# Presentation: 224974

## FIA-SRS Phase 3Vegetation Structure and Diversity Pilot StudyYear 2

- Sonja Oswalt
- Research Associate
- University of Tennessee
- Forestry, Wildlife & Fisheries Dept.

**Notes:**
Hi, My name is Sonja Oswalt.  I am sure many of you met Sharon King, my predecessor in this project.

## Outline

- What is VEG?
- Recap of 2002 South Carolina Pilot Study
- Tennessee 2003 Pilot Study—Why it Worked
- South Carolina VEG Data Analysis—A Glimpse of What’s to Come

**Notes:**
Today I’ll be briefly discussing the goals of the VEG indicator, and recapping last year’s pilot study in south Carolina.  I’ll also talk about this year’s pilot in Tennessee, and why it worked when South Carolina did not.  I’ll then be briefly covering a bit of the data analysis from the South Carolina pilot, which will be available in the next S.C. state analytical report.

## What is VEG?

- A measurement of the composition and spatial arrangement of all vascular plants on plot
- Includes all trees, shrubs, herbs, grasses, ferns, and fern allies (horsetails and club mosses)
- Examine which forest types are more prone to invasion by non-native species
- Assess which reporting areas are most diverse
- Complete sample tree diversity on plot (regeneration) allows better prediction of successional trends

**Notes:**
The vegetation structure and diversity indicator is a measurement of the composition and spatial arrangement of all vascular plant species on a plot.  This includes all life forms.  The goals of the indicator are to examine which forest types are more prone to invasion by non-native species; assess which reporting areas are most divers; and to identify regeneration for predicting successional trends.

## South Carolina 2002 RecapField Methods:

**Four subplots per plot**
**Subplot = 24’ radius circle**
**Three 1-m2 quadrats in each SP**
**Quadrats: Ground cover to 1%**
**Quadrats: Species cover to 1% **
**Subplot: List all species, cover to 1% **
**Subplot: Note layer of greatest cover for all species**
**Subplot: Total cover (of all species combined) to 1% for each layer class (1-4)**

**Notes:**
The South Carolina pilot study, conducted in 2002, used the current national protocol for the VEG indicator.  That protocol includes (read off slide)

## South Carolina 2002 RecapLogistics

**Botanist traveled with forestry crew doing P2 and other P3 variables**
**Forestry crew marked subplots for veg. work**
**Botanist collected veg. data without help from foresters**
**Crew left plot when foresters were done, regardless of Veg. Indicator completion (Subplots that were started were finished)**
**Crew traveled from Lexington/Newberry daily and returned home every night**

**Notes:**
During the 2002 pilot study, the botanist traveled with the P-2 forestry crew, who were measuring P-2 and P-3 (soils, DWD, crowns) variables.  Typically, the crew marked the subplots for the botanist, then the botanist collected the VEG data independently.  During the pilot, the botanist left when the crew was done, whether or not the VEG collection was complete.  No overnight stays were allowed, and the crews worked 8 hour days.

## South Carolina 2002 RecapCompletion Results

**Collected data on 31 out of 33 plots in 27 counties in SC **
**Completed 71 out of 124 subplots (57%)**
**	****Subplots completed (# plots):**
**4 (4)****	****	****3 (6)**
**2 (16)****	****	****1 (5)**
**Average: 2.3 subplots per plot**

**Notes:**
The 2002 pilot was not overly successful with regards to completion.  Data collection occurred on 31 subplots in 27 S.C. counties.  Of those, most were only partially completed for the VEG indicator.  Only 57% of subplots were completed.

## South Carolina 2002Why Didn’t It Work?

- More travel than plot time
- No overnight stays
- Ave. 3.3 hrs on plot/day, 4.4 hrs travel/day
- One plot: 65 min. in field, 7 hours driving (1 SP completed)
- Botanist did not adhere to time limits for species search
- Excessive keying/coding on plot

**Notes:**
Logisics represented the primary problem for the indicator.  Travel time occupied the majority of the field crews’ time.  However, the methodology is also somewhat repetitive, and the botanist tended to go over on allowed time for searching subplots for species.

