Trophic interactions confound temperature dependence to control greenhouse gas flux from shallow lakes

Thomas A. Davidson*,†, Joachim Audet*, Jens-Christian Svenning†, Torben L. Lauridsen*, Martin Søndergaard*, Frank Landkildehus*, Søren E. Larsen* & Erik Jeppesen*,‡

* Department of Bioscience and Artic Research Centre (ARC), Aarhus University, Vejlsøvej 25, 8600 Silkeborg, Denmark

† Section for Ecoinformatics and Biodiversity, Department of Bioscience, Aarhus University, Ny Munkegade 114, DK-8000 Aarhus, Denmark.

‡ Sino-Danish Centre for Education and Research, Beijing, China

Corresponding author: Thomas A. DavidsonE-mail: thd@dmu.dk

Biological Sciences: Ecology & Environmental Sciences

Keywords: Fresh waters, climate change, carbon, methane, trophic interaction, macrophytes

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Significance

We clarify the response of greenhouse gas (GHG) flux from shallow lake ecosystems to global warming.

Whilst analysis based on seasonal temperature variation suggests whole ecosystem response of GHG flux

scales with temperature, the experimental warming of 2-4ºC and 4-6 ºC showed no significant effect on GHG

fluxes. Instead, the abundance of submerged plants, which control trophic interactions in shallow lakes, was

a key driver of GHG flux. Therefore, at whole ecosystem level trophic interactions can override the cellular

level temperature dependence of the biogeochemical processes controlling GHG flux. This suggests, in

contrast to predictions based on analysis of seasonal temperature variation, that warming may not lead to

positive feedbacks to atmospheric GHG concentrations from fresh waters.

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Abstract

Fresh waters occupy 3% of the Earth’s land surface(1) but make a disproportionate contribution to

greenhouse gas (GHG) emissions(2, 3), shallow lakes, which represent 97 % of all lakes(1), are particular

efflux hotspots(3, 4). At the cellular level the biogeochemical processes controlling GHG production

(metabolism and methanogenesis) scale with temperature(5, 6). Whilst whole ecosystem studies which use

seasonal temperature variation show temperature dependence of these key biogeochemical processes(6-9),

the results of empirical studies not reliant on seasonal variation fail to convincingly demonstrate temperature

dependence(6, 10-12). Here, we address this dichotomy, highlighted by(7) using a long-running, shallow-

lake mesocosm warming experiment investigating the relationship between temperature and GHG fluxes.

Analysis of data based on the seasonal temperature variation suggests methanogenesis, ecosystem respiration

and gross primary production all scale with temperature, with sequentially lower activation energies. In

contrast experimental warming showed no positive relationship between temperature and GHG fluxes.

Instead, the abundance of submerged plants, which control trophic interactions in shallow lakes(13),

emerged as a strong determinant of ecosystem-level GHG flux. Thus, we demonstrate that relationships

established from seasonal temperature variation are unreliable predictors of ecosystem level response to

climate warming, as trophic interactions can override the intrinsic cellular-level temperature dependence(14,

15). Furthermore, our study suggests that shallow lakes, which are both by number and area by far the most

prevalent water body globally, will not exhibit positive feedbacks to atmospheric GHG concentrations as the

climate warms.

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Main Section

Fresh waters and their sediments are the location of a number of important biogeochemical processes

involved in the cycling of the three main GHGs – carbon dioxide (CO2), methane (CH4) and nitrous oxide

(N2O). Furthermore, inland waters make a disproportionate contribution to greenhouse gas (GHG)

emissions(2, 3) and shallow lakes, which are efflux hotspots(3, 4), are by far the most numerous water body

type globally, both by number and area(1). Whilst there is a great deal of uncertainty linked to estimates of

GHG flux from fresh waters(2, 16), it is clear that the way they are impacted by global warming has

considerable potential to contribute to positive or negative feedbacks to GHG concentrations.

At microbial levels the processes in question, metabolism (primary production and respiration) and

methanogenesis, are temperature dependent as their rates are controlled by biochemical kinetics(5).

