Justin M. J. Travis

Costs of dispersal

Dispersal costs can be classified into energetic, time, risk and opportunity costs and may be levied directly or deferred during departure, transfer and settlement. They may equally be incurred during life stages before the actual dispersal event through investments in special morphologies. Because costs will eventually determine the performance of dispersing individuals and the evolution of dispersal, we here provide an extensive review on the different cost types that occur during dispersal in a wide array of organisms, ranging from micro‐organisms to plants, invertebrates and vertebrates. In general, costs of transfer have been more widely documented in actively dispersing organisms, in contrast to a greater focus on costs during departure and settlement in plants and animals with a passive transfer phase. Costs related to the development of specific dispersal attributes appear to be much more prominent than previously accepted. Because costs induce trade‐offs, they give rise to covariation between dispersal and other life‐history traits at different scales of organismal organisation. The consequences of (i) the presence and magnitude of different costs during different phases of the dispersal process, and (ii) their internal organisation through covariation with other life‐history traits, are synthesised with respect to potential consequences for species conservation and the need for development of a new generation of spatial simulation models.

Identification of 100 fundamental ecological questions

Summary 1. Fundamental ecological research is both intrinsically interesting and provides the basic knowledge required to answer applied questions of importance to the management of the natural world. The 100th anniversary of the British Ecological Society in 2013 is an opportune moment to reflect on the current status of ecology as a science and look forward to high-light priorities for future work.

Habitat persistence, habitat availability and the evolution of dispersal

Many organisms live in ephemeral habitats, making dispersal a vital element of life history. Here, we investigate how dispersal rate evolves in response to habitat persistence, mean habitat availability and landscape pattern. We show that dispersal rate is generally lowered by reduced habitat availability and by longer habitat persistence. However, for habitats that persist for an average of ten times the length of a generation, we show a clear non–monotonic relationship between habitat availability and dispersal rate. Some patterns of available habitat result in populations with dispersal polymorphisms. We explain these observations as a metapopulation effect, with the rate of evolution a function of both within–population and between–population selection pressures. Individuals in corridors evolve much lower dispersal rates than those in the mainland populations, especially within long, narrow corridors. We consider the implications of the results for conservation.

Trade-offs and the evolution of life-histories during range expansion.

During range-advance, individuals on the expanding edge of the population face a unique selective environment. In this study, we use a three-trait trade-off model to explore the evolution of dispersal, reproduction and competitive ability during range expansion. We show that range expansion greatly affects the evolution of life-history traits due to differing selection pressures at the front of the range compared with those found in stationary and core populations. During range expansion, dispersal and reproduction are selected for on the expanding population front, whereas traits associated with fitness at equilibrium density (competitive ability) show dramatic declines. Additionally, we demonstrate that the presence of a competing species can considerably reduce the extent to which dispersal is selected upwards at an expanding front. These findings have important implications for understanding both the rate of spread of invasive species and the range-shifting dynamics of native species in response to climate change.

The evolution of density-dependent dispersal

Despite a large body of empirical evidence suggesting that the dispersal rates of many species depend on population density, most metapopulation models assume a density–independent rate of dispersal. Similarly, studies investigating the evolution of dispersal have concentrated almost exclusively on density–independent rates of dispersal. We develop a model that allows density–dependent dispersal strategies to evolve. Our results demonstrate that a density–dependent dispersal strategy almost always evolves and that the form of the relationship depends on reproductive rate, type of competition, size of subpopulation equilibrium densities and cost of dispersal. We suggest that future metapopulation models should account for density–dependent dispersal

Reid's paradox revisited: the evolution of dispersal kernels during range expansion.

Current approaches to modeling range advance assume that the distribution describing dispersal distances in the population (the “dispersal kernel”) is a static entity. We argue here that dispersal kernels are in fact highly dynamic during periods of range advance because density effects and spatial assortment by dispersal ability (“spatial selection”) drive the evolution of increased dispersal on the expanding front. Using a spatially explicit individual‐based model, we demonstrate this effect under a wide variety of population growth rates and dispersal costs. We then test the possibility of an evolved shift in dispersal kernels by measuring dispersal rates in individual cane toads (Bufo marinus) from invasive populations in Australia (historically, toads advanced their range at 10 km/year, but now they achieve >55 km/year in the northern part of their range). Under a common‐garden design, we found a steady increase in dispersal tendency with distance from the invasion origin. Dispersal kernels on the invading front were less kurtotic and less skewed than those from origin populations. Thus, toads have increased their rate of range expansion partly through increased dispersal on the expanding front. For accurate long‐range forecasts of range advance, we need to take into account the potential for dispersal kernels to be evolutionarily dynamic.

