American Journal of Plant Sciences, 2012, 3, 1205-1218
http://dx.doi.org/10.4236/ajps.2012.39146 Published Online September 2012 (http://www.SciRP.org/journal/ajps)
1205
The Ecological Classification of Coastal Wet Longleaf Pine
(Pinus palustris) of Florida from Reference Conditions
George L. McCaskill1, Shibu Jose2
1Forest Inventory & Analysis, Northern Research Station, USDA Forest Service, Newtown Square, USA; 2Center for Agroforestry,
School of Natural Resources, University of Missouri, Columbia, USA.
Received July 31st, 2012; revised August 28th, 2012; accepted September 10th, 2012
ABSTRACT
Tropical storms, fire, and urbanization have produced a heavily fragmented forested landscape along Florida’s Gulf
coast. The longleaf pine forest, one of the most threatened ecosystems in the US, makes up a major part of this frag-
mented landscape. These three disturbance regimes have produced a mosaic of differently-aged pine patches of single
or two cohort structures along this coastline. The major focus of our study was to determine reference ecosystem condi-
tions by assessing the soil biochemical properties, overstory stand structure, and understory plant species richness along
a patch-derived 110-year chronosequence in order to accurately evaluate on-going longleaf pine restoration projects.
This ecological dataset was also used to classify each reference patch as mesic flatwoods, wet flatwoods, or wet sa-
vanna. All of the reference locations were found to have similar soil types with no significant differences in their soil
biogeochemistry. Mean diameter-at-breast height (DBH), tree height, and patch basal area increased as mean patch age
increased. Stand growth reached a plateau around 80 - 90 years. Shrub cover was significantly higher in the mature-
aged patches (86 - 110 years) than in the young (6 - 10 years) or mid-aged (17 - 52 years) patches, despite prescribed
fire. Plant species diversity as indicated by the Shannon-Wiener index decreased with patch age. Soil biogeochemical
properties, forest structure, and understory species composition were effective for ecologically classifying our pine
patches as 55% mesic flatwoods, 20% wet flatwoods, and 25% wet savanna. Florida’s Gulf coastal wet longleaf pine
flatwoods attain a structural and plant species equilibrium between 80 - 90 years.
Keywords: Forest Structure; Species Richness; Restoration; Pine Patches; Shannon-Wiener; Soil Biogeochemistry
1. Introduction
In recent years, there has been a great effort to restore
longleaf pine (Pinus palustris Mill.) communities within
the southeastern U.S. They are one of the most threat-
ened ecosystems in the United States having less than 3%
of its original extent remaining [1]. Restoration projects
have been implemented in an effort to restore more than
405,000 ha of longleaf pine in the Southeast during the
past decade alone [2]. This effort continues with the goal
to restore an additional 1,900,000 ha by 2015 [3].
Although many past studies have focused on the un-
derstory plant communities of longleaf pine ecosystems
[4-7], less information exists on the spatial-temporal pat-
terns of understory plant species as they relate to the soil
biogeochemical properties and forest structure specifi-
cally situated within Florida’s Gulf coastal flatwoods
zone [8-10].
In addition, many researchers have classified longleaf
pine sites along the lower Gulf coastal plain utilizing
understory vegetation composition to separate one long-
leaf pine site from another [4,11,12]. A few have used
fluvial vs. upland descriptions, climatic conditions, soil
drainage patterns, and differences in soil texture to clas-
sify differently structured longleaf pine stands [13-15].
Coastal Wet Flatwoods
Most of Florida’s wet pine flatwoods are concentrated
along the 1240 km Gulf coastline, which contains
marshes, bays, and offshore islands. This coastal land-
scape is continuously shaped by active fluvial deposition
and weather processes which promote and maintain the
formation of beaches, swamps and wet mineral flats. The
topographic relief ranges from 0 to 20 m, the annual pre-
cipitation from 1300 - 1600 mm, while the average an-
nual temperature ranges between 19˚C - 21˚C. Growing
seasons are long, lasting 270 - 290 days [16]. Soil parent
material consists of marine deposits containing limestone,
marl, sand, and clay. The dominant suborders are Aquods,
Aquents, and Aquepts, which are highly acidic poorly
drained sandy soils having thermic and hyperthermic

The Ecological Classification of Coastal Wet Longleaf Pine (Pinus palustris) of Florida from Reference Conditions 1206
temperature regimes and an aquic moisture regime [17-
19].
In Florida, plant species richness increases with soil
moisture until an ecotone between mesic pine flatwoods
and Taxodium distichum swamps is reached [5,12,20,21].
This ecotone is occupied by wet flatwoods and wet sa-
vanna subtypes of the coastal pine flatwoods [14,22,23].
