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How do both autotrophic and heterotrophic successions end up having Gross Production = Respiration?

How do both autotrophic and heterotrophic successions end up having Gross Production = Respiration?


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I was reading Information theory by Eleith, Odum and Golley from different sources, one of which was Funfamentals of ecology by Odum:

… autogenic succession usually begins with an unbalanced community metabolism, where gross production, P, is either greater than or less than community respiration, R, and proceeds towards a more balanced condition, where P=R. The rate of biomass production (B/P ) increases during sucession until a stabilised system is achieved, in which a maximum of biomass (or high information content) and symbiosis between organisms are maintained per unit of available energy flow.

The succession begins with P>R in autotrophic sucession and P

I have tried to find explanatory texts both in this and other books without any success so my question is how's this balanced state achieved in both types of successions (the answer is hinted in the first paragraph which I don't quite understand)?

Related to my last post.


The author is saying that 1) Mature ecosystems tend to have a balance between production (=P) and use (=R, respiration) of biomass. This is actually tautological because the author would probably define a mature ecosystem as one where this is true (P=R).

If it starts out P > R, the autotrophs are dominant: more biomass is being produced than used up. It is possible, for a time, that P will increase as, for example, plants grow more leaves, but R is growing too, and there is an eventual limit on P, which at maximum depends on the light available to the ecosystem. As biomass grows, so does the amount of biomass to potentially decay, so eventually R will always catch up to P, until there is balance.

If it starts out P < R, that means you are using up biomass faster than you are creating it. This case is even simpler: you will gradually run out of biomass, and R will decrease.

In either case, when the author is talking about P = R, this is going to be in relative terms; there might still be variations between them, for example seasonal variation, but on average over years or decades you would expect P = R in a mature, stable ecosystem.


Autotrophic and heterotrophic contributions to short-term soil CO2 efflux following simulated summer precipitation pulses in a Mediterranean dehesa

[1] Autotrophic and heterotrophic components of soil CO2 efflux may have differential responses to environmental factors, so estimating the relative contribution of each component during summer precipitation pulses is essential to predict C balance in soils experiencing regular drought conditions. As even small summer rains induced high instantaneous soil respiration rates in Mediterranean wooded grasslands, we hypothesized that standing dead mass, surface litter, and topsoil layer could play a dominant role in the initial flush of CO2 produced immediately after soil rewetting in contrast, soil CO2 effluxes during drought periods should be mostly derived from tree root activity. In a grazed dehesa, we simulated four summer rain events and measured soil CO2 efflux discontinuously, estimating its δ 13 C through a Keeling plot nonsteady state static chamber approach. In addition, we estimated litter contribution to soil CO2 efflux and extracted soil available C fractions (K2SO4-extracted C and chloroform-fumigated extracted C). The δ 13 C-CO2 from in-tube incubated excised tree roots and rewetted root-free soil was −25.0‰ (±0.2) and −28.4‰ (±0.2), respectively. Assuming those values as end-members' sources, the autotrophic component of soil CO2 efflux was dominant during the severe drought, whereas the heterotrophic contribution dominated from the very beginning of precipitation pulses. As standing dead mass and fresh litter contribution was low (<25%) in the first day and negligible after, we concluded that CO2 efflux after rewetting was mostly derived from microbial mineralization of available soil organic C fractions.


Nitrification is a central component of the terrestrial nitrogen (N) cycle, but the contribution of autotrophic and heterotrophic nitrification to total gross nitrification remains poorly understood. To clarify their relative importance in neutral and moderate acid soils, an incubation experiment was conducted with 15 N-ammonium isotopic pool dilution techniques and combined with acetylene (C2H2, 10 Pa) as a specific inhibitor of autotrophic nitrification and sodium chlorate (NaClO3) as a potential inhibitor of heterotrophic nitrification. Additionally, CO2, N2O and CH4 fluxes were measured to identify potential side-effects of inhibitors on soil respiration and CH4 fluxes.

The presence of C2H2 completely eliminated gross nitrification in all investigated soil samples. The addition of NaClO3 affected neither gross nitrification nor gross ammonification in soils of both investigated grassland sites. This provided strong evidence that heterotrophic nitrification was not an important process in the investigated grassland soils. Acetylene but not NaClO3 decreased net CH4 uptake, likely due to homology of the enzymes ammonia monooxygenase. Overall, the present study shows a dominant role of autotrophic nitrification in gross nitrate production for both neutral and slightly acid soils and illustrates the potential of acetylene as an inhibitor of gross autotrophic nitrification.


Trophic ecology of Caribbean octocorals: autotrophic and heterotrophic seasonal trends

Studies over the past decades indicate that octocorals are becoming the dominant group in some areas of the Caribbean. Yet, basic knowledge about the trophic ecology of these organisms and their seasonal and species-specific variability is still scarce, though this might play a key role in determining their importance in benthic–pelagic coupling processes and, consequently, their role in carbon cycles. In the present study, two Caribbean gorgonian species (Plexaurella nutans and Pterogorgia anceps) were studied during an annual cycle, to assess seasonal variations in their reliance on heterotrophic versus autotrophic energy inputs. Zooplankton capture rates and bulk tissue stable isotopes were measured on a monthly basis to assess heterotrophic energy input, while autotrophic contribution was quantified monthly by Symbiodiniaceae cell densities and pigment contents, accompanied by seasonal measurements on Symbiodiniaceae (Breviolum sp.) photosynthetic performance and host respiratory demand. The results show that while autotrophy was the main energy source for both species, there was also a non-neglectable input through zooplankton that accounted for 0.2–0.8% and 0.7–3.4% of the energy demands in P. nutans and P. anceps, respectively. Our data further demonstrate that there are species-specific and seasonal differences in the contributions of these two nutrition modes, though there is no indication of shifts in the predominant mode during the year in either species. The energy inputs resulted in a positive energy balance throughout the year, with an energy surplus available for somatic growth, gonads, and/or energy reserves (e.g., lipids). However, the seasonal patterns differed between species, a feature that is most likely related to the different reproduction periods of the octocorals. Altogether, the information gathered here serves for a better understanding of the trophic ecology of mixotrophic octocorals and the seasonal variability of the nutritional modes that will define their potential impact in the carbon cycle and benthic–pelagic coupling processes of coral reefs.

