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This work was funded in part by The Leon Levy Foundation, The Wolf Creek Foundation and the National Science Foundation (ABI sustaining: DBI-1939187; MSA: DEB-2017817; computing support from CNS-1059284 and CCF-1522054). Aim Animal migration is often explained as the result of resource tracking in seasonally dynamic environments. Therefore, resource availability should influence both the distributions of migratory animals and their seasonal abundance. We examined the relationship between primary productivity and the spatio-temporal distributions of migratory birds to assess the role of energy availability in avian migration. Location North America. Time period Full annual cycle, 2011-2016. Major taxa studied Nocturnally migrating landbirds. Methods We used observations of nocturnally migrating landbirds from the eBird community-science programme to estimate weekly spatial distributions of total biomass, abundance and species richness. We related these patterns to primary productivity and seasonal productivity surplus estimated using a remotely sensed measure of vegetation greenness. Results All three avian metrics showed positive spatial associations with primary productivity, and this was more pronounced with seasonal productivity surplus. Surprisingly, biomass showed a weaker association than did abundance and richness, despite being a better indicator of energetic requirements. The strength of associations varied across seasons, being the weakest during migration. During spring migration, avian biomass increased ahead of vegetation green-up in temperate regions, a pattern also previously described for herbivorous waterfowl. In the south-eastern USA, spring green-up was instead associated with a net decrease in biomass, and winter biomass greatly exceeded that of summer, highlighting the region as a winter refuge for short-distance migrants. Main conclusions Although instantaneous energy availability is important in shaping the distribution of migratory birds, the stronger association of productivity with abundance and richness than with biomass suggests the role of additional drivers unrelated to energetic requirements that are nonetheless correlated with productivity. Given recent reports of widespread North American avifaunal declines, including many common species that winter in the south-eastern USA, understanding how anthropogenic activities are impacting winter bird populations in the region should be a research priority. Postprint Postprint

Funding: Wolf Creek Foundation; National Science Foundation. Grant Numbers: CCF-1522054, CNS-1059284, DBI-1939187, ICER-1927646; National Aeronautics and Space Administration. Grant Number: NNH12ZDA001N-ECOF; Leon Levy Foundation. 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. Postprint

1. Interactions between hosts and pathogens are dynamic at both ecological and evolutionary levels. In the resultant ‘eco-evolutionary dynamics’ ecological and evolutionary processes affect each other. For example, the house finch Haemorhous mexicanus and its recently emerged pathogen, the bacterium Mycoplasma gallisepticum, form a system in which evidence suggests that changes in bacterial virulence through time enhance levels of host immunity in ways that drive the evolution of virulence in an arms race. 2. We use data from two associated citizen science projects in order to determine whether this arms race has had any detectable effect at the population level in the north-eastern United States. 3. We used data from two citizen science projects, based on observations of birds at bird feeders, which provide information on the long-term changes in sizes of aggregations of house finches (host population density), and the probabilities that these house finches have observable disease (disease prevalence). 4. The initial emergence of M. gallisepticum caused a rapid halving of house finch densities; this was then followed by house finch populations remaining stable or slowly declining. Disease prevalence also decreased sharply after the initial emergence and has remained low, although with fluctuations through time. Surprisingly, while initially higher local disease prevalence was found at sites with higher local densities of finches, this relationship has reversed over time. 5. The ability of a vertebrate host species, with a generation time of at least 1 year, to maintain stable populations in the face of evolved higher virulence of a bacterium, with generation times measurable in minutes, suggests that genetic changes in the host are insufficient to explain the observed population-level patterns. We suggest that acquired immunity plays an important role in the observed interaction between house finches and M. gallisepticum.

Across the globe, millions of people feed wild birds and this practice has profound implications for many bird species. To better understand the effects of this supplemental feeding, many researchers have compared birds with access to inexhaustible supplemental food to those without access. However, the consistency of supplemental food availability varies with peoples’ provisioning habits because some people fill their feeders daily, while others do so sporadically. As the consistency of food availability changes, a bird’s foraging strategy, including its use of space, should change. To determine how space use varies with the consistency of supplemental food availability, we surveyed three species with access to experimental feeders that provided constant, pulsed, or no access to food. We conducted these surveys at two locations—near and far from the feeder—within nine sites to determine differences in space use among and within sites. Access to supplemental food, regardless of feeding regime, anchored the movements of each species near the feeders. However, the different feeding regimes had different effects on space use. Birds with constant access to supplemental food were continually anchored near the feeders, while the birds with pulsed access were temporarily anchored near the feeders. In one of three species (black-capped chickadee), birds concentrated in larger numbers near feeders with pulsed access when food was available compared to near feeders with constant access. Supplemental feeders act as spatial anchors but do so in different ways across species and feeding regimes with potentially varying implications for survival and population dynamics.

