Chest- and Waist-Deep Aquatic Plyometric Training and Average Force, Power, and Vertical-Jump Performance

Purpose: The purpose of the study was to compare effects of chest- and waist-deep water aquatic plyometrics on average force, power and vertical jump. Methods: Twenty-nine male and female participants were assigned to either a control group or 1 of 2 aquatic groups (waist deep and chest deep) and participated in a 6-wk, twice per wk plyometric training program. Average force and power were measured on a force plate using 3 jumps: squat, countermovement, and drop jump. Vertical-jump heights were also recorded. A repeated-measures ANOVA was used to determine significant differences between testing and groups on average force, power and vertical jump. Results: No significant differences were found with average force and power with the squat, countermovement, and vertical jumps. There were significant changes in drop jump average in the control group from the pretest to posttest. Conclusions: With the water depths chosen and held constant, there appears to be no increased benefit in performance variables

Interactions between defoliation level, species, and soil richness determine foliage production during and after simulated spruce budworm attack

Defoliation level and site type are thought to influence tree response during spruce budworm (Choristoneura fumiferana (Clemens)) outbreaks. We determined the effects of four manual defoliation treatments (0%, 50%, 100%, and 100% + bud removal of current foliage) for 3 years on foliage production of balsam fir (Abies balsamea (L.) Mill.), black spruce (Picea mariana (Mill.) Britton, Sterns & Poggenb.), and white spruce (Picea glauca (Moench) Voss) trees on four site-quality classes. After 3 years of defoliation and 2 years of recovery, foliage biomass was reduced by 34%–98%. During defoliation, the number of shoots generally increased and shoot length of spruce generally decreased, especially on rich sites. During recovery, the number of shoots increased substantially, shoot length decreased, and bud destruction reduced the number of shoots by about 50% compared with that of trees that received the 100% defoliation treatment. Defoliation did not substantially affect needle length. Trees on rich sites had two- to fourfold greater foliage production than trees on poor sites. Effects of site and defoliation differed among species, but site quality, especially nutrition, played an important role in production of shoots and needles and the tree’s ability to withstand defoliation. Black spruce had more limited ability to recover foliage biomass, only producing more shoots, whereas balsam fir and white spruce had stronger ability to recover needle and shoot length, respectively.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Spatial-Temporal Patterns of Spruce Budworm Defoliation within Plots in Québec

We investigated the spatial-temporal patterns of spruce budworm (Choristoneura fumiferana (Clem.); SBW) defoliation within 57 plots over 5 years during the current SBW outbreak in Québec. Although spatial-temporal variability of SBW defoliation has been studied at several scales, the spatial dependence between individual defoliated trees within a plot has not been quantified, and effects of defoliation level of neighboring trees have not been addressed. We used spatial autocorrelation analyses to determine patterns of defoliation of trees (clustered, dispersed, or random) for plots and for individual trees. From 28% to 47% of plots had significantly clustered defoliation during the 5 years. Plots with clustered defoliation generally had higher mean defoliation per plot and higher deviation of defoliation. At the individual-tree-level, we determined ‘hot spot trees’ (highly defoliated trees surrounded by other highly defoliated trees) and ‘cold spot trees’ (lightly defoliated trees surrounded by other lightly defoliated trees) within each plot using local Getis-Ord Gi* analysis. Results revealed that 11 to 27 plots had hot spot trees and 27% to 64% of them had mean defoliation <25%, while plots with 75% to 100% defoliation had either cold spot trees or non-significant spots, which suggested that whether defoliation was high or low enough to be a hot or cold spot depended on the defoliation level of the entire plot. We fitted individual-tree balsam fir defoliation regression models as a function of plot and surrounding tree characteristics (using search radii of 3–5 m). The best model contained plot average balsam fir defoliation and subject tree basal area, and these two variables explained 80% of the variance, which was 2% to 5% higher than the variability explained by the neighboring tree defoliation, over the 3–5 m search radii tested. We concluded that plot-level defoliation and basal area were adequate for modeling individual tree defoliation, and although clustering of defoliation was evident, larger plots were needed to determine the optimum neighborhood radius for predicting defoliation on an individual. Spatial autocorrelation analysis can serve as an objective way to quantify such ecological patterns

Development and evaluation of a biomass increment-based index for site productivity

Measures of forest productivity generally rely on site index, which can be problematic for multi-cohort and mixed-species stands. Using stand growth and dominant tree height-age (i.e., site tree) measurements from ~10,900 plot locations from Maine, Nova Scotia, New Brunswick, and Prince Edward Island, a forest productivity model for the Acadian Region was developed as a function of climate, lithology, soils, and topographic metrics. Approximately 65% of variation in observed above-ground dry-biomass growth rate (BG) was explained by a Chapman-Richards function of temperature, bedrock, soil root space, slope, and depth to water in combination with stand structure and species predictors. Productivity was then defined in terms of the predicted asymptote of BG, holding structure and species constant, which was termed biomass growth index (BGI); i.e., the site-influenced component of the BG relationship. BGI was mapped on a 20 m grid throughout the region. BGI explained 0-30% of the variability in spruce (Picea sp.) and balsam fir (Abies balsamea) site index, and had similar site index predictive performance (Âą 5%) when compared to existing land productivity classifications in each province. BGI provides a direct relationship between site variables and growth and can help guide forest management decisions and future research.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Forecasting Forest Inventory Using Imputed Tree Lists for LiDAR Grid Cells and a Tree-List Growth Model

