DOI: 10.5061/dryad.pvmcvdnxp
title: Data for: Thermal plasticity of multiple traits varies more within than between populations of <em>Plantago lanceolata</em> at its northern range edge

description: Temperature plays a pivotal role in defining the distribution of species
 and the fitness of individuals within species’ ranges. Phenotypic
 plasticity can allow individuals to cope with varying environmental
 conditions, including rapid climate change. Populations at range edges
 experience more variable conditions than core populations and thus are
 hypothesized to exhibit higher thermal plasticity. However, as the
 strength of plasticity often varies between individuals, it can also
 differ among local populations at range edges. We studied the extent of
 and variation in thermal plasticity for several traits within and between
 populations of the perennial herb Plantago lanceolata L. (Plantaginaceae)
 at its northern range edge. We sampled seeds from nine sites within a 50 x
 50 km region and grew them under three temperature regimes in a
 greenhouse. We measured traits related to size, flowering, pathogen
 responses, and inflorescence pigmentation. We expected to find higher
 plasticity in traits less strongly connected to fitness, and that
 differences between individuals would outweigh differences between
 populations in underpinning this variation in plasticity. Our results show
 thermal plasticity in leaf size and abundance, flowering probability and
 abundance, and pigmentation. Notably, we also found increased pathogen
 symptoms and higher infection rates of one of two viruses screened,
 highlighting the potential for changes in pathogen sensitivity and
 exposure under climate change. Importantly, in all traits but flower
 abundance, more variation in plasticity was attributable to differences
 within populations than between populations. Although this contribution
 was small in magnitude compared to thermal effects on traits, the higher
 intra- versus interpopulation variation in plasticity suggests that
 differences between individuals provide most of the variation in thermal
 plasticity, which may be driven by small-scale variations in habitat
 conditions, highlighting the need for conservation strategies that
 consider microhabitat variation to support short-term adaptive responses
 to thermal variability.
description: <em>Plant material</em> We
 collected seeds of <em>Plantago lanceolata</em> from nine
 local populations (habitat patches consisting of meadows or pastures) in
 Åland during August 2019 (Fig. 1; in Hällfors et al. <em>in
 prep</em> and Table S1 in ‘Supplementary Information.pdf’). In the
 Åland Islands, <em>P. lanceolata</em> occurs in a network of
 ca. 4000 meadows (Ojanen et al., 2013). We chose the seed collection sites
 for this study from nine populations in three different parts of mainland
 Åland to represent (1) a variety of areas across the Åland main island,
 (2) <em>P. lanceolata</em> habitat area extent, and (3)
 connectivity to other nearby local populations of <em>P.
 lanceolata</em> (Fig. 1 in Hällfors et al. <em>in
 prep</em>). All meadows in Åland where
 <em>P. lanceolata</em> occurs are annually surveyed for the
 presence of the butterfly species <em>Melitaea cinxia</em>,
 the larvae of which use it as their food plant, and for the fungal
 pathogen <em>Podosphaera plantaginis</em> that infects
 <em>P. lanceolata</em> (Ojanen et al., 2013). During the first
 surveys at the beginning of the 1990s, the cover of <em>P.
 lanceolata</em> and <em>Veronica spicata</em> (the other
 host species of <em>M. cinxia</em> larvae) were also measured
 and their exact locations recorded (Ojanen et al., 2013). As a proxy for
 <em>P. lanceolata</em> population size, ground cover
 (m<sup>2</sup>) of the plant has been estimated within each
 patch (Ojanen et al., 2013). Habitat area is considered the extent to
 which the two host plant species of <em>M. cinxia</em> occurs
 in a patch. This measure of habitat area can be used as a proxy for
 fragmentation and population size and thus a rough proxy for genetic
 diversity (González et al., 2020). Connectivity between patches is
 evaluated annually and can be used as a proxy for gene flow among
 populations (Hanski, 1999). As <em>P.
 lanceolata</em> was the most common or the only host species in the
 seed collection sites for this study (Ojanen et al., 2013), the habitat
 area corresponds well with the extent of the focal species. Based on the
 connectivity metrics and the species’ dispersal ability (with both
 pollination and seed dispersal mainly occurring within local populations),
 we consider each sampling site to be independent from the others and treat
 them as spatially unstructured variables in our subsequent analyses.
