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.

The topology of ecological interaction webs holds important information for theories of coevolution, biodiversity, and ecosystem stability . However, most previous network analyses solely counted the number of links and ignored variation in link strength. Because of this crude resolution, results vary with scale and sampling intensity, thus hampering a comparison of network patterns at different levels . We applied a recently developed quantitative and scale-independent analysis based on information theory to 51 mutualistic plant-animal networks, with interaction frequency as measure of link strength. Most networks were highly structured, deviating significantly from random associations. The degree of specialization was independent of network size. Pollination webs were significantly more specialized than seed-dispersal webs, and obligate symbiotic ant-plant mutualisms were more specialized than nectar-mediated facultative ones. Across networks, the average specialization of animal and plants was correlated, but is constrained by the ratio of plant to animal species involved. In pollination webs, rarely visited plants were on average more specialized than frequently attended ones, whereas specialization of pollinators was positively correlated with their interaction frequency. We conclude that quantitative specialization in ecological communities mirrors evolutionary trade-offs and constraints of web architecture. This approach can be easily expanded to other types of biological interactions.

Based on a marginal value approach, we derive a nonlinear expression for evolutionarily stable (ES) dispersal rates in a metapopulation with global dispersal. For the general case of density-dependent population growth, our analysis shows that individual dispersal rates should decrease with patch capacity and-beyond a certain threshold-increase with population density. We performed a number of spatially explicit, individual-based simulation experiments to test these predictions and to explore further the relevance of variation in the rate of population increase, density dependence, environmental fluctuations and dispersal mortality on the evolution of dispersal rates. They confirm the predictions of our analytical approach. In addition, they show that dispersal rates in metapopulations mostly depend on dispersal mortality and inter-patch variation in population density. The latter is dominantly driven by environmental fluctuations and the rate of population increase. These conclusions are not altered by the introduction of neighbourhood dispersal. With patch capacities in the order of 100 individuals, kin competition seems to be of negligible importance for ES dispersal rates except when overall dispersal rates are low.

Summary 1.Understanding the causes and consequences of dispersal remains a central topic in ecology and evolution. However, a mismatch exists between our empirical understanding of the complexity of dispersal and our representation of dispersal in models. While the empirical literature is replete with examples of condition dependence at the emigration, movement and settlement phases, models rarely incorporate realism or complexity to this degree. Nor do models often include the different costs associated with dispersal, which can themselves be linked to one or more of the three key phases. 2. Here, we propose that by explicitly accounting for emigration, movement and settlement (and the multiple costs associated with each) we can substantially improve our understanding of both the dispersal process itself and how dispersal traits trade off against other life-history characteristics. We explore some of these issues conceptually, before presenting illustrative results gained from a flexible individual-based model which incorporates considerable dispersal complexity. 3. These results emphasise the nonlinear interplay between the different dispersal stages. For example, we find that investment in movement ability (at a cost to fecundity) depends upon the propensity to emigrate (and vice versa). However, owing to selection acting at the metapopulation level as well as at the individual level, the relationship between the two is not straightforward. Importantly, the shape of the trade-off between movement ability and reproductive potential can strongly influence the joint evolution of dispersal parameters controlling the degree of investment in safer movement, the probability of emigration and the straightness of movement. 4. Our results highlight that the joint evolution of dispersal characteristics can have major implications for spatial population dynamics and we argue that, in addition to increasing our fundamental biological understanding, a new generation of dispersal modelling, which exploits recent empirical advances, can substantially improve our ability to predict and manage the response of species to environmental change.