The VIBI-FQ: a Streamlined Approach to Vegetation Monitoring in Ohio. Brian Gara Wetland Ecology Group Division of Surface Water Ohio EPA

Transcription

1 The VIBI-FQ: a Streamlined Approach to Vegetation Monitoring in Ohio Brian Gara Wetland Ecology Group Division of Surface Water Ohio EPA

2 Vegetation Index of Biotic Integrity (VIBI) Overview A level 3 tool for scoring and categorizing wetlands based on the plant community structure. Developed by John Mack at Ohio EPA. In use as part of the Ohio EPA 401 and Isolated Wetland permit programs for ~10 years. Primarily a tool for assessing mitigation success. Calculates score based on series of 10 metrics derived from field data (plant identification and cover values, woody plant DBH, standing biomass, etc.) within a standard plot. Actually 3 separate VIBIs: VIBI-E (Emergent), VIBI F (Forested), and VIBI-S (Shrub)

3 Plant community data is collected using the sampling methodology developed by the North Carolina Vegetation Survey, as described by Peet, Wentworth, and White ( A flexible, multipurpose method for recording vegetation composition and structure Castanea 63(3): )

4 Plot should be placed completely within a single wetland plant community. It should not include significant areas of different types of habitat or cross HGM boundaries.

5 20 meters 50 meters

6 All species identified to lowest taxonomic level in each of 4 intensive modules and assigned to a cover class.

7 Any new species found in 6 residual modules added and assigned a cover class value representative of the entire 6- module area.

8 In forested wetlands, all woody stems > 1 meter tall found within any of the 10 modules is assigned to a size class based on diameter at breast height (DBH) and tallied.

9 Measuring Standing Biomass For emergent wetlands, all herbaceous plant material is collected at ground level for eight 0.1 square meter clip plots, located at the opposite corners of each of the four intensive modules. Dried, weighed and recorded as grams per m 2.

10 Traditional VIBI Plant information collected from 0.1 hectare plot is used to generate 10 metrics, each worth a maximum of 10 points. Primary plant community types (emergent, scrub-shrub and forested) determine which 10 metrics are used to calculate a score. VIBI is calibrated to different plant community types, HGM classes and ecoregions, so ensuing score must be compared to a table in the VIBI manual to determine the appropriate anti-degradation quality category of the wetland. Used in permit situations where rapid assessment (ORAM) has resulted in ambiguous results and a more definitive answer is required (very rarely used in these cases). Primarily used as a mitigation monitoring tool to determine if the site is developing appropriately and ecological performance goals are being met.

11 Why Modify the Traditional VIBI? VIBI development work focused on well-established natural wetland habitats in Ohio. It is unclear how individual metrics will respond to the rapidly-developing ecosystems associated with mitigation wetlands. In early successional wetlands, it is sometimes difficult to accurately classify the type of wetland that is developing. It is important to locate the VIBI plot clearly within habitat that is meeting wetland criteria. For mitigation sites with significant hydrologic manipulation, it may take several years before the hydrology has stabilized. Many of these sites are mapped as wetland/upland mosaics. These complexities can lead to fluctuating VIBI scores in the early monitoring years of a site, which can make interpretation challenging and sometimes results in disagreements regarding site success.

12 Project Goals Modify VIBI assessment approach to make monitoring simpler and more cost effective for the regulated community. The interpretation of monitoring results should be more straightforward to reduce the conflicts between applicants and regulators. Need to clearly identify the best of the best and the worst of the worst sites. Did not want to dumb down the VIBI to the extent of losing critical information.

13 How does a trained wetland scientist distinguish good and bad sites based on visual observation of the plant community? High or low species diversity? Is the site dominated by tolerant or sensitive species?

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17 Floristic Quality Assessment Index (FQAI) Concept originally developed by Floyd Swink and Gerald Wilhelm of the Morton Arboretum in Chicago. Adapted for use in many areas of the U.S. Numeric score from 0 to 10 assigned to each plant species within a regional flora. Called Coefficient of Conservatism (C of C). Refers to the affinity of each species to a particular habitat type. Tolerant species occur in a wide variety of habitats and are assigned low scores (0-2). Sensitive species have a higher number (6+).

18 Simplified VIBI or VIBI-Floristic Quality Focuses exclusively on ecological condition as it relates to the FQAI species sensitivity concept. Only two metrics calculated: Diversity and Dominance. Each is worth 50 points, so the resultant simplified VIBI or VIBI-FQ score will be between 0 to 100, with the lowest scores reserved for the most degraded plant communities and the highest scores representative of wetland plant communities in true reference condition. Requires a subset of information already collected using traditional VIBI field protocols (species presence and cover), making it easy to back-calculate VIBI-FQ scores for sites previously monitored by the Ohio EPA Wetland Ecology Group. No need to collect biomass (clip plots) or measure woody stem DBHs. This could result in significant time and cost savings.