 However, to test that closely located sites with high connectivity values
 within regions are not more similar to each other than expected, we
 included a sensitivity analysis testing for consistency of results by
 leaving out one of the sites within the region in turn (see
 <em>Statistical analyses</em> below and results in
 ‘Supplementary Information.pdf’ Tables S2 and S3). To
 obtain a representative sample of the diversity within each population, we
 collected seeds from 17–50 individuals in each of the nine chosen sites
 (i.e., per population; individuals sampled based on availability,
 following seed-collecting guidelines; ENSCONET, 2009; Table S1 in
 ‘Supplementary Information.pdf’). We collected seeds from each mother
 individual separately to allow the seeds of different mother plants to be
 sown across all treatments to measure within-population variation in
 plasticity. We deposited voucher specimens from each sampling site at the
 herbarium of the Finnish Museum of Natural History (H
 <em>sensu</em> Thiers, 2016). We left the seeds to dry and
 ripen at room temperature for a few weeks before the start of the
 experiment.  <em>Experimental set-up and thermal
 conditions</em> We conducted the experiment in
 the Viikki Plant Growth Facilities at the University of Helsinki during
 September 2019 – February 2020, in six greenhouse compartments with three
 temperature treatments each replicated once. The temperatures chosen for
 the treatments were based on average June and July temperatures in Åland,
 1959–2018 (Fig. S1 in ‘Supplementary Information.pdf’). The Cold treatment
 day temperature was set at 17°C with night temperature 8°C; while the Mean
 temperature treatment was 20°C during daytime and 11°C at night; and the
 Warm treatment was set at 23°C during daytime and 14°C at night.
 High-pressure sodium lamps created light conditions and photoperiod were
 consistent across all treatments (18 h light and 6 h dark), with day and
 night temperatures following the photoperiod. From each
 of the nine sampled populations, we randomly chose 12 mother individuals
 to provide 12 seeds for the experiment, with the expectation that this
 would result in at least 6 germinated seedlings from 10 mother individuals
 (one for each replicate, two per treatment; a total of 1296 seeds). Seeds
 were sown in mid-September 2019 during 4 consecutive days (September
 17–20). We placed each seed individually in germination tray cells (160
 per tray) filled with a potting mix (2:1:1 of sowing mix (Kekkilä WHS
 R8017), sand (Weber 1–2 mm), and perlite). After sowing the seeds, we
 sprinkled the soil surface with a 1:1 mixture of vermiculite and sand. We
 placed the germination trays on moisture-retaining matting on the
 greenhouse benches. They were watered automatically from below every 3
 days for 3 minutes initially, but this was changed to 3 minutes every
 second day from September 24. This watering regime kept the soil moist in
 all temperature treatments.   One month after sowing,
 we chose the experimental individuals by stratified random sampling from
 the entire pool of germinated seedlings. We repotted the chosen plants
 into 0.7 L pots containing soil of 4:2:1:1 parts of potting compost
 (Kekkilä WHS R8030), perlite, fine sand, and coarse sand. We allocated one
 seedling for each of the ten randomly selected successfully germinated
 mother individuals to each treatment-by-replicate combination. When fewer
 than two individuals per mother plant germinated in a treatment, we
 randomly chose a germinated seedling from another mother plant for which
 more than two individuals had germinated or from one that had not been
 allocated to the group of ten mother individuals. Because of uneven
 germination, each treatment ended up containing representatives of 7-12
 mother plants, as sometimes there was zero or only one individual
 available from one mother plant. Therefore, the other replicate was
 randomly assigned a leftover individual from another mother. If there were
 not enough germinated seedlings from another mother individual, either,
 the replicate in question ended up with fewer individuals than the others.
 This was the case for the mean and warm treatments, where the germination
 rate was lower than in the cold treatment. Altogether
 517 plants were included in the experiment, with 82–92 individuals in each
 replicate (90 and 92 in the cold replicates; 85 and 86 in the mean
 replicates, and 82 in both warm replicates). We placed the plants on two
 benches in each replicate. They were initially watered automatically for
 three minutes every second day, but on November 14, we changed the
 watering regime to three minutes every three days, as the decline in
 sunshine towards the winter reduced evapotranspiration. On December 13, we
 changed the watering regime to 3 minutes on Mondays, Tuesdays, and
 Fridays, to ensure the plants would stay sufficiently moist until the next
 watering event. For the light spectrum of the High-Pressure Sodium lamps
 used in the greenhouses, see Guo et al. (2016).To avoid the effects of
 differing local conditions on the benches (for example, higher light and
 temperature directly underneath lamps or being closer to the windows where
 the temperature may differ slightly), we rotated the plants systematically
 3 or 4 times a month. In addition to light from lamps, there was ambient
 daylight from windows.  On November 22 and January 13,
 we fertilized the plants using Kekkilä Professional Superex NPK 12-5-27:
 2kg/1000 L = 20g/10 L bucket, with 5 x 10 L buckets per bench. We
 submerged the pots in the fertilizer for 30 minutes, after which the
 benches were drained. On the same days, we applied nematodes to the soil,
 using 1 teaspoon of nematodes in 3 L of water. We used two species of
 entomopathogenic nematodes: <em>Steinernema feltidae</em> to
 control sciarid flies and <em>Steinernema carpocapsae</em> to
 control other flies. We applied them after the fertilization and automatic
 watering. We re-fertilized four additional times, once a week from January
 30 to February 19, to induce flowering to be able to collect
 inflorescences for conducting pigmentation and reflection measurements at
 the end of the experiment. During this same period, we increased watering
 so that the plants were watered from the top using 5 L of water per
 treatment, in addition to the watering regime from underneath.
