Journal of Applied
Ecology 2006
43, 792–801
Additive partitioning of diversity across hierarchical
spatial scales in a forested landscape
Blackwell Publishing, Ltd.
SHIBI CHANDY, DAVID J. GIBSON and PHILIP A. ROBERTSON
Southern Illinois University Carbondale, Department of Plant Biology and Center for Ecology, Carbondale, IL
62901– 6509, USA
Summary
1. Ecological phenomena exist at multiple scales, but measurements of diversity
frequently consider only the smallest scale, that of the original sample plots. Additive
partitioning of diversity allows multiple, hierarchical spatial scales of analysis to reveal
the scale at which diversity is maximized.
2. We examined the spatial partitioning of diversity across 378 permanent plots established in 10 7–369-ha research natural areas (RNA) in the 294 455-ha Shawnee National
Forest, Illinois, USA. Diversity (richness and Shannon’s and Simpson’s indices) was
partitioned across four spatial scales, i.e. within and between plots, and between RNA
and natural divisions (corresponding to α and three levels of β diversity), for two strata
of vegetation (trees and woody understorey).
3. For both strata, the highest contribution to diversity measured as species richness
was between plots and between RNA. Diversity was lower than expected within plots,
although Simpson’s and Shannon’s indices achieved their maximum values at this scale.
However, Shannon’s index values were higher than Simpson’s index values at the
between-RNA scale and for all strata, indicating that the most common species were
found at this scale.
4. There was a simple asymptotic relationship between plot occupancy and local
abundance, suggesting high colonization rates and rapid colonization of open habitat
indicative of a wide niche breadth of the most abundant species.
5. Synthesis and applications. The implications of these findings are that maximum
diversity across a forested landscape is not necessarily at the scale of sampling (i.e.
within plots) but may be at higher scales corresponding to larger landscape units. Moreover, the largest contribution of richness to total diversity occurred at a larger scale than
diversity expressed using measures based upon information theory, which incorporate
species abundance and evenness. Conservation efforts seeking to preserve diversity
must identify and target the correct scales to allow effective management. In this study,
management at and within the RNA represents the most appropriate scale for conserving maximum diversity.
Key-words: α diversity, β diversity, γ diversity, information theory, landscape ecology,
partitioning, scales
Journal of Applied Ecology (2006) 43, 792–801
doi: 10.1111/j.1365-2664.2006.01178.x
Introduction
Quantifying diversity at different scales of observation
helps planning for conservation measures and the man© 2006 The Authors.
Journal compilation
© 2006 British
Ecological Society
Correspondence: David Gibson, Southern Illinois University
Carbondale, Department of Plant Biology and Center for
Ecology, Carbondale, IL 62901–6509, USA.
agement of natural systems (Summerville et al. 2003).
In recent years applied ecologists have shifted their
emphasis from management of single species within
habitats to conservation of entire communities within
regions (Olson et al. 2002; Summerville et al. 2003).
This shift has necessitated the understanding of scaledependent phenomena (Freckleton 2004), including
patterns of diversity (Auerbach & Shmida 1987;
793
Additive
partitioning of
diversity
Fig. 1. Schematic representation of the different hierarchical scales studied within the
Shawnee National Forest. The α scale is the within- and β the between-level component.
Each lower scale adds to the next hierarchical level (adapted from Wagner, Wildi &
Ewald 2000; Gering, Crist & Veech 2003).
© 2006 The Authors.
Journal compilation
© 2006 British
Ecological Society,
Journal of Applied
Ecology, 43,
792–801
Whittaker, Willis & Field 2001; Reilly, Wimberly &
Newell 2005). There is a conflict between the goals of
management and conservation and the scale at which
most ecological phenomena are described and measured. Most environmental and resource management
problems can only be dealt with at a broad scale, whereas
ecological phenomena are often studied at smaller
scales. To understand how nature works, ecologists
must consider broad-scale patterns and relate them to
fine-scale phenomena (Münzbergová 2004; Wu 1999).
Thus there is a need to quantify diversity at multiple
scales (Godfray & Lawton 2001; Whittaker, Willis &
Field 2001).
In the temperate forest ecosystems of eastern North
America, an understanding of the scaling of diversity is
of particular importance as the effects of habitat loss
and fragmentation need to be quantified (Gilliam, Turrill
& Adams 1995; Keddy & Drummond 1996; Fuller 2001).