Their overstories are dominated with varying mixtures of
Pinus palustris, Pinus elliottii, Pinus clausa var. immu-
ginata), and/or Pinus serotina [24,25]. The herbaceous
ground cover of longleaf pine flatwoods is very diverse
due to the warm temperatures and high rainfall. Andro-
pogon virginicus, Serenoa repens, Aristida stricta var.
beyrichiana, Dichanthelium spp., Solidago odora, Rhexia
alifanus, and Aster adnatus are found throughout all of
the flatwoods types [26,27]. Where fire is restricted,
Smilax pumila can be a prevalent vine species, especially
on mature mesic sites [14]. Mesic longleaf pine flat-
woods are also occupied by greater populations of oak
species (Quercus pumila or laurifolia). Wet flatwoods
have a greater presence of Lyonia lucida, Cliftonia mo-
nophylla, Nyssa sylvatica var. biflora, and Ilex glabra or
coriacea. Wet pine savannas are distinguished from wet
flatwoods by fewer overstory trees, and a greater abun-
dance of Lachnanthes caroliniana, Cyperus, Scleria,
Sarracenia, and Calopogon or Platanthera [23,26,27].
Wet pine flatwoods and wet savannas are defined as pine-
dominated, poorly drained, broad plain wetlands [14,28,
29], and represent more than one million ha in the
Southeast [30]. There are almost 200 rare vascular plant
taxa found in the various longleaf pine habitats of the
Southeastern U.S. [5,12], with the majority of them be-
ing native to Florida where they are located in these wet
pine flatwoods and their associated wetlands [5,12,14,
21,31].
Three disturbance regimes are important when identi-
fying any pattern of structure or composition within coa-
stal longleaf pine [9,10,32,33]. Hurricanes directly affect
the canopy structure of longleaf pine stands through gale-
forced winds, opening up large tracts to sunlight and sim-
plifying the structure and composition of the flora that
occupy them [8]. The extensive flooding that accompa-
nies the wind causes significant changes in both the
above and below ground site productivity [8,9,34]. Fire
impacts longleaf pine forests by reducing vegetative
competition on regeneration through the removal of shrub
size oaks and hickories [35]. Finally, anthropogenic ef-
fects from urban development, grazing, prescribed fire,
and plantation forestry can reduce the structural com-
plexity of forests and promote fragmentation within the
landscape, reducing soil productivity and plant species
diversity [36,37].
The objectives of this study were to determine refer-
ence ecosystem conditions by assessing the soil bioche-
mical properties, overstory stand structure, and under-
story plant species richness along a patch-derived 110-
year chronosequence in order to accurately evaluate on-
going longleaf pine restoration projects [15,38]. This
ecological dataset was also used to classify each refer-
ence patch as mesic flatwoods, wet flatwoods, or wet
savanna; while verifying similarities between each patch
and conditions at restoration sites of the zone. The im-
portance of this work centers on our ability to distinguish
between the varieties of longleaf pine habitats found
along Florida’s Gulf in order to accurately assess their
condition. We hypothesized that stand diameter-at-breast
height (DBH), height, basal area (BA), and volume
would increase while stand density and plant species
richness would decrease when comparing younger pine
patches with older ones. We also expected the majority
of these parameters to reach a threshold (“plateau”) as
measured from within the older-aged patches [39].
2. Materials and Methods
2.1. Study Sites
Three reference locations were established within 3
kilometers of Florida’s Gulf coastline. A strict coastal
stratification was required to insure all of the reference
locations would be exposed to similar weather conditions,
specifically addressing the fact that Florida’s Gulf lies
within a very active hurricane zone [8,15,40]. The loca-
tions were Topsail Hill State Park, St. Marks National
Wildlife Refuge, and the Chassahowitzka Wildlife Man-
agement Area of the Florida Fish and Game Commission
(Figure 1). In addition to their coastal locations, these
sites were selected because of the presence of certain
plant communities, containing similar soil conditions,
and having active longleaf pine restoration programs
[15].
This narrow zone makes up the majority of the Natural
Resource Conservation Service’s Eastern Gulf Coast
Flatwoods ecoregion (MLRA 152A) and the National
Oceanic and Atmospheric Association’s Panhandle Coast
unit of the Louisianan reserve (National Estuary and
River Reserve System). In addition, the Environmental
Protection Agency (EPA) classifies this area as the
Southern Coastal Plain (75) ecoregion, which was re-
cently subdivided into the Gulf Coast Lowlands (75-01)
and the Big Bend Karst (75-06) [41,42]. All of these fe-
deral designations make this coastal zone unique from an
ecological as well as hydrological perspective.
