Forest, Fire, and Disturbance · Forest, Fire, and Disturbance PCB 3044L The effects of prescribed fire in the Pine Flatwoods ecosystem of the UCF Arboretum Leigh Mingledorff . Abstract

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The effects of prescribed fire in the Pine Flatwoods ecosystem of the UCF Arboretum

Leigh Mingledorff

Abstract I examined the effects of prescribed fire on one section of the arboretum

(Unit 1) and compared it to an unburned section (East). Each of six

groups utilized the point-quarter sampling method in both Unit 1 and

East sections to measure the distances to canopy, sub-canopy, shrub

and dead log. Groups also measured the DBH of canopy, sub-canopy,

and dead logs. Utilizing this collected data, we were able to make

individual calculations including the average point-to-plant, average

area per plant, average densities, frequencies, and coverage

percentages for both Unit 1 and the East section. Finally, importance

values were calculated to demonstrate which species for canopy and

sub-canopy were the most influential. After examining my data and

figures, I found that the prescribed burn is useful in promoting diversity,

clearing the area of excess litter, reducing fuel and is all around

necessary for managing the pine flatwoods ecosystem.

Introduction Being fire-dependent, pine flatwoods require regular fire exposure to

properly maintain their ecosystem. Fires are important to pine flatwoods

and scrub habitats because litter is burned, fuel is reduced, nutrients

are cycled, and hardwood competitors are suppressed. Certain species

of plants require fire in order to release seeds for dispersal and

germination, particularly among pines (Flatwoods). In addition, various

species of animals, some of which are endangered, rely on the pine

flatwoods habitat for survival. Following a study performed by Martin B.

Main and Larry W. Richardson, in less than six months after a prescribed

fire, wildlife use of pine flatwoods increased (Main and Richardson).

Prescribed fires are planned in Florida “to maintain fire dependent

ecosystems and restore those outside their natural balance (Prescribed

Fire)”. The first prescribed burn in UCF history was in May of 2005 in

Unit 1. Further burns have been planned and carried out. Each ecology

lab group collected data on the areas Unit 1 and East. When compared

to Unit 1 and given that the East section was/is unburned, I predicted

that on average, the East section would have shorter distances to

canopy, sub-canopy and shrub vegetation. The burned section, Unit 1,

would have shorter distances to dead logs. I also predict that the East

section would have higher importance values among species of oaks

due to the lack of burns to remove the hardwood vegetation and that

Unit 1 would have higher importance values among pines.

Materials and Methods Each of six ecology lab groups of four to five people were given 3-4

yardsticks, a field bag containing a compass, a distance measuring tape

(in meters), a DBH or diameter at breast height measuring tape (in π

centimeters), and special tape to mark each individual area that data

collection occurred. Our data collection was in the form of point-quarter,

which is the most popular plotless sampling method and is sensitive to

non-random distributions. My group and I selected a random starting

point in the East section and marked it with our ruler, noting the cardinal

directions and dividing the area into four quadrants. For each quadrant

we measured and recorded the distance (in meters) from the center of

the marker to the center of the nearest canopy vegetation, sub-canopy

vegetation, shrub vegetation, and dead log. We measured the DBH (in π

centimeters) of the canopy, sub-canopy, and dead log. In addition we

identified the live species that were sampled and gave percentage

cover for grasses, woody plants, annuals/perennials, and open space

around the center of the marker. After all four quadrants of data for our

first sample point was collected, we marked our location with tape and

moved along in one direction to randomly choose another point when

we were out of our first sample range. We repeated the previous steps

three more times for East before sampling three more points the same

way in Unit 1.

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Discussion When regarding my calculations, I find that part of my original

hypothesis was correct and part was incorrect. When compared to the

East section, Unit 1 had longer average distances, larger average areas,

and less total density for canopy, sub-canopy, and shrub vegetation. The

East section had longer average distances and larger average areas for

dead logs. I attribute the longer average distances, larger average

areas, and less total density for canopy, sub-canopy, and shrubs of Unit

1 to the prescribed fire of May 2005, which cleared out Unit 1 of any

excess vegetation, leaving more room around each plant and less

vegetation to contribute towards total density. In addition I believe that

the prescribed fire of 2005 is responsible for the shorter average

distances to dead logs as the burn most likely aided the death of weaker

vegetation. I found that when comparing the summed importance values

for sub-canopy Oak and Pine genera, that surprisingly Unit 1 has a

higher value for Oak species and East has a higher value for Pine

species. However, the groundcover percentage estimations measured

inside the marker’s center area show that the East section had a higher

percentage of woody groundcover (40.1%) and that Unit 1 had a higher

percentage of herbaceous plants (38.9%). That would mean that

because the East has yet to be burned, that the woody trees are coming

in and establishing without a fire to keep their seedlings suppressed.