Summary Bird species’ migratory patterns have typically been studied through individual observations and historical records. In recent years, the eBird citizen science project, which solicits observations from thousands of bird watchers around the world, has opened the door for a data-driven approach to understanding the large-scale geographical movements. Here, we focus on the North American tree swallow (Tachycineta bicolor) occurrence patterns throughout the eastern USA. Migratory departure dates for this species are widely believed by both ornithologists and casual observers to vary substantially across years, but the reasons for this are largely unknown. In this work, we present evidence that maximum daily temperature is predictive of tree swallow occurrence. Because it is generally understood that species occurrence is a function of many complex, high order interactions between ecological covariates, we utilize the flexible modelling approach that is offered by random forests. Making use of recent asymptotic results, we provide formal hypothesis tests for predictive significance of various covariates and also develop and implement a permutation-based approach for formally assessing interannual variations by treating the prediction surfaces that are generated by random forests as functional data. Each of these tests suggest that maximum daily temperature is important in predicting migration patterns.

Funding: The Swedish Bird Survey was supported by grants from the Swedish Environmental Protection Agency, with additional financial and logistic support from the Regional County Boards (Länsstyrelsen). The surveys were carried out within the framework of the strategic research environment Biodiversity and Ecosystem Services in a Changing Climate (BECC). The Norwegian breeding bird monitoring (Norsk hekkefuglovervåking) was financed by the Norwegian Environment Agency and the Ministry of Climate and Environment. Our research was funded through the 2017-2018 Belmont Forum and BiodivERsA joint call for research proposals, under the BiodivScen ERA-Net COFUND programme with financial support from the Research Council of Norway (295767), the Swedish Research Council FORMAS (2018-02440, 2018-02441), the Academy of Finland (323527, 326327, 326338) and the U.S. National Science Foundation (ICER-1927646). The relationships between species abundance or occurrence versus spatial variation in climate are commonly used in species distribution models (SDMs) to forecast future distributions. Under "space-for-time-substitution", the effects of climate variation on species are assumed to be equivalent in both space and time. Two unresolved issues of space-for-time-substitution are the time period for species' responses and also the relative contributions of rapid- versus slow reactions in shaping spatial and temporal responses to climate change. To test the assumption of equivalence, we used a new approach of climate decomposition to separate variation in temperature and precipitation in Fennoscandia into spatial, temporal and spatio-temporal components over a 23-year period (1996-2018). We compiled information on land cover, topography and six components of climate for 1756 fixed route surveys and we modelled annual counts of 39 bird species breeding in the mountains of Fennoscandia. Local abundance of breeding birds was associated with the spatial components of climate as expected, but the temporal and spatio-temporal climatic variation from the current and previous breeding seasons were also important. The directions of the effects of the three climate components differed within and among species, suggesting that species can respond both rapidly and slowly to climate variation and that the responses represent different ecological processes. Thus, the assumption of equivalent species' response to spatial and temporal variation in climate was seldom met in our study system. Consequently, for the majority of our species, space-for-time substitution may only be applicable once the slow species' responses to a changing climate have occurred. Whereas forecasts for the near future need to accommodate the temporal components of climate variation. However, appropriate forecast horizons for space-for-time substitution are rarely considered and may be difficult to reliably identify. Accurately predicting change is challenging because multiple ecological processes affect species distributions at different temporal scales. Publisher PDF

