How important is long‐distance seed dispersal for the regional survival of plant species?

Long‐distance seed dispersal is generally assumed to be important for the regional survival of plant species. In this study, we quantified the importance of long‐distance seed dispersal for regional survival of plant species using wind dispersal as an example. We did this using a new approach, by first relating plant species’ dispersal traits to seed dispersal kernels and then relating the kernels to regional survival of the species. We used a recently developed and tested mechanistic seed dispersal model to calculate dispersal kernels from dispersal traits. We used data on 190 plant species and calculated their regional survival in two ways, using species distribution data from 36,800 1 km²‐grid cells and 10,754 small plots covering the Netherlands during the largest part of the 20th century. We carried out correlation and stepwise multiple regression analyses to quantify the importance of long‐distance dispersal, expressed as the 99‐percentile dispersal distance of the dispersal kernels, relative to the importance of median‐distance dispersal and other plant traits that are likely to contribute to the explanation of regional survival: plant longevity (annual, biennial, perennial), seed longevity, and plant nutrient requirement. Results show that long‐distance dispersal plays a role in determining regional survival, and is more important than median‐distance dispersal and plant longevity. However, long‐distance dispersal by wind explains only 1–3% of the variation in regional survival between species and is equally important as seed longevity and much less important than nutrient requirement. In changing landscapes such as in the Netherlands, where large‐scale eutrophication and habitat destruction took place in the 20th century, plant traits indicating ability to grow under the changed, increasingly nutrient‐rich conditions turn out to be much more important for regional survival than seed dispersal.     

种子的长距离风传播模型研究进展

 植物种子的长距离传播在物种迁移、生物入侵、保护生物学等领域有重要的生态和进化意义。种子传播有很多方式,开阔草原等地区的草本植物和许多热带和温带的树木都是通过风传播种子的。风传播的方式最适合进行种子长距离传播现象的模拟研究。种子的风传播模型是传播生态研究的一个重要领域,尤其是种子的长距离风传播模型,对于外来入侵植物的扩散和破碎化景观中植物种群的基因交流等生态过程研究举足轻重,然而国内鲜见这方面的研究成果。本文综述了种子长距离风传播现象研究的背景和意义,分析了风传播种子模型的基本形式和构成原理,并分别就现象模型和机理模型的相关研究进展进行了总结,同时指出了未来发展的几个重要方向。种子的风传播模型可以分为现象模型和机理模型两类,现象模型按种子传播核心的形式包括短尾模型、偏峰长尾模型和混合传播核心模型,后两者对于长距离传播数据的模拟可以取得很好的效果。机理模型按照模拟机制可分为欧拉对流扩散模型和拉格郎日随机模型两类。本文重点介绍了种子的长距离风传播现象的形成机理和两类机理模型的参数构成和处理方式。适合种子脱落的天气和适合传播的天气的同步性可能是形成种子长距离风传播的一个重要前提,林缘和地表存在的上升气流及大风和暴风中形成的速度梯度都可能对于种子的长距离传播有重要的作用。机理模型的操作因子主要包括生物方面的因子、气象方面的因子和地形方面的因子。同时对目前几个应用比较成功的机理模型进行了简要的介绍和评价,包括倾斜羽毛模型、对流-扩散-下降模型、无掩蔽模型、背景模型、WINDISPER及其改进模型和PAPPUS模型。最后指出,目前在风传播种子的长距离模型研究中,对草本植物种子的传播模拟的投入明显不如树木种子的长距离传播模拟,对于破碎化景观中种子长距离的风传播的研究还存在很大的差距,而对提高机理模型预测能力的高分辨率物理环境数据输入技术的需求则为多学科交叉提供了很好的机会。     

Modelling population redistribution in a leaf beetle: an evaluation of alternative dispersal functions

