Water: Monitoring & Assessment
Sampling Methods and Analysis: Vascular Plants
Below is a detailed description of the sampling methodology and analysis process used by Ohio EPA for vascular plants. Also included are the lessons learned for sampling and analysis.
Sampling Methods: Vascular Plants
A single wetland often has several vegetation classes. Even if only three main classes are identified (forested, scrub-shrub, and emergent), the wetlands included for study can exhibit multiple combinations. For example, of the twenty wetlands studied in Fennessy et al. (1998a) six combinations of vegetation classes were found: emergent, emergent/scrub-shrub, forested, forested/emergent, forested/emergent/scrub-shrub, and forested/scrub-shrub. Thus, a sampling method should be flexible enough to account for horizontal and vertical variation in vegetation.
After testing a transect-quadrat method, Ohio EPA has adapted the method used by the North Carolina Vegetation Survey as its standard vegetation sampling method (Peet et al. 1998). This is a flexible, multipurpose sampling method which can be used to sample such diverse communities as grass- and forb-dominated savannahs, dense shrub thickets, forest, and sparsely vegetated rock outcrops and has been used at over 3,000 sites for over ten years as part of the North Carolina Vegetation Survey. This method is appropriate for most types of vegetation, flexible in intensity and time commitment, compatible with other data types from other methods, and provides information on species composition across spatial scales. It also addresses the problem that processes affecting vegetation composition differ as spatial scales increase or decrease and that vegetation typically exhibits strong autocorrelation (Peet et al. 1998). Peet et al. (1998), state, "Our solution to the problems of scale and spatial auto-correlation is to adopt a modular approach to plot layout, wherein all measurements are made in plots comprised of one or more 10 x 10m quadrats or "modules" (100 m2 = 1 are = 0.01 hectare). The module size and shape were chosen to provide a convenient building block for larger plots, and because a body of data already exists for plots of some multiple of this size. The square shape is efficient to lay out, ensures the observation is typical for species interactions at that scale of observation, and avoids biases built into methods with distributed quadrats or high perimeter-to-area ratios."
(Peet et al. 1998, p. 264). Basically, the method employs a set of 10 modules in a 20m x 50m layout. Within the site to be surveyed, these 20 x 50m grids are located such that the long axis of the plot is oriented to minimize the environmental heterogeneity within the plot.
Once the plot is laid out, all species within the plot are identified, an aggregate wood stem count is made, and cover is estimated at the 0.1 hectare scale. In addition, four 10 x 10m modules are intensively sampled in a series of nested quadrats. Within these "intensive" modules, species cover class values and woody stem tallies are recorded for each module separately and for each nested quadrat separately. Figure 4 shows a hypothetical application of this method to a wetland with a forested and emergent vegetation class and an unvegetated open water area. In effect, then, the method proposed by Peet et al. incorporates use of reléves found in the Braun-Blanquet methodology in as much as the length, width, orientation, and location of the modules are qualitatively selected by the investigator based on site characteristics; however, within the modules, standard quantitative floristic and forestry information is recorded, e.g., density, basal area, cover, etc.
Once the location of the plot or plots has been selected the primary purpose of the vegetation survey is to obtain a comprehensive list of all vascular plant species growing at a particular wetland at the time of sampling and to characterize the relative dominance of these species at several levels of scale (basically herbaceous, shrub, small tree, and large tree scales, or at 1 m2, 10 m2, 100 m2, and 0.1 ha (1,000 m2 or 10 are).
All vascular species within the modules are identified to species. Immature plants or plants missing structures (e.g., fruiting bodies, etc.) that cannot be identified to species are identified to genus or family or noted as unknown. Within the intensively sampled modules, percent cover is recorded for each species within modules and nested quadrats. Cover classes suggested by Peet et al. (1998) are used as a faster and more repeatable method for assigning cover values: Class 1 (solitary /few), Class 2 (0 to 1%), Class 3 (1 to 2%), Class 4 (2-5%), Class 5 (5-10%), Class 6 (10-25%), Class 7 (25-50%), Class 8 (50-75%), Class 9 (75-95%), Class (95-99%). The midpoints of the cover classes are used to calculate percent cover, relative cover, etc.
For woody stem data (trees, shrubs and woody lianas reaching breast height or 1.4 m) are collected as counts of individuals in diameter classes. Peet et al. (1998) suggest the following diameter classes (in cm): 0-1, 1-2.5, 2.5-5, 5-10, 10-15, 15-20, 20-25, 25-30, 30-35, and 35-40, with stems greater than 40 cm counted individually and measured to the nearest centimeter. Multiple stems arising from a common root system are recorded separately if they branch below 0.5m from the ground. Peet et al. (1998) recommend that the area surveyed by stem count be adjusted based on conditions at the site, e.g., reduced to 20% of the modules for dense shrub land or increased by 200% for oak savannahs. This is easily implemented by reducing the width of the modules for woody species only.
An important part of vegetation surveys is the collection, preparation, and depositing of voucher specimens in major herbariums in order to document a permanent record of that plant at that location. Although staff resources make collecting vouchers of every vascular plant infeasible, a voucher specimen of at least 10% of the vascular plant species at any given site are collected; however, in every instance in which the identity of any species cannot be confirmed in the field, or where field personnel disagree as to the identity of a species, a voucher specimen is collected for identification in the office. In particular, difficult genuses and families, e.g., Cyperaceae, Poaceae, Ranunculaceae, Viola, Aster, Potamogeton, etc. as well as endangered, threatened, rare, or otherwise unusual plants are almost always collected for confirmation.
