\articletype Original Articles Divergent links between environmental heterogeneity and thermal plasticity across the European ranges of three Hypericum species

\articletype Original Articles Divergent links between environmental heterogeneity and thermal plasticity across the European ranges of three Hypericum species | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL Ecology and Evolution This is a preprint and has not been peer reviewed. Data may be preliminary. 9 January 2026 V1 Latest version Share on \articletype Original Articles Divergent links between environmental heterogeneity and thermal plasticity across the European ranges of three Hypericum species Authors : Susanna Koivusaari 0009-0009-3183-8870 [email protected] , Maria Hällfors 0000-0002-6890-8942 , Jan Hjort 0000-0002-4521-2088 , Marko Hyvärinen , Martti Levo , Miska Luoto 0000-0001-6203-5143 , Charlotte Møller , Oystein Opedal , Laura Pietikäinen , Andrés Romero-Bravo , and Anniina Mattila 0000-0002-6546-6528 Authors Info & Affiliations https://doi.org/10.22541/au.176796215.51512290/v1 217 views 131 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract \articletype Original Articles Intraspecific variation in phenotypic plasticity can affect the ability of populations, and thus species, to respond to environmental changes. However, the prevalence and drivers of such variation are not well known. It is often assumed that intraspecific variation in phenotypic plasticity is driven by mechanisms associated with the position of a population within the species’ geographic range and the environmental heterogeneity experienced by the population. To test the effect of these two drivers, we use a combination of germination and greenhouse experiments to measure thermal phenotypic plasticity in traits ranging from germination to flower abundance in populations of three Hypericum species sampled across their European ranges. We then relate thermal plasticity to each population’s position within the species’ range and to the environmental heterogeneity of the sampling site. Our results revealed that while average thermal plasticity in several traits was similar among the three tested Hypericum species, it varied among the conspecific populations. Specifically, populations from closer to the range edge tended to be more plastic in germination probability and plant height, while populations from more heterogeneous environments tended to be more plastic in flowering phenology, plant height, and flower abundance. Interestingly, for plasticity in germination phenology, plant height, and flower abundance, we found a substantial interactive effect with accentuated plasticity in heterogeneous sites near the range edge. This suggests that populations in heterogeneous environments at range edges may adjust to environmental change via phenotypic plasticity more effectively than are other conspecific populations. These results support both tested drivers and reveal important interactive patterns for some of the tested traits. Furthermore, they encourage further research on plasticity to consider both range position and environmental heterogeneity. \articletype Original Articles 1. Introduction Phenotypic plasticity is the genotype’s ability to produce different phenotypes in different environments (Pigliucci, 2001). In the context of global change, phenotypic plasticity is a crucial mechanism by which organisms can quickly adjust to variable and temporally unfavorable environmental conditions. Such adjustment can give populations time to disperse to more favorable areas or to adapt to changing conditions in their current habitats, thereby increasing long-term survival (Jump & Peñuelas, 2005; Nicotra et al., 2010). The degree of phenotypic plasticity can vary not only among species, but also among conspecific populations (Matesanz & Ramírez-Valiente, 2019; Van Tienderen, 1991). Furthermore, the amount of intraspecific variation can vary among species and traits (Hällfors et al., 2025; Schoen & Brown, 1991). Such intraspecific variation in phenotypic plasticity can affect the ability of a species to respond to environmental changes, such as climate change (Valladares et al., 2014). Despite its importance, data on intraspecific variation in phenotypic plasticity remain scarce (Molina-Montenegro & Naya, 2012), largely because obtaining the necessary data often requires resource-intensive experiments (Matesanz & Ramírez-Valiente, 2019). Collecting data from a wide range of species and traits to assess intraspecific patterns in phenotypic plasticity will help address this gap and allow more reliable forecasts of biotic resilience in the event of environmental changes (Valladares et al., 2014). Given the scarcity of data on intraspecific variation in phenotypic plasticity, the drivers of this variation are poorly understood. Studies aiming at explain intraspecific patterns in phenotypic plasticity have often assumed that phenotypic plasticity varies within a species’ range (e.g. Mägi et al., 2011; Zettlemoyer & Peterson, 2021). More specifically, plasticity has been proposed to differ between populations located at the core versus at the periphery of a species range, with either core populations showing greater plasticity than peripheral populations (Mägi et al., 2011), or vice versa (Zettlemoyer & Peterson, 2021). Several hypotheses have been proposed for why such patterns may emerge (reviewed by Usui et al., 2023). Some of these hypotheses relate to expected abundance patterns across species’ ranges. The abundance of a species is traditionally assumed to be highest at the core of a species range and decline towards the periphery, due to declining suitability of environmental conditions towards the periphery (Brown, 1984). As a result of more individuals and greater gene flow, core populations may possess more genetic variation allowing for greater phenotypic plasticity to evolve (Arnaud-Haond et al., 2006; Ellstrand & Elam, 1993). On the other hand, gene flow from core populations could also dilute local adaptation and reduce fitness in peripheral populations (i.e. cause gene swamping) (Usui et al., 2023). This can, rather counterintuitively, result in greater plasticity in peripheral populations if evolution of plasticity is enhanced to cover fitness losses (Chevin & Lande, 2011). Another hypothesis for how and why plasticity would vary between core and peripheral populations relates to differences in environmental heterogeneity (Zettlemoyer & Peterson, 2021). Whether occurring in space or in time, environmental heterogeneity is often expected to lead to selection for greater phenotypic plasticity (Alpert & Simms, 2002; Baythavong, 2011; Gianoli, 2004; Gianoli & González-Teuber, 2005; Sultan & Spencer, 2002; Zettlemoyer & Peterson, 2021). Several theoretical studies have found support for this expectation (e.g. Scheiner, 2013; Sultan & Spencer, 2002), but empirical studies where the position of a population within the species’ range (hereafter, range position) has been used as a proxy for the amount of environmental heterogeneity experienced by the populations, have yielded inconsistent results (Dobson & Zarnetske, 2025; Gunderson & Stillman, 2015; Kotilainen et al., 2024; Mitchell et al., 2011; Molina-Montenegro & Naya, 2012). The use of a populations’ range position as a proxy for environmental heterogeneity and thus phenotypic plasticity has often been motivated by the Climatic Variability Hypothesis (CVH, Addo-Bediako et al., 2000). According to this hypothesis, climatic heterogeneity, and thus phenotypic plasticity, should increase towards the poleward edges of a species’ range. Due to the lack of evidence for such a latitudinal pattern in phenotypic plasticity, however, recent studies have suggested that phenotypic