Convergence of potential net ecosystem production among contrasting C3 grasslands
Abstract
Metabolic theory and body size constraints on biomass production and decomposition suggest that differences in the intrinsic potential net ecosystem production (NEPPOT) should be small among contrasting C3 grasslands and therefore unable to explain the wide range in the annual apparent net ecosystem production (NEPAPP) reported by previous studies. We estimated NEPPOT for nine C3 grasslands under contrasting climate and management regimes using multiyear eddy covariance data. NEPPOT converged within a narrow range, suggesting little difference in the net carbon dioxide uptake capacity among C3 grasslands. Our results indicate a unique feature of C3 grasslands compared with other terrestrial ecosystems and suggest a state of stability in NEPPOT due to tightly coupled production and respiration processes. Consequently, the annual NEPAPP of C3 grasslands is primarily a function of seasonal and short-term environmental and management constraints, and therefore especially susceptible to changes in future climate patterns and associated adaptation of management practices.
Introduction
Grasslands cover c. 40% of the Earth's surface and are located across contrasting climatic and management gradients, which results in considerable variation in ecosystem structure, environmental conditions and disturbance regimes (White et al. 2000; Gilmanov et al. 2010). A decade of eddy covariance measurements made over C3 grasslands across the globe has revealed considerable differences in their annual apparent net ecosystem production (NEPAPP), ranging approximately from −300 to 500 g C m2 year−1 (following the ecological sign convention, the positive sign indicates net uptake of carbon dioxide (CO2)) (Novick et al. 2004; Ma et al. 2007; Wohlfahrt et al. 2008a,b; Gilmanov et al. 2010). A number of studies have shown that environmental conditions and management practices commonly account for a large proportion of the variability in the annual NEPAPP (Wohlfahrt et al. 2008a,b; Gilmanov et al. 2010; Schmitt et al. 2010; Zeeman et al. 2010). However, little is known about the extent to which contrasting C3 grassland ecosystems exhibit different potentials for NEP (NEPPOT; i.e. the maximum net CO2 uptake reached during optimum conditions) due to differences in ecosystems traits, environmental conditions and management practices. However, such understanding is necessary as variations in NEPPOT might regulate the maximum ‘baseline’ capacity for the annual net CO2 uptake, and thus, provide some additional explanatory power (supplementary to the seasonal and short-term effects from environmental and management constraints) for the observed range in annual NEPAPP among global C3 grasslands.
Although NEP is a flux that can be measured by the eddy covariance technique, it is important to realise that NEP itself is not a process per se, but rather the net result of the imbalance between the two ecosystem processes of gross ecosystem production (GEP; i.e. the micrometeorological term equivalent to gross primary production, GPP, assuming that reabsorption of respired CO2 within the canopy is negligible) and ecosystem respiration (ER) (i.e. NEP = GEP−ER) (Chapin et al. 2006). Furthermore, ER is composed of both autotrophic (Ra) and heterotrophic (Rh) respiration. After accounting for the carbon loss via Ra, which is commonly considered to be a conservative fraction of GEP (Gifford 1994), NEP is essentially the difference between the net primary production (NPP; NPP = GEP−Ra) and Rh (i.e., NEP = NPP−Rh). Remarkably, recent studies have indicated that Rh is strongly controlled by the abundance and properties of the dominant plant functional type resident in an ecosystem (Chapin 2003; Bardgett 2011). Specifically, slow-growing vegetation is known to produce low-quality litter, which decomposes more slowly compared with fast-growing vegetation types. The more labile and easily decomposable litter produced by fast-growing vegetation may also stimulate soil microbial activity through the enhanced release of labile carbon forms contained in root exudates (Hobbie 1992; De Deyn et al. 2008). In return, changes in the mineralisation rate affect nutrient availability and uptake, and thus plant productivity, which suggests a strong correlation and feedback between NPP and Rh (Hobbie 1992; De Deyn et al. 2008; Bardgett 2011). Moreover, metabolic theory suggests that body size constraints on metabolic rates of biomass production and decomposition may further condition the relationship between NPP and Rh (Brown et al. 2004; Allen et al. 2005). This is because any body size-related change in NPP rates would be counterbalanced by a subsequent adjustment of decomposer activity and associated Rh rates. Given a 10 000-fold increase in carbon turnover rates moving from tree- to phytoplankton-dominated ecosystems (Allen et al. 2005), such adjustment is likely to occur on relatively short time scales in grassland ecosystems, which are characterised by a short canopy (i.e. relatively small body size). Consequently, one might expect that ecosystems dominated by a given plant functional type might converge to a very similar NEPPOT under optimum conditions (i.e. conditions during which the imbalance between NPP and Rh is maximised by stimulation of NPP and/or suppression of Rh). This is also in line with the metabolic theory that predicts that after adjusting metabolic rates for body size and temperature, organisms should have similar rates of production (Brown et al. 2004). In contrast to similar NEPPOT under optimum conditions, NEPAPP as measured by the eddy covariance method in observational studies, should vary for a given ecosystem type in contrasting regions of the world due to the different region- and site-specific constraints on NPP and Rh.
