1
2
3
4
5
Appendix A: Canopy thinning
The treatment stream before canopy thinning (top panel) and after thinning, but before most
thinned trees were removed (bottom panel).
6
1
7
Appendix B: Methodological Details for 13CO2 Respiration Assays
Laboratory and field respiration assays allowed for successful measurement of 13C of live
8
9
bacterial biomass. We added HgCl2 as a kill control in field sampling during isotope tracer
10
releases, but control respiration incubation treatments in which all live organisms were killed
11
with HgCl2 produced the same amount of CO2 as live treatments, or even more CO2 than live
12
treatments, but showed no enrichment beyond background. These results indicated that the 13CO2
13
in the headspace of live treatments was a product of bacterial respiration, not inorganic diffusion
14
(Figure 1). We could not use the concentration data from original incubations because of
15
problems with HgCl2 effects on CO2 concentrations, so we conducted additional experiments
16
(detailed below) to parameterize models with concentration data.
17
18
19
20
21
22
23
24
25
Figure 1: Live versus killed CO2 respiration treatments from the 2009 tracer release in the
reference stream. Live treatments from 10 m downstream of the point of isotope addition (most
enriched sampling site) and 30 m downstream of the point of isotope addition (moderately
enriched sampling site) are shown with data from incubations in which microbes were killed
with HgCl2. HgCl2 treatments produced as much CO2 as live treatments, but never showed any
evidence of isotopic enrichment, demonstrating that labeled CO2 in the headspace is a product of
respiration.
100
10m
30m
Kill
d13C
75
50
25
0
1
4
7
Hours
26
2
27
28
Estimates of [CO2] for 13CO2 mixing model
We used field experiments to estimate information needed for the 13CO2 mixing model
29
that we used to calculate 13CO2 that was respired by bacteria. We ran 16 concurrent incubations
30
in sealed mason jars in the each study stream using the same methods as we did for field
31
incubations during the isotope tracer releases. In each stream, 8 jars contained coarse benthic
32
organic material (leaves) and 8 contained fine benthic organic matter (sediment). Of the 8 jars
33
for each type of organic matter, 4 were untreated and 4 were treated with sodium azide to kill all
34
microorganisms and stop respiration. From this experiment, we were able to calculate δ13C of
35
CO2 from inorganic sources (that is, not from respiration) by measuring δ13C of headspace CO2
36
in sodium azide treatments. We were also able to infer the proportion of headspace CO2 that was
37
derived from inorganic sources based on the ratio of [CO2] in treatments with sodium azide over
38
[CO2] in treatments with both inorganic diffusion and respiration. We calculated the proportion
39
of headspace CO2 from respiration by difference. Those data provided the information needed
40
for our mixing model that is described in the methods section of the paper. Values we calculated
41
from the experiment and used in the mixing model are detailed below in a table. We also
42
corrected to account for the fact that both bacterial and fungal respiration contribute to the
43
organic/respiration fraction of CO2 in the incubation headspace (f organic). We estimated a ratio
44
of respiration due to bacteria versus fungi in each organic matter type based on literature values
45
from other systems (described in the methods section) and used that ratio to correct f organic to
46
find f bacteria.
47
48
49
Stream
Blues Brook
Blues Brook
Combs Brook
Combs Brook
Organic Matter Type f inorganic
Coarse (leaves)
0.83
Fine (sediment)
0.81
Coarse (leaves)
0.86
Fine (sediment)
0.79
f organic
0.17
0.19
0.14
0.21
3
δ 13C inorganic bacteria:fungi f bacteria
-23.5
1 to 10
0.02
-23.5
1 to 1
0.10
-22.8
1 to 10
0.01
-22.8
1 to 1
0.11
50
We also used data for δ13C of CO2 from our field incubations during the isotope tracer release as
51
an estimate of the total CO2 pool (both respiration and inorganic). We collected data on the three
52
sampling days during the course of the 10-day isotope drip (Days 3, 7, 10) and during the
53
sampling day following the isotope drip (Day 14). Data for δ13C of CO2 at the two sampling
54
stations (10 m downstream of the point of isotope addition and 30 m downstream of the point of
55
isotope addition) are summarized in the following table.
56
57
Stream
Organic Matter Type
Combs Brook
Combs Brook
Blues Brook
Blues Brook
Combs Brook
Combs Brook
Blues Brook
Blues Brook
Combs Brook
Combs Brook
Blues Brook
Blues Brook
Combs Brook
Combs Brook
Blues Brook
Blues Brook
Coarse (leaves)
Coarse (leaves)
Coarse (leaves)
Coarse (leaves)
Fine (sediment)
Fine (sediment)
Fine (sediment)
Fine (sediment)
Coarse (leaves)
Coarse (leaves)
Coarse (leaves)
Coarse (leaves)
Fine (sediment)
Fine (sediment)
Fine (sediment)
Fine (sediment)
Downstream
Distance
10
30
10
30
10
30
10
30
10
30
10
30
10
30
10
30
Year
δ 13C Day 3
δ 13C Day 7
δ 13C Day 10
δ 13C Day 14
2009
2009
2009
2009
2009
2009
2009
2009
2010
2010
2010
2010
2010
2010
2010
2010
29.3
-4.1
104.3
-0.6
1.7
-9.4
29.1
-0.5
44.2
-7.7
57.3
18.1
-1.0
-4.3
-0.4
-8.5
96.7
49.2
72.4
46.4
0.2
-6.6
37.8
13.2
104.3
2.2
543.6
34.3
-0.3
-4.2
6.1
10.6
74.7
2.5
70.8
20.1
0.5
-0.5
25.4
15.2
20.8
-11.6
314.7
104.1
-5.1
-12.1
41.2
17.6
-4.1
4.2
-0.5
-4.3
-8.1
-11.3
13.3
-5.2
-6.7
-17.8
9.9
-7.6
-14.7
-13.0
-9.6
-9.6
4
58
Appendix C: Detailed description of dynamic compartment model for estimating consumer
59
turnover times
60
61
We used a linked dynamic compartment model approach based on observed isotopic
62
enrichment of food (food pool, or FP) and consumers (consumer pool, or CP). We collected data
63
during the 10 days that the system was enriched with tracer 15N and 13C and for 20 days after the
64
addition stopped and 15N and 13C declined. From this point forward, we describe terms of the
65
model for N for the sake of simplicity, but 13C and 12C could be used throughout the model
66
instead of 15N and 14N. This model is also described in detail in Dodds and others (2014).
