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Keywords:

  • Alpine species;
  • Biomass;
  • Climate change;
  • Flower production;
  • Flowering probability;
  • Precipitation;
  • Reproductive effort;
  • Temperature;
  • Veronica;
  • Viola

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgements
  9. References
  10. Supporting Information

Questions

Effects of climate on flowering performance are often investigated independently of plant size. We ask how temperature and precipitation impact flowering probability and flower production: via direct effects, size-dependent indirect effects, changes in minimum size for flowering and/or changes in reproductive investment.

Location

Twelve calcareous grasslands in western Norway (4°50′–8°45′ E, 60°20′–61°50′ N).

Methods

The investigations were carried out at the rear temperature edge of alpine plants and at the leading temperature edge of lowland plants to capture the variety of climate responses occurring in different parts of species climate niches within our study landscape. The study was conducted within a natural ‘climatic grid’ consisting of temperature gradients replicated along a precipitation gradient. In each study site, we sampled populations of two alpine (Viola biflora, Veronica alpina) and two lowland (Viola palustris, Veronica officinalis) species. The relative importance of each effect was assessed under a 2 °C increase in mean summer temperature and a 10% increase in annual precipitation.

Results

Flowering was climate- and size-dependent in all species except Viola palustris. Both direct climate effects and climate-driven variation in reproductive investment were detected for the three other species. Indirect climate effects were detected for Veronica officinalis, and climate-driven variation in minimum size for flowering in Viola biflora. Climatic responses were not consistent within or between distributional types (alpine vs lowland) or genera. A temperature increase of 2 °C was predicted to increase flower production by 22% for Veronica alpina and by 74% for Veronica officinalis. A precipitation increase of 10% had a limited impact on Viola biflora flowering probability (0.08% increase) and increased Veronica officinalis flower production by 1.7%.

Conclusions

Our study shows that climate affects flowering performance both directly and through size dependence. Understanding such size-dependent responses to climate is important for our understanding of how climate change will affect flowering performance and recruitment in plant populations.


Nomenclature
Lid & Lid

(2005)

Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgements
  9. References
  10. Supporting Information

Reproduction by seed is a key event for plant population persistence and species range dynamics. Seedling recruitment depends critically on seed availability, which in turn relies on flowering performance (Gimenez-Benavides et al. 2008). In alpine and sub-alpine habitats, flowering probability and flower production have been shown to respond to temperature (Arft et al. 1999; De Valpine & Harte 2001; Saavedra et al. 2003; Aerts et al. 2004; Gimenez-Benavides et al. 2008; Milla et al. 2009), water availability (De Valpine & Harte 2001) and precipitation (Inouye et al. 2003). Global warming projections forecast changes in both temperature and precipitation (IPCC 2007). Studying how these variables affect flowering performance is therefore important to understand potential impacts of climate change on plant population persistence and range dynamics.

Climate may affect flowering in several ways. Climate may influence flowering performance directly, but also indirectly through plant size, since reproduction is typically size-dependent, and plant size is known to be affected by temperature and water availability (De Valpine & Harte 2001; Zavaleta et al. 2003; Bloor et al. 2010; Kardol et al. 2010). Climate may also affect the minimum size for flowering, as plants often grow larger before flowering in sites with favourable growing conditions (Mendez & Karlsson 2004; Bonser & Aarssen 2009). Once the minimum size for flowering has been reached, flowering probability and flower production often increase with plant size (Obeso 2002; Mendez & Karlsson 2004; Pfeifer et al. 2006a,b). Further, climate may also alter reproductive investment (e.g. allocation to reproduction) as the relationship between reproductive output (or effort) and plant size has been shown to vary with environmental factors (Ohlson 1988; Welham & Setter 1998; Mendez & Karlsson 2004; Bonser & Aarssen 2009). Although indirect climate effects through plant size and climate-driven variation in minimum size for reproduction and in reproductive investment have been documented, flowering responses to climate are often investigated independently of plant size (but see Gimenez-Benavides et al. 2007; Mendez & Karlsson 2004; Milla et al. 2009; Pfeifer et al. 2006a).

