Dedication in memoriam: In a study assessing factors controlling the tree growth of the natural timberline in SE Australia, 40 years ago, Professor Ralph O. Slatyer planted snow gum (Eucalyptus pauciflora) saplings at several altitudes. These trees continue to grow to this day and form the basis of our current study. We dedicate this paper to the memory of Professor Slatyer who passed away on 26 July 2012.
We tested whether snow gum (Eucalyptus pauciflora) trees growing in thermally contrasting environments exhibit generalizable temperature (T) response functions of leaf respiration (R) and fluorescence (Fo). Measurements were made on pot-grown saplings and field-grown trees (growing between 1380 and 2110 m a.s.l.). Using a continuous, high-resolution protocol, we quantified T response curves of R and Fo – these data were used to identify an algorithm for modelling R–T curves at subcritical T's and establish variations in heat tolerance. For the latter, we quantified Tmax [T where R is maximal] and Tcrit [T where Fo rises rapidly]. Tmax ranged from 51 to 57 °C, varying with season (e.g. winter > summer). Tcrit ranged from 41 to 49 °C in summer and from 58 to 63 °C in winter. Thus, surprisingly, leaf energy metabolism was more heat-tolerant in trees experiencing ice-encasement in winter than warmer conditions in summer. A polynomial model fitted to log-transformed R data provided the best description of the T-sensitivity of R (between 10 and 45 °C); using these model fits, we found that the negative slope of the Q10–T relationship was greater in winter than in summer. Collectively, our results (1) highlight high-T limits of energy metabolism in E. pauciflora and (2) provide a framework for improving representation of T-responses of leaf R in predictive models.
proportional increase in respiration per 10 °C rise in temperature
rate of leaf R at Tmax
T where Fo begins to rise
leaf T where R reaches a maximal rate
Plant respiration (R) is a significant component of the global carbon cycle and contributes ca. 60 Pg of carbon (C) released into the atmosphere each year (Prentice et al. 2001), approximately seven times more than C released by fossil fuel burning (Canadell et al. 2007). At the individual plant level, 30–70% of CO2 fixed by daily photosynthesis is released by R, with leaf R contributing to ≈50% of whole-plant R under favourable conditions (Atkin, Scheurwater & Pons 2007). The close association between leaf R and environmental factors makes it crucial that environment-induced variability in R is properly incorporated into coupled climate and C-cycle models (Gifford 2003; Atkin et al. 2005a); here, describing the temperature (T) response of R of plants growing in thermally contrasting environments is of central importance.
Several simulation models (Aber & Federer 1992; Schimel et al. 1997) and global dynamic vegetation models (White, Cannell & Friend 2000; Cox 2001; Cramer et al. 2001) have assumed that R can be modelled assuming a constant Q10 of 2.0 (i.e. the T-sensitivity of R remains constant, whereby rates double per 10 °C rise). However, there is growing acceptance that this assumption is not correct (Wythers et al. 2005; King et al. 2006; Atkin et al. 2008). Rather, the shape of the T response is dynamic, reflecting the fact that the functional form of the short-term temperature response curve of R departs significantly from a simple exponential. Changes in growth T that last several days can also alter the short-term Q10 (Atkin et al. 2005a), with Q10 values often varying seasonally (Atkin, Holly & Ball 2000; Zaragoza-Castells et al. 2008); there is also evidence that Q10 values are lower in tissues where substrates and/or energy demand limit R (Atkin & Tjoelker 2003). Finally, it is well established that Q10 values decrease with short-term increases in measuring T (James 1953; Forward 1960; Tjoelker, Oleksyn & Reich 2001). Theory and empirical evidence suggests that the declining T-sensitivity of leaf R (i.e. declines in Q10 of R) as measurement T's rise is linked to shifts in the control exerted by maximum enzyme activity at low T, substrate limitations at moderate-high T, and loss of enzyme function at very high T's (Atkin & Tjoelker, 2003 and references cited therein). Failure to account for the fundamental non-exponential form of the R–T relationship leads to respiratory CO2 release being overestimated over long periods (Wythers et al. 2005; King et al. 2006; Atkin et al. 2008).
When considering how to fit curves to a non-exponential form of the R–T relationship, several alternative approaches are available that differ from a constant Q10. Using the Arrhenius approach, T-mediated variations in leaf R can be modelled using a constant activation energy (Ea) (e.g. Turnbull et al. 2005); this approach accounts for slight declines in the Q10 with increasing T (Zaragoza-Castells et al. 2008). However, the Arrhenius approach assumes that respiration is substrate saturated – in many cases, this assumption is invalid for intact leaves (Atkin, Bruhn & Tjoelker 2005b). Moreover, application of a single Ea value does not account for marked T-dependent declines in Q10 often exhibited by plants in natural ecosystems (Atkin et al. 2000; Tjoelker et al. 2001) or the fact that exposure to very high T's results in rates of leaf R decreasing (Atkin & Tjoelker 2003; Hüve et al. 2011, 2012). Thus, other approaches may be needed to account for the non-exponential form of the R–T relationship. One approach is to use a modified Arrhenius algorithm formulated by Lloyd & Taylor (1994), which was developed to model the T-dependence of soil R but has since been used to model rates of leaf R in a range of species (Turnbull et al. 2001; Griffin, Turnbull & Murthy 2002; Dillaway & Kruger 2011). Another approach is to assume that Q10 declines with increasing measurement T in a near-linear manner (Tjoelker et al. 2001). To compare model fits using these different approaches, it is essential that comparisons be made using high-resolution R–T plots (i.e. where rates of leaf R are continuously measured as leaf T increases). In addition to providing a basis for elucidating response functions over physiologically relevant T ranges, high-resolution R–T plots also provide an opportunity to assess responses to extreme T's where damage to leaf functioning is irreversible.
Recently, a continuous heating protocol was developed that delivers high-resolution data on the short-term T-response of leaf R over a wide range of T's (Hüve et al. 2011, 2012); using this method, leaf R is continuously recorded as leaves are heated at near 1 °C min−1. From the resultant R–T curves, a ‘break-point’ T can be identified, where rates of R rapidly increase before reaching a maximum at Tmax, after which rates rapidly decline; a similar phenomenon was observed earlier by Björkman (1975). The ‘break-point’ T corresponds to a similar pattern seen in the T-response of the steady state of minimal fluorescence of photosystem II (PSII) (Fo) in photosynthesis (Schreiber & Berry 1977; Havaux, Greppin & Strasser 1991; Knight & Ackerly 2002; Hüve et al. 2012). In studies assessing the T-response of Fo, a two-stage pattern is typically observed, whereby Fo rises slowly with increasing T until it reaches a critical temperature (Tcrit), after which Fo rapidly increases (Knight & Ackerly 2002). As correlations have been identified between the Tcrit of Fo and changes in the temperature response of R (Hüve et al. 2012), it seems likely that Tcrit can be used as a proxy for the T at which disruption of both electron transport in PSII and respiratory biochemical machinery occurs in organelles. In Populus tremula, Tcrit occurs at ca. 49 °C (Hüve et al. 2011), while various desert and costal Californian species exhibit Tcrit at c. 42–54 °C (Knight & Ackerly 2002). The cause of changes associated with Tcrit could include biochemical factors such as increases in cell membrane leakiness (Raison et al. 1980; Hazel 1995; Bukhov et al. 1999; Schrader et al. 2004), respiratory uncoupling and/or increased ATP demand (Skulachev 1998; Vacca et al. 2004; Hüve et al. 2011), or increased drought stress at high T's, leading to changes in photosynthetic and respiratory metabolism (Lawlor & Cornic 2002; Rennenberg et al. 2006; Slot, Zaragoza-Castells & Atkin 2008; Atkin & Macherel 2009). Importantly, application of the high-resolution method of Hüve et al. (2012, 2012,2011) has been limited to studies on biochemical processes underpinning photosynthetic and respiratory T-responses in controlled-environment plants, with no study having applied the method to assess the non-exponential form of the R–T relationship or to elucidate ecological phenomena under field conditions.
Past studies considering the extent to which the short-term T-dependence of leaf R changes through time (e.g. acclimation to seasonal changes in the abiotic environment) and/or differs among plants growing in thermally contrasting sites have relied on low-resolution datasets [e.g. R is measured at wide T intervals (Bolstad, Mitchell & Vose 1999; Tjoelker et al. 2001; Bolstad, Reich & Lee 2003; Lee, Reich & Bolstad 2005; Atkin, Scheurwater & Pons 2006)] and/or via reliance on rates of leaf R measured at different diurnal time points (Atkin et al. 2000; Zaragoza-Castells et al. 2008; Rodriguez-Calcerrada et al. 2010; Crous et al. 2011). Such datasets provide insights into approximate patterns of acclimation [i.e. modulation of the long-term T-sensitivity of R in response to shifts in growth T (Larigauderie & Körner 1995; Atkin & Tjoelker 2003)], but do not elucidate the extent to which changes in growth T result in ‘fine-scale’ changes in the shape of the short-term R–T relationship of field-grown plants. Measuring high-resolution T-response functions would enable assessment of the stability or sensitivity of Tcrit and Tmax in plants in thermally contrasting environments.
The aims of our study were to (1) assess potential sources of variation in Tcrit and Tmax in individual plants of snow gum (Eucalyptus pauciflora) in thermally contrasting environments, particularly whether altitudinal and seasonal shifts in growth T alter the shape of the T-response curve of leaf R; (2) explore the extent to which sampling and leaf heating protocols [e.g. sample chilling and storage, leaf heating rate, duration of high-temperature exposure and relative humidity (RH)] influence the characterization and reproducibility of high T-response features (Tcrit, Tmax) of leaf R; and (3) assess the R–T relationship and its mathematical description using high-resolution data at subcritical T's. Given that several simulation models (Aber & Federer 1992; Schimel et al. 1997) and global dynamic vegetation models (White et al. 2000; Cox 2001; Cramer et al. 2001) assume an exponential/near-exponential increase in leaf R with rising T, this final aim allowed us to compare and contrast the efficacy of exponential fits (using Q10 and Arrhenius approaches) with that of non-exponential forms of the R–T relationship.
Materials and Methods
Plant material, field site locations and branch sampling/storage protocols
For all experiments, we used snow gum (Eucalyptus pauciflora Sieb. ex Spreng). Snow gum was chosen owing to its naturally broad altitudinal range and evergreen leaf habit enabling sampling of leaves that have a wide range of T exposure from direct summer sun to encasement in winter ice (Slatyer 1977a,b, 1978; Slatyer & Morrow 1977; Slatyer & Ferrar 1978; Kirschbaum & Farquhar 1984; Körner & Cochrane 1985; Ball, Hodges & Laughlin 1991; Atkin et al. 2000; Ball et al. 2004, 2006).
Characterization of the short-term T-dependence of leaf respiration in darkness (R) and chlorophyll fluorescence (Fo) were conducted using both 3-year-old potted saplings and mature field-grown trees. The potted saplings were raised from seed from a population collected in Gudgenby Valley in Namadgi National Park, south eastern (SE) Australia (35°45′S/148°59′E); plants were grown outdoors (in 30-cm-diameter pots containing organic potting mix) at the Research School of Biology (RSB), Australian National University (ANU), Canberra (600 m a.s.l.) for three years until February 2010, reaching a height of approximately 0.60–0.80 m. Mean minimum and maximum air temperatures were 12.9 and 25.0 °C in the month of sampling of the potted plants (February 2010). For the field studies, leaves were sampled from mature E. pauciflora trees growing in the Thredbo Valley of Kosciuszko National Park (36°30′S/148°19′E), both at a low altitude site at the bottom of valley (1380 m a.s.l.) and at three high altitude sites (1910, 1987 and 2110 m a.s.l.) where trees experience severe winter conditions (June–September) with sub-zero T's and persistent snow pack. For the trees sampled at higher altitude sites, we used E. pauciflora trees that were planted in 1972 (as 5-year-old saplings) by Prof. R.O. Slatyer and co-workers (Ferrar, Cochrane & Slatyer 1988); these trees were from the same provenance originating from seed collected near the natural tree-line (∼1900 m a.s.l.) at nearby Charlotte Pass, NSW (36°26′S/148°20′E), providing the opportunity to assess the influence of environmental factors on phenotypic variations in leaf R–T curves, while minimizing confounding variation owing to the genotypic differences. Sampling occurred twice in winter (September 2009 and August 2010) and twice in summer (February and March 2010) from the same individual trees for each sampling period (although no samples were taken from the 1987 m altitude site in September 2009). Seasonal and diurnal environmental data for the Thredbo Valley field sites are shown in Supporting Information Figs S1 and S2. Because of the absence of main power at the Thredbo Valley field sites, sampling required that branches be detached from individual trees. At each field site, 20-cm-long terminal branches were cut using sharp secateurs (n = 3–7, depending on the site), placed in plastic bags containing moistened paper and then stored in darkness in ice before returning to the laboratory in Canberra (2.5 h drive) where all samples were kept refrigerated at 2–4 °C. Once in Canberra, 1 leaf per tree was used to quantify the T response of leaf R.
Because of the time required to complete each T-response curve (typically 3 h, allowing for cooling of the water bath at the end of each run), only ∼6–8 curves per day could be quantified. Owing to the large number of replicates sampled at the four sites in Thredbo Valley, it was therefore necessary to store branches for up to 4 d to complete measurements on all sampled trees. Given this, we assessed the effect of branch detachment and storage on respiratory metabolism. Rates of leaf R at 20 °C of four attached leaves from separate pot-grown plants were measured using the Li-Cor 6400-02B cuvette; thereafter, leaves were detached and re-measured; no effect of detaching was found (data not shown). Detaching and storing potted plant branches at 2 °C (in darkness) for up to 4 d was found to have no effect on the measured rates of leaf R (Supporting Information Fig. S3). Moreover, subsequent analysis of near 100 T-response curves from the field-collected branches revealed that Tcrit and Tmax values did not differ among leaves measured after 1, 2, 3 or 4 d after sampling (data not shown), suggesting that chilling and length of cold-storage period did not alter these key features of T-response curves. Thus, while we cannot fully rule out the possibility of storage-mediated changes in the shape of T-response curves, these tests suggested that storage of leaves at 2 °C in darkness did not alter the key components of the T-response curves identified in our study (Tcrit, Tmax and rates of R at a common measuring T). Thus, detached branches were used in all measurements. In the case of potted saplings, detached branches were also used, but without the need for storage.