## Tennessee 2003—How Did it Differ?

- Slightly Different Methodology
- Ability to work 10-hour days
- Overnight stays in working counties
- Cooperative crew members
- Strict adherence to time limits
- Collecting more, keying less...
- Data collection on paper—entry into Tally later
- Coding either en route from plot or in hotel

**Notes:**
So, how did the 2003 pilot in Tennessee differ from South Carolina?  Well, methods were slightly different, eliminating redundancy.  Also, Tennessee crews had the ability to stay overnight in working counties, and were able to work fewer days with longer hours.  Additionally, the crew members were very cooperative—all members worked together to complete the entire plot on time.  The botanist strictly adhered to time limits set for finding species, and collected unknown plants rather than keying them in the field.

## 2003 Methodology

- Four subplots per plot
- Subplot = 24’ radius circle
- Three 1-m2 quadrats in each SP
- Quadrats: Ground cover to 1%--**ELIMINATED**
- Quadrats: Species cover to 1%--**ELIMINATE**D
- 	List presence/absence of species only
- Subplot: List all species, cover to 1%
- Subplot: Note layer of greatest cover for all species
- 	**CHANGED**:  Record % cover of each species in 3 Ht classes:  0-6 ft, 6-16 ft, 16+ ft
- Subplot: Total cover (of all species combined) to 1% for each layer class (1-4)

**Notes:**
Changes in the methodology used include the elimination of the quadrat ground cover measures (covered by the soil and DWD measurements); elimination of quadrat species cover measures (don’t add additional information)—instead, presence/absence was recorded; changed the way cover is measured on the subplots (neither adds nor subtracts from time limit, but adds more detailed information.

## Collected data on 15 plots in 15 counties
60 Subplots Total, 4 non-forested:  56 Total Forested Subplots
ALL BUT ONE PLOT COMPLETED
	--Why the one?  The botanist got lost...

**Collected data on 15 plots in 15 counties**
**60 Subplots Total, 4 non-forested:  56 Total Forested Subplots**
**ALL BUT ONE PLOT COMPLETED**
**	****--Why the one?  The botanist got lost...**

**Notes:**
Tennessee’s 2003 completion results were encouraging.  Data were collected on 15 plots in 15 counties for a total of 56 forested subplots.  All but one plot were completed in their entirety.  Why the one?  I got lost...

## Why Did Tennessee Work?

**Fewer, Longer days are Key, and the ability to “trade-off” hours between days (i.e., work 12 hours one day and 8 the next)**
**Elimination of time-consuming, redundant data collection**
**Strict Adherence to Time Limits**
- Total Hours spent Traveling:  76
- Total Hours spent on Plot:  47
- Total Hours spent Pressing Plants:  3
- Total Hours spent Identifying:  In process
- Total Hours spent Entering data:  In process

**Notes:**
Why did Tennessee work when S.C. didn’t?  Fewer, longer days and increased flexibility helped a good bit, the elimination of time-consuming, redundant data collection; strict adherence to search time limits

## Per-Day Breakdown

- 5 Hours Traveling per day
- 3-4 Hours on plot per day
- 20 minutes pressing plants
- ~ 20 minutes to prepare unknowns
- ~ 30 minutes to code data
- ~ 45 minutes to enter data
- Preparing & Sending Unknowns / Entering Data
- completed in hotel during evening
- Total Time Needed Per Day:  ~ 10 Hours

**Notes:**
On average, the 2003 crew spent 5 hours traveling per day and 3-4 hours on plot per day.  Additionally, the botanist spent 20 minutes every evening pressing plants.  This suggests that crews need an average of 10 hours a day to successfully complete a full plot.