 <em>Motivation for measuring the chosen
 traits</em> Vegetative traits like the size of
 leaves and the number of leaves can tell us about the amount of resources
 that the plant is able to allocate towards biomass production. Vegetative
 traits have been shown to be rather plastic in <em>P.
 lanceolata</em>, even to the degree that this plasticity masks
 differences in local adaptation across large geographic extents (Villellas
 et al., 2021). Thus, we expect to see high plasticity in these traits for
 these high-latitude populations. By measuring
 reproductive traits like flowering and number of flowers, we could assess
 fitness-related plasticity, i.e., to what extent is the reproductive
 output is affected by differing environmental conditions. <em>P.
 lanceolata</em> has been shown to express phenotypic plasticity in
 inflorescence reflectance and color (Lacey & Herr, 2005). The
 reflectance of inflorescences affects their internal temperature when
 there is incoming solar radiation (Anderson et al., 2013). Flower
 pigmentation may adjust reflectance and flower temperature since dark
 flowers absorb more solar energy than light, reflective flowers (Jewell et
 al., 1994; McKee & Richards, 1998). Pigmentation would be affected
 by, among other things, flavonoid and hydroxycinnamic acid (HCA)
 derivatives content, and less pigmented inflorescences should reflect more
 blue light. Individuals and populations of <em>P.
 lanceolata</em> vary in their susceptibility to various fungal and
 viral pathogens (Norberg et al., 2023; Sallinen et al., 2023; Susi
 & Laine, 2015). Symptoms may be caused by a variety of pathogens,
 including fungi, but two of the most common viruses within the
 metapopulation on Åland include Plantago lanceolata latent virus (PlLV)
 and Plantago latent caulimovirus (PlCaV) (Susi et al., 2019). The
 prevalence of these two viruses varies across the local populations in
 Åland. It is not known whether temperature affects the susceptibility of
 the plants or the virulence of the pathogens, and thus whether viruses can
 differentially affect the populations and their symptoms when grown in
 different thermal conditions (Trebicki, 2020).
 <em>Trait measurements</em> To
 test thermal plasticity, we measured vegetative traits (leaf size and
 number), floral traits (flowering probability and abundance), floral bract
 pigmentation - content of both flavonoids and hydroxycinnamic acid (HCA)
 derivatives (including phenylethanoids), and floral blue-light reflection
 as a proxy of pigmentation, and the proportion of leaves showing symptoms
 of pathogen infection. We also tested for the presence of two common
 viruses: <em>Plantago lanceolata</em> latent virus (PlLV; Susi
 et al., 2017) and <em>Plantago</em> latent caulimovirus
 (PLCaV) and co-infections by the two viruses (Susi et al., 2019). The
 measured trait values across populations are given in Fig. S4 and
 separately for each population in Fig. S5 in ‘Supplementary
 Information.pdf’. See data ‘P.lanceolata_plasticity_data.csv’.
 We took leaf size measurements during three consecutive days,
 starting 45 days after sowing began. We measured the length and width in
 cm of the leaf that, based on visual inspection, appeared to be the
 longest. We counted the number of leaves on each individual plant 12 weeks
 from sowing. Simultaneously, we assessed the degree to which plants showed
 symptoms of vertical pathogen infection (pathogens that likely had arrived
 with the seeds) by separately counting the number of leaves with typical
 signs of pathogen infection symptoms. We noted the number of leaves
 showing different kinds of pathogen symptoms (red, yellow, curly,
 necrosis, undefined). We used the proportion of leaves showing any
 symptoms as a pathogen response in our analyses. The information on
 different categories of symptoms is available in the accompanying data
 (‘P.lanceolata_plasticity_data.csv’). To measure infection rate by two
 common pathogens, we also took leaf samples from each individual for virus
 detection by cutting a 1 cm<sup>2</sup> piece from a leaf of
 each individual and placing the sample in a microtube. We froze and kept
 the samples in -20°C until DNA extraction. We extracted the DNA following
 Lodhi et al. (1994). See below for a description of how viruses PlLV and
 PlCaV were detected. The pathogen responses thus measure how the load of
 pathogens introduced in the experiment through the seeds affects infection
 rate and symptoms in plants grown at different temperatures. While these
 measurements allow us to capture the outcome of pathogen effects in the
 different thermal treatments, this does not allow us to distinguish any
 differences in resistance among the plants themselves or whether the
 pathogens differed in their virulence across the different temperatures
 tested. 19 weeks into the experiment, we began
 collecting inflorescences. We collected only fresh mature inflorescences,
 just before the stigma, petals, and anthers emerged from between the
 sepals. We collected inflorescences 1–3 times per week, based on their
 availability. By week 22, we had collected inflorescences from 72, 155,
 and 130 individuals in the Cold, Mean, and Warm treatments, respectively.