The three main controls on NPP and Rh that subsequently determine NEPPOT include the stationary long-term effects from (1) environmental conditions (e.g. adaptation to annual mean air temperature, radiation, total precipitation, etc.), (2) management practices (e.g. adaptation to fertiliser input, grazing, cutting) and (3) ecosystem traits (Fig. 1). In this context, ecosystem traits are understood to encompass any plant- or soil-related ecosystem property such as species composition, maximum leaf area index (LAI), maximum leaf photosynthetic capacity, litter quality and soil nutrients. Each of these three controls may affect ecosystem production and respiration processes through modifications of the carbon assimilation, allocation and turnover mechanisms and thus may alter the intrinsic potential for net CO2 uptake.
(1)Constraints from environmental conditions, that is f(e) in eqn (1), are understood to comprise effects resulting from 1) seasonal variation in environmental conditions, which determine the length and timing of the growing season period typical for a given climate region (e.g. the occurrence of summer drought in Mediterranean climates or the existence of a winter snow cover at high latitudes/altitudes) as well as from 2) short-term weather and inter-annual deviations from the long-term mean seasonal climate (e.g. a summer drought in a temperate climate).
Previous studies have quantified and compared maxima of daily or annual GEPAPP and NEPAPP for individual years (Falge et al. 2002a,b; Gilmanov et al. 2010). However, daily maxima may be subject to spurious artefacts and data spikes due to unusual events (e.g. short-term effects from weather, disturbance, management, atmospheric phenomena, etc.) and the inherent random variability of eddy covariance flux measurements (Moncrieff et al. 1996; Massman & Lee 2002; Jolly et al. 2005; Stoy et al. 2009). Meanwhile annual estimates are likely constrained by environmental conditions and/or management (Wohlfahrt et al. 2008a; Stoy et al. 2009). Thus, neither daily nor annual maxima might serve as a suitable indicator for NEPPOT. Here, we derive a robust estimate of NEPPOT over a time frame of 30 days, which approximately spans the duration of the peak net production period as evident from the slope of the cumulative NEPAPP. The main underlying assumption is that NEPPOT is not necessarily reached in every year and therefore the true NEPPOT is best approximated from the maximum 30-day NEPAPP observed over multiple years. To our knowledge, NEPPOT by such definition has not yet been explicitly investigated for grasslands or any other terrestrial ecosystem type. In this study, we apply the outlined approach to multiyear eddy covariance data from nine C3 grassland ecosystems from three contrasting climate regions to test the hypothesis that differences in NEPPOT are small among contrasting C3 grasslands.
Methods
Site selection
The primary criteria for site selection were (1) the availability of four or more complete years of data (with two exceptions, see below) and (2) their location in one of three generalised and contrasting climate regions:
- Cold-temperate: region with continuous winter snow cover and/or freezing temperatures.
- Mediterranean: region with dry, hot summers and cool, wet winters.
- Maritime: region with ample water availability and without snow cover and/or freezing temperatures.
