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JEC578.fm Page 538 Saturday, July 14, 2001 1:20 PM
Journal of
Ecology 2001
89, 538 – 554
Multiple scale composition and spatial distribution
patterns of the north-eastern Minnesota presettlement
forest
Blackwell Science, Ltd
STEVEN K. FRIEDMAN, PETER B. REICH and LEE E. FRELICH
University of Minnesota, Department of Forest Resources, 1530 North Cleveland Avenue, St Paul, MN 55108, USA
Summary
1 Bearing tree data were used to characterize the composition and spatial structure of
the southern boreal presettlement forest in north-eastern Minnesota, United States
of America. Data collected during the General Land Office Survey (GLO) between
1853 and 1917, represents 35 324 samples (each with 1–4 trees) in a 3.2 million-hectare
landscape. Nine tree species contributed at least 1% to the overall composition. Individuals of white pine and red pine were larger than all other species and represented 9%
of the tree population, while accounting for 27% of the standing basal area. Black
spruce, paper birch and larch, the three most abundant species, collectively accounted
for 51% of the population and 38% of the basal area.
2 Eight physiographic zones were characterized by differences in glacial histories,
geological surfaces, soil complexes and topographic properties, and supported different
compositional mixes of the nine important species.
3 Fifty-six per cent of all four-tree plots had three or four individuals of a single species.
This level of conspecific aggregation is an order of magnitude greater than would be
expected based on a random distribution of the same population of trees and species.
Jack pine had the greatest plot-scale aggregation, with 45% of all plots containing jack
pine trees having three or four jack pine individuals. Jaccard association of similarity
values of species co-occurrence ranged from less than 0.05 to 0.24, indicating limited
plot–scale interspecific associations.
4 Landscape spatial patterns of the species were measured at two spatial scales, 1–10 km
and from 5 to 50 km. Conspecific autocorrelation patterns were positive while interspecific
autocorrelation patterns tended to be either neutral or negative. Hence, plots dominated
by any given species tended to spatially aggregate near other such plots, out to substantial
distances.
5 Forest tree spatial patterns reflect complex fine to landscape-scale relationships
involving environmental factors, disturbance events and regeneration strategies.
Management and long-term conservation of forest landscapes should consider multiscale patterns in order to re-establish forest structural properties eliminated following
the disruption of natural disturbance processes.
Key-words: bearing trees, hierarchical structure, landscape ecology, spatial autocorrelation, spatial scales, species coexistences
Journal of Ecology (2001) 89, 538–554
Introduction
It is well known that every species has a unique suite of
responses to environmental variation and that spatial
© 2001 British
Ecological Society
Correspondence: S.K. Friedman, Forestry Department 126
Natural Resouces, Michigan State University, East Lansing,
Michigan 48823-1312, USA. Tel.: +1 517-355-0092. Fax: +1
517- 432-1143.
distributions of species are thus individualistic by nature
(Gleason 1926). Distribution patterns reflect processes
that affect seed dispersal, safe sites for colonization,
competitive interactions that emerge at both plot and
landscape scales and sensitivity to disturbance events.
Evaluation of hierarchical organization and the associated patterns of species segregation and co-occurrence
(Urban et al. 1987) requires multiple scales of investigation because ecologically important associations,
538
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Scale-dependent
patterns in southern
boreal forests
© 2001 British
Ecological Society,
Journal of Ecology,
89, 538 – 554
processes and functions observed at one scale may not
be seen at others (Smith & Urban 1988; Wiens 1989;
Podani et al. 1993; Wiens et al. 1993).
Species-poor boreal forests have not been extensively
investigated beyond the plot-scale (but see Frelich &
Reich 1995 and Kuuluvainen et al. 1998). Although,
on first inspection forests appear to have a simple
spatial structure, investigations of a Pinus sylvestrisdominated forest in Finland (Kuuluvainen et al. 1998)
showed numerous complex fine-scale autocorrelated
spatial patterns, and suggested that small-scale patterns
would be expected to influence large-scale dynamic
processes. Pacala & Deutschman (1995) simulated spatial distribution patterns in temperate forest, finding
that the tree-scale spatial patterns could in theory influence system-scale biomass productivity. In this study,
we examine composition and spatial patterns in the
presettlement southern boreal forest of north-eastern
Minnesota, United States of America (USA), to enable
a better understanding of the initial conditions upon
which human disturbance has subsequently acted and
thus to provide a better framework for interpreting the
causes and consequences of change in present-day
forest conditions.
Colonization patterns of southern boreal species
following stand-killing fires result from their variety of
regeneration strategies. Some, such as balsam fir (Abies
balsamea (L.) Mill), white pine ( Pinus strobus L.), red
pine ( P. resinosa Ait), northern white cedar (Thuja
occidentalis L.) and white spruce (Picea glauca (Moench)
Voss) are dependent on seeds from the unburned
perimeters, whereas jack pine (Pinus banksiana Lamb),
aspen (Populus tremuloides Michx) and black spruce
( Picea mariana (Mill) B.S.P.) burn and regenerate either
from canopy-stored serotinous seeds or by vegetative
means ( Heinselman 1973, 1981). Among the conifers
decreasing colonization is expected in the order jack
pine, red pine, white pine, black spruce and balsam fir
( Thomas & Wein 1985). Several site factors, including
seed bed quality (Ahlgren & Ahlgren 1981), burn intensity (Ohmann & Grigal 1981) and competitive interaction for light and nutrient resources (Messier et al.
1999) influence the relative success of species following
recolonization and a variety of spatial patterns therefore develop across fine to landscape scales (Burnett
et al. 1998; Nichols et al. 1998).
Galipeau et al. (1997 ) attributed spatial structure in a
Quebec-Ontario boreal forest to differences in identified
colonization patterns between black spruce and balsam
fir, due to seed dispersal, bimodal temporal colonization
and delayed regeneration. Although density-dependent
responses are often cited as the probable cause, site
heterogeneity can also affect resulting spatial patterns.
Houlé (1998), for example, examined the spatialtemporal autocorrelation trends of Betula alleghaniensis
and showed that mapped seedling survival locations
were independent of the preceding year’s seed rain.
One aspect of landscape ecology that has been
largely overlooked is whether there are detectable
landscape-scale spatial patterns in forests largely undisturbed by human activities. Undisturbed forest is restricted
to small remnant patches and, even in boreal forests
where human disturbance is minimal in some places,
little is known about spatial patterns. Gustafson (1998)
suggests that there are methodological limitations to
quantification of presettlement spatial patterns. Associations of species with geological formations or soil
properties, links to disturbance histories and influences
of past land use patterns, have nevertheless been made
using historical data such as General Land Office (GLO)
records (Grimm 1984; Almendinger 1985; Iverson 1988),
albeit not at the plot and landscape scale. Quantification of these patterns would, however, provide information on spatial-temporal dynamic patterns, interactions
among species and multiple-species aggregation patterns.
