No abstract

Dispersal is universally considered important for biodiversity conservation. However, the significance of long-as opposed to short-distance dispersal is insufficiently recognized in the conservation context. Long-distance dispersal (LDD) events, although typically rare, are crucial to population spread and to maintenance of genetic connectivity. The main threats to global biodiversity involve excessive LDD of elements alien to ecosystems and insufficient dispersal of native species, for example, because of habitat fragmentation. In this paper, we attempt to bridge the gap in the treatment of LDD by reviewing the conservation issues for which LDD is most important. We then demonstrate how taking LDD into consideration can improve conservation management decisions.

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 Electronic supplementary material The online version of this article (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.

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.atmospheric turbulence ͉ conditional seed release ͉ Coupled Eulerian-Lagrangian closure (CELC) model ͉ long distance dispersal ͉ tropical forest S eed dispersal allows plants to colonize new habitats, reach sites where resources favor regeneration, and escape pests and competition with siblings and mother and sets the spatial template for all post dispersal processes (1, 2). A mechanistic understanding of seed dispersal could lead to progress on many fronts but requires models that recreate the complex interactions between plants and seed dispersal vectors. Mechanistic models are perhaps most advanced for seeds dispersed by wind (3). We use a wind dispersal model developed and validated for forests and grasslands (4-6) to compare spatial patterns of seed dispersal for factorial combinations of four key plant traits observed for two contrasting tropical tree species. These comparisons, made within the conceptual framework provided by movement ecology (7), provide insight into the complex interplay between atmospheric conditions and plant traits that influence seed dispersal by wind.Seed fate motivates seed dispersal through natural selection (2). Wind dispersal models have traditionally focused on a single aspect of seed fate, the distance moved from the mother (3). Long dispersal distances sample more potential regeneration sites and minimize negative interactions with siblings and mother. The implications of coincident arrival in close proximity have been overlooked for wind-dispersed seeds (but see ref. 8 for animal-dispersed seeds). Coincident arrival of siblings increases the potential for sibling competition and pest facilitation, reduces the number of potential regeneration sites reached, and leads to future inbreeding among adults. Thus, coincident arrival impacts seed fate negatively. Dispersal distance an...

Understanding and predicting complex biological systems are best accomplished through the synthesis and integration of information across relevant spatial, temporal and thematic scales. We propose that mechanistic transport models, which integrate atmospheric turbulence with information on relevant biological attributes, can effectively incorporate key elements of aerial transport processes at scales ranging from a few centimetres and fractions of seconds, to hundreds of kilometres and decades. This capability of mechanistic models is critically important for modelling the flow of organisms through the atmosphere because diverse aerial transport processes -such as pathogen spread, seed dispersal, spider ballooning and bird migrationare sensitive to the details of small-scale short-term turbulent deviations from the mean airflow. At the same time, all these processes are strongly influenced by the typical larger-scale variation in landscape structure, through its effects on wind flow patterns. We therefore highlight the useful coupling of detailed atmospheric models such as large eddy simulations (LES), which can provide a high-resolution description of turbulent airflow, with regional atmospheric models, which can capture the effects of landscape heterogeneity at various scales. Further progress in computational fluid dynamics (CFD) will enable rigorous exploration of transport processes in heterogeneous landscapes. KeywordsAtmospheric models, biological transport, computational fluid dynamics (CFD), large-eddy simulations (LES), long-distance dispersal, turbulence.

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 representing 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.

No abstract

Most seeds of most plant species are dispersed over distances shorter than a few dozen metres, and only very few seeds travel over long distances. While the long‐distance dispersal (LDD) of seeds is, thus, typically rare, it has disproportionately large effects on the long‐term and large‐scale dynamics of plants. Here, we first highlight the importance of LDD for various aspects of plant biology, discuss problems with quantifying LDD, and advocate a new vector‐based framework for LDD research that may help to overcome some of these problems. We then present six generalizations about LDD mechanisms that can be derived using this framework. While the framework and the generalizations are also highlighted in Nathan, R., Schurr, F.M., Spiegel, O.,et al.(2008) Mechanisms of long‐distance seed dispersal.Trends in Ecology and Evolution23, 638–647, this chapter provides a more in‐depth derivation of the framework and additional evidence for the generalizations. In particular, we present here a new meta‐analysis validating an innovative model for the allometry of seed dispersal by animals. In the second part of the chapter, we extend Nathanet al.'s (2008) discussion of the implications of the framework and generalizations for understanding LDD evolution and forecasting large‐scale dynamics of plants. In particular, we use the vector‐based framework to address two fundamental questions about LDD: can we identify all relevant LDD vectors and can plant traits influence LDD? We conclude by suggesting directions for future research on LDD.