Repeated heat fluctuations amplify growth advantages of invasive over native plants​

Repeated heat fluctuations amplify growth advantages of invasive over native plants​ | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Repeated heat fluctuations amplify growth advantages of invasive over native plants​ Jinlin Lyu, Li Huang, Xue Wenyan, Yuchao Wang, Yang Li, Ming Yue, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9377302/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Increasing climate variability is expected to impose stronger selective pressures on plant communities than gradual warming alone, yet how repeated heat stress and recovery cycles influence plant performance remains poorly understood. Here, we compared the dynamic growth responses of three invasive and three native Asteraceae species exposed to three consecutive cycles of high temperature (40°C) and ambient temperature (26°C) under controlled conditions. Using destructive sampling across seven time points, we quantified growth trajectories and biomass allocation patterns throughout the fluctuation process. Repeated heat fluctuations induced pronounced divergence in growth dynamics between invasive and native plants. Invasive species exhibited significantly accelerated biomass accumulation under fluctuating conditions compared with constant ambient conditions, whereas native species showed no corresponding increase in growth rate and instead experienced progressive growth suppression. Biomass allocation patterns also differed consistently between the two groups. Invasive plants maintained or increased allocation to aboveground tissues, resulting in sustained gains in plant height and total biomass, while native plants increasingly shifted allocation belowground without corresponding biomass gains. Trait-based percentage changes further revealed that positive responses in key growth traits were consistently greater in invasive than in native species across successive fluctuation cycles. These results demonstrate that repeated heat fluctuations can amplify performance asymmetries between invasive and native plants, highlight the importance of heatwave-like thermal regimes as a driver of invasion success under climate change. Heat fluctuations Invasive plants Native plants Growth trajectory Biomass allocation Plant functional traits Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Plant performance in a changing climate is increasingly shaped not only by shifts in mean temperature but also by changes in the temporal structure of thermal stress. In many ecosystems, plants are now exposed to recurrent heat events characterized by abrupt onset, short duration, and intermittent recovery phases, rather than sustained warming alone. Such repeated heat fluctuations represent a distinct form of abiotic stress, as they repeatedly disrupt physiological processes and growth trajectories while providing limited opportunities for full recovery between events (Baldwin et al., 2019 ; Raymond et al., 2022 ). Recent climate projections indicate that this fluctuation-dominated stress regime will become more prevalent as global temperatures rise and heatwaves increase in frequency (Sato et al., 2024 ). Understanding how plants respond to repeated thermal fluctuations, rather than single or chronic stress events, has therefore emerged as a critical challenge for predicting future ecosystem structure and function. Biological invasions are widely recognized as being particularly sensitive to climate change. Classical invasion theory proposes that invasive plant species often exhibit greater phenotypic plasticity than native species, enabling them to maintain performance under fluctuating or stressful environmental conditions (Baker, 1965 ; Richards et al., 2006 ). Numerous studies have shown that invasive plants outperform native species under sustained warming, drought, or other chronic stress regimes (Hellmann et al., 2008 ; Bradley et al., 2010 ; Welshofer et al., 2018 ). Such conditions tend to disrupt climatic regimes to which native species are historically adapted, while favoring invasive species capable of rapid adjustment (Walther et al., 2009 ; Song et al., 2010 ; Sandel et al., 2012). Consequently, climate warming is widely expected to accelerate plant invasions, yet the role of increasing climate variability, particularly recurrent heatwave events, remains far less understood. In natural ecosystems, plants are rarely exposed to a single, continuous stress event. Instead, they typically experience episodic stress alternating with periods of partial or complete recovery (Hartmann et al., 2018 ; Liu et al., 2019 ; Yao et al., 2024 ). Responses to such recurrent stress may fundamentally differ from those observed under sustained stress, potentially leading to acclimation, compensatory growth, or cumulative damage depending on the balance between stress duration and recovery capacity. Despite growing recognition of this complexity, there remains limited systematic understanding of how plant growth trajectories and functional traits evolve across multiple stress–recovery cycles, and whether invasive and native species diverge in their ability to cope with such dynamic stress regimes. A further limitation of existing research lies in its predominantly static analytical framework. Many studies assess plant traits at discrete endpoints, such as the end of a stress period or the conclusion of an experiment, thereby capturing immediate or net effects of stress but overlooking the continuous dynamics of plant responses over time (Ren et al., 2021 ; Wang et al., 2021 ; Chen et al., 2025 ; Zhou et al., 2025 ). Yet key determinants of long-term plant fitness, including temporal variation in growth rate, compensatory growth during recovery phases, and stage-specific shifts in biomass allocation, are inherently dynamic processes (Daniel, 2024; Stroud & Ratcliff, 2025 ; Justin et al., 2024). Moreover, plant responses to recurrent stress may be non-linear: early stress cycles may prioritize survival-oriented strategies, whereas repeated exposure could reveal divergence in growth-oriented adaptation. Such temporal transitions, and their potential differences between invasive and native species, remain largely unexplored. Here, we propose that repeated heatwave-like thermal fluctuations act as a dynamic environmental filter that selectively favors invasive over native plant species. Specifically, we hypothesize that such recurrent stress–recovery cycles enhance the performance advantage of invasive plants by driving divergence in growth trajectories and biomass allocation patterns over time. To test this hypothesis, we selected representative invasive and native species from the Asteraceae family and subjected them to three complete cycles of high-temperature stress and recovery under controlled growth chamber conditions. Using destructive sampling at the end of each stress and recovery phase, we quantified not only final trait values but also temporal changes in total biomass accumulation rate and percentage trait responses relative to baseline conditions. By integrating growth trajectory analysis, self-control comparisons, and functional group–level responses, this study addresses three key questions: (1) Do repeated heat fluctuations fundamentally alter plant growth trajectories over time? (2) Do invasive and native species experience net performance gains or losses under such fluctuations? and (3) How does the magnitude of performance divergence evolve across successive fluctuation cycles? By adopting a dynamic, process-based perspective, this study aims to provide new insights into how increasing climatic variability may reshape plant performance and facilitate biological invasions under future climate change. 2. Materials and Methods 2.1 Plant Materials and Cultivation The Asteraceae family contains a large number of globally invasive plant species and is therefore widely used in invasion ecology research. Accordingly, six Asteraceae species were selected for this experiment and classified into two functional groups: invasive and native, based on their biogeographic origin and invasion status. The invasive group consisted of three representative alien species, Erigeron annuus , Galinsoga quadriradiata , and Symphyotrichum subulatum , all native to the Americas and introduced into China within the past ~ 150 years through natural dispersal or unintentional human-mediated pathways. The native group comprised three congeneric native species, Artemisia lavandulifolia , Artemisia dubia , and Artemisia annua , selected to ensure broad phylogenetic comparability between functional groups. Seeds of all species were germinated and cultivated under controlled greenhouse conditions. When seedlings reached the three- to four-true-leaf stage, uniformly healthy individuals were selected and transplanted individually into round plastic pots filled with a standardized potting substrate (upper diameter × height: 12.8 cm × 10.7 cm). Each pot was treated as an independent biological replicate. 2.2 Experimental Design and Temperature Fluctuation Treatment All potted seedlings were transferred to controlled-environment growth chambers. Following transplantation, plants were acclimated for 14 days under control conditions to minimize transplant shock and establish a stable baseline growth state. Control conditions consisted of a constant diurnal temperature of 26°C, a 14 h light / 10 h dark photoperiod, and 50% relative humidity. After acclimation, plants assigned to the fluctuation treatment were subjected to three consecutive cycles of high-temperature stress and recovery (Fig. 1 ). Each cycle comprised two phases: a 14-day high-temperature phase at 40°C, followed by a 14-day recovery phase under control conditions (26°C). All other environmental parameters were maintained identical to those of the control treatment. Plants in the control group were maintained continuously under control conditions throughout the entire experimental period. It should be noted that the fluctuating treatment in this study (alternating between 40°C and 26°C) was designed to simulate natural heatwave events, which inherently involve both extreme high temperatures and intermittent recovery periods, resulting in a higher mean temperature than periods without heatwaves. Therefore, this study compares plants experiencing repeated heatwaves with those under constant ambient conditions, directly addressing the overall impact of heatwaves on plants. Disentangling the independent effects of fluctuation and mean warming was not the objective of this study. The experiment followed a completely randomized block design. For each combination of plant species (three invasive and three native species) and temperature treatment (fluctuation vs. control), a total of 168 biological replicates (pots) were established. Replicates were allocated across destructive sampling time points such that independent individuals were harvested at each time point. To minimize potential micro-environmental heterogeneity within growth chambers, pots were regularly and systematically rotated throughout the experiment. 2.3 Trait Measurement Destructive sampling was conducted at key transition points throughout the temperature fluctuation treatment: prior to the initiation of temperature fluctuations, at the end of each high-temperature phase, and at the end of each recovery phase. This resulted in a total of seven sampling time points (T1–T7). At each time point, four pots were randomly selected per species and treatment (n = 4). Immediately after harvesting, morphological and biomass traits were measured. Plant height was defined as the vertical distance from the soil surface to the highest natural growing point of the plant (cm). Root length was measured as the length of the longest root after carefully washing and spreading the root system (cm). Plants were then separated into aboveground and belowground components at the root collar. Each fraction was placed into labeled kraft paper bags, heated at 105°C for 30 min to deactivate enzymes, and subsequently dried at 80°C until constant mass (approximately 72 h). Aboveground and belowground dry masses (g) were recorded, and total biomass was calculated as their sum. Based on these measurements, three biomass allocation indices were derived: root-to-shoot ratio (belowground dry mass / aboveground dry mass), aboveground biomass ratio (aboveground dry mass / total biomass), and belowground biomass ratio (belowground dry mass / total biomass). 