Skip to content

Research at St Andrews

Accounting for preferential sampling in species distribution models

Research output: Contribution to journalArticle

DOI

Open Access permissions

Open

Author(s)

Maria Grazia Pennino, Iosu Paradinas, Janine B. Illian, Facundo Muñoz, José María Bellido, Antonio López-Quílez, David Conesa

School/Research organisations

Abstract

Species distribution models (SDMs) are now being widely used in ecology for management and conservation purposes across terrestrial, freshwater, and marine realms. The increasing interest in SDMs has drawn the attention of ecologists to spatial models and, in particular, to geostatistical models, which are used to associate observations of species occurrence or abundance with environmental covariates in a finite number of locations in order to predict where (and how much of) a species is likely to be present in unsampled locations. Standard geostatistical methodology assumes that the choice of sampling locations is independent of the values of the variable of interest. However, in natural environments, due to practical limitations related to time and financial constraints, this theoretical assumption is often violated. In fact, data commonly derive from opportunistic sampling (e.g., whale or bird watching), in which observers tend to look for a specific species in areas where they expect to find it. These are examples of what is referred to as preferential sampling, which can lead to biased predictions of the distribution of the species. The aim of this study is to discuss a SDM that addresses this problem and that it is more computationally efficient than existing MCMC methods. From a statistical point of view, we interpret the data as a marked point pattern, where the sampling locations form a point pattern and the measurements taken in those locations (i.e., species abundance or occurrence) are the associated marks. Inference and prediction of species distribution is performed using a Bayesian approach, and integrated nested Laplace approximation (INLA) methodology and software are used for model fitting to minimize the computational burden. We show that abundance is highly overestimated at low abundance locations when preferential sampling effects not accounted for, in both a simulated example and a practical application using fishery data. This highlights that ecologists should be aware of the potential bias resulting from preferential sampling and account for it in a model when a survey is based on non‐randomized and/or non‐systematic sampling.
Close

Details

Original languageEnglish
Pages (from-to)653-663
Number of pages11
JournalEcology and Evolution
Volume9
Issue number1
Early online date26 Dec 2018
DOIs
Publication statusPublished - 1 Jan 2019

    Research areas

  • Bayesian modelling, Integrated nested Laplace approximation, Point processes, Species Distribution Models (SDMs), Stochastic partial differential equation

Discover related content
Find related publications, people, projects and more using interactive charts.

View graph of relations

Related by author

  1. inlabru: an R package for Bayesian spatial modelling from ecological survey data

    Bachl, F. E., Lindgren, F., Borchers, D. L. & Illian, J. B., 21 Mar 2019, In : Methods in Ecology and Evolution. Early View, 7 p.

    Research output: Contribution to journalArticle

  2. Understanding species distribution in dynamic populations: a new approach using spatio‐temporal point process models

    Soriano-Redondo, A., Jones-Todd, C. M., Bearhop, S., Hilton, G. M., Lock, L., Stanbury, A., Votier, S. C. & Illian, J. B., 4 Mar 2019, In : Ecography. Early View

    Research output: Contribution to journalArticle

  3. Non-stationary Gaussian models with physical barriers

    Bakka, H., Vanhatalo, J., Illian, J. B., Simpson, D. & Rue, H., 18 Jan 2019, In : Spatial Statistics. In press

    Research output: Contribution to journalArticle

  4. Level set Cox processes

    Hildeman, A., Bolin, D., Wallin, J. & Illian, J. B., Dec 2018, In : Spatial Statistics. 28, p. 169-193

    Research output: Contribution to journalArticle

  5. Identifying prognostic structural features in tissue sections of colon cancer patients using point pattern analysis

    Jones-Todd, C. M., Caie, P., Illian, J. B., Stevenson, B. C., Savage, A., Harrison, D. J. & Brown, J. L., 28 Nov 2018, In : Statistics in Medicine. Early View

    Research output: Contribution to journalArticle

Related by journal

  1. Decline in abundance and apparent survival rates of fin whales (Balaenoptera physalus) in the northern Gulf of St. Lawrence

    Schleimer, A., Ramp, C., Delarue, J., Carpentier, A., Bérubé, M., Palsbøl, P. J., Sears, R. & Hammond, P. S., 15 Mar 2019, In : Ecology and Evolution. Early View, 14 p.

    Research output: Contribution to journalArticle

  2. Long-term sound and movement recording tags to study natural behavior and reaction to ship noise of seals

    Mikkelsen, L., Johnson, M., Wisniewska, D. M., van Neer, A., Siebert, U., Madsen, P. T. & Teilmann, J., 6 Feb 2019, In : Ecology and Evolution. Early View, 14 p.

    Research output: Contribution to journalArticle

  3. Repeat disturbances have cumulative impacts on stream communities

    Haghkerdar, J. M., McLachlan, J. R., Ireland, A. & Greig, H. S., 14 Feb 2019, In : Ecology and Evolution. Early View, 9 p.

    Research output: Contribution to journalArticle

  4. Social effects on fruit fly courtship song

    Marie-Orleach, L., Bailey, N. W. & Ritchie, M. G., Jan 2019, In : Ecology and Evolution. 9, 1, p. 410-416 7 p.

    Research output: Contribution to journalArticle

Related by journal

  1. Ecology and Evolution (Journal)

    Will Cresswell (Reviewer)
    28 Sep 2017

    Activity: Publication peer-review and editorial work typesPeer review of manuscripts

  2. Ecology and Evolution (Journal)

    Nora Nell Hanson (Reviewer)
    2016

    Activity: Publication peer-review and editorial work typesPeer review of manuscripts

  3. Ecology and Evolution (Journal)

    David Michael Shuker (Member of editorial board)
    2011

    Activity: Publication peer-review and editorial work typesEditor of research journal

ID: 257272223