Development of an Ecoregional Floristic Quality Assessment Method for the Northeastern United States

Faber-Langendoen, Don; Cameron, Don; Gilman, Arthur V.; Metzler, Kenneth J.; Ring, Richard M.; Sneddon, Lesley

doi:10.1656/045.026.0312

Cited by 7 publications

(10 citation statements)

References 13 publications

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“…Initial data analyses included calculation of mean water depths from quadrat sampling by habitat type, as well as calculation and comparison of percent cover for species encountered. We analyzed Floristic Quality Assessment Index (FQAI) to evaluate the nativeness of the plant community based on plant species present and their Coefficient of Conservatism scores (C) (Reznicek et al 2014, Faber-Langendoen 2018. We made FQAI comparisons across transects and habitat zones both within first year sampling and across the three-year period.…”

Section: Data Analysesmentioning

confidence: 99%

“…FQAI was calculated by using the New York State preliminary C-score list with reference to the NEIWPCC Northeast Ecoregional C-score list (NEIWPCC 2011, Reznicek et al 2014, Faber-Langendoen 2018, calculated as follows:…”

Section: Data Analysesmentioning

confidence: 99%

“…Equation 1: FQAI = Ct√Nt where Ct represents the total mean C and Nt is the total species richness (Freyman et al 2016, Faber-Langendoen 2018. The total mean C value represents the mean C metric for each quadrat within a habitat zone.…”

Section: Data Analysesmentioning

confidence: 99%

“…Equation 2: Quadrat level cover weighted mean C = ∑ t i = 0 Ci γi / ∑ t i = 0γi where ∑ Ci γi is the sum of C-scores for each species (Ci) multiplied by the percent cover of each corresponding species (γi). This is divided by the sum of the percent cover of each species (γi) (Freyman et al 2016, Faber-Langendoen 2018. We made comparisons of species abundance, FQAI, mean C, and weighted mean C across the different grouped zones and overall year to year.…”

Section: Data Analysesmentioning

confidence: 99%

“…Consideration of the preliminary site vegetative composition may be appropriate when choosing a proper metric to use for future studies. If the location is a monoculture of an invasive species, the site may have a low overall mean C or weighted C score, influenced by the dominant cover of the non-native species (Andreas et al 2004, NEIWPCC 2011, Reznicek et al 2014, Faber-Langendoen 2018. However, depending on species richness rather than dominance, results using the FQAI may be skewed toward a higher quality rating (Matthews 2003, Bourdaghs et al 2006.…”

Section: Use Of Fqai Mean C and Weighted Mean Cmentioning

confidence: 99%

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Wetland Restoration inTypha-Dominated Braddock Bay of Lake Ontario

2021

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The barrier beach at the Braddock Bay wetland on Lake Ontario was lost to erosion. Without the protective barrier, the area of vegetated wetland was reduced by wave attack. Lake-level regulation implemented in 1960 resulted in cattail (primarily hybrid Typha × glauca), invasion and loss of sedge-grass meadow habitat. As part of the Rochester Embayment Great Lakes Area of Concern, Braddock Bay was targeted for restoration by the U.S. Army Corps of Engineers. The plan included reducing coverage by cattails, construction of channels and potholes to improve wildlife access to the wetland, creation of spoil mounds along the channels to discourage growth of cattail while supporting the growth of sedge-grass meadow species, re-creation of the barrier beach, and construction of new emergent marsh. We collected vegetation data for three years following the 2016 construction activities. Post-restoration results showed that cattail cover decreased greatly across years in the cattail treatment areas, decreased in lower elevation constructed habitats, and gradually increased in higher elevation habitats. Opening of the canopy resulted in increased floating and submersed species, and constructed mounds hosted wet meadow species. Site-level weighted mean C is recommended for future data analyses, rather than FQAI or mean C, because it has no observed influence from species richness. Restoration results were affected by high lake levels in 2017, identified problems in seeding and planting, and meeting construction plans for some channel and pothole depths and mound elevations. Pre-restoration soil surveys are recommended to reduce construction problems, and adaptive management should include invasive species control.

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Section: Data Analysesmentioning

confidence: 99%

Section: Data Analysesmentioning

confidence: 99%

Section: Data Analysesmentioning

confidence: 99%

Section: Data Analysesmentioning

confidence: 99%

Section: Use Of Fqai Mean C and Weighted Mean Cmentioning

confidence: 99%

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Wetland Restoration inTypha-Dominated Braddock Bay of Lake Ontario

2021

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The potential of trait data to increase the availability of bioindicators: A case study using plant conservatism values

Crisfield

Ficken

Allen

et al. 2023

Ecological Applications

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Biological indicators are commonly used to evaluate ecosystem condition. However, their use is often constrained by the availability of information with which to assign species-specific indicator values, which reflect species' responses to the environmental conditions being evaluated by the indicator.As these responses are driven by underlying traits, and trait data for numerous species are available in publicly accessible databases, one possible approach to approximating missing bioindicator values is through traits. We used the Floristic Quality Assessment (FQA) framework and its component indicator of disturbance sensitivity, species-specific ecological conservatism scores (Cscores), as a study system to test the potential of this approach. We tested the consistency of relationships between trait values and expert-assigned C-scores and the trait-based predictability of C-scores across five regions. Furthermore, as a proof-of-concept exercise, we used a multi-trait model to try to reconstruct C-scores, and compared the model predictions to expert-assigned scores. Out of 20 traits tested, there was evidence of regional consistency for germination rate, growth rate, propagation type, dispersal unit, and leaf nitrogen. However, the individual traits showed low predictability (R 2 = 0.1-0.2) for C-scores, and a multi-trait model produced substantial classification errors; in many cases, >50% of species were misclassified. The mismatches may largely be explained by the inability to generalize regionally varying C-scores from geographically neutral/naive trait data stored in databases, and the synthetic nature of Cscores. Based on these results, we recommend possible next steps for expanding the availability of species-based bioindication frameworks such as the FQA. These steps include increasing the availability of geographic and environmental data in trait databases, incorporating data about intraspecific trait variability into these databases, conducting hypothesis-driven investigations into trait-indicator relationships, and having regional experts review our results to determine if there are patterns in the species that were correctly or incorrectly classified.

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