Long-distance dispersal to a new habitat is one of the riskiest decisions a living organism can make. From ballooning spiders dispersing from salt marshes according to local wind velocity (Bonte et al., 2007) to kittiwakes abandoning breeding sites based on reproductive success of their neighbours (Boulinier et al., 2008), migration of many animals can be described as ‘informed dispersal’ by which organisms choose when and to where they travel depending on environmental cues and stressors (Clobert et al., 2009). In contrast, movement of plants, which is mostly mediated by seed or fruit dispersal, is understood as a mostly passive process by which seeds or fruits are carried by biotic or abiotic vectors (Bullock et al., 2006; Matthysen, 2012). This contradicts the wide range of environmental sensing and responses observed in plant development and physiology. Is plant dispersal really that passive?

Wind is a particularly common plant dispersal vector. Though locally unpredictable, wind is globally prevalent and almost ubiquitous. Variation in diaspore morphology and structure can have significant impacts on wind dispersal distributions when comparing between species (Muller-Landau et al., 2008; Tamme et al., 2014) however, the relevance of morphological and structural variation within a species has been questioned for wind dispersal (Greene and Johnson, 1992; Horn et al., 2001; Nathan et al., 2002). In most cases, intraspecific variation in dispersal distances is primarily determined by environmental conditions rather than plant factors (Nathan et al., 2002; Nathan et al., 2001). For example, for tree samaras the coefficient of variation for samara terminal velocity was too small to have a significant effect on dispersal distance as variation in wind turbulence was far larger (Greene and Johnson, 1992). Additionally, in field conditions, there was no correlation between diaspore terminal velocity and the likelihood of uplifting (which is a prerequisite for long-distance dispersal) for Liriodendron tulipifera diaspores (Horn et al., 2001; Nathan et al., 2002).

However, some individual plant traits, such as plant height and diaspore appendages, can affect dispersal according to developmental programmes and in response to environmental cues (Seale and Nakayama, 2020). Bet-hedging strategies during dispersal can arise by manipulation of diaspore morphology (e.g. Imbert, 2002) and can be modified in response to nutrient availability, temperature, plant competition, or water availability (Arshad et al., 2019; Imbert and Ronce, 2001; Martorell and Martínez-López, 2014).

Modifying the timing of dispersal can also influence distances travelled. Phenological traits can ensure that plants produce and release seeds during seasonal periods that are favourable for dispersal (Heydel and Tackenberg, 2017; Wright et al., 2008). Abscission bias also affects localised timing of wind dispersal. By requiring a non-zero minimum force to detach diaspores from their parent plant, dispersal is often initiated only by greater wind speeds that result in longer flight times and further travelling distances (Bohrer et al., 2008; Greene, 2005; Maurer et al., 2013; Pazos et al., 2013; Schippers and Jongejans, 2005; Soons and Bullock, 2008; Thompson and Katul, 2013; Treep et al., 2018). For dandelions Taraxacum officinale, detachment arising from high horizontal wind speeds is thought to be important for the initiation of dispersal (Greene, 2005), whereas convective updrafts are likely to be important for long distance flight (Tackenberg et al., 2003).

Many wind dispersal processes rely on mechanical innovations to initiate dispersal or aid flight (Seale and Nakayama, 2020): for example, explosive dispersal via release of builtup pressure in the seed pod of Cardamine hirsuta (Hofhuis et al., 2016) or directed ejection of seeds to enable spinning during flight in Ruellia ciliatiflora (Cooper et al., 2018). Specific wing designs and weighting generate leading-edge vortices in autorotating samaras (Lentink et al., 2009), and pappus porosity allows the formation of a separated vortex ring (SVR) in dandelion diaspores (Cummins et al., 2018).

The dandelion SVR is a toroidal vortex that forms in the wake of the pappus generating a region of low pressure. This underlies the drag-dominated flight behaviour conferred by the pappus that allows the diaspore to fall both slowly and steadily through the air (Cummins et al., 2018; Ledda et al., 2019). SVR behaviour changes as the Reynolds number (Re) changes. Re is the ratio of inertial to viscous forces and dictates the behaviour of flow around an object. It depends on the characteristic length scale of the object (D), the velocity of fluid flow (u), and the kinematic viscosity of the fluid (ν), and is calculated as Re = uD/ν. At Re >429, the SVR becomes unsteady and vortices are chaotically shed from the pappus (Cummins et al., 2018). At the natural falling velocity of dry dandelion diaspores, Re is approximately 355, which means that the SVR remains steady during flight. SVR stability is also affected by pappus porosity and thickness of the pappus (Cummins et al., 2018; Ledda et al., 2019).

So far, the SVR has been described for the dandelion pappus in their familiar open conformation. However, in response to high humidity, the dandelion pappus can morph into a closed structure by specific radial swelling of floral tissues (Meng et al., 2016; Rothert, 1895; Seale et al., 2022; Taliev, 1894). A ring of tissue, in which vascular tissue is surrounded by the cortical tissue, sits at the centre of the pappus and acts as a structure for the pappus hairs to attach to. Differential swelling capacities of the vasculature versus the cortical and other tissues facilitates raising and lowering of the pappus hairs (Seale et al., 2022). The presence of this specific mechanism and the widespread prevalence of pappus morphing in Asteraceae lineages (Rothert, 1895; Taliev, 1894) would suggest that pappus morphing serves a function.

To understand the biological significance of pappus morphing, we examined the fluid mechanics of the dandelion diaspore during flight and detachment from the parent plant. We found that, as the pappus changes shape, the SVR shrinks in size and moves closer to the pappus. Pappus closing also decreases the drag coefficient and thus increases diaspore falling velocity. When initiating dispersal, a closed pappus reduces detachment from the parent plant. We then used a computational model of wind dispersal to understand how these features affect dispersal distributions of the dandelion. Weather conditions combine with the functional consequences of pappus morphing to alter dispersal timing and flight behaviour and enhance dispersal distances compared to hypothetical diaspores that cannot morph.

