Significance Fungi move between habitats by dispersing small spores through the atmosphere. We ask what causes some species to release spores at a specific time every day versus irregularly. We find that timing of spore release dictates how long spores remain in the atmosphere before returning to the ground: Spores released at night are likely to travel for hours while spores released during the day may linger for days. Drivers are stronger in lower, warmer latitudes. Because spores in the open atmosphere are likely to die from prolonged exposure to light and air, the timing of spore release will impact survival. We have discovered a constraint likely to shape observed patterns of spore liberation.

Abstract Fungi disperse spores to move across landscapes and spore liberation takes different patterns. Many species release spores intermittently; others release spores at specific times of day. Despite intriguing evidence of periodicity, why (and if) the timing of spore release would matter to a fungus remains an open question. Here we use state-of-the-art numerical simulations of atmospheric transport and meteorological data to follow the trajectory of many spores in the atmosphere at different times of day, seasons, and locations across North America. While individual spores follow unpredictable trajectories due to turbulence, in the aggregate patterns emerge: Statistically, spores released during the day fly for several days, whereas spores released at night return to ground within a few hours. Differences are caused by intense turbulence during the day and weak turbulence at night. The pattern is widespread but its reliability varies; for example, day/night patterns are stronger in southern regions. Results provide testable hypotheses explaining both intermittent and regular patterns of spore release as strategies to maximize spore survival in the air. Species with short-lived spores reproducing where there is strong turbulence during the day, for example in Mexico, maximize survival by releasing spores at night. Where cycles are weak, for example in Canada during fall, there is no benefit to releasing spores at the same time every day. Our data challenge the perception of fungal dispersal as risky, wasteful, and beyond control of individuals; our data suggest the timing of spore liberation may be finely tuned to maximize fitness during atmospheric transport.

A careful reading of the natural history of fungal spore liberation reveals nontrivial patterns. Spores may be released at specific times of day, perhaps driven by an internal clock, or may be released irregularly, perhaps triggered by local fluctuations in the environment. Some fungi display regular, nearly circadian rhythms (Table 1). Spores of powdery mildews are often released at midday (ref. 1 and references therein), while other fungi release spores at night or in the early morning, e.g., the plant pathogens Mycosphaerella spp. (causing leaf blotch in wheat and black leaf streak in bananas) (2⇓–4), Giberella zeae (causing rots in cereals) (5, 6), and Venturia inequalis (the apple scab) (7, 8). Tropical fungi also appear to release spores at night (9, 10). Or patterns may be more complex: The causal agent of blackleg, Leptosphaeria maculans, seems to follow different diurnal rhythms in different regions and seasons, with most spores liberated in the morning in England (11) or at night in Canada (12) or in early afternoon in Western Australia (13). In other studies of the same species in Western Australia, spores appear to be released intermittently, regardless of location or month (14, 15).

Table 1. Data on spore release patterns compiled from the literature

Many authors have attempted to connect the diversity of spore liberation patterns to specific environmental cues by testing for correlations between liberation and local temperature, humidity and wind speed, etc. (reviewed in refs. 1, 16, and 17). Correlations are sometimes found; for example, asexual spores of Helminthosporium maydis (now Bipolaris maydis) and Alternaria spp. are detached from supporting structures by intense wind gusts (e.g., refs. 18⇓–20). Often there are no obvious correlations. Despite a wealth of data describing different diurnal or nocturnal patterns of spore liberation and a nascent awareness of their significance for spore dispersal (21⇓–23) the drivers of observed patterns remain obscure (13, 16).

Dispersal through the atmosphere is assumed to be both common and dangerous. Cellular material makes up about 25% of atmospheric particulate and 3 to 11% by weight (24, 25), with crucial implications for human health, agriculture, and climate. But only a small fraction of the estimated ∼ 1 0 21 cells riding the atmosphere annually survive the journey. The main threats limiting the lifespan of spores are exposure to UV light and harsh temperatures and humidities (26, 27).

Research on atmospheric dispersal often focuses on the reach of a spore; in other words, biologists pay attention to distances traveled and seek to understand how far a spore will move before settling. Long-distance dispersal remains a particular concern (22, 28⇓–30). But a spore that dies in the atmosphere will have zero fitness, even if it ultimately settles back to the ground, and an equally important facet of successful dispersal is survival. Spores in the atmosphere may survive for days or weeks or possibly longer (31⇓⇓–34). Careful data tracking the lifetimes of individual spores in the air are lacking; spores are not easy to observe or manipulate in nature. But laboratory experiments suggest the spores of many other species are in fact quite sensitive to sunlight. The mean half-life of the thin-walled basidiospores of wood decay fungi is 1.5 h after exposure to simulated sunlight (26). Twelve hours of natural sunlight kill 99% of the conidia of Botrytis cinerea (35). Rhizoctonia solani basidiospores appear unable to tolerate more than 60 min of exposure (36) (Table 2).