 These were used to measure floral bract pigmentation (flavonoids and HCA
 derivative content) and floral blue light reflection (see below for
 description of how this was conducted). For those individuals from which
 several inflorescences were collected, we averaged all subsequent
 measurements per individual prior to statistical analyses. After
 inflorescence collection for reflectance and pigmentation analyses was
 completed, we counted the total number of flowers by tallying the
 remaining inflorescences and previously cut flowering stalks. We
 summarized flower presence by truncating flower numbers &gt;1 to
 1.  <em>Measuring floral blue light reflection
 and floral bract pigmentation proxies</em> Right
 after we collected the inflorescence, we captured images in the
 near-infrared (NIR) region using an Olympus E-M1 camera and manual
 exposure with a Sigma 30mm F2.8 DN lens to visualize reflectance in the
 floral bracts. We converted the camera with DSLR AstroTEC to full spectrum
 using quartz glass to replace the sensor's built-in UVIR-cut filter.
 Lacey and Herr (2005) have reported that temperature influences the
 reflectance of <em>P. lanceolata</em> inflorescences from
 725-850 nm in the NIR region, so we additionally used a 780 nm Heliopan
 RG780 ES 52 filter, which attenuates wavelengths below 780 nm. As a light
 source, we used a lamp with an incandescent bulb. Comparison of the NIR
 images allowed any differences in absorbance characteristics of flowers to
 be discerned. We processed the images using RawDigger: Research edition
 (Version 1.4.2). We evaluated blue channel sensor counts by selecting two
 300x300 pixel samples from each flower at the tip and the widest section
 of the flower. We used a white Teflon slab with 95% reflectance as a white
 reference for each image. We used the following equation to calculate
 percentage reflection in the NIR region, which was subsequently normalized
 across samples: *((sensor counts for flower) / (sensor
 counts for Teflon slab / 0.95)) <em>100</em>
 The inflorescences were stored for about 18 months in dry and
 dark conditions before having the opportunity to analyse phenolic
 compounds from their bracts. The extraction and analysis of phenolic
 compounds from the bracts of <em>P. lanceolata</em> followed
 the protocol of Stiles et al. (2007) with some modifications detailed
 here. Approximately 10 mg DW of bracts (previously dried at room
 temperature) from each sampled flower was soaked in 1 ml of 100% MeOH
 (CH<sub>4</sub>O, ≥99.9%, Mr = 32.04 g·mol
 <sup>−1</sup>, Sigma-Aldrich, Schnelldorf, Germany) in an
 Eppendorf tube. This sample was shaken for 0.5 min using a vortex mixer
 and ultrasonicated (K-51E, Kraintek Czech s.r.o., Hradec Králové, Czech
 Republic) for 30 min at room temperature, then macerated for 24h at -22°C.
 The liquid part was collected using a pipette, and the remaining plant
 material was re-extracted in 1 ml of aqueous 50% (v/v) MeOH solution, but
 otherwise repeating the same protocol. The liquid fractions were combined
 (in 1:1 ratio, 0.5 ml each) and filtered through a 0.2 µm PTFE syringe
 filter. The extracts of the bracts were subsequently
 analysed using the HPLC-DAD system (Agilent 1260 Infinity II, Agilent
 Technologies, Santa Clara, CA, USA) equipped with a Hypersil Gold
 chromatographic column (C18, 50 mm × 2.1 mm, 1.9 μm, Thermo Scientific,
 Waltham, NJ, USA). 3 µL of extract was injected into the system, the
 phenolic compounds were then separated in gradient of two mobile phases: A
 - water:COOH (995:5, v/v) B – acetonitrile:COOH (995:5, v/v)
 (C<sub>2</sub>H<sub>3</sub>N, ≥99.9%, Mr = 41.05
 g·mol <sup>−1</sup>, Sigma-Aldrich, Schnelldorf, Germany;
 CH<sub>2</sub>O<sub>2</sub>, Mr = 46.03 g·mol
 <sup>−1</sup>, Sigma-Aldrich, Schnelldorf, Germany).
 Specification of the mobile phase gradient: t = 0-20 min A: 100 %
 -&gt; 90 %; t = 20-35 min A: 90 % -&gt; 85 %; t = 35-45 min A: 85
 % -&gt; 70 %; t = 45-50 min A: 70 % -&gt; 0 %; t = 50-60 min A: 0
 %; t = 60-70 min A: 0 % -&gt; 100 % (+7 min. equilibration time). The
 flow was set as constant during the whole analysis (0.2 mL
 min<sup>-1</sup>), likewise the temperature of the column
 (25°C). Absorption spectra of detected compounds were recorded in the
 range from 190-750 nm, chromatograms at 270, 314, 360, 440, 530, and 680
 nm. Compounds were tentatively classified as flavonoids and
 hydroxycinnamic acid derivatives based on their absorption spectra and
 retention behaviour. For the relative quantification of phenolic compound
 content (mainly of flavonoids and hydroxycinnamic acid derivatives), the
 area under those peaks detected at 270 nm was used. The adjusted peak
 areas of compounds were adjusted by sample dry weight (DW) prior to
 further data processing (providing a measure of peak area mAU.s/ d.w.
