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"Trees Canada Identification."
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The Sibley guide to trees
\"This book covers the identification of 668 native and commonly cultivated trees found in the temperate areas of North America north of Mexico. This includes most of the continental United States and Canada, an area corresponding to the United States Department of Agriculture (USDA) plant hardiness zones 1-8\"--Introduction, p. ix.
Book
Isolation, identification and characterization of Paenibacillus polymyxa CR1 with potentials for biopesticide, biofertilization, biomass degradation and biofuel production
Background
Paenibacillus polymyxa
is a plant-growth promoting rhizobacterium that could be exploited as an environmentally friendlier alternative to chemical fertilizers and pesticides. Various strains have been isolated that can benefit agriculture through antimicrobial activity, nitrogen fixation, phosphate solubilization, plant hormone production, or lignocellulose degradation. However, no single strain has yet been identified in which all of these advantageous traits have been confirmed.
Results
P. polymyxa
CR1 was isolated from degrading corn roots from southern Ontario, Canada. It was shown to possess in vitro antagonistic activities against the common plant pathogens
Phytophthora sojae
P6497 (oomycete),
Rhizoctonia solani
1809 (basidiomycete fungus),
Cylindrocarpon destructans
2062 (ascomycete fungus),
Pseudomonas syringae
DC3000 (bacterium), and
Xanthomonas campestris
93-1 (bacterium), as well as
Bacillus cereus
(bacterium), an agent of food-borne illness.
P. polymyxa
CR1 enhanced growth of maize, potato, cucumber,
Arabidopsis
, and tomato plants; utilized atmospheric nitrogen and insoluble phosphorus; produced the phytohormone indole-3-acetic acid (IAA); and degraded and utilized the major components of lignocellulose (lignin, cellulose, and hemicellulose).
Conclusions
P. polymyxa
CR1 has multiple beneficial traits that are relevant to sustainable agriculture and the bio-economy. This strain could be developed for field application in order to control pathogens, promote plant growth, and degrade crop residues after harvest.
Journal Article
Investigating machine learning models in predicting lake water quality parameters as a 3-year moving average
Lake water quality plays a vital role in the lake ecosystem, including biotic (for living creatures, such as plants, animals, and micro-organisms) and abiotic interactions. In this research, various types of machine learning (ML) methodologies, such as classification and regression tree (CART), chi-squared automatic interaction detector (CHAID), C5 tree, quick, unbiased, and efficient statistical tree (QUEST), along with multilayer perceptron (MLP) neural network, and radial basis function (RBF) neural network, are employed to predict the concentration of water quality parameters (P, EC, TDS, pH, DO, NH3, SO4, and θ). Lake Erie is situated at the international border of the USA and Canada. The C5 tree and QUEST tree are used to classify data and predict the number of groups, while the other methods are used to predict the concentration of water quality parameters in the form of a 3-year moving average. The greater matching between the observed and predicted data of dissolved oxygen (NSE = 0.978, bias = 0.126) shows that the CART decision tree has higher accuracy in correctly detecting the concentration of this parameter. The C5 tree could identify 33 groups correctly out of 36 total groups, which shows better accuracy for the C5 tree in classifying the data for this parameter.
Journal Article
De novo transcriptome assembly and discovery of drought-responsive genes in white spruce (Picea glauca)
Forests face an escalating threat from the increasing frequency of extreme drought events driven by climate change. To address this challenge, it is crucial to understand how widely distributed species of economic or ecological importance may respond to drought stress. In this study, we examined the transcriptome of white spruce ( Picea glauca (Moench) Voss) to identify key genes and metabolic pathways involved in the species’ response to water stress. We assembled a de novo transcriptome, performed differential gene expression analyses at four time points over 22 days during a controlled drought stress experiment involving 2-year-old plants and three genetically distinct clones, and conducted gene enrichment analyses. The transcriptome assembly and gene expression analysis identified a total of 33,287 transcripts corresponding to 18,934 annotated unique genes, including 4,425 genes that are uniquely responsive to drought. Many transcripts that had predicted functions associated with photosynthesis, cell wall organization, and water transport were down-regulated under drought conditions, while transcripts linked to abscisic acid response and defense response were up-regulated. Our study highlights a previously uncharacterized effect of drought stress on lipid metabolism genes in conifers and significant changes in the expression of several transcription factors, suggesting a regulatory response potentially linked to drought response or acclimation. Our research represents a fundamental step in unraveling the molecular mechanisms underlying short-term drought responses in white spruce seedlings. In addition, it provides a valuable source of new genetic data that could contribute to genetic selection strategies aimed at enhancing the drought resistance and resilience of white spruce to changing climates.
