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

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.

[1]  R. Fisher,et al.  The Relation Between the Number of Species and the Number of Individuals in a Random Sample of an Animal Population , 1943 .

[2]  Emission Spectrum Attributed to Cadmium Fluoride , 1949, Nature.

[3]  Jacob Cohen A Coefficient of Agreement for Nominal Scales , 1960 .

[4]  C. Gini On the Characteristics of Italian Statistics , 1965 .

[5]  S. Hurlbert The Nonconcept of Species Diversity: A Critique and Alternative Parameters. , 1971, Ecology.

[6]  J. M. Scott,et al.  Reducing bird count variability by training observers , 1981 .

[7]  W. Link,et al.  Observer differences in the North American Breeding Bird Survey , 1994 .

[8]  John Bell,et al.  A review of methods for the assessment of prediction errors in conservation presence/absence models , 1997, Environmental Conservation.

[9]  J. Townshend,et al.  Global land cover classi(cid:142) cation at 1 km spatial resolution using a classi(cid:142) cation tree approach , 2004 .

[10]  S. Manel,et al.  Evaluating presence-absence models in ecology: the need to account for prevalence , 2001 .

[11]  W. Bossert,et al.  The Measurement of Diversity , 2001 .

[12]  John R. Sauer,et al.  Use of North American Breeding Bird Survey Data to Estimate Population Change for Bird Conservation Regions , 2003 .

[13]  Robert K. Colwell,et al.  INTERPOLATING, EXTRAPOLATING, AND COMPARING INCIDENCE-BASED SPECIES ACCUMULATION CURVES , 2004 .

[14]  J. Nichols,et al.  Monitoring for conservation. , 2006, Trends in ecology & evolution.

[15]  S. Wood Generalized Additive Models: An Introduction with R , 2006 .

[16]  Alan Y. Chiang,et al.  Generalized Additive Models: An Introduction With R , 2007, Technometrics.

[17]  Peter B. Pearman,et al.  Common species determine richness patterns in biodiversity indicator taxa , 2007 .

[18]  Dennis M. Wilkinson,et al.  Strong regularities in online peer production , 2008, EC '08.

[19]  M. Conroy,et al.  Modeling demographic processes in marked populations , 2009 .

[20]  R. Bonney,et al.  Citizen Science: A Developing Tool for Expanding Science Knowledge and Scientific Literacy , 2009 .

[21]  Kenneth H. Pollock,et al.  Sources of Measurement Error, Misclassification Error, and Bias in Auditory Avian Point Count Data , 2009 .

[22]  Brian L. Sullivan,et al.  eBird: A citizen-based bird observation network in the biological sciences , 2009 .

[23]  Aaron M Ellison,et al.  Observer bias and the detection of low-density populations. , 2009, Ecological applications : a publication of the Ecological Society of America.

[24]  Antoine Guisan,et al.  Reproducibility of species lists, visual cover estimates and frequency methods for recording high‐mountain vegetation , 2010 .

[25]  David N. Bonter,et al.  Citizen Science as an Ecological Research Tool: Challenges and Benefits , 2010 .

[26]  David B. Lindenmayer,et al.  The science and application of ecological monitoring , 2010 .

[27]  Damien Sulla-Menashe,et al.  MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets , 2010 .

[28]  Brian L. Sullivan,et al.  eBird: Engaging Birders in Science and Conservation , 2011, PLoS biology.

[29]  Jun Yu,et al.  Emergent Filters: Automated Data Verification in a Large-Scale Citizen Science Project , 2011, 2011 IEEE Seventh International Conference on e-Science Workshops.

[30]  William A. Link,et al.  Analysis of the North American Breeding Bird Survey Using Hierarchical Models , 2011 .

[31]  Andreas M. Ali,et al.  Acoustic monitoring in terrestrial environments using microphone arrays: applications, technological considerations and prospectus , 2011 .

[32]  Steve Kelling,et al.  Data-intensive science applied to broad-scale citizen science. , 2012, Trends in ecology & evolution.

[33]  Candie C. Wilderman,et al.  Public Participation in Scientific Research: a Framework for Deliberate Design , 2012 .

[34]  I. MacKenzie Occupancy estimation and modeling , 2013 .

[35]  Vittorio Loreto,et al.  Awareness and Learning in Participatory Noise Sensing , 2013, PloS one.

[36]  Carl F. Salk,et al.  Comparing the Quality of Crowdsourced Data Contributed by Expert and Non-Experts , 2013, PloS one.

[37]  Christoph Perger,et al.  Using control data to determine the reliability of volunteered geographic information about land cover , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[38]  Kevin Crowston,et al.  Motivation and Data Quality in a Citizen Science Game: A Design Science Evaluation , 2013, 2013 46th Hawaii International Conference on System Sciences.

[39]  Jun Yu,et al.  A Human/Computer Learning Network to Improve Biodiversity Conservation and Research , 2012, AI Mag..

[40]  Sean C. Anderson,et al.  Observer aging and long-term avian survey data quality , 2014, Ecology and evolution.

[41]  Thomas G. Dietterich,et al.  The eBird enterprise: An integrated approach to development and application of citizen science , 2014 .

[42]  David P. Anderson,et al.  Scientists@Home: What Drives the Quantity and Quality of Online Citizen Science Participation? , 2014, PloS one.

[43]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[44]  Steve Kelling,et al.  Taking a ‘Big Data’ approach to data quality in a citizen science project , 2015, Ambio.