Spatiotemporal structural equation modeling in a hierarchical Bayesian
Spatio-temporal structural equation modeling in a hierarchical Bayesian framework: ecology of wet heathlands Christian Damgaard Department of Bioscience Aarhus University Bioscience – Aarhus University
Erica tetralix is decreasing on Danish wet heathlands Cover (%) Characteristic species of wet heathlands What is the cause? What management actions may reverse the trend? Bioscience – Aarhus University
Wet heathland vegetation data The cover of the three dominating species on wet heathlands, Erica tetralix, Calluna vulgaris and Molinia caerulea, as well as all other higher plant species was determined by the pin-point method Time series data (2007 – 2014) from 39 sites with a total of 1322 plots Important: pin-point cover data allows the aggregation of species Bioscience – Aarhus University
Cover data are L or U - shaped Bioscience – Aarhus University
Joint distribution of cover data • Bioscience – Aarhus University
Spatial and temporal model (SEM) Green oval: data Black box: latent variables Black arrow: spatial proc. Red arrow: temporal proc. Bioscience – Aarhus University
Spatial and temporal model (SEM) The latent variables allow the separation of measurement errors and process errors. Only process errors are needed for predictions Bioscience – Aarhus University
Estimation Hierarchical Bayesian modelling approach MCMC - Metropolis-Hastings algorithm Statistical inferences on the parameters of interest were based on the marginal posterior distribution of the parameters Bioscience – Aarhus University
Spatial process (2007) Large uncertainty – especially Calluna (yellow dots) – history? Dwarf shrubs (Erica and Calluna) have same qualitative response, and opposite to Molinia and other plants Positive spatial effects of nitrogen deposition, p. H, sandy soils, and low precipitation on dwarf shrubs Standardized regression coefficients Bioscience – Aarhus University
Geographic latent factors Regional geographic variation (50 km scale) South Jutland behave qualitatively different Information may be used to generate new hypotheses Bioscience – Aarhus University
Temporal process (2007 - 2014) Good fit! Dwarf shrubs (Erica and Calluna) have same qualitative response as the spatial effects, and opposite to Molinia and other plants Negative effect of grazing on Erica – insufficient data resolution Standardized regression coefficients Bioscience – Aarhus University
Application: local prediction Collect data from 20 to 40 plots Only temporal process is relevant (good model fit) Prediction of the effect of local management actions Set-up of an adaptive management plan Bioscience – Aarhus University
Conclusions Important to take local spatial aggregation of plant abundance into account Plant cover data are U-shaped Possible to fit ecological models to multivariate abundance data instead of summarizing the variation by ad hoc distance methods e. g. Bray-Curtis distances in ordination techniques Important to separate measurement errors from structural uncertainty when making predictions Ecological processes are best studied using time-series data Bioscience – Aarhus University
- Slides: 13