1. Dispersal is a fundamental ecological process, so spatial models require realistic dispersal kernels. We compare five different forms for the dispersal kernel of the tansy beetle Chrysolina graminis moving between patches of its host-plant (tansy Tanacetum vulgare) in a riparian landscape. 2. Multi-patch mark-recapture data were collected every 2 weeks over 2 years within a large network of patches and from 2226 beetles. Dispersal was common (28.4% of 880 recaptures after a fortnight) and was more likely over longer intervals, out of small patches, for females and during flooding. Interpatch movement rates did not differ between years and exhibited no density dependence. Dispersal distances were similar for males and females, in both years and over all intervals, with a median dispersal distance of just 9.8 m, although a maximum of 856 m was recorded. 3. A model of dispersal, where patches competed for dispersers based on their size and distance from the beetle's source patch (scaled by the dispersal kernel) was fitted to the field data with a maximum likelihood procedure and each of five alternative kernels. The best fitting had relatively extended tails of long-distance dispersal, while Gaussian and negative exponential kernels performed worst. 4. The model suggests that females disperse more commonly than males and that both are strongly attracted to large patches but do not differ between years, which are consistent with the empirical results. Model-predicted emigration and immigration rates and dispersal phenologies match those observed, suggesting that the model captured the major drivers of tansy beetle dispersal. 5. Although negative exponential and Gaussian kernels are widely used for their simplicity, we suggest that these should not be the models of automatic choice, and that fat-tailed kernels with relatively higher proportions of long-distance dispersal may be more realistic.     

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.     

Implications of nonrandom seed abscission and global stilling for migration of wind‐dispersed plant species

Migration of plant populations is a potential survival response to climate change that depends critically on seed dispersal. Biological and physical factors determine dispersal and migration of wind‐dispersed species. Recent field and wind tunnel studies demonstrate biological adaptations that bias seed release toward conditions of higher wind velocity, promoting longer dispersal distances and faster migration. However, another suite of international studies also recently highlighted a global decrease in near‐surface wind speeds, or ‘global stilling’. This study assessed the implications of both factors on potential plant population migration rates, using a mechanistic modeling framework. Nonrandom abscission was investigated using models of three seed release mechanisms: (i) a simple drag model; (ii) a seed deflection model; and (iii) a ‘wear and tear’ model. The models generated a single functional relationship between the frequency of seed release and statistics of the near‐surface wind environment, independent of the abscission mechanism. An Inertial‐Particle, Coupled Eulerian‐Lagrangian Closure model (IP‐CELC) was used to investigate abscission effects on seed dispersal kernels and plant population migration rates under contemporary and potential future wind conditions (based on reported global stilling trends). The results confirm that nonrandom seed abscission increased dispersal distances, particularly for light seeds. The increases were mitigated by two physical feedbacks: (i) although nonrandom abscission increased the initial acceleration of seeds from rest, the sensitivity of the seed dispersal to this initial condition declined as the wind speed increased; and (ii) while nonrandom abscission increased the mean dispersal length, it reduced the kurtosis of seasonal dispersal kernels, and thus the chance of long‐distance dispersal. Wind stilling greatly reduced the modeled migration rates under biased seed release conditions. Thus, species that require high wind velocities for seed abscission could experience threshold‐like reductions in dispersal and migration potential if near‐surface wind speeds continue to decline.     

Seed dispersal in heterogeneous environments: bridging the gap between mechanistic dispersal and forest dynamics models

Seed dispersal is an important determinant of vegetation composition. We present a mechanistic model of seed dispersal by wind that incorporates heterogeneous vegetation structure. Vegetation affects wind speeds, a primary determinant of dispersal distance. Existing models combine wind speed and fall velocity of seeds. We expand on them by allowing vegetation, and thus wind profiles, to vary along seed trajectories, making the model applicable to any wind-dispersed plant in any community. Using seed trap data on seeds dispersing from forests into adjacent sites of two distinct vegetation structures, we show that our model was unbiased and accurate, even though dispersal patterns differed greatly between the two structures. Our spatially heterogeneous model performed better than models that assumed homogeneous vegetation for the same system. Its sensitivity to vegetation structure and ability to predict seed arrival when vegetation structure was incorporated demonstrates the model's utility for providing realistic estimates of seed arrival in realistic landscapes. Thus, we begin to bridge mechanistic seed dispersal and forest dynamics models. We discuss the merits of our model for incorporation into forest simulators, applications where such incorporation has been or is likely to be especially fruitful, and future model refinements to increase understanding of seed dispersal by wind.     

