Modeling leaf dispersal in mixed hardwood forests using a ballistic approach

In mixed-species stands, modeling leaf litter dispersal is important to predict the physical and chemical characteristics of the forest floor, which plays a major role in nutrient cycling and in plant population dynamics. In this study, a spatially explicit model of leaf litterfall was developed and compared with two other models. These three models were calibrated for a mixed forest of oak and beech using litterfall data from mapped forest plots. All models assumed that an allometric equation described individual leaf litter production, but they strongly differed in the modeling of the probability density of leaf shedding with distance from source trees. Two models used a negative exponential function to account for leaf dispersal with distance, and this function was allowed to vary according to wind direction in one of them. In contrast, our approach was based on a simple ballistic equation considering release height, wind speed, wind direction, and leaf fall velocity; the distributions of wind speeds and wind directions were modeled according to a Weibull and a Von Mises distribution, respectively. Using an independent validation data set, all three models provided predictions well correlated to measurements (r > 0.83); however, the two models with a direction-dependent component were slightly more accurate. In addition, parameter estimates of the ballistic model were in close agreement with a foliar litter production equation derived from the literature for beech and with wind characteristics measured during leaf litterfall for both species. Because of its mechanistic background, such a spatially explicit model might be incorporated as a litterfall module in larger models (nutrient cycling, plant population dynamics) or used to determine the manner in which patch size in mixed-species stands influences litter mixture.     

Inverse modeling for effective dispersal: Do we need tree size to estimate fecundity?

The estimation of the dispersal kernel for the seedling and sapling stages of the recruitment process was made possible through the application of inverse modeling to dispersal data. This method uses the spatial coordinates of adult trees and the counts of seedlings (or saplings) in small quadrats to estimate the dispersal kernel. The unknown number of recruits produced by an adult tree (the fecundity) is estimated - simultaneously with the dispersal kernel - via an allometric linear model relating the unknown quantity with a (easily) measured characteristic of the adult tree (usually the basal area). However, the allometric relation between tree size and reproductive success in the sapling (or seedling) stage may not be strong enough when numerous, well-documented, post-dispersal processes (such as safe-site limitation for recruitment) cause large post-dispersal seedling mortality, which is usually unrelated to the size of the tree that dispersed them. In this paper we hypothesize that when tree size and reproductive success in the seedling/sapling stage are not well correlated then the use of allometry in inverse modeling is counter-productive and may lead to poor model fits. For these special cases we suggest using a new model for effective dispersal that we term the unrestricted fecundity (UF) model that, contrary to allometric models, makes no assumptions on the fecundities; instead they are allowed to vary freely from one tree to another and even to be zero for trees that are reproductively inactive. Based on this model, we examine the hypothesis that when tree size and reproductive success are weakly correlated and the fecundities are estimated independently of tree size the goodness-of-fit and the ecological meaning of dispersal models (in the seedling or sapling stage) may be enhanced. Parameters of the UF model are estimated through the EM algorithm and their standard errors are approximated via the observed information matrix. We fit the UF model to a dataset from an expanding European beech population of central Spain as well as to a set of simulated dispersal data were the correlation between reproductive success and tree size was moderate. In comparisons with a simple allometric model, the UF model fitted the data better and the parameter estimates were less biased. We suggest using this new approach for modeling dispersal in the seedling and sapling stages when tree size (or other adult-specific covariates) is not deemed to be in strong relation to the reproductive success of adults. Models that use covariates for modeling the fecundity of adults should be preferred when reproductive success and tree size guard a strong relationship.     

Information processing in models for density-dependent emigration: A comparison

Density-dependent emigration has been recognized as a fitness enhancing strategy. Yet, especially in the modelling literature there is no consensus about how density-dependent emigration should quantitatively be incorporated into metapopulation models. In this paper we compare the performance of five different dispersal strategies (defined by the functional link between density and emigration probability). Four of these strategies are based on published functional relationships between local population density and emigration probability, one assumes density-independent dispersal. We use individual-based simulations of time-discrete metapopulation dynamics and conduct evolution experiments for a broad range of values for dispersal mortality and environmental stochasticity. For each set of these conditions we analyze the evolution of emigration rates in ‘monoculture experiments’ (with only one type of dispersal strategy used by all individuals in the metapopulation) as well as in selection experiments that allow a pair-wise comparison of the performance of each functional type. We find that a single-parameter ‘asymptotic threshold’ strategy - derived from the marginal value theorem - with a decelerating increase of emigration rate with increasing population density, out-competes any other strategy, i.e. density-independent emigration, a ‘linear threshold’ strategy and a flexible three-parameter strategy. Only when environmental conditions select for extremely high emigration probabilities (close to one), strategies may perform approximately equally. A simple threshold strategy derived for the case of continuous population growth performs even worse than the density-independent strategy. As the functional type of the dispersal function implemented in metapopulation models may severely affect predictions concerning the survival of populations, range expansion, or community changes we clearly recommend to carefully select adequate functions to model density-dependent dispersal.     