Theoretical models have elegantly linked the metabolism of individual organisms to communities and

ecosystems(5) to predict how temperature drives the global carbon cycle(8 , 9) and methane fluxes from

fresh waters(6). At the ecosystem scale, both in terrestrial and aquatic systems, empirical evidence for the

temperature dependence of metabolism and methane fluxes relies on analysis of data where the temperature

gradient is the result of seasonal variation(6-10). Whilst the statistical problems of non-independence of such

data can be obviated, other factors may invalidate the use of seasonal temperature variation to predict climate

change effects on ecological patterns and processes. Seasonal temperature change is associated with

dynamics of a number of abiotic and biotic factors that can also affect GHG fluxes, including switches in

limiting resources(15), food web structure (14) and variation in the strength and nature of trophic

interactions(17). Furthermore, ecological responses to longer-term warming may involve changes in

community composition and ecosystem structure that do not develop over the course of a single season.

Thus, the use of seasonal temperature variation to validate theoretical predictions of ecosystem response to

future climate change relies on the assumption that other factors influencing the crucial biogeochemical

processes controlling GHG flux will respond to climate change in the same way as temperature. This

assumption is questionable and has little empirical support.

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Spatial real-world data using annual means, single observations or between year data, have found a weak or

absent relationship between ambient temperature and lake pCO2, indicating that the metabolic balance of a

lake is not directly controlled by temperature(11), a phenomenon with parallels for metabolism in terrestrial

ecosystems(7) and for decomposition in soils(10). Similarly, for CH4 fluxes from lakes and wetlands

empirical data using annual means do not suggest a strong relationship between CH4 flux and ambient

temperature, either being not significant or having very little explanatory power(6, 12). Exemplifying such

alternative determinants, resource availability can control rates of methanogenesis regardless of

temperature(18). Even less is known about N2O fluxes from lakes, but denitrification and nitrification are

considered to be the main drivers of N2O production(16) and beside temperature, these two processes are

greatly influenced by oxygen, pH and carbon availability. Furthermore, the role of trophic interactions has

the potential to directly and indirectly impact the temperature-flux relationship, for example the effects of

predation pressure has been shown to cascade down to influence CO2 concentrations(17). In shallow lakes

submerged plants can be described as ecosystem engineers, having a profound structuring role in shallow

lakes, affecting almost all biological and chemical processes and influencing the biomass of organisms

across trophic levels from sediment micro-organisms to fish(13).

Here, we use the longest running freshwater mesocosm experiment in existence(19) to examine the

relationship between temperature and GHG fluxes, examining both seasonal and experimental warming

effects, and assessing the importance of other potential drivers, such as submerged macrophyte abundance.

The 8-year running time of the experiment is of crucial importance as it has provided sufficient time for the

simulated ecosystems, and in particular for the sediment compartment, to reach equilibrium with the higher

temperatures of the heated mesocosms, as would also occur in real-world ecosystems under global warming.

Notably, the 8-year running time guards against reporting short-term responses of the warming of organic-

rich sediments. Mesocosm water temperature from the three experiment treatments (Fig 1) reflects the

seasonal variation in air temperature. The reference mesocosms (termed Ref) tracked ambient temperature

and the heated treatments correspond to the Intergovernmental Panel on Climate Change (IPCC) scenario A2

(termed A2) and A2 + 50% (termed A2+) downscaled to local conditions in Denmark. The A2 and A2+

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treatments are circa 2ºC and 4ºC higher than the reference tank respectively with seasonal variation in the

temperature difference among the treatments (Fig 1) as predicted by the different climate scenarios18.

Gross primary production (GPP), ecosystem respiration (ER) and CH4 efflux tracked seasonal temperature

change, whereas this was less clear for CO2 or N2O efflux (Fig 1). There was a marked difference in the

fluxes of GHGs across the temperature treatments at the start of the growing season, in April and May. The

unheated mesocosms had lower GPP, ER and lower cover of submerged plants (Fig 1) and higher CO2 and

CH4 flux. In mid-summer, July and August, the opposite situation occurred where the heated mesocosms had

higher fluxes of GHGs compared to the unheated tanks.