Methodology for quantifying short-term temperature responses
To test whether key features of leaf T-response curves (e.g. Tcrit, Tmax and Q10 values at set T's) differ systematically among plants growing in contrasting environments, we sampled leaves from detached branches in both the 3-year-old potted saplings and the mature trees growing in the Thredbo Valley. Leaves were kept in darkness for a minimum of 30 min before each run; thereafter, darkened leaves were placed in a 15.5 × 11.0 × 6.5 cm water-jacketed, glass-topped aluminium chamber (von Caemmerer & Hubick 1989). Air within the chamber was mixed by two fans (Micronel, Fellbrook, CA, USA). The chamber T was controlled using a thermostatically controlled circulator and microprocessor (model F32-HL, JULABO Labortechnik GmbH, Seelbach, Germany); air T in the chamber was monitored/controlled using an integrated Pt100 Lemosa-type stainless steel external sensor (JULABO Labortechnik GmbH). Leaf T in the chamber was measured with a small-gauge wire copper-constantan thermocouple pressed against the lower surface of the leaf, and which was attached to a LI-6400 external thermocouple adaptor (LI6400-13, Li-Cor Inc., Lincoln, NE, USA) that enabled leaf T's to be recorded by a LI-6400XT portable gas exchange system (Li-Cor Inc.). To facilitate measurement of net CO2 exchange rates in the water-jacketed chamber, we plumbed the exiting air-stream from the chamber into ‘sample’ gas line and Infra-red gas analyzer (IRGA) of the LI-6400XT [fitted with an empty and closed 3 × 2 cm cuvette (Li-Cor 6400-02B)]. Rates of net CO2 efflux (respiration) were calculated via comparison of the ‘sample’ IRGA values with those of the ‘reference’ IRGA. Flow rates through the water-jacketed chamber (700 mol s−1) and [CO2] of the incoming air were controlled using the LI-6400XT console flow meter and 6400-01 CO2 mixer. RH in the water-jacketed chamber was controlled by routing incoming air through the LI-6400XT desiccant column.
To initiate a T-response curve experiment, a detached whole leaf was first placed into the water-jacketed chamber in darkness at the prevailing room T (near 20 °C), after which we then cooled the chamber down to a target air T of 5 °C prior to the start of each T-response run. Thereafter, air T inside the chamber was heated at a rate of 1 °C min−1. Evaporative water loss typically resulted in the realized leaf T's increasing at a slower rate than that of air T. Net CO2 release rates were recorded every 30 s (see Figs 1 & 2 for example of curves). In addition to the respiration response, steady-state fluorescence (Fo) in the presence of a low intensity far-red light pulse (necessary to maintain PSII in the oxidized state) was recorded every 30 s using a MiniPAM portable chlorophyll fluorimeter (Heinz Walz, Effeltrich, Germany) fitted to the glass surface of the leaf chamber (note: Fo measurements were not made for the winter-sampled trees in 2009) (see Fig. 1a for an example of Fo measurements). At the end of each R–T curve experiment, leaves were removed, oven-dried for 2 d (70 °C) and weighed. This allowed rates of leaf R to be expressed on a dry mass basis (nmol CO2 g−1 s−1). To facilitate conversion of rates to an area basis, we measured the leaf area (LI-3100C leaf area meter, Li-Cor Inc.) and dry mass of an adjacent leaf [to calculate the leaf mass per unit leaf area (LMA)]. These LMA values were then used to calculate rates of leaf R expressed per unit leaf area where needed (μmol CO2 m−2 s−1).
The above curves were used to (1) quantify Tcrit (i.e. T where Fo values begin to rise sharply) and Tmax [i.e. leaf T where leaf R reaches its maximum value (Rmax)] and (2) compare fits of various algorithms to the observed high-resolution R–T relationships across the full range of measurement T's below that of Tcrit (see below for details on equations used). Tcrit values were calculated by determining the leaf T where intersection occurred between two linear models fitted to the slow and rapid rise phases in Fo (Knight & Ackerly 2002) (as shown in Fig. 1a).
Control experiments: heating rate, duration and humidity effects
A series of control experiments were made using the 3-year-old potted plants to assess how robust the heating protocol was in generating reproducible R–T relationships when factors such as the rate and duration of heating were altered. Specifically, we examined (1) the reversibility (i.e. hysteresis) and reproducibility of leaf R–T responses to the rate of air T increase; (2) the effect of duration of exposure to high T's; and (3) the influence of RH on rates of leaf R measured at high T's near and above the critical temperature (Tcrit) for Fo.
To assess the influence of the rate of air T increase on the T-response of R, detached leaves were analysed using four different rates of T increase (0.3, 0.7, 1.0 and 1.5 °C min−1) in experiments on separate leaves. Rates of leaf R at two measuring T's (25 and 50 °C) during the course of each heating experiment, as well Tmax and Rmax, were compared among leaves heated at different rates.
To explore how duration of exposure to a range of high T's affects rates of leaf R, separate individual leaves were exposed to fixed air T's of 50, 55, 60 or 65 °C (corresponding to a leaf T range of c. 45–52 °C). Detached leaves were placed into the water-jacketed chamber already set to the desired air T. Rates of leaf R (at the fixed air T) and corresponding leaf T's were measured for a period of 1 h.
To assess whether rates of leaf R were reproducible under the heating protocol or resulted in hysteresis effects, leaf R at 20 °C was measured using the Li-Cor 6400-02B (3 × 2 cm) cuvette prior to and following heating. Each leaf was then placed in the water-jacketed chamber (set to an air T of 20 °C) for 15–20 min to determine R under stable conditions. Air T was then increased at 1 °C min−1 to one of four target T's ranging from 60 to 75 °C in 5 °C intervals (on separate leaves), with leaf R and steady-state fluorescence (Fo) recorded throughout. Once the target T had been reached, the leaf was removed from the water-jacketed chamber and placed into the Li-Cor 6400-02B cuvette and allowed to re-acclimate to 20 °C for over 15–20 min. Leaf R at 20 °C was then re-measured. Leaves remained in darkness throughout the entire procedure.
The influence of RH on leaf R (measured at the prevailing leaf T of each treatment) was determined at air T's of 50 and 55 °C. CO2 concentration and RH inside the water-jacketed chamber were controlled by feeding compressed air into a LI-610 portable dew-point generator (Li-Cor Inc.). The desired humidity was achieved by altering the dew-point temperature according to equations in Buck (1981). Humidified air coming out of the dew-point generator was split into a reference and sample line, with the sample line connected to the water-jacketed chamber, and then analysed for differences in [CO2] using the IRGA of a LI-6400XT portable gas exchange system. To prevent condensation within the IRGA, it was necessary to pass both the sample and the reference line through a water condenser immersed in crushed ice at the point between the chamber and IRGA. This design allowed for measurement of leaf R at high humidities but prevented measurement of leaf transpiration. Rates of leaf R were recorded once leaf T and leaf R readings had stabilized, typically between 20 and 30 min.
Quantifying T-response curves
In addition to identifying key features of T-response curves such as Tcrit and Tmax, the availability of high-resolution leaf R–T datasets also provided an opportunity to assess the suitability of a range of fitted functions to account for the non-exponential form of the R–T relationship over a wide range of non-lethal T's (i.e. below values of Tcrit and leaf T resulting in heat stress-related cellular damage). As outlined in later sections, we often observed an inflection point at leaf T's greater than 45 °C, reflecting a burst in net CO2 efflux that was often coincident with rapid increases in Fo values (indicating damage to the photosynthetic machinery) in the 3-year-old potted saplings and field-grown trees from Thredbo Valley collected in the summer months (Figs 1-3). Consequently, to determine the R–T relationship at sub-critical leaf T's, we restricted our analysis to data over the 10–45 °C leaf T range. Curve fitting was achieved using the solver function in Microsoft Excel (Microsoft Office Excel 2007, Microsoft Corporation, Richmond, WA, USA) by maximizing the r2 value of the relationship between the measured and modelled data.
