients, while biological filters (e.g.
competition or predation), may constrain such distributions
(McGill et al. 2006). In contrast, historical filters, such as disturbance or land-use history, can weaken relationships
between species distributions and environmental gradients
(Vellend et al. 2007) resulting in spatial patterns associated
with colonization (Matlack 1994; Verheyen et al. 2003; Ozinga
2011 The Authors. Journal of Ecology 2011 British Ecological Society
Forest ground-layer plant communities 765
et al. 2005; Damschen et al. 2008) and ecological drift associated with stochastic processes (Hubbell 2001; Gotelli & McGill
2006). Studies of fine-scale spatial variation in plant community structure can provide insights into how such processes
interact across scales to influence vegetation locally (Leibold
et al. 2004).
Ground-layer plant communities of herb and shrub species
comprise an important component of temperate deciduous
forest ecosystems (Gilliam 2007), but are studied less frequently and intensively compared to tree species. Therefore,
our knowledge of the physiology of individual species, and of
species interactions and dynamics along environmental gradients, as well as our ability to predict the effects of changes in
the structure of environmental gradients (i.e. due to disturbance and forest stand dynamics as well as climate change) is
comparatively limited (Whigham 2004). Within temperate
deciduous forests, ground-layer plant community structure
has been associated with land-use history (Fraterrigo, Turner
& Pearson 2006; Vellend et al. 2007), overstorey composition
and structure (Beatty 1984; Scheller & Mladenoff 2002), disturbance (Moore & Vankat 1986; Roberts 2007), competition
with tree seedlings and saplings (Miller, Mladenoff & Clayton
2002), interactions between earthworms and ungulate herbivory
(Frelich et al. 2006) and soil properties (Rogers 1982; Beatty
1984; Reed et al. 1993), as well as colonization processes and
seed dispersal mechanisms (Ehrlén & Eriksson 2000; Miller,
Mladenoff & Clayton 2002). However, a synthetic model of
the myriad of direct and indirect interactions that structure
ground-layer plant communities has yet to be tested.
The relative importance of different environmental and dispersal filters for ground-layer plant community assembly may
vary over space and time with land use and among forest
stands in different stages of development. For instance, shortdistance dispersal can constrain the ability of many forest herb
species to re-colonize recovering stands in earlier stages of
development (Matlack 1994; Verheyen et al. 2003). However,
endogenous biotic control over resource availability may
increase over time (Odum 1969; Leuschner & Rode 1999) leading to linkages between the overstorey and understorey, and
increasingly complex hierarchical interactions (Gilliam, Turrill
& Adams 1995; Miller, Mladenoff & Clayton 2002; Gilliam
2007). Indeed, spatial patterns of understorey vegetation in primary and old-growth stands are more fine grained and patchy,
with lower rates of species accumulation, relative to younger
second-growth stands (Scheller & Mladenoff 2002; Burton
et al. 2009).
Within forests, fine-scale gradients of soil resources (e.g.
moisture, N, P and Ca) and light are generated by multiple factors that interact across scales of space and time. Processes that
affect moisture balance and the supply of soil nutrients are tied
to exogenous site factors such as soil morphology and topography (Campbell & Norman 1998; Schaetzl & Anderson 2005).
Layered upon physiographic factors are endogenous gradients
related to the composition and structure of the overstorey,
affecting both the radiation regime in the understorey (Canham et al. 1994) as well as the physical and chemical properties
of soil at the scale of a single tree (c. 40 m2) to larger patches
(>1 ha). In particular, evidence suggests that tree species can
exert control over nutrient cycling via litter inputs and uptake
(Mladenoff 1987; Ferrari 1999; Lovett et al. 2004; Scharenbroch & Bockheim 2007; Weand et al. 2010). Furthermore, canopy gaps created by wind-throw and tree mortality can alter
the local microclimate, resource levels and resource heterogeneity (Moore & Vankat 1986; Mladenoff 1987). The spatial
and temporal distribution of such ‘micro-environmental gradients’ can influence patterns of competition among individuals,
as well as population and community structure (Hutchings,
John & Wijesinghe 2003; Neufeld & Young 2003). Thus, the
fine-scale spatial patterning of forest ground-layer plant communities, simply in response to resource gradients, can be quite
complex.
Relationships between functional groupings of plant species and environmental gradients can provide evidence for
environmental filtering, particularly when the traits suggest
an advantage in the associated environment (McGill et al.
2006). Morphology tends to converge among forest herbs
with similar leafing phenology suggesting that phenological
guild reflects a key ecological strategy (Givnish 1987). For
instance, spring ephemerals have relatively short leaf life
span and higher rates of photosynthesis and respiration than
early summer, late-summer and evergreen species and thus
may require greater levels of resources to grow, survive and
reproduce (Neufeld & Young 2003; Reich et al. 2003; Diaz
et al. 2004). Additionally, many forest herb species have
long pre-reproductive periods and short-distance dispersal
mechanisms (Whigham 2004). The diversity of ecological
strategies suggests the potential for niche differentiation
along environmental gradients as well as dispersal limitation
and ecological drift (Tilman 1990; Ehrlén & Eriksson 2000;
Gilbert & Lechowicz 2004; Ozinga et al. 2005; Gotelli &
McGill 2006).
Here we test the hypothesis that fine-scale variation in the
composition of ground-layer plant communities is related to
species functional groupings and environmental gradients
rather than colonization and stochastic drift in a 70- to 90year-old second-growth northern hardwood forest. Specifically, we expected that the spatial distribution of plant communities within the forest is associated with the effects of
interactions between exogenous site characteristics and the live
overstorey on the distribution of environmental and resource
gradients. We also test the hypothesis that exogenous site variables exert relatively greater control over community composition than do endogenous live overstorey structure variables.
Therefore we (i) assess the relative importance of niche partitioning along environmental gradients vs. colonization; (ii)
examine how exogenous site characteristics and the overstorey
interact to affect the spatial patterning of plant communities in
the forest understorey; and (iii) examine the relative importance of exogenous site characteristics vs. endogenous variation related to overstorey composition. We use a structural
equation modelling (SEM) framework (e.g. Grace 2006) integrating sub-hypotheses suggested by theory as well as previous
investigations (Grace et al. 2010). By examining networks
of relationships among ground-layer plant communities,
2011 The Authors. Journal of Ecology 2011 British Ecological Society, Journal of Ecology, 99, 764–776
766 J. I. Burton et al.
overstorey composition and structure and environmental variables, we quantify the relative importance of niche differentiation and colonization to better understand how the species
pool is filtered by local processes in the forest understorey.
