Ran Nathan

Spatial patterns of seed dispersal, their determinants and consequences for recruitment

Growing interest in spatial ecology is promoting new approaches to the study of seed dispersal, one of the key processes determining the spatial structure of plant populations. Seed-dispersion patterns vary among plant species, populations and individuals, at different distances from parents, different microsites and different times. Recent field studies have made progress in elucidating the mechanisms behind these patterns and the implications of these patterns for recruitment success. Together with the development and refinement of mathematical models, this promises a deeper, more mechanistic understanding of dispersal processes and their consequences.

Mechanisms of long-distance dispersal of seeds by wind

Long-distance dispersal (LDD) is central to species expansion following climate change, re-colonization of disturbed areas and control of pests. The current paradigm is that the frequency and spatial extent of LDD events are extremely difficult to predict. Here we show that mechanistic models coupling seed release and aerodynamics with turbulent transport processes provide accurate probabilistic descriptions of LDD of seeds by wind. The proposed model reliably predicts the vertical distribution of dispersed seeds of five tree species observed along a 45-m high tower in an eastern US deciduous forest. Simulations show that uplifting above the forest canopy is necessary and sufficient for LDD, hence, they provide the means to define LDD quantitatively rather than arbitrarily. Seed uplifting probability thus sets an upper bound on the probability of long-distance colonization. Uplifted yellow poplar seeds are on average lighter than seeds at the forest floor, but also include the heaviest seeds. Because uplifting probabilities are appreciable (as much as 1–5%), and tree seed crops are commonly massive, some LDD events will establish individuals that can critically affect plant dynamics on large scales.

Mechanisms of long-distance seed dispersal

Growing recognition of the importance of long-distance dispersal (LDD) of plant seeds for various ecological and evolutionary processes has led to an upsurge of research into the mechanisms underlying LDD. We summarize these findings by formulating six generalizations stating that LDD is generally more common in open terrestrial landscapes, and is typically driven by large and migratory animals, extreme meteorological phenomena, ocean currents and human transportation, each transporting a variety of seed morphologies. LDD is often associated with unusual behavior of the standard vector inferred from plant dispersal morphology, or mediated by nonstandard vectors. To advance our understanding of LDD, we advocate a vector-based research approach that identifies the significant LDD vectors and quantifies how environmental conditions modify their actions.

DETERMINANTS OF LONG-DISTANCE SEED DISPERSAL BY WIND IN GRASSLANDS

Long-distance seed dispersal is an important topic in ecology, but notoriously difficult to quantify. Previous modeling approaches have failed to simulate long-distance dispersal, and it has remained unclear which mechanisms determine long-distance dispersal and what their relative importance is. We simulated wind dispersal of grassland plant seeds with four mechanistic models of increasing complexity and realism to assess which processes and which attributes of plants and their environment determine dispersal distances. We compared simulation results of the models to each other and to data from field dispersal experiments. The more complex and realistic models predicted short-distance dispersal more accurately and were the only models able to simulate long-distance dispersal. The model comparisons showed that autocorrelated turbulent fluctuations in vertical wind velocity are the key mechanism for long-distance dispersal. Seed dispersal distances are longest under high wind velocity conditions, when mechanically produced turbulent air movements are large. Under very low wind velocity conditions seeds are dispersed farther when there is more surface heating, but never as far as during strong wind events. Model sensitivity analyses showed that mean horizontal wind velocity, seed release height, and vegetation height are crucial determinants of dispersal potential and dispersal distances. Between plant species (but not within a species), seed terminal velocity is an additional important determinant of long-distance dispersal. These results imply that seed release height is the most important plant-controlled dispersal parameter for grassland plants, and that the structure of the local vegetation greatly affects dispersal distances. Thus, management plans for grasslands should take into account that changes in vegetation structure, e.g., due to eutrophication, can reduce the seed dispersal ability of wind-dispersed plant species.

Mechanistic Analytical Models for Long‐Distance Seed Dispersal by Wind

We introduce an analytical model, the Wald analytical long‐distance dispersal (WALD) model, for estimating dispersal kernels of wind‐dispersed seeds and their escape probability from the canopy. The model is based on simplifications to well‐established three‐dimensional Lagrangian stochastic approaches for turbulent scalar transport resulting in a two‐parameter Wald (or inverse Gaussian) distribution. Unlike commonly used phenomenological models, WALD’s parameters can be estimated from the key factors affecting wind dispersal—wind statistics, seed release height, and seed terminal velocity—determined independently of dispersal data. WALD’s asymptotic power‐law tail has an exponent of −3/2, a limiting value verified by a meta‐analysis for a wide variety of measured dispersal kernels and larger than the exponent of the bivariate Student t‐test (2Dt). We tested WALD using three dispersal data sets on forest trees, heathland shrubs, and grassland forbs and compared WALD’s performance with that of other analytical mechanistic models (revised versions of the tilted Gaussian Plume model and the advection‐diffusion equation), revealing fairest agreement between WALD predictions and measurements. Analytical mechanistic models, such as WALD, combine the advantages of simplicity and mechanistic understanding and are valuable tools for modeling large‐scale, long‐term plant population dynamics.

The challenges of studying dispersal

Abstract The 2001 Annual Symposium of the British Ecological Society on Dispersal was held at the University of Reading, UK, from 3–5 April 2001.

Spread of North American wind-dispersed trees in future environments.

Despite ample research, understanding plant spread and predicting their ability to track projected climate changes remain a formidable challenge to be confronted. We modelled the spread of North American wind-dispersed trees in current and future (c. 2060) conditions, accounting for variation in 10 key dispersal, demographic and environmental factors affecting population spread. Predicted spread rates vary substantially among 12 study species, primarily due to inter-specific variation in maturation age, fecundity and seed terminal velocity. Future spread is predicted to be faster if atmospheric CO(2) enrichment would increase fecundity and advance maturation, irrespective of the projected changes in mean surface windspeed. Yet, for only a few species, predicted wind-driven spread will match future climate changes, conditioned on seed abscission occurring only in strong winds and environmental conditions favouring high survival of the farthest-dispersed seeds. Because such conditions are unlikely, North American wind-dispersed trees are expected to lag behind the projected climate range shift.

Long-distance dispersal of tree seeds by wind

Some mechanisms that promote long-distance dispersal of tree seeds by wind are explored. Winged seeds must be lifted above the canopy by updrafts to have a chance of further dispersal in high velocity horizontal winds aloft or in landscape-scale convection cells. Shear-induced turbulent eddies of a scale up to one-third of canopy height provide a lifting mechanism. Preliminary data suggest that all seeds of a given species may be viable candidates for uplift and long-distance dispersal, despite the evidence that slow-falling seeds are dispersed farther under any given wind conditions. Turbulence is argued more often and more extensively to advance long-distance dispersal than to retard it. Seeds may take advantage of ‘Bernoulli sailing’ to move with faster than average winds. Elasticity of branches and trees may play a role in regulating the release of seeds into unusually favorable winds. Dispersal is at least biphasic, and the study of long-distance dispersal calls for mixed models and mixed methods of gathering data.