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.. ,,. \ ·~· ... '.. JN 'l'HE~COURT OF COMMON PLEAS c ;,': .. GEAUGA COUNTY, OHIO STATE OF OHIO,-exrCI.:_ CASE NO. 01M0771 BETTY D. MONTGOMERY ATTORNEY GENERAL OF OHIO JUDGE FORREST W. BURT

Plaintiff,

v. JUDGMENT ENTRY HERITAGE DEVELOPMENT COMPANY et. al.

Defendants.

This matter came before this Court on Plaintiff, State of Ohio's Motion for Preliminary

Injunction. In this request, the State of Ohio asked this Court to enjoin Defendants~ Heritage

\ ) Development Company and Bainbridge Land Development Company from further activities in

the wetlands and streams situated on the 127-acre site located south of State Route 43 in

Bainbridge Township, Geauga County. A preliminary injunction hearing was conducted with all

parties present on October 22-24, 2001. At that time, the Court heard testimony from witnesses

for both sides. On November 23, 2001, both parties submitted post-hearing briefs.

Based upon the witnesses' testimony and the evidence presented, this Court finds that

Defendants' 127-acre site contains streams and wetlands that are as a matter of law, "waters of

the state". In particular, the wetlands identified by the parties as L, N, 0 and R, and stream Bare

found to be "waters of the state" pursuant to R.C. 6111.0l(H). Additionally, this Court finds that

Defendants have engaged in activities that have impacted the areas in and around the wetlands

and stream B on this site. In particular, Defendants have cut trees from wetlands, R, N, 0 and . . portions of L, and have caused soils to be placed into stream B. As a result of these impacts this

(~ Court finds that it is unrealistic that these wetlands can be restored to pre-impact conditions.

Accordingly, this Court finds that Defendants have impacted waters of the state without

pnor approval from Ohio Environmental Protection Agency. Therefore, this Court enjoins

Defendants from conducting further construction activities in or around the wetlands and stream

B on this site until they have obtained the necessary approval from the Ohio Environmental

Protection Agency or upon further order of this Court. Further, this Court directs the parties to

immediately engage in settlement negotiations to reach an appropriate resolution to this matter

and report.back to the Court within the next sixty (60) days.

IT IS SO ORDERED

/7 .~ltO~ \ JllJDGE FORREST W. BURT /-

DATE 7 I

c~-. ·~~ K~, f-l.c,. ·u ~ fh(J/Lf-.,w.·-1: 2_ ,,; 6

2 ;...\; rA IN THKCB~t ~Ft=~QM1\10N PLEAS c ffl c ... I L r - ( ~ ~ - t. ...-1 ~ co·URT ~~~-qo~~y ~wiio ------____ STATE_QF_OIDO,. .EXREL. __ tJ£rilSE--rLK1\f'HHSt&-as_e"N_o._Ql_MJ)77-l_~ ------BETTY D. MONTGOMERY, CLERK OF GOURTS ATTORNEY GENERAL OF o:H¥()\UGA cµUNTY JUDGE FORREST w. BURT

Plaintiff vs. --S HERITAGE DEVELOPMENT COMPANY, et al.

Defendants

PERMANENT INJUNCTION CONSENT ORDER

The Complaint in the above captioned matter having beea- filed herein, and the

Plaintiff, State of Ohio, by its Attorney General Betty D. Montgomery ("Plaintiff'), and J Defendants, Heritage Development Company ("Heritage") and Bainbridge Land Company,

LLC ("BLD"), (Heritage and BLD collectively referred to as "Defendants"), having

consented to the entry of this Order, hereby agree to t.i.e entry of this Consent Order to

resolve the allegations set forth in the Complaint filed by the State of Ohio. This Consent

Order i~ not to be construed as an admission of liability by the Defendants for· the allegations

stated in the Complaint.

NOW THEREFORE, upon consent of the parties hereto, it is hereby

ORDERED, ADJUDGED AND DECREED as follows: I. JURISDICTION AND VENUE c I. The Court has jurisdiction over the parties and the subject matter of this case. The Complaint states a claim upon which relief can be granted against

Defendant under Chapter 6111 of the Ohio Revised Code ("O.R.C."), and venue is proper

in this Court.

II. PARTIES

2. The provisions of this Consent Order shall apply to and be binding

upon the parties to this action, and Defendants' agents, officers, employees, assigns,

successors in interest, any transferee o! buyer of BLD's one hundred and twenty-seven

(127) acres of undeveloped real property, or any portions thereof, located along State

Route 43 in Bainbridge Township, Geauga County, Ohio which is more fully described

on Exhibit "A" attached hereto and made a part hereof (the "Site"), and any person

J acting in concert or privity with any of them. (The area for "development" by

Defendants is depicted and described on Exhibit "A"). For purpose of this Consent

Order, Bert L. Wolstein, individually, is not a participant, but to the extent that he is an

officer, agent or member of the Defendants he shall be bound like any other officer,

member or agent.

III. SATISFACTION OF LAWSUIT AND RESERVATION OF RIGHTS

3. Full compliance with the terms and conditions of this Consent Order

shall constitute full satisfaction of all civil liability by Defendants for the claims as

alleged by the Plaintiff in the Complaint against Defendants and for any civil claims for

2 violations of Defendants' General NPDES Storm Water Permit #OHR 109514 for the ('' ~ Site through the entry date of this Consent Order.

4. By the filing of this Consent Order, the Complaint of the Plaintiff and the

counterclaims of the Defendants are hereby considered to be finally determined. Defendants'

counterclaims are voluntarily dismissed with prejudice pursuant to Ohio Civil Rule 41 and

this Consent Order shall be a Final Order. Upon the filing of this Consent Order, it shall act

as a mutual release, releasing all claims, counterclaims, causes of action, losses and liabilities

arising from or relating to this case, including any claims arising under R.C. Chapter 6111

and the rules promulgated thereunder, the Defendants' administrative appeal (Case No. 01-

CT-011) and the Defendants' federal suit (Case No. l:OlCV 1901 in the U.S. District Court

for the Northern District of Ohio, Eastern Division).

5. Nothing in this Consent Order shall be construed so as to limit the

authority of the State of Ohio to take any action authorized by law against Defendants

and/or any person to enforce the Consent Order through a contempt action for violations of

this Consent Order. Nothing in this Consent Order shall be construed so as to limit the

authority of the State of Ohio to seek relief against the Defendants or other appropriate

persons for claims or conditions not alleged in the Complaint, including violations which

occur after the filing of the Complaint. Similarly, nothing in this Consent Order shall be

construed so as to limit the authority of the State of Ohio to undertake any action against

any person, including the Defendants, to eliminate or mitigate conditions that may present a

threat to the public health, welfare or the environment. Finally, nothing in this Consent

3 Order shall limit the right of the Defendants to any defenses it may have for any such

(_) claims above.

IV. PERMANENT INJUNCTION

6. Defendants are hereby permanently enjoined and immediately ordered

to comply with the requirements of R.C. Chapter 6111 and the rules adopted

thereunder, and Defendants' General NPDES Storm Water Permit for the site, except

as otherwise provided in this Consent Order.

V. INJUNCTIVE RELIEF

A. On-Site Mitigation and Work

7. Defendants are enjoined and ordered to maintain and protect the current

) and natural conditions of the remaining wetlands, streams, and upland buffer areas

within the conservation easement area on the Site, as depicted in Exhibit B. Exhibit

B is hereby made a part of this permanent injunction and fully incorporated herein.

No further tree cutting or work shall occur in the unimpacted 0.42 acre portion of

wetland "L", which is also depicted on Exhibit B.

8. Defendants are enjoined and ordered to preserve Wetland "A", as

depicted in Exhibit B, on the site in accordance with Ohio Administrative Code

("O.A.C.") Rule 3745-1-54(E)(5).

4 9. Defendants are enjoined and ordered to place all areas within the

('.___ _,, conservation easement area, as depicted in Exhibit B, in a conservation easement with

deed restrictions. Defendants shall convey to Tinkers Creek Land Conservancy the

conservation easement parcel depicted and described in Exhibit B. Defendants shall

place deed restrictions on the portion of the wetlands site described in Exhibit B that

limit the use of the property in perpetuity to conservation uses only. Additionally, the

deed restrictions shall require that the property be maintained in a natural state,

specify that trees shall not be removed, herbicide application and vegetation removal

can only be conducted upon prior approval of Ohio EPA, and that the wetlands

mitigation area and other wetlands on the site not be filled, drained or otherwise

converted. The conveyance of the conservation easement for the wetlands to be

preserved for mitigation purposes shall occur prior to any filling of wetland in the

'· ) development area of the Site.

10. Defendants shall undertake measures for invasive species control in

Wetland "G" as specified in Defendants' "Enhancement and Invasive Species Control

Plan in Wetland G"; however, in no event shall Defendants use berming or water

level manipulation as a means for such control.

B. Off-Site Mitigation

11. In accordance with O.A.C. Rule 3745-1-54(E)(5), Defendants are

enjoined and ordered to obtain 27.46 acres of preserved wetlands in the Cuyahoga

River watershed, known as the Twinsburg Bog Property and the 2.25 acre upland

5 buffer, as depicted and described in Exhibit C. Exhibit C is hereby made part of this (\, '---/ permanent injunction and fully incorporated herein. Defendants shall convey to

Tinkers Creek Land Conservancy the parcel depicted and described in Exhibit C.

Defendants shall place deed restrictions on the portion of the wetlands site described

in Exhibit C that limit the use of the property in perpetuity to conservation uses only.

Additionally, the deed restrictions shall require that the property be maintained in a

natural state, specify that trees shall not be removed, herbicides application and

vegetation removal can only be conducted upon prior approval of Ohio EPA, and that

the wetlands mitigation area and other wetlands on the site not be filled, drained or

otherwise converted. Within ninety (90) days of the entry of this Consent Order, the

purchase and transfer of the deed for the wetlands to be preserved for mitigation

purposes shall occur. Defendants shall establish a I 00 foot wide buffer on the west

side of Tinker's Creek to the south of the vernal pool area as depicted on Exhibit "C".

12. Defendants are enjoined and ordered to obtain and/or purchase

mitigation credit at the Grand River Lowland Mitigation Bank for 2.59 acres of

constructed wetlands to mitigate for 2.59 acres of Category I and 2 wetland impacts

at the Site.

13. Defendants are enjoined and ordered to mitigate for 5.22 acres of

jurisdictional Category 3 wetlands impacts at the Site in accordance with the terms

and conditions of this Consent Order and Ohio law. The 5.22 acres of restoration

shall be constructed to ensure that they develop into a mature forested Category 3

6 wetland with areas of vernal pools. The construction, maintenance and monitoring (~' '----/ requirements of the 5.22 acre wetland shall be conducted as follows:

(A) The site of restoration shall be within the Cuyahoga River watershed;

(B) Defendants shall submit for Ohio EPA approval a mitigation work

plan within 1 year of the effective date of this Consent Order that describes the proposed

mitigation site, work to be performed to construct and develop the mature forested

Category 3 wetlands and includes a 12 year monitoring plan that shall include measures

of biological integrity as indicators for determining final attainment to develop the mature

forested Category 3 wetlands;

mitigation work plan within 16 months of the effective date of the Consent Order;

(D) Defendants shall complete construction of the wetlands within 40

~ _ j months of the effective date of this Consent Order pursuant to the approved mitigation

work plan;

(E) Defendants shall meet all U.S. Army Corps of Engineers jurisdictional

requirements as stated in the 1987 Corps of Engineers Wetland Delineation Manual for a

forested wetland within 5 years of the completion of construction;

(F) Defendants shall submit to Ohio EPA a monitoring report by

December 31 for every other year for the 12 year monitoring period. The first report

shall be due the second year after construction of the site is completed. The monitoring

report shall include documentation of the then current status of the forested wetland

7 development and status. Additionally, the monitoring report shall include an evaluation (~ '--.. / of the wetland vegetation and amphibian communities and each shall be assessed and

monitored with the protocols developed for indices of biological integrity utilized by

Ohio EPA. (See Exhibit D, Mack 2001; and Micacchion, Gray and Mack 2000). At the

end of the 12 year monitoring period, the forested Category 3 wetlands shall have

achieved the minimum vegetation and amphibian indices scores required for forested

Category 3 wetlands .

(G) During the third, sixth and twelfth years following completion of

construction of the above-described work in the mitigation work plan, Defendants shall

jointly conduct a field site-view of the wetlands with the Ohio EPA. Ohio EPA may

waive the requirement of the site-view. During the site-view, Ohio EPA may make

recommendations on measures to be performed to increase the likelihood to achieve

\ ) successful restoration. Any and all reasonable measures recommended by the Ohio EPA

shall be undertaken within six (6) months of the site-visit.

(H) If the results of the final monitoring report conducted in sub-paragraph

(F) above demonstrate indices scores of biological integrity below those required for a

forested Category 3 wetland, then Defendants shall be required to perform additional

restoration work within or adjacent to the 5.22 acre site to further mitigate the work

previously performed by Defendants.

(I) Within 30 days of the determination that the final monitoring report

scores for the minimum indices of biological integrity were not achieved, or other

8 mutually agreed to period of time by the parties, Defendants shall submit to Ohio EPA r~ '---/ for approval a plan(s) for additional work, new monitoring period requirements and

schedule to perform such additional work. Sixty days after Defendants receive approval

from Ohio EPA, Defendants shall commence the additional work pursuant to Ohio EPA's

approval of Defendants additional work plan.

(J) If the 5.22 acre site does not achieve the minimum indices scores of

biological integrity of a forested Category 3 wetland, the Defendants shall repeat

subparagraph (I) above until the indices scores of biological integrity are achieved.

VI. CIVIL PENALTY

14. Defendants are hereby ordered and enjoined to pay One Million

Dollars ($1,000,000.00) to the State of Ohio pursuant to the following:

(A) Pursuant to R.C. Section 6111.09, Defendant shall pay a cash civil penalty

~ \. J of Two Hundred and Fifty Thousand dollars ($250,000.00). The penalty shall be paid by

delivering to the Attorney General's Office, c/o Jena Suhadolnik, Administrative

Assistant, Environmental Enforcement Section, 30 East Broad Street, 25th Floor,

Columbus, Ohio 43215-3428, a certified or cashier's check payable to the order of

"Treasurer State of Ohio", within ninety (90) days of entry of this Consent Order;

(B) Within the same ninety (90) day period, Defendants shall make a payment

to the OEPA, in the amount of Two Hundred and Fifty Thousand Dollars ($250,000.00)

for reimbursement of its costs in this case, the Defendants' administrative appeal (Case

No. Ol-CT-011) and the Defendants' federal suit (Case No. l:OlCV 1901 in the U.S.

9 District Court for the Northern District of Ohio, Eastern Division). The reimbursement

0 of costs shall be paid by delivering to the Attorney General's Office, c/o Jena Suhadolik,

Administrative Assistant, Environmental Enforcement Section, 30 East Broad Street,

25th Floor, Columbus, Ohio 43215-3428, a certified or cashier's check payable to the

order of "Treasurer State of Ohio"; and,

of Ohio as set forth above and in furtherance of the mutual objectives of the State of Ohio

and Defendants in improving the environment of ·.the Chagrin River Watershed,

Defendants agree to and are hereby ordered to expend funds in the amount of Five

Hundred Thousand Dollars ($500,000.00) no later than one hundred eighty (180) days

after the filing of this Consent Order on a Supplemental Environmental Project ("SEP")

to be paid into an escrow account, as designated by the Ohio EPA, for the benefit of the

) Geauga County Park District, for acquisition, leasing or financing of Bass Lake in

Munson Township, Geauga County, Ohio, for preservation by the Geauga County Park

District, in order to acquire Bass Lake from its current owners. The escrowed funds shall

be released to the Geauga County Park District upon the approval of the Director of the

Ohio Environmental Protection Agency. The property associated with the SEP shall be

administered by the Geauga County Park District for the benefit of the Geauga County

area to provide funding for the acquisition, leasing and preservation of Bass Lake and the

surrounding wetlands. In the event that the escrowed funds are not used for the Bass

10 Lake project within seven (7) years of the date of the entry of this Consent Order the

() escrowed funds shall be released to the Ohio EPA.

VII. RIGHT OF ENTRY

15. The State of Ohio, its agents and employees, shall have full access to

the locations and sites, as described in this Consent Order, at any and all reasonable times

to observe the work required by this Consent Order and as may be necessary for the

implementation of this Consent Order.

16. Nothing in this Consent Order shall be construed to limit the State's

statutory authority under R.C. Chapter 6111, the rules adopted thereunder, or any other

provision of the Revised Code, to obtain or seek access, conduct inspections or surveys,

take samples, field evaluations, and/or assessments to determine the stage of development

of the constructed/created/preserved wetlands and/or stream work at, on or near the sites

\ , ) described in this Consent Order.

VIII. MISCELLANEOUS PROVISIONS

17. Immediately upon the filing of this Consent Order, and no more than

five (5) business days after the filing thereof, Defendants shall file a notice of withdrawal

of the adjudication action in the Defendants' administrative appeal (Case No. 01-CT-011)

and the Director of the OEPA shall withdraw the proposed denial of the Section 401

Water Quality Certification and/or Isolated Wetlands Permit for the Site. Upon

withdrawal of such proposed denial, the Defendants shall within twenty (20) business

days after receiving notice of the Director's withdrawal action, voluntarily dismiss with

11 prejudice the pending suit in the United States District Court for the Northern District of

Ohio, Eastern Division, Case No. 1:01CVl901. The Defendants shall pay all Court costs

in connection with this case and the Federal Suit. Each party shall be responsible for its

respective attorneys' fees and experts' costs, except as provided by this Order.

18. All parties in this case shall cooperate fully, to execute all

complementary or supplementary documents that may be necessary and to take all

additional actions that may be deemed necessary or appropriate to give full force and

effect to the terms and conditions of this Consent Order.

19. Nothing in this Consent Order shall affect Defendants' obligation to

comply with all applicable federal, state or local laws, regulations, rules or ordinances,

including the obtaining ofany permits required by applicable federal, state or local law,

regulation, or ordinances. Specifically, Defendants shall receive all Ohio Department of \ . ) Transportation approval and/or permits before any impacts may occur to wetlands "A"

and "H" at the site for road activity along State Route 43 adjacent to Defendants'

property at issue in this case. A permit from Ohio EPA is not necessary to conduct the

work required by paragraphs seven (7) through thirteen (13) of the Consent Order.

Furthermore, Defendants are allowed to complete the development project as depicted in

Exhibit A and the 404 permit of the U. S. Army Corps of Engineers, and the State of

Ohio waives the requirements for a 40 I certification and isolated wetland permit for such

work.

12 20. Defendants shall inform the Ohio EPA of any change of its contact (\1 '---/ person, business addresses and/or telephone numbers. Unless otherwise provided in this

Consent Order, the contact persons for the Defendants and all documents required to be

submitted pursuant to this Consent Order shall be sent by certified mail to:-

John McGill 34555 Chagrin Blvd. Moreland Hills, Ohio 44022 (440) 914-4300

The contact person for the Ohio EPA for submission and approval pursuant to this

Consent Order is, and all such documents required to be submitted pursuant to this

Consent Order shall be sent by certified mail to:

Randy Bournique (or his successor) Ohio Environmental Protection Agency Central Office/Division of Surface Water 122 South Front Street Columbus, Ohio 43215 (614) 644-2001

IX. RETENTION OF JURISDICTION

21. The Court will retain jurisdiction of this action for the purpose of

enforcing and administering the parties' compliance with the terms and provisions of this

Consent Order, and to resolve disputes arising under this Consent Order. Nothing herein

alters the jurisdiction of the Environmental Review Appeals Commission under R.C. Chapter

3745.

13 X. COSTS

22. Defendants are hereby ordered to pay the court costs of this action.

23. Defendants are hereby ordered to pay the costs incurred by the OEPA

for the publication of public notice of this Consent Order in a newspaper of general

circulation. Defendants shall pay the costs associated with publication by delivering a

certified check in the amount payable to the order of"Treasurer, State of Ohio", with a

notation on the check that the check is to be deposited into the "Fund 699", to the

following address:

- .. _-· Ohio EPA, Central Office Lazarus Government Center Attn: Fiscal Officer 122 South Front Street Columbus, Ohio 43215

within thirty (30) days from the date Defendants receive notice of the costs from OEPA. )

XI. CLERK'S ENTRY OF CONSENT ORDER AND FINAL JUDGMENT

24. The parties agree and acknowledge that final approval by the Plaintiff

and Defendants and entry ohhis Consent Order is subject to the requirements of 40

C.F.R. 123.27(d)(l)(iii), which provides for public notice of the lodging of this Consent

Order, opportunity for public comment, and the consideration of any public comments by

Plaintiff. Both Plaintiff and the Defendants reserve the right to withdraw this Consent

Order based upon comments received which disclose a legal defect or disclose facts that

lead to a legal defect in this Consent Order. In the event there is no legal defect, the

14 parties shall notify the Court, within five (5) business days after the close of the thirty c (30) day comment period, and shall request that the Court issue this Consent Order as a final order. If such defect does exist, the State shall, within five (5) business days after

the close of the thirty (30) day comment period, identify the changes to the Consent

Order that are necessary to cure the defect. The State shall provide the Defendants with

the opportunity to negotiate an amendment to the Consent Order to cure any defect that

precludes the State from executing the Consent Order. Within three (3) business days

after negotiating such an amendment, the State shall execute and forward the Consent

Order as amended to counsel for the Defendants, who shall submit the Amended Consent

Order to the Court for entry as a final order. In the event that the parties cannot cure the

legal defect, the Court shall not enter this Consent Order and the parties shall jointly

move the Court to vacate the Preliminary Injunction Judgment Entry.

\. \ J 25. Pursuant to Rule 58 of the Ohio Rules of Civil Procedure, upon the

signing of this Consent Order by the Court, the Clerk is hereby directed to enter it upon

the journal. Within three (3) days of entering the judgment upon the journal, the Clerk is

hereby directed to serve upon all parties notice of the judgment and its date of entry upon

the journal in the manner prescribed by Rule 5(B) of the Ohio Rules of Civil Procedure

and note the service in the appearance docket.

15 IT IS SO ORDERED:

JUDGE~1Jfk FORREST W. BURT -... · !

APPROVED:

~---v-~ Ja~y, Esq. (0016961) Attorney for Defendants

orized Representative of Defendants

16 (,;

ATTACHMENT A 1 lXIJJtfT '!/ If BAINBRIDGE COMMERCIAL SITE BAINBRIDGE TOWNSHIP, OHIO FOR: BAINBRIDGE LAND DEVELOPMENT LLC c Situated in Bainbridge Township, County of Geauga and the State of Ohio, and known as being part of Original Bainbridge Township Lots 29 & 30, Tract 3 and being further bounded and described as follows:

Beginning for reference at an iron pin in a monument box on the centerline of Kent Road (A.K.A. Aurora Road) (S.R. 43, width varies) at the Cuyahoga County and Geauga County line, thence S 50° 21' 00" E, along the centerline of said Kent Road, a distance of 644.16 feet to a point, said point being the Principal Place of Beginning of the parcel herein described;

Thence continuing S 50° 21' 00" E, along the centerline of said Kent Road, a distance of 2567. 96 feet to a point at the northwest corner of land now or formerly owned by Mark A. and Laura . Hoehn as recorded in Vol. 806, Pg. 1199 of Geauga County Record of Deeds; ··--->..:- Thence S 39° 39' 00" W, along the northwesterly property line of said Hoehn property, and passing over an iron pin set 30.00 feet therefrom, a total distance of 330.00 feet to an iron pin set, at the southwest corner of said Hoehn property;

Thence S 50° 21' 00" E, along the southwesterly property lines of said Hoehn property, Joseph P. and Christine Sirna as recorded in Vol. 732, Pg. 192 and M. & F. Management as recorded in Vol. 964, Pg. 342 of Geauga County Record of Deeds, a distance of 820.4 7 feet to an iron pin set \ on the northwesterly line of lands now or formerly owned by Earl Mosher as recorded in Vol. ) 453, Pg. 682 of Geauga County Record of Deeds;

Thence S 57° 58' 08" W, along the northwesterly property line of said Earl Mosher, a distance of 7 .27 feet, to an iron pin set at the southwesterly corner of said Earl Mosher property;

Thence S 50° 21' 00" E, along the southwesterly property line of said Earl Mosher, a distance of 6. 7 6 feet to a 1" iron pipe found;

Thence S 32° 11' 18" E, along the southwesterly property line of said Earl Mosher, a distance of 335.95 feet to a l" iron pipe found at the southeasterly property comer of said Earl Mosher and the northwesterly corner of lands now or formerly owned by E.J. and Jane E. Burns as recorded in Vol. 710, Pg.1115 of Geauga County Record of Deeds;

Thence S 00° 47' 42" W, along the westerly line of said E.J. Burns property, a distance of207.99 feet to a point on the centerline of Lake Street (A.K.A. Lake Avenue) said point also being on the southerly line of Geauga County and the northerly line of Portage County, from said point an iron pipe was found S 0° 4 7' 42" W, 0.36 feet;

Thence N 89° 12' 06" W, along said Lake Street centerline and said County lines, a distance of 3057.13 feet to a stone in a monument box found at the Geauga County-Cuyahoga County Summit County-Portage County Corner;

1/21/02 Legal Description Bainbridge Investment-Combined Thence N 0° 13' 13" W, along the said Geauga County- Cuyahoga County Line, a distance of 2565.67 feet to a point at the southwest comer of lands now or formerly owned by Ralph Hedges as recorded in Vol. 901, Pg. 49 of Geauga County Record of Deeds, from said point an iron pin c with cap marked GWS was found N 12° 53' 22" W, 0.17 feet; Thence S 50° 49' 19" E, along the southwesterly property line of said Ralph Hedges and land now or formerly owned by New Par as recorded in Vol. 1089, Pg. 536 of Geauga County Record of Deeds, a distance of 594.89 feet to a point at said New Par southeasterly property comer, from. said point al" iron pipe was found S 83° 54'48" W, 0.22 feet;

Thence N 02° 40' 56" E, along the easterly property line of said New Par Lands and lands now or formerly owned by Joseph R. Paradise and Lisa Louise Harry as recorded in Vol. 1257, Pg. 379 of Geauga County Record of Deeds, and passing over an iron pin set 647.45 feet therefrom, a total distance of 685.00 feet to a point on the centerline of said Kent Road (S.R. 43) at the Principal Place of Beginning and containing 127.8345 acres ofland, be the same more or less but subject to all legal highways and easements of record according to a survey prepared by Hejduk Cox and Associates, Inc., June, 2000. Bearings are shown to indicate angles only and are based on the centerline of Kent Road (S.R. 43) (S 50° 21' 00" E).

Legal Description prepared by John A. Hejduk, Registered Surveyor No. 6458, of Hejduk-Cox and Associates, Inc., June 23, 2000.

··~ I

1/21/02 Legal Description Bainbridge Investment-Combined SEE SHEET C200a SEE

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·------"'BUFFER - · - · -- · - · - · - INTERMITTENT STREAM SCRUB/SHRUB - · - · - · - · - · - · - EPHEMERAL STREAM *'X-X4< "4 IMPACTED STREAM FORESTED "" *' *' - · - · - · - · - · - · - RELOCATED/CREATED SIB~ EMERGENT PLEAsE NOTE iHAT lHESE WmAND IMPAC'fS ARE ANTICIPATED WITH THE WIDENING OF SfATE ROlffE 43 WHlcti WILL BE CONDUCTED , ,, .1 , , , 1.J" , ,, ,, "" '" PRESERVED \llEJU\ND * ;.s PART OF THIS P~OJECT OR I~ lHE FUTURE ... 111111...... EN~CED WrnAND @-© STREAM DESIGN.AWN '""'"" ""'"" .., .. ,,. GREENSPACE (OUTSIDE DE.V. AREA) A-U WETLAND DESIGNATION

TOTAL ACREAGE (Ac.) SITE 127.835 CONSERVATION EASEMENT 43.05 GREENSPACE (OUTSIDE DEV. AREA) 15.00 FRONT LAWNS/LANDSCAPE 3.08 LANDSCAPE AREA (ISL.ANDS) 2.57 IMPERIOUS SURFACE 54.29 CENTER GREENSPACE 3.74 PROPOSED R/W 6.10 TOTAL AREA 127.83

TOTAL WETLANDS ON SllE 31,26 (1,g,) IMP ACTED WETLANDS 7.49 ~ AVOIDED WETLANDS 23.77 (23.35) STORMWA TER POND PLANTING 4.66

.... DENOTES WETLAND IMPACT AS CALCULATED BY USACE AND DEPA RESPECTIVELY

LAKE AVE'. 60'

PROPoaEn ON-SITE MITIGATION FOR OEPA MARKETPLACE AT FOUR CORNERS BAINBRIDdE LAND DEVELOPMENT, LLO BAINBRIDdE TOWN~H;IP_ GlEAUGA COUNTY, OHIO ATTACHMENT C

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I-Ki5N ENVIRONMENTAL ~ PROP013ED MITIGATION ~ FOR T$E ~ {bN\<"-"'°'· ITTC PARMA AND BAINBRIDGE PROJ):dT~ FOR O:E)>A 6105 Helsley Rd.• Mentor. OH 44060 AT 'tBE "TWINSBURG BOG'", 440-357-1260 • Fax 440-357-1510 dITY 0'1 TWINSBURG, ~ doum"Y. omo , ~r·, '-(~/./

ATTACHMENTD

j _..- .

I State of Ohio Wetland Ecology Group Environmental Protection Agency Division of Surface Water

Vegetation Index of Biotic Integrity (VIBI) for Wetlands:

ecoregional, hydrogeomorphic, and plant community comparisons with preliminary wetland aquatic life use designations

Final Report to U.S. EPA Grant No. CD985875-01 Testing Biological Metrics and Development of Wetland Assessment Techniques using Reference Sites Volume l

JohnJ. Mack

November 9, 2001

Governor Robert Taft 122 South Front Street, P.O. Box I049 Director Christopher Jones Columbus, Ohio 43216-1049 Acknowledgments

This work would not have been possible without the generous and continued support of the Region 5, U.S. Environmental Protection Agency Wetland Program Development Grants (Sue Elston, Catherine Garra, Lula Spruill) and the intellectual support provided by the Biological Assessment for Wetlands Workgroup (BAWWG) sponsored by U.S. EPA Headquarters. Thanks also to the many wetland interns who have provided inestimable help in the often thankless tasks of field work, data entry, and voucher management including Rick Lopez, Rob Geho, Bonnie Elfntz, Lauren Augusta, Gregg Sablak, Michael Brady, Kelly Maynard, Deni Porej, and Cynthia Caldwell. Special thanks to Michael Brady for working with me through two tremendous field seasons and learning database development on the job. Thanks to Holly Tucker, Ohio EPA, for a very thorough and critical read through of this report. and to Siobhan Fennessy, Mark Gemes, Denise Heller-Wardrop, Chris Yoder, and Jim Karr for many fruitful conversations and ideas.

11 Table of Contents

Acknowledgments ...... ii

Table of Contents ...... iii

List of Tables ...... v

List of Figures ...... vii

1.0 Introduction ......

2.0 Vegetation Sampling Methodology ...... 5

2.1 Overview ...... 5 2.2 Definitions ...... 6 2.3 Procedures ...... 6 2.3.1 Step 1. Selecting the Plot location(s) and Configuration(s) ...... 6 2.3.2 Step 2. Laying out the Plot(s ...... 7 2.3.3 Step 3. Selecting the Intensive Modules and Locating the Nested Quadrats . . . 8 2.3.4 Step 4. Measuring Vegetation ...... 8 2.3.5 Step 5. Measuring Woody Vegetation ...... 8

~ ·.;. . ·.· 2.3.6 Step 6. Measuring standing biomass ...... 9 2.3.7 Step 7. Measuring physical attributes of the site ...... 9 2.3.8 Step 8. Preserving Voucher Specimens and Assigning Voucher Numbers .... 9 2.3.9 Step 9. Assigning plants to categories ...... 10 2.3.10 Step 10. Calculating vegetation community attributes ...... 11 2.4 IBI Development Methods ...... 11 2.4.1 Site selection and classification ...... 12 2.4.2 Attribute evaluation and Metric selection ...... 16 2.4.3 Metric score and calibration ...... 18 2.5 Floristic Quality Assessment Index ...... 20 2.6 Disturbance scale ...... 21 2.7 Statistical analyses ...... 21

3.0 Vegetation IBis for Wetlands ...... 22

3.1 VIBI-EMERGENT ...... 26 3.2 VIBI-FORESTED ...... 37 3.3 VIBI-SHRUB ...... 48

...... 53

4.0 Analysis of the Vegetation IBI and ORAM scores: ecoregional, hydrogeomorphic, and plant community comparisons ...... 53

4. l. Vegetation IBI ...... 53

lll 4.2 ORAM score comparison ...... 61

5.0 Preliminary Wetland Aquatic Life Uses ...... 65

6.0 Quantitative vegetation characteristics of wetland types ...... 69

7.0 References ...... 87

IV List of Tables

Table l. Components of ecological (biological) integrity for wetlands ...... Table 2. Factors associated with wetlands that can be negatively impacted by human activities and which can cause wetlands to become degraded...... 2 Table 3. Advantages of ambient biological monitoring...... 2 Table 4. Summary of sites sampled by year to develop Wetland IBis based on vascular plants...... 4 Table 5. Cover and dbh classes ...... 9 Table 6. Interim Ohio Vegetation Community Classification ...... 13 Table 7. Interim Hydrogeomorphic (HGM) classification system for Ohio wetlands ...... 15 Table 8. Types and characteristics of attributes which can be included in biological assessments using vascular plants as a taxa group...... 16 Table 9. Categories used to classify vascular plants...... 17 Table 10. Hypotheses and assumptions about changes in vascular plant community in wetlands from human disturbance...... 17 Table 11.· Mean, standard deviation (parenthesis) and ANOV A results for 3 primary vegetation community classes for metrics used in the vegetation IBI for "reference" condition wetlands. 23 Table 12. Metrics modified or replaced in Vegetation IBI for emergent wetlands from those initially proposed in Mack et al. 2000 ...... 26 Table 13. Summary table ofregression analysis of metrics used to derive VIBI-E ...... 27 Table 14. Comparison of means (standard deviation in parenthesis) ofVIBI-E metrics by ecoregion and reference condition using analysis of variance ...... 28 Table 15. Description of metrics for VIBI-EMERGENT in the State of Ohio...... 30 Table 16. Scoring breakpoints for assigning metric scores to an emergent wetland. See Table 15 for descriptions of metric codes ...... 31 Table 17. Distribution of metric scores by site for VIBl-E ...... 32 Table 18. Percentage of metric scores by emergent wetland condition, reference versus nonreference, and ORAM v. 5.0 score...... 33 Table 19. Metrics modified or replaced in Vegetation IBI for forested wetlands from those initially proposed in Mack et al. 2000 ...... · ...... 37 Table 20. Summary table of regression analysis of metrics used to derive VIBI-F ...... 38 .Table 21. Comparison of means ofVIBI-F metrics by ecoregion and reference condition using analysis of variance ...... 39 Table 22. Description of metrics for VIBI for forested wetlands ...... 41 Table 23. Scoring breakpoints for assigning metric scores to a forested wetland. See Table 22 for descriptions of metric codes ...... 42 Table 24. Distribution of metric scores by site for VIBI-F calculated...... 43 Table 25. Percentage of metric scores by forested wetland condition, reference versus nonreference, and ORAM v. 5.0 score...... 44 Table 26. Description of metrics for VIBI -shrub...... 49 Table 27. Scoring breakpoints for assigning metric scores to a shrub wetland. See Table for descriptions of metric codes...... 50 Table 28. Distribution of metric scores by site for VIBI-SH calculated. Sites in italics are bogs and fens ...... 51 Table 29. Summary of numbers of sites by major hydrogeomorphic and plant community classes. . . . 53 Table 30. Mean and standard deviation of Vegetation IBI scores for 2 ecoregions and 2 wetland classes (reference and nonreference sites)...... 55 Table 31. Comparison of 95th percentile and quadrisection of 95'h percentile of Vegetation IBI scores by

v ecoregion for reference sites by ecoregion and all sites by ecoregion ...... 56 1 111 Table 32. Comparison of 95 h percentile and mathematical quadrisection of 95 percentile for Vegetation IBI scores for emergent, forested, and shrub wetland IBis...... 56 Table 33. Mean and standard deviation (parenthesis) and ANOV A results for metrics used in VIBI-E, VIBI-F, and VIBI-SH by reference (ref) and nonreference(non) and ecoregion categories .... 58 Table 34. Mean and standard deviation of Vegetation IBI scores ofreference wetlands for 5 dominant plant community classes in the Eastern Com Belt Plains and Erie Ontario Drift and Lake Plains ecoregions...... 58 Table 35. Comparison of95lll percentile for Vegetation IBI scores for EMERGENT wetlands ...... 59 Table 36. Mean and standard deviation of Vegetation IBI scores of all wetlands for 4 dominant hydrogeomorphic classes including fen and bog sites...... 60 Table 37. Mean and standard deviation of ORAM v. 5.0 scores for 2 ecoregions and 2 wetland classes (reference and nonreference sites) ...... 61 Table 38. Comparison of95lll percentile and quadrisection of95lll percentile of ORAM v. 5.0 scores by ecoregion for reference sites by ecoregion and all sites by ecoregion ...... 62 Table 39. Mean and standard deviation of ORAM v. 5.0 scores ofall wetlands for 4 dominant hydrogeomorphic classes...... 62 Table 40. Mean and standard deviation of ORAM v. 5.0 scores ofreference wetlands for 4 dominant plant community classes...... ~ ...... 63 Table 41. General Wetland Aquatic Life Use Designations using Vegetation !Bis...... 65 Table 42. Specific wetland use designations...... ; ...... 66 Table 43. Special wetland use designations ...... 67 Table 44. Pilot numeric biological criteria for wetlands based on Vegetation IBI breakpoints for specific plant communities and landscape positions...... 68 Table 45. Mean values (standard deviation in parenthesis) of vegetation and physical characteristics of EMERGENT MARSH communities by wetland category...... 72 Table 46. Mean values (standard deviation in parenthesis) of vegetation and physical characteristics of SWAMP FOREST communities by wetland category ...... 73 Table 47. Mean values of vegetation and physical characteristics of SHRUB SWAMP communities by wetland category...... 74 Table 48. Mean values of vegetation and physical characteristics of CALCAREOUS FEN communities by wetland category...... 75 Table 49. Mean values of vegetation and physical characteristics of SEDGE-GRASS communities (includes sedge-grass meadows and seep fens) by wetland category...... 76 Table 50. Mean values of vegetation and physical characteristics of BOG communities...... 77 Table 51. Mean importance value for selected frequently observed tree and shrub species by category of forested wetland where species observed...... 78 Table 52. Density and dominance of trees >45cm in existing forested wetland data set. All wetlands with at least one tree >45cm included in this table...... 79 Table 53. Stand characteristics of mature forested wetlands ...... ~ ...... 80 Table 54. Representative plant species of good quality emergent ...... 81 Table 55. Representative plant species of good quality forested wetlands...... 83 Table 56. Representative plant species of good quality shrub swamps...... 84 Table 57. Representative plant species of good quality sedge-grass communities...... 85

VI List of Figures

Figure 1. Ecoregions of Ohio, Indiana, and neighboring states. From Woods et al. 1998...... 3 Figure 2. Standard 20x50m (2x5) plot used in vegetation sampling as recommended by Peet et al. (l 998)5 Figure 3. Relative cover of intolerant plant species (%intolerant) versus ORAM v. 5.0 score from %intolerant metric ofVIBI-Emergent with metric score breakpoints established by graphical fitting technique...... 19 Figure 4. Relative cover of intolerant plant species versus ORAM v. 5.0 score from %intolerant metric ofVIBI-Emergent with metric score breakpoints established by mathematical quadrisection technique...... 19 Figure 5. Scatterplots of metrics used initially in the Vegetation IBI-E...... 29 Figure 6. Box and whisker plots of ORAM v. 5.0 scores by vegetation community based on wetland classes...... 34 Figure 7. Scatterplots of metrics of wetlands used to derive the Vegetation IBI for emergent wetlands ...... 35 Figure 8. Scatterplot of Vegetation IBI scores for emergent wetlands used to derive the VIBI-E ..... 36 Figure 9. Scatterplot of vegetation IBI scores for emergent wetlands...... 36 Figure 107 -Box and-whi~ker plets.of individual metric values for the VIBI-forested by reference/nonreference and ecoregion ... _...... __ ...... 45 Figure 1 L Scatterplots ofmetric values used in VIBl-Forested versus ORAM v. 5.0 score...... 46 Figure 12. Scatterplot of Vegetation IBI score versus disturbance scale for forested wetland ...... 47 Figure 13. Scatterplots of metrics used for VIBI for shrub wetlands...... 52 Figure 14. Vegetation IBI scores for wetlands in the Eastern Com Belt Plains and Erie Ontario Lake Plains plotted against ORAM v.5.0 score ...... _...... 54 Figure 15. Box and whisker plots of Vegetation IBI scores for reference (ref) and nonreference (non) wetlands by ecoregion...... _ ...... 54 ) Figure 16. Box and whisker plots of Vegetation IBI scores for hydrogeomorophic classes...... 60 Figure 17. Box and whisker plots of ORAM v. 5.0 scores for reference (ref) and nonreference (non) wetlands by ecoregion. _ ...... _ ...... _ ...... 63 Figure 18. Box and whisker plots of ORAM v. 5.0 scores by vegetation community based wetland classes...... _...... 64

Vll This page intentionally left blank.

