Growing media, feeding schedules, and container coating for Eucalyptus globulus Labill. container stock production

Present day container nursery systems for the production of Eucalyptus Dill, are reviewed and documented from the results of a comprehensive questionnaire distributed to 30 major Eucalyptus producing countries. The results of the survey showed that 41 Eucalyptus species were under production in 1990. Most of these species were grown in containers that varied in diameter, depth, diameter to depth ratio, and volume. The growing media, fertilizer types, fertilizer formulation, and the morphological standards used for the production of Eucalyptus stock for outplanting varied considerably. Two separate experiments were conducted with Eucalyptus globulus nursery stock. 1) The media and feeding schedule study tested the merits of Sphagnum peat, Vermiculite, and Perlite in various proportions as growing media under the exponential and replacement feeding schedules. Seedling height (cm), root collar diameter (mm), top dry weight (mg) and root dry weight (mg) were measured to study growth of the seedlings. 2) The media and container coating study tested the merits of radiata pine bark. Sphagnum peat, Vermiculite, and Perlite in various proportions as growing media in coated and uncoated containers. It also evaluated the effects of container coating on root growth potential and root form. In addition to the morphological attributes measured in the media and feeding schedule study, root number, and root elongation were measured. Both studies were subjected to Analyses of Variance to determine the significance of differences in growth attributes. Both studies showed that seedling height and root collar diameter are not appropriate morphological characteristics to determine or compare the size and quality of finished Eucalyptus globulus stock. A response surface showed that the highest seedling dry weight range lies between 60 to 67% Sphagnum peat, 33 to 40% Vermiculite and 0 to 3% Perlite in the experimental region. The highest predicted seedling dry weight was found at 62% Sphagnum peat and 38% Vermiculite. In each growing medium, seedlings grown under the exponential feeding schedule had a more rapid seedling dry weight gain and higher Dickson's Seedling Quality Index than those grown under the replacement feeding schedule. Eucalyptus globulus seedlings grown in coated Ventblock containers filled with various proportions of radiata pine bark. Sphagnum peat, Vermiculite, and Perlite were physiologically and morphologically superior to their counter parts grown in uncoated containers

Whole Genome Shotgun Sequencing Based Taxonomic Profiling Methods for Comparative Study of Microbial Communities

