Modeling avian full annual cycle distribution and population trends with citizen science data.

Information on species' distributions, abundances, and how they change over time is central to the study of the ecology and conservation of animal populations. This information is challenging to obtain at landscape scales across range-wide extents for two main reasons. First, landscape-scale processes that affect populations vary throughout the year and across species' ranges, requiring high-resolution, year-round data across broad, sometimes hemispheric, spatial extents. Second, while citizen science projects can collect data at these resolutions and extents, using these data requires appropriate analysis to address known sources of bias. Here, we present an analytical framework to address these challenges and generate year-round, range-wide distributional information using citizen science data. To illustrate this approach, we apply the framework to Wood Thrush (Hylocichla mustelina), a long-distance Neotropical migrant and species of conservation concern, using data from the citizen science project eBird. We estimate occurrence and abundance across a range of spatial scales throughout the annual cycle. Additionally, we generate intra-annual estimates of the range, intra-annual estimates of the associations between species and characteristics of the landscape, and interannual trends in abundance for breeding and non-breeding seasons. The range-wide population trajectories for Wood Thrush show a close correspondence between breeding and non-breeding seasons with steep declines between 2010 and 2013 followed by shallower rates of decline from 2013 to 2016. The breeding season range-wide population trajectory based on the independently collected and analyzed North American Breeding Bird Survey data also shows this pattern. The information provided here fills important knowledge gaps for Wood Thrush, especially during the less studied migration and non-breeding periods. More generally, the modeling framework presented here can be used to accurately capture landscape scale intra- and interannual distributional dynamics for broadly distributed, highly mobile species.

Robust sound event detection in bioacoustic sensor networks.

Bioacoustic sensors, sometimes known as autonomous recording units (ARUs), can record sounds of wildlife over long periods of time in scalable and minimally invasive ways. Deriving per-species abundance estimates from these sensors requires detection, classification, and quantification of animal vocalizations as individual acoustic events. Yet, variability in ambient noise, both over time and across sensors, hinders the reliability of current automated systems for sound event detection (SED), such as convolutional neural networks (CNN) in the time-frequency domain. In this article, we develop, benchmark, and combine several machine listening techniques to improve the generalizability of SED models across heterogeneous acoustic environments. As a case study, we consider the problem of detecting avian flight calls from a ten-hour recording of nocturnal bird migration, recorded by a network of six ARUs in the presence of heterogeneous background noise. Starting from a CNN yielding state-of-the-art accuracy on this task, we introduce two noise adaptation techniques, respectively integrating short-term (60 ms) and long-term (30 min) context. First, we apply per-channel energy normalization (PCEN) in the time-frequency domain, which applies short-term automatic gain control to every subband in the mel-frequency spectrogram. Secondly, we replace the last dense layer in the network by a context-adaptive neural network (CA-NN) layer, i.e. an affine layer whose weights are dynamically adapted at prediction time by an auxiliary network taking long-term summary statistics of spectrotemporal features as input. We show that PCEN reduces temporal overfitting across dawn vs. dusk audio clips whereas context adaptation on PCEN-based summary statistics reduces spatial overfitting across sensor locations. Moreover, combining them yields state-of-the-art results that are unmatched by artificial data augmentation alone. We release a pre-trained version of our best performing system under the name of BirdVoxDetect, a ready-to-use detector of avian flight calls in field recordings.

Using Semistructured Surveys to Improve Citizen Science Data for Monitoring Biodiversity.

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.

Seasonal abundance and survival of North America's migratory avifauna determined by weather radar.

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.

Building essential biodiversity variables (EBVs) of species distribution and abundance at a global scale.

