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The 2017 Chessie BIBI Update and Refinement

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The 2017 Chessie BIBI Update and Refinement The 2017 Chessie BIBI Update and Refinement Zachary M. Smith, Claire Buchanan, and Andrea Nagel AMAAB April 5th, 2018 1 Chessie BIBI Refinement • Update existing “Chessie BIBI” database with recent data (2010+) • Standardized taxa • Incorporate genus-level metrics into index if warranted • Simplify procedure for calculating index https://www.potomacriver.org/focus-areas/aquatic-life/chessie-bibi-stream-health-indicator/ 2 Chessie BIBI Refinement • Update existing “Chessie BIBI” database with recent data (2010+) • Standardized taxa • Incorporate genus-level metrics into index if warranted • Simplify procedure for calculating index • Develop a baseline against which progress in restoring stream health can be measured • Keep in mind the scale of the assessment https://www.potomacriver.org/focus-areas/aquatic-life/chessie-bibi-stream-health-indicator/ 3 4 Spatial Resolution Basin Region Bioregion 2011 2017 NAPU NCA RID VAL PIED MAC SEP 6 Taxonomic Resolution Order Family Genus 7 3 Spatial Resolutions • Basin-wide (1) • Region (2) • Bioregion (12) 45 Indices 3 Taxonomic Resolutions 8 Taxa Standardization Phylum Subphylum Class Subclass Order Suborder Family Subfamily Tribe Genus 9 Taxa Standardization Taxonomic Taxon Total Sample Percentage of Rank Taxon Presence Count Samples Reported Phylum Arthropoda 23,476 23,489 99.94 Order Total Family Total Phylum Annelida 13,182 23,489 56.12 Order Count Count Difference % Change Phylum Mollusca 9,444 23,489 40.21 Amphipoda 185,632 179,977 5,655 -3% Phylum Platyhelminthes 2,619 23,489 11.15 Architaenioglossa 1,267 1,267 0 0% Phylum Nemertea 783 23,489 3.33 Arhynchobdellida 761 761 0 0% Basommatophora 41,694 41,692 2 0% Phylum Nematoda 648 23,489 2.76 Branchiobdellida 272 207 65 -24% Phylum Nematomorpha 243 23,489 1.03 Coleoptera 472,492 472,425 67 0% Phylum Porifera 48 23,489 0.2 Collembola 3,223 1,665 1,558 -48% Phylum Cnidaria 34 23,489 0.14 Decapoda 7,958 7,941 17 0% Phylum Sarcomastigophora 33 23,489 0.14 Diptera 4,691,172 4,689,844 1,328 0% Phylum Bryozoa 3 23,489 0.01 Enchytraeida 7,019 7,019 0 0% Phylum Unidentified 2 23,489 0.01 Ephemeroptera 885,321 851,695 33,626 -4% Subphylum Hexapoda 23,456 23,489 99.86 Haplotaxida 35 32 3 -9% Subphylum Clitellata 13,141 23,489 55.95 Hemiptera 5,906 5,860 46 -1% Subphylum Crustacea 11,272 23,489 47.99 Heterostropha 78 78 0 0% Subphylum Rhabditophora 2,585 23,489 11.01 Isopoda 177,778 176,050 1,728 -1% Subphylum Chelicerata 2,377 23,489 10.12 Lepidoptera 2,117 1,138 979 -46% Subphylum Polychaeta 40 23,489 0.17 Lumbriculida 13,447 13,445 2 0% Subphylum Catenulida 14 23,489 0.06 Megaloptera 24,588 24,550 38 0% Subphylum Myriapoda 11 23,489 0.05 Neoophora 8,172 3,501 4,671 -57% Subphylum Neodermata 1 23,489 0 Neotaenioglossa 19,345 19,345 0 0% Class Insecta 23,455 23,489 99.86 Neuroptera 72 64 8 -11% Class Oligochaeta 13,001 23,489 55.35 Odonata 41,470 41,184 286 -1% Opistophora 1,621 1,621 0 0% Class Malacostraca 11,250 23,489 47.89 Plecoptera 