Table 2. Data on spore longevity compiled from the literature

While a spore must settle back to the ground before it dies from exposure, turbulence makes the duration of a spore’s journey in the atmosphere inherently unpredictable: Two identical spores released from a single sporocarp may take radically different paths (37). But the average flight time for a group of spores released simultaneously from the same location may follow a specific pattern, which is widely studied in the context of aerosol science (and named residence time or flight time; e.g., refs. 38 and 39). We use principles taken from atmospheric science to model spore flight time. The underlying dynamics are well understood: The flight time of large aerosols (diameter 5 to 20 μm, similar to a typical fungal spore) results from a balance between two opposing forces: gravity causes particles to sediment downward, and turbulence keeps them aloft (38, 39). Hence residence times for larger particles are shorter (38, 40, 41). To facilitate quantitative models, aerosol science often assumes that the dynamics have reached equilibrium, for example assuming particles take off from a large area consistently over time. But this assumption cannot hold for fungi, which are discrete organisms distributed in irregular patches, often reproducing at one or a few time points. While a recent model (42) considers particles released at one time in the idealized case of a vertically infinite neutral atmosphere (where the intensity of turbulence increases linearly with altitude and does not change in time), it cannot capture how flight times vary with geography and season. More realistic analyses of spore flight time considering variations of the state of the atmosphere in space and time require massive numerical simulations using meteorological data.

By combining state-of-the-art numerical simulations of atmospheric particle transport drawn from real weather data with simplified models of atmospheric turbulence, and by explicitly considering spore lifespan, we discover that the timing of spore liberation dramatically influences the reach of living spores. Manipulating the timing of spore liberation will dramatically influence fitness. We find the following: 1) The average duration of a spore’s flight depends on when it is released. 2) By explicitly defining fitness as the fraction of spores that return to ground while alive (within their lifetimes) we discover that patterns of spore liberation shape fitness. 3) Turbulence dominates vertical transport and thus dictates flight time in realistic conditions. 4) Turbulence is cyclical and typically stronger during the day versus at night. But the strength and reliability of its diurnal cycle vary with geography and season. 5) When and where the diurnal cycle is strong, releasing spores at specific times of day will allow a fungus to shape the duration of a spore’s journey through the atmosphere. But if the cyclical pattern of turbulence is unreliable, a direct measure of the local intensity of turbulence will be a better guide than time of day to shaping flight time and maximizing fitness. Results provide a set of testable hypotheses to understand observed patterns of spore release: 1) A regular, rhythmic release of spores is beneficial for species living in regions where the atmosphere cycles regularly. 2) For these species, short-lived spores should be released at night, while long-lived spores can be released during the day. 3) Intermittent patterns of spore release may emerge as an adaptation to an environment where the diurnal cycle of turbulence is disrupted.

Atmospheric transport is assumed to be unpredictable, and fungi are assumed to have little control over dispersal, but spore discharge itself appears finely tuned to maximize individual fitness (43⇓⇓⇓⇓–48). Our results demonstrate that although after leaving a sporocarp individual spores follow unpredictable trajectories, a fungus may still maximize survival of its progeny in the atmosphere by strategizing the timing of spore release.