We can also test for the spatially-dependent correlated
relationships across plot, region and landscape scales
predicted by theoretical work (Andren 1994; Pacala &
Deutschman 1995; Ives et al. 1998). We used GLO records,
which although not ideal are the best presettlement
forest records available, and have demonstrated value
for landscape-scale vegetation reconstruction.
The presettlement forest of the Arrowhead Region
in north-eastern Minnesota, USA ( Fig. 1) was subjected
to natural fire disturbance regimes. One of our objectives was to address the accuracy of the common preconception that because of the removal of white and
red pine by logging, these two species had dominated
the landscape (Larson 1949; Hoffman 1952; Flader
1983). Commercial logging started in the late 1800s
and its ecology is therefore poorly documented. This
paper evaluates composition, species co-occurrence and
spatial distribution patterns in the presettlement forest
to gain insight into its physiognomic characteristics.
The following questions are addressed:
• What was the composition and size structure of the
presettlement forest?
• How were the tree species associated with the physiographic zones and surface geological material?
• How were the species associated with soil texture and
permeability conditions?
• Did conspecific and interspecific spatial patterns differ
between the plot, intermediate and regional landscape
scales?
Study area
The 32 000 km2 Arrowhead Region of Minnesota (Fig. 1)
is centred at approximately 575 910 East and 5 274 896
North UTM Zone 15. The region remains primarily forested and tree species of current major importance include
aspen, pines, spruce, larch, balsam fir and paper birch.
Physiographic zones of Minnesota were mapped by
Wright (1972) following precipitation, temperature
and geological gradients which vary along both east–
west and north–south transects (see Table 1 for characteristics of the study area). The region was glaciated
until about 12 000 , and now contains numerous
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S.K. Friedman,
P.B. Reich &
L.E. Frelich
Fig. 1 North-eastern Minnesota, USA. Arrowhead Region Physiographic Zones (after Wright 1972).
Table 1 Dominant physiographic zones of north-eastern Minnesota, shown with the dominant geological materials and
dominant soil properties. Numbers in ( ) are the proportion of the study area represented by the physiographic zone. Data are from
Wright (1972)
Physiographic zone
Dominant geological material
Dominant soil properties
Border Lakes Region (26.0%)
North Shore Highlands (14.5%)
Rainy Lobe Vermilion Ground Moraine
Superior Lobe Mille Lacs-Highland
Ground Moraine
Rainy Lobe St Croix Ground Moraine
Des Moines Lobe Culver End Moraine
Holocene Peats
Rainy Lobe Vermilion Ground Moraine
Superior Lobe Outwash
Des Moines Lobe Erskine Moraine
Lake Modified Till
Coarse medium texture – low permeability
Medium texture – moderate low
permeability
Medium texture – moderate permeability
Histisols – low permeability
Histisols – low permeability
Medium texture – moderate permeability
Coarse medium texture – low permeability
Medium fine texture – moderate
low permeability
Toimi Drumlin Area (11.8%)
Aurora-Alborn Clay-till Area (4.9%)
Glacial Lakes Upham and Aitkin (5.6%)
Chisholm Embarrass Area (10.6%)
Brainerd Automba Drumlin Area (4.9%)
Beltrami Arm Glacial Lake
Agassiz (18.9%)
© 2001 British
Ecological Society,
Journal of Ecology,
89, 538 – 554
glacial moraines, glacially carved lakes, drumlins and
exposed Canadian shield bedrock. Thin mineral soils
derived from glacial tills and moraines are interspersed
with Holocene peat and alluvial deposits. The area has
a cold continental climate, with 30 years (1951–80) mean
winter ( December, January and February) and summer
( June, July and August) temperatures recorded near
the centre of the study area (Virginia, MN) of –13.4 °C
and 17.5 °C, respectively (Baker et al. 1985). The ground
is typically snow-covered from mid-November to midApril. Historically, vegetation dynamics in this southern boreal forest were driven by fires initiated during
heavy lightning storms in late summer (Heinselman
1973, 1996). Although Native Americans lived in this
region, and species composition and spatial distribution
patterns at the time of arrival of European settlers may
reflect their use of resources, their influence is not
considered here.
Methods
General Land Office records (Almendinger 1997) for
the Arrowhead Region were integrated in a multipurpose geographical information system (GIS) database. Maps depicting the location of bearing trees,
physiographic zones (Wright 1972), surficial geology
(Morey et al. 1982) and soils (Cummins & Grigal 1981)
are stored in ARC/INFO.
GLO records consist of spatially explicit information on tree species collected as part of the original
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Scale-dependent
patterns in southern
boreal forests
© 2001 British
Ecological Society,
Journal of Ecology,
89, 538 – 554
Public Land Survey of the United States which mapped
land holdings following the Northwest Ordinance of
1785. Each 6-mile (10 km) wide township-range unit,
was subdivided into 1-mile (0.6 km) sections and then
subdivided into quarter and quarter-quarter sections
of land. Data were collected at the intersecting points
within this nested mapping scheme, during a 64-year
period prior to initiation of logging and local settlement
in this area (1853–1917). Trees and/or cairns were used
to mark the intersections. Specific instructions were
issued by the Surveyors General of the United States
( White 1983) and included requirements to mark four
trees at each township section corner, and one in each
adjoining section and two at each quarter section and
meander corners, and to record species and size class
data of each tree. Although standardized instructions
were issued, the exact procedures varied with individual
survey crews.
Although biases have been identified in GLO records
(Bourdo 1956; Grimm 1981) and may constrain scientific
analysis, they represent some of the best data available
for investigating vegetation composition, distribution
patterns, and species–environment and species–species
association patterns prior to the large-scale land clearance by European settlers (Bourdo 1956; Grimm 1984;
Delcourt & Delcourt 1996). The large sample size and
landscape area considered, a small species pool and
our conservative methodology, alleviate most of the
potential bias in bearing tree data. However, statistical
analysis, was not always possible, in which case exploratory data analysis techniques were used.
Forty species were reported in the bearing tree records
for the north-eastern Minnesota presettlement forest.
As in all GLO records, taxonomic ambiguities were
plentiful, such as unreliable distinction between white
and black spruce. However, we assume that then, as
now, black spruce was far more common and that the
GLO records of Picea generally refer to Picea mariana.
Multiple species abbreviations were used for some
species, as well as a single abbreviation for many others,
and such errors were eliminated by combining entries
into meaningful taxonomic units (Almendinger 1997).
Each GLO record was viewed as being analogous to
a vegetation sample plot, although each GLO plot
includes data for between only one and four trees. The
database consists of 35 324 section and quarter section
locations covering an area approaching 3.2 million
hectares and spans across 365 townships. A total of
82 899 trees were recorded (at least one per plot), although
surveyor instructions, incomplete surveys and incomplete
data, as well as limitations encountered during the
transcription of hand-written surveyor notes and conversion to digital formats, meant that only 26% had the
maximum four trees. Most of our analyses consist of only
these 9280 plots, yielding information for 37 120 trees.