plasticity could be explained by variation in environmental heterogeneity occurring at a smaller scale (Manenti et al., 2017). Such variation may disrupt, or even override, latitudinal patterns in climatic conditions (Potter et al., 2013). Accounting for local-scale indicators of environmental heterogeneity may thus be critical to accurately predict patterns in plasticity (Potter et al., 2013; Usui et al., 2023). Here, we quantify thermal phenotypic plasticity across a set of European populations of three Hypericum species. We compare species’ average responses to temperature, intraspecific variation in those responses, and how the amount of intraspecific variation differs among species. Then, we ask whether and how 1) populations’ range position, and 2) the amount of environmental heterogeneity at the source locations of the populations are related to the degree of thermal plasticity, and 3) if there are additive or interactive effects of these two factors on plasticity. By jointly evaluating the impact of range position, environmental heterogeneity, and their interaction, we aim to avoid the common pitfalls related to only testing a single expectation or using range position as a proxy for environmental heterogeneity. Furthermore, this approach allows us to test how local-scale spatial environmental heterogeneity affects phenotypic plasticity and to uncover potential interactive effects on variation in plasticity, both of which have rarely been empirically tested (but see de la Mata et al., 2022 for a case of testing the independent impact of spatial environmental heterogeneity on plasticity). We expect to find greater plasticity either in core or peripheral populations, and in environments with greater local-scale heterogeneity. Furthermore, based on possible alternative influences of genetic diversity and gene flow at the peripheral populations, we expect that the positive effect of increased environmental heterogeneity depends on the populations’ range position either by dampening or enhancing the effect in peripheral populations. \articletype Original Articles 2. Methods \articletype Original Articles 2.1 Study species and populations Hypericum montanum (L.), H. perforatum (L.) and H. maculatum (Crantz) are perennial herbs of the Hypericaceae family. The native ranges of H. perforatum and H. maculatum cover most of Europe (Hultén & Fries, 1986) and they are commonly found in grassland habitats ( Hypericum perforatum L. in GBIF Secretariat, 2023; Hypericum maculatum Crantz in GBIF Secretariat, 2023). H. montanum , on the other hand, has a more restricted distribution (Hultén & Fries, 1986) and it occurs mainly in woodland habitats ( Hypericum montanum L. in GBIF Secretariat, 2023). While H. perforatum is facultatively apomictic, H. montanum and H. maculatum are obligate sexual reproducers (Matzk et al., 2003). All three species have relatively limited dispersal capacity (dispersal distance of 1-5 meters for 50 % and 99 % of the seeds; Lososová et al., 2023). We acquired seeds of the three species from different parts of their native ranges, totaling 38 populations (after filtering, described in Section 2.5), either by collecting them in the field or acquiring seed accessions from European seed banks (Fig. 1, Table S1). The seeds were primarily collected between 2017-2021, with one H. perforatum population collected in 2007 and one H. montanum population collected in 1998. The seed material from seed banks and our field collections was collected according to ENSCONET guidelines (generally originating from at least 50 individuals; ENSCONET, 2009). For the self-collected material, seeds were sampled per mother plant, and an equal number of seeds from each mother were pooled for each temperature treatment (see Sections 2.3 and 2.4). \articletype Original Articles Figure 1. Locations of the seed collection sites of the three studied Hypericum species in Europe. See Table S1 for more details. The mean temperature of the warmest quarter (°C) (WorldClim; Fick & Hijmans, 2017) is shown in the background. \articletype Original Articles 2.3 Germination experiments The seeds were cleaned from debris with an aspirator (Agriculex DB-1, column seed cleaner CB115009) before sowing. For each population, we sowed a maximum of 200 seeds (depending on availability) 2 mm apart from each other on 8 petri dishes (25 seeds per petri dish, diameter 5.5 cm) with 1% agar (Sigma agar, lots SLBL4283V & SLBX7044). After eight weeks of cold stratification at 4 °C, we placed the petri dishes in growth chambers (Incubator numbers: LMS Cooled COLD 13234/20P4, MEAN 13233/20P4, WARM 13235/20P4, HOT 132636/20P4, United Kingdom), set to four temperature treatments. Daytime temperatures (16 h) were set to 16 °C (cold), 20 °C (medium), 24 °C (warm) and 28 °C (hot). The night-time temperatures (8 h) were set to 10 °C below the daytime temperature. Photoperiod in all treatments was 16/8 h light/dark. Each treatment was further divided into two replicates that were located on different shelves of the same growth chamber. The choice of temperature treatments was based on data on average summer temperatures at the trailing, core, and leading areas of the study species’ ranges, as well as the predicted thermal conditions at the trailing edge in 2070, using data from WorldClim (Fick & Hijmans, 2017). \articletype Original Articles 2.4 Greenhouse experiments Seeds for the greenhouse experiments were cold-stratified for 4 weeks at 4 °C in dry paper bags. For each population, we sowed a maximum of 200 seeds (depending on availability) in 8 trays (25 seeds per tray) filled with sowing mixture (Kekkilä sowing mixture W HS R8017; KEK31116) cover by a thin layer (1-2 mm) of coarse sand. The trays were placed in greenhouse compartments with four temperature treatments identical to those used in the germination experiment, except that the night-time temperature for the vegetative stage was changed to 8 °C below the daytime temperature. Each treatment was further divided into two replicates placed in distinct greenhouse compartments. After the seeds had germinated, a maximum of 10 seedlings (depending on availability) were randomly chosen from each population and potted into individual 1L pots filled with soil (Kekkilä Professional coarse potting mixture; KEK33933). The pots were placed on a water-retaining rug on growing tables. The plants were watered by an automated watering system by soaking the rug underneath the pots. The watering schedule in each treatment was adjusted to keep the plants equally moist in all treatments. The plants were grown in the greenhouses from December 2021 to May 2022. In the end of March, the watering system broke leaving the H. perforatum plants dry for some days in replicate A. We accounted for this in the interpretation of the results. The plants were fertilized six weeks after sowing with a 0.075 % solution of Kekkilä Turve Superex (NPK 12–5–27) and subsequently every two weeks with a 0.2 % solution of the same fertilizer. To avoid any effects of differing conditions within the greenhouse compartment, the germination trays and pots were periodically rotated (dates of rotation: Dec 8, 2021, Dec 15, 2021, Dec 22, 2021, Jan 19, 2022, Jan 26, 2022, Feb 2, 2022, Mar 4, 2022, Apr 8, 2022). 