Developing Ecological Criteria for Prescribed Fire in 

South Florida Pine Rockland Ecosystems 

(USGS Open File Report OF 2006-1062) 

James R. Snyder 

Michael S. Ross 

Suzanne Koptur 

Jay P. Sah 

July 2005
Final Report 

Developing Ecological Criteria for Prescribed Fire in South Florida Pine Rockland 

Ecosystems 

USGS Open File Report OF 2006-1062 

Interagency Agreement between U.S. Geological Survey and Bureau of Land 

Management 

BLM #1422-R220A5-0012 Modification Number 2 

April 29, 1997 

Submitted by 

James R. Snyder 

USGS, Florida Integrated Science Center 

Big Cypress Field Station 

Ochopee, FL 

Michael S. Ross 

Southeast Environmental Research Center 

Florida International University 

Miami, FL 

Suzanne Koptur 

Department of Biological Sciences 

Florida International University 

Miami, FL 

Jay P. Sah 

Southeast Environmental Research Center 

Florida International University 

Miami, FL 

July 2005

ii 
Table of Contents 

Summary iv

1. Introduction 1 

2. Experimental Burns 10 

 

3. Trees 16 

 

4. Palms 29

 

5. Shrubs 43 

6. Herb layer 49 

7. Herbivory 60 

8. Biomass dynamics 79 

9. Conclusions 92 

10. Acknowledgements 93 

11. Literature Cited 94 

12. Appendices 100 

 GPS coordinates of plot corners 100 

Vascular plant species list 102 

 

iii 
Executive Summary 

The pine rocklands of South Florida, characterized by a rich herbaceous flora with 

many narrowly endemic taxa beneath an overstory of south Florida slash pine (Pinus elliottii 

var. densa), are found in three areas: the Miami Rock Ridge of southeastern peninsular 

Florida, the Lower Florida Keys, and slightly elevated portions of the southern Big Cypress 

National Preserve. Fire is an important element in these ecosystems, since in its absence the 

pine canopy is likely to be replaced by dense hardwoods, resulting in loss of the

characteristic pineland herb flora. Prescribed fire has been used in Florida Keys pine forests 

since the creation of the National Key Deer Refuge (NKDR), with the primary aim of 

reducing fuels. Because fire can also be an effective tool in shaping ecological communities,

we conducted a 4-year research study which explored a range of fire management options in 

NKDR. The intent of the study was to provide the Fish and Wildlife Service and other land 

managers with information regarding when and where to burn in order to perpetuate these 

unique forests. 

In 1998 we initiated a burning experiment in a randomized complete block design. 

Three treatments were to be carried out in a single well-defined block in each of two 

characteristic understory types during each year from 1998 through 2000. One understory 

type was characterized by a relatively sparse shrub layer and a well-developed herb layer 

("open"), and the second had a dense shrub layer and poorly developed herb layer 

("shrubby"). The three burn treatments were: (a) summer burn, (b) winter burn, and (c) no 

burn, or control. Three 1- ha plots were established in each block, and randomly assigned to 

the three treatments. Though the first year experimental burns were carried out without 

incident, constraints posed by external factors, including nationwide and statewide 

prohibitions on prescribed burning due to wildfires in other regions, delayed the experimental

burns and precluded collection of postburn data on one third of the burns. Ultimately we

burned only eleven plots, three in winter and eight in summer, over a four-year period from 

1998 to 2001. Vegetation was sampled in a stratified, nested design within 18 plots. Trees 

were sampled in a 1.0-ha plot, shrubs in twenty 50-m2 circular (radius 4 m) subplots within 

the tree plot, and the herb layer in four circular 1-m2 quadrats (radius 0.57 m) within each 

subplot. The amount of fuel in the shrub layer was estimated by applying regression models 

to plant dimensional data, and ground layer fuel was estimated by a harvest method. The 

effects of Key deer herbivory on regeneration of the understory pine rockland plant 

community after fire was studied by monitoring inside and outside exclosures established 

within two of the six blocks. 

Pine trees constituted more than half (53.3%) of the biomass, but understory fine 

fuels comprised a surprisingly high proportion of total aboveground biomass. In the three 

blocks in which paired summer and winter burns were successfully conducted, the summer

burns were more intense than the winter burns as judged by our indicators of fire intensity. 

Because of the differences in fire intensity between seasons, it was not possible to say

whether observed differences in vegetation response between summer and winter burns were 

due to season or to fire intensity. The mortality of South Florida slash pine trees was greater 

after the summer burn than the winter burn in each block, but other vegetation responses

iv 
were rarely as consistent. For instance, Metopium showed less recovery after summer burns 

in two blocks and after the winter burn in the third block. Moreover, there were instances in 

which alternative growth stages of the same species responded differently. Adult palms 

succumbed more frequently to summer than winter burns, and mortality of Coccothrinax 

exceeded that of Thrinax. In contrast, small palms recovered more readily after summer 

burns than winter burns. High intensity fires may be beneficial for some plant responses, e.g., 

the germination of Jacquemontia pentantha seeds. Deer herbivory was greater in burned 

areas, but also present on some plants in unburned control areas. 

During the course of this study a series of permanently marked vegetation plots were 

established. There are now 18 permanently marked 1.0-ha plots on Big Pine Key in which 

all trees with DBH ≥ 5 cm (> 11,400 stems) have been tagged and measured. Plots should be 

periodically re-measured to follow growth, mortality, and recruitment into the tree stratum.

Within each of the 18 plots there are also 20 marked shrub plots in which woody plants and 

palms ≥ 1 m tall have been tallied, and 80 individually marked permanent herb quadrats in 

which the abundance of all species present has been estimated. These plots will be 

immensely valuable to document future changes, whether due to prescribed fire, wildfire, 

hurricane, sea level rise, or other unforeseen disturbances. 

v 
1. INTRODUCTION 

Background 

The pine rocklands of South Florida are globally endangered ecosystems containing a 

rich herbaceous flora with many narrowly endemic taxa beneath an overstory of south Florida 

slash pine (Pinus elliottii var. densa). These once-extensive communities have borne more than 

their share of the development that has occurred in the region, and are now greatly reduced in 

area and divided into a number of smaller fragments (U.S. Fish and Wildlife Service 1999). The 

increasing influence of man in the landscape has altered many critical ecosystem processes,

including the fire regime, hydrological balance, availability of exotic seed sources, and extent of

gene flow within many key species. It is critical that a management strategy for conserving 

South Florida rocklands be developed to address these threats. 

Pine rocklands are found in three areas of South Florida (Snyder et al. 1990): the Miami

Rock Ridge of southeastern peninsular Florida, with the largest remaining area in Everglades 

National Park; the Lower Florida Keys, mostly within the National Key Deer Refuge; and the 

southern Big Cypress pinelands, entirely within Big Cypress National Preserve. The latter pine 

rocklands are perhaps better considered transitional to typical pine flatwoods to the north 

because a shallow layer of sand generally covers the limestone substrate and many of the tropical 

elements of the flora are missing. The Miami Rock Ridge and the Lower Keys pinelands grow 

on a substrate of oolitic limestone. They are characterized by a very diverse shrub layer 

dominated by hardwoods of West Indian distribution and a few species of palms. The herb layer 

is likewise diverse and contains 35 taxa endemic to southern Florida, including several species 

listed or under review for listing by the federal government (Snyder et al. 1990). Similar pine

rocklands exist in several of the Bahama islands, although the canopy tree in this case is Pinus 

caribaea var. bahamensis. 

The importance of fire as a part of this ecosystem has long been recognized (Robertson 

1953, Wade et al. 1980). In the absence of fire, a closed hardwood canopy develops and the 

characteristic pineland herb flora is lost (Robertson 1953, Alexander 1967, Loope and Dunevitz 

1981). In fact, it is the requirement of these endemic herbaceous species for fire that helped lead

to the establishment of the prescribed burning program in Everglades National Park over 50 

years ago (Robertson 1953). In the Lower Keys pinelands, the importance of fire for Key deer

habitat maintenance is well known (Dickson 1955, Alexander and Dickson 1972, Carlson 1989). 

Robertson (1955) studied the vegetation of the area as part of an analysis of South Florida 

breeding birds. Recent studies by Ross et al. (1992a,b; 1994) document the physical factors 

associated with the distribution of Lower Keys pinelands. 

Lower Keys pine rockland 

The pine forests of the Lower Keys are a small but biologically important representative 

of South Florida pine rocklands. There are less than 1000 ha of Lower Keys pinelands scattered 

over seven islands, yet this ecosystem is the major habitat for the Key deer (Dickson 1955). It is 

also home to 14 herbs endemic to South Florida, five of which occur only in these Lower Keys 

1 
settings (Avery and Loope 1980). In addition to several endemic plants under consideration for 

federal listing (e.g. Chamaecrista lineata var. keyensis, Chamaesyce deltoidea var. serpyllum, 

Linum arenicola, Melanthera parvifolia [Ross and Ruiz 1996]), two endemic butterfly taxa, the 

Florida leafwing and Bartram's hairstreak, are under consideration for listing (Wood 1994). The

larval food plant of both butterflies is Croton linearis, an herb common in Keys pinelands. 

Protection and management of the Lower Keys pinelands is made particularly difficult by 

the pattern of land ownership and development. Much of the remaining pinelands are in small,

privately owned parcels and there are private inholdings within the National Key Deer Refuge. 

Private residences embedded within the fire-sustained pineland habitat represent a classic 

example of the urban/wildland interface problem. In a more fragmented but otherwise similar 

setting in southern Miami-Dade County, an Ips beetle outbreak after Hurricane Andrew has left 

many of the isolated parcels without a single mature pine tree. The severity of the insect damage 

has been attributed to improper fire management by several researchers (Doren et al. 1993; 

Loope et al. 1994). Great expense and tenacity will be required to salvage even a remnant of the 

urban pine rockland ecosystem in Miami under current conditions. The daunting problems faced 

by resource management agencies include severe invasion by exotic trees and grasses, widely 

scattered sources of pine regeneration, and little experience with prescribed fire in such close 

quarters. The situation in the Keys is not so bleak, and the results of this study should assist in 

restoration of the Miami-Dade County pinelands. 

There has been sporadic prescribed burning in the National Key Deer Refuge since its 

creation (Berg and Wisby 1996). From 1985 to 1992 prescribed burns were conducted in the 

Refuge mainly for fuel reduction, without much consideration of ecological factors such as

season of burning. It has been shown elsewhere that season of burning can have marked 

influence on the response of pines, understory hardwoods, and herbaceous plants (Robbins and 

Myers 1992, Platt et al. 1988, Snyder 1986, Spier and Snyder 1998, Waldrop et al. 1992). There 

was no prescribed burning by U.S. Fish and Wildlife Service (FWS) staff in the Refuge from

1992-1997, in part because not enough was known about the ecological effects of prescribed fire 

in this system. 

Objective 

The objective of this study was to document the response of pine rockland vegetation to a 

range of fire management options. The ultimate goal of the project was to provide FWS and 

other land managers with information useful in deciding when and where to burn to perpetuate 

these unique pine forests. 

Study design 

Our proposal called for experimental burns to be carried out at two times of the year: 

during the rainy season, when lightning-caused fires are likely to occur (Robertson 1953, Wade

et al. 1980, Snyder 1991) and when plants are actively growing; and in the dry season, when 

plants are most dormant and burning conditions are relatively mild. For simplicity, the two 

2 
seasonal treatments are referred to as summer and winter, respectively. In addition to comparing 

season of burning, we wanted to compare burns in two contrasting types of pineland: pineland 

with relatively sparse shrub layer and a well-developed herb layer, referred to as open, and 

pineland with a dense shrub layer and a less developed herb layer, referred to as shrubby. 

On Big Pine Key we identified three blocks of open pineland and three blocks of shrubby 

pineland. Within each block we established three 1.0-ha experimental plots, one to be burned in

the summer, one to be burned in the winter, and one to remain unburned as a control (Table 1.1, 

Figure 1.1). Each year for three years the treatment burns were to be applied to the units in one 

open and one shrubby block. There were therefore a total of 18 experimental units: 2 types of 

pineland (open and shrubby) X 3 treatments (summer burn, winter burn, and unburned) X 3 

years. Postburn data were to be collected annually so that the first set of burns would have three 

years of postburn data, the second set would have two years of postburn data, and the last year’s 

burns would be followed for one year. 

The experimental plots were 100 x 100 m in most cases, but at the Poisonwood site they 

were 80 x 125 m and at the Buttonwood site they were 50 x 200 m in order to fit within available 

habitat. GPS coordinates of plot corners are given in Appendix 1. Plots were laid out with tapes 

and right angle prisms. Shrubs and herbs were sampled in circular subplots within the 1.0-ha 

plot. To locate the understory sampling points, the plots were divided into a 10 m by 10 m grid 

with a buffer 10 m wide around the periphery of the plot. Inside the buffer, potential sampling 

areas formed a checkerboard pattern so that subplots would not share a common boundary. 

Twenty sampling points were randomly chosen from the available points: 32 potential points in 

square plots, 30 points in 80 x 125 m plots, and 27 points in 50 x 200 m plots (See Figure 1.2). 

A short piece of steel reinforcement bar (rebar) was driven into the limestone in the center of

each of these 10 m by 10 m sampling areas. Shrubs were sampled in circular 50-m2 plots (radius 

4 m) centered on the rebar in each of the twenty subplots, referred to as “shrub plots.” The herb

layer was sampled in four circular 1-m2 plots (radius 0.57m) centered 2 m from the shrub plot

center in the four cardinal directions. Therefore there were 80 “herb plots” in each of the 1.0-ha

experimental units. Details of sampling methods are contained in subsequent sections of the

report. Scientific names and authorities of all vascular plant species found in the study plots are 

found in Appendix 2. 

The experimental burns were carried out by U.S. Fish and Wildlife personnel under the 

direction of James E. Durrwachter, FWS Fire Management Officer for South Florida, with 

assistance provided by The Nature Conservancy (TNC) through their fire management 

cooperative agreement with FWS. 

The first year the experimental burns were carried out without incident—two summer

burns in August and two winter burns in December. Hurricane Georges made landfall during 

mid-morning of September 25 in Key West with a minimum central pressure of 981 mb and 

maximum winds of 90 knots. Fortunately, the hurricane did not result in substantial wind 

damage and effects of saltwater from storm surge did not reach any of the study sites. The two 

summer burns the following year, 1999, also were done on schedule, but, from then on, 

circumstances beyond our control prevented us from implementing the study design as proposed. 

3 
In late summer and fall of 1999, extensive fire activity in Nevada and California shut

down prescribed burning nationwide, and by the time burning was authorized that winter, it was 

judged too dry to burn on Big Pine Key. In May 2000, prescribed burning was dealt a serious 

blow by the repercussions of an escaped prescribed fire in Bandelier National Monument, New 

Mexico. The Cerro Grande fire resulted in a nationwide prescribed fire moratorium. We could 

not do the summer burns that year and we were fortunate to get one of the postponed winter

burns from 1999 completed in December 2000. This put us effectively one year behind schedule 

in getting the treatment burns done. Because the project funding was due to run out at the end of 

2001, we opted to burn all four of the plots in the last two study areas in the summer of 2001. 

However, this was done with the understanding that we would not be able to collect the one year 

postburn data on the plots the following year. 

4 
Table 1.1. Original design of seasonal burning treatments. 

 

 

 

Site Pineland type Year Treatment Plot code

Orchid Open One

Control OC

Summer burn OS

Winter burn OW

Poisonwood Shrubby

Control PC

Summer burn PS

Winter burn PW

Iris Open Two

Control IC

Summer burn IS

Winter burn IW

Dogwood Shrubby

Control DC

Summer burn DS

Winter burn DW

Locustberry Open Three

Control LC

Summer burn LS

Winter burn LW

Buttonwood Shrubby

Control BC

Summer burn BS

 Winter burn BW

5 
Figure 1.1. Map showing location the 18 experimental plots at six sites on Big Pine Key. GPS 

coordinates of plot corners are listed in Appendix 1. 

6 
Figure 1.2a. Layout of 100x100 m plot showing 10 m buffer and location of 20 understory 

sampling locations. Used at Orchid, Iris, Dogwood, and Locustberry sites. 

7 
Figure 1.2b. Layout of 50x200 m plot showing 10 m buffer and location of 20 understory 

sampling locations. Used at Buttonwood site. 

8 
Figure 1.2c. Layout of 80x125 m plot showing 10 m buffer and location of 20 understory 

sampling locations. Used at Poisonwood site. The winter burn plot was rotated 90° in a 

clockwise direction to run east-west.

9 
2. EXPERIMENTAL BURNS

Methods

The selection of study areas was done in conjunction with the FWS staff in order to 

assure that the experimental burns could be carried out safely. When necessary, firebreaks were 

cut around the plots to prevent the fires from spreading beyond the intended target. The 

prescribed burns were carried out by FWS with assistance from TNC. 

 

We recorded four indicators of fire intensity: fire temperature, fuel consumption, and 

stem char height and canopy scorch of pine trees. During the first summer burns (Orchid and 

Poisonwood) we wrapped sets of 16 temperature pellets that melted at temperatures of 150 to 

900° F (in increments of 50° F) in heavy duty aluminum foil. One set of pellets was placed on 

the ground in each of the 20 shrub plots. Unfortunately, there was some difficulty in interpreting

the results (pellets blackened but not melted were recorded as melted) so we switched to using

steel plates with temperature-sensitive paints for the seven burns done the final 3 years of the

project. Ten paints that melted at temperatures ranging from 200 to 650o

 F in 50o F increments 

(Tempilaq, Big Three Industries) were dabbed on 7.5 cm x 7.5 cm x 3 mm steel plates. The 

plates had a small hole drilled in one corner for hanging on a wire support. The lowest 

temperature paint was placed at the bottom of the plate and the highest temperature was placed at 

the top so that melting paints did not obscure higher temperature paints. One plate was placed at 

ground level in each herb plot for a total of 80 per burn. 

Fuel loads were measured in 0.25-m2

 (0.5 x 0.5 m) clipped quadrats. Before burning, 

quadrats were located 4.5 m from the center of each shrub plot in the four cardinal directions.

After burning, the adjacent area in a clockwise direction was sampled for the remaining fuel. We

collected all live and dead plant material from the ground surface up to 1 m above ground level.

The upper size limit for dead material was 2.5 cm in diameter and for live material was 0.5 cm

diameter. The biomass from the quadrats at each shrub plot was combined and sorted into the 

following classes: forbs, ferns, graminoids, woody plants, palms, litter < 0.5 cm diameter, and 

litter ≥ 0.5 cm and < 2.5 cm. The material was dried to constant weight in a drying oven set to 

70° C. Fuel consumption was estimated by subtracting the post-burn fuel weight from the preburn fuel weight. The post-burn fuel samples were not collected after the Iris winter burn. 

Crown scorch percent and char height, two commonly used surrogates of fire intensity

(Menges & Deyrup 2001), were measured in all burned plots, and were used as estimators of 

intensity. For each pine tree, the height of charred bark and the percentage of the crown scorched 

were recorded within a month of the burn. Maximum char height was directly measured on 

individual trunks, with directional orientation noted (8 possible compass directions: N, NE, E, 

SE, S, SW, W, NW). Percent scorch, a measure of the amount of live needles killed by the fire 

(percent crown scorch volume, Fowler and Sieg 2004), was estimated to the nearest 5 or 10%. 

10 
Results and Discussion

A total of 11 of the planned 12 experimental burns were carried out (Table 2.1). In 1998, 

the summer and winter burns in Orchid and Poisonwood blocks burned on schedule. In 1999, 

only the summer burns were completed in the Iris and Dogwood blocks. The paired winter burn 

for the Iris block was completed a year late, in December 2000 and the Dogwood winter burn 

plot was never burned. In the summer of 2001, the summer and winter burn plots in the 

Locustberry and Buttonwood sites were burned. 