Prior to European settlement, eastern North America
was covered by primary deciduous forest (Braun 1950)
but several factors have led to their decline. During settlement, most of the primary forests were cleared and
much of what remains now is secondary forest (Cowell
1998). Because of the clearing of primary forest, suppression of fires and chestnut blight Endothia parasitica
and Dutch-elm disease Ophiostoma ulmi, the oak–
hickory (Quercus spp.–Carya spp.) forests of eastern
North America are fragmented, compositionally altered
and being invaded by other hardwood species such as
sugar maple Acer saccharum and beech Fagus grandifolia
(Bormann & Likens 1979; Fralish et al. 1991). Replacement of the dominant species impacts the entire ecosystem
by changing the microclimate, understorey vegetation
and fauna (Holling 2001). Fragmentation of forests
impacts the ecosystem at local, regional and global
scales (Fralish 1997).
Partitioning of total species diversity into additive
components within and between communities provides
a framework by which diversity can be measured at
different levels of organization (Lande 1996; Godfray
& Lawton 2001). The most frequently used diversity
indices (i.e. Shannon’s and Simpson’s) are based upon
information theory (Magurran 2004). These indices
measure both the number of species (species richness)
and the combined effect of richness and species relative
abundance (Pielou 1969). These diversity indices can
be used to compare patches and sites by linking spatial
scales with diversity (Whittaker 1960, 1972, 1977; Willig
2001). Whittaker (1972), for example, quantified alpha
(α), beta (β) and gamma (γ) diversity to describe diversity at different hierarchical scales, i.e. within plots,
between plots and at the landscape level, respectively
(Fig. 1). The total diversity in a pooled set of communities (γ diversity) can be partitioned into additive components within and between communities (Allan 1975;
Lande 1996), which makes it possible to calculate the
relative contribution of α, β and γ diversity across a
range of spatial scales (Wagner, Wildi & Ewald 2000;
Gering, Crist & Veech 2003).
Forest communities are dynamic, and understanding how diversity is partitioned at different scales will
help in managing and maintaining the forests effectively
across the landscape. Managers need an accurate measurement of diversity at all hierarchical scales. Partitioning of diversity helps researchers understand the scale
or scales that are most critical for determining species
composition and persistence. Identifying the scale or
scales where maximum diversity occurs over time helps
in understanding the vegetation dynamics (Small &
McCarthy 2002) and will aid in planning forest management to conserve natural levels of diversity.
Our study was designed to partition diversity in a
forested landscape at different scales. We used a randomization approach (Crist et al. 2003) to allow additive
partitioning of diversity across a large regional landscape. Specifically, to address these issues of scaling
and diversity, we partitioned diversity in 10 research
natural areas (RNA) at different sampling scales in
two natural, divisions of the Shawnee National Forest
(SNF), Illinois, USA. Maintaining a constant unit of
sampling (i.e. sample plots) while conducting analysis
of plots grouped within and between sites and natural,
divisions allowed the sample ‘grain’ (size of sample
unit) to remain invariant while changing the sample
‘focus’ (area of inference) (Scheiner et al. 2000; Rahbek
2005). The sample ‘extent’, describing the geographical
space over which comparisons were made in this study,
was the landscape represented by SNF. The question
addressed was how partitioning of species diversity
at different focal scales (plots, sites and divisions)
helps us understand diversity. In addition, we explored
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D. J. Gibson &
P. A. Robertson
the relationship between local abundance and regional
distribution across the SNF landscape to investigate
further links between local and regional scales (Freckleton et al. 2005).
Methods
 
SNF (294 455 ha) in southern Illinois includes two
natural divisions, the Ozark Hills Division (OZ,
50 000 ha, 39 807 in SNF) and the Shawnee Hills Division (SH, 243 306 ha in SNF), characterized by their
glacial history and geology (Schwegman 1973). Ten
RNA, ranging in size from 7 to 369 ha, were established
between 1989 and 1991 across these two divisions as
representatives of the communities in SNF. LaRue Pine
Hills, Ozark Hill Prairies and Atwood Ridge RNA are
part of OZ while Burke Branch, Cave Hill, Dennison
Hollow, Panther Hollow, Barker’s Bluff, Stone Face
and Whoopie Cat RNA are part of SH (Fralish 1997).
In OZ the highest altitudes are characterized by rugged
topography and underlain by cherty limestone. The
Ozark Hills are covered with up to 10 m of loess soil.