2.5 Trait measurements During the germination experiments, we recorded the number of germinated seeds weekly over four weeks, based on seedlings having a radicle longer than 2 mm. Radicle length was defined as the distance from the radicle tip to the last occurrence of visible root hairs. Once a seed had been recorded as germinated it was removed from the petri dish. In the fifth week, we performed cut-tests to determine the viability of the remaining seeds, i.e. whether the seed had germinated during the last week of the experiment, or whether it was full, empty, moldy, or infested. At this stage, seedlings with a root radicle shorter than 2 mm were also scored as germinated. Based on this information, we determined the number of viable seeds (excluding empty and infested seeds) following the recommendations of the Millennium Seed Bank (Germination testing: procedures and evaluation. Millenium Seed Bank Partnership, 2022). For estimating germination in further analyses, we included only populations where the proportion of seeds that germinated out of the total number of viable seeds was more than 5 %. This was done to exclude accessions where low germination was likely due to incomplete dormancy release. From these measurements, we derived two traits for our analyses: germination proportion, defined as the proportion of viable seeds that germinated, and germination phenology, defined as the number of days from sowing to germination for seeds that emerged during the 4-week monitoring period. During the greenhouse experiments, we measured three traits: flowering phenology (the number of days from sowing to the onset of flowering), plant height (the length of the longest branch in cm), and flower abundance (the number of flowers produced by the end of the experiment, including withered and open flowers as well as full and empty seed capsules). Plant height and flower abundance were only measured for H. montanum and H. perforatum due to time limitations during data collection. The measured trait values across populations are given in Table S2. \articletype Original Articles 2.6 Data analyses All data analyses were conducted in RStudio, version 2025.09.2 (Posit team 2025; R Core Team, 2025). \articletype Original Articles 2.6.1 Measuring range position We measured range position using two alternative metrics: 1) the geographic distance between the population and the edge of the species’ range (DRE; Fig. 2A), and 2) the climatic distance between the conditions at each source site and the average conditions across the range of the species (DCE; Fig. 2B). To calculate these metrics, we digitized distribution maps from Hultén & Fries (1986) using QGIS (Transformation type: Thin Plate Spline; Resampling method: Lanczos; Coordinate system: Arctic Polar Stereographic EPSG:3995) (QGIS.org, 2025). For DRE, we calculated the distance (in kilometers) from each study population’s geographic location to the northernmost or southernmost range edge, whichever was located closer to the population (Fig. 2A). To calculate DCE, we extracted and scaled (mean = 0, SD = 1) bioclimatic variables (WorldClim; Fick & Hijmans, 2017) for each species’ range and for each sampled population. We then conducted a Principal Component Analysis (PCA; Greenacre et al., 2022) for each species, and formed a convex hull around the first and second principal components to represent the species’ climatic niche (Fig. 2B; see loadings in Fig. S3). The DCE metric was then calculated as the climatic distance from each study population’s position to the nearest edge of the convex hull. Both metrics were finally scaled to zero mean and unit variance (mean = 0, SD = 1). The measured range position values across populations are given in Table S3. \articletype Original Articles Figure 2. Illustration of metrics used for the population’s range position and for environmental heterogeneity. Panel A illustrates distance to the southernmost or northernmost (dashed lines) range edge (DRE) with points in lighter colors indicating closer proximity to the range edge (in kilometers), panel B illustrate distance to climatic edge (DCE) with larger points showing the study populations and smaller points all sites from which climatic parameters were measured across the species’ range. Study populations in lighter colors were located closer to the species climatic edge. Panels C and D illustrate the environmental heterogeneity metrics, showcased using examples of 500 x 500 m squares around seven populations of H. montanum . Squares in panel C show land cover types (Malinowski et al., 2020) from which Shannon diversity of land cover types (SHDI) and mean perimeter-area ratio of land cover patches (PAR) were calculated (square-specific values of SHDI and PAR are shown in rows underneath each square). Squares in panel D show elevation (Copernicus Sentinel data, 2022) from which average roughness in elevation (ARE) values was calculated (square-specific values of ARE are shown underneath each square). \articletype Original Articles 2.6.2 Measuring environmental heterogeneity We measured environmental heterogeneity using three alternative metrics, two of which focus on different aspects of land cover heterogeneity, and one describing topographic heterogeneity. More specifically, we calculated the Shannon diversity of land cover types (SHDI; Fig. 2C), which represents compositional heterogeneity in land cover, mean of the perimeter-area ratio of land cover patches (PAR; Fig. 2C), which represents configurational heterogeneity in land cover, and average roughness in elevation (ARE; Fig 2D), which represents topographic heterogeneity. To calculate SHDI and PAR, we used the function calculate_lsm() from the ‘landscapemetrics’ R package (Hesselbarth et al., 2019) and to calculate ARE, we used the sa() function from the ‘geodiv’ R package (Smith et al., 2021). We calculated all three metrics within a 500-m square buffer (i.e., a square extending 500 m from the central point in all directions) around each study population. A square buffer was used to enable including whole grid cells in the buffer. Deciding the size of the square buffer was based on the estimated dispersal distance according to which the studied Hypericum species would be able to reach areas 500 meters from the seed collection site within 100 years given yearly sexual reproduction (with a maximum dispersal distance of 5 meters, Lososová et al., 2023). For SHDI and PAR, we used S2GLC 2017 land cover data (Malinowski et al., 2020) and for ARE we used EEA-10 Copernicus DEM from year 2022 (Copernicus Sentinel data, 2022), both in 10-meter resolution. All three metrics were finally scaled to zero mean and unit variance (mean = 0, SD = 1). The measured environmental heterogeneity values across populations are given in Table S3. \articletype Original Articles 2.6.3 Quantifying the effects of range position and environmental heterogeneity \articletype Original Articles 2.6.3.1 Estimating thermal plasticity We fitted generalized linear mixed effects models (GLMMs) implemented with the glmmTMB() function in the ‘glmmTMB’ R package (McGillycuddy et al., 2025) to model each trait as a population-level linear function of temperature. We allowed populations to differ both in their mean deviation and in the slope of their reaction norms by including the interaction between population and treatment as a random effect (i.e., we fitted random-regression models) (Arnold et al., 2019). To account for the nonindependence of individuals grown in the same greenhouse chamber or incubator, we included replicate ID as a random effect. We set Gaussian error distribution with an identity link function for all traits except germination, for which we set binomial error distribution with a logit link function. We treated temperature as a continuous variable, which we scaled to zero mean and unit variance (mean = 0, SD = 1) to facilitate model fitting. We evaluated normality and homoscedasticity of residuals by visually inspecting QQ-plots and by plotting residuals against fitted values and statistically using Shapiro-Wilk and Breusch-Pagan tests, and found minor deviations from normality and homoscedasticity (Figures S1-S2). Flower abundance was square-root-transformed to better meet the assumptions. We extracted the random