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ORIGINAL PAPER
Reading the landscape: temporal and spatial changesin a patterned peatland
M. K. Nungesser
Received: 29 June 2009 / Accepted: 20 August 2011 / Published online: 7 October 2011
� Springer Science+Business Media B.V. 2011
Abstract The Everglades of south Florida is a
patterned peatland that has undergone major hydro-
logic modification over the last century, including both
drainage and impoundment. The Everglades ridge and
slough patterns were originally characterized by
regularly spaced elevated ridges and tree islands
oriented parallel to water flow through interconnected
sloughs. Many areas of the remaining Everglades have
lost this patterning over time. Historical aerial pho-
tography for the years 1940, 1953, 1972, 1984, and
2004 provides source data to measure these changes
over six decades. Maps were created by digitizing the
ridges, tree islands, and sloughs in fifteen 24 km2
study plots located in the remaining Everglades, and
metrics were developed to quantify the extent and
types of changes in the patterns. Pattern metrics of
length/width ratios, number of ridges, and perimeter/
area ratios were used to define the details and
trajectories of pattern changes in the study plots from
1940 through 2004. These metrics characterized
elongation, smoothness, and abundance of ridges and
tree islands. Hierarchical agglomerative cluster anal-
ysis was used to categorize these 75 maps (15 plots by
5 years) into five categories based on a suite of metrics
of pattern quality. Nonmetric multidimensional scal-
ing, an ordination technique, confirmed that these
categories were distinct with the primary axis distin-
guished primarily by the abundance of elongated
ridges in each study plot. Strong patterns like those
described historically were characterized by numer-
ous, long ridges while degraded patterns contained
few large, irregularly shaped patches. Pattern degra-
dation usually occurred with ridges fusing into fewer,
less linear patches of emergent vegetation. Patterning
improved in some plots, probably through wetter
conditions facilitating expression of the underlying
microtopography. Trajectories showing responses of
individual study plots over the six decades indicated
that ridge and slough patterns can degrade or improve
over time scales of a decade or less. Changes in ridge
and slough patterns indicate that (1) patterns can be
lost quickly following severe peat dryout, yet (2)
patterns appear resilient at least over multi-decadal
time periods; (3) patterns can be maintained and
possibly strengthened with deeper water depths, and
(4) the sub-decadal response time of pattern changes
visible in aerial imagery is highly useful for change
detection within the landscape. This analysis suggests
that restoration of some aspects of these unique
peatland patterns may be possible within relatively
short planning time frames. Use of aerial photography
in future Everglades restoration efforts can facilitate
restoration and adaptive management by documenting
sub-decadal pattern changes in response to altered
hydrology and water management.
M. K. Nungesser (&)
Applied Sciences Bureau, Water Resources Division,
South Florida Water Management District,
West Palm Beach, FL, USA
e-mail: [email protected]
123
Wetlands Ecol Manage (2011) 19:475–493
DOI 10.1007/s11273-011-9229-z
Keywords Everglades � Patterned peatland �Landscape ecology � Hierarchical clustering �Wetland restoration � Historical analysis � Nonmetric
multidimensional scaling � Restoration ecology
Introduction
The Everglades wetlands of south Florida (USA)
(Fig. 1) are unique in the Americas and the world
partly because they are a tropical patterned peatland.
Patterns in natural environments consist of a few
primary elements that are repeated regularly and non-
randomly across the landscape, occurring in both
mineral soils and in peat soils. Patterned peatlands are
common in boreal regions (Heinselman 1963, 1965,
1970; Moore and Bellamy 1974; National Wetlands
Working Group 1988; Rydin and Jeglum 2006), but
rare elsewhere. In boreal peatlands, linear ridges lie
perpendicular to water flow, whereas linear ridges and
tree islands in the Everglades are aligned parallel to
water flow. This patterned south Florida landscape
was first described as ‘‘ridge and slough’’ in 1915
(Baldwin and Hawker 1915) and extended throughout
most of the landscape south of Lake Okeechobee
(Harshberger 1914; Baldwin and Hawker 1915; SCT
2003). Figure 2 represents the original pre-drainage
landscapes of southern Florida showing the original
extent of the ridge and slough patterning in this
simulated satellite image.
Peatlands are wetlands that develop in an environ-
ment where decomposition rates are slower than
primary production, resulting in a gradual net accu-
mulation of organic soils over time (Ingram 1983;
Frenzel 1983; Moore and Bellamy 1974; Crum 1992).
The Ridge and Slough landscape consisted of parallel,
elongated ridges of sawgrass (Cladium jamaicensis)
peat, elevated above open water sloughs inhabited by
floating water lilies (Nymphaea odorata) and sub-
merged bladderwort (Utricularia spp.). Tree islands
were a third component of the landscape, typically
long, teardrop-shaped forms, forested at the more
elevated upstream ends and tapering to shrub and
sawgrass communities along their downstream tails.
These three landscape elements were distinguished
both by vegetation and by slight elevation differences
of a few decimeters (Wright 1912; Baldwin and
Hawker 1915). Historically, water flowed into the
Everglades annually as overflow from Lake Okeecho-
bee in the wet season (May through November), then
drained gradually during the dry season (Leach et al.
1971). The Everglades drained naturally southwards
to Florida Bay or Biscayne Bay (Fig. 2).
Details of the pre-drainage Everglades are derived
from both original observations and from paleoeco-
logical analyses. Early explorers and surveyors indi-
cated that the area defined as Ridge and Slough in
Fig. 2 was characterized by ridges and sloughs
extending throughout the region, with sloughs deep
enough to allow them to paddle boats throughout the
region (Ives 1856; Dix and MacGonigle 1905; Baldwin
and Hawker 1915; McVoy et al. 2011). They did
not observe notable differences in landscape patterns
that existed even as early as the 1940s. These observers
also indicated that the landscape linearity could be
readily discerned from ground level (Smith 1848;
Ives 1856; Dix and MacGonigle 1905; Harshberger
1914; Baldwin and Hawker 1915; SCT 2003; McVoy
et al. 2011). It is assumed here that this pre-drainage
(pre-1880) Everglades landscape was configured rel-
atively uniformly across its extent, consistent with
these records.
Palynological analyses of peat cores indicate that
sloughs date back over 1000 years ago and the ridges
to four centuries ago (Bernhardt and Willard 2009).
They also indicate that responses to variations in
climate were similar in all sub-regions of the Ever-
glades (Willard et al. 2001). Hydrological alterations
initiated in the late 1880s and continuing since have
interrupted the natural seasonality, depths, and flow of
fresh water, simultaneously disrupting this regionally
synchronized response to climate. The Ridge and
Slough landscape variously has been drained, dried,
burned, compartmentalized, flooded, and enriched by
nutrients before it was studied systematically by
scientists (Alexander and Crook 1973; Doren et al.
1997; Richardson et al. 1999; Stephens and Johnson
1951; SCT 2003; McVoy et al. 2011), preventing
detailed quantitative analysis of the pre-drainage
system.