2.4 Data analysis To characterize overall growth trajectories under repeated temperature fluctuations, total biomass was analyzed across sampling time points. For functional group–level comparisons, total biomass values of the three species within each functional group (invasive or native) were averaged at each time point. Preliminary species-level analyses indicated consistent directional responses among species within each functional group; therefore, functional group means were used to emphasize general growth patterns. Linear regression was applied to examine changes in total biomass over the experimental period, with sampling time treated as an ordered variable representing progression through successive stress–recovery phases rather than absolute time. Analysis of covariance (ANCOVA) was used to test for differences in biomass accumulation rates (i.e., slopes) between temperature treatments and between functional groups. To quantify relative response intensity while minimizing inherent baseline differences among species, percentage changes in each trait were calculated relative to the initial control condition (T1) using the following formula: Percentage change = [(Mean of treatment group at time T - Mean of control group at time T1) / Mean of control group at time T1] × 100% Percentage changes were first calculated at the species level and then averaged across species within each functional group. Independent-samples t-tests were conducted at each sampling time point (T2–T7) to assess divergence in response magnitude between invasive and native plants. These comparisons were intended to identify consistent temporal patterns of divergence rather than to emphasize isolated pairwise differences. Preliminary species-level analyses indicated consistent directional responses among species within each functional group. Therefore, functional group means were used to emphasize general growth patterns, while minimizing noise associated with interspecific variability. All statistical analyses were performed using SPSS 22.0, with significance assessed at α = 0.05. Figures were generated using SigmaPlot 12.5. 3. Results 3.1 Fundamental divergence in growth trajectories under repeated heat fluctuations To evaluate long-term growth dynamics under repeated temperature fluctuations, changes in total biomass were analyzed across seven sampling time points (Fig. 2 ; Table 1 ). Linear regression revealed contrasting growth trajectories between invasive and native plants under fluctuating temperature conditions. Table 1 Difference analyses on regression parameters for total biomass vs. time Invasive plants Control Treatment Difference between slopes a = 0.0797 b = 0.0817 R 2 =0.9860 p < 0.0001 a = 0.1367 b=-0.0161 R 2 =0.9404 p = 0.0003 p = 0.005 Native plants a = 0.0928 b = 0.0595 R 2 =0.9530 p = 0.0002 a = 0.0782 b = 0.1241 R 2 =0.9421 p = 0.0003 NS Difference between slopes NS p = 0.008 For invasive plants, the slope of total biomass accumulation was significantly higher under fluctuating temperatures than under constant ambient conditions (ANCOVA, p = 0.005), indicating an accelerated biomass accumulation rate in response to repeated heat fluctuations. In contrast, no significant difference in growth slopes was detected between fluctuation and control treatments for native plants (ANCOVA, p > 0.05), suggesting that repeated temperature fluctuations did not enhance their biomass accumulation rate. Under constant ambient conditions, invasive and native plants exhibited comparable growth trajectories, with no significant difference in biomass accumulation slopes between functional groups (ANCOVA, p > 0.05). However, under fluctuating temperature regimes, growth trajectories diverged markedly: invasive plants maintained a sustained increase in total biomass across successive fluctuation cycles, whereas native plants showed a substantially reduced rate of biomass increase over time. 3.2 Differential impacts of fluctuations on plant performance: gains vs. costs Repeated heat fluctuations induced distinct patterns of trait responses in invasive and native plants, as reflected by percentage changes in morphological and biomass-related traits relative to control conditions (Fig. 3 , Fig. 4 ). Across most sampling time points, invasive plants exhibited positive percentage changes in plant height, aboveground dry mass, belowground dry mass, and total biomass under fluctuating temperatures, indicating higher trait values compared with control conditions. In contrast, responses of root length differed from other traits, showing positive values only after the first high-temperature phase and negative values during subsequent phases. Native plants displayed more variable and often negative trait responses under repeated fluctuations. Percentage changes in total biomass shifted from slightly positive values during early phases to negative values in later stages of the experiment. Although aboveground dry mass frequently remained higher in fluctuation treatments than in controls, this pattern was not accompanied by increases in plant height, which remained consistently lower under fluctuating temperatures throughout the experiment. Heatmap visualization further highlighted contrasting response profiles between functional groups (Fig. 4 ). Invasive plants generally showed positive deviations from control conditions across multiple traits and time points, whereas native plants exhibited increasing negative deviations, particularly for total biomass and root-related traits, as fluctuation cycles progressed. 3.3 Comparative performance of invasive and native plants across fluctuation cycles Comparisons of percentage trait changes between invasive and native plants revealed that performance differences intensified across successive heat fluctuation cycles (Table 2 ). Table 2 Significance comparison of percent changes in various traits between invasive and native plants across three high-temperature/ambient cycles (mean ± SE). High-temperature /ambient cycle Plant height (cm) Root length (cm) Aboveground dry weight (g) Belowground dry weight (g) Total biomass (g) Aboveground biomass ratio Belowground biomass ratio Root:shoot ratio Cycle 1 Invasive plants 25.48 ± 4.42 -30.41 ± 7.11 17.88 ± 6.95 12.53 ± 4.75 10.15 ± 2.20 7.31 ± 7.65 2.10 ± 3.35 -2.92 ± 9.18 Native plants -7.64 ± 3.58 -31.98 ± 10.54 19.28 ± 9.80 -23.64 ± 10.46 3.29 ± 10.67 16.50 ± 5.72 -26.25 ± 7.29 -35.50 ± 8.83 p 0.0089** NS NS NS NS NS 0.0448* NS Cycle 2 Invasive plants 32.69 ± 7.22 -34.08 ± 10.33 132.10 ± 45.09 10.69 ± 3.86 55.24 ± 9.75 52.79 ± 34.81 -27.78 ± 5.51 -47.98 ± 7.57 Native plants -1.94 ± 5.35 2.16 ± 0.25 8.07 ± 5.92 3.03 ± 1.48 7.42 ± 3.88 0.42 ± 1.93 -3.63 ± 4.35 -3.68 ± 6.04 p 0.0346* 0.0457* NS NS 0.0205* NS 0.0484* 0.0202* Cycle 3 Invasive plants 38.45 ± 11.96 -39.62 ± 17.91 79.25 ± 14.21 16.10 ± 8.31 53.53 ± 5.63 16.41 ± 6.68 -24.22 ± 5.27 -34.81 ± 3.34 Native plants -13.78 ± 6.42 -8.09 ± 4.20 5.11 ± 3.61 -17.01 ± 3.61 -2.53 ± 4.47 8.10 ± 2.75 -14.19 ± 5.84 -20.52 ± 5.62 p 0.0348* NS 0.0145* 0.0405* 0.0031** NS NS NS During the first fluctuation cycle, invasive plants showed significantly greater percentage increases in plant height than native plants ( p < 0.01), while differences in most biomass-related traits were not yet significant. In the second cycle, significant differences emerged in total biomass, root length, root-to-shoot ratio, and belowground biomass ratio, indicating increasing divergence in trait responses between functional groups. By the third cycle, invasive plants exhibited significantly greater values than native plants for plant height, aboveground dry mass, belowground dry mass, and total biomass ( p < 0.05). Collectively, these results demonstrate that differences between invasive and native plants were not static but increased progressively with repeated exposure to heat fluctuations, leading to pronounced divergence in growth performance by the final fluctuation cycle. 4. Discussion 4.1 Repeated Heat Fluctuations Lead to Divergent Growth Trajectories between Invasive and Native Plants By dynamically tracking plant performance across three consecutive heat–recovery cycles, this study demonstrates that repeated heat fluctuations generate contrasting growth trajectories between invasive and native plant species. Under constant ambient conditions, invasive and native plants exhibited comparable biomass accumulation rates, indicating similar growth potential in stable environments. However, under fluctuating temperature regimes, their growth trajectories diverged markedly, suggesting that performance differences arise primarily from differential responses to repeated environmental fluctuations rather than inherent growth-rate advantages. Invasive plants exhibited a significantly steeper biomass accumulation slope under fluctuating temperatures, whereas native plants showed no corresponding increase in growth rate. Similar asymmetric responses between invasive and native plants have been reported under warming scenarios, where elevated temperatures disproportionately constrain native species relative to invaders (Verlinden & Nijs, 2010 ; Walther et al., 2009 ; Liu et al., 2017 ). Our results extend these findings by demonstrating that divergence is amplified when thermal stress occurs repeatedly as stress–recovery cycles, rather than as a continuous shift in mean temperature. Analyses based on relative changes from unstressed control conditions further revealed that invasive plants generally achieved net gains in multiple growth traits following repeated fluctuations, whereas native plants increasingly deviated negatively from their baseline performance. Comparable patterns of asymmetric performance under environmental variability have been documented in studies emphasizing the role of temporal heterogeneity in shaping invasion success (Richards et al., 2006 ; Davidson et al., 2011). Together, these findings indicate that repeated heat fluctuations act as a dynamic driver that progressively amplifies growth asymmetry between invasive and native plants. Because the fluctuating treatment also involved elevated mean temperature relative to the constant control, our results should be interpreted as responses to realistic heatwave-like thermal regimes rather than isolated temperature variability alone. This distinction is important when extrapolating our findings to natural systems, where heatwaves inherently combine both extreme temperature peaks and elevated mean thermal conditions. 4.2 Divergent Growth Plasticity and Resource Allocation Strategies Drive Phenotypic Differentiation The divergent growth trajectories observed in this study are accompanied by consistent differences in trait-level responses and biomass allocation patterns between invasive and native plants. Invasive plants generally exhibited positive changes in plant height and total biomass across successive fluctuation cycles, whereas native plants showed increasingly negative responses during later stages of the experiment. Biomass allocation patterns further highlight this contrast. Under repeated heat fluctuations, invasive plants maintained or increased allocation to aboveground biomass, whereas native plants displayed progressively higher root-to-shoot ratio. Similar allocation shifts have been widely reported as plant responses to stress, reflecting trade-offs between growth and maintenance (Poorter & Navas, 2003 ; Wright et al., 2005). In stressful or resource-limited environments, increased belowground allocation is often associated with survival-oriented strategies, but may constrain overall biomass accumulation when not accompanied by sufficient carbon gain. Phenotypic plasticity has long been proposed as a key trait underlying invasion success, particularly under heterogeneous or fluctuating environmental conditions (Richards et al., 2006 ; Nicotra et al., 2010 ; Valladares et al., 2007 ). Meta-analyses and empirical syntheses indicate that invasive plants frequently exhibit greater plasticity in growth and allocation traits than native species, allowing them to sustain performance across a broader range of conditions (Davidson et al., 2011; Godoy et al., 2011 ). Consistent with this framework, our results suggest that invasive plants maintain growth-oriented allocation patterns under repeated thermal fluctuations, whereas native plants increasingly shift biomass belowground without corresponding gains in total biomass. Such differences in allocation responses, rather than superiority in any single trait, likely underpin the progressive divergence in growth performance observed between functional groups. 4.3 Fluctuation Frequency as an Intensified Environmental Filter and Its Implications for Plant Invasion Beyond individual trait responses, our findings highlight the ecological importance of repeated thermal fluctuations as an environmental filter shaping plant invasion dynamics. Traditional studies of heat stress have largely focused on plant survival or performance under single extreme events or sustained warming (Jagadish et al., 2021 ; Wang et al., 2017 ). However, increasing evidence suggests that the temporal structure of stress exposure—particularly the recurrence of stress–recovery cycles—can exert strong selective pressures on plant populations (Baldwin et al., 2019 ; Raymond et al., 2022 ). Repeated stress–recovery cycles may impose cumulative constraints on species that are unable to fully recover between stress events, leading to progressively reduced performance over time (Hoover et al., 2017 ; Gagné et al., 2022). In this study, native plants showed increasingly negative deviations from baseline growth during later fluctuation cycles, whereas invasive plants maintained stable or positive performance. Similar patterns have been observed in studies emphasizing the importance of recovery dynamics and carbon balance in determining plant performance under repeated stress (Tang et al., 2014 ; Liu et al., 2022 ). Although the experimental design included a single fluctuation regime, the progressive divergence observed across successive cycles suggests that repeated exposure to thermal stress, even at constant intensity, can amplify performance asymmetries between invasive and native species. Under future climate scenarios characterized by increased temperature variability, such dynamics may disproportionately favor species capable of sustaining growth across repeated stress episodes, thereby accelerating biological invasion processes (Bradley et al., 2010 ; Walther et al., 2009 ). 