Much biodiversity data is collected worldwide, but it remains challenging to assemble the scattered knowledge for assessing biodiversity status and trends. The concept of Essential Biodiversity Variables (EBVs) was introduced to structure biodiversity monitoring globally, and to harmonize and standardize biodiversity data from disparate sources to capture a minimum set of critical variables required to study, report and manage biodiversity change. Here, we assess the challenges of a 'Big Data' approach to building global EBV data products across taxa and spatiotemporal scales, focusing on species distribution and abundance. The majority of currently available data on species distributions derives from incidentally reported observations or from surveys where presence-only or presence-absence data are sampled repeatedly with standardized protocols. Most abundance data come from opportunistic population counts or from population time series using standardized protocols (e.g. repeated surveys of the same population from single or multiple sites). Enormous complexity exists in integrating these heterogeneous, multi-source data sets across space, time, taxa and different sampling methods. Integration of such data into global EBV data products requires correcting biases introduced by imperfect detection and varying sampling effort, dealing with different spatial resolution and extents, harmonizing measurement units from different data sources or sampling methods, applying statistical tools and models for spatial inter- or extrapolation, and quantifying sources of uncertainty and errors in data and models. To support the development of EBVs by the Group on Earth Observations Biodiversity Observation Network (GEO BON), we identify 11 key workflow steps that will operationalize the process of building EBV data products within and across research infrastructures worldwide. These workflow steps take multiple sequential activities into account, including identification and aggregation of various raw data sources, data quality control, taxonomic name matching and statistical modelling of integrated data. We illustrate these steps with concrete examples from existing citizen science and professional monitoring projects, including eBird, the Tropical Ecology Assessment and Monitoring network, the Living Planet Index and the Baltic Sea zooplankton monitoring. The identified workflow steps are applicable to both terrestrial and aquatic systems and a broad range of spatial, temporal and taxonomic scales. They depend on clear, findable and accessible metadata, and we provide an overview of current data and metadata standards. Several challenges remain to be solved for building global EBV data products: (i) developing tools and models for combining heterogeneous, multi-source data sets and filling data gaps in geographic, temporal and taxonomic coverage, (ii) integrating emerging methods and technologies for data collection such as citizen science, sensor networks, DNA-based techniques and satellite remote sensing, (iii) solving major technical issues related to data product structure, data storage, execution of workflows and the production process/cycle as well as approaching technical interoperability among research infrastructures, (iv) allowing semantic interoperability by developing and adopting standards and tools for capturing consistent data and metadata, and (v) ensuring legal interoperability by endorsing open data or data that are free from restrictions on use, modification and sharing. Addressing these challenges is critical for biodiversity research and for assessing progress towards conservation policy targets and sustainable development goals.

Dynamic conservation for migratory species.

In an era of unprecedented and rapid global change, dynamic conservation strategies that tailor the delivery of habitat to when and where it is most needed can be critical for the persistence of species, especially those with diverse and dispersed habitat requirements. We demonstrate the effectiveness of such a strategy for migratory waterbirds. We analyzed citizen science and satellite data to develop predictive models of bird populations and the availability of wetlands, which we used to determine temporal and spatial gaps in habitat during a vital stage of the annual migration. We then filled those gaps using a reverse auction marketplace to incent qualifying landowners to create temporary wetlands on their properties. This approach is a cost-effective way of adaptively meeting habitat needs for migratory species, optimizes conservation outcomes relative to investment, and can be applied broadly to other conservation challenges.

Global change and the distributional dynamics of migratory bird populations wintering in Central America.

Understanding the susceptibility of highly mobile taxa such as migratory birds to global change requires information on geographic patterns of occurrence across the annual cycle. Neotropical migrants that breed in North America and winter in Central America occur in high concentrations on their non-breeding grounds where they spend the majority of the year and where habitat loss has been associated with population declines. Here, we use eBird data to model weekly patterns of abundance and occurrence for 21 forest passerine species that winter in Central America. We estimate species' distributional dynamics across the annual cycle, which we use to determine how species are currently associated with public protected areas and projected changes in climate and land-use. The effects of global change on the non-breeding grounds is characterized by decreasing precipitation, especially during the summer, and the conversion of forest to cropland, grassland, or peri-urban. The effects of global change on the breeding grounds are characterized by increasing winter precipitation, higher temperatures, and the conversion of forest to peri-urban. During spring and autumn migration, species are projected to encounter higher temperatures, forests that have been converted to peri-urban, and increased precipitation during spring migration. Based on current distributional dynamics, susceptibility to global change is characterized by the loss of forested habitats on the non-breeding grounds, warming temperatures during migration and on the breeding grounds, and declining summer rainfall on the non-breeding grounds. Public protected areas with low and medium protection status are more prevalent on the non-breeding grounds, suggesting that management opportunities currently exist to mitigate near-term non-breeding habitat losses. These efforts would affect more individuals of more species during a longer period of the annual cycle, which may create additional opportunities for species to respond to changes in habitat or phenology that are likely to develop under climate change.

