Skip to main content

Advertisement

SpringerLink
Log in

Determining vegetation metric robustness to environmental and methodological variables

  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

Land managers need reliable metrics for assessing the quality of restorations and natural areas and prioritizing management and conservation efforts. However, it can be difficult to select metrics that are robust to sampling methods and natural environmental differences among sites, while still providing relevant information regarding ecosystem changes or stressors. We collected herbaceous-layer vegetation data in wetlands and grasslands in four regions of the USA (the Midwest, subtropical Florida, arid southwest, and coastal New England) to determine if commonly used vegetation metrics (species richness, mean coefficient of conservatism [mean C], Floristic Quality Index [FQI], abundance-weighted mean C, and percent non-native species cover) were robust to environmental and methodological variables (region, site, observer, season, and year), and to determine adequate sample sizes for each metric. We constructed linear mixed effects models to determine the influence of these environmental and methodological variables on vegetation metrics and used metric accumulation curves to determine the effect of sample size on metric values. Species richness and FQI varied among regions, and year and observer effects were also highly supported in our models. Mean C was the metric most robust to sampling variables and stabilized at less sampling effort compared to other metrics. Assessment of mean C requires sampling a small number of quadrats (e.g. 20), but assessment of species richness or FQI requires more intensive sampling, particularly in species-rich sites. Based on our analysis, we recommend caution be used when comparing metric values among sites sampled in different regions, different years, or by different observers.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Data availability

Data and R code will be uploaded to the Illinois Data Bank.

References

  • Andreas, B. K., Mack, J. J., & McCormac, J. S. (2004). Floristic quality assessment index (FQAI) for vascular plants and mosses for the State of Ohio. Ohio Environmental Protection Agency, Division of Surface Water, Wetland Ecology Group, Columbus, Ohio.

  • Bernthal, T. W. (2003). Development of a floristic quality assessment methodology for Wisconsin. In D. J. Watermolen (Ed.), Madison, WI: Wisconsin Department of Natural Resources, Bureau of Integrated Science Services. Retrieved from https://dnr.wi.gov/topic/Wetlands/documents/FQAMethodWithAcknowledgements.pdf

  • Bourdaghs, M., Johnston, C. A., & Regal, R. R. (2006). Properties and performance of the floristic quality index in Great Lakes coastal wetlands. Wetlands, 26(3), 718–735. https://doi.org/10.1672/0277-5212(2006)26[718:PAPOTF]2.0.CO;2

    Article  Google Scholar 

  • Bried, J. T., Strout, K. L., & Portante, T. (2012). Coefficients of conservatism for the vascular flora of New York and New England: inter-state comparisons and expert opinion bias. Northeastern Naturalist, 19, 101–114. https://doi.org/10.1656/045.019.s608

    Article  Google Scholar 

  • Burnham, K. P., & Anderson, D. R. (2002). Model selection and multimodal inference. Springer-Verlag. https://doi.org/10.1007/b97636

    Book  Google Scholar 

  • Chamberlain, S. J., & Ingram, H. M. (2012). Developing coefficients of conservatism to advance floristic quality assessment in the Mid-Atlantic region. The Journal of the Torrey Botanical Society, 139, 416–427. https://doi.org/10.3159/TORREY-D-12-00007.1

    Article  Google Scholar 

  • Chamberlain, S. J., & Brooks, R. P. (2016). Testing a rapid Floristic Quality Index on headwater wetlands in central Pennsylvania, USA. Ecological Indicators, 60, 1142–1149.

    Article  Google Scholar 

  • Chiarucci, A., & Palmer, M. A. (2006). The inventory and estimation of plant species richness. Encyclopedia of Life Support Systems (EOLSS). Oxford: EOLSS Publishers.

  • Chao, A., & Chiu, C. (2016). Species richness: estimation and comparison. In N. Balakrishnan, T. Colton, B. Everitt, W. Piegorsch, F. Ruggeri, J. L. Teugels (Eds.), Wiley StatsRef: Statistics Reference Online. https://doi.org/10.1002/9781118445112.stat03432.pub2

  • Choi, Y. D. (2004). Restoration ecology to the future: a call for new paradigm. Restoration Ecology, 15, 351–353. https://doi.org/10.1111/j.1526-100X.2007.00224.x

    Article  Google Scholar 

  • Cohen, M. J., Carstenn, S., & Lane, C. R. (2004). Floristic Quality Indices for biotic assessment of depressional marsh condition in Florida. Ecological Applications, 14, 784–794. https://doi.org/10.1890/02-5378

    Article  Google Scholar 

  • Connor, E. F., & McCoy, E. D. (1979). The statistics and biology of the species-area relationship. The American Naturalist, 113(6), 791–833. https://doi.org/10.1086/283438

    Article  Google Scholar 

  • Euliss, N. H., Jr., & Mushet, D. M. (2011). A multi-year comparison of IPCI scores for Prairie Pothole Wetlands: implications of temporal and spatial variation. Wetlands, 31(4), 713–723.