Most of this area was logged between 1880 and 1920
(Marini 1995; Fralish 1997). In contrast, the Shawnee
Hills are underlain with sandstone intermingled with
limestone. This region was extensively logged throughout the 18 –20th centuries with only 3 –5% of the current
forest being old growth (Parker & Ruffner 2004). Since
logging ceased in the 1930 –50s the forested areas have
been largely undisturbed.
 
© 2006 The Authors.
Journal compilation
© 2006 British
Ecological Society,
Journal of Applied
Ecology, 43,
792–801
A total of 378 plots (the census data set), each with a
radius of 11·4 or 17·8 m, was established from 1996 to
1998 in the 10 RNA. The RNA in OZ were sampled
with 11·4-m radius plots as the topography was rugged
and it was difficult to find large areas with uniform
terrain. In contrast, six of the seven RNA in SH were
sampled with 17·8-m radius plots. The terrain in Burke
Branch RNA was rugged compared with the other
RNA in SH and hence was sampled using 11·4-m radius
plots. Plots were established using a stratified random
design within each RNA and plot centres were permanently marked with a 1·5-m iron rod driven at least
50 cm into the soil. Plot locations were recorded on
hand-drawn maps or, in many cases, with a global positioning system (GPS). The numbers of plots per RNA
varied reflecting the size of the RNA. These plots were
sampled and analysed initially at the RNA scale by
Adams (1999), Grahame (1996), McCoy (1997), Shimp
(1996) and Suchecki (1999).
 
Within each plot the diameter at breast height (d.b.h.)
of each tree (woody individuals with d.b.h. ≥ 5·0 cm)
was measured. These data were used to determine tree
basal area and density in each plot. The density of the
woody understorey (woody individuals > 2·5 cm and
< 5 cm d.b.h.) was determined by counting all individuals in 5·6-m radius subplots for plots with a 11·4-m
radius and 11·4-m radius subplots for plots with a 17·8m radius in the centre of the permanent plot. The
woody understorey included tree saplings, shrubs and
woody vines. All nomenclature follows Mohlenbrock
(1986).
 
Partitioning of diversity
We followed procedures outlined (summarized below)
in Gering, Crist & Veech (2003) to partition diversity
across spatial scales in our data. Separate analyses were
conducted for the tree and woody understorey strata.
The program  (Gering & Crist 2002; Veech
et al. 2002; Crist et al. 2003; Gering, Crist & Veech
2003; Summerville et al. 2003) was used to calculate
diversity across the region, SNF. The observed α and β
diversity was computed at each focal scale, where diversity was measured as species richness (No), Shannon’s
index (H′) or Simpson’s index (λ). Shannon’s H′ and
Simpson’s λ are presented as N1 = exp[H′] and N2 = 1/
λ, respectively, to allow comparison with richness, where
N1 approximates the number of abundant species and
N2 approximates the number of very abundant species
(Hill 1973; Peet 1974). Probabilities that the observed
values for α and β diversity could have been obtained
by chance alone were obtained by bootstrapping,
allowing the statistical significance of the observed values to be tested. The density of all species from all samples at a given scale was combined to create a single
species pool. Individuals were then randomly assigned
to samples such that the initial density in the sample
was maintained but resulting in a new number of taxa.
The randomized samples were then partitioned to provide diversity measures at each scale. This randomization procedure was repeated 10 000 times to obtain
null distributions of α and β estimates for the diversity
measures at each scale of analysis. The observed values
at the scale considered were compared against expected
values generated from the null distribution obtained by
the randomization procedure. The proportion of null
values that were greater than or less than the actual
observed estimates was used to assess statistical significance. The probabilities obtained from the randomization test were interpreted as P-values as in traditional
parametric significance tests.
In this study, landscape-scale diversity across SNF is
the sum of α and β diversity, where α is the average
diversity within sampling units and β is average diversity between sampling units in the region, hence
maintaining their traditional interpretation (Allan 1975;
Wagner, Wildi & Ewald 2000). For example, α1 is the
mean within-plot diversity whereas β1 is the diversity
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Additive
partitioning of
diversity
between plots and β2 is the diversity between RNA.