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Discussion

The debate about the metabolic state of the oligotrophic ocean has evolved into a polarized dispute between two contrasting positions that are very difficult to reconcile (that is, the oligotrophic ocean is heterotrophic or it is autotrophic) and require substantial unaccounted for allochthonous inputs of either organic or inorganic nutrients 10,11 . Both positions arise from the same fundamental view that ‘the oligotrophic ocean’ is a single steady-state ecosystem whose balance of trophic processes is meaningful across scales. This perception is evident in the very discussion about its ‘metabolic state’, a property of either organisms or ecosystems whose calculation requires knowledge of the spatial and temporal scales of key trophic processes and connections in the system (production, accumulation, transport, consumption and oxidation of organic matter) 25 . However, the definition of the oligotrophic ocean is solely based on the level of productivity, controlled by the degree of water column stratification that determines nutrient limitation for the phytoplankton. Moreover, the oligotrophic ocean includes distant regions whose trophic connections are difficult to determine. Hence, estimating a single metabolic state for the global oligotrophic ocean from scattered measurements of P and R assumes scale independency in its trophic dynamics, that is, rests on the important assumptions of functional unity and steady state.

Our findings indicate that both this logic and the empirical evidence sustaining the debate are flawed. The systematic prevalence of autotrophy in the oligotrophic SATL challenges the generally accepted view that net heterotrophy prevails in in vitro NCP measurements in the oligotrophic ocean 10,11 , and the conclusion that such a prevalence is due to systematic methodological biases 11,19 . Moreover, the difference in NCP between the NAST and SATL refutes the hypothesis that a single type of balance prevails throughout the oligotrophic ocean 10,11 . The oligotrophic ocean is neither auto- nor heterotrophic, but functionally diverse. The debate has partially resulted from the use of universal P:R relationships where the heterotrophic NAST-E is over-represented.

The prevalence of R over P in the NAST implies carbon and energy deficits that need to be compensated through anoxygenic primary production, inputs of allochthonous organic matter or a higher efficiency in the use of metabolic energy. The paucity of reducing substrates and feeble redox gradients in the epipelagic zone of the oligotrophic ocean constrain the efficiency of chemoautotrophy and anoxygenic photosynthesis, whose relative contributions to euphotic zone carbon fluxes are <1% compared to oxygenic photosynthesis 26,27 , that is, too small to explain the metabolic differences between the NAST and SATL. Photoheterotrophic prokaryotes can use both light and organic matter for energy but require organic molecules as sources of carbon and electrons. Although proteorhodopsin-based and aerobic anoxygenic phototrophic bacteria are abundant and active in the oligotrophic ocean 28 , the implications of photoheterotrophy for the metabolic balance in the upper ocean are unclear. On one hand, phototrophic energy production reduces the need for aerobic respiration to sustain the maintenance costs of heterotrophs. On the other hand, photoheterotrophy increases the survival, and on occasions the growth, of prokaryotes under very limiting conditions 29 , possibly by supporting the energetic costs of processes such as active transmembrane transport, production of ectoenzymes, breakdown of complex organic molecules and cell motility 30 , but also by light induced anaplerotic CO2 fixation, which may provide up to 18% of cellular carbon demand 29 . This augmented survival, biomass production and the ability for organic matter utilization would increase the potential for heterotrophic respiration in the oligotrophic gyres. The consequences of photoheterotrophy for the metabolic cycling of carbon and the competitive advantage of planktonic bacteria 28,29,30 suggest an important, and yet unknown, role in energy and carbon balances in the oligotrophic ocean. However, it is difficult to infer the relationship between photoheterotrophy and the regional differences in R and NCP, and photoheterotrophy would not reduce the estimated deficit of organic carbon in the NAST per se.

Calculations of dissolved organic carbon import to the surface of the NAST are one to two orders of magnitude too small to support previous estimations of net heterotrophy 11 . However, the mean mixed layer (upper 50 m) in vitro NCP deficit in the NAST used in these calculations (−1 mmolC m −3 per day) 11 is ∼ 10 times higher than the −0.12 mmolO2 m −3 per day mean epipelagic NCP (average 112 m depth) estimated from our data set (Table 1 see also Fig. 5). In addition, our data show a depth-dependent distribution of NCP through the epipelagic layer, with lower heterotrophy near the 1% light depth ( ∼ 100 m depth) and in the upper 25 m ( ∼ 33% incident PAR Figs 3, 4, 5). Although we are reluctant to calculate annual balances from our data set, assuming a respiratory quotient of 0.8 11 and a mean mixed layer of 50 m 11 , 365 times our mean NCP in the upper 50 m of the NAST (−0.07±0.03 mmolO2 m −3 per day) amounts to −1.1±0.4 molC m −2 per year, that is, less than twice the 0.7 molC m −2 per year dissolved organic carbon input estimated ‘with high uncertainty’ because of the ill constrained inputs from the margins 14 . These same lateral inputs are also needed to balance phosphorous budgets in the NAST. The phytoplankton of the North and South Atlantic gyres is limited primarily by nitrogen availability 31 however, a distinctive characteristic of the NAST is that the concentration of bioavailable phosphorous in the euphotic layer is the lowest of all the ocean gyres and limits phytoplankton production 13 . The similar rates of phytoplankton carbon fixation 32 and P (Table 1) in the NAST and SATL hence concur with the similar euphotic zone concentrations of bioavailable nitrogen, but are at odds with the chronic phosphorous limitation in the NAST. The lateral input of dissolved organic phosphorous (DOP) from the shelf region of NW Africa into the gyre interior helps to balance this discrepancy 33 . DOP imported by a combination of gyre and eddy circulations is estimated to support up to 70% of the particle export over much of the gyre 33 , and should therefore impact on the balance between P and R. DOP may be directly utilized by the phytoplankton or recycled by heterotrophic bacteria, but the latter have been found to easily outcompete Prochlorococcus (the most abundant phytoplankter in the oligotrophic ocean) for DOP (ATP) uptake 34 . Estimating the metabolic impact of DOP import into the NAST requires resolving the variability of group-specific differences in DOP uptake and utilization, which might also interact with the photoheterotrophic enhancement of dissolved organic matter bioavailability, and with an anticipated increase in the open ocean phosphorous limitation with global warming 35 . A mechanistic understanding of the regional differences in community metabolism requires not only solving the spatial and temporal variation of NCP and relative inputs of allochthonous inorganic and organic nutrients within the gyres, but also the study of the multiple biochemical strategies for nutrient stress 36 and energy and carbon acquisition across spatial and temporal scales 37 .

Our results show that R heterogeneity in the oligotrophic ocean is important for NCP variability and prediction, and that global metabolic balances in the oligotrophic ocean represent the average of different metabolic states, rather than the metabolic state of a single global biome. While the potential of P:R relationships for NCP scaling remains, extrapolation should be specific to provinces showing a coherent trophic functioning 38 . The different geographic extent of these provinces requires spatially explicit approaches, that is, a trophic biogeographic partitioning of the ocean based on the spatial and temporal variability not only of P but also of R 23,38 . This is a difficult task because only the substantial P data set and reliable predictive algorithms 39 allow for comprehensive regional and seasonal depictions 39,40,41 , which is not enough to reveal the trophic diversity of the ocean. Besides, the scale dependency of ecosystem metabolism along the temporal axis is arguably as important for the highly dynamic, non-steady-state planktonic ecosystem, and particularly relevant in the context of anthropogenic change 42 . Although R is a slow variable compared with P, its temporal variability could be important for prediction of trophic dynamics over scales significant to environmental change 43 .