Journal Article
Trees of Western North America
Covering 630 species, more than any comparable field guide,Trees of Western North Americais the most comprehensive, best illustrated, and easiest-to-use book of its kind. Presenting all the native and naturalized trees of the western United States and Canada as far east as the Great Plains, the book features superior descriptions; thousands of meticulous color paintings by David More that illustrate important visual details; range maps that provide a thumbnail view of distribution for each native species; \"Quick ID\" summaries; a user-friendly layout; scientific and common names; the latest taxonomy; information on the most recently naturalized species; a key to leaves; and an introduction to tree identification, forest ecology, and plant classification and structure. The easy-to-read descriptions present details of size, shape, growth habit, bark, leaves, flowers, fruit, flowering and fruiting times, habitat, and range. Using a broad definition of a tree, the book covers many small, overlooked species normally thought of as shrubs, as well as treelike forms of cacti and yuccas. With its unmatched combination of breadth and depth, this is an essential guide for every tree lover.
The most comprehensive, best illustrated, and easiest-to-use field guide to the trees of western North AmericaCovers 630 species, more than any comparable guide, including all the native and naturalized trees of the United States and Canada as far east as the Great PlainsFeatures specially commissioned artwork, detailed descriptions, range maps for native species, up-to-date taxonomy and names, and much, much moreAn essential guide for every tree lover
eBook
Cross-species outlier detection reveals different evolutionary pressures between sister species
Lodgepole pine (Pinus contorta var. latifolia) and jack pine (Pinus banksiana) hybridize in western Canada, an area of recent mountain pine beetle range expansion. Given the heterogeneity of the environment, and indications of local adaptation, there are many unknowns regarding the response of these forests to future outbreaks. To better understand this we aim to identify genetic regions that have adaptive potential.
We used data collected on 472 single nucleotide polymorphism (SNP) loci from 576 tree samples collected across 13 lodgepole pine-dominated sites and four jack pine-dominated sites. We looked at the relationship of genetic diversity with the environment, and we identified candidate loci using both frequency-based (arlequin and bayescan) and correlation-based (matsam and bayenv) methods.
We found contrasting relationships between environmental variation and genetic diversity for the species. While we identified a number of candidate outliers (34 in lodgepole pine, 25 in jack pine, and 43 interspecific loci), we did not find any loci in common between lodgepole and jack pine. Many of the outlier loci identified were correlated with environmental variation.
Using rigorous criteria we have been able to identify potential outlier SNPs. We have also found evidence of contrasting environmental adaptations between lodgepole and jack pine which could have implications for beetle spread risk.
Journal Article
Tree-ring reconstructed megadroughts over North America since a.d. 1300
Tree-ring reconstructed summer Palmer Drought Severity Indices (PDSI) are used to identify decadal droughts more severe and prolonged than any witnessed during the instrumental period. These “megadroughts” are identified at two spatial scales, the North American continental scale (exclusive of Alaska and boreal Canada) and at the sub-continental scale over western North America. Intense decadal droughts have had significant environmental and socioeconomic impacts, as is illustrated with historical information. Only one prolonged continent-wide megadrought during the past 500 years exceeded the decadal droughts witnessed during the instrumental period, but three megadroughts occurred over the western sector of North America from a.d. 1300 to 1900. The early 20th century pluvial appears to have been unmatched at either the continental or sub-continental scale during the past 500 to 700 years. The decadal droughts of the 20th century, and the reconstructed megadroughts during the six previous centuries, all covered large sectors of western North America and in some cases extended into the eastern United States. All of these persistent decadal droughts included shorter duration cells of regional drought (sub-decadal [almost equal to] 6 years), most of which resemble the regional patterns of drought identified with monthly and annual data during the 20th century. These well-known regional drought patterns are also characterized by unique monthly precipitation climatologies. Intense sub-decadal drought shifted among these drought regions during the modern and reconstructed multi-year droughts, which prolonged large-scale drought and resulted in the regimes of megadrought.
