Global Change Biology (2015) 21, 3489–3498, doi: 10.1111/gcb.12934

Leaf-trait plasticity and species vulnerability to climate change in a Mongolian steppe PIERRE LIANCOURT1,2, BAZARTSEREN BOLDGIV3,4, DANIEL S. SONG1, LAURA A. S P E N C E 1 , 5 , B R E N T R . H E L L I K E R 1 , P E T E R S . P E T R A I T I S 1 and B R E N D A B . C A S P E R 1 1 Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA, 2Institute of Botany, Academy of Sciences of
, Czech Republic, 3Department of Biology, National University of Mongolia, Ulaanbaatar 14201, the Czech Republic, T
rebon 4 Mongolia, Academy of Natural Sciences of Drexel University, Philadelphia, PA 19103, USA, 5Sterling College, Craftsbury Common, VT, USA

Abstract Climate change is expected to modify plant assemblages in ways that will have major consequences for ecosystem functions. How climate change will affect community composition will depend on how individual species respond, which is likely related to interspecific differences in functional traits. The extraordinary plasticity of some plant traits is typically neglected in assessing how climate change will affect different species. In the Mongolian steppe, we examined whether leaf functional traits under ambient conditions and whether plasticity in these traits under altered climate could explain climate-induced biomass responses in 12 co-occurring plant species. We experimentally created three probable climate change scenarios and used a model selection procedure to determine the set of baseline traits or plasticity values that best explained biomass response. Under all climate change scenarios, plasticity for at least one leaf trait correlated with change in species performance, while functional leaf-trait values in ambient conditions did not. We demonstrate that trait plasticity could play a critical role in vulnerability of species to a rapidly changing environment. Plasticity should be considered when examining how climate change will affect plant performance, species’ niche spaces, and ecological processes that depend on plant community composition. Keywords: climate change, drought, functional traits, increased precipitation, Mongolian steppe, open-top chambers, plasticity, warming Received 28 October 2014; revised version received 23 February 2015 and accepted 25 February 2015

Introduction Global climate change is already affecting vegetation worldwide by causing shifts in the ranges (Parmesan & Yohe, 2003; Kelly & Goulden, 2008; Chen et al., 2011), phenologies (Parmesan & Yohe, 2003; Menzel et al., 2006), and abundances of species (Sturm et al., 2001). Yet predicting how plant communities will continue to change is a major challenge because responses are likely to vary widely among taxa (Grime, 1998; Lavorel & Garnier, 2002) and because taxa differ in their ecological roles and contributions to ecosystem function. Therefore, understanding whether particular plant characteristics predict species’ performance under climate change is a priority (Williams et al., 2008) and critical to our understanding of the consequences for the structure of ecosystems and the services they provide (Chapin et al., 2000). Current thinking suggests that functional traits, which vary among species, should help predict how individual species will respond to climate change (Chapin et al., Correspondence: Pierre Liancourt, tel. +420 271 015 233, fax +420 271 015 105, e-mail: [email protected]

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2000; Soudzilovskaia et al., 2013) and add predictive power to modeling future changes in vegetation composition (Pollock et al., 2012; Dubuis et al., 2013; FrenetteDussault et al., 2013). This is because sets of functional traits are thought to represent a species’ ecological niche space (McGill et al., 2006); for example, leaf traits are indicative of growth strategy and carbon economy, resource exploitation, and conservation (Reich et al., 1997; Wright et al., 2004; Pierce et al., 2013). In addition, functional trait compositions of plant communities vary systematically over climate and soil resource gradients (Reich et al., 1997; Choler, 2005; Wright et al., 2005; Ordo~ nez et al., 2009; Hudson et al., 2011). Therefore, community-level differences in functional leaf traits across spatial environmental gradients can successfully link variation in ecosystem function to variation in vegetation composition (Lavorel et al., 2006; Pakeman, 2011). Whether differences in sets of functional traits among species are predictive of differences in their performance responses to temporal climatic changes, however, has rarely been experimentally tested in the field (Soudzilovskaia et al., 2013). Intraspecific variation in traits must also be considered when evaluating the importance of functional