The cold-temperate sites included three alpine sites at different elevations as well as one continental prairie site. We also added two sites with less than 4 years of continuous data (i.e. the cold-temperate alpine site Torgnon and the maritime site Carlow) to determine whether NEPPOT could also be confidently estimated for smaller data sets. Another reason to include the Torgnon site was to obtain an elevation gradient among the cold-temperate alpine sites. The final selected grasslands were the cold-temperate alpine sites Neustift (N), Monte Bondone (MB), Torgnon (TN) and the cold-temperate prairie site Lethbridge (L), the Mediterranean sites Vaira (V) and Tojal (T), and the maritime sites Dripsey (D), Wexford (W) and Carlow (CW). In addition, the site selection contained a gradient of management intensity encompassing natural (abandoned or unmanaged) (L, TN), extensively (V, T, MB) and intensively (N, D, W, CW) managed grasslands. The site-specific details and references are summarised in Table 1.
| Site | N | MB | TN | L | V | T | D | W | CW |
|---|---|---|---|---|---|---|---|---|---|
| Country | Austria | Italy | Italy | Canada (AB) | USA (CA) | Portugal | Ireland | Ireland | Ireland |
| Coordinates |
47°07′ N 11°19′ E |
46°01′ N 11°2′ E |
45°50′ N 7°34′ E |
49°43′ N 112°56′ W |
38°41′ N 120°95′ W |
38°28′ N 8°01′ W |
51°59′ N 8°45′ W |
52°30′ N 6°40′ W |
52°52′ N 6°54′ W |
| Elevation (m a.s.l.) | 970 | 1550 | 2160 | 951 | 129 | 190 | 195 | 57 | 56 |
| Climate region | Cold-Temperate | Cold-Temperate | Cold-Temperate | Cold-Temperate | Mediterranean | Mediterranean | Maritime | Maritime | Maritime |
| Mean Ta (°C) | 6.5 | 5.5 | 3.1 | 5.4 | 16.5 | 15.5 | 9.4 | 10.1 | 9.4 |
| Mean PPT (mm) | 852 | 1189 | 920 | 402 | 562 | 669 | 1207 | 877 | 824 |
| Snow cover and/or T < 0 °C | Nov–Apr | Nov–Apr | Nov–May | Oct–Apr | none | none | none | none | none |
| Management | intensive meadow | extensive meadow | abandoned pasture | unmanaged prairie | extensive pasture | extensive pasture | intensive meadow/pasture | intensive meadow/pasture | intensive meadow/pasture |
| Nitrogen fertiliser application (kg N ha−1 year−1) | manure | low | none | none | none | none | c. 150–250 (inorganic, manure, slurry) | c. 200–300 (inorganic, manure, slurry) | c. 200 (inorganic, manure, slurry) |
| Soil type | Fluvisol | Typic Hapludalfs | Cambisol | Orthic dark-brown chernozem | Lithic haploxerepts | Luvisol | Gleysol | (Gleyic) Cambisol | Calcic Luvisol |
| Soil texture | (sandy) loam | loam | loamy sand | clay loam | silt loam | sandy (clay) loam | loam | loam | sandy loam |
| Soil C (kg C m−2) | 8.1 (0–30 cm) | 8.7 (0–20 cm) | 2.8 (0–20 cm) | 3.7 (0–10 cm) | 6.0 (0–30 cm) | 3.3 (0–30 cm) | 9.0 (0–30 cm) | 3.9 (0–10 cm) | 4.2 (0–10 cm) |
| Soil N (kg N m−2) | n.a. | 0.76 (0–20 cm) | 0.22 (0–20 cm) | n.a. | 0.60 (0–30 cm) | n.a. | 0.76 (0–30 cm) | 0.34 (0–10 cm) | 0.42 (0–10 cm) |
| Max. LAI (m2 m−2) | 5.5 | 4.7 | 2.8 | 1.2 | 2.7 | 2.3 | 2.5 | n.a. | 5.1 |
| Dominant species | Dactylis glomerata, Festuca pratensis, Phleum pratensis, Ranunculus acris, Trifolium spp. | Festuca rubra, Nardus stricta, Trifolium alpinum | Nardus stricta, Arnica montana, Trifolium alpinum, Carex sempervirens | Agropyron dasystachyum, Agropyron smithii, Vicia americana, Artemesia frigid, Koeleria cristata, Carex filifolia, Stipa comata, Stipa viridula | Brachypodium distachyon, Hypochaeris glabra, Trifolium spp., Dichelostema volubile, Erodium botrys | Avena barbata, Lusitanica spp., Vulpia spp., Medicago spp., Trifolium spp., Cynodon dactylon | Lolium perenne, Alopecurus pratensis, Holcus lanatus | Lolium perenne | Lolium perenne, Trifolium. repens |
| Data coverage | 2001–2009 | 2003–2009 | 2009–2010 | 1999–2006 | 2001–2007 | 2005–2008 | 2003–2006, 2008, 2009 | 2004–2006, 2008, 2009 | 2003, 2008 |
| References | Wohlfahrt et al. (2008b) | Marcolla et al. (2011) | Migliavacca et al. (2011a) | Flanagan & Adkinson (2011) | Ma et al. (2007) | Aires et al. (2008) | Peichl et al. (2011) | Peichl et al. (2012) | Flechard et al. (2007) |
Data
