For many avian species, spatial migration patterns remain largely undescribed, especially across hemispheric extents. Recent advancements in tracking technologies and high-resolution species distribution models (i.e., eBird Status and Trends products) provide new insights into migratory bird movements and offer a promising opportunity for integrating independent data sources to describe avian migration. Here, we present a three-stage modeling framework for estimating spatial patterns of avian migration. First, we integrate tracking and band re-encounter data to quantify migratory connectivity, defined as the relative proportions of individuals migrating between breeding and nonbreeding regions. Next, we use estimated connectivity proportions along with eBird occurrence probabilities to produce probabilistic least-cost path (LCP) indices. In a final step, we use generalized additive mixed models (GAMMs) both to evaluate the ability of LCP indices to accurately predict (i.e., as a covariate) observed locations derived from tracking and band re-encounter data sets versus pseudo-absence locations during migratory periods and to create a fully integrated (i.e., eBird occurrence, LCP, and tracking/band re-encounter data) spatial prediction index for mapping species-specific seasonal migrations. To illustrate this approach, we apply this framework to describe seasonal migrations of 12 bird species across the Western Hemisphere during pre-and postbreeding migratory periods (i.e., spring and fall, respectively). We found that including LCP indices with eBird occurrence in GAMMs generally improved the ability to accurately predict observed migratory locations compared to models with eBird occurrence alone. Using three performance metrics, the eBird + LCP model demonstrated equivalent or superior fit relative to theeBird-only model for 22 of 24 species-season GAMMs. In particular, the integrated index filled in spatial gaps for species with over-water movements and those that migrated over land where there were few eBird sightings and, thus, low predictive ability of eBird occurrence probabilities (e.g., Amazonian rainforest in South America). This methodology of combining individual-based seasonal movement data with temporally dynamic species distribution models provides a comprehensive approach to integrating multiple data types to describe broad-scale spatial patterns of animal movement. Further development and customization of this approach will continue to advance knowledge about the full annual cycle and conservation of migratory birds.
Migrating animals occur along a continuum from species that spend the nonbreeding season at a fixed location to species that are nomadic during the nonbreeding season, essentially continuously moving. Such variation is likely driven by the economics of territoriality or heterogeneity in the environment. The Snowy Owl (Bubo scandiacus) is known for its complex seasonal movements, and thus an excellent model to test these ideas, as many individuals travel unpredictably along irregular routes during both the breeding and nonbreeding seasons. Two possible explanations for this large variation in the propensity to move are (1) dominance hierarchies in which dominant individuals (adult females in this case) monopolize some key, consistent resources, and move less than subdominants; and (2) habitat heterogeneity in which individuals foraging in rich and less heterogenic environments are less mobile. We analyzed fine-scale telemetry data (global positioning system [GPS]/global system for mobile communication [GSM]) from 50 Snowy Owls tagged in eastern and central North America from 2013 to 2019, comparing space use during the winter period according to sex and age, and to land cover attributes. We used variograms to classify individuals as nomadic (58%) or range-resident (42%), and found that nomadic owls had ten times larger wintering areas than range-resident owls. The frequency of nomadism was similar in socially-dominant adult females, immatures, and males. However, nomadism increased from west to east, and north to south, and was positively associated with the use of water and negatively associated with croplands. We conclude that many individual Snowy Owls in Eastern North America are nomadic during the nonbreeding season and that movement patterns during this time are driven primarily by extrinsic factors, specifically heterogeneity in habitat and prey availability, as opposed to intrinsic factors associated with spacing behavior, such as age and sex.
Louse flies (Diptera: Hippoboscidae) are obligate ectoparasites that often cause behavioral, pathogenic, and evolutionary effects on their hosts. Interactions between ectoparasites and avian hosts, especially migrating taxa, may influence avian pathogen spread in tropical and temperate ecosystems and affect long-term survival, fitness and reproductive success. The purpose of this study was to characterize the vector-associated microbiome of ectoparasitic louse flies feeding on migrating raptors over the fall migration period. Surveys for louse flies occurred during fall migration (2015-2016) at a banding station in Pennsylvania, United States; flies were collected from seven species of migrating raptors, and we sequenced their microbial (bacteria and archaea) composition using high-throughput targeted amplicon sequencing of the 16S rRNA gene (V4 region). All louse flies collected belonged to the same species, Icosta americana. Our analysis revealed no difference in bacterial communities of louse flies retrieved from different avian host species. The louse fly microbiome was dominated by a primary endosymbiont, suggesting that louse flies maintain a core microbial structure despite receiving blood meals from different host species. Thus, our findings highlight the importance of characterizing both beneficial and potentially pathogenic endosymbionts when interpreting how vector-associated microbiomes may impact insect vectors and their avian hosts.
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