Journal Article
Molecular Detection of 10 of the Most Unwanted Alien Forest Pathogens in Canada Using Real-Time PCR
Invasive alien tree pathogens can cause significant economic losses as well as large-scale damage to natural ecosystems. Early detection to prevent their establishment and spread is an important approach used by several national plant protection organizations (NPPOs). Molecular detection tools targeting 10 of the most unwanted alien forest pathogens in Canada were developed as part of the TAIGA project (http://taigaforesthealth.com/). Forest pathogens were selected following an independent prioritization. Specific TaqMan real-time PCR detection assays were designed to function under homogeneous conditions so that they may be used in 96- or 384-well plate format arrays for high-throughput testing of large numbers of samples against multiple targets. Assays were validated for 1) specificity, 2) sensitivity, 3) precision, and 4) robustness on environmental samples. All assays were highly specific when evaluated against a panel of pure cultures of target and phylogenetically closely-related species. Sensitivity, evaluated by assessing the limit of detection (with a threshold of 95% of positive samples), was found to be between one and ten target gene region copies. Precision or repeatability of each assay revealed a mean coefficient of variation of 3.4%. All assays successfully allowed detection of target pathogen on positive environmental samples, without any non-specific amplification. These molecular detection tools will allow for rapid and reliable detection of 10 of the most unwanted alien forest pathogens in Canada.
Journal Article
Using a Trait-Based Approach to Compare Tree Species Sensitivity to Climate Change Stressors in Eastern Canada and Inform Adaptation Practices
Despite recent advances in understanding tree species sensitivities to climate change, ecological knowledge on different species remains scattered across disparate sources, precluding their inclusion in vulnerability assessments. Information on potential sensitivities is needed to identify tree species that require consideration, inform changes to current silvicultural practices and prioritize management actions. A trait-based approach was used to overcome some of the challenges involved in assessing sensitivity, providing a common framework to facilitate data integration and species comparisons. Focusing on 26 abundant tree species from eastern Canada, we developed a series of trait-based indices that capture a species’ ability to cope with three key climate change stressors—increased drought events, shifts in climatically suitable habitat, increased fire intensity and frequency. Ten indices were developed by breaking down species’ response to a stressor into its strategies, mechanisms and traits. Species-specific sensitivities varied across climate stressors but also among the various ways a species can cope with a given stressor. Of the 26 species assessed, Tsuga canadensis (L.) Carrière and Abies balsamea (L.) Mill are classified as the most sensitive species across all indices while Acer rubrum L. and Populus spp. are the least sensitive. Information was found for 95% of the trait-species combinations but the quality of available data varies between indices and species. Notably, some traits related to individual-level sensitivity to drought were poorly documented as well as deciduous species found within the temperate biome. We also discuss how our indices compare with other published indices, using drought sensitivity as an example. Finally, we discuss how the information captured by these indices can be used to inform vulnerability assessments and the development of adaptation measures for species with different management requirements under climate change.
Journal Article
Tree-CRowNN: A Network for Estimating Forest Stand Density from VHR Aerial Imagery
Estimating the number of trees within a forest stand, i.e., the forest stand density (FSD), is challenging at large scales. Recently, researchers have turned to a combination of remote sensing and machine learning techniques to derive these estimates. However, in most cases, the developed models rely heavily upon additional data such as LiDAR-based elevations or multispectral information and are mostly applied to managed environments rather than natural/mixed forests. Furthermore, they often require the time-consuming manual digitization or masking of target features, or an annotation using a bounding box rather than a simple point annotation. Here, we introduce the Tree Convolutional Row Neural Network (Tree-CRowNN), an alternative model for tree counting inspired by Multiple-Column Neural Network architecture to estimate the FSD over 12.8 m × 12.8 m plots from high-resolution RGB aerial imagery. Our model predicts the FSD with very high accuracy (MAE: ±2.1 stems/12.8 m2, RMSE: 3.0) over a range of forest conditions and shows promise in linking to Sentinel-2 imagery for broad-scale mapping (R2: 0.43, RMSE: 3.9 stems/12.8 m2). We believe that the satellite imagery linkage will be strengthened with future efforts, and transfer learning will enable the Tree-CRowNN model to predict the FSD accurately in other ecozones.
Journal Article