Towards the Automatic Classification of Avian Flight Calls for Bioacoustic Monitoring.

Automatic classification of animal vocalizations has great potential to enhance the monitoring of species movements and behaviors. This is particularly true for monitoring nocturnal bird migration, where automated classification of migrants' flight calls could yield new biological insights and conservation applications for birds that vocalize during migration. In this paper we investigate the automatic classification of bird species from flight calls, and in particular the relationship between two different problem formulations commonly found in the literature: classifying a short clip containing one of a fixed set of known species (N-class problem) and the continuous monitoring problem, the latter of which is relevant to migration monitoring. We implemented a state-of-the-art audio classification model based on unsupervised feature learning and evaluated it on three novel datasets, one for studying the N-class problem including over 5000 flight calls from 43 different species, and two realistic datasets for studying the monitoring scenario comprising hundreds of thousands of audio clips that were compiled by means of remote acoustic sensors deployed in the field during two migration seasons. We show that the model achieves high accuracy when classifying a clip to one of N known species, even for a large number of species. In contrast, the model does not perform as well in the continuous monitoring case. Through a detailed error analysis (that included full expert review of false positives and negatives) we show the model is confounded by varying background noise conditions and previously unseen vocalizations. We also show that the model needs to be parameterized and benchmarked differently for the continuous monitoring scenario. Finally, we show that despite the reduced performance, given the right conditions the model can still characterize the migration pattern of a specific species. The paper concludes with directions for future research.

Innovative Visualizations Shed Light on Avian Nocturnal Migration.

Globally, billions of flying animals undergo seasonal migrations, many of which occur at night. The temporal and spatial scales at which migrations occur and our inability to directly observe these nocturnal movements makes monitoring and characterizing this critical period in migratory animals' life cycles difficult. Remote sensing, therefore, has played an important role in our understanding of large-scale nocturnal bird migrations. Weather surveillance radar networks in Europe and North America have great potential for long-term low-cost monitoring of bird migration at scales that have previously been impossible to achieve. Such long-term monitoring, however, poses a number of challenges for the ornithological and ecological communities: how does one take advantage of this vast data resource, integrate information across multiple sensors and large spatial and temporal scales, and visually represent the data for interpretation and dissemination, considering the dynamic nature of migration? We assembled an interdisciplinary team of ecologists, meteorologists, computer scientists, and graphic designers to develop two different flow visualizations, which are interactive and open source, in order to create novel representations of broad-front nocturnal bird migration to address a primary impediment to long-term, large-scale nocturnal migration monitoring. We have applied these visualization techniques to mass bird migration events recorded by two different weather surveillance radar networks covering regions in Europe and North America. These applications show the flexibility and portability of such an approach. The visualizations provide an intuitive representation of the scale and dynamics of these complex systems, are easily accessible for a broad interest group, and are biologically insightful. Additionally, they facilitate fundamental ecological research, conservation, mitigation of human-wildlife conflicts, improvement of meteorological products, and public outreach, education, and engagement.

A characterization of autumn nocturnal migration detected by weather surveillance radars in the northeastern USA.

