@article{SorkauBochBoeddinghausetal.2018, author = {Sorkau, Elisabeth and Boch, Steffen and Boeddinghaus, Runa S. and Bonkowski, Michael and Fischer, Markus and Kandeler, Ellen and Klaus, Valentin H. and Kleinebecker, Till and Marhan, Sven and M{\"u}ller, J{\"o}rg and Prati, Daniel and Schoening, Ingo and Schrumpf, Marion and Weinert, Jan and Oelmann, Yvonne}, title = {The role of soil chemical properties, land use and plant diversity for microbial phosphorus in forest and grassland soils}, series = {Journal of plant nutrition and soil science = Zeitschrift f{\"u}r Pflanzenern{\"a}hrung und Bodenkunde}, volume = {181}, journal = {Journal of plant nutrition and soil science = Zeitschrift f{\"u}r Pflanzenern{\"a}hrung und Bodenkunde}, number = {2}, publisher = {Wiley-VCH}, address = {Weinheim}, issn = {1436-8730}, doi = {10.1002/jpln.201700082}, pages = {185 -- 197}, year = {2018}, abstract = {Management intensity modifies soil properties, e.g., organic carbon (C-org) concentrations and soil pH with potential feedbacks on plant diversity. These changes might influence microbial P concentrations (P-mic) in soil representing an important component of the Pcycle. Our objectives were to elucidate whether abiotic and biotic variables controlling P-mic concentrations in soil are the same for forests and grasslands, and to assess the effect of region and management on P-mic concentrations in forest and grassland soils as mediated by the controlling variables. In three regions of Germany, Schwabische Alb, Hanich-Dun, and Schorfheide-Chorin, we studied forest and grassland plots (each n=150) differing in plant diversity and land-use intensity. In contrast to controls of microbial biomass carbon (C-mic), P-mic was strongly influenced by soil pH, which in turn affected phosphorus (P) availability and thus microbial Puptake in forest and grassland soils. Furthermore, P-mic concentrations in forest and grassland soils increased with increasing plant diversity. Using structural equation models, we could show that soil C-org is the profound driver of plant diversity effects on P-mic in grasslands. For both forest and grassland, we found regional differences in P-mic attributable to differing environmental conditions (pH, soil moisture). Forest management and tree species showed no effect on P-mic due to a lack of effects on controlling variables (e.g., C-org). We also did not find management effects in grassland soils which might be caused by either compensation of differently directed effects across sites or by legacy effects of former fertilization constraining the relevance of actual practices. We conclude that variables controlling P-mic or C-mic in soil differ in part and that regional differences in controlling variables are more important for P-mic in soil than those induced by management.}, language = {en} } @article{MuellerPoellathMoshammeretal.2009, author = {M{\"u}ller, J{\"o}rg and Poellath, Jakob and Moshammer, Ralf and Schr{\"o}der-Esselbach, Boris}, title = {Predicting the occurrence of Middle Spotted Woodpecker Dendrocopos medius on a regional scale, using forest inventory data}, issn = {0378-1127}, doi = {10.1016/j.foreco.2008.09.023}, year = {2009}, abstract = {The Middle Spotted Woodpecker (Dendrocopos medius) is the bird species which Germany has the greatest global responsibility to protect. It is an umbrella species for the entire assemblage of animals associated with mature broadleaved trees, especially oak. Even though well studied in small to medium scale stands, the validity of habitat suitability analysis for this species in larger forests has not previously been proved. Aim of this study was to test suitability of permanent forest inventory plots for modelling its distribution in a 17,000 ha forest landscape and to derive habitat threshold values as a basis for formulating management guidelines. Based on 150 randomly selected 12.5 ha plots we identified mean age and basal area of oaks as the most important habitat factors using a backward selection logistic model. Internal validation showed an AUC of 0.89 and a R-2(N) of 0.58. Determination of thresholds using maximally selected rank statistics found higher probability of occurrence in stands with a mean age >95 years. Above that age the probability increased again in stands with more than 6.4 m(2) basal area oak/ha. Our results show that widely available forest inventory data can serve as a valuable basis for monitoring the Middle Spotted Woodpecker, either within the framework of the Natura 2000 Network, or more generally in integrated forest management with the aim of providing suitable habitats for the entire assemblage of species on old deciduous trees, especially oak.