In this study, we use a natural ‘climatic grid’ consisting of temperature gradients replicated along a precipitation gradient to investigate flowering responses to summer temperature and annual precipitation. First, we ask how do temperature and precipitation influence flowering: via direct climate effects, indirect climate effects through plant size, changes in minimum size for reproduction and/or changes in reproductive investment? Second, we assess the importance of the investigated climate effects for flowering performance under an increase of 2 °C in summer temperature and 10% in annual precipitation, corresponding to future climate projections for the study region (Hanssen-Bauer et al. 2009).

The investigations were carried out on two pairs of lowland–alpine perennial herbaceous species with contrasting climatic niches in western Norway (Viola palustris–V. biflora and Veronica officinalis–V. alpina). The species were chosen so that our climatic gradients included the rear temperature edge (sensu Hampe & Petit 2005) of the alpine species niches and the leading temperature edge (sensu Hampe & Petit 2005) of the two lowland species niches. We predict little or no response to temperature for the two alpine plants, as temperature generally does not constitute a strong abiotic limiting factor at the lower temperature margin of a species temperature niche (Brown et al. 1996). In contrast, we predict a positive temperature response for the two lowland species for which temperature stress may limit distribution towards our cold climate sites (Brown et al. 1996). We predict a positive response to precipitation for all species, as low water availability may limit flowering performance towards the drier end of our climate grid. We hypothesize that all types of investigated effects will contribute to the predicted responses mentioned above.

Methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgements
  9. References
  10. Supporting Information

Study sites

The natural climate grid combines 12 sites representing four levels of mean annual precipitation [ca. 600 (1) 1200 (2), 2000 (3) and 2700 (4) mm] and three levels of summer temperature [means of the four warmest months; ca. 6.5 (ALP), 8.5 (INT) and 10.5 (LOW) °C; Fig. 1]. The grid was designed to cover a gradient of ca. 4 °C across the boreal to low-alpine zone transition. We targeted grazed intermediate-rich meadows (Potentillo-Festucetum ovinae; G8 sensu Fremstad 1997) occurring on south-facing, shallow slopes (5–20°) with relatively rich bedrock in terms of nutrient availability. Sites were selected specifically to keep grazing regime and history, bedrock, slope, aspect and vegetation types as constant as possible. Full names and geographic coordinates of each site are available in Appendix S1. All sites were fenced in spring 2009 to avoid animal disturbance. Geographical distance between sites is on average 15 km and ranges from 175 km (LOW1 and LOW4) to 650 m (LOW2 and INT2, which are also 400 m a.s.l. apart).

image

Figure 1. Position of each site within the SEEDCLIM climate grid. Altitude is the main driver for changes in mean summer temperature, and continentality is the main driver for changes in annual precipitation within the grid, but there are interactions between the two and sites are therefore positioned in geographical space so as to decouple the two gradients as far as possible.

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We used interpolated temperature and precipitation data from the period 1961–1990 with a resolution of 100 m (Norwegian Meteorological Institute, www.met.no; see Tveito et al. 2005 for method description) for site selection and statistical analyses. The interpolated mean summer temperatures were highly correlated with on-site 2-m height temperature measurements in 2009 (Pearson correlation 0.93, n = 12). Precipitation loggers were also set up locally in 2009, but the recordings contained too many measurement errors to be used.

Species

The two alpine–lowland species pairs, Veronica alpina–V. officinalis and Viola bifloraV. palustris, were chosen so that our sites covered the rear edge of the two alpine species temperature niche and the leading edge of the two lowland species temperature niche. We chose common species within the climatic grid to maximize the number of sites where the species occurred individually and where both species of a pair occurred simultaneously. Similar branching structure within our species pairs allows for easier comparison between alpine and lowland species in further studies including morphological species traits. All four species are clonal, and often develop long lateral rhizomes and several flowering shoots on the same genet (especially the two lowland species). We therefore use the shoot as our working unit.