Materials and methods
STUDY AREA
The study area is located within the Flambeau River State Forest on
the loess plain of north-central Wisconsin, USA (Fig. 1). It is a rich,
second-growth northern mesic forest (Curtis 1959) and the site of a
long-term manipulative experiment that examines the effects of forest
structure and deer exclusion on biodiversity and ecosystem function
(e.g. Dyer et al. 2010; Stoffel et al. 2010). The field site consists of
thirty-five 80 · 80 m (0.64 ha) permanent plots distributed across a
280-ha forest landscape. Each plot contains three circular subplots of
0.05, 0.14 and 0.23 ha. Forty-two permanent 4-m2 quadrats were
established in each plot within the small (n = 10), medium (n = 16)
and large (n = 16) subplots (Fig. 2). For this study, we focus on
pre-treatment data from a subsample of nine plots representative of
the variation in overstorey composition and structure encountered
at the site. We used a random subsample of 24 quadrats from each of
the selected plots (n = 4, 7 and 13 from the small, medium and large
subplots, respectively; n = 215; one plot with 23 quadrats). This subsample was selected for more intensive sampling of environmental
variables because it was an economically and logistically feasible
sample size.
The overstorey includes one major age class of 70- to 90-year-old
northern hardwoods dominated by sugar maple (Acer saccharum),
basswood (Tilia americana) and white ash (Fraxinus americana),
although scattered trees exceeding 100 years of age can be found
(Table 1; all nomenclature follows the Flora of North America Editorial Committee 1993+). Soils are silt loams (Glossudalfs) of the
Magnor, Ossmer and Freeon series overlaying dense till, all of which
are subjected to seasonally perched (Magnor and Freeon) or high
(Ossmer) water tables. The Ossmer series is relatively well drained
compared to the Magnor and Freeon series, and distributed among
lower elevations across the site, although the elevation gradient is subtle (c. 20 m of relief among the subplots in this analysis). To index
variability in the local microclimate (e.g. attributable to soil series
as well as local topography), subplot elevation, slope and aspect
data were gathered from a digital elevation model (30 m resolution).
January and July daily high temperatures (1980–1997) average )12
and 19 C, respectively (Daymet U.S. Data Center, http://
daymet.org), and the median length of the growing season is 105
frost-free days (base temperature = 0 C, 1971–2000, Midwest
Regional Climate Center, http://mcc.sws.uiuc.edu).
FIELD DATA COLLECTION
Fig. 1. Location of study site in north-central Wisconsin. Inset shows
location within the eastern USA.
Fig. 2. Plot detail with subplot and quadrat layout. Quadrats are distributed along cardinal axes within subplots. Quadrats are 2 · 2
metres (4 m2) and spatially integrate fine-scale measurements of forest structure, microclimate, plant communities and soil properties.
Subplot sizes correspond to experimental gap treatments addressed
in subsequent studies.
Ground-layer vegetation sampling was conducted in the spring (midApril – late-May) and midsummer (mid-June – late-July) in 2006 to
account for all vascular plant species, which vary in leafing phenology. During each survey, the percentage cover of all vascular plant
taxa <1.4 m tall was estimated in each 4-m2 quadrat (Fig. 2) using
an ordinal cover class: absent, £ 0.5%, 0.5–1%, >1–2.5%, >2.5–
5%, >5–15%, >15–25%, >25–50%, >50–75%, >75–95%, >95
to < 100%, and 100% (e.g. Gauch 1982). Classes included the range
of values that is greater than the lower bound and equal to or less than
the upper bound. Seasonal data sets were merged, retaining the largest cover classes when species were observed during multiple sampling
periods. Cover classes were converted to mid-points for all analyses.
In the midsummer survey, in addition to cover estimates, tree seedlings and saplings (<3 cm d.b.h.) were tallied within each quadrat.
All saplings < 10 cm d.b.h. were tallied within subplots. We also
measured the diameter of all trees ‡ 10 cm d.b.h. (c. 1.4 m height)
within each subplot and calculated basal area as the sum of the stem
cross-sectional area of all trees (by species, from d.b.h.) scaled to a
consistent per-hectare basis (m2 ha)1) to account for the differences
in area among large, medium and small subplots.
Diffuse non-interceptance (%Light) was measured at 30, 100 and
200 cm above each quadrat in 2006 after canopy closure between 31
May and 26 July using the LAI-2000 plant canopy analyzer (LICOR, Lincoln, NE, USA). The LAI-2000 measures the integrated
gap fraction at five concentric angles simultaneously (Welles & Norman 1991). Above-canopy measurements were taken concurrently
every 15–30 s on a 31-m tower located centrally within the site.
Below-canopy measurements were matched with the closest-in-time
above-canopy measurements and %Light was calculated using the
post-processing software, FV2000 (LI-COR). All measurements were
taken under uniformly overcast or cloudy sky conditions. Trends due
2011 The Authors. Journal of Ecology 2011 British Ecological Society, Journal of Ecology, 99, 764–776
Forest ground-layer plant communities 767
Table 1. Overstorey composition (plot mean± SE of 11 most abundant tree species). Total basal area (BA) is 29 m2 ha)1, density is 445 live
stems (‡ 10 cm d.b.h.) ha)1. Foliar chemistry data (mass-based %C, %N and %Ca) are from site (n = 3 per species, collected from overstorey
tree canopies prior to senescence in September 2008) unless otherwise specified using symbols (superscripts). Foliar P values = 0.2% for all
species, except Acer rubrum (for which we do not have data). ND, no data
Percentage by species
BA
Density
C
N
Ca
Lignin
Acer saccharum
Tilia americana
Fraxinus americana
Carya cordiformis
Fraxinus nigra
Quercus rubra
Betula alleghaniensis
Acer rubrum
Tsuga canadensis
Ostrya virginiana
Populus tremuloides
56.2 ± 2.0
16.0 ± 1.3
11.8 ± 1.6
4.3 ± 0.9
3.9 ± 0.9
2.2 ± 0.6
1.6 ± 0.4
1.4 ± 0.6
0.9 ± 0.3
0.7 ± 0.2
0.6 ± 0.4
60.3 ± 1.7
10.4 ± 0.9
7.0 ± 1.0
5.0 ± 1.1
2.9 ± 0.6
0.9 ± 0.2
1.1 ± 0.2
1.6 ± 0.7
0.5 ± 0.1
3.1 ± 0.8
0.6 ± 0.5
45.2
43.5
43.9
44.2
44.9
47.6
46.1
49.9†
50.5
44.1
49.4
1.7
2.0
0.9
2.0
1.2
2.1
2.1
1.8†
1.6
1.9
2.2
1.8
3.2
2.0
1.8
1.9
0.9
1.7
ND
0.6
2.6
1.5
14.8‡
17.0†
23.6‡
ND
19.9‡
22.3‡
27.9†
18.5†
16.0†
21.1†
25.6†
Data are from the †Northeastern Ecosystem Research Cooperative foliar chemistry database (2009) and ‡S. Serbin & P. Townsend, unpubl. data.
to variation over the sampling period and among different sensors
were modelled and subsequent analyses relating transmittance to
overstorey structure and community data were performed using the
residuals.