To understand the history and impacts of hydro-
logic change on the Ridge and Slough landscape, it is
necessary to present a brief summary of water
management in the Everglades. Beginning in the
1880s, the Everglades were heavily modified for
drainage. Canals were constructed to reduce water
levels in Lake Okeechobee (Leach et al. 1971;
476 Wetlands Ecol Manage (2011) 19:475–493
123
Alexander and Crook 1973) and to drain the region
south of Lake Okeechobee. Three canals, including
the Miami Canal, bisected the Ridge and Slough
landscape (Fig. 1). These canals lowered water levels
in the marsh with effects on the peat extending
outward far from the canal (Stephens and Johnson
1951; Leach et al. 1971). The previous annual
overflow into the peatlands from Lake Okeechobee
ceased after construction of a flood protection levee
around the south end of the Lake in 1928. Farther to
the south, a highway connecting Florida’s east and
west coasts (Tamiami Trail) was constructed,
obstructing southward flow (Fig. 1). Ecosystem dam-
age from peat drainage was compounded by droughts
and extensive muck fires throughout the peatlands
(Alexander and Crook 1973).
Increasing demand for water storage for the
region’s growing human population of the region
coupled with the desire to reduce muck fires subse-
quently led to compartmentalization of the wetlands in
the 1960s, creating shallow reservoirs (water conser-
vation areas) out of the formerly continuous wetlands
(Fig. 1). The two southern Water Conservation Areas
(WCAs), WCA-3A and WCA-3B, were completed by
Fig. 1 South Florida study
area identifying Water
Conservation Areas
(WCAs) 1, 2A, 2B, 3A, 3B
(managed by South Florida
Water Management
District), and Everglades
National Park and Big
Cypress Preserve (federal
lands). Major canals, roads,
and levees are delineated
Wetlands Ecol Manage (2011) 19:475–493 477
123
mid-1967 (Leach et al. 1971) and are the focus of this
analysis. A new east–west highway (I-75) was
constructed through northern WCA-3A, and the
Miami Canal was degraded at regular intervals to
allow water movement southward. Other than rainfall,
inflow to these compartments has been restricted to
flow through the northern canals and water control
structures, the breaks in the Miami Canal, and some
inflow from the west and from newly constructed
stormwater treatment areas to the north. Because these
compartments retained water, southward landscape
slope and flow impedance produced dryer conditions
in the northern areas of the compartments and deeper
water upstream of the impoundments at the southern
ends (Sklar et al. 2002a; SCT 2003; McVoy et al.
2011). These altered conditions continue to impact this
patterned landscape while water management prac-
tices have changed over time.
Consistent with the early descriptions of the Ever-
glades, photos dating from the early 1900s show the
marked linearity of the landscape (SCT 2003; McVoy
et al. 2011). The earliest set of aerial photos covering
most of the Everglades was dated 1940 (Foster et al.
2004). Even after a half century of drainage, these
images show a landscape with linear patterns extend-
ing throughout most of southern and southeastern
Florida, suggesting resilience of the patterns. Visual
comparisons of 1940 and 2004 imagery reveal that
many formerly patterned areas are now dominated
by shallow wetlands of thick emergent species and
discontinuous patches of water. These changes prompt
the following questions: (1) How similar were the
patterns across WCA-3 in 1940? (2) When and how
did the Ridge and Slough patterns change between
1940 and today? (3) Did patterns change in similar
ways over the six decades? (4) Were pattern changes
synchronous? These questions can be addressed by an
analysis of pattern changes from historic aerial pho-
tographs and by relating these changes to preceding
hydrologic and environmental conditions.
Methods
Study sites and pattern data
Water Conservation Area 3, the focus of this analysis,
is a primary target of flow and ecological restoration in
the Everglades (C&SF 1999; USACE and SFWMD
2002) including Everglades National Park. The
dimensions of WCA-3 are approximately 63.6 km
long by 38 km wide.
Qualitative aspects of the patterning can be readily
discerned when they are strong, as in Fig. 3, or when
they have degraded severely. Patterns are considered
strong, as shown in the maps in Fig. 3a, if ridges are
elongated, numerous, aligned parallel to each other,
regularly spaced horizontally and vertically, separated
by linear sloughs, and defined by a distinct ridge-
slough boundary. Sloughs in strongly patterned
areas are linear and interconnected, of similar widths,
with few impediments to flow other than occasional
Fig. 2 The pre-drainage landscape of south Florida shown in
the center with the linear ridges and tree islands visible (McVoy
et al. 2011). The Ridge and Slough landscape extended
throughout most of the area south and southeast of Lake
Okeechobee to Florida Bay
478 Wetlands Ecol Manage (2011) 19:475–493
123
horizontal bridges between ridges. In contrast,
degraded patterns in this system, shown in Fig. 3b,
are defined by undifferentiated stands of emergent
vegetation generally covering the entire study plot,
with few open water patches.
Simple visual inspection of photos may not detect
early changes because of the complexity of the
patterns themselves and because of the relatively
subjective nature of pattern quality assessment. Quan-
titative metrics, on the other hand, can discern changes
that may identify improvement or degradation of
patterns over a time frame useful for feedback to
restoration practices. Therefore, using a time-series of
pattern maps, multivariate assessment methods were
developed to detect temporal and spatial changes in
the Everglades patterned landscapes.
It is important to note that pattern strength does not
translate directly to the health of the ecosystem;
because of decades of drainage and altered hydrology,
the remaining patterns even in the 1940s do not
represent the pristine Everglades. Instead, nearly all of
the Ridge and Slough landscape was affected by
drainage by 1940, as described in the introduction.
Within the boundary of WCA-3, three spatially
distinct flow paths were identified based upon esti-
mated elevations and landscape orientation (Sklar
et al. 2006). These transects were labeled as G, N, and I
from west to east in WCA-3, respectively (Fig. 4).
Fifteen study plots were superimposed along these
paths oriented along the landscape directionality from
north to south through WCA-3 (Rutchey et al. 2009).
The plots were laid out along each flow path orientated
parallel to the local landscape directionality with
adjustments made largely to avoid major barriers, such
as levees and large canals. Each plot was 4 km by
6 km, sized to fit multiple ridges vertically and
horizontally. Four plots were located along transect
G, six along N, and five along I. These rectangular
plots each provided focused but spatially distributed
sub-regions of the local landscape. By tracking the
same location across time, these plots were used to
determine trajectories of past changes and can be
Fig. 3 Comparisons of
good and poor patterning in
the study plots based upon
the maps generated from
aerial photographs. Darkareas are open water, lightareas are ridges/tree islands
and emergent vegetation
patches. The top images
(a) represent strongly
patterned study plots
(G1-1940, N3-1940, and
G3-1972) and the lower
(b) represent fully degraded
patterns (I4-1984, I1-2004,
and N1-1984)
Wetlands Ecol Manage (2011) 19:475–493 479
123
incorporated into monitoring plans for future man-
agement and restoration activities.
Patterns were digitized from historic aerial photog-
raphy taken at intervals representing most decades
from 1940 through 2004. The aerial photography used
in this analysis was flown in the dry seasons (January
through April) of 1940, 1953, 1972, 1984, and 2004,
which experience clearer conditions and lower humid-
ity than at other times of the year. The early imagery
was black and white (1940 and 1953), while
subsequent images were color infrared or natural
color. The 1940 images were U.S. Department of
Agriculture Soil Survey aerial photographs at a
resolution of 1:40,000, scanned to a 1-m resolution
available in jpeg format (Foster et al. 2004). Images
from the early 1950s were photographed at 1:20,000
scale and digitally scanned at a 0.3 m resolution. The
next available set of images was from 1972/1973,
available as tiff files digitally scanned at a 0.61 m
resolution from original 1:80,000 scale images. The
1984 images were scanned digitally at 1.5 m resolu-
tion from the 1984 National High Altitude
Photography five-foot resolution color infrared tiff
files. The 2004 images were Digital Ortho Quarter
Quads (DOQQs) one-meter resolution color infrared
jpeg files. All images were spatially georeferenced to
the 2004 DOQQs (T. Schall, SFWMD, pers. comm.).
