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Cazenave, X.

Publications and source records attributed to Cazenave, X..

2 recordsLinked to original sources

Studying the role of climate and sex in flowering dynamics of black poplar across time and space

Global change has a major impact on rivers and their associated terrestrial compartment, the riparian forest. The black poplar (Populus nigra L.), the dominant species of the European riparian forest, is considered an indicator of the biodiversity dynamics of these ecosystems, but is threatened with extinction in several parts of its range. The species is dioecious and shows wide variability in many traits, enabling it to adapt to a broad range of environmental conditions. A detailed characterization of this natural variability is therefore a key issue both for the conservation of natural populations and for biological engineering aimed at restoring riverbanks. Phenology is both an indicator of climate change and an adaptation lever for populations, particularly in perennial species such as trees. In this study, we characterized the spatial and temporal variability in flowering phenology by studying 13 natural black poplar populations along the Loire River, France. Flowering date and duration showed substantial variability both within and between populations, and males generally flowered earlier than females. Despite this variability, the ranking of individual trees' flowering dates was highly consistent across years. Most male-female pairs shared several days of overlapping flowering each year, and this overlap was moderately stable across years. Flowering dates advanced by about five to six days per degree Celsius during early spring, with no detectable difference in sensitivity between sexes. This work contributes to the characterization of flowering variability in an economically important tree species and may inform both the conservation of genetic resources and breeding.

plant biology↗

Combining genetic resources and elite material populations to improve the accuracy of genomic prediction in apple

Genomic selection is an attractive strategy for apple breeding that could reduce the length of breeding cycles. A possible limitation to the practical implementation of this approach lies in the creation of a training set large and diverse enough to ensure accurate predictions. In this study, we investigated the potential of combining two available populations, i.e. genetic resources and elite material, in order to obtain a large training set with a high genetic diversity. We compared the predictive ability of genomic predictions within-population, across-population or when combining both populations, and tested a model accounting for population-specific marker effects in this last case. The obtained predictive abilities were moderate to high according to the studied trait and were always highest when the two populations were combined into a unique training set. We also investigated the potential of such a training set to predict hybrids resulting from crosses between the two populations, with a focus on the method to design the training set and the best proportion of each population to optimize predictions. The measured predictive abilities were very similar for all the proportions, except for the extreme cases where only one of the two populations was used in the training set, in which case predictive abilities could be lower than when using both populations. Using an optimization algorithm to choose the genotypes in the training set also led to higher predictive abilities than when the genotypes were chosen at random. Our results provide guidelines to initiate breeding programs that use genomic selection when the implementation of the training set is a limitation.

genomics↗