Which plant species dominate? Ecologists often refer to plants as dominants but rarelydefine the term. Textbooks indicate that dominant species are the most abundant and exert the most influence or control on the habitat and other species (Carpenter 1956, Greig-Smith 1986, Ricklefs & Miller 1990). Weeds become dominant on newly disturbed ground by quickly establishing a dense carpet of seedlings. In contrast, a tussock sedge might take decades to dominate a wet meadow, doing so by building tussocks and casting an umbrella of heavy shade early in the growing season. But how abundant must it be, and how much influence must it exert to qualify as a dominant? Clarification and an objective index are needed.

How does a species dominate? Many weedy species form a monotype by crowding out competitors, while a sedge tussock forms a matrix by creating habitat for other species (Peach 2004; Arboretum Leaflet 2005-02). Dominance forms can differ with species, and a species can change its form of dominance over time (Frieswyk et al. In review). Christin Frieswyk (ibid.) has devised a way to characterize seven forms of dominance.

Three attributes of dominance:

CHARACTERIZING DOMINANCE

What easily-measured attributes help determine dominance? In wooded vegetation, foresters measure density, frequency of occurrence, and cross-sectional area of tree trunks to calculate “importance values” that quantify dominance. In herbaceous plant communities, ecologists estimate species’ percent cover. Percent cover is easily and reliably estimated (Kercher et al. 2003). Think of it as the shade cast on the ground when the sun is directly overhead. Using a 1-m2 plot frame, samplers can easily judge cover in classes, such as 0–1%, 2–5%, 5–25%, 26–50%, 51–75% and 76–100%. Given enough plots, the species with high mean cover are readily identifiable. In addition, one can distinguish species that have a tendency toward high cover, i.e., those that usually occur in the top class, in contrast to those that usually have medium cover and bell-shaped distribution data. From the same data set, the number of species in a plot can be used to characterize species with high species suppression (few neighbors in a plot), although other factors could cause low species richness. From simple, rapidly-collected data, Frieswyk worked out how to calculate a species dominance index (SDI) as the average of three attributes: mean cover (MC), mean species suppression (MSS), and tendency toward high cover (THC), as illustrated on page 2. The process is not as simple as the outcome, however. Detailed guidelines are available from the authors for calculating each attribute, scaling values from 0 to 1, and using data on frequency of occurrence to identify potentially-dominant species. Here, we concentrate on how SDI identifies dominants and characterizes their form of dominance.

Arboretum Leaflets

Which species are dominant and how do they dominate?

Leaflet Number 2005-03April 2005

1Cover (MC)Cover (MC)= mean cover

Tendency towardTendency towardHigh CoverHigh Cover

(THC) (THC)= times high cover

/times present

SpeciesSpeciesSuppressionSuppression

(MSS)(MSS)= inverse of species

richness

Attributes of dominance

Cattails photographed by Aaron Boers

2

Determining which species are dominant The process involves (1) creating a list of potential dominants, (2) computing SDI, (3) identifying the dominant species, and (4) classifying their dominance forms (see below). Among the potentially-dominant species, those with above-average SDI are considered dominant. This objective approach can be followed by different investigators, in different plant communities, and using the same or other measures of species influence. Calculations can be made for individual sampling sites, for sets of sites, or for entire regions. Frieswyk had access to data from 74 wetlands randomly chosen from the coasts of all five Great Lakes (Danz et al. 2005). In each wetland, plant cover was estimated by species in 1-m2 plots (total of 1743) that were sampled along randomly-located transects. SDI identified 38 dominants in the 74 coastal wetlands. For the collective wetlands of an individual Great Lake, SDI found 21 dominant species, and for the entire Great Lakes wetland data set, SDI found 6 dominant species. With large wetland area and many types of wetlands, it is more difficult for a species to dominate the entire region.

Quantifying species dominance as SDI:

What form of dominance does each dominant species display? With dominance indicated as the average of MC, MSS and THC, it is easy to visualize different forms of dominance. The most obvious would be a species with high values for all three attributes, but it is also possible for species to dominate just by having high MC or high THC

or high MSS. In fact, there are eight possible combinations of low vs. high values for three variables (2x2x2 = 8); only one of these (low MC, low MSS and low THC) would characterize a subordinate or non-dominant species. The other seven combinations characterize dominance. Indeed, Frieswyk found all seven forms of dominance in the Great Lakes wetland data, as follows:

The monotype form characterized 28% of cases, including most dominance by invasive Typha (Typha angustifolia and Typha ✕ glauca combined). The matrix form characterized 24% of cases, mostly those of native dominants. Dominance forms were associated with both species quality and measures of anthropogenic stress (see map on page 3, showing dominants as species codes and forms of dominance as color codes as in the above table). Thus, SDI provides a way to quantify and describe different dominance forms and is a useful indicator of vegetation condition.