The estimation of the metabolic balance of the oligotrophic ocean continues to be an important and urgent challenge for ocean biogeochemistry. Integrative in situ approaches are invaluable for constraining metabolic balances over long and large scales. However, only in vitro or other instantaneous methods may resolve the spatial heterogeneity and temporal dynamics of ecosystem processes, which is a prerequisite for prediction. Our findings call for an effort to improve the scope and resolution of R and NCP measurements in the ocean. While we are far from a mechanistic understanding of the variability of plankton trophic functioning, the scale difference between in situ and in vitro methods provides unique opportunities to derive and test system-dependent empirical models for NCP extrapolation. A better appreciation of the scaling of metabolic processes to biogeochemical fluxes is a priority to improve our prediction of the ocean’s interaction with the changing climate.


3 Determination of Metabolic Rates

3.1 Rationale for the Correction of Baroclinic Vertical Dislocations

The main challenge we faced for the calculation of depth-resolved metabolic rates from high-frequency (DO) signals in this large lake was to filter out the physical contribution to DO variations due to internal motions. The main assumption underlying the two methods proposed below is that DO fluctuations due to internal motions are dominated by vertical (baroclinic) displacements, and consequently, they are correlated with temperature fluctuations as shown in this section.

(1) (2) (3) (4)

where a1 and a2 are constants related to the ratio of mean temperature and oxygen vertical gradients. The two methods proposed here use this approximation to filter the DO signal in the frequency (scale-separation method) and time domains, respectively.

3.2 Scale-Separation Method

3.2.1 NEP From Fortnight-Scale DO Budget

In this first approach, which we call scale-separation method, we combined a fortnight-scale oxygen budget to derive NEP, with a spectral approach to remove the physical signal from the diel DO fluctuations and derive GPP and R. For the first step, we recovered the low-frequency seasonal DO signal and eliminated the oscillatory contribution of internal lake motions by low-pass filtering the temperature and DO records with a Gaussian filter with a time scale of 15 days. We consider that DO variations over these time scales are governed by the interplay between NEP, vertical turbulent diffusion, gas exchange with the atmosphere, and the intrusion of the well-oxygenated Rhône River into the lake thermocline during the stratified period (Halder et al., 2013 ). Due to this intrusion, and to the fact that surface waters leave the lake at the outflow, mass conservation imposes a continuous basin-wide hydraulic uplift.

(5)

where the indices i ± 0.5 refer to the upper and lower edges of the layer and V i is the layer volume (V i = h i A i , where h i and A i are the layer height and mean area, respectively). The layers were defined by the vertical position of the sensors, and i = 1 corresponds to the deepest sensor (30 m), and i = N is the surface layer. Q i is the intrusion flux into the layer i in m 3 s −1 . The second term on the right-hand side represents vertical turbulent diffusion (including gas exchange and mixed layer deepening), the third term represents the oxygen supply due to convergence of river waters into the layer, and the fourth term is the vertical uplift induced by this convergence. DOintr is the oxygen concentration in the intruding riverine waters.

(6)

where TML is the mixed layer temperature and Tintr is the temperature at the intrusion (turbidity maximum) at the time when the turbidity profiles were taken.

The inflow at each layer, Q i , was then determined by splitting the total inflow into the different layers and by taking the intrusion temperature at each time step into account, Tintr = γ −1 ⋅ (TRhone + (γ − 1) ⋅ TML). Finally, we assumed that river and mixed layer waters are approximately saturated in oxygen, and hence, DOintr = DOeq(T i ), which is the equilibrium oxygen concentration at in situ temperature, T i . As an upper boundary condition, we assumed that surface waters leave the lake at the outflow. River flow and temperature data were smoothed similarly to the oxygen signal, and the splitting between temperature classes was done according to the spread of measured river temperature within the averaging time window.

(7)

where A i±0.5 is the lake area at the interfaces, κ is a turbulent diffusion coefficient, and is the DO vertical gradient, calculated by centered differences. The turbulent diffusion coefficients were diagnosed by applying the heat budget method (Powell & Jassby, 1974 ) in the same fashion as Equation 5, including the effects of penetrative shortwave radiation and river intrusion. The budget was calculated with the smoothed temperature mooring time series but, in order to close the budget, the heat transfer below the deepest sensor was also quantified. For this purpose, a composite smooth temperature field was constructed below 30 m by applying a similar smoothing procedure to 401 deep temperature profiles collected from the platform with a CTD probe and the microstructure profilers.

(8)

where A0 is the lake surface area and kw is a parameterized piston velocity calculated using the surface-renewal model by Soloviev et al. ( 2007 ), which includes the contribution of wind stress, thermal convection, and bubbles to gas exchange. In order to assess the uncertainty associated with the spread of kw among the available parameterizations (Dugan et al., 2016 ), a set of parameterizations including the most popular ones was tested. The air-lake fluxes were calculated with nonsmoothed values of DO concentration and instantaneous kw, and smoothed afterward. Turbulent fluxes Fdiff between the air-lake interface and the base of the mixed layer were interpolated linearly in depth for each time step, assuming instantaneous homogenization within the mixed layer.

A mixed layer deepening (i.e., entrainment) term was also added to the diffusive flux when a deepening of the mixed layer was observed. Considering that a mixed layer deepens from at time step j to at time step j + 1, the maximum entrainment flux is (9) where is the volume-weighted mean DO concentration in the mixed layer at step j, is the mixed layer volume at the same time step, and Δt is the time interval between the two steps. The mean DO concentration in the mixed layer after entrainment was calculated as (10)

where TH stands for thermocline, which represents the water volume comprised between and , and, hence, its volume was . The entrainment flux is maximal at the bin interface located at and set to decrease linearly toward the surface and .

3.2.2 Spectral Isolation of the Diel Signal

The budget analysis allows the quantification of a seasonal-scale NEP, that is, the fortnight balance between GPP and R. However, to disentangle these terms, it is necessary to resolve the day-night oxygen cycle, in which the effect of respiration can be isolated since photosynthesis is only active during daytime (Odum, 1956 ). We propose a spectral approach to isolate the biological DO signal from physical advection. The spectral approach was introduced by Cox et al. ( 2015 ), who determined GPP and R from the amplitude of a DO signal at the diel frequency calculated in the frequency domain. However, with this method, the long-term effect of oxygen accumulation due to NEP is implicitly removed, because the differences in the amplitude of the signal during day and night are not recovered but, at best, modeled by a prescribed function (Cox et al., 2015 ). This problem is solved in our approach by recovering NEP from the fortnight-scale budget. The newly proposed spectral method yields an estimate of the daily R, and GPP is subsequently computed using the budget-derived NEP as GPP = NEP + R (R is defined positive).