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3490 P . L I A N C O U R T et al. traits as a predictor of how a species will respond to climate change (Jung et al., 2014). While it has been demonstrated that ecotypic differences among populations affect the response to climate change (e.g., Liancourt et al., 2013), the role of phenotypic plasticity, which could be crucial for buffering the detrimental effects of climate change (Frei et al., 2014; see Franks et al., 2014 for recent review), remains poorly explored. Phenotypic plasticity need not be adaptive. It can also be neutral in its consequences for performance or indicative of environmentally induced damage (Bradshaw, 1965; Alpert & Simms, 2002; Grime & Mackey, 2002; Richards et al., 2006). Therefore, the importance of trait plasticity will depend on its links to responses in plant performance under climate change (Nicotra et al., 2010). Any investigation linking functional traits to performance must realistically examine species within the context of their natural community because a changing climate will impact their performance directly or indirectly by altering the abiotic environment or biotic interactions (Pearson & Dawson, 2003; Levine et al., 2010; Liancourt et al., 2013). Therefore, to set the problem in a natural context, we conducted a field experiment within the Mongolian steppe, an understudied region of the Eurasian grassland, which is the largest grassland in the world. Mongolia is predicted to experience above-average rates of temperature increase, but, while precipitation is predicted to change over the coming century, there is no consensus on how (IPCC, 2013; but see Sato et al., 2007). Following the climate predictions for the region, we created three probable scenarios of climate change in the field to test how functional leaf traits and their plasticity could affect the responses in plant performance to climate change. The three scenarios were as follows: (i) an increase in summer temperature coupled with a reduction of summer soil moisture, (ii) an increase in summer precipitation with no change in temperature, and (iii) increases in both summer precipitation and summer temperature. We measured six commonly used functional leaf traits in 12 common and taxonomically diverse co-occurring species, both in ambient conditions (control) and under the three climate scenarios. The functional traits were as follows: leaf area per leaf (LA), specific leaf area (SLA), leaf length (LL), leaf dry matter content (LDMC), leaf nitrogen content per unit leaf mass (LNC), and leaf carbon content per unit leaf mass (LCC). Trait plasticity was defined as the change from the trait values under ambient conditions to the values under the climate scenarios. Finally, we used change in biomass from ambient conditions to climate scenarios as a measure of how a particular scenario affected plant performance.

Functional leaf traits should be good predictors of a species’ vulnerability (biomass response) to different scenarios of climate change as they have well-documented physiological foundations (Perez-Harguindeguy et al., 2013 for review). Among the traits we measured, two somewhat independent sets are recognized. The first set, SLA, LDMC, LNC, and LCC, reflects the resource exploitation/conservation tradeoff (Perez-Harguindeguy et al., 2013), whereas the second set, LL and LA, for herbaceous species, relates to plant size. Together, these leaf traits also have been suggested to approximate the stress tolerance and competitive ability of species (see Pierce et al., 2013). Foundational to our study, interspecific variation in these leaf traits has been documented along climatic gradients and across biomes with, for example, SLA increasing with precipitation or SLA and LNC decreasing with temperature (e.g., Reich et al., 1997; Wright et al., 2005; Ordo~ nez et al., 2009). For each climate change scenario in our study, we predicted which of the six measured traits would be involved in biomass response based on patterns found across existing climatic gradients. How plasticity in leaf functional traits relates to biomass response under different climate scenarios is more difficult to predict because there is a paucity of information about the relative advantages of leaf-trait plasticity with environmental change in the field (Nicotra et al., 2010). Overall, we expected that biomass response would best relate to a combination of baseline values in some traits and plasticity in others. Specifically, we predicted that: 1. Under the scenario of increased summer temperature and drought, species with conservative syndromes (low SLA, low LNC, high LDMC) and small leaves in ambient conditions would be less vulnerable (e.g., Reich et al., 1997; Wright et al., 2005; Ordo~ nez et al., 2009; Pierce et al., 2013). Reducing leaf area in order to reduce water loss and adopting more conservative leaf-trait syndromes (decreased SLA or increased LDMC) are expected to be advantageous under a drier and warmer condition. Also in response to a drier and warmer climate, trait constancy (i.e., the absence of plasticity) could be favored, as stress-tolerant species are thought to be less plastic (Grime, 1979; Chapin, 1980). 2. Under the scenario of supplemental summer precipitation, exploitative species (high SLA, high LNC, low LDMC) with large leaves should benefit the most from decreasing abiotic stress and be the least affected by the subsequent increase in competition (see Liancourt et al., 2013; Pierce et al., 2013). Given that plant competition in the Mongolian steppe increases with supplemental precipitation (Liancourt et al., 2013), increasing values of leaf traits related to © 2015 John Wiley & Sons Ltd, Global Change Biology, 21, 3489–3498

L E A F - T R A I T P L A S T I C I T Y A N D C L I M A T E C H A N G E 3491 light capture (increased LA, increased SLA) could be advantageous (see Evans & Poorter, 2001). 3. Under the scenario combining increased summer temperature and precipitation, the relationship between exploitative traits and biomass response should be stronger than when supplemental precipitation is not coupled with increased temperature. This prediction assumes that exploitative species could be colimited by both temperature and water availability in our system.