We made a trade-off between spatial extent and
spatial resolution. The study region was quite large,
and fine-scale spatial data were not available for the
entire study area. In all cases, the resolution associated
with the bearing tree locations was substantially greater
(fine-scale point-based digital maps) than the resolution
of the environmental parameters (coarse-scale polygonal
digital maps) with which we wished to link them. Map
scales also varied with each of the environmental variables.
Count data were assembled for the nine most important species and the eight most important physiographic
zones in the study region (i.e. those contributing more
than 1% to the total area). A 9 × 8 matrix was crosstabulated, using S-Plus (Statistical Sciences 1995) to give
a goodness of fit statistic to determine whether species
abundance patterns differed among the physiographic
zones. Physiographic zones were also qualitatively examined for differences in abundance of soil orders, and
area proportion of soils classified by texture and permeability classes.
GLO records included size class data (measured
in inches) for each tree recorded. Although some authors
have calculated basal area/unit area (Anderson &
Anderson 1975) using tree diameter and estimates of
distance from section corners, species mean distance
from section corners is a known bias inherent to GLO
records (Almendinger 1997). Estimation of species density
would therefore be erroneous. Diameter measurements
can, however, be used to calculate a composite species
basal area estimate (Shiver & Borders 1996).
Soil distribution patterns were defined according to
the ‘Soils and Land Surfaces of Minnesota 1980’ digital
map developed by Cummins & Grigal (1981). Soil texture and permeability conditions, classified using a fivelevel ordinal ranking scheme following a soil moisture
gradient, were superimposed on the GLO plots. Species
associations with soil texture (coarse, coarse-medium,
medium, medium-fine and organic) and permeability
classes (high, high-moderate, moderate, moderate-low
and low) were determined through goodness-of-fit tests.
Adjusted residuals (Abrams & Ruffner 1995; Agresti
1996) from this analysis were used to assess whether
species were positively, negatively or neutrally associated with soil characteristics. An inverse area-weighted
correction function was used to construct histograms
of the proportion of each species among the soil texture
and permeability classes. We did not attempt to adjust
the vegetation and soil association patterns for finescale variations known to occur in soil properties in the
study area.
Estimates of species co-occurrence were developed
from the four-tree plot data set and used to assess species
associations and their distribution on the landscape.
Jaccard association of similarity scores were used to
determine the strength of species co-associations.
Non-metric multidimensional scaling ( NMDS, PCORD Version 4.0, MjM Software, Gleneden Beach,
Oregon, USA, McCune & Mefford 1999) was then used
to assess which species were associated with other species. This technique, which benefits from being nonparametric and having relaxed assumptions (Sammon
1969; Fasham 1977; Kenkel & Orloci 1986; Green et al.
1997; McRae et al. 1998), was used to rank similarity
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S.K. Friedman,
P.B. Reich &
L.E. Frelich
Fig. 2 Species associations are shown in a non-metric
multidimensional ordination mapping. Co-occurrence rates
of species at individual plots were used to develop this figure.
© 2001 British
Ecological Society,
Journal of Ecology,
89, 538 – 554
scores and map species in ordination space. Estimates
of species co-occurrence were transformed to percentages before the analysis. NMDS was run, using the
autopilot (slow and thorough) mode, which automatically reruns the best solution (McCune, personal
communication). Autopilot was configured to run 40
iterations with real data and 50 runs with randomized data. The scree plot revealed three significant
dimensions in the ordination. The optimal graphical
representation was revealed with two dimensions
(Fig. 2) providing the most useful interpretation of
the distribution of species at this scale.
The 9280 four-tree plot records were used to determine:
(a) the average number of individuals per species per
plot; (b) the proportion of all plots where the species
occurred; (c) whether species were locally dominant at
the plot scale; and (d) whether species were distributed
across the landscape evenly among plots. Plots that
contained three or four individuals of a single species
were considered representative of aggregation locations
for that species. This is a conservative estimate of species
spatial aggregation, because the distance to individual
trees from section corner points was moderate, averaging
6.7 m for all species, and there was no indication that
trees closer to the section corner were either skipped or
not selected in favour of other trees. Binomial expectations for the percentage of plots with three or four individuals of each species were calculated following cluster
sampling for species proportions (Hayek & Buzas 1997;
Goldberg 1986).
Spatial autocorrelation was used to determine if
species distributions patterns were aggregated at scales
ranging from 1 km to 50 km. The analysis consists of
measuring the distances from a plot to all other plots,
allocating each pair to an a priori defined distance class,
and determining whether the two plots have species in
common. The analysis is iterated once for each distance class. Spatial autocorrelation with nominal data
is reported using the standard normal deviate (SND),
for which values between –1.96 and +1.96 represent the
95% confidence limits for a neutral pattern. Positive
associations are present when the SND is above +1.96
and values less than –1.96 indicate over-dispersion. The
method is useful in describing the spatial association of
species patterns with high concentrations of a single
species and for comparing plots among species on a
landscape. In patchy landscapes, SND typically decreases
as the distance class increases (Sokal & Oden 1978;
Frelich et al. 1993).
Spatial autocorrelation conducted using two different spatial lags, representing an intermediate
landscape-scale (1 km to 10 km with 1 km steps) and
a regional landscape-scale (5 km to 50 km with 5 km
steps), allowed us to determine: (a) if plots populated
by four conspecifics were autocorrelated across the landscape; and (b) if plots with four trees of one species were
spatially correlated with conspecific four-tree plots of
other species. Positive autocorrelation would indicate,
respectively, that species distribution patterns were
spatially aggregated, thus providing a measure of how
homogeneous the forest was relative to the distribution
of a given species or that the forest was heterogeneous,
possessing characteristics indicating a patterned mosaic
landscape.
We also assessed autocorrelation between high
aggregation plots (plots populated with four trees of a
single species) and mixed two-species plots (the species
in the high aggregation plots and one other, each species
being represented twice). Spatial patterns were assessed
from 1 km to 10 km distances by 1 km steps. The objective of this analysis was to determine if plots dominated
by pairs of species were spatially related to other plots
where each species was highly aggregated. The analysis
was conducted using high-aggregation plots for each
species in turn and the corresponding mixed two-species
plots. Positive autocorrelation offers a refined measure
of any mosaic spatial structure indicated in the earlier
analyses.
Results
    
Bearing tree records for north-eastern Minnesota identified nine species present in amounts greater than 1%
of the total (Table 2). Spruce was the most abundant
species, followed by paper birch (Betula papyrifera
Marsh.), larch (Larix laricina Du Roi, K. Koch), aspen
and balsam fir. Jack pine (7.8%), white pine (6.3%), white
cedar (6.1%), and red pine (2.7%) were also present in
significant amounts.
