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Despite the well-known effects of sexual selection on phenotypes, links between this evolutionary process and reproductive isolation, genomic divergence, and speciation have been difficult to establish. We unravel the genetic basis of sexually selected plumage traits to investigate their effects on reproductive isolation in barn swallows. The genetic architecture of sexual traits is characterized by 12 loci on two autosomes and the Z chromosome. Sexual trait loci exhibit signatures of divergent selection in geographic isolation and barriers to gene flow in secondary contact. Linkage disequilibrium between these genes has been maintained by selection in hybrid zones beyond what would be expected under admixture alone. Our findings reveal that selection on coupled sexual trait loci promotes reproductive isolation, providing key empirical evidence for the role of sexual selection in speciation. 

Abstract Urbanization has dramatically altered Earth's landscapes and changed a multitude of environmental factors. This has resulted in intense land‐use change, and adverse consequences such as the urban heat island effect (UHI), noise pollution, and artificial light at night (ALAN). However, there is a lack of research on the combined effects of these environmental factors on life‐history traits and fitness, and on how these interactions shape food resources and drive patterns of species persistence. Here, we systematically reviewed the literature and created a comprehensive framework of the mechanistic pathways by which urbanization affects fitness and thus favors certain species. We found that urbanization‐induced changes in urban vegetation, habitat quality, spring temperature, resource availability, acoustic environment, nighttime light, and species behaviors (e.g., laying, foraging, and communicating) influence breeding choices, optimal time windows that reduce phenological mismatch, and breeding success. Insectivorous and omnivorous species that are especially sensitive to temperature often experience advanced laying behaviors and smaller clutch sizes in urban areas. By contrast, some granivorous and omnivorous species experience little difference in clutch size and number of fledglings because urban areas make it easier to access anthropogenic food resources and to avoid predation. Furthermore, the interactive effect of land‐use change and UHI on species could be synergistic in locations where habitat loss and fragmentation are greatest and when extreme‐hot weather events take place in urban areas. However, in some instances, UHI may mitigate the impact of land‐use changes at local scales and provide suitable breeding conditions by shifting the environment to be more favorable for species' thermal limits and by extending the time window in which food resources are available in urban areas. As a result, we determined five broad directions for further research to highlight that urbanization provides a great opportunity to study environmental filtering processes and population dynamics. 

AbstractLife history theory predicts that increased investment in current offspring decreases future fecundity or survival. Avian parental investment decisions have been studied either via brood size manipulation or direct manipulation of parental energetic costs (also known as handicapping). However, we have limited experimental data on the potential interactive effects of these manipulations on parent behavior. Additionally, we know little about how these manipulations affect spatial foraging behavior away from the nest. We simultaneously manipulated brood size and parental costs (via added weight in the form of a GPS tag) in wild female barn swallows (Hirundo rustica). We measured multiple aspects of parent behavior at and away from the nest while controlling for measures of weather conditions. We found no significant interactive effects of manipulated brood size and parental costs. Both sexes increased their visitation rate with brood size, but nestlings in enlarged broods grew significantly less post-brood size manipulation than those in reduced broods. Foraging range area was highly variable among GPS-tagged females but was unaffected by brood size. As such, increased visitation rate in response to brood size may be more energetically costly for far-ranging females. GPS-tagged females did not alter their visitation rate relative to un-tagged birds, but their mates had higher visitation rates. This suggests that GPS tagging may affect some unmeasured aspect of female behavior, such as prey delivery. Our findings indicate that investigation of foraging tactics alongside visitation rate is critical to understanding parental investment and the benefits and costs of reproduction. Significance statementAvian parental investment decisions have been studied by either brood size manipulation or direct manipulation of parental costs, but rarely both simultaneously. We simultaneously manipulated brood size and parental costs (via addition of a GPS tag) in a wild avian system, allowing us to examine interactive effects of these manipulations. Additionally, studies of parental investment often examine behaviors at the nest, but measurements of parental care behavior away from the nest are rare. Our study is unique in that we measured multiple aspects of parental care, including spatial foraging behavior tracked with GPS tags. We found no interactive effects of manipulated brood size and parental costs on visitation rate or nestling growth, and spatial foraging behavior of females was individually variable. Documenting foraging tactics alongside visitation rate is critical to understanding parental investment because the same visitation rate might be more costly for far-ranging females. 

Abstract Despite the increasing feasibility of sequencing whole genomes from diverse taxa, a persistent problem in phylogenomics is the selection of appropriate genetic markers or loci for a given taxonomic group or research question. In this review, we aim to streamline the decision-making process when selecting specific markers to use in phylogenomic studies by introducing commonly used types of genomic markers, their evolutionary characteristics, and their associated uses in phylogenomics. Specifically, we review the utilities of ultraconserved elements (including flanking regions), anchored hybrid enrichment loci, conserved nonexonic elements, untranslated regions, introns, exons, mitochondrial DNA, single nucleotide polymorphisms, and anonymous regions (nonspecific regions that are evenly or randomly distributed across the genome). These various genomic elements and regions differ in their substitution rates, likelihood of neutrality or of being strongly linked to loci under selection, and mode of inheritance, each of which are important considerations in phylogenomic reconstruction. These features may give each type of marker important advantages and disadvantages depending on the biological question, number of taxa sampled, evolutionary timescale, cost effectiveness, and analytical methods used. We provide a concise outline as a resource to efficiently consider key aspects of each type of genetic marker. There are many factors to consider when designing phylogenomic studies, and this review may serve as a primer when weighing options between multiple potential phylogenomic markers. 

A goal of many research programmes in biology is to extract meaningful insights from large, complex datasets. Researchers in ecology, evolution and behavior (EEB) often grapple with long-term, observational datasets from which they construct models to test causal hypotheses about biological processes. Similarly, epidemiologists analyse large, complex observational datasets to understand the distribution and determinants of human health. A key difference in the analytical workflows for these two distinct areas of biology is the delineation of data analysis tasks and explicit use of causal directed acyclic graphs (DAGs), widely adopted by epidemiologists. Here, we review the most recent causal inference literature and describe an analytical workflow that has direct applications for EEB. We start this commentary by defining four distinct analytical tasks (description, prediction, association, causal inference). The remainder of the text is dedicated to causal inference, specifically focusing on the use of DAGs to inform the modelling strategy. Given the increasing interest in causal inference and misperceptions regarding this task, we seek to facilitate an exchange of ideas between disciplinary silos and provide an analytical framework that is particularly relevant for making causal inference from observational data.