Vlll 1.0 Introduction

A principal goal of the Clean Water Act is to maintain and restore the physical, chemical and biological integrity of the waters of the United States. 33 U.S.C. § l 25 l (a). Biological integrity has been defined as "... the capability of supporting and maintaining a balanced integrated, adaptive community of organisms G having a species composition, diversity, and functional organization comparable to that of natural habitat of the region (Karr and Dudley l 98 l ). "Integrity" or "Ecological integrity" has been defined as the sum of the earth's biological diversity and biological processes' (Table 1); the converse of ecological integrity is biotic impoverishment, which is defined as the systematic reduction in the capacity of the earth to support living systems (Karr 1993). Thus, "A biological system is healthy and has ecological integrity when its inherent potential is realized, its condition is "stable," its capacity for self-repair is maintained, and external support for maintenance is minimal. Integrity implies an unimpaired condition or quality or state of being complete and undivided (Karr, p. 1522, 1993)." The concept of integrity, and its measurement and description by biological surveys, underpins the development of biological criteria.

The factors in natural wetlands which can be degraded by human activity fall into several broad classes: biogeochemistry, habitat, hydrology, and biotic interactions (Table 2). The quantitative measurement (assessment) of the degree of integrity ofa particular natural system, and conversely the degree of impairment, degradation or impoverishment, can be attempted in many ways. The State of Ohio has successfully developed a sophisticated system using ambient biological monitoring of fish and macroinvertebrate assemblages to assess the quality of streams in Ohio: the Invertebrate Community Index (macroinvertebrates), the Index of Biological Integrity (fish), and the Modified Index of Well Being (fish) (Ohio EPA 1988a, 1988b, 1989a, 1989b; Yoder and Rankin 1995). This type of system has been used and adopted throughout North America and Europe (Karr 1993). See also Karr and Kerans (1992); Barbour et al. (1992); Bode and Novak (1995); Hornig et al. (1995); Simon and Emery (1995), Hughes et al. (1998). The statistical properties of Ohio's IBI was investigated and validated by Fore, Karr, and Loveday (1993). They concluded that the IBI could distinguish between five and six nonoverlapping categories of integrity and that the IBI is "... an effective monitoring tool that can be used to communicate qualitative assessments to the public and policy makers or to provide quantitative assessments for a legal or regulatory context based on confidence intervals or hypothesis testing procedures (Fore, Karr, and Loveday, p. 1077, 1993).

Table 1. Components of ecological (biological) integrity for wetlands. Adapted from Karr and Kerans (1992) and Karr (1993).

Biological diversity Biological Processes

Elements of biodiversity Nutrient cyclinglbiogeochemistry

Genes within populations Photosynthesis

Populations within species Water cycling/hydrological regime

Species within communities/ecosystems Evolution/speciation

Communities/ecosystems within landscapes Competition/Predation/Mutualisms

Landscapes within biosphere

Karr (1993) defines biological diversity as the variety of the earth's naturally occurring biological elements, which extend over a broad range of organization scales from genes to populations, species, assemblages, and landscapes; the complement of biological diversity (the elements) are the biological processes on which those elements depend. Table 2. Factors associated with wetlands that can be negatively impacted by human G activities and which can cause wetlands to become degraded. Adapted from lists for flowing waters from Karr and Kerans (1992), Karr et al. (1986), Ohio EPA (1988a).

factor description examples of disturbances

biogeochemistry natural patterns of that type of wetland for nutrient cycling, nutrient enrichment. sedimentation, decomposition, photosynthesis, nutrient sequestration and addition of organic or inorganic chemicals, release, aerobic/anaerobic regimes, etc. heavy metals. toxic substances, etc.

habitat natural patterns and structures of that type of wetland for mowing, grazing, farming, vehicle use, floral and fauna! communities. clearcutting, woody debris removal, shrub/sapling removal, herbaceous/aquatic bed removal, sedimentation, etc.

hydrology natural hydrologic regime of that type of wetland: frequency, ditching, tiling, dikes and weirs, additions duration, amount of inundation; sources of water, etc. of stormwater, point source discharges, filling and grading, construction of roads and railroad beds, dredging, etc.

biotic interactions natural patterns of competition, predation, disease, introduction of nuisance or nonnative parasitism, etc. species (carp, reed canary grass, purple loosestrife, European buckthom, etc.)

Table 3. Advantages of ambient biological monitoring. Adapted from Karr and Kerans (1992).

# description

Broad based ecologically

J 2 Provides biologically meaningful evaluation

3 Flexible for special needs

4 Sensitive to a broad range of degradation

5 Integrates cumulative impacts from point source, nonpoint source, hydrologic alteration, and other diverse impacts of human society

6 Integrates and evaluates the full range of classes of impacts (e.g. hydrologic modifications, habitat alterations, etc.) on biotic systems

7 Direct evaluation of resource condition

8 Easy to relate to general public

9 Overcomes many weaknesses of individual parameter by parameter approaches

10 Can assess incremental degrees and types of degradation, not just above or below some threshold

11 Can be used to assess resource trends in space or time

2 The State of Ohio's indices are codified in Ohio Administrative Code Chapter 3745-l and constitute G numeric "biological criteria" which are a part of the state's water quality standards required under the Clean Water Act. See 33 U.S.C. § 1313. Biological criteria are numerical values or narrative expressions that describe the reference biological integrity ofnatural communities (U.S. EPA 1990). It is important to stress that the overall index score resulting from an IBI, as well as each individual metric represen.t testable hypotheses as to how a natural system responds to human disturbance (Karr 1993). Attributes of natural communities are selected and predictions are made as to how the attribute will respond, e.g. increase or decrease; not change until a particular threshold is reached and then increase quickly; increase linearly, or curvilinearly, etc. Moreover, the existing biological condition ofa natural system is the integrated result of the chemical, physical, and biological processes that comprise and maintain the system, and the biological condition of the system can be conceived as the integration or result of these processes over time. The organisms, individually and as communities, are indicators of the actual conditions in that system since they inhabit the system and are subject to the variety of natural and human-caused variation (disturbance) to the system (Ohio EPA 1988a). In this regard, biological monitoring and biocriteria take advantage of this inherent integrative characteristic of the biota ofa system, whereas chemical and toxicity monitoring only represents a single point in time unless costly, continuous sampling over time is performed. Table 3 lists some of the advantages inherent in biological monitoring.

"Wetlands" are a type of water of the United States and a water of the State of Ohio. See e.g. Ohio Revised Code (ORC) §6111.0l(H), OAC Rule 3745-l-02(B)(90), 33 CFR 323.2(c). Until recently, wetlands in Ohio were only generically protected under the state's water quality standards. On May 1, 1998, the State of Ohio adopted wetland water quality standards and a wetland antidegradation rule. See OAC Rules 3745-1-50 through 3745-1-54. The water quality standards specify narrative criteria for wetlands and create the "wetland designated use." All wetlands are assigned to the "wetland designated use." However, numeric biological criteria were not proposed since they had not yet been developed.

An important feature of Ohio's current regulatory program for wetlands is found in the wetland antidegradation rule. See OAC Rule 3745-1-54. The wetland antidegradation rule categorizes wetlands based on their functions, sensitivity to disturbance, rarity and irreplaceability and scales the strictness of avoidance, minimization, and mitigation to a wetland's category. Three categories are established: Category 1 wetlands with minimal wetland function and/or integrity; Category 2 wetlands with moderate wetland function and/or integrity; and Category 3 wetlands with superior wetland function and/or integrity. The defining of these regulatory categories using actual measures of a wetland's biology and functions has been a continuing need since the adoption of the Wetland Water Quality Standards and wetland antidegradation rules. Figure 1. Ecoregions of Ohio, Indiana, and neighboring states. From Woods et al. 1998.

3 Ohio EPA began working on the development of biological criteria using vascular plants in 1996. To date, Ohio has sampled 121 different wetlands or separable plant communities within a wetland. These study sites have are located mostly in the Eastern Com Belt Plains and Erie-Ontario Lake Plains Eco regions (Figure 1) but with some sites in the Huron Erie Lake Plains, the Michigan Indiana Drift Plains, and the Western Allegheny Plateau (Table 4). These sites span the range of condition from highly degraded by human activity to relatively undisturbed, i.e. the best quality sites available or "reference conditions." This work has been funded since 1996 by several different U.S. EPA Region 5 Wetland Program Development Grants including CD995927, CD995761, CD985277, CD985276, and CD985875.

Table 4. Summary of sites sampled by year to develop Wetland IBls based on vascular plants.

total minus cumulative resampled resampled year total total sites sites

7 7 7 1997 17 24 14 3 1998 3 27 3 1999 31 58 28 3 2000 36 94 36 2001 36 130 33 3

totals 130 121 2 9

The objectives of the wetland biocriteria development project have been to develop Indices of Biotic Integrity (both interim and final) to evaluate ecological integrity of a wetland using vascular plants, macroinvertebrates and amphibians indicator taxa using an ecoregional approach and calibrate the ORAM using these IBis.

Based on preliminary results (Fennessy et al. l 998a, l 998b ), Ohio EPA concluded that vascular plants, macroinvertebrates, and amphibians could be used as indieator organisms for the development of wetland-specific IBis. Mack et al. (2000) proposed an initial vegetation index of biotic integrity (VIBI) based on data collected from 1996, 1997, 1998, and 1999 in the Eastern Com Belt Plains Ecoregion. This report reevaluates the VIBI based on an additional data set from sites in the Erie-Ontario Lake Plains ecoregion.

2 Ohio EPA has also sampled other natural and mitigation wetlands. Four mitigation wetlands were sampled in 1998 in addition to the 3 natural wetlands listed in this table. Ten mitigation wetlands were sampled in 200 I as part of a separate study of mitigation wetland performance (unpublished data). As part of an earlier study of mitigation wetlands, 17 other sites were sampled in 1995 (10 mitigation wetlands and 7 natural wetlands) (Fennessy and Roehrs 1997). Finally, as part ofa separate study of the Floristic Quality Assessment Index and riparian wetlands, IO other riparian forested wetlands were studied. Total wetlands sampled by Ohio EPA is 152 (128 natural and 24 mitigation (Fennessy et al.1998b).

4 2.0 Vegetation Sampling Methodology

2.1 Overview

Since the 1999 field season, Ohio EPA has used a plot-based vegetation sampling method3 described by Peet et al. (1998). This is a flexible, multipurpose sampling method which can be used to sample such diverse communities as grass and forb dominated savannahs, dense shrub thickets, forest, and sparsely vegetated rock outcrops. Their method has been used 50m 4 3 at thousands of sites for over ten years by the North 6 Carolina Vegetation Survey. It is appropriate for 3 most types of vegetation, flexible in intensity and 4 time commitment, and provides information on 7 species composition across spatial scales. It also addresses the problem that processes affecting vegetation composition differ as spatial scales increase or decrease and that vegetation typically exhibits strong autocorrelation.

4 In addition to the advantages already mentioned, the size and square shape of the modules provide 4 convenient building block for larger or smaller plots 10 and the square shape is efficient to lay out and ensures the observation is typical for species interactions at that scale of observation. While this method is compatible with data from other methods, it avoids _) biases built into methods with distributed quadrats or high perimeter-to-area ratios (Peet et al. 1998). Finally, there is an existing body ofliterature using plots of this type.

The most typical application of the method employs Figure 2. Standard 20x50m (2x5) plot used in vegetation a set of l O modules in a 20 x 50m layout (Figure 2). sampling as recommended by Peet et al. (1998). Modules are numbered counterclockwise moving from the "front• of the plot Once the plot is laid out, all species within the plot are to the "back; then from the back of the plot to the front. identified. For forest and shrub communities, an Module comers are number~d cloc~ise_in the_ direction of aggregate woody stem count is made. Four IO x IOm movement along the centerline. Typical intensive modules are ,,. . " . . shaded and standard intensive module nested quadrats are modules are mtens1vely sampled lil a senes of indicated by small squares. nested quadrats. Within these "intensive" modules, species cover class values (Table 5) are recorded for each module separately and for each nested quadrat separately. In effect then, this method incorporates the use ofreleves found in the Braun-Blanquet methodology in as much as the length, width, orientation, and location of the modules are qualitatively selected by the investigator based on site characteristics; however, within the modules, standard quantitative floristic and forestry information is recorded, e.g.

Refer to Mack et al. (2000) for a comparison of data from the plot-based method to data collected using transect-quadrat methods. Out of the current data set used to derive the interim VIBls, only 20 wetlands had data collected using only the earlier transect-belt method. For these 20 sites, species observed outside of the quadrats in the "belt" area of the transect were excluded from the subsequent data analysis. In in 1999 and 2001, 6 of these 20 sites were resampled using the current method.

5 l; frequency, density, basal area, cover, etc. ·.. _/ 2.2 Definitions

Module - A "module" is the basic unit of sampling under this method and consists of a l Om x l Om 2 2 2 2 quadrat. Nested quadrats of0.0lm , O.lm , lm , 10m are located in one or more comers ofa module. A sampling plot is made up of one or more modules. If the size ofa plot is lOm xlOm, then the module is also a "plot" or a "releve."

Plot - A "plot" is an area where vegetation is being sampled at a particular site. A plot is made up of one or more modules. Plots can also be called "releves."

Releve - A synonym for "plot."

2 2 2 2 Quadrat- Quadrat refers to one or more nested quadrats (O.Olm , O.lm , lm , 10m ) that are located in one or more corners of a module. Technically, the module itself is a 100m2 "quadrat" but here the term quadrat is used to describe the smaller nested quadrats (or subquadrats).

Presence - "Presence" is defined as the occurrence ofa species (based on the emergence of stem or stems) within a quadrat, module, or plot (Peet et al. 1998).

Cover - "Cover" is defined as the percentage of ground surface obscured by the vertical projection of all aboveground parts of a given species onto that surface. No single species may exceed 100% cover, though the sum of cover estimates across all species often exceeds l 00%.

2 \ Are -An "are" is one-hundredth ofa hectare (O.Olha) or 100m • A single module is 1 are. ) Hectare - A "hectare" is 10000m2 or 100 ares. A typical 2x5 plot made up of 10 modules is 0.1 hectares.

Depth (of occurrence) - "Depth" refers to the size of the subquadrat in which the presence of a species is first noted. For example, ifthe presence of species is first observed in the lm2 subquadrat, the depth of occurrence is 2.

Level (of occurrence) - A synonym for "depth."

2.3 Procedures

Sampling procedures are summarized in the following sections. Information discussed below is taken from Standardized Vegetation Sampling Procedures Field Manual VersiOn I.I, Ohio EPA Technical Report WET/200I-2 (Mack 200lb).

2.3.1 Step 1. Selecting the Plot location(s) and Configuration(s)

Plot size, shape, and location. At most sites, a "standard" plot was established consisting of a 2x5 array of lOm xlOm modules, i.e. 20m wide by 50m long (equals 1000m2 = l are= 0.1 ha), within the boundary

6 of the wetland and within each vegetation community of interest. 4 In some instances, heterogeneity of 0 vegetation or environment, researcher time, or significance of site made a standard 0.1 ha plot inappropriate or impractical. Where the standard plot would not fit or would have been inadequate or heterogeneous, the size or shape of the plot was modified to obtain a representative sample of the community of interest.

A determination was made as to the dominant, codominant and minor vegetation communities present in the wetland and what the community of interest being sampled was. In some instances, multiple plots were needed to sample wetlands with more than one dominant vegetation community. In other instances, where the wetland was dominated by a single type of community, but had a minor presence of another, the plot was located such that at least a portion of the minor community was located within the plot (but not within any of the intensive modules). This ensured that the species present in the subcommunity were identified and enumerated. This situation occurred most frequently in scrub-shrub wetlands (e.g. buttonbush swamps) located within a forest or woodlot, where a narrow forested margin was often present, or in emergent marshes where a narrow band of shrub vegetation was present along some or all of the wetland's perimeter

Subsamples and Supersamples. At a few wetlands, subsamples of especially dense shrub vegetation were used. According to Peet et al. (1998), the standard plot can be adapted for unusually high stem densities of woody vegetation, e.g. a Rosa palustris thicket, or unusually low stem densities, e.g. an oak savannah, by subsampling or supersampling the "problem" vegetation. This is accomplished by adjusting the width of the module, as measured from the centerline of the plot by the appropriate percentage. Thus, after laying out a plot in or through a Rosa palustris thicket, the shrub stratum is measured in a Sm x 1Om module by reducing the width of the module by Sm or SO% (a SO% subsample).

Plot orientation (minimizing heterogeneity). Plots were placed to minimize within-plot environmental ) heterogeneity, which implies that the long axis of the plot encountered the least possible variation in these characteristics, unless the heterogeneity in question would not affect the goal of characterizing the vegetation. In this situation, the particular heterogeneity was ignored and the long-axis of the plot was established without regard to that gradient. The most common instance of this occurrence was in zoned emergent marshes where water depth generally decreases towards the upland boundary and the vegetation is zoned in harrow bands.

2.3.2 Step 2. Laying out the Plot(s)

Once the general location, orientation, and size of the plot was determined, the plot was delineated on the ground. 1be SOm basel!ne of plot was established using a measuring tape and the compass direction noted. Marker flags were placed every !Om along this line. Next, the 20m sides of the intensive modules are located perpendicular to the centerline. At a minimum, marker flags were placed at the corners of intensive modules and frequently at the corners of every module depending on the site. The modules in the plot were numbered counterclockwise, starting with the first module on the baseline to the right of the centerline and proceeding down to the end of the centerline and then back to the baseline (Figure 2). Conversely, the corners of the modules were numbered clockwise, starting at the centerline and moving

4 Peet et al. ( 1998) recommend 1000m2 area for forest inventory of rich mesic forests and numerous North American forest studies have employed a 1000m2 plots. This size plot is similar to the area recommended by Mueller and 2 Dombois (1974), i.e. 200-500m • According to Peet et al. (1998), numerous plot configurations are possible. Where a standard 2x5 plot of I 000m2 will not fit, a 2x2 plot of 400m2 can be a good substitute. Strips of two, three, four, or five modules can also be used where homogeneity considerations limit the number of modules.

7 up or down the centerline, depending on which side of the centerline the module is located (Figure 2) to 0 avoid having nested quadrats being placed side by side.

2.3.3 Step 3. Selecting the Intensive Modules and Locating the Nested Quadrats

In a standard 2x5 plot, the intensive modules were generally located in the center of the plot, if possible, to ensure that the contents were as representative as possible and to reduce subjective bias associated with starting the tape in close proximity to these modules. Where a 2x2 array was used, every module was usually treated as an "intensive" module. Where narrower configurations like lx4 or lx5, or where a 2x3 or 2x4 plot was used, the intensive modules were usually located in the center modules and six to eight nested quadrats were measured. If other unusual conditions suggested that a specific comer would be inappropriate, alternate comers were selected.

2.3.4 Step 4. Measuring Vegetation

All vascular plant species within the modules were identified to the lowest taxonomic level possible. Immature plants or plants missing structures (e.g. fruiting bodies, etc.) that could not be identified to species were identified to genus. Otherwise, the plant was recorded as unknci\vn and notation made as to its type (graminoid, monocot, dicot, forb, family, etc.) to the extent that could be identified. If several unknowns of the same type were present but were obviously different species, they were distinguished by assigning a number, e.g., unknown grass spp. #1, #2, etc. Nomenclature in this report generally follows Gleason and Cronquist (1990).

Presence data were recorded in the form of a couplet with the first column used for the depth at which a species was first recorded as present and the second for cover. Couplet headings were the module and \ comer numbers (e.g. 2-2, 2-3, etc.), except for (where applicable) an aggregate pair headed R-R (for ) "residual") that contained species first recorded in an aggregate of modules that supplement those sampled intensively. See Peet et al. (1998) for a detailed discussion of how data is recorded. All species with stems emerging anywhere within the focal module were listed and each of these species had a depth 2 2 2 2 value of 4 (O. lm ), 3 (lm ), 2 (l0m ), or 1 (l00m ) recorded. Cover data was recorded using the cover classes in Table 5 for every species, except canopy level trees where only basal area was measured (See Step 5 for woody vegetation below). The midpoint of the cover class was used in all subsequent analyses.

2.3.5 Step 5. Measuring Woody Vegetation

For woody vegetation, stem counts were made and basal area was measured for all trees, shrubs and woody vines reaching 1.0 m, with the exception of multiple stemmed shrubs, e.g. buttonbush. Shrubs with multiple stems from the same root (genets) were counted once as a "shrub clump" and analyzed with the 0-1 cm size class. The diameter classes and midpoints in Table 5 were used, with stems greater than 40 cm counted individually and measured to the nearest tenth centimeter. The midpoints of the class were used to calculate basal area by class.

8 CJ Table 5. Cover and dbh classes recommended by Peet et al. (1998) cover dbh dbh mid point (cm) 2 class % cover midpoint class (cm) basal area (cm )

solitary or few 0.01% 0-1 0.50 0.063

2 0-1% 0.5% 2 1-2.5 1.75 0.766

3 1-2% 1.5% 3 2.5-5 3.75 3.52

4 2-5% 3.5% 4 5-10 7.50 14.1

5 5-10% 7.5% 5 10-15 12.5 39.1

6 10-25% 17.5% 6 15-20 17.5 76.6

7 25-50% 37.5% 7 20-25 22.5 126.6

8 50-75% 62.5% 8 25-30 27.5 189.1

9 75-95% 85% 9 30-35 32.5 264.1

10 95-99% 97% 10 35-40 37.5 351.6

11 >40cm individually individually

2.3.6 Step 6. Measuring standing biomass

Standing biomass (emergent wetlands only) was estimated by harvesting to ground level all plants rooted in 900cm2 quadrats located in the nest corners of the intensive modules. Samples were collected on the same day vegetation sampling of the plot was done. All plants within quadrat were cut at the soil surface ) and placed into paper sample bags. Plants were oven dried at I 05 °C for at least 24 hours and samples were then weighed and the weights recorded.

2.3.7 Step 7. Measuring physical attributes of the site

In addition to the quantitative vegetation data collected, various physical attributes of the wetland being sampled was also recorded. These include depth of standing water, depth to saturated soils, litter depth, number of tussocks and hummocks, number of standing trees, number of coarse woody debris, microhabitat interspersion, physical characteristics of soils (color, texture, redox features, etc.), pH and temperature of standing water. Soil and water samples were also collected and samples were analyzed at the Ohio EPA laboratory. Grab samples for water were collected by directly filling one quart containers with water from the wetland. Soil samples were generally located within the intensive modules of the plot unless conditions at the wetland (depth of water, substrate characteristics, etc.) made this infeasible, in which case an alternative sampling location was identified. Soil samples were taken from the top 12 cm of soil. Samples were collected using a 8.25cm x 25cm stainless steel bucket auger (AMS Soil Recovery Sampler), with a butyrate plastic liner. The auger was then inserted to half its depth, filling the liner half-way. The auger was then removed and the operation repeated (filling the liner completely). Prior to 1999, a bucket auger was not used but samples were still extracted from the top l 2cm of soil.

2.3.8 Step 8. Preserving Voucher Specimens and Assigning Voucher Numbers

Voucher specimens were collected at almost every site, especially the more taxonomically difficult genera and families. Although staff resources made collecting vouchers of every vascular plant infeasible, a voucher specimen of at least l 0% of the vascular plant species at any given site was usually collected. In

9 (~ every instance in which the identity of any species could not be confirmed in the field, or where field "'__) personnel disagreed as to the identity of a species, a voucher specimen was collected for identification in the office. In particular, difficult genuses and families, e.g., Cyperaceae and Poaceae, were frequently collected. Vouchers were retained in the Ohio EPA herbarium and specimens will also be sent to regional herbariums. ·

2.3.9 Step 9. Assigning plants to categories

After sampling, plants found in a wetland were assigned to various categories.

l. Reproductive categories. Each plant was assigned to one of three reproductive categories: monocotyledon, dicotyledon, cryptogam (ferns and fern allies).

2. Life form categories. Each plant was assigned to various "life form" categories reflective of the plants usual height, shape, or structural characteristics including the following:

a. forb - all non-grasslike plants including ferns and fem-allies;

b. graminoid - all grass-like plants including species in the Poaceae, Cyperaceae, Juncaceae, Typhaceae, and Sparganiaceae;

c. shrub - plants with woody stems that have "shrubby" growth habitat (e.g. Cephalanthus occidenta/is, A/nus spp., Salix interior, but not Salix nigra);

d. tree - woody plants which can grow into a mature forest canopy;

e. vine - plants with a climbing, twinging, or recumbent growth habitat.

3. Wetland indicator status categories. The basic wetland indicator status of the plant (UP, FACU, FAC, FACW, OBL) forthe State of Ohio as determined by appropriate U.S. Fish and Wildlife Service Publications (e.g. Reed 1988).

4. Tolerance/intolerance (to disturbance) categories. Plants with a Coefficient of Conservatism rank ofO, 1, or 2 were determined to be "tolerant;" plants with a Coefficient of Conservatism rank of 6, 7, 8, 9, or 10 were determined to be "intolerant." See discussion ofFQAI score and Coefficients of Conservatism in §2.5.

5. Taxa level categories. Plants were assigned to various taxalevel categories including the following: Carex, Scirpus, Juncus, Typha, Phalaris, Aster, Rosa, Cephalanthus, Poaceae, Cyperaceae, Asteraceae, Rosaceae, Lemnaceae

6. Indigeneity categories. Plants were assigned to one of two categories, native versus nonnative species, based on whether the species was present prior to European settlement in the State of Ohio as determined by taxonomic experts and references.

2.3.10 Step 10. Calculating vegetation community attributes

The following basic vegetation community attributes were calculated:

10 (' Number (Richness). The number of plants in various categories was counted. ~ Relative Number (Proportion). The number of plants in the various categories listed above divided by the total number plants identified to species or genus level. Alternatively, the number of plants in a category divided by the number of plants in another category, e.g. the number of shrub species divided by the number of tree species.

Coverage (Dominance). The sum of the percent coverage values for a plant species recorded for each intensive module or at the releve level (nonintensive modules ifthe plant was not observed in an intensive module). Percent cover was recorded for all species except canopy level tree species.

Relative Coverage. The sum of the percent coverage values recorded for a plant species in a plot divided by the sum of coverage values for all plant species in the plot.

Class Frequency. The number of size classes where a tree or shrub species has at least one individual of that size present in the size class.

Relative class frequency. The number of size classes that a particular woody species has individuals of that size present in divided by the total number of size classes.

Density. The number of stems per hectare of a woody plant species (tree or shrub).

Relative Density. The number of sterns per hectare of a woody plant divided by the total number of sterns per hectare of all woody plants in the plot.

Basal Area (Dominance). The basal area of woody plant species in m2 per hectare l / Relative basal Area. The basal area of woody plant species in m2 per hectare divided by the sum of the basal area of all woody plant species in the plot.

Importance value. The sum of relative frequency, relative density, and relative basal area of woody plants divided by three and variants of the importance value.

FQAI Score and variants. The FQAI score and variants of the FQAI score(% cover of plants with Cotes of0-2 (tolerant)or 6-10 (intolerant)).

2.4 IBI Development Methods

Karr et al. (1986) and Ohio EPA (1988a, l 988b) performed foundational IBI development using freshwater fish. Ohio EPA (l 988a, l 988b) developed IBis for macro in vertebrates in freshwater streams. Karr and Kerans (1992) summarized their procedure for developing a macroinvertebrate IBI for the Tennessee Valley Authority. The U.S. EPA has several guidance manuals on IBI development that recommend various procedures and methods (U.S. EPA 1990, 1998, 1999). However, there are very few published attempts to develop IBis using vascular plants as the indicator taxa (Gemes and Helgen 1999; Carlisle et al. 1999; Adam us 1996). Some of the details of the fish and macroinvertebrate methods must be adapted to wetlands (a different type of aquatic system than flowing streams) and to vascular plants (a different taxa group). What follows in these next sections is a summary of Ohio EPA's approach to developing IBis for vascular plants.

11 2.4.1 Site selection and classification

Early classification schemes employed by Ohio EPA are summarized in Mack et al. (2000). Results from Mack et al. (2000) suggest that somewhat diverse wetland types may be groupable during the development of an IBI and other groups, e.g. fen and bogs, which are generally kept separate during IBI derivation can then be "regraphed" with other types of wetlands after the VIBI score is determined since the IBI scoring process has a standardizing effect on inter-class variation. The current working hypothesis is that while certain wetland types may differ in their floras at the species or community level, these species or communities of species behave in a similar manner in response to human disturbance (Premise 11 Karr and Chu 1999). Results from Mack et al. (2000) and this report suggest that 20-30 potential hydrogeomorphic or plant community classes (Table 6 and 7) may be condensable into 4-8 classes for the purposes of vegetation IBI development and application.

Ohio EPA has developed and continues to revise plant community and hydrogeomorphic classification systems (Mack et al. 2000; Mack 2000) (Table 6 and 7). The plant community classification scheme (Table 6) is based on the primary classes in the Cowardin et al. (1979) scheme (forest, scrub-shrub, emergent). Plant communities and plant community types follow, in part, the Ohio plant community classification system developed by Anderson (1982), although Ohio EPA has added several community types (forest seeps, seep fens, sedge-grass communities, tall shrub fens) not listed in Anderson (1982).

The hydrogeomorphic (HGM) classification (Table 7) can be considered an Ohio specific scheme that Brinson (1993) recommended be developed and is adapted from a system developed for Pennsylvania by Smith et al. (1995) and Cole et al. (1997).

12 (~' '----,/ Table 6. Interim Ohio Vegetation Community Classification (modified after Anderson 1982) and from Mack (Table 6, p. 25, 2000a). Refer to Anderson (1982) for a more detailed discussion of the community types listed below. Note: the term isolated is used in an the context of hydrogeomorphic class or landscape position and should not be construed to have any bearing on legal definitions or jurisdiction.

class community type description

Forest a Swamp (1) riparian forests Communities characterized by closed canopies of tree forests (2) isolated forests species. Includes swamp forests in isolated (depressional) (3) vernal pools settings, swamp forests located on floodplains and subject to regular flooding and part of a floodplain forest matrix, and vernal pools (as defined in OAC Rule 3745-1-50) which can be considered a type of isolated swamp forest that is largely unvegetated with herbaceous vegetation. Dominant canopy trees should be specified and can be one or several of the following: pin oak, swamp white oak, maple (red or silver), ash (green, black, pumpkin), white pine, elm, swamp cottonwood, black gum, hemlock, etc.

b Forest Seeps ( 1) riparian forest Communities characterized by closed canopies of tree seeps, (2) isolated species with strong "break in the slope" groundwater forest seeps expression, mucky soils, and often densely vegetated in the herb layer with Carex spp., skunk cabbage (Symplocarpus foetidus), and often other fen associates. Can occur in isolated positions or at bases and slopes of stream valley walls.

c Tamarack- (1) Tamarack- Tamarack and other hardwood species (yellow birch, red Hardwood hardwood bog maple, blackgum, quaking aspen form a closed canopy Bog Forest over peat or muck soils with characteristic bog under5tory vegetation in a "hummock and hollow" microtopography. ') Often grades into other swamp forest types.

, ) II Shrub a Shrub (1) riparian swamps Characteristic species include willows, alders, dogwoods, swamps (2) isolated swamps swamp rose, meadow sweet. Buttonbush and alder shrub swamps have over half their cover in buttonbush or alder, respectively. May occur as narrow zones around bogs, fens, or marshes. Dominant species of shrub canopy should be specified and can be one or several of the following: buttonbush, alder, dogwood, willows, blueberries, spirea, chokeberry (Aronia melanocarpa), winterberry (//ex verticillata).

b Bog or Fen (1) tall shrub bog (2) (1) Shrub bogs have massive, continuous sphagnum Shrub tall shrub fen carpets, in addition to bog shrubs and herbs, but may Swamps grade into "boggy" mixed shrub, alder, or buttonbush swamps, or marshes but these lack sphagnum carpets; (2) Shrub fens are similar to other fen communities in the herb .. h~.-!-, layer but have continuous to partially continuous to occasionally discontinuous canopies of dense shrub vegetation. Species can include willow (Sa/ix spp.), chokeberry (Aronia melanocarpa), winterberry (//ex verticillata), catberry (Nempopanthus mucronatus), blueberries (Vaccinium spp.), Gay/ussacia baccata, alder (A/nus spp.), poison sumac (Toxicodendron vemix), viburnums, dogwoods (Comus spp.), etc.

Ill Emergent a Marshes (1) submergent Characterized by herbaceous vegetation in isolated, marsh, (2) floating- depressional settings, adjacent to or part of lakes, and leaved marsh, (3) sometimes in proximity to or in mainstem or headwater mixed emergent positions of streams but development and succession not marsh, (4) cattail influenced by perennial or nearly perennial connections to a stream. Typical species can include Sagittaria spp., Typha spp., Sparganium spp., Peltandra virginica, Pontederia cordata, Nuphar advena, Decodon verticillatus, Carex spp., Juncus spp., Scirpus spp., Cyperus spp., Eleocharis spp., Poaceae spp., various fems, Lycopus spp., Scutellaria spp., Iris spp., and other wetland forbs and floating aquatic plants.