Analysis of the seasonal data revealed temperature dependence in ecosystem-level primary production,

respiration and methanogenesis (Fig 2) with activation energies comparable to previous findings(6, 9). In

marked contrast to the seasonal data the effects of the experimental temperature treatments showed warming

to have either: no discernible effect, or a negative effect on GHG efflux. Temperature treatment had a weak

negative effect on CO2 efflux (p = 0.0361, pseudo R2= 0.058); a non-significant positive relationship with

N2O (p =0.144) and no discernible relationship with CH4 (p = 0.956) (Supplementary Table 1). Analysis of

the response of flux per unit of GPP showed a weak negative effect of temperature on CO2 per unit GPP (p =

0.0219, pseudo R2=0.059), a non-significant negative effect on N2O per unit GPP and a non-significant

negative effect on CH4 per unit GPP (p = 0.060). These results suggest that global warming will not induce

positive feedbacks via GHG efflux from shallow lakes.

Mixed-effects models were employed to investigate controls, other than temperature, of GHG dynamics in

the experimental mesocosms. The abundance of submerged plants consistently made a significant

contribution to the models explaining the fluxes for each of the GHGs (Table 1). The optimal model for CO2

flux included macrophyte abundance and net primary production (NPP) and an interaction term (p < 0.0001,

pseudo R2 = 0.62). The optimal model for CH4 flux included ER and macrophyte abundance (p = 0.0027,

pseudo R2 = 0.37) and the optimal model for N2O included NPP, ecosystem stability (ES) (see methods),

macrophyte abundance and an interaction term between macrophyte abundance and ES (p < 0.0001, pseudo

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R2 = 0.20). More abundant submerged macrophytes led to reduced fluxes of the individual GHGs (Table 1).

Total GHG flux (in CO2 equivalents) showed a strongly negative relationship with macrophyte abundance,

which was consistent across temperature treatment and season (Fig 3). Analysis of the total annual flux per

mesocosm by ANOVA showed no temperature treatment effect on total GHG flux (p = 0.25) or on

individual GHG fluxes, but a significant effect of macrophyte abundance, which explained large proportion

of the variation (p= 0.013, adjusted R2 = 0.423).

The results of this long-term mesocosm experiment show that the abundance of submerged macrophytes is

an important factor governing GHG dynamics in shallow lake ecosystems (Fig 3 & 4), which here overrides

the unequivocal temperature dependence present at the microbial-scale(5). In our study the addition of

macrophyte abundance to the models significantly improved their explanatory power, suggesting that in

addition to their contribution to production and respiration and thus NPP, macrophytes exert additional

controls on gas fluxes (Fig 4). Submerged plant abundance is the single most dominant influence on the

trophic structure and the function of shallow lake ecosystems, providing habitat for invertebrates and fish,

substrate for periphyton, affecting sediment structure and chemistry(20) and influencing a range of biological

and chemical processes(13). These include increasing invertebrate biomass, altering food web dynamics and

increasing heterogeneity of pH all of which can alter pCO2(17). Furthermore, macrophytes link the water

column to the sediments, changing sedimentary oxygen dynamics(21), reducing methanogenesis(22) and

altering the availability of useful organic substrates for methanogenic and denitrifying bacteria(18) (Fig 4).

The evidence of climate change impacts on ecology and biodiversity such as shifts in species ranges and

estimations of species loss(23), can be contrasted against the uncertainty associated with the question of how

biogeochemical processes with multiple controls taking place in complex ecosystems will respond to

increases in temperature. Our results demonstrate notwithstanding strong theoretical and experimental

evidence of temperature dependence, ecosystem responses may be complex, driven not solely by temperature

but also by trophic interactions. Therefore using seasonal temperature variation to establish ecosystem scale

response to global warming may lead to overstating the likely impacts of higher temperatures. In this case

the presence and abundance of submerged plants in shallow lakes was demonstrated to be far more important

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for GHG fluxes than relatively large temperature increases (>2ºC and >4ºC respectively for A2 and A2+ -

see supplementary methods). It is, however, difficult to predict climate change impacts on macrophyte

abundance in shallow lakes. Increased temperatures have been implicated in reducing the likelihood of a

shallow lake being macrophyte dominated(24) although the mechanisms are unclear. In contrast, at higher

latitudes, the climate change driven lengthening of the growing season is likely to allow for a greater

temporal extent of macrophyte cover. What is clear from these results is that shallow lake management

aimed at maximising submerged plant abundance could be used to reduce GHG fluxes from shallow lakes.

