Climate change impacts the dispersal of annual plants: a mechanistic approach

Climate change impacts the dispersal of annual plants: a mechanistic approach | 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 Article Climate change impacts the dispersal of annual plants: a mechanistic approach Antonio Manzaneda, Victor Valenzuela-Polo, Rocío Bolaños-Jiménez, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4346427/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Anthropogenic climate change (ACC) significantly impacts plant populations, biodiversity, and ecosystem services. The survival of plant species hinges on their ability to adapt or migrate in pace with shifting climate niches, which is key for maintaining gene flow and habitat colonization in changing environments. This study investigates the mechanistic basis of how ACC affects seed dispersal. Here, we specifically model how elevated atmospheric CO 2 levels, temperature, and drought collectively influence dispersal trait plasticity and potential across multiple species. We identified essential diaspore dispersal predictors using climate chamber experiments, fluid dynamic modelling, and wind tunnel assays. Our findings indicate a predicted dispersal distance reduction of 40% for temperate annual plants in warmer, drier, and CO 2 -rich conditions. Our results highlight the importance of inherent limits of plasticity in multiple traits for facing environmental change and the need to consider multiple environmental factors to understand dispersal in future climates. Biological sciences/Plant sciences/Plant ecology Biological sciences/Ecology/Population dynamics Figures Figure 1 Figure 2 Figure 3 Main Anthropogenic climate change (ACC) – global temperature rising due to global greenhouse gas emissions 1 – is already affecting plant populations, plant biodiversity, and the ecosystem services they provide 1,2,3 . The persistence of plant populations is contingent on their ability to adapt or migrate to follow their climatic niches across the landscape at a pace that is sufficiently responsive to changes in environmental conditions 4,5,6 . Therefore, the success or failure of plant species in the face of environmental changes is closely linked to the plasticity and adaptability of their inherent traits. Consequently, many studies have analysed the response and plasticity of plant adaptive traits – i.e., fitness-related physiological, functional, and/or life-history traits – to different drivers of ACC 3,7,8,9 . Conversely, the impact of ACC on seed dispersal-related traits (i.e., wind loading, hairs, pappus, terminal velocity, etc.) remains unclear 10,11 , despite seed dispersal being crucial for essential processes of plant population persistence in changing environments, such as gene flow, population connectance in fragmented landscapes, colonization of novel habitats, and plant natural regeneration 5,12,13 . Previous studies exploring the effects of ACC on seed-dispersal traits have primarily focused on the effect of drought, elevated temperature, or change in the wind regimes 14,15,16,17,18 . These and other studies suggest the existence of phenotypic plasticity in developing some dispersal-related traits (termed informed dispersal 16,19 ), which may allow species to disperse rapidly across heterogeneous landscapes. However, to the best of our knowledge, no previous study has integrated the study of dispersal traits and their plasticity with the analysis of the mechanistic basis of the impact of ACC's essential factors (elevated atmospheric CO 2 , temperature, and drought) to model dispersal potential and limitations at multispecific levels. This is crucial to fully comprehend the effects of ACC on the dispersal of plant populations because (i) plants perceive environmental signals in an integrated manner 2 , and (ii) trait plasticity to environmental change typically varies between plant species 3,7 , which may later influence the future dynamics and assemblies of plant communities. This study uses climate-controlled experiments in growth chambers, mechanistic fluid dynamic modelling, and wind tunnel assays to determine key trait predictors of diaspore dispersal for 14 temperate annual species growing in a Mediterranean climate as part of a Climate Change simulation study. Mediterranean basin is projected to experience more frequent heat waves, a significant increase in air temperature (ranging from 0.9ºC to 5.6ºC), and a 4–22% decrease in precipitation, depending on the emission scenario 20 . This increase in aridity 21 is already impacting Mediterranean inland ecosystems, making the herbaceous flora more vulnerable 22 . Likewise, an increase in aridity is similarly predicted in other regions of the Earth under the Mediterranean climate 23 . The main objective of this study is to investigate how key dispersal-related traits (abscission height, terminal velocity, and wing loading) vary in response to elevated CO 2 , temperature, and drought and how this may limit dispersal potential at specific and multispecific levels. Our study explicitly tests the informed dispersal hypothesis – i.e., dispersal-related traits are modified according to the environment 19 – concerning untested previous joint drivers of ACC, such as CO 2 elevation, drought, and elevated temperature, and investigates the mechanistic basis of how ACC affects main dispersal traits in temperate annual plants. According to the informed dispersal hypothesis, traits related to dispersal should enhance dispersal in stressful environmental conditions (e.g., drought and elevated temperature) through trait plasticity. However, inherent limits from trait plasticity 24 can constrain dispersal potential and prevent escape from stressful conditions. Results ACC impact on traits All traits, except wing loading, were independently significantly impacted by climate and irrigation treatment (Table 1 ). Only for the terminal velocity there was a significant interaction effect between the climate and irrigation factors (Table 1 ). Thus, when grown in either drought or enriched CO 2 conditions and elevated temperature, maternal plants developed significantly smaller (between 5–19% across treatments) and lighter diaspores (between 20–31% across treatments) than plants grown in current and well-watered conditions (Fig. 1 ; Table S2). The abscission height was significantly lower (between 24–31%) for plants grown under either water stress or higher levels of CO 2 and temperature conditions compared to plants grown in the current ambient (Fig. 1 , Table S2). Additionally, seeds originating from plants grown under climate change conditions or water stress had a shorter holding time (17–23%) than plants grown in current and well-watered conditions (Fig. 1 , Table S2). Concerning terminal velocity, the effect of climate change was significant only in water stress conditions (Fig. 1 ). Seeds from plants developing in drought fell slower (9–13%) than seeds from plants grown in optimal conditions at enriched and warmed ambient. Finally, the C/N ratio was only affected by the irrigation treatment (F 1,151 =48.71, P < 0.0001), indicating that plants growing under water restriction showed significantly lower C/N than plants growing in well-watered conditions (Fig. 1 ). Table 1 Summary of the General Lineal Mixed Models performed to analyze trait variation between two climates and two watering treatments. Significant factors (P < 0.05) are highlighted in bold type. For each statistical analysis, degrees of freedom (d.f), the F-distribution (F), the significance level (P), and the Likelihood Ratio Test (LRT) are presented. Significant (at least P < 0.05) effects are highlighted in bold. TRAITS Factors Diaspore mass Diaspore area Wing Loading Terminal Velocity (F) Abscission height (L) L/F Fixed effects d.f. F P d.f. F P d.f. F P d.f. F P d.f. F P d.f. F P Climate (C) 3,835 100.22 < 0.0001 3,821 447.8 < 0.0001 3,48 2.29 0.13 3,791 14.79 < 0.0001 3,48 39.63 < 0.0001 3,48 23.5 < 0.0001 Irrigation (I) 1,835 66.51 < 0.0001 1,821 55.79 < 0.0001 1,48 3.21 0.08 1,791 26,45 < 0.0001 1,48 50.2 < 0.0001 1,48 21.2 < 0.0001 C x I 3,835 0.001 0.99 3,820 0.47 0.493 3,48 0.65 0.42 3,791 4,24 0.04 3,48 0.37 0.545 3,48 1.82 0.18 Random effects d.f. LRT P d.f. LRT P d.f. LRT P d.f. LRT P d.f. LRT P d.f. LRT P Species 1 2773.3 < 0.0001 1 3495.3 < 0.0001 1 65.538 < 0.0001 1 1048 < 0.0001 1 41.71 < 0.0001 1 112.4 < 0.0001 At the species level, terminal velocity was significantly affected by climate change and/or soil water availability in 12 out of 14 species (Table S3). In six species, ACC effects were interactive, indicating that the impact of drought, elevated temperature, and atmCO 2 were interdependent and species-specific (Table S3; Fig. S8). For example, terminal velocity is reduced significantly in Brachypodium hybridum , Brachypodium stacei , and Eruca sativa under drought and elevated temperature and CO 2 conditions (Fig. S8). In contrast, for Avena sativa , terminal velocity is augmented in drought and ACC conditions (Fig. S8). Additionally, for species with incomplete dehiscence, diaspores of maternal plants of E. sativa and Sinapis alba grown under arid and ACC conditions exhibited lower terminal velocity than seeds of the same species grown under similar conditions (Fig. S8-S9). Regarding the height of abscission, climate change and/or drought significantly influenced the expression of this trait in all the species (Table S3, Fig. S10). This influence was mostly interactive (Table S3), yet climate change and drought typically significantly reduced abscission height (Fig. S10). Plant and dispersal trait covariation (PCA analysis) The Principal Components (PC) analysis revealed that three principal components account for approximately 83% of the total variance (Fig. S11). The first principal component strongly correlates positively with diaspore traits and terminal velocity, the second with the holding time, and the third with abscission height (Fig. S11). PC analyses performed on groups (e.g., climatic conditions) indicated that capturing variation associated with these components did not change between climatic conditions (i.e., 95% confidence interval ellipses largely overlapping, Fig. S12). The plant species that contributed to a larger extent to the first principal component was A. sativa , whereas small-seeded species such as Papaver roheas , Arabidopsis thaliana and Capsella bursa-pastoris contributed also to segregation in this component yet in the opposite direction (Fig. S12). Dispersal distance estimates Theoretical dispersal distances were significantly and additively affected by climate and drought (F 3,49 = 13.92, P < 0.0001; F 3,49 = 19.21, P 0.05). The distance at which diaspores were dispersed varied between climatic scenarios. Diaspores from maternal plants grown in climate change and drought were dispersed shorter distances (ca. 41% less) than diaspores developed in current and well-watered conditions (Fig. 2 A, 2 B). Therefore, higher dispersal distances were observed in plants grown under current and well-watered conditions compared to plants grown in the other climatic scenarios (median: 1.41 m ± interquartile range (IR): 0.42; 1.21 ± 0.39; 1.28 ± 0.58; 0.84 ± 0.53; values for current and well-watered, current and drought, climate change well-watered and climate change and drought conditions, respectively; Fig. 