Functional traits offer a rich quantitative framework for developing and testing theories in evolutionary biology, ecology and ecosystem science. However, the potential of functional traits to drive theoretical advances and refine models of global change can only be fully realised when species-level information is complete. Here we present the AVONET dataset containing comprehensive functional trait data for all birds, including six ecological variables, 11 continuous morphological traits, and information on range size and location. Raw morphological measurements are presented from 90,020 individuals of 11,009 extant bird species sampled from 181 countries. These data are also summarised as species averages in three taxonomic formats, allowing integration with a global phylogeny, geographical range maps, IUCN Red List data and the eBird citizen science database. The AVONET dataset provides the most detailed picture of continuous trait variation for any major radiation of organisms, offering a global template for testing hypotheses and exploring the evolutionary origins, structure and functioning of biodiversity.

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Bat communities in the Neotropics are some of the most speciose assemblages of mammals on Earth, with regions supporting more than 100 sympatric species with diverse feeding ecologies. Because bats are small, nocturnal, and volant, it is difficult to directly observe their feeding habits, which has resulted in their classification into broadly defined dietary guilds (e.g., insectivores, carnivores, and frugivores). Apart from these broad guilds, we lack detailed dietary information for many species and therefore have only a limited understanding of interaction networks linking bats and their diet items. In this study, we used DNA metabarcoding of plants, arthropods, and vertebrates to investigate the diets of 25 bat species from the tropical dry forests of Lamanai, Belize. Our results report some of the first detection of diet items for the focal bat taxa, adding rich and novel natural history information to the field of bat ecology. This study represents a comprehensive first effort to apply DNA metabarcoding to bat diets at Lamanai and provides a useful methodological framework for future studies testing hypotheses about coexistence and niche differentiation in the context of modern high‐throughput molecular data.

Gene tree discordance is expected in phylogenomic trees and biological processes are often invoked to explain it. However, heterogeneous levels of phylogenetic signal among individuals within datasets may cause artifactual sources of topological discordance. We examined how the information content in tips and subclades impacts topological discordance in the parrots (Order: Psittaciformes), a diverse and highly threatened clade of nearly 400 species. Using ultraconserved elements from 96% of the clade's species-level diversity, we estimated concatenated and species trees for 382 ingroup taxa. We found that discordance among tree topologies was most common at nodes dating between the late Miocene and Pliocene, and often at the taxonomic level of genus. Accordingly, we used two metrics to characterize information content in tips and assess the degree to which conflict between trees was being driven by lower quality samples. Most instances of topological conflict and non-monophyletic genera in the species tree could be objectively identified using these metrics. For subclades still discordant after tip-based filtering, we used a machine learning approach to determine whether phylogenetic signal or noise was the more important predictor of metrics supporting the alternative topologies. We found that when signal favored one of the topologies, noise was the most important variable in poorly performing models that favored the alternative topology. In sum, we show that artifactual sources of gene tree discordance, which are likely a common phenomenon in many datasets, can be distinguished from biological sources by quantifying the information content in each tip and modeling which factors support each topology.

Aim:The study of biogeographic barriers is instrumental in understanding the evolution and distribution of taxa. With the increasing availability of empirical datasets, emergent patterns can be inferred from communities by synthesizing how barriers filter and structure populations across species. We assemble phylogeographic data across a barrier and perform spatially explicit simulations, quantifying spatiotemporal patterns of divergence, the influence of traits on these patterns, and the statistical power needed to differentiate diversification modes.Taxon: Vertebrates, Invertebrates, Plants Location: North America Methods: We incorporate published datasets, from papers that match relevant keywords, to examine taxa around the Cochise Filter Barrier, separating the Sonoran and Chihuahuan Deserts of North America, to synthesize phylogeographic structuring across the communities with respect to organismal functional traits. We then use simulation and machine learning to assess the power of phylogeographic model selection.Results: Taxa distributed across the Cochise Filter Barrier show heterogeneous responses to the barrier in levels of gene flow, phylogeographic structure, divergence timing, barrier width, and divergence mechanism. These responses correlate with locomotor and thermoregulatory traits. Many taxa show a Pleistocene population genetic break, often with introgression after divergence. Allopatric isolation and isolation by environment are the primary mechanisms structuring genetic divergence within taxa. Simulations reveal that in spatially explicit isolation with migration models across the barrier, age of divergence, presence of gene flow, and presence of isolation by distance can confound the interpretation of evolutionary history and model selection by producing easily confusable results. We re-analyze five empirical genetic datasets to illustrate the utility of these simulations despite these constraints.Main Conclusions: By synthesizing phylogeographic data for the Cochise Filter Barrier, we show that barriers interact with species traits to differentiate taxa in communities over millions of years. Identifying diversification modes across the barrier for these taxa remains challenging because commonly invoked demographic models may not be identifiable across a range of likely parameter space.

