Interested in “whole” lake metabolism
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Transcript of Interested in “whole” lake metabolism
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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
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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.
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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
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Photosynthesis and respiration
• Quick review• CO2 + H2O CH2O + O2
• GPP (gross primary production) – all photosynthesis independent of its fate.
• R (total respiration, including plants and consumers) is the equation backwards.
• GPP produces O2 in the light.• R consumes O2 in both the light and
the dark
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Definitions
• GPP- gross photosynthesis• R –total respiration, including
autotrophs• RH – heterotrophic (consumer)
respiration• RA- autotrophic respiration.• NEP (net ecosystem production)=
GPP-R• NPP (net primary production) =
GPP-RA
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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, RH , RA, 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?
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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
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SondeEstimating metabolism (GPP, R, NEP)
from free-water dissolved oxygen
80%
85%
90%
95%
100%
205 206 207 208 209 210 211 212Day of Year, 2001
Dis
solv
ed O
xyge
n (%
sat
urat
ion)
0
2
4
6
Ligh
t: Ei
nste
ins
m-2 h
-1
Sunlight
DissolvedOxygen
Daytime:
R - exchange gas GPP2
dtdO
Nighttime:
R - exchange gas2
dtdO
Based on Odum (1956); Cole et al. 2000
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GLEON- Mostly we have one (really great!!) buoy per lake
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Van de Bogert et al. 2007; L&O:Methods
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Macrophyte shallows can be food web “hot spots” with different levels of GPP and R
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Plant beds can be dense
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0
3
6
9
220 221 222 223Day of Year
DO
(mg/
L)
A]
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. O2 follows tide
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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?
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Million Man March, October 16, 1995Goal: Foster a spirit of support and self-sufficiency within the black community
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Million Mom March, Mother’s Day 2000Goal: send Congress the message that women stand together for stronger national gun-control laws
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Million Sonde March, July-August 2007Goal: To determine how many sondes are needed to get a “good” estimate of lake metabolism
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SONDE water ballet- calibrate at one site; disperse to many sites; check calibration at end at one site
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Sparkling Lake
Peter Lake
8 TP (ug/L) 12.52.2 chl a (ug/L) 5.03.3 DOC (mg/L) 4.87.4 pH 7.064 Area (ha) 2.520 Max Depth
(m)19
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2
4
6
8
10
12
14
Water depth (m
)Sparkling Lake64ha
n=35
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07/19 07/20 07/21 07/22 07/23 07/24 07/25 07/26 07/27 07/285
6
7
8
9
10
11
Diss
olve
d O
xyge
n (m
g/L)
Sparkling Lake 2007
0
5
10
15
20
Wat
er D
epth
(m)
N=35
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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
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Water depth (m
)
Peter Lake2.5 ha
n=27
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Water depth (m
)
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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Rarefaction results for both lakes.
GPP, R about 50 to 100 mmol m-2d-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.
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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”
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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.
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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?
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0
100G
PP
1 2 3 4 5 6 7 8 9
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 50
Site
All Pairs
Tukey-Kramer
0.05
Peter Lake GPP aggregated by time.
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-100
0
R
1 2 3 4 5 6 7 8 9
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 50
Site
All Pairs
Tukey-Kramer
0.05
Peter Lake R aggregated by time.
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-100
0
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GP
P
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 50
Site
All PairsTukey-Kramer0.05
Sparkling Lake GPP aggregated by time.
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-100
0
100
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400R
esp
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 50
Site
All PairsTukey-Kramer0.05
Sparkling Lake R aggregated by time.
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-100
0
100
NEP
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 50
Site
All PairsTukey-Kramer0.05
-20
-10
0
10
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30
NEP
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 50Site
All PairsTukey-Kramer0.05
Sparkling NEP
Peter NEP
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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)
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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)
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Coloso et al. 2010 Aq. Science- There are depth issues too.