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Fitting complex ecological point process models with integrated nested Laplace approximation

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Fitting complex ecological point process models with integrated nested Laplace approximation. / Illian, Janine Baerbel; Martino, Sara; Sørbye, Sigrunn H.; Gallego-Fernández, Juan B.; Zunzunegui, Maria; Esquivias, M. Paz; Travis, Justin M.

In: Methods in Ecology and Evolution, Vol. 4, No. 4, 04.2013, p. 305-315.

Research output: Contribution to journalArticle

Harvard

Illian, JB, Martino, S, Sørbye, SH, Gallego-Fernández, JB, Zunzunegui, M, Esquivias, MP & Travis, JM 2013, 'Fitting complex ecological point process models with integrated nested Laplace approximation' Methods in Ecology and Evolution, vol. 4, no. 4, pp. 305-315. https://doi.org/10.1111/2041-210x.12017

APA

Illian, J. B., Martino, S., Sørbye, S. H., Gallego-Fernández, J. B., Zunzunegui, M., Esquivias, M. P., & Travis, J. M. (2013). Fitting complex ecological point process models with integrated nested Laplace approximation. Methods in Ecology and Evolution, 4(4), 305-315. https://doi.org/10.1111/2041-210x.12017

Vancouver

Illian JB, Martino S, Sørbye SH, Gallego-Fernández JB, Zunzunegui M, Esquivias MP et al. Fitting complex ecological point process models with integrated nested Laplace approximation. Methods in Ecology and Evolution. 2013 Apr;4(4):305-315. https://doi.org/10.1111/2041-210x.12017

Author

Illian, Janine Baerbel ; Martino, Sara ; Sørbye, Sigrunn H. ; Gallego-Fernández, Juan B. ; Zunzunegui, Maria ; Esquivias, M. Paz ; Travis, Justin M. / Fitting complex ecological point process models with integrated nested Laplace approximation. In: Methods in Ecology and Evolution. 2013 ; Vol. 4, No. 4. pp. 305-315.

Bibtex - Download

@article{3c83bfb9679e447aa82a2962c5afcb06,
title = "Fitting complex ecological point process models with integrated nested Laplace approximation",
abstract = "Summary1. 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.",
keywords = "Marked point patterns, Spatial modelling, Log-Gaussian Cox processes",
author = "Illian, {Janine Baerbel} and Sara Martino and S{\o}rbye, {Sigrunn H.} and Gallego-Fern{\'a}ndez, {Juan B.} and Maria Zunzunegui and Esquivias, {M. Paz} and Travis, {Justin M.}",
year = "2013",
month = "4",
doi = "10.1111/2041-210x.12017",
language = "English",
volume = "4",
pages = "305--315",
journal = "Methods in Ecology and Evolution",
issn = "2041-210X",
publisher = "John Wiley & Sons, Ltd (10.1111)",
number = "4",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Fitting complex ecological point process models with integrated nested Laplace approximation

AU - Illian, Janine Baerbel

AU - Martino, Sara

AU - Sørbye, Sigrunn H.

AU - Gallego-Fernández, Juan B.

AU - Zunzunegui, Maria

AU - Esquivias, M. Paz

AU - Travis, Justin M.

PY - 2013/4

Y1 - 2013/4

N2 - Summary1. 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.

AB - Summary1. 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.

KW - Marked point patterns

KW - Spatial modelling

KW - Log-Gaussian Cox processes

U2 - 10.1111/2041-210x.12017

DO - 10.1111/2041-210x.12017

M3 - Article

VL - 4

SP - 305

EP - 315

JO - Methods in Ecology and Evolution

T2 - Methods in Ecology and Evolution

JF - Methods in Ecology and Evolution

SN - 2041-210X

IS - 4

ER -

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ID: 46184216