All three sites have a soil moisture gradient as repre-
sented by mesic pine flatwoods, wet pine flatwoods, wet
pine savannas, and Taxodium distichum swamps. Their
common soils are described as sandy, siliceous, thermic,
aeric, acidic, and poorly drained. All three sites have
active longleaf pine restoration programs where fire has

The Ecological Classification of Coastal Wet Longleaf Pine (Pinus palustris) of Florida from Reference Conditions 1207
Topsail Hill State Preserve St Marks National Wildlife Refu
g
e
Chassahowitzka State Wildlife Area
Jacksonville
Atlantic Ocean
Tampa Bay
Gulf Coast Flatwoods with Marshes and Islands
Upland Ridges and Plains
Southwestern Flatlands
Eastern Flatlands and Upper Santa Fe Flatwoods
Everglades, Big Cypress, and Okefenokee swamps
Coastal Marshes and Islands
Okefenokee Plains
Griffith, 1994 and 2008 Environmental Protection Agency (EPA)
Key West
Figure 1. Locations of the three reference sites within the Gulf Coast Flatwoods subecoregion of Florida (Griffith et al. 1994,
2008).
and the Pickney sand series (sandy, siliceous, thermic,
cumulic, humaquepts) [18,45].
been prescribed for more than 25 years at approximately
a three-year-return interval. All of the sites are managed
by a state or federal agency to enhance habitat for
threatened species associated with longleaf pine eco-
systems.
Pine patches representing differently aged cohorts up
to 110-years of longleaf pine succession have been in-
cluded as the temporal scale applied in this study. The
reference site and chronosequencial scale were only de-
termined after an in-depth field survey of stand condi-
tions along Florida’s Gulf Coast Flatwoods zone.
The southern reference site on the spatial gradient is
the Chassahowitzka Wildlife Management Area
(28˚78′47″N, 82˚34′26″W) in Hernando County, FL. It is
approximately 12,140 ha, and the soils are dominated by
Myakka fine sands (sandy, siliceous, hyperthermic, aeric,
alaquods) and Basinger fine sands (sandy, siliceous, hy-
perthermic spodic Psammaquents) [17,43]. Even though
this site is found within the Big Bend Karst (75-06)
subecoregion, its coastal location contain vegetation and
soils with greater similarity to the other study sites lo-
cated within the neighboring Gulf Coast Lowlands (75-
01) subecoregion [42,43]. The St. Marks National Wild-
life Refuge (30˚6′18″N, 85˚11′7″W) in Wakulla and Jef-
ferson Counties, FL consists of 25,900 ha with the major
soils being the Leon series (sandy, siliceous, thermic,
aeric, alaquods) and the Scranton series (sandy, siliceous,
thermic, humaqueptic, Psammaquents); [19,44]. Topsail
Hill State Park (30˚22′15″N, 86˚16′20″W) in Walton
County, FL, contains 610 ha of some of the oldest long-
leaf pine stands in Florida. The park also contains im-
portant dune lake habitat. The soils are the Leon series
2.2. Patch Age-Tree Size Classes
The 110-year patch-derived chronosequence is based
upon measuring the selected longleaf pine patches start-
ing from six years after stand replacement to the oldest
patches (cohort) measured within our reference sites.
Each reference location contained three distinctly-aged
pine forests (one-hectare blocks) where four randomly
placed 400 m2 pine patches were measured from within
each block. The patch size was based upon earlier long-
leaf pine flatwoods research which found the average gap
size (cohort) to vary from 335 - 410 m2 within natural
pine stands [46]. The three following age-tree size class
descriptions based upon collected field data provided a
means of tying patch age to stand structure [39,47].
The young age-tree size class: A young age pine patch
exists when at least 70% of the stocking is found as seed-
lings and saplings. Any minor pole component should

The Ecological Classification of Coastal Wet Longleaf Pine (Pinus palustris) of Florida from Reference Conditions 1208
have an average DBH less than 20 cm. The mid-age-tree
size class: The mid age pine patch exists when at least
70% of the stocking is dominated by a mixture of poles
and small sawlog size trees (10 - 30 cm DBH). The ma-
ture age-tree size class: A mature pine patch exists when
at least 70% of the stocking is dominated by sawlog size
trees (30 - 45 cm DBH). For this study, a seedling is de-
fined as a woody plant that is generally less than 91.5 cm
in height, while a sapling is a woody plant with a diameter-
at-breast height (DBH) of less than 10 cm but greater
than 2.5 cm. Finally, a tree is defined as a woody plant
with a DBH of greater than 10 cm [48].
2.3. Data Collection
In order to examine natural phenomena as they exist, we
conducted a non-experimental comparative survey of the
ecological attributes from within the three reference lo-
cations. Therefore, field data collection utilized a modi-
fied nested approach to correspond with the average pine
patch size found in Florida’s coastal natural longleaf pine
flatwoods [46,49]. Each reference location had three
one-hectare blocks, representing each of the three pre-
viously defined patch age-tree size classes. Each one-
hectare block contained four randomly placed 400 m2
patches (cohort) used to take measurements [39]. Patch
size was based on earlier longleaf pine flatwoods re-
search that found the average gap size to vary from 335
to 410 m2 in natural pine stands [46]. Tree height and
DBH were measured on all trees greater than 10 cm. All
saplings were measured for height and diameter (root
collar). Patch density (trees/ha), basal area (BA) (m2/ha)
and standing volume (m3/ha) were calculated from these
data. At least 30% of the representative trees were cored
at breast height to determine patch age. The equation
used for tree volume was: Volume (V) =
(0.000078539816 * (DBH2)) * tree height [48].
