Abstract
Ecosystems around the world are connected by seasonal migration. The migrant animals themselves are influenced by migratory connectivity through effects on the individual and the population level. Measuring migratory connectivity is notoriously difficult due to the simple requirement of data conveying information about the nonbreeding distribution of many individuals from several breeding populations. Explicit integration of data derived from different methods increases the precision and the reliability of parameter estimates.We combine ring‐reencounter, stable isotope, and blood parasite data of Barn Swallows Hirundo rustica in a single integrated model to estimate migratory connectivity for three large scale breeding populations across a latitudinal gradient from Central Europe to Scandinavia. To this end, we integrated a non‐Markovian multistate mark‐recovery model for the ring‐reencounter data with normal and binomial mixture models for the stable isotope and parasite data.The integration of different data sources within a mark‐recapture modeling framework enables the most precise quantification of migratory connectivity on the given broad spatial scale. The results show that northern‐breeding populations and Southern Africa as well as southern‐breeding populations and Western–Central Africa are more strongly connected through Barn Swallow migration than central European breeding populations with any of the African wintering areas. The nonbreeding distribution of Barn Swallows from central European breeding populations seems to be a mixture of those populations breeding further north and south, indicating a migratory divide.
Highlights
Migratory connectivity describes the extent to which individuals of the same populations living in close vicinity to each other all year long or whether they are mixing with individuals from other populations at some point of their annual cycle, or in other words it is the degree to which different geographic areas are connected by migrating animals (Webster, Marra, Haig, Bensch, & Holmes, 2002)
The results show that northern-breeding populations and Southern Africa as well as southern-breeding populations and Western–Central Africa are more strongly connected through Barn Swallow migration than central European breeding populations with any of the African wintering areas
We found that most Barn Swallows belonging to Northern populations (Sweden) were wintering in Southern Africa showing relatively high migratory connectivity
Summary
Migratory connectivity describes the extent to which individuals of the same populations living in close vicinity to each other all year long or whether they are mixing with individuals from other populations at some point of their annual cycle, or in other words it is the degree to which different geographic areas are connected by migrating animals (Webster, Marra, Haig, Bensch, & Holmes, 2002). Only a single data source is used to describe the distribution of individuals of a migratory species at different times of the annual cycle, even though it has long been recognized that the combination of different data sources is important in the study of migratory connectivity (e.g., Boulet & Norris, 2006). Møller, and Van Wilgenburg (2012), one of the first studies who formally combined different data sets for the estimation of migratory connectivity, used the distribution of ring recoveries as prior for the analyses of stable isotope data. Thereby, they assumed that the spatial distribution of ring recoveries reflects the spatial distribution of the birds. The model combines the just mentioned data collected during the breeding season in Europe with published data on stable isotopes values in feathers sampled in the wintering area (Szép et al, 2009) and data on avian malaria parasites from birds sampled in sub-Saharan Africa (Beadell et al, 2009; Bensch et al, 2000; Bonneaud et al, 2009; Chasar et al, 2009; Durrant et al, 2007; Hellgren et al, 2013; Loiseau et al, 2012, 2010; Lutz et al, 2015; Marzal et al, 2011; Mendes et al, 2013; Sorensen et al, 2016; Waldenström, Bensch, Kiboi, Hasselquist, & Ottosson, 2002)
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