Total diversity at the landscape level can be described
as α1 + β1 + β2 + β3, i.e. average diversity within plots
+ diversity between plots + diversity between RNA +
diversity between the two divisions (Fig. 1). Thus, the
spatial focus varied from 0·04- to 0·1-ha plots, 7- to
217-ha sites (RNA), 39 807- and 254 648-ha divisions,
up to the spatial extent of the 294 455-ha region of SNF
(Fig. 1).
where S is the number of species observed in n plots,
and r1 is the number of species occurring in one sample
plot (Palmer 1990). First-order estimates provide the
highest levels of precision compared with other methods
of estimating total species richness (Palmer 1991).
Spatial scale
Tree layer
To partition diversity at various spatial scales, the individuals (trees or woody understorey) per species were
counted for all of the 378 plots sampled across the
region: 233 plots in OZ and 145 plots in SH. Diversity of
the tree data was also partitioned in separate analyses
of the plots in each division because the plots in the two
divisions were of different sizes (11·4- or 17·8-m radius,
respectively). The 39 plots from Burke Branch RNA
were excluded from the separate analysis of SH as the
plots in this RNA were of a smaller radius than those in
the rest of the division. These separate analyses were
conducted as a check to determine the effect of mixing
two sample plot sizes on the partitioning of diversity.
For the woody understorey, two separate analyses
were also conducted, one for OZ with plot sizes 5·6 m
and one for SH with plot sizes of 11·4 m. The 80 plots
at Atwood Ridge RNA were excluded from this separate analysis of OZ as the plots in this RNA were of a
larger radius than those in the rest of the division.
There were 66 tree species in the entire landscape
recorded from the 378 plots. Mean richness was significantly lower than expected (the pattern that would be
found if individuals and species were distributed at random) within plots (Table 1 and Fig. 2a). The highest
richness was observed between plots (β1 = 37%; Fig. 2a),
although it too was significantly less than expected.
Richness was significantly greater than expected
between RNA (β2 = 31%) and between the two regions
Results
    
Abundance–occupancy relationships
The relationship between patch occupancy and local
abundance in both strata was investigated by plotting
(i) the proportion of sites occupied (P) against local
abundance (N; basal area or density for trees and
density woody understorey) and (ii) the relationship
between local population abundance and regional population size, i.e. between N and Nτ = PN (Freckleton et al.
2005). The nature of these relationships was investigated by fitting simple linear and non-linear regressions,
with the latter being accepted when the variance explained (r2) represented an improvement of > 5% over
the linear model.
Species accumulation curves
© 2006 The Authors.
Journal compilation
© 2006 British
Ecological Society,
Journal of Applied
Ecology, 43,
792–801
Species accumulation curves were constructed to evaluate the completeness of inventories in the two strata
from the 378 plots with - (McCune & Mefford
1999). Average species accumulation curves were constructed by subsampling the entire community 500
times to determine the number of species as a function
of subsample size. First-order jack-knife estimates of
total species richness were calculated in PC-ORD as:
estimated total species richness = S + r1(n − 1)/n
Fig. 2. Partitioning of diversity in (a) tree and (b) woody
understorey strata at four scales in increasing order from plot
to the two divisions.
796
S. Chandy,
D. J. Gibson &
P. A. Robertson
Table 1. Observed and expected additive partitioning of diversity of trees at four spatial scales for the full data set (census, n = 378
plots) and separate analyses of the Ozark Hills and Shawnee Hills divisions. In all cases pairs of observed and expected values are
significantly different from each other (P < 0·0001)
Richness
Simpson’s (1/λ)
Partition/scales
Observed
Expected
Observed
Expected
Observed
Expected
Census
β3
β2
β1
α1
12·0
20·4
24·2
9·4
4·1
10·2
30·1
21·6
1·2
1·4
2·6
5·5
1·0
1·0
1·5
15·3
1·02
1·03
1·15
4·17
1·00
1·00
1·02
12·5
Ozark Hills
β2
β1
α1
14·5
31·3
8·5
5·8
32·3
15·8
1·3
2·8
2·1
1·0
1·7
11·5
1·03
1·16
4·17
1·00
1·00
10·00
6·5
14·3
20·3
1·3
2·2
5·6
1·0
1·2
13·6
1·03
1·15
4·00
1·00
1·0
10·0
Shawnee Hills (less Burke Branch)
β2
14·5
β1
16·2
α1
10·3
© 2006 The Authors.
