IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v467y2022ics0304380022000345.html
   My bibliography  Save this article

Species profiles support recommendations for quality filtering of opportunistic citizen science data

Author

Listed:
  • Van Eupen, Camille
  • Maes, Dirk
  • Herremans, Marc
  • Swinnen, Kristijn R.R.
  • Somers, Ben
  • Luca, Stijn

Abstract

Opportunistic citizen science data are commonly filtered in an attempt to improve their applicability for relating species occurrences with environmental variables. Recommendations on when and how to filter, however, have remained relatively general and associations between species traits and filtering recommendations are sparse. We collected six traits (body size, detectability, classification error rate, familiarity, reporting probability and range size) of 52 birds, 25 butterflies and 14 dragonflies. Both absolute (values not rescaled) and relative traits (values rescaled per taxonomic group) were linked to filter effects, i.e. the impact on three different measures of species distribution model performance caused by applying three different quality filters, for different degrees of sample size reduction. First, we applied multiple regressions that predicted the filter effects by either absolute (including taxonomic group) or relative traits. Second, a principal component and clustering analysis were performed to define five species profiles based on species traits that were retained after a multiple regression model selection. The analysis of the profiles indicated the relative importance of species traits and revealed new insights into the association of species traits with changes in model performance after data quality filtering. Both taxonomic group (more than absolute traits) and relative species traits (mainly classification error rate, range size and familiarity) defined the impact of data quality filtering on model performance and we discourage the selection of a quality filtering strategy based on one single species trait. Results further confirmed the importance of considering the goal of the study (i.e. increasing model discrimination capacity, sensitivity or specificity) as well as the change in sample size caused by stringent filtering. The general species knowledge amongst citizen scientists (importance of observer experience), together with the mechanism of record verification in an opportunistic data platform (importance of verifiable metadata) have the largest potential for enhancing the quality of opportunistic records.

