Search for: All records

Abstract Migrating landbirds adjust their flight and stopover behaviors to efficiently cross inhospitable geographies, such as the Gulf of Mexico and the Sahara Desert. In addition to these natural barriers, birds may increasingly encounter anthropogenic barriers created by large‐scale changes in land use. One such barrier could be the Corn Belt in the Midwest United States, where 76.4% of precolonial vegetation (forest and grassland combined) has been replaced by agricultural and urban areas, primarily corn fields. We used 5 years of data from 47 weather radar stations in the United States to compare the population‐level flight patterns of migrating landbirds crossing the Corn Belt and the forested landscapes south and north of it in spring and autumn. We also examined the impacts of the Corn Belt relative to the Gulf of Mexico on the stopover behavior of migrating birds by comparing changes in the proportion of migrants that stop to rest (stopover‐to‐passage ratio [SPR]) relative to distance from both barriers. Birds showed increased meridional airspeeds and stronger selection for tailwinds when crossing the Corn Belt compared with forested landscapes. For birds crossing the Gulf of Mexico, the highest proportion of migrants stopped to rest after crossing the Gulf, and SPR decreased sharply as distance from the shoreline increased. We did not find this pattern after migrants crossed the Corn Belt, although the SPR increased in the Corn Belt as birds approached the down‐route forest boundary in both seasons. This weaker pattern for stopover propensity after crossing the Corn Belt is likely due to its narrower width, the availability of small forest patches throughout the Corn Belt, and the subset of species affected, compared with the gulf. We recommend restoring stepping stones of forest in the Corn Belt and protecting woodlands along the Gulf Coast to help landbirds successfully negotiate both barriers. 

Abstract Understanding migratory connectivity, or the linkage of populations between seasons, is critical for effective conservation and management of migratory wildlife. A growing number of tools are available for understanding where migratory individuals and populations occur throughout the annual cycle. Integration of the diverse measures of migratory movements can help elucidate migratory connectivity patterns with methodology that accounts for differences in sampling design, directionality, effort, precision and bias inherent to each data type.The R packageMigConnectivitywas developed to estimate population‐specific connectivity and the range‐wide strength of those connections. New functions allow users to integrate intrinsic markers, tracking and long‐distance reencounter data, collected from the same or different individuals, to estimate population‐specific transition probabilities (estTransition) and the range‐wide strength of those transition probabilities (estStrength). We used simulation and real‐world case studies to explore the challenges and limitations of data integration based on data from three migratory bird species, Painted Bunting (Passerina ciris), Yellow Warbler (Setophaga petechia) and Bald Eagle (Haliaeetus leucocephalus), two of which had bidirectional data.We found data integration is useful for quantifying migratory connectivity, as single data sources are less likely to be available across the species range. Furthermore, accurate strength estimates can be obtained from either breeding‐to‐nonbreeding or nonbreeding‐to‐breeding data. For bidirectional data, integration can lead to more accurate estimates when data are available from all regions in at least one season.The ability to conduct combined analyses that account for the unique limitations and biases of each data type is a promising possibility for overcoming the challenge of range‐wide coverage that has been hard to achieve using single data types. The best‐case scenario for data integration is to have data from all regions, especially if the question is range‐wide or data are bidirectional. Multiple data types on animal movements are becoming increasingly available and integration of these growing datasets will lead to a better understanding of the full annual cycle of migratory animals.