## 2003 Time (Preliminary)

**Logistics remain the largest problem.  However, 4 10-hour days and shorter methods**
** ****help to counteract.**

**Notes:**
Logistics remain the area of least efficiency.  However, fewer, longer days help to counteract that inefficiency and shorter VEG methods help the botanist keep up with the remainder of the crew members.

## Where Are We Headed With The Data?

- South Carolina Invasive Species Report:
- An Example of Uses for VEG P3 Data

**Notes:**
So, if we are collecting the data, what is it going to be used for?  Last year’s data collection yielded some interesting preliminary results for the upcoming State Analyst Report.  Keep in mind that the sample size is small for the following data.

## Data Analysis

- Data from South Carolina P-2 Exotic Species Variable
- Data from South Carolina P-3 VEG collection
- Analyzed by Eco-region and Physiographic Section
- Two Levels:  Plot and Subplot
- SAS software, NCSS software, Arcview
- Summary statistics, differences in relative diversity, occurrence of non-native invasives

**Notes:**
Data analysis included data from P2 and P3 for 2002-2003 in South Carolina.  The data were analyzed for eco-region and for physiographic region, and were analyzed at both the plot and subplot level.  Analysis was conducted using SAS, NCSS, and arcview to produce various summary statistics related to overall diversity and non-native invasive species.

## Results (P-2 and P-3)

- Phase-2
- 505 forested plots in 46 counties in 2002-2003
- 41% contained at least one alien species
- 15% contained at least two
- 3 % contained at least 3
- <1% contained 4 or more
- Phase-3
- 102 plant families represented
- 6% of all identified species are alien
- However:  Alien species occurred in 80% of measured plots
- In contrast:  73% of native species occurred in LESS THAN 10% of all plots and 48% occurred in only ONE measured plot

**Notes:**
There were 505 forested plots in 46 counties in 2002-2003.  Of those, 41% contained at least one non-native species, 15% contained two or more, 3% contained at least 3, and less than one percent contained 4 or more (that the foresters recognized).  Phase 3 data revealed that 6% of all identified vascular plant species were alien.  However, this relatively small number of introduced species occurred in 80% of measured plots.  In contrast, 73% of native species occurred in less than 10% of all plots, with 48% occurring in only one plots.

## Mean Number of Species/Subplot by Ecological Region

**Mean Number of Species/Subplot by Ecological Region**
**(Significantly Different at ****α**** = 0.10)**

**Notes:**
Phase three data detected overall differences in diversity between 3 ecological regions in south carolina at an alpha of 0.10.  Small sample sizes limit the ability to detect differences with confidence.  However, power calculations by SAS indicate that a full 5-year dataset would detect differences with 99% confidence.

## Slide 18

| Scientific name | Common Name | Frequency (%) by Subplot |
| --- | --- | --- |
| Vitis rotundifolia Michx. | muscadine | 73.24 |
| Acer rubrum L. | red maple | 67.61 |
| Smilax glauca Walt. | cat greenbriar | 57.75 |
| Pinus taeda L. | loblolly pine | 56.34 |
| Prunus serotina Ehrh. | pond pine | 54.93 |
| Liquidambar styraciflua L. | sweetgum | 53.52 |
| Diospyros virginiana L. | common persimmon | 49.30 |
| Gelsemium sempervirens (L.) St. Hil. | evening trumpetflower | 47.89 |
| Parthenocissus quinquefolia (L.) Planch. | virginia creeper | 47.89 |
| Quercus alba L. | white oak | 42.25 |
| Nyssa sylvatica Marsh. | black gum | 40.85 |
| Quercus laurifolia Michx. | Laurel oak | 40.85 |
| Rubus argutus Link | sawtooth blackberry | 40.85 |
| Smilax rotundifolia L. | roundleaf greenbriar | 40.85 |
| Ilex opaca Ait. | American holly | 36.62 |
| Quercus nigra L. | water oak | 36.62 |
| Cornus florida L. | flowering dogwood | 35.21 |
| Quercus falcata Michx. | southern red oak | 33.80 |
| Toxicodendron radicans (L.) Kuntze | poison ivy | 33.80 |
| Vaccinium arboreum Marsh. | farkleberry | 32.39 |

**Notes:**
In South Carolina, Muscadine grape was the most common species, occurring in 73% of plots, with red maple following closely behind.  This might be of interest over time, and red maple is becoming increasingly more common in eastern forests.