Percentage volumetric soil water content (SWC) was measured at
all quadrats with a time domain reflectometer (TDR; Delta TH20,
Dynamax Inc, Houston, TX, USA) to a depth of 6 cm using site-specific calibrations (J. L. Stoffel, unpubl. data). Time domain reflectometry probes were placed in the mineral soil in two locations along the
northern boundaries of all quadrats and measurements were averaged. Measurements were taken four times during the 2006 growing
season: between 14 and 18 June (June SWC), 11 and 13 July (July
SWC), 24 and 28 July (late July SWC), and 14 and 18 Aug. (Aug.
SWC). No measurements were taken within 24 h of a heavy rain.
In October (2006), O-horizon samples were collected along four
cardinal directions 1 m from quadrats within a 33-cm diameter PVC
ring and composited for each location. Corresponding mineral soil
samples to a depth of 15 cm were collected using a 2.5-cm diameter
push probe. O-horizon samples were dried at 70 C and soil samples
were air dried and passed through a 2-mm sieve. Both O-horizon and
soil samples were ground and sent to Brookside Laboratories (New
Knoxville, OH, USA) for standard analysis of soil and tissue properties: pH, soil organic matter (OM), sulphur, Mehlich exchangeable
Ca, Mg, K and Na, cation exchange capacity (by summation),
Mehlich III extractable P, Mn, Zn, B, Cu, Fe and Al for soil, and percentage (%)N, P, K, Ca, Mg, S, B, Fe, Mn, Cu, Zn and Al for the Ohorizon. Soil nutrient concentrations were scaled up to a per-hectare
basis using regressions of bulk density (BD) on OM (n = 119 samples), BD = 0.3215 · OM)0.3434, R2 = 0.99, P < 0.001 and calculated assuming a constant coarse fraction. O-horizon N : P ratio was
calculated in order to relate ground-layer vegetation to stoichiometric
constraints on resource availability (Elser et al. 2000). In the large
subplots, additional soil cores were extracted, divided to two depths
(shallow = 0–15 cm, deep = 15–50 cm) and submitted for hydrometric soil textural analysis (i.e. %sand, silt and clay).
DISPERSAL AND FUNCTIONAL GROUPS
All herbaceous and shrub species (127 species total) were classified (i)
according to their life-form as forb, fern, graminoid, shrub or vine;
(ii) leafing phenology as spring ephemeral, early summer, late-summer, or evergreen-dimorphic (Givnish 1987); (iii) ability to reproduce
vegetatively (clonality) and (iv) seed dispersal mechanism as ballistic,
gravity ⁄ no obvious mechanism, ingested, myrmecochory, wind or
adhesive (Miller, Mladenoff & Clayton 2002). The cover and number
of species (richness) within each group was aggregated (by summation) for all quadrats.
MULTIVARIATE ANALYSIS
To examine relationships among compositional similarity, dispersal
and functional groups, and environmental variables we used simple
and partial Mantel tests using the ecodist package in R (R version
2.7.2. 2008. The R Foundation for Statistical Computing; Goslee
& Urban 2007). Simple Mantel tests test the null hypothesis of no
relationship between two similarity matrices, while partial Mantel
tests permit the inclusion of more than two similarity matrices to
examine the relationships among correlated variables (e.g. relationship of community composition to geographic distance given the
environment). The compositional similarity (Sørenson, or Bray–Curtis, index) of all pairs of quadrats was calculated as twice the sum of
the abundances of all species shared between two quadrats divided by
the sum of all species abundances occurring in the two quadrats
(McCune & Grace 2002). Matrices based on functional or dispersal
groupings and environmental variables were calculated in the same
manner, except species were replaced, respectively, with the groups
and environmental variables. Mantel correlations among the community matrix, the matrix calculated from aggregated functional groups,
and the environmental matrix provide evidence of trait-based environmental filtering, while correlations with dispersal group and geographic spatial distance matrices provide evidence for dispersal
assembly – particularly in the absence of environmental and functional group correlations. Relationships between community and
geographic distances not accounted for by the environmental, functional and dispersal trait matrices were interpreted as evidence of neutral patterns of community assembly (e.g. Tuomisto & Ruokolainen
2006).
To characterize patterns of compositional variation, we used nonmetric multi-dimensional scaling ordinations. Non-metric multidimensional scaling (NMS) is an ordination technique that iteratively
finds the solution, or axes of variation, that best capture the patterns
in the dissimilarity matrix (also using the Sørenson measure of
dissimilarity). By examining the positions of quadrats along ordination axes (NMS scores), as opposed to cover of individual species,
2011 The Authors. Journal of Ecology 2011 British Ecological Society, Journal of Ecology, 99, 764–776
768 J. I. Burton et al.
species richness or groupings of species, we can examine the main
dimensions of compositional variation in the forest. All species occurring in fewer than 5% of all quadrats were deleted prior to the ordination (80 species deleted) and species cover data were relativized using
a general relativization by column (species) totals. We further transformed the main matrix of 47 species using the arcsine square root
transformation. Our final solution was based on the rotation that
maximized the variation explained along the first axis. Pearson correlations between species abundance and NMS axis scores were examined to interpret how species composition varies along ordination
axes. Similarly, we examined the correlations among NMS axis scores
and the aggregated cover and richness of all functional and dispersal
groups, and environmental variables. Bi-variate plots were assessed
visually and data were transformed when necessary. Non-metric
multi-dimensional scaling ordinations were executed using PC-ORD
(McCune & Mefford 1999).
STRUCTURAL EQUATION MODELLING
To examine relationships among overstorey structure, environmental
gradients and ground-layer plant community composition (as
described by NMS axis scores), we used SEM. Structural equation
modelling is based on regression analysis and uses two or more structural equations to examine complex, multivariate hypotheses (Grace
2006). In contrast to multiple regression models, which assume no
directional relationships among independent variables, SEMs can
better represent the relationships among correlated variables and can
lead to different conclusions and improved interpretations (Grace &
Bollen 2005). This feature permits one to model direct and indirect
interactions among variables, allowing investigators to quantify the
relative importance of the multiple pathways by which variables may
interact in a system. Structural equation modelling tests the structure
of the overall hypothesized model by comparing the implied and
observed covariance matrices, as well as the individual ‘paths’ or ‘subhypotheses’ embedded within the model. Although a good fit between
data and an SEM by itself may not establish causation, it can provide
valuable insight into the processes associated with the pattern of
interest when theory and evidence support the hypothesized model.
We postulated an initial conceptual model (meta-model, sensu
Grace et al. 2010) based on our previous investigations, theory and a
priori knowledge (Fig. 3). In some cases, it could be argued that relationships may be reversed or reciprocal; however, we believe that in
the context of this study these premises provide reasonable starting
points for an SEM analysis. To relate this theoretical framework to
the community axis scores, we replaced conceptual variables (Fig. 3)
with measured variables. Measured variables were selected based on
the correlations of variables with NMS axis scores. Modification indices were examined, and pathways were added if they improved the
overall fit of the model. We selected the best-fitting model that both
corresponded with our understanding of the system (Fig. 3) and
explained existing patterns of spatial autocorrelation (described
below) using Akaike’s information criterion (AIC; Akaike 1974),
which favours more parsimonious models. All SEM analyses
were performed using amos 18.0 (Amos 18.0.0 1983–2009 James
L. Arbuckle).
Fig. 3. Meta-model guiding the structural equation modelling analysis of ground-layer plant communities. Boxes represent categories of
variables, while the arrows show the hypothesized relationships
among categories and the directions of those relationships. Arrow
width indicates hypothesized relative importance of the pathway.