Improving the forecast for biodiversity under climate change

BACKGROUND As global climate change accelerates, one of the most urgent tasks for the coming decades is to develop accurate predictions about biological responses to guide the effective protection of biodiversity. Predictive models in biology provide a means for scientists to project changes to species and ecosystems in response to disturbances such as climate change. Most current predictive models, however, exclude important biological mechanisms such as demography, dispersal, evolution, and species interactions. These biological mechanisms have been shown to be important in mediating past and present responses to climate change. Thus, current modeling efforts do not provide sufficiently accurate predictions. Despite the many complexities involved, biologists are rapidly developing tools that include the key biological processes needed to improve predictive accuracy. The biggest obstacle to applying these more realistic models is that the data needed to inform them are almost always missing. We suggest ways to fill this growing gap between model sophistication and information to predict and prevent the most damaging aspects of climate change for life on Earth. ADVANCES On the basis of empirical and theoretical evidence, we identify six biological mechanisms that commonly shape responses to climate change yet are too often missing from current predictive models: physiology; demography, life history, and phenology; species interactions; evolutionary potential and population differentiation; dispersal, colonization, and range dynamics; and responses to environmental variation. We prioritize the types of information needed to inform each of these mechanisms and suggest proxies for data that are missing or difficult to collect. We show that even for well-studied species, we often lack critical information that would be necessary to apply more realistic, mechanistic models. Consequently, data limitations likely override the potential gains in accuracy of more realistic models. Given the enormous challenge of collecting this detailed information on millions of species around the world, we highlight practical methods that promote the greatest gains in predictive accuracy. Trait-based approaches leverage sparse data to make more general inferences about unstudied species. Targeting species with high climate sensitivity and disproportionate ecological impact can yield important insights about future ecosystem change. Adaptive modeling schemes provide a means to target the most important data while simultaneously improving predictive accuracy. OUTLOOK Strategic collections of essential biological information will allow us to build generalizable insights that inform our broader ability to anticipate species’ responses to climate change and other human-caused disturbances. By increasing accuracy and making uncertainties explicit, scientists can deliver improved projections for biodiversity under climate change together with characterizations of uncertainty to support more informed decisions by policymakers and land managers. Toward this end, a globally coordinated effort to fill data gaps in advance of the growing climate-fueled biodiversity crisis offers substantial advantages in efficiency, coverage, and accuracy. Biologists can take advantage of the lessons learned from the Intergovernmental Panel on Climate Change’s development, coordination, and integration of climate change projections. Climate and weather projections were greatly improved by incorporating important mechanisms and testing predictions against global weather station data. Biology can do the same. We need to adopt this meteorological approach to predicting biological responses to climate change to enhance our ability to mitigate future changes to global biodiversity and the services it provides to humans. Emerging models are beginning to incorporate six key biological mechanisms that can improve predictions of biological responses to climate change. Models that include biological mechanisms have been used to project (clockwise from top) the evolution of disease-harboring mosquitoes, future environments and land use, physiological responses of invasive species such as cane toads, demographic responses of penguins to future climates, climate-dependent dispersal behavior in butterflies, and mismatched interactions between butterflies and their host plants. Despite these modeling advances, we seldom have the detailed data needed to build these models, necessitating new efforts to collect the relevant data to parameterize more biologically realistic predictive models. New biological models are incorporating the realistic processes underlying biological responses to climate change and other human-caused disturbances. However, these more realistic models require detailed information, which is lacking for most species on Earth. Current monitoring efforts mainly document changes in biodiversity, rather than collecting the mechanistic data needed to predict future changes. We describe and prioritize the biological information needed to inform more realistic projections of species’ responses to climate change. We also highlight how trait-based approaches and adaptive modeling can leverage sparse data to make broader predictions. We outline a global effort to collect the data necessary to better understand, anticipate, and reduce the damaging effects of climate change on biodiversity.

The evolution of dispersal in a metapopulation: a spatially explicit, individual-based model

Dispersal is the process that binds the subpopulations of a metapopulation together. Previous models of the evolution of dispersal have tended to be deterministic and not spatially explicit. We develop an individual–based, spatially explicit lattice model to determine how subpopulation equilibrium density, reproductive rate and form of competition affect the rate of dispersal that is selected for. For comparison, a deterministic analogue of the individual–based model is also developed. Dispersal rate is a neutral character in the deterministic model. The individual–based model makes predictions which differ significantly from its deterministic counterpart, particularly when subpopulation equilibrium densities are low. Higher rates of dispersal evolve when reproductive rate is high and subpopulation equilibrium density is small. Our results demonstrate that the propensity to disperse is not a neutral character and that deterministic models of metapopulations should be interpreted with caution.

The interplay of positive and negative species interactions across an environmental gradient: insights from an individual-based simulation model

Positive interspecific interactions are commonplace, and in recent years ecologists have begun to realize how important they can be in determining community and ecosystem dynamics. It has been predicted that net positive interactions are likely to occur in environments characterized by high abiotic stress. Although empirical field studies have started to support these predictions, little theoretical work has been carried out on the dynamic nature of these effects and their consequences for community structure. We use a simple patch-occupancy model to simulate the dynamics of a pair of species living on an environmental gradient. Each of the species can exist as either a mutualist or a cheater. The results confirm the prediction: a band of mutualists tends to occur in environmental conditions beyond the limits of the cheaters. The region between mutualists and cheaters is interesting: population density here is low. Mutualists periodically occupy this area, but are displaced by cheaters, who themselves go extinct in the absence of the mutualists. Furthermore, the existence of mutualists extends the area occupied by the cheaters, essentially increasing their realized niche. Our approach has considerable potential for improving our understanding of the balance between positive and negative interspecific interactions and for predicting the probable impacts of habitat loss and climate change on communities dominated by positive interspecific interactions.

Limited evolutionary rescue of locally adapted populations facing climate change

Dispersal is a key determinant of a populations evolutionary potential. It facilitates the propagation of beneficial alleles throughout the distributional range of spatially outspread populations and increases the speed of adaptation. However, when habitat is heterogeneous and individuals are locally adapted, dispersal may, at the same time, reduce fitness through increasing maladaptation. Here, we use a spatially explicit, allelic simulation model to quantify how these equivocal effects of dispersal affect a populations evolutionary response to changing climate. Individuals carry a diploid set of chromosomes, with alleles coding for adaptation to non-climatic environmental conditions and climatic conditions, respectively. Our model results demonstrate that the interplay between gene flow and habitat heterogeneity may decrease effective dispersal and population size to such an extent that substantially reduces the likelihood of evolutionary rescue. Importantly, even when evolutionary rescue saves a population from extinction, its spatial range following climate change may be strongly narrowed, that is, the rescue is only partial. These findings emphasize that neglecting the impact of non-climatic, local adaptation might lead to a considerable overestimation of a populations evolvability under rapid environmental change.