4.4 Study Limitations and Future Directions Several limitations should be acknowledged. First, this study focused on six species from a single plant family. While functional group–level comparisons strengthen inference regarding invasive versus native strategies, broader taxonomic sampling is required to test the generality of these patterns across plant lineages with contrasting life-history strategies (van Kleunen & Johnson, 2007 ; Schlaepfer et al., 2010 ). Second, the experiment was conducted under competition-free conditions, allowing isolation of individual-level growth responses but limiting inference about community-level outcomes. Previous studies have shown that competitive interactions can amplify asymmetries between invasive and native species under environmental stress (Niu & Wan, 2008 ; Verlinden et al., 2014 ). Future studies incorporating interspecific competition and community-level approaches will be essential for linking individual growth trajectories to population dynamics and species replacement. Overall, by integrating dynamic growth trajectories with trait-based responses, this study emphasizes that recurrent environmental fluctuations should be viewed not merely as episodic stressors, but as active filters shaping plant performance over time. These findings contribute to a growing body of literature highlighting the importance of climatic variability, rather than changes in mean conditions alone, in driving biological invasions and restructuring plant communities. We acknowledge that the fluctuating treatment in this study had a higher mean temperature than the constant control, which inherently combines the effects of both heat pulses and increased mean temperature. While this precludes the separation of these two factors, our experimental design was intentionally chosen to simulate ecologically realistic heatwave scenarios. Heatwaves in nature inherently involve both extreme peaks and elevated mean temperatures. Thus, our comparison addresses the question of how plants respond to such real-world thermal regimes. Future studies incorporating a constant warm treatment could further disentangle the independent contributions of mean warming and temperature variability. 5. Conclusion This study systematically compared the growth responses of invasive and native plants under experimentally imposed repeated fluctuations between high and normal temperatures and revealed clear, consistent divergence in their growth dynamics. Invasive plants exhibited a significantly enhanced biomass accumulation rate under fluctuating conditions, accompanied by a sustained shift in biomass allocation toward aboveground structures, indicating their capacity to maintain growth-oriented strategies across successive stress–recovery cycles. In contrast, native plants showed no improvement in growth rate under temperature fluctuations and instead displayed progressively constrained biomass accumulation, together with increased allocation to belowground components. These contrasting responses highlight that repeated thermal fluctuations amplify performance asymmetries between invasive and native plants by differentially shaping growth trajectories and allocation outcomes over time. Importantly, our results suggest that the temporal characteristics of climatic variability, particularly the recurrence of stress relative to plant recovery capacity can exert a stronger influence on plant performance than the intensity of individual stress events alone. By adopting a dynamic, process-based perspective, this study emphasizes the role of environmental fluctuation regimes as active ecological filters and provides a mechanistic framework for understanding how increasing climate variability may accelerate biological invasions and restructure plant communities under future climate change. Declarations Conflicts of Interest Statement : The authors declare no conflicts of interest. Funding: This study was supported by the Science and Technology Program of the Shaanxi Academy of Sciences (2023k-24, 2025k-07); the Natural Science Basic Research Plan of Shaanxi Province (2025JC-YBMS-206); The Science and Technology Plan Project of Xi’an (25NJSYB00011, 24NYGG0093; 24NYZZ0079). Author Contributions: Conceptualization, Wang Yuchao, Lyu Jinlin, Yue Ming; data collection and curation, Xue Wenyan, Yang Hang, Zhang Yan; results analysis and interpretation, Lyu Jinlin, Li Yang, Huang Li; writing-original draft, Lyu Jinlin; writing-review and editing, Wang Yuchao, Huang Li. All authors have read and agreed to the published version of the manuscript. References Baker HG (1965) Characteristics and modes of origin of weeds. In Baker HG, Stebbins GL (eds). The Genetics of Colonizing Species. New York, USA: Academic Press, 147–168. Baldwin JW, Dessy JB, Vecchi GA, Oppenheimer M (2019) Temporally compound heat wave events and global warming: an emerging hazard. Earth’s Future 7(4): 411–427. https://doi.org/10.1029/2018EF000989 Barros V, Melo A, Santos M, Nogueira L, Frosi G, Santos MG (2020) Different resource–use strategies of invasive and native woody species from a seasonally dry tropical forest under drought stress and recovery. Plant Physiol Bioch 147: 181–190. https://doi.org/10.1016/j.plaphy.2019.12.018 Bradley BA, Blumenthal DM, Wilcove DS, Ziska LH (2010) Predicting plant invasions in an era of global change. Trends Ecol Evol 25(5): 310–318. https://doi.org/10.1016/j.tree.2009.12.003 Chen D, Cai AM, Wang YJ, van Kleunen M (2025) Competitive Superiority of Aliens over Natives under Abiotic and Biotic Stresses in Legume and Nonlegume Woody Species. Ecology 106(11): e70252. https://doi.org/10.1002/ecy.70252 Chomel M, Lavallee JM, Alvarez–Segura N, et al. (2022) Intensive grassland management disrupts below–ground multi–trophic resource transfer in response to drought. Nat Commun 13: 6991. https://www.nature.com/articles/s41467–022–34449–5 Davidson AM, Jennions M, Nicotra AB (2015) Do invasive species show higher phenotypic plasticity than native species and, if so, is it adaptive? A meta–analysis. Ecol Lett 14(4): 419–431. https://doi.org/10.1111/j.1461–0248.2011.01596.x Gagne MA, Smith DD, Mcculloh KA (2020) Limited physiological acclimation to recurrent heatwaves in two boreal tree species. Tree Physiol 40(12): 1680–1696. https://doi.org/10.1093/treephys/tpaa102 Giejsztowt J, Classen AT, Deslippe JR (2020) Climate change and invasion may synergistically affect native plant reproduction. Ecology 101(1): e02913. https://doi.org/10.1002/ecy.2913 Godoy O, Valladares F, Castro–Díez P (2011) Multispecies comparison reveals that invasive and native plants differ in their traits but not in their plasticity. Funct Ecol 25(6): 1248–1259. https://doi.org/10.1111/j.1365–2435.2011.01886.x Godoy O, Valladares F, Castro–Dı́ez P (2012) The relative importance for plant invasiveness of trait means, and their plasticity and integration in a multivariate framework. New Phytol 195(4): 912–922. https://doi.org/10.1111/j.1469–8137.2012.04205.x Halbritter AH, Fior S, Keller I, Billeter R, Edwards PJ, Holderegger R, Karrenberg S, Pluess AR, Widmer A, Alexander JM (2018) Trait differentiation and adaptation of plants along elevation gradients. J Evolution Biol 31(6): 784–800. https://doi.org/10.1111/jeb.13262 Hartmann H, Moura CF, Anderegg WRL, et al . (2018) Research frontiers for improving our understanding of drought–induced tree and forest mortality. New Phytol 218(1): 15–28. https://doi.org/10.1111/nph.15048 He QY, Yan MJ, Miyazawa Y, Chen QW, Cheng RR, Otsuki K, Yamanaka N, Du S (2020) Sap flow changes and climatic responses over multiple–year treatment of rainfall exclusion in a sub–humid black locust plantation. Forest Ecol Manag 457:117730. https://doi.org/10.1016/j.foreco.2019.117730 Heberling JM, Fridley JD (2013) Resource–use strategies of native and invasive plants in Eastern North American forests. New Phytol 200(2): 523–533. https://doi.org/10.1111/nph.12388 Hellmann JJ, Byers JE, Bierwagen BG, Dukes JS (2008) Five potential consequences of climate change for invasive species. Conserv Biol 22(3): 534–543. https://doi.org/10.1111/j.1523–1739.2008.00951.x Higgins SI, Richardson DM (2014) Invasive plants have broader physiological niches. PNAS 111(29): 10610–10614. https://doi.org/10.1073/pnas.1406075111 Hoover DL, Knapp AK, Smith MD (2017) Photosynthetic responses of a dominant C 4 grass to an experimental heat wave are mediated by soil moisture. Oecologia 183: 303–313. https://doi.org/10.1007/s00442–016–3755–6 Jagadish SVK, Way DA, Sharkey TD (2021) Plant heat stress: concepts directing future research. Plant Cell Environ 44(7): 1992–2005. https://doi.org/10.1111/pce.14050 Keep T, Sampoux JP, Barre P, et al. (2020) To grow or survive: which are the strategies of a perennial grass to face severe seasonal stress? Funct Ecol 35(5): 1145–1158. https://doi.org/10.1111/1365–2435.13770 Laughlin DC (2024) Unifying functional and population ecology to test the adaptive value of traits. Biol Rev 99: 1976–1991. https://doi.org/10.1111/brv.13107 Lind EM, Borer E, Seabloom E, et al. (2013) Life–history constraints in grassland plant species: a growth–defence trade–off is the norm. Ecol Lett 16(4): 513–521. https://doi.org/10.1111/ele.12078 Liu HP, Able AJ, Able JA (2022) Priming crops for the future: rewiring stress memory. Trends Plant Sci 27(7): 699–716. https://doi.org/10.1016/j.tplants.2021.11.015 Liu LB, Gudmundsson L, Hauser M, Qin DH, Li SC, Seneviratne SI (2019) Revisiting assessments of ecosystem drought recovery. Environ Res Lett 14: 114028. https://doi.org/10.1088/1748–9326/ab4c61 Liu YJ, Oduor AMO, Zhang Z, Manea A, Tooth IM, Leishman MR, Xu XL, van Kleunen M (2017) Do invasive alien plants benefit more from global environmental change than native plants? Global Change Biol 23(8): 3363–3370. https://doi.org/10.1111/gcb.13579 Mccarthy M, Enquist B (2007) Consistency between an allometric approach and optimal partitioning theory in global patterns of plant biomass allocation. Funct Ecol 21(4): 713–720. https://doi.org/10.1111/j.1365–2435.2007.01276.x Nicotra AB, Atkin OK, Bonser SP, et al. (2010) Plant phenotypic plasticity in a changing climate. Trends Plant Sci 15(12): 684–692. https://doi.org/10.1016/j.tplants.2010.09.008 Niu SL, Wan SQ (2008) Warming changes plant competitive hierarchy in a temperate steppe in northern China. J Plant Ecol 1(2): 103–110. https://doi.org/10.1093/jpe/rtn003 Poorter H, Navas ML (2003) Plant growth and competition at elevated CO 2 : on winners, losers and functional groups. New Phytol 157(2): 175–198. https://doi.org/10.1046/j.1469–8137.2003.00680.x Raed H Carmen BS, Ma QY, Daniel B, Ellie B, Corey L, Kai K (2025) Amplified agricultural impacts from more frequent and intense sequential heat events. Environ Res Lett 20: 114001. https://doi.org/10.1088/1748–9326/ae06b8 Raymond C, Suarez–Gutierrez L, Kornhuber K, Pascolini-Campbell M, Sillmann J, Waliser D (2022) Increasing spatiotemporal proximity of heat and precipitation extremes in a warming world quantified by a large model ensemble. Environ Res Lett 17: 035005. https://doi.org/10.1088/1748–9326/ac5712 Ren GQ, Zou CB, Wan LY, Johnson JH, Li J, Zhu L, Qi SS, Dai ZC, Zhang HY, Du DL (2021) Interactive effect of climate warming and nitrogen deposition may shift the dynamics of native and invasive species, J Plant Ecol 14(1): 84–95. https://doi.org/10.1093/jpe/rtaa071 Richards CL, Bossdorf O, Muth NZ, Gurevitch J, Pigliucci M (2006) Jack of all trades, master of some? On the role of phenotypic plasticity in plant invasions. Ecol Lett 9, 981–993. https://doi.org/10.1111/J.1461–0248.2006.00950.X Robinson A, Lehmann J, Barriopedro D, Rahmstorf S, Coumou D (2021) Increasing heat and rainfall extremes now far outside the historical climate. npj Clim Atmos Sci 4: 45. https://doi.org/10.1038/s41612–021–00202–w Sandel B, Dangremond EM (2012) Climate change and the invasion of California by grasses. Global Change Biol 18(1): 277–289. https://doi.org/10.1111/j.1365–2486.2011.02480 Sato H, Mizoi J, Shinozaki K, Yamaguchi-Shinozaki K (2024) Complex plant responses to drought and heat stress under climate change. Plant J, 117: 1873–1892. https://doi.org/10.1111/tpj.16612 Schlaepfer DR, Glättli M, Fischer M, van Kleunen M (2010) A multispecies experiment in their native range indicates pre–adaptation of invasive alien plant species. New Phytol 185: 1087–1099. https://doi.org/10.1111/j.1469–8137.2009.03114.x Sexton JP, Mckay JK, Sala A (2002) Plasticity and genetic diversity may allow saltcedar to invade cold climates in North America. Ecol Appl 12(6): 1652–1660. Song L, Chow WS, Sun LL, Peng CL (2010) Acclimation of photosystem II to high temperature in two Wedelia species from different geographical origins: Implications for biological invasions upon global warming. J Exp Bot 61(14): 4087–4096. https://doi.org/10.1093/jxb/erq220 Stroud JT, Ratcliff WC (2025) Long–term studies provide unique insights into evolution. Nature, 639, 589–601. https://doi.org/10.1038/s41586–025–08597–9 Tang YK, Wen XF, Sun XM, Zhang XY, Wang HM (2014) The limiting effect of deep soilwater on evapotranspiration of a subtropical coniferous plantation subjected to seasonal drought. Adv Atmos Sci 31: 385–395. https://doi.org/10.1007/s00376–013–2321–y Valladares F, Gianoli E, Gómez JM (2007) Ecological limits to plant phenotypic plasticity. New Phytol 176(4): 749–763. https://doi.org/10.1111/j.1469–8137.2007.02275.x van Kleunen M, Johnson SD (2007) South African Iridaceae with rapid and profuse seedling emergence are more likely to become naturalized in other regions. J Ecol 95(4): 674–681. https://doi.org/10.1111/j.1365–2745.2007.01250.x Verlinden M, de Boeck HJ, Nijs I (2014) Climate warming alters