Great Lakes Coastal Wetland Monitoring: Support of Restoration Across the Basin

Transcription

1 : Support of Restoration Across the Basin Matthew J. Cooper University of Notre Dame, Notre Dame, IN

2 Presentation Outline Quality control and assurance Importance of basin wide monitoring Examples of our data in action (fish) Conclusions, moving forward

3 Program Approximately 1,039 wetlands over 5 years ( ) Birds, amphibians, fish, invertebrates, plants, water quality, habitat covariates

4 Quality Assurance / Quality Control System 1) Guiding documents: QAPP and SOPs 2) Ongoing and open communication among labs 3) Pre season training and certification 4) Mid season performance checks 5) Established lab specific QA systems for water quality 6) Data entry QC 7) Database audits 8) Corrective procedures (triggered by deficiencies) 9) Documentation and reporting

5 Quality Assurance / Quality Control System 1) Guiding documents: QAPP and SOPs 2) Ongoing and open communication among labs 3) Pre season training and certification 4) Mid season performance checks 5) Established lab specific QA systems for water quality 6) Data entry QC 7) Database audits 8) Corrective procedures (triggered by deficiencies) 9) Documentation and reporting

6 1) Guiding documents Quality Assurance Project Plan (QAPP, 96 pp.) Standard Operating Procedures (SOPs, 143 pp.) Working documents, 3 rd revision, continual improvement Valuable training and reference documents

7 3) Pre-season training and certification Training camps for regional teams Field personnel must pass certification exams each year Overseen by QA/QC managers or trained, experienced crew leaders

8 4) Mid-season performance checks Administered by co PI at each regional lab Comprehensive audit of performance Example performance criteria (fish) identification of plant zones (95%) correct setting of nets (100%) 20 species level IDs in the field (95%) handling of live fish (100%) determining when to retain specimens (100%)

9 7) Database audits QA/QC managers scour the database for errors and omissions Work with regional crews to correct issues Trigger corrective action procedures (re training, etc.)

10 Quality Assurance / Quality Control System 1) Guiding documents: QAPP and SOPs 2) Ongoing and open communication among labs 3) Pre season training and certification 4) Mid season performance checks 5) Established lab specific QA systems for water quality 6) Data entry QC 7) Database audits 8) Corrective procedures (triggered by deficiencies) 9) Documentation and reporting

11 Importance of basin-wide monitoring

12 Importance of basin-wide monitoring Where to restore Least or most degraded? Highest potential for improvement? What to restore Ecosystem function or populations? Habitat area? How much to restore Ecological or budgetary endpoints? Adaptive management Dynamic project objectives? Flexible methodology?

13 Importance of basin-wide monitoring Where to restore Least or most degraded? Highest potential for improvement? What to restore Ecosystem function or populations? Habitat area? How much to restore Ecological or budgetary endpoints? Adaptive management Dynamic project objectives? Flexible methodology? Sociopolitical and economic decisions All require good data

14 Importance of basin-wide monitoring Monitoring human health vs. ecosystem health Lots of data Much less data

15 Examples of our data in action Sensiba Erie Marsh Braddock Bay

16 Examples of our data in action: Sensiba Wetland Managed impoundment for waterfowl production since 1959 Aging dikes, pump, control structures Invasion by cattail and Phragmites Loss of fish and waterfowl production Restoration:

17 Examples of our data in action: Sensiba Wetland Goals Improve fish and wildlife habitat Increase waterfowl production Improve fish habitat (esp. for northern pike spawning) Strategy Update/replace infrastructure Improve water level management Construct channels to reconnect wetland to Green Bay

18 Examples of our data in action: Sensiba Wetland Pre construction sampling (2012, 2013) Post construction monitoring (2014 and beyond)

19 Examples of our data in action: Sensiba Wetland Pre restoration fish Index of Biotic Integrity (IBI) scores Inside dike: 24 (average of zones sampled) Outside dike: 44 (average of zones sampled)