Mikroorganismen, typischerweise in Form von großen Gemeinschaften aus einer Vielzahl von Spezies, sind ein allgegenwärtiger Bestandteil unserer Umwelt. Solche Gemeinschaften sind ein wesentlicher Bestandteil ihrer Umgebung und beeinflussen diese auf verschiedenen Ebenen. Besonders Wirt-assoziierte Mikroben werden wegen ihres Einflusses auf die menschliche Gesundheit intensiv untersucht. Darüber hinaus entwickelt sich ein wachsendes Interesse an mikrobiellen Gemeinschaften wegen ihrer Rolle in der Landwirtschaft, Abfalltechnik, im Bergbau und in der Biotechnologie. Metagenomik ist ein vergleichsweise neues wissenschaftliches Feld, welches mikrobielle Gemeinschaften auf der Basis von genetischem Material aus einer definierten Umgebung untersucht. Technische Fortschritte bei der DNA Sequenzierung haben es möglich gemacht, auf diese Weise taxonomisches Profiling durchzuführen, d.h. die mikrobiellen Spezies qualitativ und quantitativ zu erfassen. Bei der ``whole genome shotgun sequencing (WGS)'' Methode wird die DNA aus der Probe direkt fragmentiert und sequenziert. Taxonomische Profiling-Methoden, welche auf diesem Verfahren beruhen, sind weniger anfällig für PCR Biase im Vergleich zu anderen Methoden, wie z.B. 16S-rDNA basierten Verfahren. Allerdings stellt hierbei die enorme Größe und Redundanz der Datenbanken sowie der hohe Grad an Homologie unter den in den Datenbanken erfassten Organismen einen Nachteil dar. In dieser Arbeit stellen wir zwei rechnergestützte Verfahren vor, die beide Probleme adressieren. Die erste Methode ist ein taxonomischer Profiler, mit dem Ziel, die Mehrfachzuweisungen von Reads zu Referenzsequenzen homologer Spezies auf der Basis der unterschiedlichen Abdeckungsprofile zu korrigieren. Durch die sorgfältige Auswertung der Read-Abdeckungen werden hierbei falsch positive Referenzgenome von der Auswahl entfernt. Durch diese Filterstrategie erhöht sich die Genauigkeit und Auflösung des Verfahrens, da ein größerer Teil der Reads eindeutig einem Genom zugeordnet werden kann. Wir zeigen darüberhinaus, dass durch die Methode auch die Häufigkeiten der Organismen präziser bestimmt werden können. Die zweite Methode ist ein verteilter Read-Mapper, welcher das Problem der großen und sich häufig ändernden Referenzdatenbanken in der Metagenomik dadurch adressiert, dass die Referenzdatenbanken systematisch in Partitionen unterteilt werden. Hierdurch kann der Bedarf an Rechenzeit und Speicher für die Berechnung von Indizes um Größenordnungen verringert und Index Aktualisierungen in wenigen Minuten anstelle von Tagen durchgeführt werden. Um trotz der hohen Zahl von kleinen Indizes eine hohe Performanz beim alignieren der Reads zu erreichen, haben wir eine neue, schnelle und kompakte Filter-Datenstruktur entwickelt, den interleaved bloom filter. Dadurch sind wir in der Lage, die beschriebenen Verbesserungen beim Erzeugen und Aktualisieren der Indizes ohne Einbußen bei der Mapping-Geschwindigkeit zu erreichen.Microorganisms, typically occurring as large, species diverse communities, are a ubiquitous part of nature. These communities are a vital part of their environment, influencing it through various layers of interaction. Host-associated microbial communities are particularly scrutinized for their influence on the host’s health. Additionally, there is a growing interest in microbial communities due to their role in livestock, agriculture, waste treatment, mining, and biotechnology. Metagenomics is a relatively young scientific field that aims to study such microbial communities based on genetic material recovered directly from an environment. Advances in DNA sequencing have enabled us to perform taxonomic profiling, i.e. to identify microbial species quantitatively and qualitatively at increasing depth. In whole genome shotgun sequencing (WGS), environmental DNA is taken directly from an environment and sequenced after being fragmented, without PCR amplification. Taxonomic profiling methods based on such sequencing data introduce less PCR bias compared to their amplicon based counterparts such as 16S-rDNA based profiling methods. However, the challenges posed by the enormous and redundancy of databases and the high degree homology among reference genomes of microorganisms put WGS methods at a disadvantage. In this thesis, we will present and discuss two separate computational methods that address both challenges. The first method is a taxonomic profiler that leverages coverage landscapes created by mapping sequencing reads across reference genomes to address the challenge posed by homologous regions of genomes. By carefully evaluating the coverage profile of reference genomes we drop spurious references from consideration. This filtration strategy results in more uniquely mapping reads to the remaining reference genomes improving both the resolution and accuracy of the taxonomic profiling process. We have also shown that this method improves the quality of relative abundances assigned to each detected member organism. The second method is a distributed read mapper which addresses the issue of large and frequently changing databases by systematically partitioning it into smaller bins. It significantly reduces the time, and computational resources required to build indices from such large databases by orders of magnitudes and updates can be performed very quickly in a few minutes compared to days in earlier methods. To achieve a competitive mapping speed while maintaining many small indices, we implemented a novel, fast and lightweight filtering data structure called interleaved bloom filter. With that, we are able to achieve the described improvements in the index building and updating time without compromising the read-mapping speed