competition between two highly invasive alien plant species and dominant native competitors. Weed Res 54(3): 234–244. https://doi.org/10.1111/wre.12076 Verlinden M, Nijs I (2010) Alien plant species favoured over congeneric natives under experimental climate warming in temperate belgian climate. Biol Invasions 12(8): 2777–2787. https://doi.org/10.1007/s10530–009–9683–1 Walther GR, Roques A, Hulme PE, et al. (2009) Alien species in a warmer world： Risks and opportunities. Trends Ecol Evol 24(12): 686–693. https://doi.org/10.1016/j.tree.2009.06.008 Wan JSH, Bonser SP, Pang CK, Fazlioglu F, Rutherford S (2024) Adaptive responses to living in stressful habitats: Do invasive and native plant populations use different strategies?. Ecol Lett 27: e14419. https://doi.org/10.1111/ele.14419 Wang X, Liu FL, Jiang D (2017) Priming: a promising strategy for crop production in response to future climate. J Integr Agr 16(12): 2709–2716. https://doi.org/10.1016/S2095–3119(17)61786–6 Wang ZX, He ZS, He WM (2021). Nighttime climate warming enhances inhibitory effects of atmospheric nitrogen deposition on the success of invasive Solidago canadensis. Climatic Change 167(1/2): 20. https://doi.org/10.1007/s10584–021–03175–0 Welshofer KB, Zarnetske PL, Lany NK, Read QD (2018). Short–term responses to warming vary between native vs. exotic species and with latitude in an early successional plant community. Oecologia 187(1): 333–342. https://doi.org/10.1007/s00442–018–4111–9 Willis SG, Hulme PE (2002). Does temperature limit the invasion of Impatiens glandulifera and Heracleum mantegazzianum in the UK? Funct Ecol 16(4): 530–539. https://doi.org/10.1046/j.1365–2435.2002.00653.x Wright IJ, Reich PB, Westoby M, et al. (2004). The worldwide leaf economics spectrum. Nature 428: 821–827. https://doi.org/10.1038/nature02403 Yao Y, Fu BJ, Liu YX, Zhang Y, Ding JY, Li Y, Zhou S, Song JX, Wang S, Li CJ, Zhao WW (2024). Compound hot–dry events greatly prolong the recovery time of dryland ecosystems. Natl Sci Rev 11(10): nwae274. https://doi.org/10.1093/nsr/nwae274 Zhang XL, Yu HH, Lv T, Yang L, Liu CH, Fan SF, Yu D, Carboni M (2021). Effects of different scenarios of temperature rise and biological control agents on interactions between two noxious invasive plants. Divers Distrib 27(12): 2300–2314. https://doi.org/10.1111/ddi.13406 Zhou XH, He WM, Peng PH, Li JJ (2025). Long–term warming legacies facilitate invasive plant growth and inhibit enemy performance. J Plant Ecol 18(3): rtaf033. https://doi.org/10.1093/jpe/rtaf033 Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 30 Apr, 2026 Reviewers invited by journal 30 Apr, 2026 Editor invited by journal 11 Apr, 2026 Editor assigned by journal 11 Apr, 2026 First submitted to journal 10 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9377302","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":632356985,"identity":"00859538-f32c-4e46-8c34-53e52276fef2","order_by":0,"name":"Jinlin Lyu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIie3PMQrCQBBA0ZHAbjOYdqucYSAgFh5mg5BKQiCNlWwIWHkAO6/gERIG9Qq24gUCNhaCxmhlkTWd4P5qinkMA+By/WBDAF1CqkB4A1PWNAmsRLSEFAxlkVfrNA6/IU0EEOChYKw5shOlT5zSOBEqMjwhT4Pk3dZCNK9JZS2ZkUgA4/jYSbDUjKSi5YtgBgpHPci4GYyVSPMmWBkGoj5E5qZakQ6F7Rffw+kF54toU8hzfb3dA1/yvpMAoP64273+TJb2HZfL5frvHhp+SBItgpXjAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-3898-774X","institution":"xi'an botanical garden of shaanxi province (Institute of botany of shaanxi province)","correspondingAuthor":true,"prefix":"","firstName":"Jinlin","middleName":"","lastName":"Lyu","suffix":""},{"id":632356986,"identity":"e42d518d-0d68-41e0-8c67-5b20aff89c5e","order_by":1,"name":"Li Huang","email":"","orcid":"","institution":"Xishuangbanna Tropical Botanical Garden Chinese Academy of Sciences Key Laboratory of Tropical Forest Ecology","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Huang","suffix":""},{"id":632356987,"identity":"97667b1a-0bac-4dc3-a3a0-d922090e8dad","order_by":2,"name":"Xue Wenyan","email":"","orcid":"","institution":"xi'an botanical garden of shaanxi province","correspondingAuthor":false,"prefix":"","firstName":"Xue","middleName":"","lastName":"Wenyan","suffix":""},{"id":632356988,"identity":"98cbe874-9f91-4c40-a69f-a6302b8034a5","order_by":3,"name":"Yuchao Wang","email":"","orcid":"","institution":"xi'an botanical garden of shaanxi province","correspondingAuthor":false,"prefix":"","firstName":"Yuchao","middleName":"","lastName":"Wang","suffix":""},{"id":632356989,"identity":"b0fecf5d-fd9a-47c6-9c58-a9afb6823a51","order_by":4,"name":"Yang Li","email":"","orcid":"","institution":"xi'an botanical garden of shaanxi province","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Li","suffix":""},{"id":632356990,"identity":"4f05d905-20b2-4905-9330-2a7d8fe9b56f","order_by":5,"name":"Ming Yue","email":"","orcid":"","institution":"Northwest University","correspondingAuthor":false,"prefix":"","firstName":"Ming","middleName":"","lastName":"Yue","suffix":""},{"id":632356991,"identity":"5bb56273-548c-4525-b5aa-75bbcd8bf6c9","order_by":6,"name":"Hang Yang","email":"","orcid":"","institution":"xi'an botanical garden of shaanxi province","correspondingAuthor":false,"prefix":"","firstName":"Hang","middleName":"","lastName":"Yang","suffix":""},{"id":632356992,"identity":"a21348e4-d217-4762-921e-b85934f178a2","order_by":7,"name":"Yan Zhang","email":"","orcid":"","institution":"xi'an botanical garden of shaanxi province","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2026-04-10 09:14:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9377302/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9377302/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108756773,"identity":"92bf1bcb-9df7-4224-982c-5afdeb086cb3","added_by":"auto","created_at":"2026-05-08 05:35:17","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":77669,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram of the experimental design with repeated heat fluctuations and sampling time points.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9377302/v1/3ae849277560d4c6a0a6e152.jpg"},{"id":108756770,"identity":"4195f228-6d8b-4422-9688-30b40bb9c1c0","added_by":"auto","created_at":"2026-05-08 05:35:17","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":73088,"visible":true,"origin":"","legend":"\u003cp\u003eDynamics of total biomass in invasive and native plants in response to fluctuating high temperature and constant ambient conditions. Symbols represent mean values (±SE) for invasive (circles) and native (triangles) plants under fluctuating high-temperature (open symbols, solid lines) and constant ambient (closed symbols, dashed lines) treatments. Lines indicate significant linear regressions (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). The x-axis values (1-7) represent consecutive treatment phases: 1, initial ambient phase; 2, first high-temperature phase; 3, first recovery ambient phase; 4, second high-temperature phase; 5, second recovery ambient phase; 6, third high-temperature phase; 7, final ambient phase.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9377302/v1/926106f9771f6eccdd383f8f.jpg"},{"id":108756769,"identity":"009bb65c-3a4b-431c-953c-a009ff84c463","added_by":"auto","created_at":"2026-05-08 05:35:17","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":128083,"visible":true,"origin":"","legend":"\u003cp\u003eTrends in percent change for five growth traits of invasive and native plants under two temperature regimes. (A) Plant height, (B) root length, (C) aboveground dry weight, (D) belowground dry weight, (E) total biomass. Percent changes were calculated relative to [e.g., the initial measurements at the start of the experiment]. Data points represent mean ± SE. The x-axis values (1-6) correspond to consecutive treatment phases: 1, first high-temperature phase; 2, first recovery ambient phase; 3, second high-temperature phase; 4, second recovery ambient phase; 5, third high-temperature phase; 6, final ambient phase. Red and green lines represent invasive and native plants, respectively; solid and dashed lines represent fluctuating high-temperature treatment and constant ambient control, respectively.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9377302/v1/e70a99cbe0dace4355f7a4f8.jpg"},{"id":108806374,"identity":"e14a8db7-7878-4c3c-af0d-add545d2c680","added_by":"auto","created_at":"2026-05-08 15:28:24","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":66251,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap visualization of trait-specific responses to repeated heat fluctuations in invasive and native plants. The value in each cell is calculated as the difference in percentage change between the treatment and control groups. Blue hues indicate positive values (i.e., trait enhancement under fluctuations compared to the control), with darker blue representing larger positive effects. Red hues indicate negative values (i.e., trait suppression), with darker red representing larger negative effects.\u003c/p\u003e\n\u003cp\u003eSampling time points are labeled as: T2, T3, T4, T5, T6, T7 (corresponding to first high-temperature phase, first recovery ambient phase, second high-temperature phase, second recovery ambient phase, third high-temperature phase, final ambient phase, respectively).\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9377302/v1/193129a91acca9f35c784ddb.jpg"},{"id":108809688,"identity":"8ff58a92-d76b-45d2-83a8-c99731a8da80","added_by":"auto","created_at":"2026-05-08 15:55:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":654940,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9377302/v1/02f2f2e4-f39f-44cd-91ee-cc0f6d710564.pdf"}],"financialInterests":"","formattedTitle":"Repeated heat fluctuations amplify growth advantages of invasive over native plants​","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePlant performance in a changing climate is increasingly shaped not only by shifts in mean temperature but also by changes in the temporal structure of thermal stress. In many ecosystems, plants are now exposed to recurrent heat events characterized by abrupt onset, short duration, and intermittent recovery phases, rather than sustained warming alone. Such repeated heat fluctuations represent a distinct form of abiotic stress, as they repeatedly disrupt physiological processes and growth trajectories while providing limited opportunities for full recovery between events (Baldwin et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Raymond et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Recent climate projections indicate that this fluctuation-dominated stress regime will become more prevalent as global temperatures rise and heatwaves increase in frequency (Sato et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Understanding how plants respond to repeated thermal fluctuations, rather than single or chronic stress events, has therefore emerged as a critical challenge for predicting future ecosystem structure and function.\u003c/p\u003e \u003cp\u003eBiological invasions are widely recognized as being particularly sensitive to climate change. Classical invasion theory proposes that invasive plant species often exhibit greater phenotypic plasticity than native species, enabling them to maintain performance under fluctuating or stressful environmental conditions (Baker, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1965\u003c/span\u003e; Richards et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Numerous studies have shown that invasive plants outperform native species under sustained warming, drought, or other chronic stress regimes (Hellmann et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Bradley et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Welshofer et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Such conditions tend to disrupt climatic regimes to which native species are historically adapted, while favoring invasive species capable of rapid adjustment (Walther et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Song et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Sandel et al., 2012). Consequently, climate warming is widely expected to accelerate plant invasions, yet the role of increasing climate variability, particularly recurrent heatwave events, remains far less understood.\u003c/p\u003e \u003cp\u003eIn natural ecosystems, plants are rarely exposed to a single, continuous stress event. Instead, they typically experience episodic stress alternating with periods of partial or complete recovery (Hartmann et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yao et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Responses to such recurrent stress may fundamentally differ from those observed under sustained stress, potentially leading to acclimation, compensatory growth, or cumulative damage depending on the balance between stress duration and recovery capacity. Despite growing recognition of this complexity, there remains limited systematic understanding of how plant growth trajectories and functional traits evolve across multiple stress\u0026ndash;recovery cycles, and whether invasive and native species diverge in their ability to cope with such dynamic stress regimes.