Direct and sizedependent effects of climate on

Journal of Vegetation Science 25 (2014) 275–286

Direct and size-dependent effects of climate on flowering performance in alpine and lowland herbaceous species € ck, Tessa Bargmann & Vigdis Vandvik Eric Meineri, Olav Skarpaas, Joachim Spindelbo

Keywords Alpine species; Biomass; Climate change; Flower production; Flowering probability; Precipitation; Reproductive effort; Temperature; Veronica; Viola Nomenclature Lid & Lid (2005) Received 4 November 2011 Accepted 29 January 2013 Co-ordinating Editor: Suzanne Prober

Meineri, E. (corresponding author, meineri. € ck, J. [email protected]), Spindelbo ([email protected]), Bargmann, T. ([email protected]) & Vandvik, V. ([email protected]): Department of Biology, University of Bergen, Thormøhlensgate 53A, Postboks 7803, N-5020, Bergen, Norway Skarpaas, O. ([email protected]): Norwegian Institutes for Nature Research, en 21, N-0349, Oslo, Norway Gaustadalle € ck, J. : Faculty of Engineering and Spindelbo Science, Sogn og Fjordane University College, Postboks 133, N-6851, Sogndal, Norway

Abstract 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.

Introduction 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

Journal of Vegetation Science Doi: 10.1111/jvs.12062 © 2013 International Association for Vegetation Science

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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 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

Fig. 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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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). 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 biflora–V. 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 9 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 9 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): R2LR ¼ 1 expð2ðlogLM logL0 Þ=NÞ, 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.


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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\Terms

N

RLR2

Intercept

Shoot height

Number of leaves

Leaf length

Leaf width

Veronica alpina Veronica officinalis Viola biflora Viola palustris

165 455 91 125

0.78 0.77 0.72 0.56

0.90** 2.63*** 2.02*** 2.53***

0.01*** 0.01*** n.s. n.s.

0.09*** 0.07*** 0.21*** 0.20*

0.09** 0.07*** n.s. 0.18***

0.16** 0.19*** 0.15*** n.s.

P-levels of likelihood ratio tests: *: