Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Photosynthetic variation and responsiveness to CO2 in a widespread riparian tree

  • Shannon Dillon ,

    Roles Conceptualization, Data curation, Investigation, Methodology, Writing – original draft, Writing – review & editing

    Shannon.dillon@csiro.au

    Affiliation Genetic Diversity and Adaptation, Breakthrough genetic technologies for crop productivity, CSIRO Agriculture and Food, Canberra, ACT, Australia

  • Audrey Quentin,

    Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology

    Affiliation Landscape Intensification, CSIRO Land and Water, Hobart, TAS, Australia

  • Milos Ivković,

    Roles Data curation, Formal analysis

    Affiliation Genetic Diversity and Adaptation, Breakthrough genetic technologies for crop productivity, CSIRO Agriculture and Food, Canberra, ACT, Australia

  • Robert T. Furbank,

    Roles Investigation, Writing – review & editing

    Affiliation ARC Centre of Excellence for Translational Photosynthesis, Research School of Biology, Australian National University, Acton, ACT, Australia

  • Elizabeth Pinkard

    Roles Conceptualization, Methodology, Writing – review & editing

    Affiliation Landscape Intensification, CSIRO Land and Water, Hobart, TAS, Australia

Photosynthetic variation and responsiveness to CO2 in a widespread riparian tree

  • Shannon Dillon, 
  • Audrey Quentin, 
  • Milos Ivković, 
  • Robert T. Furbank, 
  • Elizabeth Pinkard
PLOS
x

Abstract

Phenotypic responses to rising CO2 will have consequences for the productivity and management of the world’s forests. This has been demonstrated through extensive free air and controlled environment CO2 enrichment studies. However intraspecific variation in plasticity remains poorly characterised in trees, with the capacity to produce unexpected trends in response to CO2 across a species distribution. Here we examined variation in photosynthesis traits across 43 provenances of a widespread, genetically diverse eucalypt, E. camaldulensis, under ambient and elevated CO2 conditions. Genetic variation suggestive of local adaptation was identified for some traits under ambient conditions. Evidence of genotype by CO2 interaction in responsiveness was limited, however support was identified for quantum yield (φ). In this case local adaptation was invoked to explain trends in provenance variation in response. The results suggest potential for genetic variation to influence a limited set of photosynthetic responses to rising CO2 in seedlings of E. camaldulensis, however further assessment in mature stage plants in linkage with growth and fitness traits is needed to understand whether trends in φ could have broader implications for productivity of red gum forests.

Introduction

Forest trees are foundation species in ecosystems worldwide. They are long lived, often wide spread and traverse strong environmental gradients. As a result, forest tree species frequently exhibit adaptive phenotypic clines reflecting genetic adaptations to local environment [1,2]. Such clines highlight the capacity of forests to adapt to their environment over evolutionary time scales [3], however it is less is well understood how forests will adapt to future climate change [4]. Increasing concentration of atmospheric carbon dioxide (CO2) is one of the most important global change pressures currently affecting forests, which acts directly through its effect on leaf-level gas exchange, and indirectly through its effect on climate [5]. How forest species respond to shifts in rising CO2, in interaction with broader climate variation, will have consequences for the ecological communities which they support, as well as restoration and commercial forestry [6,7].

As more species confront environmental change, it is becoming important to quantify the factors influencing their capacity to adapt and to monitor these [8,9,10]. Adaptive responses to CO2 in trees could include both evolutionary adaptations and phenotypic plasticity, and a better understanding of these effects will assist management of future forests [11,12]. The ability of an organism to change its phenotype in response to changing environment, or plasticity, is a widely recognised adaptive mechanism in plants [13,14,15], that could have particular utility mediating phenotypic adaptation in forest tree species with long generation times where rates of evolutionary adaptation may be slow compared to the velocity of environmental change [4,16]. Plastic responses will therefore be highly relevant to adaptation in forests trees over the time frame in which CO2 is projected to increase [17].

Plastic responses of morphological and physiological traits under CO2 enrichment are extensively documented in forest trees, including eucalypts [18,19,20,21,22,23,24,25]. This generally points to increased productivity and improved water use efficiency of forests driven by CO2 fertilisation [5,26,27,28]. However the extent to which CO2 stimulation effects vary among genotypes or populations, and subsequent impacts for forest productivity across a species range, is not well understood. Genetic effects determining CO2 response, or genotype by CO2 interaction (G × CO2), have been quantified in other plants [7,29,30,31,32,33], suggesting that populations or genotypes can respond in ways not predicted from generalised interpretations of CO2 response.

Characterisation of G × CO2 responses in trees is therefore warranted, and may be furthered by better understanding the processes leading to genetic variation in response. Evolutionary adaptation has been proposed as one constraint on plasticity (or adaptive plasticity) in plants where trends in phenotypic response reflect adaptation along environmental clines in nature [12,34,35,36]. Due to the breadth of environments encountered by widely distributed forest tree species in situ, local adaptation is expected to be a driver of variation in plasticity, and this has been observed for phenology, leaf and physiological traits [37,38,39]. It is less well understood whether pre-existing adaptations to environment underlie variation in population plasticity in physiological responses to CO2, although this has been suggested [7,40].

To address gaps in our understanding of population level adaptation to CO2 in forest trees, we investigated the extent of local adaptation and responsiveness to elevated CO2 for key photosynthesis traits in a wide spread, ecologically and genetically diverse eucalypt, E. camaldulensis. Traits were assessed across the species natural distribution, where we firstly explored whether genetic variation among provenances and subspecies was detectable and if so whether this variation was likely to have been influenced by adaptation to local environment. We subsequently investigated the degree to which variation in photosynthetic responses to CO2 enrichment was dependant on provenance of origin, and where G×CO2 was identified, whether there was evidence that trends in plasticity could have been constrained by pre-existing adaptations to local environment.

Methods and materials

Genetic material

In total 486 E. camaldulensis genotypes representing 43 provenances and 5 subspecies were sampled with between 5 and 12 genotypes per provenance for 401 “test” cases, and an additional 85 “control” plants with an average of 2 plants per provenance (Fig 1, Table 1). Genotypes were sampled across the natural range of E. camaldulensis, in an attempt to capture a representative sample of genetic diversity for this species. E. camaldulensis seed was obtained from the Australian Tree Seed Centre (Canberra, Australia) as provenance seed lots collected from individual mother trees in situ, with the exception of four seed lots for which seed was bulked. Within provenance each genotype represented a different seed lot, thus the design did not capture within family variation. Seeds were germinated in a native low phosphorus potting mix at ambient CO2 (400 ppm; 28°C) on a 16hr day/night light cycle. Four weeks post germination seedlings were transferred into 10cm diameter 0.75L planter bags with a native low phosphorus potting mix (1/3 of river sand, 1/3 of peat moss and 1/3 of natural compost) and moved to a glasshouse under similar atmospheric conditions (400 ppm; 24°C) and a natural 12hr day/night light cycle. Two applications of a systemic fungicide (Fongarid®, active constituent: 250g/kg Furalaxyl; dilution: 1g/L) were applied at 2 week intervals from approximately 5 weeks until 2 months of age to eliminate risk of root fungal disease.

thumbnail
Fig 1. Distribution of E. camaldulensis provenances sampled in this study.

