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.
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Aggregating three sources of long‐term trends of swallows and martins to identify priority conservation areas in the Great Lakes region
Abstract Long‐term monitoring of bird populations across scales is important in evaluating conservation targets and creating effective conservation strategies. For nearly six decades, the Breeding Bird Survey (BBS) has served as the primary broad‐scaled source of relative abundance trends of swallows and martins in North America. Recently, however, it has become possible to obtain breeding population trends using semi‐structured eBird community science data. Moreover, weather surveillance radar data of swallow and martin roosting populations yield a third complementary source of trend information.Using results from these three approaches, we propose a novel method of spatially combining estimates of percent change per year into a probability of directional agreement and/or disagreement that describes (1) the direction of the trend within a given region, (2) the amount of evidence associated with the estimate and (3) how much uncertainty surrounds it. We focus our efforts on an area of high Hirundinidae concentration in the North American Great Lakes region and predict trends from 2012 to 2022.We found a high probability of agreement between all three sources about observed declines in swallow and martin trends in the region surrounding Lake Ontario and to the west of Lake Michigan. Focusing future research on these regions could improve our understanding of these declines and help build more targeted conservation initiatives.Synthesis and applications. Our data integration methodology allows managers to identify regions that accumulate evidence of concerning trends across multiple wildlife monitoring schemes. These regions can thus be prioritized in conservation and management efforts. This approach can be generalized to other sources of long‐term monitoring data of different species, at different stages of their annual cycle, in any geographic location.
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- Award ID(s):
- 2017554
- PAR ID:
- 10653234
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Journal of Applied Ecology
- ISSN:
- 0021-8901
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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