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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(More)
Citizen-science projects engage volunteers to gather or process data to address scientific questions. But citizen-science projects vary in their ability to contribute usefully for science, conservation, or public policy. eBird has evolved from a basic citizen-science project into a collective enterprise, taking a novel approach to citizen science by(More)
Birds are unrivaled windows into biotic processes at all levels and are proven indicators of ecological well-being. Understanding the determinants of species distributions and their dynamics is an important aspect of ecology and is critical for conservation and management. Through crowdsourcing, since 2002, the eBird project has been collecting bird(More)
The Cornell Laboratory of Ornithology's mission is to interpret and conserve the earth's biological diversity through research, education, and citizen science focused on birds. Over the years, the Lab has accumulated one of the largest and longest-running collections of environmental data sets in existence. The data sets are not only large, but also have(More)
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(More)
This document describes the eBird reference data set and the processing steps taken during creation. We hope this data will be a useful resource for studying avian dynamics and for developing new ecological modeling techniques. The eBird reference data is freely available for all usages. The observational data included in the data set have data access level(More)
and predicts patterns of organism distribution and abundance, and explains the causes of these patterns. Ecological systems are extremely complex, and a multitude of processes may affect organisms (McMichael et al. 2003). These processes can vary over time (Delcourt and Delcourt 2005) and through space (Tuomisto et al. 2003). Consequently , to understand(More)
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(More)
The distributions of animal populations change and evolve through time. Migratory species exploit different habitats at different times of the year. Biotic and abiotic features that determine where a species lives vary due to natural and anthropogenic factors. This spatiotemporal variation needs to be accounted for in any modeling of species' distributions.(More)