Occurrence records (underlaid) spanning the species natural range were obtained from the Atlas of Living Australia (http://www.ala.org.au. Accessed 12 May 2016). National Surface Hydrology Polygon obtained from Geoscience Australia [82]. Figure produced using ArcGIS v. 10.3.

https://doi.org/10.1371/journal.pone.0189635.g001

thumbnail
Table 1. Provenances and subspecies of E. camaldulensis sampled for assessment at ambient and elevated CO2.

https://doi.org/10.1371/journal.pone.0189635.t001

Growth conditions

At approximately 2 months of age 486 seedlings were transferred to four controlled-environment growth chambers (PGC20 Conviron®, Winnipeg, Canada) using a randomised design. Plants were arranged in 350 x 300 mm plastic seedling trays, at nine plants per tray. Every second week over the course of the experiment, trays within each cabinet were rotated to reduce confounding position effects rather than blocking. At this time Aquasol® soluble plant fertiliser (NPK- 23:3:95 and 14 trace elements) was provided at half strength, and subsequently every month for the duration of the experiment to ensure plants were not nutrient limited. Temperature regime was set to 18°C (night) and 24°C (day), which enveloped at least one of annual mean, maximum or minimum temperature encountered naturally by any of the sampled provenances. A 16hr day/night photoperiod was applied, to approximate the natural photoperiod encountered between 37–14°S and 115–149°E. Each cabinet was fitted with Growlux fluorescent lamps (Sylvania 24W/T5/GRO, Australia) and a black-light (Sylvania FHE 28W/T5/BLB, Australia) to provide light in the red and blue regions of the spectra coinciding with the photosynthesis action spectrum, while enriching with high frequency light in the UVa spectrum recommended for normal growth of eucalypts [41]. Light intensity was implemented via an hourly step function in the morning and evening to simulate natural light conditions. Plants experienced full light, corresponding to about 1000 μmol m-2 s-1, for 7 hours each day. Each cabinet was submitted to the same photoperiod but was delayed by 2 hours between chambers to allow an intensive measurement campaign. Relative humidity in the cabinet was controlled at 50% during the day and 60% during the night by a dehumidifier (Belta 601). Plants were watered to saturation from the base daily for the first six weeks, and twice daily from seven weeks.

CO2 conditions

The cabinet experiment aimed to detect photosynthetic variation among provenances under ambient CO2 conditions, and to assess evidence of provenance by environment interactions (G × E) in response to elevated CO2 conditions. Plants were sequentially exposed to ambient [CO2] (aCO2, 400 ppm) over 10 weeks followed by elevated [CO2] (eCO2, 800 ppm) for a further 8 weeks (± 20 μmol CO2 mol−1). This included a period of two weeks to allow plants to acclimatise to the cabinets before commencing the ambient treatment. The chosen CO2 levels for each treatment were based on current and projected (yr. 2100) atmospheric CO2 levels respectively (IPCC 2014). To maintain CO2 at the desired concentration, a non-dispersive CO2 analyser (GMT220 Vaisala Carbocap® CO2; Vantaa; Finland) continuously measured and directly controlled CO2 in each chamber via injection of industrial grade compressed CO2. This was combined with a granular soda lime-based CO2 controller. In total 401”test” genotypes were subjected to both aCO2 and eCO2 phases. The sequential design of the cabinet treatments accounted for genotype effects between CO2 treatments. To account for potential ontological effects due to the treatments being measured eight weeks apart, a set of 85 “control” individuals (Table 1) were retained at aCO2 for the duration of the experiment.

Phenotypic data

Previous studies in trees have established that photosynthetic traits are responsive to changing CO2, and could therefore serve as a suitable base for assessing intraspecific variation in CO2 response. Variation in photosynthetic processes also has potential to impact growth, productivity and fitness of individual trees or forest stands, and therefore are an important trait in the context of forests growing under future CO2 conditions. A set of ten photosynthesis traits were estimated on test and control plants during the final week of each treatment phase, or within the final two weeks in the case of integrated photosynthetic traits. All measurements were performed on a fully expanded, mature leaf from the upper crown of test and control plants that had developed during the respective treatment phase. By standardising sampling of leaves at a common developmental stage we attempted to limit confounding ontological variation between treatment time points. Gas exchange measurements were performed using a Li-Cor LI-6400 portable photosynthesis instrument (Li-Cor, Inc., Lincoln, USA) within the period of peak photosynthetic activity, determined from diurnal measurements (0900–1200 hrs). Leaves in the cuvette were illuminated to saturating photon flux density (PFD) of 2000 μmol m-2 s-1, and ambient temperature (24°C). Leaves were measured at an external CO2 concentration of 400 ppm (growth CO2) during aCO2 treatment, and sequentially at both 400 ppm and 800 ppm (growth CO2 for control and test plants respectively) during the eCO2 treatment. Measuring gas exchange at two levels in the test and control plants in the elevated treatment enabled us to examine the effect of CO2 acclimation vs instantaneous enhancement of photosynthesis. In each instance light-saturated assimilation rate (Anet, μmol m−2 s−1) was recorded after an equilibration period of up to five minutes.

The response of net assimilation rate (A) to varying intercellular CO2 concentrations (ACi), and varying light intensities (A-light) were also determined in the seventh week of both CO2 treatments. Integrated measurements of photosynthesis are expected to be less variable (greater precision) than instantaneous measures (such as Anet), and can provide insight into biochemical processes underpinning CO2 assimilation. However due to the time required for these measurements this was performed on a subset of plants, 3–4 per provenance, and did not include control plants. In the ambient treatment this totalled 131 plants and in the elevated 124 plants. The A-Ci curve consisted of 15 steps of external CO2 concentrations applied in succession over 400, 350, 250, 150, 100, 50, 0, 400, 500, 750, 900, 1200, 1500, 2000, 400 ppm. Leaf photosynthesis was then measured at 12 photon flux densities over 2000, 1800, 1500, 1000, 800, 600, 400, 200, 100, 75, 50, 0 μmol m−2 s−1. Dark respiration was defined as the absolute CO2 exchange rate measured during the last step of the A-light curve. Leaves were allowed to equilibrate for 5 minutes between each step of the A-Ci and A-light curves. All measurements were performed at ambient temperature (24°C) with VPD held close to 1 kPa. Biochemical parameters were calculated from the A-Ci (Vcmax, J, TPU, Γ) and A-light (Amax, φ, Rdark, LCP, θ) curves for each genotype using established photosynthesis model equations [42](S1 Table). Because of the potential for leaf temperature to influence estimates of quantum yield [43], the relationship between leaf temperature (Tleaf) and φ at time of measurement was assessed but found not to be correlated at either ambient or elevated CO2 (aCO2: R2 = 0.002, p = 0.56; eCO2: R2 = 0.01, p = 0.249). As a quality measure A-Ci and A-light curves are presented for a subset of plants at both CO2 levels in S1 Fig. Instantaneous light-saturated assimilation rate (Anet) was also taken from the A-Ci curve at 400ppm in the aCO2 and eCO2 treatments to facilitate direct comparison with integrated biochemical traits. Trait data for all genotypes across the two CO2 treatments and controls have been made available in supplemental S1 File.