We used gap-filled flux and meteorological (precipitation, soil temperature, photosynthetically active photon flux density and volumetric soil water content) data supplied by either the principal investigators (PI) at the sites D, W, N, MB, TN, L or retrieved as Level 4 data (quality checked and gap-filled) from the CarboEurope (T, CW) and Ameriflux (V) databases, both regional networks within FLUXNET (http://fluxnet.ornl.gov/). The procedures for gap-filling and partitioning into the component fluxes GEP and ER followed the methods described by Reichstein et al. (2005) (all but L, D, W) and Barr et al. (2004) (L, D, W). Previous studies have shown consistent estimates of NEP, GEP and ER obtained by the different methods (Moffat et al. 2007; Lasslop et al. 2010). Positive NEP values indicate net CO2 uptake (i.e. GEP > ER). Although the eddy covariance technique provides estimates of the biosphere–atmosphere net ecosystem exchange of CO2 (NEE), we instead use the term NEP (with opposite sign convention to NEE) with the purpose of maintaining an ecological perspective. It is, however, important to note that due to the underlying measurement methodology, the term NEP as used here does not include (commonly small) lateral losses of respiration-derived dissolved carbon (Chapin et al. 2006). Additional supporting environmental data including information on LAI and soil properties were either obtained from the site PIs or retrieved from the respective FLUXNET databases.
Potential NEP, GEP and ER
To obtain an estimate of NEPPOT we first determined the mean NEPAPP over 30 consecutive days (i.e. the mean of 30 daily totals of NEPAPP) at each site using a moving window (with 1-day increments starting from January 1) over all available site-years. In a second step, the maximum of all 30-day averages observed across all years at each site was defined as the site-specific NEPPOT. Thus, we assume that the maximum apparent rate occurring under optimum conditions (i.e. conditions resulting in highest productivity with concurrent lowest respiration) within a multiyear time series should approach or ideally equal the potential rate. Clearly, the probability of finding the true NEPPOT increases with the number of years available at a given site. It is therefore possible that NEPPOT was underestimated at the CW and TN sites where only 2 years of data were available. Furthermore, optimum conditions may have never occurred even within a multiyear data set at a given site. In this case, however, the continued constraint on ecosystem functioning is a site characteristic and the period with the least severe constraints was, therefore, considered ‘optimum’ instead. Furthermore, we estimated 30-day means of apparent GEP and ER (GEPAPP and ERAPP) with the same approach used to determine 30-day mean NEPAPP, and denoted GEPAPP and ERAPP at the time of NEPPOT as GEPPOT and ERPOT respectively.
The averaging period (i.e. window size) of 30 days to determine the apparent mean and potential NEP, GEP and ER was chosen because: (1) the cumulative NEP curves for the grasslands commonly showed maximum slopes lasting for c. 1 month during the peak growth period in spring/summer, and (2) this reduced the impact of spurious artefacts and data spikes occurring on shorter time scales (daily to weekly) due to unusual weather (e.g. rain pulses) and management (e.g. soil disturbance from the use of heavy machinery, fertiliser application) events or short-term violation of any of the underlying assumptions in the eddy covariance theory (e.g. occurrence of advection processes, gravity waves, etc.) (Massman & Lee 2002; Jolly et al. 2005; Stoy et al. 2009). Meanwhile, a window size > 30 days was not considered to avoid interference from periodically reoccurring management practices at longer time scales. Repeating our analysis using 10- and 20-day averaging periods altered the absolute values of NEPPOT, which increased with decreasing length of the averaging period, but did not change our overall findings (see Appendix S1, Fig. S1). It is noteworthy that given the relationship between absolute potential values and the length of the averaging period, the focus in this approach is on the relative comparison of potential rates for a given averaging period, whereas the absolute values are of secondary importance.