346,056 339,074 6,982 -2% Class Gastropoda 6,705 23,489 28.55 Rhynchobdellida 653 653 0 0% Class Bivalvia 5,952 23,489 25.34 Trichoptera 966,328 964,090 2,238 0% Class Trepaxonemata 2,585 23,489 11.01 Tubificida 143,438 143,276 162 0% Class Entognatha 1,025 23,489 4.36 Unionoida 85 85 0 0% Class Hirudinea 616 23,489 2.62 Veneroida 34,694 34,635 59 0% Class Ostracoda 29 23,489 0.12 Class Maxillopoda 17 23,489 0.07 Class Branchiopoda 2 23489 0.01 10 Taxa Attributes Order Genus Source Family (%) (%) (%) RBP Mid-Atlantic MAC (Barbour et al. 0 4 17 1999) RBP Southeast NC (Barbour et al. 1999) 0 1 18 Tolerance Values DC (Bollman et al. 2010) 9 13 19 EPA (NRSA 2008) 31 53 63 WAB (WVDEP 2015) 9 69 65 PADEP (Chalfant 2009) 13 58 71 NYSDEC (Smith 2016) 0 4 55 BIBI 35 78 89 Source Order (%) Family (%) Genus (%) NYSDEC (Smith 2016) 0 4 55 DC (Bollman et al. 2010) 2 12 19 Functional Feeding RBP (Barbour et al. 1999) 19 29 43 WAB (WVDEP 2015) 35 63 74 PADEP (Chalfant 2009) 28 60 74 Groups EPA_FFG (NRSA 2008) 41 62 72 BIBI 54 77 92 Family Source Order (%) Genus (%) (%) DC (Bollman et al. 2010) 0 11 18 RBP (Barbour et al. 1999) 4 15 22 Habits WAB (WVDEP 2015) 20 62 72 EPA (NRSA 2008) 24 60 73 EPA (USEPA 2012) 0 28 71 BIBI 24 65 1184 Rarefaction 12 ICPRB Work Flow Define Disturbance Gradient Metric Sensitivity Score Metrics Rate Index 13 Define the Disturbance Gradient Reference Degraded 14 100 50 IBI SCORE 0 REF MIN MOD DEG *MIX 15 Rapid Habitat Assessment 12 16 Specific 0 1 2 3 Conductance 300 750 1,000 Disturbance Gradient Score pH 2 1 0 1 2 5 6 8.5 9.5 DO 2 0 5 16 Metric Sensitivity 20 20 20 15 15 15 10 10 10 Number of Taxa Number 5 5 5 0 0 0 REF DEG REF DEG REF DEG Weak Strong Very Strong Sensitivity Sensitivity Sensitivity 17 Standardizing Metric Values Number of Taxa %EPT 20 100 15 100 75 10 50 50 100 0 50 5 25 0 0 0 REF DEG REF DEG 18 Standardizing Metric Values Number of Taxa %EPT 100 100 50 50 0 0 REF DEG REF DEG 19 Rating Procedure 20 Results 21 Bioregion Index Scores 22 Region Index Scores 23 24 25 Jackknife Results Precision Accuracy 26 Jackknife Results Precision Accuracy 27 Jackknife Results Precision Accuracy 28 Support for Family-Level Resolution Waite, I. R., A. T. Herlihy, D. P. Larsen, N. S. Urquhart, and D. J. Klemm. 2004. The effects of macroinvertebrate taxonomic resolution in large landscape bioassessments: an example from the Mid-Atlantic Highlands, USA. Freshwater Biology 49:474–489. Melo, A. S. 2005. Effects of taxonomic and numeric resolution on the ability to detect ecological patterns at a local scale using stream macroinvertebrates. Archiv für Hydrobiologie 164:309–323. Corbi, J. J., and S. Trivinho-Strixino. 2006. Influence of taxonomic resolution of stream macroinvertebrate communities on the evaluation of different land uses. Acta Limnologica Brasiliensia 18:469–475. Mueller, M., J. Pander, and J. Geist, 2013. Taxonomic Sufficiency in Freshwater Ecosystems: Effects of Taxonomic Resolution, Functional Traits, and Data Transformation. Freshwater Science 32:762. Cuffney, T.F. and J.G. Kennen, 2017. Potential Pitfalls of Aggregating Aquatic Invertebrate Data from Multiple Agency Sources: Implications for Detecting Aquatic Assemblage Change across Alteration Gradients. Freshwater Biology. 