Statistical methods

Photosynthetic variation among provenances and subspecies.

Variation in photosynthetic traits among provenances and subspecies at ambient CO2 was first assessed via linear discriminant analyses of principal components, by applying subspecies as the grouping factor, to identify a set of multivariate discriminant functions that maximise variance among provenances, using the MASS package in R [44]. Principal components applied in this analysis were first generated on raw trait data across individuals using the “prcomp” function in base R (R Development Core Team, 2015) (S2 Table). Significance of subspecies variance across discriminant functions was assessed via MANOVA with a Wilks' Lambda test in base R. Discriminant functions and their coefficients were visualised using the ggord package in R [45].

Quantitative variation in photosynthetic traits among provenances and subspecies at ambient CO2 was subsequently examined to assess the contribution of genetic factors to trait variation across the species range. Trait data was assessed for incorrect entries and outlying values. To estimate the proportion of phenotypic variance at ambient CO2 attributable to genetic groups, or provenance effect, random effect variance components were estimated in a bivariate linear mixed model analysis as per Falconer and McKay (1996), implemented in ASReml-R Release 3 (Butler et al. 2009, R Development Core Team, 2015): where y is the vector of trait observations, b and u are vectors of fixed (cabinet and CO2) and random (provenance) effect estimates respectively, X and Z are incidence matrices for fixed and random model terms and e is a vector of random residual effects. The proportion of the total phenotypic variance (or provenance effect) (Pmr) explained by the random provenance effect variance components () at ambient CO2, was subsequently estimated following Falconer and Mackay (1996): where is the residual error variance component at ambient CO2 and n is the harmonic mean of the number of seedlings per provenance. The latter was applied to account for unbalanced data. Similar analyses were performed at the subspecies level. Provenance least squares means (or best linear unbiased estimates–BLUEs) were estimated for each trait under ambient conditions by fitting provenance as a fixed term in the linear model framework already described, and applying the predict() function in ASReml-R. This provided an adjusted mean (BLUE) for each provenance accounting for potential sources of variation in the data, subsequently applied to relate phenotypic values to environment at site of origin (see next section), deemed necessary since environmental estimates were aggregated at the provenance level. Applying the same linear model framework described above, without random effects, associations between provenance BLUEs among traits were fitted using the “lm” function in base R.

Environmental associations.

Associations between traits and environmental variables were explored, to assess potential for local adaptation to explain patterns of phenotypic variation among populations. Point estimates for environmental variables including climate, ecology and geological variables were obtained for each provenance based on geographic coordinates from the Atlas of Living Australia [46]. Principal component analyses was performed on this data set to reduce dimensionality, grouping parameters by: climate (37 variables: including rainfall, evaporation, temperature, humidity, wind and irradiance), geology (11 variables: including nutrient availability, slope, soil depth, erosion, and weathering) and ecology (4 variables: NPP, NDVI, endemism and species richness), using the “prcomp” function in base R. This produced a set of 6 orthogonal “environmental” axes explaining up to 50% of the cumulative variance (S3 Table). Associations with environment were performed using provenance BLUEs for each trait. Collinearity was detected among traits at ambient CO2, therefore we also performed environmental associations with a set of uncorrelated phenotypic variables produced via a principal components analysis upon provenance trait BLUEs using the “prcomp” function in base R. This produced a set of 3 orthogonal “phenotypic” axes, with an eigen value > 1 explaining at least 50% of the cumulative variance (S4 Table). Associations between traits or PC variables and environmental point estimates were fitted in a linear model framework using the lm() function in base R. To account for potentially neutral demographic effects on phenotypic variation among provenances the analysis was performed with and without geographic coordinates (latitude and longitude) as an additional fixed term, with the caveat that this will only account for isolation by distance.

Responses to CO2.

Analyses of overall trait responsiveness to CO2 treatment was performed separately for test and control plants, implemented in a simple linear model framework using the lm() function in base R, where treatment (ambient or elevated) and cabinet were included as a fixed terms in the model. Significance of treatment effect was obtained for each trait using the anova() function and treatment least squares means were estimated from the model intercept with the R package lsmeans [47].

Provenance by CO2 interaction (G × E) was examined in test plants via cross treatment genetic correlation (gCor), where restricted maximum likelihood variance (REML) and correlations for random provenance effects across CO2 treatments were derived using ASReml-R Release 3, using the linear mixed model framework previously described. This was implemented by fitting random effects across CO2 treatments in an unstructured correlation matrix, assuming heterogeneous variance estimates, using the corgh() function. In this way the provenance level genetic correlation was calculated for the same trait in two different CO2 environments. All effects were assumed to have heterogeneous variances across treatments, and variance correlations were estimated both for provenance and for error terms. REML derived variances and correlations were constrained to fall within the theoretically possible range from -1 to +1. Non-parametric correlation of provenance least-square BLUEs between the ambient and elevated treatments were also assessed via a Spearman’s Rank Correlation test. In the elevated treatment provenance BLUEs were estimated in same way as described for the ambient treatment. The trait reaction norm, or Δtrait, was estimated as a measure of plasticity for each provenance from the ratio of elevated to ambient treatment provenance least-square BLUEs, where negative values indicate a decrease in trait estimates in the elevated treatment. To assess possible mechanistic drivers of variation in plasticity where G × E interaction was detected, provenance level associations between Δtrait and environmental point estimates were fitted in a linear model framework using the lm() function in base R, with and without geographic coordinates (latitude and longitude) as an additional fixed term to account for potentially neutral demographic effects.

Results

Photosynthetic variation at ambient CO2

At ambient CO2 significant provenance and subspecies variation was detected, pointing to genetic factors contributing to variation in some traits across the species range. Linear discriminant analyses (LDA) identified a weak cline based on a single significant discriminant function maximising variation in photosynthetic traits at the subspecies level (S2 Fig; Wilks test p = 0.009). The discriminant function coefficients (PC1 = 0.565, PC2 = -0.009 and PC3 = -0.132) identified PC1 as primarily contributing to variation among subspecies and implicated a set of traits loading to PC1 with a correlation of 0.5 or greater (S2 Table), including φ, J, TPU, LCP, Vcmax, Amax and Rdark. In the mixed model analysis a significant proportion of phenotypic variation was explained by subspecies for several photosynthetic traits, ranging from 44 to 65 percent, namely φ, J, LCP, Γ and Rdark (Table 2), most of which were previously implicated in the LDA. In general, aggregating variance estimates at the subspecies level improved estimation of genetic effects for photosynthetic traits, with significant variation detected at the provenance level for a single trait only, J, suggesting insufficient replication to adequately capture provenance level effects for most traits. In the case of φ, θ, Γ and Rdark a moderate proportion of trait variation (13 to 22%) was explained by provenance, however large standard errors relative to these estimates deemed them insignificant. In other cases variance at the provenance level was detected relative to the standard error, but variance explained was too small to be of practical significance. Variance of raw phenotypic values within provenances grouped by subspecies for all ten traits is additionally presented (Fig 2). Covariation of provenance means under ambient CO2 identified relationships between biochemical parameters, which reflect known mechanistic dependencies (S5 Table, S3 Fig). For example, variation in quantum yield (φ) was strongly positively correlated with the maximum rate of CO2 fixation (Amax), instantaneous CO2 assimilation (Anet), rate of electron transport (J) and maximum rate of carboxylation (Vcmax).

thumbnail
Fig 2. Box plots illustrate variation among provenances, grouped by subspecies, for each photosynthetic trait, presented as the mean, 1st and 3rd quartiles of the distribution and outliers within whiskers spanning 1.5 times the interquartile range (IQR).