Journal compilation
© 2006 British
Ecological Society,
Journal of Applied
Ecology, 43,
792–801
Shannon’s (exp H′)
(β3 = 18%). In contrast, 80% of the diversity was partitioned within plots when expressed using Simpson’s
index, with small and decreasing amounts at increasing
scales. When diversity was expressed using Shannon’s
index, the pattern of observed partitioning of diversity
between scales was intermediate between that observed
using richness and Simpson’s index. Nevertheless, diversity decreased from the plot scale upwards as it did
using Simpson’s index. Diversity was, however, significantly less than expected by chance at the within-plot scale
when expressed by either Shannon’s or Simpson’s indices
(Table 1). There were only one to two (< 10%) abundant (N1, exp H′) and very abundant (N2, 1/λ) species
(Hill 1973) observed at focal scales above that of the
plots ( Table 1). In contrast, within plots there were 5·5
and 4·17 abundant and very abundant taxa, respectively,
representing 23 –58% of the observed richness.
The same patterns of partitioning of diversity were
observed when the data from each division were considered in separate analyses to maintain constant plot
size. Fifty-four and 41 tree species were recorded from
OZ and SH, respectively. The mean richness per plot
was 15·8 and 20·3 species, higher than expected (Table 1),
i.e. the pattern that would be found if individuals and
species were distributed at random. The highest richness
was observed between plots (β1 = 57·4% and 39·5%)
for both the divisions, as it was when the data from the
two divisions were analysed together. In SH, observed
diversity was significantly greater than expected between
plots for all three measures, whereas in OZ observed
diversity was less than expected at this scale when
expressed as richness but greater than expected when
expressed using Shannon’s or Simpson’s indices.
Woody understorey
There were 76 woody understorey species in 367 plots
(eleven plots of the 378 surveyed was dropped from the
analysis as no woody understorey individuals were
recorded) across the region. At the within and between
plot (α1 and β1) scales, mean richness was 7·5 and 33·8,
respectively, significantly lower than expected, indicating low diversity at these scales (Table 2). Between
RNA and between divisions, the observed diversity
was higher than expected, indicating greatest diversity
at these scales. Partitioning of diversity between spatial
scales was similar to that observed in the tree layer, with
maximum richness between plots. When measured by
Simpson’s and Shannon’s indices, the greatest diversity
occurred at the plot scale and decreased with increasing
scale (Table 2 and Fig. 2b). There was only 1.1–3.6
(< 10%) abundant (N1, exp H′) and one very abundant (N2, 1/λ) woody understorey species (Hill 1973)
observed at focal scales above that of the plots
(Table 2). In contrast, within plots (α1) there were 4·7
and 3·5 abundant and very abundant taxa, respectively,
representing 47–63% of the observed richness.
When separate analyses for 152 plots from OZ and
95 plots from SH were analysed, within-plot (α1) mean
richness was significantly lower than expected for both
the divisions. Between-plot richness was also lower than
expected for OZ and for SH. Richness was highest at
the between-RNA (β2) scale (Table 2). When measured
by Simpson’s and Shannon’s indices, the greatest diversity occurred at the between-plot and between-RNA
scales for both divisions (Table 2). This pattern was
similar to the analysis from the census data.
‒   
The most abundant species were also the most widespread across the region in both the tree and woody
understorey layers (Fig. 3). The relationship between
the proportion of sites occupied and local basal area
and density was best fit with a non-linear regression in
which occupancy showed a rapid initial increase but
797
Additive
partitioning of
diversity
Table 2. Observed and expected additive partitioning of diversity of the woody understorey at four spatial scales for the full data
set (census, n = 367 plots) and separate analyses of the Ozark Hills and Shawnee Hills divisions. In all cases pairs of observed and
expected values are significantly different from each other (P < 0·0001)
Richness
Simpson’s (1/λ)
Partition/scales
Observed
Expected
Observed
Expected
Observed
Expected
Census
β3
β2
β1
α1
11·9
22·8
33·8
7·5
4·5
10·8
44·4
16·3
1·1
1·7
3·6
4·7
1·0
1·0
2·1
11·1
1·006
1·03
1·22
3·52
1·000
1·001
1·03
10·06
Ozark Hills (less Atwood Ridge)
β2
11·9
β1
31·0
α1
5·1
4·6
31·4
12·0
1·3
4·9
3·0
1·0
2·2
8·5
1·04
1·45
2·32
1·00
1·70
8·33
Shawnee Hills
β2
β1
α1
7·6
17·3
11·1
1·5
3·4
3·2
1·1
1·9
7·9
1·06
1·30
2·70
1·003
1·04
7·69
13·2
17·3
5·5
became saturating at high densities (Fig. 3). The relationship between regional population size (i.e. NT = PN)
and local density (N), where P is the proportion of plots
occupied (Freckleton et al. 2005), was linear (data not
shown). Plots of species’ frequency distributions were
unimodal, with most species being rare, with 40 tree
and 61 woody understorey species occurring in less
than 10% of the plots. Only a few species were widely
distributed (see below).