Materials and methods

Experiment The experiment was set up in 2009 and ran for 4 years on the upper part of a steep south-facing slope in the Dalbay river valley (51° 01.4050 N 100° 45.6000 E; elevation 1800 m. a.s.l; incline ~20˚, referred to previously as the ‘upper slope’ in Liancourt et al., 2013). Regionally, the average annual air temperature is 4.5 °C, with average monthly temperatures from 21 °C (January) to 12 °C (July) (Nandintsetseg et al., 2007). Average annual precipitation over the last 40 years was 265 mm (Namkhaijanstan, 2006). The growing season is roughly mid-June to mid-August. We used hexagonal open-top chambers (OTCs) to produce the warming treatment (see Marion et al., 1997). OTCs were 1.0 m wide at the top and 1.5 m at the bottom, 40 cm tall, and made of Sun-Liteâ HP fiberglass glazing mounted on a clear Lexan frame. They elevated air temperature by an average of 1.5 °C in the day and depressed it by 0.2 °C at night (Liancourt et al., 2013). They approximately reduce the volumetric soil moisture by 30% in our system (see Liancourt et al., 2012a for an in-depth description). OTCs were set up in early June each year, after the last snow but before most species showed new growth and taken down in mid-August, until the experiment was terminated in late July 2012. Plots without OTCs had the same dimensional footprint. Supplemental precipitation alone (+Water) or in combination with an OTC (+OTC+Water) was achieved by a simulated 4.5-mm weekly rainfall event. Once a week, river water was delivered with a watering can in the evening. Watering extended for 7 weeks total in 2009, 10 weeks in 2010, nine in 2011, and seven in 2012, representing an increase above seasonal observed ambient precipitation of 15.7% (2009), 25.3% (2010), 29.6% (2011), and 30.2% (2012). Averaged over the growing season, volumetric soil moisture, measured by a portable probe (WET-2 sensor, Delta-T Devices Ltd, Cambridge, UK) at a depth of ~6 cm, was on average 23% greater in watered plots than in the unwatered plots in 2009 and 44% greater in 2010 (12.2%v vs. 9.9%v, respectively, in 2009 and 9.1%v vs. 6.3%v, respectively, in 2010, Liancourt et al., 2012a). Because the OTC increased temperatures and intercepted some rain, volumetric soil moisture was greater in the +Water plots than in the +OTC+Water plots (+Water plots: 33% greater than the controls; +OTC+Water: 23% greater than the control plots). Altogether, our experimental setup consisted in one plot each of +OTC, +Water, and +OTC+Water treatment