\u003c/p\u003e \u003cp\u003eA further limitation of existing research lies in its predominantly static analytical framework. Many studies assess plant traits at discrete endpoints, such as the end of a stress period or the conclusion of an experiment, thereby capturing immediate or net effects of stress but overlooking the continuous dynamics of plant responses over time (Ren et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Yet key determinants of long-term plant fitness, including temporal variation in growth rate, compensatory growth during recovery phases, and stage-specific shifts in biomass allocation, are inherently dynamic processes (Daniel, 2024; Stroud \u0026amp; Ratcliff, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Justin et al., 2024). Moreover, plant responses to recurrent stress may be non-linear: early stress cycles may prioritize survival-oriented strategies, whereas repeated exposure could reveal divergence in growth-oriented adaptation. Such temporal transitions, and their potential differences between invasive and native species, remain largely unexplored.\u003c/p\u003e \u003cp\u003eHere, we propose that repeated heatwave-like thermal fluctuations act as a dynamic environmental filter that selectively favors invasive over native plant species. Specifically, we hypothesize that such recurrent stress\u0026ndash;recovery cycles enhance the performance advantage of invasive plants by driving divergence in growth trajectories and biomass allocation patterns over time. To test this hypothesis, we selected representative invasive and native species from the Asteraceae family and subjected them to three complete cycles of high-temperature stress and recovery under controlled growth chamber conditions. Using destructive sampling at the end of each stress and recovery phase, we quantified not only final trait values but also temporal changes in total biomass accumulation rate and percentage trait responses relative to baseline conditions. By integrating growth trajectory analysis, self-control comparisons, and functional group\u0026ndash;level responses, this study addresses three key questions: (1) Do repeated heat fluctuations fundamentally alter plant growth trajectories over time? (2) Do invasive and native species experience net performance gains or losses under such fluctuations? and (3) How does the magnitude of performance divergence evolve across successive fluctuation cycles? By adopting a dynamic, process-based perspective, this study aims to provide new insights into how increasing climatic variability may reshape plant performance and facilitate biological invasions under future climate change.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Plant Materials and Cultivation\u003c/h2\u003e \u003cp\u003eThe Asteraceae family contains a large number of globally invasive plant species and is therefore widely used in invasion ecology research. Accordingly, six Asteraceae species were selected for this experiment and classified into two functional groups: invasive and native, based on their biogeographic origin and invasion status.\u003c/p\u003e \u003cp\u003eThe invasive group consisted of three representative alien species, \u003cem\u003eErigeron annuus\u003c/em\u003e, \u003cem\u003eGalinsoga quadriradiata\u003c/em\u003e, and \u003cem\u003eSymphyotrichum subulatum\u003c/em\u003e, all native to the Americas and introduced into China within the past ~\u0026thinsp;150 years through natural dispersal or unintentional human-mediated pathways. The native group comprised three congeneric native species, \u003cem\u003eArtemisia lavandulifolia\u003c/em\u003e, \u003cem\u003eArtemisia dubia\u003c/em\u003e, and \u003cem\u003eArtemisia annua\u003c/em\u003e, selected to ensure broad phylogenetic comparability between functional groups.\u003c/p\u003e \u003cp\u003eSeeds of all species were germinated and cultivated under controlled greenhouse conditions. When seedlings reached the three- to four-true-leaf stage, uniformly healthy individuals were selected and transplanted individually into round plastic pots filled with a standardized potting substrate (upper diameter \u0026times; height: 12.8 cm \u0026times; 10.7 cm). Each pot was treated as an independent biological replicate.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Experimental Design and Temperature Fluctuation Treatment\u003c/h2\u003e \u003cp\u003eAll potted seedlings were transferred to controlled-environment growth chambers. Following transplantation, plants were acclimated for 14 days under control conditions to minimize transplant shock and establish a stable baseline growth state. Control conditions consisted of a constant diurnal temperature of 26\u0026deg;C, a 14 h light / 10 h dark photoperiod, and 50% relative humidity.\u003c/p\u003e \u003cp\u003eAfter acclimation, plants assigned to the fluctuation treatment were subjected to three consecutive cycles of high-temperature stress and recovery (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Each cycle comprised two phases: a 14-day high-temperature phase at 40\u0026deg;C, followed by a 14-day recovery phase under control conditions (26\u0026deg;C). All other environmental parameters were maintained identical to those of the control treatment. Plants in the control group were maintained continuously under control conditions throughout the entire experimental period.\u003c/p\u003e \u003cp\u003eIt should be noted that the fluctuating treatment in this study (alternating between 40\u0026deg;C and 26\u0026deg;C) was designed to simulate natural heatwave events, which inherently involve both extreme high temperatures and intermittent recovery periods, resulting in a higher mean temperature than periods without heatwaves. Therefore, this study compares plants experiencing repeated heatwaves with those under constant ambient conditions, directly addressing the overall impact of heatwaves on plants. Disentangling the independent effects of fluctuation and mean warming was not the objective of this study.\u003c/p\u003e \u003cp\u003eThe experiment followed a completely randomized block design. For each combination of plant species (three invasive and three native species) and temperature treatment (fluctuation vs. control), a total of 168 biological replicates (pots) were established. Replicates were allocated across destructive sampling time points such that independent individuals were harvested at each time point. To minimize potential micro-environmental heterogeneity within growth chambers, pots were regularly and systematically rotated throughout the experiment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Trait Measurement\u003c/h2\u003e \u003cp\u003eDestructive sampling was conducted at key transition points throughout the temperature fluctuation treatment: prior to the initiation of temperature fluctuations, at the end of each high-temperature phase, and at the end of each recovery phase. This resulted in a total of seven sampling time points (T1\u0026ndash;T7). At each time point, four pots were randomly selected per species and treatment (n\u0026thinsp;=\u0026thinsp;4).\u003c/p\u003e \u003cp\u003eImmediately after harvesting, morphological and biomass traits were measured. Plant height was defined as the vertical distance from the soil surface to the highest natural growing point of the plant (cm). Root length was measured as the length of the longest root after carefully washing and spreading the root system (cm).\u003c/p\u003e \u003cp\u003ePlants were then separated into aboveground and belowground components at the root collar. Each fraction was placed into labeled kraft paper bags, heated at 105\u0026deg;C for 30 min to deactivate enzymes, and subsequently dried at 80\u0026deg;C until constant mass (approximately 72 h). Aboveground and belowground dry masses (g) were recorded, and total biomass was calculated as their sum.\u003c/p\u003e \u003cp\u003eBased on these measurements, three biomass allocation indices were derived: root-to-shoot ratio (belowground dry mass / aboveground dry mass), aboveground biomass ratio (aboveground dry mass / total biomass), and belowground biomass ratio (belowground dry mass / total biomass).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Data analysis\u003c/h2\u003e \u003cp\u003eTo characterize overall growth trajectories under repeated temperature fluctuations, total biomass was analyzed across sampling time points. For functional group\u0026ndash;level comparisons, total biomass values of the three species within each functional group (invasive or native) were averaged at each time point. Preliminary species-level analyses indicated consistent directional responses among species within each functional group; therefore, functional group means were used to emphasize general growth patterns.\u003c/p\u003e \u003cp\u003eLinear regression was applied to examine changes in total biomass over the experimental period, with sampling time treated as an ordered variable representing progression through successive stress\u0026ndash;recovery phases rather than absolute time. Analysis of covariance (ANCOVA) was used to test for differences in biomass accumulation rates (i.e., slopes) between temperature treatments and between functional groups.\u003c/p\u003e \u003cp\u003eTo quantify relative response intensity while minimizing inherent baseline differences among species, percentage changes in each trait were calculated relative to the initial control condition (T1) using the following formula:\u003c/p\u003e \u003cp\u003ePercentage change = [(Mean of treatment group at time T - Mean of control group at time T1) / Mean of control group at time T1] \u0026times; 100%\u003c/p\u003e \u003cp\u003ePercentage changes were first calculated at the species level and then averaged across species within each functional group. Independent-samples t-tests were conducted at each sampling time point (T2\u0026ndash;T7) to assess divergence in response magnitude between invasive and native plants. These comparisons were intended to identify consistent temporal patterns of divergence rather than to emphasize isolated pairwise differences. Preliminary species-level analyses indicated consistent directional responses among species within each functional group. Therefore, functional group means were used to emphasize general growth patterns, while minimizing noise associated with interspecific variability.\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed using SPSS 22.0, with significance assessed at α\u0026thinsp;=\u0026thinsp;0.05. Figures were generated using SigmaPlot 12.5.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Fundamental divergence in growth trajectories under repeated heat fluctuations\u003c/h2\u003e \u003cp\u003eTo evaluate long-term growth dynamics under repeated temperature fluctuations, changes in total biomass were analyzed across seven sampling time points (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Linear regression revealed contrasting growth trajectories between invasive and native plants under fluctuating temperature conditions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifference analyses on regression parameters for total biomass vs. time\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eInvasive plants\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDifference between slopes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ea\u0026thinsp;=\u0026thinsp;0.0797\u003c/p\u003e \u003cp\u003eb\u0026thinsp;=\u0026thinsp;0.0817\u003c/p\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e=0.9860\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ea\u0026thinsp;=\u0026thinsp;0.1367\u003c/p\u003e \u003cp\u003eb=-0.0161\u003c/p\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e=0.9404\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0003\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNative plants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ea\u0026thinsp;=\u0026thinsp;0.0928\u003c/p\u003e \u003cp\u003eb\u0026thinsp;=\u0026thinsp;0.0595\u003c/p\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e=0.9530\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ea\u0026thinsp;=\u0026thinsp;0.0782\u003c/p\u003e \u003cp\u003eb\u0026thinsp;=\u0026thinsp;0.1241\u003c/p\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e=0.9421\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDifference between slopes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor invasive plants, the slope of total biomass accumulation was significantly higher under fluctuating temperatures than under constant ambient conditions (ANCOVA, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005), indicating an accelerated biomass accumulation rate in response to repeated heat fluctuations. In contrast, no significant difference in growth slopes was detected between fluctuation and control treatments for native plants (ANCOVA, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), suggesting that repeated temperature fluctuations did not enhance their biomass accumulation rate.\u003c/p\u003e \u003cp\u003eUnder constant ambient conditions, invasive and native plants exhibited comparable growth trajectories, with no significant difference in biomass accumulation slopes between functional groups (ANCOVA, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, under fluctuating temperature regimes, growth trajectories diverged markedly: invasive plants maintained a sustained increase in total biomass across successive fluctuation cycles, whereas native plants showed a substantially reduced rate of biomass increase over time.