34 th Annual Meeting New England Association of Environmental Biologists 31st Annual Meeting Hotel Viking Newport, RI March 19,

Application of the Index of Biotic Similarity (B) to the Analysis of the Data Generated Carlos F. A. Pinkham Declan J. McCabe Biology Department Biology Department Norwich University St Michael’s College Northfield, VT Colchester, VT Farley Brown Johnathon L. Miller Sterling College Formerly of Craftsbury Commons, VT Geology Department Norwich University 2 by the

Outline Vermont Streams Project Index of Biotic Similarity, BioSim2, & the statistical test Results Conclusions 3

Outline Vermont Streams Project –Concept –Participants –Drainage Basins in the Project –Macroinvertebrate Techniques 4

The Streams Project is a collaborative effort involving Universities, Colleges, VT DEC, and high schools, managed by VT EPSCoR (Experimental Programs to Stimulate Competitive Research). It is dedicated to collecting high-quality data on streams in the Champlain basin while training the next generation of scientists. Ultimately this database will be instrumental in understanding watershed dynamics around the state. Vermont Streams Project Concept 5

Vermont Streams Project Participants 6

Vermont Streams Project 2008, College Baseline Study: 33 sites 6 drainage basins 7

Collected in summer-early fall (June & July up to October) Collected from representative locations in a riffle in the stream Substrate in an area about 1 square meter upstream of a 500 micron mesh D-net is thoroughly disturbed by hand Four replicates collected each replicate lasting about 30 seconds Replicates preserved individually in 75% alcohol Vermont Streams Project Macroinvertebrate Techniques Sampling 8

1)Sample is washed and spread evenly over a white, gridded tray with 16 squares. 2)Starting with random grid, it and the next 3 consecutive squares are picked clean of macroinvertebrates using a 3 diopter magnifying headset and separate light. 3)Process is continued if necessary until 300 organisms are picked. 4)Total number of squares picked is recorded. 5)Picked macroinvertebrates are preserved in 75% alcohol. 6)Macroinvertebrates are identified to genus, except Oligochaetes and Chironomids (Family). Vermont Streams Project Macroinvertebrate Techniques Processing 9

Outline Index of Biotic Similarity, BioSim2, & the Statistical Test –Brief Review –What is a Sector? –Statistically Valid Sectors 10

Index of Biotic Similarity, BioSim2, & the Statistical Test (Pinkham-Pearson Index) Brief Review Barbour et al. (1992) in a systematic comparison of the metrics proposed in EPA's rapid bioassessment protocol (Pfalkin et al., 1989), concluded that B "may be the most appropriate metric to serve as a measure of community similarity." 11

Index of Biotic Similarity, BioSim2, & the Statistical Test Brief Review 12

Brief Review Matrix of B’s Between 11 Parameters Index of Biotic Similarity, BioSim2, & the Statistical Test 13

Site Dendrogram Index of Biotic Similarity, BioSim2, & the Statistical Test Brief Review 14

Taxa Dendrogram Index of Biotic Similarity, BioSim2, & the Statistical Test Brief Review 15

What is a Sector? Index of Biotic Similarity, BioSim2, & the Statistical Test 16

Assumptions –The measurements in each site are independent –The % composition of taxa follow a normal distribution Independent Index of Biotic Similarity, BioSim2, & the Statistical Test Statistically Valid Sectors 17

Calculations Index of Biotic Similarity, BioSim2, & the Statistical Test Statistically Valid Sectors 18

Calculations H o : There is a not a significant difference between the percent compositions of taxa in the sites making up Sector 1 & Sector 2. H a : There is a significant difference between the percent compositions of taxa in the sites making up Sector 1 & Sector 2. Given H o is true then The p-value is calculated using the chi-square distribution. Index of Biotic Similarity, BioSim2, & the Statistical Test Statistically Valid Sectors 19

Outline Results –Original Macroinvertebrate Data Matrix –Macroinvertebrate % Composition Data –Statistically Valid Sector Analysis –Abundance Values for Each Sector 20

208 taxa collected at 33 sites comprising 24, 677 organisms… compressed to 83 taxa at 33 sites comprising 23,987 organisms (<97% of original) by eliminating – taxa which appeared in only one site with 30 or fewer organisms (82 taxa). – taxa which appeared in only two sites with total of 30 or fewer organisms (25 taxa). –taxa which appeared in only three sites with a total of 30 or fewer organisms (18 taxa). Results Original Macroinvertebrate Data Matrix 21