Multiple socioecological factors influence timing of natal dispersal in kangaroo rats

Natal dispersal is an important event in the life history of many species. Timing of natal dispersal can influence survivorship and subsequent reproductive success. A variety of individual proximal factors determine if and when offspring disperse from the natal territory by influencing the costs of dispersing and the benefits of delaying dispersal. I examined the influence of multiple factors on dispersal age in the banner-tailed kangaroo rat (Dipodomys spectabilis), a solitary species lacking extreme sex-biased dispersal. I used an information theoretic approach to compare Cox proportional hazards regression models of dispersal age for 121 offspring over a 3-year period consisting of low and high population densities. The top-ranked models indicated that dispersal age was influenced by a combination of socioecological factors related to resource competition, environmental conditions, kin competition, and a lesser extent sex. Circumstances that likely reduced the probability of successful dispersal such as intense resource competition at high population density and being born earlier in the breeding season when environmental conditions were poor lead to longer delays in natal dispersal. Offspring in larger litters also dispersed earlier possibly to avoid competition with kin. Sex was weakly supported in top models but may only influence dispersal age at high population densities. These results suggest that the proximal factors that trigger dispersal are influenced by a combination of multiple effects related to the costs of dispersing and the benefits of remaining at home, even in species that do not form long-term social groups or have extreme sex-biased dispersal.     

Modeling range dynamics in heterogeneous landscapes: invasion of the hemlock woolly adelgid in eastern North America

Range expansion by native and exotic species will continue to be a major component of global change. Anticipating the potential effects of changes in species distributions requires models capable of forecasting population spread across realistic, heterogeneous landscapes and subject to spatiotemporal variability in habitat suitability. Several decades of theory and model development, as well as increased computing power and availability of fine-resolution GIS data, now make such models possible. Still unanswered, however, is the question of how well this new generation of dynamic models will anticipate range expansion. Here we develop a spatially explicit stochastic model that combines dynamic dispersal and population processes with fine-resolution maps characterizing spatiotemporal heterogeneity in climate and habitat to model range expansion of the hemlock woolly adelgid (HWA; Adelges tsugae). We parameterize this model using multiyear data sets describing population and dispersal dynamics of HWA and apply it to eastern North America over a 57-year period (1951-2008). To evaluate the model, the observed pattern of spread of HWA during this same period was compared to model predictions. Our model predicts considerable heterogeneity in the risk of HWA invasion across space and through time, and it suggests that spatiotemporal variation in winter temperature, rather than hemlock abundance, exerts a primary control on the spread of HWA. Although the simulations generally matched the observed current extent of the invasion of HWA and patterns of anisotropic spread, it did not correctly predict when HWA was observed to arrive in different geographic regions. We attribute differences between the modeled and observed dynamics to an inability to capture the timing and direction of long-distance dispersal events that substantially affected the ensuing pattern of spread.     

How complex do models need to be to predict dispersal of threatened species through matrix habitats?

Persistence of species in fragmented landscapes depends on dispersal among suitable breeding sites, and dispersal is often influenced by the "matrix" habitats that lie between breeding sites. However, measuring effects of different matrix habitats on movement and incorporating those differences into spatially explicit models to predict dispersal is costly in terms of time and financial resources. Hence a key question for conservation managers is: Do more costly, complex movement models yield more accurate dispersal predictions? We compared the abilities of a range of movement models, from simple to complex, to predict the dispersal of an endangered butterfly, the Saint Francis' satyr (Neonympha mitchellii francisci). The value of more complex models differed depending on how value was assessed. Although the most complex model, based on detailed movement behaviors, best predicted observed dispersal rates, it was only slightly better than the simplest model, which was based solely on distance between sites. Consequently, a parsimony approach using information criteria favors the simplest model we examined. However, when we applied the models to a larger landscape that included proposed habitat restoration sites, in which the composition of the matrix was different than the matrix surrounding extant breeding sites, the simplest model failed to identify a potentially important dispersal barrier, open habitat that butterflies rarely enter, which may completely isolate some of the proposed restoration sites from other breeding sites. Finally, we found that, although the gain in predicting dispersal with increasing model complexity was small, so was the increase in financial cost. Furthermore, a greater fit continued to accrue with greater financial cost, and more complex models made substantially different predictions than simple models when applied to a novel landscape in which butterflies are to be reintroduced to bolster their populations. This suggests that more complex models might be justifiable on financial grounds. Our results caution against a pure parsimony approach to deciding how complex movement models need to be to accurately predict dispersal through the matrix, especially if the models are to be applied to novel or modified landscapes.     