Are the best dispersers the best colonizers? Seed mass,dispersal and establishment in <Emphasis Type="Italic">Carduus</Emphasis> thistles

Negative correlations between dispersal and establishment are often reported in the plant literature; smaller seeds tend to disperse better but germinate less well, and produce smaller seedlings. However, because dispersal capacity is often quantified using proxies, such as the settling velocity of wind-dispersed seeds, little is known about the exact shape of this negative relationship, and how it is modified by other plant traits and environmental conditions. We studied the dispersal-establishment relationship in two wind-dispersed thistles (Carduus nutans and Carduus acanthoides). We applied a mechanistic wind dispersal model (WALD) to seeds released under a range of environmental conditions, and tested germination and seedling growth under standardized conditions in a greenhouse. Dispersal distance and establishment (germination and seedling growth) were not significantly correlated, although in both species smaller seeds dispersed farther, and showed lower germination and lower seedling growth rates. This apparent paradox can partly be explained by the significant influence of other factors such as release height and environment (wind and vegetation), which explained more variation in dispersal than did terminal velocity. Another potential explanation is the variation in seed traits: germination is strongly positively related to seed mass, weakly positively related to plume loading, but not significantly related to terminal velocity. This weakening of the correlation with germination is due to additional layers of trait (co)variability: for instance, seed mass and pappus size are positively correlated, and thus big seeds partially compensate for the negative effect of seed mass with larger pappi. Our mechanistic approach can thus lead to a better understanding of both potentially opposing selection pressures on traits like seed mass, and diluting effects of other seed, plant and environmental factors.     

Effects of dispersal mechanisms on spatio-temporal development of epidemics

The nature of pathogen transport mechanisms strongly determines the spatial pattern of disease and, through this, the dynamics and persistence of epidemics in plant populations. Up to recently, the range of possible mechanisms or interactions assumed by epidemic models has been limited: either independent of the location of individuals (mean-field models) or restricted to local contacts (between nearest neighbours or decaying exponentially with distance). Real dispersal processes are likely to lie between these two extremes, and many are well described by long-tailed contact kernels such as power laws. We investigate the effect of different spatial dispersal mechanisms on the spatio-temporal spread of disease epidemics by simulating a stochastic Susceptible-infective model motivated by previous data analyses. Both long-term stationary behaviour (in the presence of a control or recovery process) and transient behaviour (which varies widely within and between epidemics) are examined. We demonstrate the relationship between epidemic size and disease pattern (characterized by spatial autocorrelation), and its dependence on dispersal and infectivity parameters. Special attention is given to boundary effects, which can decrease disease levels significantly relative to standard, periodic geometries in cases of long-distance dispersal. We propose and test a definition of transient duration which captures the dependence of transients on dispersal mechanisms. We outline an analytical approach that represents the behaviour of the spatially-explicit model, and use it to prove that the epidemic size is predicted exactly by the mean-field model (in the limit of an infinite system) when dispersal is sufficiently long ranged (i.e. when the power-law exponent a相似文献   

Seed fate and decision‐making processes in scatter‐hoarding rodents

A mechanistic understanding of seed movement and survival is important both for the development of theoretical models of plant population dynamics, spatial spread, and community assembly, and for the conservation and management of plant communities under global change. While models of wind‐borne seed dispersal have advanced rapidly over the past two decades, models for animal‐mediated dispersal have failed to make similar progress due to their dependence on interspecific interactions and complex, context‐dependent behaviours. In this review, we synthesize the literature on seed dispersal and consumption by scatter‐hoarding, granivorous rodents and outline a strategy for development of a general mechanistic seed‐fate model in these systems. Our review decomposes seed dispersal and survival into six distinct sub‐processes (exposure, harvest, allocation, preparation, placement, and recovery), and identifies nine intermediate (latent) variables that link physical state variables (e.g. seed and animal traits, habitat structure) to decisions regarding seed allocation to hoarding or consumption, cache placement and management, and deployment of radicle‐pruning or embryo excision behaviours. We also highlight specific areas where research on these intermediate relationships is needed to improve our mechanistic understanding of scatter‐hoarder behaviour. Finally, we outline a strategy to combine detailed studies on individual functional relationships with seed‐tracking experiments in an iterative, hierarchical Bayesian framework to construct, refine, and test mechanistic models for context‐dependent, scatter‐hoarder‐mediated seed fate.     