29 30 Region vs. Bioregion Regional Indices Pros Cons • Standardized • Loss of Sensitivity • Simple (Parsimony) • Large Sample Size Bioregion Index Agreement MAC SEP CA NRV UNP LNP SRV NCA NAPU SGV BLUE PIED Match 30.3% 55.9% 52.9% 55.4% 49.3% 69.7% 59.6% 51.4% 43.2% 59.3% 40.9% 52.2% Near 47.0% 41.7% 39.2% 37.6% 37.3% 28.2% 37.0% 41.5% 44.4% 36.9% 40.7% 38.5% Region Index Disagree 22.7% 2.4% 7.9% 7.0% 13.4% 2.1% 3.5% 7.1% 12.4% 3.8% 18.4% 9.3% 31 2014 “In contrast to our initial hypothesis, however, we found that the Full Region models did almost as well in predicting macroinvertebrate structure as the more area specific Individual Ecoregion models. It may, therefore, be valuable for large regional models to be developed as a good preliminary assessment of the major factors affecting the biological condition of streams in a geographic region as long as the range in natural landscape variability (e.g., climate, slope, elevation) can be minimized. The regional models can then be tested and compared against small subregional or watershed models to determine whether there is a large enough improvement in model performance or variable specificity to justify the general application of regional models or more specificity is need from local models.” https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3968005/pdf/pone.0090944.pdf 32 33 Region Index Split by Bioregion 34 Region vs. Bioregion Northern Central Appalachians (NCA) 35 Region vs. Bioregion Upper Northern Piedmont (UNP) 36 Region vs. Bioregion Southern Ridge and Valley (SRV) 37 Influence of Season 38 Influence of Season 39 Table M-2. Percent of Excellent, Good, and Fair ratings (%EGF) in Reference conditions, for the Coast and Inland region indices. Wilcoxon test p-values <0.01 are shown. * Average and StDev exclude MAC. July - November March - June Wilcoxon REF REF Bioregion %EGF n %EGF n % Change p-value BLUE 96.2% 53 95.0% 80 1.3% ns CA 85.7% 14 90.5% 21 -5.3% ns LNP 92.5% 133 84.1% 214 9.9% <0.01 MAC 90.0% 10 42.9% 7 Too few data - NAPU 56.4% 39 90.0% 50 -37.3% <0.01 NCA 95.4% 131 94.7% 187 0.8% ns NRV 80.6% 31 77.1% 118 4.6% ns PIED 92.6% 54 83.5% 79 10.8% ns SEP 100.0% 14 93.8% 32 6.7% ns SGV 88.9% 63 84.4% 64 5.3% ns SRV 90.9% 164 90.1% 232 0.9% ns UNP 81.0% 21 69.1% 55 17.2% <0.01 Average* 87.3% 86.6% StDev* 11.9% 8.0% 40 Influence of Karst 41 Regional Index Scores o Mapped by HUC12 o Area-weighted and rolled up to basin 42 Chesapeake Bay States Use Macroinvertebrate IBIs to Assess State Waters New York Pennsylvania West Virginia Maryland District of Columbia Delaware Virginia 43 Next Steps • Obtain more data • Incorporate more environmental features for defining the disturbance gradient • Use data outside of the Chesapeake Bay watershed to construct indices • Balance the number of Reference and Degraded • Assess additional data subdivisions (e.g., season, karst, and elevation) • Increase collaboration • Combine multiple models • Region + Bioregion Indices • BIBI + Additional CBP Indices 44 Acknowledgments 1.
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