The dandelion diaspore consists of an achene attached to a pappus comprising around 100 hair-like filaments (Cummins et al., 2018). The pappus is found closed in wetting conditions such as mist or rain (Rothert, 1895; Taliev, 1894; Figure 1a–c). In contrast, when dry, hairs are approximately horizontal, when measured from µCT scans at a 2 mm radius from the centre of the pappus (Figure 1—figure supplement 1a).

Figure 1 with 1 supplement see all

Download asset Open asset

To capture pappus closure dynamics, we imaged dandelion diaspores in an enclosed, water-tight chamber with moisture added via an ultrasonic humidifier for 10 or 20 min (Figure 1d–f). For both treatments, the pappus angle (the angle between outermost hairs, see Figure 1c) rapidly decreased (4.0 ± 1.4 degree min^–1) during moisture treatments, which is consistent with previous experiments (Meng et al., 2016). This continued for 5–15 min after moisture addition ceased with a final change in angle of 60°–90° (Figure 1f). To test the effect of relative humidity (RH) below 100%, samples were placed in a chamber with stable RH. At RH of 71.8 and 87.0%, modest angles changes of around 10° and 24° were observed, respectively (Figure 1—figure supplement 1b). Together, these results indicate that the dandelion pappus partially closes at high RH and rapidly closes when fully wet.

We hypothesised that pappus shape changes would modify diaspore flight and indeed found a strong effect on the falling velocity of the diaspore (Figure 2a). A decrease in pappus angle of around 100–150°, associated with wetting for 1 hr, resulted in double to triple the falling velocity (Figure 2a). This is consistent with a change in projected pappus area that occurs when the pappus closes (Figure 2—figure supplement 1a). Additionally, a small increase in mass due to the added water resulted in a 0.7 μN increase in drag for the wetted samples.

Figure 2 with 1 supplement see all

Download asset Open asset

To understand further how the pappus shape change affects the drag and falling velocity of the dandelion diaspore, we calculated the drag coefficient (C_D) and Reynolds numbers (Re) from our drop test data. The coefficient C_D is a nondimensionalised parameter indicating the drag on an object for a given projected area (in our case, the area given by the pappus hairs). For dry diaspores, we examined C_D and Re alongside data from weighted/clipped diaspores from Cummins et al., 2018 (Figure 2b). For dry diaspores, higher Re is associated with rapidly decreasing C_D before reaching a plateau. The wet diaspores with closed pappi, however, exhibit a different C_D-Re relationship with substantially lower C_D for a given Re compared to dry (weighted) diaspores with open pappi (Figure 2c). Closing the pappus therefore has two simultaneous effects: Re is increased as falling velocity increases, and the drag per unit of projected pappus area is decreased relative to a hypothetical open pappus of the same projected area. Therefore, pappus morphing is associated with shifting to a different region of the C_D-Re parameter space (Figure 2c).

An SVR forms behind the pappus due to the high porosity of the pappus combined with precise spacing of the hairs, which limits air flow through the pappus. (Cummins et al., 2018; Ledda et al., 2019). The nature of the SVR behind circular disks is strongly affected by the porosity of the disk and its projected area. Closing the pappus has several effects on the geometry: firstly, the projected area exposed to the flow reduces (Figure 2—figure supplement 1a); secondly, porosity of the pappus reduces as hairs move closer together (Figure 2c); and thirdly, the shape of the pappus becomes less disk-like and more cone-like. We observed that the combination of these effects alters the fluid mechanics around the pappus by reducing vortex size and shifting the vortex towards the pappus (Figure 2h and i).

In order to understand how pappus morphing impacted on SVR formation, we examined the dandelion pappus in dry and wet states fixed in a vertical wind tunnel (Figure 2d). Flow visualisation confirmed the presence of the SVR in both open and partially closed pappi (Figure 2e and f). We performed particle image velocimetry (PIV) (Raffel et al., 2018) to spatially resolve the velocity field of air in the region behind the pappus. Streamwise flow velocity was kept constant to give Re = 133–197 across different pappus angles. Our PIV analysis was able to detect the expected features of the SVR, such as stagnation points in the flow and characteristic vorticity profiles. (Figure 2g, Figure 2—figure supplement 1b).

In the region of space directly behind the centre of the pappus, the maximum magnitude of negative streamwise flow velocity (minimum u_z) showed no clear relationship with the pappus angle (Figure 2—figure supplement 1c). However, the location of minimum u_z correlated with pappus angle (Figure 2g). At more open pappus angles, the position of minimum u_z moved further downstream of the pappus (Figure 2g, p=0.001, R² = 0.34). Additionally, the size of the vortex significantly changes with altered pappus angle. The nominal length (see ‘Materials and methods’) of the vortex was markedly reduced at smaller pappus angles (Figure 2i, p<0.001, R² = 0.46).

From our data, it is clear that pappus morphology considerably affects the falling velocity of the diaspore and the fluid mechanics of the pappus. We also expected that the pappus shape might affect detachment of the diaspore from the parent plant. This is important because, as noted before, force thresholds on detachment are an important mechanism by which plants can bias timing of dispersal in favour of increased wind speeds (Greene, 2005; Greene et al., 2008; Schippers and Jongejans, 2005; Skarpaas et al., 2006; Soons and Bullock, 2008; Thompson and Katul, 2013; Treep et al., 2018) and is thought to promote long distance dispersal (Schippers and Jongejans, 2005; Soons and Bullock, 2008; Thompson and Katul, 2013). We placed dandelion infructescences in a wind tunnel (Figure 3—figure supplement 1a) and assessed the number of diaspores detaching at varying wind speeds (Figure 3). Hydrating samples for 1 hr by mist application significantly (p<0.001, X² = 341, df = 1) reduced detachment for the range of wind speeds tested (Figure 3c, Figure 3—figure supplement 1b). In flow with 9.1% turbulent intensity, 24% of dry diaspores remained attached at the maximum wind speed (9.8 m s^–1) compared to 50% of wet diaspores (Figure 3c). Reducing the turbulent intensity of the flow also reduced diaspore detachment (Figure 3—figure supplement 1b, c). These data indicate that hydration and air flow impact on the initiation of dispersal. This observation is consistent with detachment data for thistles (Jongejans et al., 2007; Marchetto et al., 2012; Skarpaas et al., 2006).