These 83 taxa at 33 sites comprising 23,987 organisms… further compressed to 65 taxa at 33 sites by eliminating those taxa with a sum of their % compositions over all sites that did not exceed 4%. 23,454 organisms remained (>95% of the original) Results Macroinvertebrate % Composition Data 22

AI-Ple-Pero-Isop AI-Eph-Hep-Rhi AI-Eph-Bae-Acen AI-Tri-Hyds-Che AI-Eph-Ephi-Eph AI-Tri-Hyds-Pot-f AI-Ple-Pero AI-Cole-Elm-Ord-n AI-Tri-Phi-Dol AI-Cole-Elm AI-Eph-Ephe-Ser AI-Eph-Ephe AI-Tri-Hel-Hel AI-Ple-Peri AI-Eph-Lepp-Par AI-Eph-Bae-Pse AI-Eph-Bae-Fal AI-Dip-Tab AC-Iso-Ase-Lir-l AI-Ple-Pte-Pte AI-Tri-Glo-Aga AI-Ple-Chl-All AI-Tri-Rhy-Rhy AI-Ple-Leu-Leu AI-Eph-Ephe-Eph AI-Eph-Bae-Acer AI-Ple-Cap-Cap AI-Eph-Sip-Par AI-Ple-Cap-Nem-c AI-Ple AI-Eph-Hep-Hep AI-Tri-Glo-Glo AI-Eph-Lepp-Lep AI-Meg-Cor-Nig AI-Tri-Bra-Bra AI-Eph-Hep AI-Dip-Tip-Ped AI-Cole-Pse-Pse AI-Eph-Hep-Epe AI-Eph-Ephe-Dru AI-Ple-Peri-Neo AI-Dip-Tip MG-Pul-Lym AC-Amp-Cra-Cra AI-Cole-Dyt-Dyts AI-Tri-Hyds-Hyd AI-Tri-Hyds AI-Tri AI-Tri-Hyds-Cer AI-Tri-Hyds-Arc AI-Tri-Uen-Neo AI-Dip-Tip-Ant AI-Eph-Bae AI-Tri-Phi-Chi AI-Eph-Bae-Bae AI-Dip-Cer A AI-Eph AI-Dip-Nym-Nym AI-Dip AI-Cole-Elm-Opt AC-Oli AI-Dip-Sim AI-Dip-Chi AI-Cole-Elm-Ste MB_BiR_76_ MB_ER_47_ MB_PD_48_081-A MB_R7_46_ MB_BrR_114_ MB_ByR_42_ MB_PD_48_081-B MB_HR_58_ MB_SS_87_ LR_MB_142_ LR_MB_103_ MB_LD_48_ MB_SS_87_ MB_SS_87_ MB_ByR_42_ MB_SS_87_ PB_CC_63_ LR_BR_318_ LR_FHB_321_ OC_CR_XXX_ OC_NHR-XXX LC_R7_51_ LB_MR_229_ LB_MR_288_ OC_BC_172_ LR_RB_197_ LR_EB_213_ LR_WB_215_ LR_WB_244_ LR_WB_386_ LR_BR_141_ LR_SR_139_ LR_BR_165_ Results Statistically Valid Sectors 23