Subspecies are ordered based on their approximate south to north latitudinal position.

https://doi.org/10.1371/journal.pone.0189635.g002

thumbnail
Table 2. Phenotypic variance, provenance and subspecies effects, for 10 photosynthetic traits under ambient CO2.

https://doi.org/10.1371/journal.pone.0189635.t002

Genetic variation in photosynthetic traits detected among provenances and subspecies under current CO2 conditions point to underlying genetic differences in photosynthesis that may have been driven by adaptation to environment, such as climate or geological factors, given red gum is a widespread and environmentally heterogeneous species. Local adaptation was subsequently invoked as one possible explanation for the observed phenotypic variation in several traits based on associations with environmental parameters (Table 3, Fig 3). The results suggest a relationship between environmental parameters loading to climPC2 (precipitation, temperature, seasonality, water stress index and wind; S3 Table) and traits loading to traitPC2 (φ, Amax, J and TPU; S4 Table), where increasing values of climPC2 correspond with higher winter rainfall, lower temperatures, and increasing values of traitPC2 correspond with increasing φ, J, Amax and TPU (Fig 2A). With the exception of Amax, an adaptive model for these traits is consistent with the genetic component implied from significant subspecies effects. Associations with climPC2 were also observed for individual traits loading to photoPC2 (Fig 2B, S4 Table). In addition ecological variables loading to ecolPC2 were associated with Amax, Vcmax and θ(Fig 2C and 2D). Here maximum assimilation rate was correlated with higher endemism and decreased NDVI, with the inverse relationship detected for curvature of the light-response curve. In several cases associations with climate and ecology factors were diminished after accounting for spatial factors, possibly indicating demography rather than adaptation as influencing the observed patterns, however local adaptation cannot be ruled out as a driver because environmental parameters loading to climPC1, climPC2 and ecolPC2 are significantly spatially autocorrelated (climPC1, R2 = 0.57, p < 0.001; climPC2, R2 = 0.87, p < 0.001; ecolPC2, R2 = 0.26, p < 0.001).

thumbnail
Fig 3. Association of provenance BLUEs (least square means) for selected traits and climate parameters.

For principal components, arrows against the y axis indicate the relative shift in environmental variables based on loadings with increasing values of the PC estimate. Likewise arrows against the vertical axis indicate relative shift in trait values based on loadings with decreasing values of the PC estimate.

https://doi.org/10.1371/journal.pone.0189635.g003

thumbnail
Table 3. Association of provenance level trait variation (BLUEs), including multivariate PC’s, with environmental parameters under ambient CO2.

https://doi.org/10.1371/journal.pone.0189635.t003

Photosynthetic responses to CO2

Overall response between CO2 treatments for each trait is presented in Table 4. Significant downregulation of net photosynthesis (Anet[400–400]) in test plants at elevated growth conditions relative to ambient conditions was detected, with no change observed in the corresponding controls measured at the same external [CO2] in the cuvette. Conversely, instantaneous enhancement in net photosynthesis (Anet[400–800]) was observed in both test and control plants at the second treatment point relative to the ambient treatment when measured at 800ppm [CO2] in the cuvette. A lack of change in Anet between treatments for control plants indicated that ontological effects between treatment points were not likely to confound interpretation of photosynthetic responses to CO2, in line with the expectation that age related shifts in photosynthesis traits of woody perennials are limited to major developmental transitions [48]. Although this comparison could not be made at the provenance level, because of insufficient replication of control plants within provenances, variation in response ratio (elevated Anet[400]/ambient Anet[400]) for individual plants indicated that overall response is not likely to mask genotypic variation in net photosynthesis response to elevated CO2. This was shown by way of a simple one sample t-test, which for control plants indicated the distribution of response ratio across samples was not significantly different to 1 (theoretical mean for a distribution based on plants with no CO2 response) at p < 0.05, whereas deviation from this limit among test plants was highly significant (p = 4.72e-15) (S4 Fig). Shifts in A-Ci and A-light curve derived photosynthetic traits were nominally treated as CO2 effects on the basis that these parameters will relate to changes in Anet. This is supported by downregulation of all biochemical traits in the elevated treatment relative to ambient, although this shift was not significant in the case of Γ (Table 4). Estimates of overall Anet taken from the A-Ci curve at 400ppm in both the ambient and elevated treatments also indicated a significant downturn in photosynthetic activity under CO2 enrichment (aCO2μ = 18.15, eCO2μ = 16.26, F = 8.34, p = 0.004).

thumbnail
Table 4. Trait response to CO2 across ambient (aCO2) and elevated (eCO2) treatments.

https://doi.org/10.1371/journal.pone.0189635.t004

Genotype by environment (G × E) interaction was suggested from intersection or scale change of provenance level reaction norms (Δ trait). Departure of cross treatment genetic correlation (gCor) from unity supported a G × E interaction in response to CO2 treatment for quantum yield (φ) where the correlation estimate departed from one within one standard error (Table 5). For all other traits the model converged but the estimate was at the boundary of the parameter space and standard errors could not be estimated. Gene by environment interaction was also suggested for a further five traits based on lack of significant spearman’s rank correlation for provenance BLUEs across treatments. Association of provenance level trait reaction norms and environment at provenance site of origin for traits where G × E was implicated suggest adaptive evolution (or adaptive plasticity) as a possible driver of variation in response across CO2 treatments for (Figs 4 and 5; Table 6). The strongest evidence for adaptive plasticity was detected based on environmental association with quantum yield (φ). In this case a downward shift in φunder elevated CO2 was detected in provenances originating from cooler climates, with lower summer rainfall and irradiance, relative to populations from northern latitudes (Figs 4B and 5). Response in light saturated net photosynthesis (Amax) was also negatively associated with ecolPC2, indicating provenances with higher primary productivity (based on higher NDVI) tended to respond more positively to CO2 increase relative to other sites (Fig 4A).

thumbnail
Fig 4.