Which species are dominant in Great Lakes wetlands and in what form? Across the region, 6 dominant species emerged: invasive Typha , Calamagrostis canadensis, Carex lasiocarpa, Sagittaria latifolia var. latifolia, Impatiens capensis and Carex stricta. Invasive Typha and Calamagrostis canadensis showed

Mean CoverMean Cover(MC)(MC)

(MCMC + MSSMSS + THCTHC) / 3 = SDI

Tendency towardTendency towardHigh CoverHigh Cover

(THC) (THC)

Mean SpeciesMean SpeciesSuppressionSuppression

(MSS)(MSS)

Quantifying species dominance

iT iTiT iT

Multiple dominants in a natural meadow (Photo: Aaron Boers)

3

monotype form while Carex lasiocarpa showed matrix form. Sagittaria latifolia and Impatiens capensis showed the aberrant form, and Carex stricta showed the patchy form. At the wetland scale, monotype and matrix forms were the most common. Invasive Typha and Phalaris arundinacea showed monotype form more often than matrix form. Carex stricta and Calamagrostis canadensis formed monotypes as often as matrix. Carex lasiocarpa ssp. americana and Carex lacustris showed matrix form more often than monotype. Of 35 dominant species, 19 formed monotypes, 16 matrix, 13 compressed, 13 patchy, 3 ubiquitous, 2 aberrant, and 1 diffuse. All seven forms of dominance were found. Species’ behavior often differed with location and spatial scale; thus, dominant form was a function of the species, its location and the spatial scale combined, and examining changes in dominance across space added to the understanding of that species. For example, Peltandra virginica showed both the matrix and monotype form at the wetland scale, but the compressed form at the lake scale.

Once a dominant, always a dominant? No; shifts from non-dominant to dominant can and do occur from wetland to wetland in the Great Lakes data set. Julia Wilcox's field site (above photos) experienced shifts in dominance over time and difference in dominance form from plot to plot (Table 1). In addition, Andrea Herr-Turoff and Aaron Boers (UW doctoral students) found that dominance changed from year to year in wetlands being invaded by Phalaris arundinacea and Typha spp. Thus, SDI can track shifts from non-dominant to dominant status and shifts from one dominance form to another. Changes in vegetation are clear when an species such as invasive Typha shifts from non-dominant to dominant status or when it shifts from any form to a monotype.

Table 1. Field test of herbiciding, burning, and seeding to replace Phalaris arundinacea with native plants (plots were 25 x 60 m; cf. Wilcox 2004).

Plot 1 Plot 2 Plot 3 2003 Not dominant Not dominant Not dominant2004 Patchy Monotype Monotype

Reed canary grass dominant (Photo: J. Wilcox) Reed canary grass not dominant (Photo: J. Wilcox)

In summary:• The calculation of SDI is an objective way to identify

dominant species.• SDI uses data on presence and percent cover, although

other abundance measures can be used.• Dominant species are readily characterized by their

form of dominance.• Seven forms of dominance occur in 75 Great Lakes

wetlands, with monotype and matrix forms the most common.

• Dominance form can change from place to place and over time.

• SDI objectively characterizes changes in dominants and form of dominance.

Examples of Wisconsin wetland dominants (Cc=Calamagrostis canadensis; iT=invasive Typha; Pa=Phalaris

arundinacea) and their form of dominance (colors as in table on p. 2)

ReferencesCarpenter, J. R. 1956. An Ecological Glossary. Hafner

Publishing Company, New York, New York, USA.

Danz, N.P., R.R. Regal, G.J. Niemi, V.J. Brady, T. Hollenhorst, L.B., Johnson, G.E. Host, J.M. Hanowski, C.A. Johnston, T. Brown, J. Kingston, and J.R. Kelly. 2005. Environmentally stratified sampling design for the development of Great Lakes environmental indicators. Environmental Monitoring and Assessment 102:41-65.

Frieswyk, C. B., C. Johnston, and J. B. Zedler. In review. Quantifying and qualifying dominance in vegetation. Ecological Applications.

Greig-Smith, P. 1986. Chaos or Order -- Organization. Pages 19-29 in J. Kikkawa and D. J. Anderson, editors. Community Ecology: Pattern and Process. Blackwell Scientific Publications, Melbourne, Australia.

Kercher, S. M., C. B. Frieswyk, and J. B. Zedler. 2003. Effects of sampling teams and estimation methods on the assessment of plant cover. Journal of Vegetation Science 14:899-906.

Peach, M. A. 2004. Tussock sedge meadows and topographic heterogeneity: Ecological patterns underscore the need for experimental approaches to wetland restoration despite the social barriers. M.S. Thesis, University of Wisconsin-Madison.

Ricklefs, R. E. and G. L. Miller. 1990. Ecology. W.H. Freeman and Company, New York, New York, USA.

***This Leaflet was prepared by Joy B. Zedler, Aldo Leopold Chair of Restoration Ecology, and Christin B. Frieswyk, Botany Doctoral Student, both of the University of Wisconsin-Madison, in collaboration with Carol Johnston, South Dakota State University.

Leaflets are accessible at http://botany.wisc.edu/zedler/leaflets.html

and at www.wisc.edu/arboretum

Acknowledgments

We thank Carol Johnston of South Dakota State University for inviting our participation in the Great Lakes Environmental Indicators project (GLEI). This research has been supported by a grant from the US Environmental Protection Agency’s Science to Achieve Results (STAR) Estuarine and Great Lakes (EaGLe) program through funding to the GLEI Project, US EPA Agreement EPA/R-828675. (to Gerald Niemi, Carol Johnston, and others). Findings do not necessarily reflect the views of EPA. We thank Barbara Bedford, Terry Brown, Michael Bourdaghs, and Lynn Vaccaro for collaboration on data collection. We thank the Wisconsin Coastal Management Program (contract 84003-004.40, under the National Oceanic and Atmospheric Administration, NOAA for funding to help assess progress in managing coastal wetlands and the University of Wisconsin Sea Grant Institute under grants from the National Sea Grant College Program, NOAA, U.S. Department of Commerce, and from the State of Wisconsin (federal grant NA16RG2257, project number R/LR-96). Artwork and layout provided by Kandis Elliot, Botany Department.

4Tussocks resprouting in spring after a control burn (J. Zedler)

Profile of Carex stricta showing the matrix form of dominance--it can support a dozen or more species!