by Claire Morin | 7 October 2020
Published in The Forestry Chronicle 95(3): 196-206. https://doi.org/10.5558/tfc2019-028
Forest managers have to take into account multiple objectives such as stand yield, wood quality attributes, ecological constraints and social considerations when making their decisions. The objective of the present study is to build
(i) a dynamic modulus of elasticity (MOEdyn) model and
(ii) a core wood density (WDcore) model for white spruce plantations in the Bas-Saint-Laurent Region (Québec, Canada) to quantify their inter- and intra-stand variations in order for managers to better weigh their different options.
Using data obtained from 54 sample plots in 31 white spruce plantations from Eastern Québec, the MOEdyn of 143 trees and the WDcore of 162 trees were analysed. Dendrometric and stand variables were used to build a MOEdyn linear mixed-effect model and a WDcore multiple linear regression model. The MOEdyn model explained 66.8% of the total variation, 1.1% of which originated from inter-stand variations. MOEdyn was proportional to diameter at breast height (DBH) and non-linearly decreased with tree growth rate. The WDcore model explained 16.0% of the total variation. The intra-stand variations were represented by a negative relationship between WDcore and growth rate. Inter-stand variations were accounted for by site index and altitude. The performance of the MOEdyn model was satisfactory and in accordance with the literature. However, the WDcore model was below standard, mainly as a consequence of unaccounted intra-individual variations. Nonetheless, raw simulations using these models suggest that white spruce wood from plantations may benefit from intensive forest management.
by Audrey Verreault | 7 October 2020
Published in Journal of Chemical Ecology 46(1): 84-98. https://doi.org/10.1007/s10886-019-01130-z
Non-nutritive phytochemicals (secondary metabolites and fibre) can influence plant resistance to herbivores and have ecological impacts on animal and plant population dynamics. A major hindrance to the ecological study of these phytochemicals is the uncertainty in the compounds one should measure, especially when limited by cost and expertise. With the underlying goal of identifying proxies of plant resistance to herbivores, we performed a systematic review of the effects of non-nutritive phytochemicals on consumption by leporids (rabbits and hares) and cervids (deer family). We identified 133 out of 1790 articles that fit our selection criteria (leporids = 33, cervids = 97, both herbivore types = 3). These articles cover 18 species of herbivores, on four continents. The most prevalent group of phytochemicals in the selected articles was phenolics, followed by terpenes for leporids and by fibre for cervids. In general, the results were variable but phenolic concentration seems linked with high resistance to both types of herbivores. Terpene concentration is also linked to high plant resistance; this relationship seems driven by total terpene content for cervids and specific terpenes for leporids. Tannins and fibre did not have a consistent positive effect on plant resistance. Because of the high variability in results reported and the synergistic effects of phytochemicals, we propose that the choice of chemical analyses must be tightly tailored to research objectives. While researchers pursuing ecological or evolutionary objectives should consider multiple specific analyses, researchers in applied studies could focus on a fewer number of specific analyses. An improved consideration of plant defence, based on meaningful chemical analyses, could improve studies of plant resistance and allow us to predict novel or changing plant-herbivore interactions.
by Marie-Claude Boileau | 7 October 2020
Published in Heredity 124: 562–578. https://doi.org/10.1038/s41437-019-0290-3
Genomic selection (GS) has a large potential for improving the prediction accuracy of breeding values and significantly reducing the length of breeding cycles. In this context, the choice of mating designs becomes critical to improve the efficiency of breeding operations and to obtain the largest genetic gains per time unit. Polycross mating designs have been traditionally used in tree and plant breeding to perform backward selection of the female parents. The possibility to use genetic markers for paternity identification and for building genomic prediction models should allow for a broader use of polycross tests in forward selection schemes. We compared the accuracies of genomic predictions of offspring’s breeding values from a polycross and a full-sib (partial diallel) mating design with similar genetic background in white spruce (Picea glauca). Trees were phenotyped for growth and wood quality traits, and genotyped for 4092 SNPs representing as many gene loci distributed across the 12 spruce chromosomes. For the polycross progeny test, heritability estimates were smaller, but more precise using the genomic BLUP (GBLUP) model as compared with pedigree-based models accounting for the maternal pedigree or for the reconstructed full pedigree. Cross-validations showed that GBLUP predictions were 22–52% more accurate than predictions based on the maternal pedigree, and 5–7% more accurate than predictions using the reconstructed full pedigree. The accuracies of GBLUP predictions were high and in the same range for most traits between the polycross (0.61–0.70) and full-sib progeny tests (0.61–0.74). However, higher genetic gains per time unit were expected from the polycross mating design given the shorter time needed to conduct crosses. Considering the operational advantages of the polycross design in terms of easier handling of crosses and lower associated costs for test establishment, we believe that this mating scheme offers great opportunities for the development and operational application of forward GS.
by Claire Morin | 2 October 2020
Published in ISPRS Journal of Photogrammetry and Remote Sensing 156: 14-26. https://doi.org/10.1016/j.isprsjprs.2019.07.010
Tree mortality is an important forest ecosystem variable having uses in many applications such as forest health assessment, modelling stand dynamics and productivity, or planning wood harvesting operations. Because tree mortality is a spatially and temporally erratic process, rates and spatial patterns of tree mortality are difficult to estimate with traditional inventory methods. Remote sensing imagery has the potential to detect tree mortality at spatial scales required for accurately characterizing this process (e.g., landscape, region). Many efforts have been made in this sense, mostly using pixel- or object-based methods. In this study, we explored the potential of deep Convolutional Neural Networks (CNNs) to detect and map tree health status and functional type over entire regions. To do this, we built a database of around 290,000 photo-interpreted trees that served to extract and label image windows from 20 cm-resolution digital aerial images, for use in CNN training and evaluation. In this process, we also evaluated the effect of window size and spectral channel selection on classification accuracy, and we assessed if multiple realizations of a CNN, generated using different weight initializations, can be aggregated to provide more robust predictions. Finally, we extended our model with 5 additional classes to account for the diversity of landcovers found in our study area. When predicting tree health status only (live or dead), we obtained test accuracies of up to 94%, and up to 86% when predicting functional type only (broadleaf or needleleaf). Channel selection had a limited impact on overall classification accuracy, while window size increased the ability of the CNNs to predict plant functional type. The aggregation of multiple realizations of a CNN allowed us to avoid the selection of suboptimal models and help to remove much of the speckle effect when predicting on new aerial images. Test accuracies of plant functional type and health status were not affected in the extended model and were all above 95% for the 5 extra classes. Our results demonstrate the robustness of the CNN for between-scene variations in aerial photography and also suggest that this approach can be applied at operational level to map tree mortality across extensive territories.
by Marie-Claude Boileau | 2 October 2020
Tour of Grandes-Piles forest nursery and experimental plantations, presented on August 23, 2019 during the 2019 Canadian Forest Genetics Association Conference, August 19 to 23, 2019, Lac-Delage, QC. Guide. Gouvernement du Québec, ministère des Forêts, de la Faune et des Parcs, Direction de la recherche forestière. 29 p.