Disturbance is represented in a circle and with dotted lines because
we do not measure disturbance history directly, but infer its effects
from residual spatial patterns indicating dispersal limitation.
from the model. SCR may be attributed to an unmeasured spatial
process such as dispersal limitation or disturbance history (McIntire
& Fajardo 2009). To examine the assumption of independent errors,
we fit robust semivariogram models (Cressie & Hawkings 1980) to
observed NMS axis scores and model residuals (the difference
between SEM-predicted NMS scores and observed NMS scores)
using weighted least-squares regression. Residual spatial autocorrelation was deemed ‘significant’ when fitted lines occurred outside of the
Monte Carlo random simulation envelopes (100 simulations). Spatial
analyses were performed in R, using the GeoR package (R version
2.7.2. 2008. The R Foundation for Statistical Computing).
Results
ASSOCIATIONS AMONG COMMUNITY SIMILARITY,
ENVIRONMENT AND TRAITS
Mantel tests showed that the similarity matrix calculated from
species abundances (community composition) is related to
matrices calculated from aggregated dispersal and functional
groupings, environmental variables and geographic distance
(Table 2). While the similarity matrix based on dispersal
groupings is significantly related to the matrix based on functional groups (r = 0.43, P < 0.001), both simple and partial
Mantel tests suggest that dispersal groups are more strongly
related to community composition than functional groups
(Table 2). Partial Mantel tests relating the community matrix
to the environmental matrix and geographic distance matrix
also show stronger relationships of community composition to
geographic distance (i.e. space) than environmental variables.
SPATIAL AUTOCORRELATION
ORDINATION
Spatial correlation of model residuals (SCR) indicates that one of the
main assumptions of linear modelling is not satisfied (i.e. independent
errors). Additionally, SCR indicates that one or more variables contributing to the spatial patterning of the dependent variable is missing
The three-dimensional NMS ordination of 215 quadrats
(Fig. 4) has a final stress of 23.86 and explains a total of
54.7% of the variation in the ground-layer plant community
2011 The Authors. Journal of Ecology 2011 British Ecological Society, Journal of Ecology, 99, 764–776
Community–Trait Relationships
Community M Dispersal
Community M Functional
Dispersal M Functional
Community M Dispersal|Functional
Community M Functional|Dispersal
Mantel r
P
0.42
0.35
0.43
0.32
0.21
<0.001
<0.001
<0.001
<0.001
<0.001
0.25
0.32
0.37
0.15
0.25
<0.001
<0.001
<0.001
<0.001
<0.001
Community–Environment Relationships
Community M Environment
Community M Space
Environment M Space
Community M Environment|Space
Community M Space|Environment
The community matrix was calculated from the relative abundances of all species occurring in > 5% of the quadrats (47 species); the functional group matrix was based on the aggregated
cover of life-forms (ferns, forbs, graminoids, shrubs and vines)
and phenolological guilds (spring ephemeral, early summer, latesummer and evergreen-dimorphic); the dispersal matrix was calculated based on the aggregated cover of species with adhesive,
ballistic, clonality, ingested, myrmecochory or wind seed and vegetative dispersal. The environmental matrix included all exogenous site variables; endogenous overstorey tree species and
saplings [basal area (BA) and density, respectively – saplings not
separated by species]; and environmental variables = soil and Ohorizon properties and light transmittance.
composition and structure (axis 1 = 23.7%, axis 2 = 20.3%,
axis 3 = 10.7%). NMS ordinations with stress levels > 20 are
typically considered more difficult to interpret; however, stress
is strongly related to sample size (McCune & Grace 2002) and
our sample of 215 quadrats is unusually high for community
studies. Monte Carlo randomization tests suggest that
observed patterns were unlikely due to chance (a = 0.05).
Furthermore, repeated ordinations as well as ordinations with
smaller samples, greater explained variability and lower stress
levels yield consistent patterns.
The first axis contrasts communities characterized by greater
coverage and richness of spring ephemerals from communities
composed primarily of evergreen-dimorphic species (Table 3).
The functional and dispersal groups positively associated with
axis 1 include cover and richness of spring ephemeral species
(r = 0.46 and 0.64, respectively), cover and richness of ballistic
species (r = 0.50 and 0.52, respectively), cover of gravity-dispersed species (r = 0.49) and cover of forb species (r = 0.40),
while cover and richness of evergreen-dimorphic species
(r = )0.42 and )0.43, respectively) are negatively related to
quadrat axis 1 scores (Fig. 4, Table 3).
Axis 2 contrasts diverse communities of early summer forbs
from sparsely vegetated communities composed of less
common graminoid and evergreen-dimorphic species (Fig. 4,
Table 3). Total plant species richness (r = 0.47) and richness
of herbaceous plant species (r = 0.47) are positively correlated
with axis 2. Forb richness and cover (r = 0.60 and 0.51), early
summer species cover and richness (r = 0.40 and 0.59, respec-
1.5
ESsp FRBsp
ADcov CLNsp
ADsp
FRBcov
Total richness
BALcov
Herb richness
GRVcov
SEcov
SEsp
EVsp
EVcov
0.5
–0.5
1.5
Axis 3 – 11%
+ R. cynosbati
+ A. filix-femina
Table 2. Simple and partial Mantel tests examining correlations
among similarity in species composition, dispersal and functional
traits, environmental variables and spatial coordinates
Axis 2 – 20%
+ V. pubescens
+ A. triphyllum
Forest ground-layer plant communities 769
FERNcov
FERNsp
0.5
GRVcov
SEcov
SEsp
BALsp BALcov
EVsp
SHRUBsp
–0.5
SHRUBcov
–2.5
–1.5
–0.5
0.5
1.5
+ Allium tricoccum
+ Dryopteris intermedia
Axis 1 – 24%
Fig. 4. Non-metric multi-dimensional scaling (NMS) ordination of
quadrats (N = 215) described by 47 species. Vectors from centroids
show joint axis correlations with dispersal (dashed lines, italic font)
and functional groups (solid lines, phenological guilds shown in bold
font; r ‡ 0.40). Axis labels show the percentage of variation in the
distance matrix captured by the ordination axis and main species
correlations (Table 3). Species listed along vertical axes include Viola
pubescens, Arisaema triphyllum, Athyrium filix-femina and Ribes
cynosbati. ES, early summer; FRB, forb; CLN, clonal; AD, adhesive;
BAL, ballistic; GRV, gravity; SE, spring ephemerals; and EV, evergreen-dimorphic; sp, species richness; cov, percent cover.
tively), richness of clonal species (r = 0.49) and adhesive species richness and cover (r = 0.51, for both) are positively
correlated with axis 2, while no major groups are significantly
negatively related to axis 2 (r cut off = 0.40).