Each 400 m2 measurement patch contained four 1-m2
plots randomly nested within the larger patch for under-
story plant sampling. Stem counts and percent cover of
each plant species were assessed using a modified Dau-
benmire method incorporating eight different coverages
[49-51]. The list of species is found in the Species Code
List (see Appendix A). Shannon-Weiner diversity values
were calculated for each patch [52].
2.4. Soil Sampling and Preparation
Soil samples were taken from the top 10 cm within each
1 m2 vegetation quadrat and stored at 4˚C until analysis.
Sub-samples (20 g) were analyzed for soil pH by pre-
pared slurries using a soil-to-water ratio of 1-to-2 [53],
percent organic matter (SOM) content by the Walkley-
Black method [54], and a sieved and dried (105˚C) sub-
sample was used to determine gravimetric moisture con-
tent. Net nitrogen mineralization rates (NMIN) were esti-
mated from in-situ incubation of soil samples [55,56].
Soil microbial biomass carbon (CMB) was determined by
chloroform fumigation-extraction [57].
2.5. Statistical Analyses
A three-level balanced nested plot design was incorpo-
rated into a stratified random sample in order to integrate
the different ecosystem attributes measured at different
scales, and among sites. Patches previous grouped by the
three age-tree size classes were further stratified using
the specific ages of the cohorts they contained into five
distinct time intervals (6 - 10, 17 - 34, 36 - 52, 60 - 71, 86
- 110). This allowed us to analyze changes in forest stru-
cture and plant species composition from one time inter-
val to the next [58,59].
Since we conducted a non-experimental comparative
survey of the ecological attributes of each site, the samp-
ling of these nine distinct reference sites produced a da-
taset where the normality assumption needed for the
analysis of variance (ANOVA) was not justified. There-
fore, trends over time and between variables were ob-
tained from linear polynomial regression using the gene-
ral linear model [60]. The 2nd order polynomial re-
gression equation standard form is y = a0 + a1x + a2x2 + ε.
Regression models were validated by comparing resi-
duals with predicted values along normal Q-Q plots and
comparing F-ratios to eliminate higher order terms.
Models were also tested for multicollinearity by variance
inflation factors and condition index numbers.
PC-ORD, a PC-based program [61] containing an
algorithm for Canonical Correspondence Analysis (CCA)
was used to examine the overall spatial structure of the
individual reference patches by identifying the under-
story plant species along vectors (gradients) for soil
chemical, net nitrogen mineralization, and soil microbial
biomass values found among the study sites [62]. Linear
combinations of the environmental variables were used
to maximize the separation of plant species along four
biplot axes. Site scores were derived from the weighted
averages of the associated species scores. Community
structure was illustrated by the influence of different
environmental variables upon plant species ordination
[63].
Plant species indicator analysis (IndVal) was used to
measure the level of relationship between a given plant
species to categorical units such as pine flatwoods sub-
types or patch age intervals. It was also used to attribute
different plant species to particular soil biogeochemical
conditions based on the abundance and occurrence of
those species within the selected group. Indicator values
range from 0 to 100, with “100” being a perfect indicator
and “0” a no affiliation score. Because indicator species

The Ecological Classification of Coastal Wet Longleaf Pine (Pinus palustris) of Florida from Reference Conditions 1209
analysis is a statistical inference, the Monte Carlo permu-
tation test procedure (1000 iterations) was used to estab-
lish significance of a p-value as determined by the num-
ber of random runs greater than or equal to the inferred
value (
 = 0.10). Accuracy was defined from the bino-
mial 95% confidence interval [64,65]. Hypothesis testing
for differences between field data grouped by two soil
drainage classes was accomplished by using two-sample
t-test with an alpha of 0.05 and a two-tailed confidence
interval. A Mixed model REML with F-ratios was used
to determine the power of each collected field variable
within the nested design along spatial and temporal
scales [60].
3. Results and Discussion
3.1. Soil Types
All three sites contained taxonomically similar soil types.
All of the soils had similar soil properties (sandy, acidic,
thermic, aquic, and poorly drained). The soils were also
found to be functionally equivalent (NMIN, CMB and BA);
even when compared by drainage class (Table 1). The
only significant difference was soil organic matter con-
tent between poorly drained and very poorly drained soils.
3.2. Overstory Stand Structure
A total of 36 measured pine patches resulted in 26 diffe-
rently aged cohorts along the chronosequence. Five dis-
tinct patch age intervals were identified by data analysis.