A bimodal map of ridges/tree islands and sloughs
was created for each plot for the years 1940, 1953,
1972, 1984, and 2004. Automated methods of
classification could not be used because of the
highly variable quality of the original photographs.
Therefore, ridges and tree islands appearing in each
study plot were digitized manually at a 1:5000
resolution to create a bimodal map of ridges (tree
islands are considered a special case of ridges) and
sloughs by delineating the edges of emergent
vegetation; water extent is not directly delineated
in this process. Ridges and other vegetation patches
longer than 300 m comprised the longest 25% of the
ridges in the 1940 photos and were used for the
analysis because these landscape elements visually
define the linearity of the patterns (Sklar et al.
2004). Paleoecological studies (Bernhardt and Wil-
lard 2009; Sklar et al. 2008) have revealed that these
large structures represent long-enduring structures in
the landscape. The resulting maps (5 years for each
of the 15 plots) provided a uniform data set of 75
plots for analysis. The naming convention for each
plot is the plot name with the year of the map (e.g.,
N2-1972). A copy of the full set of maps is
available from the author.
Shape and directional metrics (length, width, area,
perimeter, and orientation) were recorded for each
ridge or tree island. Length was defined as the
maximum distance between points in the ridges;
orientation was calculated from the maximum length
arc. Individual ridge values included area, perimeter,
mean width, and ratios of length/width (L/W) and
perimeter/area (P/A). The L/W ratio represents elon-
gation of the plot’s elements (larger numbers indicate
longer, thinner shapes), while the P/A ratio generally
represents smoothness of the elongated elements
(higher values indicate smoother shapes).
Plot-level summaries included mean ridge length
(L), width (W), perimeter (P), and area (A), variability
of the orientation, and the total number of ridges (n).
Plot-level means of the L/W and P/A ratios were
created (LW and PA, respectively). Two additional
metrics were calculated at the plot level: the LeWN
index (Length–width-number) and the PAN index
Fig. 4 Locations of study plots in WCA-3. Flow transects are
labeled G, N, and I from west to east, and numbered from north
to south (G 1–4, N 1–6, and I 1–5, respectively). Each plot is
4 km by 6 km in size
480 Wetlands Ecol Manage (2011) 19:475–493
123
(Perimeter-area-number) (Sklar et al. 2004). LeWN is
defined as:
LeWN ¼Xn
i¼1
L=W ð1Þ
where n is the number of polygons[300 m long and
L/W is the length/width ratio of each ridge in the plot.
PAN is defined as:
PAN ¼Xn
i¼1
P=A ð2Þ
where n is the number of polygons[300 m long and P/A
is the perimeter/area ratio of each ridge in the plot. The
LeWN and PAN indices account for the abundance of
these shapes in the plots. For example, a study plot could
have one long smooth ridge, producing high L/W and
P/A values, but lack the patterning resulting from the
frequency and regularity of these ridges in a patterned
peatland. LeWN and PAN correct for that mean by
accounting for the abundance of the ridges in a plot. Plots
with numerous elongated or smooth ridges then have
higher LeWN and PAN indices than those with fewer
ridges of similar proportions or those lacking ridges.
Features that extend beyond the edges of the plot
boundaries were identified as ‘‘edge’’ features to
contribute to the total cover, but were not used to
calculate other plot values with a few exceptions.
Because some plots contained only edge elements,
it was necessary to retain at least one feature for the
analysis; the largest vegetation patches (covering
greater than 5% of the plot area, 1.2 9 106 m2) were
retained and used for all analyses. Of the 75 plots,
22 contained only one or two features under this
constraint and 53 contained three or more features.
Because orientation did not add useful information,
these values were removed from further analysis. Plot
summary data determined previously to relate to
pattern quality throughout the Everglades (Sklar et al.
2004) (Table 1) were used for subsequent analyses.
Analytical methods
Basic descriptive and multivariate analyses were
conducted for five variables: n, LW, PA, LeWN, and
PAN (representing plot level statistics). Hierarchical
agglomerative cluster analysis was used to define
relatively homogeneous groups across time and space
using the pattern metrics. Clustering was performed
with PCOrd (McCune and Mefford 2006) using the
Sorensen (Bray-Curtis) distance measure with group
average linkage.
Ordination helped identify the important variables
that define relationships among the shape metrics.
Nonmetric multidimensional scaling (NMDS) (Kruskal
1964) was selected for ordination because it uses rank
distances, does not require linear relationships between
variables, and is generally effective for ecological data
(McCune and Grace 2002). The original data and the
cluster groups were used as the primary and secondary
matrices, respectively. A Monte Carlo test compared the
final stress produced by the real versus randomized data.
Post-analysis tests (analysis of variance, means
comparisons, and correlations) were conducted to
determine the variables that dominated the ordination
axis, which incorporates information from all the
variables. Two graphics illustrate the temporal and
spatial changes in pattern quality in the study plots.
Local changes in pattern over the study period (1940
through 2004) were displayed as linear plots of category
by year. Spatial distributions of pattern quality of the
study plots in each year across the landscape were
mapped using Inverse Distance Weighted interpolation.
Results
The plot level metrics in Table 1 indicate that study plots
varied widely by year and by plot, often by several
orders of magnitude: the number of ridges in a plot
ranged from one to 126; mean perimeter/area ratios (PA)
varied from 0.0072 to 0.3624; mean length/width ratios
(LW) were 2.327 to 11.584; LeWN ranged between
2.327 and 695.0 and PAN values from 0.0072 to 26.46.
The strongest correlations (Table 2) were between
the number of ridges (n) and LeWN (r = 0.9502) and
PA with PAN (r = 0.9461). The number of ridges
(n) was only moderately correlated with PAN (r =
0.5025), even though both PAN and LeWN include n
as a component. LW and LeWN were moderately
correlated (r = 0.4880) as were PAN with LeWN
(r = 0.5448). All other correlations were less than 0.5.
Relationships with the ordination axis are discussed
below with the NMDS results.