Furthermore, the possible contamination of the diel amplitude by physical signals was not considered by Cox et al. ( 2015 ) and is addressed here. The removal of the effect of baroclinic motions was performed with basic spectral analysis techniques (Welch, 1967 ) in four steps: (i) removal of the fraction of the oxygen variability coherent with temperature variability (Levine & Lueck, 1999 ), (ii) removal of a spectral baseline around the diel frequency, (iii) rejection of data segments where the decontaminated spectrum is lower than 1.5 times the baseline, and (iv) rejection of the data segments where the diel oxygen increase preceded the light increase by more than 2 h. R rates were finally computed by integrating the denoised spectra around the diel frequency. These calculations were done for each time step (1 h) over 256-datapoint (∼11 days) overlapping segments of DO data at different depths. The details of this procedure are outlined in the supporting information S1.

3.3 Diel Method With Time Domain Correction

Alternatively, we introduce a simpler method in which DO variations due to vertical dislocations induced by baroclinic motions are filtered directly in the time domain. With this approach, we could apply the classical diel method (Odum, 1956 ) directly to a DO signal from which the signature of internal motions has been removed. To do so, the DO records at different depths were split into 24 h segments. For each day, we calculated daily mean temperature and DO profiles (〈T〉, 〈DO〉) and the T′ and DO′ anomalies with respect to the mean profiles. Then, DO′ was fitted against a second-order polynomial of T′ (Equation 4) for each layer, in order to model the fraction of the DO variability that can be explained by vertical dislocations (). A clean DO signal for each day and depth was then obtained as . Within each day, the hourly change of DOclean presumably driven by biological production/consumption (hourly NEP, which we denote as nep) was calculated by subtracting the gas exchange and vertical diffusion terms according to Equations 5, 7, and 8. Because the fortnight-scale budget indicated that the river contribution and mixed layer deepening are minor (see Section 4) we ignored these terms here, and the hourly nep for a layer i reduced to (11)

An inverse modeling approach, applied to 24 h segments of nep, was used to determine the daily rates of GPP and R based on a fit to the light and temperature profiles following Hanson et al. ( 2008 ) and Obrador et al. ( 2014 ). The determination coefficient (R 2 ) of the DO′ signal fit to Equation 4 was used to reject data segments dominated by physical noise (i.e., with R 2 > 0.75). For these segments, and for days yielding negative values of GPP or R, we set GPP = NEP (R = 0) when NEP > 0 and R = |NEP| (GPP = 0) when NEP < 0, where NEP was calculated by averaging Equation 11 over 24 h. This was done to ensure that the oxygen mass is conserved and that the relation between the three metabolic rates (NEP = GPP − R) is always consistent. The details are outlined in the supporting information S2.


Filter-feeders have differential bottom-up impacts on green and brown food webs

Nutrient recycling by consumers can strongly impact nutrient availability for autotrophic and heterotrophic microbes, thus impacting functions such as primary production and decomposition. Filter-feeding freshwater mussels form dense, multispecies assemblages in aquatic ecosystems and have been shown to play a critical role in nutrient cycling. Mussel excretion can enhance benthic primary production and influence algal species composition. However, the role of mussels in brown or detritus-based food webs and species-specific differences has received considerably less attention. Here, using mesocosm experiments, we assessed how three species of freshwater mussels that occupy three different phylogenetic tribes influenced benthic algal accrual, ecosystem metabolism, cotton strip decomposition, leaf litter (Acer saccharum) decomposition, and litter-associated fungal biomass measured as ergosterol. Additionally, we measured mussel excretion and biodeposition rates and assessed the stoichiometry (C:N, C:P, and N:P) of the benthic algae, cotton strips, and leaf litter. In comparison to controls without mussels, generally, mussel treatments had higher benthic algal biomass composed of more diatoms, higher gross primary productivity and net ecosystem production rates, and higher cotton strip tensile strength loss, but there was not a difference in ecosystem respiration rates, leaf litter decomposition rates, or fungal biomass. Benthic algae had lower C:N and higher N:P in mussel treatment tanks and cotton strip C:N was lower in mesocosms with mussels. Our results suggest that nutrient regeneration by mussels most strongly regulates green food webs, with some impacts to brown food webs, suggesting that consumers have interactive effects on microbial functioning in freshwaters.

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Materials and methods

Study site

Our study site is a boreal, mixed black spruce–jack pine (Pinus banksiana Lamb.) site located 30 km southeast of Chibougamau, Quebec, Canada (49.693° N, 74.432° W). The site is part of the Canadian Carbon Program (formerly Fluxnet Canada Research Network see Coursolle et al. 2006) with exchanges of CO2, water vapour and energy measured continuously by the eddy covariance method and meteorological variables monitored simultaneously. Mean annual temperature is 0 °C, mean annual precipitation is 960 mm and annual growing degree days (threshold of 5 °C) is 1235 at the nearby Chapais 2 climate station (Canadian Climate Normals 1971–2000 http://climate.weatheroffice.ec.gc.ca/climate_normals/). Soil parent material is a coarse-textured glacial deposit with a moderate stone content. In most of the area, the soil is a well-drained, ferro-humic podzol, but in drainage is imperfect in some areas. Bergeron et al. (2007) provide a more detailed description of the site.

The overstory consists of 90–100-year-old black spruce and jack pine trees that became established after a wildfire interspersed with small patches of 30-year-old communities of the same species. There are a variety of overstory and understory combinations relating to tree age and soil drainage within the area, contributing to eddy covariance measurements (Table 1).

Stand attributes in 2003 for strata identified within a 500-m radius of the eddy covariance tower at a mature black spruce site in northern Quebec. A small percentage of the forest cover in both young strata was in boreal hardwoods which are not accounted for in this analysis.