2 B). At the species level, theoretical seed dispersal distances were lower in climate change conditions and drought compared to current and well-watered conditions in 12 out of 14 species (Fig. S13). For example, for U. urens , the model predicts a reduction of about 50% of the dispersal distance in climate change compared to current and well-watered conditions (Fig. S13). Only diaspores of P. rhoeas and A. sativa showed higher dispersal distance in climate change conditions (Fig. S13). In three out of four climate scenarios examined, realized dispersal distances were positive and highly correlated to theoretical dispersal distances (Fig. S14) with a slope close to 1.0. However, in the climate change and drought scenario, observed and model-predicted values were not significantly linked (Fig. S14), and theoretical values underestimated empirical ones. Dispersal predictors Multiple regression analyses revealed that the key predictors affecting diaspore dispersal were the abscission height, the terminal velocity, and their relationship (holding time; Fig. 3 and Table S4). The abscission height was positively correlated with the dispersal distance, which means that the taller the infructescence of the maternal plant, the greater the dispersal distance, and this trend was particularly strong in drought. Similarly, the lower the terminal velocity, the longer the dispersal distance. Only under the current ambient and drought, C/N ratio, diaspore area, and mass were linked to dispersal distance (Table S4). DISCUSSION A plethora of studies have documented the negative impact of ACC on plants’ physiological and functional traits for the survival of plant populations in diverse climate conditions 3,7,8,9,22 . Our research provides experimental and theoretical evidence that this also applies to diaspore-dispersal traits and the dispersal potential of annual species that have evolved under the Mediterranean climate. The increased aridity predicted in these areas 21 could restrict plants’ ability to move to more favorable ecological environments where they can thrive and reproduce under current and future climates. Our findings challenge the main assumption of the informed dispersal hypothesis, which states that traits related to dispersal should improve dispersal in stressful environmental conditions 16,19 . In particular, we have demonstrated that ACC’s drivers affect the expression of the diaspore’s features, such as the height at which diaspores are released from maternal plants and the period that diaspores are exposed to the wind flow. As a result, in warmer, drier CO 2 -enriched climates, our results predict that the dispersal distance of Mediterranean annual species will be reduced by about 40% on average. Finally, we have shown that abscission height and terminal velocity are reliable estimators of diaspore dispersal across all evaluated climatic scenarios. Trait plasticity is the major way by which plants cope with different environments 24 . The analysis of expression for dispersal-related and functional traits performed here suggests that the differential effects of temperature, enriched-CO 2 ambient, and drought on abscission height, diaspore traits, and terminal velocity limited the effect of trait plasticity to enhance dispersal. While terminal velocity decreased in climate change and water-limited conditions, which would favour dispersal distance, the lowered abscission height observed for plants grown under these conditions cancelled out the potential benefit for dispersal of producing smaller and lighter diaspores. Notably, whereas the effect of ACC on terminal velocity was variable across species (Fig. S8), the reduction in the abscission height was pervasive in most analyzed species (Fig. S10). This suggests that the contrasting plastic response observed in these two traits precluded plasticity as an effective mechanism for ensuring Mediterranean annual plants' dispersal in predicted stressful environments, which concurs with theoretical predictions of the existence of an intrinsic limit to plasticity that can constrain the adaptive potential of plants to rapid and extensive environmental changes, such as those resulting from ACC 24,25 . Similarly, our findings highlight the importance of considering multivariate trait plasticity across environments when evaluating organisms' functional responses 26 . Thus, PCA results suggest that co-expression of traits (i.e., phenotypic integration) occurs for diaspore mass, diaspore area, abscission height, wind loading, terminal velocity, and C/N ratio since all of them covary positively to PC1 across climatic scenarios; meanwhile, holding time varies orthogonally to them (Fig. S11). Hence, while correlated selection would be expected on traits that, in an integrated manner, enhance fitness-related dispersal 27 , integration should constrain plasticity because the more closely linked a trait's expression is to other traits, the harder the independent change of that trait in response to the environment 26 , as is the case here for abscission height, wing loading components, and terminal velocity. The fact that trait covariation remained constant across different climatic scenarios supports this idea (Fig. S12). It is interesting to note that the analysis carried out on individuals revealed that plant species that produce large infructescences and/or heavy diaspores, such as A. sativa, B. hybridum , or M. polymorpha , positively contributed to trait differentiation in PC1 (as shown in Fig. S12). Conversely, plant species that produce small and light diaspores, and develop shorter infructescences, such as P. rhoeas, A. thaliana , and C. bursa-pastoris , negative contributed more to such trait differentiation (as shown in Fig. S12). This indicates that there is a differential integration of dispersal traits between small and large plant species. Small plant species minimize wing loading to increase the holding time, while larger plant species depend on a complex phenotypic integration for dispersal. Our study predicts an important additive impact of ACC factors on diaspore dispersal (Fig. 2 ). In particular, the reduction in the dispersal distance is expected to be about 40% in ambient with elevated temperature, enriched-CO 2, and soil water limitation, and ca. 22–23% in the other climatic scenarios compared to the most optimal climatic one (Fig. 2 ). These findings contrast with previous research that found similar or enhanced dispersal in stressful environments 14,16,17,18 . For example,, warming increased plant height by 9% in Carduus nutans , positively affecting the spread rate 14 . In the same species, a different study showed that drought reduced plant height yet predicted dispersal distances of plants affected by water stress was similar or longer than those of plants growing with abundant water 17 . Three non-exclusive explanations are possible to explain discrepancy of our results with other studies. First, the effect of the environment on dispersal is species-specific. For example, in our study, the dispersal distance of Papaver rhoeas seeds was enhanced by ca. 24% under climate change and drought compared to current ambient and humid conditions (Fig. S13) because the abscission height was unaffected by climate change or drought (Fig. S8), being the exception to the general pattern described here. Second, in the same vein, our research has adopted an approach that includes multiple species in our models and experiments instead of focusing solely on one or a few related species. Third, previous studies have not considered the impact of CO 2 enrichment on dispersal, which is known to have net effects on plant physiology apart from temperature 2 , although our approach does not allow us to test this later effect directly. Our findings therefore strongly support the notion that ACC factors have a significant influence on limiting the dispersal distance of diaspores of annual plants. This is primarily due to a marked decrease in abscission height – in relative terms, the most important predictor of diaspore dispersal 28 – compared to a more moderate reduction in terminal velocity (Fig. 1 , Fig. 3 ). We can draw a few ecological conclusions and predictions based on our findings. Except for one case, our research suggests that the dispersal of diaspores may be significantly limited in the near future in the Mediterranean areas, where increased climate aridity is forecasted in the Mediterranean areas 20,23 . Because diaspore dispersal is a crucial factor in the population spatial spread 14,29 , most species' migration ability is expected to be restricted in the future climate forecasted for the Mediterranean areas. This, in turn, is likely to limit the plant species' capacity to track advantageous ecological niches spatially. However, dispersal involves more than just the initial movement of diaspores 30,31,32 . Assessing the demographic effects of limited initial dispersal on germination, seedling emergence, and survival in a community-level and density-dependent context would still be necessary to fully understand the impact on population spread of shortened dispersal driven by ACC. 29,33 . In addition, our results have highlighted an important implication: ACC's effects on dispersal traits are species-specific. This, in turn, affects the dispersal distances of plant species in different directions, leading to a community with many losers and few winners ( P. roheas , for example). This pattern may eventually result in the colonization of new favorable niches and drive the future assembly of annual Mediterranean plant communities. This study highlights the limitations of the adaptability of plants to rapid and significant environmental changes. It suggests that intrinsic limits to trait plasticity can restrict plants' adaptive potential. However, it must be noted that our study only employed a single or a minimal number of accessions to analyze trait expression (as required for analyzing plasticity). Therefore, genetic variation for plasticity in functional and life-history traits may exist, leading to populations exhibiting higher levels of plasticity in dispersal traits. This could allow for greater potential for dispersal and colonization in warmer and drier environments. Future studies should explore genetic variation in nature to understand the plastic responses of dispersal-related traits better. Finally, determining the importance of dispersal-competition trade-offs under climate change 35 will require more research to understand the future dynamics of the assembly of plant communities inhabiting ecosystems under changing Mediterranean-type climates. Material and Methods Study system To investigate the impact of ACC on dispersal, we have selected fourteen annual herbaceous species (Table S1 ) that typically grow in Mediterranean grasslands both in natural habitats and agrosystems, where they play an essential ecological function and services 36,37,38,39 (e.g., pollination, erosion control, carbon sequestration, etc.). These species were chosen based on their commonality in the grassland community, their range of taxonomy, and the variety and size of their diaspore features (as listed in Table S1 ). In addition, some of the species, such as Arabidopsis thaliana and Brachypodum distachyon , are recognized plant models. All these species rely on gravity or