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

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          Abstract

          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.

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          Most cited references9

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          Monitoring for conservation.

          Human-mediated environmental changes have resulted in appropriate concern for the conservation of ecological systems and have led to the development of many ecological monitoring programs worldwide. Many programs that are identified with the purpose of 'surveillance' represent an inefficient use of conservation funds and effort. Here, we revisit the 1964 paper by Platt and argue that his recommendations about the conduct of science are equally relevant to the conduct of ecological monitoring programs. In particular, we argue that monitoring should not be viewed as a stand-alone activity, but instead as a component of a larger process of either conservation-oriented science or management. Corresponding changes in monitoring focus and design would lead to substantial increases in the efficiency and usefulness of monitoring results in conservation.
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            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. Copyright © 2011 Elsevier Ltd. All rights reserved.
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              Observer bias and the detection of low-density populations.

              Monitoring programs increasingly are used to document the spread of invasive species in the hope of detecting and eradicating low-density infestations before they become established. However, interobserver variation in the detection and correct identification of low-density populations of invasive species remains largely unexplored. In this study, we compare the abilities of volunteer and experienced individuals to detect low-density populations of an actively spreading invasive species, and we explore how interobserver variation can bias estimates of the proportion of sites infested derived from occupancy models that allow for both false negative and false positive (misclassification) errors. We found that experienced individuals detected small infestations at sites where volunteers failed to find infestations. However, occupancy models erroneously suggested that experienced observers had a higher probability of falsely detecting the species as present than did volunteers. This unexpected finding is an artifact of the modeling framework and results from a failure of volunteers to detect low-density infestations rather than from false positive errors by experienced observers. Our findings reveal a potential issue with site occupancy models that can arise when volunteer and experienced observers are used together in surveys.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                9 October 2015
                2015
                : 10
                : 10
                : e0139600
                Affiliations
                [1 ]Cornell Lab of Ornithology, Cornell University, Ithaca, New York, United States of America
                [2 ]British Trust for Ornithology, Thetford, Norfolk, England, United Kingdom
                [3 ]School of Information, University of Michigan, Ann Arbor, Michigan, United States of America
                [4 ]School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, Oregon, United States of America
                [5 ]College of Information Studies, University of Maryland, College Park, Maryland, United States of America
                University of Bologna, ITALY
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: SK WKW CL WMH AJ MI DF FAL. Performed the experiments: WKW TM AJ JY. Analyzed the data: SK WKW CL WMH AJ MI JY TM DF AW CW. Contributed reagents/materials/analysis tools: WKW TM AJ JY JG. Wrote the paper: SK WKW CL WMH TM AJ MI JY JG AW CW FAL.

                Article
                PONE-D-14-33015
                10.1371/journal.pone.0139600
                4599805
                26451728
                b949d707-1a84-4909-9ebb-96c549a218b2
                Copyright @ 2015

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

                History
                : 23 July 2014
                : 15 September 2015
                Page count
                Figures: 9, Tables: 2, Pages: 20
                Funding
                This work was supported by the Leon Levy Foundation ( http://leonlevyfoundation.org), Seaver Institute, Wolf Creek Foundation, and the National Science Foundation (IIS-1238371 and IIS-1209714). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Custom metadata
                All relevant data are available from Dryad: doi: 10.5061/dryad.2k27f.

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