Because the Ridge and Slough patterns are highly
regular and anisotropic, high correlation between most
variables was expected. Strong patterns are long,
linear, and relatively smooth, while weak patterns are
Wetlands Ecol Manage (2011) 19:475–493 481
123
Ta
ble
1P
lot
sum
mar
yd
ata
for
stu
dy
plo
tsin
Wat
erC
on
serv
atio
nA
rea
3
Stu
dy
plo
t,
yea
r
nL
WP
AL
eWN
PA
NS
tud
yp
lot,
yea
r
nL
WP
AL
eWN
PA
NS
tud
yp
lot,
yea
r
nL
WP
AL
eWN
PA
N
G1
-19
40
75
5.6
70
.03
92
42
5.0
2.9
39
12
-19
40
38
.28
0.0
24
32
4.8
0.0
73
N2
-19
40
25
7.0
10
.02
57
17
5.4
0.6
43
G1
-19
53
56
5.6
30
.03
10
31
5.1
1.7
37
12
-19
53
26
.02
0.0
15
41
2.0
0.0
31
N2
-19
53
22
.95
0.0
16
25
.90
.03
2
G1
-19
72
50
6.4
70
.03
46
32
3.7
1.7
31
12
-19
72
25
.21
0.0
09
01
0.4
0.0
18
N2
-19
72
28
7.0
60
.03
12
19
7.6
0.8
73
G1
-19
84
52
6.1
10
.03
55
31
7.8
1.8
48
12
-19
84
43
.34
0.0
36
61
3.4
0.1
46
N2
-19
84
37
7.0
10
.03
33
25
9.6
1.2
30
G1
-20
04
67
6.0
60
.03
34
40
5.9
2.2
35
12
-20
04
12
4.7
90
.04
60
57
.40
.55
2N
2-2
00
42
45
.42
0.0
30
81
30
.10
.74
0
G2
-19
40
87
6.1
40
.02
88
53
4.5
2.5
02
13
-19
40
13
.49
0.0
25
83
.50
.02
6N
3-1
94
01
74
3.0
60
.01
42
53
2.0
2.4
67
G2
-19
53
21
5.9
10
.03
02
12
4.1
0.6
35
13
-19
53
12
.98
0.0
18
63
.00
.01
9N
3-1
95
31
21
1.0
70
.04
47
13
2.8
0.5
36
G2
-19
72
38
7.1
20
.03
56
27
0.6
1.3
53
13
-19
72
33
.61
0.0
16
31
0.8
0.0
49
N3
-19
72
18
8.3
40
.03
40
15
0.1
0.6
12
G2
-19
84
27
5.9
90
.03
60
16
1.7
0.9
72
13
-19
84
24
.16
0.0
41
43
.30
.08
3N
3-1
98
42
47
.20
0.0
35
61
72
.70
.85
5
G2
-20
04
33
5.3
00
.03
22
17
4.8
1.0
62
13
-20
04
44
5.4
10
.07
24
23
8.0
3.1
85
N3
-20
04
65
.47
0.0
34
93
2.8
0.2
09
G3
-19
40
12
.89
0.0
20
52
.90
.02
11
4-1
94
01
2.8
80
.01
57
2.9
0.0
16
N4
-19
40
12
.78
0.0
15
82
.30
.01
6
G3
-19
53
24
.63
0.0
29
99
.30
.06
01
4-1
95
36
3.8
30
.04
99
23
.00
.29
9N
4-1
95
33
4.0
50
.03
45
12
.10
.10
4
G3
-19
72
54
7.6
30
.03
75
41
1.9
2.0
25
14
-19
72
12
.66
0.0
14
22
.70
.01
4N
4-1
97
21
91
1.5
80
.03
75
22
0.0
0.7
12
G3
-19
84
63
7.0
60
.04
00
44
5.1
2.5
17
14
-19
84
12
.61
0.0
14
22
.60
.01
4N
4-1
98
41
55
.35
0.0
38
98
0.2
0.5
84
G3
-20
04
89
7.8
00
.05
27
69
4.6
4.6
87
14
–2
00
42
2.8
90
.03
22
5.8
0.0
64
N4
-20
04
25
6.0
90
.05
45
15
2.2
1.3
63
G4
-19
40
83
4.9
20
.02
95
40
8.8
2.4
47
15
-19
40
12
64
.27
0.0
31
15
37
.93
.91
3N
5-1
94
01
2.9
20
.02
09
2.9
0.0
21
G4
-19
53
58
6.9
60
.04
24
40
3.5
2.4
61
15
-19
53
37
.24
0.0
16
52
1.7
0.0
49
N5
-19
53
24
.73
0.0
35
09
.50
.07
0
G4
-19
72
22
5.1
50
.03
10
11
3.3
0.6
82
I5-1
97
22
8.7
80
.01
23
17
.60
.02
5N
6-1
97
22
76
.19
0.0
51
81
67
.11
.39
8
G4
-19
84
44
5.9
30
.04
59
26
1.1
2.0
18
15
-19
84
56
.28
0.0
35
13
1.4
0.1
75
N5
-19
84
16
5.3
60
.04
66
85
.80
.74
6
G4
-20
04
68
10
.22
0.0
50
86
95
.03
.45
51
5-2
00
46
5.5
60
.02
25
33
.40
.13
5N
5-2
00
45
86
.57
0.0
41
43
80
.92
.40
3
I1-1
94
09
25
.96
0.0
27
05
48
.32
.48
8N
1-1
94
03
3.4
60
.02
29
10
.40
.06
9N
6-1
94
05
43
.95
0.0
26
52
13
.21
.43
0
I1-1
95
34
4.4
70
.04
86
17
.90
.19
4N
1-1
95
32
4.4
80
.03
73
9.0
0.0
75
N6
-19
53
10
5.8
70
.04
97
58
.70
.49
7
I1-1
97
24
3.3
70
.04
10
13
.50
.16
4N
1-1
97
22
6.4
60
.00
97
12
.90
.01
9N
6-1
97
23
4.4
60
.02
18
13
.40
.06
5
I1-1
98
41
2.6
30
.01
37
2.6
0.0
14
N1
-19
84
12
.33
0.0
07
42
.30
.00
7N
6-1
98
45
4.8
70
.03
91
24
.30
.19
5
I1-2
00
41
2.3
40
.00
72
2.3
0.0
07
N1
-20
04
45
.32
0.0
21
22
1.3
0.0
85
N6
-20
04
23
.85
0.0
14
17
.70
.02
8
nn
um
ber
of
rid
ges
/tre
eis
lan
ds,
LW
mea
nle
ng
th/w
idth
rati
o,
PA
mea
np
erim
eter
/are
ara
tio
,L
eWN
LeW
Nin
dex
,an
dP
AN
PA
Nin
dex
(see
Eq
s.1
and
2)
482 Wetlands Ecol Manage (2011) 19:475–493
123
relatively irregular in shape and random in dimensions
(for very low n, patch dimensions are defined by the
dimensions of the study plot). In an unpatterned
system, one would expect lack of correlation between
shape, smoothness, length/width ratios, and perimeter/
area ratios.
The hierarchical cluster analysis was used to define
groups containing from four to 25 members (Fig. 5;
Table 3) composed of mixes of plots from multiple
years and hydrological flow paths. The analysis
showed very low chaining (2.69%). In this type of
dendogram, chaining is the addition of an item to an
existing group because it is not sufficiently different
to generate a new group. This hierarchical cluster
analysis shows low chaining, suggesting that the
groups identified in this analysis are distinct.