wind for passive dispersal, yet they do not possess any specific anemochory mechanism (e.g., wings, fluffy hairs, or pappus). However, some ( Sinapis alba and Calendula arvensis ) may have unspecific structures like hairs, elaiosomes, or carpel remains (as shown in Fig. S1 ). All the species are monocarpic and release the seeds after maturation in late spring or summer. All plant material used in the experiments was sourced from native populations in southern Spain and grown in common gardens (Semillas Silvestres S.L., Semillas Cantueso S.L., and the University of Jaén (UJA) Germplasm collection) for multiple generations, minimizing maternal effects on trait expression. In particular, selected accessions for each species were for A. thaliana , Wild Type (WT), for B. distachyon was JHIN_13, for B. stacei Alsur_1, for B. hybridum BdCzPP_11 for S. alba Laca_24. The plants used for the remaining species came from a unique source population (certificated seeds from a limited number of accessions and populations). Growth Chamber Experiments The seeds germinated in a petri dish under cold, humid, and dark conditions. Once germinated, the plants were moved to a growth chamber (FitoClima PLH Aralab®), where they were grown in a climatic SPLIT-PLOT design under controlled conditions. Maternal plants were subjected to two climatic conditions: 1. Control climatic conditions (NC hereafter): The plants were grown in current atmospheric and temperature conditions (22ºC day / 12ºC night, photoperiod 16h light/8 h night) to simulate natural conditions during early fall or early spring growing season. The atmospheric concentration of carbon dioxide (atmCO 2 ) remained constant at 415 parts per million (ppm), and the air Relative Humidity (RH) was maintained at 50%. 2. Climate Change climatic conditions (CC hereafter): The maternal plants were grown at 22ºC + 3.8 ºC day / 12ºC + 3.8ºC night, representing an increase in predictable temperature for the Mediterranean region in 2100 based on models using the RCP4.5 model of emissions 40 . The photoperiod was kept the same at 16h light/8 h night. The atmCO2 was raised to 600 ppm, considering an annual increase of 0.5% in atmCO2. This value is the average of projected CO 2 levels for the year 2100 in the Northern Hemisphere, based on several pathway species scenarios 41 . The air RH was maintained at 50%. Moreover, at each split of climatic conditions, plants underwent two treatments of irrigation: irrigation available: A. Watered regularly: well-watered (WW) hereafter. B. drought: gradual cessation of irrigation after two weeks of growth, water restricted (WR) hereafter. Soil moisture was monitored during the experiment using a soil moisture sensor EC-5 (Meter®) to reach a soil water content ca. 47% ± 2.36 in the well-watered treatment under current climate and ca. 31% ± 2.46 under climate change conditions. In the drought treatment, soil water content was ca. 15.6% ± 7.01 and 15.4% ± 6.26 for current and climate change conditions, respectively. This experimental set eventually shaped a fully crossed two-factor (climatic condition and irrigation) design of 4 treatments. For each species, we planted three biological replicates for 168 plants assayed. We grow these plants for six weeks until they flower and seed-set. Most of the species can self-fertilize. However, C. arvensis , S. alba , E. sativa , and M. silvestris require cross-pollination to produce seeds, so they were hand-pollinated to obtain seeds. Traits, diaspore, and seed measurements After the plant reached its seed-set stage, we collected trait measurements for the maternal plants and their offspring under different irrigation treatments and climate conditions. For the maternal plants, we measured the abscission height (diaspore release height) and the C/N ratio from reproductive shoots (using an elemental gas analyzer Leco® TruSpec Micro). In contrast, we measured the diaspore mass, diaspore area, and wing loading (i.e., mass/ area ratio) for the offspring in 20 diaspores per species. These later measurements were taken using a digital microscope (Dino-Lite® AD7013 MT) and ImageJ software (Fig. S2). To determine each diaspore’s terminal velocity (the maximum fall velocity when the acceleration is null), we used a vertical tunnel made of Plexiglass, 38 cm x 38 cm wide x 200 cm high (Fig. S3). We let the diaspore fall and recorded the final part of the fall using a fast camera with a 60mm, f/2.8 lens, a capture speed of 250 fps, and a frame every 0.04 seconds with an average resolution of 125.28 µm/pixel (Fig. S4). We checked that no acceleration was observed and, thus, that the terminal velocity was indeed reached. Finally, we analyzed and tracked the video frames with ImageJ software. In addition, because the seed release of some Brassicaceae species often involved the dispersal of the whole silique due to indehiscence (pers. obs; Fig. S5), we also determined the terminal velocity of diaspores (seeds along with siliques) of S. alba , E. sativa and C. bursa-pastoris . The WALD Model We fitted the mechanistic Wald analytical long-distance dispersal (WALD) model 42,43 to estimate the theoretical dispersal distance of each species from the different irrigation and climate treatments. The following expression gives the WALD model: $$k\left(r\right)={\left(\frac{\tau }{2\pi {r}^{3}}\right)}^{\frac{1}{2}}expexp \left(-\frac{{\tau \left(r-\mu \right)}^{2}}{2{\mu }^{2}r}\right)$$ 1 where µ = LU / F and τ =(L/Ψ) 2 are the scale and shape parameters, respectively, with L the abscission height, U the horizontal wind velocity, F the terminal velocity, and Ψ is the turbulence intensity of the flow. k (r) is an inverse Gaussian distribution function that estimates the probability of being dispersed to a given distance r . In the model, the value of U was set at 8.8 ms − 1 with a turbulent intensity (wind speed standard variation divided by its mean value) of 0.29 for three reasons. Firstly, this value is well above the annual average wind speed (2.5–3.5 ms − 1 ) in the central Andalucía region 44 , where the plants are initially from. This wind speed emulates the velocity during gust winds, when most of the offspring of annual herbs are dispersed (pers. obs.). Secondly, such wind speed is the maximum velocity the wind tunnel used in the study could reach, which is important for validating the observed seed dispersal (see below). Thirdly, this velocity is the point where turbulent intensity is minimized in the tunnel validation assays and to have greater control in abiotic parameters (as shown in Fig. S6). From model ( 1 ), L / F ratio denotes the flight hold time (the effective time that seeds are flying in the air). Wind tunnel experiments We conducted seed release experiments for seeds of each plant species grown under different climate conditions and irrigation treatments to validate theoretical dispersal estimates using a horizontal wind tunnel (test section area 0,24 m 2 and 200 cm long; Fig. S7). We set an average wind velocity of 8.8 ms − 1 , monitored using a hot wire anemometer (PCE 091006, Industrial Physics®) during the seed release assays. Reynolds number based on the seed’s characteristic length, Re = U A 0.5 /𝜈 , ranged between 9.8×10 3 and 1.7×10 5 , with A the tunnel area and 𝜈 the air cinematic viscosity. Thus, we placed five replicates of 20 seeds per species and experimental condition in a horizontal platform at the center of the wind tunnel outlet section, 80 cm above the floor (Fig. S7). Plattform’s shape was slender, its longitudinal length small enough to not affect the air boundary layer. Moreover, a piece of glass was placed on its top to minimize the effect of rugosity on the results. d We exposed the seeds to airflow until release, recapturing them on a white sheet (5m x 1.5m) on the ground. The sheet was grilled in calibrated regions every 5 cm. We measured the distance between each seed's departure and landing points on the ground. Data analyses To investigate the impact of climate and soil water availability on plant traits, we fitted Generalized Linear Mixed Models (GLMM) with restricted maximum likelihood (REML) estimation using the R software version 4.3.2 45 . In these analyses, the dependent variables were the diaspore traits (diaspore mass, diaspore area, wing loading, terminal velocity) and maternal plant traits (abscission height and C/N ratio). Climate (current atmosphere and temperature, and enriched atmCO 2 and elevated temperature), irrigation, with two levels (well-watered and drought), and their interaction were the main fixed effects factors. In these models, we also included species as a random factor. We conducted these analyses using the ‘lme4’ package and the statistical significance of random factors (using likelihood ratio tests, LRT) was evaluated using the ‘lmerTest’ package. Effects of primary factors and contrasts were summarized by obtaining the least-squares means (i.e., the model estimated marginal means) using the ‘emmeans’ function implemented in the ‘emmeans’ package. In addition, we fitted independent GLMMs for each species to analyze ACC impact on those traits that are important for WALD’s theoretical model, i.e., terminal velocity, and abscission height. Response variables were transformed when necessary to achieve normality and homoscedasticity requirements. To analyze the structure of the correlation between plant and diaspore traits at each ambient and watering treatment, we performed principal component analyses (PCA) using the ‘factorextra’, ‘FactorMineR’, and ‘corrplot’ packages in R software. Theoretical dispersal distance estimates were computed using the ‘rinvgauss’ and ‘dinvgauss’ functions in the ‘statmod’ package in R. We ran 1000 simulations to obtain an average dispersal distance for each experimental situation and species. Once estimated, we fitted GLMM to investigate the impact of ACC factors (ambient and irrigation and their interaction) on theoretical dispersal distances. We analyzed the relation between the theoretical dispersal distance estimates and the empirical distance values from the wind tunnel assays using correlation analyses to validate our dispersal estimates for each species, climate, and treatment. Finally, to investigate predictors of diaspore dispersal, we analyze the relationship between dispersal estimates with plant and diaspore traits for each of the climatic scenarios examined by fitting backward multiple regression analyses using the ‘lme4’ and ‘AICcmodavg’ packages in R. Declarations Acknowledgments Technical and human support provided by Centro de Instrumentación CientíficoTécnica (CICT) of Universidad de Jaén is gratefully acknowledged. FEDER funds from the European Commission (Programa Operativo FEDER Andalucía References IPCC, 2023: Summary for Policymakers. 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Informed dispersal in plants: Heterosperma pinnatum (Asteraceae) adjusts its dispersal mode to escape from competition and water stress. Oikos 123, 225–231. Teller, B.J., Campbell, C., Shea, K., 2014. Dispersal under duress: Can stress enhance the performance of a passively dispersed species? Ecology. 95: 2694–2698. Teller, B.J., Zhang, R., Shea, K., 2016. Seed release in a changing climate: initiation of movement increases spread of an invasive species under simulated climate warming. Divers. Distrib. 