Figure 3 with 1 supplement see all

Download asset Open asset

As drag, falling velocity, and diaspore detachment are strongly affected by moisture-dependent pappus morphing, we expected pappus closure would influence dispersal distances. We tested this hypothesis by numerically modelling the dispersal distance in dry or wet conditions corresponding to the open or closed state of the pappus. We first separately tested the effects of pappus closure on diaspore flight, then diaspore detachment, and finally combined these phenomena together to understand how pappus morphing affects dispersal.

We simulated wind dispersal trajectories using the Markov chain synthetic turbulence generation (STG) model previously developed and used for grassland species (Pazos et al., 2013; Soons et al., 2004). This model simulates diaspore dispersal according to instantaneous calculation of wind trajectories. Importantly, the model can be generalised to new settings using measured (or estimated) parameters and is able to capture long-distance dispersal in a realistic manner. Long-distance dispersal relies on uplifting of diaspores at early stages in the dispersal process and is primarily achieved within the model by incorporating autocorrelated horizontal and vertical turbulence generated from properties of the canopy vegetation. We used wind velocity values from the Edinburgh meteorological data separating it into two groups: dry conditions (RH < 90%) or wet conditions (RH ≥ 90%). There is a consistent negative correlation between wind speed and RH (Figure 4a, Figure 4—figure supplement 1). At high RH, when dandelion pappi are more likely to be closed, wind speeds are relatively low, although still variable (Figure 4a).

Figure 4 with 2 supplements see all

Download asset Open asset

We ran five different model simulations to explore the different functional aspects of pappus morphology on dispersal. For each model, we simulated 10,000 dispersal events and computed kernel density estimates normalised such that the integral over distance equalled one. Firstly, we simulated dispersal of dandelion diaspores with open pappi in dry weather conditions (Model 1), when wind speeds tend to be higher. As expected, most diaspores would disperse a short distance (median: 4.3 m) from the parent plant, but the model generated dispersal distances with a right-skewed tail, indicating some diaspores will disperse much longer distances (Figure 4c). It is worth noting that extremely long distances (>10,000 km) are observed in a very small number of cases. Some, though not all, of these individual simulations occurred because the diaspores hit the top of the atmospheric boundary layer during trajectory simulation, but we found that omitting such individuals from the analysis made very little difference to the kernel density estimate. The accuracy of the model at this range cannot be verified due to the impracticality of tracking such events empirically. Without a clear rationale for a specific cut-off distance, we decided to leave the model distribution unchanged to avoid misrepresentation of the model’s results.

To explore the effects of weather conditions alone on dispersal, we ran the model again using the wet weather wind speed distribution in a hypothetical scenario where the pappus remains open in these conditions (Model 2). This substantially decreased the median dispersal distance to 1.5 m (Figure 4d). It also decreased long-distance dispersers from 17.5% of simulated dispersal events in Model 1 to 5.6% in Model 2 (Figure 4e). This indicates that the lower wind speeds typical in wetter weather conditions (Figure 4a) would considerably decrease dispersal distances across the entire distribution even with the aerodynamically favourable open pappus (Figure 4c–e).

We then ran several simulations to investigate the ecological significance of closing the pappus. In Model 3, we used the wet weather wind conditions, the falling velocity of the diaspore when the pappus is closed, and the probability of a given wind speed detaching the diaspore in the first place was based on the detachment profiles observed for wet infructescences (Figure 3). Compared to keeping the pappus open during wet weather, closing the pappus reduced median dispersal from 1.5 m to 0.9 m (Figure 4c and d). However, long-distance dispersal increased from 5.6% to 7.5% of the 10,000 simulated dispersal events (Figure 4e) giving an increase of 34%.

To understand how different aspects of pappus closing affect dispersal, we separately manipulated the detachment threshold probability and the falling velocity of the diaspore. Closing the pappus only during flight (Model 4) dramatically reduced median dispersal (4.3 m to 1.4 m), while keeping the pappus closed only during detachment (Model 5) considerably increased both median and long-distance dispersal (4.3 m to 6.6 m and 17.5% vs. 21.4% respectively).

Our model simulations established probability distributions for different realistic and hypothetical scenarios for diaspores that disperse. In reality, though, humidity may modify detachment thresholds to impact the probability of initiating dispersal in the first place. To explore this possibility, we simulated a 48 hr period during which dandelion plants are likely to disperse all their diaspores (Treep et al., 2018). Each instantiation of dispersal was considered to last 1 s, and diaspores were assessed for detachment against the appropriate probability distribution (derived from Figure 3) at the randomly selected wind speed for that second. There was a considerably higher probability of detaching in Models 1 and 3 when wind speeds were selected from the dry distributions and diaspores detachment was based on having the pappus open (Figure 4f). When pappi were closed and weather was wet (Model 3), the probability of detachment dropped dramatically, which is likely due to the low wind speeds and higher detachment thresholds in these conditions. When detachment did occur, wind speeds were higher than when the pappi were open in wet weather (Figure 4g, Model 3 vs. Model 2). The data imply that dispersal in wet conditions is a rare event (Figure 4f), but that when it does occur, closing the pappus increases the chances of travelling long distances (Figure 4e, Model 3 vs. Model 2). We can conclude that closing the dandelion pappus maintains long-distance dispersal primarily via its effect on detachment (Model 4). This biases diaspore release towards dispersing in dry conditions when wind speeds tend to be higher (Figure 4g). Interestingly, for the few diaspores that do disperse in wet conditions, a greater spread of dispersal distances occurs with reduced median distances but slightly higher long-distance dispersal.