Incorporating animal behavior into seed dispersal models: implications for seed shadows

Seed dispersal fundamentally influences plant population and community dynamics but is difficult to quantify directly. Consequently, models are frequently used to describe the seed shadow (the seed deposition pattern of a plant population). For vertebrate-dispersed plants, animal behavior is known to influence seed shadows but is poorly integrated in seed dispersal models. Here, we illustrate a modeling approach that incorporates animal behavior and develop a stochastic, spatially explicit simulation model that predicts the seed shadow for a primate-dispersed tree species (Virola calophylla, Myristicaceae) at the forest stand scale. The model was parameterized from field-collected data on fruit production and seed dispersal, behaviors and movement patterns of the key disperser, the spider monkey (Ateles paniscus), densities of dispersed and non-dispersed seeds, and direct estimates of seed dispersal distances. Our model demonstrated that the spatial scale of dispersal for this V. calophylla population was large, as spider monkeys routinely dispersed seeds >100 m, a commonly used threshold for long-distance dispersal. The simulated seed shadow was heterogeneous, with high spatial variance in seed density resulting largely from behaviors and movement patterns of spider monkeys that aggregated seeds (dispersal at their sleeping sites) and that scattered seeds (dispersal during diurnal foraging and resting). The single-distribution dispersal kernels frequently used to model dispersal substantially underestimated this variance and poorly fit the simulated seed-dispersal curve, primarily because of its multimodality, and a mixture distribution always fit the simulated dispersal curve better. Both seed shadow heterogeneity and dispersal curve multimodality arose directly from these different dispersal processes generated by spider monkeys. Compared to models that did not account for disperser behavior, our modeling approach improved prediction of the seed shadow of this V. calophylla population. An important function of seed dispersal models is to use the seed shadows they predict to estimate components of plant demography, particularly seedling population dynamics and distributions. Our model demonstrated that improved seed shadow prediction for animal-dispersed plants can be accomplished by incorporating spatially explicit information on disperser behavior and movements, using scales large enough to capture routine long-distance dispersal, and using dispersal kernels, such as mixture distributions, that account for spatially aggregated dispersal.     

Improving inverse model fitting in trees—Anisotropy,multiplicative effects,and Bayes estimation

Model fitting for individual-based effects in forests has some problems. Because samples measuring the separate influence of each individual are rarely available, the measured value in the sample represents the influence of all surrounding individual trees. Therefore, it is helpful to build inverse models that use the spatial pattern of the variable as well as that of the source trees. For example, since seed dispersal is influenced by wind effects, a model is discussed describing anisotropic effects to ensure an unbiased estimate of the total fruit number. Further, we present a model describing the absorption of radiation by trees. In this case a multiplicative combination of individual effects yields the total effect. Our approach uses logarithmic transformations of the original data to model multiplicative combinations as sum of transformed single effects. For fitting model parameters we propose an approach based on Bayesian statistics, to ensure ecologically interpretable parameters.     

Consequences of ignoring dispersal variation in network models for landscape connectivity

Habitat loss and fragmentation can negatively influence population persistence and biodiversity, but the effects can be mitigated if species successfully disperse between isolated habitat patches. Network models are the primary tool for quantifying landscape connectivity, yet in practice, an overly simplistic view of species dispersal is applied. These models often ignore individual variation in dispersal ability under the assumption that all individuals move the same fixed distance with equal probability. We developed a modeling approach to address this problem. We incorporated dispersal kernels into network models to determine how individual variation in dispersal alters understanding of landscape-level connectivity and implemented our approach on a fragmented grassland landscape in Minnesota. Ignoring dispersal variation consistently overestimated a population's robustness to local extinctions and underestimated its robustness to local habitat loss. Furthermore, a simplified view of dispersal underestimated the amount of habitat substructure for small populations but overestimated habitat substructure for large populations. Our results demonstrate that considering biologically realistic dispersal alters understanding of landscape connectivity in ecological theory and conservation practice.     