The third axis distinguishes communities of fern and latesummer species from communities composed of shrub species
(Fig. 4, Table 3). Fern cover (r = 0.52) and fern species richness (r = 0.45) are positively related to axis 3 while shrub species richness (r = )0.47) and shrub cover (r = )0.47) are
negatively related to axis 3.
STRUCTURAL EQUATION MODELLING
The final structural equation models showed correspondence
between the implied and observed covariance matrices
(v2 = 1.04, d.f. = 3, P = 0.793 for axis 1; v2 = 8.73,
d.f. = 7, P = 0.273 for axis 2). No model was fit to axis 3
because Pearson correlations indicate only very weak associations with environmental variables. In SEM analysis,
2011 The Authors. Journal of Ecology 2011 British Ecological Society, Journal of Ecology, 99, 764–776
770 J. I. Burton et al.
Table 3. Correlations of species cover with ordination axis scores (by
phenological group). Only species with Pearson axis correlations (r)
‡ 0.40 are given
Spring ephemerals
Allium tricoccum
Enemion biternatum
Dicentra spp.
Early-summer species
Viola pubescens
Trientalis borealis
Ribes cynosbati
Polygonatum pubescens
Phlox divaricata
Osmorhiza spp.
Athyrium filix-femina
Late-summer species
Laportea canadensis
Evergreen-dimorphic species
Dryopteris intermedia
Anenome acutiloba
Axis 1
Axis 2
Axis 3
0.56
0.46
0.00
0.29
0.09
0.48
)0.12
)0.12
0.18
)0.13
)0.48
)0.02
)0.48
)0.07
)0.17
0.01
0.63
)0.11
0.02
0.38
0.45
0.53
)0.12
0.06
0.07
)0.50
0.08
0.10
0.05
0.50
0.08
0.12
0.47
)0.40
)0.46
)0.23
)0.15
0.26
0.28
P-values ‡ 0.05 indicate a fit between the data and the
model; therefore our P-values in addition to other fit indicators, such as AIC, suggest a good model fit. In order to simplify the structural equation models, we used statistical
composites (Verheyen et al. 2003; Grace 2006; Harrison
et al. 2006) for overstorey composition, soil texture, O-horizon properties and soil properties (chemical). Observed indicator variables for each composite were selected based on
correlations with NMS axis scores and backwards elimination multiple regression analysis. Composites were constructed for each axis using generalized linear models (SAS
GLM Procedure) and predicted scores were used as soil texture, O-horizon, and soil properties variables (Fig. 5).
A hierarchy of controls driven by soil texture and associated
effects on soil moisture, chemical properties of soil, and overstorey composition and structure, explains 62% of the total
variation in quadrat axis 1 scores (Table 4, Fig. 6). While axis
1 scores are directly related to elevation, July SWC, O-horizon
and soil properties, these direct effects are mediated by soil texture and the composition of the overstorey. Given these direct
and indirect effects, elevation has the strongest total effects on
axis 1 (standardized total effect = 0.66), followed by soil
properties (standardized total effect = 0.26) and July SWC
(standardized total effect = 0.24). The total contributions of
O-horizon properties (standardized total effect = 0.14) and
the overstorey (standardized total effect = 0.16) were relatively weak.
Interactions among soil texture, overstorey composition,
soil moisture, O-horizon properties and dispersal traits explain
41% of the variation in axis 2 scores (Table 4, Fig. 6). Axis 2
scores are directly related to soil texture, soil moisture (June),
O-horizon properties and aggregated dispersal traits. Specifically, the aggregated cover of species with gravity and ballistic
seed dispersal resolved patterns of residual spatial autocorrelation that could not be explained by environmental variables.
Fig. 5. Composite variable models relating observed soil texture,
chemical properties and O-horizon to NMS axis scores (Fig. 4). Solid
lines show positive relationships, dashed lines show negative relationships; line weights correspond to the variable importance. Standardized coefficients and significance levels (*P < 0.05, **P < 0.01,
***P < 0.001) are shown along the paths. %Siltdeep, %Silt in deep
horizons (15–50 cm); %Claydeep, %Clay in deep soil horizons;
%Claysurface, %Clay in shallow soil horizons (0–15 cm); N : P, ratio
of nitrogen to phosphorous in O-horizon; %Ca, percentage calcium
in O-horizon; pH, soil pH; CEC, soil cation exchange capacity; Ln,
indicates a natural log transformation; P, soil phosphorus; Ca, calcium; O.M., soil organic matter; see Table 1 for full tree species
nomenclature.
Additionally, June SWC and soil texture have indirect effects
on axis 2 through their effects on other variables directly
related to axis 2. O-horizon properties (standardized total
effect = 0.38), followed by gravity seed dispersal (standardized total effect = 0.22), June SWC (standardized total
effect = )0.22) and overstorey composition (standardized
total effect = 0.20) have the strongest influences on axis 2.
Discussion
Our first objective was to assess the relative importance of
environmental filtering vs. colonization processes for fine-scale
patterns of forest ground-layer plant communities in this
second-growth northern mesic forest. Results suggest that
both processes play important roles in plant community
assembly. The spatial patterning of ground-layer plant
communities is often attributed to corresponding changes in
2011 The Authors. Journal of Ecology 2011 British Ecological Society, Journal of Ecology, 99, 764–776
Forest ground-layer plant communities 771
Table 4. Structural equation model (SEM) estimates. Arrows show direction of path from variables to the left to variables to the right of the
arrow. Estimates are unstandardized coefficients, z is the test statistic, and pr ‡ |z| is the two-tailed probability under the null hypothesis of no
relationship between variables. Standardized coefficients (Std. Coeff.) are standardized by standard deviations. For axis interpretations see
Fig. 4 and Table 3
AXIS 1
Estimate
Direct effects
O-horizon fi Axis 1
Soil properties fi Axis 1
Elevation fi Axis 1
July SWC fi Axis 1
Indirect effects
Elevation fi July SWC
Elevation fi Overstorey
Elevation fi O-Horizon
Soil Texture fi July SWC
Soil Texture fi Overstorey
Soil Texture fi Soil Properties
Soil Texture fi O-Horizon
July SWC fi Overstorey
July SWC fi O-Horizon
Overstorey fi O-Horizon
Overstorey fi Soil Properties
O-Horizon fi Soil Properties
Unexplained correlations
Soil Texture M Elevation
Axis 1 M Overstorey
pr ‡ |z|
z
Std. Coeff.