The burns were carried out within a few days of a significant rain event with relatively

low in-stand wind speeds (Table 2.2). The firing patterns used in the prescribed burns resulted in 

both backing and flanking fires within the plots with flame lengths up to 8 feet and 15 feet 

respectively (Table 2.2). 

Fuels consumed in the burns were dominated by the fine litter component, with both the 

coarse litter and the live vegetation component contributing relatively little (Figure 2.1). Indices 

of fire intensity are shown in Table 2.3. For the three sites at which we had paired summer and 

winter burns (Orchid, Poisonwood, and Iris), all measures of fire intensity were greater for the 

summer burns. In the remaining five summer burns, percent scorch and char height were greater 

than the three winter burns. Some measures of fire intensity varied widely between the two 

burns done within the Locustberry and Buttonwood blocks even though they were burned on the 

same day. For example, the mean percent scorch was 84 in LW and 58 in LS, both burned on 

July 19, 2001. 

11 
12 

Table 2.1. Dates of treatment burns and names of burns in FWS fire reports. 

Site Treatment Plot Previous Burn1 Burn date Burn name

Orchid 

 Control OC 9/11/90 --- --- 

 Summer OS 9/11/90 8/16/98 AUDUBON 2A 

 Winter OW 9/11/90 12/15/98 BLVD 2 

Poisonwood 

 Control PC 7/24/86 --- --- 

 Summer PS 7/24/86 8/17/98 AUDUBON 3A 

 Winter PW 7/24/86 12/15/98 HAMMOCK 2 

Iris 

 Control IC 9/10/91 --- --- 

 Summer IS 9/10/91 6/22/99 BIGPINE 

 Winter IW 9/05/85 12/12/00 IRIS WINTE 

Dogwood 

Control DC 19882

 --- --- 

 Summer DS 1988 7/18/99 BONE2 

 Winter DW 1988 not burned --- 

Locustberry 

 Control LC 8/23/86 --- --- 

 Summer LS 8/23/86 7/19/01 PALM SUMR 

 Winter LW 8/23/86 7/19/01 PALM SUMR 

Buttonwood 

 Control BC unknown --- --- 

 Summer BS unknown 7/18/01 BOSS SUMR 

 Winter BW unknown 7/18/01 BOSS SUMR 

______________________________________________________________________ 

1

 Bergh and Wisby (1996) 

2

 No burn shown in Bergh and Wisby (1996); Ross and Ruiz (1996) list a 1988 prescribed fire 

for the area (with previous burns of 1965 and 1961) 
13 

Site Code

Years 

since last 

burn Burn Date

Days since 

last rain

Last rainfall amount (inches)

Wind 

speed 

(mph)

Te

(

Orchid

OS 8 16-Aug-98 2 0.6" 2-7 86OW 8 15-Dec-98 1 0.77" (1-3 days) 3-5 68

Poisonwood

PS 12 17-Aug-98 2 0.2" 2 days and 0.4" 3 days 3-5 PW 12 15-Dec-98 1 0.77" (1-3 days) 3-51 68

Iris

IS 8 18-Jul-99 3 0.71" 3-8 

IW 14 12-Dec-00 ND ND 3-5 N

Dogwood

DS 11 22-Jun-99 4 2.17" 4-8 N

Locustberry

LS 14 19-Jul-01 1 0.4 ND N

LW 14 19-Jul-01 1 0.4 ND N

N

N

ND

ND

Buttonwood

BS >30 18-Jul-01 1 0.22"

BW >30 18-Jul-01 1 0.22"

Table 2.2. Burn conditions for the 11 experimental burns. Data from FWS fire reports. ND=no data av

Assumed same as OW, because windspeeds outside of stand similar (8-16 and 9-15)

12First range for backing fire, second for flanking fire
Table 2.3. Indicators of fire intensity for the 11 experimental burns.

Years Fine litter Fine litter Scorch Char ht. Fire temp. Plot since last consumed consumed (%) (m) (°F) fire (g/m2) (%)

OS 8 62 2.5 — 1080 71

OW 8 46 1.7 — 894 65

PS 12 66 3.0 — 1408 64

PW 12 22 1.3 — 876 52

IS 8 55 2.1 444 626 47

IW 14 39 1.3 371 — —

DS 11 84 2.8 489 1097 74

LS 14 58 2.0 511 288 37

LW(S) 14 84 3.4 538 516 56

BS >30 66 2.6 483 742 44

BW(S) >30 74 2.7 523 1132 63

14
-200.0

0.0

200.0

400.0

600.0

800.0

1000.0

1200.0

1400.0

1600.0

1800.0

g/

m

2

OS

OW

PS

PW

IS

DS

LS

LW(S)

BS

BW(S)

Fuel consumption

Live

Fine litter

Coarse litter

Figure 2.1. Fuels consumed in experimental burn plots. Plot codes are shown in Table 2.1. 

W(S) indicates that plot was originally designated for winter burn but was burned in summer. 

15
3. TREES 

Methods 

In each of the 18 1.0-ha plots all trees (not including palms) with stem diameters ≥ 5 cm

at 1.4 m above ground (DBH) were tagged and mapped. Trees were identified with numbered 

aluminum tags attached with aluminum nails at 1.4 m above ground. DBH was measured with 

diameter calipers. Bifurcating stems were treated separately only if there was a significant gap

between them at the base (i.e. below ground attachment). 

Trees were mapped by two methods. In some plots we used measuring tapes and rightangle prisms. A tape was stretched across the plot at 50-m intervals to form a baseline. A rightangle prism was used to locate trees along this axis and a second tape was used to measure the 

distance, up to 25 m, from the baseline to the tree. On other plots tree mapping was done with a 

method based on the principles of triangulation, and adhering to the guidelines of Boose et al. 

(1998). In this method, a target tree with unknown coordinates is located, its tag number 

recorded, and a series of 3 consecutive clockwise distance measurements are taken from

reference benchmarks (0,0; 0,8; 6,0 m along plot boundaries). Once at least three targets have 

been mapped, benchmarks can be abandoned and the known trees are then used as reference

trees for other unknown targets. The angles from references to targets cannot exceed 120 or be 

below 20°. Trees downed by Hurricane Georges were mapped from their original base, their 

direction of fall recorded, and length measured. Data were analyzed using INTERPOINT 

software (Boose et al. 1998). 

Postburn canopy scorch, bole char height, and scarring measurements were taken within 

one month following the fires. Percent scorch is an estimate of the amount of live needles killed

by the fire using the following scale: 0%, 10%, 25%, 50%, 75%, 90%, 100% (percent crown 

scorch volume, Fowler and Sieg 2004). There was essentially no needle consumption in any of 

the fires. Maximum char height was directly measured on individual trunks, with directional

orientation noted (8 possible compass directions: N, NE, E, SE, S, SW, W, NW). The height of 

basal scarring ("cat faces") was also noted, along with its direction. Trees in burned plots and 

their associated control plots were checked for mortality annually. 

In the plots in which the spatial distribution of all trees was known, the spatial pattern of

fire intensity, as reflected in individual tree crown scorch and bark char height, could be 

determined by interpolation of the point measurements. We interpolated the data via a kriging 

algorithm (Maclean and Cleland 2003; Taylor et al. 2004). Using the Geostatistical function in 

ArcView 3.1, ordinary kriging was performed in two steps: semivariogram analysis (Isaaks and 

Srivastava 1989) and gridding. A semivariogram is a function fit to variances associated with all 

pairs of point measurements within bracketed zones across the range of distances present in the 

data. We applied a spherical model to fit semivariograms for both scorch percentage and char 

height. A search distance of 15m was used in the gridding process. The output was a map of 1 m

x 1m grid cells with an estimated scorch percentage and char height in each cell. 

16
Results and Discussion 

Initial Conditions

Hardwood species generally make up a minor part of the pine rockland tree layer. There 

were few hardwood trees in the open sites, and none in the Orchid control plot. Hardwoods were 

most numerous at the Buttonwood site and in the Buttonwood control plot the number of 

hardwood stems approached the number of pine trees (Figure 3.1). 

 

Metopium toxiferum, poisonwood, is the dominant hardwood species in the Keys 

pinelands, as it constitutes 75.6% of all hardwood stems. It is found in every plot except the 

Orchid control plot which is devoid of hardwood trees (Table 3.1). Other, rather minor, species 

include Conocarpus erectus, Myrica cerifera, Piscidia piscipula, and Pithecellobium 

guadalupense. A total of 18 hardwood tree species were found in the plots, and all but one 

species occurred in the Buttonwood control plot. The Buttonwood site had both the greatest 

number of hardwood trees and the highest tree species diversity. The three shrubby sites 

(Poisonwood, Dogwood, and Buttonwood) averaged 122±41 hardwood trees/ha and 6.7±1.6 

species/ha while the open sites (Orchid, Iris, Locustberry) averaged 8±2.9 trees/ha and 1.9±0.5 

species/ha. 

 

The size-class distribution of both pines and hardwood species are shown in Figure 3.2. 

Hardwoods were generally confined to the two smallest size classes (≤15 cm DBH). The 

largest diameter Metopium was 22.8 cm in the Poisonwood control plot. 

 

The density of pine trees ranged from 361 ha-1 in the Iris summer plot to 1006 ha-1 in the

Dogwood winter plot (Figure 3.1); the mean pine density was 570 ± 37.4 trees ha-1. The high 

density of pines in the Dogwood winter plot was due to very large number of pines in the 

smallest size class (Figure 3.2). The largest size-classes were best represented in the 

Poisonwood plots. The largest pines in the study were trees with DBH of 40.4 cm (PW), 39.0 

cm (PW), 38.6 cm (PC), and 38.0 cm (PS). 

Fire effects

We were able to evaluate postburn mortality of trees the year following the first seven 

burns (Table 3.2). The aboveground portions of hardwood trees were frequently killed by the 

experimental fires, although in many cases the topkilled individual survived and resprouted from 

belowground parts. In the sites with significant numbers of hardwoods, the mortality, or

topkilling, of trees ranged from 21% after the Poisonwood summer burn to 97% after the 

Dogwood summer burn. 

 

Pine mortality was higher after the summer burn than the winter burn in the three sets of 

paired burns (Table 3.2). At the Orchid site the difference was slight, but at Poisonwood and 

Iris, the mortality after summer burns was >3 times the mortality after winter burns. The 

Dogwood summer burn was the most severe burn in terms of pine mortality, with 57.1% of the 

trees dying. 

17
18

 Mortality of pines was related to diameter at the Orchid and Dogwood sites, with smaller 

trees more likely to die; there was no statistical relation between size and mortality at the 

Poisonwood and Iris sites (Table 3.3). In all seven of the burns, the degree of scorching of the 

tree crown was highly related to tree mortality whereas the height of bark char was significant 

only in four of the seven burns (Table 3.3). 

 

The spatial pattern of pine mortality related to crown scorch and char height is shown for 

the Orchid summer and winter burns in Figure 3.3. The pattern of pine mortality appeared to 

track local variation in fire intensity quite closely, though not uniformly for both indices of 

intensity. For example, the high mortality of smaller trees in the northwest corner of the Orchid 

winter burn plot appeared to be more highly related to severe crown scorch than to the height of 

bark charring. 
Table 3.1. Hardwood tree species diversity and number of stems ≥5 cm dbh per ha in the study plots.

OC OS OW PC PS PW IC IS IW DC DS DW LC LS 

No. of species 021354 42162441 No. of stems 0 8 6 26 54 74 28 5 8 65 62 60 15 2 

Species

Metopium toxiferum 6 6 24 50 50 22 3 8 51 61 54 10 2 Conocarpus erectus 2 1 18 1 Myrica cerifera 1 1 4 2 8 Psicidia piscipula 1 1 4 3 Pithecellobium guadalupense 1 2 Byrsonima lucida 2 113 Coccoloba uvifera Guapira discolor Rhizophora mangle Manikara bahamensis Guettarda scabra 2 Myrsine floridana 2 1 Psidium longipes 1 1 Reynosia septentrionalis Bumelia salicifolia 1 1Pisonia rotundata Bumelia celastrina Ficus citrifolia 

Orchid Poisonwood Iris Dogwood Locustberr

19
Table 3.2. Percent mortality of trees after 

experimental burns. In many cases, hardwood 

species resprouted from the base of the tree. 

Site Burn Pine Hardwoods

Orchid Summer 17.6 37.5*

Winter 16.0 50.0*

Poisonwood Summer 28.4 21.2

Winter 7.8 29.4

Iris Summer 5.6 60.0*

Winter 1.8 0*

Dogwood Summer 57.1 96.8

* <10 stems 

preburn 

Table 3.3. Mean DBH, scorch, and char height of surviving pine trees and trees killed by 

burns in seven of the experimental plots, with ANOVA p-value. 

DBH (cm) Scorch % Char height (m) 

Live Dead p-value Live Dead p-value Live Dead p-value

OS 13.00 10.53 <0.001 55.1 90.1 <0.001 2.33 3.22 <0.001

OW 11.93 9.49 <0.001 37.4 91.6 <0.001 1.56 2.24 <0.001

PS 13.45 12.66 0.238 55.5 93.1 <0.001 2.57 3.77 <0.001

PW 13.43 14.96 0.289 19.1 52.7 <0.001 1.29 1.48 0.361

IS 15.17 13.42 0.229 53.2 85.0 <0.001 2.05 2.50 0.164

IW 11.18 10.87 0.847 38.4 86.5 <0.001 1.30 1.55 0.421

DS 10.95 9.80 0.002 64.2 99.1 <0.001 1.81 3.60 <0.001

20
Tree density

0

200

400

600

800

1000

1200

BC BS BW DC DS DW IC IS IW LC LS LW OC OS OW PC PS PW

Sites

Tr

ees (ind/ha)

PINELL

HW-SPP

Figure 3.1. Density of pines and hardwood species in the 18 experimental plots. 

21
OC - Tree density

0

20

40

60

80

100

120

140

160

1 2 3 4 5 6

DBH - Class

Trees (in

d/h

a)

PINELL

HW

OS Tree density

0

50

100

150

200

1 2 3 4 5 6

DBH Class

Trees (in

d/h

a)

PINELL

HW

OW - Tree density

0

50

100

150

200

250

300

350

1 2 3 456

DBH Class

Trees (in

d/h

a)

PINELL

HW

Figure 3.2. Size-class distributions of pines and hardwoods in the study plots. DBH class 1 = 5-

10 cm, 2 = 10.1-15.0 cm, 3 = 15.1-20.0 cm, 4 = 20.1-25.0 cm, 5 = 25.1-30 cm, 6 = > 30 cm. 

22
PC - Tree density

0

20

40

60

80

100

120

140

160

180

1 2 3 4 5 6

DBH Class

Trees (in

d/h

a)

PINELL

HW

PS - Tree density

0

50

100

150

200

250

1 2 345 6

DBH Class

Trees (in

d/h

a)

PINELL

HW

PW - Tree density

0

20

40

60

80

100

120

140

160

180

1 2 3 4 5 6

DBH Class

Trees (in

d/h

a)

PINELL

HW

Figure 3.2, continued. 

23
IC - Tree density

0

50

100

150

200

250

1 2 3 4 5 6

DBh Class

Tr

e

e

s (ind/h

a)

PINELL

HW

IS - Tree density

0

20

40

60

80

100

120

1 2 345 6

DBH Class

Trees (in

d/h

a)

PINELL

HW

IW - Tree density

0

50

100

150

200

250

300

350

400

123 4 5 6

DBH Class

Trees (in

d/h

a)

PINELL

HW

Figure 3.2, continued. 

24
DC - Tree density

0

100

200

300

400

500

1 2 3 4 5 6

DBH Class

Trees (in

d/h

a)

PINELL

HW

DS - Tree density

0

50

100

150

200

250

300

350

400

1 2 3 4 5 6

DBH Class

Tr

e

e

s (ind/ha)

PINELL

HW

DW - Tree density

0

100

200

300

400

500

600

700

1 2 3 4 5 6

DBH Class

Trees (ind/ha)

PINELL

HW

Figure 3.2, continued. 

25
LC - Tree density

0

50

100

150

200

123 4 5 6

DBH Class

Trees (In

d/h

a)

PINELL

HW

LS - Tree density

0

20

40

60

80

100

120

140

160

1 2 345 6

DBH Class

Trees (in

d/h

a)

PINELL

HW

LW - Tree density

0

50

100

150

200

1 2 3 4 5 6

DBH Class

Trees (in

d/h

a)

PINELL

HW

Figure 3.2, continued. 

26
BC - Tree density

0

50

100

150

200

250

300

350

400

123 456

DBH Class

Trees (in

d/h

a)

PINELL

HW

BS - Tree density

0

50

100

150

200

250

300

1 2 3 4 5 6

DBH Class

Trees (in

d/h

a)

PINELL

HW

BW Tree density

0

50

100

150

200

250

300

350

400

12345 6

DBH Class

Trees (in

d/h

a)

PINELL

HW

Figure 3.2, continued. 

27
Figure 3.3. Maps of canopy scorch and char height in Orchid winter and summer burn 

plots. See text for methods. Locations and size classes of live and dead pine trees shown. 

28
4. PALMS 

Methods

Palms were not sampled in the same manner as trees. All palms with a total 

height (height of the tallest leaf) ≥ 1 m were measured within the 20 shrub plots within 

each 1.0-ha plot before burning. For each individual we recorded the species, the total 

height, and the height of the apical meristem, or stem height. Palms were re-sampled at 

one-year intervals after the burns on the same schedule as hardwood shrubs: Orchid and 

Poisonwood plots were sampled four times, the Iris and Dogwood control and summer

burn plots were sampled three times, the Iris winter burn plot was sampled twice and the 

Dogwood winter and all the Locustberry and Buttonwood plots were sampled only once

before the burns. 

Our method of recording palms before and after fire allowed us to determine firecaused mortality of only those palms whose apical height was at least 1 m. For palms 

with apical heights < 1 m, it is possible that after the burn reduced leaf size, or drooping 

leaves, might cause a plant to not be counted even though the plant was still alive. 

Height growth of Coccothrinax and Thrinax is rather slow (Cooley 2004), so that by 

comparing the density of palms with stems ≥ 1 m tall before the burn to the density 1 yr 

after the burn, we can estimate the number of palms killed by the fire. The growth of

palms into the ≥ 1m size class during the year after the fire could contribute to a minor 

underestimation of mortality. 

Results and Discussion

Initial Conditions

Thrinax morrissii, the Keys thatch palm, and Coccothrinax argentata, the silver

palm, both tropical species, are the dominant palms in the Keys pinelands and are 

common in all of the plots (Table 4.1). Serenoa repens, saw palmetto, is a temperate 

palm with an unusual horizontal creeping stem. It is a codominant in the Iris and 

Poisonwood sites but is present in all the study plots. A fourth species, also temperate in 

distribution, Sabal palmetto, the cabbage palm, is infrequent on Big Pine Key. 

The density of palms ≥1m in height ranged from <2000 to >5000 individuals per 

hectare. Densities were generally <3000/ha in open sites and >3000/ha in shrubby sites. 

The highest density was in the Poisonwood control plot, with 5150 palms/ha and the

lowest was in the Orchid control plot, with 1880 palms/ha. Overall, the mean Thrinax

density was 1600 palms/ha and the density of Coccothrinax was 1000 palms/ha. Thrinax 

densities were greater than Coccothrinax in all plots except the Poisonwood control and 

the Iris winter plots; Thrinax was six times more abundant than Coccothrinax in the

Orchid site. Serenoa reached its highest densities, 1760 palms/ha, at the Iris site. In the 

lower Keys pinelands Serenoa is generally found around the edges of depressions or the 

wetter parts of pinelands. Sabal is found sporadically in the plots and only 21 individuals 

were encountered in 360 shrub plots. 