Biogeographic barriers are considered important in initiating speciation through geographic isolation, but they rarely indiscriminately and completely reduce gene flow across entire communities. Explicitly demonstrating which factors are associated with gene‐flow levels across barriers would help elucidate how speciation is initiated and isolation maintained. Here, we investigated the association of behavioral isolation on population differentiation in Northern Cardinals (Cardinalis cardinalis) distributed across the Cochise Filter Barrier, a region of transitional habitat which separates the Sonoran and Chihuahuan deserts of North America. Using genomewide markers, we modeled demographic history by fitting the data to isolation and isolation‐with‐migration models. The best‐fit model indicated that desert populations diverged in the Pleistocene with low, historic, and asymmetric gene flow across the barrier. We then tested behavioral isolation using reciprocal call‐broadcast experiments to compare song recognition between deserts, controlling for song dialect changes within deserts. We found that male Northern Cardinals in both deserts were most aggressive to local songs and failed to recognize across‐barrier songs. A correlation of genomic differentiation and strong song discrimination is consistent with a model where speciation is initiated across a barrier and maintained by behavioral isolation.

Vocalizations in animals, particularly birds, are critically important behaviors that influence their reproductive fitness. While recordings of bioacoustic data have been captured and stored in collections for decades, the automated extraction of data from these recordings has only recently been facilitated by artificial intelligence methods. These have yet to be evaluated with respect to accuracy of different automation strategies and features. Here, we use a recently published machine learning framework to extract syllables from ten bird species ranging in their phylogenetic relatedness from 1 to 85 million years, to compare how phylogenetic relatedness influences accuracy. We also evaluate the utility of applying trained models to novel species. Our results indicate that model performance is best on conspecifics, with accuracy progressively decreasing as phylogenetic distance increases between taxa. However, we also find that the application of models trained on multiple distantly related species can improve the overall accuracy to levels near that of training and analyzing a model on the same species. When planning big-data bioacoustics studies, care must be taken in sample design to maximize sample size and minimize human labor without sacrificing accuracy.

Spatial models show that genetic differentiation between populations can be explained by factors ranging from geographic distance to environmental resistance across the landscape. However, genomes exhibit a landscape of differentiation, which could indicate that multiple spatial models better explain divergence in different portions of the genome. We test whether alternative geographic predictors of intraspecific differentiation vary across the genome in ten bird species that co-occur in Sonoran and Chihuahuan Deserts of North America. Using population-level genomic data, we characterized the genomic landscapes across species and modeled five predictors that represented historical and contemporary mechanisms. The characteristics of genomic landscapes differed across the ten species, influenced by varying levels of population structuring and admixture between deserts. General dissimilarity matrix modeling indicated that the best-fit models differed from the whole genome and partitions along the genome. Almost all of the historical and contemporary mechanisms were important in explaining genetic distance, but particularly historical and contemporary environment, while contemporary abundance, position of the barrier to gene flow, and distance explained relatively less. Individual species have significantly different patterns of genomic variation. These results illustrate that the genomic landscape of differentiation was influenced by alternative geographic factors operating on different portions of the genome.