Review
Special Issue: Ecological and evolutionary informatics
Data-intensive science applied to broad-scale citizen science

https://doi.org/10.1016/j.tree.2011.11.006Get rights and content

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.

Section snippets

The need for citizen-science data in ecology

The conservation of species begins with an understanding of the distribution, abundance, habitat preferences and movements of organisms across wide geographic areas and over long periods of time. Although inroads have been made into automating the collection of information on species occurrence 1, 2, 3, typically only human observers can reliably identify organisms to the species level [4]. Intensive surveys made by expert observers can accurately identify patterns of species occurrence within

Designing and implementing broad-scale citizen-science projects

Maximizing the information obtained from broad-scale citizen-science projects that gather species occurrence data depends on finding the proper balance between data quantity and quality. Quantity is important, because a sufficiently large volume of data with relatively lower per-datum information content can contain more information for broad-scale species distribution estimates than a smaller amount of higher quality data [20]. High data quality is essential and requires attention to the

Extracting ecological and evolutionary insights during analysis

Effective research requires clearly articulated questions. Typically, these questions are formulated prior to data collection, allowing the data collection processes to be tailored to help answer the questions identified 10, 24. Under such circumstances, the types of analysis used will typically be those conventionally used by ecologists (Hochachka, W.M. et al., unpublished data). By contrast, many broad-scale observational data sets, which include citizen-science data, are collected with only

Concluding remarks

In this review, we have illustrated some of the unique challenges inherent in broad-scale citizen-science data sets and how novel data-intensive techniques can overcome these challenges. Although our discussion has focused on one citizen-science project, eBird, the general approaches that we have taken to designing the data collection and analyses processes are more widely applicable. Our experience has shown that an uncomplicated protocol and appropriate rewards for volunteer participation is

Acknowledgements

We thank R. Bonney, T. Dietterich, B. Sullivan and three anonymous reviewers for improving the clarity of our article. This work was funded by the Leon Levy Foundation, Wolf Creek Foundation and the National Science Foundation (Grant Numbers OCI-0830944, CCF-0832782, ITR-0427914, DBI-1049363, DBI-0542868, DUE-0734857, IIS-0748626, IIS-0844546, IIS-0612031, IIS-1050422, IIS-0905385, IIS-0746500, AGS-0835821, CNS-0751152, CNS-0855167, TG-DEB110008).

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