Discussion Our results demonstrate that the time of the day when spores are released dramatically affects their fitness. Spores released during the day tend to be transported higher up in the atmosphere and return to the ground after several days. But many spores can survive only a few hours in the open atmosphere and would die before returning to the ground. Hence, we predict that to maximize survival short-lived spores should be typically launched at night. Many tropical fungi release spores preferentially at night or in the early morning (9, 10), as do pathogens including Mycosphaerella fijiensis (2, 3), and R. solani (61) (Table 1). Measures of M. fijiensis and R. solani’s survival after exposure to sunlight suggest spores’ lifetimes in the open atmosphere are in fact very short, consistent with our prediction (36, 62). But why are some species releasing spores during the day? Our results show that fitness peaks at night for short-lived spores; however, fitness flattens out as spores are more long lived (see SI Appendix, Fig. S2 for results with τ = 2 d and τ = 2 wk). In other words, spores that are adapted to survive in the atmosphere for weeks can be released at any time of the day including when turbulence is maximum during the day. Because survival in the air is not limiting, long-lived spores may be released in conditions that maximize a different aspect of fitness, for example, distance traveled. To maximize distance traveled, these spores should be released in strong turbulent conditions, typically found during the day. Consistent with this hypothesis, asexual spores of various species including Alternaria spp. are liberated by winds in the afternoon (Table 1); Alternaria spores may also be long lived (63) [e.g., Alternaria porri spores can survive for hundreds of days (64)]. B. cinerea also appears to liberate spores predominantly during the day (65, 66); however, its conidia do not appear particularly long lived (35): This species may have evolved different adaptations to return to the ground in timely fashion. Releasing spores at a specific time of the day modulates the expected fraction of spores surviving the journey, suggesting that fungi may tie spore liberation to an internal clock. Interestingly, spore production in species like Neurospora crassa is indeed regulated by a circadian clock (67, 68), but whether this provides any selective advantage has been hitherto unclear. Our results suggest that producing spores at certain times of the day may be instrumental to releasing them at times that maximize chances of survival. A clock is not always a useful predictor of fitness: In cases where the atmospheric cycle is disrupted, turbulence varies irregularly, and releasing spores at the same time every day may lead to massive losses. In regions where the cycle is disrupted, short-lived spores should be released intermittently, whenever turbulence is weak, whereas long-lived spores can be released in conditions of intense turbulence. Interestingly, intermittent patterns of spore liberation are observed for many species (ref. 17 and references therein). A particularly clear correlation is observed between long-lived asexual spores and wind. Asexual spores of the species H. maydis (now B. maydis) are among those considered to be capable of withstanding atmospheric conditions experienced during continental-scale dispersal (69). Abscission of these spores occurs only when wind velocity exceeds 10 m/s (70). This is consistent with our hypothesis, because large air speed close to a substrate is usually associated to intense turbulent wind gusts. Whether other meteorological variables, e.g., temperature and humidity, are sensed by the fungi and how they dictate spore release is still poorly understood (16). Whether a clock-based or a sensation-based strategy is more effective will depend on the environment that a species is adapted to live within. To compare these two alternative strategies, we introduced the parameter p w which distinguishes between regions with regular vs. intermittent atmospheric conditions. If patterns of spore liberation are shaped by the need to maximize spore survival in the atmosphere, species adapted to regions where the diurnal cycle is strong (where p w ∼ 1 ) may release spores at the same time every day, for example releasing short-lived spores at night. In these regions, spores may be released at the same time every day either by using time of the day as a cue or by responding to, e.g., temperature or wind speed, which will also vary regularly. But species adapted to regions with a weak diurnal cycle (where p w ∼ 0 ) will be more likely to liberate spores intermittently in response to local cues that provide information about turbulence intensity directly. The value of p w that marks the transition from clock-based to sensation-based strategies will vary from species to species, but the qualitative pattern is robust. Globally distributed fungi may adapt to local environments by evolving different patterns of spore liberation in different parts of the world. The genetic model N. crassa would provide an ideal test of whether species can evolve different spore liberation patterns at different latitudes. It grows as far north as Alaska but also in New Mexico and Louisiana and near the equator in places like Indonesia, India, and the Caribbean. Spores are produced according to an intrinsic circadian rhythm, but whether the fungus uses different spore liberation strategies across its range is to the best of our knowledge an unasked question. Spore dispersal is generally regarded as dangerous and fundamentally wasteful. Our results demonstrate that although fungi do lose control over individual spores, they can still maximize fitness in the atmosphere by manipulating the timing of spore release. Indeed, the timing of spore liberation dictates the fraction of spores that survive their journey in the open atmosphere. In other words, the ensemble statistics of spore flight time keep memory of the initial conditions that spores meet when they first reach the open air. These results partially reconcile two contrasting aspects of fungal spore dispersal: microscopic optimization vs. large-scale uncertainty. On the one hand, at micrometer to centimeter scales, fungi have evolved fascinating adaptations to maximize the efficiency of the microscopic mechanism of discharge (reviewed in ref. 48). The ascomycetes, an enormously large and diverse phylum, fire sexual spores from a pressurized cell finely tuned to minimize dissipation (43⇓⇓⇓–47). The basidiomycetes, a similarly diverse phylum, eject spores using a surface tension catapult and achieve precise control of spore range immediately after discharge (71⇓⇓⇓⇓⇓–77). These adaptations suggest spore dispersal is under considerable selective pressure. But optimization during spore discharge appears a stark contrast to the fact that, once spores reach dispersive airflows, their fate is dictated by a series of stochastic events and appears entirely out of control of any individual parent. Fungi may acknowledge uncertainty by producing extraordinarily large numbers of propagules; fungal migration appears wasteful and fundamentally different from the ordered migration of mammals (78). But fungi may also use their exquisite control of discharge to release spores when the chances of spores’ survival in the atmosphere are greatest. Our study points to a previously unsuspected connection between patterns of diurnal or nocturnal release and longevity. But release and longevity are rarely considered simultaneously. There is a great need for data, and these data may confirm fungal movements through the atmosphere as less chaotic than they appear.