0.18
0.40
0.05
0.04
2.01
6.39
6.45
3.99
0.044
<0.001
<0.001
<0.001
0.10
0.26
0.38
0.20
0.24
0.07
0.03
2.34
)0.48
0.43
)0.38
0.03
0.02
0.19
0.40
0.16
6.35
15.31
5.06
2.20
)4.25
3.61
)3.28
3.27
2.69
3.07
6.83
2.06
<0.001
<0.001
<0.001
0.028
<0.001
<0.001
0.001
0.001
0.007
0.002
<0.001
0.039
0.42
0.77
0.44
0.15
)0.18
0.20
)0.19
0.16
0.16
0.25
0.47
0.14
0.40
0.05
5.85
5.61
<0.001
<0.001
0.44
0.44
AXIS 2
Estimate
z
pr ‡ |z|
Std. Coeff.
Direct effects
O-Horizon fi Axis 2
June SWC fi Axis 2
sqrt(Gravity) fi Axis 2
sqrt(Ballistic) fi Axis 2
Soil Texture fi Axis 2
0.72
)0.02
0.06
0.07
0.44
6.24
)2.37
3.83
2.93
2.11
<0.001
0.018
<0.001
0.003
0.035
0.38
)0.13
0.21
0.17
0.12
Indirect effects
Soil Texture fi June SWC
Soil Texture fi Overstorey
Soil Texture fi O-Horizon
June SWC fi O-Horizon
Overstorey fi O-Horizon
O-Horizon fi Soil Properties
)4.43
0.85
)0.44
)0.02
0.52
0.17
)2.74
7.13
)3.50
)4.25
8.24
3.17
0.006
<0.001
<0.001
<0.001
<0.001
0.002
)0.18
0.44
)0.23
)0.25
0.53
0.20
Unexplained correlations
Axis 2 M Overstorey
sqrt(Gravity) M sqrt(Ballistic)
sqrt(Gravity) M Overstorey
sqrt(Ballistic) M Overstorey
O-Horizon M sqrt(Gravity)
Soil Texture M sqrt(Gravity)
Soil Texture M sqrt(Ballistic)
sqrt(Ballistic) M Soil Properties
0.03
0.88
0.09
0.12
)0.10
0.09
0.08
0.05
3.53
4.19
2.45
4.36
)2.87
3.83
4.40
2.48
<0.001
<0.001
0.014
<0.001
0.004
<0.001
<0.001
0.013
0.26
0.29
0.16
0.30
)0.19
0.27
0.32
0.15
July SWC = soil water content measure between 24 and 28 July, June SWC measured between 14 and 18 June. For construction of
Overstorey, Soil Texture, Soil Properties and O-Horizon Properties composite variables see Fig. 5.
environmental conditions and dispersal limitation (Nekola &
White 1999; Miller, Mladenoff & Clayton 2002; Gilbert &
Lechowicz 2004). Although stochastic processes resulting in
ecological drift can produce strong levels of dispersal limitation
(Hubbell 2001), the relationship between dispersal strategies
and community patterns that we observed suggests that these
2011 The Authors. Journal of Ecology 2011 British Ecological Society, Journal of Ecology, 99, 764–776
772 J. I. Burton et al.
SWCJuly
R2 = 0.25
Soil
texture
Elev.
Overstorey
R2 = 0.63
O–horizon
R2 = 0.47
Soil
properties
R2 = 0.40
Axis 1
R2 = 0.62
SWCJune
R2 = 0.03
Soil
texture
Overstorey
R2 = 0.19
O–horizon
R2 = 0.29
Soil
properties
R2 = 0.34
Seed Dispersal
Ballistic
Gravity
Axis 2
R2 = 0.41
Fig. 6. Results of structural equation modelling analysis (v2 = 1.04,
d.f. = 3, P = 0.793, AIC = 67 for axis 1 and v2 = 8.73, d.f. = 7,
P = 0.273, AIC = 63 for axis 2). Straight, single-headed arrows
show directed effects of one variable on another; curved, doubleheaded arrows indicate unanalysed correlations between variables.
Dashed lines are negative relationships and solid lines show positive
relationships; weights correspond with the strength of the standardized coefficients (Table 4). R2 values show the proportion of variance
explained for each endogenous variable by all variables with direct
arrows to it. Composite variables (Figs 4 and 5) are indicated within
hexagons; observed variables are in rectangles. Unanalysed correlations among the aggregated cover of species with ballistic and gravity
seed dispersal, the overstorey and environmental variables in the
model for axis 2 are not shown graphically. SWC, soil water content;
Elev., elevation.
spatial patterns are not generated solely by stochastic processes
of ecological drift. In particular, dispersal limitation appears
relatively more important for species with short-distance dispersal mechanisms (i.e. gravity or ballistics) than species with
long-range seed dispersal (i.e. wind, adhesion or ingestion).
Ozinga et al. (2005) observed similar colonization constraints
along environmental gradients spanning the entire biogeographic region of the Netherlands. This pattern, in conjunction
with the observed correlations among community-aggregated
dispersal and functional groupings, suggests that competition–
colonization trade-offs (Tilman 1990), or tolerance–fecundity
trade-offs associated with seed size (Ehrlén & Eriksson 2000;
Muller-Landau 2010), may play an important role in the
spatio-temporal patterning of ground-layer plant communities. Furthermore, our results suggest that nearly a century
after the original logging disturbance, ground-layer plant com-
munities are not at competitive equilibrium with the environment.
Patterns of compositional variation can be difficult to detect
in the forest understorey, particularly at the fine grain and
large extent examined here (i.e. n = 215, 4-m2 quadrats distributed across a 280-ha research site). Using ordinations of
quadrats in species space, we identified three dimensions of
compositional variation that interact to produce the observed
mosaic of plant communities (Fig. 4, Table 3). The transition
from evergreen-dimorphic to deciduous and spring ephemeral
species along axis one has been seen elsewhere (Scheller &
Mladenoff 2002; Graves, Peet & White 2006; Burton et al.
2009), and suggests that phenological guilds not only permit
species to partition temporal variability in the environment
(e.g. Givnish 1987), but also spatial gradients in soil nutrients
(mainly phosphorus, in this case). The pattern of environmental sorting may reflect trade-offs between survival in stressful,
resource-poor environments and growth in resource-rich
environments (e.g. Reich et al. 2003; Diaz et al. 2004). The
stronger relationship of spring ephemerals rather than evergreen-dimorphic species with community axis scores may
reflect the relatively high whole-plant compensation points of
spring ephemerals (Givnish 1987). By contrast, evergreendimorphic species appear in a broader range of environmental
conditions.
The orthogonal patterns of increasing cover of early summer
species contrasting with less common graminoid species and
forbs such as Arisaema triphyllum, albeit modest, is similar to
patterns shown with gradients in the severity of exotic earthworm invasion in forests of the northern Great Lakes region
(e.g. Frelich et al. 2006). After accounting for environmental
influences, patterns of residual spatial autocorrelation are
related to the abundance of species with short-distance ballistic
and gravity seed dispersal mechanisms (Fig. 6). This ‘colonization deficit’ (sensu Ozinga et al. 2009) cannot be attributed to
changes in the availability of dispersal vectors because these
two mechanisms do not rely upon external agents or fragmentation in this contiguous forest (but see Damschen et al. 2008).
