Spatial and competition effects in tree breeding

FACUNDO MUÑOZ; LEOPOLDO SANCHEZ; CAPPA EDUARDO PABLO

Arcachon

Conferencia; IUFRO Conference Genomics and forest tree genetics; 2016

Local environmental variation is well known to bias genetic estimates if not accounted for properly. Several authors have recommended to routinely include spatial effects in models for genetic evaluation of trees (e.g. Gilmour et al. 1997; Dutkowski et al. 2002). In contrast, the competition among trees is a known issue but much less frequently addressed and studied (Muir 2005; Cappa and Cantet 2008; Costa e Silva and Kerr 2013). It produces a negative autocorrelation among neighbouring trees, which can compensate in part the spatial effect, making both effects more difficult to detect and separate. Moreover, it can have an important impact in the response to selection if not accounted for. First, because of biased genetic estimates. But most importantly, because directand competition additive genetic effects are often antagonistic. Hence, selecting the best genotypes frequently means also selecting the most competitive individuals, which is not necessarily the optimal strategy. In addition, any phenotypic measure could potentially benefit from spatial and competition adjustments, delivering records that are less prone to bias by uncontrolled or hidden environmental factors, and therefore with clearer genetic signal for further use in association and genomic predictions. In this talk, we discuss diagnostic tools and procedures, as well as current implementations of spatial and competition models available in the Free and Open Source Software R-package breedR.