To understand the dispersal distribution of dandelion diaspores across all possible weather conditions, we combined the distribution of Models 1 and 3 weighted by the frequency of wet or dry weather conditions in our meteorological data, and also conditioned on the probability of detachment in such conditions (Figure 4—figure supplement 2). This kernel density estimate therefore represents the realistic dispersal distribution of diaspores given that they detach from the parent plant and that the pappus morphs depending on the humidity. We compared this to a hypothetical version in which the pappus cannot morph by combining Models 1 and 2 in the same manner. In our dataset, dry weather is three times more frequent than wet. Diaspores are 8 times less likely to detach in wet weather versus dry when morphing occurs and 2.5 times less likely when the pappus remains open. As a result dispersal during wet weather is rare in both morphing and open conditions so the distribution from Model 1 therefore dominated the overall kernel density estimates in both cases. To compare the models we found that the Jensen–Shannon divergence was 0.034 indicating that the two distributions follow essentially similar shapes (Figure 4—figure supplement 2a and b). Examining how the models varied, we found that morphing led to a 6% increase in long-distance dispersal (from 26.7% to 28.5% of seeds) (Figure 4—figure supplement 2d) and a 23% increase in median dispersal (from 3.9 m to 4.8 m) compared to a pappus that was always open (Figure 4—figure supplement 2c).

Structure is intimately linked to function in engineering and in biology. While biological systems have had millions of years of evolution to improve functional outcomes, evolution must also balance many survival and reproductive requirements which may lead to trade-offs. The dandelion diaspore achieves functionality during flight by constructing a filamentous pappus whose porosity allows an SVR to form. However, diaspore flight is not favourable in all circumstances, so we conjecture that the pappus morphs and modifies its structure to alter the flight characteristics.

As the pappus closes, the SVR decreases in size and shifts its position. Morphing via angular rotation of the pappus hairs is not only a convenient mechanism but also has functional consequences. If pappus hairs remained open but shortened in length, the drag per unit area would be greater than for a cone-like morphology with the same projected area. By generating a cone-like shape instead of a disk-like shape, the pappus becomes more streamlined. Together, the fluid mechanics of the closed pappus leads to significant changes in diaspore falling velocity and detachment from the parent plant.

Dispersing far from a parent is often considered favourable as resource and kin competition are reduced (Saastamoinen et al., 2018), but competition is not the only factor affecting site suitability. Soil moisture levels are temporally and spatially variable (Vinnikov et al., 1996), more than characteristics such as light and temperature (Silvertown et al., 2015), meaning that optimal germination sites and times may vary considerably with changing weather and microenvironments. Our data suggest that dandelions have evolved pappus morphing because it biases detachment toward favourable dispersal times, which enhances both median and long-distance dispersal.

Moisture-induced pappus morphing may thus be a form of informed dispersal, by which dispersal is modified according to environmental cues signalling more favourable conditions (Clobert et al., 2009). In contrast to the popular perception that plant migration is a passive response to wind and other vectors, an explorative literature review has revealed otherwise (Seale and Nakayama, 2020). Plants are not able to make conscious decisions influencing their behaviour, but can form mechanical or chemical structures that respond differentially to the environment. There are two types of informed dispersal in plants: developmental adaptation of heteromorphic diaspores to environmental stresses (Martorell and Martínez-López, 2014), which acts over a longer time scale responding to stable environmental conditions; and highly temporal and local responses such as the fire-induced dispersal of Banksia (He et al., 2011), and the temperature-controlled dehiscence of Brassica siliques (Li et al., 2018), as well as the dynamic pappus morphing observed here. Such short-term regulation may be particularly beneficial in responding to unpredictable or fast-changing conditions.

The effects of pappus closure on detachment and on flight counteract one another to some extent as diaspores that detach when wet are more likely to do so when wind speeds are higher, but a closed pappus will cause them to fall faster. However, the low likelihood of detaching at low windspeeds when dry (due to higher average wind speeds) and when wet (due to pappus closure) means that dispersal largely occurs when wind speeds are high. These higher wind speeds appear to be more significant than the change in falling velocity as the overall effect of a morphing pappus is to increase median and long-distance dispersal (Figure 4, S5). The effect on long-distance dispersal is particularly significant as this has a disproportionate effect on ecological distributions and is often the major driver of range expansion (Clark, 1998; Kot et al., 1996; Trakhtenbrot et al., 2005).

Pappus morphing may have additional functions beyond detachment, flight, and dispersal, such as protection from damage, or to aid germination. The morphing phenomenon occurs in many (perhaps most) pappus-bearing diaspores but is conspicuously missing from some representatives of the Asteraceae (e.g. Leontodon, Hieracium, Erigeron) (Rothert, 1895; Taliev, 1894). A comparative approach examining the ecological and phenological features of pappose fruits that do or do not morph may yield greater insight into the roles of pappus morphing.

We have tested the ecological implications of this biomechanical response to moisture and suggested that pappus closure acts to tune dispersal promoting long-distance dispersal by responding to moisture conditions. As many species modify their seed or fruit structure in response to water, this might be a general mechanism to ensure dispersal at appropriate times.

Considering how prevalent environmentally induced plasticity is in plant structure and its development, understanding of structural functions within environmental contexts is critical to predict the ecological consequences of climate change (Denny and Helmuth, 2009). Insights into ecological and environmental drivers in turn enable us to comprehend selective pressure that has acted and will act to evolve plant forms and their functions.