AI-Ple-Pero-Isop AI-Eph-Hep-Rhi AI-Eph-Bae-Acen AI-Tri-Hyds-Che AI-Eph-Ephi-Eph AI-Tri-Hyds-Pot-f AI-Ple-Pero AI-Cole-Elm-Ord-n AI-Tri-Phi-Dol AI-Cole-Elm AI-Eph-Ephe-Ser AI-Eph-Ephe AI-Tri-Hel-Hel AI-Ple-Peri AI-Eph-Lepp-Par AI-Eph-Bae-Pse AI-Eph-Bae-Fal AI-Dip-Tab AC-Iso-Ase-Lir-l AI-Ple-Pte-Pte AI-Tri-Glo-Aga AI-Ple-Chl-All AI-Tri-Rhy-Rhy AI-Ple-Leu-Leu AI-Eph-Ephe-Eph AI-Eph-Bae-Acer AI-Ple-Cap-Cap AI-Eph-Sip-Par AI-Ple-Cap-Nem-c AI-Ple AI-Eph-Hep-Hep AI-Tri-Glo-Glo AI-Eph-Lepp-Lep AI-Meg-Cor-Nig AI-Tri-Bra-Bra AI-Eph-Hep AI-Dip-Tip-Ped AI-Cole-Pse-Pse AI-Eph-Hep-Epe AI-Eph-Ephe-Dru AI-Ple-Peri-Neoe AI-Dip-Tip MG-Pul-Lym AC-Amp-Cra-Cra AI-Cole-Dyt-Dyts AI-Tri-Hyds-Hyd AI-Tri-Hyds AI-Tri AI-Tri-Hyds-Cer AI-Tri-Hyds-Arc AI-Tri-Uen-Neo AI-Dip-Tip-Ant AI-Eph-Bae AI-Tri-Phi-Chi AI-Eph-Bae-Bae AI-Dip-Cer A AI-Eph AI-Dip-Nym-Nym AI-Dip AI-Cole-Elm-Opt AC-Oli AI-Dip-Sim AI-Dip-Chi AI-Cole-Elm-Ste MB_BiR_76_ MB_ER_47_ MB_PD_48_081-A MB_R7_46_ MB_BrR_114_ MB_ByR_42_ MB_PD_48_081-B MB_HR_58_ MB_SS_87_ LR_MB_142_ LR_MB_103_ MB_LD_48_ MB_SS_87_ MB_SS_87_ MB_ByR_42_ MB_SS_87_ PB_CC_63_ LR_BR_318_ LR_FHB_321_ OC_CR_XXX_ OC_NHR-XXX LC_R7_51_ LB_MR_229_ LB_MR_288_ OC_BC_172_ LR_RB_197_ LR_EB_213_ LR_WB_215_ LR_WB_244_ LR_WB_386_ LR_BR_141_ LR_SR_139_ LR_BR_165_ Results Statistically Valid Sectors (Cont’d) 24

AI-Ple-Pero-Isop AI-Eph-Hep-Rhi AI-Eph-Bae-Acen AI-Tri-Hyds-Che AI-Eph-Ephi-Eph AI-Tri-Glo-Aga AI-Ple-Chl-All AI-Tri-Rhy-Rhy AI-Ple-Leu-Leu AI-Eph-Ephe-Eph AI-Eph-Bae-Acer AI-Ple-Cap-Cap AI-Eph-Sip-Par AI-Tri-Glo-Glo AI-Eph-Lepp-Lep AI-Meg-Cor-Nig AI-Tri-Bra-Bra AI-Eph-Hep AI-Dip-Tip-Ped AI-Cole-Pse-Pse AI-Eph-Hep-Epe AI-Eph-Ephe-Dru AI-Ple-Peri-Neoe AI-Dip-Tip MG-Pul-Lym AC-Amp-Cra-Cra AI-Cole-Dyt-Dyts AI-Tri-Hyds-Hyd AI-Tri-Hyds AI-Tri AI-Tri-Hyds-Cer AI-Tri-Hyds-Arc AI-Tri-Uen-Neo AI-Dip-Tip-Ant AI-Eph-Bae AI-Tri-Phi-Chi AI-Eph-Bae-Bae AI-Dip-Cer A AI-Eph AI-Dip-Nym-Nym AI-Dip-Sim AI-Dip-Chi AI-Cole-Elm-Ste MB_BiR_76_081 MB_ER_47_081 MB_PD_48_081-A Absent to Mostly MB_R7_46_081 Absent Mostly Absent Mostly Rare Absent Rare to Abs-Rare Mostly Rare LowerUpper MB_BrR_114_081 Common MB_ByR_42_081 MB_PD_48_081-B MB_HR_58_081 MB_SS_87_081 Absent Absent to Mostly Absent to Mostly LR_MB_142_081 Mostly Absent Mostly Rare Absent Abs-UncomCommon Abs-UncomUpper LR_MB_103_081 MB_LD_48_081 MB_SS_87_083 Mostly Absent to Rare to MB_SS_87_084 Absent Absent to Abs-Rare Mostly Abs-Rare Lower CommonAbs-Rare Common MB_ByR_42_082 Mostly Rare Absent MB_SS_87_082 PB_CC_63_081 LR_BR_318_081 LR_FHB_321_081 Absent Absent toAbsent MostlyAbsent to Mostly OC_CR_XXX_081 Abs-Uncom Mostly Rareto AbsentMostly RareCommonAbs-Uncom Upper OC_NHR-XXX-081 Uncommon LC_R7_51_081 LB_MR_229_081 Rare to Mostly Absent to Rare to LB_MR_288_081 Absent Abs-Uncom Abs-Rare Common AbsentAbs-RareCommonAbs-Rare Common OC_BC_172_081 LR_RB_197_081 Absent Abs-Rare Abs-Uncom Rare toAbs-Rare Mostly Abs-Rare Mostly LR_EB_213_081 Uncommon Lower Upper LR_WB_215_081 Rare to Absent Absent to Mostly LR_WB_244_081 Common Mostly Absent Abs-Uncom toAbsentCommon Mostly Rare Mostly Absent Lower LR_WB_386_081 Rare LR_BR_141_081 Absent to Rare to Mostly Absent to Rare to LR_SR_139_081 Mostly AbsentMostly Rare Abs-Uncom Common AbsentAbs-UncomCommon UncommonCommon LR_BR_165_081 Results Abundance Values for Each Sector 25