A Short Note on Short Dispersal Events

We study how the speed of spread for an integrodifference equation depends on the dispersal pattern of individuals. When the dispersal kernel has finite variance, the central limit theorem states that convolutions of the kernel with itself will approach a suitably chosen Gaussian distribution. Despite this fact, the speed of spread cannot be obtained from the Gaussian approximation. We give several examples and explanations for this fact. We then use the kurtosis of the kernel to derive an improved approximation that shows a very good fit to all the kernels tested. We apply the theory to one well-studied data set of dispersal of Drosophila pseudoobscura and to two one-parameter families of theoretical dispersal kernels. In particular, we find kernels that, despite having compact support, have a faster speed of spread than the Gaussian kernel.     

Why trees migrate so fast: confronting theory with dispersal biology and the paleorecord

Reid's paradox describes the fact that classical models cannot account for the rapid (10(2)-10(3) m yr-1) spread of trees at the end of the Pleistocene. I use field estimates of seed dispersal with an integrodifference equation and simulation models of population growth to show that dispersal data are compatible with rapid spread. Dispersal estimates lay to rest the possibility that rapid spread occurred by diffusion. The integrodifference model predicts that, if the seed shadow has a long 'fat' tail, then rapid spread is possible, despite short average dispersal distances. It further predicts that velocity is more sensitive to life history than is classical diffusion. Application of such models is frustrated because the tail of the seed shadow cannot be fitted to data. However, the data can be used to test a 'long-distance' hypothesis against alternative ('local') models of dispersal using Akaike's Information Criterion and likelihood ratio tests. Tests show that data are consistent with >10% of seed dispersed as a long (10(2) m) fat-tailed kernel. Models based on such kernels predict spread as rapid as that inferred from the pollen record. If fat-tailed dispersal explains these rapid rates, then it is surprising not to see large differences in velocities among taxa with contrasting life histories. The inference of rapid spread, together with lack of obvious life-history effects, suggests velocities may have not reached their potentials, being stalled by rates of climate change, geography, or both.     

Long distance seed dispersal by wind: measuring and modelling the tail of the curve

The size and shape of the tail of the seed dispersal curve is important in determining the spatial dynamics of plants, but is difficult to quantify. We devised an experimental protocol to measure long-distance dispersal which involved measuring dispersal by wind from isolated individuals at a range of distances from the source, but maintaining a large and constant sampling intensity at each distance. Seeds were trapped up to 80 m from the plants, the furthest a dispersal curve for an individual plant has been measured for a non-tree species. Standard empirical negative exponential and inverse power models were fitted using likelihood methods. The latter always had a better fit than the former, but in most cases neither described the data well, and strongly under-estimated the tail of the dispersal curve. An alternative model formulation with two kernel components had a much better fit in most cases and described the tail data more accurately. Mechanistic models provide an alternative to direct measurement of dispersal. However, while a previous mechanistic model accurately predicted the modal dispersal distance, it always under-predicted the measured tail. Long-distance dispersal may be caused by rare extremes in horizontal wind speed or turbulence. Therefore, under-estimation of the tail by standard empirical models and mechanistic models may indicate a lack of flexibility to take account of such extremes. Future studies should examine carefully whether the widely used exponential and power models are, in fact, valid, and investigate alternative models. Received: 7 March 1999 / Accepted: 2 April 2000     

When will plant morphology affect the shape of a seed dispersal "kernel"?

Most models of dispersal assume that plants are point sources. In reality, the scale in height over which seed sources are distributed is often of the same order as the scale in distance over which most individual seeds are dispersed. But is this sufficient to affect the fundamental shapes of dispersal frequency distributions? Most published conclusions about the effects of canopy structure on dispersal are subjective. A model is developed to explore the consequences of plant canopies for the shapes of whole-plant seed dispersal "kernels". The canopies were described by simple geometric shapes, while an empirical probability density function (PDF) was used for dispersal from a point source. It was found that the resulting whole-plant PDF for dispersal distance was almost invariably peaked, whereas the PDF for the density of seed rain (as would be measured by pitfall traps) could either be peaked or monotonic according to the canopy shape, position of seeds in the canopy, and mean dispersal distance. The shapes of kernels from whole plants (distributed seed sources) can be very different from those derived from a point source under certain circumstances.     