Table 1. Comparison of key features of temperature (T) response curves of 3-year-old pot-grown Eucalyptus pauciflora saplings (site: Canberra) and field-grown trees (site: Thredbo Valley) sampled along an altitudinal transect at different times of year
Field site sampling date
Field site altitude (m a.s.l.)
LMA (g m−2)
R at 25 °C (μmol CO2 m−2 s−1)
Rmax (μmol CO2 m−2 s−1)
Parameters shown are as follows: leaf mass per area (LMA), fluorescence yield of dark adapted leaves (Tcrit of Fo), leaf T where respiration rates were maximal (Tmax) and leaf R (μmol CO2 m−2 s−1) at two measuring T's (25 °C and Tmax). Values are means of 3–7 individual replicates, unless otherwise stated (±SE). No data on Tcrit values were available for the first field sampling in the winter of 2009 (September).
203 ± 13
46.9 ± 2.5
51.5 ± 1.2
1.2 ± 0.1
6.6 ± 0.3
Thredbo Valley, Kosciuszko National Park, NSW
September 2009 (winter)
306 ± 18
58.4 ± 0.5
3.7 ± 0.2
28.7 ± 1.6
279 ± 8
56.9 ± 0.6
3.5 ± 0.4
27.3 ± 2.1
282 ± 12
57.5 ± 0.7
2.6 ± 0.3
24.1 ± 0.9
February 2010 (summer)
282 ± 11
48.7 ± 4.4
55.9 ± 1.3
3.3 ± 0.3
25.5 ± 1.4
255 ± 12
46.3 ± 4.7
55.6 ± 1.3
2.8 ± 0.3
24.4 ± 1.5
272 ± 18
45.2 ± 2.2
55.4 ± 0.8
2.7 ± 0.2
23.0 ± 1.4
260 ± 14
45.2 (n = 2)
52.3 ± 1.7
2.5 ± 0.2
18.0 ± 1.5
March 2010 (summer)
260 ± 8
41.1 ± 4.3
55.0 ± 0.9
3.7 ± 0.3
23.3 ± 0.8
275 ± 15
45.8 ± 2.4
56.3 ± 1.0
3.5 ± 0.3
22.9 ± 1.2
282 ± 15
45.3 ± 2.4
53.3 ± 0.9
3.6 ± 0.4
24.2 ± 1.1
294 ± 13
44.7 ± 4.1
54.8 ± 0.8
2.3 ± 0.1
17.8 ± 1.3
August 2010 (winter)
329 ± 4
63.1 ± 2.7
56.0 ± 0.5
4.0 ± 0.3
25.8 ± 1.8
319 ± 14
58.7 ± 1.3
57.6 ± 0.7
3.6 ± 0.3
24.0 ± 2.1
308 ± 12
58.1 ± 1.7
57.1 ± 0.3
3.1 ± 0.4
20.3 ± 1.0
290 ± 16
58.0 ± 0.6
55.2 ± 1.6
2.9 ± 0.3
19.2 ± 1.7
Given our high-resolution R–T curve data over the 10–45 °C range, we compared model fits obtained using several alternative algorithms. The first approach involved calculating an exponential rise in respiration based on a single Q10 value. Single Q10 values are often used in simulation models (Aber & Federer 1992; Schimel et al. 1997) and global dynamic vegetation models (White et al. 2000; Cox 2001; Cramer et al. 2001), and thus it is of interest to compare fits made assuming fixed Q10 values with other models where the T-dependence is not exponential. Using this approach, leaf R can be calculated from a reference value using the following equation:
Another commonly used method of modelling T-responses of leaf R is to use an Arrhenius function as shown here:
where RT is the rate of respiration at a given measurement T, is the measured rate of R at a reference temperature Tref (K), Ea (J mol−1) is the activation energy of respiration and r is the universal gas constant (8.314 J mol−1 K−1). More importantly, as reported in Zaragoza-Castells et al. (2008), application of a fixed Ea value allows for moderate decreases in the Q10 of leaf R with increasing T. A constant Ea cannot, however, account for those scenarios where the temperature dependence of the Q10 is substantial [e.g. in earlier work on snow gum (Atkin et al. 2000)]. Thus, to model data where the R–T relationship is markedly non-exponential, alternative equations are needed. Here, one approach is to use a modified Arrhenius algorithm formulated by Lloyd & Taylor (1994) that allows Ea to vary inversely with T as shown here:
where T0 is a temperature between T and 0 K (measured in K) (as defined by Lloyd & Taylor 1994). An alternative to Arrhenius theory to account for markedly non-exponential R–T relationships is to permit the Q10 to decline with increasing T as observed empirically in a meta-analysis of published values across a range of ecosystems (Tjoelker et al. 2001; Atkin & Tjoelker 2003). To do this, one can use the following equation detailed in Atkin et al. (2005b):
where x and y are constants that describe a linear decline in Q10 with increasing T. Alternatively, leaf R can be modelled using the following polynomial equation fitted to plots of log R versus T (Atkin et al. 2005b):
where T is the leaf temperature (°C), and a, b and c are the coefficients that describe the T-response of the natural log of R (note that the coefficient a corresponds to the natural log of R at 0 °C). The differential of Eqn (6) can be used to model the Q10 value at any given T:
To replace a constant Q10 with T-dependent coefficients of a polynomial, rearrangement of Eqns (1) and (7) is needed. Using algebra (see Supporting Information), it is possible to model R from a reference T according to
where RT is the modelled rate of respiration at a desired T (°C), is the measured rate of respiration at a known T [Tref (°C)], and b and c are coefficients from the polynomial model.
All statistical analyses (e.g. anovas and post hoc tests) were conducted using IBM SPSS (V19) (SPSS Inc., Chicago, IL, USA). Details on the tests used are provided in the relevant Result sections. The homogeneity of the data was assessed using Levene's test of equality of error variances. One tailed t-tests were used to assess the impact of the heating protocol up to a pre-determined temperature on leaf R at 20 °C as we were only interested in damage, that is, reductions in measured leaf R. The r2 values presented in Supporting Information Table S1 were determined from first principles in Microsoft Excel by direct comparison of the modelled and measured values of leaf R.
Comparison of T-response curves of plants in contrasting environments
Results from potted plants and field-grown trees provided data from which to quantify key features of T-response curves at high T's (e.g. Tmax of leaf R and Tcrit of Fo). Here, we provide an overview of the data generated using the high-resolution heating protocol, using both 3-year-old pot-grown plants and branches from field-grown trees to illustrate characteristic responses. Thereafter, we address the question of whether responses to Tcrit and Tmax values differ systematically among E. pauciflora saplings/trees sampled along the 730 m altitude gradient (1380–2110 m a.s.l.) in a sub-alpine/alpine region of SE Australia.