Results of a means comparison test for variables by
category appear in Table 4. Categories 1 and 5 differed
Table 2 Correlations between variables (all plots, all years) and the Nonmetric Multidimensional Scaling axis 1 for the pattern
metrics in Water Conservation Area 3
n PA LW LeWN PAN Axis 1
n 1.0000
PA 0.2891 1.0000
LW 0.3265 0.2283 1.0000
LeWN 0.9502 0.3511 0.4880 1.0000
PAN 0.5025 0.9461 0.2317 0.4558 1.0000
Axis 1 0.8544 0.3362 0.6508 0.8739 0.4438 1.0000
Strong(5)
Good(4)
Moderate(3)
Poor(2)
Degraded(1)
Distance (Objective Function)
Information Remaining (%)
1.3E-06
100
2.8E-01
75
5.7E-01
50
8.5E-01
25
1.1E+00
0
G1_1940G4_1940G1_2004G4_1953G3_1972G3_1984N5_2004G2_1940I1_1940I5_1940N3_1940G3_2004G4_2004G1_1953G1_1984G1_1972G2_1972G4_1984N2_1984I3_2004N4_1972N6_1940G2_1953N2_2004G4_1972N3_1953G2_1984N5_1972G2_2004N2_1940N3_1984N2_1972N3_1972N4_2004I2_2004N6_1953N4_1984N5_1984G3_1940I4_1940N5_1940I3_1953N4_1940I1_1984I4_1984I4_1972I1_2004N1_1984I3_1940G3_1953N5_1953N1_1953I2_1972I3_1984N6_2004I1_1972I2_1984N4_1953N6_1972I3_1972N1_1940I2_1953N1_1972I4_2004N2_1953I1_1953I5_1972I2_1940I5_1953N1_2004I4_1953N6_1984I5_1984I5_2004N3_2004
Fig. 5 Tree diagram of study plots and pattern categories
produced by hierarchical agglomerative cluster analysis of
the variables n, LW, PA, LeWN, and PAN (see Table 1).
Correspondence of the pattern categories is identified along the
left axis of the diagram. The study plots in each category are
listed in Table 3
Wetlands Ecol Manage (2011) 19:475–493 483
123
significantly for all variables, but had different rela-
tionships with variables in the other categories; some
variables varied distinctly between categories, while
others did not. The number of ridges in a plot differed
between categories 5, 4, and 3 or under. Similar
patterns occurred for LeWN and LW. PA fell into two
groups with category 5 and 1 being distinct. PAN
values differed for category 5, but not for the other four
categories (1 through 4). The strongly patterned plots
share high values among the pattern metrics and the
poorly patterned plots share low values.
The top cluster in Fig. 5 contains fourteen plots
with high LeWN, PAN, and n values (Table 3). These
plots exhibit strongly linear vegetation patches, even
spacing between patches, and elongated and intercon-
nected open water patches. The cluster second from
the bottom in Fig. 5 consists of eleven plots with the
lowest values of n, PA, LW, LeWN, and PAN. These
plots contain scattered patches of open water among a
continuous stand of emergent vegetation. The three
other categories fall numerically in between the two
extremes. The second cluster from the top in Fig. 5 has
n, LeWN, and PAN values less than half those of the
top cluster, and LW is a bit larger than that of the top
cluster. Emergent vegetation in this second cluster
retains linearity but some of the patches have
coalesced, and open water sloughs have become
somewhat less connected. The bottom cluster in
Fig. 5 contains 25 plots with low values of n, PA,
PAN, and LeWN, with moderate LW, suggesting that
they have some linear structures but these are
degraded. The middle cluster in Fig. 5 has only four
plots with intermediate values of n, PA, LW, LeWN,
and PAN. These contain a mix of less linear ridges and
sloughs showing expansion and increased intercon-
nection of ridges with a resulting loss of connected
sloughs. From these metrics and visual inspection,
these clusters were ranked from 1 through 5, repre-
senting poor to strong patterning, respectively. The
classes noted along the left side of the cluster diagram
(Fig. 5) indicate the pattern categories.
Table 3 Pattern categories derived from agglomerative hier-
archical cluster analysis of variables shown in Table 1, and
memberships of each category for study plots by year
1 2 3 4 5
G3-1940 G3-1953 I2-2004 G1-1953 G1-1940
I1-1984 I1-1953 N4-1984 G1-1972 G1-2004
I1-2004 I1-1972 N5-1984 G1-1984 G2-1940
I3-1940 I2-1940 N6-1953 G2-1953 G3-1972
I3-1953 I2-1953 G2-1972 G3-1984
I4-1940 I2-1972 G2-1984 G3-2004
I4-1972 I2-1984 G2-2004 G4-1940
I4-1984 I3-1972 G4-1972 G4-1953
N1-1984 I3-1984 G4-1984 G4-2004
N4-1940 I4-1953 I3-2004 I1-1940
N5-1940 I4-2004 N2-1940 I5-1940
I5-1953 N2-1972 N3-1940
I5-1972 N2-1984 N5-2004
I5-1984 N2-2004
I5-2004 N3-1953
N1-1940 N3-1972
N1-1953 N3-1984
N1-1972 N4-1972
N1-2004 N4-2004
N2-1953 N5-1972
N3-2004 N6-1940
N4-1953
N5-1953
N6-1972
N6-1984
N6-2004
Table 4 Means comparisons of variables used in classifications of pattern quality
Category n plots Ridges/plot PA LW LeWN PAN
5 14 77.8 (a) 0.0606 (a) 6.48 (a) 487.85 (a) 4.5427 (a)
4 21 32.4 (b) 0.0376 (a,b) 6.66 (a) 203.38 (b) 1.2198 (b)
3 4 13.3 (c) 0.0453 (a,b) 5.34 (a,b) 70.54 (c) 0.5946 (a,b)
2 25 3.2 (c) 0.0271 (a,b) 4.98 (b) 15.96 (c) 0.0941 (b)
1 11 1.0 (c) 0.0158 (b) 2.77 (c) 2.77 (c) 0.0158 (b)
Letters in parentheses indicate the group memberships; those that are the same indicate no significant differences for that variable in
the category
484 Wetlands Ecol Manage (2011) 19:475–493
123
Means comparison tests indicated that each cate-
gory consisted of significantly different values of
many of the component variables (Table 4). The
values of LeWN and n for categories 5 and 4 differ
from those in categories 1 through 3. PAN values for
categories 5 and 3 differ from those in categories 1, 2,
and 4. Categories 1 and 5 differ for PA, and LW of
categories 4 and 5 differ from categories 1 and 2. It
should be noted that the number of ridges per patch
(n) is not significantly different from each other in
categories 1 and 2 (Table 4); some of the increase in
values of the variables in these two categories results
from canals, roads, and utility lines that divide what
would otherwise be one single patch of emergent
vegetation into several.
The G flow-way (western WCA-3) plots dominated
categories 4 and 5, representing good to strong
patterning. Three of the four plots in category 3 were
in the southern N flow-way, while category 2 (poor
patterning) contained members predominantly from
1953 and 1972 in the I and N flow-ways. Nearly all of
the degraded plots (category 1) were in the I flow-way,
located the farthest east in the WCA. Patterns in the
1940 plots were most commonly classified as either
degraded or strong: six were classified as strongly
patterned (category 5) and five were classified as
degraded (category 1) in 1940 (Table 3).
The best ordination using NMDS produced a single
axis (Fig. 6a) that organized the plots and retained the
groups created by the cluster analysis. These catego-
ries were distinct and lacked overlap. A Monte Carlo
test comparing real versus randomized data indicated
that the real data produced a much lower stress value
(1.139 after 107 iterations; final instability 0.00000)
(Fig. 6b). The first axis accounted for almost 90% of
the variance in the data (r2 = 0.894), suggesting that
the combined variables produced a generally linear
structure.
