2016
DOI: 10.1007/s10342-016-0984-5
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Adapting Douglas-fir forestry in Central Europe: evaluation, application, and uncertainty analysis of a genetically based model

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Cited by 50 publications
(46 citation statements)
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“…Therefore, their true dispersal capacity has not yet been manifested; thus, these results are biased by the highest uncertainty among the species studied. Moreover, the lower performance of P. menziesii is connected with mismatch in climate conditions between source and target localities, and therefore, lower level of adaptation (Chakraborty et al., ).…”
Section: Discussionmentioning
confidence: 99%
“…Therefore, their true dispersal capacity has not yet been manifested; thus, these results are biased by the highest uncertainty among the species studied. Moreover, the lower performance of P. menziesii is connected with mismatch in climate conditions between source and target localities, and therefore, lower level of adaptation (Chakraborty et al., ).…”
Section: Discussionmentioning
confidence: 99%
“…In a recent study, we developed two universal response functions (URFs), that predict the intraspecific phenotypic variation of functional traits such as dominant tree height and basal area of the North American Douglas-fir Pseudotsuga menziesii for plantations in central Europe as a response to climatic drivers (Chakraborty et al 2015(Chakraborty et al , 2016. In URFs, the relationships between two functional traits (tree height and basal area) and the environmental variables are mathematically specified as causal relations allowing a mechanistic understanding of trait variation and species distribution although the model formulations are correlative and empirical in nature (Dormann et al 2012).…”
Section: Introductionmentioning
confidence: 99%
“…In addition, recent occurrence based-SDMs (Boiffin et al 2017) calibrated with presence/ absence data in North America failed to predict the observed occurrence of Douglas-fir in Europe, thus questioning the validity of such traditional models when extrapolated. Our universal response functions (URFs) (Wang et al 2010), are based on extensive provenance trial data from Austria and southern Germany and can be applied at any putative planting site in Europe (Chakraborty et al 2015) to predict future growth performance and to identify the most suitable planting stock for stand establishment (Chakraborty et al 2016). The URFs combine genetic effects, i.e.…”
Section: Introductionmentioning
confidence: 99%
“…In Europe, excluding the most northern parts, provenance trials and practical experience cover mostly coastal provenances. In a future climate these experiences would be advantageous for more northern growing sites (Eilmann et al 2013;Isaac-Renton et al 2014;Chakraborty et al 2016). In Norway, Finland and Sweden, provenance trials found the interior provenances to be superior to the coastal (Kurkela 1981;Magnesen 1987;Martinsson and Kollenmark 2001).…”
Section: Introductionmentioning
confidence: 99%