regression-slope coefficients for each population, converted them to absolute values and used them as estimates of the magnitude of the population-specific plastic response to temperature in the subsequent analyses. The measured plasticity values across populations are given in Table S3. We used AIC (Akaike’s Information Criterion) comparisons to assess statistical support for population-specific responses to temperature by comparing models including only a random intercept to models including both a random intercept and a random slope for the population. \articletype Original Articles 2.6.3.2 Selecting metrics with the highest explanatory power for variation in thermal plasticity We fitted linear models (LMs) to test the effect of the two range position metrics (DRE and DCE) and the three environmental-heterogeneity metrics (SHDI, PAR, and ARE) on thermal plasticity. For each trait, we fitted twelve models in total, to test all combinations of one range position and one environmental-heterogeneity variable, as well as their interaction. Thus, each model included a maximum of two explanatory variables and their interaction. We also tested whether accounting for non-independence between the populations of the same species improved the models but found no qualitative change in the results (see Table S4 and Table S5). We computed Spearman correlations among explanatory variables using the cor_test() function in the ‘rstatix’ R package (Kassambara, 2025), and all were below 0.7, indicating no problematic multicollinearity (Dormann et al., 2013). We ranked the models based on AIC values, and for each trait selected the model with the lowest AIC value (ΔAIC >2; Burnham & Anderson, 2004; Symonds & Moussalli, 2011). If the highest-ranked model included an interaction, this parameter was also incorporated into the final models. If there were no detectable differences between the models, we still selected the one with the lowest AIC value for further investigation and assessed case by case whether any of the competing models provided additional insight. We evaluated normality and homoscedasticity of residuals by visually inspecting QQ-plots and by plotting residuals against fitted values and statistically using Shapiro-Wilk and Breusch-Pagan tests, and found no major deviation from normality and homoscedasticity (Figures S4-S5). \articletype Original Articles 2.6.2.3 Analyzing the effect of range position and environmental heterogeneity To analyze the effect of range position and environmental heterogeneity on among-population thermal plasticity, we assessed the parameter estimates of the models selected in the previous step (Section 2.6.2.2). Additionally, we examined the amount of variance explained by each term by calculating their relative importances with the calc.relimp() function from the ‘relaimpo’ R package (Groemping, 2005). Predictions were produced using the ggpredict() function in the ‘ggeffects’ R package (Lüdecke, 2018). \articletype Original Articles 3. Results 3.1 Thermal plasticity and its variation among populations The germination probability of H. perforatum seeds decreased by on average of 8.1 % per 1°C increase in temperature (p < 0.001; Table 1). In addition to this, for all three species, the timing of germination and flowering advanced, on average, with increasing temperature (Table 1). Specifically, the timing of germination advanced 0.5 days for H. montanum (p < 0.05), and 0.7 days for H. perforatum (p < 0.001) and H. maculatum (p < 0.001) per 1°C. Moreover, H. montanum plants flowered 3.6 days (p < 0.001), H. perforatum 4 days (p < 0.001), and H. maculatum 3.5 days (p < 0.001) earlier per 1°C (Table 1). For the remaining species-trait combinations, we failed to detect thermal responses across populations. The average thermal responses were rather similar across species for all traits except germination probability, with a 12.1 %, 97.3 %, and 51.5 % lower germination probability in the warmest thermal treatment (28 °C) compared to the coldest (16 °C) thermal treatment across populations of H. montanum, perforatum , and H. maculatum , respectively. All species exhibited population-specific responses of germination probability, plant height, and flower abundance, as indicated by support for the models including both random intercepts and slopes for population (Table 1). For germination probability, the population-specific responses varied from a 19.7 % reduction per 1°C to an 8.6 % increase per 1°C (Table 1; Fig. 3 A-C), for plant height from a 4.1 cm reduction per 1°C to 2.58 cm increase per 1°C (Table 1; Fig 3 J-K), and for flower abundance from 1.5 flower decrease per 1°C to 0.2 flower increase per 1°C (Table 1; Fig 3 L-M). The thermal responses in germination phenology were more uniform among populations, varying from an advance of 1 day/°C to a delay of 0.05 days/°C (Table 1; Fig. 3 D-F), with statistical support for variation in germination phenology only for H. maculatum . For flowering phenology, the population-specific responses ranged from a 5.7 to a 1.9-day advance per 1°C (Table 1; Fig 3 G-I), providing strong evidence of intraspecific variation in thermal responses of H. maculatum and H. perforatum . For all traits but flowering phenology, intraspecific variation in thermal responses was greatest in H. montanum . Table 1. Parameter estimates from models describing plastic responses to temperature treatments. The estimates and standard errors shown here have been back-transformed to the original scale. SE = standard error in the average thermal responses of the species; SD pop = standard deviation of the population-specific thermal responses; Min pop = minimum of the population-specific thermal responses; Max pop = maximum of the population-specific thermal responses; ΔAIC = difference in the AIC (Akaike’s Information Criterion) value between the model including both random intercept and slope and only random intercept for population, with values greater than 2 indicating support for the random-slope component. \articletype Original Articles Trait Species Term Estimate SE t-value p-value SD pop Min pop Max pop ΔAIC Germination probability (log-odds/°C) H. montanum (Intercept) -1.489 0.151 -11.324 <0.001 Treatment -0.010 0.048 -0.205 0.838 0.078 -0.107 0.087 6.47 H. perforatum (Intercept) 1.307 0.181 -2.318 <0.05 Treatment -0.078 0.021 -3.749 <0.001 0.053 -0.178 0.032 23.72 H. maculatum (Intercept) 1.162 0.212 1.139 0.255 Treatment -0.042 0.038 -1.110 0.267 0.052 -0.141 0.034 13.05 Germination phenology (days/°C) H. montanum (Intercept) 24.879 1.536 8.225 <0.001 Treatment -0.548 0.239 -2.290 <0.05 0.301 -0.831 0.048 0.41 H. perforatum (Intercept) 32.721 0.747 26.190 <0.001 Treatment -0.650 0.152 -4.268 <0.001 0.163 -0.948 -0.394 0.70 H. maculatum (Intercept) 32.045 1.027 17.225 <0.001 Treatment -0.656 0.167 -3.939 <0.001 0.061 -0.722 -0.499 2.80 Flowering phenology (days/°C) H. montanum (Intercept) 199.452 4.101 29.950 <0.001 Treatment -3.614 0.883 -4.091 <0.001 0.598 -4.382 -2.819 0.80 H. perforatum (Intercept) 209.075 3.008 41.557 <0.001 Treatment -3.992 0.592 -6.748 <0.001 1.190 -5.676 -1.883 20.7 H. maculatum (Intercept) 192.556 2.798 40.328 <0.001 Treatment -3.493 0.805 -4.339 <0.001 1.404 -5.447 -1.944 14.2 Plant height (cm/°C) H. montanum (Intercept) 102.402 6.622 9.814 <0.001 Treatment -1.765 1.250 -1.412 0.158 1.602 -4.092 -0.148 15.8 H. perforatum (Intercept) 47.673 2.536 24.896 <0.001 Treatment 0.734 0.506 1.452 0.147 0.883 -0.74 2.57 33.5 Flower abundance (flowers/°C) H. montanum (Intercept) 277.689 3.633 4.258 <0.001 Treatment -0.162 0.203 -0.899 0.369 0.629 -1.464 0.001 28.6 H. perforatum (Intercept) 84.213 3.165 4.950 <0.001 Treatment -0.000 0.051 -0.078 0.938 0.133 -0.350 0.196 29.1 Figure 3. Predicted population-specific effects of