They were Young (6 - 10 years), Young-Midaged (17 -
34 years), Midaged (36 - 53 years), Mid-Mature (60 - 71
years), and Mature (86 - 110 years). The mean patch
DBH, height, BA, and volume increased significantly
among the five time intervals (Table 2). For example, the
mean DBH for patches between 6 - 10 years after estab-
lishment was approximately 6.0 cm, 20 - 25 cm for the
patches 35 - 52 years, and greater than 30.0 cm for the
patches greater than 85 years (mature age). Height, BA,
and volume exhibited similar results, even though stand
density was highly variable with no identifiable temporal
patterns (Table 2).
Polynomial regression analysis revealed all of the
stand variables, except for stand density, increased with
patch age. Patch mean DBH and height increased with
age until they reached an asymptote at 85 - 90 yrs. Stand
basal area and volume followed similar regression curves
as with DBH and height (Figure 2). The diameter distri-
bution of trees by patch age interval reflected the in-
crease in diameter (Figure 3).
Table 1. Soil and stand properties between the three reference sites.
Location Soil Great Group Soil Texture (Top
10 cm) Moisture Regime Temperature Regime Drainage Class
Chassahowitzka Wildlife
Management Area
Psammaquent Sandy Aquic Hyperthermic
Very poorly
drained
Alaquod Sandy Aquic Hyperthermic Poorly drained
St. Marks National Wildlife
Refuge
Psammaquent Sandy Aquic Thermic
Very poorly
drained
Alaquod Sandy Aquic Thermic Poorly drained
Topsail Hill State Preserve
Humaquept Sandy Aquic Thermic
Very poorly
drained
Alaquod Sandy Aquic Thermic Poorly drained
Stand Basal Area and Soil Biochemical Properties (Mean Values*)
Drainage Class Stand Basal Area
(m2/ha) pH-log [H+]
Net Nitrogen
Mineralization Rates
(mg N/kg–1 soil/month–1)
Microbial Biomass Carbon
(mg C/kg–1 soil)
Very poorly drained 6.5a 4.4a 11.6a 374.3a
Poorly drained 8.3a 4.5a 9.9a 356.1a
*Means between drainage classes followed by the same lower case letters are not significantly different (alpha = 0.05).

The Ecological Classification of Coastal Wet Longleaf Pine (Pinus palustris) of Florida from Reference Conditions 1210
Table 2. Stand attributes and specie s ri c hness by patch age interval.
Patch Age
Interval
(years)
Patch Age Class Mean Patch
Diameter (cm)
Mean Patch
Height (m)
Patch
Density
(trees/ha)
Patch Basal Area
(m2/ha)
Patch Volume
(m3/ha)
Shannon-Wiener
Diversity H'
6 - 10 Young 5.1 (0.53) 2.5 (0.32) 258 (20.1) 0.12 (0.02) 9.3 (0.10) 1.96 (0.05)
17 - 34 Young/Mid 19.1 (0.77) 10.2 (0.25) 293 (20.0) 6.81 (0.48) 75.5 (5.59) 2.07 (0.05)
36 - 52 Mid-Age 25.5 (1.04) 15.4 (0.80) 211 (30.4) 8.56 (1.21) 138.3 (22.61) 1.75 (0.06)
60 - 71 Mid/Mature 29.6 (1.54) 15.4 (0.59) 229 (23.6) 11.59 (1.30) 186.9 (23.78) 1.75 (0.07)
86 - 110 Mature 29.9 (1.2) 16.6 (0.46) 190 (8.7) 11.83 (0.46) 214.3 (6.91) 1.44 (0.04)
The sample size for stand data by age class was n ≥ 6; and for the vegetation-soils data n ≥ 12.
y = -0.0043x
2
+ 0.7571x + 0.4671
R
2
= 0.75
p < 0.0013
0
5
10
15
20
25
30
35
40
45
50
025 50 75100125
Mean Patch DBH (cm)
y = -0.0026x
2
+ 0.4109x + 0.9501
R
2
= 0.72
p < 0.0004
0
2
4
6
8
10
12
14
16
18
20
22
0255075100 125
M e an Pa t c h He ig ht ( m)
y = -0.0018x
2
+ 0.3297x -1.9355
R
2
= 0.46
p < 0.0025
0
5
10
15
20
25
025 50 75100125
Mean Patch Basal Area ( m
2
/ ha)
Mean Patch Age (Years)
y = -0.0115x
2
+ 3.6533x -24.902
R
2
= 0.76
p <0 .0004
0
50
100
150
200
250
300
350
0255075100 125
Mean Patch V olume(m
3
/ ha)
Mean Patch Age (Years)
Figure 2. Mean stand DBH, height, BA, and volume along a 110-year longleaf pine chronosequence as measured from 26
differently aged pine patches.