In the tree layer, Quercus alba was both the most
frequent species occurring in 69% of the plots and the
most abundant (mean basal area 4·8 m2 ha−1, density
65 individuals ha−1). In addition to Q. alba, four other
trees occurred in > 40% of the plots with a basal area
> 1·0 m2 ha−1 (Carya glabra, Quercus rubra, Quercus
velutina and Sassafras albidum). Cornus florida was
widespread (63% occurrence) but not abundant (0·6 m2
ha−1), although its density was high (110 individuals ha−1).
Pinus echinata and Magnolia acuminata were restricted
to OZ, and Quercus prinus was present only on the dry
upper slopes of both the divisions.
In the woody understorey, C. florida and S. albidum
were the most widespread (42% and 38% of plots,
respectively) and the most abundant species (678 and
419 individuals ha−1, respectively). Other widespread
species in the woody understorey included Fagus grandifolia, Ostrya virginiana and Acer saccharum, occurring in 25%, 23% and 21% of the plots, with a density of
309, 247 and 181 individuals ha−1, respectively.
  
© 2006 The Authors.
Journal compilation
© 2006 British
Ecological Society,
Journal of Applied
Ecology, 43,
792–801
Shannon’s (exp H′)
The species accumulation curves for both the tree and
the woody understorey strata were starting to flatten
towards a potential asymptote at our level of sampling
intensity of 378 plots (Fig. 4). First-order jack-knife
estimates of total species richness were 74 and 94 taxa
in the tree and woody understorey strata, respectively.
The observed richness of 66 and 76 tree and woody
understorey taxa thus represented 89% and 81%,
respectively, of the estimated total species richness.
Discussion
We have identified the focal scales at which vegetation
diversity occurs in SNF. This is important because it is
argued that different processes determine diversity at
different scales (Crawley & Harral 2001; Collins, Glenn
& Briggs 2002). Our study is the first to partition diversity between different forest strata at a range of spatial
scales (from 0·04-ha plots to a 294 000-ha landscape).
The only comparable studies have been on the partitioning of insect diversity across two ecoregions in the
mid-western USA (Gering, Crist & Veech 2003) and
studies of agricultural fields in Switzerland (Wagner,
Wildi & Ewald 2000) and Germany (Roschewitz et al.
2005). Gering, Crist & Veech’s (2003) study indicated
that the largest scale was most relevant for conservation and restoration of beetles. Wagner, Wildi & Ewald’s
(2000) study indicated that between-patch diversity
contributed the most to overall plant diversity in an
agricultural landscape, whereas Roschewitz et al.
(2005) showed that arable weed diversity was maintained by high within- and between-field heterogeneity
(β diversity). The results of our study are comparable to
Wagner, Wildi & Ewald’s (2000) in that we also found
intermediate scales to have significantly higher than
expected species richness and contain the largest proportion of diversity. Maximum richness was primarily
between plots (β1) for both tree and woody understorey strata, followed secondarily by the between-RNA
scale (β2; Tables 1 and 2). Bedrock defines the two
divisions in SNF, which differ in species composition
(Parker & Ruffner 2004), whereas the RNA were established to preserve both representative and, in some
cases, unique features of the landscape. For example,
798
S. Chandy,
D. J. Gibson &
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Fig. 4. Species accumulation curves for (a) trees and (b)
woody understorey sampled in the Shawnee National Forest.
*First-order jack-knife estimate of total richness.
Fig. 3. Abundance–occupancy plots for (a) tree basal area
(r 2 = 0·70, P1,64 < 0·0001), (b) tree density (r 2 = 0·86, P1,64 <
0·0001) and (c) woody understorey density (r 2 = 0·90,
P2,73 < 0·0001) from all plots.
© 2006 The Authors.
Journal compilation
© 2006 British
Ecological Society,
Journal of Applied
Ecology, 43,
792–801
LaRue Pine Hills RNA in OZ contains the only native
stands of Pinus echinata in the region (Parker & Ruffner
2004).
