•
•
•
•
•
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 highfrequency measurements- a GLEON goal
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.
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
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
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
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?
Why free-water measurements?




No container effects
High temporal resolution
Offers promise of
integrating over large(r)
area
Advances in technology
have made
measurements easier,
cheaper, more reliable
Estimating metabolism (GPP, R, NEP)
from free-water dissolved oxygen
Dissolved Oxygen
(% saturation)
Sunlight
6
Dissolved
Oxygen
95%
4
90%
2
85%
80%
0
205
206
207
208
209
210
Day of Year, 2001
Nighttime:
Daytime:
dO 2
 gas exchange - R
dt
dO 2
 GPP  gas exchange - R
dt
Based on Odum (1956); Cole et al. 2000
211
212
Light: Einsteins m -2 h-1
100%
GLEON- Mostly we have one (really great!!) buoy per lake
Van de Bogert et al. 2007; L&O:Methods
Macrophyte shallows can be food web “hot spots” with
different levels of GPP and R
Plant beds can be dense
DO (mg/L)
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)
9
A]
6
3
0
220
221
222
223
Day of Year
In Trapa, no GPP in the water column; lots
of R. O2 follows tide
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?
Million Man March, October 16, 1995
Goal: Foster a spirit of support and selfsufficiency within the black community
Million Mom March, Mother’s Day 2000
Goal: send Congress the message that women stand
together for stronger national gun-control laws
Million Sonde March, July-August 2007
Goal: To determine how many sondes are
needed to get a “good” estimate of lake
metabolism
NDE water ballet- calibrate at one site; disperse
many sites; check calibration at end at one site
Sparkling
Lake
Peter
Lake
8
TP
2.2
chl a
(ug/L)
5.0
3.3
DOC
(mg/L)
4.8
7.4
64
20
(ug/L)
pH
Area
(ha)
Max Depth
(m)
12.5
7.0
2.5
19
Sparkling Lake
64ha
n=35
14
12
8
6
4
2
Water depth (m)
10
Sparkling Lake 2007
20
10
15
9
8
10
7
5
6
5
07/19 07/20 07/21 07/22 07/23
N=35
07/24 07/25 07/26 07/27
07/28
0
Water Depth (m)
Dissolved Oxygen (mg/L)
11
GPP
R
NEP
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.
Peter Lake
2.5 ha
n=27
14
12
8
6
4
2
Water depth (m)
10
Water depth (m)
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.
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.
Location, rather than date, accounts for most of
the explained variance. The explained variance is
only about 20-30% of the total variance.
Van de Bogert et al. 2012
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.
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.
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.
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.
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.
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”
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.
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?
GPP
100
50
27
26
25
24
23
22
21
20
19
18
17
16
15
14
13
12
11
10
9
8
7
6
5
4
3
2
1
0
All Pairs
Tukey-Kramer
Site
Peter Lake GPP aggregated by time.
0.05
R
0
50
27
26
25
24
23
22
21
20
19
18
17
16
15
14
13
12
11
10
9
8
7
6
5
4
3
2
1
-100
All Pairs
Tukey-Kramer
Site
Peter Lake R aggregated by time.
0.05
GPP
200
100
0
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
-100
Site
All Pairs
Tukey-Kramer
0.05
Sparkling Lake GPP aggregated by time.
400
Resp
300
200
100
0
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
-100
Site
Sparkling Lake R aggregated by time.
All Pairs
Tukey-Kramer
0.05
-20
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
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
NEP
100
0
Sparkling
NEP
-100
Site
Site
All Pairs
Tukey-Kramer
0.05
30
20
Peter
NEP
10
0
-10
All Pairs
Tukey-Kramer
0.05
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)
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)
Coloso et al. 2010 Aq. Science- There are depth issues too.