29
The size–class distributions of Thrinax and Coccothrinax are shown in Figure 4.1. 

Typically there is a decrease in the number of individuals as the size increases.

Exceptions include Thrinax in PC, BC, and BW plots and Coccothrinax in IC, LC, and 

BW plots. Thrinax dominates the larger size classes. The tallest Thrinax recorded in the

study was 5.50 m tall and the tallest Coccothrinax was 4.05 m. The leaves and stem 

diameter of Thrinax are also larger so it has a larger mass than Coccothrinax (Cooley 

2004). 

Fire Effects

 Mortality of Thrinax and Coccothrinax is shown in Figure 4.2 for the seven burns 

in which one-year postburn data were collected. There were three pairs of 

summer/winter burns and in two of the cases there was higher mortality due to the 

summer burns (Orchid and Poisonwood) and in one case there was higher mortality after

the winter burn (Iris). The seasonal pattern was the same for both species of palm, 

although the mortality of Coccothrinax was substantially greater than Thrinax at the 

Orchid site. The most striking difference between the two species was in the Dogwood 

summer burn in which more than half the Coccothrinax died, but only 2.7% of the 

Thrinax were killed. 

The weighted mortality (summing the palms killed in the plots and dividing by 

the number of pre-burn palms) is shown in Table 4.2. There was little difference between 

summer and winter burns for Thrinax, but there was substantially greater mortality after

summer burns for Coccothrinax. Overall mortality of Coccothrinax was about three 

times greater than that of Thrinax (32.5% vs. 10.3%). 

Cooley’s (2004) study provides partially independent estimates of mortality 

because they are based on marked individuals in the four plots burned the last year of the 

project (Buttonwood and Locustberry plots) as well as the Iris winter plot which is 

included in our analysis. Based on plants with apical heights > 1m, Cooley found 20.9% 

mortality of Coccothrinax and 9.6% mortality of Thrinax, somewhat less than in the

results reported here. The high mortality rates for these tropical palms differ from 

general lack of mortality seen when Serenoa (Abrahamson 1984) and Sabal (McPherson 

and Williams 1998) are burned. 

Because our sampling protocol for palms was based on individuals with leaves at 

least 1 m above ground level we cannot determine whether reductions in numbers of

palms in the smaller size classes was due to mortality or simply reduced leaf size after

burns. There is mortality of these smaller individuals based on field observations and

Cooley’s (2004) results based on marked plants. Cooley found lower mortality for plants 

with stem heights ≤ 1 m than for larger plants. Based on 121 marked Thrinax and 144 

marked Coccothrinax, mostly from the Locustberry and Poisonwood plots burned in the 

summer, Cooley documented 11.1% mortality for Coccothrinax and 6.6% mortality for

Thrinax. 

30
31

While it is not possible to determine mortality of smaller individuals by our 

sampling method, we are able to compare the impact of burning on the numbers of 

individuals reaching certain size limits. The postburn recovery of smaller Thrinax and 

Coccothrinax, those which met our sampling criterion of ≥1 m total height and whose 

stem height is ≤ 1m, is shown in Figure 4.3. In the three pairs of summer and winter 

burns, the recovery of palms one year after burning was always greater after the summer 

burn, this being most pronounced at the Iris site. The same pattern held after three years 

at the Orchid and Poisonwood sites. This result indicates that there are different factors 

affecting larger palms than smaller palms because in the Orchid and Poisonwood plots 

there was greater mortality of plants with stems > 1 m tall after summer burns than winter 

burns. 

Serenoa has the unique habit among North American palms of a horizontal, 

branching stem. Because of this habit, most of the stems are less than 50 cm above 

ground, and in this study only 2 individuals had apical meristems > 1 m above ground 

level. Therefore the postburn recovery of Serenoa can be compared to that of the smaller 

Thrinax and Coccothrinax. Unlike the two common tropical palms, there is greater 

recovery of Serenoa one year after winter burns at the Poisonwood and Iris sites (Figure 

4.4). To add additional confusion, however, the pattern is reversed by the third year at 

the Poisonwood site so that the summer burn has greater recovery. Therefore, at the two 

sites where we have paired summer and winter burns and three years of postburn data, the 

smaller palms recover more after summer than winter burns by year three (Figures 4.3 & 

4.4). 

Serenoa appears to be more fire-adapted than Thrinax or Coccothrinax, based on 

mean percent recovery of stems < 1 m tall one year after burning: Serenoa 86.0±4.27, 

Thrinax, 64.3±4.94; and Coccothrinax, 55.6±4.77. 

 
32

OC OS OW PC PS PW IC IS IW DC DS DW LC LS LW BC 

Thrinax morrisii 160 231 225 185 159 126 137 68 62 199 148 186 151 178 164 257 Coccothrinax argentata 23 38 35 300 158 132 32 31 79 106 105 104 63 64 92 101 Serenoa repens 5 58 8 30 97 106 83 176 128 10 29 4 6 11 7 2 Sabal palmetto 0021 1124 10 0000000

Total 188 327 270 516 415 365 254 279 279 315 282 294 220 253 263 360 

Table 4.1. Density of palms (total height ≥ 1 m) in the study plots.

Orchid Poisonwood Iris Dogwood Locustberry But

Summer burns Winter burns All burns

Thrinax 10.8 9.3 10.3Coccothrinax 41.0 15.0 32.5Table 4.2. Mortality (%) of palms with stems ≥ 1 m tall.
Figure 4.1. (Following six pages). Size-class distribution of Thrinax and Coccothrinax in the 

study plots. Size-class based on total height, or height of tallest leaf. 

Size-classes: 

1=1.0-1.49m 

2=1.5-1.99m 

3=2.0-2.49m 

4=2.5-2.99m 

5=3.0-3.49m 

6=3.5-3.99m 

7=4.0-4.49m 

8=4.5-4.99m 

33
OC Palms

0

200

400

600

800

1000

1 2 3 4 5 6 7 8

Height size class

No./h

a

Coccothrinax

Thrinax

OS Palms

0

200

400

600

800

1000

12345678

Height size class

N

o./h

a

Coccothrinax

Thrinax

OW Palms

0

200

400

600

800

1000

1234 5678

Height size class

N

o./h

a

Coccothrinax

Thrinax

Figure 4.1, continued. 

34
PC Palms

0

200

400

600

800

1000

1200

1400

1600

1 234 5 6 7 8

Heightsize class

N

o./ha

Coccothrinax

Thrinax

PS Palms

0

200

400

600

800

1000

1 234 567 8

Height size class

N

o./ha

Coccothrinax

Thrinax

PW Palms

0

200

400

600

800

1000

1 2 3 4 5 6 7 8

Height size class

N

o./ha

Coccothrinax

Thrinax

Figure 4.1, continued. 

35
IC Palms

0

200

400

600

800

1000

1 2 3456 7 8

Height size class

N

o./ha

Coccothrinax

Thrinax

IS Palms

0

200

400

600

800

1000

1 2 3 4 5 6 7 8

Height size class

N

o./h

a

Coccothrinax

Thrinax

IW Palms

0

200

400

600

800

1000

1 2 3 4 567 8

Height size-class

N

o./ha

Coccothrinax

Thrinax

Figure 4.1, continued. 

36
DC Palms

0

200

400

600

800

1000

1 2 3 4 5 6 7 8

Height size class

N

o./ha

Coccothrinax

Thrinax

DS Palms

0

200

400

600

800

1000

1 2 3 4 5 6 7 8

Height size class

N

o./h

a

Coccothrinax

Thrinax

DW Palms

0

200

400

600

800

1000

1 234 5 6 7 8

Height size class

No./h

a

Coccothrinax

Thrinax

Figure 4.1, continued. 

37
LC Palms

0

200

400

600

800

1000

1 234 567 8

Height size class

N

o./ha

Coccothrinax

Thrinax

LS Palms

0

200

400

600

800

1000

12345678

Height size class

N

o./ha

Coccothrinax

Thrinax

LW Palms

0

200

400

600

800

1000

1 2 3 456 7 8

Height size class

N

o./ha

Coccothrinax

Thrinax

Figure 4.1, continued. 

38
BC Palms

0

200

400

600

800

1000

1 2 3 4 5 6 7 8

Height size class

No./h

a

Coccothrinax

Thrinax

BS Palms

0

200

400

600

800

1000

1 234 5 6 7 8

Height size class

N

o./h

a

Coccothrinax

Thrinax

BW Palms

0

200

400

600

800

1000

123 456 7 8

Height size-class

N

o./ha

Coccothrinax

Thrinax

Figure 4.1, continued.

39
Thrinax Mortality

0

10

20

30

40

50

60

70

80

S W S W* S* W* S

Orchid Poisonwood Iris Dogwood

P

erc

e

nt M

ort

ality

Coccothrinax Mortality

0

10

20

30

40

50

60

S* W* S W S* W S

Orchid Poisonwood Iris Dogwood

P

erc

e

nt Mort

ality

Figure 4.2. Mortality of palms with apical heights ≥ 1 m. *Plots with < 10 individuals. 

40
Thrinax - Postburn recovery

0

10

20

30

40

50

60

70

80

90

100

S W S W S W S

Orchid Poisonwood Iris Dogwood

P

erc

e

nt initial d

e

n

sity

1 yr

3yr

Coccothrinax - Postburn recovery

0

10

20

30

40

50

60

70

80

90

100

S W S W S W S

Orchid Poisonwood Iris Dogwood

P

erc

e

nt of initial d

e

n

sity

1 yr

3 yr

Figure 4.3. Number of palms with total heights ≥ 1 m and apical heights ≤ 1 m one and three 

years after burns, expressed as percentage of number of palms meeting these criteria before the 

burns. The Iris and Dogwood plots were not sampled 3 years after burning. 

41
Serenoa - Postburn recovery

0

10

20

30

40

50

60

70

80

90

100

S W* S W S W S

Orchid Poisonwood Iris Dogwood

P

erc

e

nt of initial d

e

n

sity

1yr

3 yr

Figure 4.4. Number of palms with total heights ≥ 1 m one and three years after burns, expressed 

as percentage of number of palms with total heights ≥ 1 m before the burns. The Iris and 

Dogwood plots were not sampled 3 years after burning. 

*The Orchid winter plot had < 10 individuals. 

42
5. SHRUBS

Methods

 Woody plants ≥ 1 m tall and with DBH < 5 cm were sampled in 20 circular subplots with 

radius of 4 m and an area of 50 m2

. The location of these shrub plots is shown in Figure 1.2. 

The species and height of each plant was recorded. For species with tree-like form (e.g., 

Metopium), the DBH was recorded if it was ≥ 1.0 cm. For species with shrubby growth form

(e.g., Myrica), the maximum crown width and its perpendicular were measured. Shrubs were resampled at one-year intervals after the burns on the same schedule as palms: Orchid and 

Poisonwood plots were sampled a total of four times, the Iris and Dogwood control and summer 

burn plots were sampled three times, the Iris winter burn plot was sampled twice and the

Dogwood winter and all the Locustberry and Buttonwood plots were sampled just once. 

Results and Discussion

Initial Conditions

 

A total of 34 woody species were found in the shrub plots. Pithecellobium guadelupense, 

Pisonia rotundata, Metopium toxiferum, and Psidium longipes were found in all the study plots 

(Table 5.1). Metopium is the most common hardwood tree in the pinelands and the other three 

species are shrubs or small trees in the Florida Keys. Other common species included Myrsine

floridana, Byrsonima lucida, and Myrica cerifera. All the species have tropical distributions 

with the exception of Myrica. 

The density of hardwood stems was 2.5 times greater in the shrubby plots than the open 

plots (shrubby 5170±570, open 2050±230 stems/ha; t-test p = 0.0004). Pithecellobium was the 

dominant species in terms of stem density, accounting for over one third of all the stems (1315 

stems/ha). 

 

The number of shrub species found in shrubby plots was greater than found in the open 

plots (shrubby 14.4±1.17 species, open 10.3±1.15 species; t-test, p=0.024). The Iris control plot 

had unusually high species diversity for an open plot due to the presence of single individuals of

five species. Conversely, the Dogwood winter burn plot had a low diversity for a shrubby plot. 

Fire effects

The vast majority of shrub stems were topkilled by the fires, but all species are capable of 

regrowth from basal or belowground parts. All of the species can therefore be described as

sprouters rather than seeders. One judge of the fire impact is the number of stems ≥ 1 m tall one 

year after the burn relative to the initial number of stems. Most of the stems recorded after the

burns were resprouts from topkilled individuals, although there were instances where some

stems, particularly larger stems or plants in areas of very low fuel loads, survived the fire. The

percent recovery of initial stem number one year after burning is shown in Table 5.2 for the 

43
44

seven most common species. Species vary from having very few stems ≥ 1 m tall one year after 

burning, as in the case of Myrsine, Pithecellobium, and Psidium, to those species such as Myrica

and Pisonia in which 30% or more of the initial number of stems are found a year later. 

There is also variability among burns within species. Figure 5.1 compares the recovery 

of the three most common species after different seasons of burns. There are three pairs of 

winter/summer burns that can be compared in the Orchid, Poisonwood, and Iris sites. Our 

measures of fire severity (Table 2.3) indicated more severe fires in the summer than the winter in 

all three cases and summer fires are generally considered more stressful to hardwoods than 

winter fires (Robbins and Myers 1992). In spite of this, Metopium had a higher proportion of 

live stems after the summer burn in the Poisonwood site and Pisonia had more stems 1 year after 

summer burns at both Orchid and Poisonwood. The Dogwood summer burn (for which there 

was no paired winter burn) was the most stressful burn for four of the seven species (Table 5.2). 

We are able to compare the recovery of stem numbers 3 years after burning in the paired 

plots at Orchid and Poisonwood (Table 5.3). Byrsonima, Metopium, and Pisonia all increased to 

reach about half the initial number of stems on average. The recovery of Myrsine and Psidium

after fire is particularly slow. Myrica, on the other hand, because of prolific root sprouting 

actually had more stems 3 years after burning than before the fires. 

The relative recovery of the three dominant species after winter and summer burns at the 

Orchid and Poisonwood sites is the same at 3 yr after burning as it was 1 yr postburn (Figure 

5.2). The recovery of Metopium and Pithecellobium is less after the summer burn than the 

winter burn at the Orchid site, but at the Poisonwood site recovery was greater after the summer 

burns. Pisonia showed more vigorous recovery after summer burns at both sites. The number of 

stems of Pithecellobium increased dramatically between the first and third year after burning 

(from 1.3% of the initial number to 23.7%), although at three years postburn it was still 

considerably less recovered than four of the six other common species (Table 5.3). The increase 

between years was much more modest for Metopium (from 14.9 to 49.3) and Pisonia (from 28.4 

to 54.9%). 

 

 
Table 5.1. Shrub species diversity and number of stems > 1 m tall (per 1000 m2) in the study plots before treatments.

OC OS OW PC PS PW IC IS IW DC DS DW LC LS 

No. of species 9 13 9 12 17 17 18 9 10 12 17 9 6 No. of stems 135 293 296 649 813 651 245 200 166 303 409 316 133 2

Species

Acacia pinetorum 2 1 3112 1Annona glabra 1 6 3Byrsonima lucida 8 8 22 10 25 25 13 23 15 16 20 14 Coccoloba diversifolia 1 1 1Coccoloba uvifera 2 1 Conocarpus erectus 8 1 3 11 94 5 Croton linearis 13 19 7 12 Crossopetalum rhacoma 1 1 1 1 Eugenia axillaris 4 1 8 9 9 15449 3 Eugenia foetida 10 5 40 1 1 Ficus citrifolia 1 1 1 Guettarda scabra 11 1 4 17 8 Manilkara bahamensis 3 2 Metopium toxiferum 10 26 14 25 63 44 25 29 35 18 21 58 14 Morinda royoc 6 1Myrica cerifera 55 26 5 19 40 9 15 6 25 17 7 Myrsine floridana 7 38 71 51 4 203 42 89 37 5 22 4 Piscidia piscipula 2 Pisonia rotundata 17 34 37 59 70 32 31 14 26 57 42 47 30 Pithecellobium guadalupense 23 55 91 468 561 180 42 13 18 105 206 117 51 Psidium longipes 58 54 3335 12591 66 45 55 31 Randia aculeata 2 2 14 3 1 1 Reynosia septentrionalis 1 6 Sophora tomentosa Ximenia americana 1 1Other species: Bumelia celastrina, BC (4); Chrysobalanus icaco , DS (1); Chrysophylum oliviforme , DS (1); Guapira disc

keyensis , BC (1); Rhizophora mangle , BC (2); Schinus terebinthifolius (non-native), BS (1); Simaruba glauca , PS (3); Un

Orchid Poisonwood Iris Dogwood Locust

45
Site Burn

Byrsonima Metopium Myrica Myrsine Pisonia Pithecell. Psidium

Orchid Summer * 7.7 10.9 0.0 26.5 1.8 1.9

Winter 9.1 14.3 34.6 0.0 16.2 2.2 0.0

Poisonwood Summer 4.0 23.8 52.6 * 42.9 0.5 *

Winter 12.0 13.6 45.0 3.9 28.1 0.6 8.3

Iris Summer 13.0 0.0 13.3 0.0 21.4 0.0 *

Winter 13.3 20.0 * 2.7 69.2 11.1 *

Dogwood Summer 0.0 0.0 23.5 0.0 21.4 0.0 2.2

Mean 8.6 11.3 30.0 1.1 32.3 2.3 3.1

* <10 stems preburn

Table 5.2. Number of shrub stems >1 m tall one year after burning as percentage of initial number.

Species

Site Burn

Byrsonima Metopium Myrica Myrsine Pisonia Pithecell. Psidium

Orchid Summer * 23.1 50.9 0.0 64.7 7.3 7.4

Winter 40.9 50.0 100.0 4.2 40.5 23.1 3.0

Poisonwood Summer 64.0 65.1 147.4 * 61.4 36.4 *

Winter 44.0 59.1 145.0 34.0 53.1 27.8 8.3

Mean 49.6 49.3 110.8 12.7 54.9 23.7 6.2

* <10 stems preburn

Species

Table 5.3. Number of shrub stems >1 m tall three years after burning as percentage of initial number.

46
Metopium

0.0

5.0

10.0

15.0

20.0

25.0

Summer Winter Summer Winter Summer Winter Summer

Orchid Poisonwood Iris Dogwood

Percent

Pisonia

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

Summer Winter Summer Winter Summer Winter Summer

Orchid Poisonwood Iris Dogwood

Percent

Pithecellobium

0.0

2.0

4.0

6.0

8.0

10.0

12.0

Summer Winter Summer Winter Summer Winter Summer

Orchid Poisonwood Iris Dogwood

Percent

Figure 5.1. Hardwood stems >1m tall one year postburn as percent of initial number. 

47
Metopium

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

Summer Winter Summer Winter

Orchid Poisonwood

Percent of initial densit

y

1 yr

3 yr

Pisonia

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

Summer Winter Summer Winter

Orchid Poisonwood

Percent of initial density

1 yr

3 yr

Pithecellobium

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

Summer Winter Summer Winter

Orchid Poisonwood

Percent of initial density

1 yr

3 yr

Figure 5.2. Comparison of one-year and three-year postburn recovery of hardwood 

stems. 