This southern-boreal forest was composed of small
and medium-sized trees but red pine and white pine
were substantially larger than the other species in this
region (Table 2). In the presettlement record, 22% of
the red pine and 28% of the white pine were at least
50 cm in size measured at 1.4 m above the ground,
whereas other species were represented predominantly
by smaller trees. Although white pine represented only
6% of the trees, it accounted for 20% of the total basal
area in the bearing tree records. Red pine produced 7%
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Scale-dependent
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Table 2 Composition, mean d.b.h. and basal area of the nine species present in the presettlement forest with greater than 1% of
the total number of trees
Species
Percentage
composition
Mean d.b.h.
(cm)
Percentage of individual
≥ 50 cm d.b.h.
Percentage of
total basal area
Black spruce
Paper birch
Larch
Aspen
Balsam fir
Jack pine
White pine
Northern white cedar
Red pine
Other
20.7
15.1
15.0
10.8
9.4
7.8
6.3
6.1
2.7
6.1
18.2
21.0
19.2
18.3
17.1
19.0
40.0
22.4
37.0
–
0.3
1.4
0.6
1.1
0.1
0.6
27.6
1.7
22.0
–
13.4
14.0
11.0
7.7
5.2
5.7
20.1
6.0
7.3
9.6
Table 3 Species percentage composition (first number in paired columns) and percentage basal area (second number in paired
columns) of the presettlement tree species within the primary physiographic zones
Physiographic zones
Species
Jack pine
Red pine
Aspen
Paper birch
Northern
white cedar
White pine
Balsam fir
Spruce spp.
Larch
Border
Lakes
Region
North
Shore
Highlands
Toimi
Drumlin
Area
Aurora
Alborn
Clay-till
Area
Chisholm
Embarrass
Area
Brainerd
Automba
Drumlin
Area
Beltrami
Arm Glacial
Lake
Agassiz
17.5
3.8
13.4
17.2
3.4
13.4
11.5
8.8
14.5
3.9
0.1
0.3
4.9
19.3
10.0
0.1
0.4
6.3
19.7
9.5
10.0
1.0
5.4
16.6
4.4
7.6
3.5
5.2
16.4
3.9
3.4
3.0
13.2
16.7
4.0
3.9
7.7
12.8
17.1
4.3
0.2
1.0
5.1
9.2
8.4
0.2
3.6
5.6
13.8
10.3
10.4
5.7
8.6
16.7
6.4
9.9
16.5
5.6
14.9
5.2
0.9
1.0
12.4
17.5
6.9
0.6
1.9
10.1
15.5
5.5
5.2
3.5
18.8
9.4
7.7
4.8
11.6
12.8
7.4
8.8
5.2
8.8
20.7
9.1
19.2
4.6
15.3
7.8
10.9
18.1
18.0
9.1
23.4
10.3
14.0
7.3
7.6
7.5
29.4
16.7
23.8
4.0
18.9
15.2
4.3
4.2
23.4
24.6
16.6
1.9
14.0
19.1
2.7
4.5
22.5
43.2
14.1
1.9
14.4
31.9
6.0
8.5
19.0
14.9
18.2
3.9
10.7
12.4
9.4
4.4
10.8
23.5
31.4
2.4
5.5
13.2
3.6
9.9
22.6
14.7
15.2
5.9
16.6
12.3
of the basal area while contributing only 3% of the
composition. Collectively, the three most abundant
species, black spruce, paper birch and larch (51% of all
trees), accounted for 38% of the total basal area of
standing timber over the entire landscape (Table 2).
     
 
© 2001 British
Ecological Society,
Journal of Ecology,
89, 538 – 554
Glacial
Lakes
Upham
and Aitkin
The eight physiographic zones (Fig. 1) included in this
analysis are characterized by differences in the abundance and basal area of bearing tree species, surficial
geological materials, soil texture, soil permeability and
climatic conditions, but were uniformly dominated by
the same nine species (average of 95% of the composition in each zone). Each physiographic zone, however,
supported a different mixture of the species and proportional amounts of basal area (Table 3), indicating
that the presettlement forest composition and structure was heterogeneous. Variations can be seen for
instance in abundance of larch, jack pine and balsam fir
among the Glacial Lakes Upham and Aitkin, the Toimi
Drumlin Area, North Shore Highlands and the Border
Lakes Regions (Table 3) and particularly in percentage
basal area contributions of red pine and white pine in
each of the physiographic zones. Chi-square tests demonstrated that the species composition and basal area distribution patterns ( χ2 = 191.6 and 26.9, respectively,
with d.f. = 63) were significantly different among zones.
Heterogeneous environmental characteristics including substrate properties (Table 1), climate and varying
disturbance histories are reflected in the species distribution patterns. For example, larch was abundant
(43.2%) and jack pine rare (0.2%) in Glacial Lakes
Upham and Aitkin, an area dominated by histisols with
low permeability.
     
Soil texture and permeability are often correlated soil
properties, but their correlation with the soil-map units
was far from universal. However, each soil-mapping
unit contains numerous fine-scale inclusions which are
integrated into single discrete classes of soil texture and
permeability.
Despite this unqualified environmental heterogeneity,
statistically significant patterns of association among
the species and the soil factors were identified through
goodness-of-fit tests. Species abundance patterns varied
among the soil texture and permeability classes, and
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L.E. Frelich
Table 4 Species percentage composition within soil texture classes. Positive (+), negative (–), and neutral associations (n) were
assigned following a test for conditional independence, based on adjusted residual values exceeding |3| indicating a lack of fit of
the HO in that cell
Soil texture class
Species
( percentage of area)
Coarse (6.9%) Coarse-medium (44.4%) Medium (26.3%) Medium-fine (12.3%) Organic (10.2%)
Jack pine
Red pine
Aspen
Paper birch
Northern white cedar
White pine
Balsam fir
Spruce spp.
Larch
11.6+
4.0+
9.6 –
15.8+
5.3 –
7.5+
8.7 –
21.4n
16.1n
12.0+
3.6+
12.5+
18.2+
5.8 –
6.2 –
11.6+
20.1–
10.0 –
2.8 –
2.3 –
6.6 –
19.9+
7.8+
9.8+
10.0n
22.9+
17.9+
3.7–
1.2 –
20.5+
9.3 –
9.7+
6.4 –
11.5+
22.2n
15.6n
1.5–
0.9–
7.6–
10.4–
5.6–
2.9–
4.1–
29.4+
37.6+
Table 5 Species percentage composition within soil permeability classes. Conventions as in Table 4
Soil permeability class
Species
( percentage of area)
High (2.8%)
High-moderate (1.8%)
Moderate (24.5%)
Moderate-low (53.4%)
Low (17.5%)
Jack pine
Red pine
Aspen
Paper birch
Northern white cedar
White pine
Balsam fir
Spruce spp.