AI-Ple-Pero-Isop AI-Eph-Hep-Rhi AI-Eph-Bae-Acen AI-Tri-Hyds-Che AI-Eph-Ephe-Eph AI-Tri-Glo-Aga AI-Ple-Chl-All AI-Tri-Rhy-Rhy AI-Ple-Leu-Leu AI-Eph-Ephe-Eph AI-Eph-Bae-Acer AI-Ple-Cap-Cap AI-Eph-Sip-Par AI-Tri-Glo-Glo AI-Eph-Lepp-Lep AI-Meg-Cor-Nig AI-Tri-Bra-Bra AI-Eph-Hep AI-Dip-Tip-Ped AI-Cole-Pse-Pse AI-Eph-Hep-Epe AI-Eph-Ephe-Dru AI-Ple-Peri-Neoe AI-Dip-Tip MG-Pul-Lym AC-Amp-Cra-Cra AI-Cole-Dyt-Dyts AI-Tri-Hyds-Hyd AI-Tri-Hyds AI-Tri AI-Tri-Hyds-Cer AI-Tri-Hyds-Arc AI-Tri-Uen-Neo AI-Dip-Tip-Ant AI-Eph-Bae AI-Tri-Phi-Chi AI-Eph-Bae-Bae AI-Dip-Cer A AI-Eph AI-Dip-Nym-Nym AI-Dip-Sim AI-Dip-Chi AI-Cole-Elm-Ste MB_BiR_76_081 low elev high impact MB_ER_47_081 MB_PD_48_081-A Mostly MB_R7_46_081 Rare to Upper MB_BrR_114_081 Common MB_ByR_42_081 MB_PD_48_081-B MB_HR_58_081 MB_SS_87_081 Absent to Mostly Low elev mod impact LR_MB_142_081 Common Upper LR_MB_103_081 MB_LD_48_081 Low elev some impact MB_SS_87_083 Absent to Rare to MB_SS_87_084 Common MB_ByR_42_082 MB_SS_87_082 PB_CC_63_081 LR_BR_318_081 Hi elev mod impact LR_FHB_321_081 Absent to Mostly OC_CR_624_081 Common Upper OC_NHR LC_R7_51_081 LB_MR_229_081 Rare to Absent to Rare to Hi elev some imp LB_MR_288_081 Common OC_BC_172_081 LR_RB_197_081 Mostly Hi elev hi impact LR_EB_213_081 Upper LR_WB_215_081 Rare to Absent to Hi elev lo impact LR_WB_244_081 Common LR_WB_386_081 LR_BR_141_081 Rare to Absent to Rare to Hi elev some imp LR_SR_139_081 Common LR_BR_165_081 Results Abundance Values for Each Sector Low elev High imp Low elev mod imp Low elev some imp High elev mod imp High elev some imp High elev High imp High elev lo imp High elev some imp 26

Four major site sets (clusters of sites) were identified. These four site sets could be distinguished on the basis of as few as 25 taxa. These 25 taxa included taxa sets (clusters of taxa) of pollution intolerant, intermediate and tolerant organisms, thus… These four site sets could be assessed for impact on the basis of as few as 25 taxa. It is not at all unreasonable to have HS students master the consistent identification of these 25 taxa and thus be in a position to assist the professional effort by state DECs/DEMs to assess stream quality on an ongoing basis. Results Major Conclusions 27

Acknowledgements The authors wish to thank: The EPSCoR 2008 & 2009 Baccalaureate College Development (BCD) Faculty Support Streams Project Grants under NSF Grant Number, EPS

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