Communityor stratum . %area . Basal area (m 2 ha − 1 ) . MeanDBH . Fol.biomass(Mg ha − 1 ) . Branchbiomass(Mg ha − 1 ) . Stemsurfacearea(m 2 ha − 1 ) . Totalbiomass(Mg ha − 1 ) .
Total . Blackspruce(%) . Jackpine(%) .
Young, lowland 6 21 99 0 9 11.1 9.1 3862 89.7
Young, upland 14 17 97 2 8 9.9 7.5 3297 71.2
Semi-mature, lowland 5 24 81 19 9 12.8 10.2 4580 100.1
Semi-mature, upland 3 29 90 10 13 12.1 12.4 5138 136.7
Mature, lowland 44 28 85 15 13 11.7 11.8 4706 130.2
Mature, upland 30 17 72 28 13 6.5 7.1 2879 82.7
Communityor stratum . %area . Basal area (m 2 ha − 1 ) . MeanDBH . Fol.biomass(Mg ha − 1 ) . Branchbiomass(Mg ha − 1 ) . Stemsurfacearea(m 2 ha − 1 ) . Totalbiomass(Mg ha − 1 ) .
Total . Blackspruce(%) . Jackpine(%) .
Young, lowland 6 21 99 0 9 11.1 9.1 3862 89.7
Young, upland 14 17 97 2 8 9.9 7.5 3297 71.2
Semi-mature, lowland 5 24 81 19 9 12.8 10.2 4580 100.1
Semi-mature, upland 3 29 90 10 13 12.1 12.4 5138 136.7
Mature, lowland 44 28 85 15 13 11.7 11.8 4706 130.2
Mature, upland 30 17 72 28 13 6.5 7.1 2879 82.7

Stand attributes in 2003 for strata identified within a 500-m radius of the eddy covariance tower at a mature black spruce site in northern Quebec. A small percentage of the forest cover in both young strata was in boreal hardwoods which are not accounted for in this analysis.

Communityor stratum . %area . Basal area (m 2 ha − 1 ) . MeanDBH . Fol.biomass(Mg ha − 1 ) . Branchbiomass(Mg ha − 1 ) . Stemsurfacearea(m 2 ha − 1 ) . Totalbiomass(Mg ha − 1 ) .
Total . Blackspruce(%) . Jackpine(%) .
Young, lowland 6 21 99 0 9 11.1 9.1 3862 89.7
Young, upland 14 17 97 2 8 9.9 7.5 3297 71.2
Semi-mature, lowland 5 24 81 19 9 12.8 10.2 4580 100.1
Semi-mature, upland 3 29 90 10 13 12.1 12.4 5138 136.7
Mature, lowland 44 28 85 15 13 11.7 11.8 4706 130.2
Mature, upland 30 17 72 28 13 6.5 7.1 2879 82.7
Communityor stratum . %area . Basal area (m 2 ha − 1 ) . MeanDBH . Fol.biomass(Mg ha − 1 ) . Branchbiomass(Mg ha − 1 ) . Stemsurfacearea(m 2 ha − 1 ) . Totalbiomass(Mg ha − 1 ) .
Total . Blackspruce(%) . Jackpine(%) .
Young, lowland 6 21 99 0 9 11.1 9.1 3862 89.7
Young, upland 14 17 97 2 8 9.9 7.5 3297 71.2
Semi-mature, lowland 5 24 81 19 9 12.8 10.2 4580 100.1
Semi-mature, upland 3 29 90 10 13 12.1 12.4 5138 136.7
Mature, lowland 44 28 85 15 13 11.7 11.8 4706 130.2
Mature, upland 30 17 72 28 13 6.5 7.1 2879 82.7

Plot-based sampling of trees and litter fall

Twelve circular 400-m 2 field plots were established in a stratified sampling design based on the stand types observed from aerial photographs. We scaled up to the site by assigning weights to strata to represent a 500-m radius circle around the tower. Diameter at breast height (DBH) was measured and species noted for all trees >1.3 m in height at plot establishment in 2003, and height was measured for a sample of trees in each plot. DBH was re-measured in 2008 and tree mortality was noted. Litter fall was collected in early spring and late autumn in eight traps per plot (trap size was 2862 cm 2 ) at four of the 12 plots (the intensively monitored plots) representing stand types covering 70% of the footprint area. The litter was oven-dried, separated into foliage, twigs, cones and bark by species and weighed.

Eddy covariance

A complete description of the eddy covariance and CO2 profile systems can be found in Bergeron et al. (2007). Briefly, half-hourly net CO2 flux between the ecosystem and atmosphere (Fce) was measured by eddy covariance using a 3-D sonic anemometer–thermometer (model CSAT3, Campbell Scientific Canada, Edmonton, AB, Canada) and a closed path infrared gas analyser (LI-7000, LI-COR Inc., Lincoln, NE). In addition, the change in the amount of CO2 stored in the air beneath the eddy covariance sensors was monitored for each half hour using a five-height profile system equipped with a LI-6262 gas analyser (LI-COR Inc., Lincoln, NE). Net ecosystem exchange (NEE) was computed as the sum of Fce and change in CO2 storage. Nighttime NEE when the friction velocity exceeded 0.25 m s − 1 ( Bergeron et al. 2007) was used to estimate ecosystem respiration.

Meteorological measurements

Bergeron et al. (2007) described meteorological data collected at the study site. Following is a brief description of the meteorological data used in this study. Air temperature (Ta) was measured at a height of 24 m with a shielded thermistor (model HMP45C, CSC). Soil temperature at 5 cm below the active moss layer (Ts5cm) was measured with thermistors (model 107, CSC) located under a relatively closed canopy with feather moss as ground cover and under a relatively open canopy with lichens as the ground cover. In both cases, temperature was measured every 5 s and averaged for each half hour.

Automated stem and branch CO2 efflux measurements

The term CO2 efflux is used instead of respiration for woody components in recognition of the potential for transport of CO2 in xylem water from the location where the stem or branch efflux is measured ( Teskey et al. 2008).

Automated CO2 efflux measurements of woody tissues were taken during four field campaigns in the 2005 growing season. We measured CO2 efflux rates at breast height and at mid-crown of two black spruce trees, at breast height of two jack pine trees and on two branches of a single black spruce tree. These trees were accessible from a scaffold tower located ∼30 m north of the flux tower. Chambers at breast height enclosed 61.5 cm 2 of stem surface and chambers at mid-crown enclosed 30.75 cm 2 . Base plates made of closed cell neoprene and aluminium plates were fixed to the stem with hose clamps, and putty and silicone grease were used to make airtight seals with stems. Chambers made from acrylic tubes were fastened to the base plates for the duration of a field campaign. Branch chambers completely enclosed a 10-cm woody branch segment. Solenoids under the control of a data logger (Model CR-10, Campbell Scientific Inc., Chatham, ON, Canada) determined the chamber through which air was directed. A pump pushed air from a mixing box to the chamber, while another pump pulled air from the efflux chamber to the gas analyser. The mixing box dampens the temporal variability of CO2 concentration, thereby ensuring that air entering the respiration chambers is similar to air entering the reference chamber of the gas analyser. Air flow rates were controlled by mass flow controllers (Type 1179A, MKS Instruments, Andover, MA, USA). Flow rates were set at 0.25 l min − 1 . Preliminary tests indicated that flux measurements stabilized within 20 min at this flow rate. Stem temperature was measured with a copper–constantan thermocouple adjacent to the chambers while CO2 flux was being observed. Carbon dioxide efflux from woody tissues was estimated from differential measurements of an infrared gas analyser (Li-6252, LI-COR Inc., Lincoln, NE, USA).