Examples of T-response curves obtained with the high-resolution heating protocol are shown in Figs 1 and 2 (pot-grown and field-grown leaves, respectively), where leaves were exposed to increases of air T of 1 °C min. Figure 1a shows a representative leaf R–T curve of a single pot-grown plant, while individual leaf R–T plots (shown on Rmax-normalized basis) for eight leaves of pot-grown plants are shown in Fig. 1c; the latter is shown to illustrate how the shape of R–T curves vary among plants grown under common conditions. Figure 2a also shows an individual leaf R–T plot for a field-grown tree (sampled at 1910 m a.s.l. in winter of 2009). Shown in Figs 1a and 2a are model curves fitted to leaf R–T data over the sub-critical 10–45 °C range. These model fits were based on a second-order polynomial equation fitted to plots of the log R against T (Eqn (6)) (e.g. Fig. 1b). In all curves, rates of leaf R increased with rising leaf T's, with maximum rates (i.e. Rmax) occurring at a very high leaf T's (Tmax) that were always >50 °C (Table 1). Interestingly, while the T-dependence of leaf R decreased steadily over the 10–45 °C range, it often increased (i.e. an inflection point in the data) as T's approached the Tmax. In other words, there was an apparent ‘burst’ in leaf R (increased net CO2 efflux) as leaf T's approached Tmax (Figs 1a & 2a). This is further illustrated by the Arrhenius plot shown in Fig. 2b, where the T-dependence of R increased sharply as T's approached Tmax [with the apparent activation energy (Ea) of R increasing from 47 to 125 kJ mol−1 as leaf T's exceeded 50 °C – these burst-dependent Ea values are similar to those reported by Hüve et al. (2012)]. In our study, such ‘bursts’ were common, both in potted and in field-grown plants.
Averaged across all 3-year-old pot-grown plants (Fig. 1c), Tmax of leaf R was 51.5 ± 1.2 °C (n = 8; Table 1); individual Tmax values ranged from 4 to −55 °C, with six of the eight replicates exhibiting Tmax values >50 °C (Fig. 1c). For mature trees sampled in the Thredbo Valley, Tmax values were 56.0 ± 0.3 °C (n = 92) when averaged across all sites and seasons (Table 1), with individual tree values ranging from 47 to 62 °C. Although Tmax values differed significantly among seasons in the Thredbo Valley as detected by a two-way anova with altitude and season as factors (d.f. = 1,79; F = 12.90, P = 0.001), such differences between the seasons were relatively minor [i.e. there was only an ~2 °C difference in Tmax values between summer (54.8 ± 0.4 °C) and winter (57.1 ± 0.4 °C)]. Similarly, there was little effect of altitude on Tmax values (P > 0.05, when comparing values averaged across seasons), ranging from 55.2 ± 0.7 °C at 1380 m a.s.l. to 56.5 ± 0.7 °C at 1987 m a.s.l. Thus, notwithstanding the differences in average Tmax values exhibited by the 3-year-old pot-grown plants and their mature tree counterparts, overall the data suggest that marked environmental gradients in the Thredbo Valley (both seasonal and spatial) had relatively little impact on Tmax.
In addition to recording T-responses of leaf R, the heating protocol also provided an opportunity to assess the high T tolerance of PSII (via monitoring of Fo values). Figure 1a shows the T-response of Fo for an individual leaf of a 3-year-old pot-grown plant, with Fo values beginning to slowly rise at T's near 40 °C, with a more rapid increase occurring near 50 °C. For the pot-grown plants, average Tcrit values (see Fig. 1a) were 46.9 ± 2.5 °C (individual values ranged from 40 to 51 °C), with Tcrit always being less than Tmax (Table 1). In Thredbo Valley trees, altitude had little impact on Tcrit values, suggesting that the T where disruption to PSII occurred was invariant among leaves collected from low and high altitudes. Averaged across all sites, Tcrit values (Table 1) were similar across the two summer sampling months [Tcrit: 46.5 ± 1.6 °C (n = 10; February 2010) and 44.2 ± 1.6 °C (n = 12; March 2010)]. These summer Tcrit values were always less than Tmax [Tmax: 54.8 ± 0.8 °C (n = 22; February 2010) and 54.8 ± 0.5 °C (n = 25; March 2010)]. By contrast, Tcrit was always greater than Tmax in winter [averaged across sites in August 2010: Tcrit: 60.2 ± 1.1 °C (n = 14) versus Tmax: 56.9 ± 0.5 °C (n = 28)]. Importantly, Tcrit values were much higher in winter than in summer. This finding is further illustrated by Fo–T curves for a single tree sampled at the 1987 m a.s.l. site in March (summer) and August (winter) 2010; in summer, Fo values increased markedly when the leaf experienced T's of >46 °C, whereas in winter, leaf T's near 60 °C were required to trigger a rise in Fo (Fig. 3). Thus, PSII was more heat-tolerant in trees experiencing sub-zero temperatures in winter ice-encasement than the warmer conditions in summer.
Table 1 shows the average area-based rates of leaf R at 25 °C and at Tmax (i.e. Rmax). For the 3-year-old pot-grown saplings, rates of leaf R were 1.2 ± 0.1 μmol CO2 m−2 s−1 at 25 °C and 6.6 ± 0.3 μmol CO2 m−2 s−1 at the Tmax. By contrast, rates of leaf R were markedly higher in leaves from the Thredbo Valley, where rates of leaf R at 25 °C ranged from 2.3 to 4.0 μmol CO2 m−2 s−1 and Rmax ranged from 18 to 29 μmol CO2 m−2 s−1. Associated with the higher rates of area-based rates of respiration in the Thredbo Valley trees (compared with the 3-year-old pot-grown saplings) were higher ratios of leaf area to mass (LMA). A two-way anova detected a significant influence of altitude (d.f. = 3,82; F = 10.553; P < 0.001) and sampling date (d.f. = 3,82; F = 3.276; P < 0.05) on rates of leaf R at 25 °C and Rmax; however, differences in leaf R at 25 °C and Rmax were relatively minor, both when comparing seasons (Fig. 4a) and sites (Table 1).
Protocol control experiments
In the protocol, leaves were heated at a rate of 1 °C min−1. To determine whether the rate of heating influenced rates of leaf R and the Tmax of R, we quantified the effect of four heating rates (air T increasing at 0.3, 0.7, 1.0 and 1.5 °C min−1) on the shape of leaf R–T curves, using leaves from the 3-year-old pot-grown saplings. Heating rates between 0.3 and 1.5 °C min−1 had no effect on R25, Rmax or Tmax parameters derived using the continuous heating protocol (Table 2; one-way anova – d.f. = 3,25; P > 0.05). Averaged across the four heating rates, Tmax and Rmax values were 53.8 ± 1.0 °C and 6.1 ± 0.9 μmol CO2 m−2 s−1, respectively. These results are not significantly different from the data for pot-grown plants presented in Table 1 (t-test: P > 0.05).