Associations between photosynthetic responses, a) ΔAmax and b) Δφ, between CO2 regimes for test plants. The dashed lines at Δtrait = 1 is the expected response ratio if no change is observed between treatment.

https://doi.org/10.1371/journal.pone.0189635.g004

thumbnail
Fig 5.

Association between quantum yield (φ) and mean annual temperature at site of origin across for the a) ambient and b) elevated [CO2] treatments, and c) relationship between φresponse ratio (Δφ) and provenance mean annual temperature. Units for WorldClim temperature data are in oC*10.

https://doi.org/10.1371/journal.pone.0189635.g005

thumbnail
Table 6. Association of trait response, where G×E was supported or suggested, and multivariate environmental parameters at provenance site of origin.

https://doi.org/10.1371/journal.pone.0189635.t006

Discussion

Photosynthetic variation at ambient CO2

Adaptive clines along environmental gradients for growth and phenology traits are widely observed in trees [49,50,51]. Although genetic variation in photosynthetic traits has been reported in crops and undomesticated plants [52], natural variation among populations has less commonly been examined in trees, or indeed eucalypts [22,53], and consequently our understanding of the extent of local adaptation in these traits in widespread tree species is limited. Here we shed light on genetic factors contributing to variation in photosynthetic traits based on significant subspecies variation for several biochemical drivers of photosynthesis. In several cases associations with environment at site of origin suggest clines in trait variation could have resulted from local adaptation.

Associations with environment for quantum yield (φ) and other traits (Amax, TPU and J) suggest increased photosynthetic capacity under well-watered conditions in seedlings originating from cooler, temperate climates with decreased irradiance, possibly reflecting an adaptive cline (Fig 2A). Adaptive clines in some of these photosynthesis traits have been suggested in other organisms, for example increased drought tolerance has been affiliated with decreased φ in European beech (Aranda et al. 2014). Light saturated photosynthesis (Amax) has been shown to be highest in poplar and spruce originating from cooler habitats [54,55], while temperature sensitivity in trees and other photosynthetic organisms suggest φ can be tightly optimised to suit local conditions [56,57,58,59]. Quantifiable adaptation of phenotype to local environment could have implications for forests under forecasted climate redistributions. For example, persistence of locally adapted populations could be impacted if phenotypes linked with productivity, such as photosynthesis, are maladapted under future conditions [16,60]. It has therefore been recommended that patterns of phenotypic and genetic adaptation should be applied to improve prediction of forest responses to climate change [61]. While it is unclear whether our findings in seedlings would extrapolate to mature forests [48,62], or indeed whether the observed variation in photosynthetic traits would directly link to variation in productivity or fitness [63,64,65], it does suggest a basis for further consideration of photosynthetic traits when assessing adaptive potential.

CO2 response and adaptive plasticity

Overall response measured at 400ppm in both ambient and elevated treatments indicated down regulation of net photosynthesis (Anet) at elevated CO2, but not in control plants (Table 4). The same trend was detected when assessing overall Anet (at 400ppm) taken from the A-Ci curve. Down regulation of net photosynthesis also coincided with a decrease in biochemical processes of photosynthesis, pointing to a general down turn in photosynthetic activity under elevated CO2. This is consistent with accounts of acclimation of photosynthesis to growth under elevated CO2 in other plants [66,67,68,69,70], which has been suggested to result from a combination of nutrient and sink limitations [71].

Evidence for a G×CO2 interaction was detected for a single biochemical parameter of photosynthesis, quantum yield (φ), but was suggested for other photosynthetic traits. Previously, the effect of genotype on photosynthesis in response to CO2 was found to be limited in a less diverse sampling within the sub species E. c. camaldulensis [22], though genetic variation in plasticity of leaf physiological traits has been detected in other eucalypts [38,72,73,74]. Correlations detected between Δφ and climate factors loading to climPC2 indicate that adaptation to local environment could be a determinant of responsiveness of quantum yield under CO2 enrichment (Table 6). The cline in Δφ implied downregulation of photon conversion during photosynthesis under elevated CO2 in provenances originating from temperate (sub sp. camaldulensis) or arid zones (sub sp. arida and refulgens) which on average have cooler climates, with lower summer rainfall and irradiance, relative to provenances at more northern latitudes (sub sp. obtusa, simulata and refulgens) (Fig 4B). Component loadings identified mean annual temperature as the variable most strongly contributing to this cline (Table 5C, S3 Table). The cline in responsiveness along this temperature gradient was inversely correlated with provenance level trends in φ detected under ambient conditions (Figs 3 and 5A), suggesting that CO2 responsiveness of φ may be constrained by pre-existing environmentally prescribed genetic adaptation of this trait to temperature (and other factors) in situ. We note that shading or other factors related to growth rate are not implicated because φin the elevated treatment showed no correlation with final plant height (R2 = 0.004, p = 0.68). Genetic adaptation of plasticity (or adaptive plasticity) in quantum yield (φ) has been proposed in other plants, though this is the first report relating φ to variation in CO2 response [75,76]. The biological mechanism by which temperature optimisation of φ could impact this trait under higher CO2 levels is not resolved here, but warrants further investigation.

Our findings point to the potential for genetic variation among populations, possibly in response to environmental adaptation, to constrain responsiveness to CO2 enrichment in at least one photosynthetic trait in E. camaldulensis seedlings. This is a potentially significant finding as it suggests that photosynthetic efficiency of populations could respond differently to CO2 enrichment and in ways not predicted from generalised species responses. Although photosynthetic variation has been directly linked to fitness in some plants [77,78], the implications of adaptive plasticity in CO2 response for productivity of mature forests or plantations under prolonged CO2 enrichment remain unclear [63,64,65]. A more exhaustive assessment linking variation in photosynthesis response to CO2 with growth and fitness traits, including trees at later developmental stages and over longer exposure periods, is needed to better understand this. In addition, interactions between CO2 response and other climate variables were not assessed here but will need to be considered [79,80,81]. Future experiments addressing G×CO2 would benefit from increased replication of genotypes within provenances to improve power to detect genetic effects for highly variable instantaneous, and to a lesser extent integrated, estimates of photosynthesis.

Supporting information

S1 Fig. Subspecies biplot of the first two discriminant functions determined PCA of individual trait values under ambient [CO2].

Discriminant function coefficients are plotted for each PC, scaled to the discriminant function axes, indicating their relative importance in defining subspecies groups.

https://doi.org/10.1371/journal.pone.0189635.s001

(TIF)

S2 Fig.

Representative A-Ci (a-b) and light (c-d) curves for a subset of eight genotypes spanning the range of the φ parameter estimate in the ambient and elevated CO2 treatments indicate the quality of data from which biochemical parameters of photosynthesis were estimated. φ estimates for individual trees are as follows: Balranald—tree 1 (0.033), Palmer River—tree 7 (0.044), Nyngan—tree 7 (0.047), Arrowsmith Lake—tree 1 (0.050); Minderoo–tree 2 (0.053), Station Creek–tree 1 (0.055), Nullagine Creek–tree 2 (0.058), Station Creek–tree 3 (0.067). Inset, initial slope between 0 and 500 ppm [CO2] for the A-Ci curve, and 0 and 500 photon flux for the light curve.

https://doi.org/10.1371/journal.pone.0189635.s002

(TIF)

S3 Fig. Provenance level trait-trait correlation plots for photosynthetic traits, with the following naming convention changes: f = quantum yield (φ), q = curvature of the light-response curve (θ) and G = compensation point (Γ).

https://doi.org/10.1371/journal.pone.0189635.s003

(TIFF)

S4 Fig. Box plots illustrating the distribution of individual plant response ratios for Anet (elevated Anet[400]/ambient Anet[400]), for all control and test plants in this study.

