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。     

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.     

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     

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.     

Dandelion Seed Dispersal: The Horizontal Wind Speed Does Not Matter for Long-Distance Dispersal - it is Updraft!

Abstract: Long-distance dispersal of seeds (LDD) surely affects most ecological and evolutionary processes related to plant species. Hence, numerous attempts to quantify LDD have been made and, especially for wind dispersal, several simulation models have been developed. However, the mechanisms promoting LDD by wind still remain ambiguous and the effects of different weather conditions on LDD, although recognized as important, have only rarely been investigated. Here we examine the influence of wind speed and updrafts on dispersal of dandelion ( Taraxacum officinale agg.), a typical wind-dispersed herb of open habitats. We used PAPPUS, a weather-sensitive mechanistic simulation model of wind dispersal, which considers frequency distribution of weather conditions during the period the simulation refers to. A simulation for the 4-month shedding period of dandelion shows that high wind speed does not promote LDD. In contrast, vertical turbulence, especially convective updrafts, are of overwhelming importance. Mainly caused by updrafts, in the simulations more than 0.05 % of dandelion seeds were dispersed beyond 100 m, a distance commonly used to define LDD. We conclude that long-distance dispersal of seeds of herbaceous species with falling velocities < 0.5 - 1.0 ms^-1 is mainly caused by convective updrafts.     

Towards a mechanistic understanding of dispersal evolution in plants: conservation implications

Aim A species’ dispersal characteristics will play a key role in determining its likely fate during a period of environmental change. However, these characteristics are not constant within a species – instead, there is often both considerable interpopulation and interindividual variability. Also changes in selection pressures can result in the evolution of dispersal characteristics, with knock‐on consequences for a species’ population dynamics. Our aim here is to make our theoretical understanding of dispersal evolution more conservation‐relevant by moving beyond the rather abstract, phenomenological models that have dominated the literature towards a more mechanism‐based approach. Methods We introduce a continuous‐space, individual‐based model for wind‐dispersed plants where release height is determined by an individual’s ‘genotype’. A mechanistic wind dispersal model is used to simulate seed dispersal. Selection acts on variation in release height that is generated through mutation. Results We confirm that, when habitat is fragmented, both evolutionary rescue and evolutionary suicide remain possible outcomes when a mechanistic dispersal model is used. We also demonstrate the potential for what we term evolutionary entrapment. A population that under some conditions can evolve to be sufficiently dispersive that it expands rapidly across a fragmented landscape can, under different conditions, become trapped by a combination of limited dispersal and a large gap between patches. Conclusions While developing evolutionary models to be used as conservation tools is undoubtedly a challenge, we believe that, with a concerted collaborative effort linking the knowledge and methods of ecologists, evolutionary biologists and geneticists, it is an achievable aim.     

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.     

Effects of seed morphology and orientation on secondary seed dispersal by wind

传播体形态与方位对二次种子风力传播的影响     

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.     

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.     

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.     

Plant propagation fronts and wind dispersal: an analytical model to upscale from seconds to decades using superstatistics

Scale separation crossing many orders of magnitude is a consistent challenge in the ecological sciences. Wind dispersal of seed that generates plant propagation fronts is a typical case where timescales range from less than a second for fast turbulent processes to interannual timescales governing plant growth and climatic forcing. We show that the scale separation can be overcome by developing mechanistic and statistical links between processes at the different timescales. A mechanistic model is used to scale up from the turbulent regime to hourly timescales, while a superstatistical approach is used to relate the half-hourly timescales to annual vegetation migration speeds. We derive a semianalytical model to predict vegetation front movement as a function of wind-forcing statistics and characteristics of the species being dispersed. This model achieves better than order-of-magnitude agreement in a case study of tree dispersal from the early Holocene, a marked improvement over diffusion models. Plant migration is shown to depend nonlinearly on the wind environment forcing the movement but linearly on most physiological parameters. Applications of these analytical results to parameterizing models of plant dispersion and the implications of the superstatistical approach for addressing other ecological problems plagued by similar "dimensionality curses" are outlined.     

