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Spatio-Temporal Structural Equation Modeling in a Hierarchical Bayesian Framework: What Controls Wet Heathland Vegetation?

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Spatio-Temporal Structural Equation Modeling in a Hierarchical Bayesian Framework : What Controls Wet Heathland Vegetation? / Damgaard, Christian Frølund.

In: Ecosystems, Vol. 22, No. 1, 01.2019, p. 152-164.

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@article{81d338fc814b4d4cad355318db72df81,
title = "Spatio-Temporal Structural Equation Modeling in a Hierarchical Bayesian Framework: What Controls Wet Heathland Vegetation?",
abstract = "Structural equation models are highly suited for evaluating ecosystem-level hypotheses, but to be effective, structural equation models need to be able to accommodate spatial and temporal data. Here, the importance of different abiotic and biotic drivers on wet heathland vegetation is investigated using a spatio-temporal structural equation model in a hierarchical Bayesian framework. Ecological data from 39 Danish sites, each with several wet heathland plots, were sampled in the period 2007–2014. Including resampling over the years, 1322 plots were sampled. Plant cover was measured using the pin-point method and the joint distribution of the key plant species in the wet heathland ecosystem, Erica tetralix, Calluna vulgaris, Molinia caerulea, and an aggregate class of other higher plants was estimated assuming a Dirichlet-multinomial mixture distribution. The investigated drivers of wet heathland vegetation include nitrogen deposition, soil type, pH, precipitation and grazing. Generally, the two dwarf shrubs, E. tetralix and C. vulgaris, responded in qualitatively similar ways to the abiotic variables and qualitatively oppositely to the way the grass M. caerulea and the aggregate class of other higher plants. Furthermore, the spatial effects were qualitatively similar to the temporal effects. The two dwarf shrub species were most likely positively affected by nitrogen deposition, soil pH, sandy soils, low precipitation, and the absence of grazing. The study demonstrated that important insight on ecosystem dynamics and regulation can be obtained by spatial and temporal structural equation modeling in a hierarchical Bayesian framework and that the proper statistical modeling of the joint species abundance is a key feature of such models. Furthermore, the advantages of partitioning different types of uncertainties become clear when the fitted structural equation model is used for predictive purposes at a specific site.",
keywords = "hierarchical Bayesian models, joint distribution of plant abundance, pin-point cover data, spatial and temporal variation of plant cover, structural equation modeling, wet heathland vegetation",
author = "Damgaard, {Christian Fr{\o}lund}",
year = "2019",
month = jan,
doi = "10.1007/s10021-018-0259-8",
language = "English",
volume = "22",
pages = "152--164",
journal = "Ecosystems",
issn = "1432-9840",
publisher = "Springer New York LLC",
number = "1",

}

RIS

TY - JOUR

T1 - Spatio-Temporal Structural Equation Modeling in a Hierarchical Bayesian Framework

T2 - What Controls Wet Heathland Vegetation?

AU - Damgaard, Christian Frølund

PY - 2019/1

Y1 - 2019/1

N2 - Structural equation models are highly suited for evaluating ecosystem-level hypotheses, but to be effective, structural equation models need to be able to accommodate spatial and temporal data. Here, the importance of different abiotic and biotic drivers on wet heathland vegetation is investigated using a spatio-temporal structural equation model in a hierarchical Bayesian framework. Ecological data from 39 Danish sites, each with several wet heathland plots, were sampled in the period 2007–2014. Including resampling over the years, 1322 plots were sampled. Plant cover was measured using the pin-point method and the joint distribution of the key plant species in the wet heathland ecosystem, Erica tetralix, Calluna vulgaris, Molinia caerulea, and an aggregate class of other higher plants was estimated assuming a Dirichlet-multinomial mixture distribution. The investigated drivers of wet heathland vegetation include nitrogen deposition, soil type, pH, precipitation and grazing. Generally, the two dwarf shrubs, E. tetralix and C. vulgaris, responded in qualitatively similar ways to the abiotic variables and qualitatively oppositely to the way the grass M. caerulea and the aggregate class of other higher plants. Furthermore, the spatial effects were qualitatively similar to the temporal effects. The two dwarf shrub species were most likely positively affected by nitrogen deposition, soil pH, sandy soils, low precipitation, and the absence of grazing. The study demonstrated that important insight on ecosystem dynamics and regulation can be obtained by spatial and temporal structural equation modeling in a hierarchical Bayesian framework and that the proper statistical modeling of the joint species abundance is a key feature of such models. Furthermore, the advantages of partitioning different types of uncertainties become clear when the fitted structural equation model is used for predictive purposes at a specific site.

AB - Structural equation models are highly suited for evaluating ecosystem-level hypotheses, but to be effective, structural equation models need to be able to accommodate spatial and temporal data. Here, the importance of different abiotic and biotic drivers on wet heathland vegetation is investigated using a spatio-temporal structural equation model in a hierarchical Bayesian framework. Ecological data from 39 Danish sites, each with several wet heathland plots, were sampled in the period 2007–2014. Including resampling over the years, 1322 plots were sampled. Plant cover was measured using the pin-point method and the joint distribution of the key plant species in the wet heathland ecosystem, Erica tetralix, Calluna vulgaris, Molinia caerulea, and an aggregate class of other higher plants was estimated assuming a Dirichlet-multinomial mixture distribution. The investigated drivers of wet heathland vegetation include nitrogen deposition, soil type, pH, precipitation and grazing. Generally, the two dwarf shrubs, E. tetralix and C. vulgaris, responded in qualitatively similar ways to the abiotic variables and qualitatively oppositely to the way the grass M. caerulea and the aggregate class of other higher plants. Furthermore, the spatial effects were qualitatively similar to the temporal effects. The two dwarf shrub species were most likely positively affected by nitrogen deposition, soil pH, sandy soils, low precipitation, and the absence of grazing. The study demonstrated that important insight on ecosystem dynamics and regulation can be obtained by spatial and temporal structural equation modeling in a hierarchical Bayesian framework and that the proper statistical modeling of the joint species abundance is a key feature of such models. Furthermore, the advantages of partitioning different types of uncertainties become clear when the fitted structural equation model is used for predictive purposes at a specific site.

KW - hierarchical Bayesian models

KW - joint distribution of plant abundance

KW - pin-point cover data

KW - spatial and temporal variation of plant cover

KW - structural equation modeling

KW - wet heathland vegetation

UR - http://www.scopus.com/inward/record.url?scp=85047294076&partnerID=8YFLogxK

U2 - 10.1007/s10021-018-0259-8

DO - 10.1007/s10021-018-0259-8

M3 - Journal article

VL - 22

SP - 152

EP - 164

JO - Ecosystems

JF - Ecosystems

SN - 1432-9840

IS - 1

ER -