Funding: We also acknowledge the Scientific Project of the State Order of the Government of Russian Federation to Lomonosov Moscow State University No. 121032300105-0 for participating in EBBA2 data. Our research was funded through the 2017–2018 Belmont Forum and BiodivERsA joint call for research proposals, under the BiodivScen ERA—Net COFUND program, with the following funding organizations: the Academy of Finland (Univ. Turku: 326327, Univ. Helsinki: 326338), the Swedish Research Council (Swedish Univ. Agric. Sci: 2018–02440, Lund Univ.: 2018–02441), the Research Council of Norway (Norwegian Instit. for Nature Res., 295767), and the National Science Foundation (Cornell Univ., ICER-1927646), and we also acknowledge the Swedish Environmental Protection Agency. Wetland bird species have been declining in population size worldwide as climate warming and land-use change affect their suitable habitats. We used species distribution models (SDMs) to predict changes in range dynamics for 64 non-passerine wetland birds breeding in Europe, including range size, position of centroid, and margins. We fitted the SDMs with data collected for the first European Breeding Bird Atlas and climate and land-use data to predict distributional changes over a century (the 1970s–2070s). The predicted annual changes were then compared to observed annual changes in range size and range centroid over a time period of 30 years using data from the second European Breeding Bird Atlas. Our models successfully predicted ca. 75% of the 64 bird species to contract their breeding range in the future, while the remaining species (mostly southerly breeding species) were predicted to expand their breeding ranges northward. The northern margins of southerly species and southern margins of northerly species, both, predicted to shift northward. Predicted changes in range size and shifts in range centroids were broadly positively associated with the observed changes, although some species deviated markedly from the predictions. The predicted average shift in core distributions was ca. 5 km yr−1 towards the north (5% northeast, 45% north, and 40% northwest), compared to a slower observed average shift of ca. 3.9 km yr−1. Predicted changes in range centroids were generally larger than observed changes, which suggests that bird distribution changes may lag behind environmental changes leading to 'climate debt'. We suggest that predictions of SDMs should be viewed as qualitative rather than quantitative outcomes, indicating that care should be taken concerning single species. Still, our results highlight the urgent need for management actions such as wetland creation and restoration to improve wetland birds' resilience to the expected environmental changes in the future. Publisher PDF

Climate change induced phenological shifts in primary productivity result in trophic mismatches for many organisms1–4, with broad implications for ecosystem structure and function. For birds that have a synchronized timing of migration with resource availability, the likelihood that trophic mismatches may generate a phenological response in migration timing increases with climate change5. Despite the importance of a holistic understanding of such systems at large spatial and temporal scales, particularly given a rapidly changing climate, analyses are few, primarily because of limitations in the access to appropriate data. Here we use 24 years of remotely sensed data collected by weather surveillance radar to quantify the response of a nocturnal avian migration system within the contiguous United States to changes in temperature. The average peak migration timing advanced in spring and autumn, and these changes were generally more rapid at higher latitudes. During spring and autumn, warmer seasons were predictive of earlier peak migration dates. Decadal changes in surface temperatures predicted spring changes in migratory timing, with greater warming related to earlier arrivals. This study represents one of the first system-wide examinations during two seasons and comprises measures from hundreds of species that describe migratory timing across a continent. Our findings provide evidence of spatially dynamic phenological shifts that result from climate change. Climate change affects the timing of bird migration, which can lead to mismatch with resource availability. Migration occurred earlier in spring and autumn in the United States during the past 24 years; warming led to later arrival in the western Unites States and earlier arrival in the rest of the country.

Modeling the distribution of a data-poor species is challenging due to a reliance on unstructured data that often lacks relevant information on sampling and produces coarse-resolution outputs of varying accuracy. Data on sampling effort associated with higher-quality, semi-structured data derived from some community science programs can be used to produce more precise models of distribution, albeit at a cost of using fewer data. Here, we used semi-structured data to model the seasonal ranges of the Plain Tyrannulet (Inezia inornata), a poorly known Austral–Neotropical migrant, and compared predictive performance to models built with the full unstructured dataset of the species. By comparing these models, we examined the relatively unexplored tradeoff between data quality and data quantity for modeling of a data-sparse species. We found that models using semi-structured data outperformed unstructured-data models in the predictive accuracy metrics (mean squared error, area under the curve, kappa, sensitivity, and specificity), despite using only 30% of the available detection records. Moreover, semi-structured models were more biologically accurate, indicating that the tyrannulet favors arboreal habitats in dry and hot lowlands during the breeding season (Chaco region) and is associated with proximity to rivers in tropical and wet areas during the nonbreeding season (Pantanal, Beni, and southwest Amazonia). We demonstrate that more detailed insights into distributional patterns can be gained from even small quantities of data when the data are analyzed appropriately. The use of semi-structured data promises to be of wide applicability even for data-poor bird species, helping refine information on distribution and habitat use, needed for effective assessments of conservation status. LAY SUMMARY Modeling the distributions of poorly known species is compromised by the sparse and noisy available data, often leading to coarse-resolution models. Semi-structured community science data, capable of accounting for sources of biases, can provide more accurate insights into species' distributions, but their effectiveness remains unclear with small datasets. We evaluated the performance of models built with semi-structured data for a data-poor species (Plain Tyrannulet) against models built with all the available records of the tyrannulet that maximize sample size but for which variation in the sampling process could not be corrected. The predictive accuracy of models was better when using semi-structured data, even at the expense of a 70% to 72% reduction in the number of detection records. We demonstrated that improved information on distribution and habitat use can result from even small quantities of high-quality data, information that is critical for an effective conservation assessment of currently poorly known species.