temperature (°C) on the five studied traits. A-C germination probability; D-F germination phenology; and G-I flowering phenology for H. montanum (blue), H. perforatum (red), and H. maculatum (green). J-K plant height and L-M number of flowers for H. montanum and H. perforatum . The estimates and standard errors shown here have been back-transformed to the original scale. Points show the measured trait values in each temperature treatment, and lines show the predicted trait values across temperature treatments and their 95 % confidence intervals. Points and lines for different populations are shown within each plot with different shades of color. ΔAIC > 2 indicates statistical support for among-populational differences in thermal responses. \articletype Original Articles 3.3 Effect of range position and environmental heterogeneity on thermal plasticity In the highest-ranked (lowest AIC) models, distance to range edge (DRE) represented range position for plasticity in plant height and flower abundance, while distance to the climatic edge (DCE) represented range position for plasticity in germination probability, germination phenology, and flowering phenology (Table 2). Similarly, environmental heterogeneity was best captured by the mean perimeter-area ratio of land cover patches (PAR) for plant height, the Shannon diversity of land cover types (SHDI) for germination probability, and average roughness in elevation (ARE) for germination phenology, flowering phenology, and flower abundance (Table 2). The pairwise interaction between range position and environmental heterogeneity was retained in the best-ranked models for plasticity in germination phenology, plant height, and flower abundance (Table 2). The correlations between the range position and environmental heterogeneity metrics as well as within them were low, with the highest correlation emerged between distance to range edge (DRE) and average roughness in elevation (ARE) (ρ = -0.35; Table S6). For plasticity in germination phenology and flower abundance, the highest-ranked model was supported over all alternative candidate models (Table 2). For the other studied traits, there was also support for additional models (6, 2, and 1 additional models for plasticity in germination probability, flowering phenology and plant height, respectively; Table 2). Across these alternative models, the effects of range position and environmental heterogeneity were largely consistent (Table S7): for plasticity in flowering phenology, both effect sizes and directions remained unchanged; for plant height, support for the effect of environmental heterogeneity increased to moderate (p < 0.1); and for germination, the effect of range position decreased when measured using distance to range edge (DRE) compared to using distance to climatic edge (DCE), as included in the highest-ranked model. The highest-ranked models provided moderate evidence for independent effects of range position and environmental heterogeneity on plasticity in flowering phenology and plant height (Table 3; Fig 4). Populations located farther from the edge were less plastic in height (Est. = -2.15; p < 0.05; Table 3), while populations located in more heterogeneous environments were more plastic in flowering phenology (Est. = 2.52; p < 0.05; Table 3). Furthermore, plasticity in germination probability tended to vary with range position, and plasticity in plant height and flower abundance tended to vary with environmental heterogeneity. Populations located farther from the edge were less plastic in germination probability (Est. = -0.06; p < 0.1; Table 3), while populations in more heterogeneous environments were more plastic in plant height (Est. = 1.67; p < 0.1) and flower abundance (Est. = 0.44; p < 0.1). For those traits for which the highest-ranked model included an interaction between range position and environmental heterogeneity metrics (germination phenology, plant height, and flower abundance) the interaction term was negative (Est. = -0.29; p <0.05, Est. = -2.68; p < 0.05, Est. = -1.21; p < 0.05 respectively; Table 3). This means that there was a tendency for populations closer to the edge having greater plasticity at greater environmental heterogeneity, and vice versa in populations towards the core areas (Fig. 4). We found differences among traits in how much variation in plasticity was explained by the predictors. The percentage of variation in plasticity explained was lowest for germination probability (12 %; Fig. 5), and highest for plant height (62.6 %) and flower abundance (62.6 %). Furthermore, we found differences in which of the predictors explained most of the plasticity variation. For traits for which models included an interaction between range position and environmental heterogeneity, the interaction effect explained the majority of the variation. The percentage of variance explained by the interaction term was particularly high in the models for plant height (41.01 %) and flower abundance (55.8 %), and moderate for germination phenology (19.6%). For the traits for which the highest-ranking models did not include an interaction term, range position was more important for germination probability while environmental heterogeneity was more important for flowering phenology. Table 2. Model selection results for models testing the effect of range position and environmental heterogeneity on trait plasticity. Each tested model included one variable from each group, with one version of the model including their interaction. DRE = distance to range edge; DCE = distance to climatic edge; SHDI = Shannon diversity of land cover types; PAR = mean perimeter-area ratio of land cover patches; ARE = average roughness in topography; ΔAIC = difference in the AIC (Akaike’s Information Criterion) value between the model compared to the highest-ranked model (ΔAIC = 0.0). \articletype Original Articles Range position metric used Environmental heterogeneity metric used Interaction included Plasticity in germination probability Plasticity in germination phenology Plasticity in flowering phenology Plasticity in plant height Plasticity in flower abundance ΔAIC ΔAIC ΔAIC ΔAIC ΔAIC DRE SHDI No 1.4 5.0 6.3 10.2 10.5 DRE SHDI Yes 1.6 3.8 6.9 12.0 11.7 DRE PAR No 2.3 5.0 3.2 9.1 11.4 DRE PAR Yes 1.7 4.4 3.1 0.0 7.8 DRE ARE No 2.3 5.1 0.8 10.3 11.7 DRE ARE Yes 4.0 5.4 2.7 0.3 0.0 DCE SHDI No 0.0 5.7 6.0 12.0 11.2 DCE SHDI Yes 2.0 3.9 7.7 12.5 13.2 DCE PAR No 0.5 5.6 3.6 10.3 11.6 DCE PAR Yes 1.9 7.4 5.4 12.2 13.2 DCE ARE No 1.3 5.7 0.0 11.9 12.1 DCE ARE Yes 3.2 0.0 1.3 12.5 12.7 \articletype Original Articles Table 3. Parameter estimates from the highest-ranked models testing the effect of range position (RP) and environmental heterogeneity (EH) on trait plasticity. SE = standard error. \articletype Original Articles Trait plasticity Term Estimate SE t-value p-value Plasticity in germination probability Intercept 0.312 0.033 9.580 <0.001 EH -0.038 0.033 -1.151 0.258 RP -0.059 0.033 -1.770 <0.1 Plasticity in germination phenology Intercept 2.660 0.115 23.125 <0.001 EH -0.147 0.119 -1.232 0.228 RP 0.078 0.120 0.652 0.520 EH:RP -0.296 0.107 -2.768 <0.05 Plasticity in flowering phenology Intercept 15.578 0.883 17.636 <0.001 EH 2.524 0.906 2.786 <0.05 RP -1.071 0.906 -1.182 0.253 Plasticity in plant height Intercept 4.075 0.804 5.070 <0.001 EH 1.660 0.844 1.968 <0.1 RP -2.146 0.852 -2.520 <0.05 EH:RP -2.679 0.772 -3.471 <0.01 Plasticity in flower abundance Intercept 1.216 0.228 5.333 <0.001 EH 0.439 0.240 1.828 <0.1 RP -0.385 0.240 -1.603 0.137 EH:RP -1.212 0.300 -4.046 <0.01 \articletype Original Articles Figure 4. Predictions from the highest-ranked models testing the effect of range position (RP) and environmental heterogeneity (EH) on trait plasticity, with population-specific plasticity