22, 708–716. Seale, M., Nakayama, N., 2020. From passive to informed: mechanical mechanisms of seed dispersal. New Phytol. 225, 653–658. Ali, E., W. Cramer, J. Carnicer, E. Georgopoulou, N.J.M. Hilmi, G. Le Cozannet, and P. Lionello, 2022: Cross-Chapter Paper 4: Mediterranean Region. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. Pörtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegría, M. Craig, S. Langsdorf, S. Löschke, V. Möller, A. Okem, B. Rama (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA, pp. 2233–2272, doi:10.1017/9781009325844.021. Essa, Y.H., Hirschi, M., Thiery, W. et al. 2023. Drought characteristics in Mediterranean under future climate change. npj Clim Atmos Sci 6, 133: https://doi.org/10.1038/s41612-023-00458-4 Golodets, C., Sternberg, M., Kigel, J., Boeken, B., Henkin, Z., Seligman, N.G., Ungar, E.D., 2015. Climate change scenarios of herbaceous production along an aridity gradient: vulnerability increases with aridity. Oecologia 177, 971–979. del Pozo A, Brunel-Saldias N, Engler A, Ortega-Farias S, Acevedo-Opazo C, Lobos GA, Jara-Rojas R, Molina-Montenegro MA. Climate Change Impacts and Adaptation Strategies of Agriculture in Mediterranean-Climate Regions (MCRs). Sustainability. 2019; 11:2769. Valladares, F., Gianoli, E., Gómez, J.M., 2007. Ecological limits to plant phenotypic plasticity. New Phytol. 176, 749–763. Matesanz, S., Gianoli, E., & Valladares, F. 2010. Global change and the evolution of phenotypic plasticity in plants. Annals of the New York Academy of Sciences, 1206, 35-55. Matthew E. Nielsen, Daniel R. Papaj. 2022. Why study plasticity in multiple traits? New hypotheses for how phenotypically plastic traits interact during development and selection. Evolution, 76: 858–869. Chen, C., and Giladi, I. 2020. Variation in morphological traits affects dispersal and seedling emergence in dispersive diaspores of Geropogon hybridus . American Journal of Botany , 107 : 436 444. Thomson, F. J., Moles, A. T., Auld, T. D., & Kingsford, R. T. 2011. Seed dispersal distance is more strongly correlated with plant height than with seed mass. Journal of Ecology, 99, 1299-1307. Zhu, J., Lukić, N., Pagel, J., & Schurr, F. M. 2023. Density dependence of seed dispersal and fecundity profoundly alters the spread dynamics of plant populations. Journal of Ecology , 111 , 1735-1748. Moles, A. T., & Westoby, M. 2006. Seed size and plant strategy across the whole life cycle. Oikos, 113, 91-105. Manzaneda, A.J., Rey, P.J., Alcántara, J.M., 2009. Conflicting selection on diaspore traits limits the evolutionary potential of seed dispersal by ants. J. Evol. Biol. 22, 1407–1417. Rey, P. J., & Alcántara, J. M. 2000. Recruitment dynamics of a fleshy-fruited plant ( Olea europaea ): Connecting patterns of seed dispersal to seedling establishment. Journal of Ecology, 88, 622-633. Caplat, P., Nathan, R., Buckley, Y.M., 2012. Seed terminal velocity, wind turbulence, and demography drive the spread of an invasive tree in an analytical model. Ecology 93, 368–377. Pigliucci, M. 2005. Evolution of phenotypic plasticity: where are we going now?. Trends in Ecology and Evolution, 20: 481-486. Urban M.C., Tewksbury J. J. and Kimberly S. 2012. On a collision course: competition and dispersal differences create no-analogue communities and cause extinctions during climate change. Proc. R. Soc. B.2792072-2080. Baets, S. D., Poesen, J., Knapen, A., & Galindo, P. 2007. Impact of root architecture on the erosion‐reducing potential of roots during concentrated flow. Earth Surface Processes and Landforms: The Journal of the British Geomorphological Research Group, 32(9), 1323-1345. Vukicevich, E., Lowery, T., Bowen, P., Úrbez-Torres, J. R., & Hart, M. 2016. Cover crops to increase soil microbial diversity and mitigate decline in perennial agriculture. A review. Agronomy for Sustainable Development, 36, 1-14. Mallinger, R. E., Franco, J. G., Prischmann-Voldseth, D. A., & Prasifka, J. R. 2019. Annual cover crops for managed and wild bees: Optimal plant mixtures depend on pollinator enhancement goals. Agriculture, ecosystems & environment, 273, 107-116. Carbonell-Bojollo et al., 2020. Efficient groundcovers in Mediterranean olive groves under changing climate. In: S. Kumar et al. (eds.), Resources Use Efficiency in Agriculture. Springer Nature Singapore Pte Ltd. 2020 pp. 729-760. Cramer, W., Guiot, J., Fader, M., Garrabou, J., Gattuso, J. P., Iglesias, A., ... & Xoplaki, E. 2018. Climate change and interconnected risks to sustainable development in the Mediterranean. Nature Climate Change, 8(11), 972-980. Szulejko, J. E., Kumar, P., Deep, A., & Kim, K. H. 2017. Global warming projections to 2100 using simple CO2 greenhouse gas modeling and comments on CO2 climate sensitivity factor. Atmospheric Pollution Research, 8(1), 136-140. Wald, A. 2004. Sequential analysis. Dover Phoenix, New York, New York, USA. Katul, G. G., A. Porporato, R. Nathan, M. Siqueira, M. B.Soons, D. Poggi, H. S. Horn, and S. A. Levin. 2005. Mechanistic analytical models for long-distance seed dispersal by wind. American Naturalist 166:368–381. Badger, J., Bauwens, I., Casso, P., Davis, N., Hahmann, A., Hansen, S. B. K., ... & Volker, P. 2019. Global wind atlas 3.0. World Bank Group https://globalwindatlas.info. R Core Team. 2022. R: A language and environment for statistical computing (Version 4.3.2) [Software]. R Foundation for Statistical Computing. Additional Declarations There is NO Competing Interest. Supplementary Files SPLMDispersal.docx Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-4346427","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":300231630,"identity":"6f40a50d-c59f-46d8-a9bc-c6e10ecdd1eb","order_by":0,"name":"Antonio Manzaneda","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYLCChAIGOQaGAyRpMWAwJlELgwFDYgPRivmnHX4m8cDALn3DwcMPHxcw2NgT1CJxO81MIsEgOXfDgWPGxjMY0oiw7naCsUGCATNQywEzaR6GwwkEdcjfTv8M1FKfbnDg+PffPAz/CTvM4HaO4YMEg8MJBgfOmDHzMBxgJOgww9s5hUAtxw1nHjhTLM1jkEzYL3K3gWH1o6Janu/G8Y2feSrsCDsMASQOgNxJggZgDBF00SgYBaNgFIxUAABhpj7DGEK3dgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-9384-7910","institution":"University of Jaen","correspondingAuthor":true,"prefix":"","firstName":"Antonio","middleName":"","lastName":"Manzaneda","suffix":""},{"id":300231632,"identity":"4559fcb8-a05a-4c0b-a4c5-edddb8cf44b3","order_by":1,"name":"Victor Valenzuela-Polo","email":"","orcid":"","institution":"University of Jaen","correspondingAuthor":false,"prefix":"","firstName":"Victor","middleName":"","lastName":"Valenzuela-Polo","suffix":""},{"id":300231635,"identity":"72cc2fea-4602-4683-a9c9-b0a366622a23","order_by":2,"name":"Rocío Bolaños-Jiménez","email":"","orcid":"","institution":"University of Jaen","correspondingAuthor":false,"prefix":"","firstName":"Rocío","middleName":"","lastName":"Bolaños-Jiménez","suffix":""},{"id":300231638,"identity":"ebc2d490-4078-49ef-af22-c41c5642c7f6","order_by":3,"name":"Pedro J. Rey","email":"","orcid":"https://orcid.org/0000-0001-5550-0393","institution":"Universidad de Jaén","correspondingAuthor":false,"prefix":"","firstName":"Pedro","middleName":"J.","lastName":"Rey","suffix":""},{"id":300231642,"identity":"05a5003e-3c58-4d01-8b63-0fa1f4a34edb","order_by":4,"name":"Julio Alcantara","email":"","orcid":"https://orcid.org/0000-0002-8003-7844","institution":"Universidad de Jaen","correspondingAuthor":false,"prefix":"","firstName":"Julio","middleName":"","lastName":"Alcantara","suffix":""}],"badges":[],"createdAt":"2024-04-30 05:35:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4346427/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4346427/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56227833,"identity":"386d8d14-b93e-4ce2-879f-e4c7f6833ca7","added_by":"auto","created_at":"2024-05-10 06:32:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":73210,"visible":true,"origin":"","legend":"\u003cp\u003eVariation of plant traits between experimental climate (NC: current atmosphere and temperature; CC: climate change) and irrigation treatments: drought (red lines on graphs) vs. well-watered (blue lines). Values in the graphs are the model-adjusted means and 1SE for each variable. Graph letters depict significant differences (P\u0026lt;0.05) in post hoc comparisons.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4346427/v1/35374afedaf45ae352335c3a.png"},{"id":56227834,"identity":"f7b5371a-36ee-42f7-9b3f-7c393dbf39e6","added_by":"auto","created_at":"2024-05-10 06:32:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":51795,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Theoretical dispersal distances estimated from Wald’s model (density inverse Gaussian) at the community level. The vertical dashed lines on the graph indicate the median dispersal distance values for each ambient. (B) Density plot of median dispersal distances in climate and irrigation treatment at a specific level.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4346427/v1/42ce38d4c8ec3102222a77d0.png"},{"id":56227835,"identity":"7294f4b4-b4b2-4a08-9d30-2f41016ae456","added_by":"auto","created_at":"2024-05-10 06:32:26","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":26206,"visible":true,"origin":"","legend":"\u003cp\u003eCoefficients plot for multiple regression conducted to assess the relative importance of several traits as dispersal predictors. The X-axis is a standardized regression coefficient, and the Y-axis is a predictor variable. The dashed line represents the zero line. Thick bars on points depict standard errors, while thinner lines represent 95 % CI.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4346427/v1/dc2a6e748f1295d980fcb2ef.png"},{"id":56228913,"identity":"0bf8ce14-ffbf-45c4-9cfa-9606a9e08134","added_by":"auto","created_at":"2024-05-10 06:48:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":627187,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4346427/v1/70e7ec36-ee88-4da4-8443-a66eb3746189.pdf"},{"id":56227836,"identity":"7255626b-44ce-475d-8986-87aca14580e3","added_by":"auto","created_at":"2024-05-10 06:32:29","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":13679329,"visible":true,"origin":"","legend":"","description":"","filename":"SPLMDispersal.docx","url":"https://assets-eu.researchsquare.com/files/rs-4346427/v1/96ae68ad2caf6b19e095532b.