Climate warming is driving changes in species distributions, although many species show a so-called climatic debt, where their range shifts lag behind the fast shift in temperature isoclines. Protected areas (PAs) may impact the rate of distribution changes both positively and negatively. At the cold edges of species distributions, PAs can facilitate species distribution changes by increasing the colonization required for distribution change. At the warm edges, PAs can mitigate the loss of species, by reducing the local extinction of vulnerable species. To assess the importance of PAs to affect species distribution change, we evaluated the changes in a non-breeding waterbird community as a response to temperature increase and PA status, using changes of species occurrence in the Western-Palearctic over 25 years (97 species, 7,071 sites, 39 countries, 1993– 2017). We used a community temperature index (CTI) framework based on species thermal affinities to investigate the species turn-over induced by temperature increase. In addition, we measured whether the thermal community adjustment was led by cold-dwelling species extinction and/or warm-dwelling species colonization, by modelling the change in standard deviation of the CTI (CTIsd). Using linear mixed-effects models, we investigated whether communities within PAs had lower climatic debt and different patterns of community change regarding the local PA surface. Thanks to the combined use of the CTI and CTIsd, we found that communities inside PAs had more species, higher colonization, lower extinction and the climatic debt was 16% lower than outside PAs. The results suggest the importance of PAs to facilitate warm-dwelling species colonization and attenuate cold-dwelling species extinction. The community adjustment was however not sufficiently fast to keep pace with the strong temperature increase in central and northeastern Western-Palearctic regions. Our study underlines the potential of the combined CTI and CTIsd metrics to understand the colonization-extinction patterns driven by climate warming.

Choosy females drive isolation Rapid radiations of recently diverged species represent an excellent opportunity for exploring drivers of speciation. The capuchino seedeaters, a group of South American birds, include a number of species that, in the field, are often discernable only through male plumage and song. Turbek et al. used genomes and behavioral experiments to identify potential isolating factors in two members of this group and found that, though entirely sympatric, females mated only with conspecific males and that only a few genes differed between the species (see the Perspective by Jarvis). Thus, a small reshuffling of genes and reinforcement through mate choice has driven divergence in these overlapping and very similar species. Science , this issue p. eabc0256 ; see also p. 1312

The COVID-19 pandemic has likely affected natural systems around the world; the curtailment of human activity has also affected the collection of data needed to identify the indirect effects of this pandemic on natural systems. We describe how the outbreak of COVID-19 disease, and associated stay-at-home orders in four political regions, have affected the quantity and quality of data collected by participants in one volunteer-based bird monitoring project, eBird. The four regions were selected both for their early and prolonged periods of mandated changes to human activity, and because of the high densities of observations collected. We compared the months of April 2020 with April in previous years. The most notable change was in the landscapes in which observations were made: in all but one region human-dominated landscapes were proportionally more common in the data in April 2020, and observations made near the rarer wetland habitat were less prevalent. We also found subtler changes in quantity of data collected, as well as in observer effort within observation periods. Finally, we found that these effects of COVID-19 disease varied across the political units, and thus we conclude that any analyses of eBird data will require region-specific examination of whether there have been any changes to the data collection process during the COVID-19 pandemic that would need to be taken into account., Funding for this work came from the Wolf Creek Foundation, The Leon Levy Foundation, The Packard Foundation, and the National Science Foundation [grants ITR-0427914, DBI-0542868, IIS-0612031, ABI-1356308, CCF-1522054, ICR-1927646]. ICR-1927646 funding to Cornell University was through the 2017-2018 Belmont Forum and BiodivERsA joint call for research proposals, under the BiodivScen ERA-Net COFUND program, with financial support from the Academy of Finland (AKA, Univ. Turku: 326327, Univ. Helsinki: 326338), the Swedish Research Council (Formas, SLU: 2018-02440, Lund Univ.: 2018-02441), the Research Council of Norway (Forskningsrådet, NINA: 295767) and the U.S. National Science Foundation.