Viola biflora is common in moist and relatively rich mountain habitats and is found in snowbeds and leesides, grazed upland pastures, stream banks and birch forests. Viola palustris grows on moist soils and is common in moist pastures, meadows, forests, mires and stream banks. Veronica alpina is found in upland habitats and is common in snowbeds, upland forests, grasslands and stream banks. Veronica officinalis is found on shallow well-drained soils within pastures and meadows, along road verges and in grazed forests and uplands (Lid & Lid 2005; Mossberg & Stenberg 2007).

Plant trait sampling

At each site, we selected five blocks of ca. 5 m² each within an area of ca. 30 m². Blocks were chosen to be as similar as possible in terms of vegetation structure, slope and aspect. Within each block, five 25 cm × 25 cm plots were placed systematically, with occurrence of the target community and/or one or more of our four target species (see Appendix S1 for species occurrence) as acceptance criteria. For Veronica shoots (ramets), we recorded shoot height, length and width of the largest leaf, and number of leaves, flowers, buds and capsules. For Viola shoots, we recorded length of the longest leaf stalk, length and width of the largest leaf, number of leaves, flowers, buds and capsules, and height of the highest reproductive organ. Flowering probability was calculated as the proportion of shoots in each plot that had a reproductive organ, while flower production was calculated as the sum of buds, flowers and capsules per shoot, excluding the non-flowering shoots. This trait was not considered for Viola palustris as it mostly produced a single flower. Hereafter, we refer to this data set as the ‘demography data’.

Additionally, we collected 14–23 genets of each target species outside the blocks by repeatedly dropping a 50 cm × 50 cm quadrat on the ground and harvesting all genets in the quadrat until we had collected at least ten genets of each of the focal species occurring in each site. Shoots (ramets) of these genets were measured in the same way as in the demography data. The different plant parts were weighed to estimate the vegetative biomass of those occurring within the blocks (hereafter ‘biomass data’).

Statistical analyses

Vegetative biomass (hereafter ‘biomass’), as a measure of size for the shoots in the demography data, was estimated from the biomass data using linear mixed effect models (see Pinheiro & Bates 2000 for details) and was modelled as a function of the non-reproductive traits. All models were nested on site and genet to account for repeated measurements. To assess the goodness-of-fit of these models, we calculated an R2 analogue based on likelihood ratios (Magee 1990): inline image, where logLM is the log-likelihood of the model, logL0 is the log-likelihood of the null model with a fixed intercept and random intercepts for sites and individuals, and N is the number of observations. We then used these models (Table 1) to estimate the biomass of shoots in the demography data, while correcting for random effects of site.

Table 1. Fixed effects coefficients of mixed effects models used to estimate biomass (BM) for the four focal species with sample sizes (N) and RLR2 based on likelihood ratios (see Methods for details). The response variable is log2(BM (mg)) and shoot height, leaf length and leaf width are expressed in mm
Species\TermsNRLR2InterceptShoot heightNumber of leavesLeaf lengthLeaf width
  1. P-levels of likelihood ratio tests: *: <0.05, **: <0.01, ***: <0.001; n.s.: not significant.

Veronica alpina 1650.780.90**0.01***0.09***0.09**0.16**
Veronica officinalis 4550.772.63***0.01***0.07***0.07***0.19***
Viola biflora 910.722.02***n.s.0.21***n.s.0.15***
Viola palustris 1250.562.53***n.s.0.20*0.18***n.s.

Generalized linear mixed effect models (see Pinheiro & Bates 2000 for details) were used to investigate direct climate effects as well as climate-driven changes in minimum size for reproduction (hereafter referred to as additive size and climate effects) and in reproductive investment (hereafter referred to as interactive size and climate effects) in the demography data. In these models, flowering probability and flower production were regressed against biomass, summer temperature, annual precipitation and potential interactions between biomass and climate variables. Hereafter, we refer to these models as ‘flowering probability’ and ‘flower production’ models, respectively.

Plant size responses to climate were investigated in the demography data set using linear mixed effect models with biomass as the response variable and summer temperature and annual precipitation as explanatory variables. Hereafter we refer to these models as ‘Biomass’ models.