Unveiling human-assisted dispersal mechanisms in invasive alien insects: Integration of spatial stochastic simulation and phenology models

Capturing the spread of biological invasions in heterogeneous landscapes is a complex modelling task where information on both dispersal and population dynamics needs to be integrated. Spatial stochastic simulation and phenology models have rarely been combined to assist in the study of human-assisted long-distance dispersal events.Here we develop a process-based spatially explicit landscape-extent simulation model that considers the spread and detection of invasive insects. Natural and human-assisted dispersal mechanisms are modelled with an individual-based approach using negative exponential and negative power law dispersal kernels and gravity models. The model incorporates a phenology sub-model that uses daily temperature grids for the prediction and timing of the population dynamics in each habitat patch. The model was applied to the study of the invasion by the important maize pest western corn rootworm (WCR) Diabrotica virgifera ssp. virgifera in Europe. We parameterized and validated the model using maximum likelihood and simulation methods from the historical invasion of WCR in Austria.WCR was found to follow stratified dispersal where international transport networks in the Danube basin played a key role in the occurrence of long-distance dispersal events. Detection measures were found to be effective and altitude had a significant effect on limiting the spread of WCR. Spatial stochastic simulation combined with phenology models, maximum likelihood methods and predicted versus observed regression showed a high degree of flexibility that captured the salient features of WCR spread in Austria. This modelling approach is useful because it allows to fully exploit and the often limited and heterogeneous information available regarding the population dynamics and dispersal of alien invasive insects.     

Probability distributions and statistical inference for axial data

Observations on axes which lack information on the direction of propagation are referred to as axial data. Such data are often encountered in enviromental sciences, e.g. observations on propagations of cracks or on faults in mining walls. Even though such observations are recorded as angles, circular probability models are inappropriate for such data since the constraint that observations lie only in [0, π) needs to be enforced. Probability models for such axial data are argued here to have a general structure stemming from that of wrapping a circular distribution on a semi-circle. In particular, we consider the most popular circular model, the von Mises or circular normal distribution, and derive the corresponding axial normal distribution. Certain properties of this distribution are established. Maximum likelihood estimation of its parameters are shown to be surprisingly, in contrast to trigonometric moment estimation, numerically quite appealing. Finally we illustrate our results by several real life axial data sets. Received: September 2004/ Revised: December 2004     

Planning for Climate Change: Identifying Minimum-Dispersal Corridors for the Cape Proteaceae

Abstract:  Climate change poses a challenge to the conventional approach to biodiversity conservation, which relies on fixed protected areas, because the changing climate is expected to shift the distribution of suitable areas for many species. Some species will persist only if they can colonize new areas, although in some cases their dispersal abilities may be very limited. To address this problem we devised a quantitative method for identifying multiple corridors of connectivity through shifting habitat suitabilities that seeks to minimize dispersal demands first and then the area of land required. We applied the method to Proteaceae mapped on a 1-minute grid for the western part of the Cape Floristic Region of South Africa, to supplement the existing protected areas, using Worldmap software. Our goal was to represent each species in at least 35 grid cells (approximately 100 km²) at all times between 2000 and 2050 despite climate change. Although it was possible to achieve the goal at reasonable cost, caution will be needed in applying our method to reserves or other conservation investments until there is further information to support or refine the climate-change models and the species' habitat-suitability and dispersal models.     

Genes and song: genetic and social connections in fragmented habitat in a woodland bird with limited dispersal

Understanding the processes leading to population declines in fragmented landscapes is essential for successful conservation management. However, isolating the influence of disparate processes, and dispersal in particular, is challenging. The Grey Shrike-thrush, Colluricincla harmonica, is a sedentary woodland-dependent songbird, with learned vocalizations whose incidence in suitable habitat patches falls disproportionally with decline in tree cover in the landscape. Although it has been suggested that gaps in tree cover might act as barriers to its dispersal, the species remains in many remnants of native vegetation in agricultural landscapes, suggesting that it may have responded to habitat removal and fragmentation by maintaining or even increasing dispersal distances. We quantified population connectivity of the Grey Shrike-thrush in a system fragmented over more than 120 years using genetic (microsatellites) and acoustic (song types) data. First, we tested for population genetic and acoustic structure at regional and local scales in search of barriers to dispersal or gene flow and signals of local spatial structuring indicative of restricted dispersal or localized acoustic similarity. Then we tested for effects of habitat loss and fragmentation on genetic and acoustic connectivity by fitting alternative models of mobility (isolation-by-distance [the null model] and reduced and increased movement models) across treeless vs. treed areas. Birds within -5 km of each other had more similar genotypes and song types than those farther away, suggesting that dispersal and song matching are limited in the region. Despite restricted dispersal detected for females (but not males), populations appeared to be connected by gene flow and displayed some cultural (acoustic) connectivity across the region. Fragmentation did not appear to impact greatly the dispersal of the Grey Shrike-thrush: none of the mobility models fit the genetic distances of males, whereas for females, an isolation-by-distance model could not be rejected in favor of the models of reduced or increased movement through treeless gaps. However, dissimilarities of the song types were more consistent with the model of reduced cultural connectivity through treeless areas, suggesting that fragmentation impedes song type sharing in the Grey Shrike-thrush. Our paper demonstrates that habitat fragmentation hinders important population processes in an Australian woodland bird even though its dispersal is not detectably impacted.     

