Spatio-temporal modelling of the effect of selected environmental and land-use factors on acid grassland vegetation

Christian Damgaard*

*Corresponding author for this work

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    2 Citations (Scopus)
    22 Downloads (Pure)

    Abstract

    Acid grasslands are threatened both by agricultural intensification with nutrient addition and increased livestock densities as well as by land abandonment. In order to understand and quantify the effect of selected environmental and land-use factors on the observed variation and changes in the vegetation of acid grasslands, large-scale spatial and temporal pin-point plant cover monitoring data are fitted in a structural equation model. The important sources of measurement and sampling uncertainties have been included using a hierarchical model structure. Furthermore, uncertainties associated with the measurement and sampling are separated from the process uncertainty, which is important when generating ecological predictions that may feed into local conservation management decisions. Generally, increasing atmospheric nitrogen deposition led to more grass-dominated acid grassland habitats at the expense of the cover of forbs. Sandy soils were relatively more acidic, and the effects of soil type on the vegetation include both direct effects of soil type and indirect effects mediated by the effect of soil type on soil pH. Both soil type and soil pH affected the vegetation of acid grasslands. Even though only a relatively small proportion of the temporal variation in cover was explained by the model, it would still be useful to quantify the uncertainties when using the model for generating local ecological predictions and adaptive management plans.

    Original languageEnglish
    JournalJournal of Plant Ecology
    Volume15
    Issue2
    Pages (from-to)253-264
    Number of pages12
    ISSN1752-9921
    DOIs
    Publication statusPublished - Apr 2022

    Keywords

    • acid grassland vegetation
    • hierarchical Bayesian models
    • joint distribution of plant abundance
    • pin-point cover data
    • spatial and temporal variation of cover
    • structural equation modelling

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