Figure 2 illustrates how the investigated effects were identified. A direct climate effect is indicated by responses to at least one of the two climate variables in the flowering probability and/or flower production models (Fig. 2a). An indirect climate effect through plant size is indicated by responses of flowering probability and/or flower production to biomass, combined with a biomass response to climate in the biomass models (Fig. 2b). Climate-driven variation in minimum size for reproduction (additive size and climate effects) is indicated by a response of flowering probability to both biomass and climate (Fig. 2c), as an effect of climate shifts the size-dependent curve for flowering probability towards smaller or larger sizes. Finally, climate-driven variation in reproductive investment (interactive size and climate effects) is indicated by interactions between biomass and climate in the flowering probability and/or flower production models (Fig. 2d,e), since an interaction between biomass and climate indicates that a change in climate causes a shift in the slope describing the relationship between flowering probability or flower production and plant size.

image

Figure 2. Hypothetical effects of climate and plant size on flowering performance (flowering probability and flower production). Direct climate effects are indicated by a main effect of temperature or precipitation on flowering probability and/or flower production (a), while indirect climate effects through plant size are indicated by a main effect of climate (temperature and/or precipitation) on biomass and a main effect of biomass on flowering probability and/or flower production (b). A change in minimum size for reproduction with climate (additive size and climate effect) is indicated by main effects of both biomass and climate in the flowering probability models (c). Changes in reproductive investment with climate (interactive size and climate effect) are indicated by interactions between climate variables and biomass, and can be investigated for both flower probability (d) and flower production (e).

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All four types of effect can act individually or in concert. For example, an indirect climate effect through plant size in addition to a direct climate effect and an additive size and climate effect would be indicated by a response of biomass to climate and responses of flowering probability to both biomass and to climate. Note that in such cases we cannot separate the additive size and climate effect from the direct climate effect.

The two alpine species were only present in the alpine sites and in one of the intermediate temperature sites (INT3, see Appendix S1). Thus, the variation in summer temperature is dependent on one site only for the two alpine species. We therefore investigated the responses to annual precipitation and summer temperature in separate models for these species. When assessing the response to annual precipitation, we removed plots from the intermediate temperature site (INT3, see Fig. 1) to avoid incorporating the variance caused by temperature differences between the two sites of the third precipitation level. Similarly, we analysed responses to mean summer temperature using the sites INT3 and ALP3 (ca. 2000 mm annual precipitation for both sites; Fig. 1). In these models, mean summer temperature was expressed as a categorical variable.

Biomass was log2 transformed for all analyses to meet the normality assumption. Annual precipitation was expressed in metres in the regressions, so that it obtained coefficients with a similar scale to the other predictors. The inclusion of quadratic terms was tested for the two climate variables in all models, if suggested by visual inspection of the data. All models were selected using likelihood ratio tests in a step-wise backward selection process. Flowering probability, flower production and biomass models were nested hierarchically on sites, blocks and plots to account for data set structure. We assumed binomial error distributions for the flowering probability model, Poisson error distributions for the flower production models, and Gaussian distributions for the biomass models.

We assessed the relative importance of the investigated responses for an increase of 2 °C in summer temperature and a 10% increase in annual precipitation using the model predictions. To do so, we calculated the absolute change in flowering probability and/or flower production due to each variable, and then assessed its relative contribution compared to the sum of the absolute changes (see Appendix S2 for detailed methods and equations). For the lowland species, we based our calculation on the coldest wettest site where the species occurred (leading edge), and for the alpine species, on the driest site (rear edge).

In addition to mixed models, we explored structural equation modelling (SEM, see Fox 1980 for detailed method) as a method for assessing direct and indirect climate effects. These analyses were inferior to mixed models, as they could not easily account for random variance components. We present one example in Appendix S3 for comparative purposes.

All analyses were carried out in R (v. 2.13.1; R Foundation for Statistical Computing, Vienna, AT), using the packages nlme (v. 3.1-97; Biomass model) and lme4 (v. 0.999375-33; all other models).