Species-area relationships from a spatially explicit neutral model in an infinite landscape

We use recently developed technical methods to study species–area relationships from a spatially explicit extension of Hubbell's neutral model on an infinite landscape. Our model includes variable dispersal distances and exhibits qualitatively different behaviour from the cases of nearest-neighbour dispersal and finite periodic landscapes that have previously been studied. We show that different dispersal distances and even different dispersal kernels produce identical species–area curves up to rescaling of the two axes. This scaling property provides a straightforward method for fitting the model to empirical data. The species–area curves display all three phases observed empirically and enable the exponent describing the power law relationship for species–area curves to be identified as the gradient at the central phase. This exponent can take all values between 0 and 1 and is given by a simple function of the speciation rate, independent of all other model variables.     

Saddle-Point Approximations,Integrodifference Equations,and Invasions

Invasion, the growth in numbers and spatial spread of a population over time, is a fundamental process in ecology. Governments and businesses expend vast sums to prevent and control invasions of pests and pestilences and to promote invasions of endangered species and biological control agents. Many mathematical models of biological invasions use nonlinear integrodifference equations to describe the growth and dispersal processes and to predict the speed of invasion fronts. Linear models have received less attention, perhaps because they are difficult to simulate for large times. In this paper, we use the saddle-point method, alias the method of steepest descent, to derive asymptotic approximations for the solutions of linear integrodifference equations. We work through five examples, for Gaussian, Laplace, and uniform dispersal kernels in one dimension and for asymmetric Gaussian and radially symmetric Laplace kernels in two dimensions. Our approximations are extremely close to the exact solutions, even for intermediate times. We also employ an empirical saddle-point approximation to predict densities using dispersal data. We use our approximations to examine the effects of censored dispersal data on estimates of invasion speed and population density.     

Assessing Intraspecific Variation in Effective Dispersal Along an Altitudinal Gradient: A Test in Two Mediterranean High-Mountain Plants

Background

Plant recruitment depends among other factors on environmental conditions and their variation at different spatial scales. Characterizing dispersal in contrasting environments may thus be necessary to understand natural intraspecific variation in the processes underlying recruitment. Silene ciliata and Armeria caespitosa are two representative species of cryophilic pastures above the tree line in Mediterranean high mountains. No explicit estimations of dispersal kernels have been made so far for these or other high-mountain plants. Such data could help to predict their dispersal and recruitment patterns in a context of changing environments under ongoing global warming.

Methods

We used an inverse modelling approach to analyse effective seed dispersal patterns in five populations of both Silene ciliata and Armeria caespitosa along an altitudinal gradient in Sierra de Guadarrama (Madrid, Spain). We considered four commonly employed two-dimensional seedling dispersal kernels exponential-power, 2Dt, WALD and log-normal.

Key Results

No single kernel function provided the best fit across all populations, although estimated mean dispersal distances were short (<1 m) in all cases. S. ciliata did not exhibit significant among-population variation in mean dispersal distance, whereas significant differences in mean dispersal distance were found in A. caespitosa. Both S. ciliata and A. caespitosa exhibited among-population variation in the fecundity parameter and lacked significant variation in kernel shape.

Conclusions

This study illustrates the complexity of intraspecific variation in the processes underlying recruitment, showing that effective dispersal kernels can remain relatively invariant across populations within particular species, even if there are strong variations in demographic structure and/or physical environment among populations, while the invariant dispersal assumption may not hold for other species in the same environment. Our results call for a case-by-case analysis in a wider range of plant taxa and environments to assess the prevalence and magnitude of intraspecific dispersal variation.     