The NMDS axis was highly correlated with the
LeWN index (r = 0.8739) and n (r = 0.8544)
(Table 2). The correlation with LW was also high
(r = 0.6508). The strength of the correlations with PA
and PAN were lower (r \ 0.5). These correlations
indicate that patterning strength is dominated by both
the linearity of the ridges and the higher number of
ridges per unit area measured in these plots. Smooth-
ness of the edges alone does not contribute as much as
the linearity to the assessment of pattern strength,
although it also characterizes the Ridge and Slough
landscape. The ordination confirmed and clarified the
validity of the pattern classes (1–5) produced by the
cluster analysis (Fig. 5; Table 3).
In general, pattern quality can be estimated by the
number of ridges and the value of LeWN in a plot.
Plots with 20 or more long ridges tended to be those
with good to strong patterns (categories 4 and 5;
Tables 1, 3). When plots contained fewer than ten long
ridges, their pattern quality was poor or degraded
(categories 1 and 2). Plots with LeWN values greater
than 100 showed good or strong patterns, while those
under 50 were poor or degraded. Similarly, values of
PAN that exceeded 1.0 were strongly patterned, and
1
5
4
3
2
0 1.0-1.00
50
75
25
Ran
k
Distance in Ordination Space
Dimensions
Str
ess
Real Data Randomized Data
Maximum
Mean
Minimum
Fig. 6 Ordination diagram from nonmetric multi-dimensional
scaling analysis (a). The input matrix contained the same
metrics used for the hierarchical clustering (Fig. 5) plus a
variable defining the cluster categories 1–5 for each study plot.
The diagram represents the distances in ordination space (x-
axis) plotted against the rank order of each study plot (1 through
75) (y-axis), with the categories identified by symbol. Stress (b),
an inverse measure of fit to the data (McCune and Grace 2002),
compares the real data to randomized data using a Monte Carlo
test. Stress is nearly monotonic and significantly different from
random (P \ 0.05). NMDS supports the associations produced
by hierarchical clustering
Wetlands Ecol Manage (2011) 19:475–493 485
123
those below 0.01 were poorly patterned. For both
n and LeWN, 10–20 ridges or LeWN values of 50–100
were moderately patterned.
Temporal trends
Time series of pattern changes in each plot were
illustrated with temporal trajectories of each plot using
the categories derived from the hierarchical clusters.
These trajectories provide information about the
stability of patterns since 1940 and identify individual
plot responses to changing environmental conditions.
These temporal trajectories are displayed in Fig. 7.
Patterns in all plots changed over time by at least
one category, and most show a combination of both
improvement and degradation (Fig. 7). From 1940 to
2004, overall pattern quality in five plots declined,
four remained unchanged, and six improved. By 2004,
most individual plots fell into different categories than
they had in 1940. Over the six decades, eight plots
(G3, N3, N4, N5, N6, I1, I3, and I5) moved among two
or more categories from either good to poor or poor to
good patterning. The other seven plots (G1, G2, G4,
N1, N2, I2, and I4) changed by only one category.
Plots G1, G2, and G4 remained in the strongly
patterned categories 4 and 5 from 1940 through
2004. Plots N1, I2, and I4 remained in the lowest
categories 1 and 2 throughout the 60 years. Plots N3,
N6, I1, and I5 were classified with good or strong
patterns in 1940 but their patterns degraded over time.
In contrast, plots G3, N4, N5, I2, and I3 improved in
patterning from 1940 through 2004. These trajectories
indicate that pattern quality was dynamic in most of
the Ridge and Slough peatland over the six decades.
To some extent, pattern categories grouped
together along flow-ways. Most of the G flow-way
plots were classified as categories 4 and 5 across all
time periods. Plots G1, G2, and G4 retained patterning
over the six decades while those in the N and I flow-
ways changed markedly. The N flow-way plots were
evenly distributed among declining, improving, and
no change. Most of the I plots fell into the lowest
categories, 1 and 2 (Table 3; Fig. 8).
Spatial trends
The spatial relationships of pattern quality (Fig. 8)
show regions where the Ridge and Slough patterns
were better or poorer than others and suggest potential
environmental conditions affecting patterns. In 1940,
plots with good patterns appeared in two regions, one
at the south end of WCA-3 immediately north of the
Tamiami Trail (plots G4, N5, and I5). The second area
of good patterning ranged from plots G1 and G2
northeastward to N2, N3, and I1 (Fig. 8). The other
seven plots had poor patterning (categories 1 or 2) in
the first aerial photographs and were located away
from Tamiami Trail and to the east and to the far north.
Plot I1 remained in the lowest categories consistently
from 1940 through 2004. The divergence of pattern
quality in 1940 indicated that the earliest photos
captured a Ridge and Slough landscape that was
already significantly altered from pre-drainage condi-
tions (McVoy et al. 2011).
By 1952, patterning had degraded throughout the
eastern flow-ways; all of the I plots fell into categories
1 and 2, and most of the N flow-way plots were also
classified as category 2. Only plots G1, G2, G4, and
N3 remained in categories 4 or 5. Plot I5 just north of
Tamiami Trail had degraded from category 5 to 2 and
N6 had declined from category 4 to category 3.
Following compartmentalization in the mid-1960s,
the 1972 patterns in N2, N4, N5, and G3 had improved
from 1953, although N6 had degraded in what became
WCA-3B. All four G flow-way plots and four of the
six plots in the N flow-way had good or strong
patterning (G1–G4 and N2–N5), while all the eastern
plots, I1–I5, plus N1 and N6 were classified as poor or
degraded patterns. During the 1960s, newly con-
structed openings in the Miami Canal allowed addi-
tional water to flow southward again, which would
have rehydrated plot N2. The L-67 levees and canal
impounded water to the north, where G4 and N5 would
have been most affected by higher water depths. Both
of these structural changes increased water depths in
portions of the WCA. South of the newly constructed
L-67 levee, the peatland grew increasingly dry, and
from then through 2004, all three plots in WCA-3B
(N6, I4, and I5) remained in the poor or degraded
categories.
In 1984, poor patterns remained east of Miami
Canal and south of the L-67 levee, while the G flow-
way and the middle four plots of the N flow-way
continued in the moderate to strong categories. Pattern
categories had improved again by 2004 even in some
of the eastern plots (I2 and I3), and four plots were
characterized with good or strong patterns (G1, G3,
G4, and N5). Only N3 in central WCA-3A degraded
486 Wetlands Ecol Manage (2011) 19:475–493
123
by two categories from 1984 to 2004, having already
declined from category 5 in 1940.
