Which factors shape animals' migration movements across large geographical scales, how different migratory strategies emerge between populations, and how these may affect population dynamics are central questions in the field of animal migration [1] that only large-scale studies of migration patterns across a species' range can answer [2]. To address these questions, we track the migration of 270 Atlantic puffins Fratercula arctica, a red-listed, declining seabird, across their entire breeding range. We investigate the role of demographic, geographical, and environmental variables in driving spatial and behavioral differences on an ocean-basin scale by measuring puffins' among-colony differences in migratory routes and day-to-day behavior (estimated with individual daily activity budgets and energy expenditure). We show that competition and local winter resource availability are important drivers of migratory movements, with birds from larger colonies or with poorer local winter conditions migrating further and visiting less-productive waters; this in turn led to differences in flight activity and energy expenditure. Other behavioral differences emerge with latitude, with foraging effort and energy expenditure increasing when birds winter further north in colder waters. Importantly, these ocean-wide migration patterns can ultimately be linked with breeding performance: colony productivity is negatively associated with wintering latitude, population size, and migration distance, which demonstrates the cost of competition and migration on future breeding and the link between non-breeding and breeding periods. Our results help us to understand the drivers of animal migration and have important implications for population dynamics and the conservation of migratory species.

Abstract. Population-level estimates of species' distributions can reveal fundamental ecological processes and facilitate conservation. However, these may be difficult to obtain for mobile species, especially colonial central-place foragers (CCPFs; e.g., bats, corvids, social insects), because it is often impractical to determine the provenance of individuals observed beyond breeding sites. Moreover, some CCPFs, especially in the marine realm (e.g., pinnipeds, turtles, and seabirds) are difficult to observe because they range tens to ten thousands of kilometers from their colonies. It is hypothesized that the distribution of CCPFs depends largely on habitat availability and intraspecific competition. Modeling these effects may therefore allow distributions to be estimated from samples of individual spatial usage. Such data can be obtained for an increasing number of species using tracking technology. However, techniques for estimating population-level distributions using the telemetry data are poorly developed. This is of concern because many marine CCPFs, such as seabirds, are threatened by anthropogenic activities. Here, we aim to estimate the distribution at sea of four seabird species, foraging from approximately 5,500 breeding sites in Britain and Ireland. To do so, we GPS-tracked a sample of 230 European Shags Phalacrocorax aristotelis, 464 Black-legged Kittiwakes Rissa tridactyla, 178 Common Murres Uria aalge, and 281 Razorbills Alca torda from 13, 20, 12, and 14 colonies, respectively. Using Poisson point process habitat use models, we show that distribution at sea is dependent on (1) density-dependent competition among sympatric conspecifics (all species) and parapatric conspecifics (Kittiwakes and Murres); (2) habitat accessibility and coastal geometry, such that birds travel further from colonies with limited access to the sea; and (3) regional habitat availability. Using these models, we predict space use by birds from unobserved colonies and thereby map the distribution at sea of each species at both the colony and regional level. Space use by all four species' British breeding populations is concentrated in the coastal waters of Scotland, highlighting the need for robust conservation measures in this area. The techniques we present are applicable to any CCPF.

Abstract1. To prevent further global declines in biodiversity, identifying and understanding key habitats is crucial for successful conservation strategies. For example, globally, seabird populations are under threat and animal movement data can identify key at-sea areas and provide valuable information on the state of marine ecosystems. To date, in order to locate these areas, studies have used global positioning system (GPS) to record position and are sometimes combined with time-depth recorder (TDR) devices to identify diving activity associated with foraging, a crucial aspect of at-sea behaviour. However, the use of additional devices such as TDRs can be expensive, logistically difficult and may adversely affect the animal. Alternatively, behaviours may be resolved from measurements derived from the movement data alone. However, this behavioural analysis frequently lacks validation data for locations predicted as foraging (or other behaviours).2. Here, we address these issues using a combined GPS and TDR dataset from 108 individuals by training deep learning models to predict diving in European shags, common guillemots and razorbills. We validate our predictions using withheld data, producing quantitative assessment of predictive accuracy. The variables used to train these models are those recorded solely by the GPS device: variation in longitude and latitude, altitude and coverage ratio (proportion of possible fixes acquired within a set window of time).3. Different combinations of these variables were used to explore the qualities of different models, with the optimum models for all species predicting non-diving and diving behaviour correctly over 94% and 80% of the time, respectively. We also demonstrate the superior predictive ability of these supervised deep learning models over other commonly used behavioural prediction methods such as hidden Markov models.4. Mapping these predictions provides useful insights into the foraging activity of a range of seabird species, highlighting important at sea locations. These models have the potential to be used to analyse historic GPS datasets and further our understanding of how environmental changes have affected these seabirds over time. K E Y W O R D Scommon guillemot, European shag, foraging, machine learning, prediction, razorbill, time-depth recorder This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Lay SummarySex segregation, competition and differences in individual quality may drive dispersive migration in birds and affect their fitness. Atlantic puffins tracked for up to 6 years followed remarkably different migration routes, but individuals followed the same route every year. Although random dispersion and sex segregation could not explain the patterns observed, birds visiting the Mediterranean Sea foraged more and had a higher breeding success than birds remaining locally or visiting the Atlantic Ocean.