Invasion by extremes: population spread with variation in dispersal and reproduction

For populations having dispersal described by fat-tailed kernels (kernels with tails that are not exponentially bounded), asymptotic population spread rates cannot be estimated by traditional models because these models predict continually accelerating (asymptotically infinite) invasion. The impossible predictions come from the fact that the fat-tailed kernels fitted to dispersal data have a quality (nondiscrete individuals and, thus, no moment-generating function) that never applies to data. Real organisms produce finite (and random) numbers of offspring; thus, an empirical moment-generating function can always be determined. Using an alternative method to estimate spread rates in terms of extreme dispersal events, we show that finite estimates can be derived for fat-tailed kernels, and we demonstrate how variable reproduction modifies these rates. Whereas the traditional models define spread rate as the speed of an advancing front describing the expected density of individuals, our alternative definition for spread rate is the expected velocity for the location of the furthest-forward individual in the population. The asymptotic wave speed for a constant net reproductive rate R0 is approximated as (1/T)(piuR)/2)(1/2) m yr(-1), where T is generation time, and u is a distance parameter (m2) of Clark et al.'s 2Dt model having shape parameter p = 1. From fitted dispersal kernels with fat tails and infinite variance, we derive finite rates of spread and a simple method for numerical estimation. Fitted kernels, with infinite variance, yield distributions of rates of spread that are asymptotically normal and, thus, have finite moments. Variable reproduction can profoundly affect rates of spread. By incorporating the variance in reproduction that results from variable life span, we estimate much lower rates than predicted by the standard approach, which assumes a constant net reproductive rate. Using basic life-history data for trees, we show these estimated rates to be lower than expected from previous analytical models and as interpreted from paleorecords of forest spread at the end of the Pleistocene. Our results suggest reexamination of past rates of spread and the potential for future response to climate change.     

Predicting dispersal of auto‐gyrating fruit in tropical trees: a case study from the Dipterocarpaceae

Seed dispersal governs the distribution of plant propagules in the landscape and hence forms the template on which density‐dependent processes act. Dispersal is therefore a vital component of many species coexistence and forest dynamics models and is of applied value in understanding forest regeneration. Research on the processes that facilitate forest regeneration and restoration is given further weight in the context of widespread loss and degradation of tropical forests, and provides impetus to improve estimates of seed dispersal for tropical forest trees. South‐East Asian lowland rainforests, which have been subject to severe degradation, are dominated by trees of the Dipterocarpaceae family which constitute over 40% of forest biomass. Dipterocarp dispersal is generally considered to be poor given their large, gyration‐dispersed fruits. However, there is wide variability in fruit size and morphology which we hypothesize mechanistically underpins dispersal potential through the lift provided to seeds mediated by the wings. We explored experimentally how the ratio of fruit wing area to mass (“inverse wing loading,” IWL) explains variation in seed dispersal kernels among 13 dipterocarp species by releasing fruit from a canopy tower. Horizontal seed dispersal distances increased with IWL, especially at high wind speeds. Seed dispersal of all species was predominantly local, with 90% of seed dispersing <10 m, although maximum dispersal distances varied widely among species. We present a generic seed dispersal model for dipterocarps based on attributes of seed morphology and provide modeled seed dispersal kernels for all dipterocarp species with IWLs of 1–50, representing 75% of species in Borneo.     

Dispersal patterns, dispersal mechanisms, and invasion wave speeds for invasive thistles

Understanding and predicting population spread rates is an important problem in basic and applied ecology. In this article, we link estimates of invasion wave speeds to species traits and environmental conditions. We present detailed field studies of wind dispersal and compare nonparametric (i.e., data-based) and mechanistic (fluid dynamics model-based) dispersal kernel and spread rate estimates for two important invasive weeds, Carduus nutans and Carduus acanthoides. A high-effort trapping design revealed highly leptokurtic dispersal distributions, with seeds caught up to 96 m from the source, far further than mean dispersal distances (approx. 2 m). Nonparametric wave speed estimates are highly sensitive to sampling effort. Mechanistic estimates are insensitive to sampling because they are obtained from independent data and more useful because they are based on the dispersal mechanism. Over a wide range of realistic conditions, mechanistic spread rate estimates were most sensitive to high winds and low seed settling velocities. The combination of integrodifference equations and mechanistic dispersal models is a powerful tool for estimating invasion spread rates and for linking these estimates to characteristics of the species and the environment.