Background Knowledge on migration patterns and flyways is a key for understanding the dynamics of migratory populations and evolution of migratory behaviour. Bird migration is usually considered to be movements between breeding and wintering areas, while less attention has been paid to other long-distance movements such as moult migration. Methods We use high-resolution satellite-tracking data from 58 taiga bean geese Anser fabalis fabalis from the years 2019–2020, to study their moult migration during breeding season. We show the moulting sites, estimate the migratory connectivity between the breeding and the moulting sites, and estimate the utilization distributions during moult. We reveal migration routes and compare the length and timing of migration between moult migrants and successful breeders. Results All satellite-tracked non-breeding and unsuccessfully breeding taiga bean geese migrated annually to the island of Novaya Zemlya in the high Arctic for wing moult, meaning that a large part of the population gathers at the moulting sites outside the breeding range annually for approximately three months. Migratory connectivity between breeding and moulting sites was very low (rm =  − 0.001, 95% CI − 0.1562–0.2897), indicating that individuals from different breeding grounds mix with each other on the moulting sites. Moult migrants began fall migration later in autumn than successful breeders, and their overall annual migration distance was over twofold compared to the successful breeders. Conclusions Regular moult migration makes the Arctic an equally relevant habitat for the taiga bean goose population as their boreal breeding and temperate wintering grounds, and links ecological communities in these biomes. Moult migration plays an important role in the movement patterns and spatio-temporal distribution of the population. Low migratory connectivity between breeding and moulting sites can potentially contribute to the gene flow within the population. Moult migration to the high Arctic exposes the population to the rapid impacts of global warming to Arctic ecosystems. Additionally, Novaya Zemlya holds radioactive contaminants from various sources, which might still pose a threat to moult migrants. Generally, these results show that moult migration may essentially contribute to the way we should consider bird migration and migratory flyways.

1. Knowledge concerning spatio-temporal distributions of populations is a prerequisite for successful conservation and management of migratory animals.Achieving cost-effective monitoring of large-scale movements is often difficult due to lack of effective and inexpensive methods.2. Taiga bean goose Anser fabalis fabalis and tundra bean goose A. f. rossicus offer an excellent example of a challenging management situation with harvested migratory populations. The subspecies have different conservation statuses and population trends. However, their distribution overlaps during migration to an unknown extent, which, together with their similar appearance, has created a conservation-management dilemma.3. Gaussian process (GP) models are widely adopted in the field of statistics and machine learning, but have seldom been applied in ecology so far. We introduce the R package gplite for GP modelling and use it in our case study together with birdwatcher observation data to study spatio-temporal differences between bean goose subspecies during migration in Finland in 2011-2019.4. We demonstrate that GP modelling offers a flexible and effective tool for analysing heterogeneous data collected by citizens. The analysis reveals spatial and temporal distribution differences between the two bean goose subspecies in Finland. Taiga bean goose migrates through the entire country, whereas tundra bean goose occurs only in a small area in south-eastern Finland and migrates later than taiga bean goose. Synthesis and applications.Within the studied bean goose populations, harvest can be targeted at abundant tundra bean goose by restricting hunting to southeastern Finland and to the end of the migration period. In general, our approach combining citizen science data with GP modelling can be applied to study spatiotemporal distributions of various populations and thus help in solving challenging management situations. The introduced R package gplite can be applied | 1147

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Migratory connectivity is a metric of the co-occurrence of migratory animals originating from different breeding sites, and like their spatial dispersion, can vary substantially during the annual cycle. Together, both these properties affect the optimal times and sites of population censusing. We tracked taiga bean geese (Anser fabalis fabalis) during 2014-2021 to study their migratory connectivity and nonbreeding movements and determine optimal periods to assess the size of their main flyway population. We also compared available census data with tracking data, to examine how well two existing censuses covered the population. Daily Mantel's correlation between breeding and nonbreeding sites lay between 0 and 0.5 during most of the nonbreeding

Migratory divides separate populations of migratory animals, facilitating the evolution of intraspecific differences in migration strategies. The optimal migration theory suggests differing migration strategies for birds using different flyways (with different habitats), but the knowledge regarding the impact of the flyway to the individual migration strategies is scarce. We used satellite tracking and neckband resightings to unravel the structure of the migratory divide between two European flyway populations of greylag geese <em>Anser anser</em>. We modelled satellite tracking data using Gaussian processes to study migration strategies of birds using different flyways. The mean posterior probability for an individual to migrate along the Western Flyway decreased gradually from 0.98 to 0.06 within the Baltic Sea coast, revealing a gradual migratory divide. In addition, migration strategies differed between the flyways. The birds using Western Flyway migrated earlier in autumn, performed longer annual migration and made a clear stopover during migration, whereas the birds using Central Flyway flew directly to their wintering sites. The gradual migratory divide that also divided migration strategies provides exciting insights to ecological and evolutionary factors behind migratory divides. Gaussian processes enabled modelling of detailed migration strategies, encouraging their future usage in movement ecology.

Migratory connectivity is a metric of the co-occurrence of migratory animals originating from different breeding sites, and like their spatio-temporal distributions, can vary substantially during the annual cycle. Together, both these properties affect the optimal times and sites of population censusing. We tracked taiga bean geese (Anser fabalis fabalis) during 2014-2021 to study their migratory connectivity and non-breeding movements, and determine optimal periods to assess the size of their main flyway population. We also compared available census data with tracking data, to examine how well two existing censuses covered the population. Daily Mantels correlation between breeding and non-breeding sites lay between 0 and 0.5 during most of the non-breeding season, implying birds from different breeding areas were not strongly separated other times in the annual cycle. However, the connectivity was higher among birds from the westernmost breeding areas compared to the birds breeding elsewhere. Daily Minimum Convex Polygons showed tracked birds were highly aggregated at census times, confirming their utility. The number of tracked birds absent at count sites during the censuses however exceeded numbers double-counted at several sites, indicating that censuses might have underestimated the true population size. Our results show that connectivity can vary in different times during the non-breeding period, and should be studied throughout the annual cycle. Our results also confirm previous studies, which have found that estimates using marked individuals usually produce higher population size estimates than total counts. This should be considered when using total counts to assess population sizes in the future.

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