© 2015 John Wiley & Sons Ltd, Global Change Biology, 21, 3489–3498

and an unaltered control plot (referred to as ‘ambient conditions’), organized in a single fenced 9 9 9 m block; there were seven replicate blocks each separated by at least 30 m. All plots and treatments were in the same locations all 4 years. The 12 target species are all long-lived perennials common to the mountain steppe. Four of them are grasses (Agropyron cristatum, Festuca lenensis, Koeleria macrantha, & Stipa krylovii). The others are a legume (Oxytropis strobilacea), five nonleguminous forbs (Artemisia commutata, Aster alpinus, Potentilla acaulis, Potentilla sericea, & Pulsatilla turczaninovii), and two sub-shrubs (Artemisia frigida & Thymus gobicus) (see Table S1 for the proportion of the total biomass per plot accounted for by the target species, and Table S2 for the average species richness in the experimental plots). Sedges (Carex spp.), although the most abundant by percent cover (Liancourt et al., 2012b), were not included, as the three species are difficult to distinguish morphologically. The functional leaf-trait measurements were performed at the peak of the vegetation season, in the second year of the experiment (2010). A total of three-to-ten fully developed leaves (including the petioles for eudicots) were collected from one to three adult individuals of each species in each plot, depending on species abundance and leaf size, and combined for trait measurements. Thus, for each target species present in a replicate plot, a single value was obtained for each trait (see Table S3 for the number of plots per treatment where the species were sampled for trait measurements). For all traits but LDMC, the measurements followed standard protocols (see Perez-Harguindeguy et al., 2013 for review). For LDMC, fresh mass was measured using a partial rehydration method in the field (Vaieretti et al., 2007), and dry mass was determined from oven-dried leaves (48 h, 80 °C) in the laboratory. LNC and LCC were measured at the Department of Earth and Environmental Sciences, University of Pennsylvania, with an elemental analyzer (Costech Analytical Technologies, CA, USA). Trait values measured in each experimental condition for the 12 species are given in Tables S4–S7. All live aboveground biomass was harvested at the end of the experiment, in late July 2012, from a 50 9 100 cm area in each plot. The short sides of this sampling area were parallel to opposite (parallel) sides of the hexagonal plot, and the area was centered side-to-side within the plot in both directions. Plants were cut at ground level, sorted by species, air-dried in the field, and oven-dried in the laboratory (36 h, 80 °C) before weighing. The biomass of a target species in a plot was obtained by adding the aboveground biomass of all individuals of that species.

Response variables Baseline functional trait values were those measured in control plots (ambient conditions). Plasticity under a particular scenario was calculated for a given trait for a given species as follows, using ln-transformed trait values: Plasticity ¼ Effect sizeijk ¼ ½meani ðIn - trait j treatment Þ  meani ðIn - trait j control Þ=SD pooled

3492 P . L I A N C O U R T et al. where i is a species, j is a trait, and k is one of the three scenarios of climate change. ‘meani (ln-trait j treatment)’ was the mean ln-transformed trait value for the trait j measured on a given species i in a given treatment (+OTC, +Water or +OTC+ Water); ‘meani (ln-trait j control)’ was the mean ln-transformed trait value for the trait j measured on a given species i in the control plots; SDpooled was the standard deviation of ln-transformed trait values for the trait j under both control and the particular treatment condition to which it is being compared, pooled together for this particular species i. It is unlikely that a genotype turnover occurred between treatments after 2 years of experiment. Therefore, by combining the leaves of several adult individuals/genotypes per plots, we assume that the variation in trait values observed between the control and a particular treatment condition is primarily the result of a plastic response. A positive plasticity (i.e., positive effect size value) indicates for that species an increase in the value of a trait j expressed in standard deviation units, and a negative value indicates a decrease. A second measure of plasticity used the absolute value of the effect size, that is, |plasticity| = |Effect size ijk|. |Plasticity| was used to indicate how much each of the species changed from controls without reference to the direction of change. Biomass response exhibited by each species in each scenario after 4 years was also calculated using effect size in comparison with controls. Biomass response ¼ Effect sizeik ¼ ½meani ðIn - biomass treatment Þ  meani ðIn - biomass control Þ=SD pooled where ‘meani (ln-biomass treatment)’ was the mean total aboveground biomass (ln-transformed) for given species i averaged across plots and measured in a given treatment (+OTC, +Water or +OTC+Water); ‘meani (ln-biomass control)’ was the equivalent for the control plots. Biomass response is therefore the response of each species i to one of the scenarios k, expressed in standard deviation units. Positive values indicate that species i increased in total aboveground biomass under a particular scenario k compared to the ambient conditions, and negative values indicate the opposite.

Statistical analyses Baseline trait values for each of the 12 species were considered using first the actual ln-transformed values of all six traits in ambient conditions (control plots), and then also as quantified in multivariate space using principal component analysis (PCA). The PCA axes were rotated (using VARIMAX) to improve correlations between suites of traits and the first two axes of the PCA. Traits correlated with either axis 1 or axis 2 were identified by Pearson’s correlation analyses, based on the scores of the 12 species in all seven replicate plots. We ran two sets of multiple regression models to evaluate whether baseline traits and/or trait plasticity explained interspecific variation in biomass response under each of the three climate change scenarios. An exhaustive screening method was undertaken to explore the candidate set of models (Calcagno & de Mazancourt, 2010). The first set of models was considered as baseline traits only, to test whether they predict the