13 Table 6. Interim Ohio Vegetation Community Classification (modified after Anderson 1982) and from Mack (Table 6, p. 25, 2000a). Refer to Anderson (1982) for a more detailed discussion of the community types listed below. Note: the term isolated is used in an the context of hydrogeom~rphic class or landscape position and should not be construed to have any bearing on legal definitions or jurisdiction.

class community type description

Ill Emergent b Sedge-grass (1) Wet Prairies Communities dominated by sedges and grasses with other Communities including slough prairie or fen-associate forbs. (1) Wet prairies are grass-bluejoint characterized by Calamogrostis spp, Spartina pectinata, prairies, (2) Sedge and Carex spp. as well as other "prairie" forbs and grasses meadows, (3) Seep like Lythrum a/atum, Pycnanthemum virginianum, Uatris fens spicata, Silphium terebinthinaceum, etc.. (2) Sedge Meadows are dominated by various Carex spp. including Carex /acustris, Carex stricta, Carex trichocarpa, and Carex atherodes. Wet prairies may grade into sedge meadows. Sedge meadows may also be components of mixed emergent marshes. (3) Seep fens are groundwater driven emergent wetlands that occur at breaks in slope or bases of floodplain slopes with many plants associated with calcareous fens (llld) types but lacking in calciphile fen obligates like shrubby cinquefoil. Seep fens have many fen associates like Carex stricta, Carex lepta/ea, Carex interior, Solidago patula, Aster puniceus, as well as other sedge-meadow, marsh, and wet prairie plants. Communities with species assemblages similar to seep fens but without strong ground water inputs, should be classified as sedge meadows.

c Herbaceous Types of riverine Characterized by perennial or nearly perennial surface Riverine communities: (1) water connection to streams or rivers and large annual Communities submergent, (2) sediment movements. Riverine communities may occur floating-leaved, (3) over the entire breadth of slow streams or may be mixed emergent, restricted to the slower waters in shallower or more . and (4) water-willow protected areas. Species assemblages may be similar to other non-riverine marsh communities and may also include a strong presence of shrub species. They are commonly bordered by deeper or more rapidly flowing water. Riverine communities include stands in water which flows either all or part (e.g. oxbows) of a year, / usually every year. Headwater marshes which normally display very slow flowage are excluded but the distinction between a marsh and riverine systems is not always clear.

d Fens (1) Cinquefoil-Sedge Characterized by mineral-rich, nutrient poor groundwater Fen, (2) Tamarack inputs or on margins of glacial kettle lakes with strong Fen, (3) Arbor Vitae presence of obligate calciphile plant species like Gaca/ia Fen plantaginea, Carex flava, Carex sterilis, Deschampsia flexuosa, Eleocharis rostellata, Eriophorum viridicarinatum, Pamassia glauca, Potentilla fruticosa, Rhynchospora capillacea, Solidago ohioensis, Triglochin spp.

e Bogs (1) Sphagnum bog, Both bog communities are characterized by continuous (2) Leatherleaf bog carpets of Sphagnum and/or other acidophilic mosses. Large bog systems often include areas of floating-leaved marsh in peripheral moats or in the center as well as other marsh or shrub dominated areas. Refer to Anderson (1982) for discussion of these community types.

14 (': Table 7. Interim Hydrogeomorphic {HGM) classification system for Ohio wetlands v1.1 (October ~/ 2000) modified after Mack (Table 1, p. 5, 2000a) and adapted from Smith et al. (1995) and Cole et al. (1997). Note: the term isolated is used in an the context of hydrogeomorphic class or landscape position and should not be construed to have any bearing on legal definitions or jurisdiction.

class subclass dominant description soils

Isolated (A) closed (1) organic Wetland is in an isolated landscape position and not Depression (B) open soils, associated with a stream, river, or lake. Wetland may be (2)mineral "closed" (without discemable surface water inlets or outlets) soils or "open" (with inlets or outlets), but precipitation, overland flow, and/or interflow are primary water sources and evapotranspiration in the growing season is the dominant hydrodynamic. Wetland may have organic (peat, muck) or mineral soils and may have shallow groundwater, surface water (including precipitation), or both as sources of hydrology

II lmpoundment (A) beaver (1) organic Wetland is impounded by beaver or human activity. Fringing (B) human soils, wetlands around some reservoirs can be classified there. (2) mineral soils

Ill Riparian (A) Headwater (1) organic Wetland is associated with a stream or river in a headwater Depression (1" or 2nd order) soils, or mainstem floodplain position and receives hydrologic (2) mineral inputs from annual or regular flooding. If evapotranspiration (B) Mainstem soils in the growing season is the dominant hydrodynamic , (3"' order or wetland is "riparian-depression." If ground water is an larger) important hydrologic input in addition to surface water, wetland is "riparian-groundwater. Wetland may have organic (peat, muck) or mineral soils.

IV Riverine (A) Headwater (1) organic Wetland located within the defined banks or channel of a :1 I (1" or 2nd order) soils, stream or river and is not a Riparian headwater wetland (2) mineral (B) Mainstem soils (3"' order or larger)

v Slope (A) riparian (1) organic Wetland located on a topographic slope with break-in-the (B) isolated soils, slope ground water inputs with unidirectional flow of water (C) fringing (2) mineral and in isolated landscape position (isolated) OR associated soils with stream or river (riparian) OR associated with a lake, pond or reservoir (fringing)

VI Fringing (A) ground water (1) organic Wetland associated with a kettle lake (other than lake Erie), (B) surface soils, or large pond, divided into subclasses based on predominant water (2) mineral _hydrology (groundwater, surface water or ~th) ar:id soil type (C) both soils

VII Coastal (A) unrestricted, Wetland is associated with the coast of lake Erie and its (B) restricted hydrology is unrestricted by human activity, OR restricted by (C) estuarine human activity, OR associated with the reaches of rivers and streams flowing into lake Erie and affected by short and long term lake levels

15 2.4.2 Attribute evaluation and Metric selection

After initial classification and during classification iterations, potential ecological or biological attributes 0 of the taxa group are identified and evaluated (Barbour et al. 1995). 5 Potential attributes are initially selected a priori and should include aspects of the community structure, taxonomic composition, individual condition, and biological processes (Table 8; Karr and Kerans 1992; Barbour et al. 1995).

Barbour et al. (l 995) state that a useful attribute has five general characteristics:

l. Relevant to the biological community under study and to the specified program objectives;

2. Sensitive to stressors;

3. Able to provide a response that can be discriminated from natural variation;

4. Environmentally benign to measure in the aquatic environment; and

5. Cost-effective to sample.

Table 8. Types and characteristics of attributes which can be included in biological assessments using vascular plants as a taxa group. Adapted from Barbour et al. 1995.

type possible attributes

community structure taxa richness, relative I ) cover, density, dominance taxonomic composition identity, floristic quality (FOAi), tolerance or intolerance of key taxa

individual condition disease, anomalies, contaminant levels

biological-processes productivity, trophic dynamics, nutrient cycling

Ohio EPA evaluated sampling costs and sampling time (including travel to and from study sites) for vegetation sampling during the 2001 field season. One time equipment purchases to perform sampling describe in this report total approximately $12,000. Annual supply costs are approximately $2,000. During the 200 l field season, an experienced sampling team sampled 44 sites with 4 7 plots over 28 field days. A normal sampling team was comprised of one full time biologist and two interns. Average person hours per site and per plot was 6.5 and 6.1 hours, respectively for the biologist, and 11.6 and l 0.8, hours, respectively for the interns. Cost per site and per plot, excluding soil and water sample analytical costs was approximately $402 and $376, respectively. Average lab costs for soil and water sample analysis at Ohio EPA's laboratory were $394 per plot for soil samples and $154 per plot for water samples ( 48 samples, and 36 water samples).

Ohio EPA evaluated a suite of potential attributes based on the biological information collected. The target taxa group (vascular plants) was classified into several categories (Table 9). Then possible changes

5 In this report, "attribute" is defined as a measurable characteristic of the biological community, and "metric" is defined as an attribute that changes in some predictable way in response to increased human disturbance (Karr and Chu I 997). "Metric" is also used to refer to an attribute that has been included as a component of a multimetric IBI.

16 to disturbance (increase, decrease, etc.) and types of changes (linear, curvilinear, dose-response) were proposed. These constitute testable hypotheses and assumptions which go into making up the completed IBI (Table IO). Data from wetlands representing a range of disturbance were then evaluated for c ecologically meaningful and explainable trends. These procedures can be summarized as follows:

Step l Classify organisms such that attributes span range of types, trophic levels, strata (horizontal, vertical), reproductive strategies, ecological affinities, age classes

Step 2 Propose working hypotheses for potential attributes

Step 3 Use graphical techniques, descriptive statistics, regression analysis, etc. to evaluate attributes from data set of reference and nonreference wetlands.

Step 4 Select "successful" attributes

In general, successful attributes were those where ecologically meaningful linear or curvilinear dose response or other ecologically meaningful relationships were observed across a gradient of human disturbance. See Results section for additional discussion.

Table 9. Categories used to classify vascular plants. Adapted from Karr and Kerans (1992).

# category type

Taxagroup dicots, monocots, certain genera (e.g. Carex), certain families or family groups (e.g. Poaceae, cryptograms), etc.

2 Life Form forb, graminoid, shrub, tree, aquatic, etc.

) 3 Indicator Status wetland indicator status, e.g. FAC, OBL, FACW, etc.

4 Age (size) class what size, and presumably age, class a tree is a member of

5 Ecological affinity Coefficient of conservatism assigned to plant species by Floristic Quality Assessment Index.

Table 10. Hypotheses and assumptions about changes in vascular plant community in wetlands from human disturbance. Adapted from Karr and Kerans (1992).

# hypothesized changes caused by human disturbance of natural wetlands

number of species and those of specific taxa groups declines

2 abundance (dominance) or numbers of intolerant species declines

3 abundance (dominance) or numbers of tolerant species increases

4 proportions or abundance of plants with narrow ecological affinities declines

5 overall ftoristic quality of plant community declines

6 primary productivity increases

7 proportions or abundances of plants with particular wetland affinities (obligate, facultative) changes relative to reference conditions

17 Table 10. Hypotheses and assumptions about changes in ""'j vascular plant community in wetlands from human C) disturbance. Adapted from Karr and Kerans (1992). # hypothesized changes caused by human disturbance of natural wetlands

8 proportions or abundances of plants with certain life forms (e.g. forb, graminoid, shrub or tree) or reproductive classes changes relative to reference conditions

9 proportions of individuals (relative density) or relative dominance (basal area) in woody species age classes increases changes relative to reference conditions

10 changes in community heterogeneity relative to reference condition

·2.4.3 Metric score and calibration

Once an ecological attribute is selected as a "metric", a score must be assigned based on the measurement of that attribute by a biological survey of the wetland. U.S. EPA ( 1998, 1999) outlines several methods for determining scoring criteria including an "all-sites" and "percentage of standard" method followed by trisection or quadrisection of the distribution. Karr et al. (1986) and Ohio EPA (1988a, l 988b) used trisection of data from reference sites to derive !Bis for fish in freshwater streams. Ohio EPA (1988a, l 988b) used quadrisection in the derivation of IBis for macro invertebrates in freshwater streams. Hughes et al. (1998) used the 95lh percentile of values from all sites as the most sensitive index. Mack et al. (2000) evaluated both trisection and quadrisection in developing the interim VIBI for Ohio wetlands. The relative position of the wetlands remained the same regardless whether the VIBI score distribution was ) trisected or quadrisected. Because of the clear advantages of quadrisection using a 0, 3, 7, 10 scoring scale (e.g. more intuitive 100 point scale, more graphical "spread", etc.), only quadrisection was used in this paper.

U.S. EPA (p. 9-10, 1999) states that recent data from various states is supportive of the "all-sites" approach and that ideally, a composite of all sites representing a gradient of conditions is used which represent a dose-response relationship; however, this approach depends on whether both reference and non-reference sites can be incorporated into the data set. Karr et al.(1986), Ohio EPA (1988a, l 988b ), and others used a reference site approach followed by trisection or quadrisection. Reference sites are defined as sites lacking obvious or discernible human cultural influence or the /east-impacted systems available in particular landscape. In the case of stream IBis based on fish or macroinvertebrate assemblages, data' from reference streams was plotted against stream drainage area to accounrfor natural variability based on stream size and landscape position. A "maximum species richness line" (MSRL) is then fitted to the resulting distribution such that 95% of the data points fall below the line. The distribution is then trisected or quadrisected below this line (Ohio EPA l 988a, l 988b; Yoder and Rankin 1995; Barbour et al. 1995). Since no similar defining 11 }!:-axis" equivalent to stream drainage area has been identified for wetlands when vascular plants are being used as an indicator assemblage, the step of fitting the MSRL to the distribution can be omitted, and the 95% point identified numerically and the measurements below this point are quadrisected. The main difference is the omission, in the "reference sites" method, of sites with human cultural influences.

Mack et al. (2000) calculated VIBI scores using an "all-sites" and a "reference-sites" method. The main effect was an overall suppression in the VIBI score when only least-impacted reference sites were used; the relative position of sites did not vary noticeably. Karr and Chu ( 1999) state that sectioning of data sets into approximately equal thirds or fourths is appropriate where monotonic or linear distributions are observed in the metric values. Where distributions are not monotonic or linear, they recommend using natural breaks in the distribution to determine scores

18 Low quality sites are often more difficult to locate and obtain access to than high quality sites. Many high quality wetlands are located on public lands where research access is easily obtained; low quality sites are often on private lands and locating them and obtaining access is more difficult. Because of this, 0 low quality sites are under represented in the existing data set, especially for forested and shrub dominated communities. The difficulty in finding low quality forest or shrub wetlands is further compounded by the fact that forest or shrub wetlands, when very degraded, often lose their woody component altogether and resemble low quality emergent marshes.

Because of this problem, the "all-sites" and "reference sites" methods are further evaluated in this report to determine which is most appropriate. When the "all-sites" method was used, all the sites within the class representing a gradient of disturbance were analyzed together. When the "reference-sites" method was used, all the sites within the class representing least-impacted or "reference" conditions were analyzed together;

The measurement of a particular metric was made at each wetland and the 95th percentile of the measurement was calculated. The 95th percentile is used as the upper reference limit and the range of scores below this score was quadrisected. The 95th percentile was 0.7 - calculated using the PERCENTILE function of 0.6 - EXCEL. Two methods were used to c 0.5 - quadrisect the distribution: mathematical ~ Q) 0.4 - quadrisection and graphically-fitted 0 £ 0.3 - quadrisection. Mathematical quadrisection 0~ simply mathematically divides the distribution 0.2 - . 10 into four equal parts. Sites with measurements 0.1 - . - 7 .. 3 above the fourth quartile below the 95th 0.0 - ••• ... . . ' n j I I I I - I I I I I I I percentile received a score of l 0, sites within 0 10 20 30 40 50 60 70 80 90 100 the third received a score of 7, sites within the ORAM v5 second received a score of 3 and sites within the first quartile received a score ofO. Figure 3. Relative cover of intolerant plant species (%intolerant) versus ORAM v. Graphically-fitted quadrisection breaks the 5.0 score from %intolerant metric of VIBl-Emergent with metric score breakpoints distribution into sections at points in the established by graphical fitting technique. distribution that conform to observed changes in the attributes and assigns scores ofO, 3, 7, or 10 based on whether the attribute is in the first, second, third, or fourth section of the distribution. This method was used when the distribution was not linear or obvious breaks 0.7 - . in metric values were apparent. The 0.6 - differences in the two methods are readily c 0.5 - . apparent when actual data and breakpoints are ~ Q) 0.4 - 10 compared. Ci . . c 0.3 - 7 0~ . Figures 3 and 4 show the values for the 0.2 - . . 3 %intolerant metric in the VIBI-E where it was 0.1 - . . determined that graphical fitting was more ...... 0 0.0 - ... .. "' . . appropriate than mathematical quadrisection. I I I I I I I I I I I 0 10 20 30 40 50 60 70 80 90 100 Figure 3 has graphically fitted breakpoints; ORAM v5 Figure 4 has scoring mathematically

quadrisected breakpoints. It is readily Figure 4. Relative cover of intolerant plant species versus ORAM v. 5.0 score from apparent that this distribution is not linear but %intolerant metric of VIBl-Emergent with metric score breakpoints established by rather represents a threshold where high to mathematical quadrisection technique. moderate disturbance results in scores of 0 to

19 nearly 0 with an abrupt increase in scores as disturbance decreases. Mathematically derived breakpoints result in too many sites receiving a score ofO when they are functioning at a higher level. Graphically () fitting the breakpoints to the distribution considerably improves the metric's performance. 2.5 Floristic Quality Assessment Index

Ohio EPA has previously investigated the use of the Floristic Quality Assessment Index (FQAI) and its relationship to wetland disturbance and quality in Fennessy et al. (1998a and 1998b). Mack et al. 2000 found that the FQAI score and variants of that score were very strongly correlated with measures of wetland disturbance.

The FQAI was first devised by Swink and Wilhelm (1979) for plants in the Chicago region and later explored by Wilhelm and Ladd (1988) and Wilhelm and Masters (1995) and adapted to Michigan (Herman et al. 1993) and Northern Ohio (Andreas and Lichvar 1995). Its use is being explored in other parts of the United States (Ladd, in prep.). The principal concept underlying the FQAI is that the "quality" of a natural community can be objectively evaluated by examining the degree of ecological conservatism (or tolerance) of the plants species in that community, regardless of the type of community or the abundance, dominance, growth form, etc. of the plants that comprise it. Fennessy et al. (1998a, l 998b) found significant correlations between a wetland's FQAI score and the degree of human disturbance at the site.

A floristic quality index is developed by assigning a numeric score from 0 to l 0 to the entire flora growing in a specified geographical region. This score is called a "coefficient of conservatism" or "C of C," and represents the degree of conservatism (or tolerance) displayed by that species in relation to all other species of the region (Wilhelm and Ladd 1988; Wilhelm and Masters 1995; Andreas and Lich var 1995). The C of C's of all the species identified as growing at a particular site are summed and divided by the square root of the total species identified, N, or

,· . I Eqn. 1

where I = the FQAI score, c0 = the coefficient of conservatism of a plant, and N = the total number of native species at the site being evaluated. Note that the FQAI excludes nonnative plants from the calculation of the index.

For the purposes ofIBI development, the FQAI can be conceptualized as a weighted richness metric. Richness (total number of species, number of species in a taxa or functional group, etc.) is one of the oldest concepts used in ecology to distinguish communities (Krebs 1999), and is often used as a useful attribute in IBI development.

Although the assigning of the CofC is a subjective, although not arbitrary decision made by a person or group of persons intimately familiar with the flora of a region or state based on their knowledge of the narrowness or breadth of a plant's ecological tolerances, once this decision is made, the index is both objective and consistent. In effect, FQAI "front-loads" the subjectivity during the development of the system itself: users of the index are required to apply it objectively and consistently. Any inherent biases in assigning a particular CofC to particular plants, occur uniformly and the relative comparison of site A to Site B to Site C is not affected.

In using the FQAI to develop VIBis, Ohio EPA has used the FQAI system developed by Andreas a,nd Lichvar (1995) and the CofC's proposed by them for this regional flora. Most of the wetlands studied to date occur within or near this region and it was determined that until a statewide FQAI was developed, this is the best system presently available.

20 2.6 Disturbance scale

As was mentioned previously, no similar defining "x-axis" equivalent to stream drainage area has been 0 identified for wetlands when vascular plants are being used as an indicator assemblage. Therefore, an important part of the development of the VIBI has been the concomitant development of a semi quantitative "disturbance scale." Fennessy et al. (1998b) developed a qualitative 0 to 10 scale of degree of disturbance. Fennessy et al. (Figure 2.2, l 998a) developed a 4-tiered ranking system for evaluating the degree of human disturbance. In addition, Fennessy et al. (1998a) investigated the responsiveness of the score from ORAM v. 3.0 to disturbance and found significant correlations.

Gemes and Helgen ( 1999) classified sites by type of disturbance (none, agricultural, storm water) and used a scoring scheme which assigned a 0, 2, 4, or 8 depending on the degree of human influence, 0 being equal to reference conditions and 8 being very disturbed. In addition, they considered three other disturbance factors (hydrologic alteration and miscellaneous influences, known historic influences, and the quality of the immediate buffer around the site). These factors were assigned scores ofO, I, 2, or 3, with 3 being the most severely disturbed.

Carlisle et al. (1999) developed a multivariable habitat quality/disturbance ranking system for use in their development of IBis for vascular plants and macroinvertebrates in Massachusetts coastal marshes. In addition, Ohio EPA has had considerable experience and success in developing a qualitative habitat evaluation index (QHEI) for streams and in correlating this index to IBis (Rankin 1989; 1995).

The ORAM v. 5.0 is similar to these other ranking systems in that it functions as an ecological integrity and disturbance scale in addition to being a regulatory classification tool. It expressly addresses disturbances to wetland hydrology and habitat, presence or lack of buffers, intensity of surrounding land use, presence and abundance of invasive plant species, disturbances to substrates, and overall wetland quality. Finally, the ORAM v. 5.0 was designed to "relativize" differences in wetland type and HGM class during the assessment of the wetland and assigning of a score. Thus, the user is asked to evaluate a 'j J wetland in relation to other ecologically and hydrogeomorphically similar wetlands. Once a "score" is assigned to a particular wetland, it is comparable to other scores of other different types of wetlands. By "controlling" for these variables up-front in the disturbance scale, the number of wetland classes needed to develop an IBI can be limited. This results in wetlands occupying the same ranking "space" on the disturbance scale regardless of differences in hydrology and landscape position.

The ORAM v. 5.0 appears to be useful as a regulatory categorization tool as well as for evaluating the degree of human dii'turbance, or conversely, a qualitative assessment of the ecological integrity (intactness) of the wetland. Hence, the score has been used as the disturbance scale in the development of the vegetation IBis presented here.

2.7 Statistical analyses

Minitab v. 12.0 for Windows was used to perform all statistical tests. Descriptive statistics, box and whisker plots, regression analysis, analysis of variance, multiple comparison tests, and t tests were used to explore and evaluate the biological attributes measured for VIBI development, the VIBis, physical parameters, and the ORAM v. 5.0 scores.

21 3.0 Vegetation IBis for Wetlands

Vegetation IBis have been previously proposed for three dominant plant community types: emergent, forest, and shrub (Mack et al. (2000). These community types corresponded to the first broad community division of the Cowardin et al. (l 979) classification scheme, e.g. the "palustrine" system was divided into palustrine forested, palustrine emergent, and palustrine shrub wetlands. There were several reasons these three broad classes were adopted. First, some metrics used in the Vegetation IBis were specific to a 2 particular type of wetland. For example, average standing biomass (g/m ) data in the form of clip plots was only collected at emergent wetland types; forestry metrics like relative density of trees in the 10 to 25 cm size classes could only be collected in sites dominated by woody vegetation. Therefore, metrics based on these attributes were inapplicable to some types of wetlands.

Second, it is obvious that there is a large amount of natural variation in species composition and ecosystem functions between communities dominated by herbaceous vegetation versus communities dominated by woody vegetation (Cronk and Fennessy 2001). Classifying at this broad level helped to reduce the effect of this natural variability to identify community attributes that were varying due to human disturbances.

Finally, a statistical comparison was performed of the mean values of the shared metrics between emergent, forest, and shrub communities (Table 11). Significant differences were observed for 5 "core" metrics common to the vegetation IBis (dicot, hydrophyte, FQAJ, %tolerant, and %intolerant). The successionally and/or floristically intermediate position of shrub wetlands between emergent and forested wetlands is also suggested by this comparison of mean metric scores.

Each of the subsequent sections presents the Vegetation Index of Biotic Integrity (VIBI) for the emergent, forested, and shrub wetland vegetation classes. Figures and tables supporting each VIBI are presented after the text in §§3.l, 3.2, and 3.3. An overall analysis of the behavior of the three VIBis is discussed in 1 §4.0. ) The individual metrics that make up the VIBl-E, VIBl-F, and VIBI-SH were selected to represent a number oftaxa groups and taxa levels, plant life forms, tolerance or intolerance to disturbance, and community level variables. Metrics were selected that had linear, curvilinear, threshold, or other discernible relationship to the disturbance gradient. An important step in the development of an IBI is to test the metrics initially developed against data collected from additional sites, or from sites in different ecoregions, or from sites of a different HGM or plant community class, to determine whether the relationship observed between that metric and human disturbance continues to be present or was simply an artifact of the prior data set.

The Vegetation IBis proposed in Mack et al. (2000) were based on data collected from wetlands located in·the Eastern Corn Belt Plains (ECBP) Ecoregion of western and central Ohio. Since 1996, 509 vascular plant species have been identified in the quadrats and plots used in this study. Of these 509 species, 394 had relative cover values >l.0%. Wetlands in the Erie Ontario Lake Plains (EOLP) ecoregion (glaciated Allegheny Plateau and Lake Plains) were sampled during the 2000 field season.6 Data from the EOLP wetlands were tested against the ECBP wetlands data for differences based on ecoregion. Graphical analysis, regression analysis, and analysis of variance and multiple comparison tests were used to test the previously developed Vegetation IBI.

6 Wetlands in the Erie Ontario Drift and Lake Plains were also sampled during the 2001 field season, but this data was not able to be analyzed in time for this report.

22 Table 11. Mean, standard deviation (parenthesis) and ANOVA results for 3 primary vegetation community classes for metrics used in the vegetation 181 for "reference" condition wetlands, i.e. wetlands lacking in obvious human cultural influences. Means with shared letters were not significantly different after Tukey's Honest Significant Difference test. Metrics with asterisks were compared using 2-sample t test. Means in bold face paired for purposes of developing VIBl-SH (See § 3.3 below).

Emergent Forested Shrub metric results N=S N=13 N=12

no. carex spp. df=32, F=1.73, p=0.690 3.9(1.6) 3.2(2.7) 3.0(1.8)

no. dicot spp. df=32, F=3.56, p=0.041 23.0(5.2)a 30.6(6.9)b 26.6(6.5)ab

no. shrub spp df=32, F=0.04, p=0.958 5.4(2.5) 5.1(2.9) 5.1(1.9)

no. hydrophyte spp. df=32, F=8.59, p=0.001 32.0(7.3)a 20.1(8.5)b 19.9(5.1)b

no. Rosaceae spp. df=32, F=1.51, p=0.238 1.9(1.0) 2.9(1.6) 2.8(1.4)

FOAi score df=32, F=2.52, p=0.097 23.4(4.8)ab 25.9(3.9)a 22.5(2.9)bc

% tolerant spp. df=32, F=5.27, p=0.011 0.19(0.14)a 0.17(0.08)a 0.06(0.05)b

% intolerant spp. df=32, F=4.90, p=0.014 0.22(0.15)a 0.41(0.22)b 0.50(0.19)b

%invasive graminoids df=32, F=3.56, p=0.041 0.030(0.055)a 0.00004 0.003(0.008)b (0.00002)b

*shrub density df=20, t=-0.82, p=0.42 na 1533(1358) 2004(1404)

*small tree df=17, t=0.06, p=0.95 na 0.101(0.05) 0.099(0.09)

*maximum IV df=19, t=-1.70, p=0.11 na 1.14(0.28) 1.39(0.41)

An important factor in the development of these IBis has been the consistent use of certain decision rules. Even small wetlands can have more than one plant community even at the broad level of just three classes, emergent, forest, and shrub. Some of the "rules" for how data is analyzed and used are found in the sampling procedures for locating sample plots. These specify how to sample various wetlands and plant communities within them (Mack 200lb). Beyond these, certain methodological issues arise when deciding how to use the data to develop a vegetation-based wetland IBL Additional and different issues arise when deciding how to use the same data to assess the level of "impairment" or to assign a regulatory category of a wetland.

For example, a wetland has two co-dominant plant communities, a shallowly inundated to saturated wet woods and a deeper buttonbush pool that has no forest canopy above it. Floristically and structurally the two communities are different but comprise a single "wetland" in the sense of draw.ing a boundary between wetland areas and upland areas. Several approaches to sampling this wetland are possible: sample only one of the two communities; sample each community separately with completely separated plots; sample each community separately with adjoining plots; sample mostly in one community but include a portion of the other in the sampling plot. Depending on how the community was sampled, additional issues arise as to how to use the data in the development of a vegetation IBI versus how to use the data to assess the wetland from a condition assessment/use attainment perspective. An answer to the IBI development issue may not answer the use attainment application issue.

Returning to the wetland above, suppose you choose to sample each community with two completely

23 separated plots. Now you want to use this data to develop a vegetation IBL Since this is a single wetland, should you combine the data sets? If you do, how do you graph or analyze the data? With the forest community data set or the shrub community data set? If the data is combined, doesn't this give 0 this type of wetland an advantage over a wetland with only one of these communities in the IBI development process. If you analyze data from each community separately and only include the relevant portion in the relevant data set (forest and shrub), what happens ifthere are disturbances that have only degraded one but not the other community? Should there be a separate disturbance score for each community to reflect this? What if, instead of using this data to develop an IBI, you want to assess the condition of this wetland. Do you combine the data sets or keep them separate? Combining them may result in a higher IBI score. Do you calculate separate IBI .scores and use the higher of the two scores to assess condition? Again, what ifthe disturbance is localized to one but not the other community/portion of the wetland?

Applying the use attainment paradigm to wetlands may also present some methodological difficulties that prevent a mechanical approach to its use when working in wetland versus stream systems. Streams are more defined and definable. Sampling is clearly confined within two banks and some linear reach. Attainment/nonattainment areas are defined by river miles. Wetlands come in very large to very small sizes. Single to multiple communities, gradients (physical, temporal) of inundation, saturation. Disturbances may only effect a portion of wetland or a portion of one community in a wetland. In this situation, is the wetland in attainment or nonattainment or partial attainment? How do you define an area of nonattainment?

In response to questions like this, certain "decision rules" have been adopted to ensure the consistent development and use of a vegetation IBI.

l. IBI Development Rule. Co-dominant communities within a single wetland should be sampled with completely separate plots, or at a minimum, adjoining plots that allow the data set to be kept separate, and data from each should be analyzed as if it were the only community present. Thus, forested ) wetland data sets should only be graphed and analyzed with other forested wetland data sets when developing and selecting metrics and metric scoring breakpoints to develop a vegetation IBL Wetlands with a single dominant community with small amounts of other communities, e.g. the buttonbush swamp with a narrow forested margin, the emergent marsh with a narrow shrub margin or small pool with floating aquatic plants, should be sampled using the plot location rules in the Field Manual (Mack 2001 b) which require that the marginal community be included in the plot but not be the focus of the intensive modules.

2. Assessment Rule. HOWEVER, from a bioassessment, use attainment, or antidegradation categorization perspective, a single wetland with two co-dominant communities should be assessed or categorized by looking at the result that gives you the best answer, e.g. the forested community has a Category 3 VIBI score while the buttonbush community has a Category 2 VIBI score: the wetland is categorized as a Category 3 wetland.

3. IBI Development and Assessment Rule. Large wetland complexes with multiple subcommunities should generally be sampled with multiple plots in order to reflect the overall diversity of the complex and the data from these plots should be added together and analyzed as ifit were a single wetland. Note: rule 1 applies if there are ·codominant emergent, forest, or shrub classes; this rule addresses multiple communities within a class. For example, Singer Lake Bog7 is a several hundred acre bog complex with areas of continuous Sphagnous carpets dominated by leatherleaf and cranberry,

Vegetation 181 scores Singer Lake Bog were as follows: Floating-leaved marsh/decodon marsh plot=66; LeatherleafBog plot=60, combined data set of both plots=80.

24 floating-leaved marshes, open water moats and channels, decodon marshes, mixed shrub communities, etc. A complete assessment of this complex would require data from most of these communities and would likely require two to several sampling plots.

4. Certain problems with codominant communities can be solved by using the scoring boundary rules developed for ORAM v. 5.0 regulatory categorization purposes (Mack 200 la). In order to use the ORAM as a rapid assessment tool, a "scoring boundary" needs to be established in order to determine what is being assessed and what is not. The main rule is that where strong changes in hydrology occur, wetland areas can be scored separately even if they are contiguous to each other. Thus, where a wetland can be split into separate scorable areas, separate sample plots should be established in each scoring area and the data evaluated, analyzed and used as if they were two geographically separated wetlands. For example, Watercress Marsh is a large wetland complex at the headwaters of the Mahoning River in Columbiana County. A large, sloped, tall shrub fen is present on one side of the complex; the rest of the marsh is primarily a cattail or floating leaved marsh with shrubby margins. The hydrology of the fen is driven by calcareous ground water expressing along the slope. The marsh areas receive this ground water but are also fed by run off from the watershed. The marsh is also very disturbed by nutrient enrichment from nearby farms and former road construction; the fen appears to be largely intact and very floristically diverse. Because of the hydrologic discontinuity at the base of the slope fen to the flat marsh, separate scoring boundaries can be established around these two hydrogeomorphically (and floristically) distinct communities.

With these preliminary matters addressed, the following sections present the Vegetation IBis developed for three classes of wetland plant communities: emergent, forest, and shrub.

\ )

25 3.1 VIBI-EMERGENT

In Mack et al. (2000), a Vegetation IBI was proposed for "emergent" wetland types (VIBI-EMERGENT or VIBl-E) that included ten metrics: number of carex spp., number of dicot spp., number of shrub spp. divided by the total spp., FQAI score,% cover of tolerant spp., % cover of intolerant spp., % cover of 2 FAC spp., median% cover ofgraminoid spp., average standing biomass (g/m ) and heterogeneity. Of these original ten metrics, 7 were determined to be still valid and usable, 6 unchanged and 1 with slight modifications; 3 metrics were rejected and replaced with new metrics.

Table 12. Metrics modified or replaced in Vegetation 181 for emergent wetlands from those initially proposed in Mack et al. 2000.

code metric description

%FAG relative cover of plants Metric replaced. Overall Lack of discernible with FAG wetland indicator relationship between metric and disturbance status gradient. Replaced with a richness metric of number of FAGW and OBL species.

%graminoid median of the relative Metric replaced. Abrupt curvilinear cover of graminoid plant relationship observable but natural variability species and the large number of disturbed wetlands with low metric values resulted in too many scores of 7 or 10 and a skewing of overall VIBI scores. Replaced with relative cover of invasive graminoid species.

biomass average standing biomass Metric modified. High quality graminoid (g/m2) dominated communities with relatively high standing biomass obscured predictable trend observed in this metric when initially adopted. Modified by using maximum standing biomass sampled instead of average of all biomass samples dampened this natural variability in productivity.

heterogeneity heterogeneity index Metric replaced. Only weak relationship (Simpson's D) between metric and disturbance gradient and the large number of disturbed wetlands with low metric values resulted in too many scores of 7 or 10 and a skewing of overall VIBI scores. Replaced with Rosaceae species richness metric.

The originally proposed metrics and replacement metrics were evaluated using the techniques outlined in the methods section and also for their continued robustness and sensitivity based on the inclusion in the data set of additional emergent wetlands from the EOLP ecoregion. None of the metrics were rejected solely based on obvious ecoregional differences.8 Table 12 and Figure 5 summarize the rejected metrics and the reasons for rejection.

Table 15 describes the metrics currently included in the VIBI-E. The six metrics retained from the initial VIBI-E continue to perform strongly especially number of carex and dicot species, FQAI score, and relative cover of tolerant and intolerant plant species. Predictable and statistically significant linear and curvilinear relationships between human disturbance, as measured by the ORAM v. 5.0 score, and the current metrics continue to be observed (Figure 6, Table 13).

The means of the rejected metrics for wetlands in the ECBP and EOLP were compared. None of the means were significantly different except the %graminoid metric: %FAC (df=l 7, t=l .53, p=0.14); %graminoid (df=l 7, t=2.25, p=0.038; biomass (df=6, t=0.72, p=0.50); heterogeneity (df=22, t=0.28, p=0.79).

26 Table 13. Summary table of regression analysis of metrics used to derive VIBl-E. N=25, df=24 for all metrics except maximum biomass (N=24, 0 df=23). See Table 15 for descriptions of metrics.

F p R,

carex 11.71 0.002 33.7%

dicot 40.22 <0.001 63.6%

shrub/tot 11.8 0.002 33.9%

hydrophyte 82.99 <0.001 78.3%

rosaceae 12.41 0.002 35.0%

FOAi 79.25 <0.001 77.5%

%tolerant 18.66 <0.001 44.8%

%intolerant 18.17 <0.001 44.1%

%invasive graminoids 6.67 <0.017 22.5%

maximum biomass 8.82 0.007 28.6%

Results from an analysis of variance using ecoregion and reference condition as independent variables were mixed and should be considered preliminary given the small sample sizes for 3 of the 4 categories. Eight metrics had significant differences: carex, dicot, shrub/tot, hydrophyte, FQAI, %tolerant, %intolerant and %intolerant (Table 14, Figure 7). Most of these differences were due to differences between reference and nonreference wetlands and not to ecoregional differences. \ ) For the dicot metric, the mean number of dicot species in reference wetlands in the ECBP region was significantly higher (by 4 species) than the EOLP region; there was no similar difference between nonreference wetlands in these ecoregions. It is unclear whether this is a true ecoregional difference or an artifact of the data set since the number of dicot species in reference wetlands was significantly higher than nonreference wetlands regardless of ecoregion.

For the shrub/tot metric, all four means were significantly different from each other with the means in both reference and nonreference ECBP wetlands being lower than the means of reference and nonreference wetlands in the EOLP region. This pattern could be problematic for the future use of this metrjc since alternate scoring breakpoints may need to be derived for each ecoregion. However, these differences may disappear as additional sites are added to the data set. Assuming the differences are real; the present use of this metric with a combined data set ofEOLP and ECBP would result in a slight bias· against ECBP wetlands since the EOLP wetlands might shift the scoring breakpoints up. The behavior of this metric will be investigated further as data from the 2001 field season is included.

For the hydrophyte metric, all means were again significantly different from each other, although ref ECBP and ref-EOLP wetlands only differed by 5.5 species. A larger difference was observed between non-ECBP and non-EOLP wetlands (8.8 species) but this was likely an artifact of the data set since the non-reference EOLP wetlands were of considerably better quality than many of the non-reference ECBP wetlands in the data set. Again, assuming the differences are real, the present use of this metric with a combined data set of EOLP and ECBP would result in a slight bias against ECBP wetlands since the EOLP wetlands might shift the scoring breakpoints up. The behavior of this metric will be investigated further as data from the 200 l field season is included.

27 The differences in FQAI metric means can probably be discounted as an artifact of the current data set for the reasons discussed in the hydrophyte metric since the highly disturbed nonreference ECBP wetlands Q sampled had considerably lower FQAI scores than the non-EOLP sites sampled.