Table 2. Measured rate of increase of leaf temperature (T), rates of leaf respiration (R) of pot-grown E. pauciflora saplings at 25 °C and Tmax (Rmax), and Tmax values for T-response curves of leaf R conducted at different rates of air T increase (n = 5–8; ± SE)
Programmed rate of air T increase (°C min−1)
Measured rate of leaf T increase (°C min−1)
R at 25 °C (μmol CO2 m−2 s−1)
Rmax (μmol CO2 m−2 s−1)
1.28 ± 0.07
1.5 ± 0.3
6.1 ± 0.7
54.0 ± 1.6
0.81 ± 0.04
1.9 ± 0.3
7.7 ± 1.4
50.6 ± 1.6
0.58 ± 0.02
1.7 ± 0.1
6.4 ± 0.9
54.7 ± 2.8
0.26 ± 0.01
1.3 ± 0.2
4.8 ± 0.3
55.5 ± 1.9
We also addressed whether duration of heat exposure at high T impacts on measured rates of leaf R. In experiments such as that shown in Fig. 1, leaves were exposed to increasing leaf T's that exceeded Tcrit for several minutes. Rates of leaf R initially increased up to Tmax, then rapidly declined as leaf T's exceeded Tmax (Figs 1 & 2). To disentangle the effects of increasing T from that of duration of heat exposure, we exposed leaves of 3-year-old pot-grown E. pauciflora saplings to one of four high air T's (50–65 °C in 5 °C intervals) for up to 60 min. Particular focus was placed on assessing the effect of exposure to leaf T's between Tcrit and Tmax (i.e. c. 47–52 °C). Figure 5 shows leaf T's and corresponding rates of leaf R after 5 and 60 min of T treatment at the set air T. For all treatments, leaf T remained consistently below air T, owing to evaporative cooling. Leaf T's in each air T treatment were close to or exceeded the Tcrit of the potted saplings (47 °C), and Tmax of leaf R (52 °C) was exceeded in the hottest treatment (i.e. air T of 65 °C). A significant interaction effect of air T treatment and duration of heating on measured leaf T was detected (d.f. = 3, 16; F = 4.046; P < 0.05). This reflects the increase in leaf T after 60 min in the hottest air T treatment (Fig. 4a), not observed in other treatments. At air T's of 50, 55 and 60 °C, evaporative water loss resulted in leaf T-values being maintained in relatively narrow range. Importantly, leaf R measured after 60 min exhibited significant declines in all treatments (Fig. 5b), confirmed by a significant time effect detected by repeated measures anova (d.f. = 1, 16; F = 67.382; P < 0.001). Repeated measures anova revealed a significant interaction effect of time of exposure and air T on leaf R (d.f. = 3, 16; F = 5.600; P < 0.01) which declined by 25% at 50 °C and by 95% at 65 °C at 60 compared to 5 min of treatment (Fig. 5b). In leaves exposed to air T's of 50 and 55 °C, leaf R declined linearly over time after an initial 5 min of equilibration in the chamber (data not shown). By contrast, the decline in leaf R accelerated between 5 and 15 min in the two hotter treatments (air T's of 60 and 65 °C); indeed, more than 50% of the overall decline occurred within the first 15 min of 60 and 65 °C air T treatment (data not shown). Taken together, these control experiments demonstrate (1) that R was not inhibited by 5 min of heat treatment (48–53 °C); (2) that R was inhibited when leaf T was held above Tcrit for extended periods (>5 min); and (3) the extent of inhibition of R depended on the duration of high-T exposure and leaf T.
To assess whether the heating protocol resulted in hysteresis effects, leaf R at 20 °C of the 3-year-old potted saplings was measured (using the Li-Cor 6400-02B cuvette) prior to and after heating leaves throughout a range of leaf T's that ended at high-temperature values that were either below or above Tcrit. In the two heat treatments where leaf T did not exceed 48 °C (i.e. Tcrit in the 3-year-old saplings), no significant difference was found in leaf R at 20 °C measured before and after the heating protocol treatment (Table 3). However, when leaf T increased above 48 °C approaching Tmax (i.e. those leaves were exposed to the full range of rising air T's to 74 °C), significantly lower rates of leaf R at 20 °C were observed after heating (48% reduction; Table 3). Thus, in the 3-year-old potted saplings, irreversible damage to the respiratory apparatus occurred when leaf T's exceeded Tcrit.
Table 3. Rates of leaf respiration (R) measured at a leaf temperature (T) of 20 °C of pot-grown E. pauciflora saplings before and after exposure to a heating protocol where air T's were increased at 1 °C min−1, starting at 20 °C and finishing at a maximum air T of 60, 67, or 74 °C (corresponding with leaf T's of 45–52 °C) (n = 4; ± SE)
Max air temperature treatment (°C)
Max leaf temperature (°C)
R at 20 °C (μmol CO2 m−2 s−1)
P (paired t-test)
Reported P-values correspond to results from one-tailed paired t-tests used to detect any significant decrease in R at 20 °C owing to heat treatment.
44.8 ± 0.9
2.1 ± 0.2
2.0 ± 0.4
47.7 ± 0.7
1.7 ± 0.2
1.7 ± 0.2
52.3 ± 0.8
1.6 ± 0.2
0.8 ± 0.2
The process of heating to very high T's resulted in leaves being exposed to very low RH, which, in turn, increased water loss by evaporation and promoted tissue desiccation. To assess whether tissue desiccation (evaporative water loss) accelerated loss of respiratory function at high air and leaf T's, we altered RH in the sample air-stream and determined leaf R at T's near Tcrit (Supporting Information Fig. S4). Variations in RH had little effect on leaf R except with dry air (0% RH) and leaf T's of 46 °C (post hoc Tukey's test, P < 0.05). Collectively, these results indicate that evaporative water loss was unlikely to influence our estimate of the T response of leaf R below 45 °C.
Modelling of respiration: comparison of model fits to high-resolution data
We compared fits obtained using a range of approaches to describe the R–T relationship. This was accomplished using high-resolution leaf R–T data obtained from the 3-year-old pot-grown saplings. Using Eqns (1) and (2) to model T-responses, average fixed Q10 and Ea values were 2.01 ± 0.04 and 54.6 ± 1.5 kJ mol−1, respectively. We then compared the difference between model and actual rates of leaf R over the 10–45 °C range (Fig. 6). All models provided reasonably good fits over the 25–45 °C T range (r2 values for all fits >0.99), whereas all models over-predict R at low leaf T's. Importantly, however, the degree of overestimation was markedly higher when assuming fixed Q10 and Ea values, compared with models that allowed for non-exponential forms of the R–T relationship in which temperature sensitivity (instantaneous slope) varies with change in measurement temperature. Perhaps not surprisingly, the best agreement between model and observation across the range of measurement temperatures was obtained using a second-order polynomial (Eqn (6)), a three-parameter model (i.e. all others were two-parameter models). Consequently, we used Eqns (6) and (7) to describe the T-dependence of leaf R (Supporting Information Fig. S5) and the Q10 (Supporting Information Fig. S6), respectively, in the field-collected material for leaf T's < 45 °C (below the value of Tcrit and heat injury, irrespective of whether leaves were sampled in summer or winter).
Q10 values declined with increasing measurement T, but the instantaneous Q10–T responses did not consistently differ with altitude (Supporting Information Fig. S6). However, Q10–T relationships differed between summer and winter sampling periods with a greater T-dependent decline in Q10 in winter than in summer (Fig. 4b). We observed small seasonal shifts in the shape of the T-response curves of R, while R at 25 °C was unchanged (Table 1).
Given the paucity of empirical data on high-temperature responses of leaf R, the nature of thermal limits to respiratory function in plants remains largely uncharacterized. In addition, there is growing acceptance that being able to predict the response of leaf R to changes in temperature is crucial to understanding how future changes in climate will impact on ecosystem functioning (Gifford 2003; Wythers et al. 2005; King et al. 2006; Atkin et al. 2008; Sitch et al. 2008). While it is known that in many cases, leaf R cannot be modelled assuming a constant Q10 of 2.0 (Wythers et al. 2005; King et al. 2006; Atkin et al. 2008), our ability to more accurately model the T-dependence of leaf R has been limited by the lack of high-resolution T-response data throughout a wide range of T's. Given the above, we tested whether snow gum trees exhibit generalizable temperature (T) response functions of leaf respiration (R) and fluorescence (Fo), and if so, whether key features of those T-response curves (e.g. Tcrit, Tmax and Q10) differ systematically among plants growing in contrasting environments.