It is more likely associated with lower rates of colonization for
these species and the fine-scale connectivity of suitable microsites (Ehrlén & Eriksson 2000) in this contiguous forest. Additionally, it is possible that environmental variables such as soil
moisture and O-horizon %Ca increase earthworm activity
(Hobbie et al. 2006) to result in local extirpation of such
species (Hale, Frelich & Reich 2005), producing the observed
spatial patterns. Interactions with herbivores, such as whitetailed deer, may further exacerbate this problem (Frelich et al.
2006). Future studies will examine this hypothesis directly.
ENVIRONMENTAL FILTERING
We hypothesized that interactions among exogenous site characteristics and overstorey structure, through their effects on
the distribution of resources within the forest, affect the spatial
patterning of plant communities in the forest understorey.
Thus, embedded within the overall hypothesis that such
interactions exist were several sub-hypotheses related to the
2011 The Authors. Journal of Ecology 2011 British Ecological Society, Journal of Ecology, 99, 764–776
Forest ground-layer plant communities 773
nature of the interactions among environmental variables
(Fig. 3). Because some of our sub-hypotheses were not
supported by the data, our results suggest a more complex
version of the original conceptual model. The main differences
between the original and revised meta-models are (i) a pathway
from soil moisture to O-horizon properties; (ii) a pathway
from O-horizon properties to ground-layer plant communities;
and (iii) a lack of important pathways to and from light transmittance. The paths from soil moisture to O-horizon properties likely reflect the positive relationship between soil moisture
and nitrogen and phosphorus mineralization in well-drained
soils (Fig. 6; Mladenoff 1987) and denitrification in saturated
soils (Fig. 6; Christensen, Simkins & Tiedje 1990). Similarly,
the paths from O-horizon properties to plant communities
likely indicate the important effects of litter quality on the rates
at which nutrients become available to plants for uptake
(Ferrari 1999; Lovett et al. 2004). However, because this study
covered a single growing season, results from modelling
may be different in another year, although we expect a similar
structure.
Because of the important role of canopy gaps in temperate
deciduous forests (e.g. Moore & Vankat 1986) and because
light is generally thought to be the most important limiting
resource in forest understoreys (Neufeld & Young 2003), we
expected light transmittance to be related to the spatial patterning of ground-layer plant communities (Graves, Peet &
White 2006). We also expected light transmittance to be related
to the composition and structure of the overstorey (Canham
et al. 1994), although we observed neither pattern. The importance of light gradients may be restricted to stands with a
greater range of values, including lower minimum and higher
maximum transmittances, such as old growth stands with canopy gaps created by the fall of old, large trees (Dahir & Lorimer 1996). For instance, Scheller & Mladenoff (2002) observed
that the variability of light transmittance within a stand was
associated with differences in the spatial patterning of understorey plant communities between old-growth and mature second-growth stands. Old-growth stands exhibit greater levels of
within-stand heterogeneity in light transmittance than secondgrowth stands (CV = 65% vs. 32%) and a corresponding
finer-grained mosaic of ground-layer plant communities. The
variability in light transmittance here is more similar to that in
the even-aged stands of Scheller & Mladenoff (2002) than the
old growth (CV = 42% in the present study). Thus, linkages
between overstorey and ground-layer vegetation due to canopy gaps may also be restricted to older stands (Gilliam, Turrill
& Adams 1995). Indeed, northern hardwood stands can exist
in the mature, self-thinning phase for 100–150 years before
transitioning to uneven-aged status (Frelich 2002). The timing
of this transition likely depends upon the rate of autogenic succession and the occurrence of disturbance, which can accelerate or hinder the development of stand structure.
It is also possible that ground-layer plant communities in
younger stands respond to variation in light transmittance
induced by variation in leafing phenology among tree species
early in the growing season (e.g. Lopez et al. 2008). For
instance, although hemlock trees did not appear to influence
light transmittance at the peak of the growing season (maximum leaf area index), the reduction in light transmittance in
the neighbourhood of hemlock trees in the spring may be
another pathway by which hemlock discourages spring ephemeral species with high whole-plant compensation points. Alternatively, current instrumentation available for measuring light
transmittance may not be sensitive enough to detect subtle variation in light levels in deeply shaded understoreys (less than c.
6%; Machado & Reich 1999; Tobin & Reich 2009). Controlled
and manipulative studies examining limitations of instrumentation and the role of light transmittance integrated over the
course of the growing season in structuring understorey plant
communities are required to better understand the importance
of light in a dimly lit forest understorey.
EXOGENOUS VARIABLES
The important direct and indirect effects of site variables
and soil moisture in both structural equation models provides confirmatory evidence for the hypothesis that exogenous variables exert control over resources and groundlayer plant communities in this 70- to 90-year-old secondgrowth forest. However, the relative importance of exogenous vs. endogenous processes varied between the two axes
modelled, suggesting that the distribution of resources
affecting ground-layer plant communities is not exclusively
driven by exogenous site variables. The effects of site variables, and elevation in particular, on soil and O-horizon
properties were more important than overstorey composition for axis 1, while the effects of overstorey composition
on the O-horizon was cumulatively more important for
axis 2. Our results are consistent with those of Smith et al.
(2008), who showed that in the absence of disturbance,
forest understorey vegetation was related to soil properties
and drought. Leuschner & Lendzion (2009) also reported
that the soil microclimate and chemical properties were of
primary importance to the distribution of forest herbs,
while light transmittance was of secondary importance. It
remains unclear, for the present study, whether the effects
of moisture levels on axis 1 were related to drought stress
and ⁄ or the effects of moisture on nutrient acquisition (i.e.
low levels of soil moisture decrease nutrient mineralization
rates, mass flow and diffusion, increasing the importance
of root interception for nutrient acquisition; Lambers, Chapin & Pons 1998).
ENDOGENOUS VARIABLES
O-horizon and soil properties driving patterns of groundlayer plant communities were also related to the composition and structure of the live overstorey. While effects of
tree species on soil resource gradients through their differences in litter chemistry have been observed elsewhere
(Ferrari 1999; Scharenbroch & Bockheim 2007; Weand
et al. 2010), few studies have simultaneously shown how
these effects can affect forest understorey plant communities (e.g. Beatty 1984). Based on these results, we propose
2011 The Authors. Journal of Ecology 2011 British Ecological Society, Journal of Ecology, 99, 764–776
774 J. I. Burton et al.
that the effects of overstorey composition and structure on
soil resources is an additional mechanism for observed
linkages between overstorey and understorey vegetation
later in succession (e.g. Gilliam, Turrill & Adams 1995;
Gilliam 2007). Such linkages are likely diminished in younger stands and particularly in second-growth stands due to
the decreased importance of foundational species, such as
hemlock and other conifer species, in second-growth northern hardwood stands (e.g. Schulte et al. 2007). However,
the soil legacy of a greater abundance of these species historically may be causing some of the observed patterns
that cannot be linked to the overstorey. For instance, the
effects of Roman agriculture on soils and plant communities in forests that were last farmed nearly two millennia
ago remain detectable (Dupouey et al. 2002).