Table 14. Comparison of means (standard deviation in parenthesis) of VIBl-E metrics by ecoregion and reference condition using analysis of variance (N=25, df=24 for all metrics except maximum biomass (N=24, df=23)). Means without shared letters are significantly different at p<0.05 based on Tukey's Honest Significant Difference test.

non-EC BP non-EOLP ref-EC BP ref-EOLP ANOVA results N=13 N=3 N=5 N=4

carex df=24, F=7.79, p=0.001 1.5(0.98)a 1.0(1.0)a 4.2(1.8)b 3.3(1.3)b

dicot df=24, F=6.75, p=0.002 11.7(7.1 )a 14.0(4.4)a 25.2(6.1)b 21.3(2.5)bc

shrub/tot df=24, F=11.59, p<0.001 0.02(0.03)a 0.18(0.09)b 0.10(0.06)c 0.16(0.09)d

hydrophyte df=24, F=15.06, p<0.001 13.2(6.6)a 22.0(1.7)b 29.0(8.4)c 34.5(4.0)d

rosaceae df=24, F=2.45, p=0.092 0.6(0.96) 1.0(1.0) 1.8(1.3) 1.8(0.5)

FOAi df=24, F=14.02, p<0.001 11.7(4.3)a 16.8(1.4)b 24.5(6.5)c 23.1 (0.95)c

%tolerant df=24, F=6.07, p=0.004 0.53(0.24)a 0.44(0.15)a 0.11 (0.08)b 0.26(0.16)b

%intolerant df=24, F=9.03, p<0.001 0.03(0.04)a 0.04(0.05)a 0.33(0.20)b 0.18(0.19)bc

%invasive graminoids df=24, F=1.63, p=0.211 0.21(0.26) 0.23(0.16) 0.01(0.01) 0.05(0.08)

maximum biomass df=23, F=2.24, p=0.115 1616(628) 1408(1054) 1231(758) 508(146)

J:or the %tolerant metric, only differences between reference and nonreference wetlands were significant. \ No significant ecoregional differences were present. /) Finally, for the %intolerant metric the ref-ECBP wetlands had an average higher score than the ref-EOLP wetlands, the same pattern observed in the dicot metric. It is unclear whether this is a real difference or simply due to the relatively small sample sizes. No ecoregional differences were observed in the nonreference ECBP or EOLP classes. The behavior of this metric will be investigated further as data from the 200 l field season is included.

Metric values were converted to IBI scores by quadrisecting the 95lh percentile of the values of that metric data. Table 16 lists the scoring breakpoints used to assign a score to a particular metric value. The scores of each metric were evaluated (Table 17). Scores were distributed appropriately when the percentage of scores was calculated by qualitative categories (very poor, poor, fair; good, reference), reference versus nonreference, and ORAM v. 5.0 scoring categories (0-35, 36-59, 60+) (Table 18).

A Vegetation Index of Biotic Integrity for wetlands dominated by emergent vegetation communities was calculated by summing the individual metric scores. The VIBI scores for each wetland were replotted against the ORAM score (Figure 8) (df=24, F=l06.49, p<0.001, R2=82.2%). The overall behavior of the VIBI-Emergent is very satisfactory. Reference sites receive high to very high scores and the VIBI-E is able to distinguish a full range of sites from very highly disturbed (lowest score= 3) to very high quality sites (highest score = 97).

Although emergent fens and bogs were excluded from the data set during the derivation of the VIBI-E because of their exceptional floristic characteristics, calculating a VIBI score for these sites using the metrics and scoring criteria derived without them appeared to result in appropriate scores for the fen and bog sites, with the exception of Springville Marsh (Table 17). This was probably due to how Springville

28 Marsh was sampled since only very small relict areas of fen vegetation exist in a sea of narrow-leaved cattail, however, the sampling plot focused on this relict vegetation. Thus, until a separate fen/bog VIBI is developed, this technique appears to provide an adequate assessment method for these class of c wetlands, since the VIBI-E appears to accurately assess their relative integrity/disturbance levels and rank them appropriately in the class of emergent wetlands.

Overall characteristics and behavior of the VIBI-E in relation to ecoregional, hydrogeomorphic, and plant community classes is found in §4.0. A important development in the study of emergent wetlands in this report has been the evaluation of the emergent class and whether "mixed emergent marsh" communities and sedge-grass communities (fens, seep fens, sedge meadows, wet prairies) can be· grouped. This issue is explored in detail in §4.0 below.

0.5 +non-EC +non-EC .. 1500 • • non-EOL • non-EOL o.4 (>re~ECBP • (> (>re~ • •ref. • re~EOLP . ~0.3 ~000 (> E ~ .Q • ~02 • .0 (> Q) • • E ~500 •• ~ • 0.1 • (> • •• • . ,, ~~ 0.0 .., .. 0 0 10 20 30 40 50 60 70 80 90100 0 10 203040 50 60 7080 90100 ORAM v5 ORAM v5

1.0 0.4 +non-EC +non-EC 0.9 • • non-EOL • • non-EOL 0.8 0.3 (>re~ECBP (>re~ECBP • re~EOLP >-0.7 • re~EOLP u ~0.6 <(02 fo.s • IL ;f' a;0.4~ • (> Qj0.3 0.1 -<=02 ....• <\_._ (> 0.1 0.0 _ 0.0 0 10 20 30 40 50 60 70 80 90100 0 10 20 30 40 50 60 70 80 90100 ORAM v5 ORAMv5

Figure 5. Scatterplots of metrics used initially in the Vegetation 181-E proposed in Mack et al. (2000).

29 Table 15. Description of metrics for VIBl-EMERGENT in the State of Ohio. r~ ,_J (+)or(-) as disturbance metric code type increases description

number of carex spp. carex richness decrease Number of Carex spp. present at a site.

number of dicot spp. dicot richness decrease Number of dicot (dicotyledon) spp. present at a site.

shrub species/total shrub/tot richness ratio decrease Number of shrub species divided by the total species number of species.

number of FACW and hydrophyte richness decrease Number of plants with a Facultative Wet (FACW) OBLspp. or Obligate (OBL) wetland indicator status present at a site.

number of Rosaceae rosa richness decrease Number of species in the Rose (Rosaceae) spp. family present at the site.

FQAI score FQAI weighted decrease The Floristic Quality Assessment Index score richness calculated in accordance with Andreas and index Lichvar (1995). See §2.3 for description.

relative cover of %intolerant dominance decrease Percent coverage of plants in herb and shrub intolerant plants ratio stratums with a CofC of 6,7,8,9 and 10 divided by total percent coverage of all plants.

relative cover tolerant %tolerant dominance increase Percent coverage of plants in herb and shrub plant species ratio stratums with a CofC of 0, 1, and 2 divided by total percent coverage of all plants .

relative cover of %gram dominance increase The relative coverage of Typha sp., Phalaris invasive graminoid arundinacea, and Phragmites austra/is. plant species l J maximum standing biomass primary increase The grams per square meter of largest clip plot " biomass production sample collected at each emergent wetland.

30 Table 16. Scoring breakpoints for assigning metric scores to an emergent wetland. See Table 15 0 for descriptions of metric codes. 95'" metric percentile quadrisection method score O score 3 score 7 score 10

carex 5.7 mathematical quadrisection 0-1 2-3 4 ~5

dicot 26.7 graphical fitting 0-9 10-14 15-23 ~24

shrub/tot 0.225 mathematical quadrisection 0-0.056 0.0561-0.112 0.1121-0.169 0.1691-1.0

hydrophyte 40.0 mathematical quadrisection 0-10 11-20 21-30 ~31

rosa 3.0 mathematical quadrisection 0-1 2 3 ~4

FQAI 28.3 graphical fitting 0-9.9 10.0-14.3 14.4-21.4 ~21.5

• %intolerant 0.423 mathematical quadrisection 0-0.106 0.1061-0.211 0.2111-0.317 0.3171-1.0

%tolerant 0.797 mathematical quadrisection 0.5981-1.0 0.3981-0.598 0.1991-0.398 0-0.199

\ \. ) %invasive 0.592 graphical fitting 0.31-1.0 0.151-0.3 0.031-0.15 0-0.03 graminoids

biomass 2435 mathematical quadrisection ~1827 1219-1826 610-1218 0-609

31 Table 17. Distribution of metric scores by site for VIBl-E. Sites in italics are fen or bog sites Ci excluded from the derivation of the scoring breakpoints but then evaluated using the VIBl-E. ORAM no. of "O" no. of "3" no. of "7" no. of "10" site condition v5.0 VIBl-E scores scores scores scores

American Legion fair 40 41 3 2 4 Bates Creek good 59 53 0 5 4 Beaver Creek good 55 50 0 5 3 2 Berger Rd poor 24.5 6 7 3 0 0 Birkner Pond poor 30 27 5 2 2 Bloomville Swamp fair 36 19. 2 7 0 Calamus 1997 reference 73 67 2 2 5 County Rd 200 very poor 19 13 8 0 Daughmer reference 69 87 0 0 9 Dever fair 22 15 4 5 0 Eagle Cr Beaver reference 71 64 0 5 4 Eagle Cr Bog reference 81 67 2 2 5 Guilford Lake good 45.5 51 2 0 6 2 Herrick Fen fair 61 53 3 0 3 4 Keller Low fair 35 26 3 5 Kinnikinnick reference 66 60 3 5 Kiser Lake reference 71 88 0 0 9 LaRue Emergent fair 28 33 4 3 2 Lawrence Low 1 poor 34 29 4 4 Marsh Wetlands reference 78 83 0 2 7 j Mishne 1999 very poor 19.5 3 9 0 0 Mud Lake Bog reference 91 78 0 3 6 Palmer Rd very poor 16.5 9 7 3 0 0 Rickenbacker reference 51.5 61 0 2 6 2 Scofield fair 40 46 3 4 2 Silver Lake fair 82 87 0 0 9 Singer Lake Bog reference 86 77 0 2 7 Springville Marsh fair 50 73 0 2 2 6 Stages Pond poor 42 12 6 4 0 0 Tinkers Creek reference 80.5 70 3 5 Watercress Marsh fair 60 37 5 3 total 73 77 60 100 %total 23% 25% 19% 32%

32 Table 18. Percentage of metric scores by emergent wetland condition, reference versus nonreference, and ORAM v. 5.0 score. 0 % of "O" scores % of "3" scores % of "7" scores % of "10" scores very poor 33% 6% 0% 1%

poor 30% 17% 5% 2%

fair 29% 42% 35% 16%

good 3% 16% 27% 10%

reference 5% 19% 33% 71%

nonreference 93% 71% 52% 36%

reference 7% 29% 48% 74%

0-35 ORAM score 70% 35% 12% 5%

26-59 ORAM score 19% 39% 45% 19%

60+ ORAM score 11% 26% 43% 76%

\ ~j'

33 6 3J 5- 4 E;:;:J x e 3- ~al- "'0 lL CJ 2 ~ 0 BBQ 10 0 9 $ rm-ECBPrm-EOlPrd-E

1.0 3J U9 us -U7 c U6 0 al 8 0 ~U5 $ U4 'O :§ ~0.3 Q 10 L1 U2 U1 ~ 8 8 0 ~ uo rm-ECBP rm-EOlPrd-E

U3 U7 us- co.s- :§ U2 :c ~U4 2 :§ U3 c ~0.1 DB *0.2 B U1 Q 0.0 c::::i D e uo- 0 rm-ECBPrmEOl.Prd-E

«! 1.0 U9 g 15 0.8 2 3J EU7 .c E U6 "'"- e al CJ 15,us -0 ~ ~0.4 B .c"' 10- E0.3 ~ 0.2- 8 Q8 U1- ~ 0.0 0 rm-ECBPrm-EOlPrd-E

3 zro ., 20'.Xl ~ 2 CD "'E19:Xl 0 0 .,"' ~10)) e 1 "' E 9:XJ ~~g bl 0 0 rm-ECBPrmEOl.Prd-E

Figure 6_ Box and whisker plots of ORAM v_ 5.0 scores by vegetation community based on wetland classes. Refer to Table 6 for a description of these classes. A line is drawn across the box at the median_ The bottom of the box is at the first quartile (Q1 ). and the top is at the third quartile value. The whiskers are the lines that extend from the top and bottom of the box to the adjacent values. The adjacent values are the lowest and the highest observations that are still inside the region defined by the following limts: Lower Limit=Q1-1.5(Q3-Q1 ); Upper Limit=Q3+ 1.5(Q3-Q1 ). 34 "' • G 10- • x ~ .. 5• • u • • ""· .. .,., 10. o·,,,_...... ;;;...... o•• - 0 10:ii«19JflJ.7t18)t;JJ.1(0 0 10iiJ40~e)'70s)90'100 rnAM vs rnAM vs

~...r------.--~.----- ...1.0 -----.------.. 0.8 t • 25 \. c 0.7 .. • • a! 0.6 .. •• • ... • .! Q.5 •• .9 •• • / • rl' • • ;fl. 0.3 •• ...... 02 • • • • rl' ...... oo·...... ·....• ...... 0 102D:l040508l708J9>100 o iiit:11«>soai'108lm100 rnAM vs rnAM vs

.,_ n7 • 0.6 • • :§ 0.2• .. ~ 0.5. :0 • • a> Q.4 • • 2 • ~o.3' .. -; 0.1• ~0.2• • • I • ·' ., . •• • rl' • • ~ • ~i oo• QO ... J ;, ., SJ fD 70 8) !II 100 0 10 ., 100 0 -··.... ii Ci si "' "'.,!II rnAM vs rnAM vs

QJ ., • ., .. • • E :§ 0.2 ~ 0.5 • :0 • .,"'•• • ·2 '• •I ~ 0.3 • -; 0.1 • • • ·=.,,.,. 0.2 I • I • ., • •o ... QO 10 ., 9) ., 10 0 10 ., 9) &J 0 » ... "' !II "" » ... "' "' .. rnAM vs rnAM vs

,,.,, • .. .,~ ZDl • .."' 2 •••• ~ 1SIJJ rl' • u •• • .,.. n • • x ""' • • e •• "' E • • • ... . - "" • ..

0 1020:1140508)708)9)100 0 ., ., ., &J ., » .. "' .. "" rnAM vs rnAM vs

Figure 7. Scatterplots of metrics of wetlands used to derive the Vegetation IBI for emergent wetlands. 35 100 • non-ECBP 90 G ¢0 • non-EOLP -c Ill 0 ref-ECBP <1> 80 CJ) ..... Ill ref-EOLP 70 Ill <1> 0 E Ill w 60 0 0 50 • • Ill en • c 40 • 0 • :+:: • ctl 30 • <1> • • -CJ) 20 • • ~ 10 • • • • 0 • 0 10 20 30 40 50 60 70 80 90 100 ORAM v5

Figure 8. Scatterplot of Vegetation 181 scores for emergent wetlands used to derive the VIBl-E (df=24, F=106.49, p<0.001, R2=82.2%). ECBP =Eastern Corn Belt Plains Ecoregion; EOLP =Erie Ontario Lake Plains Ecoregion; ref=reference wetlands, i.e. wetlands lacking in obvious human cultural influences; non=nonreference wetlands.

36 G

\ ;i 100 / • bog 90 dist-fen +-' 00 Gil • c 0 Gil fen Q) 80 Gil .._O'l • 0 non Q) 70 0 • 0 0 E 0 • w 60 0 0 O• 50 0 0 en ¢ c 40 ¢ 0 ¢ :p ¢ +-'co 30 Q) 0 °o. O'l 20 0 00 ~ 0 10 0 0 0 0 0 10 20 30 40 50 60 70 80 90 100 ORAM v5

Figure 9. Scatterplot of vegetation IBI scores for emergent wetlands with fen and bog wetlands included. See Table 5 for plant community descriptions. bog=wetlands that are classified as bogs; fen=wetlands that are classified as fens, dist-fen=fens that have been disturbed by human activities; non=wetlands not classified as fens or bogs.

37 3.2 VIBl-FORESTED

In Mack et al. (2000), a Vegetation IBI was proposed for "forested" wetland types (VIBl-FOREST or G VIBI-F) that included ten metrics: number of dicot spp., number of shrub spp. divided by number of tree spp., number ofFAC spp. divided by the total number ofspp., FQAI score,% cover of tolerant spp.,

% cover of intolerant spp., % cover of OBL spp., relative density of small trees, log10 of shrub density, and heterogeneity. Of these original ten metrics, 7 were determined to be still valid and usable, 5 unchanged and 2 with slight modifications, and 3 were rejected and replaced with new metrics.

The originally proposed metrics and replacement metrics were evaluated using the techniques outlined in the methods section and also for their continued robustness and sensitivity based on the inclusion in the data set of additional forested wetlands from the EOLP ecoregion. None of the metrics were rejected solely based on obvious ecoregional differences. Table 19 and Figure l 0 summarize the modified or rejected metrics and the reasons for the change.

Table 22 describes the metrics currently included in the VIBI-F. The seven metrics retained from the initial VIBI-F continue to perform strongly, especially, number of dicot species, FQAI score, relative cover of tolerant and intolerant plant species, small_ tree, and shrub density. Predictable and statistically significant linear and curvilinear relationships between human disturbance, as measured by the ORAM v. 5.0 score, and the current metrics continued to be observed (Figure 11, Table 20).

Table 19. Metrics modified or replaced in Vegetation IBI for forested wetlands from those initially proposed in Mack et al. 2000.

code metric description

shrub/tree ratio of number of shrub Metric modified. Better fit when simpler shrub species divided by number species richness metric used instead of of tree species previously used ratio metric.

FAG/tot ratio of number of plants Metric replaced. Overall Lack of discernible with a FAG wetland relationship between metric and disturbance indicator status divided by gradient. Replaced with hydrophyte richness the total number of species metric counting numbers of FAGW and OBL at the site plant species per site.

%0BL relative cover of plants Metric replaced. Determined to be too with an OBL wetland redundant with the hydrophyte metric (number indicator status of FAGW and OBL spp.). Replaced with Rosaceae species richness metric.

shrub density the log10 of shrub density Metric modified. Better fit when simpler shrub density without log10 conversion used.

heterogeneity heterogeneity index Metric replaced. Only weak relationship (Simpson's 0) (see Krebs between metric and disturbance gradient. 1999) Replaced with quantitative forestry metric of maximum importance value of a species at a site.

38 Table 20. Summary table of regression analysis of metrics used to derive VIBl-F. N=29, df=28 for G all metrics. See Table 23 for descriptions of metrics.

F p R'

di cot 15.69 <0.001 36.8%

shrub 17.42 <0.001 39.2%

hydrophyte 10.71 0.003 28.4%

rosaceae 4.89 0.036 15.3%

FOAi 48.39 <0.001 64.2%

%tolerant 12.48 0.001 31.6%

%intolerant 9.74 0.004 26.5%

small tree 17.00 <0.001 38.6% .···;i.~., shrub density 12.40 0.002 31.5%

maximum IV 8.54 0.007 24.0%

Results from an analysis of variance using ecoregion and reference condition as independent variables should be considered preliminary given the small sample sizes for 3 of the 4 categories. Only four metrics had significant differences (FQAI, %intolerant, small tree and shrub density) while two other metrics showed marginally significant differences (dicot and hydrophyte) (Table 21). None of the significant or marginally significant mean differences, except for hydrophyte, were due to ecoregional \ ) differences in the mean metric values when only reference sites were compared. There were differences between nonreference wetlands based on ecoregion for the %intolerant, small tree, and shrub density metrics. It is unclear whether this is a real difference or simply due to wetlands sampled to date. The nonreference EOLP wetlands sampled in 2000 tended to be of overall better quality and an effort was made during the 2001 field season to sample the very wor-Se type of wetlands in the EOLP ecoregion.

There were some graphical differences between means of the reference wetlands based on ecoregion but these differences did not trend consistently, i.e. one ecoregion consistently with better scores than the other. Thus, in conclusion, little clear ecoregional variation is apparent from an examination of mean values of the metrics that make up the vegetation IBI for forested wetlands.

Metric values were converted to IBI scores by quadrisecting the 95th percentile of the values of that metric data. Table 23 lists the scoring breakpoints used to assign a score to a particular metric value. The scores of each metric were evaluated (Tables 24 and 25). Scores were distributed appropriately when the percentage of scores was evaluated by qualitative categories (very poor, poor, fair, good, reference), reference versus nonreference, and ORAM v. 5.0 scoring categories (0-35, 36-59, 60+).

39 Table 21. Comparison of means of VIBl-F metrics by ecoregion and reference condition using analysis of variance (N=29). Means without shared letters are significantly different G, at p<0.05 based on Tukey's Honest Significant Difference test, except for dicot and hydrophyte.

non-ECBP non-EOLP ref-ECBP ref-EOLP ANOVA results N=11 N=S N=S N=8

dicot df=28, F=2.93, p=0.053 24.8(8.1 )ac 19.8(7.6)a 33.0(9.4)b 29.0(4.5)bc

shrub df=28, F=2.20, p=0.113 2.5(2.2) 3.6(2.6) 5.3(3.2) 5.0(2.4)

hydrophyte df=28, F=2.54, p=0.079 13.6(4.2)a 18.2(4.9)a 23.3(13. 7)b 18.0(4.9)a

rosaceae df=28, F=0.62, p=0.608 2.2(2.3) 1.8(1.1) 3.3(1.5) 2.8(1.4)

FOAi df=28, F=6.73, p=0.002 17.7(4.9)a 18.6(5.6)a 24.2(4.5)b 26.7(4.0)b

%tolerant df=28, F=2.19, p=0.114 0.31(0.18) 0.33(0.29) 0.18(0.03) 0.14(0.09)

%intolerant df=28, F=5. 75, p=0.004 0.06(0.09)a 0.34(0.36)b 0.32(0.06)b 0.43(0.25)b

small tree df=28, F=3.91, p=0.020 0.26(0.13)a 0.17(0.17)b 0.08(0.05)b 0.11(0.05)b

shrub density df=28, f=4.77, p=0.009 92(224)a 893(999)b 1798(1911)c 1402(1004)c

maximum IV df=28, F=1.23, p=0.320 1.445(0.436) 1.498(0.467) 1.285(0.141) 1.148(0.337)

A Vegetation Index of Biotic Integrity for wetlands dominated by forest vegetation communities was calculated by summing the individual metric scores. The VIBI scores for each wetland were replotted against the ORAM score (Figure 11) (df=28, F=53.81, p<0.001, R2=66.6%). The overall behavior of the VIBI-Emergent is very satisfactory. Reference sites receive high to very high scores and the VIBI-E is \ able to distinguish a full range of sites from very highly disturbed (lowest score = 6) to very high quality ) .· sites (highest score = 94).

Several points can be made in summarizing the characteristics of the VIBI-F. First, as a minor point, one site, Killdeer Plains, has been reclassified as a nonreference site for this report. This site is locate in a woodlot southwest of Harpster in the Killdeer Plains Wildlife Area. The_ wetland is located in the center of the woodlot on the east side and extends past the woodlot edge into a pasture/former farmer field. The woodlot itself has an overall, subtle degraded appearance being depauperate in spring wildflowers and with very high coverages of poison ivy. Given the lack of any blatantly obvious human disturbances, this site was classified as reference in Mack et al. (2000) even though it did not otherwise appear to be a reference site. Its very low VIBI score (33) is a definite outlier in the data set and therefore this site has been reclassified as a nonreference site.

Second, three sites that had mid-range ORAM v5.0 scores had very high VIBI scores (Hempelman, City of Mansfield, and Blackjack Rd front) (Table 24). All three sites had disturbances in or near the wetland which lowered their ORAM v. 5.0 score. City of Mansfield has a ditch and a pond dug into part of the wetland and a buried sewer line through part of the wetland; Blackjack Rd wa5 a mature forest that was clearcut about 15 years ago and is located next to a road; Hempelman is located in a small woodlot with narrow buffers on two sides, a road next to or through the wetland, and intensive farming just outside the woodlot. Despite these disturbances City of Mansfield and Blackjack Rd. continue to exhibit very high levels of floral quality; during sampling of Blackjack Rd., a new population and Knox County record of the state endangered plant Carex crus-corvi was discovered. City of Mansfield has a very diverse open woodland canopy with rich shrub and herbaceous understory. The reason for the high VIBI score for Hempelman is less clear, but may be due to changes in sampling method: Hempelman was sampled in 1997 using the quadrat/transect method (see Mack et al. 2000 for a discussion of this), and this may have

40 resulted in over-estimates of metric values.

Third, although very low quality forested wetlands exist, it is much more difficult to locate them on the 0 landscape since when forested wetlands become very disturbed they tend to have had their trees completely removed and thus have the appearance of emergent marshes or shrub swamps. Given that the climatological climax landscape in Ohio is deciduous forest (Shane 1987; Webb et al. 1983), and that most of Ohio was forested at the time of settlement (Gordon 1966), it can be argued that degraded emergent marshes in known, previously forested areas constitute the bottom of the scale for forested wetlands. Such a site was actually sampled in 2000 (US 42 wetland). This was a riparian forested wetland impoundment area for the Charles Mills reservoir in Richland County that was clearcut about 15 years ago. Stumps with stump sprouts are present but the site has converted to an open water and reed canary grass (Phalaris arundinacea) dominated wetland: it looks like a very disturbed emergent marsh, but for the stumps and stump sprouts.

The continued existence of a forest canopy in the wetland, which is usually associated with some upland forest buffer, also likely ameliorates the effect of human disturbance and increases the overall integrity of even degraded forested systems. Thus, it is expected that, in general, the most highly disturbed, but still tree-covered, forested wetlands will on average exhibit higher degrees of quality than the most highly disturbed emergent wetlands.

Fourth, Mack et al. (2000) discussed the existence of unvegetated versus vegetated forested wetlands. Although not analyzed further in this report, this continues to be a difference observed in the field during sampling of forested wetlands. The metrics presently included in the VIBI-F were selected because they work in both types of systems. In sampling unvegetated forested wetlands, it is very important to include within the sampling plot a considerable portion of the edges of these wetlands (especially herbaceous and shrub vegetation) as well as vegetation growing on bases of trees and hummocks.

\ i J

41 Table22. Description of metrics for VIBI for forested wetlands.

(+)or(·) as disturbance 0 metric code type increases description

number of dicot spp. dicot richness decrease Number of dicot (dicotyledon) spp. present at a site.

number of shrub spp. shrub richness decrease Number of shrub species present at a site.

number of FACW and hydrophyte richness decrease Number of plant species with a facultative-wet or OBL plant species obligate wetland indicator status at a site.

number of Rose rosaceae richness decrease Number of species in the Rose Family familyspp. (Rosaceae) present at a site.

FQAI score FQAI weighted decrease The Floristic Quality Assessment Index score richness index calculated in accordance with Andreas and Ladd (1995). See §2.3 for discussion

relative cover of %intol dominance decrease Percent coverage of plants in the herb and shrub "intolerant" plants ratio stratum with CofCs of 6, 7, 8, 9, or 10 divided by the total percent coverage of all plants. "CofC" means the an individual plant species' "Coefficient of Conservatism".

relative cover of %tol dominance increase Percent coverage of plants in the herb and shrub "tolerant plant ratio stratum with Cotes of 0, 1, or 2 divided by the species total percent coverage of all plants. "CofC" means the an individual plant species' "Coefficient of Conservatism".

relative density..of small tree density ratio increase The density (stems/ha) of a tree species in size trees in 10-25cm size classes between 10 and 25 cm dbh divided by the classes density of all trees.

\ shrub density shrub density density decrease The density (stems/ha) of shrub species. ) (stems/ha) maximum modified max IV mod importance increase The maximum modified importance value of a importance value of a value species at a site calculated by summing relative species at a site size class frequency, relative density, and relative basal area of a species, but not dividing this sum by3.

42 Table 23. Scoring breakpoints for assigning metric scores to a forested wetland. See Table 22 (--,, for descriptions of metric codes. '--j 95•• metric percentile quadrisection method score 0 score 3 score 7 score 10

dicot 37.2 graphical fitting 0-15 16-25 26-29 30+

shrub 8.0 graphical fitting 0-1 2 3-4 5+

hydrophyte 24.6 graphical fitting 0-9 10-14 15-20 21+

rosaceae 5.6 graphical fitting 0 2-3 4+

FQAI 30.1 graphical fitting 0-14.0 14.1-19.0 19.1-24.0 24.1+

%intolerant 0.602 graphical fitting 0-0.035 0.0351-0.12 0.121-0.3 0.31-1.0

%tolerant 0.737 mathematical quadrisection 0.4511-1.0' o.·3011-0.451 0.1501-0.301 0-0.150

small tree 0.411 mathematical quadrisection 0.3241-1.0 0.2161-0.324 0.1081-0.216 0-0.108 \ ) shrub 2990 graphical fitting 0-99 100-399 400-999 1000+ density

max IV 2.033 graphical fitting 1.61+ 1.31-1.6 1.21-1.3 0-1.2

43 Table 24. Distribution of metric scores by site for VIBl-F calculated. Sites in italics are bog or fen sites.

0 ORAM no. of "O" no. of "3" no. of "7" no. of "10" site condition v5.0 VIBl-F scores scores scores scores

Ackerman poor 24 6 8 2 0 0

Big Woods reference 68.5 68 0 2 6 2

Blackjack Rd (front) fair 55.5 91 0 0 3 7

Brown Lake Bog reference 79 90 0 B

City of Mansfield good 53 91 0 0 3 7

Collier Woods reference 73.5 64 0 3 5 2

Eagle Cr. Vernal reference 64 76 0 0 8 2

Flowing Well fair 46 23 5 3 2 0

Fowler Woods reference 79 81 0 4 5

Gahanna Woods 4th good 67.5 53 0 5 4

Graham Rd. very poor 28.5 6 8 2 0 0

Hempel man good 48 85 0 0 5 5

Johnson Rd. very poor 21 6 8 2 0 0

Killbuck Creek fair 33 32 2 6 2 0

Killdeer Plains fair 53.5 33 3 4 3 0

LaRueWoods fair 55 43 3 3 2 2

Lawrence Woods High reference 73 87 0 2 7

Lawrence Woods Low 2 fair 43 71 2 3 5 0

Leafy Oak reference 78 94 0 0 2 8

Mentor Marsh fair 34 44 2 3 5 0

N. Kingsville S. Barr. Sw. reference 67 77 0 2 3 5

Orange Rd. fair 45 37 3 3 4 0

Oyer Tamarack reference

Pallister reference 74 81 0 4 5

Pawnee Rd. reference 70 79 0 3 0 7

Sawmill good 52 63 3 2 4

Tipp-Elizabeth Rd. poor 29 29 4 4

Townline Rd. good 61 70 0 3 3 4

US42 poor 31 16 7 2 0

White Pine Bog reference 83 88 0 0 4 6

total 56 62 84 98 %total 19% 21% 28% 33%

44 Table 25. Percentage of metric scores by forested wetland condition, reference versus nonreference, and ORAM v. 5.0 score. 0 % of "O" scores % of "3" scores % of "7" scores % of "10" scores very poor 29% 6% 0% 0%

poor 34% 13% 1% 2%

fair 30% 34% 27% 9%

good 7% 24% 24% 21%

reference 0% 23% 48% 67%

non reference 95% 66% 45% 29%

reference 5% 34% 55% 71%

0-35 ORAM score 30% 34% 27% 9%

36-59 ORAM score 64% 32% 18% 19%

60+ ORAM score 5% 34% 55% 71%

45 QB Q1 ., Q7 Cl. - Q6 Cl. c: "'., ~ 0.5 ~ 0.4 4 0 0 <.> Q - OJ :0-20 ~ 1f/- 0.2 0.1 10 $ B0 0.0 • ~ -BllPs rer-Hu> rB-ffiLP nn-8JLP uf-EllP u/.ffiLP -""' refXeco -""' refXeco

B. Q9 QB

Cl. • c 0.7 Cl. ~ 0.6 "' 5 .a • ..! 0.5 £0.4 2 c: .c: 3 -- 0.3 "' ~g ?ft 0.2 4 Q1. B QO 0 ut-ii&> -EDP ft:n-B:>LP~o rllf-BiP r4'-8)LP ~enn-IDLP r•-ffiLP -""' refXeco refXeco

.. QS

CD Q.4 I ~ 35 ,.. f Cl. - Ql e 2i «i "C,.. E a2 .c: .. 15 Q1 g \ 8 D$ e ~ ~ QO ~ -EDP n:n-B'.>LP nt·BiP rd'-EDLP _m,, nn-iiJLP rtt-Bu> Hl-IDLP refXeco refXeco

!ml

~ 5 ~- .. "'c: .. .. 3Dl .. "C .. 3 ..<.> .g 3JXl .. 2 e .t: 1• QB "'""'

~DI I I I -I 808I I - I -BB' -ED!P rllf-BB" r9-EDLP -EDP -"''" rllf-BB' ,_...fDLP refXeco refXeco

., 21

>-u >< ~"' .. u. E 1.1 80 10 60 Q6 ~ -BB' nn-BlLP rS-BB' rct-eoLP -EDP ...,...EDLP rllf-Hlf' ut-EDLP refXeco refXeco

Figure 10. Box and whisker plots of individual metric values for the VIBl-forested by reference/nonreference and ecoregion (ECBP=Eastem Com Belt Plains; EOLP=Erie Ontario Lake Plains). A line is drawn acro,%'~the box at the median. The bottom of the box is at the first quartile (01 ), and the top is at the third quartile value. The whiskers are the lines tha7extend from the top and bottom of the box to the adjacent values. The adjacent values are the lowest and the highest observations that are still inside the region defined by the following limits: Lower Limit=O 1-1.5(03- 01 ); Upper Limit=03+1.5(03-01 ). • 0.8 ... 07 • cl 0. • - 0.6 c • • 0. • ., •• .. •• e o.s • • 0"' I ~. ~ 0,4 u 0 • • • .. "c: 0.3 • 20 • • • • I • "" 0.2 ...... 0.1 • • .. . .. 10 •• • 00 . .• .; . . • 20 ., .. 70 ., 20 ., ...... 70 ., CRAM vS CRAM vS

09 • •• 08 • • • Q.7 0. 8. c 0. .. 06 • .. Q5 • •• .c ,. • .! ·- £0.4 2 • • • .s:: 3 • •• • c "' • • • ~ ~~ • • ~ .. • • 01 • .. • - 00 ..... • .,; .,•. . . ., . . ., ...... 70 .. "' ...• .. ... 70 CRAM vs CRAM vs

" OS • • • ., 3S ., o• >. !" •• • 0. - 03 • e " • .. • • • >- • E 02 • .s::" • • 15 • •• • 01 • •- 1 .. t ~,..... • ~ I I \ ··' • ) •• 00 • • ., ,,; .. rii 70 20 .. ... 70 .. CRAM vS CRAM vs

•• '°" • .,~ s • • • =:-""'., ., c ., • • CD :mJ • CD 3 u .c" • • •• ml •• ., 2 2 "' • -• •• • .r:: ., e 10XJ • ... • • • .:, .. •••- • - 0 ., ., 20 .. 70 20··- "' ...- ..- 70 CRAM vS CRAM vS

., • 21 • • I • • • • > ••• • • I • • - 1.6 i5 20 .. • " •• I ... • • "'E "- • • • • • • • • • ··~ • ., • • • • • • 06 • ., ., • 20 ...... 70 20 ...... 70 ., CRAM vS CRAM vS

Figure 11. Scatterplots of metric values used in VIBl-Forested versus ORAM v. 5.0 score. 47 0

100 <> • non-ECBP 90 •• <> a a • non-EOLP 80 • a a a <> ref-ECBP aa a ref-EOLP 70 • a <> ._Q) <> 0 60 • (.) Cf) 50 • LL _!... 40 • • a:i • > 30 • • • 20 • • 10 0 •• • 0 10 20 30 40 50 60 70 80 90 100 h igh disturbance low disturbance low integrity high integrity

Figure 12. Scatterplot of Vegetation 181 score versus disturbance scale (ORAM v.5.0 score) for forested wetland (df=28, F=53.81, p<0.001, R2=66.6%). non=nonreference wetlands; ref=reference wetlands; ECBP=Eastem Corn Belt Plains; EOLP=Erie Ontario Lake Plains. ) I

48 3.3 VIBI-SHRUB

Development of the VIBI-SHRUB (VIBI-SH) (formerly VIBI-Scrub-Shrub or VIBI-SS) was complicated 0 by the fact that initially many shrub communities were classified as forested since they were located entirely within upland or wetland forests. The scrub-shrub class did not begin to emerge until data exploration of potential attributes for forest IBis revealed a group of sites which did not appear to "fit." The dominant character of the vegetation communities at these was reconsidered and it became apparent that they were more properly classified as shrub dominated communities without a closed canopy of trees, although a "forested" margin was often present and they were frequently located within tracts of forest.

Another complication was the lack of a suite of sites which represented the entire disturbance gradient form highly disturbed to very undisturbed. All of the shrub wetlands were in "good" or "reference" condition; the low end of the scale was missing in this data set which made it impossible to apply the attribute evaluation "rules" used to identify candidate metrics (i.e. linear or curvilinear relationships, etc.).

Shrub communities in Ohio are usually an intermediate successional step between herbaceous and forested communities, however they can be very stable (in time) and extensive (in area) features on the landscape and aie generally treated as a distinct, identifiable community (Anderson 1982; Cowardin et al. 1979). Because shrub communities are intermediate between herbaceous and forested communities, a solution to the problems discussed above was to evaluate the shrub sites in conjunction with the metric values for forested and emergent communities. Thus, the shrub wetlands were compared with forested and emergent wetlands. Metrics where the shrub community sites "fit" into these other data sets were selected for use. "Fit" was determined by graphing shrub metric values with the emergent and forested data sets (Figure 13) and by comparing mean values of metrics for emergent, forest, and shrub communities to identify where significant differences between these classes occurred (refer to Table 11 in §3.0). This analysis resulted in the use of 5 forested (shrub, rosaceae, hydrophyte, small tree, shrub density) and 3 emergent metrics (carex, dicot, FQAI) (Table 26, Figure 13). Because the distributions and ) mean values for %tolerant and %intolerant metrics were so different for shrub wetlands, only the shrub wetland values for these two metrics were used to calculate the metric scoring breakpoints (Table 27).