Simpson’s and Shannon’s indices when expressed as
a percentage were always highest at the plot scale, indicating maximum richness and evenness of species at
this scale. Shannon’s index was proportionally lower
than Simpson’s index at the plot scale, indicating that
the most common species were not evenly distributed
at this scale (Magurran 1988; Gering, Crist & Veech
2003). Simpson’s index is the probability of drawing at
random from a sample two individuals belonging to the
same species (Gering, Crist & Veech 2003; Magurran
1988); as such it is more sensitive than Shannon’s index
to changes in the abundance of common species. In
contrast, Shannon’s index is sensitive to rare species
(Peet 1974; Magurran 1988). Expressed as N1 (i.e. exp H′)
and N2 (i.e. 1/λ), Shannon’s and Simpson’s indices
reflect the number of relatively abundant and very
abundant species, respectively (Hill 1973). Both these
indices were higher at the α scale than the β scales
because of local dominance and evenness of several
species, including oaks and maples, giving them high
information content at the plot scale. Crist et al. (2003)
note that contrasting partitions of species richness and
Shannon’s index values reflect patterns of species dominance or rarity. Our observations are consistent with
those of scale-related diversity relationships of forest
Lepidoptera made by Summerville et al. (2003), suggesting that local factors determine species abundance
at the within-plot scale whereas patterns of richness are
determined at larger scales.
As in other systems and with other organisms (Gibson,
Ely & Collins 1999; Collins, Glenn & Briggs 2002;
Heino 2005), the most abundant species were also the
most widespread in both strata (Fig. 3), indicating a
link between local population processes and regional
dynamics (Freckleton et al. 2005). The relatively few
widespread, albeit abundant, species (e.g. Quercus alba
and Cornus florida) and preponderance of infrequent
species is consistent with the findings from partitioning
diversity in which maximum richness was observed at
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© 2006 The Authors.
Journal compilation
© 2006 British
Ecological Society,
Journal of Applied
Ecology, 43,
792–801
the most local focal scale. The existence of a positive
abundance–occupancy relationship provides generality
for the findings presented here, and has implications
for the species assemblages of the region (Gaston &
Blackburn 2000). Several hypotheses have been proposed
to account for abundance–occupancy relationships
(Gaston & Blackburn 2000). The pattern observed for
the forest species here (a positive abundance–occupancy
relationship and a unimodal species frequency distribution) is most consistent with Brown’s (1984) nichebased model predicting interspecific variation in realized
niche breadths in which both regional distribution
and local abundance reflect the degree to which local
environmental conditions meet species’ requirements
(Heino 2005). This pattern is consistent with models in
which colonization rates are sufficiently high across the
region so that all empty sites are immediately occupied
(Freckleton et al. 2005). Indeed, the arrival rate of
potential colonists to a site can directly affect scaledependent relationships between local and regional
diversity (Fukami 2004). When total diversity declines,
the relative contribution of diversity at the smallest
spatial scales to total diversity is maximized. The asymptotic relationship between mean local abundance and
proportion of plots occupied implies a threshold above
which increased local abundance does not allow increased
site occupancy, consistent with a habitat-filling model
(Freckleton et al. 2005).
Low richness at the smallest scales could be the result
of a sampling effect, habitat fragmentation, low dispersal rates or microsite variation. Individual plots may
not have been large enough to physically include all tree
and woody understorey species (i.e. a sampling effect;
Small & McCarthy 2002). However, this type of sampling effect is taken into account in the randomization
part of the partitioning procedure. Expected richness
of an individual plot cannot exceed the number of individuals in the plot, which is less than the total richness
across the landscape (66 and 76 tree and woody understorey taxa, respectively, mean density of 49 ± 1·6 and
53 ± 2·7 trees and woody understorey individuals
per plot, respectively). The fragmented nature of the
eastern deciduous forest reduces regional colonization
processes at the local scale and could reduce plot-scale
richness (Fralish et al. 1991; Fralish 1997; Friedman,
Reich & Frelich 2001). Although our study does not
provide a test of this idea, our findings are consistent
with models that postulate the operation of regional
biogeographical processes governing levels of local
diversity (Loreau 2000). Huston (1999) suggested competition, predation and environmental variability can
reduce diversity at small scales, while mutualism and
productivity can increase diversity. Low levels of local
diversity, as we found for plot-scale richness, are considered in Hubbell’s (2001) unified neutral model of
diversity to be indicative of low rates of dispersal among
species across the metacommunity represented by the
landscape as a unit. A third factor that could contribute
to low richness at the within- and between-plot scales
(α and β1) is microsite variation between plots (Frelich,
Machado & Reich 2003). At the larger scales all types
of microsites were likely to be adequately represented,
leading to high diversity (Beatty 2003). Studies that
focus entirely at the plot scale are limited in the extent
to which generalizations can be made to larger scales
(Weiher & Howe 2003).