Larch
10.8+
5.1+
8.5 –
13.4 –
6.2n
5.2 –
8.2 –
23.5n
19.1+
11.9+
3.6+
10.1–
16.8n
4.9 –
8.4+
9.0
20.6 –
14.8 –
11.0+
3.5+
14.0+
16.4n
6.1–
5.6n
10.2n
21.5 –
11.7–
0.6 –
1.0 –
9.0 –
18.7+
8.8+
10.6+
12.8+
20.8 –
17.7+
2.0–
0.6–
5.5–
7.5–
6.0n
1.8–
4.1–
30.9+
41.6+
positive, negative and neutral associations between the
species and environmental conditions were detected
( Tables 4 and 5, Fig. 3). Ten of the 25 potential soilclass combinations (five texture classes × five permeability classes) occurred in this landscape and each of
these showed a distinct pattern of species abundance
( Fig. 3). Three general species groups were identified
from this analysis. Jack and red pine were both positively
associated with the better drained, coarse-textured soils,
in direct contrast to spruce and larch, which maintained
a positive association for the organic soils found in
hydric landscape settings. The remaining five species
(aspen, balsam fir, paper birch, northern white cedar
and white pine) were more variable, although they
generally exhibited neutral or negative associations
with low permeability, organic soils, as well as with highly
permeable soils, and tended to be positive in at least
some instances with intermediate soil texture and
permeability classes.
‒  
© 2001 British
Ecological Society,
Journal of Ecology,
89, 538 – 554
Jaccard coefficients of interspecific association were
calculated using the four-tree plot records (9280 plots)
as a measure of the co-occurrence among species.
Although a larger pool of 18 species was used to calculate
these coefficients, the additional nine, which did not
contribute at least 1% to the forest composition, all
showed very low interspecific association. The results
that follow pertain only to the nine most important
species.
Four arbitrary levels of interspecific association
strength were defined based upon the resulting range of
coefficients: high (> 0.24), moderate (0.24–0.10), low
(0.09–0.05) and very low (< 0.05). The nine species
formed 32 interspecific pairings (out of 36 potential),
none of which were in the high association category; 10
were moderate, 12 were low associations, and 10 were
very low. Spruce and larch, the two species most strongly
associated with the hydric and mesic soils, co-occurred
more frequently than any other species pair (Table 6).
Species distribution patterns were influenced by both
species–species associations and environmental conditions, as can be seen by comparing the NMDS ordination and individual species distribution maps. NMDS
methodology ordinates species associations based upon
plot-scale species co-occurrence similarity ranks ( Fig. 2),
and thus in this case represents a finer scale assessment
of species distribution patterns than do the individual
species distribution maps (Fig. 4). The relatively large
distances between species in the ordination is due to
the tendency for conspecifics to aggregate and for
interspecific co-occurrence to be unusual (Table 6). The
ordination was sensitive to an environmental gradient
reflecting soil moisture conditions, so that sites where
spruce and larch were abundant are located on the
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patterns in southern
boreal forests
Fig. 3 Histograms of species distributions patterns illustrating the response to soil texture and soil permeability conditions. An inverse area weighted
corrected factor was used to eliminate the bias associated with unequal area of soil surfaces.
© 2001 British
Ecological Society,
Journal of Ecology,
89, 538 – 554
ordination close to one another and in the opposite
corner from jack pine and red pine, while aspen, paper
birch, balsam fir, white pine and northern white cedar,
which occupy sites of more intermediate soil moisture
conditions, are located in the central portion.
Maps illustrating the distribution patterns of each of
the individual species, using plots where the species was
present at least three times, illustrate more generalized
distribution patterns over the entire regional scale
(Fig. 4). Although the species distributions patterns
were generally overlapping, it is clear that each species
was also associated with broader-scale environmental
conditions. For example, jack pine (Fig. 4a) and red
pine (Fig. 4d) were more abundant in the northern portion of the study area (Border Lakes Region) where
soils are rocky and thin, whereas the greatest concentration of white pine (Fig. 4b) and balsam fir (Fig. 4f )
occurred within the North Shore Highlands with intermediate soil texture and permeability and climatic
conditions moderated by Lake Superior lake effects.
Aspen (Fig. 4e), while distributed across the entire
region, had a high concentration in the north-west
on lacustrine soils along the southern shore of Lake
Vermillion. Even the most abundant species, black
spruce (Fig. 4h), and larch (Fig. 4i) show areas of high
concentration related to specific environmental settings
associated with the Toimi Drumlins and the Glacial
Lakes Upham and Aitkin physiographic zones ( Fig. 1).
-  
Using the 9280 plots with four individuals, we present
several metrics describing the relative frequency, plotscale and landscape-scale species aggregation patterns
JEC578.fm Page 546 Saturday, July 14, 2001 1:20 PM
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S.K. Friedman,
P.B. Reich &
L.E. Frelich
Table 6 Species co-occurrence classes, based upon the Jaccard coefficient of similarity. No species pairings were found in the high
association class (> 0.24). Species pairs are listed in decreasing rank order from highest to lowest association
Moderate association (0.24 – 0.10)
Low association (0.9 – 0.05)
Very low association (< 0.05)
Spruce – larch
Balsam fir – paper birch
Spruce – paper birch
Aspen – paper birch
Spruce – balsam fir
Paper birch – white pine
Aspen – jack pine
Red pine – white pine
Paper birch – northern white cedar
Aspen – balsam fir
Balsam fir – white pine
Balsam fir – northern white cedar
Aspen – spruce
Aspen – white pine
Spruce – northern white cedar
Larch – balsam fir
Paper birch – jack pine
Spruce – white pine
Aspen – larch
Spruce – jack pine
Jack pine – red pine
Red pine – paper birch
Aspen – red pine
Jack pine – larch
White pine – northern white cedar
Aspen – northern white cedar
Jack pine – balsam fir
Red pine – balsam fir
Red pine – spruce
Jack pine – white pine
Red pine – larch
Red pine – northern white cedar
©
2001
British
Fig.
4 Distribution
maps of Jack pine (a), white pine (b), white cedar (c), red pine (d), aspen (e), balsam fir (f ), paper birch (g), black spruce (h) and larch
Ecological
Society,
(i). Only those
plots with three and four individuals of the same species at the plot are shown.
Journal of Ecology,
89, 538 – 554
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Scale-dependent
patterns in southern
boreal forests
© 2001 British
Ecological Society,
Journal of Ecology,
89, 538 – 554
Table 7 Species frequency, dominance and aggregation patterns among the 9280 plots with four individuals. For all species, the
importance of per plot clustering is substantially higher than expected by random chance. Numerical-dominance is defined as an
occurrence of three or four trees of the same species at a plot (columns C and D). Aggregation is defined (column F) as the ratio
between the number of all plots where the species was dominant (column D) and the binomial expectation of three or four
individuals of the species being present at any plot (column E)
Species
Average
number of
individuals
per plot
where present
A
Frequency of
plots where
the species is
present
B
Plots where
present
C
All plots
D
Binomial
expectation
of 3 or 4
individuals of
the species per
plot for all
plots
E
Jack pine
Red pine
Aspen
Paper birch
Northern white cedar
White pine
Balsam fir
Larch
Spruce spp.
