No abstract

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.

Aim Ecological data collected by the general public are valuable for addressing a wide range of ecological research and conservation planning, and there has been a rapid increase in the scope and volume of data available. However, data from eBird or other large‐scale projects with volunteer observers typically present several challenges that can impede robust ecological inferences. These challenges include spatial bias, variation in effort and species reporting bias. Innovation We use the example of estimating species distributions with data from eBird, a community science or citizen science (CS) project. We estimate two widely used metrics of species distributions: encounter rate and occupancy probability. For each metric, we critically assess the impact of data processing steps that either degrade or refine the data used in the analyses. CS data density varies widely across the globe, so we also test whether differences in model performance are robust to sample size. Main conclusions Model performance improved when data processing and analytical methods addressed the challenges arising from CS data; however, the degree of improvement varied with species and data density. The largest gains we observed in model performance were achieved with 1) the use of complete checklists (where observers report all the species they detect and identify, allowing non‐detections to be inferred) and 2) the use of covariates describing variation in effort and detectability for each checklist. Occupancy models were more robust to a lack of complete checklists. Improvements in model performance with data refinement were more evident with larger sample sizes. In general, we found that the value of each refinement varied by situation and we encourage researchers to assess the benefits in other scenarios. These approaches will enable researchers to more effectively harness the vast ecological knowledge that exists within CS data for conservation and basic research.

Metabolism is the link between ecology and physiology-it dictates the flow of energy through individuals and across trophic levels. Much of the predictive power of metabolic theories of ecology derives from the scaling relationship between organismal size and metabolic rate. There is growing evidence that this scaling relationship is not universal, but we have little knowledge of how it has evolved over macroevolutionary time. Here we develop a novel phylogenetic comparative method to investigate how often and in which clades the macroevolutionary dynamics of the metabolic scaling have changed. We find strong evidence that the metabolic scaling relationship has shifted multiple times across the vertebrate phylogeny. However, shifts are rare and otherwise strongly constrained. Importantly, both the estimated slope and intercept values vary widely across regimes, with slopes that spanned across theoretically predicted values such as 2/3 or 3/4. We further tested whether traits such as ecto-/endothermy, genome size, and quadratic curvature with body mass (i.e., energetic constraints at extreme body sizes) could explain the observed pattern of shifts. Though these factors help explain some of the variation in scaling parameters, much of the remaining variation remains elusive. Our results lay the groundwork for further exploration of the evolutionary and ecological drivers of major transitions in metabolic strategy and for harnessing this information to improve macroecological predictions.

Competitive exclusion and habitat filtering influence community assembly, but ecologists and evolutionary biologists have not reached consensus on how to quantify patterns that would reveal the action of these processes. Currently, at least 22 α‐diversity and 10 β‐diversity metrics of community phylogenetic structure can be combined with nine null models (eight for β‐diversity metrics), providing 278 potentially distinct approaches to test for phylogenetic clustering and overdispersion. Selecting the appropriate approach for a study is daunting. First, we describe similarities among metrics and null models across variance in phylogeny size and shape, species abundance, and species richness. Second, we develop spatially explicit, individual‐based simulations of neutral, competitive exclusion, or habitat filtering community assembly, and quantify the performance (type I and II error rates) of all 278 metric and null model combinations against each assembly process. Many α‐diversity metrics and null models are at least functionally equivalent, reducing the number of truly unique metrics to 12 and the number of unique metric + null model combinations to 72. An even smaller subset of metric and null model combinations showed robust statistical performance. For α‐diversity metrics, phylogenetic diversity and mean nearest taxon distance were best able to detect habitat filtering, while mean pairwise phylogenetic distance‐based metrics were best able to detect competitive exclusion. Overall, β‐diversity metrics tended to have greater power to detect habitat filtering and competitive exclusion than α‐diversity metrics, but had higher type 1 error in some cases. Across both α‐ and β‐diversity metrics, null model selection affected type I error rates more than metric selection. A null model that maintained species richness, and approximately maintained species occurrence frequency and abundance across sites, exhibited low type I and II error rates. This regional null model simulates neutral dispersal of individuals into local communities by sampling from a regional species pool. We introduce a flexible new R package, metricTester, to facilitate robust analyses of method performance.

Summary1. Plant traits vary widely across species and underpin differences in ecological strategy. Despite centuries of interest, the contributions of different evolutionary lineages to modern-day functional diversity remain poorly quantified. 2. Expanding data bases of plant traits plus rapidly improving phylogenies enable for the first time a data-driven global picture of plant functional diversity across the major clades of higher plants. We mapped five key traits relevant to metabolism, resource competition and reproductive strategy onto a phylogeny across 48324 vascular plant species world-wide, along with climate and biogeographic data. Using a novel metric, we test whether major plant lineages are functionally distinctive. We then highlight the trait-lineage combinations that are most functionally distinctive within the present-day spread of ecological strategies. 3. For some trait-clade combinations, knowing the clade of a species conveys little information to neo-and palaeo-ecologists. In other trait-clade combinations, the clade identity can be highly revealing, especially informative clade-trait combinations include Proteaceae, which is highly distinctive, representing the global slow extreme of the leaf economic spectrum. Magnoliidae and Rosidae contribute large leaf sizes and seed masses and have distinctively warm, wet climatic distributions.

The high avian biodiversity present in the Neotropical region offers a great opportunity to explore the ecology of host-parasite relationships. We present a survey of avian haemoparasites in a megadiverse country and explore how parasite prevalences are related to physical and ecological host characteristics. Using light microscopy, we documented the presence of haemoparasites in over 2000 individuals belonging to 246 species of wild birds, from nine localities and several ecosystems of Colombia. We analysed the prevalence of six avian haemoparasite taxa in relation to elevation and the following host traits: nest height, nest type, foraging strata, primary diet, sociality, migratory behaviour, and participation in mixed species flocks. Our analyses indicate significant associations between both mixed species flocks and nest height and Haemoproteus and Leucocytozoon prevalence. The prevalence of Leucocytozoon increased with elevation, whereas the prevalence of Trypanosoma and microfilariae decreased. Plasmodium and Haemoproteus prevalence did not vary significantly with elevation; in fact, both parasites were found up to 3300m above sea level. The distribution of parasite prevalence across the phylogeny of bird species included in this study showed little host phylogenetic signal indicating that infection rates in this system are evolutionarily labile. Vector distribution as well as the biology of transmission and the maintenance of populations of avian haemoparasites deserve more detailed study in this system.

Glucocorticoid (GC) hormones are important phenotypic mediators across vertebrates, but their circulating concentrations can vary markedly. Here we investigate macroevolutionary patterning in GC levels across tetrapods by testing seven specific hypotheses about GC variation and evaluating whether the supported hypotheses reveal consistent patterns in GC evolution. If selection generally favors the "supportive" role of GCs in responding effectively to challenges, then baseline and/or stress-induced GCs may be higher in challenging contexts. Alternatively, if selection generally favors "protection" from GCinduced costs, GCs may be lower in environments where challenges are more common or severe. The predictors of baseline GCs were all consistent with supportive effects: levels were higher in smaller organisms and in those inhabiting more energetically demanding environments. During breeding, baseline GCs were also higher in populations and species with fewer lifetime opportunities to reproduce. The predictors of stress-induced GCs were instead more consistent with the protection hypothesis: during breeding, levels were lower in organisms with fewer lifetime reproductive opportunities. Overall, these patterns indicate a surprising degree of consistency in how some selective pressures shape GCs across broad taxonomic scales; at the same time, in challenging environments selection appears to operate on baseline and stress-induced GCs in distinct ways.