Dispersal of seed via overland flow is clearly a form of hydrochory, and could incorporate both nautochory (the dispersal of floating seeds at the surface of a water column) [27] or bythisochory (dispersal of non-floating seeds along the base of a water column) [28]. Dispersal in overland flow, however, has characteristics that differentiate it significantly from typical hydrochory along a stream network or within wetlands. These characteristics include the following mechanisms: (i) the initiation of dispersal relies on the occurrence of relatively infrequent intense rainfall events that generate sufficient overland flow to move seeds (by comparison, in most streams and rivers, flow is perennial or nearly so, and the initiation of hydrochory relies on primary or secondary transport of seeds to the flow channel); (ii) the termination of dispersal is dictated by seed trapping or the cessation of overland flow (by comparison, stranding of seed on river banks or floating vegetation, or burial of seeds that change their density over time are the primary modes of termination of in-channel hydrochory) [29, 30]; (iii) flow is not confined to the vicinity of the channel network, and consequently (iv) overland flow can lead to long-distance seed dispersal, over shorter length-scales but also a less-constrained areal extent than hydrochory within rivers.
seed dispersal mechanisms pdf free
Download File: https://tiotrapfante.blogspot.com/?to=2vCotK
This study proceeds in three parts: (i) a review of the relevant flow generation and seed characteristics that influence secondary dispersal by overland flow; (ii) extension of existing seed transport theories to overland flow in sparse canopies, and an illustration of theoretical results from this extension; and (iii) a discussion of the implications of these results for spatial ecology in drylands.
Seed dispersal by overland flow is influenced by other seed characteristics. Larger seeds are less likely to be mobilized [15, 18, 19, 51, 52]. More intense storms are more likely to mobilize seeds [19]. Several species have adaptations such as awns, hairs, and pappi that enhance seeds trapping [51], some are preferentially dispersed into cracks [15], and some excrete mucilage when wet [15, 19, 51, 54]: adaptations that tend to prevent dispersal by water (although some studies suggest that mucilage increases seed buoyancy and promotes dispersal in runoff [54]).
In summary, several empirical studies suggest that: (i) seeds will float; (ii) transport initiation is a critical stage of dispersal; (iii) transport initiation is less likely for larger seeds; and (iv) adaptations that increase the likelihood of seed trapping influence transport. These common findings provide the minimum input to the development of theoretical descriptions of seed transport in overland flow.
CELC may be run for an ensemble of seeds through Monte Carlo simulations of the travel paths, yielding a probabilistic description of seed transport distances from a single seed source position. The resulting probability density function (PDF) of seed originating from any individual point is known as the dispersal kernel. Kernels provide a parsimonious description of dispersal and can be directly incorporated into spatial models of the plant population, as has been done in a number of recent studies [70, 71].
In the overland flow problem, the dispersal kernel is spatially heterogeneous and will vary for each potential point of seed release depending on the flow experienced locally at that point, and the downslope distance to vegetated patches that intercept flow and seeds. This situation offers three possible approaches for the spatial representation of dispersal: (i) a description of only mean transport lengths initiated from every point in space (at a manageable computational cost, but at the cost of preserving only one moment of the dispersal kernels); (ii) the generation of an individual dispersal kernel for every potential release point, which can then be spatially summed to obtain the final distribution of dispersed seeds throughout the domain (comprehensive, but with a high computational cost); or (iii) in landscapes with strong spatial organization (i.e. a consistent length-scale between vegetated patches) and strong trapping of seed by vegetation, multiple dispersal events may cause the cumulative dispersal lengthscales to converge. In these landscapes, it might be possible to generate an effective dispersal kernel that could be used to approximate seed transport as a low-dimensional basis for modeling - especially if such models are subjected to spatially periodic boundary conditions.