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Differential impacts of fluctuations on plant performance: gains vs. costs\u003c/h2\u003e \u003cp\u003eRepeated heat fluctuations induced distinct patterns of trait responses in invasive and native plants, as reflected by percentage changes in morphological and biomass-related traits relative to control conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAcross most sampling time points, invasive plants exhibited positive percentage changes in plant height, aboveground dry mass, belowground dry mass, and total biomass under fluctuating temperatures, indicating higher trait values compared with control conditions. In contrast, responses of root length differed from other traits, showing positive values only after the first high-temperature phase and negative values during subsequent phases.\u003c/p\u003e \u003cp\u003eNative plants displayed more variable and often negative trait responses under repeated fluctuations. Percentage changes in total biomass shifted from slightly positive values during early phases to negative values in later stages of the experiment. Although aboveground dry mass frequently remained higher in fluctuation treatments than in controls, this pattern was not accompanied by increases in plant height, which remained consistently lower under fluctuating temperatures throughout the experiment.\u003c/p\u003e \u003cp\u003eHeatmap visualization further highlighted contrasting response profiles between functional groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Invasive plants generally showed positive deviations from control conditions across multiple traits and time points, whereas native plants exhibited increasing negative deviations, particularly for total biomass and root-related traits, as fluctuation cycles progressed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Comparative performance of invasive and native plants across fluctuation cycles\u003c/h2\u003e \u003cp\u003eComparisons of percentage trait changes between invasive and native plants revealed that performance differences intensified across successive heat fluctuation cycles (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSignificance comparison of percent changes in various traits between invasive and native plants across three high-temperature/ambient cycles (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-temperature\u003c/p\u003e \u003cp\u003e/ambient cycle\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlant height (cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRoot length (cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAboveground dry weight (g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBelowground dry weight (g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTotal biomass (g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAboveground biomass ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eBelowground biomass ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRoot:shoot ratio\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCycle 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInvasive plants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.48\u0026thinsp;\u0026plusmn;\u0026thinsp;4.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-30.41\u0026thinsp;\u0026plusmn;\u0026thinsp;7.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.88\u0026thinsp;\u0026plusmn;\u0026thinsp;6.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.53\u0026thinsp;\u0026plusmn;\u0026thinsp;4.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.15\u0026thinsp;\u0026plusmn;\u0026thinsp;2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.31\u0026thinsp;\u0026plusmn;\u0026thinsp;7.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.10\u0026thinsp;\u0026plusmn;\u0026thinsp;3.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-2.92\u0026thinsp;\u0026plusmn;\u0026thinsp;9.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNative plants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7.64\u0026thinsp;\u0026plusmn;\u0026thinsp;3.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-31.98\u0026thinsp;\u0026plusmn;\u0026thinsp;10.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.28\u0026thinsp;\u0026plusmn;\u0026thinsp;9.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-23.64\u0026thinsp;\u0026plusmn;\u0026thinsp;10.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.29\u0026thinsp;\u0026plusmn;\u0026thinsp;10.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16.50\u0026thinsp;\u0026plusmn;\u0026thinsp;5.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-26.25\u0026thinsp;\u0026plusmn;\u0026thinsp;7.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-35.50\u0026thinsp;\u0026plusmn;\u0026thinsp;8.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0089**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0448*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCycle 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInvasive plants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.69\u0026thinsp;\u0026plusmn;\u0026thinsp;7.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-34.08\u0026thinsp;\u0026plusmn;\u0026thinsp;10.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e132.10\u0026thinsp;\u0026plusmn;\u0026thinsp;45.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.69\u0026thinsp;\u0026plusmn;\u0026thinsp;3.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e55.24\u0026thinsp;\u0026plusmn;\u0026thinsp;9.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e52.79\u0026thinsp;\u0026plusmn;\u0026thinsp;34.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-27.78\u0026thinsp;\u0026plusmn;\u0026thinsp;5.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-47.98\u0026thinsp;\u0026plusmn;\u0026thinsp;7.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNative plants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.94\u0026thinsp;\u0026plusmn;\u0026thinsp;5.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.07\u0026thinsp;\u0026plusmn;\u0026thinsp;5.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.03\u0026thinsp;\u0026plusmn;\u0026thinsp;1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.42\u0026thinsp;\u0026plusmn;\u0026thinsp;3.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.42\u0026thinsp;\u0026plusmn;\u0026thinsp;1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-3.63\u0026thinsp;\u0026plusmn;\u0026thinsp;4.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-3.68\u0026thinsp;\u0026plusmn;\u0026thinsp;6.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0346*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0457*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0205*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0484*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0202*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCycle 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInvasive plants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.45\u0026thinsp;\u0026plusmn;\u0026thinsp;11.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-39.62\u0026thinsp;\u0026plusmn;\u0026thinsp;17.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79.25\u0026thinsp;\u0026plusmn;\u0026thinsp;14.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.10\u0026thinsp;\u0026plusmn;\u0026thinsp;8.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e53.53\u0026thinsp;\u0026plusmn;\u0026thinsp;5.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16.41\u0026thinsp;\u0026plusmn;\u0026thinsp;6.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-24.22\u0026thinsp;\u0026plusmn;\u0026thinsp;5.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-34.81\u0026thinsp;\u0026plusmn;\u0026thinsp;3.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNative plants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-13.78\u0026thinsp;\u0026plusmn;\u0026thinsp;6.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-8.09\u0026thinsp;\u0026plusmn;\u0026thinsp;4.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.11\u0026thinsp;\u0026plusmn;\u0026thinsp;3.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-17.01\u0026thinsp;\u0026plusmn;\u0026thinsp;3.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-2.53\u0026thinsp;\u0026plusmn;\u0026thinsp;4.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.10\u0026thinsp;\u0026plusmn;\u0026thinsp;2.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-14.19\u0026thinsp;\u0026plusmn;\u0026thinsp;5.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-20.52\u0026thinsp;\u0026plusmn;\u0026thinsp;5.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0348*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0145*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0405*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0031**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDuring the first fluctuation cycle, invasive plants showed significantly greater percentage increases in plant height than native plants (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while differences in most biomass-related traits were not yet significant. In the second cycle, significant differences emerged in total biomass, root length, root-to-shoot ratio, and belowground biomass ratio, indicating increasing divergence in trait responses between functional groups. By the third cycle, invasive plants exhibited significantly greater values than native plants for plant height, aboveground dry mass, belowground dry mass, and total biomass (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eCollectively, these results demonstrate that differences between invasive and native plants were not static but increased progressively with repeated exposure to heat fluctuations, leading to pronounced divergence in growth performance by the final fluctuation cycle.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Repeated Heat Fluctuations Lead to Divergent Growth Trajectories between Invasive and Native Plants\u003c/h2\u003e \u003cp\u003eBy dynamically tracking plant performance across three consecutive heat\u0026ndash;recovery cycles, this study demonstrates that repeated heat fluctuations generate contrasting growth trajectories between invasive and native plant species. Under constant ambient conditions, invasive and native plants exhibited comparable biomass accumulation rates, indicating similar growth potential in stable environments. However, under fluctuating temperature regimes, their growth trajectories diverged markedly, suggesting that performance differences arise primarily from differential responses to repeated environmental fluctuations rather than inherent growth-rate advantages.\u003c/p\u003e \u003cp\u003eInvasive plants exhibited a significantly steeper biomass accumulation slope under fluctuating temperatures, whereas native plants showed no corresponding increase in growth rate. Similar asymmetric responses between invasive and native plants have been reported under warming scenarios, where elevated temperatures disproportionately constrain native species relative to invaders (Verlinden \u0026amp; Nijs, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Walther et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Our results extend these findings by demonstrating that divergence is amplified when thermal stress occurs repeatedly as stress\u0026ndash;recovery cycles, rather than as a continuous shift in mean temperature.\u003c/p\u003e \u003cp\u003eAnalyses based on relative changes from unstressed control conditions further revealed that invasive plants generally achieved net gains in multiple growth traits following repeated fluctuations, whereas native plants increasingly deviated negatively from their baseline performance. Comparable patterns of asymmetric performance under environmental variability have been documented in studies emphasizing the role of temporal heterogeneity in shaping invasion success (Richards et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Davidson et al., 2011). Together, these findings indicate that repeated heat fluctuations act as a dynamic driver that progressively amplifies growth asymmetry between invasive and native plants. Because the fluctuating treatment also involved elevated mean temperature relative to the constant control, our results should be interpreted as responses to realistic heatwave-like thermal regimes rather than isolated temperature variability alone. This distinction is important when extrapolating our findings to natural systems, where heatwaves inherently combine both extreme temperature peaks and elevated mean thermal conditions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Divergent Growth Plasticity and Resource Allocation Strategies Drive Phenotypic Differentiation\u003c/h2\u003e \u003cp\u003eThe divergent growth trajectories observed in this study are accompanied by consistent differences in trait-level responses and biomass allocation patterns between invasive and native plants. Invasive plants generally exhibited positive changes in plant height and total biomass across successive fluctuation cycles, whereas native plants showed increasingly negative responses during later stages of the experiment.\u003c/p\u003e \u003cp\u003eBiomass allocation patterns further highlight this contrast. Under repeated heat fluctuations, invasive plants maintained or increased allocation to aboveground biomass, whereas native plants displayed progressively higher root-to-shoot ratio. Similar allocation shifts have been widely reported as plant responses to stress, reflecting trade-offs between growth and maintenance (Poorter \u0026amp; Navas, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Wright et al., 2005). In stressful or resource-limited environments, increased belowground allocation is often associated with survival-oriented strategies, but may constrain overall biomass accumulation when not accompanied by sufficient carbon gain.