Billions of birds migrate at night over North America each year. However, few studies have described the phenology of these movements, such as magnitudes, directions, and speeds, for more than one migration season and at regional scales. In this study, we characterize density, direction, and speed of nocturnally migrating birds using data from 13 weather surveillance radars in the autumns of 2010 and 2011 in the northeastern USA. After screening radar data to remove precipitation, we applied a recently developed algorithm for characterizing velocity profiles with previously developed methods to document bird migration. Many hourly radar scans contained windborne "contamination," and these scans also exhibited generally low overall reflectivities. Hourly scans dominated by birds showed nightly and seasonal patterns that differed markedly from those of low reflectivity scans. Bird migration occurred during many nights, but a smaller number of nights with large movements of birds defined regional nocturnal migration. Densities varied by date, time, and location but peaked in the second and third deciles of night during the autumn period when the most birds were migrating. Migration track (the direction to which birds moved) shifted within nights from south-southwesterly to southwesterly during the seasonal migration peaks; this shift was not consistent with a similar shift in wind direction. Migration speeds varied within nights, although not closely with wind speed. Airspeeds increased during the night; groundspeeds were highest between the second and third deciles of night, when the greatest density of birds was migrating. Airspeeds and groundspeeds increased during the fall season, although groundspeeds fluctuated considerably with prevailing winds. Significant positive correlations characterized relationships among bird densities at southern coastal radar stations and northern inland radar stations. The quantitative descriptions of broadscale nocturnal migration patterns presented here will be essential for biological and conservation applications. These descriptions help to define migration phenology in time and space, fill knowledge gaps in avian annual cycles, and are useful for monitoring long-term population trends of migrants. Furthermore, these descriptions will aid in assessing potential risks to migrants, particularly from structures with which birds collide and artificial lighting that disorients migrants.

Convergence of broad-scale migration strategies in terrestrial birds.

Migration is a common strategy used by birds that breed in seasonal environments. Selection for greater migration efficiency is likely to be stronger for terrestrial species whose migration strategies require non-stop transoceanic crossings. If multiple species use the same transoceanic flyway, then we expect the migration strategies of these species to converge geographically towards the most optimal solution. We test this by examining population-level migration trajectories within the Western Hemisphere for 118 migratory species using occurrence information from eBird. Geographical convergence of migration strategies was evident within specific terrestrial regions where geomorphological features such as mountains or isthmuses constrained overland migration. Convergence was also evident for transoceanic migrants that crossed the Gulf of Mexico or Atlantic Ocean. Here, annual population-level movements were characterized by clockwise looped trajectories, which resulted in faster but more circuitous journeys in the spring and more direct journeys in the autumn. These findings suggest that the unique constraints and requirements associated with transoceanic migration have promoted the spatial convergence of migration strategies. The combination of seasonal atmospheric and environmental conditions that has facilitated the use of similar broad-scale migration strategies may be especially prone to disruption under climate and land-use change.

Abundance models improve spatial and temporal prioritization of conservation resources.

Conservation prioritization requires knowledge about organism distribution and density. This information is often inferred from models that estimate the probability of species occurrence rather than from models that estimate species abundance, because abundance data are harder to obtain and model. However, occurrence and abundance may not display similar patterns and therefore development of robust, scalable, abundance models is critical to ensuring that scarce conservation resources are applied where they can have the greatest benefits. Motivated by a dynamic land conservation program, we develop and assess a general method for modeling relative abundance using citizen science monitoring data. Weekly estimates of relative abundance and occurrence were compared for prioritizing times and locations of conservation actions for migratory waterbird species in California, USA. We found that abundance estimates consistently provided better rankings of observed counts than occurrence estimates. Additionally, the relationship between abundance and occurrence was nonlinear and varied by species and season. Across species, locations prioritized by occurrence models had only 10-58% overlap with locations prioritized by abundance models, highlighting that occurrence models will not typically identify the locations of highest abundance that are vital for conservation of populations.

Taking a 'Big Data' approach to data quality in a citizen science project.

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.

Can Observation Skills of Citizen Scientists Be Estimated Using Species Accumulation Curves?

Volunteers are increasingly being recruited into citizen science projects to collect observations for scientific studies. An additional goal of these projects is to engage and educate these volunteers. Thus, there are few barriers to participation resulting in volunteer observers with varying ability to complete the project's tasks. To improve the quality of a citizen science project's outcomes it would be useful to account for inter-observer variation, and to assess the rarely tested presumption that participating in a citizen science projects results in volunteers becoming better observers. Here we present a method for indexing observer variability based on the data routinely submitted by observers participating in the citizen science project eBird, a broad-scale monitoring project in which observers collect and submit lists of the bird species observed while birding. Our method for indexing observer variability uses species accumulation curves, lines that describe how the total number of species reported increase with increasing time spent in collecting observations. We find that differences in species accumulation curves among observers equates to higher rates of species accumulation, particularly for harder-to-identify species, and reveals increased species accumulation rates with continued participation. We suggest that these properties of our analysis provide a measure of observer skill, and that the potential to derive post-hoc data-derived measurements of participant ability should be more widely explored by analysts of data from citizen science projects. We see the potential for inferential results from analyses of citizen science data to be improved by accounting for observer skill.

