Matthew C. Van de Bogert 1 Ph.D. Candidate University of Wisconsin – Madison And

1. Confronting within lake heterogeneity:How many sensors does it take to measure whole-lake metabolism? Matthew C. Van de Bogert 1 Ph.D. Candidate University of Wisconsin – Madison And Steve Carpenter 1, Darren Bade 2, Jon Cole 3, Paul Hanson 1, Tim Kratz 1, Owen Langman 1, and Mike Pace 4 1 Center for Limnology, University of Wisconsin – Madison, USA 2 Kent State University, Kent, Ohio, USA 3 Institute of Ecosystem Studies, Millbrook, NY, USA 4 University of Virginia, Charlottesville, VA, USA

2. PART 1 Just how variable is metabolism throughout a lake? Are there any emergent patterns?

3. Growing evidence of heterogeneity in metabolism Van de Bogert, et al., Limnol. Oceanogr.: Methods 5, 2007, 145–155 Lauster, et al., Can. J. Fish Aquat. Sci. Vol. 63, 2006

4. Wisconsin

5. Water Depth (m) Sparkling Lake Peter Lake

6. 36 Oxygen Sensors for 10 days Water Depth (m)

7. 27 Oxygen Sensors for 10 days Water Depth (m)

8. Dissolved Oxygen, mg L-1

9. Average 24hr Range of Dissolved OxygenOver 10-day deployment mg L-1

10. From Dissolved Oxygen To Metabolism

11. Nighttime: Daytime: Estimating metabolism (GPP, R, NEP) from free-water dissolved oxygen Based on Odum (1956)

12. There are cases where this model works well…

13. But also cases when it doesn’t Dissolved oxygen (uM) Day of Year, 2007 (24 hour period)

14. All pelagic sites

15. Mean of pelagic sites

16. Mean of littoral sites

17. Mean of transition zones

18. Lake-wide average(area weighted)

19. Apply Odum’s metabolism model to these curves…

20. mmol O2 m-2 d-1 Sparkling Lake10 day mean and standard deviation Apply Odum’s metabolism model to these curves…

21. mmol O2 m-2 d-1 Peter Lake10 day mean and standard deviation Apply Odum’s metabolism model to these curves…

22. PART 1: Recap • We set out to gain a better understanding of spatial heterogeneity in lake metabolism estimates • We found that the amplitude of diel cycles varies in space, with some recognizable patters • Amplitude is greater near shore than in the center for both lakes • There is greater heterogeneity among littoral sites than among pelagic sites. • Noisy data (due to movement of unique water masses past individual sensors?) interferes with the assumptions of the classic Odum metabolism model. • Aggregating data reduces “noise”. Patterns of aggregated DO through time more closely resemble patterns one would expect based on what we know about metabolism. • Metabolism calculations partitioned by lake region, show pelagic metabolism (GPP & R) to be lower than littoral metabolism. NEP, however, appears less sensitive to differences in lake region.

23. PART 2: So what’s one to do? • Just how many sensors are needed? • And should I put them all in one basket?

24. Rarefaction Peter Lake Sparkling Lake

25. Example: for n=3 100Unique Combinations of 3 sensors 100“Lake-wide” estimates X 100 Mean and variance Repeat for all n, eg. 1 through 36

26. Uncertainty declines with increased spatial sampling mmol O2 m-2 d-1 (mean & standard deviation) Number of sensors aggregated for whole-lake estimate

27. Do we really need all these?

28. Lessons in Heterogeneity

29. Lessons in Heterogeneity Pelagic regions are fairly homogenous

30. Lessons in Heterogeneity Pelagic regions are fairly homogenous “Hotspots” occur in the littoral zone Pelagic is not the same as littoral

31. If I had only 2 sensors… mmol O2 m-2 d-1 Strategic placement can cut uncertainty nearly in half.

33. Final Answer? • Additional sensors provide more accurate and precise estimates of whole-lake metabolism. • However, currently they are costly and cumbersome to operate in large numbers. • Strategic placement of a few (2 or 3) sensors may significantly improve whole-lake metabolism estimates (reducing bias and uncertainty). • It is unlikely that a single sensor alone would provide an accurate estimate of whole-lake metabolism in these systems.

34. Caveats(when might a single sensor be ok?) • Perhaps in long-term studies in which researchers desire an index of change over time. • In this case, metabolism estimates should be reported as indices and likely neither whole-lake nor only pelagic estimates. • More work is needed on systems of varying sizes. • Other Ideas?

35. PART 3: Day versus Night Respiration (4 slides left) • When using oxygen data to calculate GPP and R, ecologists assume daytime R = nighttime R. • This assumption is made not for a strong belief it is true, but for a lack of quantitative ability to measure daytime R.

36. Cumulative O2 Respiredmmol m-2 Hours Past Sunset Nighttime Respiration – Lake Average Estimate of Daylight R Respiration rate (slope of line) decreases over the course of darkness

37. Whole-Lake (Epilimnetic) Dark Respiration(all days of deployment) Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 Day 8 Cumulative O2 Respiredmmol m-2 Day 9 Day 10 Day 11 Hours Past Sunset

38. Summary for 10 Days in Sparkling • Ratio of “daytime” R to mean nighttime R, is 1.7 +/- 0.23 (s.d.). • 24 hour respiration is 1.45 times greater than previously thought. • If NEP = 0, GPP is also 1.45 times greater. • If NEP < 0, our underestimate of GPP will be greater • If NEP > 0, our underestimate of GPP will be smaller

39. THANK YOU! John Walker,USGS Jim Hodgson, St. Norbert College Jim ColosoInstitute of Ecosystem Studies Luke Winslow, Ryan Kroiss, UW-Madison Dorothy and Eugene Grant Scholarship fund UW Trout Lake Field Station University of Notre Dame Environmental Research Center (UNDERC) GLEON for funding to attend the GLEON 7 Conference.

40. aka The Million Sonde March

41. Van de Bogert, et al., Limnol. Oceanogr.: Methods 5, 2007, 145–155