Costs and benefits of non‐random seed release for long‐distance dispersal in wind‐dispersed plant species

The dispersal ability of plants is a major factor driving ecological responses to global change. In wind‐dispersed plant species, non‐random seed release in relation to wind speeds has been identified as a major determinant of dispersal distances. However, little information is available about the costs and benefits of non‐random abscission and the consequences of timing for dispersal distances. We asked: 1) to what extent is non‐random abscission able to promote long‐distance dispersal and what is the effect of potentially increased pre‐dispersal risk costs? 2) Which meteorological factors and respective timescales are important for maximizing dispersal? These questions were addressed by combining a mechanistic modelling approach and field data collection for herbaceous wind‐dispersed species. Model optimization with a dynamic dispersal approach using measured hourly wind speed showed that plants can increase long‐distance dispersal by developing a hard wind speed threshold below which no seeds are released. At the same time, increased risk costs limit the possibilities for dispersal distance gain and reduce the optimum level of the wind speed threshold, in our case (under representative Dutch meteorological conditions) to a threshold of 5–6 m s^–1. The frequency and predictability (auto‐correlation in time) of pre‐dispersal seed‐loss had a major impact on optimal non‐random abscission functions and resulting dispersal distances. We observed a similar, but more gradual, bias towards higher wind speeds in six out of seven wind‐dispersed species under natural conditions. This confirmed that non‐random abscission exists in many species and that, under local Dutch meteorological conditions, abscission was biased towards winds exceeding 5–6 m s^–1. We conclude that timing of seed release can vastly enhance dispersal distances in wind‐dispersed species, but increased risk costs may greatly limit the benefits of selecting wind conditions for long‐distance dispersal, leading to moderate seed abscission thresholds, depending on local meteorological conditions and disturbances.     

Are Plant Species Able to Keep Pace with the Rapidly Changing Climate?

Future climate change is predicted to advance faster than the postglacial warming. Migration may therefore become a key driver for future development of biodiversity and ecosystem functioning. For 140 European plant species we computed past range shifts since the last glacial maximum and future range shifts for a variety of Intergovernmental Panel on Climate Change (IPCC) scenarios and global circulation models (GCMs). Range shift rates were estimated by means of species distribution modelling (SDM). With process-based seed dispersal models we estimated species-specific migration rates for 27 dispersal modes addressing dispersal by wind (anemochory) for different wind conditions, as well as dispersal by mammals (dispersal on animal''s coat – epizoochory and dispersal by animals after feeding and digestion – endozoochory) considering different animal species. Our process-based modelled migration rates generally exceeded the postglacial range shift rates indicating that the process-based models we used are capable of predicting migration rates that are in accordance with realized past migration. For most of the considered species, the modelled migration rates were considerably lower than the expected future climate change induced range shift rates. This implies that most plant species will not entirely be able to follow future climate-change-induced range shifts due to dispersal limitation. Animals with large day- and home-ranges are highly important for achieving high migration rates for many plant species, whereas anemochory is relevant for only few species.     

Modelling mangrove propagule dispersal trajectories using high‐resolution estimates of ocean surface winds and currents

Mangrove forests are systems that provide ecosystem services and rely on floating propagules of which the dispersal trajectories are determined by ocean currents and winds. Quantitating connectivity of mangrove patches is an important conservation concern. Current estimates of connectivity, however, fail to integrate the link between ocean currents at different spatial scales and dispersal trajectories. Here, we use high‐resolution estimates of ocean currents and surface winds from meteorological and oceanographic analyses, in conjunction with experimental data on propagule traits (e.g., density, size, and shape) and dispersal vector properties (e.g., strength and direction of water and wind currents). We incorporate these data in a dispersal model to illustrate the potential effect of wind on dispersal trajectories of hydrochorous propagules from different mangrove species. We focus on the Western Indian Ocean, including the Mozambique Channel, which has received much attention because of its reported oceanic complexity, to illustrate the effect of oceanic features such as eddy currents and tides. In spite of the complex pattern of ocean surface currents and winds, some propagules are able to cross the Mozambique Channel. Eddy currents and tides may delay arrival at a suitable site. Experimentally demonstrated differences in wind sensitivity among propagule types were shown to affect the probability of departure and the shape of dispersal trajectories. The model could be used to reconstruct current fluxes of mangrove propagules that may help explain past and current distributions of mangrove forests and assess the potential for natural expansion of these forests.     