67
We tracked the mass of each isotope (either 15N and 14N) over time in each pool and
68
gains and losses of isotopes were based on uptake (U15N and U14N) and loss (L15N and L14N) of
69
both isotopes expressed as mass per unit area per unit time (t). These uptake and loss fluxes were
70
calculated by tracking the 15N and 14N in the consumer pool (CP15N and CP14N, respectively) as a
71
function of the δ15N of up to three potential food pools labeled a, b, and c (FPδ15N,a ; FPδ15N,b;
72
FPδ15N,c).
73
The composite food pool δ15N (𝐹𝑃𝛿15𝑁 ) was estimated by weighting the δ15N of
74
individual diet sources (𝐹𝑃𝛿15𝑁,𝑎 ) by the proportion of that food source in the consumer diet (𝑃𝑎 ;
75
Eq. 1). The proportion of each food source was estimated using prior knowledge of consumer
76
diets or by functional feeding group classification. For organisms that were presumed to eat
77
fewer than three food sources, the number of sources was reduced accordingly.
𝐹𝑃𝛿15𝑁 = 𝐹𝑃𝛿15𝑁,𝑎 × 𝑃𝑎 + 𝐹𝑃𝛿15𝑁,𝑏 × 𝑃𝑏 + 𝐹𝑃𝛿15𝑁,𝑐 × 𝑃𝑐
78
79
(1)
5
80
The 15N atomic ratios of the FP (ARFP) and CP (ARCP) were calculated from δ15N values to
81
allow separate mass-balance tracking of 15N and 14N based on the ratio of 15N and 14N in the
82
atmosphere of 0.003663:
𝐴𝑅𝐹𝑃 = 𝐹𝑃
83
𝐹𝑃15𝑁
14𝑁 + 𝐹𝑃15𝑁
= (𝐹𝑃𝛿15𝑁 /1000) × 0.003663 + 0.003663
(2)
84
At each sampling point, the 15N and 14N fluxes into the consumer pool were assumed to be
85
proportional to the atomic ratio of the food pool(s), and N uptake and loss were the sum of the
86
15
87
N and 14N uptake rates (U15N and U14N , respectively):
𝑈 = 𝑈15𝑁 + 𝑈14𝑁
(3)
88
We used the change in the consumer pool of 15N between time steps 1 and 2, representing the
89
time between sampling, to calculate the new size of the consumer pool at time step 2 (CP15N, t=2)
90
from net uptake, which depends on ARFP and the uptake from the FP over time (t) as well as the
91
loss from the CP (L), the atomic ratio of the consumer pool (ARCP), and time (Eq. 4):
92
93
94
𝐶𝑃15𝑁,𝑡=2 = 𝐶𝑃15𝑁,𝑡=1 + (𝑈 × 𝐴𝑅𝐹𝑃 × 𝑡) − (𝐿 × 𝐴𝑅𝐶𝑃 × 𝑡)
(4)
Similarly, net uptake of 14N was calculated as:
𝐶𝑃14𝑁,𝑡=2 = 𝐶𝑃14𝑁,𝑡=1 + [𝑈 × (1 − 𝐴𝑅𝐹𝑃) × 𝑡] − [𝐿 × (1 − 𝐴𝑅𝐶𝑃) × 𝑡]
(5)
95
The calculated ARCP can lead to model instability if the time steps are too large (i.e., many days
96
between samples). Thus, ARCP was reset to the observed value at each sampled time step in the
97
model. In our study systems, biomass of CP was constant over the time period, then U = L.
98
We created a multiplier (M) to evaluate the degree to which we were not accounting for
99
δ15N label mismatch in the food pool by adjusting the peak food source label to fit the observed
100
peak animal isotopic signal. The bounds on the range of M values were set such that the lowest
101
possible value for M adjusted the food source to match the δ15N of its consumer (CP15N/FP15N).
6
102
In contrast, the highest M value was dependent on the measured or calculated δ15N-NH4+ in the
103
water column during the experiment (water15N/FP15N).
104
The model was created in Microsoft Excel and the “Solver” function was used to fit
105
observed to modeled values of δ15N by minimizing the sum of square of errors and changing U,
106
U/L, and M. Because there was no change in biomass over the experiment, U/L was set to 1. We
107
observed the model output graphically to assess the quality of fit.
108
7