eBird Data Research Articles

Climate-based prioritization of data collection for monitoring wintering birds in Latin America

SummaryRecent studies have highlighted the threat that climate change poses to species, as areas of climatic suitability contract or shift across the landscape. North American Neotropical long-distant migrant bird species present a unique problem compared to sedentary species because climate change may differ significantly across their breeding and wintering grounds. Studying the potential future distributions of these birds is challenging on many levels, including the fact that our understanding of the wintering grounds of these species is quite poor. To address this issue, we analyse available eBird data during the winter season in the Western Hemisphere in an effort to further promote and direct citizen science efforts to focus on areas that are climatically undersampled. We used Mobility-Oriented Parity (MOP) to understand the areas where climates are most dissimilar from climates sampled by existing eBird checklists, creating a map that ranks the western hemisphere at a 10 km resolution for climatic sampling during the winter season. We found that parts of Mexico and Central America, areas of Colombia, almost the entire Amazon Basin, coastal Peru and Chile, and northern Argentina are climatically undersampled. As a test case, we then used the map of survey priorities to simulate additional sampling in Colombia and recalculated the rankings. Guiding additional sampling with the priorities reduced climate dissimilarities between sampled and unsampled grid cells more than when additional sampling expanded in proportion to current sampling efforts or based on geographic undersampling. Analyses of sampling coverage in environmental space, such as this, will be a useful tool for targeting monitoring effort for bird species.

Using eBird data to model population change of migratory bird species

Using eBird data to model population change of migratory bird species

Novel measures of continental‐scale avian migration phenology related to proximate environmental cues

AbstractRapid changes in seasonal avian migrations provide compelling examples of biotic response to climate change. Seasonal waves of land surface phenology and temperature are thought to be primary exogenous cues that migrants use to fine tune migration timing. Exploration of the role that these cues play in regulating migration timing requires better spatial, temporal, and taxonomic sampling than is typically available. We analyze weather surveillance radar (WSR) and eBird citizen science data in an effort to understand the macroscale relationships among migration phenology, seasonal waves of land surface phenology and seasonality of temperature. We demonstrate that both WSR and eBird data provide similar estimates of migration timing at local (within 100 km) and macro scales (>20° of latitude). These measures of avian phenology were positively correlated with spring temperatures and land surface phenology (extended spring index of first leaf date), which followed a south to north wave. Start‐of‐spring, estimated from a remotely sensed index of vegetation greenness, did not show the expected pattern from south to north and appears unlikely to be a useful exogenous migration cue within the study region. Future analyses of WSR and eBird data have the potential to provide high‐resolution phenology data that may be useful in understanding spatiotemporal dynamics of migration systems and the response of these systems to climate change.

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.

Efficacy of eBird data as an aid in conservation planning and monitoring

One of the world's largest citizen science projects is eBird, a database that has been used primarily to address questions of bird distributions and abundance over large spatial scales. However, addressing finer-scale questions is also possible, depending on survey coverage and whether assumptions and limitations are matched to the scale of inferences. Our objective was to determine if the eBird database could be used to develop estimates of bird abundance and diversity comparable to those from standardized shorebird surveys. We compared a year of standardized shorebird surveys by trained observers at Snook Islands Natural Area located in Palm Beach County, Florida, to a year of eBird observations from the same site. Total species richness derived from eBird (25 species) was higher than that from standardized surveys (20 species). Similarly, we found the Shannon diversity index calculated from eBird was higher (2.81) than the same index calculated from standardized surveys (2.21; P < 0.001). The higher diversity and species richness may reflect the greater effort of eBird participants (35,289 person-hours) compared to our standard surveys (2126 person-hours). We found only a slight difference in parameter estimates between data obtained from eBird and from standardized surveys. Potential use and value of eBird as a tool for land managers and conservationists may be greater than currently realized, but studies conducted in a wider range of ecosystems and locations are needed to develop generalizations.

EBird: Curating Citizen Science Data for Use by Diverse Communities

In this paper we describe eBird, a highly successful citizen science project. With over 150,000 participants worldwide and an accumulation of over 140,000,000 bird observations globally in the last decade, eBird has evolved into a major tool for scientific investigations in diverse fields such as ornithology, computer science, statistics, ecology and climate change. eBird’s impact in scientific research is grounded in careful data curation practices that pay attention to all stages of the data lifecycle, and attend to the needs of stakeholders engaged in that data lifecycle. We describe the important aspects of eBird, paying particular attention to the mechanisms to improve data quality; describe the data products that are available to the global community; investigate some aspects of the downloading community; and demonstrate significant results that derive from the use of openly-available eBird data.