The role of model selection in describing stochastic ecological processes

A great deal of variation in ecological processes can be generated through variation in environmental conditions. Models describing the underlying processes should be able to account for this variation. However, models may not be flexible enough, so that different realizations of a process may be better described by different models. This may lead to uncertainty in model selection.
Here we examine the question of whether two empirical models can provide consistent fits to different realizations of a process affected by environmental variation. We further examine the sensitivity of the model predictions to the amount of data available and the selection of the model. To study this, we simulated pollen dispersal patterns under varying wind conditions and then investigated whether the datasets consistently supported the same model. The role of the model selection and the impact of the spatial range over which the dispersal distances were observed were assessed by comparing model predictions at long dispersal distances.
There was no consistent pattern of one model providing a better fit than the other across simulations. The model providing better fit varied depending on the range of distances over which the dispersal patterns were observed, and on the amount of long-distance dispersal. The model predictions were found to be very sensitive to the selection of the model.
The variation between datasets produced with the same underlying mechanisms cannot be easily described using one model, which also limits our ability to reliably predict the underlying process. Therefore, the amount of information about a model choice provided by an individual field study may be rather limited. If we are to understand processes that are affected by environmental variation then we have to observe the range of possible outcomes of the processes under varying spatio-temporal conditions.     

Testing a mechanistic dispersal model against a dispersal experiment with a wind‐dispersed moss

Wind is the main dispersal agent for a wide array of species and for these species the environmental conditions under which diaspores are released can potentially modify the dispersal kernel substantially. Little is known about how bryophytes regulate spore release, but conditions affecting peristome movements and vibration of the seta may be important. We modelled airborne spore dispersal of the bryophyte species Discelium nudum (spore diameter 25 μm), in four different release scenarios, using a Lagrangian stochastic dispersion model and meteorological data. We tested the model predictions against experimental data on colonization success at five distances (5, 10, 30, 50 and 100 m) and eight directions from a translocated point source during seven two‐day periods. The model predictions were generally successful in describing the observed colonization patterns, especially beyond 10 m. In the laboratory we established spore release thresholds; horizontal wind speed sd > 0.25 m s^?1 induced the seta to vibrate and in relative humidity < 75% the peristome was open. Our dispersal model predicts that the proportion of spores dispersing beyond 100 m is almost twice as large if the spores are released under turbulent conditions than under more stable conditions. However, including release thresholds improved the fit of the model to the colonization data only minimally, with roughly the same amount of variation explained by the most constrained scenario (assuming both vibration of the seta and an open peristome) and the scenario assuming random release. Model predictions under realised experimental conditions suggest that we had a low statistical power to rank the release scenarios due to the lack of measurements of the absolute rate of spore release. Our results hint at the importance of release conditions, but also highlight the challenges in dispersal experiments intended for validating mechanistic dispersal models.     

A mathematical and statistical framework for modelling dispersal

Mechanistic and phenomenological dispersal modelling of organisms has long been an area of intensive research. Recently, there has been an increased interest in intermediate models between the two. Intermediate models include major mechanisms that affect dispersal, in addition to the dispersal curve of a phenomenological model. Here we review and describe the mathematical and statistical framework for phenomenological dispersal modelling. In the mathematical development we describe modelling of dispersal in two dimensions from a point source, and in one dimension from a line or area source. In the statistical development we describe applicable observation distributions, and the procedures of model fitting, comparison, checking, and prediction. The procedures are also demonstrated using data from dispersal experiments. The data are hierarchically structured, and hence, we fit hierarchical models. The Bayesian modelling approach is applied, which allows us to show the uncertainty in the parameter estimates and in predictions. Finally, we show how to account for the effect of wind speed on the estimates of the dispersal parameters. This serves as an example of how to strengthen the coupling in the modelling between the phenomenon observed in an experiment and the underlying process – something that should be striven for in the statistical modelling of dispersal.