Individual animal fitness can be strongly influenced by the ability to recognise habitat features which may be beneficial. Many studies focus on the effects of habitat on annual reproductive rate, even though adult survival is typically a greater influence on fitness and population growth in vertebrate species with intermediate to long lifespans. Understanding the effects of preferred habitat on individuals over the annual cycle is therefore necessary to predict its influences on individual fitness. This is particularly true in species that are resident and territorial year-round in the temperate zone, which may face potential trade-offs between habitat that maximises reproduction and that which maximises non-breeding season (‘over-winter’) survival. We used a 37-year study of Song Sparrows Melospiza melodia residing territorially year-round on a small island to examine what habitat features influenced adult over-winter survival, how site-specific variation in adult survival versus annual reproductive rate influenced long-term habitat preference, and if preferred sites conferred on average higher individual fitness. Habitat features such as area of shrub cover and exposure to intertidal coastline predicted adult over-winter survival independent of individual age or sex, population size, or winter weather. Long-term habitat preference (measured as occupation rate) was better predicted by site-specific annual reproductive rate than by expected over-winter survival, but preferred sites maximised fitness on average over the entire annual cycle,. Although adult over-winter survival had a greater influence on population growth (λ) than reproductive rate, the influence of reproductive rate on λ increased in preferred sites because site-specific variation in reproductive rate was higher than variation in expected over-winter survival. Because preferred habitats tended to have higher mean site-specific reproductive and adult survival rates, territorial birds in this population do not appear to experience seasonal trade-offs in preferred habitat, but are predicted to incur substantial fitness costs of settling in less-preferred sites. This article is protected by copyright. All rights reserved.

After recovery, house finches ( Haemorhous mexicanus) reinfected with the same Mycoplasma gallisepticum strain remain partially resistant to reinfection for at least 14 mo in that they recover from reinfection much more rapidly than do Mycoplasma gallisepticum-naïve birds. To test the response of birds to reinfection with a heterologous strain we performed two experiments. In a first experiment we exposed birds to one of three strains that differed in virulence. After they had recovered all were reinfected with the most virulent-strain available at the time of the experiment. In a second experiment we infected and later reinfected house finches with one of two Mycoplasma gallisepticum strains whereby we switched the order of the strain used. In both experiments, disease in birds reinfected with a more-virulent strain caused more-severe disease. Our data suggest that the observed increase in Mycoplasma gallisepticum virulence, once the disease has become endemic in free-ranging house finches is-in part-driven by increased resistance of recovered birds to strains of equal or lower virulence.

Conventional surveys designed to monitor common and widespread species may fail to adequately track population changes of rare or patchily distributed species that are often of high conservation concern. We evaluated the performance of a new monitoring approach that employs both a spatially balanced sampling design and a targeted survey protocol designed to estimate population trends of one such patchily distributed species, the Golden-winged Warbler (Vermivora chrysoptera), in the Appalachian Mountains Bird Conservation Region (BCR 28), USA. Our spatially balanced survey consisted of 105 sample quads (one-quarter Delorme Atlas pages) across the current range of Golden-winged Warblers within BCR 28, each with five sample points located in early successional habitat. From 2009 to 2013, collaborators visited each sample point once per year during the peak breeding season and conducted a 17-min survey consisting of passive observation and playback of conspecific songs and mobbing vocalizations. We used multi-season, single-species occupancy models to estimate probability of quad occupancy, detection probability, and occupancy dynamics for Golden-winged Warblers and closely related Blue-winged Warblers (Vermivora cyanoptera). Our survey protocol resulted in high estimates of detection probability for Golden-winged (92%) and Blue-winged (79%) warblers, with 47% and 56% of quads estimated to be initially occupied, respectively. Derived population trend estimates (λ) indicated an average decline in population of 6% for Golden-winged Warblers and 7% for Blue-winged Warblers, resulting in estimated 21% and 22% declines, respectively, in quad occupancy after 5 yr. Our results demonstrate that coupling a spatially balanced survey design in appropriate habitat with a playback protocol to increase detection rates is a viable strategy for tracking populations of Golden-winged Warblers in the Appalachian Mountains BCR. Similar survey methods should be considered for other rare, declining, or patchily distributed bird species that require targeted monitoring.

