Both plant genotype and yearly abiotic variation affect herbivore population sizes, but long-term data have rarely been used to contrast the relative contributions of each. Using a hierarchical Bayesian model, we directly compare effects of these two factors on the population size of a common herbivore, Aceria parapopuli, on Populus angustifolia × fremontii F(1) hybrid trees growing in a common garden across 8 years. Several patterns emerged. First, the Bayesian posterior estimates of tree genotype effects on mite gall number ranged from 0.0043 to 229 on a linear scale. Second, year effect sizes across 8 years of study ranged from 0.133 to 1.895. Third, in comparing the magnitudes of genotypic versus yearly variation, we found that genotypic variation was over 130 times greater than variation among years. Fourth, precipitation in the previous year negatively affected gall abundances, but was minimal compared to tree genotype effects. These findings demonstrate the relative importance of tree genotypic variation in determining herbivore population size. However, given the demonstrated sensitivity of cottonwoods to drought, the loss of individual tree genotypes from an altered climate would have catastrophic impacts on mites that are dependent upon these genotypes for their survival.
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http://dx.doi.org/10.1007/s00442-011-2108-8 | DOI Listing |
PLoS Genet
January 2025
Génétique Quantitative et Evolution - Le Moulon, INRAE, CNRS, AgroParisTech, Université Paris-Saclay, Gif-sur-Yvette, France.
Elucidating the genetic components of plant genotype-by-environment interactions is of key importance in the context of increasing climatic instability, diversification of agricultural practices and pest pressure due to phytosanitary treatment limitations. The genotypic response to environmental stresses can be investigated through multi-environment trials (METs). However, genome-wide association studies (GWAS) of MET data are significantly more complex than that of single environments.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Agricultural Production, College of Agricultural and Environmental Sciences, Makerere University, Kampala, Uganda.
Soybean is a globally important industrial, food, and cash crop. Despite its importance in present and future economies, its production is severely hampered by bruchids (Callosobruchus chinensis), a destructive storage insect pest, causing considerable yield losses. Therefore, the identification of genomic regions and candidate genes associated with bruchid resistance in soybean is crucial as it helps breeders to develop new soybean varieties with improved resistance and quality.
View Article and Find Full Text PDFAppl Environ Microbiol
January 2025
Tohoku Research Center, Forestry and Forest Products Research Institute, Forest Research and Management Organization, Morioka, Japan.
Unlabelled: , a white-colored truffle that is endemic to Japan, is promising for culinary purposes due to its unique aroma. We were able to cultivate in plantations of inoculated seedlings for the first time. Ascocarps were found after 43 months at one site and after 61 months at another.
View Article and Find Full Text PDFMol Ecol
January 2025
Department of Crop Protection, Hochschule Geisenheim University, Geisenheim, Germany.
Herbivorous insects need to cope with changing host plant biochemistry caused by abiotic and biotic impacts, to meet their dietary requirements. Larvae of the multivoltine European grapevine moth Lobesia botrana, one of the main insect pests in viticulture, feed on both flowers and berries. The nutritional value and defence compounds of these organs are changing with plant phenology and are affected by climate change which may accordingly alter plant-insect interactions.
View Article and Find Full Text PDFPlant Commun
January 2025
National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Hongshan Laboratory, Hubei, China. Electronic address:
In the face of climate change and the growing global population, there is an urgent need to accelerate the development of high-yielding crop varieties. To this end, vast amounts of genotype-to-phenotype data have been collected, and many machine learning (ML) models have been developed to predict phenotype from a given genotype. However, the requirement for high densities of single-nucleotide polymorphisms (SNPs) and the labor-intensive collection of phenotypic data are hampering the use of these models to advance breeding.
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