3.3. Understory Plants
The three reference sites shared more than 45 plant spe-
cies in common (Appendix A). The three most common
understory species were Ilex glabra, Quercus pumila,
and Serenoa repens. A species found in rare numbers
among sites was Xyris caroliniana, a wetland indicator.
The abundance of grasses and forbs decreased while the
abundance of shrubs increased over the chronosequence
(p < 0.05; Figure 4). The Shannon-Wiener diversity in-
dex decreased as patch age increased, while having a range
from 2.07 - 1.44 for the dataset (Table 2; Figure 5).
3.4. Site Classification
Smilax pumila, Hypericum hypericoides, and Gaylussa-
cia frondosa were the dominant plant species indicators
for mesic flatwoods (p ≤ 0.038), Aristida stricta var. bey-
richiana, and Dichanthelium ovale were the dominant
plant species indicators for the wet flatwoods subtype (p
≤ 0.001), while Lachnanthes caroliniana and Scleria
cilliata were the dominant plant species indicators for the
wet savanna subtype (p ≤ 0.009; Table 3). Twenty (20)
patches were classified as mesic flatwoods, 7 patches as
wet flatwoods, and 9 patches as wet savanna.

The Ecological Classification of Coastal Wet Longleaf Pine (Pinus palustris) of Florida from Reference Conditions 1211
0%
5%
10%
15%
20%
25%
30%
35%
10 15 20 2530 35 40
Relat i ve Frequency (% )
Diam e t er (cm ) di stributi on in 17-34 y ear o l d
patches
0%
5%
10%
15%
20%
25%
30%
35%
10 15 20 25 30 35 40 45
Relat i ve F requency (%)
Diam eter (cm) di stri bu t i on i n 36-52 year ol d
patches
0%
5%
10%
15%
20%
25%
30%
35%
10 15 20 25 30 3540 45
Relat i ve F requency (% )
D i am et er (cm) d i st ri but i on in 60-71 y ear ol d
patches
0%
5%
10%
15%
20%
25%
30%
35%
10 15 20 25 30 35 40 45
Relative Freq uency (%)
Diameter (cm) distribution in 86-110 year old
patches
Figure 3. Diameter distribution of trees 10 cm d.b.h. and greater within the four patch age intervals as measured from 26
differently aged pine patches.
0
10
20
30
40
50
Gras sesForbsS hrubsVinesB are Ground
Perc ent Co ver (%)
Vegetative Typ e
6-1 7 yea rs24-52 years60-110 years
c
b
a
a
a
bb
d
a
aa
a
c
d
d
Figure 4. Composition of understory vegetation by patch
age interval.
y = -0.0075x + 2.2424
R²= 0.68
p < 0.0001
1.00
1.20
1.40
1.60
1.80
2.00
2.20
2.40
0 204060801001
Shannon-Wiener Diversity H'
20
Patch Age (Years)
Figure 5. Shannon-Wiener Diversity index along a 110-year
longleaf pine chronosequence as measured from 26 diffe-
rently aged pine patches.

The Ecological Classification of Coastal Wet Longleaf Pine (Pinus palustris) of Florida from Reference Conditions 1212
Table 3. Plant indicator values (IndVal*) (percent of perfect indication) with associated environmental variable by pine flat-
woods type. P-values represent the proportion of randomized runs (1000) equal to or less than observed values (
 = 0.1).
Pine Subtype
Pine Subtype Plant Species Mesic Wet FlatwoodsWet SavannaSD P-Value Veg Type
Mesic Flatwoods Smilax pumila 25 1 5 4.69 0.038 Vine
Hypericum hypericoides 17 1 0 3.08 0.024 Forb
Gaylussacia frondosa 16 0 4 3.3 0.057 Shrub
Pteridium aquilinum 12 0 1 3 0.066 Fern
Wet Flatwoods Lachnanthes caroliana 0 52 4 3.57 0.001 Forb
Arisitida beyrichiana 0 36 0 3.51 0.001 Grass
Dichanthelium ovale 6 36 7 4.41 0.007 Grass
Cyperus ssp. 1 11 1 2.67 0.088 Grass
Wet Savanna Il ex gl abra 19 13 38 3.55 0.009 Shrub
Scleria ssp. 17 3 29 3.31 0.014 Grass
*INDICATOR VALUES (% of perfect indication based on combining the values for relative abundance and relative frequency) n = 48.
3.5. Discussion
There are four assumptions which must be met in order
to insure the credible use of space-for-time substitutions
(chronosequence) when studying ecosystem change [66].
They include having strong similarities in vegetative
composition, soil properties, and climatic patterns, while
sharing the same position in the landscape. There is also
a need to have an extensive knowledge of the land-use
history of each site. The use of a chronosequence to study
secondary succession in coastal longleaf pine patches
was justified given the close similarities (greater than 45
common species) in plant species composition, their al-
most identical soil properties found at each site, their
location within the same climatic zone, their equivalent
positions on the landscape, and the known 25 year land-
use history of each reference site [67-69].