The Source data for all figures has been deposited to Zenodo, doi: https://doi.org/10.5281/zenodo.7038366.

1. 1. Seale M

   (2022) Zenodo

   Environmental morphing enables informed dispersal of the dandelion diaspore.

   https://doi.org/10.5281/zenodo.7038366

1. 1. Arshad W
   2. Sperber K
   3. Steinbrecher T
   4. Nichols B
   5. Jansen VAA
   6. Leubner-Metzger G
   7. Mummenhoff K

   (2019) Dispersal biophysics and adaptive significance of dimorphic diaspores in the annual aethionema arabicum (brassicaceae)

   The New Phytologist 221:1434–1446.

   https://doi.org/10.1111/nph.15490

   • PubMed
   • Google Scholar
2. 1. Bohrer G
   2. Katul GG
   3. Nathan R
   4. Walko RL
   5. Avissar R

   (2008) Effects of canopy heterogeneity, seed abscission and inertia on wind-driven dispersal kernels of tree seeds

   Journal of Ecology 96:569–580.

   https://doi.org/10.1111/j.1365-2745.2008.01368.x

   • Google Scholar
3. 1. Bonte D
   2. Bossuyt B
   3. Lens L

   (2007) Aerial dispersal plasticity under different wind velocities in a salt marsh wolf spider

   Behavioral Ecology 18:438–443.

   https://doi.org/10.1093/beheco/arl103

   • Google Scholar
4. 1. Boulinier T
   2. McCoy KD
   3. Yoccoz NG
   4. Gasparini J
   5. Tveraa T

   (2008) Public information affects breeding dispersal in a colonial bird: kittiwakes cue on neighbours

   Biology Letters 4:538–540.

   https://doi.org/10.1098/rsbl.2008.0291

   • PubMed
   • Google Scholar
5. 1. Bullock JM
   2. Shea K
   3. Skarpaas O

   (2006) Measuring plant dispersal: an introduction to field methods and experimental design

   Plant Ecology 186:217–234.

   https://doi.org/10.1007/s11258-006-9124-5

   • Google Scholar
6. 1. Clark JS

   (1998) Why trees migrate so fast: confronting theory with dispersal biology and the paleorecord

   The American Naturalist 152:204–224.

   https://doi.org/10.1086/286162

   • PubMed
   • Google Scholar
7. 1. Clobert J
   2. Le Galliard JF
   3. Cote J
   4. Meylan S
   5. Massot M

   (2009) Informed dispersal, heterogeneity in animal dispersal syndromes and the dynamics of spatially structured populations

   Ecology Letters 12:197–209.

   https://doi.org/10.1111/j.1461-0248.2008.01267.x

   • PubMed
   • Google Scholar
8. 1. Cooper ES
   2. Mosher MA
   3. Cross CM
   4. Whitaker DL

   (2018) Gyroscopic stabilization minimizes DraG on ruellia ciliatiflora seeds

   Journal of the Royal Society, Interface 15:20170901.

   https://doi.org/10.1098/rsif.2017.0901

   • PubMed
   • Google Scholar
9. 1. Cummins C
   2. Seale M
   3. Macente A
   4. Certini D
   5. Mastropaolo E
   6. Viola IM
   7. Nakayama N

   (2018) A separated vortex ring underlies the flight of the dandelion

   Nature 562:414–418.

   https://doi.org/10.1038/s41586-018-0604-2

   • PubMed
   • Google Scholar
10. 1. Denny M
    2. Helmuth B

    (2009) Confronting the physiological bottleneck: a challenge from ecomechanics

    Integrative and Comparative Biology 49:197–201.

    https://doi.org/10.1093/icb/icp070

    • PubMed
    • Google Scholar
11. Conference

    1. Ghanadi F
    2. Emes M
    3. Yu J
    4. Arjomandi M
    5. Kelso R

    (2017) Investigation of the atmospheric boundary layer characteristics on gust factor for the calculation of wind load

    SOLARPACES 2016 Abu Dhabi, United Arab Emirates.

    https://doi.org/10.1063/1.4984496

    • Google Scholar
12. 1. Greene DF
    2. Johnson EA

    (1992) Can the variation in Samara mass and terminal velocity on an individual plant affect the distribution of dispersal distances?

    The American Naturalist 139:825–838.

    https://doi.org/10.1086/285359

    • Google Scholar
13. 1. Greene DF

    (2005) The role of abscission in long-distance seed dispersal by the wind

    Ecology 86:3105–3110.

    https://doi.org/10.1890/04-1430

    • Google Scholar
14. 1. Greene DF
    2. Quesada M
    3. Calogeropoulos C

    (2008) Dispersal of seeds by the tropical sea breeze

    Ecology 89:118–125.

    https://doi.org/10.1890/06-0781.1

    • PubMed
    • Google Scholar
15. 1. He T
    2. Lamont BB
    3. Downes KS

    (2011) Banksia born to burn

    The New Phytologist 191:184–196.

    https://doi.org/10.1111/j.1469-8137.2011.03663.x

    • PubMed
    • Google Scholar
16. 1. Heydel F
    2. Tackenberg O

    (2017) How are the phenologies of ripening and seed release affected by species’ ecology and evolution?