Connecting Inventory Information Sources for Landscape Level Analyses

In forest landscape level analyses, forest information is commonly represented by separate polygons, defined by differences in species composition, stand structure, crown closure, and productivity. The simplest approach to projecting yield of stands over the land base is to create an aggregated yield table, weighted by area of each stand type (groups of polygons with similar attributes) as a means of projecting future volume per ha and other attributes. At the other end of complexity, each polygon is projected forward, using a particular management pathway where a record of each tree (and other elements) is maintained. Polygons may also be subdivided and/or recombined based on changes over time, and on features identified on other data sources (e.g., soils maps). As information needs increase, the trend has been toward the more complex approach to landscape level analysis. However, data are commonly limited, in terms of attributes, space, time, and management pathways represented. As a result, most resource managers rely on the very simple projection of forests in time, using an aggregated yield table. Others try to represent this spatial complexity via spatial mapping using polygons defined on aerial photography or other remotely sensed media. Gains have been made in presenting the spatial maps in Geographic Information Systems, and in producing models for a variety of attributes and management pathways, often by producing hybrid models. However, improved linkages between models, ground data, and spatial maps are needed, as are statements of model accuracy at larger spatial and temporal scales. For Canada, the spatial and temporal scales are particularly of interest, since the forested area is very large, and tree species have long life spans. This study discusses and compares commonly used methods to link data sources, using a small land area of about 5,000 ha located in British Columbia, Canada.This is the author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by the FBMIS Group and can be found at: http://cms1.gre.ac.uk/conferences/iufro/fbmis/FBMISCov.htm.Keywords: projection of forest land, landscape level analysis, linkages across scale

Developing Gut Microbiota Exerts Colonisation Resistance to Clostridium (syn. Clostridioides) difficile in Piglets

Clostridium (syn. Clostridioides) difficile is considered a pioneer colonizer and may cause gut infection in neonatal piglets. The aim of this study was to explore the microbiota-C. difficile associations in pigs. We used the DNA from the faeces of four sows collected during the periparturient period and from two to three of their piglets (collected weekly until nine weeks of age) for the determination of bacterial community composition (sequencing) and C. difficile concentration (qPCR). Furthermore, C. difficile-negative faeces were enriched in a growth medium, followed by qPCR to verify the presence of this bacterium. Clostridium-sensu-stricto-1 and Lactobacillus spp. predominated the gut microbiota of the sows and their offspring. C. difficile was detected at least once in the faeces of all sows during the entire sampling period, albeit at low concentrations. Suckling piglets harboured C. difficile in high concentrations (up to log10 9.29 copy number/g faeces), which gradually decreased as the piglets aged. Enrichment revealed the presence of C. difficile in previously C. difficile-negative sow and offspring faeces. In suckling piglets, the C. difficile level was negatively correlated with carbohydrate-fermenting bacteria, and it was positively associated with potential pathogens. Shannon and richness diversity indices were negatively associated with the C. difficile counts in suckling piglets. This study showed that gut microbiota seems to set conditions for colonisation resistance against C. difficile in the offspring. However, this conclusion requires further research to include host-specific factors

Gender differences in domains of job satisfaction: evidence from doctoral graduates from Australian universities

Based on data from a study of graduates from PhD programs at Australia’s Group of Eight (Go8) universities, a gender gap in job satisfaction domains is estimated using a Mann-Whitney U test. Findings from the aggregate model show significant gender differences in only 5 out of 17 domains of job satisfaction. Further, separate analyses by age, employment status and family type/living arrangement broadly support the absence of gender differences in domains of job satisfaction. For aspects of job satisfaction that show significant gender differential it is found that males are more satisfied than females with their hours worked, opportunity for career advancement and workload, whereas females are more satisfied than males with their relationship with co-workers and contribution to society. This implies that males are more satisfied with intrinsic dimensions of job satisfaction while females are more satisfied with extrinsic aspects of job satisfaction

Evaluation of the spatial linear model, random forest and gradient nearest-neighbour methods for imputing potential productivity and biomass of the Pacific Northwest forests

