Ana Trakhtenbrot

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.

Mechanistic models of seed dispersal by wind

Over the past century, various mechanistic models have been developed to estimate the magnitude of seed dispersal by wind, and to elucidate the relative importance of physical and biological factors affecting this passive transport process. The conceptual development has progressed from ballistic models, through models incorporating vertically variable mean horizontal windspeed and turbulent excursions, to models accounting for discrepancies between airflow and seed motion. Over hourly timescales, accounting for turbulent fluctuations in the vertical velocity component generally leads to a power-law dispersal kernel that is censored by an exponential cutoff far from the seed source. The parameters of this kernel vary with the flow field inside the canopy and the seed terminal velocity. Over the timescale of a dispersal season, with mean wind statistics derived from an “extreme-value” distribution, these distribution-tail effects are compounded by turbulent diffusion to yield seed dispersal distances that are two to three orders of magnitude longer than the corresponding ballistic models. These findings from analytic models engendered explicit simulations of the effects of turbulence on seed dispersal using computationally intensive fluid dynamics tools. This development marks a bifurcation in the approaches to wind dispersal, seeking either finer resolution of the dispersal mechanism at the scale of a single dispersal event, or mechanistically derived analytical dispersal kernels needed to resolve long-term and large-scale processes such as meta-population dynamics and range expansion. Because seed dispersal by wind is molded by processes operating over multiple scales, new insights will require novel theoretical tactics that blend these two approaches while preserving the key interactions across scales.

Understanding strategies for seed dispersal by wind under contrasting atmospheric conditions

Traits associated with seed dispersal vary tremendously among sympatric wind-dispersed plants. We used two contrasting tropical tree species, seed traps, micrometeorology, and a mechanistic model to evaluate how variation in four key traits affects seed dispersal by wind. The conceptual framework of movement ecology, wherein external factors (wind) interact with internal factors (plant traits) that enable movement and determine when and where movement occurs, fully captures the variable inputs and outputs of wind dispersal models and informs their interpretation. We used model calculations to evaluate the spatial pattern of dispersed seeds for the 16 factorial combinations of four traits. The study species differed dramatically in traits related to the timing of seed release, and a strong species by season interaction affected most aspects of the spatial pattern of dispersed seeds. A rich interplay among plant traits and seasonal differences in atmospheric conditions caused this interaction. Several of the same plant traits are crucial for both seed dispersal and other aspects of life history variation. Observed traits that limit dispersal are likely to be constrained by their life history consequences.

ENVIRONMENTAL CLUSTER ANALYSIS AS A TOOL FOR SELECTING COMPLEMENTARY NETWORKS OF CONSERVATION SITES

Identifying networks of sites that efficiently represent the biotic diversity of larger regions is a prerequisite for effective conservation planning. Recent approaches for systematic conservation planning rely on the idea of complementarity, i.e., the selection of networks of sites that complement each other in their species composition. A major obstacle in applying these methods is the need for detailed data on species distribution, which is usually not available. In this study, we test the hypothesis that cluster analysis based on environmental variables (rainfall, temperature, and lithology) can be used to identify sets of complementary sites that efficiently represent regional species diversity. The performance of this approach (which we term environmental cluster analysis, ECA) is evaluated using an extensive database of the flora of Israel as a test case. Our results indicate that the ECA performed significantly better than a random null model in repre- senting regional floristic diversity. Moreover, sites representing regions (clusters) defined by the ECA were more efficient in capturing the floristic diversity of Israel than sites representing the floristic regions of the area. In contrast to our expectation, the efficiency of the ECA was particularly pronounced in the analysis of rare species. The main mechanism behind the superiority of the ECA was its ability to identify sets of sites characterized by high turnover of species, rather than individually rich sites. The overall results suggest that ECA may serve as an important tool for the identification of complementary networks of conservation sites.

Dissipation Intermittency Increases Long-Distance Dispersal of Heavy Particles in the Canopy Sublayer

The dispersion of heavy particles such as seeds within canopies is evaluated using Lagrangian stochastic trajectory models, laboratory, and field experiments. Inclusion of turbulent kinetic energy dissipation rate intermittency is shown to increase long-distance dispersal (LDD) by contributing to the intermittent ejection of particles to regions of high mean velocity outside the canopy volume. Model evaluation against controlled flume experiments, featuring a dense rod canopy, detailed flow measurements, and imaged trajectories of spherical particles, demonstrates that superimposing a terminal velocity on the fluid velocity is insufficient to determine the particle dispersal kernel. Modifying the trajectory model by adding dissipation intermittency is found to be significant for dispersal predictions along with the addition of inertial and crossing trajectories’ effects. Comparison with manual seed-release experiments in a forest using wind-dispersed seeds shows that the model captures most of the measured kernels when accepted uncertainties in plant area index and friction velocity are considered. Unlike the flume experiments, the model modifications for several wind-dispersed seeds have minor effects on short-distance dispersal. A large increase was predicted in LDD when including dissipation intermittency for the forest experiment. The main results suggest that fitting or calibrating models to the ‘main body’ of measured kernels may not offer extrapolating foresight to LDD predictions. As inertial effects were found mostly negligible in the field conditions here, the extended trajectory model requires specifying only the seed’s terminal velocity and a constant variance of the normalized dissipation rate. Therefore, the proposed modifications can be readily applied to classical trajectory models so as to improve LDD predictions.