    Oikos 126:738–747.

    https://doi.org/10.1111/oik.03442

    • Google Scholar
17. 1. Hofhuis H
    2. Moulton D
    3. Lessinnes T
    4. Routier-Kierzkowska AL
    5. Bomphrey RJ
    6. Mosca G
    7. Reinhardt H
    8. Sarchet P
    9. Gan X
    10. Tsiantis M
    11. Ventikos Y
    12. Walker S
    13. Goriely A
    14. Smith R
    15. Hay A

    (2016) Morphomechanical innovation drives explosive seed dispersal

    Cell 166:222–233.

    https://doi.org/10.1016/j.cell.2016.05.002

    • PubMed
    • Google Scholar
18. 1. Horn HS
    2. Nathan RAN
    3. Kaplan SR

    (2001) Long-Distance dispersal of tree seeds by wind

    Ecological Research 16:877–885.

    https://doi.org/10.1046/j.1440-1703.2001.00456.x

    • Google Scholar
19. 1. Imbert E
    2. Ronce O

    (2001) Phenotypic plasticity for dispersal ability in the seed heteromorphic crepis sancta (asteraceae)

    Oikos 93:126–134.

    https://doi.org/10.1034/j.1600-0706.2001.930114.x

    • Google Scholar
20. 1. Imbert E

    (2002) Ecological consequences and ontogeny of seed heteromorphism

    Perspectives in Plant Ecology, Evolution and Systematics 5:13–36.

    https://doi.org/10.1078/1433-8319-00021

    • Google Scholar
21. 1. Jackson CH

    (2016) Flexsurv: a platform for parametric survival modeling in R

    Journal of Statistical Software 70:1–33.

    https://doi.org/10.18637/jss.v070.i08

    • PubMed
    • Google Scholar
22. 1. Jongejans E
    2. Pedatella NM
    3. Shea K
    4. Skarpaas O
    5. Auhl R

    (2007) Seed release by invasive thistles: the impact of plant and environmental factors

    Proceedings. Biological Sciences 274:2457–2464.

    https://doi.org/10.1098/rspb.2007.0190

    • PubMed
    • Google Scholar
23. 1. Kot M
    2. Lewis MA
    3. van den Driessche P

    (1996) Dispersal data and the spread of invading organisms

    Ecology 77:2027–2042.

    https://doi.org/10.2307/2265698

    • Google Scholar
24. 1. Ledda PG
    2. Siconolfi L
    3. Viola F
    4. Camarri S
    5. Gallaire F

    (2019) Flow dynamics of a dandelion pappus: a linear stability approach

    Physical Review Fluids 4:71901.

    https://doi.org/10.1103/PhysRevFluids.4.071901

    • Google Scholar
25. 1. Lentink D
    2. Dickson WB
    3. van Leeuwen JL
    4. Dickinson MH

    (2009) Leading-edge vortices elevate lift of autorotating plant seeds

    Science 324:1438–1440.

    https://doi.org/10.1126/science.1174196

    • PubMed
    • Google Scholar
26. 1. Li XR
    2. Deb J
    3. Kumar SV
    4. Østergaard L

    (2018) Temperature modulates tissue-specification program to control fruit dehiscence in Brassicaceae

    Molecular Plant 11:598–606.

    https://doi.org/10.1016/j.molp.2018.01.003

    • PubMed
    • Google Scholar
27. 1. Marchetto KM
    2. Jongejans E
    3. Shea K
    4. Auhl R

    (2012) Water loss from flower heads predicts seed release in two invasive thistles

    Plant Ecology & Diversity 5:57–65.

    https://doi.org/10.1080/17550874.2012.667841

    • Google Scholar
28. 1. Martorell C
    2. Martínez-López M

    (2014) Informed dispersal in plants: heterosperma pinnatum (asteraceae) adjusts its dispersal mode to escape from competition and water stress

    Oikos 123:225–231.

    https://doi.org/10.1111/j.1600-0706.2013.00715.x

    • Google Scholar
29. Book

    1. Matthysen E

    (2012)

    Multicausality of dispersal: a review

    In: Clobert J, Baguette M, Benton TG, Bullock JM, editors. Dispersal Ecology and Evolution. Oxford University Press. pp. 3–18.

    • Google Scholar
30. 1. Maurer KD
    2. Bohrer G
    3. Medvigy D
    4. Wright SJ
    5. Thompson K

    (2013) The timing of abscission affects dispersal distance in a wind-dispersed tropical tree

    Functional Ecology 27:208–218.

    https://doi.org/10.1111/1365-2435.12028

    • Google Scholar
31. 1. Meng Q
    2. Wang Q
    3. Zhao K
    4. Wang P
    5. Liu P
    6. Liu H
    7. Jiang L

    (2016) Hydroactuated configuration alteration of fibrous dandelion pappi: toward self-controllable transport behavior

    Advanced Functional Materials 26:7378–7385.

    https://doi.org/10.1002/adfm.201602596

    • Google Scholar
32. 1. Muller-Landau HC
    2. Wright SJ
    3. Calderón O
    4. Condit R
    5. Hubbell SP

    (2008) Interspecific variation in primary seed dispersal in a tropical forest

    Journal of Ecology 96:653–667.

    https://doi.org/10.1111/j.1365-2745.2008.01399.x

    • Google Scholar
33. 1. Nathan Ran
    2. Safriel UN
    3. Noy-Meir I

    (2001) Field validation and sensitivity analysis of a mechanistic model for tree seed dispersal by wind

    Ecology 82:374–388.

    https://doi.org/10.1890/0012-9658(2001)082[0374:FVASAO]2.0.CO;2

    • Google Scholar
34. 1. Nathan R
    2. Katul GG
    3. Horn HS
    4. Thomas SM
    5. Oren R
    6. Avissar R
    7. Pacala SW
    8. Levin SA

    (2002) Mechanisms of long-distance dispersal of seeds by wind

    Nature 418:409–413.

    https://doi.org/10.1038/nature00844

    • PubMed
    • Google Scholar
35. 1. Pazos GE
    2. Greene DF
    3. Katul G
    4. Bertiller MB
    5. Soons MB
    6. Jongejans E

    (2013) Seed dispersal by wind: towards a conceptual framework of seed abscission and its contribution to long-distance dispersal

    Journal of Ecology 101:889–904.

    https://doi.org/10.1111/1365-2745.12103

    • Google Scholar
36. Book

    1. Raffel M
    2. Willert CE
    3. Scarano F
    4. Kähler CJ
    5. Wereley ST
    6. Kompenhans J

    (2018) Particle Image Velocimetry

    Springer.

    https://doi.org/10.1007/978-3-319-68852-7

    • Google Scholar
37. 1. Rothert W

    (1895)

    On the hygroscopic tissue of the compositae pappus

    Bot Zentralblatt 63:320–324.

