Data from: Evidence of a spatial auto-correlation in the browsing level of four major European tree species
Cite this dataset
Hagen, Robert; Suchant, Rudi (2021). Data from: Evidence of a spatial auto-correlation in the browsing level of four major European tree species [Dataset]. Dryad. https://doi.org/10.5061/dryad.4xgxd256m
Moran's I of the browsing level of fir, spruce, beech and oak in the Federal State of Baden-Württemberg
Data supporting the results publised in "Evidence of a spatial auto-correlation in the browsing level of four major European tree species" by Hagen and Suchant (2020).
The contribution of spatial processes to the spatial patterns of ecological systems is widely recognised, but spatial patterns in the ecology of plant-herbivore interactions have rarely been investigated quantitatively owing to limited budget and time associated with ecological research. Studies of the level of browsing on various tree species reported either no spatial auto-correlation or a small effect size. Further, the effects of disturbance events, such as hurricanes, which create large forest openings on spatial patterns of herbivory are not well understood. In this study, we used forest inventory data obtained from the federal state of Baden-Württemberg (Southern Germany) between 2001 and 2009 (grid size: 100×200 m) and thus, after hurricane Lothar struck Southern Germany in 1999. We investigated whether the browsing level of trees (height <= 130 cm) in one location is independent of that of the neighbourhood. Our analyses of 1.758.622 saplings (187.632 sampling units) of oak (Quercus), fir (Abies), spruce (Picea) and beech (Fagus) revealed that the browsing level is characterised by a short distance spatial auto-correlation. The application of indicator variables based on browsed saplings should account for the spatial pattern as the latter may affects the results and therefore also the conclusions of the analysis.
The data in Tab. 1 were used to plot Figs. 2 and 3 of the main manuscript entitled "Evidence of a spatial auto-correlation in the browsing level of four major European tree species" and Fig. S4 of the supplementary material. The columns read as follow: Year, Tree species, Total number of sampling units, Total number of saplings, browsing level, Morans's I of the browsing level for the neighborhood of 100 m, Twice the square root of the estimated variance (TSREV) of Moran's I (Browsing 100 m), Morans's I of the browsing level for the neighborhood of 200 m, TSREV of Moran's I (Browsing 200 m), Morans's I of the sapling density for the neighborhood of 100 m, TSREV of Moran's I (Saplings 100 m), Morans's I of the sapling density for the neighborhood of 200 m, TSREV of Moran's I (Saplings 200 m), proportion of hunting grounds in Baden-Württemberg (Forstliches Gutachten) that documented a high browsing level
The data in Tab.2 were visualised in Figs. S2 and S3 (supplementary material). The columns read as follow: Lag length in meter, included observations, estimate of Moran's I, estimated variance, p-value, tree species, year.
Files are stored as comma-seperated-value-data sheets (csv-files) using the dot (.) as decimal seperator and the semicolon (;) to divide columns from eachother.
Missing data in Tab. 1 reflect, that those data were neither part of the analysis nor used for drawing figures. The single NA-value in Tab. 1 (column "TSREV - Saplings (200 m) reflects the low amount of included observations for this calculation (N=13))