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgements
  9. References
  10. Supporting Information

Responses to climate and size

Size was important for the flowering performance of all species, and climate affected flowering of all species except Viola palustris (Table 2). Responses varied between species, however, and direct, indirect, additive and interactive climate effects were detected (Table 2). For Viola biflora we found a direct, unimodal relationship between flowering probability and precipitation (Table 2, cf. Fig. 2a). Furthermore, we found both additive effects (Table 2, cf. Fig. 2c) and a negative interactive size and climate effect (Table 2, cf. Figs 2d and 3), suggesting that minimum plant size for flowering was smaller in the wetter sites. However, once minimum size for reproduction was reached, individuals tended to invest more resources into flowering (relative to biomass) at the drier sites. The two Veronica species showed clear responses to temperature, and responded by changing the number of flowers produced, rather than the probability of flowering. For Veronica alpina, we found a direct positive effect of temperature (Table 2, Fig. 4) and a negative interactive size and temperature effect, suggesting a lower reproductive investment towards the warmer sites (Table 2, cf. Fig. 2e). Consequently, small plants produced more flowers in the warmer sites, whereas big plants produced more flowers in the colder sites (Fig. 4). For the flower production of Veronica officinalis, we found both a direct and an indirect positive effect of temperature (Table 2; cf. Figs 2a,b and 5a,b). This species also responded directly to increasing annual precipitation (Table 2) and reduced its reproductive investment as precipitation increased, as indicated by a negative interaction between biomass and precipitation (Table 2, cf. Fig. 2e). While big plants produced more flowers in the dry sites, small plants produced a comparable number of flowers in both wet and dry sites (Fig. 5c).

Table 2. Fixed effects coefficients in mixed effects models used to estimate size and climate effect on flowering and climate effect on size
ModelSpecies\TermsInterceptSizeTempPrecPrec²Size:TempSize:Prec
  1. P-level: *: <0.05, **: <0.01, ***: <0.001; –: not investigated; n.s.: not significant.

  2. Size: log2 vegetative biomass (mg); Prec: annual precipitation (m); Temp: mean summer temperature (°C). For Veronica alpina and Viola biflora, intercepts and coefficients for biomass are extracted using the whole datas et when none of the climate variables were retained in the respective models investigating temperature or precipitation effects. We otherwise indicate the intercepts and biomass coefficients of the model in which a climatic variable was retained (see Methods). Sample sizes (N) in biomass and flowering probability (same data set) and flower production models, respectively: Veronica alpina: 218, 74 (temperature effect); N = 602, 67 (precipitation effect); Veronica officinalis: 1568, 301; Viola biflora: 735, 66 (temperature effect), 1210, 60 (precipitation effect); Viola palustris: 836, NA (generally one flower per plant).

Flowering probability Veronica alpina −15.55***3.00***n.s.n.s.n.s.n.s.n.s.
Veronica officinalis −10.01***1.24***n.s.n.s.n.s.n.s.n.s.
Viola biflora −33.82***4.15***n.s.22.43***−4.76**n.s.−1.60**
Viola palustris −8.85***0.89***n.s.n.s.n.s.n.s.n.s.
Flower production Veronica alpina −1.49*0.54***2.53*n.s.n.s.−0.41**n.s.
Veronica officinalis −2.20*0.35***0.21**0.60*n.s.n.s.−0.08*
Viola biflora 0.35***n.s.n.s.n.s.n.s.n.s.n.s.
Biomass Veronica alpina 3.80***n.s.n.s.n.s.
Veronica officinalis 0.970.55*n.s.n.s.
Viola biflora 4.52***n.s.n.s.n.s.
Viola palustris 5.32***n.s.n.s.n.s.
image

Figure 3. Viola biflora flowering probability as a function of biomass at three precipitation levels. Curves: model predictions. Dots and vertical segments: mean values of the five quintiles of biomass at each site in the demography data and their SE. The coefficients used for the regression equation can be found in Table 2. Precipitation was set to the precipitation value of the site ‘ALP1’ for dry predictions, ‘ALP2’ for Mid-dry predictions and ‘ALP3’ for Mid-wet predictions. P-values levels of the model parameters are given in Table 2.