values in blue ( H. montanum ), red ( H. perforatum ), and green ( H. maculatum ). For RP and EH metrics, value -1 indicates the lowest distance to edge/lowest amount of environmental heterogeneity and value 1 the greatest distance to edge/highest amount of environmental heterogeneity. A-B germination probability; C-E germination phenology; and F-G flowering phenology; H-J branch length and K-M number of flowers. Statistically significant p-values are shown with p < 0.01 = **; p < 0.05 = *; p < 0.1 = +. \articletype Original Articles Figure 5. Variance partitioning of models testing the effects of range position and environmental heterogeneity on trait plasticity. Stacked bars represent the proportion of total variance in plasticity (R²) attributable to each predictor, in addition to unexplained variance. \articletype Original Articles 4. Discussion \articletype Original Articles 4.1 Thermal plasticity and its variation among populations Our result revealed detectable thermal plasticity in germination and flowering phenology for all three species, and for H. perforatum in germination probability. Consistent with other studies, warmer temperatures led to earlier germination and flowering (Collins et al., 2025; de Villemereuil et al., 2018; Haggerty & Galloway, 2011), and to a reduced probability of germination (Vázquez-Ramírez & Venn, 2025). For the remaining traits (plant height and flower abundance) we did not detect thermal responses across populations. These patterns support the idea that plasticity is often trait-specific (Arnold et al., 2022; Hällfors et al., 2025; Ren et al., 2020), with phenological traits, in particular, being highly sensitive to temperature (Cleland et al., 2007; Menzel et al., 2020; Roslin et al., 2021). While we found little variation in average thermal responses among the species for most of the traits, we did detect variation among conspecific populations for the majority of them. For germination probability, plant height, and flower abundance, conspecific populations of all three species differed in their thermal responses, while for flowering phenology, such variation was evident for H. perforatum and H. maculatum , and for germination phenology for H. maculatum . For all of these species-trait combinations, the populations varied both in the magnitude and direction of the thermal response (Table 1; Fig 3). Interestingly, in all traits but flowering phenology, we found most variation among the populations of H. montanum , potentially indicating stronger local adaptation induced by the more sparse occurrence, and thus more limited gene flow between the populations of the species. Overall, our findings align with a recent meta-analysis indicating that intraspecific variation in plasticity is common in plants (Matesanz & Ramírez-Valiente, 2019) and highlight the importance of accounting not only for the average responses of the species, but also for intraspecific variation. High intraspecific variation in plasticity can allow some populations to respond adaptively to climate change (Walter et al., 2023) rendering species-level assumptions prone to erroneous predictions. Thus, these results suggest that the three Hypericum species possess genetic variation for diverse plastic responses that could enable some of the populations to adjust more efficiently to environmental changes induced by climate change. Because the tested temperatures covered a wide range of current and potential future growing season temperatures across the species ranges, we consider it unlikely that a wider range of temperatures would have revealed substantial additional plasticity. However, exploring potential nonlinear responses could uncover plasticity that remained undetected in our analysis (Arnold et al., 2022). To avoid overcomplicated models, we extracted the slope of the linear response to then use for our subsequent test of the roles of range position and environmental heterogeneity. Thus, examining non-linear effects was beyond the scope of this particular study. Future studies should, however, account for potential nonlinear responses to temperature to investigate the extent of thermal plasticity. 4.2 Relationship between thermal plasticity and range position We found moderate evidence that plasticity in plant height and germination probability declined with increasing distance to the range edge (Table 3; Fig 4). Populations located closer to the edge of the species’ range were more plastic in plant height and germination probability than those located closer to the core. For the other three traits (germination phenology, flowering phenology and flower abundance) range position had no detectable independent effect. As outlined in the introduction, phenotypic plasticity is often hypothesized to vary within species ranges either by peripheral populations showing lower plasticity (Mägi et al., 2011), or greater plasticity (Zettlemoyer & Peterson, 2021), in comparison to core populations. Our results concerning thermal plasticity in plant height and germination probability, together with e.g. Lázaro-Nogal et al. (2015), thus provide support for the latter hypothesis. The trend of increasing plasticity towards the periphery could be connected to patterns in species’ abundances and, potentially simultaneously, to gradients in environmental heterogeneity aligning with range position. Although empirical evidence remains contested (Dallas et al., 2020), the abundance of species is often assumed to be highest at the core of a species range and decline towards the periphery, due to a similar pattern in environmental suitability (Brown, 1984). Assuming this applies, in less abundant peripheral populations, greater plasticity may evolve as a consequence of directional selection maintained by maladaptive gene flow from the more abundant core populations (Chevin & Lande, 2011). Additionally, greater seasonal variation in climatic conditions at the poleward edges could favor the selection of plasticity in peripheral populations (Addo-Bediako et al., 2000). Since we did not measure the amount of genetic variation within the sampled populations nor seasonal variation in climatic conditions, we can, however, only speculate on whether these mechanisms relate to the patterns we observed. 4.3 Relationship between thermal plasticity and environmental heterogeneity Plasticity in flowering phenology, plant height and flower abundance tended to increase with environmental heterogeneity, although with variable statistical support (Table 3; Fig 4). Thus, populations located in more heterogeneous landscapes responded more plastically to temperature increase by, for instance, advancing flowering more than populations located in more homogenous landscapes. This could be a result of the offspring of plant individuals in the populations located in the more heterogeneous landscapes needing to germinate in contrasting environmental patches compared to the parental plant, which in turn may result in selection for plasticity (Alpert & Simms, 2002). For the remaining two traits – germination probability and germination phenology – environmental heterogeneity did not have a detectable independent effect. The results for plasticity in flowering phenology, plant height and flower abundance are in line with previous theoretical work indicating that environmental heterogeneity can favor phenotypic plasticity (Scheiner, 2013; Sultan & Spencer, 2002). However, most previous empirical studies testing the effect of environmental heterogeneity on plasticity have focused on temporal environmental heterogeneity rather than spatial environmental heterogeneity (e.g. Gianoli, 2004; Gianoli & González-Teuber, 2005; Lázaro-Nogal et al., 2015). In the rare cases where the effect of spatial environmental heterogeneity has