Experimental manipulation of seed shadows of an Afrotropical tree determines drivers of recruitment

The loss of animals in tropical forests may alter seed dispersal patterns and reduce seedling recruitment of tree species, but direct experimental evidence is scarce. We manipulated dispersal patterns of Manilkara mabokeensis, a monkey-dispersed tree, to assess the extent to which spatial distributions of seeds drive seedling recruitment. Based on the natural seed shadow, we created seed distributions with seeds deposited under the canopy ("no dispersal"), with declining density from the tree ("natural dispersal"), and at uniform densities ("good dispersal"). These distributions mimicked dispersal patterns that could occur with the extirpation of monkeys, low levels of hunting, and high rates of seed dispersal. We monitored seedling emergence and survival for 18 months and recorded the number of leaves and damage to leaves. "Good dispersal" increased seedling survival by 26%, and "no dispersal" decreased survival by 78%, relative to "natural dispersal." Using a mixed-effects survival model, we decoupled the distance and density components of the seed shadow: seedling survival depended on the seed density, but not on the distance from the tree. Although community seedling diversity tended to decrease with longer dispersal distances, we found no conclusive evidence that patterns of seed dispersal influence the diversity of the seedling community. Local seed dispersal does affect seedling recruitment and survival, with better dispersal resulting in higher seedling recruitment; hence the loss of dispersal services that comes with the reduction or extirpation of seed dispersers will decrease regeneration of some tree species.     

Dispersal, competition, and shifting patterns of diversity in a degraded oak savanna

Diversity is a balance between processes that add and limit species (e.g., dispersal vs. competition), but reconciling their contributions remains a challenge. Recruit-ment-based models predict that dispersal barriers are most limiting for diversity, while competition-based models predict that dispersal matters only when competition is minimized. Testing these models is difficult because their influence varies with scale and site productivity. In a degraded oak savanna, we used plot-level (seed additions, burning) and site-level (proportions of regional functional groups found locally) analyses in areas with variable soil depth to examine how dispersal and competition influence diversity. At the plot level, added species persisted where they were formerly absent, but few established naturally despite fire-induced resource enrichment and nearby populations, revealing the importance of dispersal limitation for diversity. This result did not vary with soil depth or standing crop. Although competition could not prevent establishment in unburned plots, it significantly lowered survival, indicating that resource limitations exacerbate dispersal inefficiencies. At the site level, the concordance between regional and local diversity for native species was associated with soil depth heterogeneity, not dispersal or competition. This suggests that persistence is determined primarily by the influence of the environment on population demographics. Given that the formation of new populations is unlikely, those remaining appear to be confined to optimal habitat where they resist competitive or stochastic displacement, possibly explaining why species loss is rare despite substantial habitat loss and invasion. For exotics, there was no relationship between diversity and soil depth heterogeneity. Annuals with presumed dispersal capabilities were significantly overrepresented in all sites while perennial forbs, the largest regional functional group, were significantly underrepresented. We interpret the native-exotic discrepancies as reflecting the recent arrival of exotics (150 years ago), suggesting that local establishment occurs slowly even for species with regional prevalence. The accumulation lag may be explained by the need for founder populations to be demographically stable; otherwise persistence requires continual immigration favoring overrepresentation by dispersers. Our findings support the view that dispersal limitation restricts diversity within plant communities, but suggests that the impacts of environment on demographic performance ultimately determine the pattern and rate of community assembly.     