O-horizon and soil properties related to the cycling of
nitrogen and phosphorus (i.e. O-horizon N:P, soil P, soil
and O-horizon Ca) were the most important environmental
variables driving the observed compositional patterns. Local
soil nutrient status can affect competitive hierarchies, herbivory, soil microbial communities (i.e. mycorrhizae, pathogens) and promote exotic species invasion (Gilliam 2006).
Interestingly, nitrogen and phosphorus were associated with
different, orthogonal patterns of compositional variation.
O-horizon and soil properties associated with the nitrogen
cycle interacted with the soil moisture regime to affect the
distribution of early summer species. Thus, reduction of
heterogeneity in O-horizon and soil properties through the
simplification of overstorey composition and structure coupled with nitrogen deposition could result in corresponding
homogeneity in ground-layer plant communities (Gilliam
2006; Bobbink et al. 2010).
A transition from communities of evergreen-dimorphic
species and spring ephemerals was associated with a gradient
in soil phosphorus. Soil phosphorus becomes progressively
depleted with substrate age, thus phosphorous limitation is
generally thought to be less important in northern temperate
zones than in older, weathered soils of the tropics (Walker &
Syers 1976; Vitousek & Farrington 1997; Lambers et al.
2008). However, our results, along with the results of Naples
& Fisk (2009), suggest that vegetation, or perhaps the distributions of some species, in northern temperate forests may
be limited by phosphorous (Weand et al. 2010). Furthermore, this 70- to 90-year-old forest includes scattered trees
> 100 years of age, and this oldest age class suggests the site
was not completely clear-cut and heavily burned, as often
occurred in this region following logging of original stands.
Therefore, our study also suggests that P-limitation may not
be restricted to earlier stages (c. 30 years old) of stand development (Naples & Fisk 2009). Phosphorus was the most variable soil property that we observed (range = 9.5–
220.4 kg ha)1, CV = 79%). The relationship between soil P
and spring ephemeral cover may explain the observations of
Rogers (1982), who observed that across the entire Lake
States region, vernal herb communities were sparser and less
diverse on sites with coarse-textured, nutrient-poor soil
with slow rates of decomposition and evergreen trees.
UNEXPLAINED CORRELATIONS
While many of the relationships among the overstorey, soil
and O-horizon properties and ground-layer plant communities
could be specified in the model, the interactions are complex
and several additional ‘unanalysed’ correlations had to be controlled for in the model. Unanalysed correlations may be spurious, but they may also indicate a common unobserved driver.
For example, a significant unanalysed correlation between
overstorey composition and understorey plant community
NMS axis 1 scores was observed (Fig. 6). This relationship
may indicate an additional unobserved pathway by which
these species affect ground-layer plant communities (e.g. overstorey phenology, disturbance history or soil microbial communities).
Conclusion
Our results suggest that the spatial patterning of ground-layer
plant communities in this 70- to 90-year-old, second-growth
northern hardwood forest is driven jointly by colonization processes and environmental filtering. Although it has been nearly
a century since the last major disturbance to this forest (nearcomplete logging), it appears that ground-layer plant communities have not achieved competitive equilibrium. While it is
also possible that competitive equilibrium is a rare state under
the natural disturbance regime, and that ground-layer plant
meta-communities exist in a dynamic equilibrium resulting
from fluctuating environmental conditions (e.g. Connell 1978;
Hanski 1998; Leibold et al. 2004), the observed patterns of colonization are not related to gap dynamics. The speed of re-colonization likely depends on the severity of disturbance to the
overstorey, understorey (amount of vegetation removed) and
forest floor (Roberts 2007). We do not possess specific records
of the logging operations at this site; however, disturbance to
the overstorey was often severe during the logging era of the
early 1900s, leaving residual stands of saplings and small trees
(poles), and was often followed by slash fires. The observed
even-aged structure, with the exception of a few older trees
> 100 years suggests that this was the case here. Areas surrounding residual poles may have served as refugia for slowly
colonizing plants – disturbance was likely less severe and competition with shrubs and saplings may be suppressed in such
areas. In addition to recovering from the original logging event
more slowly, species with short-distance dispersal may be disproportionately affected by exotic earthworm invasions and
herbivory by overpopulated white-tailed deer (Odocoileus virginianus) as a result of their low migration rates.
Fine-scale variation in plant communities was also related
to environmental filters resulting from complex interactions
among site variables, the composition of the overstorey, and
O-horizon and soil properties. Much of the variation in soil
and O-horizon properties driving ground-layer plant communities is related to exogenous site variables such as soil texture
and soil moisture. Relative to exogenous variables, endogenous processes played an important but lesser role in regulating the distribution of resources, and thus ground-layer plant
2011 The Authors. Journal of Ecology 2011 British Ecological Society, Journal of Ecology, 99, 764–776
Forest ground-layer plant communities 775
communities. Relationships among tree species, O-horizon
and soil properties may have been more important historically
when co-dominant species (i.e. hemlock and yellow birch) were
locally more abundant. The relative importance of endogenous
processes in second-growth stands is thus governed by the
composition of the residual stand, the distribution of such
biological and ecological legacies and the rate of structural
development. Because internal regulation by endogenous processes may be diminished in such second-growth stands due to
the removal of coarse woody debris and the selective removal
of tree species, they may be more sensitive to climate change
and other external stressors than primary forests that were
never logged and old-growth forests with higher levels of heterogeneity. Long-term manipulative field experiments coupled
with physiological studies of plant resource economics, along
with strictly confirmatory SEM analyses performed on independent data sets are required to confirm the causal mechanisms discussed.
Acknowledgements
Funding was provided by grants from USDA McIntire-Stennis to D.J.M. and
J.I.B.; the National Research Initiative of the USDA Cooperative State
Research, Education and Extension Service to D.J.M., C.G. Lorimer and S.T.
Gower; Wisconsin Department of Natural Resources (WDNR) Division of
Forestry & Bureau of Integrated Science Services to D.J.M. and J.A.F.; and the
Garden Club of America Fellowship in Ecological Restoration to J.I.B. We
gratefully acknowledge the contributions of J.H. Dyer, T.D. Hayes, E.F. Latty,
J.L. Stoffel and numerous undergraduate assistants; as well as D. Hvizdak and
A. Voightlander (U.S. NRCS) and E. Padley (WDNR) for classifying the soil
profiles. For feedback on earlier versions, we thank E.I. Damschen, T.J. Givnish, S.T. Gower, E.L. Kruger, C.G. Lorimer, A.E. Sabo, P.A. Townsend, the
Handling Editor and two anonymous referees.
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Received 23 June 2010; accepted 27 January 2011
Handling Editor: Frank Gilliam
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