This fits well with established management aims of increasing macrophyte cover for maintenance of

ecological quality and biodiversity in shallow lakes. Submerged plant diversity may also be an important

factor shaping ecosystem processes as more diverse communities are, not only generally more abundant but,

are also likely to maintain higher abundance for a longer periods both within and between years(25) which

our findings suggest would reduce GHG efflux.

These results are highly important as at a global scale shallow lakes are overwhelmingly the most common

lake type(1). Their response to current and future warming may therefore have influential feedbacks,

potentially negative or positive, to the global climate system. Unfortunately, however, there has hitherto been

a high degree of uncertainty in estimates of GHG fluxes from fresh waters(26). Our results indicate that not

accounting for trophic dynamics and specifically variation in macrophyte cover may be a major contributor

to this uncertainty. Furthermore, the experimental results highlight a weakness in the use of seasonal

temperature variation to validate theoretical predictions of temperature dependence and to predict the likely

effects of climate change and processes that are controlled by multiple, often interacting, factors.

Methods

Experimental setup and data

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The climate change mesocosm at Lemming, Central Jutland, Denmark (56°14′N, 9°31′E) has been running

continuously since August 2003 and is the longest running lake mesocosm experiment investigating the

effects of climate change. It consists of fully mixed, outdoor, flow-through mesocosms (diameter 1.9 m,

water depth 1 m, retention time ~2.5 months). There are four replicates of each temperature scenario which

are: unheated reference mesocosm (Ref); scenario A2 - (A2) downscaled to local condition in Denmark at a

monthly basis and scenario A2 +50% (A2+), full details can be found in(27) and the seasonal among

temperature differences can be seen in Figure 1.

Aqueous N2O, CH4 and CO2 in the mesocosms were determined monthly between March and September

2011 and bimonthly from September 2011 to January 2012, eight years after the initiation of the experiment.

Direct measurements of N2O, CH4 and CO2 were performed by headspace equilibration following the method

described in(28). Briefly, a thermo-insulated 1.2L bottle was carefully filled with water sampled at the water

surface. Thereafter, the bottle was rapidly capped with a specially designed lid equipped with rubber septa to

enable the introduction of a 50 mL headspace of ambient air using a syringe. The bottle was then vigorously

shaken for 120s to allow for equilibration between gas and water phases. Samples of both the headspace and

the ambient air were taken and brought to the lab for analysis. For each tank duplicate equilibrations were

done. The temperature of the water was recorded at the time of sampling. The gas samples of N2O, CH4 and

CO2 were stored at room temperature and in the dark before further processing, typically after 3-5 days.

Nitrous oxide, CH4 and CO2 were determined on a GC (see supplementary methods for details).

Gas fluxes of N2O, CH4 and CO2 were estimated from aqueous concentrations (see supplementary methods).

Dissolved Oxygen (DO) and water temperature were measured every 30 minutes over the sampling period.

Gross primary production (GPP) and ecosystem respiration (ER) were estimated using the formulas of (29)

and(30). See supplementary methods for details.

Macrophyte abundance was quantified as per cent volume inhabited (PVI) of the water column. Percentage

cover and height of the submerged plants was assessed allowing estimation of the proportion of the

mesocosms occupied by submerged plants. Plant abundance was assessed every two weeks and the closest

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sample point closest to the gas sample was used in the analysis. If there were two equidistant points then the

macrophytes abundance was extrapolated from those points.

Tanks were defined as being stable (ES = 1) or unstable (ES =0) depending on the degree of change in

macrophyte abundance between sampling periods. If a tank underwent a sharp change in the macrophyte

abundance then it was classified as unstable. A sharp change was defined as a greater than 100 % reduction

in PVI, having started at greater than 20% and accompanied by a reduction in pH of at least 1 unit between

sampling occasions.