Improving Your Chances: Boosting Citizen Science Discovery

Citizen scientists are playing an increasing role in helping collect, process, and/or analyze data used to study a variety of scientific phenomena. We address the problem of identifying tasks that are rewarding to the citizen scientists, which results in greater participation, leading to more data and better models. We apply our methodology to eBird, whose participants are avid birders interested in observing different species while contributing to science. In order to improve the birders' chances of meeting their goals, we consider the following probabilistic maximum coverage problem: Given a set of locations, select a subset of size k, such that the birders maximize the expected number of observed species by visiting such locations. We also consider a secondary objective that gives preference to birding sites not previously visited. We consider two variants of the probabilistic maximum coverage problem, provide a theoretical analysis, describe several algorithms with provable approximation guarantees, as well as heuristic approaches, and provide empirical results using eBird data. Our algorithms are fast and provide high quality recommendations.

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.

Adaptive Spatio-Temporal Exploratory Models: Hemisphere-wide species distributions from massively crowdsourced eBird data

Broad-scale spatiotemporal processes in conservation and sustainability science, such as continent-wide animal movement, occur across a range of spatial and temporal scales. Understanding these processes at multiple scales is crucial for developing and coordinating conservation strategies across national boundaries. In this paper we propose a general class of models we call AdaSTEM, for Adaptive Spatio-Temporal Exploratory Models, that are able to exploit variation in the density of observations while adapting to multiple scales in space and time. We show that this framework is able to efficiently discover multiscale structure when it is present, while retaining predictive performance when absent. We provide an empirical comparison and analysis, offer theoretical insights from the ensemble loss decomposition, and deploy AdaSTEM to estimate the spatiotemporal distribution of Barn Swallow (Hirundo rustica) across the Western Hemisphere using massively crowdsourced eBird data.

Modeling species distribution dynamics with SpatioTemoral Exploratory Models: Discovering patterns and processes of broad-scale avian migrations

The distributions of animal populations are not static. During regular migratory movements species exploit different habitats. This spatiotemporal variation needs to be accounted for when modeling a species’ distribution and is essential for developing conservation strategies for widespread species, and especially for migratory species. Attempts to design conservation landscapes across large regions based on models of distributions in a single season or a small region may not fully reflect the limiting factors that are driving population declines. Our goal is to predict and explore patterns of species’ occurrence and local habitat usage across broad landscapes. We use data from eBird (http://www.ebird.org), an online citizen science bird-monitoring project and environmental descriptions from continent-wide covariates linked through observation location and time. These covariates include remotely sensed habitat information from the National Land Cover Database and vegetation phenology from MODIS. We model species occurrence with the SpatioTemporal Exploratory Model (STEM), an ensemble model designed to adapt to non-stationary spatiotemporal processes. This is accomplished by creating a large ensemble of local models, each restricted to a local spatial and temporal region. Within each region a user specified predictive model associates the predictors with the response. Patterns modeled locally “scale up” via ensemble averaging to larger scales. Here we analyze eBird data to study broad-scale movements of bird populations throughout the year. We use STEM built with decision trees to adapt to a wide variety avian migration patterns without requiring a detailed understanding of the underlying dynamic local processes. We demonstrate how eBird data are capable of resolving the changing distributions of birds through their migrations. Then we illustrate how seasonal variation in habitat association can be identified and explored. These tools provide valuable information for generating hypotheses and making inference about the processes driving dynamic species distributional patterns.

A method for measuring the relative information content of data from different monitoring protocols

Summary1. Species monitoring is an essential component of assessing conservation status, predicting effects of habitat change and establishing management and conservation priorities. The pervasive access to the Internet has led to the development of several extensive monitoring projects that engage massive networks of volunteers who provide observations following relatively unstructured protocols. However, the value of these data is largely unknown. 2. We develop a novel cross‐data validation method for measuring the value of survey data from one source (e.g. an Internet checklist program) relative to a second, benchmark data source. The method fits a model to the data of interest and validates the model using benchmark data, allowing us to isolate the training data's information content from its biases. We also define a data efficiency ratio to quantify the relative efficiency of the data sources. 3. We apply our cross‐data validation method to quantify the value of data collected in eBird – a western hemisphere, year‐round citizen science bird checklist project – relative to data from the highly standardized North American Breeding Bird Survey (BBS). The results show that eBird data contain information similar in quality to that in BBS data, while the information per BBS datum is higher. 4. We suggest that these methods have more general use in evaluating the suitability of sources of data for addressing specific questions for taxa of interest.

EBird: A citizen-based bird observation network in the biological sciences

New technologies are rapidly changing the way we collect, archive, analyze, and share scientific data. For example, over the next several years it is estimated that more than one billion autonomous sensors will be deployed over large spatial and temporal scales, and will gather vast quantities of data. Networks of human observers play a major role in gathering scientific data, and whether in astronomy, meteorology, or observations of nature, they continue to contribute significantly. In this paper we present an innovative use of the Internet and information technologies that better enhances the opportunity for citizens to contribute their observations to science and the conservation of bird populations. eBird is building a web-enabled community of bird watchers who collect, manage, and store their observations in a globally accessible unified database. Through its development as a tool that addresses the needs of the birding community, eBird sustains and grows participation. Birders, scientists, and conservationists are using eBird data worldwide to better understand avian biological patterns and the environmental and anthropogenic factors that influence them. Developing and shaping this network over time, eBird has created a near real-time avian data resource producing millions of observations per year.