Suggested Citation

  • Van Eupen, Camille & Maes, Dirk & Herremans, Marc & Swinnen, Kristijn R.R. & Somers, Ben & Luca, Stijn, 2022. "Species profiles support recommendations for quality filtering of opportunistic citizen science data," Ecological Modelling, Elsevier, vol. 467(C).
    • Handle: RePEc:eee:ecomod:v:467:y:2022:i:c:s0304380022000345
    DOI: 10.1016/j.ecolmodel.2022.109910
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380022000345
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2022.109910?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Van Eupen, Camille & Maes, Dirk & Herremans, Marc & Swinnen, Kristijn R.R. & Somers, Ben & Luca, Stijn, 2021. "The impact of data quality filtering of opportunistic citizen science data on species distribution model performance," Ecological Modelling, Elsevier, vol. 444(C).
    2. Lê, Sébastien & Josse, Julie & Husson, François, 2008. "FactoMineR: An R Package for Multivariate Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i01).
    3. Silvia Ferrari & Francisco Cribari-Neto, 2004. "Beta Regression for Modelling Rates and Proportions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 31(7), pages 799-815.
    4. Tim Newbold & Lawrence N. Hudson & Samantha L. L. Hill & Sara Contu & Igor Lysenko & Rebecca A. Senior & Luca Börger & Dominic J. Bennett & Argyrios Choimes & Ben Collen & Julie Day & Adriana De Palma, 2015. "Global effects of land use on local terrestrial biodiversity," Nature, Nature, vol. 520(7545), pages 45-50, April.
    5. Cribari-Neto, Francisco & Zeileis, Achim, 2010. "Beta Regression in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 34(i02).
    6. Rutten, Anneleen & Casaer, Jim & Swinnen, Kristijn R.R. & Herremans, Marc & Leirs, Herwig, 2019. "Future distribution of wild boar in a highly anthropogenic landscape: Models combining hunting bag and citizen science data," Ecological Modelling, Elsevier, vol. 411(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Paulus, Anne & Hagemann, Nina & Baaken, Marieke C. & Roilo, Stephanie & Alarcón-Segura, Viviana & Cord, Anna F. & Beckmann, Michael, 2022. "Landscape context and farm characteristics are key to farmers' adoption of agri-environmental schemes," Land Use Policy, Elsevier, vol. 121(C).
    2. Ameztegui, Aitor & Coll, Lluís & Messier, Christian, 2015. "Modelling the effect of climate-induced changes in recruitment and juvenile growth on mixed-forest dynamics: The case of montane–subalpine Pyrenean ecotones," Ecological Modelling, Elsevier, vol. 313(C), pages 84-93.
    3. Grün, Bettina & Kosmidis, Ioannis & Zeileis, Achim, 2012. "Extended Beta Regression in R: Shaken, Stirred, Mixed, and Partitioned," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i11).
    4. Jillian M Rung & Leonard H Epstein, 2020. "Translating episodic future thinking manipulations for clinical use: Development of a clinical control," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-15, August.
    5. Zhang, Dengjun & Xie, Yifan, 2022. "Customer environmental concerns and profit margin: Evidence from manufacturing firms," Journal of Economics and Business, Elsevier, vol. 120(C).
    6. Buntaine, Mark T., 2011. "Does the Asian Development Bank Respond to Past Environmental Performance when Allocating Environmentally Risky Financing?," World Development, Elsevier, vol. 39(3), pages 336-350, March.
    7. Yukako Sado-Inamura & Kensuke Fukushi, 2018. "Considering Water Quality of Urban Rivers from the Perspectives of Unpleasant Odor," Sustainability, MDPI, vol. 10(3), pages 1-14, February.
    8. Li-Chu Chien, 2013. "Multiple deletion diagnostics in beta regression models," Computational Statistics, Springer, vol. 28(4), pages 1639-1661, August.
    9. Dengjun Zhang, 2022. "Capacity utilization under credit constraints: A firm‐level study of Latin American manufacturing," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 1367-1386, January.
    10. Jodrá, P. & Jiménez-Gamero, M.D., 2016. "A note on the Log-Lindley distribution," Insurance: Mathematics and Economics, Elsevier, vol. 71(C), pages 189-194.
    11. López Prol, Javier & Zilberman, David, 2023. "No alarms and no surprises: Dynamics of renewable energy curtailment in California," Energy Economics, Elsevier, vol. 126(C).
    12. Van Eupen, Camille & Maes, Dirk & Herremans, Marc & Swinnen, Kristijn R.R. & Somers, Ben & Luca, Stijn, 2021. "The impact of data quality filtering of opportunistic citizen science data on species distribution model performance," Ecological Modelling, Elsevier, vol. 444(C).
    13. Abbasiharofteh, Milad & Kogler, Dieter F. & Lengyel, Balázs, 2023. "Atypical combinations of technologies in regional co-inventor networks," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 52(10), pages 1-1.
    14. Frank A. La Sorte & Alison Johnston & Toby R. Ault, 2021. "Global trends in the frequency and duration of temperature extremes," Climatic Change, Springer, vol. 166(1), pages 1-14, May.
    15. Pablo Mitnik & Sunyoung Baek, 2013. "The Kumaraswamy distribution: median-dispersion re-parameterizations for regression modeling and simulation-based estimation," Statistical Papers, Springer, vol. 54(1), pages 177-192, February.
    16. Barbiero, Tommaso & Grillenzoni, Carlo, 2019. "A statistical analysis of the energy effectiveness of building refurbishment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    17. Tariq Maqsood & Mark Edwards & Ioanna Ioannou & Ioannis Kosmidis & Tiziana Rossetto & Neil Corby, 2016. "Seismic vulnerability functions for Australian buildings by using GEM empirical vulnerability assessment guidelines," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 80(3), pages 1625-1650, February.
    18. Steven B Kim & Dong Sub Kim & Xiaoming Mo, 2021. "An image segmentation technique with statistical strategies for pesticide efficacy assessment," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-12, March.
    19. Johnson, Caroline A. & Flage, Roger & Guikema, Seth D., 2019. "Characterising the robustness of coupled power-law networks," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    20. Antonio Calcagnì & Luigi Lombardi, 2022. "Modeling random and non-random decision uncertainty in ratings data: a fuzzy beta model," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(1), pages 145-173, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecomod:v:467:y:2022:i:c:s0304380022000345. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.