 mg). <em>Detecting viruses</em>
 We detected PlLV and PlCaV using specific primers (for PlLV:
 PiLVi2_forward_1 5′-GTGTTTAACAATGAAGTGAGCC-3′C-3′ and PiLVi2_reverse_4
 5′-AATCCATCCACACATCCAATC-3′; and for PlCaV forward primer
 5′-AGGAGATGCCCATACTTTACC-3′ and reverse primer
 ′-GACTTGCCAGAACCTGATTTAC-3′) as in Sallinen et al. (2020). We ran PCR
 reactions to detect viruses in a ﬁnal volume of 10 μL containing 1 μL of
 DNA, and GoTaq Green® polymerase 5x Mastermix (Promega Corporation, USA)
 according to the manufacturer’s instructions. We subjected samples to
 initial denaturation at 95 °C for 2 min, following 35 cycles of
 denaturation at 95 °C for 40 s, annealing at 60 °C for 40 s, and extension
 at 72 °C for 1 min with a ﬁnal extension step of 72 °C for 5 min. The PCR
 product sizes were 117 bp for PlLV and 100 bp for PlCaV. We resolved the
 amplicons on a 1.2% agarose gel and visualized them using the Gel Doc XR
 System (Bio-Rad Laboratories, Inc., USA).
 <em>Statistical analyses</em> Our
 main aim was to assess, for the different traits, 1) the degree of thermal
 plasticity and 2) the origin of the majority of the variation in this
 plasticity, i.e., whether most variation in plasticity could be assigned
 to the population level or to the mother individual level. A mixed
 modelling approach allowed questions 1) and 2) to be assessed
 simultaneously, by choosing <em>temperature treatment</em> as
 a fixed effect and <em>population</em> and <em>mother
 individual</em> as nested random effects (Arnold et al., 2019).
 Here, the fixed effect describes the overall average response to the
 temperature treatment of the focal population, while the random effects
 describe how much variation is partitioned among
 <em>populations</em> and <em>mother
 individuals</em>, i.e., the contribution of variation in response
 attributable to interpopulation and intrapopulation differences,
 respectively. Using this approach, we were able to answer the main
 research questions concerning the contribution to variation in the
 measured traits by different local populations and mother individuals as
 opposed to the average value of all populations. Simultaneously, we
 accounted for non-independence between plants grown in the same greenhouse
 chamber by also using the greenhouse chamber as a random effect
 (<em>Replicate</em>). We evaluated the independence of the
 populations within each of the three main sampling areas by their
 connectivity values (Fig. 1 in Hällfors et al. <em>in
 prep</em>). For populations with relatively high connectivity values
 and situated in close proximity to each other (the region containing
 populations G, E, and F), we conducted a sensitivity analysis to test the
 consistency of the results. We left out one of the highly connected
 populations at a time and compared the model estimates, posterior
 probabilities, and standard deviations (Tables S2 and S3 in ‘Supplementary
 Information.pdf’).  We used the
 <em>brms</em> package (version 2.15.0; Bürkner, 2021) in the R
 environment (R version 4.2.2; R Core team, 2023) to fit generalised and
 linear mixed-effect models (GLMMs and LMMs) in a Bayesian framework. We
 modelled <em>leaf size, total flavonoid content, HCA
 content</em> and <em>blue light reflectance</em> with a
 Gaussian error distribution, <em>number of leaves</em> with a
 negative binomial, <em>flowering probability</em>, PLCaV
 infection and PlLV infection with a Bernoulli error distribution,
 <em>number of flowers</em> and <em>number of
 infections</em> with a zero-inflated Poisson distribution, and the
 <em>proportion of leaves with pathogen responses</em> with a
 binomial error distribution. We estimated leaf size as the product of leaf
 length and leaf width to obtain a rough estimate in
 cm<sup>2</sup>. These values were divided by 100 and
 square-root transformed, providing a better posterior predictive fit of
 the resulting model. See analysis code
 ‘P.lanceolata_plasticity_brms_analysis.R’. For all
 models, we used informative prior distributions, which were needed to
 avoid computational issues. The informative prior distributions were
 justified for use here because the magnitude of the effects was expected
 to be small for all response variables. For the fixed effects and the
 random effects, we used Gaussian distributions with a mean of zero for
 both and a standard deviation of 2 and 1, respectively. However, for the
 models on the HCA derivative and flavonoid content, we set the standard
 deviation to 100 for the fixed effects and 50 for the random effects to
 account for the larger scale of these response variables. For the
 intercept, we used Student’s t as defined by the default settings in
 <em>brms</em> (Bürkner, 2021). For all models, we ran four
 chains with 4000 iterations, including a warm-up of 2000 iterations and a
 thinning rate of 1. Thus, we obtained 8000 posterior samples for each
 model. For error distributions with an additional
 parameter to the location parameter, we considered a version of the model
 where the additional parameter was allowed to vary by treatment
 temperature. For response variables with a Gaussian error distribution
 (<em>leaf size</em>, <em>flavonoid</em> and
 <em>HCA content</em>, <em>blue light
 reflectance</em>), the additional parameter was the
 <em>residual standard deviation (σ)</em>. For the
 <em>number of flowers</em> and <em>number of
 infections</em> modelled with a zero-inflated Poisson distribution,
 the additional parameter was the <em>zero-inflation
 probability</em>.  For the <em>number of leaves</em>,
 modelled using a negative binomial distribution, the additional parameter
 allowed to vary was <em>shape</em>. We compared the basic
 models to those models including this varying error distribution using the
 <em>loo_compare</em> function within the
 <em>brms</em> package, which compares the predictive
 performance of the models with leave-one-out cross-validation (Vehtari et
 al., 2017). Because of computational issues, the comparison of the number
 of leaves was conducted using the widely applicable information criterion
 (WAIC; Vehtari et al., 2017). If the additional model did not perform
 better (uncertainty intervals overlapped), we used the basic model for
 interpretation and result presentation. We evaluated
 model convergence by investigating whether the Rhat values were
 &lt;1.05 and the bulk effective sample size and tail effective sample
 size were each &gt;400. We also visually inspected plots of posterior
 predictive checks using 10 posterior samples to identify potential
 discrepancies between the observed and predicted data.