biomass response without including plasticity. We ran the multiple regression procedure using the trait values themselves (ln-transformed) and again using the species’ mean score on the first two PCA axes to represent baseline trait values. The second set of models included both baseline traits, plasticity and |plasticity| values together. For consistency with the first set of models, we first used the trait values in ambient conditions (ln-transformed) and then again used the species’ mean scores on the first two PCA axes as baseline trait values. Multiple regressions were used to find the combinations of baseline trait values, plasticity in each of the six traits, and | plasticity| in each of the six traits that best explained variation among species in biomass response. The use of absolute values for plasticity allows accounting for parabolic (nonlinear) relationship between trait response to a scenario k and biomass change. A negative relationship between |plasticity| and biomass response indicates that constancy (i.e., no change) on the trait considered is favored under scenario k. A positive relationship indicates that any change in the trait values is favored under scenario k. However, if the best-fit model for a given scenario k included both plasticity and |plasticity| for the same trait, it was ignored as this is not biologically sound, and the second best model was interpreted. Finally, once the best-fit model was identified for a scenario k, we used Pearson’s correlation analyses to test for the correlation among predictors and calculated the variance inflation factor (VIF) to evaluate the risk of multicollinearity before interpreting the value and sign of the parameter estimates. Variance components were calculated for each predictor variable after the best model under each climate change scenario was identified using Akaike’s weights (Burnham & Anderson, 2002). Multiple regressions were performed in R (version 2.13.2; R Development Core Team, 2013) using glmulti package (Calcagno & de Mazancourt, 2010). PCA and Pearson’s correlations were performed with JMP 8.0 (SAS Institute, 2008).

Results The species varied substantially in their baseline trait values under ambient conditions as shown by PCA (Fig. 1). The first and second PCA axes account for 37.3% and 35.9%, respectively, of the total measured variance in all leaf traits among species. LDMC, LNC, and to a lesser extent LCC are correlated with the first axis, while LL is correlated with axis 2 (Fig. 1). SLA and LA are correlated with both axes, but their loading is greater on axis 2 (Fig. 1). High LDMC, high LCC, but low LNC characterize leaves of the grasses Festuca lenensis and Stipa krylovii. The forb Thymus gobicus has particularly small leaves, and the forbs Aster alpinus and Artemisia commutata have the lowest LDMC but the highest LNC. Other species exhibit intermediate levels of the measured traits (Fig. 1; Table S4). Models constructed from values of all six baseline traits poorly predicted biomass response among the 12 species. For the warmer and drier climate change sce© 2015 John Wiley & Sons Ltd, Global Change Biology, 21, 3489–3498

L E A F - T R A I T P L A S T I C I T Y A N D C L I M A T E C H A N G E 3493 nario (+OTC), most species decreased in biomass (Fig. 2); the best model identified biomass response to be negatively correlated with SLA (only), explaining 23% of the variance in biomass response, but this model

Fig. 1 Ordination diagram of the two-first axes of the principal component analysis (PCA) based on ln-transformed trait values. Values in the table are the Pearson’s correlation coefficients between the PCA scores of the 12 species on the first two axes and the trait values. *** indicate P < 0.001 for the loading of the leaf traits on axes 1 and 2. Values in bold indicate which axis is more correlated to a particular trait. Centroids of each species are indicated by the dot. Species codes are as follows: AGR_CRI: Agropyron cristatum; ART_COM: Artemisia commutata; ART_FRI: Artemisia frigida; AST_ALP: Aster alpinus; FES_LEN: Festuca lenensis; KOE_MAC: Koeleria macrantha; OXY_STR: Oxytropis strobilacea; POT_ACA: Potentilla acaulis; POT_SER: Potentilla sericea; PUL_TUR: Pulsatilla turczaninovii; STI_KRY: Stipa krylovii; THY_GOB: Thymus gobicus.

was marginally significant only (P = 0.06). Similarly, with supplemental precipitation (+Water), biomass response was negatively correlated with LDMC (only), explaining 25% of the variance, but again the model was marginally significant (P = 0.06). For +OTC+Water, no significant model involving one or more baseline traits was identified (P > 0.24). Both under +Water and under +OTC+Water treatments, some species increased in biomass and others decreased (Fig. 2). Likewise, baseline traits proved unimportant in models built from both baseline traits and plasticity values; the best model for each climate change scenario failed to include baseline trait as explaining a significant fraction of the variation in biomass response. This was true not only when PCA axes were used as estimates of baseline traits (Table S8) but also when the mean baseline values for all six traits were used. The two approaches gave the same best model, each lacking baseline traits, under each climate scenario (Table 1). In contrast, trait plasticity explains a large component of the variance among species in biomass response under all three climate scenarios. With +OTC, 49.5% of the variance among species in biomass response was explained by their absolute change in LL (Table 1, Fig. 3a). That is, species with the most plasticity in LL, regardless of the direction of change in LL, showed the most reduction in biomass under the stress of elevated temperatures and reduced water. The second best model was interpreted for +Water because the best-fit model included both plasticity and |plasticity| for the same trait (Table S8). With +Water, biomass response was negatively correlated with plasticity in LL (Table 1, Fig. 3b) and positively correlated

Fig. 2 Species-specific biomass responses to the three scenarios of climate change. The biomass responses are expressed in standard deviation units. © 2015 John Wiley & Sons Ltd, Global Change Biology, 21, 3489–3498

3494 P . L I A N C O U R T et al. Table 1 Best models used to estimate the biomass response to increased temperature and drought (+OTC), to supplemental precipitation (+Water), and to the scenario combining increased temperature and supplemental precipitation (+OTC+Water). For each scenario, the adjusted R2 for the best model, the estimate, and the P-value of each predictor are reported in the table Predictors

Estimates

P-values

Adjusted R2

Increased temperature and drought (+OTC) |Plasticity| LL 0.974 0.0051 0.517 Supplemental precipitation (+Water) Plasticity LL 0.649 < 0.001 0.769 Plasticity LA 0.827 < 0.001 Increased temperature and supplemental precipitation (+OTC+Water) Plasticity LL 0.407 0.0461 0.651 Plasticity SLA 0.445 0.0063 |Plasticity| LCC 0.884 0.006

with plasticity in LA (Fig. 3c), with these explaining 47.5 and 30.0%, respectively, of the variance in biomass response. Plasticity in LL and plasticity in LA were themselves correlated (R = 0.62, P = 0.03); however, the variance inflation factor was close to 1 (VIF = 1.47), suggesting a low risk of collinearity. The best model for +OTC+Water identified plasticity in LL and SLA and |plasticity| in LCC as best explanatory variables (Table 1, Table S8), accounting for 45.9, 12.3, and 9.4%, respectively, of the variance among species in biomass response. Biomass response decreased with increasing LL, as it did with supplemental precipitation only (Fig. 3d). Biomass response increased with increasing SLA (Fig. 3e) and with absolute change in LCC, regardless of the direction of change (Fig. 3f). No two of these three plasticity variables were correlated.

Discussion Baseline values of leaf traits under present-day climatic conditions did not predict how species performed under particular scenarios of climate change. However, we found that plasticity in leaf traits due to climate manipulations, or in some cases the lack of plasticity, was associated with changes in performance in response to our climate manipulations (see also Warren & Lake, 2012). Thus, our results provide strong experimental support for recent conjectures about trait plasticity having an important role in explaining performance under climate change (Nicotra et al., 2010). How plasticity correlates with climate-induced changes in biomass depends on the climate change scenario and the trait in question. In particular, our study demonstrates that the most successful plasticity strat-

egy depends on temperature and on soil moisture. With elevated temperatures and drier conditions (i.e., in the OTC alone treatment), most species decreased in aboveground biomass, yet the species that showed the least change in biomass, and thus appeared to be the most stress tolerant, were those that showed the highest degree of constancy in LL (Grime, 1979). Note, however, that the most tolerant species could have expressed plasticity on traits that we did not measure, such as root traits, for example, that are likely to be of primary importance in water-limited system such as the Mongolian steppe (Comas et al., 2013). In contrast, with supplemental precipitation, regardless of whether temperatures were elevated or not, the change in biomass was related to plasticity in multiple traits. Climate likely influences plasticity both directly through changes to the abiotic environment and indirectly through altered intensities of biotic interactions. We think that the negative correlation between biomass response and plasticity in LL identified both under +Water and under +OTC+Water reflects the outcome of increased competition for light with water addition. Indeed, we documented increased competition with water addition in a prior experiment with Festuca lenensis (Liancourt et al., 2013). An increase in LL under shaded conditions (leaf blade length for grasses and mainly petiole length for dicots) is a well-documented response for non-shade-tolerant species (Lambers et al., 2008). Positive correlations between biomass response and plasticity in LA (+Water) likely reflect increases in leaf surface area that provides these species greater competitive advantage. We assume that the benefits of increased light interception that comes with broader leaves offset the cost of any increased water loss through greater transpirational surface area. The positive correlation between plasticity in SLA and biomass response with supplemental precipitation and elevated temperatures (+OTC+Water) is consistent with an increase in resource acquisition/exploitation and increased growth rate as documented in other studies (Atkin et al., 2006; see also Perez-Harguindeguy et al., 2013 for review). Additionally, the positive correlation between |plasticity| in LCC, regardless of the direction of LCC change, and biomass response under +OTC+Water, suggests that these coexisting species use a variety of successful strategies involving carbon acquisition and carbon economy. Note, however, that | plasticity| in LCC explains much less variation than plasticity in LL or SLA. Divergent carbon strategies among co-occurring species are consistent with findings for Mediterranean old-field successional plant communities where the relationship between growth rate and LCC varied among closely related species occurring along a successional gradient (Kazakou et al., 2006); in © 2015 John Wiley & Sons Ltd, Global Change Biology, 21, 3489–3498