19 Diversity Metric Standard FQAI calculation for all species recorded in 20M x 50M sample plot: (Sum of C of C values)/ (total number of species) For the Ohio EPA reference dataset of ~300 natural wetlands, the FQAI score for most sites fell within a range of 10 to 30. Therefore, this metric is calculated as follows: ((FQAI 10)/20) x 50 * FQAI scores below 10 receive 0 metric points; those above 30 receive the maximum of 50.

20 Scatterplot of FQAI vs ORAM: Natural Wetlands Monitored ( ) (N = 261) The regression equation is FQAI = ORAM Predictor Coef SE Coef T P Constant ORAM S = R-Sq = 50.7% R-Sq(adj) = 50.5% FQAI ORAM

21 Dominance Metric Relative cover values for each species are multiplied with its assigned C of C value. These individual species cover-weighted C of C scores are summed for the entire sample plot to create a combined site cover-weighted C of C. The dominance metric is calculated as follows: (cover-weighted C of C/6) x 50 * Cover-weighted C of C values 6 and above are considered to be indicative of sites dominated by sensitive species and receive the maximum of 50 points.

22 Cover-Weighted C of C Scatterplot of Cover-Weighted C of C vs ORAM: Natural Wetlands Monitored ( ) The regression equation is Weighted C of C = ORAM Predictor Coef SE Coef T P Constant ORAM S = R-Sq = 41.3% R-Sq(adj) = 41.0% (N = 261) ORAM

23 Scatterplot of VIBI-FQ vs ORAM: Natural Wetlands Monitored ( ) (N=261) The regression equation is VIBI-FQ = ORAM Predictor Coef SE Coef T P Constant ORAM S = R-Sq = 56.9% R-Sq(adj) = 56.7% VIBI-FQ ORAM

24 Scatterplot of VIBI-FQ vs VIBI: Natural Wetlands Monitored ( ) (N=261) The regression equation is VIBI-FQ = VIBI Predictor Coef SE Coef T P Constant VIBI S = R-Sq = 71.7% R-Sq(adj) = 71.5% VIBI-FQ VIBI

25 ORAM = 21.5 (Category 1) VIBI = 13 (Category 1) VIBI-FQ = 2 (Category 1)

26 ORAM = 82.5 (Category 3) VIBI = 80 (Category 3) VIBI-FQ = 90 (Category 3)

27 National Wetland Condition Assessment (NWCA): Ohio Intensification Study

28 Scatterplot of VIBI-FQ vs ORAM: NWCA Intensification ( ) (N = 50) The regression equation is VIBI-FQ = ORAM Predictor Coef SE Coef T P Constant ORAM S = R-Sq = 60.7% R-Sq(adj) = 59.9% 60 VIBI-FQ ORAM

29 Scatterplot of VIBI-FQ vs VIBI: NWCA Intensification ( ) (N = 50) The regression equation is The regression equation is VIBI-FQ = VIBI VIBI-FQ = VIBI Predictor Coef SE Coef T P Predictor Coef SE Coef T P Constant Constant VIBI VIBI S = R-Sq = 60.6% R-Sq(adj) = 59.8% S = R-Sq = 57.0% R-Sq(adj) = 56.0% 60 VIBI-FQ VIBI

30 VIBI-FQ vs. Traditional VIBI for Mitigation Monitoring Less information needs to be collected with the VIBI-FQ. All species within 20M x 50M VIBI plot are identified and assigned cover classes, but no clip plots and no DBH measurements necessary. VIBI-FQ scores are much easier to interpret. As a site develops, diversity should increase and the predominance of sensitive species (i.e. higher C of C s) should also increase. Including upland habitat or multiple plant community types within a single vegetation plot can have major consequences for the traditional VIBI score and how it is interpreted. Crossing the plant community boundaries with the VIBI plot should have little or no effect on the VIBI-FQ.