The study of aggressive interactions between species has, to date, usually been restricted to interactions among small numbers of ecologically close competitors. Nothing is known about interspecific dominance hierarchies that include numerous, ecologically varied species. Such hierarchies are of interest because they could be used to address a variety of research questions, e.g. do similarly ranked species tend to avoid each other in time or space, and what will happen when such species come into contact as climates change? Here, we propose a method for creating a continental-scale hierarchy, and we make initial analyses based on this hierarchy. We quantified the extent to which a dominance hierarchy of feeder birds was linear, as intransitivities can promote local species9 coexistence. Using the existing network of citizen scientists participating in Project FeederWatch, we collected the data with which to create a continent-spanning interspecific dominance hierarchy that included species that do not currently have overlapping geographic distributions. Overall, the hierarchy was nearly linear, and largely predicted by body mass, although there were clade-specific deviations from the average mass-dominance relationship. Most of the small number of intransitive relationships in the hierarchy were based on small samples of observations. Few observations were made of interactions between close relatives and ecological competitors like Melanerpes woodpeckers and chickadees, as such species often have only marginally overlapping geographic distributions. Yet, these species9 ranks--emergent properties of the interaction network--were usually in agreement with published literature on dominance relationships between them.

Citizen science data are increasingly making valuable contributions to ecological studies. However, many citizen science surveys are also designed to encourage wide participation and therefore the participants have a range of natural history expertise, leading to variation and potentially bias in the data. We assessed a recently proposed measure of observer expertise, calculated based on the average numbers of species recorded by observers. We investigated if this observer expertise score is associated with how often an observer reports any individual species. Species reporting rates increased monotonically with the observer's expertise score for 197 of 200 species, suggesting that this expertise score describes inter-observer variation in the detectability of individual species. Expertise scores were incorporated into single-species occupancy models as a covariate, to explain inter-observer variation in detectability. Including expertise as a detectability covariate led to improved model fit and improved predictive performance on validation data. The expertise score had a large effect on the estimated detectability, comparable in magnitude to the effect of the duration of the observation period. Expertise scores were also included into single-species occupancy models that estimated seasonal patterns in species occupancy and seasonal expertise effects. The addition of a seasonal effect of expertise led to improved model fit and increased predictive performance on validation data. The seasonal expertise accounted for bias that may be introduced by seasonal differences in the effect of expertise, caused by changes in the environment or species behaviour. Measures of observer expertise included in models as a covariate can account for heterogeneity and bias introduced by variable expertise, although in this example the differences in estimated occupancy were small. This method of incorporating observer expertise can be used in any regression model of species occurrence, occupancy, abundance, or density to produce more reliable ecological inference and may be most important where citizen science schemes encourage wide participation. Overall, the results highlight the value of recording observer identity and other detectability covariates, to control for sources of bias associated with the observation process.

Ensuring that conservation decisions are informed by the best available data is a fundamental challenge in the face of rapid global environmental change. Too often, new science is not easily or quickly translated into conservation action. Traditional approaches to data collection and science delivery may be both inefficient and insufficient, as conservation practitioners need access to salient, credible, and legitimate data to take action. Open access data could serve as a tool to help bridge the gap between science and action, by providing conservation practitioners with access to relevant data in near real time. Broad-scale citizen-science data represent a fast-growing resource for open access databases, providing relevant and appropriately scaled data on organisms, much in the way autonomous sensors do so on the environment. Several such datasets are now broadly available, yet documentation of their application to conservation is rare. Here we use eBird, a project where individuals around the world submit data on bird distribution and abundance, as an example of how citizen-science data can be used to achieve tangible conservation science and action at local, regional, and global scales. Our examination illustrates how these data can be strategically applied to improve our understanding of spatial and temporal distributions of birds, the impacts of anthropogenic change on ecological systems, and creative conservation solutions to complex problems. We raise awareness of the types of conservation action now happening with citizen-science data, and discuss the benefits, limitations, and caveats of this approach.