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Climate change impacts the dispersal of annual plants: a mechanistic approach","fulltext":[{"header":"Main","content":"\u003cp\u003eAnthropogenic climate change (ACC) \u0026ndash; global temperature rising due to global greenhouse gas emissions\u003csup\u003e1\u003c/sup\u003e \u0026ndash; is already affecting plant populations, plant biodiversity, and the ecosystem services they provide\u003csup\u003e1,2,3\u003c/sup\u003e. The persistence of plant populations is contingent on their ability to adapt or migrate to follow their climatic niches across the landscape at a pace that is sufficiently responsive to changes in environmental conditions\u003csup\u003e4,5,6\u003c/sup\u003e. Therefore, the success or failure of plant species in the face of environmental changes is closely linked to the plasticity and adaptability of their inherent traits. Consequently, many studies have analysed the response and plasticity of plant adaptive traits \u0026ndash; i.e., fitness-related physiological, functional, and/or life-history traits \u0026ndash; to different drivers of ACC\u003csup\u003e3,7,8,9\u003c/sup\u003e. Conversely, the impact of ACC on seed dispersal-related traits (i.e., wind loading, hairs, pappus, terminal velocity, etc.) remains unclear\u003csup\u003e10,11\u003c/sup\u003e, despite seed dispersal being crucial for essential processes of plant population persistence in changing environments, such as gene flow, population connectance in fragmented landscapes, colonization of novel habitats, and plant natural regeneration\u003csup\u003e5,12,13\u003c/sup\u003e. Previous studies exploring the effects of ACC on seed-dispersal traits have primarily focused on the effect of drought, elevated temperature, or change in the wind regimes\u003csup\u003e14,15,16,17,18\u003c/sup\u003e. These and other studies suggest the existence of phenotypic plasticity in developing some dispersal-related traits (termed informed dispersal\u003csup\u003e16,19\u003c/sup\u003e), which may allow species to disperse rapidly across heterogeneous landscapes. However, to the best of our knowledge, no previous study has integrated the study of dispersal traits and their plasticity with the analysis of the mechanistic basis of the impact of ACC's essential factors (elevated atmospheric CO\u003csub\u003e2\u003c/sub\u003e, temperature, and drought) to model dispersal potential and limitations at multispecific levels. This is crucial to fully comprehend the effects of ACC on the dispersal of plant populations because (i) plants perceive environmental signals in an integrated manner\u003csup\u003e2\u003c/sup\u003e, and (ii) trait plasticity to environmental change typically varies between plant species\u003csup\u003e3,7\u003c/sup\u003e, which may later influence the future dynamics and assemblies of plant communities.\u003c/p\u003e \u003cp\u003eThis study uses climate-controlled experiments in growth chambers, mechanistic fluid dynamic modelling, and wind tunnel assays to determine key trait predictors of diaspore dispersal for 14 temperate annual species growing in a Mediterranean climate as part of a Climate Change simulation study. Mediterranean basin is projected to experience more frequent heat waves, a significant increase in air temperature (ranging from 0.9\u0026ordm;C to 5.6\u0026ordm;C), and a 4\u0026ndash;22% decrease in precipitation, depending on the emission scenario\u003csup\u003e20\u003c/sup\u003e. This increase in aridity\u003csup\u003e21\u003c/sup\u003e is already impacting Mediterranean inland ecosystems, making the herbaceous flora more vulnerable\u003csup\u003e22\u003c/sup\u003e. Likewise, an increase in aridity is similarly predicted in other regions of the Earth under the Mediterranean climate\u003csup\u003e23\u003c/sup\u003e. The main objective of this study is to investigate how key dispersal-related traits (abscission height, terminal velocity, and wing loading) vary in response to elevated CO\u003csub\u003e2\u003c/sub\u003e, temperature, and drought and how this may limit dispersal potential at specific and multispecific levels. Our study explicitly tests the informed dispersal hypothesis \u0026ndash; i.e., dispersal-related traits are modified according to the environment\u003csup\u003e19\u003c/sup\u003e \u0026ndash; concerning untested previous joint drivers of ACC, such as CO\u003csub\u003e2\u003c/sub\u003e elevation, drought, and elevated temperature, and investigates the mechanistic basis of how ACC affects main dispersal traits in temperate annual plants. According to the informed dispersal hypothesis, traits related to dispersal should enhance dispersal in stressful environmental conditions (e.g., drought and elevated temperature) through trait plasticity. However, inherent limits from trait plasticity\u003csup\u003e24\u003c/sup\u003e can constrain dispersal potential and prevent escape from stressful conditions.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eACC impact on traits\u003c/h2\u003e \u003cp\u003eAll traits, except wing loading, were independently significantly impacted by climate and irrigation treatment (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Only for the terminal velocity there was a significant interaction effect between the climate and irrigation factors (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Thus, when grown in either drought or enriched CO\u003csub\u003e2\u003c/sub\u003e conditions and elevated temperature, maternal plants developed significantly smaller (between 5\u0026ndash;19% across treatments) and lighter diaspores (between 20\u0026ndash;31% across treatments) than plants grown in current and well-watered conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Table S2). The abscission height was significantly lower (between 24\u0026ndash;31%) for plants grown under either water stress or higher levels of CO\u003csub\u003e2\u003c/sub\u003e and temperature conditions compared to plants grown in the current ambient (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table S2). Additionally, seeds originating from plants grown under climate change conditions or water stress had a shorter holding time (17\u0026ndash;23%) than plants grown in current and well-watered conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table S2). Concerning terminal velocity, the effect of climate change was significant only in water stress conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Seeds from plants developing in drought fell slower (9\u0026ndash;13%) than seeds from plants grown in optimal conditions at enriched and warmed ambient. Finally, the C/N ratio was only affected by the irrigation treatment (F\u003csub\u003e1,151\u003c/sub\u003e=48.71, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), indicating that plants growing under water restriction showed significantly lower C/N than plants growing in well-watered conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003eSummary of the General Lineal Mixed Models performed to analyze trait variation between two climates and two watering treatments. Significant factors (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) are highlighted in bold type. For each statistical analysis, degrees of freedom (d.f), the F-distribution (F), the significance level (P), and the Likelihood Ratio Test (LRT) are presented. Significant (at least P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) effects are highlighted in bold.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"19\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"18\" nameend=\"c19\" namest=\"c2\"\u003e \u003cp\u003eTRAITS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003eDiaspore mass\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e\u003cem\u003eDiaspore area\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e\u003cem\u003eWing\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eLoading\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003e\u003cem\u003eTerminal\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eVelocity (F)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003e\u003cem\u003eAbscission\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eheight (L)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c19\" namest=\"c17\"\u003e \u003cp\u003e\u003cem\u003eL/F\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFixed effects\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ed.f.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ed.f.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ed.f.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003ed.f.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cem\u003ed.f.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u003cem\u003ed.f.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClimate (C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e447.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3,48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3,791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e14.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e3,48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e39.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e3,48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e23.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIrrigation (I)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1,48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1,791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e26,45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1,48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e50.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e1,48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e21.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC x I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3,48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3,791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e4,24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e3,48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e3,48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRandom effects\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ed.f.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eLRT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ed.f.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eLRT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ed.f.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eLRT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003ed.f.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cem\u003eLRT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cem\u003ed.f.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cem\u003eLRT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u003cem\u003ed.f.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e\u003cem\u003eLRT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2773.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3495.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e65.538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e41.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e112.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\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\u003e \u003c/p\u003e \u003cp\u003eAt the species level, terminal velocity was significantly affected by climate change and/or soil water availability in 12 out of 14 species (Table S3). In six species, ACC effects were interactive, indicating that the impact of drought, elevated temperature, and atmCO\u003csub\u003e2\u003c/sub\u003e were interdependent and species-specific (Table S3; Fig. S8). For example, terminal velocity is reduced significantly in \u003cem\u003eBrachypodium hybridum\u003c/em\u003e, \u003cem\u003eBrachypodium stacei\u003c/em\u003e, and \u003cem\u003eEruca sativa\u003c/em\u003e under drought and elevated temperature and CO\u003csub\u003e2\u003c/sub\u003e conditions (Fig. S8). In contrast, for \u003cem\u003eAvena sativa\u003c/em\u003e, terminal velocity is augmented in drought and ACC conditions (Fig. S8). Additionally, for species with incomplete dehiscence, diaspores of maternal plants of \u003cem\u003eE. sativa\u003c/em\u003e and \u003cem\u003eSinapis alba\u003c/em\u003e grown under arid and ACC conditions exhibited lower terminal velocity than seeds of the same species grown under similar conditions (Fig. S8-S9). Regarding the height of abscission, climate change and/or drought significantly influenced the expression of this trait in all the species (Table S3, Fig. S10). This influence was mostly interactive (Table S3), yet climate change and drought typically significantly reduced abscission height (Fig. S10).