\u003c/p\u003e \u003cp\u003ePhenotypic plasticity has long been proposed as a key trait underlying invasion success, particularly under heterogeneous or fluctuating environmental conditions (Richards et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Nicotra et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Valladares et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Meta-analyses and empirical syntheses indicate that invasive plants frequently exhibit greater plasticity in growth and allocation traits than native species, allowing them to sustain performance across a broader range of conditions (Davidson et al., 2011; Godoy et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Consistent with this framework, our results suggest that invasive plants maintain growth-oriented allocation patterns under repeated thermal fluctuations, whereas native plants increasingly shift biomass belowground without corresponding gains in total biomass. Such differences in allocation responses, rather than superiority in any single trait, likely underpin the progressive divergence in growth performance observed between functional groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Fluctuation Frequency as an Intensified Environmental Filter and Its Implications for Plant Invasion\u003c/h2\u003e \u003cp\u003eBeyond individual trait responses, our findings highlight the ecological importance of repeated thermal fluctuations as an environmental filter shaping plant invasion dynamics. Traditional studies of heat stress have largely focused on plant survival or performance under single extreme events or sustained warming (Jagadish et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, increasing evidence suggests that the temporal structure of stress exposure\u0026mdash;particularly the recurrence of stress\u0026ndash;recovery cycles\u0026mdash;can exert strong selective pressures on plant populations (Baldwin et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Raymond et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRepeated stress\u0026ndash;recovery cycles may impose cumulative constraints on species that are unable to fully recover between stress events, leading to progressively reduced performance over time (Hoover et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Gagn\u0026eacute; et al., 2022). In this study, native plants showed increasingly negative deviations from baseline growth during later fluctuation cycles, whereas invasive plants maintained stable or positive performance. Similar patterns have been observed in studies emphasizing the importance of recovery dynamics and carbon balance in determining plant performance under repeated stress (Tang et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough the experimental design included a single fluctuation regime, the progressive divergence observed across successive cycles suggests that repeated exposure to thermal stress, even at constant intensity, can amplify performance asymmetries between invasive and native species. Under future climate scenarios characterized by increased temperature variability, such dynamics may disproportionately favor species capable of sustaining growth across repeated stress episodes, thereby accelerating biological invasion processes (Bradley et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Walther et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Study Limitations and Future Directions\u003c/h2\u003e \u003cp\u003eSeveral limitations should be acknowledged. First, this study focused on six species from a single plant family. While functional group\u0026ndash;level comparisons strengthen inference regarding invasive versus native strategies, broader taxonomic sampling is required to test the generality of these patterns across plant lineages with contrasting life-history strategies (van Kleunen \u0026amp; Johnson, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Schlaepfer et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Second, the experiment was conducted under competition-free conditions, allowing isolation of individual-level growth responses but limiting inference about community-level outcomes. Previous studies have shown that competitive interactions can amplify asymmetries between invasive and native species under environmental stress (Niu \u0026amp; Wan, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Verlinden et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Future studies incorporating interspecific competition and community-level approaches will be essential for linking individual growth trajectories to population dynamics and species replacement.\u003c/p\u003e \u003cp\u003eOverall, by integrating dynamic growth trajectories with trait-based responses, this study emphasizes that recurrent environmental fluctuations should be viewed not merely as episodic stressors, but as active filters shaping plant performance over time. These findings contribute to a growing body of literature highlighting the importance of climatic variability, rather than changes in mean conditions alone, in driving biological invasions and restructuring plant communities.\u003c/p\u003e \u003cp\u003eWe acknowledge that the fluctuating treatment in this study had a higher mean temperature than the constant control, which inherently combines the effects of both heat pulses and increased mean temperature. While this precludes the separation of these two factors, our experimental design was intentionally chosen to simulate ecologically realistic heatwave scenarios. Heatwaves in nature inherently involve both extreme peaks and elevated mean temperatures. Thus, our comparison addresses the question of how plants respond to such real-world thermal regimes. Future studies incorporating a constant warm treatment could further disentangle the independent contributions of mean warming and temperature variability.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study systematically compared the growth responses of invasive and native plants under experimentally imposed repeated fluctuations between high and normal temperatures and revealed clear, consistent divergence in their growth dynamics. Invasive plants exhibited a significantly enhanced biomass accumulation rate under fluctuating conditions, accompanied by a sustained shift in biomass allocation toward aboveground structures, indicating their capacity to maintain growth-oriented strategies across successive stress\u0026ndash;recovery cycles. In contrast, native plants showed no improvement in growth rate under temperature fluctuations and instead displayed progressively constrained biomass accumulation, together with increased allocation to belowground components. These contrasting responses highlight that repeated thermal fluctuations amplify performance asymmetries between invasive and native plants by differentially shaping growth trajectories and allocation outcomes over time. Importantly, our results suggest that the temporal characteristics of climatic variability, particularly the recurrence of stress relative to plant recovery capacity can exert a stronger influence on plant performance than the intensity of individual stress events alone. By adopting a dynamic, process-based perspective, this study emphasizes the role of environmental fluctuation regimes as active ecological filters and provides a mechanistic framework for understanding how increasing climate variability may accelerate biological invasions and restructure plant communities under future climate change.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflicts of Interest\u003c/h2\u003e \u003cp\u003e \u003cb\u003eStatement\u003c/b\u003e: The authors declare no conflicts of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis study was supported by the Science and Technology Program of the Shaanxi Academy of Sciences (2023k-24, 2025k-07); the Natural Science Basic Research Plan of Shaanxi Province (2025JC-YBMS-206); The Science and Technology Plan Project of Xi\u0026rsquo;an (25NJSYB00011, 24NYGG0093; 24NYZZ0079).\u003c/p\u003e\u003ch2\u003eAuthor Contributions:\u003c/h2\u003e \u003cp\u003eConceptualization, Wang Yuchao, Lyu Jinlin, Yue Ming; data collection and curation, Xue Wenyan, Yang Hang, Zhang Yan; results analysis and interpretation, Lyu Jinlin, Li Yang, Huang Li; writing-original draft, Lyu Jinlin; writing-review and editing, Wang Yuchao, Huang Li. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBaker HG (1965) Characteristics and modes of origin of weeds. In Baker HG, Stebbins GL (eds). The Genetics of Colonizing Species. New York, USA: Academic Press, 147\u0026ndash;168.\u003c/li\u003e\n\u003cli\u003eBaldwin JW, Dessy JB, Vecchi GA, Oppenheimer M (2019) Temporally compound heat wave events and global warming: an emerging hazard. Earth\u0026rsquo;s Future 7(4): 411\u0026ndash;427. https://doi.org/10.1029/2018EF000989\u003c/li\u003e\n\u003cli\u003eBarros V, Melo A, Santos M, Nogueira L, Frosi G, Santos MG (2020) Different resource\u0026ndash;use strategies of invasive and native woody species from a seasonally dry tropical forest under drought stress and recovery. Plant Physiol Bioch 147: 181\u0026ndash;190. https://doi.org/10.1016/j.plaphy.2019.12.018\u003c/li\u003e\n\u003cli\u003eBradley BA, Blumenthal DM, Wilcove DS, Ziska LH (2010) Predicting plant invasions in an era of global change. Trends Ecol Evol 25(5): 310\u0026ndash;318. https://doi.org/10.1016/j.tree.2009.12.003\u003c/li\u003e\n\u003cli\u003eChen D, Cai AM, Wang YJ, van Kleunen M (2025) Competitive Superiority of Aliens over Natives under Abiotic and Biotic Stresses in Legume and Nonlegume Woody Species. Ecology 106(11): e70252. https://doi.org/10.1002/ecy.70252\u003c/li\u003e\n\u003cli\u003eChomel M, Lavallee JM, Alvarez\u0026ndash;Segura N, \u003cem\u003eet al.\u003c/em\u003e (2022) Intensive grassland management disrupts below\u0026ndash;ground multi\u0026ndash;trophic resource transfer in response to drought. Nat Commun 13: 6991. https://www.nature.com/articles/s41467\u0026ndash;022\u0026ndash;34449\u0026ndash;5\u003c/li\u003e\n\u003cli\u003eDavidson AM, Jennions M, Nicotra AB (2015) Do invasive species show higher phenotypic plasticity than native species and, if so, is it adaptive? A meta\u0026ndash;analysis. Ecol Lett 14(4): 419\u0026ndash;431. https://doi.org/10.1111/j.1461\u0026ndash;0248.2011.01596.x\u003c/li\u003e\n\u003cli\u003eGagne MA, Smith DD, Mcculloh KA (2020) Limited physiological acclimation to recurrent heatwaves in two boreal tree species. Tree Physiol 40(12): 1680\u0026ndash;1696. https://doi.org/10.1093/treephys/tpaa102\u003c/li\u003e\n\u003cli\u003eGiejsztowt J, Classen AT, Deslippe JR (2020) Climate change and invasion may synergistically affect native plant reproduction. Ecology 101(1): e02913. https://doi.org/10.1002/ecy.2913\u003c/li\u003e\n\u003cli\u003eGodoy O, Valladares F, Castro\u0026ndash;D\u0026iacute;ez P (2011) Multispecies comparison reveals that invasive and native plants differ in their traits but not in their plasticity. Funct Ecol 25(6): 1248\u0026ndash;1259. https://doi.org/10.1111/j.1365\u0026ndash;2435.2011.01886.x\u003c/li\u003e\n\u003cli\u003eGodoy O, Valladares F, Castro\u0026ndash;Dı́ez P (2012) The relative importance for plant invasiveness of trait means, and their plasticity and integration in a multivariate framework. New Phytol 195(4): 912\u0026ndash;922. https://doi.org/10.1111/j.1469\u0026ndash;8137.2012.04205.x\u003c/li\u003e\n\u003cli\u003eHalbritter AH, Fior S, Keller I, Billeter R, Edwards PJ, Holderegger R, Karrenberg S, Pluess AR, Widmer A, Alexander JM (2018) Trait differentiation and adaptation of plants along elevation gradients. J Evolution Biol 31(6): 784\u0026ndash;800. https://doi.org/10.1111/jeb.13262\u003c/li\u003e\n\u003cli\u003eHartmann H, Moura CF, Anderegg WRL, \u003cem\u003eet al\u003c/em\u003e. (2018) Research frontiers for improving our understanding of drought\u0026ndash;induced tree and forest mortality. New Phytol 218(1): 15\u0026ndash;28. https://doi.org/10.1111/nph.15048\u003c/li\u003e\n\u003cli\u003eHe QY, Yan MJ, Miyazawa Y, Chen QW, Cheng RR, Otsuki K, Yamanaka N, Du S (2020) Sap flow changes and climatic responses over multiple\u0026ndash;year treatment of rainfall exclusion in a sub\u0026ndash;humid black locust plantation. Forest Ecol Manag 457:117730. https://doi.org/10.1016/j.foreco.2019.117730\u003c/li\u003e\n\u003cli\u003eHeberling JM, Fridley JD (2013) Resource\u0026ndash;use strategies of native and invasive plants in Eastern North American forests. New Phytol 200(2): 523\u0026ndash;533. https://doi.org/10.1111/nph.12388\u003c/li\u003e\n\u003cli\u003eHellmann JJ, Byers JE, Bierwagen BG, Dukes JS (2008) Five potential consequences of climate change for invasive species. Conserv Biol 22(3): 534\u0026ndash;543. https://doi.org/10.1111/j.1523\u0026ndash;1739.2008.00951.x\u003c/li\u003e\n\u003cli\u003eHiggins SI, Richardson DM (2014) Invasive plants have broader physiological niches. PNAS 111(29): 10610\u0026ndash;10614. https://doi.org/10.1073/pnas.1406075111\u003c/li\u003e\n\u003cli\u003eHoover DL, Knapp AK, Smith MD (2017) Photosynthetic responses of a dominant C\u003csub\u003e4\u003c/sub\u003e grass to an experimental heat wave are mediated by soil moisture. Oecologia 183: 303\u0026ndash;313. https://doi.org/10.1007/s00442\u0026ndash;016\u0026ndash;3755\u0026ndash;6\u003c/li\u003e\n\u003cli\u003eJagadish SVK, Way DA, Sharkey TD (2021) Plant heat stress: concepts directing future research. Plant Cell Environ 44(7): 1992\u0026ndash;2005. https://doi.org/10.1111/pce.14050\u003c/li\u003e\n\u003cli\u003eKeep T, Sampoux JP, Barre P, \u003cem\u003eet al.