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image

Figure 4. Model prediction of Veronica alpina flower production response to size and temperature. The coefficients used for the regression equation can be found in Table 2.

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image

Figure 5. Veronica officinalis flower production response to size, temperature (a) and precipitation (c), and size response to temperature (b). Curves: model predictions, dots and vertical segments: mean and associated SE of each site. The coefficients used for the regression equations can be found in Table 2. Plot (a): precipitation was set to its mean and temperature was either set to the mean temperature of the lowland sites (black curve) or the mean temperature of the intermediate sites (grey curve). Plot (c): temperature was set to its mean and precipitation was either set to the mean precipitation of the sites receiving <1.5 m of annual precipitation (dry sites) or set to the mean precipitation of sites receiving more than 1.5 m of annual precipitation (wet sites).

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Predicted changes in flowering performance and effect contributions

For Viola biflora, an increase of 10% in annual precipitation is predicted to increase flowering probability by only 0.08%. The direct response to increasing annual precipitation, combined with the induced reduction in minimum size for reproduction, had a stronger impact on the flowering probability (62.8% of the total effect; Fig. 6a) than the decrease in reproductive investment (37.2%; Fig. 6a); however, the net effects were modest and tended to cancel each other out. For Veronica alpina, flower production is predicted to increase by one flower per shoot on average, i.e. ca. 22%, in response to a 2 °C increase in temperature. The impact of the direct effect of higher temperature on flower production (50.5%; Fig. 6b) was comparable to that of decreased reproductive investment (49.5%; Fig. 6b). For Veronica officinalis, increasing summer temperature by 2 °C is predicted to increase flower production by 5.27 flowers per shoot (i.e. 74%), whereas increasing annual precipitation by 10% would increase flower production by 0.12 flowers per shoot (i.e. 1.7%). The direct response to increasing temperature constituted 42.0% of the total temperature impact, compared to only 10.3% for the indirect positive temperature effect through size (Fig. 6c). The direct positive response to increasing precipitation and the lower reproductive investment under increased precipitation were of similar magnitude (24.4% and 23.2%; Fig. 6c). Altogether, increasing temperature (52.3%; Fig. 6c) was slightly more important than increasing precipitation (47.6%; Fig. 6c).

image

Figure 6. Contributions of the detected climate effects to changes in flowering performance for the focal species. See Fig. 1 for descriptions of different potential climate effects on flowering. The relative contribution of each variable is calculated as the proportional contribution to the total change in flowering probability or flower production (absolute numbers) for an increase of 2 °C in mean summer temperature (c) and 10% in annual precipitation. For Veronica alpina (b) the relative contribution of each effect is based on the difference in flower production between the sites ALP3 and INT3 (Appendix S1; see Methods). For Viola biflora (a), the calculations are based on the drier alpine site where the species occurs (ALP1; Appendix S1). For Veronica officinalis (c), the calculations are based on the colder and wetter site where the species occurs (INT4; Appendix1). The arrow ‘Size to Flower probability’ in (a) and (b) has a standard width since no climate effects on size were detected. The width of the arrow ‘Size to Flower production’ in (c) is a function of the indirect effect of temperature mediated through size.

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Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgements
  9. References
  10. Supporting Information

Our study shows that both temperature and precipitation impact flowering performance. However, the different responses to climate were complex and tended to act in opposite directions. Our results suggest a positive response to increasing temperature for the flower production of the alpine and lowland Veronica species, but not for the two Viola spp., rejecting our hypothesis of weak or negative responses to temperature for alpine species in contrast to positive responses for lowland species. This result suggests that population responses to climate change, at least in terms of flowering performance, cannot be predicted from the population position within the species range (leading vs rear edge) (see also Abeli et al. 2012).

While several precipitation effects were found, they overall did not strongly affect flowering performance. Responses were species-specific and the positive (direct) and negative (reproductive investment) effects found for both Viola biflora and Veronica officinalis tended to cancel each other out. Our prediction of a general positive impact of higher annual precipitation on flowering is therefore not corroborated.