Respiration at high temperatures
Over 23% of the Earth's land surface exhibits maximum air T > 40 °C (Larcher 2004); in such habitats, sun-exposed leaves can be 10 °C hotter than the surrounding air (Gates 1965; Gates, Alderfer & Taylor 1968; Singsaas et al. 1999; Wise et al. 2004), resulting in measured leaf T's exceeding 50 °C (Hamerlynck & Knapp 1994; Valladares, Gianoli & Gomez 2007). Our study demonstrates that leaves may survive short-term exposure to these temperatures (see Table 3). However, under full sun conditions, especially in the absence of wind (Vogel 2009), exposure at time intervals sufficient to cause irreversible damage are possible in ecosystems at low latitude sites. Although the necessary conditions may be uncommon, it is likely that they act as a selection pressure especially as high T's are likely to become more common in the future (Meehl & Tebaldi 2004; IPCC 2007; Battisti & Naylor 2009; Ganguly et al. 2009). Past studies have addressed the issue of physiological function, particularly photosynthesis, at critically high T's (Berry & Björkman 1980; Havaux 1992, Havaux 1993, Havaux & Gruszecki 1993; Valladares & Pearcy 1997; Knight & Ackerly 2002; Hozain et al. 2010; Hüve et al. 2011, 2012). Our study adds to these studies via characterization of the respiratory response under a wide range of environmental conditions.
It is worth mentioning that although it is convenient to determine Tcrit and Tmax simultaneously, these parameters reflect properties of two different organelles, that is, chloroplasts and mitochondria, respectively. It is reasonable to assume that because Tcrit is associated with changes in chloroplast membrane leakiness (Raison et al. 1980; Hazel 1995; Bukhov et al. 1999; Schrader et al. 2004), it may also reflect changes in mitochondrial membranes. This assumption is further strengthened by correlations found between the temperature response of Fo and R (Hüve et al. 2012).
Overall, our results demonstrate that respiratory function is impaired when leaves are exposed to very high leaf T's. However, the impact of high leaf T's depends on the T-value and duration of exposure. So long as exposure up to Tcrit was short-lived (<5 min), function was unaffected. When leaf T's exceeded Tcrit for more than 5 min, then R declined. At T's between Tcrit and Tmax, there was a burst in respiratory CO2 efflux with rising T (Figs 1a & 2a) and an inflection point in the Arrhenius function (Fig. 2b). The fact that Fo values rose rapidly at T's > Tcrit suggests that other factors in addition to enzyme kinetics (T-responses) apply.
In the heating protocol used in our study, it is likely that heat-induced change in membrane properties (as assessed by determination of Tcrit) was the primary impact of the high T stress (Björkman et al. 1980; Hazel 1995; Sung et al. 2003). T-dependent changes in membrane properties often manifest in an abrupt change in the slope of Arrhenius plots (Berry & Raison 1981). Such a loss in membrane integrity could result in respiratory CO2 release becoming uncoupled from mitochondrial electron transport (Skulachev 1998), with the respiratory ‘burst’ (Figs 1 & 2) reflecting removal of feedback controls by adenylate limitations (Dry et al. 1987). In this scenario, the rise in measured rates of leaf R at leaf T's > 47 °C might not necessarily reflect an increase in the rate of ATP synthesis. Alternatively, heat-induced cellular damage might increase the demand for ATP – if so, the ‘burst’ may reflect an attempt at cellular repair prior to Tmax being reached. Either explanation would provide a mechanism to account for accelerated and variable rates of respiratory CO2 release at high T's.
Thermotolerance of photosynthesis: surprising seasonal variations
An unexpected result of our study was the finding that the critical leaf T where Fo rises rapidly (Tcrit) was markedly greater in August (winter) than the two summer sampling months (February and March) in trees growing along the altitudinal transect. In winter, E. pauciflora experiences very low day and night T's in the Thredbo Valley (Supporting Information Figs S1 & S2), with trees growing at the highest sites being snowbound and exposed to blizzard conditions for extended periods. Given this, one might have expected that acclimation to low-T conditions would have been associated with a decrease in tolerance of high T events (Raison, Berry & Bjorkman 1979; Knight & Ackerly 2002). Why did we observe the opposite? Often, studies assessing seasonal changes in thermotolerance have used plants growing in environments where daytime T's remain above freezing (e.g. Loveys, Egerton & Ball 2006); by contrast, in winter, leaves of our field-grown trees experienced near constant sub-zero temperatures for several days before sampling, with leaves being encased in ice on the day of sampling in August 2010. E. pauciflora is known to form a special chlorophyll complex in the winter, which functions to dissipate excess light as heat (Gilmore & Ball 2000). Whether this state is also able to confer greater thermal stability in the dark has not been investigated. While the cold-hard band forms as part of the cold acclimation by E. pauciflora in winter, pigment complex reorganization has also been observed in cold acclimation in Pinus sylvestris (Ottander, Campbell & Oquist 1995). Further work is needed to establish whether there is a link between pigment reorganization in cold-acclimated evergreen leaves and altered thermal tolerance evident as a change in Tcrit.
Assessment of heating protocol
Our results demonstrate that the Hüve et al.'s (2011, 2011,2012) high-resolution protocol provides a robust, reproducible method for quantifying and modelling the shape of the T response of leaf R, up to leaf T's of c. 45 °C and less than Tcrit. At leaf T's above Tcrit (ca. 45 °C in all measurements, excluding the winter-sampled leaves), increased duration of high-T heating progressively impaired respiratory function. This complements other studies that have observed T-induced bursts in steady-state fluorescence and leaf R (Havaux et al. 1991; Knight & Ackerly 2002; Hüve et al. 2011, 2012) at comparable T's. The fact that a combination of high-T heating and low RH led to a significant rise in leaf R (Supporting Information Fig. S4) suggests that leaf desiccation, in part, may have contributed to the ‘burst’ in leaf R at T's > 45 °C. Indeed, past work has reported a biphasic response to tissue relative water content (RWC) whereby severe drying results in an increase in rates of leaf R, albeit over longer time scales than used in our study (Flexas et al. 2005). Given this, the effect of heat stress on leaf R may be linked to damage to membranes caused by tissue desiccation. Support for this suggestion comes from work with thylakoid membranes that showed that the ability of those membranes to withstand high T stress was influenced by the surrounding RH (Valladares & Pearcy 1997). Moreover, a link between T tolerance and membrane properties has been demonstrated in comparing desiccation-tolerant and desiccation-sensitive pea seeds (Stupnikova et al. 2006).
Given the importance of duration of high-T treatment on rates of leaf R (Fig. 5b), one might have expected that slow heating rates would have altered the shape of the resultant R–T curves; however, this was only found to be the case when leaf T's exceeded 45 °C in the pot-grown saplings [an encouraging finding as many studies (e.g. Hüve et al. 2011) use a standard heating rate of 1 °C min−1]. By contrast, duration of exposure impacts was observed in leaves >Tcrit, perhaps reflecting the high rates of evaporative water loss from leaves at these high T's, leading to desiccation-related injury or membrane disruption in leaves exposed to very high T's for time periods greater than 5–10 min.
