PredictingEllenberg's soil moisture indicator value in theBavarianAlps using additive georegression

Abstract

Questions: Can forest site characteristics be used to predict Ellenberg indicator values for soil moisture? Which is the best averaged mean value for modelling? Does the distribution of soil moisture depend on spatial information? Location: Bavarian Alps, Germany.Methods: We used topographic, climatic and edaphic variables to model the mean soil moisture value as found on 1505 forest plots from the database WINALPecobase. All predictor variables were taken from area-wide geodata layers so that the model can be applied to some 250 000 ha of forest in the target region. We adopted methods developed in species distribution modelling to regionalize Ellenberg indicator values. Therefore, we use the additive georegression framework for spatial prediction of Ellenberg values with the R-library mboost, which is a feasible way to consider environmental effects, spatial autocorrelation, predictor interactions and non-stationarity simultaneously in our data. The framework is much more flexible than established statistical and machine-learning models in species distribution modelling. We estimated five different mboost models reflecting different model structures on 50 bootstrap samples in each case.Results: Median R 2 values calculated on independent test samples ranged from 0.28 to 0.45. Our results show a significant influence of interactions and nonstationarity in addition to environmental covariates. Unweighted mean indicator values can be modelled better than abundance-weighted values, and the consideration of bryophytes did not improve model performance. Partial response curves indicate meaningful dependencies between moisture indicator values and environmental covariates. However, mean indicator values <4.5 and >6.0 could not be modelled correctly, since they were poorly represented in our calibration sample. The final map represents high-resolution information of site hydrological conditions. Conclusions:Indicator values offer an effect-oriented alternative to physicallybased hydrological models to predict water-related site conditions, even at landscape scale. The presented approach is applicable to all kinds of Ellenberg indicator values. Therefore, it is a significant step towards a new generation of models of forest site types and potential natural vegetation.

Does soil moisture overrule temperature dependence of soil respiration in Mediterranean riparian forests?

“…Soil moisture also affects plant composition and productivity (Häring et al, 2013) and thus controls the quantity and quality of both soil organic matter (SOM) and root exudate supply (Rustad et al, 2000).…”

Changes in forest floor and tree vegetation in the level II monitoring plot in Valgunde parish

“…The increase in herb and moss projection cover could be a result of decrease in tree and shrub projection cover, thus allowing more sunlight to understorey vegetation. In forestry, bioindication with the mean values of EIVs has been used to assess the site productivity (Bergès et al, 2006) and to predict herb biomass production (Axmanová et al, 2012), water relationships in soils (Häring et al, 2013) and atmospheric deposition (Dobben et al, 1999). Some studies suggest that bioindication can perform better than instrumental measurements and predicts plant species occurrence more accurately (Dupré & Diekmann, 1998;Jarvis et al, 2016).…”

Predictive modelling and monitoring of Ellenberg moisture value validates restoration success in floodplain forests

“…Häring et al. (), who made a spatial prediction of mF for the Bavarian Alps, showed that the moisture value must be modelled using a combination of predictors. In floodplains we are dealing with an interplay between hydromorphic processes (e.g.…”

“…Ellenberg moisture value is a good indicator for hydrological conditions (Häring et al. ) and ecological change, but limitations do exist, as described in detail in Diekmann () and Ellenberg et al. ().…”

“…Average Ellenberg indicator values for moisture of herbaceous plants provide a summary of vegetation response to water availability and excess water, and have frequently been used to detect hydrological changes in Central European grassland and forest vegetation (Häring et al. ). Thus, using Ellenberg moisture value as an indicator for compositional changes induced by a changed hydrological regime is a promising alternative for predictions, particularly in a riparian forest (Dancau ; Ellenberg et al.…”