2.4
1.9
2.0
1.8
1.9
1.7
1.7
2.2
2.1
13.3%
6.0%
21.2%
34.0%
13.5%
15.0%
21.7%
28.2%
41.1%
45.1%
26.2%
31.7%
21.9%
26.7%
19.7%
18.3%
36.6%
33.2%
6.0%
1.6%
6.7%
7.4%
3.6%
3.0%
4.0%
10.3%
13.6%
0.21%
0.009%
0.53%
1.4%
0.11%
0.12%
0.31%
1.4%
3.7%
Percentage of plots
where numerically
dominant
( Table 7). Two factors influence the proportion of all
plots where a species was present (which we call frequency, column B), i.e. the total population of each
species (out of the 37 120-tree pool, which we call total
abundance) and their degree of plot aggregation. We
arbitrarily define plot scale numerical dominance as an
occurrence of three or four trees of the same species at
a plot (columns C and D). Aggregation is defined
(column F) as the ratio between the proportion of all
plots where the species was dominant (column D) and
the binomial expectation of the species being dominant
at any plot (column E). Ratios exceeding 1 indicate
clumped distributions whereas ratios less than 1 indicate over-dispersion.
If species were distributed randomly, each would be
expected to be numerically dominant in a small fraction of all plots but that fraction would vary tremendously between species. Spruce for example (due solely
to its high abundance) would be expected to dominate
plots approximately 400 times more often than red pine
(3.7% of all plots vs. 0.009% Table 7). However, all species tended to aggregate, i.e. to dominate plots far more
frequently than expected randomly (Table 7, column
F). Red pine tended to aggregate at the highest rate,
approximately 180 times more often than would occur
at random, with jack pine, white pine and northern
white cedar also occurring in aggregations far more
often (25 – 33 times) than expected. In contrast, spruce,
paper birch and larch occurred in aggregations less
than eight times as frequently as would be expected by
chance ( Table 7, column F). This strong tendency of all
species to aggregate results in an impressively high proportion (56%) of all plots being dominated by a single
species, given that a value of less than 8% would be
expected if all distributions were random.
Using plot-scale frequency of occurrence as a
measure of species distributions, the two most widely
Aggregation,
i.e. ratio of
observed numerical
dominance: the
level predicted
by the binomial
probability
F
28.6
177.8
12.6
5.2
32.7
25.0
12.9
7.5
3.7
distributed species were spruce and paper birch, which
were found in 41% and 34% of all plots, respectively.
The distribution of each species was primarily driven
by its total abundance but aggregation also is influential. Paper birch was thus more widely distributed
than larch (34% vs. 28% of all plots), despite a similar
total abundance (15%), because it tended to aggregate
less frequently (Table 7, column F). Red pine was the
least widely distributed of the major species (6% of all
plots).
The species also differed in their tendencies to be
numerically dominant in plots in which they occurred
(Table 7, column C), as well as in relation to all plots
(Table 7, column D). Jack pine had by far the highest
relative numerical dominance of plots where it was
found (45% of all plots with jack pine had three or four
jack pine individuals). This measure of numerical dominance is sensitive to both the aggregation tendency and
the overall abundance. Red pine was the most aggregated, but its population size was very low and thus
it dominated 26% of the sites where it occurred,
which is still a very high percentage considering its
overall abundance. In essence, its very low numbers
made it less likely to dominate sites where it occurred,
despite high aggregation. The opposite situation existed
for spruce; it was the most abundant and least aggregated, and yet still numerically dominated 33% of all
plots where it was present. Birch, white pine and fir
numerically dominated roughly one-fifth of the sites
where they were present, due to a combination of intermediate frequency and aggregation rates.
Not surprisingly, spruce, as the most abundant species,
numerically dominated 13.6% of all plots (Table 7, column D). The greater aggregation of larch than birch led
to a wider distribution of the latter (see above) but also
resulted in larch numerically dominating 10.3% of all
plots compared with 7.4% for birch. Red pine numerically
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S.K. Friedman,
P.B. Reich &
L.E. Frelich
Fig. 5 Intraspecific spatial autocorrelation correlograms, pairing all monospecific four-tree plots of each species. Individual
species data are from a subset of the 9280 four-tree plots that contain location information for a single species.
dominated 1.6% of all plots, with the remaining species numerically dominating between 3% and 7% of all
plots.
-  
© 2001 British
Ecological Society,
Journal of Ecology,
89, 538 – 554
Positive spatial association was consistently found for
all conspecifics across the landscape when measured at
either the intermediate (1–10 km) or regional landscape (5–50 km) scales (Fig. 5). This pattern was
evident among the presettlement forest species until
extremely large distances, often beyond 20 km, were
encountered. The strongest positive conspecific spatial
patterns were found for aspen, larch and jack pine at
both scales (Fig. 5). Spruce, balsam fir and white pine
also consistently exhibited significant positive intraspecific spatial associations on both scales. Red pine,
paper birch and northern white cedar were found to
have weaker positive intraspecific spatial association,
although these were often significant.
Examination of interspecific patterns (Fig. 6) revealed
substantially less positive spatial autocorrelation and
patterns of species spatial autocorrelations were not
consistent across all distance classes. Most interspecific pairings were found to exhibit neutral or slightly
negative spatial patterns across all distance classes.
Three species pairs, however, have strikingly different
patterns. Spruce–larch (Fig. 6a) are shown initially to
be neutrally autocorrelated to 4000 m, yet beyond this
distance (out to 25 km) were positively associated at
both scales (Fig. 6b). Aspen–birch and aspen–balsam
fir exhibited negative associations at distance classes
greater than 2000 m (Fig. 6a and Fig. 6b).
Species showing similar associations for soil texture
and permeability site conditions (Fig. 3), such as jack
pine and red pine on coarse soils, or aspen, paper birch,
white pine and northern white cedar on intermediately
moist soils, were found to have negative or neutral interspecific spatial autocorrelation. These results indicated
that although species might have similar associations
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Scale-dependent
patterns in southern
boreal forests
Fig. 6 Interspecific spatial autocorrelation correlograms, pairing monospecific four-tree plots of two species. Individual species
data are from a subset of the 9280 four-tree plots that contain location information for a single species.
© 2001 British
Ecological Society,
Journal of Ecology,
89, 538 – 554
for environmental factors, different factors may be
involved in the regulation of spatial distribution patterns
within similar environment settings. An alternative interpretation is that the mono-dominant aggregations are
themselves randomly mixed. Thus, the expected interspecific spatial association suggested strictly by common
preferences for similar site conditions (Fig. 3) was not a
reliable predictor of spatial association among the species.
Autocorrelation patterns between high aggregation
plots (plots with four conspecifics) and mixed twospecies plots were conducted to examine spatial dependency patterns across transition zones where both species
occurred (Fig. 7). In all but a few cases, species combinations were found to have neutral association patterns
at spatial lags from 1 to 10 km. Departure from this
pattern is seen within a few species-pairs, for example
spruce–balsam fir and jack pine–aspen (Fig. 7b,d), which
exhibit positive association for one to three consecutive
distance classes. Patterns were also asymmetric, in the
sense that traversing from high aggregation plots of
white pine to mixed species plots of white pine and
paper birch was not the same pattern as going from the
mixed species plots to high aggregation plots of birch.