Statistical methods

The statistical modelling approach had two stages: first we tested for a temperature treatment effect on each

GHG flux using linear mixed model with sampling occasion as a random effect. The performance of the

random effect was assessed by model comparison based on likelihood ratio (LR), the effect of temperature

treatment was assessed by comparison with a null model using LR (supplementary methods). The second

stage involved determining the optimal model explaining the flux of each GHG. Here, we used LR to

compare models, starting with a full model, including quadratic terms and interactions, sequentially dropping

the least significant variable until all remaining covariables were significant(31). Model fit was estimated as

pseudo-R2, details of programmes and packages used can be found in supplementary methods. For the

calculation of total annual flux, values for October and December were derived by linear interpolation of the

adjacent months.

Acknowledgements

This work was supported by CLEAR (a Villum Kann Rasmussen Centre of Excellence on lake restoration); the Danish

Council for Strategic Research (Centre for Regional Change in the Earth System - CRES—contract no: DSF-EnMi 09-

066868) and MARS (Managing Aquatic ecosystems and water Resources under multiple Stress), EU 7th Framework

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Programme, Contract No.: 603378. TD was supported by CIRCE funded by the AU ideas programme and EU Marie-

Curie fellowship (IEF - 255180 - PRECISE). JCS was supported by the European Research Council (ERC-2012-StG-

310886-HISTFUNC). We are very grateful for the highly efficient technical assistance of Dorte Nedegaard who

performed the analysis of the gas concentrations, Kirsten Landkildehus Thomsen for field assistance and to Tinna

Christensen and Juliane Wischnewski for help with the figure preparation.

Author contribution

EJ, TLL, FL and MS conceived and designed the mesocosm experiment. TD, JA and EJ conceived and designed the

GHG study. TD, TLL, JA & FL collected the data. EJ & SEL calculated the productivity and respiration rates. JA

calculated the gas fluxes and TD analysed the data. TD wrote the manuscript with contributions from JA, EJ and JCS.

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Figure legends

Figure 1 – Seasonal variation of key variables and GHG fluxes by temperature treatment:

Mean and standard error per experimental warming treatment for temperature, gross primary production,

ecosystem respiration, net primary production, macrophyte abundance and chlorophyll-a biomass, CO2, CH4,

N2O & total greenhouse gas flux in CO2 equivalents. The experimental treatments were ambient temperature

(Ref) – blue, A2 scenario (+2-4ºC) – green and A2+ scenario (+4-6 ºC) - red.

Figure 2. Apparent temperature dependence of primary production, respiration and methanogenesis.

Seasonal temperature variation used to characterise ecosystem level temperature dependence using mixed

effects models fitting a Boltzmann-Arrhenius function for the twelve tanks over the nine sampling occasions.

for a) gross primary production, b) ecosystem respiration and c) methanogenesis. The samples are divided by

season – winter, spring/autumn and summer and by temperature treatment - ambient temperature (Ref) –

blue, A2 scenario (+2-4ºC) – green and A2+ scenario (+4-6 ºC) – red. The solid line represents the fitted

value for the mixed effects models.

Figure 3. Total macrophyte abundance against total greenhouse gas flux:

GHG flux is in CO2 equivalents given for each month. The solid line is a least squares regression line for

illustration and the dashed line represents the divide between GHG sink and source. Each point represents a

single experimental tank in this plot and they are coloured by treatment: ambient temperature (Ref) – blue,

A2 scenario (+2-4ºC) – green and A2+ scenario (+4-6 ºC) – red.

Figure 4. Diagrammatic representation of differences in patterns of processes that control GHG

dynamics between high and low macrophyte cover.

The width of the arrow represents the volume of gas efflux or influx. Panel a has high macrophyte cover and

is a sink for CO2 and a reduced source of CH4 and N2O; whereas panel b has low macrophyte cover with a

high efflux of CO2 and CH4 and a small efflux of N2O.

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