    Article  Google Scholar 

  • Faber-Langendoen, D., Cameron, D., Gilman, A. V., Metzler, K. J., Ring, R. M., & Sneddon, L. (2019a). Development of an ecoregional floristic quality assessment method for the Northeastern United States. Northeastern Naturalist, 26(3), 593–608. https://doi.org/10.1656/045.026.0312

    Article  Google Scholar 

  • Faber-Langendoen, D., Lemly, J., Nichols, W., Rocchio, J., Walz, K., & Smyth, R. (2019b). Development and evaluation of NatureServe’s multi-metric ecological integrity assessment method for wetland ecosystems. Ecological Indicators, 104, 764–775.

    Article  Google Scholar 

  • GeoSystems Analysis. (2014). Middle Rio Grande restoration project monitoring and adaptive management plan, Update: April 2014. Prepared for the U.S. Army Corps of Engineers, Albuquerque District. Prepared by GeoSystems Analysis, Inc. Albuquerque, NM. April 2014. URS Contract No. 25008873.

  • Gotelli, N. J., & Colwell, R. K. (2001). Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness. Ecology Letters, 4(4), 379–391. https://doi.org/10.1046/j.1461-0248.2001.00230.x

    Article  Google Scholar 

  • Grace, J. B., Allain, L., & Allen, C. (2000). Factors associated with plant species richness in a coastal tall-grass prairie. Journal of Vegetation Science, 11, 443–452. https://doi.org/10.2307/3236637

    Article  Google Scholar 

  • Herman, K., Masters, L. A., Penskar, M. R., Reznicek, A. A., Wilhelm, G. S., & Brodoqicz, W. R. (1997). Floristic quality assessment: development and application in the state of Michigan (USA). Natural Areas Journal, 17(3), 265–279. Retrieved from www.jstor.org/stable/43911686

  • Herman, B. D., Madsen, J. D., & Ervin, G. N. (2006). Development of coefficients of conservatism for wetland vascular flora of north central Mississippi. MS thesis, Mississippi State, MS.

  • Johnston, C. A., Ghioca, D. M., Tulbure, M., Bedford, B. L., Bourdaghs, M., Frieswyk, C. B., Vaccaro, L., & Zedler, J. B. (2008). Partitioning vegetation response to anthropogenic stress to develop multi-taxa wetland indicators. Ecological Applications, 18, 983–1001.

    Article  Google Scholar 

  • Lopez, R. D., & Fennessy, S. M. (2002). Testing the floristic quality assessment index as an indicator of wetland condition. Ecological Applications, 12, 487–497. https://doi.org/10.1890/1051-0761(2002)012[0487:TTFQAI]2.0.CO;2

    Article  Google Scholar 

  • Mabry, C., Golay, M. E., Lock, D., & Thompson, J. R. (2018). Validating the use of coefficients of conservatism to assess forest herbaceous layer quality in upland mesic forests. Natural Areas Journal, 38, 6–14.

    Article  Google Scholar 

  • MacArthur, R. H., & Wilson, E. O. (1967). The theory of island biogeography. Princeton University Press.

    Google Scholar 

  • Matthews, J. W. (2003). Assessment of the floristic quality index for use in Illinois, USA, wetlands. Natural Areas Journal, 23(1), 53–60.