   

Our study helps to advance the understanding of
partitioning of diversity at different scales and strata
across a large regional landscape by determining the
focal scale at which the greatest contribution to total
diversity occurs. This study unifies the disparate
approaches to studying species diversity and composition, as all measures of diversity (especially β diversity)
are expressed in the same unit. For all the strata, the
highest richness was at the between-plot scale (β1), followed by the between-RNA scale (β2; Tables 1 and 2).
The RNA were established to be ecological representatives of the larger landscape (USDA 1986) and this is
well illustrated from our results and the partitioning of
diversity. At the landscape scale, the species’ accumulation curves reflect adequate sampling as more than
80% richness in the tree and woody understorey strata
were captured (Fig. 4). Although the RNA were established based on canopy cover and basal area of dominant
trees, our results show that partitioning of diversity was
the same for both strata, indicating that both are similarly structured ecologically in this region. Richness
was lower than expected at the plot scale because the
majority of diversity is at higher scales that cannot be
captured in the smaller individual plots (Weiher &
Howe 2003). There was an increasing disparity in the
extent to which observed diversity exceeded expected
diversity with increasing spatial scale (Tables 1 and 2).
At the smallest scales, heterogeneity in distribution
leads to exclusion of some taxa from local patches,
resulting in observed diversity being significantly less
than expected at the plot scale. With increasing scale
there is an increasingly even distribution of taxa, allowing observed diversity to exceed expected. Thus, as scale
increases, the apparent dominance of a few species that
is seen at local scales becomes less apparent (Summerville
et al. 2003).
Anthropogenic factors and suppression of natural
disturbances, for example fire, have contributed to the
fragmentation of the forest and composition change
throughout North America (Cocke, Fulé & Crouse
2005). There is also increasing concern over the conversion of oak–hickory forests to sugar maple forests
(Iverson 1994; Washburn & Arthur 2003). The relevant
point in this study is that richness was highest at the
between-plot scale, followed by richness at the betweenRNA scale. The eastern deciduous forest occupies a
vast area in North America and there is considerable
variation in species composition and abundance (Braun
800
S. Chandy,
D. J. Gibson &
P. A. Robertson
1950). For the preservation of the total diversity (γ
diversity) in the landscape it is important to recognize
and conserve diversity at the appropriate scales of
interest (Keddy & Drummond 1996; Whittaker, Willis
& Field 2001). By considering scales from the plot level
to the landscape level this study has identified where
diversity is maximized. When considering conservation or ecosystem restoration in this region, our results
suggest that it is primarily within RNA that attention
should be focused for conserving diversity. The RNA
represent important conservation areas of the landscape and the high levels of diversity that we observed
among sample plots allow managers and researchers to
focus at a logistically tractable scale. However, some
caution has to be exercised in making this interpretation
because, as unique areas, the RNA may not be representative of the landscape as a whole. The RNA were
initially identified on the basis of unique biotic features,
including high species richness in some cases, and such
areas, including many biodiversity hot spots (Latimer,
Silander & Cowling 2005), merit high levels of protection. However, results such as those presented here
reinforce the special nature of charismatic areas. Sampling in less high-profile areas of the landscape, outside
the RNA in this case, would be necessary to assess the
contribution of the background landscape matrix to
regional diversity.
Acknowledgements
We thank the USDA Forest Service for partially funding the project, Eric Adams, Roger McCoy, Anthony
Grahame, Jody Shimp and Paul Suchecki for collecting
the data, Yohanes Honu and Allan Dzurny for their
help in the field, and Loretta Battaglia, Kevin Davie,
Tadashi Fukami, Yohanes Honu, Michael Hutchings,
Peter Minchin, Charles Ruffner, Dale Vitt and Natalie
West for comments on the manuscript. A special thanks
to Joe Veech for the program , helping with
the analysis and comments on the manuscript.
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Editor: Rob Freckleton