been tested, plasticity has been found to be driven by the spatial scale rather than the magnitude of environmental heterogeneity surrounding the population (de la Mata et al., 2022). Our study thus represents one of the first empirical studies finding signatures of greater plasticity in more spatially heterogeneous environments. \articletype Original Articles 4.4 Interactive effect of range position and environmental heterogeneity on thermal plasticity We found moderate evidence for range position and environmental heterogeneity interactively affecting plasticity in all of the three traits where the highest-ranked model included the interaction, i.e. plasticity in germination phenology, plant height and flower abundance. Furthermore, we found the direction of the interaction to be consistent across the three traits: an increase in environmental heterogeneity was linked to an increase in plasticity in peripheral populations, but to a decrease in plasticity in core populations. This interaction was the only pathway through which range position and environmental heterogeneity affected plasticity in germination phenology. For plasticity in plant height, both range position and environmental heterogeneity also had an independent effect, and for plasticity in flower abundance, range position additionally had an independent effect. Based on previous hypotheses about how range position and environmental heterogeneity may affect plasticity, we formulated two alternative hypotheses (see Introduction ) for the direction of the interaction. First, we hypothesized that the positive impact of increased environmental heterogeneity on plasticity may be dampened in peripheral populations as, e.g., lower genetic variation may restrict the evolutionary potential for increasing phenotypic plasticity (Arnaud-Haond et al., 2006; Ellstrand & Elam, 1993). Second, we hypothesized that the positive impact of increased environmental heterogeneity on plasticity may be further enhanced in peripheral populations if, e.g., directional selection, maintained by maladaptive gene flow from central populations (Chevin & Lande, 2011), selects for the evolution of higher plasticity. While the exacerbating impact of environmental heterogeneity towards the peripheral populations was in line with our second hypothesis, the abating effect towards the core was not. Some studies have, however, suggested that selection may work against plasticity in highly variable environments due to high levels of environmental stress caused by the varying conditions (Mägi et al., 2011). In such environments, the energetic costs of maintaining flexibility may outweigh its benefits, making a less plastic, more robust phenotype a more successful strategy (Schneider, 2022). In the case of higher abundance at the core areas of species’ ranges, core populations may have higher connectivity and gene flow, which could help maintain genetic variation for selection to act upon (Eckert et al., 2008), potentially making core populations particularly capable of responding to those high-levels of environmental stress. However, because we did not collect data on genetic variation within populations nor on species-specific environmental stress conditions, we can only speculate whether these mechanisms could cause a negative effect of environmental heterogeneity on plasticity in core populations. Nevertheless, the divergent links between environmental heterogeneity and plasticity across the species’ ranges underscore the complexity of variation in thermal plasticity across species ranges, and accentuates the substantial knowledge gaps that still remain unresolved. \articletype Original Articles 4.4 To what extent was plasticity predicted? Our results revealed substantial differences among traits in how much variation in plasticity could be explained by range position, environmental heterogeneity, and their interaction. The explanatory power was particularly high for plasticity in plant height (62.6 %) and flower abundance (62.5 %), for which the interaction between range position and environmental heterogeneity accounted for most of the explained variation (41 % and 55.8 %, respectively), highlighting its crucial role in predicting plasticity in those traits. In contrast, for the other three traits, a large proportion of the variation (65.6 - 88 %) remained unexplained. Similar trait-specific patterns have been reported, for instance, in a recent global meta-analysis, where an effect of latitude was found only for certain traits when assessing plasticity across many species and studies (Dobson & Zarnetske, 2025). Furthermore, Manenti et al. (2017) showed that environmental heterogeneity had weak and trait-specific effects on plasticity in three sympatric Drosophila species. We can speculate that the trait-specific effects of range position and environmental heterogeneity on plasticity are related to how closely the traits are linked to fitness, although they may also indicate that additional factors influencing plasticity were not included in our models. For instance, it is possible that some of the unexplained variation could arise from stochastic processes such as genetic drift (Lenormand et al., 2009), which are not straightforward to incorporate into analyses on intraspecific plasticity using designs such as ours. Furthermore, for some traits, environmental heterogeneity that occurs at a spatial scale smaller than in which it was measured here (10 x 10 meters) may determine the degree of plasticity. However, such data are rarely available, especially for spatial extents as large as in ours. Finally, it is possible that some of the unexplained variation originates from intrinsic differences among the tested species. Due to the relatively low number of studied populations we were not able to assess whether the effect of range position and environmental heterogeneity differ among the species. However, it is possible that in the more sparsely occurring H. montanum , reduced connectivity among populations may lead to lower gene flow and stronger local adaptation, potentially resulting in more pronounced differences in thermal plasticity between peripheral and core populations compared to the more widespread H. perforatum and H. maculatum . Additionally, the fact that H. montanum and H. maculatum are strict sexual reproducers, while H. perforatum is facultatively apomictic, could lead to differences in the genetic constitution of the species (Schoen & Brown, 1991) and thus in how the populations of the species respond to selection, specifically rendering H. montanum and H. maculatum populations more sensitive to selection. Future studies should investigate whether thermal responses differ between common versus rarer species. Such work could reveal important information about distinct drivers of intraspecific patterns among species and could be especially useful for informing conservation decisions. \articletype Original Articles 4.5 Does range position constitute a proxy for local-scale environmental heterogeneity? Our results revealed low correlations between the two range position and three environmental heterogeneity metrics. These results suggest that, at least in our study system, and in terms of spatial environmental heterogeneity, range position does not constitute a functional proxy for local-scale environmental heterogeneity. This result gives support for earlier studies suggesting that local-scale environmental heterogeneity may disrupt latitudinal patterns in environmental heterogeneity (Manenti et al., 2017). However, when comparing the relative importances of range position and local-scale environmental heterogeneity, we did not find clear evidence, across traits, that one would be more important than the other and thus that local-scale