Manual stem CO2 efflux measurements

Stem CO2 efflux rates were measured manually at breast height on eight black spruce and 12 jack pine trees having wide ranges of DBH and at mid-crown location for two of the black spruce trees and six of the jack pine trees on 7 days during the 2005 growing season. Base plates identical to those used with the automated system were attached to stems and remained in place for the 2005 growing season. A chamber identical to those used with the automated system was attached to the base plate to make a measurement and then removed. Trees sampled at mid-crown locations were located approximately 200–500 m from the scaffold tower along an access road to permit the use of a bucket truck. Carbon dioxide efflux from stems was measured with a LI-COR 6200 portable photosynthesis system (LI-COR, Lincoln NE, USA) operating in closed mode. Stem temperature was measured with a copper–constantan thermocouple adjacent to the chambers while CO2 flux was being observed.

Manual branch CO2 efflux measurements

Efflux rates of branches from six black spruce and six jack pine trees were measured on 7 days during the 2005 growing season using a LI-COR 6200 (LI-COR Inc., Lincoln NE, USA) fitted with a chamber identical to the one used with the automated system. Branches were cut with a pole pruner, and their cut end was immediately immersed in water and re-cut. Several measurements of efflux were taken at different locations on the foliage-free parts of each branch. Measurements on a branch were completed within 1 h of cutting. A total of 82 branch segments were measured. Branch temperature was measured with a copper–constantan thermocouple inserted under the bark near the chamber immediately after the efflux measurement was completed.

Foliar respiration measurements

We measured foliar respiration rates of 1-year-old shoots on 4 days in August and September of 2005 using an LCA-4 portable gas exchange system (ADC Inc., Herts, England) and the cylindrical conifer foliage chamber wrapped in aluminium foil. Two branches per tree from four black spruce and three jack pine trees were cut with a pole pruner, and the cut end was immersed in water and re-cut. In total, 21 black spruce and 21 jack pine shoots were measured. Dry masses of needles and twigs (65 °C) were determined in the laboratory.

Automated soil CO2 efflux measurements

Soil CO2 efflux was measured continuously from 6 May to 2 November 2005 using a non-steady-state, automated chamber system a complete description of the system and its operation can be found in Bergeron et al. (2009) and Gaumont-Guay et al. (2008). Briefly, six chambers were attached to the system at start up and three chambers were added on 15 June. Chambers were located within a 700-m 2 area 80 m south of the flux tower. Ground vegetation was feather moss inside five chambers, lichen inside two chambers and sphagnum inside the remaining two chambers. The distribution of chambers among ground vegetation types is approximately proportional to the cover of these ground vegetation types in the footprint of the tower. Shrubs were excluded from collars. Each chamber was measured once or twice every half hour.

Trenched plot soil CO2 efflux

Within each of the four intensively monitored plots, three pairs of 2-m 2 micro-plots were delineated, and one micro-plot from each pair was trenched to a depth of 60 cm in August 2003, lined with a geotextile and backfilled. Three PVC collars were permanently installed in each micro-plot. Soil CO2 efflux was measured monthly during growing seasons in trenched (Rtrenched) and untrenched (Runtrenched) plots with a LI-COR 6400 using the 6400-09 soil CO2 chamber placed on PVC collars. Soil temperature at 10 cm depth was measured at the same time using a calibrated thermometer.

Calculation of stand-level autotrophic and ecosystem CO2 efflux and estimation of uncertainty

Stem efflux

We estimated the uncertainty of stem CO2 efflux by propagating sampling error within the calculations. For each species, we first averaged the R15 values from the three measurements taken on a tree and on a date, the tree × date being considered as our experimental unit, and used the resulting values to compute a population mean and standard deviation (SD). These values were used to compute the error term shown in Table 2. The values of the SD were then used in a simplified scaling-up procedure that was repeated 1000 times in a Monte Carlo approach in which the estimates of R15 were varied randomly within their probability distribution. Results were used to compute the standard error (SE) of the estimate shown in Table 3. The uncertainty in stem surface area estimates was not incorporated into this uncertainty calculation.

Annual C efflux rates in 2005 for aboveground tree components at a mature black spruce site in northern Quebec.

. Stem efflux(g C m −2 stemsurface year −1 ) . Branch efflux(g C kg − 1 DW year −1 ) . Foliage respiration(g C kg − 1 DW year −1 ) .
Black spruce 169.2 (59.6) 151 (81.4) 137.7 (52.0)
Jack pine 200.5 (69.0) 226.7 (113.2) 94.1 (28.8)
. Stem efflux(g C m −2 stemsurface year −1 ) . Branch efflux(g C kg − 1 DW year −1 ) . Foliage respiration(g C kg − 1 DW year −1 ) .
Black spruce 169.2 (59.6) 151 (81.4) 137.7 (52.0)
Jack pine 200.5 (69.0) 226.7 (113.2) 94.1 (28.8)

Annual C efflux rates in 2005 for aboveground tree components at a mature black spruce site in northern Quebec.

. Stem efflux(g C m −2 stemsurface year −1 ) . Branch efflux(g C kg − 1 DW year −1 ) . Foliage respiration(g C kg − 1 DW year −1 ) .
Black spruce 169.2 (59.6) 151 (81.4) 137.7 (52.0)
Jack pine 200.5 (69.0) 226.7 (113.2) 94.1 (28.8)
. Stem efflux(g C m −2 stemsurface year −1 ) . Branch efflux(g C kg − 1 DW year −1 ) . Foliage respiration(g C kg − 1 DW year −1 ) .
Black spruce 169.2 (59.6) 151 (81.4) 137.7 (52.0)
Jack pine 200.5 (69.0) 226.7 (113.2) 94.1 (28.8)

Chamber-based estimates of ecosystem-scale annual respiratory fluxes in 2005 and SE of estimates at a mature black spruce site in northern Quebec. Also shown are the relative contributions of living ecosystem components to the variance of autotrophic respiration.

Ecosystem component . Total . Percentage contributionto variance of Ra .
Mg C ha −1 year −1 .
Stem 0.76 ± 0.36 11
Branch 0.50 ± 0.50 22
Foliage 1.28 ± 0.62 33
Roots 1 1.70 ± 0.61 33
Moss 2 0.68 ± 0.09 1
Autotrophic 4.92 ± 1.06
Heterotrophic 1 5.40 ± 0.92
Ecosystem 10.32 ± 1.82
Ecosystem component . Total . Percentage contributionto variance of Ra .
Mg C ha −1 year −1 .
Stem 0.76 ± 0.36 11
Branch 0.50 ± 0.50 22
Foliage 1.28 ± 0.62 33
Roots 1 1.70 ± 0.61 33
Moss 2 0.68 ± 0.09 1
Autotrophic 4.92 ± 1.06
Heterotrophic 1 5.40 ± 0.92
Ecosystem 10.32 ± 1.82

Estimated by partitioning annual soil respiration (7.1 ± 0.95 Mg C ha −1 year −1 ) into root and heterotrophic respiration based on the ratio of trenched to untrenched plot respiration rates (0.76).