Metric scores for shrub wetlands were calculated by using the scoring breakpoints derived for the emergent and forested metrics (Table 27). A Vegetation Index of Biotic Integrity for wetlands dominated by shrub vegetation communities was calculated by summing the individual metric scores (Table 28).

49 Table 26. Description of metrics for VIBI -shrub. Group refers to which data set (E=emergent, 0 F=forested, SH=shrub) the shrub wetland data was included with to derive the VIBl-SH. (+)or(-) as disturbance code type group increases description

number of Carex spp. carex richness E+SH decrease Number of Carex spp. present at a site.

number of dicot spp. di cot richness E+SH decrease Number of dicot (dicotyledon) spp. present at a site.

number of shrub spp. shrub richness F+SH decrease Number of shrub species present at a site.

number of FACW and hydrophyte richness F+SH decrease Number of plant species with a OBL plant species facultative-wet or obligate wetland indicator status at a site.

number of Rose family rosaceae richness F+SH decrease Number of species in the Rose Family spp. (Rosaceae) present at a site.

FQAI score FQAI weighted E+SH decrease The Floristic Quality Assessment lngex richness score calculated in accordance with index Andreas and Ladd (1995).

relative cover of %intolerant dominance SH decrease Percent coverage of plants in the herb "intolerant" plants ratio and shrub stratum with CofCs of 6, 7, 8, 9, and 10 divided by the total percent coverage of all plants. "CofC" me13ns the an individual plant species' "Coefficient of Conservatism"..

relative cover of "tolerant %tol dominance SH increase Percent coverage of plants in the herb plant species ratio and shrub stratum with CofCs of 0, 1, or 2 divided by the total percent coverage of all plants. "CofC" means the an j " individual plant species' "Coefficient of Conservatism".

relative density of trees in small tree density F+SH increase The density (stems/ha) of tree species in 10-25cm size classes ratio size classes between 10 and 25 cm dbh divided by the total density of trees.

shrub density (stems/ha) shrub density F+SH decrease The density (stems/ha) of shrub species. density

50 Table 27. Scoring breakpoints for assigning metric scores to a shrub wetland. See Table for 0 descriptions of metric codes. 95•• metric percentile quadrisection method score O score 3 score 7 score 10

carex 5.7 graphical fitting 0-1 2-3 4 ~5 emergent distribution

dicot 26.7 graphical fitting 0-9 10-14 15-23 ~24 emergent distribution

shrub 8.0 graphical fitting 0-1 2 3-4 5+ forest distribution

hydrophyte 27.0 graphical fitting 0-9 10-14 15-20 21+ forest distribution

rosaceae 5.6 graphical fitting 0 1 . 2-3 4+ forest distribution

FQAI 28.3 graphical fitting 0-9.9 10.0-14.3 14.4-21.4 ~21.5 emergent distribution

%intolerant 0.838 mathematical quadrisection 0-0.210 0.2101-0.419 0.4191-0.629 0.6291-1.0 only shrub site distribution

%tolerant 0.206 mathematical quadrisection 0.1551-1.0 0.1031-0.155 0.0521-0.103 0-0.055 only shrub site distribution

'I / small tree 0.411 mathematical quadrisection 0.3241-1.0 0.2161-0.324 0.1081-0.216 0-0.108 forest distribution

shrub 2990 graphical fitting 0-99 100-399 400-999 1000+ density forest distribution

51 Table28. Distribution of metric scores by site for VIBl-SH calculated. Sites in italics are bogs and fens 0 ORAM no. of "O" no. of"3" no. of "7" no. of "10" site condition v5.0 VIBl-SH scores scores scores scores

2 Meadows Swamp good 49 71 0 2 5 3

Area K reference 61.5 84 0 3 6

Blackjack Rd (back) good 67 94 0 0 2 8

Blanchard Oxbow fair 48 27 5 2 3 0

Burton Lakes Vernal reference 67 77 2 6

Callahan good 57.5 58 2 6

Cessna reference 61 78 0 5 4

Drew Woods reference 70 80 0 2 2 6

Fowler Woods BBS reference 79 68 5 3

FriedsBog reference 76 86 0 2 0 8

Gahanna 1st reference 82.5 94 0 0 2 8

Grand R. Terraces reference 74 91 0 0 3 7

Keller High reference 65.5 65 2 0 5 3

King Hollow Rd poor 28 27 6 2

Koe/liker Fen reference .12 74 3 5

McKee Bog good 56 94 0 0 2 8

McKinley fair 37.5 33 4 3 2

Oyer Wood Frog reference 69 68 5 3

Route 29 reference 59 75 0 5 4

Slate Run reference 71 88 0 0 4 6

Swamp Cottonwood reference 75 94 0 0 2 8

The Rookery reference 69 71 0 2 5 3

Towners Woods reference 74 54 3 3 3

Townline BBS good 61 57 2 2 3 3

Watercress Marsh Fen reference 77.5 85 0 3 3 4

Wilson Swamp reference 77 66 3 5

total 29 31 83 117 %total 11% 12% 32% 45%

52 ~~· x 5 ~ 4 "'u 3 <> <»• <» <>¢-

1525 3.5 45 5565 75 85 15 25 35 45 55 65 75 85 ORAMv5 ORAMv5

. 0.8 30 0.7 l!;'J l?~=t 0.6 a. <>··&~·. * ~ ~ ~ ~ 0.5 :;:-20 .. * * 0 <><>8 <> <> ~ a; 0.4 * u <> ~0.3 ** =o 10 <><» <> <> <><> ~02 ** * 0.1 *.** itt,*· *M; * <> 0.0 ** • I - • 15 25 35 45 55 65 75 85 :iii 30 ..,j 50 60 ro 8'.) ORAMv5 ORAMv5

. 0.9 0.8 l!::J 0.7 . *: § a. 7 ...*• * * :;:- 6 ~ 0.6 (DOS .:*.a.* .D 5 *\.. * . *" :g 0.4 "'4 * -II- • -:;; 3 * ~g; 2 * *V .*·Jti: * . 0.1 * *** •• 0.0 20 30 40 50 60 70 8'.) 20 30 40 50* 60 70 8'.) ORAMv5 ORAMv5

05 ., . l!::J 0.4 l!::J "'4 * * * * ., .,.,o.J ** :5 3 **" * ~2 * * 2 * **-** *·* !:'"' . *. "** "' * * -~* *... i8E- • * ~1 \""*X** * 0.0 * . :*• 20 30 40 50 60 70 8'.) 20 30 40 50 60 70 8'.) ORAMv5 ORAMv5

45 500J ------.§ . ·* FOies ~ •s ..,:ij3000"' "* -g 2000 * ·t. .w•t• .c lt·*. . **'·* ., 1000 * 2030-40508)708) 2030405060708'.l ORAMv5 ORAMv5

Figure 13. Scatterplots of metrics used for VIBI for shrub wetlands.

53 4.0 Analysis of the Vegetation IBI and ORAM scores: ecoregional, hydrogeomorphic, and plant community comparisons

0 4.1. Vegetation IBI

The characteristics of the Vegetation Indices of Biotic Integrity (VIBis) were compared using ecoregional, hydrogeomorphic (HGM) and plant community classification schemes as major sorting variables. Only wetlands from the Eastern Com Belt Plains or Erie Ontario Drift and Lake Plains ecoregions were included in this analysis (N=83), however, 3 sites from the Michigan Indiana Drift and Lake Plains, l coastal marsh, and l site from the Western Allegheny Plateau have been sampled. Table 29 summarizes the sites currently sampled (through the 20001 field season) for present or future use in developing vegetation IBis.

Table 29. Summary of numbers of sites by major hydrogeomorphic and plant community classes. Numbers in parentheses are numbers including plots from 2001 field season. Data from 2001 field season was not analyzed in this report Hydrogeomorphic Classes N I Plant Community Classes N isolated depression 57(69} various bog communities 6(7} I'h1 isolated flats 1(2} \11'£),j various fen communities 6(11) riparian mainstem depression 8(12} marshes (all types} 23(36)

riparian headwater depression 5(8} sedge-grass communities 3(6) ~ riparian headwater groundwater 3 shrub swamps 20(23} P.?' m '~, slope (riparian and isolated} 8(17} swamp forests 30(38}

fringing 3 ,/ 11 impoundment 2 ,~.:lri'Sl coastal 1(5} ii TOTAL 88(121) fr~J 88(121)

The Vegetation IBI scores for all wetlands and classes of wetlands in the Eastern Com Belt Plains and Erie Ontario Drift and Lake Plains were plotted together (Figure 14). Very strong linear trends were observed (df=82, F=l 74.95, p<0.001, R2=68.4%). While this is a not unexpected result given that the VIBI metrics were selected based on their relationship to the ORAM v. 5.0 score used as a disturbance scale, this graph can be considered a summation of the relationship each metric has had with the disturbance scale. The IBI scoring technique has a standardizing effect that dampens individual metric variability.

54 100 .6. • bog 1' ¢ .6. .., ~ ~ 0 90 ¢ dist-bog * *'\"* 0 dist-fen 20 • * 10 •• • * • 0 • ** 0 10 20 30 40 50 60 70 80 90 100 high disturbance low disturbance low integrity high integrity

Figure 14. Vegetation IBI scores for wetlands in the Eastern Corn Belt Plains and Erie Ontario Lake Plains plotted against ORAM v.5.0 score. Refer to Table 6 for descriptions of the plant communities. "dist-fen" and dist-bog" refer to fens or bogs that have been disturbed by human activity. Results from linear regression of this distribution highly significant (df=82, F=174.95, p<0.001, R"=68.4%).

100 90

Q) 80 0 u 70 m"' 60 Q $ c 50 .Q 10 40 .. QI 30 Cl Q) > 20 10 0

Q. Q. CD ..J u 0 ~ ~ 0 g c

Figure 15. Box and whisker plots of Vegetation IBI scores for reference (ref) and nonreference (non) wetlands by ecoregion. ECBP= Eastern Com Belt Plains, EOLP= Erie Ontario Lake Plains. Means are indicated by solid circles. A line is drawn across the box at the median. The bottom of the box is at the first quartile (01 ), and the top is at the third quartile (03) value. The whiskers are the lines that extend from the top and bottom of the box to the adjacent values. The adjacent values are the lowest and highest observations that are still inside the region defined by the following limits: Lower Limit= 01 - 1.5 (03 - 01 ); Upper Limit= 03 + 1.5 (03 - 01 ). Outliers are points outside of the lower and upper limits and are plotted with asterisks (*).

55 Comparisons of the mean Vegetation IBI scores for wetlands in the Eastern Com Belt Plains (ECBP and Erie Ontario Drift and Lake Plains (EOLP) ecoregions showed scores were significantly different from each other based on reference condition, but ecoregional differences were only noted between the nonreference ECBP and nonreference EOLP categories (df=72, F=l9.79, p<0.001) (Table 30, Figure 15). Thus, on average, there were no significant differences in mean scores for reference wetlands in this data set for the two ecoregions when VIBI scores for all classes are pooled. Only small differences were noted 1 when the 95 h percentiles of the two ecoregions were compared with ECBP distribution being slightly higher than the EOLP distribution. This is opposite what would be expected.

Table 30. Mean and standard deviation of Vegetation 181 scores for 2 ecoregions and 2 wetland classes (reference and nonreference sites). Bogs and calcareous fens from both ecoregions were excluded from the analysis. Means with shared letters were not significantly different at p<0.05 after analysis of variance followed by Tu key's HSD multiple comparison test.

mean stdev N

nonreference ECBP 38.1a 26.3 31

nonreference EOLP 50.7b 22.1 10

reference ECBP 76.9c 13.1 17

reference EOLP 78.3c 9.4 15

The quadrisected distributions of the 95lh percentile of each of the dominant vegetation communities . (emergent, forest, shrub) was also evaluated. Little ecoregional variation was observed (Table 31) and very similar scores and breakpoints were calculate for each community type (Table 32). However, very marked ecoregional separation is present within the emergent class when only "marsh" communities in ) the two ecoregions are compared (Table 35 and see discussion below separating emergent marsh communities and emergent sedge-grass, fen, and bog communities). Considering only reference quality marshes in the two ecoregions, the following breakpoints were calculated when the 95lh percentile was q uadrisected:

ECBP Reference Marshes 0-16 17-34 35-51 52+ EOLP Reference Marshes 0-20 21-41 42-62 63+

Mean VIBI scores for emergent marshes were also different based on ecoregion (ECBP=69 .0 n=2; EOLP=80.7 n=3) although these differences were not significant probably due to the admittedly small sample sizes. 9 In conclusion, within the limits of the current data s.et, there appear to be noticeable ecoregional differences in emergent marshes that should be accounted for when establishing numeric biological criteria.

9 A review of Table 35 will also reveal that a similar discrepancy in breakpoints is also apparent when sedge grass, fen, and bog communities are compared. It is felt that this difference is due to the lack of reference quality fen and sedge grass sites in the EOLP region. Additional reference quality sites in the EOLP region were sampled in 200 I. Also, similar large differences were not observed when ecoregional differences in the mean VIBI scores ofreference forested and reference shrub wetlands were evaluated: mean VIBI-F scores: ref-ECBP=78.3, refEOLP=81.7, not significant at p<0.05; mean VIBI-SH scores: ref-ECBP=75.0, ref-EOLP=77.8, not significant at p<0.05.

56 1 Table 31. Comparison of 95 h percentile and quadrisection of 95lh percentile of Vegetation 181 scores by ecoregion for reference sites by ecoregion and all sites by () ecoregion, excluding fen and bog wetlands. _/

reference reference point all sites all sites point EOLP ECBP difference EOLP ECBP difference

95"' percentile 90.3 94.0 -3.7 90.8 93.0 -2.2

1•1 quarter 22.6 23.5 -0.9 22.7 23.3 -0.6

2nd quarter 45.2 47.0 -1.8 45.4 46.5 -1.1

3"' quarter 67.7 70.5 -2.8 68.1 69.8 -1.7

Table 32. Comparison of 951h percentile and mathematical quadrisection of 95lh percentile for Vegetation 181 scores for emergent, forested, and shrub wetland 181s.

95"' percentile 1st quarter 2•• quarter 3"' quarter

Emergent All Sites 93 23 47 70

All ECBP sites 93 23 47 70

All EOLP sites 84 21 42 63

Reference. ECBP sites 96 24 48 72

Reference EOLP sites 86 21 43 64

average 90 23 45 68

Forest All Sites 91 23 46 68

All ECBP sites 92 23 46 69

All EOLP sites 90 23 45 68

Reference ECBP sites 93 23 46 69

Reference EOLP sites 89 22 45 67

average 91 23 46 68

Shrub All Sites 91 23 47 68

All ECBP sites 91 23 45 68

All EOLP sites 89 22 45 67

Reference ECBP sites 92 23 46 69

Reference EOLP sites 89 22 45 67

average 90 23 45 68

57 Individual metrics that make up the VIBl-E, VIBI-F, and VIBI-SH were also compared (Table 33). Of the ten metrics evaluated, 9 metrics showed significant differences between reference and nonreference wetlands. Only 4 metrics had apparent ecoregional differences (dicot, hydrophyte, %intolerant, shrub 0 density). Of these 4 metrics, only the differences in %intolerant and shrub density appear to be possible ecoregionally-based differences. The difference in dicot and hydrophyte metrics is opposite the expected trend since the reference ECBP sites on average are higher than the reference EOLP sites. This is likely to be an artifact of the current data set.

The differences in the %intolerant metric may in fact be due to EOLP wetlands having higher relative cover of plants with coefficients of conservatism of 6 to 1O; or at least this would not be an unexpected result, given the more "boreal" character of wetlands in northeast Ohio. The differences in shrub density between ref-ECBP and ref-EOLP wetlands is not as easily explainable. It may be a real difference or again an artifact of the current data set. The behavior of all the metrics used will continue to be evaluated as data from 2001 and subsequent years are incorporated into the VIBI data set.

The mean ORAM scores of reference wetlands of different dominant vegetation communities was also compared. While there were no significant differences between these classes when reference sites were compared (df=40, F=l.27, p=0.300), sites dominated by bog, fen, and sedge-grass communities had noticeably higher VIBI scores (Table 34). The lack of significance may be due to the very small sample sizes when only reference sites are considered, especially for the sedge-grass communities.

It has been expected throughout the development of the VIBI, that bogs and calcareous fens would likely need to be classified and treated separately, but the high scores for sedge-grass communities is a new factor. In order to further explore this classification issue, the 95lh percentiles of the VIBI distributions forall emergent sites, marshes only, and fen/bog/sedge-grass communities were compared. Very stark differences in the 95lh percentiles and the upper end of the quadrisected distribution were apparent (Table 35). Including fen, bog and sedge-grass communities in the distribution raises the 3rd quarter breakpoint \ for marshes by 12 points from 58 (marshes only) to 70 (all sites). The 3rc1 quarter breakpoint for just the ) fen, bog and sedge-grass communities is a score of72, 14 points higher than the 3rc1 quarter breakpoint for marshes only. Therefore, separate numeric standards appear to be necessary for establishing wetland habitat uses for these two emergent community groups.

58 Table 33. Mean and standard deviation (parenthesis) and ANOVA results for metrics used in 0 VIBl-E, VIBl-F, and VIBl-SH by reference (ref) and nonreference(non) and ecoregion categories (ECBP=Eastern Corn Belt Plains, EOLP= Erie Ontario Drift and Lake Plains. Means with shared letters were not significantly different after Tukey's HSD multiple comparison test. Results from biomass metric not reported because of insufficient sample size (n<4) in two of the four categories.

metric ANOVA results nonECBP nonEOLP refECBP refEOLP

dicot spp. df=72, F=7.58, p<0.001 18.7(9.6)a 17.4(6.5)a 28.9(6.2)b 24.7(6.9)c

hydrophyte spp. df=72, F=6.39, p=0.001 14.4(6.4)a 19.4(8.9)b 24.0(9.0)c 20.5(7.8)d

rosaceae spp. df=72, F=2.77, p=0.048 1.5(1.8)a 1.7(1.4)a 2.8(1.4)b 2.3(1.1 )b

FQAI df=72, F=16.32, p<0.001 15.7(5.9)a 17.4(4.2)a 23.2(4.2)b 25.1(3.9)b

%tolerant df=72, F=9.49, p<0.001 0.36(0.26)a 0.35(0.23)a 0.10(0.07)b 0.13(0.10)b

%intolerant df=72, F=10.10, p<0.001 0.13(0.19)a 0.22(0.30)a 0.37(0.15)b 0.48(0.25)c

%invasive graminoids df=72, F=5.82, p=0.001 0.11 (0.19)a 0.23(0.27)b 0.005(0.009)c 0.003(0.01 O)c

small tree density df=43, F=3.40, p=0.027 0.21(0.13)a 0.18(0.19)a 0.11 (0.08)b 0.09(0.06)b

shrub density df=43, F=6.59, p=0.001 458(739)a 523(644)a 1433(1089)b 2305(1714)c

max importance value df=45, F=1. 72, p=0.178 1.41(0.46) 1.54(0.44) 1.43(0.30) 1.13(0.39)

Table 34. Mean and standard deviation of Vegetation IBI scores of reference wetlands for 5 dominant plant community classes in the Eastern Corn Belt Plains and Erie Ontario Drift and Lake Plains ecoregions. Sedge-grass meadow wetlands include wet prairies, seep fens and other sedge and grass dominated wetlands. Means were not significantly different (p=0.35) after analysis of variance.

mean stdev N

bog 84.8 10.1 5

fen 83.0 8.0 4

sedge-grass 85.3 16.9 3

marsh 76.0 7.5 5

shrub swamp 73.7 13.2 12

swamp forest 80.1 8.8 12

59 0 1 Table 35. Comparison of 95 h percentile for Vegetation 181 scores for EMERGENT wetlands.

description Table 6 Grouped by 95lh 1st 2·• 3"' code percentile quarter quarter quarter

All Emergent Ill All Sites 93 23 47 70

All ECBP sites 93 23 47 70

All EOLP sites 84 21 42 63

Reference ECBP sites 96 24 48 72

Reference EOLP sites 86 21 43 64

average 90 23 45 68

Marshes only Illa All Sites 78 19 39 58

All ECBP sites 69 17 34 51

All EOLP sites 84 21 42 63

Reference ECBP sites 70 17 35 52

Reference EOLP sites 86 22 43 65

average 77 19 39 58

) Fen, bog, and lllb, c,d,e All Sites 96 24 48 72

sedge-grass All ECBP sites 96 24 48 72

communities All EOLP sites 79 20 40 59

Reference ECBP sites 96 24 48 72

Reference EOLP sites 82 20 40 60

average 89 22 45 67

60 Finally, the Vegetation IBI score ofreference 0 wetlands based on hydrogeomorphic class was analyzed. In order to ensure a large enough sample size, some classes were grouped 100...r~~~~~~~~~~~~~~~~~~---, together. With regards to HGM classes, no 90 statistically significant differences in mean ~ 80 8 70 VIBI scores were observed for reference rn wetlands (df=40, F=l.25, p=0.304) and all m so c 50 wetlands (df=82, F=l.45, p=0.235) (Table .Q 10 40 36). However, there is a very large difference Ci) 30 Cl in mean scores and noticeable graphical ~ 20 separation in box and whisker plots (Figure 10

l 6) for riparian mainstem depressions versus 0 ~~~~~~~~~~~~~~~~~~~~ the other HGM classes. All of the riparian m .c"' mainstem depression sites in the current data iiic. 0::: set are forest or shrub dominated communities. Figure 16. Box and whisker plots of Vegetation 181 scores for hydrogeomorophic classes. "isol depress"=isolated depressions; "ripar-head"= riparian headwater depressions; "ripar-main-dep"=riparian mainstem depressions; "slope-fringe"= slope Despite the lack of statistical significance, or lacustrine fringe (primarily fens) wetlands. Means are indicated by solid circles. A when means from all sites are considered the line is drawn across the box at the median. The bottom of the box is at the first quartile (01 ), and the top is at the third quartile (03) value. The whiskers are the mean VIBI score for riparian mainstem lines that extend from the top and bottom bf the box to the adjacent values. The depressions is 17 to 28 points lower; for adjacent values are the lowest and highest observations that are still inside the reference sites, it is 14 to 17 points lower. region defined by the following limits: Lower Limit= 01 - 1.5 (03 - 01 ); Upper Limit = 03 + 1.5 (03 - 01 ). OuUiers are points outside of the lower and upper limits and This difference is too large to ignore and may are plotted with asterisks (*). require separate VIBI breakpoints for this class. The 95lh percentile of riparian mainstem depressions is 65.3 (n=8). Quadrisecting this distribution results in substantially lower breakpoints than for other forested and shrub wetlands (Table 32). The lower overall scores for riparian mainstem depressions are also amenable to an ecological explanation: . these are communities which would often be subject to annual to multi-annual flood events such that there floras would have strong representation from plants tolerant of this recurring natural disturbance. In comparison to inland wetlands with more stable hydrologies, this shift to a high-quality, naturally disturbance tolerant flora, may result in lower VIBI scores. The same situation may occur in the coastal marsh setting where wetland annuals with relatively low coefficients of conservatism can occur in diverse coastal marsh plant communities.

Table 36. Mean and standard deviation of Vegetation IBI scores of all wetlands for 4 dominant hydrogeomorphic classes including Jen and bog sites. One headwater impoundment was grouped in the riparian headwater category. No means were significantly different (p<0.05) after analysis of variance.

ALL SITES ALL SITES REFERENCE REFERENCE mean N mean N

isolated depression 62.6(28.0) 56 79.1(11.3) 30

riparian mainstem depression 38.9(17.9) 8 65.0(1.4) 2

riparian-headwater-depression and 55.9(25.3) 9 80.7(5.5) 3 riparian-headwateriJroundwater

slope and fringing 66.9(25.1) 10 82.0(11.8) 6

In conclusion, there appear to be no ecoregional differences in Vegetation IBI scores of reference

61 wetlands in the current data set with the exception of emergent marsh communities; ecoregional 0 differences between nonreference wetlands are attributable to the current data set which is lacking very highly disturbed EOLP wetlands. Such sites were sampled during the 200 l field season and it is expected that nonreference wetlands in both regions will have similar scores once these new sites are incorporated into the data set. Vegetationally, emergent marshes, swamp forests (wet woods, vernal pools and other depressional wetlands) and shrub swamps have very similar average VIBI scores. Calcareous fens, bogs, and sedge-grass dominated emergent wetlands need to be separately classified. Considering hydrogeomorphic classes, little or no differences are apparent between classes except for riparian mainstem depression wetlands, which appear to require separate classification and numeric criteria breakpoints.

4.2 ORAM score comparison

The characteristics of the Ohio Rapid Assessment Method for Wetlands (ORAM) v. 5.0 scores were compared using ecoregional, hydrogeomorphic (HGM) and plant community classification schemes as major sorting variables.

Comparing mean ORAM v. 5.0 scores for wetlands in the Eastern Com Belt Plains (ECBP and Erie Ontario Drift and Lake Plains (EOLP) ecoregions, scores were significantly different from each other reference versus nonreference categories, but only nonreference wetlands showed significant ecoregional differences (Table 37, Figure 17). Nonreference wetlands selected for sampling in the EOLP region during the 2000 field season tended to be of better quality; highly degraded sites were not sampled, and therefore, this difference may be an artifact of the current data set. Attempts were made to include highly degraded EOLP wetlands during the 2001 field season.

In addition to a comparison of mean differences in ORAM v. 5.0 scores, the 95lh percentile of the ORAM score distributions was analyzed since the sectioning ofthis number is a standard IBI development technique for determining breaks between IBI classifications. In comparing reference sites between the ecoregions, differences in breakpoints were minor varying from 0. 7 to 2.2 points for the quadrisected 95lh percentile (Table 38). There was up to a 2.9 point difference in the 95lh percentiles for the two regions. Assuming the ecoregional differences are real and not an artifact of the current data set, using these

Table 37. Mean and standard deviation of ORAM v. 5.0 scores for 2 ecoregions and 2 wetland classes (reference and nonreference sites). Fen and bog sites from both ecoregions were excluded from the analysis. Means with shared letters were not significantly different at p<0.05 after analysis of variance followed by Tu key's HSD multiple · · · ··· · comparison test.

mean stdev N

nonreference ECBP 39.8a 13.7 31

nonreference EOLP 48.6b 15.1 10

reference ECBP 68.9c 7.8 17

reference EOLP 73.Sc 6.0 15

62 1 1 Table 38. Comparison of 95 h percentile and quadrisection of 95 h percentile of ORAM 0 v. 5.0 scores by ecoregion for reference sites by ecoregion and all sites by ecoregion, excluding fen and bog wetlands.

reference reference point all sites all sites point EOLP ECBP difference EOLP ECBP difference

95"' percentile 81.6 78.7 2.9 80.9 76.7 4.2

1'' quarter 20.4 19.7 0.7 20.2 19.2 1.0

2"" quarter 40.8 38.4 2.4 40.5 38.4 2.1

3rd quarter 61.2 59.0 2.2 60.8 57.5 3.3

N 15 17 25 48

Table 39. Mean and standard deviation of ORAM v. 5.0 scores of all wetlands for 4 dominant hydrogeomorphic classes. One headwater impoundment was grouped in the riparian headwater category. Means with shared letters were not significantly different at p<0.05 after analysis of variance followed by Tukey's HSD multiple comparison test.

mean stdev N

isolated depression (includes 1 57.5a 19.3 56 isolated flats)

riparian mainstem depression 45.4a 19.0 8

\ riparian-headwater-depression and 55.3a 20.2 9 riparian-headwater-groundwater

slope and fringing 63.7a 14.0 10

results to set ORAM scoring breakpoints10 would result in the following:

ECBP 0-19.7 (Category 1) 19.8-59.0 (Category 2) 59.1 +(Category 3) EOLP 0-20.4 (Category 1) 20.5-61.2 (Category 2) 61.2+ (Category 3)

Comparing these breakpoints to. the ones proposed in Mack (2000), results in a 1 point lowering of the Category 2/3 breakpoint for ECBP wetlands and a 1 point increase in the Category 2/3 breakpoint for the EOLP wetlands. It should be noted that mechanical quadrisection of the ORAM v. 5.0 distribution was rejected in Mack et al. (2000) and breakpoints were graphically fitted since the l st quarter breakpoint was too low and virtually no highly disturbed (category 1) wetlands would exist if mathematically quadrisected breakpoints were used.

The ORAM score of reference wetlands based on hydrogeomorphic and plant community classification scheme (Tables 5 and 6) was analyzed. In order to ensure a large enough sample size, some classes were grouped together. With regards to HGM classes, no statistically significant differences in mean ORAM

10 ORAM breakpoints proposed in Mack et al. (2000) were as follows: 0-29.9 (Category I), 30-34.9 (Category 1-2 "gray" zone, 35.0-59.9 (Category 2), 60.0-64.9 (Category 2-3 "gray" zone), 65.0-100 (Category 3 ).

63 scores were observed for reference wetlands (df=38, F=0.60, p=0.618) and all wetlands (df=82, F=l.45, 0 p=0.235) (Table 39). The mean ORAM scores of reference wetlands of different dominant vegetation communities were also compared. There were significant differences between each category when all sites (both reference and nonreference were compared (df=82, F=4.68, p=0.001); however, when only reference wetlands were compared, there were no significant differences, although bogs on average scored 4-6 points higher than the other classes (df=40, F=0.39, p=0.853) (Table 40).

The results of the HGM and plant community comparison make sense and were expected since the questions that make up the ORAM are either insensitive to such differences or, to be properly answered, require the rater to explicitly rate wetlands in relation to others with the same HGM or plant community

Table 40. Mean and standard deviation of ORAM v. 5.0 scores of reference wetlands for 4 dominant plant community classes. Several sedge-grass meadow wetlands were included in the marsh category. Means were not significantly different at (p=0.853) after analysis of variance.

mean stdev N

bog 75.8 12.9 5

fen 70.5 13.9 4

sedge-grass 71.2 12.0 5

marsh 68.3 2.5 3 ) shrub swamp 69.4 7.0 11

swamp forest 71.3 7.7 13

classes. For example, some metrics in the ORAM (e.g. Metric 1 wetland size, Metric 2 buffers and intensity of surrounding land uses, Metric 4a substrate disturbance, Metric 6b plan view interspersion, Metric 6c invasive plant coverage) can be answered objectively without any reference to HGM or plant community class of the wetland being evaluated. Other Metrics (e.g. Metric 3e hydrologic intactness, Metric 4b habitat development, Metric 4c habitat intactness) expressly require the rater to rate the wetland only in relation to wetlands of the same type, region, class, etc., thus expressly standardizing the ORAM score such that wetlands of different types receive a similar score (Mack 2000).

In conclusion, ORAM v. 5.0 performed as it was intentionally designed to perform, with an overall insensitivity to regional, hydrogeomorphic, and plant community differences in wetlands. The method includes questions which by their nature are insensitive to these potential differences, or requires the user to explicitly compare the wetland being assessed to other wetlands with the same regional, hydrogeomorphic, or plant community characteristics. The only real exception to this are the "special wetland communities" listed in Metric 5 of ORAM v. 5.0, in particular the bog and fen communities, which showed a marked graphical separation in their box and whisker plots especially when both reference and nonreference sites were analyzed together. However, this was also an intended and expected result of the ORAM v. 5.0 which was designed to ensure that special communities of this type in Ohio consistently received high scores and were appropriately categorized. Significant ecoregional

64 differences in mean scores of nonreference 85 0 wetlands were observed in the current data set but this may be an artifact of the wetlands 75 currently included in the data set. Significant 65 $ lO > differences in mean scores of references were 55 not detected although there mean scores for ~ ~ ~ 45 reference wetlands in the EOLP were higher 0 than reference wetlands in ECBP. This may be 35 . due to the fact that, on average, wetlands in the G 25 Eastern Com Belt Plains ecoregion tend to be more disturbed. Regionally, the Eastern Com 15 Q. Q. Q. ID ..J 6 () 0 Belt Plains tends to be a much more fragmented w w ~ ..!. ..!. 0 landscape overwhelming developed as active c e e agricultural land. However, when the 95th Figure 17. Box and whisker plots of ORAM v. 5.0 scores for reference (ref) and percentiles of the ORAM distributions for the nonreference (non) wetlands by ecoregion. ECBP= Eastern Com Belt Plains, two ecoregions are compared the differences EOLP= Erie Ontario lake Plains. Means are indicated by solid circles. A line is are much less apparent, such that the best _ drawn across the box at the median. The bottom of the box is at the first quartile (01), and the top is at the third quartile (03) value. The whiskers are the lines that quality wetlands in the ECBP ecoregion are as extend from the top and bottom of the box to the adjacent values. The adjacent good as the best quality wetlands in the EOLP, values are the lowest and highest observations that are still inside the region defined by the following limits: Lower limit= 01 - 1.5 (03 - 01 ); Upper limit= 03 + 1.5 (03 there are just fewer intact systems in the ECBP - 01 ). Outliers are points outside of the lower and upper limits and are plotted with ecoregion. asterisks (*). ·

1 )

It) > 60

-~ 50 Q'. ~ ~ 0 .• 40

30 ~

c ., 0 .i;; g- D"' !!. .. e"' ~ ~ .2 r o_ ,,"' D E ~ ~ ~ Figure 18. Box and whisker plots of ORAM v. 5.0 scores by vegetation community based wetland classes. Refer to Table 5 for a description of these classes. The mean is indicated by a solid dot. A line is drawn across the box at the median. The bottom of the box is at the first quartile (01 ), and the top is at the third quartile (03) value. The whiskers are the lines that extend from the top and bottom of the box to the adjacent values. The adjacent values are the lowest and highest observations that are still inside the region defined by the following limits: Lower Limit= 01 - 1.5 (03 - 01 ); Upper Limit = 03 + 1.5 (03 - 01 ).

65 5.0 Preliminary Wetland Aquatic Life Uses

A main purpose of developing wetland specific IBis has been to specify numeric biological criteria for wetlands that correspond to various wetland designated uses. At the present time, Ohio law lists a single designated use for wetlands, the "wetland designated use" (OAC Rule 3745-1-52) which a wetland has merely by meeting the definition ofa wetland in OAC Rule 3745-1-50. The development ofa numeric IBI based on wetland vegetation is sufficiently advanced to attempt a preliminary outline of wetland aquatic life uses with associated numeric criteria. Ultimately, standards like these would be incorporated into the State of Ohio's water quality standards just as standards for streams have been previously promulgated.

The uses discussed below should be considered to be a preliminary attempt at developing specific wetland use designations. There should be every expectation that major or minor changes will be made as this topic is discussed and refined. The Wetland Aquatic Life Uses (ALUSs) follow a format equivalent to stream aquatic life uses. General use designations are defined in Table 41.

Table 41. General Wetland Aquatic Life Use Designations using Vegetation IBls.

code designation definition

SWLH Superior Wetland Habitat Wetlands that are capable of supporting and maintaining a superior or unusual community of vascular plants having a species composition, diversity, and functional organization comparable to the vegetation 181 score of at least3 times the quadrisected 95"' percentile distribution as specified in Table 44 below.

WLH Wetland Habitat Wetlands that are capable of supporting and maintaining a balanced, integrated, adaptive community of vascular plants having a species composition, diversity, and functional organization comparable to the vegetation 181 score of at least 2 times the quadrisected 95"' percentile distribution as specified in Table 44 below.

) RWLH Restorable Wetland Habitat Wetlands which are degraded but have a reasonable potential for regaining the capability of supporting and maintaining a balanced, integrated, adaptive community of vascular plants having a species composition, diversity, and functional organization comparable to the vegetation 181 score of at least 1 times the·quadrisected 95"' percentile distribution as specified in Table 44 below.

LWLH Limited Wetland Habitat Wetlands which are seriously degraded and which do not have a reasonable potential for regaining the capability of supporting and maintaining a balanced, integrated, adaptive community of vascular plants having a species composition, diversity, and functional organization comparable to the vegetation 181 score of less than 1 times the quadrisected 95"' percentile distribution as specified in Table 44 below.

Once a general use designation is assigned, a specific Wetland Aquatic Life use is designated. This specific use incorporates aspects of the classification schemes in Table 6 and Table 7 and provides hydrogeomorphic and dominant vegetation characteristics of the range of wetlands present in the State of Ohio. The specific uses are summarized in Table 42. The specific uses correspond to the three dominant plant communities (forest, emergent, shrub), the seven main landscape positions (isolated, riparian, slope, 11 coastal (Lake Erie), lacustrine (non-Lake Erie), impoundment, and riverine). Most of the landscape position information is specified as a numeric specific use designation modifier in Table 42. Finally, in addition, to the general and specific uses, special uses are proposed in Table 43.

II There are five possible kinds of slope wetlands are forest seeps, seep fens, sedge-meadows, tall shrub fens, and fens.

