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Duane R. Diefenbach
Carolyn Mahan


June 2002


National Park Service
Northeast Region, Philadelphia Support Office
Stewardship and Partnerships
U.S. Custom House
200 Chestnut Street
Philadelphia, PA 19106



Executive Summary

The National Park Service is committed to inventorying and monitoring the natural resources under its stewardship. The work reported here contributes to the refinement of the process of data collection so that it can provide the specific information needed for the optimal management of those resources.

In March of 2000, a group of natural resource experts met to develop objectives for Shenandoah National Park’s Vegetation Inventory and Monitoring Program. This report combines two papers about that project. Carolyn Mahan, Assistant Professor of Biology at Penn State University, reported on the workshop itself, which resulted in the identification of specific management and sampling objectives in three areas of interest: general forest trends, (e.g., tree species composition and tree growth rates.), forest health (e.g., trends in hemlock woolly adelgid infestation), and special and unique ecosystems and species (e.g., trends in abundance of endangered plant species).

Duane Diefenbach, U.S. Geological Survey, Pennsylvania Cooperative Fish and Wildlife Research Unit, conducted a statistical evaluation of vegetation data collected at the park from 1997 through 2000 to determine whether the objectives stated at the workshop could be met, and recommended adjustments to the sampling design.

The data used in the evaluation were collected as part of the park’s Long Term Ecological Monitoring System (LTEMS) program from 1987 to 2000 to estimate basal area (m2/ha) of trees (>5 m tall), stem density (stems/ha) of shrubs and saplings (1-5 m tall), and stem density (stems/ha) of seedlings (woody vegetation <1 m tall). Also, data collected at the Big Meadows site were used to estimate changes in shrub coverage before and after treatment by burning. The coefficient of variation (CV = standard error/mean x 100%) was used as a measure of the precision of an estimate. A CV < 10% is generally considered necessary for research, a CV < 25% is recommended for management, and a CV 50% is usually sufficient for pilot studies. The species for which basal area and stem density were calculated were determined in consultation with park staff. All forest cover types were sampled >2 times during 1987-2000, although they were not sampled during the same year such that parkwide estimates for any given year could be calculated. These data provided variances that were incorporated into a power analysis to assess whether the current LTEMS and Big Meadows sampling designs could meet stated inventory and monitoring objectives.

The following objectives, established during the workshop, were evaluated:

1. Data collected for the LTEMS program should ensure a 90% probability of detecting a >50% change in the basal area or stem density of any woody plant species (in a given size class) within any one forest cover type over a five-year period ( =0.20). The ability of current sampling efforts to meet this objective were assessed by calculating power curves for tree basal area, shrub and sapling stem density, and seedling stem density.

2. Data collected for the LTEMS program should ensure an 80% probability of detecting a >20% change in the coverage of a particular exotic species parkwide over a five-year period ( =0.20). The ability of the current LTEMS program at Shenandoah NP to meet this objective was assessed using the power curves calculated above for changes in seedling and sapling stem density of tree-of-heaven (Ailanthus altissima). Stem density was used as an indicator of areal coverage for this species.

3. Data collected for the LTEMS program should ensure an 80% probability of detecting a >20% change parkwide in species affected by disease or insects over a five-year period ( =0.20). The ability of the current LTEMS program at Shenandoah NP to meet this objective was assessed using the power curves calculated above for changes in stem density of flowering dogwood (Cornus florida) and basal area of all oak species.

4. Monitoring of shrub coverage at Big Meadows should ensure a 95% probability of detecting a >40% reduction in shrub coverage over a five-year period ( =0.15). The program TRENDS was used to estimate the statistical power to detect these changes.

For basal area, most CVs were <40% for species in forest cover types where they were dominant (e.g., northern red oak [Quercus rubra] in northern red oak cover types). Declines in oaks and the decline of Virginia pine (Pinus virginiana) and pitch pine (Pinus rigida) were evident from the changes in estimated basal area between sampling periods. Because most basal area measurements are >20 m2/ha for species in their primary forest cover types (e.g., yellow poplar in cove hardwoods, northern red oak in chestnut oak cover type, etc.), current sampling effort should have >90% power to detect changes in basal area of 50% for dominant species.

For stem density of shrubs and saplings, most CVs were >50% (range 31-1,169%). The power analysis suggested that stem density changes of >2,000 stems/ha had >90% probability of being detected. Because most stem densities during both sampling periods were <1,000 stems/ha, current sample sizes are inadequate to detect important changes in stem density of shrubs and saplings.

Stem density of seedlings was extremely variable, and the power analysis suggested that only extremely large changes in stem density (>70,000 stems/ha) could be detected under the current sampling effort. Moreover, large enough sample sizes likely cannot be obtained to meet stated objectives because of the inherent variability of these data.

Increases in stem density for tree-of-heaven >1m tall (sapling) ranged from 0-143 stems/ha (Appendix G). The ability to detect such changes is poor (power < 70%) even if sample sizes were tripled.

Shrub stem densities for flowering dogwood ranged from 15.7 to 536.1 stems/ha. Under current sampling efforts, power was estimated as >80% for changes >1,000 stems/ha. Consequently, the sampling effort would have to increase 2-3 times current levels to detect ~100% changes in current densities.

The effect of gypsy moth on oak abundance, as measured by changes in basal area for all oak species, has a good chance of being detected under current sample sizes. Mean stem densities of oak saplings ranged from 0 to 871 stems/ha, and thus the ability to detect only large changes in stem densities (>1,000 stems/ha) for saplings will likely have acceptable power.

The current sampling design for Big Meadows provided estimates of total shrub coverage (all species combined) and of changes in shrub coverage, with CVs < 20%. Although estimates of coverage for individual shrub species were not precise (CVs > 30%), biologically important changes in overall shrub coverage should be detected under the current sampling design.

To meet the monitoring objectives developed at the workshop, recommendations for the most important changes to the LTEMS program at Shenandoah NP are listed here. Additional recommendations are detailed in the report.

1. A sampling design needs to be implemented that will permit parkwide estimates of vegetation parameters for a given point in time. Presently, changes in basal area or stem density can be estimated within each forest cover type, but cannot be estimated across all cover types for the same time period because each forest cover type is visited in a different year.

2. Requirements to monitor the spatial distribution of forest cover types should be investigated before implementing changes to the sampling design. Traditional stratified sampling designs cannot incorporate changes in the distribution of cover types over time.

3. Sample sizes need to be increased such that all strata contain >1 plot. Sample sizes overall may need to be increased, depending on the selected sampling design, to meet objectives for detecting changes in stem density of shrubs and saplings.

4. Trees within plots should continue to be permanently marked with unique identifiers to reduce misidentification and data collection errors.

5. An electronic field-based data entry system should be fully implemented to speed data collection, reduce data entry errors, and eliminate transcription errors that may occur with a paper system.

6. The purpose and need to collect seedling stem densities should be reviewed. It is unlikely that it will be possible to obtain adequate sample sizes to detect biologically important changes in seedling density or abundance.


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