L E A F - T R A I T P L A S T I C I T Y A N D C L I M A T E C H A N G E 3495

(a)

(b)

(d)

(c)

(e)

(f)

Fig. 3 Regression plots of biomass response against different measures of plasticity. Biomass response and plasticity measures are all expressed in standard deviation units. Panels for each of the three scenarios show the unique relationship between the responses of the 12 species and responses in a particular predictor while controlling for responses in any other significant predictor under the respective scenario. Panel a: |plasticity| in LL under the warmer and drier scenario (+OTC). For the supplemental precipitation scenario (+Water), Panel b: plasticity in LL, and Panel c: plasticity on LA. For the scenario +OTC+Water, Panel d: plasticity in LL, Panel e: plasticity in SLA, and Panel f: |plasticity| in LCC.

some species, an increase in growth rate was associated with an increase in LCC, and in others, it was associated with a decrease in LCC. Increased biomass can be achieved either by increasing leaf-level carbon-rich structural compounds (cellulose or lignin) or storage (lipids, carbohydrates) or by adding new growth at the expense of leaf-level carbon content (Poorter et al., 1992). Regardless of exactly how it is achieved, increased growth rate appears key for gaining a com© 2015 John Wiley & Sons Ltd, Global Change Biology, 21, 3489–3498

petitive advantage under this scenario coupling supplemental precipitation and increased temperatures. We predicted that the relationship between exploitative traits and biomass response should be stronger with warming and supplemental precipitation than with supplemental precipitation alone. The fact that plasticity involving SLA and LCC was identified in +OTC+Water but not in +Water suggests this is the case. We think that the added effect of +OTC was medi-

3496 P . L I A N C O U R T et al. ated directly through temperature and that species taking advantage of this scenario were apparently growthlimited by both water availability and temperature. We can eliminate indirect effects of +OTC on nitrogen availability or plant–plant competition based on results of a companion study (Liancourt et al., 2013). A direct effect of temperature is in line with previous controlled condition experiments documenting the plasticity in SLA and its relationship with relative growth rate with increasing growth temperature (Atkin et al., 2006). Altogether, these results suggest that plasticity in different leaf traits is likely to confer a competitive advantage under different climate change scenarios. It is important to note that our approach to quantifying plasticity did not distinguish clones or other genetically related individuals, such as half-siblings, and therefore did not allow exploring further whether plasticity was purely environmentally driven or had a genetic component (Scheiner, 1993). Moreover, a high degree of trait variability among families within species and opposite reaction norms of different genotypes to our scenarios could have increased the overall intraspecific trait variation (SDpooled), resulting in our underestimating plasticity. Even so, the observed patterns based on only 12 species are striking, and our key finding that trait plasticity is an important determinant of species’ susceptibility to climate change suggests new and interesting avenues for broader scale comparisons. We see three possible reasons why baseline leaftrait values in ambient conditions offered poor predictive power for biomass response to climate change, unlike what could be assumed from interspecific variation in leaf traits documented along climatic gradients (Reich et al., 1997; Wright et al., 2005; Ordo~ nez et al., 2009). Firstly, results from experiments, such as ours, that are conducted at a single site run the risk of not being generalizable or predictive of typical responses observed across larger geographic scales. The second possible reason may relate to whether the plant community was assembled through strong habitat filtering processes (Weiher & Keddy, 1999; Cornwell et al., 2006), whereby the habitat selects for a set of species that are somewhat functionally equivalent, referred to as an equalizing mechanism (sensu Chesson, 2000). However, the taxonomic and functional diversity of our study species and, in particular, their species-specific response to our climate treatments clearly argues against such an explanation (Fig. 2). Moreover, strong habitat filtering is thought to be particularly unlikely in temporally fluctuating environments (Chesson, 2000; Adler et al., 2006), and we view the Mongolian steppe as exhibiting substantial year-to-year differences in climatic conditions (Davi et al., 2010). Indeed, it has been suggested that tempo-