}, language = {en} } @article{MuellerSchroederEsselbachMueller2009, author = {M{\"u}ller, Daniel and Schr{\"o}der-Esselbach, Boris and M{\"u}ller, J{\"o}rg}, title = {Modelling habitat selection of the cryptic Hazel Grouse Bonasa bonasia in a montane forest}, issn = {0021-8375}, doi = {10.1007/s10336-009-0390-6}, year = {2009}, abstract = {The Hazel Grouse Bonasa bonasia is strongly affected by forest dynamics, and populations in many areas within Europe are declining. As a result of the 'wilding' concept implemented in the National Park Bavarian Forest, this area is one of the refuges for the species in Germany. Even though the effects of prevailing processes make the situation there particularly interesting, no recent investigation about habitat selection in the rapidly changing environment of the national park has been undertaken. We modelled the species-habitat relationship to derive the important habitat features in the national park as well as factors and critical threshold for monitoring, and to evaluate the predictive power of models based on field surveys compared to an analysis of infrared aerial photographs. We conducted our surveys on 49 plots of 25 ha each where Hazel Grouse was recorded and on an equally sized set of plots with no grouse occurrence, and used this dataset to build a predictive habitat-suitability model using logistic regression with backward stepwise variable selection. Habitat heterogeneity, stand structure, presence of mountain ash and willow, root plates, forest aisles, and young broadleaf stands proved to be predictive habitat variables. After internal validation via bootstrapping, our model shows an AUC value of 0.91 and a correct classification rate of 87\%. Considering the methodological difficulties attached to backward selection, we applied Bayesian model averaging as an alternative. This multi-model approach also yielded similar results. To derive simple thresholds for important predictors as a basis for management decisions, we alternatively ran tree-based modelling, which also leads to a very similar selection of predictors. Performance of our different survey approaches was assessed by comparing two independent models with a model including both data resources: one constructed only from field survey data, the other based on data derived from aerial photographs. Models based on field data seem to perform slightly better than those based on aerial photography, but models using both predictor datasets provided the highest predictive accuracy.}, language = {en} } @article{HothornMuellerSchroederetal.2011, author = {Hothorn, Torsten and M{\"u}ller, J{\"o}rg and Schroeder, Boris and Kneib, Thomas and Brandl, Roland}, title = {Decomposing environmental, spatial, and spatiotemporal components of species distributions}, series = {Ecological monographs : a publication of the Ecological Society of America.}, volume = {81}, journal = {Ecological monographs : a publication of the Ecological Society of America.}, number = {2}, publisher = {Wiley}, address = {Washington}, issn = {0012-9615}, doi = {10.1890/10-0602.1}, pages = {329 -- 347}, year = {2011}, abstract = {Species distribution models are an important tool to predict the impact of global change on species distributional ranges and community assemblages. Although considerable progress has been made in the statistical modeling during the last decade, many approaches still ignore important features of species distributions, such as nonlinearity and interactions between predictors, spatial autocorrelation, and nonstationarity, or at most incorporate only some of these features. Ecologists, however, require a modeling framework that simultaneously addresses all these features flexibly and consistently. Here we describe such an approach that allows the estimation of the global effects of environmental variables in addition to local components dealing with spatiotemporal autocorrelation as well as nonstationary effects. The local components can be used to infer unknown spatiotemporal processes; the global component describes how the species is influenced by the environment and can be used for predictions, allowing the fitting of many well-known regression relationships, ranging from simple linear models to complex decision trees or from additive models to models inspired by machine learning procedures. The reliability of spatiotemporal predictions can be qualitatively predicted by separately evaluating the importance of local and global effects. We demonstrate the potential of the new approach by modeling the breeding distribution of the Red Kite (Milvus milvus), a bird of prey occurring predominantly in Western Europe, based on presence/absence data from two mapping campaigns using grids of 40 km 2 in Bavaria. The global component of the model selected seven environmental variables extracted from the CORINE and WorldClim databases to predict Red Kite breeding. The effect of altitude was found to be nonstationary in space, and in addition, the data were spatially autocorrelated, which suggests that a species distribution model that does not allow for spatially varying effects and spatial autocorrelation would have ignored important processes determining the distribution of Red Kite breeding across Bavaria. Thus, predictions from standard species distribution models that do not allow for real-world complexities may be considerably erroneous. Our analysis of Red Kite breeding exemplifies the potential of the innovative approach for species distribution models. The method is also applicable to modeling count data.}, language = {en} }