Materials and Methods Lagrangian Simulations with Meteorological Data Using HYSPLIT. To compute the statistics of flight times, we follow many particles released from a given location at different times using the HYSPLIT model (79), an open source code developed at the Air Resource Laboratory of the NOAA (ARL-NOAA) in the United States. We modeled spores as passive tracers with an additional gravitational settling velocity. Spores are transported by the wind, whose velocity is obtained from meteorological datasets on a large-scale grid with resolution 32 km, and turbulence on smaller scales is modeled as a correlated stochastic process, so spore trajectories can be computed through a Langevin equation d P ( t ) d t = V meteo [ P ( t ) , t ] + V ′ [ P ( t ) , t ] + V G , [1]where P is the three-dimensional instantaneous location of a spore, V meteo is the large-scale wind velocity from the North American Regional Reanalyses (NARR) (80), V ′ is a realization of the stochastic turbulent fluctuation (81), and V G is the gravitational settling velocity. NARR is an extended dataset of meteorological variables on a regular grid covering the whole North American continent resulting from a matching procedure between outputs of numerical models and sparse observations of many atmospheric variables. NARR data are given on a Lambert conformal grid with 309 × 237 horizontal points and 24 levels on a vertical pressure-sigma coordinates system. The nominal horizontal resolution is 32 km. Starting from 1979 to today the state of the atmosphere is available at a time resolution of 3 h. The variance of turbulent fluctuations in the vertical direction depends on height and is modeled with semiempirical expressions that vary with the state of the atmosphere, i.e., stable vs. unstable (see SI Appendix for details) (82, 83). The specific choice for the closure as well as the dependence on altitude only weakly affects fitness (SI Appendix). Spores settle with a constant downward speed or gravitational settling velocity, V G . Fitness at 6 h is insensitive to V G for typical spores, V G ≲ 12 mm/s (SI Appendix, Fig. S3). As a reference, this value of gravitational settling velocity corresponds to a sphere with the density of water and diameter 20 μm. Gravitational settling affects the process of escape into the stratosphere relevant for fitness of long-lived spores (SI Appendix, Figs. S1 and S2). Finally, dry deposition to the ground is computed assuming that the flux of spores j ( x , t ) to the ground is proportional to concentration of spores close to the soil θ ( x , t ) : j ( x , t ) = V d θ ( x , t ) | z = 0 , where V d , the deposition velocity, was taken equal to the gravitational settling, as appropriate for spherical particles larger than about 1 μm (38). A more detailed modeling of deposition on the canopy including dependence on spore shape is left for future studies. Fig. 3 shows that deposition velocities affect fitness quantitatively, but do not affect the qualitative patterns. In our model, the gravitational/deposition velocity is the only parameter of the dynamics that depends on the fungal species. Wet deposition may decrease the flight time in daytime releases, but does not change the general conclusions unless fungi are able to release spores right before rain. There is some evidence that this may be true for some species, and we will treat this fascinating possibility elsewhere. One-Dimensional Model of Spore Transport. In the Eulerian framework, the concentration of passive tracers advected by a short-correlated velocity field in one dimension follows the well-known Fokker–Plank equation ∂ t θ ( z , t ) = ∂ z [ D ( z ) ∂ z θ ] + v D ∂ z θ (37, 84), where θ ( z , t ) is the average concentration of spores at altitude z and time t; D ( z ) is the vertical eddy diffusivity, for which we use the closures implemented in the HYSPLIT simulations (SI Appendix); and v D is the sedimentation velocity. We compared fitness obtained through this simplified one-dimensional model that neglects the horizontal dynamics entirely to the results of the full HYSPLIT model. The one-dimensional model captures well the importance of stability in determining fitness and reproduces the oscillations observed in the full simulations. We impose reflecting boundary conditions at the top of the domain (25 km), and we remove a fraction of spores localized at z < 50 m according with the deposition velocity, as done by the HYSPLIT model. We adopt a finite-volume scheme with regular cells of Δ z = 5 m and a Runge–Kutta algorithm of fourth order for time marching, with time step Δ t chosen according to the Courant criterion Δ t = min D i Δ z 2 8 D i , where D i = D ( z i ) is the eddy diffusivity evaluated at the center of the ith vertical cell. Regression. To determine whether time of the day or vertical turbulence holds the most reliable information about fitness, we perform nonparametric regression using regularized least squares with kernels (85⇓–87). We target a function that predicts fitness χ from input data x, where x represents either time of day or turbulence intensity at spore release. A kernel function defines similarity between different input points; here we use a Gaussian kernel k ( x , x ′ ) = e − ‖ x − x ′ ‖ 2 / ( 2 π β 2 ) . Given a training set ( x , χ ) = ( x 1 , . . , x N , χ 1 , … , χ N ) , the solution to the regularized least-squares problem predicts fitness for a new value of the input data x new , f ( x n e w ) = k ( x n e w , x ) ( K + λ n I ) − 1 χ , [2]where K is the matrix whose entries are the values of the kernel function calculated over the training points K i j = k ( x i , x j ) . We use Eq. 2 to predict fitness and evaluate the error as the square difference between the prediction f ( x new ) and the actual value of fitness computed through our HYSPLIT simulations, χ new . We choose the hyperparameters of the model, λ and β, to minimize the validation error using fourfold cross-validation. We split our 9,600 simulations (10 locations × 4 mo × 30 d × 8 releases per day) into 240 test points (1 location × 1 mo × 30 d × 8 releases per day) and a random subsample from the remaining 9,360 simulations was split 3:1 between training and validation. We use a random subsample of 1,000 (for t) and 1,500 (for w) simulations. We compute the mean square error over the validation set using Eq. 2 and repeat the procedure varying systematically the hyperparameters to identify the region in the parameter space that provides minimum validation error. We then compute the mean square error on the test data and we repeat the procedure over the 40 different ways to split the dataset into test and training, and we average to obtain the test error. The difference in the test error, defined as < ( χ test − f ( x test ) ) 2 > , for x = time of day and x = turbulence intensity, with the hyperparameters selected as above, is plotted in Fig. 5F, normalized with the variance of fitness calculated over the test set. Data Availability. All data and scripts are available in SI Appendix, and the list and content of files are described in SI Appendix. Simulations of spore transport in the atmosphere are generated through the HYSPLIT transport and dispersion model, freely available from the NOAA ARL: https://www.ready.noaa.gov/HYSPLIT.php. Data on turbulence intensity are obtained from the National Centers for Environmental Prediction (NCEP) Reanalysis dataset, which is freely available from the NOAA/Office of Oceanic and Atmospheric Research (OAR)/Earth System Research Laboratory (ESRL) Physical Sciences Division (PSD), Boulder, CO: https://www.esrl.noaa.gov/psd/.