Statistical analysis
Significant differences in the potential rates across sites were tested by assessing their respective underlying non-normally distributed (Shapiro–Wilk test, P < 0.05) data points (n = 30 values of daily totals) using the nonparametric Kruskal–Wallis one-way analysis of variance (anova) on ranks followed by a Bonferroni-type multiple comparison. Using the comparably less and more conservative Tukey–Kramer and Scheffé multiple comparison tests, respectively, did not alter the results. With the same approach, the mean environmental conditions during the period in which NEPPOT occurred were also determined and compared. According to the partial autocorrelation function (PACF) plots, daily NEP replicate samples were independent over the investigated 30-day period at all sites except for one (W). Thus, in addition, we conducted a sign test (which makes very few assumptions about the nature of the underlying distribution) for differences in the medians using a piecewise linear nonparametric empirical cumulative distribution function (ECDF) (Samuels & Witmer 2002). In this approach, NEPPOT values originate from the distribution of the annual maximum 30-day mean NEPAPP over all site-years. As an additional advantage, this approach also adjusts for the unequal numbers of data-years among sites and its effect on the probability of NEPPOT occurring (i.e. being reached) at a given site within the site-specific available number of data-years.
Results
Environmental conditions
Multiyear averages (n = 2–9) of daily environmental variables (as described by f(e) in eqn 1) showed distinct and characteristic patterns for the three climate regions (Fig. 2). Daily precipitation was highest and evenly distributed throughout the year at the maritime sites, and lowest at the cold-temperate prairie site. In contrast to the summer peak in precipitation at the other sites, the two Mediterranean sites experienced a prolonged period with reduced precipitation during the summer. The peak and amplitude of daily soil temperature were greatest at the Mediterranean sites and smallest at the maritime sites. Soil temperature remained above freezing temperatures at all times at both the Mediterranean and the maritime sites. Among the cold-temperate alpine sites, a clear elevation gradient was noted for the onset of soil thawing and summer soil temperature maxima. The multiyear average of maximum daily photosynthetically active photon flux density was lowest at the maritime sites and highest at the Mediterranean sites. Sufficient soil water availability all year round at the maritime sites was in contrast to summer water deficits occurring at the Mediterranean sites. Overall, the maritime sites experienced lower environmental constraints on net CO2 uptake compared with the cold-temperate (extended snow cover) and Mediterranean (pronounced summer water stress) regions.
Maximum LAI estimates ranged from 1.2 to 5.5 m2 m−2 (Table 1). Furthermore, the seasonal patterns of LAI development varied among the different grasslands, with maximum LAI occurring in spring at the Mediterranean sites, whereas LAI at the other sites peaked in summer and was occasionally characterised by periodic reduction caused by management practices (i.e. cutting/grazing) (Fig. 2).
Cumulative apparent NEP
Cumulative NEPAPP showed characteristic temporal patterns for each climate region (e.g. steady net CO2 loss during the dry summer and winter periods at the Mediterranean and cold-temperate sites respectively) and management regimes (e.g. temporary net CO2 loss following grazing and harvest events) (Fig. 3). Annual sums of NEPAPP ranged widely from a net source of −108 g C m−2 year−1 to a net sink of 404 g C m−2 year−1 across all sites and years. Averaged for each climate region, mean (± standard deviation, SD) annual NEPAPP of 222 ± 104 g C m−2 year−1 was higher in the maritime region than in the Mediterranean (64 ± 70 g C m−2 year−1) and cold-temperate regions (60 ± 98 g C m−2 year−1).
Potential NEP, GEP and ER
Averaged over 30 days, values for NEPPOT were within a narrow range of 4.6–6.0 g C m−2 day−1 and not significantly different (P > 0.05 based on results from both the various multiple comparison tests and the sign test) for six of nine sites (Fig. 4). The remaining three sites (TN, T, and CW) were those at which NEPPOT was estimated from four or fewer site-years, with the lowest rate of 3.3 g C m−2 day−1 occurring at T and CW. The mean (± SD) NEPPOT across the six converging sites was 5.3 ± 0.5 g C m−2 day−1.
The timing (defined as the day-of-year, DOY, representing the centre point of the NEPPOT period) at which NEPPOT occurred spanned from DOY 97 at the Mediterranean site V to DOY 198 at the cold-temperate prairie site L, and correlated with the length of the snow cover (and/or freezing temperatures) period (Fig. 4a, see also Table 1 and Fig. 2).