Assumes that half of moss GPP estimated to be 1.36 Mg ± 0.19 C ha −1 year −1 ( Bergeron et al. 2009) was respired.

Chamber-based estimates of ecosystem-scale annual respiratory fluxes in 2005 and SE of estimates at a mature black spruce site in northern Quebec. Also shown are the relative contributions of living ecosystem components to the variance of autotrophic respiration.

Ecosystem component . Total . Percentage contributionto variance of Ra .
Mg C ha −1 year −1 .
Stem 0.76 ± 0.36 11
Branch 0.50 ± 0.50 22
Foliage 1.28 ± 0.62 33
Roots 1 1.70 ± 0.61 33
Moss 2 0.68 ± 0.09 1
Autotrophic 4.92 ± 1.06
Heterotrophic 1 5.40 ± 0.92
Ecosystem 10.32 ± 1.82
Ecosystem component . Total . Percentage contributionto variance of Ra .
Mg C ha −1 year −1 .
Stem 0.76 ± 0.36 11
Branch 0.50 ± 0.50 22
Foliage 1.28 ± 0.62 33
Roots 1 1.70 ± 0.61 33
Moss 2 0.68 ± 0.09 1
Autotrophic 4.92 ± 1.06
Heterotrophic 1 5.40 ± 0.92
Ecosystem 10.32 ± 1.82

Estimated by partitioning annual soil respiration (7.1 ± 0.95 Mg C ha −1 year −1 ) into root and heterotrophic respiration based on the ratio of trenched to untrenched plot respiration rates (0.76).

Assumes that half of moss GPP estimated to be 1.36 Mg ± 0.19 C ha −1 year −1 ( Bergeron et al. 2009) was respired.

Branch CO2 efflux

Manual measurements of CO2 efflux from branch segments differed from those of stem segments by not repeating measurements at particular locations on each sampling day, except for the two branch segments attached to the automated system, and therefore, our analyses of seasonal variation and scaling from measurements to annual estimates of CO2 efflux per kilogramme of branch biomass differed. We expressed branch CO2 efflux on a dry mass basis. The treatment of automated and manual measurements to make half-hourly and annual estimates of branch CO2 efflux was analogous to our treatment of stem CO2 efflux except that we assumed a Q10 of 2.0 for both species and we scaled up to the stand using branch biomass in six branch diameter classes. Accordingly, CO2 efflux measurements collected for each species on each sampling day were sorted into the following diameter classes: 0.1–0.45, 0.46–0.65, 0.66–0.85, 0.86–1.5, 1.6–2.0 and >2.0 cm and averaged before estimating R15 using Eq. ( 1). We estimated branch biomass in permanent sample plots with allometric equations found in Lambert et al. (2005) and partitioned branch biomass among diameter classes using ratios derived from detailed branch mass measurements made by Bernier et al. (2007).

We estimated the uncertainty of branch CO2 efflux by propagating its sampling error with the calculations. The uncertainty in branch CO2 efflux estimates was calculated for each species from R15 measurements. For each species, we first computed the variance of values of R15 by branch diameter class and computed the SD of branch respiration as the square root of the sum of these variances. These values were used to compute the error term shown in Table 2. The values of SD were then used in a simplified scaling-up procedure that was repeated 1000 times in a Monte Carlo approach in which the estimates of R15 were varied randomly within their probability distribution. Results were used to compute the value of SE shown in Table 3. We did not incorporate an error estimate for our values of branch biomass by diameter class in the uncertainty calculation.

Foliar respiration

Respiration rate of 1-year-old and older foliage was estimated using average R15 for measurements made in September and October, assuming a Q10 of 2.0. Half-hourly mean air temperature at mid-canopy, measured at the eddy covariance tower, and foliar biomass in plots, estimated using equations found in Lambert et al. (2005), were used to scale up to the site. Respiration of current-year foliage was estimated in two parts the first part was estimated in the same way as older foliage and an estimate of growth respiration was added. We estimated growth respiration by multiplying growth respiration coefficients found in Chung and Barnes (1977) by our estimates of current-year foliar biomass (estimation of current-year foliar biomass explained below).

We estimated the uncertainty of foliage CO2 efflux by propagating its sampling error within the calculations. For each species, we first averaged the R15 values from the three twig-level measurements taken on a tree on a date and on a branch, the tree × date × branch being considered as our experimental unit, and used the resulting values to compute a population mean and SD. These values were used to compute the error term shown in Table 2. The values of the SD were then used in a simplified scaling-up procedure that was repeated 1000 times in a Monte Carlo approach in which the estimates of R15 were varied randomly within their probability distribution. Results were used to compute the value of SE shown in Table 3. We did not incorporate an error estimate for our value of leaf biomass per hectare in the uncertainty calculation.

Soil CO2 efflux

The average annual ratio of Rtrenched/Runtrenched for 2005 was used to partition annual soil respiration estimated by Bergeron et al. (2009) into root and heterotrophic respiration.

Uncertainty in the estimate of annual soil respiration was estimated using a Monte Carlo approach, based on recommendations of Yanai et al. (http://www.esf.edu/for/yanai/Uncertainty/Estimating%20Uncertainty.pdf) whereby half-hourly soil respiration was computed 1000 times, each time changing parameters given their known or assumed probability distribution. When the half-hourly estimate of soil respiration was based on observation, a sampling error of 20% corresponding to 2 SD was used. When the half-hourly soil respiration value was estimated by gap filling, the random deviation was based on the root mean square error of the relevant temperature function. These procedures were repeated for each automated chamber. Uncertainty in the spatial scaling factor derived from the ratio of automated to manual soil respiration measurements was also taken into account when propagating errors from chambers to the site.

Net primary productivity

NPP for each of the 12 plots in the tower footprint was estimated for each component. Plot values were weighted according to the contribution of their stand type to the footprint area around the eddy covariance system.

Periodic stem biomass production was estimated as the difference in biomass estimates made using the allometric equations of Lambert et al. (2005) for measurements made in 2003 and 2008 on trees living at the second measurement. The periodic stem production was divided by the number of growing seasons in the interval to estimate mean annual stem production. No tree in any permanent sample plot was measured for the first time in 2008.

Uncertainty in the stem biomass production value was calculated using a Monte Carlo approach whereby biomass estimates were recalculated 1000 times for each plot, each time using randomly varying values of parameters for the allometric equations, based on the SE of estimates of each parameter ( Lambert et al. 2005), and assuming a normal distribution of this error. The results were used to calculate the SE of the stem NPP estimates. Because of the small number of plots in each stand type (usually only one or two), this approach did not take into account the sampling variability within each stand type.

