IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0141416.html
   My bibliography  Save this article

On Extrapolating Past the Range of Observed Data When Making Statistical Predictions in Ecology

Author

Listed:
  • Paul B Conn
  • Devin S Johnson
  • Peter L Boveng

Abstract

Ecologists are increasingly using statistical models to predict animal abundance and occurrence in unsampled locations. The reliability of such predictions depends on a number of factors, including sample size, how far prediction locations are from the observed data, and similarity of predictive covariates in locations where data are gathered to locations where predictions are desired. In this paper, we propose extending Cook’s notion of an independent variable hull (IVH), developed originally for application with linear regression models, to generalized regression models as a way to help assess the potential reliability of predictions in unsampled areas. Predictions occurring inside the generalized independent variable hull (gIVH) can be regarded as interpolations, while predictions occurring outside the gIVH can be regarded as extrapolations worthy of additional investigation or skepticism. We conduct a simulation study to demonstrate the usefulness of this metric for limiting the scope of spatial inference when conducting model-based abundance estimation from survey counts. In this case, limiting inference to the gIVH substantially reduces bias, especially when survey designs are spatially imbalanced. We also demonstrate the utility of the gIVH in diagnosing problematic extrapolations when estimating the relative abundance of ribbon seals in the Bering Sea as a function of predictive covariates. We suggest that ecologists routinely use diagnostics such as the gIVH to help gauge the reliability of predictions from statistical models (such as generalized linear, generalized additive, and spatio-temporal regression models).

Suggested Citation

  • Paul B Conn & Devin S Johnson & Peter L Boveng, 2015. "On Extrapolating Past the Range of Observed Data When Making Statistical Predictions in Ecology," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-16, October.
  • Handle: RePEc:plo:pone00:0141416
    DOI: 10.1371/journal.pone.0141416
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0141416
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0141416&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0141416?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. A. S. C. Ehrenberg & J. A. Bound, 1993. "Predictability and Prediction," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 156(2), pages 167-194, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Adrian Vickers, 2019. "An Evaluation of Survival Curve Extrapolation Techniques Using Long-Term Observational Cancer Data," Medical Decision Making, , vol. 39(8), pages 926-938, November.
    2. Meridith L Bartley & Ephraim M Hanks & Erin M Schliep & Patricia A Soranno & Tyler Wagner, 2019. "Identifying and characterizing extrapolation in multivariate response data," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-20, December.

    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. David Hand & Niall Adams, 2000. "Defining attributes for scorecard construction in credit scoring," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(5), pages 527-540.
    2. Phua, Peilin & Kennedy, Rachel & Trinh, Giang & Page, Bill & Hartnett, Nicole, 2020. "Examining older consumers’ loyalty towards older brands in grocery retailing," Journal of Retailing and Consumer Services, Elsevier, vol. 52(C).
    3. Lindsay, R. Murray, 1995. "Reconsidering the status of tests of significance: An alternative criterion of adequacy," Accounting, Organizations and Society, Elsevier, vol. 20(1), pages 35-53, January.
    4. R. Murray Lindsay, 1994. "Publication System Biases Associated with the Statistical Testing Paradigm," Contemporary Accounting Research, John Wiley & Sons, vol. 11(1), pages 33-57, June.
    5. Hubbard, Raymond & Lindsay, R. Murray, 2013. "The significant difference paradigm promotes bad science," Journal of Business Research, Elsevier, vol. 66(9), pages 1393-1397.
    6. Salisu, Afees A. & Olaniran, Abeeb & Tchankam, Jean Paul, 2022. "Oil tail risk and the tail risk of the US Dollar exchange rates," Energy Economics, Elsevier, vol. 109(C).
    7. Page, Bill & Sharp, Anne & Lockshin, Larry & Sorensen, Herb, 2018. "Parents and children in supermarkets: Incidence and influence," Journal of Retailing and Consumer Services, Elsevier, vol. 40(C), pages 31-39.
    8. Gavin Lees & Maxwell Winchester & Sidath Silva, 2016. "Demographic product segmentation in financial services products in Australia and New Zealand," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 21(3), pages 240-250, September.
    9. Jan Svanberg & Tohid Ardeshiri & Isak Samsten & Peter Öhman & Presha E. Neidermeyer & Tarek Rana & Natalia Semenova & Mats Danielson, 2022. "Corporate governance performance ratings with machine learning," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(1), pages 50-68, January.
    10. Hubbard, Raymond & Lindsay, R. Murray, 2013. "From significant difference to significant sameness: Proposing a paradigm shift in business research," Journal of Business Research, Elsevier, vol. 66(9), pages 1377-1388.
    11. Hubbard, Raymond & Vetter, Daniel E., 1996. "An empirical comparison of published replication research in accounting, economics, finance, management, and marketing," Journal of Business Research, Elsevier, vol. 35(2), pages 153-164, February.
    12. Zachary Anesbury & Maxwell Winchester & Rachel Kennedy, 2017. "Brand user profiles seldom change and seldom differ," Marketing Letters, Springer, vol. 28(4), pages 523-535, December.
    13. Gaunt, J. L. & Riley, Janet & Stein, A. & Penning de Vries, F. W. T., 1997. "Requirements for effective modelling strategies," Agricultural Systems, Elsevier, vol. 54(2), pages 153-168, June.
    14. Jella Pfeiffer & Thies Pfeiffer & Martin Meißner & Elisa Weiß, 2020. "Eye-Tracking-Based Classification of Information Search Behavior Using Machine Learning: Evidence from Experiments in Physical Shops and Virtual Reality Shopping Environments," Information Systems Research, INFORMS, vol. 31(3), pages 675-691, September.
    15. Uncles, Mark D. & Kwok, Simon, 2013. "Designing research with in-built differentiated replication," Journal of Business Research, Elsevier, vol. 66(9), pages 1398-1405.

    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:pone00:0141416. 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.