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Fitting complex ecological point process models with integrated nested Laplace approximation

Research output: Contribution to journalArticle

Author(s)

Janine Baerbel Illian, Sara Martino, Sigrunn H. Sørbye, Juan B. Gallego-Fernández, Maria Zunzunegui, M. Paz Esquivias, Justin M. Travis

School/Research organisations

Abstract

Summary
1. We highlight an emerging statistical method, integrated nested Laplace approximation (INLA), which is ideally suited for fitting complex models to many of the rich spatial data sets that ecologists wish to analyse.
2. INLA is an approximation method that nevertheless provides very exact estimates. In this article, we describe the INLA methodology highlighting where it offers opportunities for drawing inference from (spatial) ecological data that would previously have been too complex to make practical model fitting feasible.
3. We use INLA to fit a complex joint model to the spatial pattern formed by a plant species, Thymus carnosus, as well as to the health status of each individual.
4. The key ecological result revealed by our spatial analysis of these data, relates to the distance-to-water covariate. We find that T. carnosus plants are generally healthier when they are further away from the water.
5. We suggest that this may be the result of a combination of (1) plants having alternative rooting strategies depending on how close to water they grow and (2) the rooting strategy determining how well the plants were able to tolerate an unusually dry summer.
6. We anticipate INLA becoming widely used within spatial ecological analysis over the next decade and suggest that both ecologists and statisticians will benefit greatly from working collaboratively to further develop and apply these emerging statistical methods.
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Details

Original languageEnglish
Pages (from-to)305-315
Number of pages11
JournalMethods in Ecology and Evolution
Volume4
Issue number4
Early online date19 Feb 2013
DOIs
Publication statusPublished - Apr 2013

    Research areas

  • Marked point patterns, Spatial modelling, Log-Gaussian Cox processes

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