20 Examples of our data in action: Sensiba Wetland Pre restoration fish Index of Biotic Integrity (IBI) scores Inside dike: 24 (average of zones sampled) Outside dike: 44 (average of zones sampled) No. of wetlands Southern Green Bay n= Lake Michigan n=47 Basin n= IBI Score IBI Score IBI Score

21 Examples of our data in action: Sensiba Wetland Pre restoration fish community inside dike Brook stickleback 82% Central mudminnow 7% Fathead minnow 6% Green sunfish 4% Northern redbelly dace <1% Golden shiner <1% Yellow perch <1% Black bullhead <1%

22 Examples of our data in action: Sensiba Wetland Pre restoration fish community outside dike Yellow perch 22% Green sunfish 16% Emerald shiner 14% White sucker 13% Banded killifish 13% Central mudminnow 5% Bluegill 4% Pumpkinseed 3% Brook stickleback 1% Rock bass 1% Black bullhead 1% Brown bullhead 1% Longnose gar 1% Blacknose shiner 1% Smallmouth bass 1% Plus >7,000 YOY Yellow perch!

23 Examples of our data in action: Sensiba Wetland Pre vs. post construction monitoring Was the restoration successful? Is adaptive management necessary? What was learned? IBI Score?

24 Examples of our data in action: Braddock Bay Sensiba Erie Marsh Braddock Bay

25 Examples of our data in action: Braddock Bay

26 Examples of our data in action: Braddock Bay Restoration: >1.0 acre of wetland lost year -1 >1 acre lost per year Figure: U.S. Army Corps. Of Engineers

27 Examples of our data in action: Braddock Bay Restore barrier to reduce erosion Restore 185 acres of marsh Create channels and potholes for black tern nesting and northern pike spawning Restore native meadow marsh by removing invasive plants Feasibility study and environmental assessment: 2013 Construction: ~2014 Figure: U.S. Army Corps. Of Engineers

28 Examples of our data in action: Braddock Bay Pre restoration (2012) fish IBI score: 36 No. of wetlands Southern Lake Ontario n= IBI Score Lake Ontario n= IBI Score Basin n= IBI Score

29 Examples of our data in action: Braddock Bay Pre restoration (2012) fish species richness: 16 No. of wetlands Species richness Pumpkinseed 40% Bluegill 17% Yellow perch 14% Brown bullhead 8% Bowfin 5% Goldfish 4% Banded killifish 4% Emerald shiner 2% Black crappie 2% Logperch 1% Northern pike <1% 5 other species <1% >1,000 Largemouth bass YOY

30 Examples of our data in action: Braddock Bay Remaining wetland supports a diverse and healthy fish community Restoration is likely to increase the value of the wetland to Lk. Ontario ecosystem Monitoring will assess restoration success and adaptive management needs May contribute to de listing of BUIs for Rochester embayment AOC

31 Examples of our data in action: Erie Marsh Sensiba Erie Marsh Braddock Bay

32 Examples of our data in action: Erie Marsh Managed by TNC and the Erie Shooting and Fishing Club 2,270 acres (990 acres of diked marsh) 11% of southeastern Michigan s remaining marshland Managed for waterfowl production since late 1800 s Adjacent to major urban centers nature.org

33 Examples of our data in action: Erie Marsh Restoration: Erie Shooting and Fishing Club Restoration Strategy: Improve water management Install fish passage structures Extensive Phragmites control nature.org

34 Examples of our data in action: Erie Marsh Annual sampling since 2011 (pre project sampling) Surrounding wetlands sampled according to probabilistic sampling design Assess changes resulting from restoration Inform ongoing and future management of Erie Marsh

35 Monitoring program supports restoration efforts basin wide Pre and post restoration monitoring Informs adaptive management decisions Sampling design allows each project to be evaluated at multiple geographic and temporal scales Conclusions: 3 restoration examples presented were planned prior to basin wide monitoring program New restoration efforts will utilize monitoring data for strategic planning Ecosystem restoration is expensive plans should be based on appropriate data to maximize efficient use of resources

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