48
6. HERB LAYER

Methods

We sampled the herb layer vegetation in 80 1.0-m2

 plots in each of the 18 

treatment plots. Four circular plots were located within each of the 20 shrub plots,

centered 2 m from the shrub plot center in the four cardinal compass headings. A large 

galvanized nail marked the herb plot center and we sampled in a 0.57 m radius around the 

center point for a sample area of 1.0 m2

. The herb layer consisted of all herbaceous

plants as well as individuals of palms and woody species whose height was < 1.0 m.

Within each herb plot we listed each species present and estimated its cover 

according to the following cover classes: (1 = <1%, 2 = 1-5%, 3 = 5-10%, 4 = 10-25%, 5

= 25-50%, 6 = > 50%). Cover classes were converted to percent cover for analysis by 

using the midpoint of each range. The treatment plots were sampled shortly before the 

burns and at one-year intervals thereafter. The control plots were generally sampled at 

the same time as the summer burn plots. The Orchid and Poisonwood winter plots were 

sampled twice before burning and three years after burning. All sampling events are

shown in Table 6.1. The December preburn samples in Orchid and Poisonwood winter 

plots were used in analyses presented here because subsequent sampling was also done in 

the winter. 

We applied non-metric multidimensional scaling (NMS: McCune and Grace 

2002), using the PC-ORD statistical package (McCune & Medford 1999), to characterize 

and visualize the variation in plant species composition among sites. NMS is an 

ordination method that arranges sites in a few dimensions, such that the distances among 

sites in ordination space match as closely as possible the rank order of their paired 

dissimilarities in species composition. An optimal solution is achieved via an iterative 

algorithm that reduces “stress” between the ordination and the matrix of site-by-site 

dissimilarities. We derived an estimate of dissimilarity among sites by applying the 

Bray-Curtis distance metric to untransformed species cover values. To reduce undue

influence from very rare species, taxa that appeared in 5% of the plots or fewer were 

eliminated from the analysis. Ordinations were applied to two data sets. (1) To examine 

background variation in species composition across pine forests on Big Pine Key, we

ordinated mean species cover for the 18 plots in their pre-burn condition. (2) To examine

the temporal sequence in species composition associated with fire, we ordinated data 

from the 24 plot-by-year combinations from the Orchid and Poisonwood blocks during 

the years 1998-2001. In this case, rather than using stand means, we analyzed a data set 

in which mean species cover from the 20 subplots per stand (representing 4 quadrats 

each) were entered separately. 

Use of subplot data, as described above, also provided the replication necessary to 

decompose the effects on species composition associated with Block (Poisonwood or 

Orchid), Burn Season (Summer, Winter, or Control) and Year (1998-2001) within a split

plot design, using an analytical method developed by Anderson (2001, McArdle and 

Anderson 2001). The method is equivalent to a non-parametric ANOVA, and need not be 

49
based on a variance-covariance site matrix. Incorporation of a dissimilarity matrix based 

on Bray-Curtis distances, as we do here, eliminates problems associated with the undue

influence of zero values and minor species in applications of MANOVA, for instance, to 

typical species-by-site data sets (Legendre and Anderson 1999). 

Results and Discussion

Initial Conditions

A total of 163 taxa were identified in the herb plots during the course of the study: 

5 ferns and fern allies, 77 species of forbs (including 14 vine species), 33 species of 

graminoids (grasses and sedges), 4 palms, and 44 woody species. Most of the woody 

species were smaller individuals of species found in the shrub and tree layers, although a 

few were small-statured woody species that rarely or never reach a meter in height (e.g., 

Catesbaea parviflora, Crossopetalum ilicifolium, and Licania michauxii). The best 

represented families in the herb layer were Poaceae (22 spp.), Asteraceae (17 spp.), 

Fabaceae (15 spp.), Cyperaceae (11 spp.), and Euphorbiaceae (11 spp.). The herb layer 

species diversity was higher in the plots in open sites than in the shrubby plots, with an 

average of about 14 more species present in the open plots (75.0 ± 3.00 species. in open 

vs. 61.4 ± 2.70 species. in shrubby plots, p=0.004). 

The mean herb cover of the 18 plots before burning was 21.5%. Woody species 

contributed 9.3%, grasses and sedges 5.0%, forbs and ferns 4.5%, and palm seedlings 

2.7%. As one would expect due to the competition from hardwood growth, the herb layer 

cover was greater in the open plots than the shrubby plots (24.7 ± 1.27% in open vs. 

18.3 ± 1.59% in shrubby plots, p=0.006; Figure 6.1). Of the shrubby sites, Poisonwood 

had the highest cover of herbaceous species (Figure 6.1). Grasses were particularly

prominent at the Iris site. 

The NMS ordination of the pre-burn herb layer from all 18 plots is illustrated in

Figure 6.2. The two-axis ordination represents the variability in the data set quite well, as 

indicated by a low “stress” value of 10.8. The clustering of plots within individual blocks 

is evident, indicating a considerable degree of spatial patterning within the Big Pine Key 

forest, and suggesting that our blocking layout was effective in capturing that variation. 

Plots in shrubby blocks (Poisonwood, Dogwood, and Buttonwood) generally occupy the 

upper half of the ordination, while open blocks (Iris, Orchid, and Locustberry) occupy the 

lower half. The ordination therefore reinforces the idea that development of the shrub 

layer affects the composition of the herb layer in these forests.

Fire effects

The impact of fire on the herb layer is relatively minor and short-lived, relative to 

impacts on trees and shrubs. The percent cover before and after fire in several plots is 

shown in Figure 6.3. There was a substantial decrease in herb layer cover the first year 

50
after the Orchid winter burn, but not in any of the other burns. The grasses showed a 

decrease in cover after burning in all four of the Orchid and Poisonwood plots, but by the 

third year the cover had returned to pre-burn levels. Normally, one would not expect it to 

take three years for grass cover to respond and it is not clear why this happened here. At

the Iris site, the site with the greatest pre-burn graminoid cover, the cover returned to 

initial values within the year after burning. 

Fire-associated changes in vegetation at the community level were addressed

exclusively with data from the Orchid and Poisonwood blocks, where fires were 

successfully completed in the first year of the project, thereby enabling us to track

vegetation composition from its pre-burn condition through the species assemblages that 

developed over the next three years. Analysis of these data is summarized in Table 6.2. 

The 3-way interaction is not a significant source of variation, but two of the 2-way 

interactions were significant, or nearly so: Block x Season and Season x Year. In 

indicating that the year-to-year compositional trajectories of the Control, Summer, and 

Winter burn treatments differed, the S x Y interaction is of particular interest. 

The analysis of varance results can be examined visually in the NMS ordination 

diagram (Figure 6.4). As described earlier, the position of each plot-by-year combination

in Figure 6.4 represents the centroid of 20 subplot-by-year positions. Time vectors are 

included in the figure to illustrate the developmental sequence within individual plots. 

As in the ordination of all pre-burn stands (Figure 6.2), “stress” is low (11.2), and plots 

from each block form distinct groups in ordination space. The near-significant S x Y 

interaction is manifested by more subtle differences in the temporal sequences among 

treatments. In the Control plots, ordination distances between successive years are 

usually short, indicating little change in composition, with the exception of 1998-99 in 

PC. We believe the 1998 position of this plot to have been strongly impacted by its 

January sampling date, when many of the herbaceous perennials had not fully emerged. 

Other than this segment, the Control plots trace closed circles in time. In comparison, the 

burned plots follow a series of trajectories, some (e.g., PS and PW) moving increasingly 

away from their point of origin in 1998, others (OS and OW) generally returning toward 

their 1998 position by Year 3 after fire, with relatively long distances between successive

years. Nevertheless, it is clear that temporal changes due to fire were small in 

comparison to the among-plot differences in initial composition, and that a longer 

monitoring period is necessary to gain perspective on plant community changes 

following fire, which are likely to unfold over decades, not years. 

Turnover in species composition in the herb layer resulting from the burns was

minor. A few new species were recorded in plots after the initial sampling, but these 

were mostly single seedlings of woody species. One interesting case, however, is that of 

Basiphyllaea corallicola, a rare terrestrial orchid (Brown 2002). It is a small plant, at the 

most a few decimeters tall, with narrow, grass-like leaves. It occurred sporadically in a 

few of the plots, but it is unlikely that it was a response to burning. It was found in the 

IW plot one year after burning, the PW plot two years after burning, the OS plot 3 years 

after burning, and in the unburned IC plot in 2001 but not in previous years. 

51
52

Jacquemontia pentanthos, skyblue clustervine, is a twining herbaceous member 

of the Convolvulaceae that exhibits fire-stimulated germination. We observed numerous 

seedlings in the months immediately following some of the burns. In the initial herb 

sampling, Jacquemontia was found only at single points in the Dogwood and Iris control 

plots. After burning it appeared at the Poisonwood site in dramatic fashion (Table 6.3). 

One year after the summer burn, Jacquemontia appeared in almost half the sampling 

locations and had a respectable cover of 1.6%. The response was not as strong after the 

winter burn, but pronounced nonetheless. The best explanation for this phenomenon is 

germination from a soil seed bank. The congeneric J. curtisii has been shown to have 

fire-stimulated germination in mainland South Florida slash pinelands (Spier and Snyder 

1998). In that study, winter burns, which had higher temperatures, resulted in more 

germination than summer burns. It is possible that the difference in germination between 

seasons with J. pentanthos in Big Pine Key pinelands was due to differences in fire 

intensity, but we do not have sufficient data to draw that conclusion. 

 One herb species was studied in detail by a graduate student during the course of 

this study. Hong Liu looked at fire influences on Chamaecrista lineata var. keyensis, the 

endemic Big Pine partridge pea (Liu 2003). She found that Chamaecrista had higher 

mortality after summer than winter burns (Orchid, Poisonwood, and Iris sites) and 

suggested that this was due to either hotter fires in the summer or to inherent seasonal 

variation in plant vulnerability. It is considered unlikely that differences in fire 

temperatures between seasons is the primary explanation because no relationship between 

fire temperature and individual plant mortality was found (Liu et al. 2005). 

 
2

S

20

25-May18-May24-May

27-Sep

15-Nov

15-Nov16-Nov

12-Jun15-Jun14-Jun14-Jun18-Oct12-Oct

6-Sep7-Sep

7-Jun

1-Oct

6-Jul

END

24-May18-May24-May

27-Sep

15-Nov

15-Nov16-Nov

53

PLOT 1998 1999 2000

START

11-Jun15-Jun14-Jun13-Jun18-Oct12-Oct

6-Sep7-Sep

7-Jun

3-Jul

5-Jul

BURNED

12-Dec

START END START END BURNED START END BURNED START END

OC 5-Mar 6-Mar 25-Aug 28-Aug 5-Sep 5-Sep

OS 7-May 15-May 16-Aug 26-Aug 26-Aug 31-Aug 31-Aug

OW 29-May 30-May 3-Dec 4-Dec 15-Dec 4-Nov 6-Nov 16-Nov 16-Nov

PC 29-Jan 20-Feb 28-Aug 1-Sep 8-Sep 8-Sep

PS 8-May 9-May 17-Aug 27-Aug 27-Aug 6-Sep 6-Sep

PW 15-May 15-Jun 2-Dec 3-Dec 15-Dec 5-Nov 9-Nov 17-Nov 17-Nov

IC 11-Jun 11-Jun 6-Jul 7-Jul

IS 2-Jun 4-Jun 14-Jul 30-Jun 6-Jul

IW 25-Jun 25-Jun 30-Nov 30-Nov

DC 11-Jun 8-Jul 30-Jun 30-Jun

DS 4-Jun 6-Jun 22-Jun 29-Jun 30-Jun

DW 25-Jun 7-Jul 1-Dec 1-Dec

LC 16-May 16-Jun

LS 26-May 26-May

LW 16-May 16-Jun

8-Jun

26-May

8-Jun

26-May

8-Jun7-Jun

BC

BS

BW

Table 6.1. Herb layer sampling dates.
Table 6.2. Non-parametric ANOVA on Bray-Curtis distances for species assemblages in 

two Blocks (Poisonwood and Orchid) undergoing three different Seasonal treatments 

(Control, Summer and Winter burning) during four years (1998, 1999, 2000 and 2001). 

Source df SS MS F P (MC) Error term

Block (B) 1 145,337.76 145,337.76 56.69 0.01 Residual 

Season (S) 2 71,122.71 35,561.36 1.33 0.20 B x S 

B x S 2 53,457.21 26,728.60 10.42 0.001 Residual 

Year (Y) 3 17,787.60 5,929.20 2.71 0.001 B x Y 

B x Y 3 6,549.54 2,183.18 1.31 0.14 B x S x Y 

S x Y 6 13,791.56 2,298.59 1.37 0.06 B x S x Y 

B x S x Y 6 10,027.64 1,671.27 0.65 0.99 Residual 

Residual 456 1,169,048.52 2,563.70

Total 479 1,487,119.56

54
Pre 1 yr post 2 yr post 3 yr post Pre (May) Pre (Dec.) 1 yr post 2 yr post 3 yr post

Frequency (%) 0 45 50 45 0 0 20 20 15

Cover (%) 0 1.62 1.43 0.86 0 0 0.06 0.15 0.08

Summer burn plot Winter burn plot

Table 6.3. Frequency and mean cover of Jacquemontia pentanthos at the Poisonwood site before and after

experimental burns. Jacquemontia did not appear in the control plot. Frequency based on number of shrub 

sampling points at which it occurred.

55
Herb layer percent cover

0

5

10

15

20

25

30

35

C S W C S W C S W C S W C S W C S W

Buttonwood Dogwood Iris Locustberry Orchid Poisonwood

C

over (

%)

FORB GRAMINOID PALM WOODY

Figure 6.1. Mean herb layer cover in the 18 experimental plots before burns. The forb 

category includes ferns, graminoids are grasses and sedges. C= control, S= summer burn, 

and W= winter burn plots at the six study sites. 

56
-1.6 -1.4 -1.2 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0

Axis-1

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

Axis-2

Control Summer Winter

 Buttonwood

 Dogwood 

 Iris

 Locustberry

 Orchid

 Poisonwood

Figure 6.2. NMS ordination of pre-burn plots. 

57
0.00

10.00

20.00

30.00

40.00

50.00

C

over (

%)

Pre 1 yr 2 yr 3 yr Pre 1 yr 2 yr 3 yr

Summer burn Winter burn

Herb layer cover - Orchid site

FORB GRAMINOID PALM WOODY

0.00

10.00

20.00

30.00

40.00

C

over (

%)

Pre 1 yr 2 yr 3 yr Pre 1 yr 2 yr 3 yr

Summer burn Winter burn

Herb layer cover - Poisonwood site

FORB GRAMINOID PALM WOODY

0.00

10.00

20.00

30.00

40.00

Co

ver (

%)

Pre 1 yr 2 yr Pre Pre 1 yr

Summer Winter

Herb layer cover - Iris site

FORB GRAMINOID PALM WOODY

Figure 6.3. Mean herb layer cover over time. The Iris winter plot was sampled two years 

before and one year after the burn. 

58
98May

99Aug

00Aug

01Sep

-1.2 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2

Axis 1

-1.4

-1.2

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

Axis 2

 PC

 PS

 PW

 OC

 OS

 OW

98Mar

99Aug

00Sep

01Oct

98May

99Nov

00Nov

01Nov

98Jan

99Aug

00Sep

01Oct

98May

99Aug 00Sep

01Sep

98May

99Nov

00Nov 01Nov

Stress = 11.2

Figure 6.4. NMS ordination of Poisonwood (P) and Orchid (O) plots. The position of each 

plot-by-year combination represents the centroid of 20 subplot-by-year positions. The stress

value is a measure of how well or poorly the distances in the ordination diagram correspond 

to the dissimilarity values calculated from the original data. It is generally accepted that a 

value below 15 represents a good fit. 

59
7. HERBIVORY 

Fire and mammalian herbivory have diverse consequences on vegetation via direct 

and indirect modification of bottom-up resources (Danell et al.1985). Consequences of both 

forces may affect plant architecture, phenology, and leaf quality, as well as the dynamics of 

the community. Fire and herbivory have been regarded as alternate, and sometimes similar, 

selection pressures (Paige 1992). The interaction of fire and herbivory has been studied in 

some systems, but few studies have looked at the whole plant community. 

Season of burning can have marked influence on the response of pine forest plants 

(Platt et al. 1988, Snyder 1986, Spier and Snyder 1998, Waldrop et al. 1992), and there is 

some evidence that season of burn can affect the level of herbivory on certain plants. Whitetailed deer ate more buds of Pityopsis graminifolia following January fires than growing 

season fires, although shoot production of this species was greatest following May burns, the 

time when the longleaf pine savannahs are most likely to burn (Brewer and Platt 1994). 

Seasonal movements of Key deer influenced by the availability of foods and fresh water may 

result in differences in damage to plants with different reproductive phenologies 

experiencing different seasonal burn regimes. 

Fire and Key Deer Habitat 

The importance of fire for Key deer habitat maintenance is well known (Dickson 

1955, Alexander and Dickson 1972, Carlson 1989, Carlson et al. 1993). Robertson (1955) 

studied the vegetation of the area as part of an analysis of South Florida breeding birds. 

Recent studies (Ross et al.1992a,b; Rocca 1997) document the physical factors associated

with the distribution of Lower Keys pinelands. Fire is essential in maintaining pine rockland

as a pine-dominated forest; without periodic fires, succession proceeds to hardwood 

hammock with a relict overstory of pine (Robertson 1955, Alexander 1967, Snyder et al. 

1990). 

The Key deer, Odocoileus virginianus clavium, is the smallest subspecies of the 

North American white-tailed deer. The Key deer is both federally and state listed as 

endangered due to the continual reduction of its habitat and changes in the quality of its

habitat due to human influences. Key deer utilize a variety of habitats within their range; 

pine rocklands are important to Key deer because they are sites with freshwater sources

essential to their existence (Folk et al. 1991). Although mangroves constitute a large 

percentage of the diet of Key deer, many of their other preferred food plants (such as 

Pithecellobium keyense, Acacia pinetorum, Morinda royoc, and Stylosanthes hamata) are 

found in pine rockland habitat (Klimstra and Dooley 1990). A wide variety of pine rockland 

plants are utilized by Key deer to at least a moderate extent (Dooley 1975). 

Pine rockland plant production is stimulated by fire, and plants resprouting after fire 

may be exceptionally palatable to browsing herbivores, with soft new shoots and leaves. 

Some woody species show significant increases in nutritive value following fire, though 

these changes only last a matter of months (Carlson 1989). The diet of the Key deer varies 

seasonally, and availability of special foods influences Key deer movements (Carlson et al.

1993; Klimstra and Dooley 1990). Controlled burning of pine rockland 

60
habitat can improve habitat conditions for Key deer and may have positive effects on 

population size (Klimstra 1986). Carlson (1989) monitored regrowth of seven woody species 

and browsing by Key deer on those species in burned and unburned areas of the Refuge, and 

found that those woody species important in the diet of the deer recover within two years of 

the fire. He reported that herbaceous species responded quickly, with little change in species 

richness or frequency of occurrence of important diet and endemic species. This comparison

was between burned (wet season) and unburned pine rockland sites, and did not involve deer 

exclusion. An earlier, more limited study (Dickson 1955) came to a similar conclusion for 

several species studied. Our experimental study provides some real data on this important 

subject, especially at a time when Key deer populations are at an all-time high. 

Hypotheses

Hypothesis 1 – Key deer shape the pine rockland plant community via selective 

herbivory. 

Hypothesis 2 – Perennial plants preferred by Key deer will achieve greater coverage

in exclosures. 

Hypothesis 3 – The season of burn will affect how a plant species compensates for 

tissue lost to deer herbivory. 