Documenting stewardship responsibilities across the annual cycle for birds on U.S. public lands.

In the face of global environmental change, the importance of protected areas in biological management and conservation is expected to grow. Birds have played an important role as biological indicators of the effectiveness of protected areas, but with little consideration given to where species occur outside the breeding season. We estimated weekly probability of occurrence for 308 bird species throughout the year within protected areas in the western contiguous USA using eBird occurrence data for the combined period 2004 to 2011. We classified species based on their annual patterns of occurrence on lands having intermediate conservation mandates (GAP status 2 and 3) administered by the Bureau of Land Management (BLM) and the United States Forest Service (USFS). We identified species having consistent annual association with one agency, and species whose associations across the annual cycle switched between agencies. BLM and USFS GAP status 2 and 3 lands contained low to moderate proportions of species occurrences, with proportions highest for species that occurred year-round or only during the summer. We identified two groups of species whose annual movements resulted in changes in stewardship responsibilities: (1) year-round species that occurred on USFS lands during the breeding season and BLM lands during the nonbreeding season; and (2) summer species that occurred on USFS lands during the breeding season and BLM lands during spring and autumn migration. Species that switched agencies had broad distributions, bred on high-elevation USFS lands, were not more likely to be identified as species of special management concern, and migrated short (year-round species) to long distances (summer species). Our findings suggest cooperative efforts that address the requirements of short-distance migratory species on GAP status 2 lands (n = 20 species) and GAP status 3 lands (n = 24) and long-distance migratory species on GAP status 2 lands (n = 9) would likely benefit their populations. Such efforts may prove especially relevant for species whose seasonal movements result in associations with different environments containing contrasting global change processes and management mandates.

Migration timing and its determinants for nocturnal migratory birds during autumn migration.

1. Migration is a common strategy used by birds that breed in seasonal environments, and multiple environmental and biological factors determine the timing of migration. How these factors operate in combination during autumn migration, which is considered to be under weaker time constraints relative to spring migration, is not clear. 2. Here, we examine the patterns and determinants of migration timing for nocturnal migrants during autumn migration in the north-eastern USA using nocturnal reflectivity data from 12 weather surveillance radar stations and modelled diurnal probability of occurrence for 142 species of nocturnal migrants. We first model the capacity of seasonal atmospheric conditions (wind and precipitation) and ecological productivity (vegetation greenness) to predict autumn migration intensity. We then test predictions, formulated under optimal migration theory, on how migration timing should be related to assemblage-level estimates of body size and total migration distance within the context of dietary guild (insectivore and omnivore) and level of dietary plasticity during autumn migration. 3. Our results indicate seasonal declines in ecological productivity delineate the beginning and end of peak migration, whose intensity is best predicted by the velocity of winds at migration altitudes. Insectivorous migrants departed earlier in the season and, consistent with our predictions, large-bodied and long-distance insectivorous migrants departed the earliest. Contrary to our predictions, large-bodied and some long-distance omnivorous migrants departed later in the season, patterns that were replicated in part by insectivorous migrants that displayed dietary plasticity during autumn migration. 4. Our findings indicate migration timing in the region is dictated by optimality strategies, modified based on the breadth and flexibility of migrant's foraging diets, with declining ecological productivity defining possible resource thresholds during which migration occurs when winds at migration altitudes are mild. These observations provide the basis to assess how avian migration strategies may be affected by adjustments in seasonal patterns of atmospheric circulation and ecological productivity that may occur under global climate change.

Seasonal changes in the altitudinal distribution of nocturnally migrating birds during autumn migration.