Discussion
Paleoecological evidence indicates that sloughs and
ridges have remained in place for at least four
centuries (Bernhardt and Willard 2009). Additional
evidence suggests that before the landscape was
modified by drainage, broad-scale sub-environments
in the Everglades responded similarly to climate
fluctuations (Willard et al. 2001). Both of these
conditions appear to have changed over the last
century. Distinctions between ridges and sloughs
disappeared in large portions of the former patterned
0
6
Cat
ego
ry
0
6
Cat
ego
ry
0
6C
ateg
ory
0
6
Cat
ego
ry
0
6
Cat
ego
ry
0
6
Cat
ego
ry
0
6
Cat
ego
ry
0
6
Cat
ego
ry
0
6
Cat
ego
ry
0
6
Cat
ego
ry0
6
Cat
ego
ry
0
6
Cat
ego
ry
0
6
Cat
ego
ry
0
6
Cat
ego
ry
0
6
Cat
ego
ry
G1
G2
G3
G4 N6
N5
N4
N3
N2
N1
I1
I2
I3
I4
I5
0
6
0
6
0
6
0
6
0
6
0
6
0
6
1940 1953 1972 1984 2004
0
6
0
6
Cat
ego
ry
0
6
0
6
0
6
0
6
0
6
0
6
1940 1953 1972 1984 20041940 1953 1972 1984 2004
1940 1953 1972 1984 2004 1940 1953 1972 1984 2004 1940 1953 1972 1984 2004
1940 1953 1972 1984 2004 1940 1953 1972 1984 2004 1940 1953 1972 1984 2004
1940 1953 1972 1984 2004 1940 1953 1972 1984 2004 1940 1953 1972 1984 2004
1940 1953 1972 1984 2004 1940 1953 1972 1984 2004
1940 1953 1972 1984 2004
Fig. 7 Trajectories of patterns over time. Categorical values
are displayed from 1 to 5, with higher numbers indicating
stronger patterns. Categories were derived from hierarchical
agglomerative clustering and ordination for the years 1940,
1953, 1972, 1984, and 2004
Wetlands Ecol Manage (2011) 19:475–493 487
123
landscape, and those patterns that remain have
responded very differently across the landscape.
As noted in the introduction, early written records
in the pre-drainage period indicate that the patterns in
the Ridge and Slough landscape were similar through-
out its extent. From this evidence, one can reasonably
assume that neighboring plots in the Ridge and Slough
landscape would have resembled each other in the
original system. After 50 or more years of severely
altered natural flow into the Everglades, only a few of
the study plots in 1940 resembled their neighbors
(Fig. 8). Over the next six decades, many plots
differed from their neighbors in pattern quality and
most adjacent plots differed in their pattern trajectories
(Figs. 7, 8). For example, while plots N2 and I1
displayed good to strong patterning in 1940, their
trajectories diverged after 1953; plot N2 improved
again following the opening of gaps in the Miami
Canal, which was designed to improve hydration
southward, while I1 continued to degrade. In adjacent
plots G4 and N6, G4 remained well-patterned while
N6 degraded. In contrast, N4 and N5 had almost
identical trajectories from 1940 to 2004, improving
greatly from degraded conditions; N6 and I5 resem-
bled each other in starting as patterned then degrading
rapidly and remaining degraded. G1, G2, and G4
remained strongly patterned over this period. These
findings confirm observations made by Willard and
colleagues (2001) that sub-environments in the Ever-
glades are now responding to localized fluctuations in
hydroperiod rather than their original synchronized
responses to climate shifts. Pre-drainage patterns may
have varied over time, but information for a wide
variety of sites is not available.
Annual precipitation variability may have played a
role in altering Ridge and Slough patterns, though
minor, because highly variable rainfall is typical of the
south Florida climate. Climate records for WCA-3
show 10 years of below-normal rainfall prior to 1940
and a continuing dryer than average period into the
1950s (Leach et al. 1971; SFWMD 2006). The 1940
and 1953 patterns may show evidence of these drier
climate periods, exacerbating changes produced by the
50 years of drainage leading up to 1940. Rainfall in
Fig. 8 Spatial distribution
of pattern quality by year.
Light areas represent higher
values (5 = white) and darkrepresents low values
(1 = black). Mapping used
inverse distance weighted
interpolation to indicate
spatial distributions of study
plot patterns by year
488 Wetlands Ecol Manage (2011) 19:475–493
123
the 3 years immediately prior to 1972, 1984, and 2004
was somewhat higher than century average (SFWMD
2006), and these remaining patterns may reflect
elements of higher water depths on vegetation pat-
terns. It is likely that the landscape responds to rainfall
within a few years, but if rainfall were the main driver
of landscape pattern shifts, then the entire landscape
should have improved or degraded in synchrony, shifts
that are not supported by this analysis. Bernhardt and
Willard (2009) have reported that vegetation in the
Everglades, including WCA-3, shifted to a dryer
community in the early 1900s in spite of rainfall
generally producing a relatively wetter climate over-
all. They concluded that hydrologic modifications
were responsible for the dryer regional conditions.
The lack of temporal and spatial synchronicity in
pattern changes among adjacent study plots and those
along the same flow paths suggests several hypothe-
ses. One is that these pattern shifts are driven primarily
by local environmental conditions (e.g., distance to
canal or levee, local water depths) rather by than a
single regional driver such as droughts in south
Florida. Locations of plots with poor and degraded
patterning (N1, I1, I2, I3, and I4, categories 1 and 2) in
northern WCA-3A generally correspond to areas
reported to have experienced substantial peat subsi-
dence from drainage, oxidation, and burning (Ste-
phens and Johnson 1951). Peat loss of up to 0.76 m has
been estimated throughout northern and eastern WCA-
3A and northern WCA-3B (Komlos et al. 2008;
Desmond 2007).
Hydrologic data extending through the period
covered by this analysis are rare. However, historic
depth data from stage gauge 65 in southwestern WCA-
3 are available beginning in 1953; this gauge measures
stages that would influence plots G3, G4, and N5.
These stage data, smoothed and adjusted for ground
elevation to produce water depths (Fig. 9), suggest
three different ongoing depth regimes in southern
WCA-3 from 1953 through 2004. The first depths of
approximately 15 cm from 1953 to 1962 predate
compartmentalization and reflect the severe drainage
of the Everglades. Following compartmentalization in
1965, average depths at this gauge were maintained at
approximately 35 cm through 1990. After 1990,
modified hydrologic operations produced depths of
approximately 50 cm from 1990 through 2004. In the
nearly flat Everglades, these slight differences in water
depths support significantly different vegetation com-
munities (Kushlan 1990) which can be detected on
aerial imagery and were used to define ridge-slough
boundaries. The timing of these increasing depths
corresponds to the improving pattern qualities
reflected in this analysis for southern WCA-3A,
particularly for plots G3, G4, and N5.
Compartmentalization increased water depths in
southern WCA-3A; by 1972, structures had been in
place for 6–8 years or longer. Ridge and Slough
patterns improved in areas associated with deeper
water in 1972, particularly in the plots upstream of the
L-67 canals (G3, N4, and N5). While the ridge, slough,
and tree island patterns themselves appear to have
Fig. 9 Annual water depths
at gauge 64 (25�5803100,80�4001000, NGVD), near
study plot G3 in WCA-3A
(see Fig. 4). The point
values are estimated from
historical data (McVoy,
pers. comm.) for pre- and
post-drainage dates; the
smoothed curve represents a
240-day running mean depth
based on monitoring data
(SFWMD 2006). The
arrows indicate years for
which aerial photos were
mapped for use in this
analysis
Wetlands Ecol Manage (2011) 19:475–493 489
123
strengthened based upon their edge boundaries, deeper
water from impoundment has been implicated for
destruction of tree islands (Sklar and Van der Valk
2002b). It is possible that tree island vegetation,
particularly the longer-lived forest species, had
adapted to lower water depths. With a time lag for
various species to adapt to higher water levels or to be
replaced by more flood-tolerant species, forests may
eventually adjust to the deeper water. It is also possible
that tree island vegetation communities require a
regular periodicity of seasonal high and low water
depths to thrive, which the present water management
schedules do not provide. The present analysis con-
siders only the shapes of the structures and their
changes over time. Further research is needed to
characterize detailed features of the vegetation on the
ridges and tree islands and in the sloughs in these study
plots. Additional traits may need to be considered for
more detailed pattern quality assessment.