Hypothesis 4 – The longer deer are excluded from sites, the greater the coverage of 

preferred perennials. Woody species may eventually outcompete shorter 

herbaceous species. 

Hypothesis 5 – The interaction of fire (season of burn) and deer herbivory will have a 

significant effect on plant community composition in Lower Keys pine 

rockland habitat. 

Methods 

After the first-year treatment fires, while monitoring herb and shrub plots, we noticed 

many signs of deer activity and herbivory. We took advantage of the second-year 

experimental burns to study the effects of Key deer herbivory on regeneration of the

understory pine rockland plant community after fire, contrasting burns in different seasons 

with unburned control areas. 

In two blocks (Dogwood and Iris) we had three burn units: 1999 summer burn, 1999 

winter burn, and control (unburned). These two blocks differed in their time-since-burn and 

openness (Dogwood was considerably more overgrown and had not been burned in 11 years; 

Iris block was more open, and had been burned either 8 (IW) or 14 (IC and IS) years earlier. 

The first small exclosures were erected July and August (1999) following the summer

burns in June and July 1999, in summer burn and control units. In only one of the blocks 

(Iris) did the winter burn take place as planned. The Dogwood winter burn was not 

completed within the framework of this project, so data for winter burn effects represent only 

one block. 

Within each burn unit (1 ha), twenty shrub plots (4 m in diameter) had previously 

61
been located stratified-randomly and four herb plots (1 m2

) were nested within each of the 

twenty shrub plots for the main fire and vegetation study. The remaining twelve plots in the 

stratified array (Figure 1.2a) were used for this Key deer herbivory study, with two of the

four herb plots fenced and two left open within each shrub plot. The central points of each 

plot were marked with rebar, using a wire to mark the radius of the circular plot when 

sampling. 

Small exclosures (circles of 4 ft high field fence, each with a diameter of ca 1.5 m,

surrounding 1 m2

 circular plots with diameters of 1.2 m) were erected. Two exclosures were 

placed at opposite cardinal directions 4 m from the central point of the shrub plot, and 2 nonfenced control plots at the other cardinal directions. Exclosures were sturdy (staked with 4 

rebar) yet easy to open for sampling (the edges overlapped, with plastic covered heavy wire 

loops as latches). Most importantly, they were effective at keeping Key deer out of the herb 

plots contained within. Construction of exclosures in pine rockland was challenging because 

most rebar posts must be placed in solid rock using a heavy-duty hammer drill with attendant

power source (a gas-powered generator in a wheelbarrow). 

We sampled understory vegetation using methods similar to those used in our fire 

study (reported elsewhere), to facilitate comparison inside and outside the exclosures. 

Within each burn unit and the unburned control unit, 24 herb plots inside exclosures and 24 

unfenced plots were sampled. We sampled all plants encountered in our plots (171 taxa 

total) when measuring abundance and cover of shrubs and herbs (Table 7.1). For monitoring 

plant height and herbivory we tagged individuals of 33 focal taxa (Table 7.1), of which only 

20 were represented by more than 10 individuals over all the plots. These focal taxa were 

chosen to represent both plants that Key Deer had been observed to eat and species that 

appeared unpalatable. 

In each plot, we collected the standard herb data as in the fire project, listing all

species (< 1 m tall) present in the plot, counting the number of individuals or ramets, and 

estimating cover using a scale (1 = <1%, 2 = 1-5%, 3 = 5-10%, 4 = 10-25%, 5 = 25-50%, 6 =

> 50%). For focal species (Table 7.1) we tagged up to five individual plants with bird bands 

(yellow bands inside the exclosures, green bands in unfenced control plots ) and measured 

plant height (the longest stem length), number of stems, number of flowers/ inflorescences 

(or fruits/infructescences), and recorded evidence of herbivory by deer and other herbivores. 

We sampled plots 6 months and one year after fire treatments and fencing. 

The omission of the winter fire from the Dogwood block gave us an unbalanced 

design, but for comparisons of stem length, density, and cover, data were combined for both 

blocks as blocks were not significantly different. However, open pine rockland sites (Iris 

block) have greater species diversity than overgrown sites (Dogwood block), so comparisons 

of diversity were made only for Iris block, as combining data would have misrepresented 

potential differences between fire treatments. 

Univariate Analysis of Variance (ANOVA) was used to evaluate effects of fire season 

(burn) and treatment (fenced vs. open) on plant height, density, and cover of individual 

species, and species diversity of the community. Two non-metric multivariate analyses,

62
analysis of similarity (ANOSIM) and non-metric multi-dimensional scaling (MDS) were 

carried out on species abundance and cover matrices using PRIMER 5.2.9. (Clarke and 

Gorley 2001). Species abundance and cover were scaled up to the sub-plot (or shrub plot) 

level. Variable “site” was created to consolidate block and fire treatment. Since ANOSIM 

can only analyze two independent factors at a time, we first examined the effects of site and

exclosure, then the effects of block and fire treatment. The procedure generates a plot in 

which the separation between groups can be visualized, and higher values of the statistic, R, 

indicate better separation between groups. 

Results and Discussion

We recorded a great deal of damage to plants in the burned sites, both in nonexcluded plots, and in the buffer zone around the herb plots just inside the exclosures (deer 

muzzles can reach in several centimeters through the fencing). Evidence of deer activity was 

greater in burned areas where plants were regrowing, though we also observed deer damage

on some plants in unburned control areas. 

Most forbs with an erect flowering stem are subject to clipping by Key deer, and we 

observed many individuals in burned and control areas with upright stems removed. Most 

stems (> 60% of individuals in burned areas) of the endemic Chamaecrista keyensis were 

nipped off several centimeters from the ground if the plants are in the open, whereas the 

same species in the exclosures are undamaged by deer. Leaf-tying caterpillars appeared to

damage both groups equally, but these effects were not quantified. Aletris lutea, Pinguicula 

pumila, Pityopsis graminifolia, Polygala boykinii, Ruellia caroliniana, and Vernonia 

blodgetii all experienced clipping by deer, though these taxa, with the exception of Ruellia, 

were not abundant enough in our experimental plots to get tagged and monitored 

individually. 

The herb layer is not the only stratum affected by deer herbivory. Deer seemed to 

especially feed on new leaves and flowers produced by growing shoots. Seedlings, as well as

shoots on saplings, of woody Pithecellobium guadalupense were eaten by Key deer outside 

exclosures. Key deer also fed upon new leaves of silver palm (Coccothrinax argentata) and 

thatch palm (Thrinax morrissii). Leaves and meristems were damaged significantly more

when plants were outside exclosures. Species morphologically armed for protection were

also eaten by Key deer. We have seen adults carefully eat the tender leaves of prickly Acacia 

pinetorum, and there are many individuals of thistles (Cirsium horridulum) with basal 

rosettes missing. The endemic Catesbya parviflora (Rubiaceae), a very spiny shrub of short 

stature, was often seen with browsed stems. 

A comparison of stem lengths of all species showed significant interaction of both 

fire and exclosure (p = .006, Table 7.2). Mean stem length was slightly lower within 

exclosures for plots in unburned control units, but for both winter and summer burn units, 

stem length was substantially greater within exclosures than in open plots (Figure 7.1). 

Stem length was substantially greater for several twining leguminous species inside 

the exclosures (Galactia parvifolia, Rhynchosia parviflora, and Stylosanthes hamata) than 

63
for counterparts in the open (Figure 7.2). Some other vines appeared to be protected from

deer browsing: Smilax havanensis (Liliaceae) showed no difference in control areas, yet 

vines were much longer in exclosures in both summer and winter burn units; but no 

differences were seen in Mikania scandens (Asteraceae), Morinda royoc (Rubiaceae), and 

Jacquemontia pentanthos (Convolvulaceae). 

Density (number of individuals per m2

) showed no effect of exclosure, but a nearly 

significant (p = .08) effect of burn (Table 7.3). It is surprising that more individuals are 

present after winter burns than summer burns or controls (Figure 7.3), but since cover is less 

(see below) perhaps there is more room for establishment of new seedlings and ramets. 

Univariate analysis of cover similarly shows no significant effect of exclosure, but a

significant effect of burn (Table 7.4). Cover is lower in burn units than unburned controls 

after one year (though summer burn unit plots nearly recover fully compared with fenced 

unburned control plots), but interestingly the open plots in both summer and winter burn 

have equivalent cover. Exclosures seem to make a big difference after summer burn, with

fenced plots showing substantially greater cover than open plots (Figure 7.4) but the 

differences are not statistically significant. 

Species richness can be compared within Iris block only, and treatment effects appear 

to be significant (p = 0.016, Table 7.5): There is no significant interaction between burn and 

treatment, and for summer burn, winter burn, and unburned control plots, the mean number 

of species is greater in exclosure plots (Figure 7.5). The greatest mean number of species 

were found after summer burns in exclosures. 

Using multivariate techniques, only block and fire treatment effects were significant, 

while exclosure effects were not. This pattern was reflected in values of R, and to some

degree P values of the ANOSIM statistics (Table 7.6). Specifically, factors of site, block and 

fire treatment had much higher R values than exclosure. Higher R values indicate better 

separation between the groups. Results of MDS agreed with that of ANOSIM. 

While many other studies have focused on deer use of plants and the effects of plants

on deer movement, none have measured, with controls, the effects that Key deer have on the 

vegetation, and on specific plant taxa. Fire intensifies deer interest in plants and herbivory in 

its aftermath, and our design allowed us to detect short-term effects of deer grazing on plant 

establishment, growth, and the composition of the plant community. The interaction of fire 

and herbivory reveals some surprising things about plant community and population 

dynamics in pine rockland habitat. 

The Key deer now enjoy a protected existence as an endangered species inhabiting 

the Key Deer Refuge, but their major perils are still related to the presence of human beings 

(vehicular traffic and loss of habitat). The deer may have been over-protected with respect to 

excluding fire from the area. The small-scale fires employed in our experiment, coupled with 

Key deer numbers at an all-time high, may exaggerate the effects on the small areas full of 

luxuriant regrowth after fire. Continuing studies of this type will be important as fire 

management involves more frequent fires of larger extent. 

64
Table 7.1 – Plant species of pine rockland habitat encountered in this study. Species of 

special interest (individual stems tagged in plots) indicated in bold type. * indicates rare 

species. # indicates species reported or previously observed to be eaten by Key deer. Burn 

units are: DC = Dogwood Control, DS = Dogwood Summer Burn [Dogwood Block did not 

receive a winter burn]; IC = Iris Control, IS = Iris Summer, IW = Iris Winter. 

Scientific name Family Burn Units 

occurring 

No. tagged 

Abildgaardia ovata Cyperaceae IC, IS, IW 10 

#Acacia pinetorum Fabaceae IS, DS 0

Acalypha chamaedrifolia Euphorbiaceae IC, IS, IW 4 

Agalinis keyensis Scrophulariaceae DC, DS, IC, IS, IW

(all) 

36 

#Aletris bracteata Liliaceae IS 1

Andropogon cabanisii Poaceae DC, DS, IC, IW 5 

Andropogon glomeratus Poaceae DC, IS, IW 3 

Andropogon virginicus Poaceae IS, IW 0

Anemia adiantifolia Schizaeaceae DC, DS, IC, IS, IW

(all) 

11 

Angadenia berteri Apocynaceae DC 0

Aristida purpurascens Poaceae DC, DS, IC, IS, IW

(all) 

3 

Aster dumosus Asteraceae IC, IS, IW 4 

Aster tenuifolius Asteraceae IW 0

Bletia purpurea Orchidaceae IS, IW 1

Borreria terminalis Rubiaceae DC, DS, IS, IW 2 

Bumelia salicifolia Sapotaceae DS 1

Byrsonima lucida Malpighiaceae DC, DS, IC, IS, IW

(all) 

43 

Cassia bahamensis Fabaceae IC 4

Cassytha filiformis Lauraceae DC, DS, IC, IS, IW

(all) 

1 

Centrosema virginianum Fabaceae DC, DS, IC, IS, IW

(all) 

47 

Chamaecrista aspera Fabaceae IS, IW 2

*#Chamaecrista lineata Fabaceae DC, DS, IC, IS, IW

(all) 

84 

*Chamaesyce adenoptera Euphorbiaceae DS, IC, IS, IW 5 

*Chamaesyce deltoidea Euphorbiaceae DC, IC 9

Chiococca pinetorum Rubiaceae DC, DS, IC, IS, IW

(all) 

12 

Cirsium horridulum Asteraceae DC, IC, IS, IW 2 

Cladium jamaicense Cyperaceae IC, IS, IW 4 

65
Scientific name Family Burn Units 

occurring 

No. tagged 

Cnidoscolus stimulosus Euphorbiaceae IW 0

Coccoloba diversifolia Polygonaceae DC 1

Coccoloba uvifera Polygonaceae DC, DS, IS 3 

Scientific name Family Burn Units 

occurring 

No. tagged 

Coccothrinax argentata Arecaceae DC, DS, IC, IS, IW

(all) 

50 

Crossopetalum ilicifolium Celastraceae IW 0

Crossopetalum rhacoma Celastraceae IC, IS 8

Crotolaria maritima Fabaceae DC, IC, IS, IW 4 

#Croton linearis Euphorbiaceae DC, DS, IC, IS, IW

(all) 

73 

Cynanchum blodgettii Asclepiadaceae IC, IW 0

Cynoctonum sessilifolium Loganiaceae DC, IC, IS, IW 7 

Dichanthelium dichotomum Poaceae IS 0

*Dichromena floridensis Cyperaceae DC, DS, IC, IS, IW

(all) 

111 

Dyschoriste oblongifolia Acanthaceae IS 2

Echites umbellata Apocynaceae DC, DS, IC, IS, IW

(all) 

2 

Eleocharis caribaea Cyperaceae IS 0

Eragrostis elliottii Poaceae IC, IW 0

Ernodea littoralis Rubiaceae DC, DS, IC 5 

Eugenia axillaris Myrtaceae IC 1

Eugenia foetida Myrtaceae IC 1

Eustachys petraea Poaceae IC, IS, IW 5 

Evolvulus sericeus Convolvulaceae DC, IC, IS, IW 15 

Fimbristylis castanea Cyperaceae DC 0

Flaveria linearis Asteraceae IC, IS, IW 1 

#Galactia parvifolia Fabaceae DC, DS, IC, IS, IW

(all) 

58 

Guettarda scabra Rubiaceae DC, DS 10

Habenaria quinqueseta Orchidaceae DC 1

Hedyotis nigricans Rubiaceae DC, DS, IC, IS 3 

#Heterotheca graminifolia Asteraceae DC, DS, IC, IS 4 

Hypoxis wrightii Hypoxidaceae DC, DS, IC, IS, IW

(all) 

14 

Indigofera miniata Fabaceae IS 0

Jacquemontia pentanthos Convolvulaceae DS, IC 4

Liatris laevigata Asteraceae DC, DS, IC, IS, IW

(all) 

11 

Manilkara bahamensis Sapotaceae DC 1

*Melanthera parvifolia Asteraceae IS, IW 13

66
Scientific name Family Burn Units 

occurring 

No. tagged 

Metopium toxiferum Anacardiaceae DC, DS, IC, IS, IW

(all) 

20 

Mikania scandens Asteraceae IS, IW 4

#Morinda royoc Rubiaceae DC, DS, IC, IS, IW

(all) 

127 

Muhlenbergia filipes Poaceae IS, IW 0

Myrica cerifera Myricaceae DC, DS, IC, IS 10 

Scientific name Family Burn Units 

occurring 

No. tagged 

Myrsine floridana Myrsinaceae DC, DS, IC, IS, IW

(all) 

15 

Panicum virgatum Poaceae DS, IC, IS, IW 0 

Paspalum sp. Poaceae DC, DS, IC, IS, IW

(all) 

5 

Phyllanthus pentaphyllus Euphorbiaceae DC, DS, IC, IS, IW

(all) 

18 

Pinus elliottii Pinaceae DC, IC, IS, IW 12 

Pinguicula pumila Lentibulariaceae IS, IW 1

Piriqueta caroliniana Turneraceae DC, DS, IC, IS, IW

(all) 

26 

Piscidia piscipula Fabaceae IC 0

Pisonia rotundata Nyctaginaceae DC, DS, IC, IS, IW

(all) 

48 

#Pithecellobium 

guadalupense 

Fabaceae DC, DS, IC, IS, IW

(all) 

60 

Pluchea rosea Asteraceae IW 0

Polygala boykinii Polygalaceae DS, IS, IW 2 

Polygala grandiflora Polygalaceae DC, DS, IC, IS, IW

(all) 

4 

Psidium longipes Myrtaceae DC, DS, IC, IS 6 

Pteridium aquilinum Dennstaedtiaceae DS 0

Pteris bahamensis Adiantaceae DC, DS, IC, IS 4 

Pterocaulon pycnostachyum Asteraceae DC 1

Randia aculeata Rubiaceae DC, DS, IC, IS, IW

(all) 

22 

Reynosia septentrionalis Rhamnaceae DS, IC 3

Rhynchosia parvifolia Fabaceae DC, DS, IC, IS, IW

(all) 

17 

#Ruellia caroliniensis Acanthaceae DC, DS, IC, IS, IW

(all) 

55 

Sabal palmetto Arecaceae IC, IS, IW 3 

Sachsia polycephala Asteraceae IS, IW 7

Samolus ebracteatus Primulaceae IW 0

67
Scientific name Family Burn Units 

occurring 

No. tagged 

Schizachyrium gracile Asteraceae DC, DS, IC, IS, IW

(all) 

3 

Schizachyrium rhizomatum Asteraceae IC, IS, IW 4 

Schizachyrium semiberbe Asteraceae DC, DS, IC, IS, IW

(all) 

3 

Schoenus nigricans Cyperaceae IW 0

Scleria verticillata Cyperaceae IS, IW 4

Serenoa repens Arecaceae IS, IW 33

Sisyrinchium miamiense Iridaceae DC, DS, IC, IS, IW

(all) 

0 

#Smilax havanensis Smilacaceae DC, DS, IC, IS, IW

(all) 

102 

Scientific name Family Burn Units 

occurring 

No. tagged 

Solidago stricta Asteraceae IC, IS, IW 3 

Sophora tomentosa Fabaceae DS 1

Sorghastrum secundum Asteraceae DC, DS, IC, IS, IW

(all) 

88 

*#Stylosanthes hamata Fabaceae IC, IW 3

Thrinax morrisii Arecaceae DC, DS, IC, IS, IW

(all) 

44 

Tragia saxicola Euphorbiaceae DC, DS, IC 7 

#Vernonia blodgettii Asteraceae DC, IC, IS, IW 2 

Ximenia americana Olacaceae IW 1

68
Table 7.2. Univariate Analysis of Variance of mean stem length for all species one year 

post-burn. TRT = treatment, O = open (unfenced), X = exclosure (fenced), BURN = season 

of fire (control, summer, winter). 

Source df F p power

Model 5 6.04 < ,0001 0.996

TRT (O,X) 1 9.31 0.002 0.862 

BURN (C,S,W) 2 5.42 0.004 0.847 

TRT x BURN 2 5.20 0.006* 0.831 

Table 7.3. Univariate ANOVA for Density of Selected Species at 1 yr. Sp = species, other 

abbreviations as in Table 7.2. 