The distribution of response ratio across samples was not significantly different to 1 (theoretical mean for a distribution based on plants with no CO2 response, dashed line), whereas mean response ratio for test plants was significantly greater than 1. Plots present the mean, 1st and 3rd quartiles of the distribution and outliers within whiskers spanning 1.5 times the interquartile range (IQR).

https://doi.org/10.1371/journal.pone.0189635.s004

(TIFF)

S1 File. Raw trait data for individual genotypes collected across two CO2 treatments.

https://doi.org/10.1371/journal.pone.0189635.s005

(CSV)

S1 Table. Photosynthetic parameters measured.

https://doi.org/10.1371/journal.pone.0189635.s006

(DOCX)

S2 Table. Principal component loadings for the first 3 principal components derived from individual plant measurements measured at ambient CO2 (400ppm).

https://doi.org/10.1371/journal.pone.0189635.s007

(DOCX)

S3 Table. Principal component loadings for environmental parameters.

https://doi.org/10.1371/journal.pone.0189635.s008

(DOCX)

S4 Table. Principal component loadings for the first 3 principal components derived from trait provenance BLUEs estimated at ambient CO2 (400ppm).

https://doi.org/10.1371/journal.pone.0189635.s009

(DOCX)

S5 Table. Provenance level correlations between photosynthetic traits (BLUEs) at ambient CO2.

https://doi.org/10.1371/journal.pone.0189635.s010

(DOCX)

Acknowledgments

This research was supported by funding from the CSIRO Transformational Biology Catalytic Platform. Experiments utilised the infrastructure of the Australian Plant Phenomics Facility, Canberra Australia. Thanks go to the staff at the APPF Canberra for their assistance in maintaining optimal conditions during the cabinet experiment, and to Alexie Papanicolaou, Benjamin Ferre, Bala Thumma who assisted with plant measurements.