The lack of prescribed fire for the East section of the arboretum has

affected the pine flatwoods ecosystem there by letting vegetation grow

unchecked. Thus there is a larger density of vegetation. The East section

had a smaller amount of species listed than Unit 1 for sub-canopy and

shrub, indicating the East section has less diversity. Influence-wise,

Slash Pine (Pinus elliottii) is the dominant (and only species listed) for

canopy plants and Sand Pine (Pinus clausa) has the leading importance

value for sub-canopy. Otherwise, Saw Palmetto (Serenoa repens) had the

highest absolute density.

Unit 1, when compared to the East section, is characterized by longer

average distances point-to-plant, larger mean areas around vegetation,

and less overall density of vegetation. Saw palmetto (Serenoa repens)

has the highest absolute density. Slash Pine (Pinus elliottii) has the

highest importance value for canopy plants and Sabal Palm (Sabal

palmetto) has the highest value for sub-canopy. Unit 1 had a larger

amount of species listed than the East section for sub-canopy and shrub,

indicating more diversity among species is present in the burned area.

After reviewing my calculations and graphs I created, I am not overly

confident in the validity of the data. Considering I did not collect this

data alone or with a professional organization, I am not certain whether

my classmates correctly identified each plant or even correctly

measured each plant’s distance or DBH. When reviewing the raw data

for Unit 1 sub-canopy, I see that one listing of Sabal Palmetto was listed

to have a DBH of 47.5, which would have been a mature tree as mature

heights differ between diameters of 30-61 cm (Wade and Langdon). In

addition, approximately 42% of sampled points from Unit 1 sub-canopy

were discarded and not entered due to their distance being over 50

meters from the center of the marker. The sub-canopy trees located

outside of the 50 meter distance could have significantly altered my

calculations, were measurements given for their DBH. Otherwise, I

believe leaving out that 42% of sub-canopy trees were more than 50

meters away affected the mean distance point-to-plant, making the

average for sub-canopy distances smaller than it should have been.

With regard to Unit 1, based on the longer mean point-to-plant

distances, the larger average areas per plant, and the smaller amount of

density occupied but with more diversity, I strongly believe prescribed

burns are most definitely a useful tool in managing the UCF arboretum.

In addition, the knowledge that fire is essential to maintain the pine

flatwoods ecosystem supports my conclusion.

As far as recommendations go, I am unsure of the previous lengths of

preparation for prescribed burns on the UCF campus, however I did

note that some of the trees in Unit 1 had sustained fire damage. When

planning a fire, considering the weather is one of the most important

things to do in terms of a successful burn and proper smoke

management (Wade). It is obvious that there needs to be a plan laid out

to carry out the prescribed fire properly, when burning the fire must be

monitored for any irregularities, and a backup plan in case anything

goes awry. Evidence suggests that “a single fire may not be effective in

restoring flatwoods (Abrahamson)”. Therefore, planning for future burns

for other areas of the arboretum, including additional future burns of

Unit 1 is key.