Direct climate effects

Direct positive impacts of temperature on flower production were the overriding effects for the two Veronica species, although only for small to medium-sized plants for Veronica alpina. While this is in line with studies that find increased flowering under experimental warming (Arft et al. 1999; De Valpine & Harte 2001; Aerts et al. 2004), it contrasts several gradient studies that report decreasing flowering towards lower altitudes (e.g. Gimenez-Benavides et al. 2007, 2008; Milla et al. 2009). The latter studies suggest lower water availability in the warmer plots as a potential cause for this pattern. Our results suggest that this apparent negative impact of higher temperatures in conventional altitudinal gradient studies can be accounted for by a confounding effect of decreasing precipitation and hence water availability towards lower altitudes (Körner 2003) and that, once this is controlled for, higher temperature has a positive impact on flower production. In line with our results, McCain & Colwell (2011) show that species altitudinal range response to temperature may differ with precipitation. Such confounding effects may conceivably also occur for several other vital rates, suggesting caution in the interpretation of altitudinal gradient studies in terms of actual underlying environmental drivers (e.g. temperature).

Flowering probability of the alpine Viola showed a direct unimodal response to annual precipitation, and small individuals of the lowland Veronica increased their flower production with higher annual precipitation. For these species, reduced water availability may have inhibited flowering in the drier part of the climate grid (Saavedra et al. 2003; Gimenez-Benavides et al. 2007, 2008; Milla et al. 2009), as we hypothesized. The decline in flowering probability at the wet end of the gradient for the alpine species was less expected. Our study does not enable us to identify the potential biological factors that may explain this pattern (but see Larcher 2003 for potential explanations). However, such a decline is in accordance with a spatial modelling study carried out in the same landscape where we found the spatial distribution of Viola biflora to be negatively affected by high precipitation (Meineri et al. 2012).

Indirect climate effects through size

The indirect responses to temperature mediated through plant size that we found for Veronica officinalis is in line with previous studies that generally report an increase of growth and biomass towards warmer temperatures (Zavaleta et al. 2003; Penuelas et al. 2004; Hollister et al. 2005; Bloor et al. 2010; Wu et al. 2011). However, we expected similar patterns for the other focal species, as well as a biomass increase with higher precipitation (Zavaleta et al. 2003; Wu et al. 2011). Since flowering probability and/or flower production increased with increasing biomass for all four focal species (biomass data, including variation within sites), the lack of indirect climate effects can be linked to an absence of significant variation in mean biomass across sites. Several experimental warming and/or watering studies report substantial variation in biomass responses to treatments across species (De Valpine & Harte 2001; Kardol et al. 2010) and functional types (Chapin et al. 1995; Zavaleta et al. 2003). This suggests that the importance of the indirect response to climate mediated through plant size is species-specific and mostly depends on the strength and direction of species biomass responses to environmental variation.

Additive and interactive effects of climate and size

For Viola biflora, the minimum size for reproduction was lower for individuals growing in the wetter sites, and this effect, combined with the direct effect of annual precipitation, was the most important. Generally, the minimum size for flowering is described to decrease with environmental stress (Mendez & Karlsson 2004; Bonser & Aarssen 2009), suggesting a protective strategy, as delaying flowering in unfavourable conditions would result in no reproduction at all if the plant dies back or fails to reach its threshold size for reproduction (Bonser & Aarssen 2009). Thus, our results imply that excess precipitation may be more detrimental to flowering than drought for Viola biflora.

Interactive effects of climate and size were the second most important climate effects for Viola biflora, Veronica alpina and Veronica officinalis. Between-population variation in the relationship between reproductive effort and vegetative biomass is common in perennial herbaceous plants, and suggests a decreasing reproductive investment as available resources decline and the probability of mortality increases (Ohlson 1988; Welham & Setter 1998; Mendez & Karlsson 2004). For Veronica alpina, lower reproductive investment towards lower altitudes could be related to increasing competitive interactions from the lowland flora towards lower altitudes (Choler et al. 2001), where the species may allocate more resources to vegetative growth to improve competitive ability. For Viola biflora, this result is in line with the direct and additive size and precipitation effects, all pointing to a negative impact of high precipitation. However, in Veronica officinalis the decline in reproductive investment as annual precipitation increases is not in line with the hypothesis of increasing reproductive investment towards more favourable environments.

