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Predicting E llenberg's soil moisture indicator value in the B avarian A lps using additive georegression
Author(s) -
Häring Tim,
Reger Birgit,
Ewald Jörg,
Hothorn Torsten,
Schröder Boris
Publication year - 2013
Publication title -
applied vegetation science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.096
H-Index - 64
eISSN - 1654-109X
pISSN - 1402-2001
DOI - 10.1111/j.1654-109x.2012.01210.x
Subject(s) - edaphic , indicator value , covariate , environmental science , spatial analysis , statistics , water content , soil science , environmental niche modelling , autocorrelation , mathematics , ecology , soil water , geology , habitat , geotechnical engineering , biology , ecological niche
Questions Can forest site characteristics be used to predict E llenberg 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 B avarian A lps, G ermany. Methods We used topographic, climatic and edaphic variables to model the mean soil moisture value as found on 1505 forest plots from the database WINALP ecobase. 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 E llenberg indicator values. Therefore, we use the additive georegression framework for spatial prediction of E llenberg 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 non‐stationarity 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 physically‐based hydrological models to predict water‐related site conditions, even at landscape scale. The presented approach is applicable to all kinds of E llenberg indicator values. Therefore, it is a significant step towards a new generation of models of forest site types and potential natural vegetation.