Appendix Table 1.0: Equations 1-3 for Unit 1 and East

Unit 1 East

Eqn. 1: Mean Point‐to‐Plant Distance

Canopy 12.856 10.784

Sub‐Canopy 12.470 8.829

Shrub 2.228 2.187

Dead Log 7.184 11.311

Eqn. 2: Mean Area Per Plant

Canopy 165.267 116.288

Sub‐Canopy 155.507 77.951

Shrub 4.964 4.783

Dead Log 51.612 127.948

Eqn.3: Total Density

Canopy 60.508 85.994

Sub‐Canopy 64.306 128.285

Shrub 2014.511 2090.775

Dead Log 193.752 78.157

Table 2.0: Equations 4 and 5 for Unit 1

Unit 1 Species Eqn. 4: Relative

Density Eqn. 5: Absolute

Density

Canopy Pinus elliottii 1.000 60.508Sub‐Canopy Gordonia lasanthus 0.024 1.531

Myrica cerifera 0.048 3.062

Pinus clausa 0.048 3.062

Pinus elliottii 0.190 12.249

Quercus chapmanii 0.214 13.780

Quercus geminata 0.024 1.531

Quercus myrtifolia 0.214 13.780

Quercus virginiana 0.119 7.655

Rhus coppalinum 0.024 1.531

Sabal palmetto 0.095 6.124Shrub Andropogon capillipes 0.013 25.181

Asimina reticulata 0.013 25.181

Baccharis halimifolia 0.013 25.181

Befaria racemosa 0.013 25.181

Ilex glabra 0.038 75.544

Lyonia ferruginea 0.025 50.363

Lyonia lucida 0.188 377.721

Lyonia mariana 0.125 251.814






Sabal palmetto 0.013 25.181

Serenoa repens 0.388 780.623

Table 2.1: Equations 4 and 5 for East

East Species Eqn. 4: Relative

Density Eqn. 5: Absolute

Density

Canopy Pinus elliottii 1.000 85.994Sub‐Canopy Myrica cerifera 0.028 3.614



Pinus serotina 0.014 1.807




Quercus virginiana 0.056 7.227Shrub Asimina reticulata 0.048 100.760



Licania michauxii 0.012 25.190








Table 3.0: Equations 6 and 7 for Unit 1

Unit 1 Species Eqn. 6: Frequency Eqn. 7: Relative

Frequency

Canopy Pinus elliottii 1.000 1.000Sub‐Canopy Gordonia lasanthus 0.048 0.029








Rhus coppalinum 0.048 0.029

Sabal palmetto 0.095 0.059Shrub Andropogon capillipes 0.048 0.020

Asimina reticulata 0.048 0.020

Baccharis halimifolia 0.048 0.020



Lyonia ferruginea 0.095 0.039








Sabal palmetto 0.048 0.020


Table 3.1: Equations 6 and 7 for East

East Species Eqn. 6: Frequency Eqn. 7: Relative

Frequency

Canopy Pinus elliottii 1.000 1.000Sub‐Canopy Myrica cerifera 0.095 0.054



Pinus serotina 0.048 0.027




Quercus virginiana 0.143 0.081Shrub Asimina reticulata 0.190 0.070



Licania michauxii 0.048 0.018








Table 4.0: Equations 8-10 for Unit 1

Unit 1 Species Eqn. 8: Coverage Eqn. 9: Relative

Coverage Eqn. 10:

Importance Value

Canopy Pinus elliottii 37968.508 1.000 3.000Sub‐Canopy Gordonia lasanthus 0.301 0.000 0.053

Myrica cerifera 32.047 0.005 0.111

Pinus clausa 41.571 0.006 0.113

Pinus elliottii 334.287 0.049 0.416

Quercus chapmanii 128.477 0.019 0.410

Quercus geminata 10.113 0.001 0.055

Quercus myrtifolia 245.710 0.036 0.486

Quercus virginiana 286.331 0.042 0.308

Rhus copallinum 5.820 0.001 0.054

Sabal palmetto 5701.728 0.840 0.994

Table 4.1: Equations 8-10 for East

East Species Eqn.8: Coverage Eqn.9: Relative

Coverage Eqn.10:

Importance Value

Canopy Pinus elliottii 32443.026 1.000 3.000Sub‐Canopy Myrica cerifera 60.765 0.019 0.101

Pinus clausa 2401.707 0.757 1.657

Pinus elliottii 221.717 0.070 0.304

Pinus serotina 59.956 0.019 0.060

Quercus chapmanii 0.128 0.000 0.041

Quercus geminata 5.974 0.002 0.057

Quercus myrtifolia 262.548 0.083 0.592

Quercus virginiana 158.596 0.050 0.187

Table 5.0 Average percentage groundcover recorded within the center of the marker

Average Percentage Groundcover

Unit 1 East

Grass 38.9 27.7

Woody 32.4 40.1

Annuals/ Perennials 3.9 4.0

Open Space 24.8 27.2

Literature Cited Abrahamson, Warren G., and Christy R. Abrahamson. "Effects of Fire on Long-

Unburned Florida Uplands." Journal of Vegetation Science 7 (1996): 565-74.

JSTOR. 30 Nov. 2008 <http://www.jstor.org/stable/3236306>.

"Flatwoods." Florida 4-H Forest Ecology. University of Florida. 30 Nov. 2008

<http://www.sfrc.ufl.edu/4h/ecosystems/flatwoods/flatwoods.html>.

Main, Martin B., and Larry W. Richardson. "Response of Wildlife to Prescribed Fire in

Southwest Florida Pine Flatwoods." Wildlife Society Bulletin 30 (2002): 213-21.

JSTOR. 30 Nov. 2008 <http://www.jstor.org/stable/3784656>.

"Prescribed Fire." Fire & Aviation Management. US Forest Service. 30 Nov. 2008

<http://www.fs.fed.us/fire/fireuse/rxfire/rx_index.html>.

Wade, Dale D. "A Guide for Prescribed Fire in Southern Forests." Florida

Environments Online. 1989. University of Florida. 30 Nov. 2008

<http://fulltext10.fcla.edu/cgi/t/text/text-

idx?c=feol&idno=uf00000100&format=jpg>.

Wade, Dale D., and Gordon Langdon. "Cabbage Palmetto." Northeastern Area.

USDA Forest Service. 30 Nov. 2008

Documents

Forest, Fire, and Disturbance · Forest, Fire, and Disturbance PCB 3044L The effects of prescribed fire in the Pine Flatwoods ecosystem of the UCF Arboretum Leigh Mingledorff . Abstract