 In addition to interpreting the results based on mean posterior
 probability distribution and credible intervals, we used the
 <em>hypothesis</em> function in the <em>brms</em>
 package to interpret the models <em>vis-à-vis</em> the effect
 of temperature treatment. This function computes an evidence ratio for a
 one-sided hypothesis. In other words, we asked what the posterior
 probability is of the response being bigger (or in the case of phenolic
 absorbance where the direction of the effect was negative across
 treatments, smaller) than zero: a) for the mean temperature, b) between
 the mean and the warm temperature, and c) in the warm
 temperature. We did a correlation analysis using the
 <em>ggpairs</em> function from the <em>GGally</em>
 package (Schloerke et al., 2024) on all measured traits, both across and
 within treatments, to evaluate the degree to which, e.g., high values in
 certain traits correlate with high values in others. To
 estimate the effect on variance in thermal plasticity resulting from
 intrapopulation and interpopulation differences, we compared the estimated
 standard deviation attributable to the random effect of population and
 mother individual.
 <strong>References</strong>
 Anderson, E. R., Lovin, M. E., Richter, S. J., & Lacey,
 E. P. (2013). Multiple Plantago species (Plantaginaceae) modify floral
 reflectance and color in response to thermal change. <em>American
 Journal of Botany</em>, <em>100</em>(12), 2485–2493.
 https://doi.org/10.3732/ajb.1300180 Arnold, P. A., Kruuk, L. E. B., & Nicotra, A. B. (2019). How to analyse plant phenotypic plasticity in response to a changing climate. <em>New Phytologist</em>, <em>222</em>(3), 1235–1241. https://doi.org/10.1111/nph.15656 Bürkner, P.-C. (2021). Bayesian Item Response Modeling in R with brms and Stan. <em>Journal of Statistical Software</em>, <em>100</em>(5), 1–54. https://doi.org/10.18637/jss.v100.i05 ENSCONET (2009) ENSCONET Seed Collecting Manual for Wild Species. Accessed in 2025 at: https://brahmsonline.kew.org/Content/Projects/msbp/resources/Training/ENSCONET_Collecting_protocol_English.pdf González, A. V., Gómez-Silva, V., Ramírez, M. J., & Fontúrbel, F. E. (2020). Meta-analysis of the differential effects of habitat fragmentation and degradation on plant genetic diversity. <em>Conservation Biology</em>, <em>34</em>(3), 711–720. https://doi.org/10.1111/cobi.13422 Guo, X., Hao, X., Zheng, J. M., Little, C., & Khosla, S. (2016). Response of greenhouse mini-cucumber to different vertical spectra of LED lighting under overhead high pressure sodium and plasma lighting. <em>Acta Horticulturae</em>, <em>1134</em>, 87–94. https://doi.org/10.17660/ActaHortic.2016.1134.12 Hanski, I. (1999). Habitat Connectivity, Habitat Continuity, and Metapopulations in Dynamic Landscapes. <em>Oikos</em>, <em>87</em>(2), 209–219. https://doi.org/10.2307/3546736 Jewell, J., McKEE, J., & Richards, A. J. (1994). The keel colour polymorphism in Lotus corniculatus L.: Differences in internal flower temperatures. <em>New Phytologist</em>, <em>128</em>(2), 363–368. https://doi.org/10.1111/j.1469-8137.1994.tb04020.x Jousimo, J., Tack, A. J. M., Ovaskainen, O., Mononen, T., Susi, H., Tollenaere, C., & Laine, A.-L. (2014). Ecological and evolutionary effects of fragmentation on infectious disease dynamics. <em>Science</em>, <em>344</em>(6189), 1289–1293. https://doi.org/10.1126/science.1253621 Lacey, E. P., & Herr, D. (2005). Phenotypic plasticity, parental effects, and parental care in plants? I. An examination of spike reflectance in Plantago lanceolata (Plantaginaceae). <em>American Journal of Botany</em>, <em>92</em>(6), 920–930. https://doi.org/10.3732/ajb.92.6.920 Lodhi, M. A., Ye, G.-N., Weeden, N. F., & Reisch, B. I. (1994). A simple and efficient method for DNA extraction from grapevine cultivars andVitis species. <em>Plant Molecular Biology Reporter</em>, <em>12</em>(1), 6–13. https://doi.org/10.1007/BF02668658 Mckee, J., & Richards, A. J. (1998). Effect of flower structure and flower colour on intrafloral warming and pollen germination and pollen-tube growth in winter flowering Crocus L. (Iridaceae). <em>Botanical Journal of the Linnean Society</em>, <em>128</em>(4), 369–384. https://doi.org/10.1111/j.1095-8339.1998.tb02127.x Norberg, A., Susi, H., Sallinen, S., Baran, P., Clark, N. J., & Laine, A.