IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0113905.html
   My bibliography  Save this article

Can Wide Consultation Help with Setting Priorities for Large-Scale Biodiversity Monitoring Programs?

Author

Listed:
  • Frédéric Boivin
  • Anouk Simard
  • Pedro Peres-Neto

Abstract

Climate and other global change phenomena affecting biodiversity require monitoring to track ecosystem changes and guide policy and management actions. Designing a biodiversity monitoring program is a difficult task that requires making decisions that often lack consensus due to budgetary constrains. As monitoring programs require long-term investment, they also require strong and continuing support from all interested parties. As such, stakeholder consultation is key to identify priorities and make sound design decisions that have as much support as possible. Here, we present the results of a consultation conducted to serve as an aid for designing a large-scale biodiversity monitoring program for the province of Québec (Canada). The consultation took the form of a survey with 13 discrete choices involving tradeoffs in respect to design priorities and 10 demographic questions (e.g., age, profession). The survey was sent to thousands of individuals having expected interests and knowledge about biodiversity and was completed by 621 participants. Overall, consensuses were few and it appeared difficult to create a design fulfilling the priorities of the majority. Most participants wanted 1) a monitoring design covering the entire territory and focusing on natural habitats; 2) a focus on species related to ecosystem services, on threatened and on invasive species. The only demographic characteristic that was related to the type of prioritization was the declared level of knowledge in biodiversity (null to high), but even then the influence was quite small.

Suggested Citation

  • Frédéric Boivin & Anouk Simard & Pedro Peres-Neto, 2014. "Can Wide Consultation Help with Setting Priorities for Large-Scale Biodiversity Monitoring Programs?," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-17, December.
    • Handle: RePEc:plo:pone00:0113905
    DOI: 10.1371/journal.pone.0113905
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0113905
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0113905&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0113905?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
    ---><---

    References listed on IDEAS

    as
    1. Peres-Neto, Pedro R. & Jackson, Donald A. & Somers, Keith M., 2005. "How many principal components? stopping rules for determining the number of non-trivial axes revisited," Computational Statistics & Data Analysis, Elsevier, vol. 49(4), pages 974-997, June.
    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. Claudio Morana, 2014. "Factor Vector Autoregressive Estimation of Heteroskedastic Persistent and Non Persistent Processes Subject to Structural Breaks," Working Papers 273, University of Milano-Bicocca, Department of Economics, revised May 2014.
    2. Kim, Hyun Hak & Swanson, Norman R., 2018. "Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods," International Journal of Forecasting, Elsevier, vol. 34(2), pages 339-354.
    3. Joy R. Petway & Yu-Pin Lin & Rainer F. Wunderlich, 2019. "Analyzing Opinions on Sustainable Agriculture: Toward Increasing Farmer Knowledge of Organic Practices in Taiwan-Yuanli Township," Sustainability, MDPI, vol. 11(14), pages 1-27, July.
    4. Jonas Eberle & Renier Myburgh & Dirk Ahrens, 2014. "The Evolution of Morphospace in Phytophagous Scarab Chafers: No Competition - No Divergence?," PLOS ONE, Public Library of Science, vol. 9(5), pages 1-16, May.
    5. Johann Fuchs & Doris Söhnlein & Brigitte Weber & Enzo Weber, 2018. "Stochastic Forecasting of Labor Supply and Population: An Integrated Model," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 37(1), pages 33-58, February.
    6. Hauck, Jana & Suess-Reyes, Julia & Beck, Susanne & Prügl, Reinhard & Frank, Hermann, 2016. "Measuring socioemotional wealth in family-owned and -managed firms: A validation and short form of the FIBER Scale," Journal of Family Business Strategy, Elsevier, vol. 7(3), pages 133-148.
    7. Volker G. Kuppelwieser & Aleksa-Carina Putinas & Marina Bastounis, 2019. "Toward Application and Testing of Measurement Scales and an Example," Sociological Methods & Research, , vol. 48(2), pages 326-349, May.
    8. Leise Kelli de Oliveira & Carla de Oliveira Leite Nascimento & Paulo Renato de Sousa & Paulo Tarso Vilela de Resende & Francisco Gildemir Ferreira da Silva, 2019. "Transport Service Provider Perception of Barriers and Urban Freight Policies in Brazil," Sustainability, MDPI, vol. 11(24), pages 1-17, December.
    9. Ard H. J. den Reijer & Pieter W. Otter & Jan P. A. M. Jacobs, 2024. "An heuristic scree plot criterion for the number of factors," Statistical Papers, Springer, vol. 65(6), pages 3991-4000, August.
    10. David Juárez-Varón & Victoria Tur-Viñes & Alejandro Rabasa-Dolado & Kristina Polotskaya, 2020. "An Adaptive Machine Learning Methodology Applied to Neuromarketing Analysis: Prediction of Consumer Behaviour Regarding the Key Elements of the Packaging Design of an Educational Toy," Social Sciences, MDPI, vol. 9(9), pages 1-23, September.
    11. Gang Li & Chu Zhang, 2010. "On the Number of State Variables in Options Pricing," Management Science, INFORMS, vol. 56(11), pages 2058-2075, November.
    12. Edoardo Saccenti & Marieke E. Timmerman, 2017. "Considering Horn’s Parallel Analysis from a Random Matrix Theory Point of View," Psychometrika, Springer;The Psychometric Society, vol. 82(1), pages 186-209, March.
    13. repec:jss:jstsof:46:i04 is not listed on IDEAS
    14. Dray, Stephane, 2008. "On the number of principal components: A test of dimensionality based on measurements of similarity between matrices," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 2228-2237, January.
    15. repec:dgr:rugsom:14008-eef is not listed on IDEAS
    16. Psaradakis, Zacharias & Vávra, Marián, 2014. "On testing for nonlinearity in multivariate time series," Economics Letters, Elsevier, vol. 125(1), pages 1-4.
    17. Bauer, Jan O. & Drabant, Bernhard, 2021. "Principal loading analysis," Journal of Multivariate Analysis, Elsevier, vol. 184(C).
    18. Qingyong Wang & Hong-Ning Dai & Hao Wang, 2017. "A Smart MCDM Framework to Evaluate the Impact of Air Pollution on City Sustainability: A Case Study from China," Sustainability, MDPI, vol. 9(6), pages 1-17, May.
    19. Paweł Gajewski, 2017. "Sources of Regional Inflation in Poland," Eastern European Economics, Taylor & Francis Journals, vol. 55(3), pages 261-276, May.
    20. Cumming, J.A. & Wooff, D.A., 2007. "Dimension reduction via principal variables," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 550-565, September.
    21. Maria Iannario & Rosaria Romano & Domenico Vistocco, 2023. "Dyadic analysis for multi-block data in sport surveys analytics," Annals of Operations Research, Springer, vol. 325(1), pages 701-714, June.
    22. Marian Vavra, 2013. "Testing for linear and Markov switching DSGE models," Working and Discussion Papers WP 3/2013, Research Department, National Bank of Slovakia.

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0113905. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.