The migration of birds from temperate and arctic breeding grounds to lower latitudes for the non-breeding season is a major global wildlife event, comprising billions of birds and providing an important component of global ecosystems. Some of these movements are truly amazing – some 12 gram birds fly 3000km non-stop to reach their non-breeding grounds. The majority of inter-continental terrestrial migrations are undertaken by songbirds, which migrate across broad fronts, often stopping to refuel on their journey.
Despite intensive study on the breeding grounds, and to a lesser extent the non-breeding grounds and stop-over sites, research to simulate the migratory journeys themselves, or to test theoretic models of migration for such species, is rare. A generic model of migration has never been applied to songbirds undertaking the Europe- trans-Saharan migration; this is a major objective of this proposal. In light of projections of climate and land-use changes on the breeding, non-breeding and stop-over grounds of these species, such models are urgently required. Migrants could be especially vulnerable to climate change given their reliance on the linkage between widely-separated areas, which are potentially undergoing very different changes. The main limitation to developing and testing models of songbird migration has been an inability to monitor individual movements so as to understand their routes and strategies. The recent development of geolocator trackers, which record time and location and can be used on the smaller species that comprise the majority of migrants, has provided data to test migration models for the first time. Here, we will collate tracking, and extensive ringing and observation data for trans-Saharan migrants, to better understand their migratory routes and decisions. Simultaneously, we will develop flight models for individual species, which consider species-specific physiology and form to determine their flight-range potential. We will use the outputs in spatially-explicit dynamic programming (DP) models, and will test their ability to replicate observed patterns of migration. This will build on earlier work modelling optimal migration using very simple systems. We have already developed pilot flight range models that replicate well the timing and routes of migration of tracked individuals of species with near-linear migrations. Building on these data, we will use DP models, with realistic landscape resources/costs, to evaluate optimal migratory routes and refuelling locations given temporally-constrained destination rewards (i.e., likely breeding success). We will consider landscapes with dynamic resource availability, based on factors such as species-specific habitat preferences and likely food availability (based on weather and NDVI), and will include factors such as wind direction, location (relative to time of year) and an individual’s energy stores to determine whether they should stay or, if not, where they should move to. We will use these models to explore inter-annual variation in arrival dates at migratory end-points, to aid understanding of what drives phenological changes in migratory species, and to test theories of what determines migratory decisions. Modelling formalises our understanding of migration, making explicit our assumptions and any gaps in available data. Crucially, it can also inform our understanding of the migratory process and how that process will be influenced by future environmental changes. The end product will be a much better understanding of the drivers of the routes and strategies of long-distance migrants, and a modelling framework that can be applied to a wide suite of migratory passerines in different regions, or under scenarios of climate and land-use change, to simulate consequences for migratory journeys.