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003ePlant and dispersal trait covariation (PCA analysis)\u003c/h2\u003e \u003cp\u003eThe Principal Components (PC) analysis revealed that three principal components account for approximately 83% of the total variance (Fig. S11). The first principal component strongly correlates positively with diaspore traits and terminal velocity, the second with the holding time, and the third with abscission height (Fig. S11). PC analyses performed on groups (e.g., climatic conditions) indicated that capturing variation associated with these components did not change between climatic conditions (i.e., 95% confidence interval ellipses largely overlapping, Fig. S12). The plant species that contributed to a larger extent to the first principal component was \u003cem\u003eA. sativa\u003c/em\u003e, whereas small-seeded species such as \u003cem\u003ePapaver roheas\u003c/em\u003e, \u003cem\u003eArabidopsis thaliana\u003c/em\u003e and \u003cem\u003eCapsella bursa-pastoris\u003c/em\u003e contributed also to segregation in this component yet in the opposite direction (Fig. S12).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDispersal distance estimates\u003c/h2\u003e \u003cp\u003eTheoretical dispersal distances were significantly and additively affected by climate and drought (F\u003csub\u003e3,49\u003c/sub\u003e= 13.92, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; F\u003csub\u003e3,49\u003c/sub\u003e= 19.21, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Generalized Linear Mixed Models (GLMM) results for Climate and Irrigation factors respectively; interaction term P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The distance at which diaspores were dispersed varied between climatic scenarios. Diaspores from maternal plants grown in climate change and drought were dispersed shorter distances (ca. 41% less) than diaspores developed in current and well-watered conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Therefore, higher dispersal distances were observed in plants grown under current and well-watered conditions compared to plants grown in the other climatic scenarios (median: 1.41 m\u0026thinsp;\u0026plusmn;\u0026thinsp;interquartile range (IR): 0.42; 1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39; 1.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58; 0.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53; values for current and well-watered, current and drought, climate change well-watered and climate change and drought conditions, respectively; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAt the species level, theoretical seed dispersal distances were lower in climate change conditions and drought compared to current and well-watered conditions in 12 out of 14 species (Fig. S13). For example, for \u003cem\u003eU. urens\u003c/em\u003e, the model predicts a reduction of about 50% of the dispersal distance in climate change compared to current and well-watered conditions (Fig. S13). Only diaspores of \u003cem\u003eP. rhoeas\u003c/em\u003e and \u003cem\u003eA. sativa\u003c/em\u003e showed higher dispersal distance in climate change conditions (Fig. S13).\u003c/p\u003e \u003cp\u003eIn three out of four climate scenarios examined, realized dispersal distances were positive and highly correlated to theoretical dispersal distances (Fig. S14) with a slope close to 1.0. However, in the climate change and drought scenario, observed and model-predicted values were not significantly linked (Fig. S14), and theoretical values underestimated empirical ones.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eDispersal predictors\u003c/h2\u003e \u003cp\u003eMultiple regression analyses revealed that the key predictors affecting diaspore dispersal were the abscission height, the terminal velocity, and their relationship (holding time; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table S4). The abscission height was positively correlated with the dispersal distance, which means that the taller the infructescence of the maternal plant, the greater the dispersal distance, and this trend was particularly strong in drought. Similarly, the lower the terminal velocity, the longer the dispersal distance. Only under the current ambient and drought, C/N ratio, diaspore area, and mass were linked to dispersal distance (Table S4).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eA plethora of studies have documented the negative impact of ACC on plants\u0026rsquo; physiological and functional traits for the survival of plant populations in diverse climate conditions\u003csup\u003e3,7,8,9,22\u003c/sup\u003e. Our research provides experimental and theoretical evidence that this also applies to diaspore-dispersal traits and the dispersal potential of annual species that have evolved under the Mediterranean climate. The increased aridity predicted in these areas\u003csup\u003e21\u003c/sup\u003e could restrict plants\u0026rsquo; ability to move to more favorable ecological environments where they can thrive and reproduce under current and future climates. Our findings challenge the main assumption of the informed dispersal hypothesis, which states that traits related to dispersal should improve dispersal in stressful environmental conditions\u003csup\u003e16,19\u003c/sup\u003e. In particular, we have demonstrated that ACC\u0026rsquo;s drivers affect the expression of the diaspore\u0026rsquo;s features, such as the height at which diaspores are released from maternal plants and the period that diaspores are exposed to the wind flow. As a result, in warmer, drier CO\u003csub\u003e2\u003c/sub\u003e-enriched climates, our results predict that the dispersal distance of Mediterranean annual species will be reduced by about 40% on average. Finally, we have shown that abscission height and terminal velocity are reliable estimators of diaspore dispersal across all evaluated climatic scenarios.\u003c/p\u003e \u003cp\u003eTrait plasticity is the major way by which plants cope with different environments\u003csup\u003e24\u003c/sup\u003e. The analysis of expression for dispersal-related and functional traits performed here suggests that the differential effects of temperature, enriched-CO\u003csub\u003e2\u003c/sub\u003e ambient, and drought on abscission height, diaspore traits, and terminal velocity limited the effect of trait plasticity to enhance dispersal. While terminal velocity decreased in climate change and water-limited conditions, which would favour dispersal distance, the lowered abscission height observed for plants grown under these conditions cancelled out the potential benefit for dispersal of producing smaller and lighter diaspores. Notably, whereas the effect of ACC on terminal velocity was variable across species (Fig. S8), the reduction in the abscission height was pervasive in most analyzed species (Fig. S10). This suggests that the contrasting plastic response observed in these two traits precluded plasticity as an effective mechanism for ensuring Mediterranean annual plants' dispersal in predicted stressful environments, which concurs with theoretical predictions of the existence of an intrinsic limit to plasticity that can constrain the adaptive potential of plants to rapid and extensive environmental changes, such as those resulting from ACC\u003csup\u003e24,25\u003c/sup\u003e. Similarly, our findings highlight the importance of considering multivariate trait plasticity across environments when evaluating organisms' functional responses\u003csup\u003e26\u003c/sup\u003e. Thus, PCA results suggest that co-expression of traits (i.e., phenotypic integration) occurs for diaspore mass, diaspore area, abscission height, wind loading, terminal velocity, and C/N ratio since all of them covary positively to PC1 across climatic scenarios; meanwhile, holding time varies orthogonally to them (Fig. S11). Hence, while correlated selection would be expected on traits that, in an integrated manner, enhance fitness-related dispersal\u003csup\u003e27\u003c/sup\u003e, integration should constrain plasticity because the more closely linked a trait's expression is to other traits, the harder the independent change of that trait in response to the environment\u003csup\u003e26\u003c/sup\u003e, as is the case here for abscission height, wing loading components, and terminal velocity. The fact that trait covariation remained constant across different climatic scenarios supports this idea (Fig. S12).\u003c/p\u003e \u003cp\u003eIt is interesting to note that the analysis carried out on individuals revealed that plant species that produce large infructescences and/or heavy diaspores, such as A. sativa, \u003cem\u003eB. hybridum\u003c/em\u003e, or \u003cem\u003eM. polymorpha\u003c/em\u003e, positively contributed to trait differentiation in PC1 (as shown in Fig. S12). Conversely, plant species that produce small and light diaspores, and develop shorter infructescences, such as \u003cem\u003eP. rhoeas, A. thaliana\u003c/em\u003e, and \u003cem\u003eC. bursa-pastoris\u003c/em\u003e, negative contributed more to such trait differentiation (as shown in Fig. S12). This indicates that there is a differential integration of dispersal traits between small and large plant species. Small plant species minimize wing loading to increase the holding time, while larger plant species depend on a complex phenotypic integration for dispersal.\u003c/p\u003e \u003cp\u003eOur study predicts an important additive impact of ACC factors on diaspore dispersal (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In particular, the reduction in the dispersal distance is expected to be about 40% in ambient with elevated temperature, enriched-CO\u003csub\u003e2,\u003c/sub\u003e and soil water limitation, and ca. 22\u0026ndash;23% in the other climatic scenarios compared to the most optimal climatic one (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These findings contrast with previous research that found similar or enhanced dispersal in stressful environments\u003csup\u003e14,16,17,18\u003c/sup\u003e. For example,, warming increased plant height by 9% in \u003cem\u003eCarduus nutans\u003c/em\u003e, positively affecting the spread rate\u003csup\u003e14\u003c/sup\u003e. In the same species, a different study showed that drought reduced plant height yet predicted dispersal distances of plants affected by water stress was similar or longer than those of plants growing with abundant water\u003csup\u003e17\u003c/sup\u003e. Three non-exclusive explanations are possible to explain discrepancy of our results with other studies. First, the effect of the environment on dispersal is species-specific. For example, in our study, the dispersal distance of \u003cem\u003ePapaver rhoeas\u003c/em\u003e seeds was enhanced by ca. 24% under climate change and drought compared to current ambient and humid conditions (Fig. S13) because the abscission height was unaffected by climate change or drought (Fig. S8), being the exception to the general pattern described here. Second, in the same vein, our research has adopted an approach that includes multiple species in our models and experiments