Source df MS F p

Model 272 13.5 1.66 < 0.0001

Species 93 18.1 2.21 < 0.0001

TRT 1 4.1 0.51 0.477

Burn 2 20.6 2.53 0.080

Sp * TRT 57 5.5 0.68 0.968 

Sp * Burn 71 9.8 1.2 0.128 

TRT * Burn 2 .5 0.06 0.944 

Sp*TRT*Burn 43 3.7 0.46 0.999

Table 7.4. Univariate ANOVA for Cover of all species at 1 yr 

Source df MS F p

Model 5 1.3 1.70 0.131

TRT 1 0.4 0.52 0.47

Burn 2 2.1 2.90 0.05 *

TRT * Burn 2 0.54 0.73 0.50 

69
Table 7.5 – Univariate ANOVA for species richness (Iris Block only) at 1 yr 

Source df MS F p

Model 5 158.8 1.65 0.151

Burn 2 68.8 0.71 0.491

TRT 1 572.0 5.95 0.016

Burn * TRT 2 42.2 0.44 0.646 

Table 7.6. Statistics from Two-way crossed ANOSIM. 

factor Based on plant cover Based on plant abundance 

R P R P

site 0.481 0.001 0.353 0.001 

exclosure 0.103 0.001 0.025 0.146

Block 0.653 0.001 0.442 0.001 

Fire treatment 0.36 0.001 0.189 0.001 

70
Figure 7.1. Mean stem length of selected species one year after treatment + S.E. all spp. 

71
Figure 7.2. Mean stem length of leguminous vines one year after treatment. 

a) Centrosema virginiana 

0

5

10

15

20

25

30

35

40

control summer winter

Burn Units and Treatments

M

ean ste

m l

e

n

gth (c

m)

open

exclosure

b) Galactia parvifolia

0

10

20

30

40

50

60

70

control summer winter

Burn Units and Treatments

Mean stem len

gth (c

m)

open

exclosure

 

c) Rhynchosia parviflora 

0

5

10

15

20

25

30

35

40

control summer winter

Burn Units and Treatments

Mean stem len

gth (c

m)

open

exclosure

72
Figure 7.3. Mean Density of Selected Species one year after treatment + S.E. 

73
Figure 7.4. Mean cover score for all species at one year after treatment + S.E. 

74
Figure 7.5. Species richness after one year (Iris Block only) + S.E. 

75
Figure 7.6. MDS 2-D ordination plots based on plant species cover in key deer herbivory 

plots in Dogwood and Iris blocks. Sample similarity was measured based on Bray-Curtis 

Coefficients on non-transformed data. Points were labeled with A) block; B) fire treatment; 

C) exclosure status. 

A. 

dogwood

iris

Stress: 0.24

control

burned

Stress: 0.24

B. 

C. 

open

fence

Stress: 0.24

76
Figure 7.7. MDS 2-D ordination plots based on plant species abundance in key deer 

herbivory plots in Dogwood and Iris blocks. Sample similarity was measured based on BrayCurtis Coefficients on non-transformed data. Points were labeled with A) block; B) fire 

treatment; C) exclosure status. 

dogwood

iris

Stress: 0.27

A. 

control

burned

Stress: 0.27

B. 

open

fence

Stress: 0.27

C. 

77
Figure 7.8. MDS 2-D ordination plots with points labeled with fire treatment and exclosure 

status. Sample similarity was measured based on Bray-Curtis Coefficients on nontransformed data. A) Data based on plant cover; B) Data based on plant abundance. 

A. 

control open

control fence

burned open

burned fence

Stress: 0.24

B. 

control open

control fence

burned open

burned fence

Stress: 0.27

78
8. BIOMASS DYNAMICS 

Introduction

Through the process of photosynthesis, forests utilize the energy of the sun to fix

atmospheric carbon into biomass. Eventually, a fire occurs and some of the stored carbon 

and potential energy are released back into the environment. The fire cycle is central to the

evolution and function of pine forest flora and fauna worldwide; locally, its manipulation is 

the primary tool available for managing the structure and composition of south Florida pine

rocklands, including those of the Florida Keys (Alexander 1967, Alexander and Dickson 

1972, Snyder et al. 1990, Slocum et al. 2003). The development of strategies to manipulate 

the fire cycle effectively requires a comprehensive understanding of fuel dynamics and the 

effect of fuels on fire behavior. 

Immediately after a fire, available fuels are often insufficient to carry a second fire. 

The rate of accumulation of fuel loads in forest stands is thus an important determinant of fire 

frequency and severity (Davis and Cooper 1963; Paatalo 1998; Cochrane et al. 1999). As

such, fire return interval varies among forest types, based on site characteristics that affect 

productivity (Beaty and Taylor 2001; Stephens 2001). Information regarding the nature and 

distribution of fuels in relation to fire intervals has been used to project how rapidly fires will 

spread, their intensity, and ultimately their ecological effects (Rothermel 1972; Kauffman et

al. 1994; Paatalo 1998; Baeza et al. 2002). 

Season of fire is also an important component in the fire regime of south Florida pine 

forests, as it may influence fuel consumption, fire intensity, and forest development during 

the post-burn period (Kaufman and Martin 1989; Robbins and Myers 1992; Spier and Snyder 

1998; Negrón-Ortiz and Gorchov 2000; Ryan 2002; Sparks et al. 2002). Fire intensity is 

usually, though not invariably, higher in dry- than wet-season fires (Menges and Deyrup 

2001). In addition to its effects on existing vegetation, a very intense fire may hinder 

recovery of the plant community (Spier and Snyder 1998), thereby constraining the 

development of fuel loads in the post-burn period. The effect of fire seasonality on 

development of fuel loads in Florida Keys pine forests has not been studied directly; models

developed more generally for the southeastern U.S are applied, but may not fit the 

peculiarities of Keys’ climatic and edaphic conditions well. 

A mean fire return interval may be defined for any location, but spatial and temporal 

variability around this central tendency is typically large, with significant implications for

fire behavior. A long interval between fires usually facilitates fuel accumulation, mostly in 

the form of fine and dead biomass, resulting in intense fire (Rothermel 1972; Van Wilgen 

1982; Hobbs and Gimingham 1984; Reggan et al. 1988; Bradstock et al. 1998). On the other 

hand, a short fire interval drastically reduces the biomass (Van Wilgen and Kruger 1981). In 

general, plant fuel loads increase with time after burning and then become constant (Chaffey 

and Grant 2000; Baeza et al. 2002; McCaw et al. 2002). Researchers in several ecosystems

have reported that after some minimum critical period, structural changes in vegetation and 

hence in fuel distribution occur, and that these changes are particularly relevant for fire 

behavior in prescribed burning management (Baeza et al. 2002). The detection of this critical 

79
period in the fuel cycle is important, especially where the main strategy of prescribed burning 

is fuel reduction (Rothermel 1972; Koehler 1993). Fuel reduction has been an important 

consideration in the management of Florida Keys pine forests, where prescribed burning has 

been applied sporadically since the creation of National Key Deer Wildlife Refugee in 1957 

(Bergh and Wisby 1996). From 1985 to 1992, the primary objective of burns conducted in 

the Refuge was to reduce fuels and to prevent intense wildfires that would cause excessive 

pine mortality. After this active period of management, the prescribed fire program was 

inactive for about 6 years, resulting in forests of various stages of development following 

fire. 

In this study, our objectives were: i) to estimate understory and overstory biomass in 

pine rockland forests, ii) to examine the effects of season on understory fuel consumption, 

and iii) to examine the dynamics of understory fuel load accumulation after fire. 

Methods 

The study design and site descriptions have been presented thoroughly in earlier 

chapters. We were able to estimate the major aboveground biomass components (described 

below) at least once in 18 stands, which varied initially from 8 to more than 30 years in the 

time since their most recent fire (Table 8.1). Eleven of the 18 stands were burned under 

prescription during the study, as described previously. In these, we were able to examine the 

amount and types of fuel consumed and, in all except those burned during the final year of 

the project, the early stages of post-fire fuel accumulation. 

Biomass estimation

We estimated biomass within the following broad structural categories: i) ground 

layer, ii) shrubs, iii) palms iv) pines, and v) hardwood trees. Methods of biomass estimation 

differed slightly among the groups, and are described below. 

Ground layer: Ground layer fuels (consisting of litter and vegetation <1 m in height) 

were measured in all plots, including Control plots, prior to the burns in each block. In the 

11 plots subjected to experimental burning, ground fuels were measured again within one 

month following the fire, allowing estimation of fuel consumption. Finally ground fuels 

were collected in all 18 plots at the end of the project (2000), enabling us to directly assess

the accumulation of fuels over the course of time. We located 40 1-m2

 plots within each 1 ha 

burn unit. Ground fuel plots were centered on the 20 shrub plots, whose location had been 

determined by stratified random selection; two ground fuel plots were distributed at 4.5 m 

distance from the shrub plot center in opposite directions. One previously delineated 0.25-m2

sub-plot per plot was harvested during each sampling event. 

Plot harvest was accomplished by (1) excising stems at ground level and collecting all 

standing plant material less than 1 m in total height, including palms, and (2) collection of all 

undecomposed litter and coarse woody debris <2.5 cm in diameter. Samples were separated 

into live and dead fuels, and further into the following categories: forbs, ferns, graminoids 

80
(including grasses, sedges and grass-like forbs), woody plants, palms, litter <0.5 cm, and 

litter ≥0.5 cm but < 2.5 cm in diameter. Separated samples were oven dried for 48 hrs at 70o

C and weighed. 

Shrub fuels: Shrub biomass was estimated by applying regression models to plant 

dimensional data. Within each plot, plant dimensions of woody stems >1 m height and <5 cm

diameter rooted within 20 circular, 4-meter radius plots were measured. Measurements 

differed for shrub-like and tree-like species. For shrub-like species, we treated clumping 

stems as a single individual, and measured height and two crown diameters (the longest, and 

its perpendicular), whereas for tree-like plants we measured total height and DBH. 

Regression models based on these measurements were developed from a sample of about 15 

stems of each of the major shrub species, collected outside the study plots on Big Pine Key

(Sah et al. 2004). 

Palms: Total palm biomass was calculated as the summation of trunk and leaf

biomass, which were estimated separately from palm dimensional data, as described below. 

Because height is often a good predictor of palm biomass (e.g., Frangi and Lugo, 1985), we 

measured total height and apical height (height to the apical meristem) of each individual 

belonging to 4 palm species (Coccothrinax argentata, Sabal palmetto, Serenoa repens and

Thrinax morrisii) within the shrub plots described above, regardless of stem diameter. For

biomass estimation purposes, we categorized palm individuals into ten size-classes based on 

apical height. 

To estimate the biomass of palm trunks, it was necessary to estimate the bole volume 

and wood specific density for each tree sampled. Assuming a cylindrical form for a palm 

trunk, we calculated volume (v = π(d/2)2

h) of each individual tree, where h was the measured

apical height, and d was the mean basal diameter (measured at 10 cm above palm-butt) 

obtained from 3 to 5 randomly selected individuals in each of the ten height classes. Wood 

specific density (SD) was calculated for a pie-slice or cylinder excised from the trunk of six 

trees, three each of Coccothrinax argentata and Thrinax morrisii, using the oven dry method 

(Barajas-Morales 1987). Wood sections were dried at 70o

C to a constant weight. Their 

volumes were determined by the mass of water displaced when the sample was completely

submerged in a beaker of water placed on a digital balance of 2000g capacity. The increase 

in weight in grams was equivalent to the volume of the palm slice in cubic centimeters. 

Specific density was calculated as the ratio of the oven-dry mass divided by the volume of 

the wood samples. Trunk biomass was then calculated for each tree as the product of wood 

specific density and bole volume. 

Palm leaf (blade + petiole) biomass was estimated according to methods described in 

Cooley (2004). She found that height was a poor predictor of palm leaf biomass, and 

developed equations based on crown area and leaf number, which were parameters that were 

not included in the shrub sampling protocols. Therefore, in order to obtain estimates of live

and dead leaf biomass for C. argentata and T. morrisii, we multiplied the number of 

censused individuals in each palm size-class by the mean live and dead standing leaf biomass 

of an average individual within the class. We determined the mean leaf biomass in each size 

class by applying Cooley’s best regression equations to ten randomly chosen individuals per 

81
class at six sites (Cooley 2004). 

Pines: Pine sapling (height >1 m and DBH <5 cm) biomass was estimated by using a

regression developed locally for Pinus elliottii var. densa stems of this size Independent 

variables tested were height and DBH. 13 individuals were measured, collected, and 

weighed from several locations within the study area. The best regression model was applied 

to dimensional data collected from the shrub plots, in which height and DBH of pine saplings 

were measured. 

Pine tree biomass was estimated using equations developed by Gholz and Fisher 

(1982) for various tree components. The regression models were applied to the tree DBH 

data described in Chapter 3. Using DBH as a predictor, we calculated the dry weight of stem 

bark and wood, live and dead branches, and total foliage, and summed them to derive a total 

aboveground biomass of pine trees. 

Hardwood trees: Biomass of hardwood trees (DBH >5 cm) was estimated using a 

generalized equation developed by Brown et al. (1989) for dry tropical forests. The 

regression equation was applied to the hardwood tree DBH data, as described above for 

pines. 

 

We defined available fine fuels, or fuel loads, to include: (1) total ground layer 

biomass (live and dead stems <1 m height + litter and coarse woody debris in the forest 

floor), (2) shrub leaves and twigs < 1⁄4 inch in diameter, (3) palm leaves and petioles, and (4) 

pine sapling needles and twigs. 

Analytical methods

We used one-way analysis of variance (one-way ANOVA) to assess the significance 

of differences among blocks in terms of total biomass and available fuel loads. Considering 

the pre-burn data, we applied a space-for-time substitution at the plot level to assess the 

association (Pearson’s r) between several biomass components and time since fire. One-way 

ANOVA was also used to evaluate differences among burned plots in terms of ground fuel 

consumption. Relationships of ground fuel consumption with preburn ground fuel and time

since last fire were examined by fitting a linear regression equation to the plot-level data 

using standard least squares techniques. Only two blocks, Orchid and Poisonwood, were 

burned in both wet and dry seasons. Two-way analysis of variance (two-way ANOVA) was 

used to examine the effects of Block and Season on ground fuel consumption calculated for 

20 shrub subplots in the four burned plots in these blocks. 

Stepwise-multiple regression with forward selection was used to examine the effects 

of local variation in preburn fuel and fire intensity on ground fuel consumption. Independent 

variables were preburn ground fuel, scorch percentage and char height, calculated for 20 

shrub subplots in the two Orchid and two Poisonwood plots. At each step, the entered 

predictor was evaluated for significance on the basis of the ‘t’ statistic (p <0.05) and by its 

contribution to the total variation explained in the model. Finally, only the significant 

82
predictors were retained in the model. For each model, the assumptions of least-square 

regression were tested by examining the normality of standard residuals. Statistical analyses

were carried out using STATISTICA version 6.0 (StatSoft, Inc. 2001). 

Results and Discussion

Pre-burn biomass and fuel loading

Mean total aboveground biomass in Big Pine Key pine forests (including ground 

fuels) averaged 76.2 Mg ha-1 (Figure 8.1). Among-block variation was significant (F5, 12 = 

17.1, p <0.001), with pine forests in the Poisonwood block exceeding other local forests in 

biomass (105.4 Mg ha-1). Pine trees constituted more than half (53.3%) of the biomass, and 

palms, the dominant understory contributor, accounted for 18.7%. Broad-leaved shrubs and 

ground layer fuels constituted 4.2% and 21.8% of total biomass. Hardwood trees, though 

well-distributed throughout the forests, comprised a relatively small percentage (1.4%) of 

stand biomass. Biomass in the relatively well-developed Poisonwood block was comparable 

to Snyder’s (1986) estimates for Long Pine Key in Everglades National Park (75-90 Mg/ha in 

standing vegetation, 10-17 Mg/ha in litter). However, biomass in both of these south Florida 

pine forests is far less than in 25 year-old north Florida slash pine plantations, in which total 

aboveground biomass values >150 Mg/ha are attained (Gholz and Fisher 1982). 

Total stand biomass was uncorrelated with time since fire (r = +0.12, p>.05), but the

biomass of palms, broad-leaved shrubs and hardwood trees increased with fire-free period (r

= +0.62, +0.59, and +0.75, respectively; p<.05 for all three). The correlation analyses imply, 

albeit indirectly, that palms, shrubs, and hardwood trees are dynamic structural components, 

i.e., they are relatively sensitive to non-stand replacing fires, but recover quickly thereafter. 

This is surely true for shrubs and hardwoods, which resprout prolifically after fire, but palms

grow very slowly (Cooley 2004) and do not resprout; thus, the strong relationship between 

total biomass and time since last fire in this group may result from some covariant factor, 

perhaps related to long-term fire history. Direct information regarding mortality and postfire 

recovery in these groups is presented in Chapters 3-5 of this report. 

Available fine fuels in lower Keys pine forests constituted 30% of the total 

aboveground biomass (Figure 8.2), a proportion in substantial agreement with Gholz and 

Fisher (1982). On Big Pine Key, about three-fourth of available fine fuels prior to the 

experimental fires were in the ground layer. Palm leaves were 21.5% of available fine fuels, 

and broad-leaved shrubs comprised another 5.3%. Pine saplings contributed very little to the 

total fuel loads (<1%). One fine fuel category not included in Figure 8.2 is attached pine 

needles in the forest canopy, because little is directly consumed in most prescribed fires. 

This fuel component comprises only 5-10% (among-stand range: 1.4-2.5 Mg/ha-1) of the 

available fine fuel. 

Among-block differences in fuel loads were significant (F5, 12 = 18.0, p <0.001), with 

the Locustberry block (14.3 Mg ha-1) containing only about half the fuel loads present in the 

Poisonwood and Buttonwood blocks (28.8 Mg ha-1 each) (Figure 8.2). Fuel loads generally

83
increased from the most recently burned stand (Iris-8) to the longest unburned community 

(Buttonwood). However, the correlation of available fuels with time since fire was nonsignificant (r = 0.20), primarily because of anomalously low fuel loads in stands burned 14 

years before sampling in the Locustberry block and one portion of the Iris block. We suspect 

that these two areas may have burned intensely during their most recent fire, perhaps 

resulting in high mortality of palms, broadleaved shrubs, and mature pine trees, and therefore 

slow and patchy accumulation of ground fuels compared to neighboring forests. 

Ground fuel consumption and recovery 

During the study period, a total of 11 plots, including at least one plot per block, were 

burned (Table 8.1). Eight plots were burned in the summer (June-August), and three plots, 

one each in the Iris, Orchid and Poisonwood blocks, were burned in early winter (December). 

Based on post-fire and pre-fire sample weights, prescribed fire consumed an average of

56.8% of the ground fuel across all plots. Among-plot differences in the rate of fuel

consumption were significant (Figure 8.3). However, such differences were not related to 

time since last fire (n = 10, R2

 = 0.066, p = 0.475). Percentage ground fuel consumption 

tended to be higher where pre-burn ground fuels were most abundant (Figure 8.4), but the 

relationship was not significant at the plot level (n = 10, R2

 = 0.175, p = 0.228). Likewise, at 

the plot level, percentage consumption of ground fuels was not related to crown scorch 

percentage or char height, two commonly used surrogates of fire intensity (e.g., Menges &

Deyrup 2001). 

In two blocks, Orchid and Poisonwood, plots were burned in two different seasons. 

Two-way analysis of variance indicated that the percentage of ground fuels consumed was

significantly affected by both Block (F=6.98, p=.010) and Season (F=6.62, p=.012), but the 

Block x Season interaction was non-significant. As illustrated in Figure 8.5, consumption 

percentages were slightly higher in summer than winter fires, and in the Orchid than the 

Poisonwood Block. 

The analyses described above focus at the plot level, where patterns evident at a 

smaller scale may be averaged out, potentially obscuring important relationships. To 

examine ground fuel consumption at a finer scale, we used multiple regression to assess how 

ground fuel consumption calculated individually for each of the twenty shrub plots per stand

was affected by (1) the initial, pre-burn quantities of ground fuel present in each plot, and (2) 

plot estimates for char height and canopy scorch (see Chapter 2 for methods of estimation). 