References

  1. 1. Alberto FJ, Aitken SN, Alia R, Gonzalez-Martinez SC, Hanninen H, et al. (2013) Potential for evolutionary responses to climate change evidence from tree populations. Global Change Biology 19: 1645–1661. pmid:23505261
  2. 2. Savolainen O, Pyhajarvi T, Knurr T (2007) Gene flow and local adaptation in trees. Annual Review of Ecology Evolution and Systematics 38: 595–619.
  3. 3. Neale DB, Kremer A (2011) Forest tree genomics: growing resources and applications. Nature Reviews Genetics 12: 111–122. pmid:21245829
  4. 4. Aitken SN, Yeaman S, Holliday JA, Wang TL, Curtis-McLane S (2008) Adaptation, migration or extirpation: climate change outcomes for tree populations. Evolutionary Applications 1: 95–111. pmid:25567494
  5. 5. Miller AD, Dietze MC, DeLucia EH, Anderson-Teixeira KJ (2016) Alteration of forest succession and carbon cycling under elevated CO2. Global Change Biology 22: 351–363. pmid:26316364
  6. 6. Aspinwall MJ, Loik ME, Resco De Dios V, Tjoelker MG, Payton PR, et al. (2015) Utilizing intraspecific variation in phenotypic plasticity to bolster agricultural and forest productivity under climate change. Plant, Cell & Environment 38: 1752–1764.
  7. 7. Ward JK, Strain BR (1997) Effects of low and elevated CO2 partial pressure on growth and reproduction of Arabidopsis thaliana from different elevations. Plant, Cell & Environment 20: 254–260.
  8. 8. Hansen MM, Olivieri I, Waller DM, Nielsen EE, Ge MWG (2012) Monitoring adaptive genetic responses to environmental change. Molecular Ecology 21: 1311–1329. pmid:22269082
  9. 9. Sgro CM, Lowe AJ, Hoffmann AA (2011) Building evolutionary resilience for conserving biodiversity under climate change. Evolutionary Applications 4: 326–337. pmid:25567976
  10. 10. Hoffmann A, Griffin P, Dillon S, Catullo R, Rane R, et al. (2015) A framework for incorporating evolutionary genomics into biodiversity conservation and management. Climate Change Responses 2: 1–24.
  11. 11. Anderson JT, Panetta AM, Mitchell-Olds T (2012) Evolutionary and Ecological Responses to Anthropogenic Climate Change: Update on Anthropogenic Climate Change. Plant Physiology 160: 1728–1740. pmid:23043078
  12. 12. Forsman A (2015) Rethinking phenotypic plasticity and its consequences for individuals, populations and species. Heredity 115: 276–284. pmid:25293873
  13. 13. Anderson JT, Gezon ZJ (2015) Plasticity in functional traits in the context of climate change: a case study of the subalpine forb Boechera stricta (Brassicaceae). Global Change Biology 21: 1689–1703. pmid:25470363
  14. 14. Van Kleunen M, Fischer M (2005) Constraints on the evolution of adaptive phenotypic plasticity in plants. New Phytologist 166: 49–60. pmid:15760350
  15. 15. Waples RS (2016) How Plasticity and Evolution Work in the Real World. Journal of Heredity 107: 1–2. pmid:26671767
  16. 16. Aitken SN, Whitlock MC (2013) Assisted Gene Flow to Facilitate Local Adaptation to Climate Change. Annual Review of Ecology, Evolution, and Systematics, Vol 44 44: 367.
  17. 17. IPCC (2014) Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. 151 p.
  18. 18. Atwell BJ, Henery ML, Ball MC (2009) Does soil nitrogen influence growth, water transport and survival of snow gum (Eucalyptus pauciflora Sieber ex Sprengel.) under CO2 enrichment? Plant Cell and Environment 32: 553–566.
  19. 19. Atwell BJ, Henery ML, Rogers GS, Seneweera SP, Treadwell M, et al. (2007) Canopy development and hydraulic function in Eucalyptus tereticornis grown in drought in CO2-enriched atmospheres. Functional Plant Biology 34: 1137–1149.
  20. 20. Ayub G, Smith RA, Tissue DT, Atkin OK (2011) Impacts of drought on leaf respiration in darkness and light in Eucalyptus saligna exposed to industrial-age atmospheric CO2 and growth temperature. New Phytologist 190: 1003–1018. pmid:21434926
  21. 21. Barton CVM, Duursma RA, Medlyn BE, Ellsworth DS, Eamus D, et al. (2012) Effects of elevated atmospheric CO2 on instantaneous transpiration efficiency at leaf and canopy scales in Eucalyptus saligna. Global Change Biology 18: 585–595.
  22. 22. Blackman CJ, Aspinwall MJ, Resco de Dios V, Smith RA, Tissue DT (2016) Leaf photosynthetic, economics and hydraulic traits are decoupled among genotypes of a widespread species of eucalypt grown under ambient and elevated CO2. Functional Ecology: n/a-n/a.
  23. 23. Hirano A, Hongo I, Koike T (2012) Morphological and physiological responses of Siebold's beech (Fagus crenata) seedlings grown under CO2 concentrations ranging from pre-industrial to expected future levels. Landscape and Ecological Engineering 8: 59–67.
  24. 24. Smith RA, Lewis JD, Ghannoum O, Tissue DT (2012) Leaf structural responses to pre-industrial, current and elevated atmospheric CO2 and temperature affect leaf function in Eucalyptus sideroxylon. Functional Plant Biology 39: 285–296.
  25. 25. Xu C-Y, Salih A, Ghannoum O, Tissue DT (2012) Leaf structural characteristics are less important than leaf chemical properties in determining the response of leaf mass per area and photosynthesis of Eucalyptus saligna to industrial-age changes in CO2 and temperature. Journal of Experimental Botany 63: 5829–5841. pmid:22915750
  26. 26. DeLucia EH, Hamilton JG, Naidu SL, Thomas RB, Andrews JA, et al. (1999) Net Primary Production of a Forest Ecosystem with Experimental CO2 Enrichment. Science 284: 1177–1179. pmid:10325230
  27. 27. Keenan TF, Hollinger DY, Bohrer G, Dragoni D, Munger JW, et al. (2013) Increase in forest water-use efficiency as atmospheric carbon dioxide concentrations rise. Nature 499: 324–327. pmid:23842499
  28. 28. Norby RJ, De Kauwe MG, Domingues TF, Duursma RA, Ellsworth DS, et al. (2016) Model–data synthesis for the next generation of forest free-air CO2 enrichment (FACE) experiments. New Phytologist 209: 17–28. pmid:26249015
  29. 29. Anderson PD, Houpis JLJ, Anschel DJ, Pushnik JC (2001) Among- and within-provenance variability of Pinus ponderosa (Pinaceae) seedling response to long-term elevated CO2 exposure. Madrono 48: 51–61.
  30. 30. Franco AC, Rossatto DR, de Carvalho Ramos Silva L, da Silva Ferreira C (2014) Cerrado vegetation and global change: the role of functional types, resource availability and disturbance in regulating plant community responses to rising CO2 levels and climate warming. Theoretical and Experimental Plant Physiology 26: 19–38.
  31. 31. Leverenz JW, Bruhn D, Saxe H (2000) Responses of two provenances of Fagus sylvatica seedlings to a combination of four temperature and two CO2 treatments during their first growing season: Gas exchange of leaves and roots. New Phytologist 144: 437–454.
  32. 32. McKiernan AB, O'Reilly-Wapstra JM, Price C, Davies NW, Potts BM, et al. (2012) Stability of Plant Defensive Traits Among Populations in Two Eucalyptus Species Under Elevated Carbon Dioxide. Journal of Chemical Ecology 38: 204–212. pmid:22318433
  33. 33. Spinnler D, Egli P, Korner C (2003) Provenance effects and allometry in beech and spruce under elevated CO2 and nitrogen on two different forest soils. Basic and Applied Ecology 4: 467–478.
  34. 34. Nicotra AB, Atkin OK, Bonser SP, Davidson AM, Finnegan EJ, et al. (2010) Plant phenotypic plasticity in a changing climate. Trends in Plant Science 15: 684–692. pmid:20970368
  35. 35. Nicotra AB, Segal DL, Hoyle GL, Schrey AW, Verhoeven KJF, et al. (2015) Adaptive plasticity and epigenetic variation in response to warming in an Alpine plant. Ecology and Evolution 5: 634–647. pmid:25691987
  36. 36. Pigliucci M (2005) Evolution of phenotypic plasticity: where are we going now? Trends in Ecology & Evolution 20: 481–486.
  37. 37. Benomar L, Lamhamedi MS, Rainville A, Beaulieu J, Bousquet J, et al. (2016) Genetic Adaptation vs. Ecophysiological Plasticity of Photosynthetic-Related Traits in Young Picea glauca Trees along a Regional Climatic Gradient. Frontiers in Plant Science 7: 48. pmid:26870067
  38. 38. McLean EH, Prober SM, Stock WD, Steane DA, Potts BM, et al. (2014) Plasticity of functional traits varies clinally along a rainfall gradient in Eucalyptus tricarpa. Plant, Cell & Environment 37.
  39. 39. Mimura M, Aitken SN (2010) Local adaptation at the range peripheries of Sitka spruce. Journal of Evolutionary Biology 23: 249–258. pmid:20021549