ral environmental heterogeneity is likely to stabilize coexistence of species with contrasting responses to climate (Adler et al., 2006). A third reason could be that the functional leaf traits we measured, while relevant for distinguishing contrasted vegetation across gradients or across biomes (Reich et al., 1997; Wright et al., 2005; Ordo~ nez et al., 2009), are not sufficient to grasp the diversity of strategies and mechanisms of adaptation among coexisting species in our system (Ackerly, 2004). Traits measured on multiple plant organs (e.g., roots, stems, seeds) might prove additionally useful in this regard (Soudzilovskaia et al., 2013; Laughlin, 2014). Overall, we show species-specific responses to climate, increased competition with supplemental precipitation, and how trait constancy may limit decreased population growth with unfavorable temporal environmental changes. These key findings provide compelling evidence for two predictions of the storage effect theory (Chesson, 2000; Adler et al., 2006): species-specific responses to climate and covariation between the environmental conditions and competition (Chesson, 2000). For testing the third prediction of the storage effect model – buffered population growth (Chesson, 2000) – a longer term experiment would be necessary to fully document that trait constancy is important to avoid negative population growth rates in bad years (Alpert & Simms, 2002; Grime & Mackey, 2002). We conclude that phenotypic plasticity has profound implications for species coexistence within the steppe and should be considered when evaluating the niche space a species occupies (Chevin et al., 2010; Nicotra et al., 2010). In contrast, it could prove difficult to predict the relative performance of coexisting species under future climate change from functional leaf characters measured before the change. Our approach of coupling trait values with plasticity in those traits holds greater promise, but experimentation, preferably at larger spatial scales, may be required to resolve their predictive power. Identifying vulnerable species from those who will cope or even benefit from climate change is of primary importance because local increase or decrease in species abundance, as well as how their traits change, should have immediate consequences on ecosystem functions and services (Chapin et al., 2000).

Acknowledgements We thank S. Undrakhbold and A. Lkhagva, the research camp staff, and the US and Mongolian undergraduates who spent their summers in Dalbay. We are particularly grateful to J. Mortensen and D. Brickley for their help throughout the project. We thank R. Michalet and J-P. Maalouf for providing important feedback on the early version of the manuscript, J. Cowles and the reviewers for their thorough and helpful reviews. Support © 2015 John Wiley & Sons Ltd, Global Change Biology, 21, 3489–3498

L E A F - T R A I T P L A S T I C I T Y A N D C L I M A T E C H A N G E 3497 for this research and the PIRE Mongolia project was provided by the U.S. National Science Foundation (OISE 0729786). P. Liancourt received additional support from the European Union’s Seventh Framework Programme for research, technological development, and demonstration under Grant Agreement No. GA-2010-267243 – PLANT FELLOWS.

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Supporting Information Additional Supporting Information may be found in the online version of this article: Table S1. Proportion of the total biomass per plots accounted for by the 12 selected species (%). Table S2. Average species richness in the experimental plots. Table S3. Number of plots per treatment where the species were sampled for trait measurements. Table S4. Mean trait values (untransformed) and standard errors (SE) for the 12 species in ambient conditions (control plots). Table S5. Mean trait values (untransformed) and standard errors (SE) for the 12 species in the +Water plots. Table S6. Mean trait values (untransformed) and standard errors (SE) for the 12 species in the +OTC plots. Table S7. Mean trait values (untransformed) and standard errors (SE) for the 12 species in the +OTC+Water plots. Table S8. Five best models used to estimate the biomass response to increased temperature and drought (+OTC), to supplemental precipitation (+Water), and to the scenario combining increased temperature and supplemental precipitation (+OTC+Water).

© 2015 John Wiley & Sons Ltd, Global Change Biology, 21, 3489–3498