31 VIBI-FQ vs. Traditional VIBI for Mitigation Monitoring (continued) Setting scoring ranges is necessary to establish appropriate mitigation goals. Based on comparison of traditional VIBI with VIBI- FQ scores for 315 natural wetlands, the following is the proposed scoring range breakdown for the VIBI-FQ: Poor Condition (Category 1) = 0 to < 20 Fair Condition (Modified Category 2) = 20 to < 40 Good Condition (Category 2) = 40 to < 60 Excellent Condition (Category 3) = 60 to 100 Typical mitigation sites in Ohio would have a target score of 40 to ensure a good condition wetland is achieved. A minimum score for each individual parameter is required to ensure that a site does not achieve the overall numeric goal, even though it is lacking in one of the two metrics. Proposed minimum scores would be: - FQAI > 12 (or metric score of 5) - Weighted C of C > 1.2 (or metric score of 10)

32 Boxplot of Traditional VIBI Score vs. Proposed VIBI-FQ Scoring Ranges 100 Traditional VIBI Score to < to < to < to 100 (N = 35) (N = 86) (N = 79) (N = 115) VIBI-FQ Scoring Ranges

33 VIBI-FQ vs. Traditional VIBI Scoring Comparisons for Natural Wetlands (N =315) Proposed VIBI Number Traditional VIBI Category Scoring Range of Sites to <20 [ Poor (Cat 1)] to <40 [ Fair (Cat 2)] to <60 [ Good (Cat 2)] to 100 [ Excellent (Cat 3)]

34 Example of VIBI-FQ for Monitoring a Mitigation Bank

35 Example of VIBI-FQ for Monitoring a Mitigation Bank

36 Example of VIBI-FQ for Monitoring a Mitigation Bank

37 Example of VIBI-FQ for Monitoring a Mitigation Bank Mean VIBI-FQ scores are calculated for each plant community type.

38 Specific Examples of the VIBI-FQ in Action 2011 National Wetland Condition (NWCA) Assessment data Evaluation of brownfield wetland restoration project Monitoring grant-funded 319 wetland restoration projects to determine success (2016) 2016 NWCA Reference Wetland Monitoring

39 National Wetland Condition Assessment

40 National Wetland Condition Assessment

41 National Wetland Condition Assessment US VIBI-FQ 100 1) 2011 National Wetland Condition Assessment data 80 2) Evaluation of brownfield wetland restoration 60 project ) Monitoring grant-funded 319 wetland restoration projects to determine success (2016). 20 Boxplot of US VIBI-FQ vs. VMMI Condition Poor Fair VMMI Condition Good (N= 349) (N= 198) (N= 420)

42 National Wetland Condition Assessment U.S. EPA VMMI = ( Good ) Ohio EPA VIBI-FQ = 18 ( Poor )

43 Brownfield Remediation with Associated Wetland Restoration

44 Allied Chemical & Ironton Coke

45 Allied Chemical & Ironton Coke

46 Allied Chemical & Ironton Coke Scrub Shrub Wetland: VIBI-FQ = 31 ( Fair Quality ) Emergent Wetland: VIBI-FQ = 4 ( Poor Quality ) Forested Wetland: VIBI-FQ = 29 ( Fair Quality )

47 Grant-Funded Wetland Restoration Monitoring (e.g., 319, GLRI, etc.) o In 2015, a total of six 319/GLRI restoration projects were monitored using the VIBI-FQ. o Five sites scored as fair and one scored as good. o Provided an excellent method for documenting appropriate plant community development and justifying project costs.

48 2016 NWCA Reference Wetland Monitoring In 2016, three wetlands were chosen to be surveyed by U.S. EPA Region 5 staff. Detailed GIS analysis was conducted to select least disturbed wetland sites located in the Eastern Corn Belt Ecoregion of Ohio. Lawrence Woods State Nature Preserve Sears Woods State Nature Preserve Cedar Bog State Nature Preserve NWCA protocols were used to monitor each wetland, along with VIBI-FQ.

49 VIBI-FQ = 81 Lawrence Woods State Nature Preserve

50 VIBI-FQ = 82 Sears Woods State Nature Preserve

51 VIBI-FQ = 100 Cedar Bog State Nature Preserve

52 Excel Spreadsheets for Calculating VIBI Scores

53 VIBI-FQ Summary A simplified approach for the assessment of plant communities. Focuses exclusively on diversity and dominance as they relate to the FQAI species sensitivity concept. Resultant Simplified VIBI or VIBI-FQ score correlates closely with traditional VIBI and ORAM assessments completed on Ohio wetlands. Easier to conduct, calculate and interpret than the traditional VIBI, with very little (if any) loss of information. Allows for apples to apples comparison between sites for the purposes of ranking potential grant-funded preservation projects. Can be used in either upland or wetland plant communities. Consistently identifies the best of the best and the worst of the worst. Can be used to monitor plant communities outside of Ohio, if a FQAI document has been developed for a particular region.

54 Questions???