Dispersal and migration are superficially similar large-scale movements, but which appear to differ in terms of inter-individual behavioural synchronization. Seasonal migration is a striking example of coordinated behaviour, enabling animal populations to track spatio-temporal variation in ecological conditions. By contrast, for dispersal, while social context may influence an individual's emigration and settlement decisions, transience is believed to be mostly a solitary behaviour. Here, we review differences in drivers that may explain why migration appears to be more synchronized than dispersal. We derive the prediction that the contrast in the importance of behavioural synchronization between dispersal and migration is linked to differences in the selection pressures that drive their respective evolution. Although documented examples of collective dispersal are rare, this behaviour may be more common than currently believed, with important consequences for eco-evolutionary dynamics. Crucially, to date, there is little available theory for predicting when we should expect collective dispersal to evolve, and we also lack empirical data to test predictions across species. By reviewing the state of the art in research on migration and collective movements, we identify how we can harness these advances, both in terms of theory and data collection, to broaden our understanding of synchronized dispersal and its importance in the context of global change.

Understanding how animals select for habitat and foraging resources therein is a crucial component of basic and applied ecology. Th e selection process is typically infl uenced by a variety of environmental conditions including the spatial and temporal variation in the quantity and quality of food resources, predation or disturbance risks, and inter-and intraspecifi c competition. Indeed, some of the most commonly employed ecological theories used to describe how animals choose foraging sites are: nutrient intake maximisation, density-dependent habitat selection, central-place foraging, and predation risk eff ects. Even though these theories are not mutually exclusive, rarely are multiple theoretical models considered concomitantly to assess which theory, or combination thereof, best predicts observed changes in habitat selection over space and time. Here, we tested which of the above theories best-predicted habitat selection of Svalbard-breeding pink-footed geese at their main spring migration stopover site in mid-Norway by computing a series of resource selection functions (RSFs) and their predictive ability ( k -fold cross validation scores). At this stopover site geese fuel intensively as a preparation for breeding and further migration. We found that the predation risk model and a combination of the density-dependent and central-place foraging models best-predicted habitat selection during stopover as geese selected for larger fi elds where predation risk is typically lower and selection for foraging sites changed as a function of both distance to the roost site (i.e. central-place) and changes in local density. In contrast to many other studies, the nutritional value of the available food resources did not appear to be a major limiting factor as geese used diff erent food resources proportional to their availability. Our study shows that in an agricultural landscape where nutritional value of food resources is homogeneously high and resource availability changes rapidly; foraging behaviour of geese is largely a tradeoff between fast refuelling and disturbance/predator avoidance.

To robustly predict the effects of disturbance and ecosystem changes on species, it is necessary to produce structurally realistic models with high predictive power and flexibility. To ensure that these models reflect the natural conditions necessary for reliable prediction, models must be informed and tested using relevant empirical observations. Patternoriented modelling (POM) offers a systematic framework for employing empirical patterns throughout the modelling process and has been coupled with complex systems modelling, such as in agent-based models (ABMs). However, while the production of ABMs has been rising rapidly, the explicit use of POM has not increased. Challenges with identifying patterns and an absence of specific guidelines on how to implement empirical observations may limit the accessibility of POM and lead to the production of models which lack a systematic consideration of reality. This review serves to provide guidance on how to identify and apply patterns following a POM approach in ABMs (POM-ABMs), specifically addressing: where in the ecological hierarchy can we find patterns; what kinds of patterns are useful; how should simulations and observations be compared; and when in the modelling cycle are patterns used? The guidance and examples provided herein are intended to encourage the application of POM and inspire efficient identification and implementation of patterns for both new and experienced modellers alike. Additionally, by generalising patterns found especially useful for POM-ABM development, these guidelines provide practical help for the identification of data gaps and guide the collection of observations useful for the development and verification of predictive models. Improving the accessibility and explicitness of POM could facilitate the production of robust and structurally realistic models in the ecological community, contributing to the advancement of predictive ecology at large.

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The migration strategy of many capital breeders is to garner body stores along the flyway at distinct stopover sites. The rate at which they can fuel is likely to be strongly influenced by a range of factors, such as physiology, food availability, time available for foraging and perceived predation. We analysed the foraging behaviour and fuel accumulation of pink-footed geese, an Arctic capital breeder, at their mid-flyway spring stopover site and evaluated to what extent their behaviour and fuelling were related to physiological and external factors and how it differed from other stopovers along the flyway. We found that fuel accumulation rates of geese at the mid-flyway site were limited by habitat availability rather than by digestive constraints. However, as the time available for foraging increased over the stopover season, geese were able to keep constant fuelling rate. Putting this in perspective, geese increased their daily net energy intake along the flyway corresponding to the increase in time available for foraging. The net energy intake per hour of foraging remained the same. Geese showed differences in their reaction to predators/disturbance between the sites, taking higher risks particularly at the final stopover site. Hence, perceived predation along the flyway may force birds to postpone the final fuel accumulation to the last stopover along the flyway. Flexibility in behaviour appears to be an important trait to ensure fitness in this capital breeder. Our findings are based on a new, improved method for estimating fuel accumulation of animals foraging in heterogeneous landscapes based on data obtained from satellite telemetry and habitat specific intake rates.Because some animals use stored fat and protein as an important energy source during their migration and breeding, studying their foraging behaviour and fuel accumulation is necessary in order to understand their migratory behaviour (Sapir et al. 2011). Arctic-nesting birds migrating in steps have a limited time to prepare for migration and subsequent breeding because the time window where conditions are suitable at each stopover site as well as for breeding is short (Ankney and MacInnes 1978, Alerstam and Lindström 1990, Prop and Black 1998, Drent et al. 2003. A common migration strategy is therefore to garner body stores along the flyway at distinct stopover sites in order to commence breeding soon after arrival -a strategy called capital breeding Daan 1980, Klaassen et al. 2006a). Stopover sites should provide sufficient food to allow for refuelling during their stay (Bauer et al. 2006). However, the optimal foraging conditions for the animals are limited in time and space and therefore the strategy they should employ in order to forage optimally is likely to depend on a range of factors whose importance may vary within a stopover season as well as among different stopover sites along the migratory route.

Many goose species feed on agricultural land, and with growing goose numbers, conflicts with agriculture are increasing. One possible solution is to designate refuge areas where farmers are paid to leave geese undisturbed. Here, we present a generic modelling tool that can be used to designate the best locations for refuges and to gauge the area needed to accommodate the geese. With a species distribution model, locations are ranked according to goose suitability. The size of the area to be designated as refuge can be chosen by including more or less suitable locations. A resource depletion model is then used to estimate whether enough resources are available within the designated refuge to accommodate all geese, taking into account the dynamics of food resources, including depletion by geese. We illustrate this with the management scheme for pink-footed goose Anser brachyrhynchus implemented in Norway. Here, all geese can be accommodated, but damage levels appear to depend on weather, land use and refuge size.Electronic supplementary materialThe online version of this article (doi:10.1007/s13280-017-0899-5) contains supplementary material, which is available to authorized users.

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