Finally, data on standing biomass for emergent wetlands is collected. This data can be used in several ways. Biomass production in emergent wetlands dominated by herbaceous vegetation is estimated by harvesting 900 cm2 quadrats in each wetland. The quadrats are located within the intensive modules of each plot. The plants within each quadrat are cut at the soil surface and placed into paper bags. In the lab, plants are oven dried at 105 °C for at least 24 hours, and then weighed.
Lessons Learned for Vascular Plants
Floristic Quality Assessment Indexes
Ohio EPA has found that the FQAI score and subscores of the FQAI, e.g., percent coverage of plants with Coefficients of Conservatism of 0, 1, or 2, is a very successful attribute and metric for detecting disturbance in wetlands (Figures 4 and 5).
See the following references:
Andreas, Barbara., and R. Lichvar. 1995. A Floristic Quality Assessment System for Northern Ohio. Wetlands Research Program Technical Report WRP-DE-8. U.S. Army Corps of Engineers, Waterways Experiment Station.
Herman, Kim D., Linda A. Masters, Michael R. Penskar, Anton A. Reznicek, Gerould S. Wilhelm, and William W. Brodowicz. 1996. Floristic quality assessment with wetland categories and computer application programs for the state of Michigan. Michigan Department of Natural Resources, Wildlife Division, Natural Heritage Program.
Wilhelm, Gerould S., and D. Ladd. 1988. Natural area assessment in the Chicago region. Transactions 53rd North American Wildlife and Natural Resources Conference 361- 375.
Wilhelm, Gerould S., and L.A. Masters. 1995. Floristic quality assessment in the Chicago region and application computer programs. Morton Arboretum, Lisle, ILL.
Semiquantitative Disturbance/Integrity Scales
Ohio EPA has good success in developing a semiquantitative disturbance/biological integrity scale called the Ohio Rapid Assessment Method for Wetlands v. 5.0. Until such time as more quantitative variables like percent impervious surface are found, this type of tool is a good candidate for the problematic x-axis in wetland biocriteria development. See also "Plants and Aquatic Invertebrates as Indicators of Wetland Biological Integrity in Waquoit Bay Watershed, Cape Code," Carlisle, Bruce K., Anna L. Hicks, Jan P. Smith, Samuel R. Garcia, and Bryan G. Largay. Environment Cape Code 2(2):30-60 (1999), where a similar system was used to rank levels of disturbance.
Classification is definitely an iterative process. Investigators should definitely consider a Hydrogeomorphic (HGM) classification scheme if one has been developed for their region of interest, at least as a starting point. However, the experience in Ohio suggests that grosser classes based on dominant vegetation (emergent, scrub-shrub, forested, etc.) may work also. A goal of a cost-effective Biocriteria program is to have the fewest classes that provide the most cost-effective feedback. With vegetation, data from Ohio is suggesting somewhat diverse wetland types may be "clumpable," since even though their floras are different at the species level, the quality/responsiveness of their unique floras to human disturbance is equivalent. This is also a concern in states with high degrees of wetland loss where two few wetlands of a particular HGM class remain to analyze as a separate class.
Field and Lab Methods
After experimenting with both transect/quadrat and releve-style plot methods, Ohio has adopted a plot-based method that allows for a qualitative stratification of wetland by dominant vegetation communities. This method appears to be flexible and adaptable to unique site conditions, provides dominance data for all species in all strata, provides data that is intercomparable with other common methods, is relatively easy to learn, and is relatively fast and cost effective (up to 2 to 3 plots can be completed in a day).
Whatever sampling method is adopted it is essential that dominance and density information (cover, basal area of trees, stems per unit area, relative cover, relative density, importance values, etc.) be collected. Many of the most successful attributes Ohio has found in developing a vegetation IBI are based on cover data of the herb and shrub layers and density data of the shrub and tree layers.
Definitely consider using cover classes in general and a class scheme that works on a doubling principle to aid in consistent inter-investigator usage, e.g., see Peet et al. 1998. Then use the mid points of the class for your analysis. This seemed to really help with consistent usage and smoothing out the roughness in cover data.
Finally, it is recommended that initially the sampling method should "over"-stratify in both the vertical and horizontal dimensions until it can be determined which strata and communities are responding best to human disturbance. Ohio has found that the herb and shrub (subcanopy layers) seem to respond the best, although some intermediate tree size classes (e.g., 10 to 25 cm dbh) also appear to be responsive.
Overstratifying horizontally may also make sense at the reference development stage; however, ultimately the decision whether to split or clump communities depends on whether this is necessary to detect the disturbances. "Homogenizing" community types by placing a releve or transect across them (e.g., aquatic bed to emergent to shrub zone) can be appropriate if splitting doesn't matter to detect the disturbance. The caveat of course is that you can't separate the data set later if you detect something of interest in one of the clumped communities.
Vouchers and QAQC
Based on Ohio's experience voucher as much as you can for later confirmation in the lab and deposit vouchers in local and regional herbariums. Definitely, collect all Cyperaceae, Poaceae and Juncaceae and also consider collecting shrubs genuses and families (Salix, Viburnum, Vaccinium, Rosa, Alder, etc.) Polygonum spp., Aster spp., Viola spp., and Cryptograms.