The paucity of positive autocorrelation associations
indicates that smooth transitions between two species
were not common in the presettlement forest. Rather
areas with high concentrations of a single species were
found to be independent of mixed-species zones.
As a result of differences among species in overall
frequency, plot-scale aggregation and landscape-scale
spatial dependence, we can classify the nine species into
three rough groups: (a) species which aggregated markedly
and had patchy landscape distribution patterns ( jack,
red and white pine, northern white cedar); (b) species
with intermediate levels of plot aggregation coupled
with intermediate landscape distributions (paper birch,
aspen and balsam fir); and (c) species that had the
lowest (but still high) degree of aggregation and broad
JEC578.fm Page 550 Saturday, July 14, 2001 1:20 PM
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S.K. Friedman,
P.B. Reich &
L.E. Frelich
Fig. 7 Mixed species spatial autocorrelation correlograms, pairing monospecific four-tree plots with plots containing two species,
each occurring twice.
unconstrained distribution patterns reflecting high
abundance (spruce and larch).
Discussion
© 2001 British
Ecological Society,
Journal of Ecology,
89, 538 – 554
Much of the natural history and ecology of the southern
boreal forest of north-eastern Minnesota has been understood for some time (Ohmann & Ream 1971; Heinselman
1973, 1981, 1996; Ohmann & Grigal 1979), but this study
has used a hierarchical approach (Allen & Starr 1982;
Urban et al. 1987; Levin 1992) designed to improve this
knowledge base.
The origin of the complex, spatially structured
patterns is based on the regional geology, soils, disturbance regimes, species life histories and regeneration
strategies. However, the results indicate that abiotic
environmental heterogeneity in this low-diversity forest was sufficient to influence species distribution
patterns at fine and landscape scales. Significant spatial
relationships occurred at the finest plot-scale where
species aggregation was substantial and these patterns
potentially influenced the landscape-level spatial
patterns through regeneration processes that extended
across relatively large spatial domains.
  
Although the presettlement forest had relatively few
tree species, numerous compositional, abundance and
spatial patterns existed that collectively made this forest
a complex system. The observed distribution patterns
of the species were most likely influenced by spatial heterogeneity in biogeophysical properties and disturbance
histories of the physiographic zones.
Understanding why white pine and red pine were less
abundant yet much larger than the other species ( Table 3)
requires a comparison of species growth characteristics
and adaptations to fire. Both pines can live longer than
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Scale-dependent
patterns in southern
boreal forests
most of the sympatric upland species by almost 100 years,
and their maximum life expectancy, approximately
300 – 350 years, approaches the longest fire return interval in this region (Heinselman 1973; Elliott-Fisk 1988).
Secondly, morphological characteristics of both white
and red pine as mature trees are conducive to surviving
fire of moderate to low intensity. These features include
thick fire-resistant bark that provides protection to
the cambium, limited amounts of volatile resins, and
self-pruning (Fowells 1965). Heinselman (1973) used
tree rings and fire scars to construct stand origin maps
of the Boundary Waters Canoe Area Wilderness. In
many places, multiple fire scars corresponded to different fires, indicating that trees of these species survived
multiple fire events (Heinselman 1973). Individuals
of these species may also have inhabited sites that
were less likely to burn or were likely to burn less
intensely during fires than several of the other species
(Carlson, unpublished data).
-  
  
© 2001 British
Ecological Society,
Journal of Ecology,
89, 538 – 554
Environmental conditions favouring plant species coexistence include widespread seed dispersal, frequent small
scale disturbance events, wide ecological niche and
unrestricted access to suitable microsites (Schmida &
Ellner 1984; Green 1989; McLaughlin & Roughgarden
1993; Lavorel et al. 1994). Some characteristics of the
study area appear to favour co-occurrence, yet the degree
to which it developed at the four-tree plot scale was low.
Although the species have marked preferences, most
can occupy any of the available environmental conditions present at the plot-scale. Since all of the requisite
conditions for species co-occurrence exist, why was plotscale aggregation among conspecifics more common
that expected under random conditions?
Plot-scale species aggregation patterns suggest strong
fine-scale clustering of favourable regeneration safe
sites for every species, coincident with patchy seed availability. All of the upland species are prolific seed producers. Both vegetative and sexual regeneration are
well represented in this suite of species; however, it is
worth noting that the five most abundant species are
capable of vegetative regeneration while the less abundant species are not. Effective seed rain dispersal distances are generally short, conforming to the negative
exponential function (Lavorel et al. 1994; Cornett et al.
1997) that promotes conspecific aggregation. Additionally many of the species vegetatively regenerate prior
to fires (aspen, balsam fir, larch, black spruce, northern
white cedar, paper birch), and following fires (aspen
and paper birch), even if the above ground portion of
the tree is killed (Heinselman 1981; Elliott-Fisk 1988).
Below-ground reproductive tissue that survives lethal
temperatures may represent an analogue of advanced
regeneration normally associated with gap located
seedlings and saplings. Regeneration strategies therefore suggest that subsequent generations should occur
in close proximity to mature trees. When regeneration
occurs prior to fires, whether seed-based or vegetatively,
new recruits would be likely to occur within spatially
autocorrelated distances of the parent trees, generating
aggregated spatial configurations. Following fires, similar
tendencies could increase the likelihood of aggregated
patterns as well, although different species are likely to
regenerate.
Jack pine and black spruce provide contrasting
examples of regeneration strategies that may promote
aggregating tendencies. Jack pine is reproductively
mature at 20–30 years (Benzie 1977) and in this region
the fire return interval in jack pine stands was about
50 years (Heinselman 1973). Seed rain from serotinous
cones begins quickly following fires, providing a competitive advantage over seed of other species having
delayed dispersal strategies. The marked tendency for
jack pine to occupy well-drained, coarse-textured soils
and to reproduce from mature individuals following
crown fire may explain its very high aggregation rate.
While jack pine can not reproduce vegetatively, black
spruce is capable of regenerating by layering prior to
fires, is relatively shade tolerant, and thus may regenerate from seeds between disturbance events, and also has
semiserotinous cones providing reproductive opportunity in varying habitats. These characteristics may
help explain its much lower, but still positive, tendency
to aggregate than jack pine.
Aggregation by balsam fir, northern white cedar and
white pine is most likely to have developed due to
slightly different processes. Following fire, these species
are not capable of vegetative regeneration from roots or
stumps. Burn patterns, natural fire breaks and species
life spans could collectively allow small old growth
patches of these species to form in refugia in various
locations on the landscape. These species are also
moderately to very shade-tolerant as seedlings, and cast
deep shade as individual mature trees or within patches
(Cornett et al. 1998; Machado 1999). Thus, these species
are more likely to regenerate beneath or near themselves under long return intervals than the other species.
