1. Interested in “whole” lake metabolism Metabolism means primary production and respiration. Primary production is one of the basal resources for the food web. Respiration tell us how much is catabolized and what is left for export and storage. Metabolism explains the net gas balance And, metabolism is well suited to high- frequency measurements- a GLEON goal

2. Million Sonde March Matthew C. Van de Bogert, Darren L. Bade, Stephen R. Carpenter, Jonathan J. Cole, Michael L. Pace, Paul C. Hanson, and Owen C. Langman Lots of authors. This talk for the GLEON Workshop, Lake Sunappe, January 2013.

3. Talk based on a recent paper Spatial heterogeneity strongly affects estimates of ecosystem metabolism in two north temperate lakes Van de Bogert et al. 2012 Limnol. Oceanogr. We will discuss how having lots of sonde spatially arrayed in a lake affects estimates of GPP, R and NEP

4. Photosynthesis and respiration Quick review CO 2 + H 2 O  CH 2 O + O 2 GPP (gross primary production) – all photosynthesis independent of its fate. R (total respiration, including plants and consumers) is the equation backwards. GPP produces O 2 in the light. R consumes O 2 in both the light and the dark

5. Definitions GPP- gross photosynthesis R –total respiration, including autotrophs R H – heterotrophic (consumer) respiration R A - autotrophic respiration. NEP (net ecosystem production)= GPP-R NPP (net primary production) = GPP-R A

6. Quiz A darkened bottle is suspended in a lake for a few hours. The observed change in dissolved oxygen (DO) represents which process: GPP, R, R H, R A, NEP or NPP A clear bottle is suspended in the lake during daylight. Which process is measured with the DO change. A dark and light bottle are suspended for 24-h, how do I calculate GPP and R and NEP?

7. Why free-water measurements? YSI  No container effects  High temporal resolution  Offers promise of integrating over large(r) area  Advances in technology have made measurements easier, cheaper, more reliable

8. Sonde Estimating metabolism (GPP, R, NEP) from free-water dissolved oxygen Daytime: Nighttime: Based on Odum (1956); Cole et al. 2000

9. GLEON- Mostly we have one (really great!!) buoy per lake

10. Van de Bogert et al. 2007; L&O:Methods

11. Macrophyte shallows can be food web “hot spots” with different levels of GPP and R

12. Plant beds can be dense

13. DO cycles differently in Trapa beds (RED) than in main river channel (BLUE) or in SAV beds (GREEN) (Caraco and Cole 2003; Goodwin et al. 2008) In Trapa, no GPP in the water column; lots of R. O 2 follows tide

14. Sonde site might matter Where there is spatial heterogeneity, the location matters. Where mixing is not instantaneous, the location matters. The Hudson is an extreme case, but… Nice example for lakes in Lauster et al. (2006) These examples just compare a couple of sites. What if you had LOTs of sonde sites?

15. Million Man March, October 16, 1995 Goal: Foster a spirit of support and self- sufficiency within the black community

16. Million Mom March, Mother’s Day 2000 Goal: send Congress the message that women stand together for stronger national gun-control laws

17. Million Sonde March, July-August 2007 Goal: To determine how many sondes are needed to get a “good” estimate of lake metabolism

19. SONDE water ballet- calibrate at one site; disperse to many sites; check calibration at end at one site

20. Sparkling Lake Peter Lake 8 TP (ug/L) 12.5 2.2 chl a (ug/L) 5.0 3.3 DOC (mg/L) 4.8 7.4pH7.0 64 Area (ha) 2.5 20 Max Depth (m) 19

21. Water depth (m) Sparkling Lake 64ha n=35

22. Water Depth (m) N=35

23. Sparkling Lake- 35 sonde sites. Large spatial variation in daily estimates of GPP and R. NEP less variable but still variable- sign changes with space. GPP R NEP

24. Water depth (m) Peter Lake 2.5 ha n=27

25. Water depth (m)

26. Peter Lake- 27 sonde sites. Large spatial variation in daily estimates of GPP and R. NEP less variable but still variable- sign changes with space.

27. If you have only one sonde site, you have got problems. Significant (ANOVA) difference among sites and days for both lakes for GPP and R and for NEP in Peter L. NEP in Sparkling did not vary among sites. Site and day together account for 25 to 63% of total variance. Site, rather than day is the lion’s share of the explained variance in both lakes. This is disturbing. And, the variance is not just significantly different, the variance is huge among sites.

28. Van de Bogert et al. 2012 Location, rather than date, accounts for most of the explained variance. The explained variance is only about 20-30% of the total variance.

29. Between 14 and 37% of the sites were statistically different from the lake wide mean (both lakes combined) for GPP and R. NEP not as bad.

31. Littoral v pelagic- surprising results. Areal GPP and R, GREATER in pelagic than littoral sites in BOTH LAKES. 25 to 47% higher. Pelagic (areal) NEP > littoral in Sparkling, but not Peter. Volumetric rates (GPP,R and NEP) greater in littoral than pelagic in Sparkling Lake No difference in volumetric rates for Peter.

32. Variability and number of sondes Let’s take a statisitcal sampling of the sonde data Rarefaction approach Goal: identify how combining data from multiple sensors influences the precision of the lake-wide metabolism estimates.

33. Rarefaction pseudocode 1 ) Calculate metabolism (GPP, R, NEP) for each site and day. 2) For k = 1 to n, where n equals the total number of sites where sensors were deployed; a) Randomly choose k sites from the pool of n sites. b) Calculate average metabolism values (GPP, R, NEP) using the subset of k sensors. c) Repeat 2a and 2b using another random subset of sites; continue repeating the procedure until either 1000 unique subsets have been sampled or the maximum number of subsets (given by the binomial coefficient nCk) has been reached, whichever is less. d) Calculate the mean and standard deviation of the repeated measures of the mean values using k sites. e) Repeat 2a through 2d for the next value of k.

34. Rarefaction results for both lakes. GPP, R about 50 to 100 mmol m -2 d -1 in either lake. To get SD to 20% of mean takes a lot of sondes. Fewer for NEP. Dashed lines are SD attributable only to DATE.

35. Suppose we make some rules about choosing sites Instead of choosing sites randomly, specify the mix of pelagic and littoral sites. Then basically repeat the previous analysis. “Habitat targeted rarefaction”

36. GPP results shown but R and NEP similar. Van de Bogert et al. IN PRESS. Lowest SD occurs when proportion pelagic of deployed sondes matches the proportion of pelagic area in the lake.

37. What this means With limited numbers of sondes, placement matters In Peter Lake maybe 5 to 7 sensors, placed correctly, gives a low enough SD for GPP, R and NEP In Sparkling Lake, this takes 10 to 14 sensors. Maybe we are asking the wrong questions with sondes. Maybe sondes can’t give you good daily values for a whole lake? How well do we do over sites if we average over time?

38. Peter Lake GPP aggregated by time.

39. Peter Lake R aggregated by time.

40. Sparkling Lake GPP aggregated by time.

41. Sparkling Lake R aggregated by time.

42. Sparkling NEP Peter NEP

43. Implications for GLEON The Million Sonde March is potentially problematic for GLEON. We probably should not expect to produce meaningful daily or sub-daily estimates of GPP or R at the scale of the whole lake from a single site. We probably can produce meaningful seasonal or monthly means from single sites. (Maybe)

44. Questions for GLEONITES and GLEONOIDS Which mechanisms are responsible for the spatial variability in sonde metabolism estimates It his variability real? Would good physical models of water movement make the variability go away (I say no to this one)

45. Coloso et al. 2010 Aq. Science- There are depth issues too.