The regression analyses were applied to combined data from the four burned plots in the 

Orchid and Poisonwood blocks. These data represent a broad range of pine rockland 

conditions, in that they included both summer and winter fires, carried out in relatively open 

(Orchid) and closed (Poisonwood) understory conditions. Among the independent variables,

pre-burn ground fuels (p=.0004) and char height (p=.006) were significantly related to fuel

consumption, but crown scorch was unrelated (p=.10). The equation for the 2-factor model

was: 

% consumed = 11.80 + 0.15 * (pre-burn fuels, g/m2

) + 6.7 * (char height, m), R2 = .306 

84
Thus, percent consumption increased with ground fuel mass and with fire intensity. Figure 

8.6, which is based on a distance weighted least squares function applied to the raw data, 

clarifies the influence of both factors on fuel consumption. The function suggests that the 

effect of both ground fuel mass and fire intensity are greatest when the other factor is low. 

The assessment of post-fire ground fuel dynamics requires a space-for-time 

substitution, in that a temporal sequence is inferred from ground fuel biomass estimates for

plots burned in different years, where the estimates were made during a common period, i.e., 

at the conclusion of the project. Figure 8.7 presents ground fuel loads for seven plots that 

varied in time since prescribed fire from 1-3 years. While the distribution suggests some

buildup of ground fuels during this early stage in recovery, the accumulation rate can be

calculated directly for each plot. On average, estimated accumulation rate for ground fuels at 

these sites was 0.53 Mg/ha/year (SE = 0.54). These estimates, which range from 1.20 

Mg/ha/yr in the 3 year-old PS plot to -0.26 Mg/ha/yr in 2 year-old IS, indicate very high 

levels of site variation. Negative values may result from relatively high decomposition rates 

(Snyder 1986), or they may simply reflect random microsite variation among adjacent 

quadrats, to which the sequential harvest method we used is especially sensitive (see 

Methods). In the slash pine plantations studied by Gholz and Fisher (1982), ground fuels

accumulated at a constant rate slightly exceeding 1 Mg/ha/yr through age 35. 

Shrub layer fuel dynamics

Our sampling protocols did not allow a direct estimation of the consumption of shrub 

fuels. More specifically, we could not simply subtract pre-burn from post-burn standing crop, 

as we had done for ground fuels, because of the complicating effects of re-growth during the 

one-year period prior to initial post-burn sampling. However, for plots burned early in the 

project (PS, PW, OS, OW, DS, IS, and IW), it was possible to estimate shrub fuel 

accumulation during Year 2 and in some cases Year 3 after fire. Figure 8.8 displays the

decline in fine fuels associated with palms and broad-leaved shrubs from the pre-fire

condition to Year 1 post-fire, and then the recovery through Year 3 across all available sites. 

The initial decline in palm fuels was less steep than for broad-leaved shrubs (70% and 88%, 

respectively), probably because surviving palms leafed out rapidly from pre-fire meristems, 

and were immediately incorporated in the shrub layer. In contrast, broad-leaved shrubs were 

usually top-killed; when they survived, they generally re-sprouted from the base of the

original shoots, and did not reach 1 meter height by the end of Year 1. After Year 1, both

groups recovered rapidly, with palms and shrubs reaching 70% and 47%, respectively, of 

their pre-fire fine fuel biomass by Year 3. Cooley (2004) demonstrated that attached palm 

fine fuels tended to reach an asymptote well within a decade after fire in the Keys, while fine 

fuels associated with broad-leaved shrubs may take slightly longer to reach a maximum (Sah 

et al. 2004). 

Conclusion 

Fine fuels represent a very substantial proportion of the total aboveground biomass in 

Florida Keys pine forests. For this reason, fuel regulation via prescribed fire may be

particular critical if catastrophic, stand-replacing fires are to be avoided. The presence in the 

85
86

landscape of a rich pine rockland understory flora provides some evidence that this sort of 

regulation took place naturally, or perhaps with the involvement of aboriginal inhabitants. 

Today, the constraints imposed by encroaching residential users make lightning a less viable 

source of ignition, and place a heavy burden on resource managers to design fuel 

management techniques that allow humans to coexist with a healthy pine rockland 

ecosystem. 

In conjunction with earlier work reported by Cooley (2004) and Sah et al. (2004), the 

data reported here form an initial basis for developing such techniques. Our data provide 

evidence of rates of accumulation in several major fuel components, as well as standing 

stocks of fuels in forests of variable history. The data also suggest that fuel accumulation 

and season of burn do affect fire behavior, and perhaps vegetation response. Most of all, the 

results suggest that we need to become more analytical about the relationships between fire 

weather, fuel amounts, forest structure, and fuel conditions, and go beyond simplifications 

that have been depended on till now. 
Table 8.1: Study sites with burning date and sampling years. * Unburned, + Immediate postburn, @ Annual po

Year of sampling 

Site Shrub, Palms & Saplings Grou

Burning Treatment Plot ID Year when plots were last burn Date of experimental burn 1998 1999 2000 2001 1998 1

Control BC Before 1970 Not burned * 

Buttonwood Summer BS Before 1970 7/8/2001 * 

Winter BW Before 1970 7/8/2001 * 

Control DC 1988 Not burned * * * 

Dogwood Summer DS 1988 6/22/1999 * @ @ 

Winter DW 1988 Not burned * 

Control IC 1991 Not burned * * * 

Iris Summer IS 1991 7/14/1999 * @ @ 

Winter IW 1985 12/12/2000 * @ 

Control LC 1986 Not burned * 

Locust Berry Summer LS 1986 7/19/2001 * 

Winter LW 1986 7/19/2001 * 

Control OC 1990 Not burned * * * * * 

Orchid Summer OS 1990 8/16/1998 * @ @ @ * + 

Winter OW 1990 12/15/1998 * @ @ @ * + 

Control PC 1986 Not burned * * * * * 

Poisonwood Summer PS 1986 8/17/1998 * @ @ @ * + 

Winter PW 1986 12/15/1998 * @ @ @ * + 

87
Total Biomass

0

20

40

60

80

100

120

Iris-8 Orchid-8 Dogwood-11 Poisonwood-12 Iris-14 Locustberry-14 Buttonwood-30

Sites

Bio

mass (

M

g/h

a)

HW trees

Pine trees

Pine saplings

Palms

Shrubs

Ground layer

Figure 8.1. Total biomass in six blocks of Big Pine Key pine forests sampled prior to 

experimental fire. The numbers associated with site name represent time (years) since last 

fire. Pine forests in the Iris Block consist of two zones, one was burned 8 years and the 

other 14 years before recent sampling. 

Total fuel

0

5

10

15

20

25

30

35

Iris-8 Orchid-8 Dogwood-11 Poisonwood-12 Iris-14 Locustberry-14 Buttonwood-30

Sites

Fuel (

Mg/ha)

Pine saplings

Palms

Shrubs

Ground layer

Figure 8.2. Total available fuels in six blocks of Big Pine Key pine forests sampled prior 

to experimental fire. The numbers associated with site name represent time (years) since 

last fire. Pine forests in the Iris Block consist of two zones, one was burned 8 years and 

the other 14 years before recent sampling. 

88
Ground fuel consumption

0

10

20

30

40

50

60

70

80

Iris (8) Orchid (8) Dogwood (11) Poisonwood (12) Locust Berry (14) Buttonwood (30)

Sites

Fuel cons

u

m

e

d (

%)

Figure 8.3. Mean percentage of ground fuel consumption during experimental burnings 

in six blocks of Big Pine Key pine forests. The numbers in parenthesis are time (years) 

since last fire. 

Ground fuel consumption

R2 = 0.1753

0

10

20

30

40

50

60

70

80

0.0 0.5 1.0 1.5 2.0 2.5 3.0

Preburn ground fuel (kg/m2)

F

u

el consu

mption (%)

 

Figure 8.4. Mean percentage of ground fuel consumption during experimental burnings 

in relation to pre-burn ground fuel present in Big Pine Key pine forests. 

89
Ground fuel consumption

0

10

20

30

40

50

60

70

80

Orchid (8) Poisonwood (12)

Sites

Fu

el cons

u

m

e

d (

%)

Summer

Winter

Figure 8.5. Mean percentage of ground fuel consumption in the pine forests of Orchid 

and Poisonwood blocks, burned in two different seasons, summer and winter. The 

numbers in parenthesis are time (years) since last fire. 

Figure 8.6. Mean percentage of ground fuel consumption in relation to pre-burn ground 

fuel and char height during the experimental burning in the pine forests of Big Pine Key. 

90
Ground fuel accumulation

-100

-50

0

50

100

150

200

250

300

350

400

DS (2) IS (2) OS (3) OW (3) PS (3) PW (3)

Plots with time (year) since experimental burn

Ground f

u

el (

g

m/m2)

Figure 8.7. Ground fuel accumulation after experimental burn in the pine forests of Big

Pine Key. Numbers in parenthesis are the time since experimental burn. 

Broad-leaved shrub and palm fuel consumption & accumulation

Pre-burn 123

Preburn & time (year) since experimental burn

0

1

2

3

4

5

6

F

uel bio

mass (

M

g/h

a)

Shrubs fine fuel

 Palm fuel

Figure 8.8. Shrub fine fuel and palm fuel prior to and 1-3 years after experimental burn 

in the pine forests of Big Pine Key.

91
9. CONCLUSIONS 

The task of prescribed burning is not easy—witness the difficulty we had in

getting a series of simple experimental burns done on schedule. External factors delayed 

the experimental treatments and did not allow us to collect postburn data on one third of 

the burns. 

In the three blocks in which paired summer and winter burns were successfully 

carried out, the summer burns had more intense fires as judged by our indicators of fire 

intensity (crown scorch, height of bark char, fire temperature, fuel consumption). Under

other circumstances that might not always be the case. 

Because the summer burns were more intense than the winter burns, it is not 

possible to say whether differences in vegetation response between summer and winter 

burns are due to season or to fire intensity. 

The mortality of South Florida slash pine trees was greater after the summer burn 

than the winter burn in each block, but there was rarely such a consistent pattern seen for

other vegetation responses. One need only look at the recovery of stem numbers of 

common shrub species after burning: Metopium showed less recovery after summer 

burns in Orchid and Iris and after the winter burn in Poisonwood. Pisonia, while it also 

had less recovery after the summer burn in Iris and the winter burn in Poisonwood, 

showed more recovery after the summer burn at Orchid. 

The same species may respond differently to the same burns depending on the 

growth stage of the plant. Adult palms were generally more likely to succumb to summer

than winter burns, and Coccothrinax was much more likely to die than Thrinax. 

However, the recovery of small palms was greater after summer burns than winter burns. 

Higher intensity fires may be beneficial for some plant responses such as 

germination of Jacquemontia pentantha seeds. 

Fine fuels represent a substantial proportion of the total aboveground biomass in 

Lower Keys pine rocklands. 

During the course of this study a series of permanently marked vegetation plots 

were established. There are now 18 permanently marked 1.0-ha plots on Big Pine Key in 

which all trees with DBH ≥ 5 cm were tagged and measured. Plots should be 

periodically re-measured to follow growth, mortality, and recruitment into the tree

stratum. More than 11,400 trees were tagged. Similarly, within each of the 18 plots there 

are 20 marked shrub plots in which woody plants and palms ≥ 1 m tall were tallied, and

80 individually marked permanent herb quadrats in which all the species were 

enumerated. These plots will be immensely valuable to document change, whether due 

to prescribed fire, wildfire, hurricane, sea level rise, or some other unforeseen 

disturbance. 

92
10. ACKNOWLEDGEMENTS 

Our partners in this research were The Nature Conservancy (Chris Bergh, Laura 

Flynn, Doria Gordon, Jackie Vlcek, Dave Reed) and the National Key Deer Refuge, U.S. 

Fish and Wildlife Service (Refuge managers Barry Stieglitz and Jim Halpin). Jim 

Durrwachter (FMO) and his capable staff from the Florida Panther National Wildlife

Refuge conducted the treatment burns. Chris Borg was our project manager, and Hong 

Liu was the graduate research assistant on this project. Holly Belles and Marlena 

Hovorka helped in setting up the plots. We thank Elizabeth Bergh and Angela Foreman;

fire project interns Sahara Moon Chapotin, Joanne Singfield, Jenny McCune, Jana Mott, 

Christie Whelan, and Sarah Naylor; and visiting scientists John Geiger, Alan Herndon, 

Pablo Ruiz, Jed Redwine, Yuria Cardel, Mariana Cuautle, and Cecilia Diaz-Castelazo for

field assistance. David Jones produced the species list. Hillary Cooley provided data on 

palms. Susanna Stoffella assisted in the analysis of herb layer data. This research was

funded by the U.S. Department of the Interior Joint Fire Program (CA-1445-CA09-95-

0112) and a Florida International University Foundation Provost Summer Research 

Grant. 

93
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99
 12. APPENDICES 

Appendix 1. GPS coordinates for corners of 18 experimental plots. Projection=geographic, 

Datum=WGS-84, Units=Decimal Degrees. 

BLOCK PLOT CORNER XCOORD YCOORD GPSDATE GPSTIME

Buttonwood BC SW -81.374275 24.688400 5/9/2000 11:20:02 AM

Buttonwood BC SE -81.373774 24.688390 5/9/2000 12:15:25 PM

Buttonwood BC NW -81.374170 24.690219 5/9/2000 11:10:45 AM

Buttonwood BC NE -81.373674 24.690202 5/9/2000 11:53:47 AM

Buttonwood BS SW -81.373314 24.688323 5/9/2000 12:20:17 PM

Buttonwood BS SE -81.372809 24.688335 5/9/2000 12:24:27 PM

Buttonwood BS NW -81.373322 24.690143 5/9/2000 11:44:22 AM

Buttonwood BS NE -81.372825 24.690140 5/9/2000 12:39:31 PM

Buttonwood BW SW -81.372412 24.688730 5/9/2000 12:32:27 PM

Buttonwood BW SE -81.371934 24.688788 5/9/2000 02:09:34 PM

Buttonwood BW NW -81.372638 24.690542 5/9/2000 12:41:31 PM

Buttonwood BW NE -81.372146 24.690583 5/9/2000 02:02:15 PM

Dogwood DC SW -81.376248 24.702965 5/16/2000 03:01:39 PM

Dogwood DC SE -81.375267 24.703045 5/16/2000 02:56:34 PM

Dogwood DC NW -81.376337 24.703868 5/16/2000 03:07:10 PM

Dogwood DC NE -81.375368 24.703944 5/16/2000 02:49:38 PM

Dogwood DS SW -81.376411 24.704210 5/16/2000 03:25:43 PM

Dogwood DS SE -81.375434 24.704305 5/16/2000 03:14:46 PM

Dogwood DS NW -81.376530 24.705116 5/16/2000 03:22:29 PM

Dogwood DS NE -81.375531 24.705195 5/16/2000 03:18:51 PM

Dogwood DW SW -81.377874 24.704241 5/16/2000 03:32:48 PM

Dogwood DW SE -81.376896 24.704327 5/16/2000 03:29:28 PM

Dogwood DW NW -81.377938 24.705134 5/16/2000 03:36:13 PM

Dogwood DW NE -81.376979 24.705210 5/16/2000 03:41:06 PM

Iris IC SW -81.386260 24.713014 5/16/2000 02:07:39 PM

Iris IC SE -81.385289 24.713087 5/16/2000 02:25:19 PM

Iris IC NW -81.386338 24.713908 5/16/2000 02:15:09 PM

Iris IC NE -81.385346 24.713978 5/16/2000 02:20:19 PM

Iris IS SW -81.387693 24.712974 5/16/2000 01:46:06 PM

Iris IS SE -81.386686 24.713072 5/16/2000 02:04:41 PM

Iris IS NW -81.387743 24.713876 5/16/2000 01:53:32 PM

Iris IS NE -81.386749 24.713921 5/16/2000 01:58:42 PM

Iris IW SW -81.390951 24.711854 5/16/2000 01:17:13 PM

Iris IW SE -81.389956 24.711922 5/16/2000 01:24:38 PM

Iris IW NW -81.391027 24.712744 5/16/2000 01:12:04 PM

Iris IW NE -81.390036 24.712820 5/16/2000 01:31:46 PM

Locustberry LC SW -81.379506 24.701753 5/9/2000 09:32:50 AM

Locustberry LC SE -81.378517 24.701767 5/9/2000 09:37:06 AM

Locustberry LC NW -81.379518 24.702656 5/9/2000 09:45:53 AM

100
101

BLOCK PLOT CORNER XCOORD YCOORD GPSDATE GPSTIME 

Locustberry LC NE -81.378535 24.702669 5/9/2000 09:41:25 AM 

Locustberry LS SW -81.379698 24.700574 5/9/2000 09:17:24 AM 

Locustberry LS SE -81.378716 24.700622 5/9/2000 09:20:42 AM 

Locustberry LS NW -81.379752 24.701471 5/9/2000 09:30:56 AM 

Locustberry LS NE -81.378772 24.701521 5/9/2000 09:25:11 AM 

Locustberry LW SW -81.379690 24.699302 5/9/2000 08:55:43 AM 

Locustberry LW SE -81.378694 24.699326 5/9/2000 09:01:20 AM 

Locustberry LW NW -81.379712 24.700205 5/9/2000 09:09:16 AM 

Locustberry LW NE -81.378733 24.700233 5/9/2000 09:05:39 AM 

Orchid OC SW -81.380276 24.707566 5/9/2000 03:20:12 PM 

Orchid OC SE -81.379288 24.707630 5/9/2000 03:15:53 PM 

Orchid OC NW -81.380343 24.708464 5/9/2000 03:04:40 PM 

Orchid OC NE -81.379354 24.708532 5/9/2000 03:10:05 PM 

Orchid OS SW -81.382444 24.710979 5/9/2000 04:20:30 PM 

Orchid OS SE -81.381465 24.711095 5/9/2000 04:16:46 PM 

Orchid OS NW -81.382561 24.711882 5/9/2000 04:08:16 PM 

Orchid OS NE -81.381586 24.711986 5/9/2000 04:12:53 PM 

Orchid OW SW -81.380998 24.709664 5/9/2000 02:36:46 PM 

Orchid OW SE -81.380015 24.709725 5/9/2000 02:42:07 PM 

Orchid OW NW -81.381069 24.710551 5/9/2000 02:52:54 PM 

Orchid OW NE -81.380088 24.710622 5/9/2000 02:46:13 PM 

Poisonwood PC SW -81.388575 24.701740 5/16/2000 11:10:50 AM 

Poisonwood PC SE -81.387801 24.701816 5/16/2000 11:06:13 AM 

Poisonwood PC NW -81.388720 24.702863 5/16/2000 11:19:02 AM 

Poisonwood PC NE -81.387931 24.702940 5/16/2000 11:24:41 AM 

Poisonwood PS SW -81.388352 24.699000 5/16/2000 10:50:31 AM 

Poisonwood PS SE -81.387660 24.699358 5/16/2000 10:45:00 AM 

Poisonwood PS NW -81.388949 24.699974 5/16/2000 10:57:49 AM 

Poisonwood PS NE -81.388273 24.700340 5/16/2000 10:35:46 AM 

Poisonwood PW SW -81.389021 24.700742 5/16/2000 10:16:26 AM 

Poisonwood PW SE -81.387798 24.700848 5/16/2000 10:03:11 AM 

Poisonwood PW NW -81.389095 24.701463 5/16/2000 10:21:38 AM 

Poisonwood PW NE -81.387861 24.701565 5/16/2000 10:29:07 AM 
BIG PINE FIRE ECOLOGY STUDY 

Vascular plant species 

Reference Codes: (1) Wunderlin, Richard P. 1998. Guide to the Vascular Plants of Florida. University Press ofFlorida, Gainesville. 806 pp.; (2) Long, Robert W. and Olga Lakela. 1976. A Flora of Tropical Florida: A Manual of the Seed Plants and Ferns of Southern Peninsular Florida. Banyan Books, Miami. 962 pp. ; (3) Correll, Donovan S. and Helen B. Correll. 1982 (Reprint 1996). Flora of the Bahama Archipelago. A.R.G. Gantner Verlag KG, Vaduz,Liechtenstein. 1692 pp.; and (4) Lellinger, David B. 1985. A Field Manual of the Ferns & Fern-allies of the United States & Canada. Smithsonian Institution Press, Washington, D.C. 389 pp. 