Wind plays a significant role in the flight altitudes selected by nocturnally migrating birds. At mid-latitudes in the Northern Hemisphere, atmospheric conditions are dictated by the polar-front jet stream, whose amplitude increases in the autumn. One consequence for migratory birds is that the region's prevailing westerly winds become progressively stronger at higher migration altitudes. We expect this seasonality in wind speed to result in migrants occupying progressively lower flight altitudes, which we test using density estimates of nocturnal migrants at 100 m altitudinal intervals from 12 weather surveillance radar stations located in the northeastern USA. Contrary to our expectations, median migration altitudes deviated little across the season, and the variance was lower during the middle of the season and higher during the beginning and especially the end of the season. Early-season migrants included small- to intermediate-sized long-distance migrants in the orders Charadriiformes and Passeriformes, and late-season migrants included large-bodied and intermediate-distance migrants in the order Anseriformes. Therefore, seasonality in the composition of migratory species, and related variation in migration strategies and behaviours, resulted in a convex-concave bounded distribution of migration altitudes. Our results provide a basis for assessing the implications for migratory bird populations of changes in mid-latitude atmospheric conditions probably occurring under global climate change.

Spring phenology of ecological productivity contributes to the use of looped migration strategies by birds.

Migration is a common strategy used by birds that breed in seasonal environments. The patterns and determinants of migration routes, however, remain poorly understood. Recent empirical analyses have demonstrated that the locations of two North America migration flyways (eastern and western) shift seasonally, reflecting the influence of looped migration strategies. For the eastern but not western flyway, seasonal variation in atmospheric circulation has been identified as an explanation. Here, we test an alternative explanation based on the phenology of ecological productivity, which may be of greater relevance in western North America, where phenology is more broadly dictated by elevation. Migrants in the western flyway selected lower-elevation spring routes that were wetter, greener and more productive, and higher-elevation autumn routes that were less green and less productive, but probably more direct. Migrants in the eastern flyway showed little season variation but maintained associations with maximum regional greenness. Our findings suggest the annual phenology of ecological productivity is associated with en route timing in both flyways, and the spring phenology of ecological productivity contributes to the use of looped strategies in the western flyway. This fine-tuned spatial synchronization may be disrupted when changing climate induces a mismatch between food availability and needs.

Population-level scaling of avian migration speed with body size and migration distance for powered fliers.

Optimal migration theory suggests specific scaling relationships between body size and migration speed for individual birds based on the minimization of time, energy, and risk. Here we test if the quantitative predictions originating from this theory can be detected when migration decisions are integrated across individuals. We estimated population-level migration trajectories and daily migration speeds for the combined period 2007-2011 using the eBird data set. We considered 102 North American bird species that use flapping or powered flight during migration. Many species, especially in eastern North America, had looped migration trajectories that traced a clockwise path with an eastward shift during autumn migration. Population-level migration speeds decelerated rapidly going into the breeding season, and accelerated more slowly during the transition to autumn migration. In accordance with time minimization predictions, spring migration speeds were faster than autumn migration speeds. In agreement with optimality predictions, migration speeds of powered flyers scaled negatively with body mass similarly during spring and autumn migration. Powered fliers with longer migration journeys also had faster migration speeds, a relationship that was more pronounced during spring migration. Our findings indicate that powered fliers employed a migration strategy that, when examined at the population level, was in compliance with optimality predictions. These results suggest that the integration of migration decisions across individuals does result in population-level patterns that agree with theoretical expectations developed at the individual level, indicating a role for optimal migration theory in describing the mechanisms underlying broadscale patterns of avian migration for species that use powered flight.

Data-intensive science applied to broad-scale citizen science.

Identifying ecological patterns across broad spatial and temporal extents requires novel approaches and methods for acquiring, integrating and modeling massive quantities of diverse data. For example, a growing number of research projects engage continent-wide networks of volunteers ('citizen-scientists') to collect species occurrence data. Although these data are information rich, they present numerous challenges in project design, implementation and analysis, which include: developing data collection tools that maximize data quantity while maintaining high standards of data quality, and applying new analytical and visualization techniques that can accurately reveal patterns in these data. Here, we describe how advances in data-intensive science provide accurate estimates in species distributions at continental scales by identifying complex environmental associations.