There are numerous sources of data relevant for studies of the effects of climate change. Here we provide a classification of such data and their advantages and drawbacks. Long-term data can be classified into data from long-term population studies, nest record schemes, bird surveys, breeding and winter atlases, bird ringing information, bird ringing stations, bird observation depositories, and museum collections. Such data differ enormously in quality, but also in their usefulness. They range from long-term population studies of individually banded birds that can be used for genetic, evolution, and ecology studies to studies of presence/absence data. There are relatively few individual-based population studies while databases of records of individuals are much more abundant, albeit still useful for many purposes. Different databases also vary consistently in data quality, and there is scope for methodological studies and comparative analyses of multiple data bases.

While networked sensors are becoming a ubiquitous part of many human lives, their applications to the study of wild animals have been largely limited to off‐the‐shelf and stand‐alone technologies such as web cameras. However, purpose‐designed systems, applying features found in Internet‐of‐Things devices, enable more efficient gathering, managing, and disseminating of a diverse array of data needed to study the life histories of wild animals. We illustrate these claims based on our development of a system of networked nest boxes that we created to study nesting birds in urban environments. This system uses general‐purpose processors within nest boxes to perform edge computing to control data acquisition, processing, and management from multiple sensors. A central data‐management system permits easy access to all data, once downloaded, which has facilitated our uses to date of this system for formal university‐ and school‐level education, and informal science education.

Biodiversity is being lost at an unprecedented rate, and monitoring is crucial for understanding the causal drivers and assessing solutions. Most biodiversity monitoring data are collected by volunteers through citizen science projects, and often crucial information is lacking to account for the inevitable biases that observers introduce during data collection. We contend that citizen science projects intended to support biodiversity monitoring must gather information about the observation process as well as species occurrence. We illustrate this using eBird, a global citizen science project that collects information on bird occurrences as well as vital contextual information on the observation process while maintaining broad participation. Our fundamental argument is that regardless of what species are being monitored, when citizen science projects collect a small set of basic information about how participants make their observations, the scientific value of the data collected will be dramatically improved.

1. Backyard bird feeding is one of the most common ways people engage with wildlife in many parts of the world. Given its scale, it can have profound consequences for the ecology of feeder birds and their behaviour. While previous work has primarily explored socio‐demographic factors associated with bird feeding, how observations of nature at backyard feeders (e.g. changes in feeder bird abundance, interaction with natural enemies and weather) influence people's propensity to feed birds remain largely unknown. 2. We examined the association between peoples’ observations at their backyard feeders and their emotions and behaviours related to providing food to birds. We conducted an online survey of a subset of United States participants in Project FeederWatch, a large‐scale citizen science project. 3. Overwhelmingly, respondents (n = 1,176) reported taking actions, such as managing predators or maintaining feeders, in response to observable natural factors (e.g. increased incidence of disease, the presence of predators, increased bird abundance). Additionally, respondents described a variety of emotional responses to the scenarios of depredation or disease at their feeders, some of which (particularly anger) had a small association with whether a respondent would take action in response. Respondents generally believed that their bird feeding benefits backyard birds (e.g. by improving overwinter survival and overall health), and indicated that natural factors (e.g. bird abundance, disease prevalence) and abiotic factors (i.e. cold temperature) had more of an influence on how much they feed birds than internal constraints such as time and money. 4. These findings suggest that human behaviour with respect to bird feeding is coupled to observations of nature, which could lead to feedbacks between provisioning intensity and ecological dynamics. Overall, our results have important implications for bird conservation and for understanding the potential benefits that humans receive from provisioning birds.

Citizen science data are valuable for addressing a wide range of ecological research questions, and there has been a rapid increase in the scope and volume of data available. However, data from large-scale citizen science projects typically present a number of challenges that can inhibit robust ecological inferences. These challenges include: species bias, spatial bias, and variation in effort.To demonstrate addressing key challenges in analysing citizen science data, we use the example of estimating species distributions with data from eBird, a large semi-structured citizen science project. We estimate two widely applied metrics of species distributions: encounter rate and occupancy probability. For each metric, we assess the impact of data processing steps that either degrade or refine the data used in the analyses. We also test whether differences in model performance are maintained at different sample sizes.Model performance improved when data processing and analytical methods addressed the challenges arising from citizen science data. The largest gains in model performance were achieved with: 1) the use of complete checklists (where observers report all the species they detect and identify); 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 and effort variables. Improvements in model performance with data refinement were more evident with larger sample sizes.Here, we describe processes to refine semi-structured citizen science data to estimate species distributions. We demonstrate the value of complete checklists, which can inform the design and adaptation of citizen science projects. We also demonstrate the value of information on effort. The methods we have outlined are also likely to improve other forms of inference, and will enable researchers to conduct robust analyses and harness the vast ecological knowledge that exists within citizen science data.