NEPPOT occurred at the time when the 30-day mean GEPAPP was equal (i.e. at MB, TN, L, V, T and D) or close to (i.e. at N, W and CW) its maximum (Fig. 5a). NEPPOT occurred close to or lagged (by 17 ± 13 days) the maximum of the 30-day mean ERAPP at the unmanaged sites and extensive pastures (i.e. TN, V, T and L). In contrast, at the meadows and intensively managed pastures (i.e. N, MB, D, W and CW), the timing of NEPPOT preceded the peak of 30-day mean ERAPP by 57 ± 39 days. GEPPOT and ERPOT ranged from 6.4 to 12.6 g C m−2 day−1 and from 3.1 to 6.7 g C m−2 day−1, respectively (Appendix S1, Table S1), and showed a strong linear relationship among sites (Fig. 5b).
No convergence or any consistent pattern was observed among the various environmental parameters within the period in which NEPPOT occurred (Appendix S1, Fig. S2). At the maritime sites, NEPPOT occurred during periods that were cooler and wetter and received less radiation compared with the other regions.
Discussion
Convergence of potential NEP
While previous synthesis studies of grassland and other ecosystem types commonly compared apparent rates in CO2 exchange (e.g. Falge et al. 2002a,b; Lindroth et al. 2008; Gilmanov et al. 2010; Lund et al. 2010; Moors et al. 2010), we are unaware of any study investigating the potential NEP based on multiyear eddy covariance data sets. Such an analysis has been hampered by the limited number of sites that could provide five or more years of continuous data, which, as suggested by our study, are necessary to estimate NEPPOT. The convergence of NEPPOT observed among the six sites with ≥ 5 site-years of data available suggests that contrasting C3 grasslands generally hold a similar potential for net CO2 uptake despite vast differences in ecosystem traits, environmental conditions and management practices.
The considerable differences in magnitude and patterns of the environmental variables among the investigated sites indicate that the convergence of NEPPOT was not related to a convergence of the underlying environmental conditions among climate regions (Figs 2 and S2). The apparent lack of universal optimum environmental conditions for NEPPOT suggests that the maximum imbalance (i.e. NEPPOT) between production and decomposition rates in C3 grasslands is determined by site-specific optimum combinations of site, climate and management characteristics.
Furthermore, it is noteworthy that NEPPOT was identified to occur at different times among the sites spanning from spring at the Mediterranean sites to late summer at the cold-temperate sites. The different timing of NEPPOT and its temporal relationships with maximum GEPAPP and ERAPP reflect the impact of the site-specific seasonal and short-term constraints from environmental conditions and management events on production and decomposition processes. Specifically, the lag of maximum ERAPP compared with NEPPOT (and GEPPOT) in the managed systems was likely a disturbance effect from the harvest events, demonstrating a functional difference to the extensive systems where maximum ERAPP and GEPAPP more or less coincided with NEPPOT. The wide range and climate region-specific timing of the optimum CO2 uptake among the three climate regions entail further implications regarding the impact of future changes in climate and management on the carbon sink strength of C3 grasslands, because non-optimum conditions (i.e. droughts, heat waves, cold spells, grazing and harvest events) during these peak productivity periods may considerably reduce the annual NEPAPP (see also Craine et al. 2012).
One possible explanation for the convergence in NEPPOT might be the fast and tightly coupled metabolic rates of the carbon cycle in C3 grassland ecosystems due to the relatively small body size of grasses and forbs (Brown et al. 2004; Allen et al. 2005). A close connection among grassland GEP, Ra and Rh through the amount of biomass and litter production has been previously highlighted in other studies (Bahn et al. 2008; Gilmanov et al. 2010; Schmitt et al. 2010; Migliavacca et al. 2011b; Peichl et al. 2011). Moreover, in our study, we observed that those sites with higher GEP also showed higher ER rates (with regards to both apparent maxima and at the time when NEPPOT was reached), which demonstrates the tight coupling of these two processes. However, Ra is commonly a conservative fraction of GEP (Gifford 1994), and therefore unlikely to be the process responsible for major differences in the carbon budget among sites. In contrast, Rh is affected by various factors including soil microbial abundance and activity, the supply of decomposable material as well as soil environmental conditions (Chapin 2003; Davidson et al. 2006), and is therefore more likely to vary independently of GEP among different ecosystems. This supports the argument that any differences in NEPPOT would essentially result from a change in the imbalance between NPP and Rh. However, given the strong correlation between NPP and Rh highlighted in previous studies (Hobbie 1992; De Deyn et al. 2008; Bardgett 2011), NEPPOT may remain insensitive to a concurrent increase (or decrease) in NPP and Rh, which, consequently, may explain the convergence of NEPPOT among different sites as a result of a self-regulating nutrient and litter supply (i.e. amount and quality) – demand mechanism. Nevertheless, given the current lack of suitable data (i.e. high-resolution time series of NPP and Rh) to further examine underlying mechanisms, additional evidence is required to corroborate this hypothesis in future work.
