Identifying migratory birds' population bottlenecks in time and space

Ecological markers to monitor migratory bird populations: Integrating citizen science and transboundary management for conservation purposes

Countries share responsibility for the management and conservation of migratory bird species. However, a limited understanding of population dynamics hampers the implementation of harvest and transboundary management. Age-ratios and population density can be useful indicators to assess population dynamics to improve management and conservation actions. Here, the dynamics of an Atlantic population of Common quail Coturnix coturnix, using 32,508 quail samples and 4814 hunter questionnaires over a 20-year period (1996-2016) served as a comparative study for examining age-ratio patterns related to different geographic zones, population density and weather parameters. Results show that age-ratios varied over zones and years, specifically age-ratio 1 (AR1), used as an index of late breeding attempts, varied from 0.1 to 0.21. Age-ratio 2 (AR2), a surrogate of central recruitment, varied from 0.16 to 0.66. Finally, age-ratio 3 (AR3), used as an indicator of the population's annual breeding success, varied from 3.69 to 6.68. Age-ratio is linked to internal and external factors (i.e. effect of rainfall, variations over time and density-dependent relationships) depicting how quail age groups make segregated migration in time and space. Quail age groups perform a complex pattern of migration because of entwined changes in abundance, migration routes and timing, influencing population connectivity and dynamics. Our findings highlight the relevance of citizen science and transboundary agreements to improve management and conservation measures of migrant species. Administrations and policy-makers in developed and developing countries must coordinate efforts to engage hunters in a participatory management systems to achieve sustainability.

Localizing and Classifying Adaptive Targets with Trend Filtered Regression

Identifying genomic locations of natural selection from sequence data is an ongoing challenge in population genetics. Current methods utilizing information combined from several summary statistics typically assume no correlation of summary statistics regardless of the genomic location from which they are calculated. However, due to linkage disequilibrium, summary statistics calculated at nearby genomic positions are highly correlated. We introduce an approach termed Trendsetter that accounts for the similarity of statistics calculated from adjacent genomic regions through trend filtering, while reducing the effects of multicollinearity through regularization. Our penalized regression framework has high power to detect sweeps, is capable of classifying sweep regions as either hard or soft, and can be applied to other selection scenarios as well. We find that Trendsetter is robust to both extensive missing data and strong background selection, and has comparable power to similar current approaches. Moreover, the model learned by Trendsetter can be viewed as a set of curves modeling the spatial distribution of summary statistics in the genome. Application to human genomic data revealed positively selected regions previously discovered such as LCT in Europeans and EDAR in East Asians. We also identified a number of novel candidates and show that populations with greater relatedness share more sweep signals.