While many observations of species are being collected by citizen science projects worldwide, it can be challenging to identify projects collecting data that effectively monitor biodiversity. Over the past several years the allure of taking a “Big Data” approach has provided the opportunity to gather massive quantities of observations via the Internet, too often with insufficient information to describe how the observations were made. Information about species populations — where and when they occur and how many of them are there — (i.e., the signal) can be lost because insufficient information is gathered to account for the inherent biases in data collection (i.e., the noise). Here we suggest that citizen science projects that have succeeded in motivating large numbers of participants, must consider factors that influence the ecological process that affect species populations as well as the observation process that determines how observations are made. Those citizen science projects that collect sufficient contextual information describing the observation process can be used to generate increasingly accurate information about the distribution and abundance of organisms. We illustrate this using eBird as a case study, describing how this citizen science platform is able to collect vital contextual information on the observation process while maintaining a broad global constituency of participants. We highlight how eBird provides information with which to generate biodiversity indicators — specifically distribution, abundance, and habitat associations — across the entire annual cycle, even for populations of long distance migratory birds, a highly challenging taxon.

Avian migration is one of Earth's largest processes of biomass transport, involving billions of birds. We estimated continental biomass flows of nocturnal avian migrants across the contiguous United States using a network of 143 weather radars. We show that, relative to biomass leaving in autumn, proportionally more biomass returned in spring across the southern United States than across the northern United States. Neotropical migrants apparently achieved higher survival during the combined migration and non-breeding period, despite an average three- to fourfold longer migration distance, compared with a more northern assemblage of mostly temperate-wintering migrants. Additional mortality expected with longer migration distances was probably offset by high survival in the (sub)tropics. Nearctic-Neotropical migrants relying on a 'higher survivorship' life-history strategy may be particularly sensitive to variations in survival on the overwintering grounds, highlighting the need to identify and conserve important non-breeding habitats.

Data from well-designed experiments provide the strongest evidence of causation in biodiversity studies. However, for many species the collection of these data is not scalable to the spatial and temporal extents required to understand patterns at the population level. Only data collected from citizen science projects can gather sufficient quantities of data, but data collected from volunteers are inherently noisy and heterogeneous. Here we describe a ‘Big Data’ approach to improve the data quality in eBird, a global citizen science project that gathers bird observations. First, eBird’s data submission design ensures that all data meet high standards of completeness and accuracy. Second, we take a ‘sensor calibration’ approach to measure individual variation in eBird participant’s ability to detect and identify birds. Third, we use species distribution models to fill in data gaps. Finally, we provide examples of novel analyses exploring population-level patterns in bird distributions.

Many passerines that typically migrate at night also engage in migratory flights just after sunrise. These widely observed “morning flights” often involve birds flying in directions other than those aimed toward their ultimate destinations, especially in coastal areas. Morning flights have received little formal investigation, and their study may improve our understanding of how birds orient themselves during and after nocturnal movements and how they use stopover habitat. We studied autumn morning flights in the northeastern United States to identify associations between the number of birds undertaking morning flights and the magnitude of nocturnal migratory movements, nocturnal winds, and local topography. Our analyses included observations of more than 15,000 passerines at 7 locations. We found positive relationships between morning flight size and nocturnal migration density and winds aloft: Significantly more birds flew following larger nocturnal movements, quantified from weather surveillan...

Landscape metrics are commonly used indicators of ecological pattern and processes in ecological modelling. Numerous landscape metrics are available, making the selection of appropriate metrics a common challenge in model development. In this paper, we tested the performance of methods for preselecting sets of three landscape metrics for use in modelling species richness of six groups of organisms (woody plants, orchids, orthopterans, amphibians, reptiles, and small terrestrial birds) and overall species richness in a Mediterranean forest landscape. The tested methods included expert knowledge, decision tree analysis, principal component analysis, and principal component regression. They were compared with random choice and optimal sets, which were evaluated by testing all possible combinations of metrics. All pre-selection methods performed significantly worse than the optimal sets. The statistical approaches performed slightly better than random choice that in turn performed slightly better than sets derived by expert knowledge. We concluded that the process of selecting the most appropriate landscape metrics for modelling biodiversity is not trivial and that shortcuts to systematic evaluation of metrics should not be expected to identify appropriate indicators.