-L. (2023). Direct and indirect viral associations predict coexistence in wild plant virus communities. <em>Current Biology</em>, <em>33</em>(9), 1665-1676.e4. https://doi.org/10.1016/j.cub.2023.03.022 Ojanen, S. P., Nieminen, M., Meyke, E., Pöyry, J., & Hanski, I. (2013). Long-term metapopulation study of the Glanville fritillary butterfly (Melitaea cinxia): Survey methods, data management, and long-term population trends. <em>Ecology and Evolution</em>, <em>3</em>(11), 3713–3737. https://doi.org/10.1002/ece3.733 R Core Team (2024). <em>R: a language and environment for statistical computing.</em> Vienna: R Foundation for Statistical Computing Sallinen, S., Norberg, A., Susi, H., & Laine, A.-L. (2020). Intraspecific host variation plays a key role in virus community assembly. <em>Nature Communications</em>, <em>11</em>(1), 5610. https://doi.org/10.1038/s41467-020-19273-z Sallinen, S., Susi, H., Halliday, F., & Laine, A.-L. (2023). Altered within- and between-host transmission under coinfection underpin parasite co-occurrence patterns in the wild. <em>Evolutionary Ecology</em>, <em>37</em>(1), 131–151. https://doi.org/10.1007/s10682-022-10182-9 Schloerke, B., Cook, D., Larmarange, J., Briatte, F., Marbach, M., Thoen, E., Elberg, A., Toomet, O., Crowley, J., Hofmann, H., & Wickham, H. (2024). <em>GGally: Extension to “ggplot2”</em> (Version 2.2.1). https://cran.r-project.org/web/packages/GGally/index.html Stiles, E. A., Cech, N. B., Dee, S. M., & Lacey, E. P. (2007). Temperature-sensitive anthocyanin production in flowers of Plantago lanceolata. <em>Physiologia Plantarum</em>, <em>129</em>(4), 756–765. https://doi.org/10.1111/j.1399-3054.2007.00855.x Susi, H., Filloux, D., Frilander, M. J., Roumagnac, P., & Laine, A.-L. (2019). Diverse and variable virus communities in wild plant populations revealed by metagenomic tools. <em>PeerJ</em>, <em>7</em>, e6140. https://doi.org/10.7717/peerj.6140 Susi, H., & Laine, A.-L. (2015). The effectiveness and costs of pathogen resistance strategies in a perennial plant. <em>Journal of Ecology</em>, <em>103</em>(2), 303–315. https://doi.org/10.1111/1365-2745.12373 Susi, H., Laine, A.-L., Filloux, D., Kraberger, S., Farkas, K., Bernardo, P., Frilander, M. J., Martin, D. P., Varsani, A., & Roumagnac, P. (2017). Genome sequences of a capulavirus infecting Plantago lanceolata in the Åland archipelago of Finland. <em>Archives of Virology</em>, <em>162</em>(7), 2041–2045. https://doi.org/10.1007/s00705-017-3298-0 Trebicki, P. (2020). Climate change and plant virus epidemiology. <em>Virus Research</em>, 286, 198059. https://doi.org/10.1016/j.virusres.2020.198059 Thiers B. (2016). <em>Index herbariorum: a global directory of public herbaria and associated staff.</em> New York Botanical Garden’s Virtual Herbarium. Vehtari, A., Gelman, A., & Gabry, J. (2017). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. <em>Statistics and Computing</em>, <em>27</em>(5), 1413–1432. https://doi.org/10.1007/s11222-016-9696-4 Villellas, J., Ehrlén, J., Crone, E. E., Csergő, A. M., Garcia, M. B., Laine, A.-L., Roach, D. A., Salguero-Gómez, R., Wardle, G. M., Childs, D. Z., Elderd, B. D., Finn, A., Munné-Bosch, S., Bachelot, B., Bódis, J., Bucharova, A., Caruso, C. M., Catford, J. A., Coghill, M., … Buckley, Y. M. (2021). Phenotypic plasticity masks range-wide genetic differentiation for vegetative but not reproductive traits in a short-lived plant. <em>Ecology Letters</em>, <em>24</em>(11), 2378–2393. https://doi.org/10.1111/ele.13858
description: # Data for: Thermal plasticity of multiple traits varies more within than
 between populations of *Plantago lanceolata* at its northern range edge
 Dataset DOI: [10.5061/dryad.pvmcvdnxp](10.5061/dryad.pvmcvdnxp) ##
 Description of the data and file structure We studied the extent of and
 variation in thermal plasticity for several traits within and between
 populations of the perennial herb *Plantago lanceolata* L.