Moreover, in those locations that were by chance
missed by fire, species may have had greater opportunities
to regenerate and influence the local spatial patterns of
other species. All three species sometimes occurred in
habitats that were less prone to high-intensity fires.
  -

Positive conspecific and neutral interspecific spatial
patterns characterized the landscape-scale configuration of the presettlement forest. Temporal and spatial
autocorrelation of disturbance patches overlaid on the
physiographic features of a landscape can theoretically
explain aggregations (Moloney & Levin 1996) and may
well have been at work in this case. Landscape heterogeneity, simplistically divided between suitable and
unsuitable sites, can regulate successful vegetation
JEC578.fm Page 552 Saturday, July 14, 2001 1:20 PM
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S.K. Friedman,
P.B. Reich &
L.E. Frelich
establishment following disturbance. Aggregation of
individual species throughout the landscape increases
when the suitable sites occur in a contagious spatial
distribution. In effect, once established in suitable sites,
feedback processes may influence successive establishment, leading to greater aggregating and potential
enhancements to sites that make them more suitable.
Alternatively, when suitable sites are aggregated in only
a small fraction of the landscape, conspecific aggregating may decrease if the effective colonization rate is
reduced (Andren 1994; Ives et al. 1998). The proportion
of seedling safe-sites varies temporally because disturbance processes restructure (both temporally and
spatially) the proportion of these sites on the landscape.
There was an underlying mosaic composed of cooccurring species spatially distributed with neutral spatial association patterns within the landscape matrix, as
indicated by the autocorrelograms (Figs 5, 6 and 7).
The matrix was shown to be dominated by species that
maintained strong conspecific positive spatial autocorrelation patterns over large distances (Fig. 5). Thus, we
view spatial autocorrelation as a parallel and indirect
measurement of habitat variation when assessed at the
intermediate and landscape scale, rather than a method
of patch delineation (McGarigal & Marks 1995).
All nine important species tended to occur in plots
with conspecific individuals far more often than would
occur at random, and more than half of all plots were
dominated by a single species. Moreover, the likelihood
of high concentrations of different species at fine scales
was low. Over 1–10 km and larger scales, positive spatial
autocorrelation by a given species was common. Why
would this have occurred? As suggested earlier, the
presettlement forest could be considered substantially
more homogeneous at subregional scales than at either
fine or regional scales. At the largest scale considered in
this study, the Arrowhead Region is made up of zones
that differ in geology and hydrology, which produces a
regional patchy environment. Much of the Arrowhead
Region also includes a very high degree of fine-scale
heterogeneity, such as is due to close juxtaposition of
wetland and upland microsites. Perhaps at subregional
scales, such as physiographic provinces or smaller 10–
100 km2 zones, the fine-scale patchwork repeats itself
within a unit that is somewhat more homogeneous in
terms of geology, topography and soils than the entire
region. Such a zone would then tend to have a high
abundance of a given species. The landscape mosaic in
the presettlement forest was composed of numerous
zones where each species was capable of dominating
large but highly discontinuous expanses of habitat. Interspersed within these zones were areas dominated by
other species, but at densities lower than necessary to
form positive autocorrelated species assemblages.
© 2001 British
Ecological Society,
Journal of Ecology,
89, 538 – 554
Conclusions
It is apparent that this low diversity forest included
complex spatially structured properties. We were able to
define numerous examples of fine-scale and landscapescale composition and spatial patterns, which were likely
to be related to patchiness of environmental factors,
disturbance (fire), and their relation to species regeneration strategies. Fire was spatially and temporally
heterogeneous and controlled the vegetation dynamics
in this landscape (Heinselman 1973), probably influencing species composition, abundance patterns, regeneration strategies, and thus the spatial structure across
each of these scales.
At the plot scale, each species responds to local disturbances bounded by tolerances to disturbance form
and intensity and regeneration potential. These processes link plot and landscape spatial scales by coupling
regeneration strategies and dispersal mechanisms of
the mature trees to the future generations. Several authors
have recently shown that plot-scale spatial aggregation
patterns can help explain landscape patterns (Green 1989;
Andren 1994; Moloney & Levin 1996; Ives et al. 1998).
White pine was the most highly sought after species
present in the presettlement forest. The benefits derived
from this species have left a legacy for the economic
development of Minnesota and throughout the Great
Lakes region. However, this development had an associated cost, a substantially reduced population of the
species on the landscape (White Pine Regeneration
Strategies Work Group 1996). Currently public concern
and agency leadership are leading the drive to re-establish
white pine to its former status in Minnesota forests.
White pine, in the area investigated in this report, was a
significant component of the forest community because
of its size and dominant growth form rather than its
local abundance, which was patchily distributed. This
historical framework should not be ignored in planning and managing for white pine in the future.
More generally, the main issues raised in this study
are relevant to the development of forest management
plans examined from local and regional perspectives.
Effective conservation of forest biodiversity may be
enhanced by consideration of the spatial structural
characteristics of the presettlement forests, in addition
to composition. Adherence to management policies
targeting single species that disregard landscape-scale
compositional, structural and functional diversity may
fail to achieve long-term conservation objectives. Since
spatial patterns are scale-dependent, planning should
follow multiscale approaches addressing local and
regional landscape-scale issues.
Landscape structure is dependent upon local, regional
and global scale processes. Hierarchy theory that
attempts to link scale, process and ecological response,
provides a conceptual basis for the design and analysis
of complex systems (O’Neill et al. 1986; Levin 1992). However, critically important multiple scale investigations
remain rare due to the numerous difficulties associated
with defining and explaining ecological processes and
properties that develop across scale boundaries. For
example, regional disturbance patterns simultaneously
have an impact on forests at several scales (individual
JEC578.fm Page 553 Saturday, July 14, 2001 1:20 PM
553
Scale-dependent
patterns in southern
boreal forests
trees, patches and landscape-wide systems), resulting
in small-scale gap dynamics, homogeneous but patchy
forest age structure, and variation in the local to regional
biodiversity patterns. Furthermore, understanding how
organisms respond to scale-independent processes is
limited initially by the spatial and temporal scale from
which we observe processes and secondly because our
interpretations and predictions at best are extrapolations often implying erroneous linear relationships.
Conservation of dwindling natural resources, species
population dynamics, community composition and
structure and nutrient cycling are all potentially affected
by scale-dependent ecological processes that develop
at scales other than where we can observe them.
New research focusing on disturbance ecology and
landscape-scale spatial-temporal patterns is required
to improve our understanding of this complex issue.
Management of species, landscapes and ecosystems
will benefit from the new information.
Acknowledgements
We wish to thank the Wilderness Research Foundation
of Ely, Minnesota, the National Science Foundation,
the National Council for Air and Stream Improvement, and the McIntire-Stennis programs for financial
assistance. We also appreciate the editorial assistance
provided by David Gibson, Lindsay Haddon and several anonymous reviewers.
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