environmental heterogeneity would override the impact of range position. In fact, their substantial interactive role in explaining variation in plasticity for two of the traits suggests that range position and environmental heterogeneity may best predict plasticity when they are both accounted for. \articletype Original Articles 5. Conclusions and future directions Phenotypic plasticity is expected to play a key role in the performance of populations under climate change. Our study demonstrates that while thermal phenotypic plasticity in most of the studied traits was similar among the three Hypericum species examined, it varied among the populations of each species. Such variation may result in differences in the ability of the populations, and subsequently the species, to cope with the impacts of climate change. In future studies, it will be important to connect data such as ours with data on observed and expected changes in thermal environments to better understand and predict historical and future trajectories for populations and how this varies across the species’ range. Importantly, our results provide interesting indications that both a population’s position within the species’ range and the local-scale environmental heterogeneity surrounding the population may simultaneously, and also perhaps mechanistically interactively, be connected to plasticity. More specifically, our study indicates that plasticity may vary along several interacting environmental axes, and particularly that higher plasticity in edge populations in heterogenous environments can confer them with an advantage to cope with changes in climatic conditions through plastic responses. Overall, our results highlight the need for integrated research that jointly assesses the roles of populations’ position within range and environmental heterogeneity in determining the degree of phenotypic plasticity. This could potentially help address the inconsistencies that remain especially among the studies assessing the effect of range position. Moreover, the causal mechanism of why the populations’ position within range is connected to plasticity should be investigated further. This would improve our understanding of geographic patterns in the plasticity of species and populations, their ability to respond to environmental change via adaptive plastic responses, and ultimately help predict the future viability of biodiversity under continued and intensifying environmental change. \articletype Original Articles Author contributions S. H. M. Koivusaari: Conceptualization (lead), data curation (lead), formal analysis (lead); funding acquisition (equal), investigation (equal), methodology (lead); project administration (lead), software (lead), validation (lead); writing – original draft (lead); writing – review and editing (lead). M. H. Hällfors: Conceptualization (equal), data curation (equal), formal analysis (supporting), funding acquisition (equal), investigation (lead), methodology (equal), project administration (equal), resources (supporting), supervision (lead), validation (supporting), writing – original draft (equal); writing – review and editing (lead). J. Hjort: Conceptualization (supporting), supervision (equal), writing – review and editing (supporting). M.-T. Hyvärinen: Funding acquisition (equal), investigation (supporting), project administration (supporting), supervision (equal), writing – review and editing (supporting). M. Levo: Investigation (supporting); writing – review and editing (supporting). M. Luoto: Supervision (supporting); writing – review and editing (supporting). C. Møller: Writing – review and editing (equal). Ø. Opedal: Investigation (supporting), methodology (equal), software (supporting), supervision (equal), writing – review and editing (equal). L. Pietikäinen: Data curation (equal), investigation (lead), writing – review and editing (supporting). A. Romero-Bravo: Investigation (supporting), writing – review and editing (equal). A. L. K. Mattila: Conceptualization (equal), data curation (lead), formal analysis (supporting), funding acquisition (supporting), investigation (lead), methodology (equal), project administration (equal), resources (supporting), supervision (lead), validation (supporting), writing – original draft (equal); writing – review and editing (lead). \articletype Original Articles Acknowledgements We are grateful to the seed banks that provided seeds for the studies: Millennium Seed Bank, Meise Botanic Garden, Musée national d’historie naturelle, and Conservatoire Botanique National Alpin, Giardino Botanico Alpino Saussurea, University Botanic Gardens Ljubljana, Späth-Arboretum der Humboldt-Universität zu Berlin, Natural History Museum Oslo, Botanischer Garten der Karl-Franzens-Universität Graz, Jardin du Lautaret, Tallinn Botanic Garden, University of Salzburg Botanical Garden, Jardin botanique alpin - ville de Meyrin, Jardin botanique de Neuchâtel, Ringve Botanical Garden - NTNU University Museum Norway and Leśny Bank Genów Kostrzyca. We thank Denis Larpin, Christian Chauplannaz and Frederic Marquis for collecting seeds in France, and Sandrine Godefroid for collecting seeds in Belgium. Mari Miranto, Annisa Satyanti, and Marita Tiiri provided support in seed acquisition and germination testing, and several students took part in the collection of data in the green houses: Milja Laurila, Leire Borge Rouco, Ilona Kortelahti, Adam Bloch, Zhenzhen Liu, and Amaia Gonzaga Roa. The collection of fresh seeds of H. montanum in Finland, where the species is protected, was approved by the Centre for Economic Development, Transport and the Environment (UUDELY/8787/2021). The experiments were supported by funding from the Research Council of Finland (grant 331527 and 330739). SHM additionally acknowledges funding by the Research Council of Finland (grant 360742), Kone Foundation, Societas pro Fauna et Flora Fennica, Waldemar von Frenckells stiftelse and Nordenskiöld-Samfundet, MHH by the Research Council of Finland (grant 360742), LP by Jenny and Antti Wihuri Foundation and ALKM by the Kone Foundation. This study was also made possible through co-funding by the European Union (Horizon Europe, OBSGESSION-project, No 101134954). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Executive Agency. 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Collection Ecology and Evolution Keywords ecological experiment plants population ecology statistical terrestrial Authors Affiliations Susanna Koivusaari 0009-0009-3183-8870 [email protected] University of Helsinki View all articles by this author Maria Hällfors 0000-0002-6890-8942 Finnish Environment Institute View all articles by this author Jan Hjort 0000-0002-4521-2088 University of Oulu View all articles by this author Marko Hyvärinen Finnish Museum of Natural History View all articles by this author Martti Levo University of Helsinki View all articles by this author Miska Luoto 0000-0001-6203-5143 University of Helsinki View all articles by this author Charlotte Møller Finnish Museum of Natural History View all articles by this author Oystein Opedal University of Helsinki View all articles by this author Laura Pietikäinen Finnish Museum of Natural History View all articles by this author Andrés Romero-Bravo University of Sussex View all articles by this author Anniina Mattila 0000-0002-6546-6528 Finnish Museum of Natural History View all articles by this author Metrics & Citations Metrics Article Usage 217 views 131 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Susanna Koivusaari, Maria Hällfors, Jan Hjort, et al. \articletype Original Articles Divergent links between environmental heterogeneity and thermal plasticity across the European ranges of three Hypericum species. 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