Another possible explanation that might support the convergence of NEPPOT in C3 grasslands is that, in comparison with other terrestrial ecosystem types, the differences in ecosystem traits are relatively small among contrasting C3 grasslands. The range in vegetation (i.e. living plants) properties such as canopy height, LAI, rooting depth and biomass pool is limited and additional non-vegetation controls (such as woody debris pools affecting Rh in forests or water table position and microtopography controlling production and decomposition in peatlands) are basically absent. In addition, grasses die back annually because of their life form and provide easily degradable organic matter, that in combination with shorter canopy and rooting systems, allow for fast belowground transport and remineralisation of assimilated carbon. Moreover, grasslands are characterised by relatively small differences in body size and associated constraints on production and decomposition (Allen et al. 2005). In contrast, forested ecosystems exhibit a greater span in vegetation properties in relation to stand age and produce tall woody plants with prolonged life times. Furthermore, forested ecosystems build up large amounts of biomass and dead organic material over time, which results in a temporal lag and decoupling of production and respiratory processes on longer (seasonal to multiannual) time scales at both plant and ecosystem levels. Similar scenarios regarding the build-up and delay in turnover of dead organic material apply to peatlands (Limpens et al. 2008). For example, maximum monthly NEPAPP data over 5 years from four temperate pine chronosequence sites (spanning stand ages from 3 to 69 years) (Peichl et al. 2010a) indicated a wide range of 1.6–8.3 g C m−2 day−1 as approximation for NEPPOT. Given the same climate and management regimes, this range is solely related to different ecosystem traits (e.g. forest age, LAI, biomass and woody debris pools) among the four forest stands. Thus, ecosystem traits that are critical determinants of NEPPOT vary much less widely in C3 grasslands compared with other biomes, which may facilitate the convergence of NEPPOT. However, while variations in the three controls (i.e. ecosystem traits, environmental conditions, management) did not cause any apparent difference in NEPPOT among the grassland sites in our study, the extent of compensating effects among these three controls and their individual impact on NEPPOT remains unclear.
Implications for the annual CO2 sink–source strength of contrasting global C3 grasslands
The convergence of NEPPOT implies that there is little difference in the net CO2 uptake capacity predetermining the annual CO2 sink–source strength of C3 grasslands and that the range of the latter should be small among global C3 grasslands under optimum conditions. In reality, however, seasonal and short-term constraints from environmental conditions specific to each climate region (e.g. the presence and duration of snow cover, extreme temperatures, summer water deficits and the length of the growing season, Fig. 2) and from management practices (e.g. harvest, grazing, reseeding) reduce NEPPOT to climate region-specific and site-specific NEPAPP. Thus, given the lack of differences in NEPPOT, it follows that these seasonal and short-term environmental and management constraints may be considered as the major controls of the variations in the annual net CO2 uptake among global C3 grasslands. Moreover, given the interactive effects between weather/climate and associated adaptation in management (i.e. shifts in frequency and timing of management events) (Wohlfahrt et al. 2008a,b), the CO2 exchange of C3 grasslands might be especially sensitive to future climatic changes, which modify the occurrence patterns of these seasonal and short-term environmental and management constraints. This finding has also implications with regards to improving process-based models. With a few exceptions (e.g. Hidy et al. 2012), the focus has been on implementing complex processes that describe ecosystem functioning. However, given the convergence of NEPPOT, it follows that 1) C3 grasslands may be treated as one functional type and 2) that adequately simulating the impacts from the seasonal and short-term environmental and management constraints (i.e. f(e) and f(m) in eqn 1) should be the primary concern of future model improvement (Groenendijk et al. 2011).