instead of focusing solely on one or a few related species. Third, previous studies have not considered the impact of CO\u003csub\u003e2\u003c/sub\u003e enrichment on dispersal, which is known to have net effects on plant physiology apart from temperature\u003csup\u003e2\u003c/sup\u003e, although our approach does not allow us to test this later effect directly. Our findings therefore strongly support the notion that ACC factors have a significant influence on limiting the dispersal distance of diaspores of annual plants. This is primarily due to a marked decrease in abscission height \u0026ndash; in relative terms, the most important predictor of diaspore dispersal\u003csup\u003e28\u003c/sup\u003e \u0026ndash; compared to a more moderate reduction in terminal velocity (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe can draw a few ecological conclusions and predictions based on our findings. Except for one case, our research suggests that the dispersal of diaspores may be significantly limited in the near future in the Mediterranean areas, where increased climate aridity is forecasted in the Mediterranean areas\u003csup\u003e20,23\u003c/sup\u003e. Because diaspore dispersal is a crucial factor in the population spatial spread \u003csup\u003e14,29\u003c/sup\u003e, most species' migration ability is expected to be restricted in the future climate forecasted for the Mediterranean areas. This, in turn, is likely to limit the plant species' capacity to track advantageous ecological niches spatially. However, dispersal involves more than just the initial movement of diaspores\u003csup\u003e30,31,32\u003c/sup\u003e. Assessing the demographic effects of limited initial dispersal on germination, seedling emergence, and survival in a community-level and density-dependent context would still be necessary to fully understand the impact on population spread of shortened dispersal driven by ACC. \u003csup\u003e29,33\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn addition, our results have highlighted an important implication: ACC's effects on dispersal traits are species-specific. This, in turn, affects the dispersal distances of plant species in different directions, leading to a community with many losers and few winners (\u003cem\u003eP. roheas\u003c/em\u003e, for example). This pattern may eventually result in the colonization of new favorable niches and drive the future assembly of annual Mediterranean plant communities.\u003c/p\u003e \u003cp\u003eThis study highlights the limitations of the adaptability of plants to rapid and significant environmental changes. It suggests that intrinsic limits to trait plasticity can restrict plants' adaptive potential. However, it must be noted that our study only employed a single or a minimal number of accessions to analyze trait expression (as required for analyzing plasticity). Therefore, genetic variation for plasticity in functional and life-history traits may exist, leading to populations exhibiting higher levels of plasticity in dispersal traits. This could allow for greater potential for dispersal and colonization in warmer and drier environments. Future studies should explore genetic variation in nature to understand the plastic responses of dispersal-related traits better.\u003c/p\u003e \u003cp\u003eFinally, determining the importance of dispersal-competition trade-offs under climate change\u003csup\u003e35\u003c/sup\u003e will require more research to understand the future dynamics of the assembly of plant communities inhabiting ecosystems under changing Mediterranean-type climates.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStudy system\u003c/h2\u003e \u003cp\u003eTo investigate the impact of ACC on dispersal, we have selected fourteen annual herbaceous species (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) that typically grow in Mediterranean grasslands both in natural habitats and agrosystems, where they play an essential ecological function and services\u003csup\u003e36,37,38,39\u003c/sup\u003e (e.g., pollination, erosion control, carbon sequestration, etc.). These species were chosen based on their commonality in the grassland community, their range of taxonomy, and the variety and size of their diaspore features (as listed in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). In addition, some of the species, such as \u003cem\u003eArabidopsis thaliana\u003c/em\u003e and \u003cem\u003eBrachypodum distachyon\u003c/em\u003e, are recognized plant models. All these species rely on gravity or wind for passive dispersal, yet they do not possess any specific anemochory mechanism (e.g., wings, fluffy hairs, or pappus). However, some (\u003cem\u003eSinapis alba\u003c/em\u003e and \u003cem\u003eCalendula arvensis\u003c/em\u003e) may have unspecific structures like hairs, elaiosomes, or carpel remains (as shown in Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). All the species are monocarpic and release the seeds after maturation in late spring or summer.\u003c/p\u003e \u003cp\u003eAll plant material used in the experiments was sourced from native populations in southern Spain and grown in common gardens (Semillas Silvestres S.L., Semillas Cantueso S.L., and the University of Ja\u0026eacute;n (UJA) Germplasm collection) for multiple generations, minimizing maternal effects on trait expression. In particular, selected accessions for each species were for \u003cem\u003eA. thaliana\u003c/em\u003e, Wild Type (WT), for \u003cem\u003eB. distachyon\u003c/em\u003e was JHIN_13, for \u003cem\u003eB. stacei\u003c/em\u003e Alsur_1, for \u003cem\u003eB. hybridum\u003c/em\u003e BdCzPP_11 for \u003cem\u003eS. alba\u003c/em\u003e Laca_24. The plants used for the remaining species came from a unique source population (certificated seeds from a limited number of accessions and populations).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eGrowth Chamber Experiments\u003c/h2\u003e \u003cp\u003eThe seeds germinated in a petri dish under cold, humid, and dark conditions. Once germinated, the plants were moved to a growth chamber (FitoClima PLH Aralab\u0026reg;), where they were grown in a climatic SPLIT-PLOT design under controlled conditions. Maternal plants were subjected to two climatic conditions: 1. Control climatic conditions (NC hereafter): The plants were grown in current atmospheric and temperature conditions (22\u0026ordm;C day / 12\u0026ordm;C night, photoperiod 16h light/8 h night) to simulate natural conditions during early fall or early spring growing season. The atmospheric concentration of carbon dioxide (atmCO\u003csub\u003e2\u003c/sub\u003e) remained constant at 415 parts per million (ppm), and the air Relative Humidity (RH) was maintained at 50%. 2. Climate Change climatic conditions (CC hereafter): The maternal plants were grown at 22\u0026ordm;C\u0026thinsp;+\u0026thinsp;3.8 \u0026ordm;C day / 12\u0026ordm;C\u0026thinsp;+\u0026thinsp;3.8\u0026ordm;C night, representing an increase in predictable temperature for the Mediterranean region in 2100 based on models using the RCP4.5 model of emissions\u003csup\u003e40\u003c/sup\u003e. The photoperiod was kept the same at 16h light/8 h night. The atmCO2 was raised to 600 ppm, considering an annual increase of 0.5% in atmCO2. This value is the average of projected CO\u003csub\u003e2\u003c/sub\u003e levels for the year 2100 in the Northern Hemisphere, based on several pathway species scenarios\u003csup\u003e41\u003c/sup\u003e. The air RH was maintained at 50%. Moreover, at each split of climatic conditions, plants underwent two treatments of irrigation: irrigation available: A. Watered regularly: well-watered (WW) hereafter. B. drought: gradual cessation of irrigation after two weeks of growth, water restricted (WR) hereafter. Soil moisture was monitored during the experiment using a soil moisture sensor EC-5 (Meter\u0026reg;) to reach a soil water content ca. 47% \u0026plusmn; 2.36 in the well-watered treatment under current climate and ca. 31% \u0026plusmn; 2.46 under climate change conditions. In the drought treatment, soil water content was ca. 15.6% \u0026plusmn; 7.01 and 15.4% \u0026plusmn; 6.26 for current and climate change conditions, respectively. This experimental set eventually shaped a fully crossed two-factor (climatic condition and irrigation) design of 4 treatments. For each species, we planted three biological replicates for 168 plants assayed. We grow these plants for six weeks until they flower and seed-set. Most of the species can self-fertilize. However, \u003cem\u003eC. arvensis\u003c/em\u003e, \u003cem\u003eS. alba\u003c/em\u003e, \u003cem\u003eE. sativa\u003c/em\u003e, and \u003cem\u003eM. silvestris\u003c/em\u003e require cross-pollination to produce seeds, so they were hand-pollinated to obtain seeds.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eTraits, diaspore, and seed measurements\u003c/h2\u003e \u003cp\u003eAfter the plant reached its seed-set stage, we collected trait measurements for the maternal plants and their offspring under different irrigation treatments and climate conditions. For the maternal plants, we measured the abscission height (diaspore release height) and the C/N ratio from reproductive shoots (using an elemental gas analyzer Leco\u0026reg; TruSpec Micro). In contrast, we measured the diaspore mass, diaspore area, and wing loading (i.e., mass/ area ratio) for the offspring in 20 diaspores per species. These later measurements were taken using a digital microscope (Dino-Lite\u0026reg; AD7013 MT) and ImageJ software (Fig. S2). To determine each diaspore\u0026rsquo;s terminal velocity (the maximum fall velocity when the acceleration is null), we used a vertical tunnel made of Plexiglass, 38 cm x 38 cm wide x 200 cm high (Fig. S3). We let the diaspore fall and recorded the final part of the fall using a fast camera with a 60mm, f/2.8 lens, a capture speed of 250 fps, and a frame every 0.04 seconds with an average resolution of 125.28 \u0026micro;m/pixel (Fig. S4). We checked that no acceleration was observed and, thus, that the terminal velocity was indeed reached. Finally, we analyzed and tracked the video frames with ImageJ software. In addition, because the seed release of some Brassicaceae species often involved the dispersal of the whole silique due to indehiscence (pers. obs; Fig. S5), we also determined the terminal velocity of diaspores (seeds along with siliques) of \u003cem\u003eS. alba\u003c/em\u003e, \u003cem\u003eE. sativa\u003c/em\u003e and \u003cem\u003eC. bursa-pastoris\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eThe WALD Model\u003c/h2\u003e \u003cp\u003eWe fitted the mechanistic Wald analytical long-distance dispersal (WALD) model\u003csup\u003e42,43\u003c/sup\u003e to estimate the theoretical dispersal distance of each species from the different irrigation and climate treatments. The following expression gives the WALD model:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$k\\left(r\\right)={\\left(\\frac{\\tau }{2\\pi {r}^{3}}\\right)}^{\\frac{1}{2}}expexp \\left(-\\frac{{\\tau \\left(r-\\mu \\right)}^{2}}{2{\\mu }^{2}r}\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u0026micro;\u0026thinsp;=\u0026thinsp;\u003cem\u003eLU\u003c/em\u003e / \u003cem\u003eF\u003c/em\u003e and τ =(L/Ψ)\u003csup\u003e2\u003c/sup\u003e are the scale and shape parameters, respectively, with \u003cem\u003eL\u003c/em\u003e the abscission height, \u003cem\u003eU\u003c/em\u003e the horizontal wind velocity, \u003cem\u003eF\u003c/em\u003e the terminal velocity, and Ψ is the turbulence intensity of the flow. \u003cem\u003ek\u003c/em\u003e(r) is an inverse Gaussian distribution function that estimates the probability of being dispersed to a given distance \u003cem\u003er\u003c/em\u003e. In the model, the value of \u003cem\u003eU\u003c/em\u003e was set at 8.8 ms\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e with a turbulent intensity (wind speed standard variation divided by its mean value) of 0.29 for three reasons. Firstly, this value is well above the annual average wind speed (2.5\u0026ndash;3.5 ms\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) in the central Andaluc\u0026iacute;a region\u003csup\u003e44\u003c/sup\u003e, where the plants are initially from. This wind speed emulates the velocity during gust winds, when most of the offspring of annual herbs are dispersed (pers. obs.). Secondly, such wind speed is the maximum velocity the wind tunnel used in the study could reach, which is important for validating the observed seed dispersal (see below). Thirdly, this velocity is the point where turbulent intensity is minimized in the tunnel validation assays and to have greater control in abiotic parameters (as shown in Fig. S6). From model (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), \u003cem\u003eL\u003c/em\u003e/\u003cem\u003eF\u003c/em\u003e ratio denotes the flight hold time (the effective time that seeds are flying in the air).