Mean annual foliar production was estimated for black spruce and jack pine as equal to mean leaf litter fall measured in the four intensively monitored plots. We found that stem production in the plots where litter fall was measured differed from the site mean and that a close relationship existed between foliar production and stem production in these plots. We fit a quadratic least squares regression to the litter fall and stem production data of the intensively monitored plots and used this relationship to estimate foliar production in the other eight plots. Branch production was estimated from foliar production using ratios reported by Bernier et al. (2007) for these species at this site.

Uncertainty in litter production was estimated through a Monte Carlo approach in which the SEs of the regression parameters of the foliar production versus stem production relationship were used to modify the production estimates in 1000 successive runs. These estimates were then used to produce a value of SE for this term. Uncertainty in our estimate of annual branch production is proportional to the uncertainty in our estimate of foliage production because of our method of estimation.

Mean yearly coarse root biomass increment was estimated from the successive (2003 and 2008) DBH measurements using allometric equations for jack pine ( Morrison 1974) and black spruce ( Czapowskyj et al. 1985). We equated this increment to coarse root NPP by assuming an absence of coarse root mortality and turnover. Uncertainty in our estimate of coarse root production was calculated by relating coarse root production directly to stem production through the coupling of the species-specific allometric equations for coarse roots and stem and by using this relationship to propagate the uncertainty in stem wood production to coarse root production in a 1000-run Monte Carlo. Results from the Monte Carlo runs were used to compute the SE of coarse root production.

Fine root productivity was estimated as the product of fine root biomass and fine root turnover rate. We used a fine root turnover estimate of 0.35 year − 1 computed from measurements made at the nearby Lac Tirasse experimental site, a site of similar forest composition and properties ( Bernier et al. 2005). The Lac Tirasse fine root turnover rate was computed from measurements of fine root productivity (unpublished) obtained as in Bernier and Robitaille (2004) from minirhizotron data and measurements of fine root biomass obtained from root cores ( Bernier et al. 2005). Fine root biomass was measured at the current site in soil cores obtained on the periphery of the four intensively monitored plots. The soil cores (5 cm in diameter, 30 cm in depth) were obtained during the 2005, 2006 and 2007 growing seasons (n = 40 at each plot). We used the resulting mean value of 185 g biomass m − 2 year −1 (92.5 g C m −2 year −1 ) as the site-level estimate of fine root biomass.

Uncertainty around the site-level fine root NPP was calculated using the plot-level sampling error. We calculated the SD of the fine root biomass measurements for each of the four plots and injected this plot-level variability in the scaling-up procedure to the site-level NPP using a Monte Carlo approach in which the estimate of fine root biomass in each plot was varied randomly within its probability distribution. The SE was calculated on the sample of 1000 runs. We did not incorporate an error estimate for our value of fine root turnover into our uncertainty calculation.

Comparing chamber measurements to flux tower estimates of ecosystem respiration

We summed respiration estimates of aboveground components and soil respiration to make chamber-based estimates of Re. We estimated the uncertainty of the chamber estimates of Re using a Monte Carlo approach. In each of 1000 runs, the value of annual respiration of each component was varied randomly according to its SE of estimate that had been determined as described above. The chamber-based estimate of Re is compared with the eddy covariance-based estimate of Re published previously by Bergeron et al. (2008). Only respiration contributes to nighttime measurements of NEE by the eddy covariance method (half-hourly average PAR24m < 5 μmol m −2 s − 1 ). Bergeron et al. (2008) used nighttime values of NEE to establish relationships with soil temperature at 5 cm depth and used these relationships with daytime soil temperature to estimate half-hourly ecosystem respiration in daylight. Uncertainty of the eddy covariance-based estimate of ecosystem respiration was calculated using a Monte Carlo approach. For half hours with measured ecosystem respiration (night), 2 SD of measured values was assumed to correspond to a 20% sampling error, and for gap-filled half hours and daytime half hours, the root mean square error of the appropriate respiration–soil temperature equation was used to derive random variation in ecosystem respiration. Additional details can be found in Bergeron et al. (2008). In addition, a second eddy covariance-based estimate of Re was obtained by correcting for the lack of closure in the energy budget (82% closure Bergeron et al. 2009) as suggested by Barr et al. (2006). Energy budget closure was estimated using nighttime (incoming PAR < 5 μmol m −2 s − 1 ) warm season (Ta > 0 °C and Ts > 2 °C) half-hourly measurements and was applied uniformly to the whole dataset.

Comparisons between ecological and eddy covariance-based estimates of Re were made by comparing the overlap of their probability distributions, as determined from their means and SEs, assuming normal distributions. We assessed the probability that the value of each estimate fell within the expected range of the other estimate.


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Acknowledgements

We would like to acknowledge the exceptional assistance and support of the United States Antarctic Program support staff at Palmer station as well as the entire Palmer LTER science team. We are particularly thankful to Professor Hugh Ducklow for his help with bacteria productivity measurements. We also thank Professor John Dacey for his assistance during sampling, Stefanie Strebel for her assistance at Palmer station, and Zoe Sandwith for her assistance in measuring triple oxygen isotopes. We are very grateful to Professor Paul Falkowski for letting us use his facilities and instrument to measure cyclic electron flow, and to Dr Benjamin Bailleul for his expert advice and patient help for cyclic electron flow measurements. We also wish to thank Professor Maria Prokopenko for her helpful comments. This study was supported by funds from the US National Science Foundation (Award numbers 1040965 and 1043593). Funding to P.D.T. was provided by the Natural Science and Engineering Research Council of Canada.

Please note: Wiley Blackwell are not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing material) should be directed to the New Phytologist Central Office.

Fig. S1 Map of the sampling region around Palmer station.

Fig. S2 Depth profiles of temperature, salinity and density at LTER-station B.

Fig. S3 Mixed layer depths throughout the season determined by density profiles.

Fig. S4 Concentrations of major nutrients throughout the season (CO2 in μmol kg SW −1 and NO3 − , SiO2, PO4 3− in μmol l −1 ).

Fig. S5 Chlorophyll a normalized gross photosynthesis (as measured with 18 O incubations) vs the average PAR during the incubation.

Fig. S6 Bacterial respiration during the season (10-m depth, LTER – station B) measured with thymidine incorporation.

Notes S1 14 C net primary production – correction for the respiration (R) of the unlabelled carbon during the 24 h incubations.

Notes S2 Determining GPP and NCP rates from triple oxygen isotope and dO2/Ar.

Notes S3 Electron flow measurements – correction of the artefact.

Notes S4 Bacteria productivity – thymidine measurements.

Table S1 Summary of data from incubations with H2 18 O

Table S2 Summary of data to estimate Gross and Net production from dO2/Ar and triple isotope composition

Table S3 Nonsteady-states rates of Gross and Net community production assuming the samples are below or above the mixed layer

Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.


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