Purnot obs

CLASS FAMILY SP CODE SCIENTIFIC NAME AUTHOR CITATION REF 

PTERIDOPHYTE PTERIDACEAE ACRDAN Acrostichum danaeifolium Langsd. & Fisch. 1,4 PTERIDOPHYTE SCHIZAEACEAE ANEADI Anemia adiantifolia (L.) Sw. 1,4PTERIDOPHYTE PSILOTACEAE PSINUD Psilotum nudum (L.) Beauv. 1,4 PTERIDOPHYTE DENNSTAEDTIACEAE PTEAQU Pteridium aquilinum (L.) Kuhn 1PTERIDOPHYTE PTERIDACEAE PTEBAH Pteris bahamensis (J. Agardh) Fee 1,4 PTERIDOPHYTE THELYPTERIDACEAE THEKUN Thelypteris kunthii (Desv.) C.V. Morton 1,4 

GYMNOSPERM PINACEAE PINELL Pinus elliottii Engelm. 1MONOCOT CYPERACEAE ABIOVA Abildgaardia ovata (Burm. F.) Kral 1 

MONOCOT AGAVACEAE AGADEC Agave decipiens Baker 1 

MONOCOT LILIACEAE ALEBRA Aletris bracteata Northr. 1 MONOCOT POACEAE ANDCAB Andropogon cabanisii Hack. 2 1AMONOCOT POACEAE ANDGLO Andropogon glomeratus (Walter) Britton et al. 1MONOCOT POACEAE ANDVIR Andropogon virginicus L. 1MONOCOT POACEAE ARIPUR Aristida purpurascens Poir. 1MONOCOT ORCHIDACEAE BASCOR Basiphyllaea corallicola (Small) Ames 1 MONOCOT ORCHIDACEAE BLEPUR Bletia purpurea (Lam.) DC. 1 

MONOCOT POACEAE CENECH Cenchrus echinatus L. 1 

MONOCOT CYPERACEAE CLAJAM Cladium jamaicense Crantz 1 MONOCOT ARECACEAE COCARG Coccothrinax argentata (Jacq.) L.H. Bailey 1 

MONOCOT POACEAE DICDIC Dichanthelium dichotomum (L.) Gould 1 

102
CLASS FAMILY SP CODE SCIENTIFIC NAME AUTHOR CITATION REF 

MONOCOT CYPERACEAE DICFLO Dichromena floridensis Britton ex Small 2 1Rh

ex 

MONOCOT CYPERACEAE ELECAR Eleocharis caribaea Torr. 1 MONOCOT POACEAE ERAELL Eragrostis elliottii S. Watson 1 MONOCOT POACEAE EUSPET Eustachys petraea (Sw.) Desv. 1 MONOCOT CYPERACEAE FIMCAS Fimbristylis castanea (Michx.) Vahl 2 1F. MONOCOT CYPERACEAE FIMSPA Fimbristylis spathacea Roth. 2 1F. MONOCOT ORCHIDACEAE HABQUI Habenaria quinqueseta (Michx.) Eaton 1 MONOCOT HYPOXIDACEAE HYPWRI Hypoxis wrightii (Baker) Brackett 1MONOCOT POACEAE MUHFIL Muhlenbergia filipes M.A. Curtis 2 1M

filip

MONOCOT POACEAE PANPOR Panicum portoricense Desv. ex Ham. 2 1Di

ex H

MONOCOT POACEAE PANRIG Panicum rigidulum Bosc ex Nees 1 MONOCOT POACEAE PANVIR Panicum virgatum L. 1 

MONOCOT POACEAE PASCAE Paspalum caespitosum Flugge 1 

MONOCOT POACEAE PASMON Paspalum monostachyum Vasey ex Chapm. 1 MONOCOT POACEAE PASSET Paspalum setaceum Michx. 1MONOCOT CYPERACEAE RHYMIC Rhynchospora microcarpa Baldwin ex A. Gray 1 MONOCOT CYPERACEAE RHYODO Rhynchospora odorata C. Wright ex Griseb. 1MONOCOT ARECACEAE SABPAL Sabal palmetto (Walter) Lodd. ex

Schult. & Schult. f. 

1

MONOCOT ALISMATACEAE SAGLAN Sagittaria lancifolia subsp. 

lancifolia 

L. 1 

MONOCOT POACEAE SCHGRA Schizachyrium gracile (Spreng.) Nash 1 MONOCOT POACEAE SCHRHI Schizachyrium rhizomatum (Swallen) Gould 2 1S. 

(Mi

MONOCOT POACEAE SCHSEM Schizachyrium semiberbe Nees 2 1S. MONOCOT CYPERACEAE SCHNIG Schoenus nigricans L. 1MONOCOT CYPERACEAE SCLVER Scleria verticillata Muhl. Ex Willd. 1MONOCOT ARECACEAE SERREP Serenoa repens (W. Bartram) Small 1 

103
CLASS FAMILY SP CODE SCIENTIFIC NAME AUTHOR CITATION REF 

MONOCOT IRIDACEAE SISMIA Sisyrinchium miamiense E.P. Bicknell 1S. 

atla

MONOCOT SMILACACEAE SMIHAV Smilax havanensis Jacq. 1MONOCOT POACEAE SORSEC Sorghastrum secundum (Elliott) Nash 1MONOCOT ORCHIDACEAE SPITOR Spiranthes torta (Thunb.) Garay & 

H.R. Sweet 

1

MONOCOT POACEAE SPODOM Sporobolus domingensis (Trin.) Kunth 1 

MONOCOT ARECACEAE THRMOR Thrinax morrisii H. Wendl. 1

MONOCOT BROMELIACEAE TILCIR Tillandsia circinata Schltdl. 2 1T. MONOCOT BROMELIACEAE TILFLE Tillandsia flexuosa Sw. 1 MONOCOT BROMELIACEAE TILUTR Tillandsia utriculata L. 1 MONOCOT BROMELIACEAE TILVAL Tillandsia valenzuelana Rich. 2 1T. 

MONOCOT POACEAE TRIFLO Tripsacum floridanum Porter ex Vasey 1

MONOCOT TYPHACEAE TYPDOM Typha domingensis Pers. 1 MONOCOT ORCHIDACEAE VANBAR Vanilla barbellata Rchb. f. 1 

DICOT FABACEAE ACAPIN Acacia pinetorum F.J. Herm. 1DICOT EUPHORBIACEAE ACACHA Acalypha chamaedrifolia (Lam.) Mull. Arg. 1 DICOT SCROPHULARIACEAE AGAKEY Agalinis keyensis Pennell 1A

filif

DICOT SCROPHULARIACEAE AGAMAR Agalinis maritima (Raf.) Raf. 1 

DICOT APOCYNACEAE ANGBER Angadenia berteri (A. DC.) Miers 1 DICOT ANNONACEAE ANNGLA Annona glabra L. 1DICOT EUPHORBIACEAE ARGBLO Argythamnia blodgettii (Torr. ex Chapm.) 

Chapm.

1

DICOT ASCLEPIADACEAE ASCVIR Asclepias viridis Walter 1DICOT ASTERACEAE ASTDUM Aster dumosus L. 1DICOT ASTERACEAE ASTTEN Aster tenuifolius L. 1

DICOT AVICENNIACEAE AVIGER Avicennia germinans (L.) L. 1 

DICOT SCROPHULARIACEAE BACMON Bacopa monnieri (L.) Pennell 1 DICOT RUBIACEAE BORTER Borreria terminalis Small 2 1Sp

Kar

104
CLASS FAMILY SP CODE SCIENTIFIC NAME AUTHOR CITATION REF 

DICOT BORAGINACEAE BOUCASS Bourreria cassinifolia (A. Rich.) Griseb. 1 DICOT SCROPHULARIACEAE BUCFLO Buchnera floridana Gand. 2 1B. 

DICOT SAPOTACEAE BUMCEL Bumelia celastrina Kunth 2 1Si

T.D

DICOT SAPOTACEAE BUMSAL Bumelia salicifolia (L.) Sw. 3 1Si

LamDC

DICOT BURSERACEAE BURSIM Bursera simaruba (L.) Sarg. 1 

DICOT MALPIGHIACEAE BYRLUC Byrsonima lucida (Mill.) DC. 1

DICOT FABACEAE CAEPAU Caesalpinia pauciflora (Griseb.) C. Wright 1 DICOT SCROPHULARIACEAE CAPBIF Capraria biflora L. 1 

DICOT FABACEAE CASBAH Cassia bahamensis Mill. 2 1Se

& B

DICOT LAURACEAE CASFIL Cassytha filiformis L. 1

DICOT CASUARINACEAE CASEQU Casuarina equisetifolia L. 1 

DICOT RUBIACEAE CATPAR Catesbaea parviflora Sw. 1 DICOT APIACEAE CENASI Centella asiatica (L.) Urb. 1DICOT FABACEAE CENVIR Centrosema virginianum (L.) Benth. 1DICOT FABACEAE CHAASP Chamaecrista aspera (Muhl. ex Elliott) 

Greene 

1C. (MuBar

DICOT FABACEAE CHALIN Chamaecrista lineata (Sw.) Greene 1 DICOT EUPHORBIACEAE CHAADE Chamaesyce adenoptera

subsp. pergamena 

(Bertol.) Small/(Small) Burch

2 1C. 

DICOT EUPHORBIACEAE CHADEL Chamaesyce deltoidea (Engelm. ex Chapm.) 

Small 

1

DICOT EUPHORBIACEAE CHAPOR Chamaesyce porteriana Small 1DICOT ASTERACEAE CHADEN Chaptalia dentata (L.) Cassini 2 DICOT RUBIACEAE CHIALB Chiococca alba (L.) Hitchc. 1 DICOT RUBIACEAE CHIPIN Chiococca pinetorum Britton 2 1C. 

Wu

DICOT CHRYSOBALANACEAE CHRICA Chrysobalanus icaco L. 1

105
CLASS FAMILY SP CODE SCIENTIFIC NAME AUTHOR CITATION REF 

DICOT SAPOTACEAE CHROLI Chrysophyllum oliviforme L. 1 DICOT ASTERACEAE CIRHOR Cirsium horridulum Michx. 1DICOT EUPHORBIACEAE CNISTI Cnidoscolus stimulosus (Michx.) Engelm. & 

A. Gray

1

DICOT POLYGONACEAE COCDIV Coccoloba diversifolia Jacq. 1DICOT POLYGONACEAE COCUVI Coccoloba uvifera (L.) L. 1 DICOT COMBRETACEAE CONERE Conocarpus erectus L. 1DICOT ASTERACEAE CORLEA Coreopsis leavenworthii Torr. & A. Gray 1 DICOT CELASTRACEAE CROILI Crossopetalum ilicifolium (Por.) Kuntze 1DICOT CELASTRACEAE CRORHA Crossopetalum rhacoma Crantz 1DICOT FABACEAE CROMAR Crotalaria maritima Chapm. 2 1C. DICOT EUPHORBIACEAE CROGLA Croton glandulosus L. 1DICOT EUPHORBIACEAE CROLIN Croton linearis Jacq. 1DICOT ASCLEPIADACEAE CYNBLO Cynanchum blodgettii (A. Gray) Shinners 1DICOT LOGANIACEAE CYNSES Cynoctonum sessilifolium J.F. Gmel. 2 1M

Don 

DICOT EBENACEAE DIOVIR Diospyros virginiana L. 1DICOT ACANTHACEAE DYSOBL Dyschoriste oblongifolia (Michx.) Kuntze 1 DICOT APOCYNACEAE ECHUMB Echites umbellata Jacq. 1DICOT RUBIACEAE ERIFRU Erithalis fruticosa L. 1DICOT RUBIACEAE ERNLIT Ernodea littoralis Sw. 1DICOT MYRTACEAE EUGAXI Eugenia axillaris (Sw.) Willd. 1DICOT MYRTACEAE EUGFOE Eugenia foetida Pers. 1

DICOT GENTIANACEAE EUSEXA Eustoma exaltatum (L.) Salisb. ex G. Don 1 

DICOT CONVOLVULACEAE EVOGRI Evolvulus grisebachii Peter 1DICOT CONVOLVULACEAE EVOSER Evolvulus sericeus Sw. 1 

DICOT MORACEAE FICAUR Ficus aurea Nutt. 1 

DICOT MORACEAE FICCIT Ficus citrifolia Mill. 1DICOT ASTERACEAE FLALIN Flaveria linearis Lag. 1DICOT FABACEAE GALPAR Galactia parvifolia A. Rich. 2 1C. DICOT NYCTAGINACEAE GUADIS Guapira discolor (Spreng.) Little 1DICOT RUBIACEAE GUESCA Guettarda scabra (L.) Vent. 1

106
CLASS FAMILY SP CODE SCIENTIFIC NAME AUTHOR CITATION REF 

DICOT RUBIACEAE HEDNIG Hedyotis nigricans (Lam.) Fosberg 1 DICOT BORAGINACEAE HELPOL Heliotropium polyphyllum Lehm. 1DICOT ASTERACEAE HETGRA Heterotheca graminifolia (Michx.) Shinners 2 1Pi

Nut

DICOT FABACEAE INDMIN Indigofera miniata Ortega 1DICOT CONVOLVULACEAE IPOMOEA IpomoeaDICOT CONVOLVULACEAE JACPEN Jacquemontia pentanthos (Jacq.) G. Don 1 DICOT THEOPHRASTACEAE JACKEY Jacquinia keyensis Mez 1 DICOT VERBENACEAE LANINV Lantana involucrata L. 1 DICOT FABACEAE LEULEU Leucaena leucocephala (Lam.) de Wit 1 

DICOT ASTERACEAE LIAGRA Liatris gracilis Pursh 1DICOT ASTERACEAE LIALAE Liatris laevigata Nutt. 1L. 

Cha(Nu

DICOT CHRYSOBALANACEAE LICMIC Licania michauxii Prance 1DICOT LINACEAE LINARE Linum arenicola (Small) H.J.P. Winkler 1

DICOT CAMPANULACEAE LOBGLA Lobelia glandulosa Walt. 1 DICOT ONAGRACEAE LUDMIC Ludwigia microcarpa Michx. 1 

DICOT SAPOTACEAE MANBAH Manilkara bahamensis (Baker) H.J. Lam & B. 

Meeuse 

2 1M

DubCro

DICOT ASTERACEAE MELPAR Melanthera parvifolia Small 2 1MDICOT ANACARDIACEAE METTOX Metopium toxiferum (L.) Krug & Urb. 1 DICOT ASTERACEAE MIKSCA Mikania scandens (L.) Willd. 1DICOT RUBIACEAE MORROY Morinda royoc L. 1DICOT MYRICACEAE MYRCER Myrica cerifera L. 1 DICOT MYRSINACEAE MYRFLO Myrsine floridana A. DC. 3 1Ra

gui

DICOT FABACEAE NEPPUB Neptunia pubescens Benth. 1

DICOT CACTACEAE OPUSTR Opuntia stricta (Haw.) Haw. 1 

DICOT PASSIFLORACEAE PASSUB Passiflora suberosa L. 1

DICOT ASTERACEAE PECLEP Pectis leptocephala (Cass.) Urb. 2 1P. 

107
CLASS FAMILY SP CODE SCIENTIFIC NAME AUTHOR CITATION REF 

DICOT EUPHORBIACEAE PHYPEN Phyllanthus pentaphyllus C. Wright ex Griseb. 1 DICOT SOLANACEAE PHYANG Physalis angustifolia Nutt. 1 DICOT SOLANACEAE PHYARE Physalis arenicola Kearney 1 

DICOT SOLANACEAE PHYVIS Physalis viscosa L. 2 1P. DICOT LENTIBULARIACEAE PINPUM Pinguicula pumila Michx. 1DICOT TURNERACEAE PIRCAR Piriqueta caroliniana (Walter) Urb. 1DICOT FABACEAE PISPIS Piscidia piscipula (L.) Sarg. 1 DICOT NYCTAGINACEAE PISROT Pisonia rotundata Griseb. 1DICOT FABACEAE PITGUA Pithecellobium

guadalupense 

(Pers.) Chapm. 1,2

Ros

DICOT ASTERACEAE PLUROS Pluchea rosea R.K. Godfrey 1DICOT EUPHORBIACEAE POIPIN Poinsettia pinetorum Small 1DICOT POLYGALACEAE POLBOY Polygala boykinii Nutt. 1DICOT POLYGALACEAE POLGRA Polygala grandiflora Walter 1DICOT MYRTACEAE PSILON Psidium longipes (O. Berg.) McVaugh 1DICOT ASTERACEAE PTEPYC Pterocaulon pycnostachyum (Michx.) Elliott 1DICOT RUBIACEAE RANACU Randia aculeata L. 1 DICOT RHAMNACEAE REYSEP Reynosia septentrionalis Urb. 1

DICOT RHIZOPHORACEAE RHIMAN Rhizophora mangle l. 1 

DICOT ANACARDIACEAE RHURAD Rhus radicans L. 3 1,2

Kun

DICOT FABACEAE RHYPAR Rhynchosia parvifolia DC. 1DICOT ACANTHACEAE RUECAR Ruellia caroliniensis (J.F. Gmel.) Steud. 1DICOT GENTIANACEAE SABSTE Sabatia stellaris Pursh 1DICOT ASTERACEAE SACPOL Sachsia polycephala Griseb. 1DICOT PRIMULACEAE SAMEBR Samolus ebracteatus Kunth 1DICOT ANACARDIACEAE SCHTER Schinus terebinthifolius Raddi 1

DICOT LAMIACEAE SCUHAV Scutellaria havanensis Jacq. 1 DICOT MALVACEAE SIDELL Sida elliottii Torr. & A. Gray 1 

DICOT SIMAROUBACEAE SIMGLA Simarouba glauca DC. 1

DICOT SOLANACEAE SOLBAH Solanum bahamense L. 1 

DICOT SOLANACEAE SOLDON Solanum donianum Walp. 3 1S. 

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CLASS FAMILY SP CODE SCIENTIFIC NAME AUTHOR CITATION REF 

DICOT SOLANACEAE SOLERI Solanum erianthum D. Don 1 DICOT ASTERACEAE SOLSTR Solidago stricta Aiton 1DICOT FABACEAE SOPTOM Sophora tomentosa L. 1DICOT RUBIACEAE STRMAR Strumpfia maritima Jacq. 1 

DICOT FABACEAE STYHAM Stylosanthes hamata (L.) Taub. 1 DICOT MELIACEAE SWIMAH Swietenia mahagoni (L.) Jacq. 1 DICOT EUPHORBIACEAE TRASAX Tragia saxicola Small 1DICOT APOCYNACEAE URELUT Urechites lutea (L.) Britton 2 1Pe

& W

DICOT ASTERACEAE VERBLO Vernonia blodgettii Small 1DICOT OLACACEAE XIMAME Ximenia americana L. 1 

109