  40. 40. Leakey ADB, Lau JA (2012) Evolutionary context for understanding and manipulating plant responses to past, present and future atmospheric [CO2]. Philosophical Transactions of the Royal Society of London B: Biological Sciences 367: 613–629. pmid:22232771
  41. 41. Close DC, Beadle CL (2003) The ecophysiology of foliar anthocyanin. The Botanical Review 69: 149–161.
  42. 42. Sharkey TD, Bernacchi CJ, Farquhar GD, Singsaas EL (2007) Fitting photosynthetic carbon dioxide response curves for C3 leaves. Plant, Cell & Environment 30: 1035–1040.
  43. 43. Ehleringer J, Björkman O (1977) Quantum Yields for CO2 Uptake in C3 and C4 Plants: Dependence on Temperature, CO2, and O2 Concentration. Plant Physiology 59: 86–90. pmid:16659794
  44. 44. Venables WN, Ripley BD (2002) Modern Applied Statistics with S. Fourth Edition. New York: Springer.
  45. 45. Beck WM (2016) ggord: Ordination Plots with ggplot2. In: 0.11.9000 Rpv, editor.
  46. 46. ALA (2015) Atlas of Living Australia occurrence download at http://www.ala.org.au. Accessed 24 Janurary 2015.
  47. 47. Lenth RV (2016) Least-Squares Means: The R Package lsmeans. 2016 69: 33.
  48. 48. Bond BJ (2000) Age-related changes in photosynthesis of woody plants. Trends in Plant Science 5: 349–353. pmid:10908880
  49. 49. Vitasse Y, Delzon S, Bresson CC, Michalet R, Kremer A (2009) Altitudinal differentiation in growth and phenology among populations of temperate-zone tree species growing in a common garden. Canadian Journal of Forest Research 39: 1259–1269.
  50. 50. Weber JC, Larwanou M, Abasse TA, Kalinganire A (2008) Growth and survival of Prosopis africana provenances tested in Niger and related to rainfall gradients in the West African Sahel. Forest Ecology and Management 256: 585–592.
  51. 51. Woods EC, Hastings AP, Turley NE, Heard SB, Agrawal AA (2012) Adaptive geographical clines in the growth and defense of a native plant. Ecological Monographs 82: 149–168.
  52. 52. Flood PJ, Harbinson J, Aarts MGM (2011) Natural genetic variation in plant photosynthesis. Trends in Plant Science 16: 327–335. pmid:21435936
  53. 53. Anderson J, Williams J, Kriedemann P, Austin M, Farquhar G (1996) Correlations Between Carbon Isotope Discrimination and Climate of Native Habitats for Diverse Eucalypt Taxa Growing in a Common Garden. Functional Plant Biology 23: 311–320.
  54. 54. Oleksyn J, Modrzýnski J, Tjoelker MG, Zytkowiak R, Reich PB, et al. (1998) Growth and physiology of Picea abies populations from elevational transects: common garden evidence for altitudinal ecotypes and cold adaptation. Functional Ecology 12: 573–590.
  55. 55. Silim SN, Ryan N, Kubien DS (2010) Temperature responses of photosynthesis and respiration in Populus balsamifera L.: acclimation versus adaptation. Photosynthesis Research 104: 19–30. pmid:20112068
  56. 56. Ball MC, Hodges VS, Laughlin GP (1991) Cold-Induced Photoinhibition Limits Regeneration of Snow Gum at Tree-Line. Functional Ecology 5: 663–668.
  57. 57. Braun V, Neuner G (2004) Response of effective quantum yield of photosystem 2 to in situ temperature in three alpine plants. Photosynthetica 42: 607–613.
  58. 58. Dongsansuk A, Lütz C, Neuner G (2013) Effects of temperature and irradiance on quantum yield of PSII photochemistry and xanthophyll cycle in a tropical and a temperate species. Photosynthetica 51: 13–21.
  59. 59. Howells EJ, Beltran VH, Larsen NW, Bay LK, Willis BL, et al. (2012) Coral thermal tolerance shaped by local adaptation of photosymbionts. Nature Clim Change 2: 116–120.
  60. 60. Bradley St Clair J, Howe GT (2007) Genetic maladaptation of coastal Douglas-fir seedlings to future climates. Global Change Biology 13: 1441–1454.
  61. 61. Booth TH, Broadhurst LM, Pinkard E, Prober SM, Dillon SK, et al. (2015) Native forests and climate change: Lessons from eucalypts. Forest Ecology and Management 347: 18–29.
  62. 62. Grulke NE, Retzlaff WA (2001) Changes in physiological attributes of ponderosa pine from seedling to mature tree. Tree Physiology 21: 275–286. pmid:11262919
  63. 63. Gunderson CA, Wullschleger SD (1994) Photosynthetic acclimation in trees to rising atmospheric CO2: A broader perspective. Photosynthesis Research 39: 369–388. pmid:24311130
  64. 64. Lawlor DW, Mitchell RAC (1991) The effects of increasing CO2 on crop photosynthesis and productivity: a review of field studies. Plant, Cell & Environment 14: 807–818.
  65. 65. Norby RJ, Gunderson CA, Wullschleger SD, O'Neill EG, McCracken MK (1992) Productivity and compensatory responses of yellow-poplar trees in elevated C02. Nature 357: 322–324.
  66. 66. Rey A, Jarvis PG (1998) Long-term photosynthetic acclimation to increased atmospheric CO2 concentration in young birch (Betula pendula) trees. Tree Physiology 18: 441–450. pmid:12651355
  67. 67. Ainsworth EA, Long SP (2005) What have we learned from 15 years of free-air CO2 enrichment (FACE)? A meta-analytic review of the responses of photosynthesis, canopy properties and plant production to rising CO2. New Phytologist 165: 351–372. pmid:15720649
  68. 68. Bigras FJ, Bertrand A (2006) Responses of Picea mariana to elevated CO2 concentration during growth, cold hardening and dehardening: phenology, cold tolerance, photosynthesis and growth. Tree Physiology 26: 875–888. pmid:16585033
  69. 69. Faria T, Wilkins D, Besford RT, Vaz M, Pereira JS, et al. (1996) Growth at elevated CO2 leads to down-regulation of photosynthesis and altered response to high temperature in Quercus suber L. seedlings. Journal of Experimental Botany 47: 1755–1761.
  70. 70. Sanz-Sáez Á, Erice G, Aranjuelo I, Nogués S, Irigoyen JJ, et al. (2010) Photosynthetic down-regulation under elevated CO2 exposure can be prevented by nitrogen supply in nodulated alfalfa. Journal of Plant Physiology 167: 1558–1565. pmid:20708820
  71. 71. Ainsworth EA, Rogers A, Nelson R, Long SP (2004) Testing the “source–sink” hypothesis of down-regulation of photosynthesis in elevated [CO2] in the field with single gene substitutions in Glycine max. Agricultural and Forest Meteorology 122: 85–94.
  72. 72. Costa e Silva J, Potts BM, Dutkowski GW (2006) Genotype by environment interaction for growth of Eucalyptus globulus in Australia. Tree Genetics & Genomes 2: 61–75.
  73. 73. Morshet S (1981) Physiological Activity, in a Semiarid Environment, of Eucalyptus camaldulensis Dehn. from Two Provenances. Australian Journal of Botany 29: 97–110.
  74. 74. Pinyopusarerk K, Doran JC, Williams ER, Wasuwanich P (1996) Variation in growth of Eucalyptus camaldulensis provenances in Thailand. Forest Ecology and Management 87: 63–73.
  75. 75. Molina-Montenegro MA, Salgado-Luarte C, Oses R, Torres-Díaz C (2013) Is Physiological Performance a Good Predictor for Fitness? Insights from an Invasive Plant Species. PLoS ONE 8: e76432. pmid:24204626
  76. 76. van Rooijen R, Aarts MGM, Harbinson J (2015) Natural Genetic Variation for Acclimation of Photosynthetic Light Use Efficiency to Growth Irradiance in Arabidopsis. Plant Physiology 167: 1412–1429. pmid:25670817
  77. 77. Arntz AM, DeLucia EH, Jordan N (1998) Contribution of photosynthetic rate to growth and reproduction in Amaranthus hybridus. Oecologia 117: 323–330. pmid:28307911
  78. 78. McAllister CA, Knapp AK, Maragni LA (1998) Is leaf-level photosynthesis related to plant success in a highly productive grassland? Oecologia 117: 40–46. pmid:28308504
  79. 79. Ainsworth EA, Rogers A (2007) The response of photosynthesis and stomatal conductance to rising [CO2]: mechanisms and environmental interactions. Plant, Cell & Environment 30: 258–270.
  80. 80. Idso KE, Idso SB (1994) Plant responses to atmospheric CO2 enrichment in the face of environmental constraints: a review of the past 10 years' research. Agricultural and Forest Meteorology 69: 153–203.
  81. 81. Wullschleger SD, Tschaplinski TJ, Norby RJ (2002) Plant water relations at elevated CO2– implications for water-limited environments. Plant, Cell & Environment 25: 319–331.
  82. 82. Crossman S, Li O (2004) Surface Hydrology Polygons (National). In: Department of Industry TaR, editor: Geoscience Australia.