Acknowledgments This work was supported by the Agence Nationale de la Recherche Investissements d’Avenir Université Côte d’Azur IDEX project JEDI ANR-15-IDEX-01; by CNRS Projet International de cooperation scientifique “2FORECAST”; by the Thomas Jefferson Fund, a program of the French American Cultural Exchange Foundation; and by National Institute of Food and Agriculture/US Department of Agriculture Hatch project 1013478. We gratefully acknowledge F. Cassola for his suggestions on how to set up the HYSPLIT model; and support for computational resources from Istituto Nazionale di Fisica Nucleare and Consorzio Interuniversitario del Nord-Est per il Calcolo Automatico; and the NOAA Air Resources Laboratory for the provision of the HYSPLIT transport and dispersion model. NCEP Reanalysis data were provided by the NOAA/OAR/ESRL PSD, Boulder, CO, from their website at https://www.esrl.noaa.gov/psd/.

Footnotes Author contributions: D.L.O., J.G., A.M., A.P., and A.S. designed research; D.L.O., J.G., A.M., A.P., and A.S. performed research; D.L.O. and A.S. contributed new reagents/analytic tools; D.L.O. and A.S. analyzed data; and D.L.O., J.G., A.M., A.P., and A.S. wrote the paper.

The authors declare no competing interest.

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