There were six major hurricanes which passed through
our study sites during the 2004-2005 field seasons. The
use of a strict coastline stratification proved to be effect-
tive at limiting the differences between the reference
sites from the impacts of high winds and flooding on the
forest canopies and soil properties of each site. The re-
sults on stand attributes, understory species diversity, and
diameter distributions verify the effectiveness of the
patch age intervals at stratifying the dataset (Table 2;
Figure 3).
In response to criticisms against the use of the buried
bag technique and the determination of field net miner-
alization rates instead of gross nitrogen fluxes [70,71],
there was no need to determine the absolute (gross) le-
vels of nitrogen uptake in this study. The wetland con-
ditions of the sampled soils made the comparative mea-
surement of ammonium more important then nitrate.
When the purpose of the study is to compare similar for-
ested wetland sites, it is perfectly justified to use poly-
ethylene bags to determine the net nitrogen mineraliza-
tion rates. The wetland conditions make the use of the
ion exchange-resin bag technique very limited since the
resin bags favor the collection of nitrate, and under esti-
mate the levels of ammonium [70,72]. The use of poly-
ethylene bags preserved the assessment of ammonium in
saturated soils [73]. The plant uptake of nitrogen was less
important since plants can compete for nitrate easier than
they can for ammonium, which is the preferred source of
nitrogen for microbes [74]. Wienhold (2007) found in-
situ estimates are more reflective of field conditions than
either anaerobic estimates or laboratory incubations [75].
The overstory variables of mean patch DBH, patch BA,
volume, and to a lesser degree patch height exhibited
strong positive relationships with the age of the pine
patches between 6 - 110 years. But, patch tree density
showed no clear pattern along the chronosequence, ow-
ing to the high variability found within the patches along
Florida’s Gulf. This is a reasonable result given the num-
ber of major hurricanes which impacted this landscape
just prior to measurement. Patch tree density was con-
tinuously impacted by this disturbance regime during the
life of the study. The ecological dataset showed most of
the growth variables reaching an asymptote around 80 -
90 years. When the measured stand data from these pine
patches was compared to growth and yield data from a
group of thinned natural longleaf pine stands from across
the eastern Gulf, our patches were found to have lower
basal area (14 m2 vs. 25 m2) at age 30, but comparable
stand volumes (150 m3 vs. 130 m3) at age 60 [76]. Our
restoration threshold of 80-90 years was found to have a
regional difference with the threshold age of 110 years
for longleaf pine ecosystems in Texas, reported by Chap-
man (1909) [77].
Prescribed fire on a three year return-interval did not
prevent shrub species from increasing or graminoid spe-

The Ecological Classification of Coastal Wet Longleaf Pine (Pinus palustris) of Florida from Reference Conditions 1213
cies from declining as the age of the pine patch increased.
This result could be explained by lower intensive pre-
scribed fires having less of an effect within the wet con-
ditions encountered at our reference sites.
The vegetative and environmental variables collected
from the reference sites were effective for ecologically
classifying all of the patches. However, soil properties
were stronger determinants of specific ecosystem condi-
tions than were patch age determinations (Table 1; Fig-
ure 6).
4. Conclusions
All of the sites were found to have functionally equi-
valent soils and shared more than 40 plant species in
common. Patch DBH, height, and basal area increased
until 80 - 90 years when they reached a plateau. Shrub
species were significantly higher in the mature-aged pat-
ches compared to either the young or mid-aged patches.
These combined results infer that Florida’s Gulf coastal
wet longleaf pine flatwoods attain a structural and plant
species equilibrium at approximately 80 - 90 years. Soil
biochemical properties, forest structure, and understory
species composition were effective for ecologically clas-
sifying our pine patches as 55% mesic flatwoods, 20%
wet flatwoods, and 25% wet savanna within Florida’s
highly disturbed Gulf coast.
One area of this research warrants further attention.
Our research found that plant species classified as “shrubs”
dominated the mature-aged stands even with aggressive
fire management programs. Many of these “woody”
plant species do not have pioneer patterns similar to Ilex
Axis 2Soil pH
Pine Flat Type
1 Mesic Flatwoods
2 Wet Flatwoods
3 Wet Savanna
Axis 1
CMB
SOM
Soil Moisture
Figure 6. Pine flatwoods type determined by a four-dimen-
sional ordination biplot derived from Canonical Correspon-
dence Analysis (CCA) of 144 plots using understory plant
species abundance and soil biogeochemical data (SOM, soil
organic matter; CMB, microbial biomass carbon) from the
three reference sites .
glabra, Serenoa repens, or Quercus pumila. They never
dominated the site. There should be studies that focus on
these lesser known woody species and their possible be-
nefits to mature longleaf pine forest ecosystems.