    • Google Scholar
38. 1. Saastamoinen M
    2. Bocedi G
    3. Cote J
    4. Legrand D
    5. Guillaume F
    6. Wheat CW
    7. Fronhofer EA
    8. Garcia C
    9. Henry R
    10. Husby A
    11. Baguette M
    12. Bonte D
    13. Coulon A
    14. Kokko H
    15. Matthysen E
    16. Niitepõld K
    17. Nonaka E
    18. Stevens VM
    19. Travis JMJ
    20. Donohue K
    21. Bullock JM
    22. Del Mar Delgado M

    (2018) Genetics of dispersal

    Biological Reviews of the Cambridge Philosophical Society 93:574–599.

    https://doi.org/10.1111/brv.12356

    • PubMed
    • Google Scholar
39. 1. Schippers P
    2. Jongejans E

    (2005) Release thresholds strongly determine the range of seed dispersal by wind

    Ecological Modelling 185:93–103.

    https://doi.org/10.1016/j.ecolmodel.2004.11.018

    • Google Scholar
40. 1. Seale M
    2. Nakayama N

    (2020) From passive to informed: mechanical mechanisms of seed dispersal

    The New Phytologist 225:653–658.

    https://doi.org/10.1111/nph.16110

    • PubMed
    • Google Scholar
41. 1. Seale M
    2. Kiss A
    3. Bovio S
    4. Viola IM
    5. Mastropaolo E
    6. Boudaoud A
    7. Nakayama N

    (2022) Dandelion pappus morphing is actuated by radially patterned material swelling

    Nature Communications 13:2498.

    https://doi.org/10.1038/s41467-022-30245-3

    • PubMed
    • Google Scholar
42. 1. Sentelhas PC
    2. Dalla Marta A
    3. Orlandini S
    4. Santos EA
    5. Gillespie TJ
    6. Gleason ML

    (2008) Suitability of relative humidity as an estimator of leaf wetness duration

    Agricultural and Forest Meteorology 148:392–400.

    https://doi.org/10.1016/j.agrformet.2007.09.011

    • Google Scholar
43. 1. Silvertown J
    2. Araya Y
    3. Gowing D
    4. Cornwell W

    (2015) Hydrological niches in terrestrial plant communities: a review

    Journal of Ecology 103:93–108.

    https://doi.org/10.1111/1365-2745.12332

    • Google Scholar
44. 1. Skarpaas O
    2. Auhl R
    3. Shea K

    (2006) Environmental variability and the initiation of dispersal: turbulence strongly increases seed release

    Proceedings. Biological Sciences 273:751–756.

    https://doi.org/10.1098/rspb.2005.3366

    • PubMed
    • Google Scholar
45. 1. Soons MB
    2. Heil GW
    3. Nathan R
    4. Katul GG

    (2004) Determinants of long-distance seed dispersal by wind in grasslands

    Ecology 85:3056–3068.

    https://doi.org/10.1890/03-0522

    • Google Scholar
46. 1. Soons MB
    2. Bullock JM

    (2008) Non-random seed abscission, long-distance wind dispersal and plant migration rates

    Journal of Ecology 96:581–590.

    https://doi.org/10.1111/j.1365-2745.2008.01370.x

    • Google Scholar
47. 1. Tackenberg O
    2. Poschlod P
    3. Kahmen S

    (2003) Dandelion seed dispersal: the horizontal wind speed does not matter for long-distance dispersal-it is updraft!

    Plant Biology 5:451–454.

    https://doi.org/10.1055/s-2003-44789

    • Google Scholar
48. Conference

    1. Taliev V

    (1894)

    On the hygroscopic tissue of the Compositae pappus

    Proc Soc Nat Imp Univ Kazan. pp. 1–38.

    • Google Scholar
49. 1. Tamme R
    2. Götzenberger L
    3. Zobel M
    4. Bullock JM
    5. Hooftman DAP
    6. Kaasik A
    7. Pärtel M

    (2014) Predicting species’ maximum dispersal distances from simple plant traits

    Ecology 95:505–513.

    https://doi.org/10.1890/13-1000.1

    • PubMed
    • Google Scholar
50. 1. Thielicke W
    2. Stamhuis EJ

    (2014) PIVlab – towards user-friendly, affordable and accurate digital particle image velocimetry in matlab

    Journal of Open Research Software 2:jors.

    https://doi.org/10.5334/jors.bl

    • Google Scholar
51. 1. Thompson SE
    2. Katul GG

    (2013) Implications of nonrandom seed abscission and global stilling for migration of wind-dispersed plant species

    Global Change Biology 19:1720–1735.

    https://doi.org/10.1111/gcb.12173

    • PubMed
    • Google Scholar
52. 1. Trakhtenbrot A
    2. Nathan R
    3. Perry G
    4. Richardson DM

    (2005) The importance of long-distance dispersal in biodiversity conservation

    Diversity and Distributions 11:173–181.

    https://doi.org/10.1111/j.1366-9516.2005.00156.x

    • Google Scholar
53. 1. Treep J
    2. de Jager M
    3. Kuiper LS
    4. Duman T
    5. Katul GG
    6. Soons MB

    (2018) Costs and benefits of non-random seed release for long-distance dispersal in wind-dispersed plant species

    Oikos 127:1330–1343.

    https://doi.org/10.1111/oik.04430

    • Google Scholar
54. 1. Vinnikov KY
    2. Robock A
    3. Speranskaya NA
    4. Schlosser CA