Increasingly, forest management and conservation plans require spatially explicit information within a management or conservation unit. Forest biomass and potential productivity are critical variables for forest planning and assessment in the Pacific Northwest. Their values are often estimated from ground-measured sample data. For unsampled locations, forest analysts and planners lack forest productivity and biomass values, so values must be predicted. Using simulated data and forest inventory and analysis data collected in Oregon and Washington, we examined the performance of the spatial linear model (SLM), random forest (RF) and gradient nearest neighbour (GNN) for mapping and estimating biomass and potential productivity of Pacific Northwest forests. Simulations of artificial populations and subsamplings of forest biomass and productivity data showed that the SLM had smaller empirical root-mean-squared prediction errors (RMSPE) for a wide variety of data types, with generally less bias and better interval coverage than RFand GNN. These patterns held for both point predictions and for population averages, with the SLM reducing RMSPE by 30.0 and 52.6 per cent over two GNN methods in predicting point estimates for forest biomass and potential productivity

A Comparison of the Spatial Linear Model to Nearest Neighbor (k-NN) Methods for Forestry Applications

Forest surveys provide critical information for many diverse interests. Data are often collected from samples, and from these samples, maps of resources and estimates of aerial totals or averages are required. In this paper, two approaches for mapping and estimating totals; the spatial linear model (SLM) and k-NN (k-Nearest Neighbor) are compared, theoretically, through simulations, and as applied to real forestry data. While both methods have desirable properties, a review shows that the SLM has prediction optimality properties, and can be quite robust. Simulations of artificial populations and resamplings of real forestry data show that the SLM has smaller empirical root-mean-squared prediction errors (RMSPE) for a wide variety of data types, with generally less bias and better interval coverage than k-NN. These patterns held for both point predictions and for population totals or averages, with the SLM reducing RMSPE from 9% to 67% over some popular k-NN methods, with SLM also more robust to spatially imbalanced sampling. Estimating prediction standard errors remains a problem for k-NN predictors, despite recent attempts using model-based methods. Our conclusions are that the SLM should generally be used rather than k-NN if the goal is accurate mapping or estimation of population totals or averages

A comparison of selected parametric and non-paramentric imputation methods for estimating forest biomass and basal area

Various methods have been used to estimate the amount of above ground forest biomass across landscapes and to create biomass maps for specific stands or pixels across ownership or project areas. Without an accurate estimation method, land managers might end up with incorrect biomass estimate maps, which could lead them to make poorer decisions in their future management plans. The goal of this study was to compare various imputation methods to predict forest biomass and basal area, at a project planning scale (<20,000 acres) on the Malheur National Forest, located in eastern Oregon, USA. We examined the predictive performance of linear regression, geographic weighted regression (GWR), gradient nearest neighbor (GNN), most similar neighbor (MSN), random forest imputation, and k-nearest neighbor (k-nn) to estimate biomass (tons/acre) and basal area (sq. feet per acre) across 19,000 acres on the Malheur National Forest. To test the different methods, a combination of ground inventory plots, light detection and ranging (LiDAR) data, satellite imagery, and climate data was analyzed, and their root mean square error (RMSE) and bias were calculated. Results indicate that for biomass prediction, the k-nn (k = 5) had the lowest RMSE and least amount of bias. The second most accurate method consisted of the k-nn (k = 3), followed by the GWR model, and the random forest imputation. For basal area prediction, the GWR model had the lowest RMSE and least amount of bias. The second most accurate method was k-nn (k = 5), followed by k-nn (k = 3), and the random forest method. For both metrics, the GNN method was the least accurate based on the ranking of RMSE and bias.This is the publisher’s final pdf. The published article is copyrighted by the author(s) and published by Scientific Research Publishing. The published article can be found at: http://www.scirp.org/Keywords: Geographic Weighted Regression, LiDAR, Random Forest, Biomass, Gradient Nearest Neighbor, Most Similar Neighbor, K-Nearest NeighborKeywords: Geographic Weighted Regression, LiDAR, Random Forest, Biomass, Gradient Nearest Neighbor, Most Similar Neighbor, K-Nearest Neighbo