    Article  Google Scholar 

  • Matthews, J. W., Spyreas, G., & Endress, A. G. (2009). Trajectories of vegetation-based indicators used to assess wetland restoration progress. Ecological Applications, 19, 2093–2107. https://doi.org/10.1890/08-1371.1

    Article  Google Scholar 

  • Miller, S. J., & Wardrop, D. H. (2006). Adapting the floristic quality assessment index to indicate anthropogenic disturbance in central Pennsylvania wetlands. Ecological Indicators, 6(2), 313–326. https://doi.org/10.1016/j.ecolind.2005.03.012

    Article  Google Scholar 

  • Mortellaro, S., Barry, M., Gann, G., Zahina, J., Channon, S., Hilsenbeck, C., Scofield, D., Wilder, G., & Wilhelm, G. S. (2012). Coefficients of conservatism values and the floristic quality index for the vascular plants of south Florida. Southeastern Naturalist, 11(3), 1–62. https://doi.org/10.1656/058.011.m301

    Article  Google Scholar 

  • Muldavin, E. H., Milford, E. R., Umbreit, N. E., & Chauvin, Y. D. (2017). Long- term outcomes of natural-process riparian restoration on a regulated river site: the Rio Grande Albuquerque overbank project after 16 years. Ecological Restoration, 35(4), 341–353. https://doi.org/10.3368/er.35.4.341

    Article  Google Scholar 

  • National Weather Service. (2018). West central & southwest Florida 2018 annual climate summary and tables. National Oceanic and Atmospheric Administration. Retrieved from https://www.weather.gov/tbw/2018climate#ARCF1

  • Opedal, H. Ø., Armbruster, W. S., & Graae, B. J. (2015). Linking small-scale topography with microclimate, plant species diversity and intra-specific trait variation in an alpine landscape. Plant Ecology and Diversity, 8(3), 305–315. https://doi.org/10.1080/17550874.2014.987330

    Article  Google Scholar 

  • Palmer, M. W., & White, P. S. (1994). Scale dependence and the species-area relationship. The American Naturalist, 144(5), 717–740. https://doi.org/10.1086/285704

    Article  Google Scholar 

  • R Core Team. (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Retrieved from https://www.R-project.org/

  • Reemts, C. M., & Eidson, J. A. (2019). Choosing plant diversity metrics: a tallgrass prairie case study. Ecological Restoration, 37(4), 233–245. Retrieved from https://muse.jhu.edu/article/738228/pdf

  • Rhode Island Department of Environmental Management. (2020). Overview of climate in Rhode Island. Retrieved from http://www.dem.ri.gov/climate/climate-overview-ri.php

  • Ricklefs, R. E., Qian, H., & White, P. S. (2004). The region effect on mesoscale plant species richness between eastern Asia and eastern North America. Ecography, 27, 129–136. https://doi.org/10.1111/j.0906-7590.2004.03789.x

    Article  Google Scholar 

  • Ruhlman, J., Gass, L., & Middleton, B. (2012). Arizona /New Mexico Plateau Ecoregion. In B. M. Sleeter, T. S. Wilson, & W. Acevedo (Eds.), Status and Trends of Land Change in the Western United States – 1973–2000. U.S. Geological Survey Professional Paper 1794-A.

  • Ruiz-Jaen, M. C., & Aide, T. M. (2005). Restoration success: how is it being measured? Restoration Ecology, 13(3), 569–577. https://doi.org/10.1111/j.1526-100X.2005.00072.x

    Article  Google Scholar 

  • Spyreas, G. (2016). Scale and sampling effects on floristic quality. PLoS One, 11(8), e0160693. https://doi.org/10.1371/journal.pone.0160693

    Article  CAS  Google Scholar 

  • Spyreas, G. (2019). Floristic Quality Assessment: a critique, a defense, and a primer. Ecosphere, 10(8), e02825. https://doi.org/10.1002/ecs2.2825

    Article  Google Scholar 

  • Stern, J. L. (2020). Determining vegetation metric robustness to environmental and methodological variables, and coefficients of conservatism for the flora of the Middle Rio Grande, New Mexico. University of Illinois at Urbana Champaign. http://hdl.handle.net/2142/108137

  • Swink, F., & Wilhelm, G. S. (1979). Plants of the Chicago region, third ed., revised and expanded edition with keys. Lisle, IL: The Morton Arboretum.

  • Swink, F., & Wilhelm, G. S. (1994). Plants of the Chicago region (4th ed.). Indiana Academy of Science.

    Google Scholar 

  • Taddeo, S., & Dronova, I. (2018). Indicators of vegetation development in restored wetlands. Ecological Indicators, 94(1), 454–467. https://doi.org/10.1016/j.ecolind.2018.07.010

    Article  Google Scholar 

  • Taft, J. B., Wilhelm, G. S., Ladd, D. M., & Masters, L. A. (1997). Floristic quality assessment for vegetation in Illinois, a method for assessing vegetation integrity. Erigenia, 15, 3–95.