 (Plantaginaceae) at its northern range edge. We sampled seeds from nine
 sites within a 50 x 50 km region and grew them under three temperature
 regimes in a greenhouse. We measured traits related to size, flowering,
 pathogen responses, and inflorescence pigmentation. Data used in the
 manuscript: Hällfors, M.H., Robson T.M., Burg S., Pentikäinen S.,
 Koivusaari S. H. M., Luoto M., Nezval J., Pech R., Saastamoinen M.,
 Schulman L., Sirén J., Susi H.: Thermal plasticity of multiple traits
 varies more within than between populations of Plantago lanceolata at its
 northern range edge. ### Files and variables #### File:
 P.lanceolata_plasticity_brms_analysis.R **Description:** R code for
 conducting brms models on the data. Uses the data
 ‘P.lanceolata_plasticity_data.csv” #### File:
 P.lanceolata_plasticity_data.csv **Description:** Data collected in
 temperature experiment. Used in R script
 “P.lanceolata_plasticity_brms_analysis.R”. ##### Variables *
 UniqueID: unique code for the plant individual measured in the experiment
 * MotherPlant: ID of the mother plant from which the seed was grown *
 TreatTemp: Categorical value of treatment condition in which the plant was
 grown. Cold = 17°C day / 8°C night; Mean 20°C day / 11°C night; Warm =
 23°C day / 14°C night. * Replicate: Each treatment was replicated once in
 another growing chamber. C-A= Cold room A; C-B= Cold room B; M-A= Mean
 room A; M-B= Mean room B; W-A= Warm room A; W-B= Warm room B. *
 Population: Source population (see ‘Supplementary Information.docx’ Table
 S1 for description). * LeafLength_mm: The length in cm of the leaf that,
 based on visual inspection, appeared to be the longest, measured 45 days
 from sowing. * LeafWidth_mm: The width in cm of the leaf that, based on
 visual inspection, appeared to be the longest, measured 45 days from
 sowing. * LeafSize: product of ‘Leaf_Length_mm’ and ‘Leaf_Width_mm” * 100
 * LeafAbund: Number of leaves produced by the individual by the time of
 counting, 12 weeks from sowing. * Flowered: Binary variable on whether the
 individual produced flowers during the experiment; 1=yes, 0=no. *
 FlorAbund: Number of flowers produced by the individual during the
 experiment. * ReflBlueMn: Reflection of blue light in the NIR region. See
 the methods description for how this was obtained. * TotalFlav: Total
 amount of compounds tentatively classified as flavonoid derivatives based
 on their absorption spectra and retention behaviour. See the methods
 description for how this was obtained. * TotalHCA: Total amount of
 compounds tentatively classified as hydroxycinnamic acid derivatives based
 on their absorption spectra and retention behaviour. See the methods
 description for how this was obtained. * SymptomLeafAbund: Number of
 leaves showing symptoms. * SymptomLeafAbund_Yellow: Number of leaves
 showing symptoms creating a yellow color upon visual inspection. *
 SymptomLeafAbund_Red: Number of leaves showing symptoms creating a red
 color upon visual inspection. * SymptomLeafAbund_Necrosis: Number of
 leaves showing symptoms of necrosis upon visual inspection. *
 SymptomLeafAbund_Curly: Number of leaves showing symptoms of leaves
 curling up upon visual inspection. * SymptomLeafAbund_Unknown: Number of
 leaves showing symptoms that could not be classified upon visual
 inspection. * PlLV: Binary variable indicating whether a Plantago
 lanceolata latent virus infection was detected (1) or not (0). NA
 indicates that the individual's samples were not tested or the test
 did not succeed. See the methods description for how this was obtained. *
 Caulimo: Binary variable indicating whether a Plantago latent caulimovirus
 infection was detected (1) or not (0). NA indicates that the
 individual's samples were not tested or the test did not succeed. See
 the methods description for how this was obtained. * NumbInf: Total number
 of infections (0, 1, or 2); based on the sum of columns “PlLV” and
 “Caulimo”. #### File: Supplementary_Information.pdf
 **Description:** Document containing supplementary figures and tables for
 the publication Hällfors, M.H., Robson T.M., Burg S., Pentikäinen S.,
 Koivusaari S. H. M., Luoto M., Nezval J., Pech R., Saastamoinen M.,
 Schulman L., Sirén J., Susi H.: Thermal plasticity of multiple traits
 varies more within than between populations of *Plantago lanceolata* at
 its northern range edge. Contains Figures S1-S7, Table S1-S3, and
 References. ## Code/software The R environment, and packages brms,
 parallel, dplyr, devtools, and cmdstanr ## Access information Other
 publicly accessible locations of the data: * NA Data was derived from the
 following sources: * NA