Here the first possibility is explored using CELC to estimate mean seed transport distances given the location of the seeds following primary dispersal. As explored in Section Adapting CELC for Buoyant Seed Transport, the ensemble mean of all seed trajectories provides a reasonable description of the population-level transport because the low velocities of overland flow minimize the potential for turbulent spreading of dispersed seeds (in comparison to wind-dispersal in forested landscapes). A similar approach was adopted by Trakhtenbrot et al. [72] to address the characteristics of seed dispersal from uniform canopies in heterogeneous (hilly) terrain. The cumulative effects of multiple storms on seed distribution are also explored to assess the feasibility of the third case. Although it is not implemented in this study, additional drivers of variability in dispersal length-scale could be readily coupled to CELC and used to drive the definition of spatially-varying kernels for heterogeneous landscapes. Naturally, these additional drivers are site- or problem-specific and thus lie outside the scope of this review.
Here u B and v B are the vertically-averaged bulk velocities in the x and y directions which constrain the turbulence statistics and are obtained from the Eulerian flow field (Equation 1), the α and β terms are estimated using the solution of Thomson [67], and the terms dξ x and dξ y are normally distributed stochastic increments with mean zero and standard deviation of dt, that reflect the turbulent velocity fluctuations in the x and y directions. Under the assumption that seeds rapidly reach the surface of the flow and that the vertical turbulent fluctuations are strongly damped by presence of a shallow free surface flow, the Thomson solution simplifies to:
These relations were derived for planar homogeneous boundary layer flows where the turbulence is fully developed so that mechanical production of turbulent kinetic energy scales with u* 3. Equations 4 - 9 in conjunction with the Eulerian velocity fields (obtained from solution of Equation 1) form the BOB-CELC model. BOB-CELC is solved by integrating the seed transport equations throughout the space-time field of the velocity as illustrated for a one-dimensional case in Figure 2. The Lagrangian equations that form BOB-CELC are greatly simplified compared to the three-dimensional atmospheric flow scenario for which CELC was originally derived. In the three dimensional case, vertical velocity fluctuations exert a dramatic influence on the particle motion. Here it is assumed that vertical turbulent fluctuations do not exert a significant influence on the motion of a fluid particle confined to the surface of the flow (and hence to any seed motion). The scaling in equation (9) provides a first order rationale for this simplification: lateral and longitudinal fluctuations are leading order terms compared to the vertical fluctuations, which are confined to a small range in h, where the vertical velocity variance distribution within h is significantly damped by the presence of the no slip boundary at the ground and free water surface at the top.
Schematic of seed transport behavior within overland flow as modeled by BOB-CELC. (A) Conceptual diagram of assumed seed transport mechanism of simple advection at the surface of the overland flow, for all regions in which the flow depth is sufficient to mobilize the seed. (B) Illustration of spacetime plots of the flow velocity, with a 1D Lagrangian seed path illustrated by red dots. (C) Rather than a dispersal kernel, a spatially distributed set of seed displacements as a function of the starting position is generated through advective transport.
This result requires discussion, since the finding of minimal variance in seed dispersal length-scales appears counter-intuitive. This result, however, should not be interpreted as indicating that seed transport in overland flow is entirely deterministic. Instead, it indicates that turbulence within the flow trajectories is not the major source of variance in dispersal length-scales in shallow overland flow. This contrasts markedly with wind dispersal, in which turbulence is a major driver of variability in dispersal length-scales. However, the distinction between the two cases can be readily interpreted in terms of the differences in the Reynolds numbers of the flow: on the order of 100-102 for shallow overland flow, and on the order of 105-106 for wind dispersal: this suggests that travel variances due to turbulence should be many times smaller in overland flow than in wind dispersed cases. However, other sources of variability in dispersal length-scales can and should be considered when modeling seed dispersal in overland flow. Two likely sources of such variability include the time at which dispersal is initiated (the results here assumed simultaneous mobilization of all seeds at a given location), and variability in the termination of transport by the trapping of seeds. Each of these sources of variability can be readily incorporated into BOB-CELC. However, the physical basis for the parameterization of stochastic transport initiation and termination of seeds remains unclear, and further research is required. For this reason, we have retained only the most elementary descriptions of a single transport initiation time, along with a highly simplified treatment of seed trapping as described below. 2ff7e9595c
Comments