    (1996) Scales of temporal and spatial variability of midlatitude soil moisture

    Journal of Geophysical Research 101:7163–7174.

    https://doi.org/10.1029/95JD02753

    • Google Scholar
55. 1. Westerweel J
    2. Scarano F

    (2005) Universal outlier detection for Piv data

    Experiments in Fluids 39:1096–1100.

    https://doi.org/10.1007/s00348-005-0016-6

    • Google Scholar
56. 1. Wexler A
    2. Hasegawa S

    (1954) Relative humidity-temperature relationships of some saturated salt solutions in the temperature range 0 degree to 50 degrees C

    Journal of Research of the National Bureau of Standards 53:19.

    https://doi.org/10.6028/jres.053.003

    • Google Scholar
57. 1. Wilson JD
    2. Ward DP
    3. Thurtell GW
    4. Kidd GE

    (1982) Statistics of atmospheric turbulence within and above a corn canopy

    Boundary-Layer Meteorology 24:495–519.

    https://doi.org/10.1007/BF00120736

    • Google Scholar
58. 1. Wright SJ
    2. Trakhtenbrot A
    3. Bohrer G
    4. Detto M
    5. Katul GG
    6. Horvitz N
    7. Muller-landau HC
    8. Jones FA
    9. Nathan R

    (2008) Understanding strategies for seed dispersal by wind under contrasting atmospheric conditions

    PNAS 105:19084–19089.

    https://doi.org/10.1073/pnas.0802697105

    • Google Scholar
59. 1. Zhdanov O
    2. Blatt MR
    3. Zare-Behtash H
    4. Busse A

    (2021) Wind-evoked anemotropism affects the morphology and mechanical properties of arabidopsis

    Journal of Experimental Botany 72:1906–1918.

    https://doi.org/10.1093/jxb/eraa541

    • PubMed
    • Google Scholar

Author details

1. Madeleine Seale

   1. School of Biological Sciences, Institute of Molecular Plant Sciences, University of Edinburgh, Edinburgh, United Kingdom
   2. Centre for Synthetic and Systems Biology, University of Edinburgh, Edinburgh, United Kingdom
   3. School of Engineering, Institute for Integrated Micro and Nano Systems, University of Edinburgh, Edinburgh, United Kingdom
   4. Department of Plant Sciences, University of Oxford, Oxford, United Kingdom

   Contribution

   Data curation, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

2. Oleksandr Zhdanov

   1. James Watt School of Engineering, University of Glasgow, Glasgow, United Kingdom
   2. Laboratory of Plant Physiology and Biophysics, Bower Building, University of Glasgow, Glasgow, United Kingdom

   Contribution

   Formal analysis, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1742-9765

3. Merel B Soons

   Ecology & Biodiversity group, Utrecht University, Utrecht, Netherlands

   Contribution

   Software, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

4. Cathal Cummins

   1. School of Biological Sciences, Institute of Molecular Plant Sciences, University of Edinburgh, Edinburgh, United Kingdom
   2. Centre for Synthetic and Systems Biology, University of Edinburgh, Edinburgh, United Kingdom
   3. School of Engineering, Institute for Energy Systems, University of Edinburgh, Edinburgh, United Kingdom

   Present address

   1. Maxwell Institute for Mathematical Sciences, Department of Mathematics and Institute for Infrastructure and Environment, Heriot‐Watt University, Edinburgh, United Kingdom
   2. School of Energy, Geosciences, Infrastructure and Environment, Heriot‐Watt University, Edinburgh, United Kingdom

   Contribution

   Formal analysis, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

5. Erika Kroll

   School of Biological Sciences, Institute of Molecular Plant Sciences, University of Edinburgh, Edinburgh, United Kingdom

   Contribution

   Formal analysis, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8832-7208

6. Michael R Blatt

   Laboratory of Plant Physiology and Biophysics, Bower Building, University of Glasgow, Glasgow, United Kingdom

   Contribution

   Resources, Formal analysis, Supervision, Funding acquisition, Methodology, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

7. Hossein Zare-Behtash

   James Watt School of Engineering, University of Glasgow, Glasgow, United Kingdom

   Contribution

   Resources, Formal analysis, Supervision, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

8. Angela Busse

   James Watt School of Engineering, University of Glasgow, Glasgow, United Kingdom

   Contribution

   Resources, Formal analysis, Supervision, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

9. Enrico Mastropaolo (deceased)

   School of Engineering, Institute for Integrated Micro and Nano Systems, University of Edinburgh, Edinburgh, United Kingdom

   Contribution

   Conceptualization, Formal analysis, Supervision, Funding acquisition, Project administration

   Competing interests

   No competing interests declared

10. James M Bullock

    UK Centre for Ecology & Hydrology, Wallingford, United Kingdom

    Contribution

    Formal analysis, Funding acquisition, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

11. Ignazio M Viola

    School of Engineering, Institute for Energy Systems, University of Edinburgh, Edinburgh, United Kingdom

    Contribution

    Conceptualization, Formal analysis, Supervision, Funding acquisition, Methodology, Project administration, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-3831-8423

12. Naomi Nakayama

    1. School of Biological Sciences, Institute of Molecular Plant Sciences, University of Edinburgh, Edinburgh, United Kingdom
    2. Centre for Synthetic and Systems Biology, University of Edinburgh, Edinburgh, United Kingdom
    3. Centre for Science at Extreme Conditions, University of Edinburgh, Edinburgh, United Kingdom
    4. Department of Bioengineering, Imperial College London, South Kensington, United Kingdom

    Contribution

    Conceptualization, Resources, Formal analysis, Supervision, Funding acquisition, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

    For correspondence

    n.nakayama@imperial.ac.uk

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-9390-3545

• 1,962

  views

• 233

  downloads

• 6

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.