Tree crown ratio models for multi-species and multi-layered stands of southeastern British Columbia

The ratio of live crown length to tree height (crown ratio; CR) is often used as an important predictor variable for tree level growth equations, particularly for multi-species and multi-layered stands. Also, CR indicates tree vigour and can be an important habitat variable. Measurement of CR for each tree can be time-consuming and difficult to obtain in very dense stands and for very tall trees where the base of live crown is obscured. Models to predict CR from size, competition and site variables were developed for several coniferous and one hardwood tree species growing in multi-species and multi-layered forest stands (complex stands) of southeastern British Columbia. Simple correlations indicated the expected relationships of CR decreasing with increasing height, and with increasing competition. A logistic model form was used to constrain predicted CR values to the interval [0,1]. Also, predictors were divided into tree size, stand competition, and site measures, and the contribution of each set of contributors was examined. For all models, height was an important predictor. The stand competition measure, basal area of larger trees, contributed significantly to predicting CR given that crown competition factor was also included as a measure of competition. Logical trends in CR versus size and competition variable groups were reflected by the models; site variable slightly improved predictions for some species. Much of the variability in CR was not accounted for, indicating that other variables are important for explaining CR changes in these complex stands.Keywords: basal area of larger trees, multi-species stands, crown ratio, multi-layered stand

Mapping and imputing potential productivity of Pacific Northwest forests using climate variables

Regional estimation of potential forest productivity is important to diverse applications, including biofuels supply, carbon sequestration, and projections of forest growth. Using PRISM (Parameter-elevation Regressions on Independent Slopes Model) climate and productivity data measured on a grid of 3356 Forest Inventory and Analysis plots in Oregon and Washington, we evaluated four possible imputation methods to estimate potential forest productivity: nearest neighbour, multiple linear regression, thin plate spline functions, and a spatial autoregressive model. Productivity, measured by potential mean annual increment at culmination, is explained by the interaction of annual temperature, precipitation, and climate moisture index. The data were randomly divided into 2237 reference plots and 1119 target plots 30 times. Each imputation method was evaluated by calculating the coefficient of determination, bias, and root mean square error of both the target and reference data set and was also tested for evidence of spatial autocorrelation. Potential forest productivity maps of culmination potential mean annual increment were produced for all Oregon and Washington timberland.L'estimation regionale de la productivite forestiere potentielle est importante pour diverses applications, y compris les stocks de biocarburants, la sequestration du carbone et les projections de la croissance forestiere. A l'aide des donnees climatiques PRISM (Parameter-elevation Regressions on Independent Slopes Model) et des donnees de productivite mesurees sur une grille de 3356 placettes du programme d'analyse et d'invcntaire forestiers dans les Etats de l'Oregon et de Washington, nous evaluons quatre methodes d'imputation pour estimer la productivite forestiere potentielle :le plus proche voisin, la regression lineaire multiple, les fonctions dinterpolation spline et un modele spatial autoregressif. Mesuree par I'accroisscmeru annuel moyen potentiel (AAMP) maximum, la productivite est expliquee par I'interaction de la temperature annuelle, des precipitations et de I'indice d'humidite du climat. Les donnees ont etc repartics au hasard dans 2237 placettes de reference et 1119 placettes cibles une trentaine de fois. En plus d'etre testee pour la presence d'autocorrelation spatiale, chaque methode d'imputation a ete evaluee a l'aide du coefficient de determination, du biais et de l'erreur quadratique moyenne calculecs pour les ensembles de donnees cibles et de reference. Les cartes de productivite forestiere potentielle de I' AAMP maximum ont ete produites pour I'ensemble du territoire forestier exploitable des Etats de l'Oregon et de Washington