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eWind tunnel experiments\u003c/h2\u003e \u003cp\u003eWe conducted seed release experiments for seeds of each plant species grown under different climate conditions and irrigation treatments to validate theoretical dispersal estimates using a horizontal wind tunnel (test section area 0,24 m\u003csup\u003e2\u003c/sup\u003e and 200 cm long; Fig. S7). We set an average wind velocity of 8.8 ms\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, monitored using a hot wire anemometer (PCE 091006, Industrial Physics\u0026reg;) during the seed release assays. Reynolds number based on the seed\u0026rsquo;s characteristic length, \u003cem\u003eRe\u0026thinsp;=\u0026thinsp;U A\u003c/em\u003e\u003csup\u003e\u003cem\u003e0.5\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/\u0026#120584;\u003c/em\u003e, ranged between 9.8\u0026times;10\u003csup\u003e3\u003c/sup\u003e and 1.7\u0026times;10\u003csup\u003e5\u003c/sup\u003e, with \u003cem\u003eA\u003c/em\u003e the tunnel area and \u003cem\u003e\u0026#120584;\u003c/em\u003e the air cinematic viscosity. Thus, we placed five replicates of 20 seeds per species and experimental condition in a horizontal platform at the center of the wind tunnel outlet section, 80 cm above the floor (Fig. S7). Plattform\u0026rsquo;s shape was slender, its longitudinal length small enough to not affect the air boundary layer. Moreover, a piece of glass was placed on its top to minimize the effect of rugosity on the results. d We exposed the seeds to airflow until release, recapturing them on a white sheet (5m x 1.5m) on the ground. The sheet was grilled in calibrated regions every 5 cm. We measured the distance between each seed's departure and landing points on the ground.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eData analyses\u003c/h2\u003e \u003cp\u003eTo investigate the impact of climate and soil water availability on plant traits, we fitted Generalized Linear Mixed Models (GLMM) with restricted maximum likelihood (REML) estimation using the R software version 4.3.2\u003csup\u003e45\u003c/sup\u003e. In these analyses, the dependent variables were the diaspore traits (diaspore mass, diaspore area, wing loading, terminal velocity) and maternal plant traits (abscission height and C/N ratio). Climate (current atmosphere and temperature, and enriched atmCO\u003csub\u003e2\u003c/sub\u003e and elevated temperature), irrigation, with two levels (well-watered and drought), and their interaction were the main fixed effects factors. In these models, we also included species as a random factor. We conducted these analyses using the \u0026lsquo;lme4\u0026rsquo; package and the statistical significance of random factors (using likelihood ratio tests, LRT) was evaluated using the \u0026lsquo;lmerTest\u0026rsquo; package. Effects of primary factors and contrasts were summarized by obtaining the least-squares means (i.e., the model estimated marginal means) using the \u0026lsquo;emmeans\u0026rsquo; function implemented in the \u0026lsquo;emmeans\u0026rsquo; package. In addition, we fitted independent GLMMs for each species to analyze ACC impact on those traits that are important for WALD\u0026rsquo;s theoretical model, i.e., terminal velocity, and abscission height. Response variables were transformed when necessary to achieve normality and homoscedasticity requirements.\u003c/p\u003e \u003cp\u003eTo analyze the structure of the correlation between plant and diaspore traits at each ambient and watering treatment, we performed principal component analyses (PCA) using the \u0026lsquo;factorextra\u0026rsquo;, \u0026lsquo;FactorMineR\u0026rsquo;, and \u0026lsquo;corrplot\u0026rsquo; packages in R software.\u003c/p\u003e \u003cp\u003eTheoretical dispersal distance estimates were computed using the \u0026lsquo;rinvgauss\u0026rsquo; and \u0026lsquo;dinvgauss\u0026rsquo; functions in the \u0026lsquo;statmod\u0026rsquo; package in R. We ran 1000 simulations to obtain an average dispersal distance for each experimental situation and species. Once estimated, we fitted GLMM to investigate the impact of ACC factors (ambient and irrigation and their interaction) on theoretical dispersal distances. We analyzed the relation between the theoretical dispersal distance estimates and the empirical distance values from the wind tunnel assays using correlation analyses to validate our dispersal estimates for each species, climate, and treatment.\u003c/p\u003e \u003cp\u003eFinally, to investigate predictors of diaspore dispersal, we analyze the relationship between dispersal estimates with plant and diaspore traits for each of the climatic scenarios examined by fitting backward multiple regression analyses using the \u0026lsquo;lme4\u0026rsquo; and \u0026lsquo;AICcmodavg\u0026rsquo; packages in R.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eTechnical and human support provided by Centro de Instrumentaci\u0026oacute;n Cient\u0026iacute;ficoT\u0026eacute;cnica (CICT) of Universidad de Ja\u0026eacute;n is gratefully acknowledged. FEDER funds from the European Commission (Programa Operativo FEDER Andaluc\u0026iacute;a\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eIPCC, 2023: Summary for Policymakers. In: Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II, and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, H. Lee and J. Romero (eds.)]. IPCC, Geneva, Switzerland, pp. 1-34, doi: 10.59327/IPCC/AR6-9789291691647.001\u003c/li\u003e\n\u003cli\u003eLobell, D.B. y Gourdji, S.M. (2012). The influence of climate change on global crop productivity. Plant Physiology 160: 1686-1697.\u003c/li\u003e\n\u003cli\u003eParmesan, C., Hanley, M.E. 2015. Plants and climate change: complexities and surprises, \u003cem\u003eAnnals of Botany\u003c/em\u003e, Volume 116, Issue 6, November 2015, Pages 849\u0026ndash;864.\u003c/li\u003e\n\u003cli\u003eDavis MB, Shaw RG. (2001). 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A., \u0026amp; Prasifka, J. R. 2019. Annual cover crops for managed and wild bees: Optimal plant mixtures depend on pollinator enhancement goals. Agriculture, ecosystems \u0026amp; environment, 273, 107-116.\u003c/li\u003e\n\u003cli\u003eCarbonell-Bojollo et al., 2020. Efficient groundcovers in Mediterranean olive groves under changing climate. In: S. Kumar et al. (eds.), Resources Use Efficiency in Agriculture. Springer Nature Singapore Pte Ltd. 2020 pp. 729-760.\u003c/li\u003e\n\u003cli\u003eCramer, W., Guiot, J., Fader, M., Garrabou, J., Gattuso, J. P., Iglesias, A., ... \u0026amp; Xoplaki, E. 2018. Climate change and interconnected risks to sustainable development in the Mediterranean. Nature Climate Change, 8(11), 972-980.\u003c/li\u003e\n\u003cli\u003eSzulejko, J. E., Kumar, P., Deep, A., \u0026amp; Kim, K. H. 2017. Global warming projections to 2100 using simple CO2 greenhouse gas modeling and comments on CO2 climate sensitivity factor. Atmospheric Pollution Research, 8(1), 136-140.\u003c/li\u003e\n\u003cli\u003eWald, A. 2004. Sequential analysis. Dover Phoenix, New York, New York, USA.\u003c/li\u003e\n\u003cli\u003eKatul, G. G., A. Porporato, R. Nathan, M. Siqueira, M. B.Soons, D. Poggi, H. S. Horn, and S. A. Levin. 2005. Mechanistic analytical models for long-distance seed dispersal by wind. American Naturalist 166:368\u0026ndash;381.\u003c/li\u003e\n\u003cli\u003eBadger, J., Bauwens, I., Casso, P., Davis, N., Hahmann, A., Hansen, S. B. K., ... \u0026amp; Volker, P. 2019. Global wind atlas 3.0. World Bank Group https://globalwindatlas.info.\u003c/li\u003e\n\u003cli\u003eR Core Team. 2022. R: A language and environment for statistical computing (Version 4.3.2) [Software]. R Foundation for Statistical Computing.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4346427/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4346427/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAnthropogenic climate change (ACC) significantly impacts plant populations, biodiversity, and ecosystem services. The survival of plant species hinges on their ability to adapt or migrate in pace with shifting climate niches, which is key for maintaining gene flow and habitat colonization in changing environments. This study investigates the mechanistic basis of how ACC affects seed dispersal. Here, we specifically model how elevated atmospheric CO\u003csub\u003e2\u003c/sub\u003e levels, temperature, and drought collectively influence dispersal trait plasticity and potential across multiple species. We identified essential diaspore dispersal predictors using climate chamber experiments, fluid dynamic modelling, and wind tunnel assays. Our findings indicate a predicted dispersal distance reduction of 40% for temperate annual plants in warmer, drier, and CO\u003csub\u003e2\u003c/sub\u003e-rich conditions. Our results highlight the importance of inherent limits of plasticity in multiple traits for facing environmental change and the need to consider multiple environmental factors to understand dispersal in future climates.\u003c/p\u003e","manuscriptTitle":"Climate change impacts the dispersal of annual plants: a mechanistic approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-10 06:32:20","doi":"10.21203/rs.3.rs-4346427/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"463d4189-62fb-4a4a-8846-a1716990098a","owner":[],"postedDate":"May 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":31678641,"name":"Biological sciences/Plant sciences/Plant ecology"},{"id":31678643,"name":"Biological sciences/Ecology/Population dynamics"}],"tags":[],"updatedAt":"2025-08-12T10:30:43+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-10 06:32:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4346427","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4346427","identity":"rs-4346427","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}