66 Table 42. Specific wetland use designations.

Use code specific use designation Landscape position use designation modifier

la Swamp forest (1) riparian headwater depression, (2) riparian mainstem, depression (3) isolated depression, (4) lacustrine, (5) human impoundment, (6) beaver impoundment

lb Vernal pool

le Forest seeps (1) riparian (2) isolated (3) lacustrine

Id Tamarack-hardwood bog

Ila Mixed shrub swamp (1) riparian headwater depression, (2) riparian mainstem, depression (3) isolated depression, (4) lacustrine, (5) human impoundment, (6) beaver impoundment

llb Bultoribush swamp (1) riparian headwater depression, (2) riparian mainstem, ·:w . .,.- - -· depression (3) isolated depression, (4) lacustrine, (5) human impoundment, (6) beaver impoundment

lie Alder swamp (1) riparian headwater depression, (2) riparian mainstem, depression (3) isolated depression, (4) lacustrine, (5) human impoundment, (6) beaver imp

lld Tall shrub bog

lie Tall shrub fen (1) riparian (2) isolated (3) lacustrine

Illa Marshes (includes submergent, floating- (1) riparian headwater depression, (2) riparian mainstem, leaved, mixed emergent, and cattail) depression (3) isolated depression, (4) lacustrine, (5) human impoundment, (6) beaver impoundment

) lllb Sedge-grass communities (includes wet (1) riparian headwater depression, (2) riparian mainstem, prairies, sedge meadows, and seep fens) depression (3) isolated depression, (4) lacustrine, (5) human impoundment; (6) beaver impoundment

Ille Riverine marsh communities (includes submergent, floating-leaved, mixed emergent and various intennixed shrub communities

llld Fens (includes cinquefoil-fens, tamarack fens, (1) riparian (2) isolated (3) lacustrine arbor vitae fens)

Ille Bogs (includes sphagnum bogs, leatherleaf bogs, but not tamarack-hardwood bogs (le) or tall shrub bogs (lld)

IV Coastal marshes (1) restricted, (2) unrestricted, (3) estuarine

67 0 Table 43. Special wetland use designations.

subscript special uses description

A recreation wetlands with known recreational uses including hunting, fishing, birdwatching, etc. that are publicly available

B education wetlands with known educational uses, e.g. nature centers, schools, etc.

c fish reproduction habitat wetlands that provide important reproductive habitat for fish

D bird habitat wetlands that provide important breeding and nonbreeding habitat for birds

E Hood storage wetlands located in landscape positions such that they have Hood retention functions

F water quality wetlands located in landscape positions such that they can improvement perform water quality improvement functions for streams, lakes, or other wetlands

Table 44 provides pilot numeric biological criteria for wetlands based on Vegetation IBI scores for specific wetland plant communities and landscape positions (hydrogeomorphic class). Because no ecoregional differences in Vegetation IBis appear to be present at this time, the numeric criteria apply to wetlands in all ecoregions of Ohio. However, as discussed in §4.0, differences have been noted based on landscape position and plant community type. Separate numeric criteria are proposed for classes where differences appear to be present as discussed in preceding sections.

An example in how to assign a Wetland Aquatic Life use with Tables 35-38 may be helpful. The wetland being evaluated is a pumpkin ash (Fraxinus profunda) swamp in Fowler Woods State Nature Preserve. This is a swamp forest in an non-riparian landscape position. After a detailed vegetation survey, a Vegetation IBI score of81 is calculated. Referring to Table 36, this wetland receives a specific use designation of Ia3 (swamp forest-isolated depression). Referring to Table 38, a Vegetation IBI score of 81 is in the EWLH (Exceptional Wetland Habitat) use scoring range. Finally, Table 37 is consulted and it is determined that the wetland has educational uses as a state nature preserve that is open to the public. The Wetland Aquatic Life use designation can then summarized as,

SWLP-Ia38

where SWLH=means Superior Wetland Habitat, Ia3=Isolated Swamp Forest, and the

subscript8 =education use.

The primary purpose of numeric wetland biocriteria is to assess the ecological integrity, or conversely the level of impairment, of a wetland. The primary purpose of the specific uses is to identify the type and landscape position of the wetland. This provides that wetlands within a class are compared for the purpose of assessing their relative quality and functions. It also allows the tracking of impacts to· determine whether wetlands of certain types are being lost from the landscape of Ohio, and whether these wetlands are being replaced through creation or restoration. The Wetland ALUSs are designed to generally correspond to the antidegradation categories listed in OAC Rule 3745-1-54: Category I, 2, and 3. However, there may be some instances where a wetland shows moderate to substantial impairment under the Wetland ALUSs but is still categorized as a Category 2 or 3 wetland under the antidegradation rule because it exhibits one or several valuable functions at moderate to superior levels, e.g. flood retention. Any confusion this situation might engender should be alleviated by the "special uses" listed in Table 43 since this should serve as an "alert" for antidegradation review purposes.

68 Table 44. Pilot numeric biological criteria for wetlands based on Vegetation 181 breakpoints for 0 specific plant communities and landscape positions. "tbd"=to be developed. Landscape specific use position plant community code{s) LQWLH RWLH WLH SWLH

Riparian swamp forests la2, lla2, llb2, 0-16 17-33 34-50 51-100 mainstem shrub swamps llc2 depressions

All landscape swamp forests all use codes 0-22 23-45 46-66 67-100 positions vernal pool except la2, except riparian shrub swamp lla2, llb2, llc2 main stem depressions

All landscape marshes llla-ECBP 0-16 17-33 34-50 51-100 positions except coastal llla-EOLP 0-20 21-41 42-62 63-100 and riverine

All landscape bog Id, lld, lie, 0-23 24-47 48-71 72-100 positions fen lllb, llld, Ille sedge-grass

Coastal all all use codes· tbd tbd tbd tbd

Riverine all nfa tbd tbd tbd tbd

)

69 6.0 Quantitative vegetation characteristics of wetland types.

The final section of this report includes a series of tables summarizing quantitative characteristics of wetland plant communities. Since 1996, Ohio EPA has collected quantitative vegetation data in order to 0 develop wetland specific IBis using vascular plants. This data also has other uses. It provides numeric characteristics of the wetland plant communities sampled such that they can be compared to other types of wetland and terrestrial plant communities from a plant community ecology or phytosociological perspective.

Several of the tables below describe characteristics of wetland plant communities sorted by the wetlands's regulatory category under OAC Rule 3745-1-54. One of the requirements of this rule is that compensatory mitigation for unavoidable impacts to a wetland must restore or create a wetland of equal or higher category. 12 The following tables may be useful in providing quantitative ecological performance targets for mitigation projects or wetland mitigation banks. The variables summarized in these tables include the some of the component metrics that make up the Vegetation IBI as well as other vegetation and physical parameters.

With the exception of a few sites, the data summarized in these tables was collected using plots that were generally O.lha in area. Cover of plants found in the herb and shrub layer was estimated at the l00m2 level (generally lOmX!Om quadrats) and then converted to relative cover by dividing the total cover of a particular species by the cover of all species identified in the plot. Stand data for shrub and forest wetlands was derived from complete woody stem counts were made in these plots. Other vegetation sampling methods that have a similar sampling intensity and yield similar data should generally be comparable to these tables (Peet et al. 1998).

Tables 45, 46, 47, 48, 49, and 50 provided quantitative vegetation characteristics for several conunon wetland plant communities found in Ohio: emergent marshes, swamp forests (including vernal pools), shrub swamps, sedge-grass communities, calcareous fens, and various bog communities (leatherleaf bogs, sphagnum bogs, tall shrub bogs). Characteristics are sorted using the regulatory category of the wetlands in the current data set. Regulatory category was determined using the categorization rules found in Mack (200 l ). These tables should be useful in evaluating the success of mitigation projects, especially projects aimed at restoring or creating emergent marshes, swamp forests, and shrub swamps. They may also aid in evaluating the quality or regulatory category of a particular wetland.

Of note in these tables is the inclusion of several physical parameters found in natural wetlands including number of tussocks, hummocks, standing dead, and coarse woody debris, summer13 depth of water and depth to saturated soils, litter depth, and average microtopographic score. Higher quality wetlands are heterogeneous at the microtopographic (sub-meter to sub-I 00m2 level), a fact that is often overlooked in the design and construction of restoration and creation projects. They also often have very shallow (less than 30cm depth) water or saturated soils closer to the surface present during mid to late summer. Lower quality wetlands often have the depth to saturated soils >30cm or have no water or much deeper water (often an artificial impoundment effect) during mid-summer. A common flaw in restoration and creation projects is to have water that is too deep (in effect a pond) or saturated soil conditions that retreat too far below the soil surface during the growing season.

12 In the case of Category l and Category 2 wetlands, the mitigation wetland must be Category 2 or 3 quality; in the case of Category 3 wetlands, the wetland must be of Category 3 quality.

13 Parameters generally measured between mid June to the end of August.

70 Table 5 l provides mean importance values 14 for woody plant species (trees and shrubs) frequently observed in shrub or forest wetland plant communities, Several points can be readily made from this table. First, the presence and importance ofred and silver maple (Acer rubrum, A. saccarhinum), elms (Ulmus americana, U. rubra), and green ash (Fraxinus pensylvanica) are not, by themselves, indicative of c the relative quality of a forested wetland. Both low, medium and high quality forested wetlands can be dominated by these species, although a decline in importance, especially for green ash, appears to occur as quality increases.

Second, the presence and importance of certain species including red and swamp white oak (Quercus rubra, Q. bico/or), pumpkin ash (Fraxinus profunda), swamp cottonwood (Populus heterophylla) and other mesic forest species (Carpinus caroliniana, Fagus grandifolia, Tilia americana}1 5 and hickory species (Carya spp.) appear to be indicative of higher quality forested wetland.

Third, the shrub strata within a forested wetland appears to be a sensitive indicator of quality with the shrub community largely disappearing in low quality forested wetlands and constituting a vary diverse subcanopy community in medium to high quality forested wetlands. Several species including chokebeny (Aronia melanocarpa), catbeny (Nemopanthus mucronata), poison sumac (Toxicodendron vernix) and highbush bluebeny (Vaccinium corymbosum) appear to be distinctive of Category 3 forested wetlands, especially in the Lake Plains and Glaciated Allegheny Plateau of northeast Ohio.

Tables 52 and 53 quantify stand characteristics of mature to old growth forested wetlands. Mature forested wetlands are defined in the Narrative Questions of the Ohio Rapid Assessment for Wetlands as forested wetlands with more the 50% of the canopy dominated by trees with diameters at breast height >45cm (Mack 2001 ). In preparing these tables, the stand characteristics of all forested wetlands with at least one tree >45cm dbh were analyzed (Table 52). In addition, the woody stem counts made in the plots are recorded for each lOmxlOm.module allowing the mapping of position oflarge trees across the plot (Figures 19 and 20). Because trees are very long-lived, a mature and diverse canopy of trees can persist in a forested wetland even after very substantial human disturbances have degraded the herb, shrub, and fauna! communities. \ J Finally, Tables 54, 55, 56, and 57 provide lists of representative plant species for good quality emergent marsh, swamp forest, shrub swamps, and sedge-grass communities. These lists were compiled from the species list and the relative dominance of these species in wetlands sampled from 1996-2000. "Good quality" is a relative term but includes plant species found in wetlands that are capable of supporting and maintaining a balanced, integrated, adaptive community of vascular plants. It can be equated with Category 2 (excluding degraded Category 2s) and Category 3 wetlands as they are defined in OAC Rule 3745-1-54. These lists should not be considered inclusive or exclusive lists but typical lists of the plants often observed in these wetland plant communities sampled to date.

14 Importance value is calculated by summing the relative class frequency, relative density and relative basal area and dividing by 3. Relative class frequency (See §2.3.10) is substituted for relative frequency since relative frequency is not easily calculated using the plot based sampling method.

15 Sometimes located near the wetland edges or on microtopographic rises within a wetland.

71 \ C)

.____ .., ___ ~Om baseline

Figure 19. Approximate location of trees >45cm dbh in modules of 20mx50m plot at Big Woods Preserve, Miami County, Ohio. Big Woods is a category 3 vernal pool forested wetland.

\ )

Figure 20. Approximate location of trees >45cm in modules of 20mx50m plot at Orange Road site, Delaware County, Ohio. Orange Road is a degraded category 2 forested wetland with a persistent mature canopy. 72 Table 45. Mean values (standard deviation in parenthesis) of vegetation and physical characteristics of EMERGENT MARSH communities by wetland category. Sampling intensity generally 0.1 ha plot with 8 nested quad rats in accordance with methods in §2.0. 0 Analysis of variance followed by Tukey's HSD test used to explore differences between categories. Means without shared letters significantly different at p<0,05.

Restorable parameter Category 1 Category 2 Category2 Category 3

Vegetation IBI score 15(10)a 34(8)b 50(19)b 78(7)c

FQAI score 9(2)a 16(2)b 16(4)b 22(2)c

Total species 13(5)a 21(4)b 30(9)c 38(3)d

Graminoid species 6(3) 5(2) 8(3) 10(1)

Forb species 6(3)a 14(4)b 17(8)bc 19(3)c

Shrub species 0.2(0.4)a 2(2)ab 2(2)b 6(3)c

Cryptogam species O(O)a 0.3(0.6)a 0.3(0.S)a 1(0.8)b

Dicot species 6(3)a 14(4)b 18(6)bc 22(3)c

Monocot species 7(4)a 7(1)a 11(5)b 15(1)b

FACW and OBL species 9(4)a 18(6)b 22(9)b 31(3)c

relative cover forb species 0.28(0.28)a- 0.70(0.10)bc 0.52(0.23)b 0.75(0.18)c

relative cover graminoid species 0.68(0.28)a 0.28(0.11 )b 0.39(0.25)b 0.19(0.18)b

relative cover shrub species 0.002(0.006)a 0.01(0.02)a 0.06(0.05)b 0.05(0.008)c

relative cover tolerant species 0.61(0.24)a 0.42(0.17)b 0.41 (0.21 )b 0.17(0.12)c

', relative cover intolerant species 0.008(0.01 )a 0.009(0.005)a 0.05(0.04)b 0.26(0.17)c J relative cover invasive grass spp. 0.29(0.26) 0.31(0.28) 0.15(0.23) 0.01(0.02)

2 mean standing biomass (g/m > 903(551)a 1007(477)b 463(265)c 259(155)c

2 tussocks per are (100m ) 0 0 0 29

2 hummocks per are (100m ) 0 0 2.5 0.4

standing dead per hectare 0 0 6 125

woody debris per hectare 0 133 93 125

mean summer water depth (cm) 0 23 13 28

mean litter depth (cm) 6 nd 1.5

mean summer depth to saturated soils (cm) >30 nd 20 nd

mean microhabitat interspersion score nd 0.1 0.9 2.7

73 Table 46. Mean values {standard deviation in parenthesis) of vegetation and physical characteristics of SWAMP FOREST communities by (, wetland category. Sampling intensity generally 0.1 ha plot with 8 nested ·~_) quadrats in accordance with methods in §2.0. Analysis of variance followed by Tukey's HSD test used to explore differences between categories. Means without shared letters significantly different at p<0.05. There was only on 1 "restorable Category 2" swamp forest so this category was not analyzed. separately.

parameter Category 1 Category 2 Category 3

Vegetation 181 score 13(10)a 53(25)b 76(14)c

FOAi score 12(2)a 22(2)b 26(5)c

Total species 20(7)a 34(7)b 39(10)b

Graminoid species 2(2) 5(3) 6(4)

Forb species 10(6)a 13(3)b 17(5)c

Shrub species 0.6(0.9)a 4(2)b 5(3)b

Tree species 6(3)a 10(3)b 9(3)b

Cryptogam species O(O)a 1(1)a 2(2)b

Dicot species 16(7)a 27(1)b 29(7)b

Monocot species 3(2)a 6(2)b 8(5)b

FACW and OBL species 10(3)a 16(4)b 20(7)c

relative cover forb species 0.52(0.17) 0.50(0.27) 0.45(0.19)

relative cover graminoid species 0.29(0.26) 0.15(0.14) 0.20(0.15) ) relative cover shrub species 0.07(0.16)a 0.10(0.12)a 0.25(0.18)b

relative cover tolerant species 0.43(o.25)a 0.32(0.19)b 0.15(0.07)c

relative cover intolerant species 0.09(0.19)a 0.17(0.25)a 0.38(0.22)b

relative cover invasive grass spp. 0.22(0.30)a 0.07(0.15)b <0.0001b

2 tussocks per are (100m ) 0(0) 4(10) 28(37)

2 hummocks per are (100m ) 0(0) 2(5) 5(5)

standing dead per hectare 75(66) 50(57) 25(39)

. woody debris per hectare 208(95) . 263(265) 256(217) ,-;,

mean summer water depth (cm) 0(0) 0.4(0.8) 5.9(7.3)

mean litter depth (cm) 2.1(2.0) 1.2(1.1) 1.9(1.8)

mean summer depth to saturated soils (cm) >30 22(13) 11(12)

mean microhabitat interspersion score nd 2.7(2.5) 5.2(2.5)

74 Table 47. Mean values of vegetation and physical characteristics of SHRUB SWAMP communities by wetland category. Sampling intensity generally 0.1 ha plot with 8 0 nested quadrats in accordance with methods in §2.0. "nd" = no data available. Restorable parameter Category 1 Category 2 Category 2 Category3

Vegetation IBI score nd 27 59 76

FQAI score nd 17 19 22

Total species nd 22 31 31

Graminoid species nd 3 5

Forb species nd 8 15 12

Shrub species nd 4 5

Tree species nd 9 7 6

Cryptogam species nd nd 0.3 1.4

Dicot species nd 20 24 23

Monocot species nd 2 6 7

FACW and OBL species nd 8 19 18

relative cover forb species nd 0.57 0.58 0.24

relative cover graminoid species nd 0.02 0.05 0.13

relative cover shrub species nd 0.17 0.32 0.52

relative cover tolerant species nd 0.13 0.09 0.04

'] relative cover intolerant species nd 0.18 0.36 0.58 / relative cover invasive grass spp. nd nd 0.01 0.0004

2 tussocks per are (100m ) nd nd 6 51

2 hummocks per are (100m ) nd nd 10 6

standing dead per hectare nd nd 88 45

woody debris per hectare nd nd 269 350

mean summer water depth (cm) nd nd 7 10

mean litter depth (cm) nd nd 1.6 3.1

mean summer depth to saturated soils (cm) nd nd '" 26 18

mean microhabitat interspersion score nd nd nd 4.9

75 Table 48. Mean values of vegetation and physical characteristics of CALCAREOUS FEN communities by wetland category. Sampling intensity generally 0.1 ha plot with 8 nested quad rats in accordance 0 with methods in §2.0. All calcareous fens are Category 3. disturbed high quality parameter fen fen

Vegetation IBI score 71 84

FOAi score 31 34

Total species 48 56

Graminoid species 14 14

Forb species 23 27

Shrub species 7 12

Cryptogam species 2 2

Dicot species 30 36

Monocot species 16 18

FACW and OBL species 36 44

rel

relative cover graminoid species 0.71 0.32

relative cover shrub species 0.05 0.35

relative cover tolerant species 0.47 0.10

relative cover intolerant species 0.32 0.52 \ ) relative cover invasive grass spp. 0.45 0.02

2 mean standing biomass (g/m ) 981 798

2 tussocks per are (100m ) 64 204

2 hummocks per are (100m ) 0 0.7

standing dead per hectare 0 0

woody debris per hectare 0 0

mean summer water depth (cm) 9 0

mean litter depth (cm) nd 0.3

mean summer depth to saturated soils (cm) 12 0

. mean microhabitat interspersion score nd 8.8

76 Table 49. Mean values of vegetation and physical characteristics of SEDGE-GRASS communities (includes sedge-grass meadows and seep fens) by wetland category. Sampling intensity generally 0.1 ha 0 plot with 8 nested quadrats in accordance with methods in §2.0. All calcareous fens are Category 3.

Restorable parameter Category 2 Category3

Vegetation IBI score 28 85

FQAI score 12 29

Total species 23 47

Graminoid species 9 16

Forb species 10 25

Shrub species 5

Cryptogam species nd 0.3

Dicot species 13 28

Monocot species 10 18

FACW and OBL species 14 33

relative cover forb species 0.28 0.33

relative cover graminoid species 0.64 0.61

relative cover shrub species 0.04 0.03

relative cover tolerant species 0.67 0.09

\ relative cover intolerant species 0.05 0.41 ) relative cover invasive grass spp. 0.03 0.01

2 mean standing biomass (g/m ) 861 944

2 tussocks per are (100m ) nd 32

2 hummocks per are (100m ) nd 46

standing dead per hectare nd 13

woody debris per hectare nd 0

mean summer water depth (cm) nd 2

mean litter depth (cm) nd 0.4

mean summer depth to saturated soils (cm) nd 9

mean microhabitat interspersion score nd nd

77 Table 50. Mean values of vegetation and physical characteristics of BOG communities. Sampling intensity generally 0.1 ha plot with 8 nested quadrats 0 in accordance with methods in §2.0. All bogs were Category 3 wetlands.

parameter Bog values

Vegetation 181 score 85

FOAi score 29

Total species 35

Graminoid species 7

Farb species 15

Shrub species 8

Cryptogam species 2

Dicot species 23

Monaco! species 9

FACW and OBL species 26

relative cover forb species 0.43

relative cover graminoid species 0.18

relative cover shrub species 0.34

relative cover tolerant species 0.07

relative cover intolerant species 0.65

relative cover invasive grass spp. 0.0005

2 mean standing biomass (g/m ) 256

2 tussocks per are (100m ) 42

2 hummocks per are (100m ) 15

standing dead per hectare 22

woody debris per hectare 53

mean summer water depth (cm) 6

mean litter depth (cm) 0.08

mean summer depth to saturated soils (cm) 0

mean microhabitat interspersion score 7

78 Table 51. Mean importance value for selected frequently observed tree and shrub species by category of forested wetland where species observed. Carya spp. include C. cordiformis, C. glabra, C. /aciniosa, C. ova/is, C. ovata, and C. tomentosa. Cornus spp. include C. amomum, C. drummondii, and C. racemosa. 0 species Category 1 Category 2 Category 3 TREES

Acer rubrum 0.003 0.307 0.154

Acer saccarhinum 0.186 0.163 0.306

Carpinus caroliniana 0.035 0.019

Carya spp.* 0.039 0.008

Fagus grandifolia 0.003 0.083

Fraxinus nigra 0.288 0.067 0.021

Fraxinus pennsylvanica 0.442 0.184 0.126

Fraxinus profunda 0.107

Populus deltoides 0.351 0.127 0.158

Populus heterophylla 0.024

Quercus bicolor 0.102 0.057

Quercus palustris 0.066 0.107 0.018

Quercus rubra 0.003 0.007

Salix nigra 0.280 0.012

Tilia americana 0.022 0.009

Ulmus americana 0.051 0.098 0.075

Ulmus rubra 0.013 0.049 0.047

SHRUBS

Alnus incana 0.120 0.008

Aronia melanocarpa 0.024

Cephalanthus occidentalis 0.153 0.122

Comusspp.** 0.024 0.015 0.031

!lex verticillata 0.098 0.136

Lindera benzoin 0.055 0.132

Nemopanthus mucronata 0.014

Rosa palustris 0.009 0.018

Sambucus canadensis 0.002 0.006

Toxicodendron vemix 0.007

Vaccinium corymbosum 0.064

Viburnum recognitum 0.067 0.100

79 Table 52. Density and dominance of trees >45cm in existing forested wetland data set. All wetlands with at least one tree >45cm included in this table. Sampling intensity generally 0.1 ha plot in accordance with methods in §2.0. Site names in boldface type are mature forested wetlands.

0 density ratio ratio stems/ha >45cm dominance >45cm >45cm stems/all basal area basal/total site dbh stems m 2/ha basal comments

Ackerman 33 0.004 0.5 0.029

Big Woods 90 0.108 17.1 0.427

Blackjack Rd (front) 0 0.0

Brown Lake Bog 30 0.034 4.2 0.132

City of Mansfield 20 0.042 3.3 0.125

Collier Woods 70 0.089 14.6 0.348

Eagle Cr. Vernal 50 0.096 8.4 0.454

Flowing Well 57 0.076 11.3 0.375

Fowler Woods 60 0.042 11.0 0.220

Gahanna Woods 4th 10 0.038 2.1 0.139

Graham Rd. 40 0.023 9.5 0.286 non-riparian. only large Popu/us deltoides

Hempelman 10 0.006 1.5 0.080

Johnson Rd. 50 0.043 8.0 0.262 non-riparian. only large Populus deltoides

Killbuck Creek 10 0.007 1.8 0.059

Killdeer Plains 100 0.097 19.6 0.474 LaRueWoods 17 0.024 2.5 0.252

Lawrence Woods High 37 0.085 6.9 0.422

Lawrence Woods Low 2 40 0.321 14.1 0.429

Leafy Oak 0 0.0

Mentor Marsh 0 0.0

N. Kingsville S. Barr. Sw. 10 0.007 1.6 0.046

Orange Rd. 60 0.042 12.7 0.253

Oyer Tamarack 10 0.005 1.5 0.182

Pallister 100 0.049 15.0 0.334

Pawnee Rd. 80 0.036 17.2 0.396

Sawmill 0 0

US42 0 0

Tipp-Elizabeth Rd. 0 0

Townline Rd. 0 0

White Pine Bog 70 0.053 16.8 0.381

80 Table 53. Stand characteristics of mature forested wetlands. 0 characteristic range density >45cm dbh trees (stems/ha) 50-100

density ratio (stems/ha >45cm trees to stems/ha of all trees) 0.04-0.11

dominance >45cm trees (basal area at breast height m2/ha) 8.4-19.6

dominance ratio (basal area >45cm trees to basal area of all trees) 0.22-0.47

avg. no. of stems >45cm per plot (typically 20mx50m) 7.1

avg. no. of stems >45cm per 10mx10m module in plot 0.83

. )

81 Table 54, B~l2~~~Cliili~ 12h1Dl ~12~5'i~~ g! SU2S2d guiJlillC ~Dl~lll~Dl Dl'1C~b~~. ~CHUii~~ lilmilit: li!QUUW w121:g"l&1:ii:ii 0 Alnus incana Betulaceae shrub dicot Amphicarpaea bracteata Fabaceae forb dicot Angelica atropurpurea Apiaceae forb di cot Asclepias incarnata Asclepiadaceae forb dicot Bidens cernua Asteraceae forb dicot Boehmeria cyclindrica Urticaceae forb dicot Carex alata . Cyperaceae graminoid monocot Carex comosa Cyperaceae graminoid monocot Carex hystericina Cyperaceae graminoid monocot Carex lacustris Cyperaceae graminoid monocot Carex lurida Cyperaceae graminoid monocot Carex projecta Cyperaceae graminoid monocot Carex scoparia Cyperaceae graminoid mono cot Carex tribuloides Cyperaceae graminoid monocot Carex vulpinoidea Cyperaceae graminoid monocot Cephalanthus occidentalis Rubiaceae shrub di cot Ceratophytlum echinatum Ceratophytlaceae forb dicot Chelone glabra Scrophulariaceae forb di cot Cicuta bulbifera Apiaceae forb dicot Cornus amomum Comaceae shrub di cot Cornus sericea Comaceae shrub dicot Cyperus erythrorhizos Cyperaceae graminoid monocot Cyperus odoratus Cyperaceae graminoid monocot Decodon verticillatus Lythraceae forb dicot Dulichium arundinaceum Cyperaceae graminoid monocot Eleocharis palustris Cyperaceae graminoid monocot Elodea canadensis Hydrocharitaceae forb monocot Eupatorium maculatum Asteraceae forb di cot Eupatorium perfoliatum Asteraceae forb dicot Galium tinctorium Rubiaceae forb dicot Hibiscus laevis Malvaceae forb dicot Hibiscus moscheutos Malvaceae forb dicot Iris versicolor lridaceae forb monocot Juncus canadensis Juncaceae graminoid monocot Juncus interior Juncaceae graminoid monocot j J Juncus nodosus Juncaceae graminoid monocot Lemna trisulca Lemnaceae forb monocot Lycopus rubellus Lamiaceae forb dicot Lysimachia terrestris Primulaceae forb dicot Lysimachia thyrsiflora Primulaceae forb dicot Nuphar advena Nymphaceae forb monocot Nymphaea odorata Nymphaceae forb monocot Onoclea sensiblis Aspleniaceae fem cryptogam Peltandra virginica Araceae forb monocot Pilea fontana Urticaceae forb di cot Polygonum amphibium Polygonaceae forb dicot Polygonum arifolium Polygonaceae forb dicot Polygonum punctatum Polygonaceae forb dicot Pontederia cordata Pontederiaceae forb monocot Potamogeton epihydrus Potamogetonaceae forb monocot Potamogeton foliosus Potamogetonaceae forb monocot Potamogeton nodusus Potamogetonaceae forb dicot Ranunuculus longirostris Ranunculaceae forb dicot Rosa palustris Rosaceae shrub di cot Rumex orbiculatus Polygonaceae forb dicot Rumex verticillatus Polygonaceae forb dicot Sagittaria brevirostra Alismataceae forb monocot Sagittaria calycina Alismataceae forb monocot Sagittaria latifolia Alismataceae forb monocot Salix discolor Salicaceae shrub dicot Salixexigua Salicaceae shrub dicot Salixsericea Salicaceae shrub dicot Scirpus Huviatilis Cyperaceae graminoid monocot Scirpus validus Cyperaceae graminoid monocot Scutellaria galericulata Lamiaceae forb dicot Scutellaria lateriHora Lamiaceae forb dicot Sium suave Apiaceae forb dicot Sparganium americanum Sparganiaceae graminoid monocot Sparganium eurycarpum Sparganiaceae graminoid monocot

82 Table 54, B~l2~~~Dlii11i!£~ 121illll :il2~5'i~~ gf gggsf S1Uillill£ ~m~cg~cl WilC:ib~:z. :n;u~liis::a mmillt lits: mew UH2al "lil:ii:ii Spirea alba Rosaceae shrub dicot Spirodela polyrhiza Lemnaceae forb monocot 0 Stachys palustris Lamiaceae forb di cot Thelypteris palustris Thelypteridaceae fern cryptogam Toxicodendron vernix Anacardiaceae shrub dicot Typha latifolia Typhaceae graminoid monocot Urtica dioica Urticaceae forb dicot Utricularia vulgaris Lentibulariaceae forb mono cot Vaccinium corymbosum Ericaceae shrub dicot Verbena hastata Verbenaceae forb dicot Veronica anagallis-aquatica Scrophulariaceae forb dicot Veronica scutellata Scrophulariaceae forb di cot Viburnum recognitum Caprifoliaceae shrub dicot Wolffia brasiliensis Lemnaceae forb monocot Wolffia columbiana Lemnaceae forb monocot

83 Table 55. Representatjye plant specjes of good gua!jty forested wetlands.

species famify r;te toap reprq class Acerrubrum Aceraceae tree dicot 0 Acer saccarhinum Aceraceae tree di cot Apios americana Fabaceae forb di cot Arisaema triphyllum Araceae forb monocot Aronia melanocarpa Rosaceae shrub dicot Betula alleghaniensis Betulaceae tree dicot Boehmeria cylindrica Urticaceae forb dicot Caltha palustris Ranunculaceae forb di cot Carex bromoides Cyperaceae graminoid monocot Carex crinita Cyperaceae graminoid monocot Carex crus-corvi Cyperaceae graminoid monocot Carex grayii Cyperaceae graminoid monocot Carex hyalinolepis Cyperaceae graminoid monocot Carex intumescens Cyperaceae graminoid monocot Carex lupulina Cyperaceae graminoid monocot Carex prasina Cyperaceae graminoid monocot Carex seorsa Cyperaceae graminoid monocot Carex stipata Cyperaceae graminoid monocot Carex tribuloides Cyperaceae graminoid monocot Carex tuckermanii Cyperaceae graminoid monocot Carex vesicaria Cyperaceae graminoid monocot Carpinus caroliniana Betulaceae tree dicot Carya laciniosa Juglandaceae tree dicot Carya ovata Juglandaceae tree dicot Cinna arundinacea Poaceae graminoid monocot Circaea lutetiana Onagraceae forb dicot Coptis trifolia Ranuncalaceae forb dicot Dryopteris carthusiana Aspleniaceae forb cryptogam Dryopteris cristata Aspleniaceae forb dicot Festuca subverticillata Poaceae graminoid monocot Fraxinus nigra Oleaceae tree dicot Fraxinus pennsylvanica Oleaceae tree dicot Fraxinus profunda Oleaceae tree dicot Glyceria septentrionalis Poaceae graminoid monocot Hydrocotyle americanum Apiaceae forb di cot Hydrophyllum virginianum Hydrophyllaceae forb di cot !lex verticillata Aquifoliaceae shrub di cot laportea canadensis Urticaceae forb dicot Leersia virginica Poaceae graminoid monocot Lindera benzoin Lauraceae shrub di cot lobelia cardinalis Campanulaceae forb dicot Lysimachia ciliata Primulaceae forb di cot Maianthemum canadense liliaceae forb mono cot Nemopanthus mucronatus Aquifoliaceae shrub dicot Nyssa sylvatica Comaceae tree di cot Osmunda cinnamomea Osmundaceae forb cryptogam Osmunda regalis Osmundaceae forb cryptogam Pinus strobus Pinaceae tree gymnosperm Poa alsodes Poaceae graminoid monocot Polygonum hydropiperoides Polygonaceae forb dicot Polygonum punctatum Polygcinaeeae forb. dicot Populus heterophylla Salicaceae tree dicot Quercus bicolor Fagaceae tree di cot Quercus rubra Fagaceae tree dicot Sambucus canadensis Sambucaceae shrub dicot Smilacina stellata liliaceae forb monocot Symplocarpus foetidus Araceae forb monocot Trientalis borealis Primulaceae forb di cot Ulmus americana Ulmaceae tree dicot Vaccinium corymbosum Ericaceae shrub di cot Viburnum dentatum Caprifoliaceae shrub dicot Viola cucullata Violaceae forb dicot

84 Table 56. Representative plant species of good quality shrub swamps. species family life form repro class Acerrubrum Aceraceae tree dicot Acer saccarhinum Aceraceae tree dicot 0 Aronia melanocarpa Rosaceae shrub dicot Betula alleghaniensis Betulaceae tree dicot Bidens connata Asteraceae forb dicot Bidens discoidea Asteraceae forb dicot Boehmeria cylindrica Urticaceae forb dicot Carex bromoides Cyperaceae graminoid monocot Carex crus-corvi Cyperaceae graminoid monocot Carex decomposita Cyperaceae graminoid monocot Carex grayii Cyperaceae graminoid monocot Carex hyalinolepis Cyperaceae graminoid monocot Carex intumescens Cyperaceae graminoid monocot Carex laevivaginata Cyperaceae graminoid monocot Carex muskingumensis Cyperaceae graminoid monocot Carex prasina Cyperaceae graminoid monocot Carex seorsa Cyperaceae graminoid monocot Carex tribuloides Cyperaceae graminoid monocot Carex typhina Cyperaceae graminoid monocot Carex vesicaria Cyperaceae graminoid monocot Carpinus caroliniana Betulaceae tree dicot Cephalanthus occidentalis Rubiaceae shrub dicot Chelone glabra Scophulariaceae forb di cot Cinna arundinacea Poaceae graminoid monocot Dryopteris carthusiana Aspleniaceae forb cryptogam Festuca subverticillata Poaceae graminoid monocot Fraxinus pennsylv{lnica Oleaceae tree dicot Galium asprellum Rubiaceae forb dicot Galium tinctorium Rubiaceae forb dicot Galium tiiflorum Rubiaceae forb dicot Glyceria septentrionalis Poaceae graminoid monocot Impatiens capensis Balsaminaceae forb di cot Iris versicolor lridaceae forb monocot Lemna trisulca Lemnaceae forb monocot Lindera benzoin Lauraceae shrub dicot Lobelia cardinalis Campanulaceae forb dicot \ .I Lysimachia terrestris Primulaceae forb dicot I ' Lysimachia thyrsiflora Primulaceae forb di cot Nyssa sylvatica Comaceae tree dicot Osmunda cinnamomea Osmundaceae forb cryptogam Osmunda regalis Osmundaceae forb cryptogam Polygonum hydropiperoides Polygonaceae forb di cot Populus heterophylla Salicaceae tree dicot Quercus bicolor Fagaceae tree dicot Ranunculus flabellaris Ranunculaceae forb di cot Ranunculus hispidus nitidus Ranunculaceae forb di cot Ribes americanum Grossulariaceae shrub dicot Rosa palustris Rosaceae shrub dicot Rubus hispidus Rosaceae forb dicot Scutellaria lateriflora Lamiaceae forb di cot Siumsuave Apiaceae forb dicot Symplocarpus foetidus Araceae .· forb monocot Ulmus americana Ulmaceae tree di cot Vaccinium corymbosum Ericaceae shrub dicot Viburnum dentatum Caprifoliaceae shrub dicot

85 Table 57. Representative plant species of good quality sedge-grass commynjtjes. species family life form repro class c Agrimonia gryposepala Rosaceae forb dicot Angelica atropurpurea Apiaceae forb dicot Asclepias incarnata Asclepiadaceae forb dicot Aster novae-angliae Asteraceae forb dicot Aster puniceus Asteraceae forb dicot Bromus ciliatus Poaceae graminoid monocot Calamagrostis stricta Poaceae graminoid mono cot Calamagrostis canadensis Poaceae graminoid mono cot Campanula aperinoides Campanulaceae forb dicot Carex aquatilis Cyperaceae graminoid monocot Carex atherodes Cyperaceae graminoid monocot Carex hystericina Cyperaceae graminoid monocot Carex interior Cyperaceae graminoid monocot Carex lacustris Cyperaceae graminoid monocot Carex pellita Cyperaceae graminoid monocot Carex trichocarpa Cyperaceae graminoid monocot Carex sartwellii Cyperaceae graminoid monocot Carex scoparia Cyperaceae graminoid monocot Carex stricta Cyperaceae graminoid monocot Carex suberecta Cyperaceae graminoid monocot Carex tenera Cyperaceae graminoid monocot Carex tetanica Cyperaceae graminoid monocot Carex annectens Cyperaceae graminoid monocot Cephalanthus occidentalis Rubiaceae shrub dicot Cirsium muticum Asteraceae forb dicot Camus amomum Comaceae shrub di cot Camus racemosa Cornaceae shrub dicot Camus sericea Comaceae shrub dicot Corylus americana Betulaceae shrub dicot Cyperus odoratus Cyperaceae graminoid monocot Eleocharis tenuis borealis Cyperaceae graminoid monocot Eupatorium maculatum Asteraceae forb dicot Filipendula rubra Rosaceae forb di cot Galium asprellum Rubiaceae forb dicot Galium obtusum Rubiaceae forb dicot Juncus acuminatus Juncaceae graminoid monocot Juncus brachycephalus Juncaceae graminoid monocot Juncus canadensis Juncaceae graminoid monocot Juncus dudleyi Juncaceae graminoid monocot Lathyrus palustris Fabaceae forb dicot Lobelia kalmii Campanulaceae forb dicot Lobelia siphilitica Campanulaceae forb dicot Lycopus americanus Lamiaceae forb dicot Lycopus uniftorus Lamiaceae forb di cot Lysimachia ciliata Primulaceae forb dicot · Lythrum alatum Lythraceae forb dicot Mimulus ringens Scrophulariaceae forb dicot Osmunda regalis Osmundaceae forb cryptogam Pycnanthemum virginianum Lamiaceae forb di cot Rosa palustris Rosaceae shrub dicot Rudbeckia fulgida Asteraceae forb dicot Salix amygdaloides Salicaceae shrub di cot Salix bebbiana Salicaceae shrub dicot Salix discolor Salicaceae shrub dicot Salix eriocephala Salicaceae shrub dicot Salix exigua Salicaceae shrub dicot Salix humulis Salicaceae shrub dicot Salix sericea Salicaceae shrub dicot Sanguisorba canadenis Rosaceae forb dicot Saxifraga pennsylvanica Saxifragaceae forb dicot Scirpus acutus Cyperaceae graminoid monocot Scirpus pungens Cyperaceae graminoid monocot Scleria verticillata Cyperaceae graminoid monocot Solidago ohioensis Asteraceae forb dicot Solidago patula Asteraceae forb dicot Solidago rugosa Asteraceae forb dicot Sorghastrum nutans Poaceae graminoid monocot Spartina pectinata Poaceae graminoid monocot Spirea alba Rosaceae shrub dicot

86 Table 57. Representative plant species of good quality sedge-grass communities. species family life fonn repro class Stellaria longifolia Caryophyllaceae forb di cot Thelypteris palustris Thelypteridaceae forb cryptogam c forb Tradescantia ohioensis Commelinaceae monocot

87 7.0 References

Adamus, Paul R. I 996. Bioindicators for assessing ecological integrity of prairie wetlands. U.S. ' Environmental Protection Agency, National Health and Environmental Effects Laboratory, Western G Ecology Division, Corvallis, Oregon 97333. EP A/600/R-96/082 July I 996.