The limited impact of NEPPOT on annual NEPAPP suggests that ecosystem traits, being one of the three main controls on NEPPOT, are also not probably the key determinants of the annual NEPAPP in C3 grasslands. This lack of impact from ecosystem traits on the annual grassland NEPAPP is in contrast to other terrestrial ecosystem types. In forests, for instance, considerable differences in forest stand characteristics (e.g. stand age, plant functional type, biomass pools, LAI) were reported as primary controls on annual NEPAPP over climatic controls (Luyssaert et al. 2007; Lindroth et al. 2008; Peichl et al. 2010a,b; Drake et al. 2011). Similarly, ecosystem traits (i.e. water table depth, species composition, LAI, microtopography, nutrient status) were noted as important controls on growing season and annual NEPAPP in peatland ecosystems (Humphreys et al. 2006; Lund et al. 2010; Sonnentag et al. 2010). However, Limpens et al. (2008) previously argued that the NEPAPP of undisturbed peatlands (that are similar to grasslands in that they are characterised by a limited range of biomass and LAI) should be less variable than forests, and instead should be primarily controlled by environmental conditions. Species and location were suggested as the primary controls on differences in growing season NEPAPP among croplands (Moors et al. 2010). Thus, the limited effect of ecosystem traits on the cumulative seasonal and annual NEPAPP might be a unique feature of C3 grasslands.
On the basis of this first work to quantify and compare NEPPOT using multiyear eddy covariance data, we conclude that NEPPOT converges within a narrow range among contrasting C3 grasslands given long-term observations. This finding indicates a general state of stability in grassland NEPPOT during optimum conditions most likely due to body size and supply–demand-related metabolic constraints on production and decomposition processes. Given the wide range in annual NEPAPP compared with the narrow range of NEPPOT reported in this study, we conclude that the control of NEPPOT on the annual NEPAPP is limited. This implies that the variation of the annual sink–source strength for atmospheric CO2 among global C3 grasslands is primarily a function of seasonal and short-term constraints from environmental conditions and site-specific management practices, that is, the time-dependent functions f(e) and f(m) in eqn (1). Compared with other ecosystems, the CO2 exchange of C3 grasslands might therefore be especially susceptible to the direct and indirect effects from expected future changes in climate and associated (adaptive) changes in management.
Acknowledgements
The study site Neustift has received partial funding from the Austrian Ministry for Science and Research within the programme Sparkling Science (SPA/03 112/GrassClim), the European Community's Fifth and Seventh Framework Programme under grant agreements EVK2-CT2001-00125 and n° 244122, the Austrian National Science Fund (FWF) under grant agreements P17560-B03, P19849-B16 and P23267-B16, and the Tyrolean Science Fund under grant agreements UNI-404-33, UNI-404-486 and UNI-404-1083. Research at the site Monte Bondone was supported by the EU project GHG Europe (EU contract No. 244122) and the CARBOITALY project funded by the Italian government. Activities at the Torgnon site were supported by PhenoALP, an Interreg project co-funded by the European Regional Development Fund, under the operational program for cross-border cooperation Italy-France (ALCOTRA) 2007–2013. For the Lethbridge site, funding was provided by a Discovery grant from the Natural Sciences and Engineering Research Council of Canada. Research at the Vaira site was supported by the US Department of Energy Terrestrial Carbon Program, grant No. DE-FG03-00ER63013 and DE-SC0005130. The Tojal site has received funding from the European Commission through the Project CarboEurope-IP. The study sites Dripsey and Wexford were financed by the Irish Government under the National Development Plan 2000–2006 (Grant No. 2001-CC/CD-(5/7)) and the Science, Technology, Research and Innovation for the Environment (STRIVE) programme 2007–2013 (Grant No. 2008-CCRP-1.1A) which is managed by the Environmental Protection Agency. The Carlow grassland site was funded by the EC-FP5 “GreenGrass” project and the EC-FP6 “CarboEurope” project. We thank Bo Ranneby and Kristi Kuljus for helpful discussions during the preparation of the manuscript. We are also grateful to the editor and three anonymous referees for insightful and constructive comments on the manuscript.
Authorship
All authors agree to the submission of this manuscript. MP conceived the study, analysed the data, wrote the paper; OS conceived the study, assisted with paper writing, provided substantial intellectual input; GW conceived the study, provided data, provided substantial intellectual input; LBF conceived the study, provided data, provided substantial intellectual input; DDB, GK, MG, DG, BM, CP provided data and substantial intellectual input; MBJ provided data; MM and MS provided substantial intellectual input.