    Google Scholar 

  • Taft, J. B., Hauser, C., & Robertson, K. (2006). Estimating floristic integrity in tallgrass prairie. Biological Conservation, 131(1), 42–51. https://doi.org/10.1016/j.biocon.2006.02.006

    Article  Google Scholar 

  • Tangen, B. A., Butler, M. G., & Ell, M. J. (2003). Weak correspondence between macroinvertebrate assemblages and land use in Prairie Pothole Region wetlands, USA. Wetlands, 23, 104–115.

    Article  Google Scholar 

  • U.S. Army Corps of Engineers. (2008). Orland tract section 206, aquatic ecosystem restorations. Specifications document W912P6–08-C-0030. Chicago District.

  • U.S. Department of Agriculture. (2000). Soil survey of Glades County, Florida. In cooperation with the University of Florida, Institute of Food and Agricultural Sciences, Agricultural Experiment Stations, and Soil Science Department, and the Florida Department of Agriculture and Consumer Services. Retrieved from https://www.nrcs.usda.gov/Internet/FSE_MANUSCRIPTS/florida/FL043/0/Glades.pdf

  • U.S. Department of Agriculture. (2009). Rio Grande soil series. Retrieved from https://soilseries.sc.egov.usda.gov/OSD_Docs/R/RIO_GRANDE.html

  • U.S. Environmental Protection Agency. (2013). Primary distinguishing characteristics of level III ecoregions of the continental United States. Corvallis, OR: U.S. EPA Office of Research and Development (ORD). Retrieved from https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states

  • Wilhelm, G. S., & Ladd, D. (1988). Natural area assessment in the Chicago region. In Transactions of the 53rd North American Wildlife and Natural Resources Conference (pp. 361–375), Louisville, KY.

  • Wilhelm, G. S., & Masters, L. A. (1995). Floristic quality assessment in the Chicago region and application computer programs. Lisle, IL: The Morton Arboretum. Retrieved from http://www.conservationresearchinstitute.org/assets/chicagoareafqa.pdf

  • Wilhelm, G. S., & Rericha, L. (2017). Flora of the Chicago region: A floristic and ecological synthesis. Indianapolis, IN: Indiana Academy of Science.

Download references

Acknowledgements

We thank D. Zaya for assistance with R code; C. Ihssen for help with data management; R. Baranowski, D. Galloway, D. Larsen, D. Lattuca, J. Morton, L. Oliver, D. Price, K. Raposa, R. Sliwinski, L. Spencer, W. Widener, and J. Zylka for assistance with site selection and/or field sampling; C. Castle, B. Charles, S. Tillman, and J. Zinnen for field assistance and manuscript edits; B. Molano-Flores and G. Spyreas for comments on the manuscript, and the U.S. Army Corps of Engineers (USACE) Chicago District, the USACE New England District, the USACE Albuquerque District, the USACE Jacksonville District, the South Florida Water Management District, the Narragansett Bay National Estuarine Research Reserve, the Indiana Department of Natural Resources, the Rhode Island Department of Environmental Management, and Narragansett Bay Save the Bay for property access and equipment use.

Funding

This project was funded by the US Army Corps of Engineers Ecosystem Management and Restoration Research Program (EMRRP) through the Environmental Laboratory of the US Army Corps of Engineers.

Author information

Authors and Affiliations

  1. Department of Natural Resources and Environmental Sciences, University of Illinois At Urbana-Champaign, 1102 S. Goodwin Ave, Urbana, IL, 61801, USA

    Jessica L. Stern & Jeffrey W. Matthews

  2. Environmental Laboratory, US Army Engineer Research and Development Center, 3909 Halls Ferry Road, Vicksburg, MS, 39180, USA

    Brook D. Herman

Authors
  1. Jessica L. Stern
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Brook D. Herman
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jeffrey W. Matthews
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

B.D.H. and J.W.M. secured funding and designed the study. B.D.H, J.L.S., and J.W.M. performed fieldwork. J.L.S. conducted statistical analysis, prepared figures and tables, and wrote the manuscript with supervision from J.W.M. All authors have reviewed the manuscript and approve the final version.

Corresponding author

Correspondence to Jessica L. Stern.

Ethics declarations

Ethics approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Conflict of interest

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Stern, J.L., Herman, B.D. & Matthews, J.W. Determining vegetation metric robustness to environmental and methodological variables. Environ Monit Assess 193, 647 (2021). https://doi.org/10.1007/s10661-021-09445-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-021-09445-9

Keywords

Search

Navigation