Managing plant population spread: prediction and analysis using a simple model

Models can be used to direct the management of population spread for the control of invasives or to encourage species of conservation value. Analytical models are attractive because of their theoretical basis and limited data requirements, but there is concern that their simplicity may limit their practical utility. We address the applied use of simple models in a study of a declining annual herb, Rhinanthus minor. We parameterized a population-spread model using field data on demography and dispersal for four management systems: grazed only (GR), hay-cut once (H1), hay-cut twice (H2), and hay-cut with autumn grazing (HG). Within a replicated experiment we measured spread rates of introduced R. minor populations over eight years. The modeled and measured spread rates were very similar in terms of both patterns of management effects and absolute values, so that in both cases HG > H2, H1 > GR. The treatments affected both dispersal and demography (establishment and survival) and so we used decomposition approaches to analyze the major causes of differences in population spread. Increased dispersal under hay-cutting was more important than demographic changes and accounted for approximately 70% of the differences in spread rate between the hay-cut and grazed-only treatments. Furthermore, management effects on the tail of the dispersal curve were by far the most critical in governing spread. This study suggests that simple models can be used to inform practical conservation management, and we demonstrate straightforward uses of our model to predict the impacts of different management strategies. While simple models can give accurate projections, we emphasize that they must be parameterized with high-quality data gathered at the appropriate spatial scale.     

Sex-specific effects of the local social environment on juvenile post-fledging dispersal in great tits

An individual’s decision to disperse from the natal habitat can affect its future fitness prospects. Especially in species with sex-biased dispersal, we expect the cost–benefit balance for dispersal to vary according to the social environment (e.g., local sex ratio and density). However, little is known about the social factors affecting dispersal decisions and about the temporal and spatial patterns of the dispersal process. In our study, we investigated experimentally the effects of the social environment on post-fledging dispersal of juvenile great tits by simultaneously manipulating the density and sex ratio of fledglings within forest plots. We expected young females in the post-fledging period mainly to compete for resources related to food and, as they are subordinate to males, we predicted higher female dispersal from male-biased plots. Juvenile males compete for vacant territories already in late summer and autumn; thus, we predicted increased male dispersal from high density and male-biased plots. We found that juvenile females had a higher probability to leave male-biased plots and had dispersed further from male-biased plots in the later post-fledging phase when juvenile males start to become territorial and more aggressive. Juvenile males were least likely to leave male-biased plots and had smallest dispersal distances from female-biased plots early after fledging. The results suggest that the social environment differentially affected the costs and benefits of philopatry for male and female juveniles. The local sex ratio of individuals is thus an important social trait to be considered for understanding sex-specific dispersal processes.     

Sine-skewed axial distributions with an application for fallen tree data

An axis is an undirected line where there is no reason to distinguish one end of the line from the other. Phenomena in nature that can be described as axial data are numerous. In this paper a method of trigonometric moments for the axial normal or axial von Mises distribution as an alternative to the method by Arnold and SenGupta (Environ Ecol Stat 13:271–285, 2006) is discussed. Sine-skewed axial Jones–Pewsey, von Mises and wrapped Cauchy distributions are introduced as special cases of a more general construction of skew axial distributions. As an example the methods are applied to a data set which consists of the orientations of logs on the floor of a primeval spruce forest.     

Estimating dispersal potential for marine larvae: dynamic models applied to scleractinian corals

Dispersal influences ecological dynamics, evolution, biogeography, and biodiversity conservation, but models of larval dispersal in marine organisms make simplifying assumptions that are likely to approximate poorly the temporal dynamics of larval survival and capacity for settlement. In particular, larval mortality rates are typically assumed to be constant throughout larval life; and all larvae are frequently assumed to acquire and lose competence at the same time. To improve upon these assumptions, we here develop simple models of dispersal potential that incorporate rates of mortality, and acquisition and loss of settlement competence. We fit these models to empirical competence and survival data for five scleractinian coral species, to test the models' ability to characterize empirical survival and competence patterns, and to estimate the dispersal potential implied by those patterns. The models fit the data well, incorporating qualitative features of competence and survival that traditional approaches to modeling dispersal do not, with important implications for dispersal potential. Most notably, there was high within-cohort variation in the duration of the competent period in all species, and this variation increases both self-recruitment and long-distance dispersal compared with models assuming a fixed competent period. These findings help to explain the seeming paradox of high genetic population structure, coupled with large geographic range size, observed in many coral species. More broadly, our approach offers a way to parsimoniously account for variation in competence dynamics in dispersal models, a phenomenon that our results suggest has important effects on patterns of connectivity in marine metapopulations.