Anderson, Dennis M. 1982. Plant Communities of Ohio: A preliminary classification and description. Division of Natural Areas and Preserves, Ohio Department of Natural Resources, Columbus, Ohio.

Andreas, Barbara K., and R. W. Lichvar. 1995. A Floristic Quality Assessment System for Northern Ohio. Wetlands Research Program Technical Report WRP-DE-8. U.S. Army Corps of Engineers, Waterways Experiment Station.

Barbour, Michael T., James B. Stribling, and James R. Karr. 1995. Multimetric approach for establishing and measuring biological condition. In Biological Assessment and Criteria, Tools for Water Resource Planning and Decision Making, Eds. Wayne S. Davis and Thomas P. Simon, CRC Press, Inc.

Barbour, Michael T., James L. Plafkin, Brian P. Bradley, Carol G. Graves, and Robert W. Wisseman. 1992. Evaluation of EP A's rapid bioassessment benthic metrics: metric redundancy and variability among reference sites. Environmental Toxicology and Chemistry, 11:437-449.

Bode, Robert W. and Margaret A. Novak. 1995. Development and application of biological impairment criteria for rivers and streams in New York state. in Biological Assessment and Criteria, Tools for Water Resource Planning and Decision Making, Eds. Wayne S. Davis and Thomas P. Simon, CRC Press, Inc.

Brinson, Mark M. 1993. A hydrogeomorphic classification for wetlands. U.S. Army Corps of Engineers Waterways Experiment Station. Wetlands Research Program Technical Report WRP-DE-4. Vicksburg, MS.

Carlisle, Bruce K., Anna L. Hicks, Jan P. Smith, Samuel R. Garcia, and Bryan G. Largay. 1999. Plants and Aquatic Invertebrat$!S as Indicators of Wetland Biological Integrity in Waquoit Bay Watershed, Cape Code, Environment Cape Code 2(2):30-60.

Cowardin, Lewis M., Virginia Carter, Francis C. Golet, and Edward T. LaRoe. 1979. Classification of Wetlands and Deepwater Habitats of the United States. U.S. Department of the Interior, Fish and Wildlife Service, Office of Biological Services, Washington, D.C. FWS/OBS-79/31.

Cronk, Julie K. and M. Siobhan Fennessy. 2001. Wetland Plants: Biology and Ecology. CRC Press LLC, Boca Raton, Florida.

Fennessy, M. Siobhan, Michael A. Gray, and Ricardo D. Lopez (1998a). An ecological assessment of wetlands using reference sites Volume 1: Final Report, Volume 2: Appendices. Final Report to U.S. Environmental Protection Agency. Wetlands Unit, Division of Surface Water. Grant CD995761-0l.

Fennessy, M. Siobhan, Robert Geho, Bonny Elfritz, and Ricardo Lopez. l 998b. Testing the Floristic Quality Assessment Index as an Indicator of Riparian Wetland Disturbance. Final Report to U.S. Environmental Protection Agency. Wetlands Unit, Division of Surface Water. Grant CD995927.

Fore, Leska K., James R. Karr, and Loveday L. Conquest. 1994. Statistical properties of an index of biological integrity used to evaluate water resources. Canadian Journal of Fisheries and Aquatic Science, 51: 1077-1087.

Gemes, Mark C. and Judy C. Helgen. 1999. Indexes of Biotic Integrity (IBI) for Wetlands: Vegetation

88 and Invertebrate IBI's. Final Report to U.S. EPA, Assistance Number CD995525-0l, April 1999. Minnesota Pollution Control Agency, Environmental Outcomes Division, St. Paul, MN.

Gordon, Robert B. 1966. Natural vegetation of 0 hio at the time of the earliest land surveys. Ohio Biological Survey, Columbus, Ohio.

Gleason, Henry A., and Arthur Cronquist. 1991. Manual of Vascular Plants of Northeastern United States and Adjacent Canada. The New York Botanical Garden, Bronx, New York.

Herman, Kim D., Linda A. Masters, Michael R. Penskar, Anton A. Reznicek, Gerould S. Wilhelm, and William W. Brodowicz. 1996. Floristic quality assessment with wetland categories and computer application programs for the state of Michigan. Michigan Department of Natural Resources, Wildlife Division, Natural Heritage Program.

Hornig, C. Evan, Charles W. Bayer, Steve R. Twidwell, Jack R. Davis, Roy J. Kleinsasser, Gordon W. Linam, and Kevin B. Mayes. 1995. Development ofregionally based biological criteria in Texas. in Biological Assessment and Criteria, Tools for Water Resource Planning and Decision Making, Eds. Wayne S. Davis and Thomas P. Simon, CRC Press, Inc.

Hughes, Robert M., Philip R. Kaufman, Alan T. Herlihy; Thomas M. Kincaid, Lou Reynolds, and David P. Larsen. 1998. A process for developing and evaluating indices offish assemblage integrity. Canadian Journal ofFisheries and Aquatic Sciences, 55:1618-1631.

Karr, James R. 1993. Defining and assessing ecological integrity: beyond water quality. Environmental Toxicology and Chemistry, 12:1521-1531.

Karr, James.R., and Ellen W.Chu. 1997. Biological monitoring and assessment: Using multimetric indexes effectively. U.S. Environmental Protection Agency, EPA 235-R97-00l. University of \ i Washington, Seattle. / Karr, James R., and Ellen W. Chu. l 999. ·Restoring life in running waters: better biological monitoring. Island Press, Washington, D.C.

Karr, James R., and Daniel R. Dudley. 1981. Ecological perspective on water quality goals. Environmental Management 5(1):55-68.

Karr, James R., and Billie L. Kerans. 1992. Components of biological integrity: their definition and use in development of an invertebrate IBL In Proceedings of the 1991 Midwest Pollution Control Biologists Meeting: Environmental Indicators Measurement Endpoints. T.P. Simon and W. S. Davis, eds. U.S. Environmental Protection Agency, Region V, Environmental Sciences 'E>ivision, Chicago, Ill. EPA 905/R.-92/003.

Krebbs, Charles J. 1999. Ecological Methodology, 2"d Ed. Addison-Welsey Educational Publishers, Inc., Menlo Park, CA.

Ladd. D.M. in prep. The Missouri floristic quality assessment system. The Nature Conservancy, St. Louis, MO.

Mack, John J., Mick Micacchion, Lauren D. Augusta, and Gregg R. Sablak. 2000. Vegetation Indices of Biotic Integrity (VIBI) for Wetlands and Calibration of the Ohio Rapid Assessment Method for Wetlands v. 5.0. Final Report to U.S. EPA Grant No. CD985276, Interim Report to U.S. EPA Grant No. CD985875, Volume l. Ohio Environmental Protection Agency, Division of Surface Water, Columbus, Ohio.

89 Mack, John J. 2000a. Developing Wetland Rapid Assessment Protocols Using a Hydrogeomorphic Approach. Final Report to U.S. EPA Grant No. CD985277-0l. Ohio Environmental Protection Agency, Division of Surface Water, Columbus, Ohio. ;. C ------. 2000b. ORAM v.5.0 Quantitative Score Calibration August 15, 2000. Ohio Environmental Protection Agency, Division of Surface Water, Columbus, Ohio.

------. 200la. Ohio Rapid Assessment Method for Wetlands v. 5.0, User's Manual and Scoring Forms. Ohio EPA Technical Report WET/2001-1. Ohio Environmental Protection Agency, Division of Surface Water, Columbus, Ohio.

------. 200lb. Standardized Vegetation Sampling Procedures Field Manual v. I. I. Ohio EPA Technical Report WET/2001-3. Ohio Environmental Protection Agency, Division of Surface Water, 401 Wetland Ecology Unit, Columbus, Ohio.

Ohio BP A l 988a. Biological Criteria for the Protection of Aquatic Life: Volume I. The role of biological data in water quality assessment. Ohio Environmental Protection Agency, Ecological Assessment Section, Division of Water Quality Planning and Assessment, Columbus, Ohio.

_ - -Ohio BP A l 988b. Biological Criteria for the Protection of Aquatic Life: Volume IL Users Manual for biological field assessment of Ohio surface waters. Ohio Environmental Protection Agency, Ecological Assessment Section, Division of Water Quality Planning and Assessment, Columbus, Ohio.

Ohio BP A l 989a. September 30, 1989 Addendum to Biological Criteria for the Protection of Aquatic Life: Volume II, 1988. Ohio Environmental Protection Agency, Ecological Assessment Section, Division of Water Qulllity Planning and Assessment, Columbus, Ohio.

Ohio EPA l 989b. Biological Criteria for the Protection of Aquatic Life: Volume III. Standardized Biological Field Sampling and Laboratory Methods for Assessing Fish and Macroinvertebrate ) Communities. Ohio Environmental Protection Agency, Ecological Assessment Section, Division of Water Quality Planning and Assessment, Columbus, Ohio.

Omernik, J. M. 1987. Ecoregions of the conterminous United States. Annals of the Association of American Geographers 77: 118-125.

Peet, Robert K., Thomas R. Wentworth, and Peter S. White. 1988. A flexible, multipurpose method for recording vegetation composition and structure. Castanea 63(3): 262-274.

Peet, Robert K., Thomas R. Wentworth, and Peter S. White. 1997. The North Carolina Vegetation Survey Protocol: a flexible, multipurpose method for recording vegetation composition and structure. Field Manual. Available from anonymous at ftp://stipa.bio.unc.edu/NC_Veg_Survey\ Sampling\ Protocol.doc.

Rankin, Edward T. 1989. The Qualitative Habitat Evaluation Index [QHEI]: Rationale, Methods, and Application. Ecological Assessment Section, Division of Water Quality Planning and Assessment, Ohio Environmental Protection Agency, Columbus, Ohio.

Rankin, Edward T. 1995. Habitat indices in water resource quality assessments. In Biological Assessment and Criteria, Tools for Water Resource Planning and Decision Making, Eds. Wayne S. Davis and Thomas P. Simon, CRC Press, Inc.

Reed, Porter B., Jr. 1988. National List of Plant Species that Occur in Wetlands: Northeast (Region I). U.S. Department of the Interior, Fish and Wildlife Service, Research and Development, Washington, D.C.

90 Shane, Linda C. K.. 1987. Late-glacial vegetational and climatic history of the Allegheny Plateau and the fill Plains of Ohio and Indiana, U.S.A. Boreas 16: 1-20.

Simon, Thomas P. and Erich B. Emery. 1995. Modification and assessment of an index of biotic in!egrity to quantify water resource quality in great rivers. Regulated Rivers: Research & Management 1 ;:283-298.

Swi11k, F. and G. Wilhelm. 1979. Plants of the Chicago Region 3rd Ed. Morton Arboretum, Lisle, Ill.

U.S. EPA. 1990. Biological Criteria. National Program Guidance for Surface Waters, Office of Water, Regulations and Standards (WH-585), U.S. Environmental Protection Agency, Washington, D.C., April 1990. EPA-440/5-90-004.

U.S. EPA. 1998. Lake and Reservoir Bioassessment and Biocriteria. Technical Guidance Document. Office of Water, U.S. Environmental Protection Agency, Washington, D.C., August 1998. EPA 841-B- 98-007.

U.S. EPA. 1999. Rapid Bioassessment Protocols for Use in Wadeable Streams and Rivers. Periphyton, Benthic Macroinvertebrates and Fish, 2"d Ed .. Office of Water, U.S. Environmental Protection Agency, Washington, D.C., July 1999. EPA 841-B-99-002.

Webb, T. III, E. J. Cushing, and H. E. Wright, Jr. 1983. Holocene Changes in the Vegetation-of the Midwest. In Late-Quaternary Environments of the United States. Ed. H. E. Wright, Jr. University of Minnesota Press, Minneapolis, Minnesota.

Wilhelm, Gerould S., and D. Ladd. 1988. Natural area assessment in the Chicago region. Transactions 53rd North American Wildlife and Natural Resources Conference pgs. 361- 375.

Wilhelm, Gerould S., and L.A. Masters. 1995. Floristic quality assessment in the Chicago region and / application computer programs. Morton Arboretum, Lisle, ILL.

Woods, Alan J., James M. Omernik, C. Scott Brockman, Timothy D. Gerber, William D. Hosteter, and Sandra H. Azevedo. 1998. Ecoregions oflndiana and Ohio (Map) and Supplementary Text.

Yoder, Chris 0, and Edward T. Rankin. 1995. Biological Criteria Program Development and Implementation In Ohio. In Biological Assessment and Criteria, Tools for Water Resource Planning and Decision Making, Eds. Wayne S. Davis and Thomas P. Simon, CRC Press, Inc.

91 .. ·.. ;.·_·._· .. ·.·.-..·-···· - .

State of Ohio Ecological Assessment Section and Wetland Ecology Unit Environmental Protection Agency Division ofSwface Water

Final Report to U.S. EPA Grant No. CD985276 Interim Report to U.S. EPA Grant No. CD985875 Volume2:

Amphibian and Macroinvertebrate Attributes for Ohio Wetlands •,

Mick Micacchion Michael A. Gray and JobnJ.Mack

August 1, 2000

122 South Front Street, P.O. Box 1049 Columbus, Ohio 43216-1049 ) 1.0 Introduction

A principal goal of the Clean Water Act is to maintain and restore the physical, chemical and biological integrity of the waters of the United States. 33 U.S.C. §12Sl(a). Biological integrity has been defined as 11 •••the capability of supporting and maintaining a balanced integrated, adaptive community of organis~s having a species composition, diversity, and functional organiz.ation comparable to that of natural habitat of the region (Karr and Dudley 1981).

More recently, Karr (1993) has used the term "ecological integrity" and defined it as the sum of the earth's biological diversity and biological processes1 (fable 2); the converse of ecological integrity is biotic impoverishment, which is defined as the systematic reduction in the capacity of the earth to support living systems. Thus, 11A biological system is healthy and has ecological integrity when its inherent potential is realized, its condition is "stable, 11 its capacity for self-fepair is maintained, and external support for maintenance is minimal. Integrity implies an unimpaired condition or quality·or state of being complete and undivided (Karr, p. 1522, 1993)." The concept of integrity, and its measurement and description by biological smveys, underpins the development of biological criteria. • · 1 . The.factors which can be degraded by human activity in natural wetlands fall into several broad classes {fable 1). The quantitative measurement (assessment) of the degree of integrity of a particular natural system, and conversely the degree of impairment, degradation or impoverishment, can be attempted in many ways. The State of Ohio has successfully developed a sophisticated system using ambient· biological monitoring of fish and macroinvertebrate assemblages to assess the quality of streams and lakes in Ohio (the Invertebrate Community Index (macroinvertebrates), the Index of Biological Integrity (fish), and the Modified Index of Well Being (fish) (Ohio EPA 1988a, 1988b, 1989a, 1989b; Yoder and Rankin 1995). The State of Ohio's system was based on methods and results first published by Karr et al. (1986). This type of system is often referred to as an "Index of Biotic Integrity" and has been used and adopted throughout North America and Europe (Karr 1993). See also Karr and Kerans (1992); Barbour et al. (1992); Bode and Novak (1995); Hornig et al. (1995); Simon and Emery (1995), Hughes et al. (1998). The statistical properties of Ohio's IDI was investigated by Fo~ Karr, and Loveday (1993).. They ~ncluded that the IBI could distinguish between five and six nonoverlapping categories of integrity and that the IBI is "•. .an effective monitoring tool that can be used to communicate qualitative assessments to the public and policy makers or to provide quantitative assessments for a legal or regulatory context based on confidence intetvals or hypothesis testing procedures (Fo~ Karr, and Loveday, p. 1077, 1993). .

. Karr (1993) defines biological diversity as the variety of the earth's naturally occuring biologfcal elements, which extend over a broad range of oiganlzation scales from genes to populations, species, assemblages, and landscapes; the complement of biological diversity (the elements) are the biologlcal processes on which those elements depend.

1 0 Table 1. Factors associated with wetlands that can be negatively Impacted by human activities causing wetland degradation. Ada~ted from lists for flowing waters from Karr and Kerans (1992), Karr et al. (1986), Ohio EPA {1988a).

factor description examples Of dlstur.banc:eS

biogeochemlstry natural patterns of that type of weUand for nutrient cycling, nutrient enrichment, sedimentation, addition of organic decomposition, photosynthesis, nutrient sequestration and or Inorganic chemicals, heavy metals, toxic substances release, aerobic/anaerobic regimes, etc. etc. '

habitat substrate disturbance and habitat alterations mowing, grazing, fanning, vehicle use, clearcutting woody debris removal, shrub/sapling removal, ' herbaceous/aquatic bed mmova~ sedimentation,, etc.

hydrology natural hydrologic regime of that type of wetland: frequency, ditching, tiling, dikes and weirs, additions of stormwater, duration, amount of Inundation; sources of water, etc. point source cfischarges, fiUing and grading, oonstruction of roads and raDroad beds, dredging, etc.

biotic Interactions competition, predation, disease, parasitism, Introduction of nuisance or nonnative species (carj>, reed canary grass, purple loosestrffe, European · buckthom), etc. • •

Table 2. Components of ecological (biological) integrity for. wetlands.· Adapted from Karr and Kerans (1992), Karr (1993).

· Blologlcal diversity Biological Processes ) Elements of biodiversity Nutrient cydinglblogeochemistry Genes within populations Photosynthesis

Populations within species Water cycling/hydrological regime

Species within communities/ecosystems Evolution/speclatlon

Communities/ecosystems within landscapes CompetitionlPreationlMutuaflSITIS

Landscapes within blo-sphere

Table 3. Advantanges of ambient biological monitoring. Adapted from Karr and Kerans (1992). ·

I description

1 Broad based ecologically

2 Provides blologlcally meaningful evaluation

3 Flexlble for special needs

4 Sensitive to a broad range of degradation

5 Integrates cumulative Impacts from point source, nonpolnt source, hydrologlc alteration, and other cfiverse Impacts of human society

6 Integrates and evaluates the full range of classes of hnpads (e.g. hydrologlc mod"lfications, habitat ~rations, etc.) on biotic systems

7 Direct evaluation of re&Ourc:e condition

8 Easy to relate to general public 2 .<.•

0 Table 3. Advantanges of ambient biological monitoring. Adapted from Karr and Kerans {1992).

# description

9 Overcomes many weakne$ses of lncflVidual parameter by parameter approaches

10 can assess Incremental degrees and types of degradation, not just above or below some threshold

11 can be used to assess resource trends In space or time

The State of Ohio's indices are codified in Ohio Administrative Code Chapter 3745-1 and constitute numeric "biological criteria" which are a part of the state's water quality standards required under the Cleari Water Act.. See 33 U.S.C. § 1313: Biological criteria are numerical values or narrative expressions that describe the reference biological integrity of natural communities (U.S. EPA 1990). It is important to stresS that the overall index score resulting from ~ m~ as well as each individual metric represent testable hypotheses as to how a natural system responds to human disturbance (Karr 1993). Attn'butes of "'"natural communities are selected and predictions are made as to how the attn'bute will respond, e.g. increase or decrease; not change until a particular threshold is reached and then increase quickly; increase linearly, or curvilineal, etc. Moreover, the existing biological condition of a natural syStefil is the integrated result of the chemical, physical, and biological processes that comprise and maintain the system, and the biological condition of the system can be conceived as the integration or result of these processes over time (Ohio EPA 1988a). The organisms, individually and as communiti~ are indicators of the actual conditions in that system since they inhabit the system and are subject to the varietr of natural and human-caused variation (disturbance) to the system (Ohio EPA 1988a). In this re~ biological monitoring and biocriterla take advantage of this inherent integrative characteristic of the biota of a system, whereas chemical and toxicity monitoring only represents a single point in time unless · costly, continuous sampling over time is ped'ormed (fable 3).

"Wetlands" are a type of water of the United States and a water of the State of Ohio under federal and state law. See e.g. Ohio Revised Code (ORC) §6111.0l(H), OAC Rule 3745-1-02(BX90), 33 CFR 323.2(c). Until recently, wetlands in Ohio were only generically protected under state's water quality standards. On May 1, 1998, the State of Ohio adopted wetland water quality standards and a wetland antidegradation rule. OAC Rules 3745-1-50 through 3745-1-54. The water quality standards specify namltive criteria for wetlands and created the "wetland designated use.~ All jurisdictional wetl3:11ds are assigned the "wetland designated use." However, numeric criteria were not proposed since they bad not yet been developed.

Ohio began working on the development ofbiological criteria for wetlands in 1996. To date. Ohio has sampled over 60 different wetlands located primarily in the Eastern Combelt Plains Ecoregion located in central and western Ohio. These wetlands have included depressional emergent, forested, and scrub- . shrub wetlands, floodplain wetlands, fens. kettle lakes, and seep wetlands. The wetlands being studied span the range of condition from •impacted" (Le., those that have sustained a relatively high level of disturbance) to •1e8st-impaired" (i.e., the best quality sites available). This woik haS been funded since 1996 by several different U.S. EPA Region S Wetland Program Development Grants including CD995927, CD995761, CD985277, CD985276, and CD985875. Based on preliminmy results (Fennessy et al.1998a 1998b), Ohio EPA concluded that vascular plants, macroinvertebrates, and ampht'bians could be used as indicator organisms for the development of wetland-specific IBis

3 c, The objectives of the wetland biocriteria development project are as follows: 1. To develop Indices of Biotic Integrity (both interim and final) to evaluate ecological integrity of a wetland using vascular plants, macroinvertebrates and amphibians indicator taxa. ,

2. To identify and describe reference wetlands in the Ohio's four main ecoregions Eastern Combelt Plains, Erie/Ontario Drift and Lake Plain, Huron-Erie Lake Plain, and Western Allegheny Plateau.

3. To continue to assess and cahorate the Ohio Rapid Assessment Method, and to test and refine breakpoints between the wetland categories as specified under the Wetland Antidegradation Rule (see below).

A key feature of Ohio's current regulatory program for wetlands is found in the wetland antidegradation rule. OAC Rule 3745-1-54. The.wetland antidegradation rule categorizes}Vetlands based on their functions, sensitivity to disturbance, rarity and irreplaceability and scales the strictness of avo.idance, _ minimization, and mitigation to a wetland's category. Three categories were established: Category 1 wetlands with minimal wetland function and/or integrity; Category 2 wetlands with moderate wetland function and/or integrity,. and Categocy 3 wetlands with superior wetland function and/or integrity. A wetland is assigned to one of these three categories "•. .as determined by an appropriate wetland evaluation methodology acceptable to the director." OAC Rule 3745-1-54(C)(1Xa), (C)(2Xa), and (C)(3Xa). During the rule development process, Ohio BPA began developing its own wetland ev8.Iuation methodology known now as the Ohio Rapid Assessment Method (ORAM) for wetlands. The ORAM is a rapid, semiquantitative, wetland ranking tool. See discussion below and Ohio BPA (2000). )

The ORAM is designed to categorize a wetland based on whether it is a particular type ofwetland (e.g. fen, bog, old growth forest, etc.) or contains threatened or endangered species, or based on its "score." Fennessy et al. (1998a) found significant correlations between a wetland's score on the ORAM and the wetlands tiiological qualify and/or degree of disturbance. The initial scoring ranges proposed in Fennessy et al. (1998a) were·descriptively derived from a sample of wetlands scored using the ORAM and the professional judgment of The Ohio Rapid Assessment Worlcgroup (Fennessy et al. 1998a). Recahoration of the scoring ranges using actual measures of a wetland's biology and functions has been a continuing need since the adoption of the Wetland Wat.er Quality Standards and Wetland Antidegration rules and the use of"draft" versions of the ORAM (versions 3.0, 4.0, and 4.1) in regulatory decision making. .

This report discusses the development of macroinvertebrate and ampluoian attn.cutes for forested and emergent wetlands in relation to distwbance gradients as measured by ORAM.

;; "hie data analyzed in this report was collected by Ohio EPA from wetlands located in the Eastern Com · Belt Plains Ecoregion of Ohio in 1996, 1997, 1998, and 1999. Ohio EPA is sampling wetlands in the · Erie-Ontario Lake Plains Ecoregion (Lake Plains and glaciated Allegheny Plateau) during the 2000 and 2001 field seasons. A description of the 1998 and 1999 sites is included in Appendix A. The 1996 and 1997 sites were descn1>ed in Fennessy et al (1998a)

4 c

2.0 Methods

2.1 Field Methods: Amphibian and Macroinvertebrate Sampling

The Ohio EPA began evaluating wetland macroinvertebrate and amphl"bian sampling methods in 1996. A variety of sampling methods including artificial substrate samplers (HD samplers), funnel trapping, and qualitative sampling with dip nets were evaluated (see Fennessy 1998 a). In addition to the sampling method, the time of year to sample, the intensity, frequency, and_duration of sampling were evaluated.

We found that funnel traps were effective in sampling both the macroinvertebrate and ampb.J."bians found in wetlands. The following methods ~cussion pertains to the sampling of both ampht"bians and· macroinvertebrates. Each time awetland was sample

On our first visit to the wetland we measured the distance around the wetted perimeter. We then marked our sampling siteS at a location every 10% of the distance around the wetland. Ten traps were \ used in each wetland. The traps were placed on the bottom near the water edge where the trap was almost ) completely submerged. Access to the surface reduces mortali1y of ampht"bians during late spring and ·summer sampling. The traps were left in the wetland undisturbed for 24 hours. It is important to collect organisms that have diurnal and nocturnal activity patterns. No bait was used in the. traps. The macroinvertebrates and amphl"bians that entered the traps did so by chance as they Swain or crawled about the wetland. The contents of each trap were collected and processed separately to allow analysis of individual trap results. Organisms that could be readily identified and counted in the field were noted in a field book and retwned to the wetland. The remaining contents of each trap were preserved in individual · I liter plastic bottles. Macroinvertebrates were preserved in 70 % ethyl alcohol for long term storage. Where amphl"bians constituted a large portion of the catch, formalin was used as the preservative. ·

· Each time traps were deployed in a wetland, a qualitative sample was collected. Qualitative sampling involved the collection of amphl"bians and macroinvertebrates from all available aquatic natural wetland habitat features. This was achieved by using triangular ring frame 30-mesh dipnets and manually picking substrates with forceps. The goal was to compile a comprehensive species/taxa list of macroinvertebrates and amphl"bians from the site. A minimum of thirty minutes was spent collecting the , qualitative sample. Sampling continued until the field crew determined that further sampling would yield : ' . few new taxa. At least one specimen of all taxa collected during the qualitative sampling was preserved in a jar with 95% ethyl alcohol for later identification in the laboratory.

Each wetland was sampled three times between late Februaty and July. The early spring sample allows sampling of adult Ambystomatid salamanders and early spring macroinvertebrates such as faiiy shrimp which are present in the wetland for a limited time. Adult salamanders enter wetlands to breed following the first few warm, rainy nights of late winter to early spring. The actual timing of their arrival is highly weather dependent and varies greatly by year and location.

5 }: C A second sample was collected in April in order to collect adult frogs as they enter the wetland to breed. We also collected a variety ofmacroinvertebrates and amphibian larvae. The third sample was collected between late May to early July. Salamander larvae, frog tadpoles and a variety of adult and larval macroinvertebrates were collected. Salamander larvae were generally mature enough for identification purposes. The collection of mature salamander larvae was important as confirmation of the presence of a particular species in the wetland. The collection of adult salamanders from a wetland was sporadic. The adults remain in the wetland for a short period and the timing of their arrival was unpredictable. The salamander larvae take several months to mature so they have a longer sampling window.

2.2 Laboratory Methods

Upon submission to the laboratocy, all funnel trap and qualitative samples were assigned a unique lab number for tracking purposes. The contents of each funnel trap were processed individually so 1hat each site had ten quantitative samples to process for each of three collection dates. Samples preserved in formalin were washed with water and transferred to 70 % ethyl alcohol before the contents were identified.

All organisms within each funnel trap sample were identified and counted. Procedures for the identification of macroinvertebiates followed procedures used by Ohio EPA for the stream sampling program (Ohio EPA 1987). When a specific organism was especially numerous, usually in excess of· 1900 mdividuals per trap, a sample splitter was used to produce a manageable numbCr for processing. \ i The count was then multiplied by the dilution ratio to produce the total taxa count (Ohio BPA 1987). All other organisms within the sample were counted prior to splitting. The numbers of each taxa in each trap were entered into our database along with the time trapped so that a relative abundance, number per hour of trapping. could be calculated.

All samples w~re processed by the same individual so that the same level of taxonomic resolution was applied consistently for all samples. Macroinvertebrates were identified using procedmes and taxonomic keys specified in Ohio EPA (1987). The following Table 2.2 summarizes the level of taxonomy applied to specific groups, The raw data for all 1998 and 1999 samples is included in the Appendix. Data from 1996 and 1997 sites was included in Fennessy (1998 a). The identification of adult and larval . salamanders was based on keys foWld in Pfingsten and Downs(l989). Adult frog and tadpole identifications were based oii. Walker(l946). "

Table 2.2 Levels of Taxonomic Resolution

Taxonomic group Common name Level of Taxonomy

sponges Coelenterata Ihydra 1~genus

6 0 Annelida (Oligochaeta) freshwater worms Class

Annelida (Hirudinea) leeches species

Bryozoa moss animalcules species

Platyhelminthes(Turbellaria) flatworms class

Crustacea(Eubranchiopoda) fairy and clam shrimp species

Cmstacea(Cladocera) water flea order

Crustacea(Ostracoda) seed shrimp order

Crustacea(Copepoda) copepod order

Crustacea(lsopoda) sowbug genus

-Crustacea(Amphipoda) scuds genus/species .

Crustacea(Decapoda) crayfish species

Hydracarina water mite class - Insecta(Ephemeropteni) mayfly genus

Insecta{Odonata) dragonfly and damselfly genuS/species

Insecta{Plecopteta) stonefly species

Insecta(Hemiptera) true bugs genus

Insecta(Megaloptera) fishfly species

Insecta(Neuroptera) spongillafly genus - Insecta(Trichoptera) caddisfly genus/species

lnsecta(Coleoptera) beetles genus

Insecta(Diptera) fly genus/ Chironomidae-species

Gastropoda snails genus

Pelecypoda fing~I clams genus

':. i

2.3 IBI Development Methods

7 0 Karr et al. (1986) and Ohio EPA (1988a, 1988b) performed foundational IBI development using freshwater fish. Ohio EPA (1988a, l 988b) developed IBls for macro invertebrates in freshwater streams. Karr and Kerans (1992) summarized their procedure for developing a macroinvertebrate IBI for the Tennessee Valley Authority. The U.S. EPA has several guidance manuals on IBI development that recommend various procedures and methods (U.S. EPA 1990, 1998, 1999). Methods developed for fish and macroinvertebrates in streams need to be adapted for use in wetlands. What follows in these next sections is a summary of Ohio EP A's appro~h to developing IBis for ampln"bians and macro invertebrates.

1.4 Site selection and classification

Site selection and classification for IBI development is an iterative process (U.S. EPA 1999), but generally, two methods can be employed: a priori classification or a posteriori classification. Multimetric IBI approaches to developing biocriteria generally employ what could be called an iterative a priori classification approach.- 'This -has been the approach taken by Ohio EPA. A goal of a cost effective bfocriteria program is to have.the fewest classes that provide the most cost-effective'feedback. Data to date is suggesting somewhat diverse w~9 types may be "clumpable." The hypothesis currently being evaluated is that even though the floras. of hydrogeomorphically or floristically distinct wetlands are different at the species level, the quality and/or responsiveness of their unique floras to human disturbance is equivalent. This is especially a. concern where there are too few remaining examples of a particular class of wetlands to "fill out" a class: e.g. undisturbed wet prairies were once an abundant type of wetland that has now virtually disappeared from the landscape of Ohio.

Ohio EPA initially classified wetlands using a hydrogeomorphic criterion and a tloristic criterion: depressional versus riparian and emergent versus forested. A wetland classification scheme is presented in Table 2.4.

In 1999, after additional analysis and review of the resuJts·:from Fennessy et al. (1998a and 1998b), Ohio EPA added an additional selection criteria of relative disturbance since it appeared that very high quality emergent wetlands and very low to moderate quality forested wetlands were missing from the data set (Ohio EPA 1999). The weiland's score on the Ohio Rapid Assessment Method was used as a screening tool to determine the relative amount of disturbance in the wetland. Ohio EPA has sampled but has not completed analysis of several kettle lakes, fens, and bogs.

,. '•

8 0 Table 2.4 Wetland classification scheme employed by Ohio EPA during 1996-98 Amphibian and Macroinvertebrate attribute development

criterion classes description

hydrogeomorphlc depressional- Generally corresponds to "depressionar HGM class In Brinson (p. 20, Table 3, evapotranspiration 1993). Ohio EPA defines as Isolated wetlands not hydrologically connected to stmams or lakes; preclpltationfevapotransplratlon driven hydrology during growing season; typically seasonally-lundated or saturated. Brinson (1993) Includes •break-In-the-slope• seep wetlands In this category.

riparian- Closest analog to HGM Classes In Brinson (p. 22, Table 3, 1993) Is "Riverine - evapotranspiration Middle Gradient Landfonn. • Ohio EPA defines as located on floodplain or adjacent to perennial stream where wetland receives annual to biannual surface water Inputs from spring flooding, but where hydrology Is otherwise dominated by evapotransplration during the growing season.

riparian-headwater Class under consideration. Possibly much more extensive type of system pre seWement before lnaeases in hydrologic loadings In streams from extensive channefl%3tlon, ditching and b1ing. Preliminary definition: weUands located In the headwaters of streams or rivers. often with open water areas and outflow via low gradient, perennial stream. to streams or other wetland syStems. Examples: Cuyahoga Wetlands In Geauga County; Watercress Marsh Col~la County. Contains both depressional and riverine features?

riverine Class under consideration. Wetlands with pereMial surface water connections to a stream. Possibly much more extensive type of system pre settlement, now uncommon. No wetlands of this type have been studied.

kettle lakes Class under consideration. Conceptualized as "Jaqie•, depresslonal wetland. Does this system respond cfdferently than Isolated emeigent or sc:ruMhrub. Some overlap with fens and bogs. Degraded keltle lakes often had bog and \ ) fen type systems on their margins. In Ohio, not usually large enough to have •1acustr1ne• character In sense of Brinson's (1993) HGM classlfication.

fens and bogs Class under consideration. True fens and ombrotrophic bogs. In Ohio, flora of bogs and fens often near southern limits Of range, therefore, considered rare. Questions: do these form a class unto themselves or are they "depresslonal" par excellence?

Lake Erie coastal Class under consideration. True lacustrine wetlands. Lake Erle water levels wetlands are the wetland's primary hydrological Influence, I.e. the wetland is hydrologically unrestricted (no lakeward or upland border alterations), or the wetland can be characterized as an •estuarine• wetland with lake and river lnfluenoed hydrology. These Include sandbar deposition wetlands, estuarine wetlands, river mouth weffands, or those dominated by submersed aquatic vegetation. . .

floristlc emergent Wetland Is dominated by or has a codominatB plant community consisting of herbaceous vegetation. GeneraUy equivalent to Cowanfan et al. (1979) •emergent" class, but may Include "aquatic bed" also.

forested Wetland Is dominated by or has a codomlnate plant community consisting of a closed canopy of tree species. Generally equivalent to Cowardin et al (1979) "forested" class..

scrub-shrub Wetland Is dominated by or has acodOmtnate plant community consisting of a shrub or small tree species. Generally equivalent to Cowarcfin et al (1979) "scrub-shrub. class.