Modelling the temperature response of respiration
Given that the leaf R–T relationship is non-exponential over a broad range of T's, it is apparent that the instantaneous T-sensitivity (i.e. Q10) should decline with increasing measurement T at temperatures below Tcrit (Tjoelker et al. 2001; Atkin et al. 2005b). While various mathematical formulations were broadly suitable over the T range (10–45 °C), models with fixed T-dependent coefficients (i.e. Arrhenius and single Q10 models) showed increasing divergence between observed and predicted values over the full range of T's compared to those with T-dependent coefficients [modified Arrhenius (Lloyd & Taylor 1994), T-dependent Q10 (Tjoelker et al. 2001; Atkin et al. 2005b)]. Given the limitations of all models, particularly those based on a simple exponential T-response function, an empirical polynomial model fit (Eqns (5) and (6)) to the full range of observed log-transformed R data (10–45 °C) provides a suitable mathematical description of the underlying T-sensitivity of R. The observed high-resolution R data confirm that the intrinsic T-sensitivity of R declines with increasing measurement T (Tjoelker et al. 2001; Atkin & Tjoelker 2003). Examination of the near 100 R–T functions provided further evidence for a T-dependent Q10 (Tjoelker et al. 2001; Atkin et al. 2005b) in evergreen leaves across an altitudinal gradient and range of environmental conditions. The consistent over-prediction of R by the models at low T's could reflect a greater degree of error because of the low measured rates of R but could also indicate transition to a gel phase in the mitochondrial membranes at low T (Hazel 1995), leading to a reduction in R not captured by the form of the model. Regardless of the underlying cause of the divergence between model fits and observed rates at low T's (i.e. near 10 °C), by adopting a temperature-dependent Q10, R can be accurately predicted between 15 and 45 °C knowing R at any reference T in this range (Fig. 6).
To what extent is our finding that leaf R–T curves are best predicted by a polynomial equation of use to the wider modelling community? In most modelling scenarios, rates of leaf R at a given T (RT) are predicted using rates of leaf R at a reference T () combined with descriptor of how will vary with measurement T. For example, in JULES (Joint UK Land Environment Simulator – land surface model of the latest Hadley Centre climate model), RTref is predicted based on an assumed relationship with leaf N concentration and photosynthetic capacity (Cox 2001); thereafter, JULES assumes that leaf R varies with T assuming a constant Q10 of 2.0. Our study suggests that an alternative is to use the approach outlined in Eqn (8), using empirically determined coefficients derived from model fitting to high-resolution T-response functions. We observed that the coefficients of the polynomial models differed among plants (Fig. 1c) and environments, particularly between winter and summer, as reflected in the seasonally different Q10–T relationships (Fig. 4; Supporting Information Table S1). Consequently, modelling of R is potentially complicated by seasonal shifts in the shape of R–T curves, requiring further studies of patterns of Q10–T relationships in plants from a wide range of ecosystems. The results highlight the limitations of applying any one set of T-coefficients in empirically fitted models to describe T-response.
Seasonal and environmental changes in the temperature response of R
Shifts in the shape of the temperature–response functions were observed between summer and winter and among altitudes (Fig. 4, Supporting Information Figs S4 & S5), suggesting acclimation in this species, E. pauciflora. In general, the leaves sampled in summer demonstrated a lower sensitivity of Q10 to increases in T (i.e. the Q10–T response is flatter) than leaves sampled in winter. This effect was more pronounced in the lower altitude sites, but no overall pattern in the Q10 response to altitude was evident. The observed seasonal shifts in Q10 without changes in the value of R at low T's (Fig. 4) are consistent with type I acclimation (Atkin & Tjoelker 2003). By contrast, high altitude sites consistently exhibited higher rates of R than the lowest altitude site without concomitant shifts in Q10, a type II acclimation response. This interpretation is supported by other studies which examined acclimation in the T response of R in Populus balsamifera (Silim, Ryan & Kubien 2010) and Pinus banksiana (Tjoelker et al. 2008, 2009).
In our study, seasonal changes in R–T relationships were not associated with marked changes in specific rates of leaf R at moderate T's that might be expected assuming temperature was the predominate cause of acclimation. Past work on E. pauciflora saplings found cold-grown plants exhibit higher rates of leaf R than their warm-grown counterparts (e.g. winter versus summer; ambient versus elevated growth T) (Atkin et al. 2000; Bruhn et al. 2007). However, among these contrasting altitudinal sites, seasonal changes in temperature would be expected to be concurrent with changes in any number of environmental factors that could influence functioning of E. pauciflora, including precipitation, water availability, ice encasement, snow pack and cold hardening (Slatyer 1977b).
The use of the continuous heating protocol in determining high-resolution T response of R has a number of applications. The determination of high-temperature characteristics such as Tcrit and Tmax, as well as characterization of respiratory CO2 efflux provides insight into thermotolerance at extreme T's, which may indicate potential roles of membrane properties and dehydration in high-T injury. At sub-critical T's (less than 45 °C), this technique enables determination of the instantaneous response of R and its T-dependence, each expressed as a continuous function of measurement T. Fitting an empirical, second-order polynomial to log-transformed R data provided a means to quantify T-dependence of the Q10 (Atkin et al. (2005b). The data obtained from E. pauciflora plants grown in pots and field-grown trees along an elevational environmental gradient confirmed a general T-dependent decline in T-sensitivity of leaf R. However, examination of nearly 100 T-response curves of this species revealed both plant to plant and environmental (particularly summer versus winter) variation in shape of the R–T response function and its high-T features.
A generalized form of the empirical model (Eqn (8)) would enable calculation of estimated R based on a reference value of R at a known T, and thus be of use in modelling. However, the suitability of the parameter estimates beyond the individual plants (perhaps species) for which they were determined remains unknown. The challenge now will be to apply the protocols outlined in our study to a wider range of plant species to assess whether there are predictable patterns in the shape of leaf R–T curves among the key biomes of the world. With such data, we will be able to more fully explore implications of variations in such relationships on ecosystem responses to climate change and improve estimates of climate–carbon cycle dynamics.
Finally, it is crucial that a proper characterization of the T response of R is included when modelling changes in carbon fluxes with T. The T optimum of photosynthesis generally matches mean day T (Slatyer 1978; Berry & Björkman 1980). However, at T's above this optimum, photosynthetic assimilation is inhibited (Law & Crafts-Brandner 1999) while R will continue to increase in a predictable manner up to a critical T. Depending on the extent of light inhibition of leaf R (Atkin et al. 1998; Hoefnagel, Atkin & Wiskich 1998; Wang et al. 2001), such increases in rates of leaf R in the T range above the photosynthesis optimum [but below degradation T's of respiration (i.e. Tcrit)] could have marked negative impacts on daily carbon gain (Salvucci & Crafts-Brandner 2004; Hozain et al. 2010). These divergent responses of leaf R and photosynthesis to high T's events need to be considered when modelling responses of plants to heat wave events.
This work was funded by grants from the Australian Research Council (ARC FT0991448 and DP0986823). The field work was undertaken under NSW Government Scientific Licences, SL100403 and S13095. The authors thank Mr Peter Cochrane (Director, Australian National Parks) for giving his time to identify the location of the above tree-line snow gums, planted in 1972 by Professor Ralph Slatyer and co-workers. Thanks also to Ari Kornfeld for the assistance with the algebraic transformation necessary to produce Eqn (8) and to two anonymous reviewers for useful comments on the manuscript. Finally, we thank Professor Slatyer, who recently passed away, for donating log-books on documenting the location and past growth characteristics of each individual tree at the high altitude sites.