Prediction of conelet mortality rates in interior lodgepole pine using phenomic, nutritional, and environmental data

Interior lodgepole pine [Pinus contorta Dougl. var. latifolia Engelm.], is a keystone species in western Canada and the northwest United States, contributing significantly to natural forest ecosystems and commercial forestry. However, unstable seed yields driven by high conelet (first-year ovulate strobilus) mortality rates (CMR) hinder effective management and breeding efforts in Alberta, Canada. This study explores the potential of different sources of information, such as multispectral imagery, foliar nutrient analysis and microclimatic data, to predict reproductive success across three seed orchards in Alberta. The orchards are designed for deployment at different elevations, were established using different methods (seedling versus grafting), and are different ages. Phenotypic assessments focusing on conelet counts and CMR were conducted in each orchard over a three-year period. Environmental conditions were monitored using HOBO data loggers, and multispectral unmanned aerial vehicle (UAV) imagery (2x/year) was used to provide high-resolution vegetative indices. Foliar nutrient profiles were also analyzed using inductively coupled plasma optical emission spectroscopy (ICP-OES). The datasets were integrated into a Bayesian mixed model framework to identify key predictors that provide genetic and environmental context. Results revealed that genetic factors (clone- or family-based effects) accounted for up to 13% of the phenotypic variance, while environmental contributions ranged from 15% to 27%. Multi-source models improved prediction accuracy in specific scenarios, mainly through genotype-by-predictor interactions. Among all datasets, the fall multispectral imagery showed the greatest potential, with increases of 2% to 6%, in predictive accuracy from the baseline. Predicting the performance of new genotypes remained challenging, highlighting the need for genomic data integration.