9 .·.-.;::•"• ·-··;o. •·~·· "••• •.•.•..• ••• -··- - . ····--· •""·-· • -··

2.5 Attribute evaluation and Metric selection

After initial classification and during classification iterations, potential ecological or biological attributes of the taxa group are identified and evaluated (Barbour et al. 1995).2 Potential attributes are initiaIIy selected a priori and should include aspects of the community structure, taxonomic composition, individual condition, and biological process~ (Table 2.5; Karr and Kerans 1992; Barbour et al. 1995).

Table 2.5 Types and characteristics of attributes which can be included in biological assessments. Adapted from Barbour et al. 1995.

type possible attributes oomnwnlty &tructuns taxa richness, relative . abundance .

taxonomic composition Amphibian quality assessment Index, tolerance or intolerance of keytaxa

lndMdual condition disease. anomalies, contaminant levels

biological processes productivity, trophic / dynamics

Barbour et al. (1995) state that a useful attnl>ute has five general characteristics:

1. Relevant to the biological community under study and to the specified program objectives;

2. Sensitive to stressors;

3. Able to provide a response that can be discriminated from natural variation;

4. ·Environmentally benign to measure in the aquatic environment; and

5. Cost-effective to sample.

. . Wrth these principles in mind Ohio EPA evaluated a suite of potential attributes based on the biological information collected. Data from wetlands representing a range of disturbance was then evaluated for ecologically meaningful and explainable trends.

These procedures can be summariz.ed as follows: ti .

2 In this report, •attributes" are defines as a characteristic of the biological community of a wetland that changes In some predictable way In response to Increased human disturbance: a "metric" Is an auribute that has been Included as component of multimetric IBL

10 () Step 1 Classify organisms such that attributes span range of types, trophic levels, reproductive strategies, ecological affinities, age classes

Step 2 Propose working hypotheses for potential attributes

Step 3 Use graphical techniques, descriptive statistics, regression analysis, etc. to evaluate attn"butes from data set .of reference and nonreference wetlands.

Step 4 Select "successful" attnl>utes

2.6 Metric score and calibration

The analysis of macroinve~brate and amphibian data. is underway and specific metrics have not been .. selected. The results of the preliminary analysis of the data and selection of potential attnl>utes 'is included in the following section. A description ofthe steps in tlte process of metric development is in OEP A( 2000).

\· ' )

11 G 3.0 Data Analysis · Since Ohio EPA began monitoring amphibians and macroinvertebrates in wetlands 60 sites within the Eastern Corn Belt Plains ecoregion (western and central Ohio) have been sampled. A total of 1800 traps have been deployed for. 43,200 hours ( 5 trap years). The identification and enumeration of all organisms collected in the 1800 trap and 180 qualitative samples has been completed. An additional 19 wetlands in the Erie/Ontario Lake and Drift Plains ecoregion (NE Ohio) are ~ing monitored this field season.

To date, most of our efforts have focused on the field collection and laboratory processing of samples. Detailed analysis of the data is ongoing. It is becoming clear that many factors need to be considered when developing metrics for wetland fauna.

First, little information exists on the sensitivities of wetland amphtoians and macroinvertebrates to varying types and levels of disturbances. Conversely, much is known aboUt stream systems and their biology. However, biological responses to human disturbance understood for streams do not translate well to wetland situations. For example, tiie sensitivity of stream organisms to low dissolve

Much of the development of indices for stream biota is based on biological community responses to point source discharges. Most Ohio wetlands do not receive effluent from point source discharges so information lea.med from work on streams is not applicable to wetland biota.. Consequently, assessment ·of wetland biota sensitivity to effluent toxicity cannot be studied and applied widely nor does it have broad application. The inherent differences in lotic and lentic systems results in further variation in biological responses to stressors.

Many of the stressors observed in our study sites are the result of landscape level changes that affect hydrology, habitat, biological community composition, sedimentation rates, wetland size, connectivity, energy flow and water quality. The development of reliable biological indicators requires a predictable response to these stressors. These stressors include increased nutrient enrichment resulting in eutrophification, disruptions of the natural hydologic regime due to draining, increas~ runofi; grading, water level controls, mosquito abat.ement efforts, logging, increased imperviousness ofthe watershed, increased isolation due to surrounding development and intensive land uses, dredge and fill activities, introduction of exotics, mowing and others.

Hydrologic changes can disrupt nutrient and energy flow. Aerobic decomposition ofleaf litter dming the dry period is an important source of nutrients in seasonal wetlands. If a wetland is pezmanently inundated the nutrients locked up in undecomposed substrates become unavailable for primary production. This affects the whole energy cycle of the wetland and dictates the composition ofthe . biological community that resides there.

Additionally, hydro logic stressors can result in an increased or decreased wetland hydroperiod or more rapid and frequent fluctuations in wetland water levels. These marked changes in hydrology can have major effects on their flora and fauna.

One difficulty with assessing the biological community responses to disturbance is that disturbance often affects wetland classification. For example, forested wetlands may develop emergent w~d

12 G characteristics as canopy cover is reduced in response to disturbances. A permanent emergent marsh may become a semi-permanent wetland as a result of drainage activities. This potential for changing of classes as disturbance increases must be accounted for in any comparisons between wetland biological communities.

Several macroinvertebrate and amphtoian population attributes appear to be responding to disturbance gradients. Further analysis of the data should clarify these associations and identify other potential metrics.

3.1 Amphibian Data Analysis

Amphtoian assemblages in wetlands are made up of a relatively small number of taxa especially when compared to the number of plant or macroinvertebrate species found in wetlands or fish and macroinvertebrate specie:; found in·streams. To date, in Ohio, _after monitoring 60 wetlands of varying types and sizes we have encountered 15 species of amphtl>ians and two hybrids. In comparison Ohio streams are home to 165 fish species and more than 1375 inacroinv~rtebrate tax.a. We are finding the -- sm.an number of taxa may make the opportunity for development of metrics and indices for amplnoians in wetlands somewhat limited. The collected data is currently being reviewed for all possible correlations.

However, we have been ~le to explore some preliminmy relationships between the collected amplnl>ian data and the level of disturbance in some study wetlands. For the amphtl>ian analysis the study wetlands ) have been limited to depressional systems and separated into three classes: forested; scrub-shrub; and emergent The intent of the classification scheme was to place th<:' wetlands in a groups with comparable attn"butes. Special wetland communities: bogs;fens; wet prairies; riverine and kettle lake fringe wetlands have been removed from the data set to hold variables to a minimum. Analysis of the vegetation data has shown clear biotic differences between forested, scrub-shrub and emergent classifications and preliminary analysis of the amphtl>ian data indicates this is true of that community also.

In a manner similar to that done for plants (Andreas and Lichvar, 1995) we have used the varying sensitivity to disturbance and other habitat requirements to place individual amphtl>ian species within. a range of tolerance coefficients from 1 to· 10. Lower numbers indicate that those species are adapted to a greater degree of disturbance and broader range of habitat features and those species assigned higher n~bers are less tolerant of disturbance and have more specific habitat requirements. The tolerance · coefficients were developed after reviewing numerous texts about the life histories of each species and based on the experience of the researchers both 1hrough the years of this study and throughout their careers. The species encountered in wetlands and their proposed tolerance coefficients along with some rationale are contained in Table 3.1.

. The tolerance coefficients were used to develop what we are calling an amplnoian quality assessment index (AQAI) for each wetland sampled. The index is developed by first summing

13 .. -- .. -·---· -··· -·------... _. __ .. __ ·--·-··----·---·-

0 Table 3.1 Amphibian Tolerance Coefficients Species Tolerance Rationale Coefficient Ambystoma maculatum 8 Spotted salamanders have only been found in least disturbed wetlands or moderately disturbed wetlands where the disturbance bas been recent Ambystoma tigrinum 6 Tiger salamanders have been found in a range of wetlands but require deep, long lasting hydrology and intact upland habitat in 1he area. This salamander is listed as an Ohio "special intcn:st" species due to declining populations. Ambystoma jeffersonianum 5 The jeffcrson salamander and associated hybrids require intact wooded habitat adjacent to breeding pools with low to mcludes A. platincum and complex f . . modc:ratc levels of disturbance. A. trcmblayr) ..

Ambystoma texan~ 4 This is the most ubiquitous of the mole salamanders and will tolcratc wetlands with relatively short hydro-periods. Notophthalmus viridescens 9 Red spotted newts arc cxtrc:mely intolerant of disturbance, and found only in well buffcrCd intact wetlands. Rana sylvatica 7 A woodland fiog dependent on areas adjacent to wetlands with maturcwoodsandrcquircspoolswithminimaldistllrbancc Rana catesbeiana 2 Widely spread. most common in marshes but can be found in forested and saulrshrob sites. Tolerant of most common distwbancc fBctors. - Rana clamitans 3· This fiog species is found in a wide range of wetlands and is tolerant of most disturbances. Rana pipiens 2 This fiog species is not very demanding in selection of breeding sites and can be found in wetlands within a wide range disturbance levels.

Pseudacris crucifer 2 · Rcproduccs in a range of sites, .JQS.in rcquircmc:at is CllO~ water for breeding cycle and some suitable adjacent habitat Pseudacris triseriata 3 This species is slightly less tolerant of disturbance then the closely related Pseudaais crucifcr

Hyla versicolor 5 Tree fiogs n:qaire some shrubs or trees adjacent to breeding pools and an: less tolcnmt of other disturbances than most anurans.

Bufo sp. (Bufo amcricanus and Bufo 1 Toads require little except enough water to allow for their short woodhousci fowafi tadpoles arc reproductive cycle and will tolerate disturbances other indistinguishable) amphtoians cannot.

14 0 the number of individuals from all species trapped at a wetland to develop a total. Then the number of individuals from each species is multiplied by its tolerance coefficient and those sums are added to yield a second total. The second total is divided by the first total to yield the AQAI for that wetland. This index represents the average tolerance coefficient of individuals trapped at that wetland throughout the sampling season. Information on study wetlands and their AQAI and ORAM scores is presented in Table 3.2.

Table 3.2. Study Wetlands Classification and Scoring Wetland Class Year ORAM AQAI . Big Woods F 1999 68.5 6.58 Collier Woods F 1999 73.5 6.96

Graham.Road F 1999 28.5 2.45 , - Johnson Road .F 1999 21.0 2.43

Killdeer Plains F 1999 53.5 . 6.23 Lawrence Low 2 F 1999 43.0 3.82 Tipp-Efuabeth F 1999 29.0 2.70 Ackerman F 1997 21.0 2.43 Flowing Well F 1997 43.0 521 Hebron F 1997 54.5 238 Hempleman F 1997 40.0 2.50 Keller HQ F 1997 64.5 6.40 Lawrence High F 1997. 73.0 5.41 Leafy Oak F 1997 78 4.96 Leafy Oak F 1996 78 5.13 Sawmill Road F 1997 50.5 4.00 Sawmill Road F 1996 50.5 4.00 AreaK SS 1999 59.5 2.89 Drew Woods SS 1999 72.0 3.66 Oyer Frog Swamp SS 1999 68.5. 6.80

15 !.' 0 TheRookecy SS 1999 70.0 3.67 Scofield SS 1999' 43.0 2.08 Slate Run SS 1999 76.0 5.43 TwoMeadQws SS 1999 40.5 2.33 Callahan SS 1997 56.5 5.78 .. Cessna SS 1996 56.5 3.07 Gahanna Woods SS 1996 62.5 3.40 SR29 SS 1997 58.0 4.26 Berger Road E 1999 22.5 2.01 . . Bloomville E 1999 40.0 2.00 Larue Emergent E 1999 40.0 2.00 Palmer Road E 1999 16.5 2.00 Stages Pond E 1999 43.0 2.00 Calamus .E 1997 63.0 3.00 Calamus E 1996 63.0 3.31 CR200 E 1997 18 5.59 CR200 E 1996 18 4.01 Dever E 1997 22 4.75 KellerLQ E 1997 22 3.42 Lawrence Low E 1997 34 4.07 Mishne E 1996 18.5 2.00 Rickenbacker E 1996 64.5 2.78

When the AQAI is compared to the ORAM scores for study wetlands some interesting trends and correlations can be observed. It appears the AQAI is a tool that can be used to provide information on the condition of some types of wetlands based on the resident and breeding am.plnoian communities. For instance, Figure 3.1.1 illustrates the relationship between the AQAI and the ORAM score5 for the forested sites sampled in 1999. This shows a robust

16 0 correlation and indicates that changes in disturbance levels offorested study wetlands are strongly reflected in the corresponding AQAI scores.

Tabulating the AQAI for all forested sites sampled since the beginning of the study and comparing them to their ORAM scores is reflected in Figure 3.1.2. While not as strong a trend as is demonstrated when looking at the 1999 forested sites alone, there is still a good correlation. It appears that the AQAI may be a predictor of forested wetland condition.

When the AQAI is graphed against ORAM scores for 1999 scrub-shrub sites tfiere is a strong correlation as illustrated in Figme 3.1.3. It appears that as disturbance increases lower AQAI scores are to be expected. When ORAM scores for scrub-shrub sites from all years is compared to the AQAI sites there is still a good correlation (see Figure 3 .1.4). Two sites with higher than predicted AQAI scores make this correlation weaker than it would be otherwise•. These two sites had unusually large proportions of the populations dominated by species with high tolerance coefficients. - ··

1271 of the 1378 amplu"bians sampled at Oyer Frog Swamp were wood frogs, Rana sylvatica which have a tolerance coefficient of 7. With such a large portion of the amphibian community scoring at 7 it is ·no wonder the overall AQAI score was 6.8 and slightly higher than what would have been projected by Oyer Frog Swamp's ORAM score of 68.5. The CaUahan wetland had an

\ abmdant population ofred-spotted newts, Notophthalmus viridescens that accounted for 33 of I -- ' . the 87 individuarampbibians trapped there. With their tolerance coefficient of9, the newts drove up the AQAI score higher than what would otherwise be anticipated by Callahan's ORAM score of 56.5.

It should also be pointed out that our data set bad no scrub-shrub sites that bad low ORAM scores. All scrub-shrub sites sampled had ORAM scores of 40.5 or above. This means only scrub-shrub sites of moderate to low levels of disturbance were sampled. This information should be keep in mind when interpreting the AQAI to ORAM relationships for the scrub-shrub class.

Combining the AQAI scores for 811 forested and scrub-shrob sites and comparing them to corresponding ORAM scores is illustrated in Figme 3.1.5. This graph still shows a f.airly strong correlation and a clear trend of higher AQAI scores as wetland disturbance decreases, as measured by the ORAM scores. However~ it does appear that the AQAI is a better indicator of wetland condition when used to compare wetlands within the same wetland class.

When emergent sites are evaluated using the AQAI the correlations seem to break down (Figure 3.1.6). It is expected that emergent sites would have lower AQAI scores in general. In Ohio it seems most emergent sites are emergent because of some type of relatively major disturbance. There is generally not appropriate habi1at for amphibians with moderate to high tolerance coefficients such as ambystomid salamanders, newt:S or tree frogs in these types of wetlands. All emergent wetlands sampled in 1999 had AQAI scores between 2.00 and 2.01. ~well

17 0

Figure 3.1.2

Regression Plot

Y = 0.951907 + 6.61E--02X All Forested Sites R-Sq = 59.4 % .. 7· • • • • 6

5 ~ <( 4

20 . 30 40 50 60 70 80 ORAM

'•~ i .

21 illustrates the inability of most emergent sites to support amplnoians that are relatively intolerant to disturbance.

Of the emergent sites sampled in other years that had high AQAI scores but low ORAM scores . ' it was generally because there was intact upland habitat nearby. The proximity to appropriate upland habitat allowed migration to the wetlands by amphibians to complete their breeding cycle. For example, County Road 200 wetland is an approximately one half acre depressional pool that is located at the end of agricultural fields and adjacent t6 a road. This wetland has minimal buffers between it and the intensive land uses that surround it. Yet it has forested upland habitat within close enough proximity and is deep enough to support an abundant breeding population of tiger salamanders (91 of 108 individual amphibians), AI!zbystoma tigrinum, (tolerance coefficient of 6). Apparently, the tiger salamanders are migrating to the pool from the upland woods that is many hundreds of feet removed from the wetland. This domination of the amphibian population by tiger salamanders resulted in AQAI scores of 5.59 in 1997 and 4.01 in 1996, much·higher than --would be anticipated based on its ORAM score of 18.

Likewise, Lawrel}.ce Woods Low wetland scores 34 on the ORAM and has anAQAI score of 4.07. This wetland, too, has nearby forested upland habitat as well as other forested and scrub sbrub wetland pools that have breeding populations of tiger salamanders. During migration these salamanders are able to travel to the Lawrence Woods Low wetland, breed and deposit eggs. This wetland tiger salamanders accounted for 58 of the 114 ampln"bians sampled and were instrumental in elevating the AQAI score higher than would be anticipated.

Dever wetland is a small cattail, Typha latifolia dominated wetland located among active agricultural row cropping. The wetland scores 22 on the ORAM yet scores a 4. 75 ~n the AQAI. At Dever, tree frogs, Hyla versicolor (tolerance coefficient of 5) accounted for 11 of the 12 individual amplnDians sampled. The site bas just enough trees and shrubs on the perimeter and narrow buffers to provide habitat for breeding tree frogs and tli.eir larvae domimrted the meager amplnoian population sampled.

For now, it appears the AQAI may be a good indicator of disturbance levels in forested and scrub-shrub systems but does not work well for emergent sites. As stated earlier, WOik on the AQAI is preliminary and will be explored more thoroughly in the remainder ofthis grant cycle. - As well, other ampln"bian attributes will be identified from the data set and researched for their ' J)otential ~ as metrics toward development of an amplnoian mr.

Some amplnoian species are only showing up in wetlands with moderate to low levels of disturbance and may serve as indicators of intolerance. Newts, spotted salamanders, and wood frogs all are species that we have only encountered in wetlands with moderate to extremely low levels of disturbance. In those wetlands with moderate levels of disturbance where 1hese species have been found the disturbances have been recent. Over time these species might be expected to disapl'e8! from those wetlands that were recently disturbed. This could be predicted as the

18 ...... /'• .. -.....

0 continued affects of the disturbance impact their abilities to reproduce and maintain viable populations. The presence/absence and relative numbers of these species in a wetland may be attributes that will serve as metrics in development of an amphibian IBI.

There is some thought that the small numl?er of amphibian taxa in wetlands may limit index development to a few metrics. To compensate, amplnoian metrics might be linked with metrics from other taxa groups to develop an index. One other taxa group we have information on is wetland fish. The traps we use for ampbtoians and macroinvertebrates are essentially minnow traps and do a goad job of monitoring the resident fish community. We have kept records of the fish present in the traps. The fish data will be reviewed to determine if fish attnoutes might be combined with amphibian attnoutes to help develop a multi-metric index. .

At this time, fish that appear to be intolerant to disturbances in wetlands and may be indicators of wetland quality include central mudminnow, Umbra limi, brook stickleback, Culaea f!zConstans, Iowa darter, Etheostoma exile, grass pickerel, Esox americanus, northern pike, Esox lucius, blackstripe topminnow, Fundulus notatus, 'blacknose dace, Rhinichthys atratulus, lake chubsucker, Erimyzon oblongus, tadpole madtom, Noturus gyrinus, wannouth sunfish, Lepomis gulosus, and bowfin, Amia calva. Those fish species that appear to be tolerant ofmany wetland disturbances and would be considered tolerant include carp, Cyprinus carpio, green sunfish, Lepomis cyanellus, bluegill, Lepomis macrochirus, fathead minnow, Pimephal~ promelas, bluntnose minnow, Pimephales notatus, black bullhead, Ameiurus melas, and yellow bullh~ "'-- J Ameiurus nataliS.

Additionally, where predacious fish are present in wetlands the amphibian data of those wetlands will be reviewed. Ifthe fish are working to suppress amphibian populations this will need to be reflected in the biological 35Sessment of those wetlands. The presence/absence of predacious fish may be another attnoute that lends itself to metric development or may serve more broadly for wetland classification.

Results from the 19 wetlands being studied this year in the.Erie/Ontario Lake and Drift Plains will be added to the database to aid future data analysis. These sites should fiU in gaps in the data and provide additionally insights into trends already established. The data should also allow us to start to recogniz.e biological attribute differences that are ecoregional in origin.

19 0

Figure 3.1.1

Regression Plot Y =4.87E-02 + 9.73E-02X 1999 Forested Sites R-Sq =94~ %

. 6

\ ) ~ 5 <( 4

20 30 40 50 60 70 ORAM

20 s.:•-....~··..: ..... -·.•-···,.• ....._._. -...... __ • -··· -

Figure 3.1.2

Regression Plot

Y =0.951907 + 6.61E-02X All Forested Sites R-Sq=59.4%

7· • • • 6

\ ) 5 ~ <( 4

3 ··- 2

20 . 30 40 60 60 70 80 ORAM.

21 ..... _._ ...... _...... ·-····· .. ·-··· ·-· ...... _.. ..

-.- Figure 3.1.3

Regression Plot y =-1.48578 + 8.68E-02X 1999 Scrub-Shrub Sites R-Sq = 52.5 %

7 •

• \ \ / 5 ~ <( 4

3 .._ 2

40 60 60 70 80 ORAM

22 ~':.;:: ..•::" .. :.:.-.-.-~ .;,:;• ..:·. :. ."'. .:. -..... ·~ ...· .. :.~ ::~ ·.:-. ·:.-;... ~.:. :.·.,.-..;..:;:.... .'. :~:....:. ·•. :... .-. -~·-.: .• :• ..- ··=-·~:.- ..· .. :..::..·· .• -.·: ••• :.·:· ...... - ·- ... , ••·••... -•· .. ·- - '- -·- ._:...... ·-- ·, --·-· :·~. ·.:.- ..: ...... - ·- .. ·- ....- •. ,.,,..-.. "'·-'''-~ •. -. "- -·· -·- _..__ -· ...... -···· -··- -.- ...... -.... -.· •...... :-..

Figure 3.1.4

All Scrub-Shrub Sites .. •

6 • • • ) / 5 <(- 0 <( 4 • • • • 3 • • • 2 • 40 50 60 70 - 80 ORAM

23 / /·-- -··· ·- . I . I 0

Figure 3.1.5

24 Figure 3.1.6

All Emergent Sites

'• 6 •

5 •

\ \ j -<( • 0 4 • <( • • 3 • •

2 • • • • • 15 25 35 .. 45 55 65 ORAM

. ' 25 0 3.2 Macroinvertebrate Data Analysis

The following is a summary of preliminary data analysis for depressional wetlands monitored in 1996-1999. The wetlands were classified by predominant vegetation type, either forested or emergent A number of attributes of wetland macroinvertebra.te populations were examined in relation to a measure of environmental disturbance for-the emergent and forested classes of wetlands. We used the Ohio Rapid Assessment Method for Wetlands (ORAM v. 5.0) as a quantitative measure of disturbance. ~or a discussion of ORAM see OEPA (2000). The first step in the development of metrics is to select potential attributes of the biological community that respond to stressors in a predictable manner (Karr and Kerans, 1992). The selection of ea~h attnoute is a testable hypothesis. To date we have only looked at potential attnoutes based of the taxonomic composition of the macroinvertebrate community. A number ofpotential attnoutes based on tolerancefmtolerance level of macroinvertebrates and trophic level have not yet been studied. .

Two additional wetland classification variables need to be examined. The size ofa wetland can influence macroinvertebrate diversity ( WISSinger et al. 1999). The hydroperiod duration of a wetland can also impact themacroinvertebra.te comm~ty of the wetland (Schneider et al. 1999). The impact of both of these variables need to be evaluated independent of disturbance level. The primary difficulty with introducing additional wetland classification variables is that the data set within some classes becomes too small to evaluate. As adqitional sites are studied the impact of the5e variables will become easier: to study.

We have looked at plots of a number of macroinvertebra.te population attnoutes using the wetland ORAM score for the x axis value. A seasonal analysis of the data using data for each of the three sampling periods was done. Quantitative featmes of the data such as the percentage of Crustacea within a sample were compared for all sites within a vegetation class. Qualitative aspects of the data such as total taxa richness and taxa richness for specific groups was evaluated seasonally as well as pooled for all samples.

For emergent wetlands the percentage of Diptera in the early spring sample appears to be-related - to ORAM score. The higher quality (low disturbance) sites supported a greater number of dipterans (Figure 3.2.l). Most other attributes for emergent wetlands are based on taxa richness measures that are based on pooled data from all samples. Total taxa richness appears to be related to ORAM score (Figme 3.2.2). Higher quality sites support greater macroinv~brate : : diversity. ORAM measures habitat diversity so it is expected that sites wi1h greater habitat ·diversity will support greater macroinvertebrate diversity. The emergent wetlands with low disturbance support more Chironomidae tax.a, Crustacea taxa, and Tanypodinae taxa (Figures 3.2.3, 3.2.4 and 3.2.5). The Tanypodinae are predacious midges. A greater divemty of predacious midges may be based on a greater diversity ofprey species as well as habitat diversity in the higher quality sites. ·

In forested wetlands one potential attribute is based on the dominance of a few taxa in the

26 0 sample. The percentage of a funnel trap sample made up of the predominant three taxa was plotted in relation to the ORAM score (Figure 3.2.6). Higher quality sites with low disturbance should not be dominated by a few very abundant taxa. Disturbance creates opportunities for a opportunistic species that can become very abundant While the results for the spring sample are not strongly correlated, there is a tendency for higher quality sites to have reduced dominance. The timing of the sampling for this attribute is very important Virtually all of the sites are strongly dominated by fairy shrimp in the early spring . The fairy shrimp c0mplete their life cycle prior to the entrance of large ·numbers of predators in the wetland. The dominance attribute is best measured after the ~ shrimp are gone and an array of predators have entered the . wetland. The dominance attribute in the summer appears to have a stronger signal (Figure 3.2.9). The data point for the outlier in the upper right comer of the graph is from a site in which the samples contained large numbers of crayfish. Previous results indicate that an abundance of crayfish in the traps reduces the number of other organisms in the traps due to predation · (Fennessy et al l 998a). Other potential attributes are based on the percentage of the sample comprised ofChironomidae or Dytiscidae (Figure 3.2.7 and 3.2.8). Dytiscidae are one·ofthe top .. level macroinvertebrate predators in the wetland. A stronger signal for these attributes may be achieved if a measure of diversity is incorporated into the attribute. Future analysis will look at this.

27 Figure ~.2.1 Emergent Wetlands- Early Spring

.A. 40

30 ) A.. ...cu Cl) ~ 20 Q. -c A. ~ 10 A. A. .... A. 0 ~ .. A. A. , .

-·10 10 20 30 40 50 60 70 80

ORAMv5.0

28 O·

Figure 3.2.2 Emergent Wetlands- Pooled Data ('II Samples 110 ..

100

80 cu >< ""l!'". cu I- 70

s0 I- 60

. 30 10 20 30 40 50 60 70 80. ;? . ORAMv5.0

29 ()~./

Figure 3.2.3 Emergent Wetlands- Pooled Data All sample$

cu x cu 30 I- -0 A - -E 0 . A. ...5. 20 -.c 0 A A-= A A• 10 A

0 10 20 30 40 50 60 70 80

ORAMv5.0

30 0

Figure 3.2.4 Emergent Wetlan~s- Pooled Data All Samples

5 A"

4 A AA cu >

1 AA A

10 20 30 40 50 60 70 80 - ORAMv5.0

31 Figure 3.2.5 Emergent Wetlands- Pooled Data All Samples 14 . 12 .....

as 8 J as>< I- =·c 6 -"C a.0 ~ 4 A c as I-_ A 2 A A A 0 ...

-2 10 20 30 40 50 60 70 80

ORAMv5.0

32 Figure 3.2.6 Forested Wetlands- Spring 100 ..... - ...... A ..... A...... A 80 ......

...... cu ...... >< ...... cu 60 I- M ' ...... c cu ...... E ...... E 40 0 c ......

20 30 40 50 60 70 80 ORAMv5.0

33 0

Figure 3.2. 7 Forested Wetlands- Spring 5 ... 4 ...

3 \ CD ) cu 'tJ -E 0 c 2 0 a...... c- ... ·O . 'ff!. 1 ...... A ...... A 0 ...... ~ ..... -

-1 20 .· 30 40 50 60 70 80 ORAMv5.0

34 ·35 36 c 3.3 Future Work Much data analysis remains to be done. We need to look at the distribution of macroinvertebrates across wetland classification and disturbance levels to recognize the tolerance of specific taxa to disturbance. A example is in the Chironomidae family. We collect Tanytarsini and Cricotopus sp. midges from wetlands but from our stream work we know that Tanytarsini midges are intolerant and Cricotopus are tolerant. The presence/absence or relative abundance of tolerant and especially intolerant taxa have been the basis for many metrics in other habitats. They should be useful in wetland metric development when we have sufficient information about the tolerance of wetland biota to disturbance. -

The stratification of the data based on wetland classification needs ftnther study. The size of the wetland and the length of the hydroperiod are two variables that need to be examined. We will examine these in the future especially as further sampling expands our database. We will also look for eco_!'Cgional differences in wetland fmma that may impact metric development. Most metrics should behave in a similar manner across ecoregional boundaries Adjustments in scoring criteria for the individwil. metrics are all that is usually necess~ to account for ecoregional differences, however fauna distnoutional patterns could require broader changes.

: ..

37 0 4.0 References

Andreas, Barbara K., and R. W. Lichvar. 1995. A Florlstic Quality Assessment System for Northern Ohio. Wetlands Research Program Technical Report WRP-DE-8. U.S. Army Corps of Engineers, Watexways E?cl>eriment Station.

Barbour, Michael T., James B. Stribling, and James R Karr. 1995. Multimetric approach for establishing and measuring biological condition. In Biological Assessment and Criteria, Tools for Water Resource Planning and Decision Making, Eds. Wayne S. Davis and Thomas P. Simon, CRC Press, Inc.

Barbour, Michael T., James L Plafkin, Brian P. Bradley, Carol G. Graves, and Robert W. WISSeman. 1992.. Evaluation of EP A's rapid bioassessment benthic metrics: metric redundancy and variability among reference sites. Environmental Toxicology and Chemistry,_ 11:437-449.

_Bode, Robert W. ~d MMgaret A. Novak. 1995. Development and application of biological iJ!tpainnent criteria for rivers and streams in New York state. in Biological Assessment and Criteria, Tools for Water Resource Planning and Decision Making, Eds. Wayne S. Davis and Thomas P. S~on, CRC Press, Inc.

Brinson, Mark M. 1993. A hydrogeomqr.phic classification for wetlands. U.S. Army Corps of Engineers Wateiways Experiment Station. Wetlands Research Program Technical Report WRP-DE-4. Vicksburg, MS. ' ) Cowardin, Lewis M., Vrrginia Carter, Francis C. Golet, and Edward T. LaRoe. 1979. Classification of Wetlands and Deepwater Habitats of the United States. U.S. Department ofthe Interior, Fish and Wildlife Service, Office ofBiological Seivices, Washington, D.C. FWS/OBS-79/31.

Fennessy, M Siobhan, Michael A. Gray, and Ricardo D. Lopez (1998a). An Ecological Assessment of Wetlands Using Reference Sites Volume 1: Final Report, Volume 2: Appendices. Final Report to·u.s. Environmental Protection Agency. Wetlands Unit, Division of Surface Water. Grant CD995761-0l.

Fennessy, M Siobhan, Robert Geho, Bonny Elfiitz, and Ricardo Lopez. 1998b. Testing the Floristic Qualey Assessment Index as an Indicator ofRiparian Wetland Disturbance. Final Report to u~s~ Environmental Protection Agency. Wetlands Unit, Division of Surface Water. Grant CD995927.

Hughes, Robert M., Philip R Kau1inan, Alan T. Herlihy, Thomas M. Kincaid, Lou Reynolds, and David P. Larsen. 1998. A process for developing and evaluating indices of fish assemblage integrity • . <;anadian Journal of Fisheries and Aquatic Sciences, 55:1618-1631.

·Hornig, C. Evan, Charles W. Bayer, Steve R Twidwell, Jack R Davis, Roy J. Kleinsasser, Gordon W. Linam, and Kevin B. Mayes. 1995. Development of regionally based biological criteria in Texas. in Biological Assessment and Criteria, Tools for Water Resource Planning and Decision Making, Eds. Wayne S. Davis and Thomas P. Simon, CRC Press, Inc.

Karr, James R 1993. Defining and assessing ecological integrity: beyond water qualify. Environmental Toxicology and Chemistry, 12:1521-1531.

38 (J Karr, James~ and Daniel R Dudley. 1981. Ecological perspective on water quality goals. Environmental Management 5(1):55-68.

Karr, James~ and Billie L. Kerans. 1992. Components of biological integrity: their definition and use in development of an invertebrate IBI. In Proceedings of the 1991 Midwest Pollution Control Biologists Meeting: Environmental Indicators Measurement Endpoints. T.P. Simon and W. S. Davis. eds. U.S. Environmental Protection Agency, Region V, Environmental Sciences Division, Chicago, III. EPA 905/R..-92/003. .

Ohio EPA. 1987. Biological Criteria for the Protection ofAquatic Life: Volume Il. Users Manual for the Biological Field Assessment of Ohio Surface Waters. Ohio Environmental Protection Agency. Division of Water Quality Monitoring and Assessment, Surface Water Section, Columbus, Ohio

Ohio EPA. 1988a. Biological Criteria· for the Protection ofAquatic Life: Volume I. The role of biological data in water quality assessment.. Ohio Ellvironmental Protection Agency, Ecologi~ - Assessment Section, Division of Water Quality Planning and Assessment, Columbus, Ohio.

Ohio EPA. 1988b. Biological Criteria for the Protection ofAquatic Life: Volunie Il. Users Manual for biological field assessment of Ohio surface waters. Ohio Environmental Protection Agency, Ecological Assessment Section, Division of Water Quality Planning and Assessment, Columbus, Ohio.·

) Ohio EPA. 1989a. September 30, 1989 Addendum to Biological Criteria for the Protection ofAquatic Life: Volume II, 1988. Ohio Environmental Protection Agency, Ecological Assessment Section, Division of Water Quality Planning and Assessment, Columbus, Ohio.

Ohio EPA. 1989b. Biological Criteria for the Protection of Aquatic Life: Volume ID. Standardized Biological Field Sampling and Laboratory Methods for Assessing Fish and Macroinvertebra.te Communities. Ohio Environmental ProteCtion Agency, Ecological Assessment Section, Division of Water Quality Planning and Assessment, Columbus, Ohio.

Ohio EPA. 1997. Quality Assuranc;e Project Plan. An Ecological Assessment of Wetlands Using Reference Sites, Wetland Program Development Grants CD985276 and CD985761, August 1997~ Ohio Environmental Protection Agency, 401/Wetlands Unit, Columbus, Ohio ·

I Ohio EPA. 1999. Quality Assurance Project Plan, Testing Biological Metrics and Development of Wetland Assessment Techniques Using Reference Sites, Wetland Program Development Grants . CD985875, February 1999. Ohio Environmental Protection Agency, 401/Wetlands Unit, Columbus, Ohio.

Ohio EPA. 2000. Final Report to U.S.EP A Grant No. CD985276. Interim Report to U.S.EPA Grant No. CD985875. Volume I: Vegetation Indices ofBioticintegrity(VIBI) for Wetlands and Cah"bration ofthe Ohio Rapid AssesSment Method for Wetlands v. 5.0. August 2000. Ohio Environmental Protection Agency, Division of Surfuce Water. Columbus, Ohio

· - Pfingsten, Ralph A., and Downs, Floyd L. 1989. Salamanders of Ohio. Bulletin of the Ohio Biological SUIVey. Volume 7#2. Columbus, Ohio ·

39 . ·'.'.. ,, ...... ~· ...

U Schneider, Daniel W. 1999. Snowmelt Ponds in Wisconsin: Influence ofHyclroperiod on the Invertebrate Community Structure. In Batzer, Donald P, Rader, Russel B., and Wissinger, Scott A. Invertebrates in Freshwater Wetlands ofNorth America: Ecology and Management. John Wiley & Sons, Inc.

Simon, Thomas P. and Erich B. Emexy. 1995. Modification and assessment of an index of biotic integrity to quantify water resource quality in great rivers. Regulated Rivers: Research & Management 11:283-298.

U.S. EPA. 1998. Lake and Reservoir Bioassessment and Biocriteria. Technical Guidance Document. Office ofWater, U.S. Environmental Protection Agency, Washington, D.C., August 1998. EPA 841-B- 98-007. . U.S.-EPA. 1999. Rapid Bioassessment Protocols for Use fu Wadeable Streams and Rivers. Periphyton, Benthic Macroinvertebrates and Fish, 2m Ed.. Office of Water, U.S. Environmental Protection Agency, Washington, D.C., July 1999. EPA 841-B-99-002. . Walker, Charles F. 1946. The Amphibians of Ohio. Part 1. The Frogs and Toads. Ohio State Museuni Science. Bulletin. Vol. 1 # 3.

WISSinger, Scott, Bohonak, Andy, Whiteman, Howard, and Brown, Wendy. 1999. Subalpine Wetlands in ";, Colorado: Habitat Permanence, Salamander Predation and Invertebrate Communities. In Invertebrates in J Freshwater Wetlands ofNorth America: Ecology and Management. Eds Batzer, DOnald P., Rader, Russell B., and WISSinger, Scott A John Wtley & Sons, Inc. ·

Yoder, Chris 0, and Edward T. R.an1.dn. 1995. Biological Criteria Program Development and Implementation In Ohio. In Biological Assessment and Criteria, Tools for Water Resource Planning and Decision Making, Eds. Wayne S. Davis and Thomas P. Simon, CRC Press, Inc. '