5. Acknowledgements
We would like to thank Leda Suydan & Tom Ervin of
Topsail Hill Preserve State Park, Joe Reinman of the St.
Marks National Wildlife Refuge, and Mike Wichrowski
& Paul Hansen of the Chassahowitzka Wildlife Man-
agement Area of Florida’s Fish & Wildlife Commission
for providing assistance with establishment of the refe-
rence sites and obtaining the research permits.
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The Ecological Classification of Coastal Wet Longleaf Pine (Pinus palustris) of Florida from Reference Conditions 1217
Appendix A
Species Code List Table A-1. Species list.
Scientific name Code Common name
Shrubs
Asiminaincana Asin Wooly paw paw
Cyrillaracemiflora Cyra Titi
Gaylussaciadumosa Gadu Drawf huckleberry
Gaylussacia frondosa Gafr Dangleberry
Ilex coriacea Ilca Large gallberry
Ilex glabra Ilgl Gallberry
Ilex vomitoria Ilvo Yaupon
Kalmia hirsuta Kahi Hairy wicky
Licaniamichauxii Limi Gopher apple
Lyonia lucida Lylu Fetterbush
Magnolia virginiana Mavi sweet bay
Myricacerifera Myce Wax myrtle
Photiniapyrifolia Phpy Red choke berry
Quercus pumila Qupu Running oak
Serenoa repens Sere Saw palmetto
Stillangiasylvatica Stsy Queens delight
Vacciniumspp. Vacc Blueberry spp
Grasses
Andropogon virginicus Anvi Bluestem grasses
Aristida stricta var. beyrichiana Arbe Wiregrass
Calamovilfacurtissii Cacu Curtis sandgrass
Cteniumaromaticum Ctar Toothache grass
Cyperus Cype Sedge spp
Eragrostisspectabilis Ersp Purple lovegrass
Dichanthelium ovale Dich Eggleaf witch grass
Panicum - Dichanthelium Pani Panicumspp
Dichantheliumerectifolium Paer Erect leaf witchgrass
Panicumlaxiflorum Pala Velvet Witchgrass
Scleriassp. Scle Nutrushspp
Xyris caroliniana Xyca Yellow eyed grass
Forbs
Asclepiasviridula Asvi Southern milkweed
Aster adnatus Asad Scaleleaf aster
Aster eryngiifolius Aser Thistleleaf aster
Aster reticulatus Asre White top aster
Aster tortifolius Asto Dixie aster
Carphephorouspseudoliatris Caps Bristleleafchaffhead
Carphephorusodoratissimus Caod Deer tongue
Chrysopsis Chry Silkgrassspp
Conyzacanadensis Coca Canadian horseweed

The Ecological Classification of Coastal Wet Longleaf Pine (Pinus palustris) of Florida from Reference Conditions 1218
Continued
Coreopsis linifolia Coli Texas tickseed
Desmodiumrotundifolium Dero Tricklyfoil
Droseracapillaris Drca Pink sundew
Elephantopustomentosus Elto Devils grandmother
Eupatorium capillifoliu m Euca Dog fennel
Eupatorium compositifolium Euco Yankee weed
Eupatoriummohrii Eumo Mohr’s thoroughwort
Eupatoriumpilosum Eupi Rough Boneset
Euthamiagraminifolia Eugr Flat top goldenrod
Gelsemiumsempervirens Gese Yellow jessamine
Gratiolahispida Grhi Rough Hedgehyssop
Hypericum hypericoides Hyhy St. Andrews cross
Hypoxissessilis Hyse Glossyseed yellow stargrass
Hypoxisspp. Hypo Stargrassspp
Lachnanthes caroliniana Laca Carolina redroot
Lachnocaulon anceps Laan Whitehead bogbutton
Lecheapulchella Lepu Leggett’s pineweed
Liatrisgracilis Ligr Slender gayfeather
Liatristenuifolia Lite Shortleaf gayfeather
Mimosa quadrivalvis Miqu Sensitive brier
Oenotherafruticosa Oefr Evening primrose
Opuntiahumifusa Ophu Prickly pear
Pityopsisgraminifolia Pigr Silkgrass
Pterocaulonpycnostachyum Ptpy Blackroot
Rhexia alifanus Rhal Meadow beauty
Rhexiapetiolata Rhpe Fringed meadow beauty
Sabatiabrevifolia Sabr Shortleaf Rosegentian
Seymeriacassioides Seca Yaupon Blacksenna
Smilax laurifolia Smla Laurel green brier
Smilax pumila Smpu Green brier
Solidago odora Sood goldenrod
Stylismapatens Stpa Coastal plain dawn flower
Tragiaurens Trur Wavyleafnoseburn
Verbena brasiliensis Vebr Brazilian vervain
Viola septemloba Vise Blue violet
Vitisrotundifolia Viro Muscadine