A method to forecast forest inventory variables derived from light detection and ranging (LiDAR) would increase the usefulness of such data in future forest management. We evaluated the accuracy of forecasted inventory from imputed tree lists for LiDAR grid cells (20 × 20 m) in spruce (Picea sp.) plantations and tree growth predicted using a locally calibrated tree-list growth model. Tree lists were imputed by matching measurements from a library of sample plots with grid cells based on planted species and the smallest sum of squared difference between six inventory variables. Total and merchantable basal area, total and merchantable volume, Lorey’s height, and quadratic mean diameter increments predicted using imputed tree lists were highly correlated (0.75–0.86) with those from measured tree lists in 98 validation plots. Percent root mean squared error ranged from 12.8–49.0% but was much lower (4.9–13.5%) for plots with ≤10% LiDAR-derived error for all plot-matched variables. When compared with volumes from 15 blocks harvested 3–5 years after LiDAR acquisition, average forecasted volume differed by only 1.5%. To demonstrate the novel application of this method for operational management decisions, annual commercial thinning was planned at grid-cell resolution from 2018–2020 using forecasted inventory variables and commercial thinning eligibility rules

Imputing Tree Lists for New Brunswick Spruce Plantations Through Nearest-Neighbor Matching of Airborne Laser Scan and Inventory Plot Data

Light detection and ranging (LiDAR) has greatly improved the spatial resolution and accuracy of operational forest inventories. However, a cost-effective method to impute species-specific tree-level inventory is needed, to be used as input to tree or stand growth models to project single-point-in-time LiDAR estimates. We evaluated a method to match stand structural variables estimated from LiDAR to those in a library of over 5,500 sample plot measurements to impute tree lists for LiDAR grid cells across 83,000 ha of spruce (Picea sp.) plantations. Matches were determined based on planted species and minimum sum of squared difference between 6 inventory variables. Forest inventory variables obtained by the plot matches were highly correlated (r = 0.91–0.99) with those measured on 98 validation plots. Basal area distributions derived from plot matching were statistically equivalent to those observed on the validation plots 86% of the time (α = 0.05). When we aggregated the predictions for all validation plots, there was minimal difference between predicted and actual basal area distributions by planted species and species compositions were similar. Plot matching is a valid method to impute tree lists for LiDAR cells that combine the wealth of existing plot data with high resolution LiDAR-derived variables

Advancing the application of remote sensing for forest information needs in Canada : lessons learned from a national collaboration of university, industrial and government stakeholders

The Canadian forest sector requires detailed information regarding the amount and characteristics of the forest resource. To address these needs, inventory systems that spatially quantify timber and other forest related ecosystem services are required, that are accurate, comprehensive and timely. The Assessment of Wood properties using Remote Sensing (AWARE) was a five-year project involving collaboration between seven Canadian universities, and seven forest companies with support provided by provincial and federal forestry agencies and other non-for-profit forestry focused organisations. AWARE provided methods and tools to enhance the characterization of forests at national, landscape and individual tree scales. The project supported 24 post-doctoral fellows, PhD and MSc students that examined the roles that advanced three-dimensional remote sensing technologies can play in the development of accurate forest inventory systems across Canada. In this review we examine the AWARE research project, review research highlights, key outcomes, future research needs, and provide an assessment of successes and challenges the project faced over its five-year lifetime.Le secteur forestier canadien a besoin d’information détaillée au sujet de la quantité et des caractéristiques des ressources forestières. Pour répondre à de tels besoins, des systèmes d’inventaire exacts, complets et opportuns qui quantifient spatialement le bois d’œuvre et les autres services écosystémiques liés aux forêts sont nécessaires. Le projet quinquennal AWARE (Assessment of Wood Attributes using Remote sEnsing [évaluation des attributs du bois à l’aide de la télédétection]) était une collaboration entre sept universités canadiennes et sept entreprises forestières soutenue par des organismes forestiers provinciaux et fédéraux et d’autres organismes sans but lucratif-axés sur la foresterie. AWARE a fourni des méthodes et des outils pour améliorer la caractérisation des forêts à une échelle nationale, du paysage et de l’arbre individuel. Vingt-quatre boursiers de recherches postdoctorales et étudiants au doctorat et à la maîtrise se sont associés au projet et ont examiné les rôles que les technologies de télédétection tridimensionnelle (3D) de pointe peuvent jouer dans la conception de systèmes d’inventaire forestier précis partout au Canada. Dans le présent article de revue, nous nous penchons sur le projet de recherche AWARE, les points saillants de la recherche, les résultats clés et les besoins futurs en recherche et présentons une évaluation des réussites et des défis auxquels le projet a été confronté au cours de ses cinq ans