## Alien Species Detection

- P-2:  Japanese honeysuckle (*Lonicera japonica*) was most common (Frequency = 31.88%)
- However, P-2 can ONLY detect up to 4 per plot, and only those “On the List” (total of 16)
- P-3:  Japanese honeysuckle was most common (Frequency = 45.16%), which corresponds to the P-2 data
- P-3 can detect as many non-native potentially invasive species as are present (total of 27), making it a more sensitive method for detection

**Notes:**
Of the non-native species, Japanese honeysuckle was the most common, as detected in both P-2 and P-3 data.  However, the P2 dataset can only detect up to 4 species per plot, and those species must be both on the “LIST” provided, and the foresters must be able to recognize them.  In contrast, P3 can detect as many introduced species as are present (in our case we detected 11 more), and presumably the botanist will be able to confidently identify them or will collect them for later identification.  Therefore, P3 is a more sensitive method for on-plot detection.  The combined datasets allow for verification of both datasets.

## Effects of Disturbance

- 	Logistic regression indicated that distance of a plot from agricultural land was significant in explaining the presence of exotic species on a plot
-  (p < 0.001)

**Notes:**
Logistic regression indicated that the distance of a plot from agricultural land was significant in explaining the presence of exotic species on a plot. 

## Atlantic Coastal 

**Proportion of plots containing exotic species in each major ecological region**

- Atlantic Coastal
- Plain  23%

**Notes:**
67% of sampled plots in the Piedmont contained at least 1 non-native species, 23% of plots in the These differences may be due to land use, differences in overall species richness as we saw earlier, site productivity, or other environmental differences.  Future measurements combining the P-3 VEG data with P-3 soil and DWD measurements, and utilizing larger sample sizes, may enable us to isolate some of these variables for identifying potential trends.

## South Carolina 2002/2003 Data Conclusions

- Non-native Species constitute a substantial threat to forest health in South Carolina
- Phase-3 data indicate that although non-natives comprise a small % of vascular plants, those few species are alarmingly widespread
- As the more detailed P-3 data continues to be collected, examination of edge-related disturbances may give insight into the impacts of types and patterns of disturbance on the establishment and reproduction of vascular plant species

**Notes:**
Based on the available data, non-native species constitute a substantial threat to forest health in South Carolina.  Phase-3 VEG data indicate that although non-native comprise a small % of vascular plants, those few species are alarmingly widespread.  As the more detailed P-3 data continues to be collected, examination of edge-related disturbances may give insight into the impacts of types and patterns of disturbance on the establishment and reproduction of vascular plant species.

## Conclusion

- With increased efficiency in protocol and fewer but longer days in a week, the P-3 VEG collection is possible in the south
- P-3 VEG can provide additional information regarding the spread of non-native species, the impacts of disturbance on vascular plant composition, and differences in regional diversity

**Notes:**
So, it looks like the increased efficiency in the protocol, when combined with flexibility in the field, make P-3 VEG possible in the south.  This variable adds information regarding the spread of non-native species, impacts of disturbance on vascular plant composition, and differences in regional diversity.

## Acknowledgements

- University of Tennessee, Department of Forestry, Wildlife, & Fisheries, Dr. George Hopper-Department Head
- USDA Forest Service Southern Research Station FIA, John Kelly-Acting Project Leader
- South Carolina State Forestry Commission
- Tennessee Division of Forestry
- Sharon King, Jeff Turner, Beth Schulz, Cindy Aulbach, Byron Rominger, John Mullins, Anita Rose, TDF Crew Members

## Questions?