Another hypothesis is that the patterning responses
may be related to varying resilience or to environ-
mental drivers that differ geographically. Some driv-
ers may be difficult to discern, such as localized
upwelling of groundwater from highly porous surface
rock formations. Others may relate to unexpected or
presently unknown feedback mechanisms, as they do
in boreal bogs (e.g., retention of ice cores inside
hummocks; Nungesser 2003). At present, relation-
ships between the patterns and hydrology in the
Everglades are proceeding through investigations of
driving mechanisms and feedbacks (e.g., Larsen et al.
2007, 2011; Larsen and Harvey 2011, 2010; Noe et al.
2010; Ross et al. 2006; Watts et al. 2010). Paleoeco-
logical investigations are attempting to decipher pre-
drainage ecosystem structures (e.g., Willard et al.
2006; Bernhardt and Willard 2009; Rutchey et al.
2009) and their changes and driving factors.
This analysis indicates that the ridge-slough bound-
aries can change over a few years (less than a decade)
as measured by the length, width, perimeter, area, and
abundance metrics. The potential for these relatively
long-lived landscape features (centuries long; Willard
et al. 2001) to vary rapidly enough in their shape
metrics to be easily measured at a decadal or shorter
time frame is helpful for restoration monitoring and
for adaptive management. While the exact physical
and biological mechanisms responsible for pattern
generation and maintenance at these scales have yet to
be determined, the rapid response of vegetation and
landscape features to changes in the environment,
particularly water depths, has been seen elsewhere in
the remaining Everglades in slough vegetation com-
munities (Zweig and Kitchens 2008) and in tree
islands (K. Rutchey, pers. comm.). Research suggests
that this microtopography is sensitive to hydroperiod
and flow (Larsen et al. 2011; Larsen and Harvey 2010,
2011; Harvey et al. 2009; Noe et al. 2010; SCT 2003;
Watts et al. 2010) and possibly to nutrient distribution
(Larsen et al. 2011; Ross et al. 2006).
It is not clear why emergent vegetation patterns in
WCA-3 have expanded and contracted, but interac-
tions between vegetation and water depths are prob-
ably responsible. Research on expansion and
contraction of boreal hummocks and hollows, micro-
topographic features in bogs, have indicated that
microtopography responded to long-term climatic
shifts of wetter and dryer periods (Walker and Walker
1961; Conway 1948). The elevated hummocks con-
tracted and wet hollows expanded in wetter periods
and the reverse occurred in dryer periods. Similar
results have been reported for ridge and tree island
expansion and contraction in the Everglades (Bern-
hardt and Willard 2009; Willard et al. 2001). The
Everglades landscape has retained some microtopo-
graphic differentiation (McVoy, pers. comm.), so
these slight elevation differences may contribute to the
rapid response of pattern changes to water depths in
several ways. Dryer conditions may allow sawgrass
and other predominantly ridge species to expand into
former sloughs, while wetter conditions can drive the
plants back to their former sites on peat ridges; these
shifts have been observed in paleoecological analyses
in the southern Everglades (Rutchey et al. 2009).
Anecdotally, fire scars on the aerial photographs
occasionally reveal the underlying microtopography
(ridges that were visible in earlier photographs; pers.
obs.) even though surface vegetation communities do
not appear to differ noticeably. These elevation
differences may also contribute to rebuilding micro-
relief. If the microtopography is produced by auto-
genic properties of the vegetation species interacting
with hydrology, as it is in boreal peatlands (Nungesser
2003), then the remnant microtopography may expe-
dite restoration of the patterning under appropriate
hydrologic conditions when peat-generating species
are present. The remaining peat microtopography in
the Ridge and Slough landscape may provide a
platform to restore the original sloughs and ridges/
490 Wetlands Ecol Manage (2011) 19:475–493
123
tree islands. Future research will focus on more fine-
scaled analysis of pattern changes.
Knowledge of the ways in which patterns change
over time can suggest methods to actively improve
patterning. Removal of hydrologic barriers (decom-
partmentalization) would allow water to flow unhin-
dered across the landscape. Water released from the
northern boundary of WCA-3 could conceivably flow
then across the full width of WCA-3A and -3B
southward through Everglades National Park into
Florida Bay. Flow itself may contribute greatly to
improving patterning (SCT 2003) and if more natural
flow simultaneously improves water depths, then this
analysis suggests that the degraded Ridge and Slough
patterns may improve over time, though perhaps in
limited areas of WCA-3. Areas with the strongest
remaining patterns (categories 4 and 5) may be easier
to restore than those that are more degraded so long as
the landscape retains some of the initial microtopo-
graphic relief. Building up from existing microtopog-
raphy should occur more readily than reconstructing
microtopography from an undifferentiated peat sur-
face. However, if patterns respond in a hysteretic
fashion, as has been suggested in recent research
(Watts et al. 2010), then restoration of flows and
depths may not succeed in improving the patterns
rapidly or without substantial intervention.
Conclusions
Changes in the Ridge and Slough patterns over the last
six decades indicate that (1) patterns can be lost
quickly following severe peat dryout, (2) patterns
appear resilient at least over multi-decadal time
periods, (3) patterns can be maintained and possibly
strengthened with deeper water depths, and (4) the
sub-decadal response time of pattern changes detect-
able from aerial imagery is highly useful for change
detection within the landscape.
These time series suggest that excessive drainage
degrades and ultimately destroys Ridge and Slough
patterns, while rehydration and adequate water depths
may retain patterns under some conditions. Timing
and duration of water depths may be useful in
regulating the Ridge and Slough plant communities,
though the short-term connection to improved micro-
topography is not certain. The role of flow in these
peatlands is very important (SCT 2003), but was not
addressed here because historic flow data are
unavailable.
Shape metrics discussed here provide a tool to track
future, as well as historic, pattern changes in the Ridge
and Slough landscape. Because restoration plans
include obtaining repeated and regular high resolution
aerial photography, the imagery should be available
for monitoring these pattern changes in the future. The
time series of aerial photographic records provides a
unique transcript of the physiognomy of the Ever-
glades Ridge and Slough landscape, providing clues to
resilience and adaptations of the landscape and
suggesting that the patterns are both responsive over
the short term while enduring over decades. When
peatlands dry out, the visual patterns can be obliterated
rapidly, yet experience from the last century shows
that the original ridges and sloughs in areas subjected
to drainage occasionally may still be detectable from
photography. The long-term retention of some aspects
of the patterning is an encouraging sign for restoration
success. The documentation that significant improve-
ment in the patterns is possible within a few decades is
among the first evidence suggesting that restoration of
some aspects of these unique peatland patterns may be
possible within relatively short planning time frames.
Acknowledgments I would like to thank several colleagues
for their suggestions, technical help, and ideas during the
development of this manuscript: Drs. Christopher McVoy, Steve
Friedman, Colin Saunders, and Tom Dreschel. Malak Ali’s skill
in digitizing the imagery and Sue Hohner’s technical guidance
in GIS and spatial analysis were invaluable. I also appreciate the
comments of Dr. Fred Sklar and two anonymous reviewers. This
research was funded in part by the South Florida Water
Management District. Funding was provided by the South
Florida Water Management District and the Restoration
Coordination and Verification program of the Comprehensive
Everglades Restoration Plan.
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