\u003c/em\u003e (2020) To grow or survive: which are the strategies of a perennial grass to face severe seasonal stress? Funct Ecol 35(5): 1145\u0026ndash;1158. https://doi.org/10.1111/1365\u0026ndash;2435.13770\u003c/li\u003e\n\u003cli\u003eLaughlin DC (2024) Unifying functional and population ecology to test the adaptive value of traits. Biol Rev 99: 1976\u0026ndash;1991. https://doi.org/10.1111/brv.13107\u003c/li\u003e\n\u003cli\u003eLind EM, Borer E, Seabloom E, \u003cem\u003eet al.\u003c/em\u003e (2013) Life\u0026ndash;history constraints in grassland plant species: a growth\u0026ndash;defence trade\u0026ndash;off is the norm. Ecol Lett 16(4): 513\u0026ndash;521. https://doi.org/10.1111/ele.12078\u003c/li\u003e\n\u003cli\u003eLiu HP, Able AJ, Able JA (2022) Priming crops for the future: rewiring stress memory. Trends Plant Sci 27(7): 699\u0026ndash;716. https://doi.org/10.1016/j.tplants.2021.11.015\u003c/li\u003e\n\u003cli\u003eLiu LB, Gudmundsson L, Hauser M, Qin DH, Li SC, Seneviratne SI (2019) Revisiting assessments of ecosystem drought recovery. Environ Res Lett 14: 114028. https://doi.org/10.1088/1748\u0026ndash;9326/ab4c61\u003c/li\u003e\n\u003cli\u003eLiu YJ, Oduor AMO, Zhang Z, Manea A, Tooth IM, Leishman MR, Xu XL, van Kleunen M (2017) Do invasive alien plants benefit more from global environmental change than native plants? Global Change Biol 23(8): 3363\u0026ndash;3370. https://doi.org/10.1111/gcb.13579\u003c/li\u003e\n\u003cli\u003eMccarthy M, Enquist B (2007) Consistency between an allometric approach and optimal partitioning theory in global patterns of plant biomass allocation. Funct Ecol 21(4): 713\u0026ndash;720. https://doi.org/10.1111/j.1365\u0026ndash;2435.2007.01276.x\u003c/li\u003e\n\u003cli\u003eNicotra AB, Atkin OK, Bonser SP, \u003cem\u003eet al.\u003c/em\u003e (2010) Plant phenotypic plasticity in a changing climate. Trends Plant Sci 15(12): 684\u0026ndash;692. https://doi.org/10.1016/j.tplants.2010.09.008\u003c/li\u003e\n\u003cli\u003eNiu SL, Wan SQ (2008) Warming changes plant competitive hierarchy in a temperate steppe in northern China. J Plant Ecol 1(2): 103\u0026ndash;110. https://doi.org/10.1093/jpe/rtn003\u003c/li\u003e\n\u003cli\u003ePoorter H, Navas ML (2003) Plant growth and competition at elevated CO\u003csub\u003e2\u003c/sub\u003e: on winners, losers and functional groups. New Phytol 157(2): 175\u0026ndash;198. https://doi.org/10.1046/j.1469\u0026ndash;8137.2003.00680.x\u003c/li\u003e\n\u003cli\u003eRaed H Carmen BS, Ma QY, Daniel B, Ellie B, Corey L, Kai K (2025) Amplified agricultural impacts from more frequent and intense sequential heat events. Environ Res Lett 20: 114001. https://doi.org/10.1088/1748\u0026ndash;9326/ae06b8\u003c/li\u003e\n\u003cli\u003eRaymond C, Suarez\u0026ndash;Gutierrez L, Kornhuber K, Pascolini-Campbell M, Sillmann J, Waliser D (2022) Increasing spatiotemporal proximity of heat and precipitation extremes in a warming world quantified by a large model ensemble. Environ Res Lett 17: 035005. https://doi.org/10.1088/1748\u0026ndash;9326/ac5712\u003c/li\u003e\n\u003cli\u003eRen GQ, Zou CB, Wan LY, Johnson JH, Li J, Zhu L, Qi SS, Dai ZC, Zhang HY, Du DL (2021) Interactive effect of climate warming and nitrogen deposition may shift the dynamics of native and invasive species, J Plant Ecol 14(1): 84\u0026ndash;95. https://doi.org/10.1093/jpe/rtaa071\u003c/li\u003e\n\u003cli\u003eRichards CL, Bossdorf O, Muth NZ, Gurevitch J, Pigliucci M (2006) Jack of all trades, master of some? On the role of phenotypic plasticity in plant invasions. Ecol Lett 9, 981\u0026ndash;993. https://doi.org/10.1111/J.1461\u0026ndash;0248.2006.00950.X\u003c/li\u003e\n\u003cli\u003eRobinson A, Lehmann J, Barriopedro D, Rahmstorf S, Coumou D (2021) Increasing heat and rainfall extremes now far outside the historical climate. npj Clim Atmos Sci 4: 45. https://doi.org/10.1038/s41612\u0026ndash;021\u0026ndash;00202\u0026ndash;w\u003c/li\u003e\n\u003cli\u003eSandel B, Dangremond EM (2012) Climate change and the invasion of California by grasses. Global Change Biol 18(1): 277\u0026ndash;289. https://doi.org/10.1111/j.1365\u0026ndash;2486.2011.02480\u003c/li\u003e\n\u003cli\u003eSato H, Mizoi J, Shinozaki K, Yamaguchi-Shinozaki K (2024) Complex plant responses to drought and heat stress under climate change. Plant J, 117: 1873\u0026ndash;1892. https://doi.org/10.1111/tpj.16612\u003c/li\u003e\n\u003cli\u003eSchlaepfer DR, Gl\u0026auml;ttli M, Fischer M, van Kleunen M (2010) A multispecies experiment in their native range indicates pre\u0026ndash;adaptation of invasive alien plant species. New Phytol 185: 1087\u0026ndash;1099. https://doi.org/10.1111/j.1469\u0026ndash;8137.2009.03114.x\u003c/li\u003e\n\u003cli\u003eSexton JP, Mckay JK, Sala A (2002) Plasticity and genetic diversity may allow saltcedar to invade cold climates in North America. Ecol Appl 12(6): 1652\u0026ndash;1660.\u003c/li\u003e\n\u003cli\u003eSong L, Chow WS, Sun LL, Peng CL (2010) Acclimation of photosystem II to high temperature in two Wedelia species from different geographical origins: Implications for biological invasions upon global warming. J Exp Bot 61(14): 4087\u0026ndash;4096. https://doi.org/10.1093/jxb/erq220\u003c/li\u003e\n\u003cli\u003eStroud JT, Ratcliff WC (2025) Long\u0026ndash;term studies provide unique insights into evolution. Nature, 639, 589\u0026ndash;601. https://doi.org/10.1038/s41586\u0026ndash;025\u0026ndash;08597\u0026ndash;9\u003c/li\u003e\n\u003cli\u003eTang YK, Wen XF, Sun XM, Zhang XY, Wang HM (2014) The limiting effect of deep soilwater on evapotranspiration of a subtropical coniferous plantation subjected to seasonal drought. Adv Atmos Sci 31: 385\u0026ndash;395. https://doi.org/10.1007/s00376\u0026ndash;013\u0026ndash;2321\u0026ndash;y\u003c/li\u003e\n\u003cli\u003eValladares F, Gianoli E, Gómez JM (2007) Ecological limits to plant phenotypic plasticity. New Phytol 176(4): 749\u0026ndash;763. https://doi.org/10.1111/j.1469\u0026ndash;8137.2007.02275.x\u003c/li\u003e\n\u003cli\u003evan Kleunen M, Johnson SD (2007) South African Iridaceae with rapid and profuse seedling emergence are more likely to become naturalized in other regions. J Ecol 95(4): 674\u0026ndash;681. https://doi.org/10.1111/j.1365\u0026ndash;2745.2007.01250.x\u003c/li\u003e\n\u003cli\u003eVerlinden M, de Boeck HJ, Nijs I (2014) Climate warming alters competition between two highly invasive alien plant species and dominant native competitors. Weed Res 54(3): 234\u0026ndash;244. https://doi.org/10.1111/wre.12076\u003c/li\u003e\n\u003cli\u003eVerlinden M, Nijs I (2010) Alien plant species favoured over congeneric natives under experimental climate warming in temperate belgian climate. Biol Invasions 12(8): 2777\u0026ndash;2787. https://doi.org/10.1007/s10530\u0026ndash;009\u0026ndash;9683\u0026ndash;1\u003c/li\u003e\n\u003cli\u003eWalther GR, Roques A, Hulme PE, \u003cem\u003eet al.\u003c/em\u003e (2009) Alien species in a warmer world： Risks and opportunities. Trends Ecol Evol 24(12): 686\u0026ndash;693. https://doi.org/10.1016/j.tree.2009.06.008\u003c/li\u003e\n\u003cli\u003eWan JSH, Bonser SP, Pang CK, Fazlioglu F, Rutherford S (2024) Adaptive responses to living in stressful habitats: Do invasive and native plant populations use different strategies?. Ecol Lett 27: e14419. https://doi.org/10.1111/ele.14419\u003c/li\u003e\n\u003cli\u003eWang X, Liu FL, Jiang D (2017) Priming: a promising strategy for crop production in response to future climate. J Integr Agr 16(12): 2709\u0026ndash;2716. https://doi.org/10.1016/S2095\u0026ndash;3119(17)61786\u0026ndash;6\u003c/li\u003e\n\u003cli\u003eWang ZX, He ZS, He WM (2021). Nighttime climate warming enhances inhibitory effects of atmospheric nitrogen deposition on the success of invasive Solidago canadensis. Climatic Change 167(1/2): 20. https://doi.org/10.1007/s10584\u0026ndash;021\u0026ndash;03175\u0026ndash;0\u003c/li\u003e\n\u003cli\u003eWelshofer KB, Zarnetske PL, Lany NK, Read QD (2018). Short\u0026ndash;term responses to warming vary between native vs. exotic species and with latitude in an early successional plant community. Oecologia 187(1): 333\u0026ndash;342. https://doi.org/10.1007/s00442\u0026ndash;018\u0026ndash;4111\u0026ndash;9\u003c/li\u003e\n\u003cli\u003eWillis SG, Hulme PE (2002). Does temperature limit the invasion of Impatiens glandulifera and Heracleum mantegazzianum in the UK? Funct Ecol 16(4): 530\u0026ndash;539. https://doi.org/10.1046/j.1365\u0026ndash;2435.2002.00653.x\u003c/li\u003e\n\u003cli\u003eWright IJ, Reich PB, Westoby M, \u003cem\u003eet al.\u003c/em\u003e (2004). The worldwide leaf economics spectrum. Nature 428: 821\u0026ndash;827. https://doi.org/10.1038/nature02403\u003c/li\u003e\n\u003cli\u003eYao Y, Fu BJ, Liu YX, Zhang Y, Ding JY, Li Y, Zhou S, Song JX, Wang S, Li CJ, Zhao WW (2024). Compound hot\u0026ndash;dry events greatly prolong the recovery time of dryland ecosystems. Natl Sci Rev 11(10): nwae274. https://doi.org/10.1093/nsr/nwae274\u003c/li\u003e\n\u003cli\u003eZhang XL, Yu HH, Lv T, Yang L, Liu CH, Fan SF, Yu D, Carboni M (2021). Effects of different scenarios of temperature rise and biological control agents on interactions between two noxious invasive plants. Divers Distrib 27(12): 2300\u0026ndash;2314. https://doi.org/10.1111/ddi.13406\u003c/li\u003e\n\u003cli\u003eZhou XH, He WM, Peng PH, Li JJ (2025). Long\u0026ndash;term warming legacies facilitate invasive plant growth and inhibit enemy performance. J Plant Ecol 18(3): rtaf033. https://doi.org/10.1093/jpe/rtaf033\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"biological-invasions","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"binv","sideBox":"Learn more about [Biological Invasions](https://www.springer.com/journal/10530)","snPcode":"10530","submissionUrl":"https://submission.nature.com/new-submission/10530/3","title":"Biological Invasions","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Heat fluctuations, Invasive plants, Native plants, Growth trajectory, Biomass allocation; Plant functional traits","lastPublishedDoi":"10.21203/rs.3.rs-9377302/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9377302/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIncreasing climate variability is expected to impose stronger selective pressures on plant communities than gradual warming alone, yet how repeated heat stress and recovery cycles influence plant performance remains poorly understood. Here, we compared the dynamic growth responses of three invasive and three native Asteraceae species exposed to three consecutive cycles of high temperature (40\u0026deg;C) and ambient temperature (26\u0026deg;C) under controlled conditions. Using destructive sampling across seven time points, we quantified growth trajectories and biomass allocation patterns throughout the fluctuation process. Repeated heat fluctuations induced pronounced divergence in growth dynamics between invasive and native plants. Invasive species exhibited significantly accelerated biomass accumulation under fluctuating conditions compared with constant ambient conditions, whereas native species showed no corresponding increase in growth rate and instead experienced progressive growth suppression. Biomass allocation patterns also differed consistently between the two groups. Invasive plants maintained or increased allocation to aboveground tissues, resulting in sustained gains in plant height and total biomass, while native plants increasingly shifted allocation belowground without corresponding biomass gains. Trait-based percentage changes further revealed that positive responses in key growth traits were consistently greater in invasive than in native species across successive fluctuation cycles. These results demonstrate that repeated heat fluctuations can amplify performance asymmetries between invasive and native plants, highlight the importance of heatwave-like thermal regimes as a driver of invasion success under climate change.\u003c/p\u003e","manuscriptTitle":"Repeated heat fluctuations amplify growth advantages of invasive over native plants​","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-08 05:35:00","doi":"10.21203/rs.3.rs-9377302/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2026-04-30T12:48:33+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-30T10:01:47+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Biological Invasions","date":"2026-04-11T15:36:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-11T14:34:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Biological Invasions","date":"2026-04-10T04:42:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"biological-invasions","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"binv","sideBox":"Learn more about [Biological Invasions](https://www.springer.com/journal/10530)","snPcode":"10530","submissionUrl":"https://submission.nature.com/new-submission/10530/3","title":"Biological Invasions","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d042264f-d616-445f-aa6a-413227906f48","owner":[],"postedDate":"May 8th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"","date":"2026-04-30T12:48:33+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-30T10:01:47+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-08T05:35:00+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-08 05:35:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9377302","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9377302","identity":"rs-9377302","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}