Evidence for environment-induced variation in reproductive investment is scarce and often relies on a limited number of sites (Mendez & Karlsson 2004). To our knowledge, none of these studies have investigated the impact of precipitation on reproductive investment. Our consistent results for Veronica officinalis and Viola biflora may suggest a general decline in reproductive investment with increasing precipitation. In wetter sites, increased biomass (Zavaleta et al. 2003; Wu et al. 2011) may enhance competition from the surrounding vegetation, causing species to allocate more resources into vegetative growth in order to improve their competitive abilities (similar to the reduced reproductive investment of Veronica alpina in the warmer sites).

Although we consider these to be the most likely interpretations of the additive and interactive effects for our species, one should be aware that additive and interactive effects are not completely independent. Consider the alpine species Viola biflora; for this species, we did not find plant size to vary with climate. This implies that a change in minimum size for reproduction should induce a change in reproductive investment and vice versa, as any of the two effects alone would otherwise cause a change in plant size. These two effects should therefore be interpreted carefully.

Limitations and further research

Our study highlights how climate responses can be disentangled into several underlying processes. Effects were species-specific, however, and further studies involving more species and functional groups are needed to build a general understanding of the size dependence of climate change impacts on flowering. Further, it would be valuable to investigate effects of plant size on other vital processes such as seed dormancy, germination and seedling establishment (e.g. Guo et al. 2010).

In this study we did not account for between- and within-year climatic variability. However, short-term climate variability strongly affects flowering rates and flowering phenology (Pfeifer et al. 2006a,b; Bloor et al. 2010), as well as growth and biomass (De Valpine & Harte 2001; Zavaleta et al. 2003; Bloor et al. 2010). We cannot exclude the possibility that such short-term climatic variations may have affected our results.

Community-scale implications

The species-specific responses to climate that we found may have important implications at the community level. The vegetation-level implications will however depend on the role of flowering and seed production for population persistence and range shift under current and future climates. In a community where flowering performance is critical for population growth and species persistence, our results suggest that climate warming is likely to cause a drastic change in community composition. On the other hand, if sexual reproduction, and thereby flowering performance, is less important, climate effects on other life-history stages will determine the net impacts of climate on species ranges and community composition.

Conclusion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgements
  9. References
  10. Supporting Information

For most of our focal species, flowering performance was driven by both size and climate, and our results suggest a range of potential ways in which climate, together with size, may affect the flowering of our focal species. Although direct responses to climate seem to have the strongest influence on the flowering performance of our focal species, changes in minimum size for reproduction and reproductive investment also had substantial effects, and an indirect response to climate mediated through size was also important for one of our species, Veronica officinalis. Understanding such size-dependent responses to climate is important for our understanding of how climate change will affect flowering performance and recruitment in plant populations and communities.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgements
  9. References
  10. Supporting Information

This work is funded by NFR NORKLIMA (project SeedClim, 184912/S30) and ‘Olav Grolle Olsens legat’ at the University of Bergen. We thank the landowners for access to the field sites, Simon Le Mellec, Christine Pötsch, Keno Ferter and Nina Mahler for field assistance, Kari Klanderud, Deborah Goldberg, Zuzana Munzbergova and anonymous reviewers for helpful comments on the manuscript, and Cathy Jenks for language revision.

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  6. Discussion
  7. Conclusion
  8. Acknowledgements
  9. References
  10. Supporting Information
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Supporting Information

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgements
  9. References
  10. Supporting Information
FilenameFormatSizeDescription
jvs12062-sup-0001-AppendixS1.pdfapplication/PDF5KAppendix S1. Table of site geographic coordinates and species occurrence.
jvs12062-sup-0002-AppendixS2.pdfapplication/PDF73KAppendix S2. Calculation of the relative importance of the investigated effects.
jvs12062-sup-0003-AppendixS3.pdfapplication/PDF15KAppendix S3. Structural equation modelling analysis for Veronica officinalis.

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