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Abstract
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.
Highlights
The conservation of species begins with an understanding of the patterns of distribution, abundance, and movements of individuals
To illustrate the results of our estimates of observer variability, we present results from observations made in Bird Conservation Region 30, the New England and Mid-Atlantic Coast of the United States
As in the results presented for the occurrence estimates, these distribution estimates use spatio-temporal exploratory model (STEM) to standardize the effects of search effort as the probabilities that a typical eBird participant would count enough individual American Robins (Turdus migratorius) for each abundance class on a search conducted from 7 to 8 a.m. while traveling 1 km at the given location and time of year
Summary
The conservation of species begins with an understanding of the patterns of distribution, abundance, and movements of individuals These patterns are driven by an interacting series of climatic, geological, ecological, and anthropogenic processes operating simultaneously across a range of spatial and temporal scales (Bell 2012). By comparing these patterns across a range of spatial and temporal scales can we begin to identify the interacting role of these processes. To study and understand entire ecological systems, data must be collected at fine resolutions over broad spatial and temporal extents, for wideranging species. Citizen science projects have emerged as an efficient way to gather such data by engaging a large number of people and compiling their ecological observations, and the fastest growth in species’ distribution data comes from volunteers participating in citizen science projects (Pimm et al 2014)
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