Spatio-temporal modelling of the effect of selected environmental and land-use factors on acid grassland vegetation
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...
Gespeichert in:
Veröffentlicht in: | Journal of plant ecology 2022-04, Vol.15 (2), p.253-264 |
---|---|
1. Verfasser: | |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | 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. |
---|---|
ISSN: | 1752-993X 1752-9921 1752-993X |
DOI: | 10.1093/jpe/rtab088 |