Hundreds Of Trees Research Articles

Temperature-related Saccharification of Delignified Sawdust Materials from the Lagos Lagoon in Nigeria

Sawdust, a product of the forest industry is mostly left untreated as solid waste. This phenomenon is well observed along the Lagos Lagoon in Nigeria where hundreds of trees are cut daily by sawmills to deliver wood for mainly the furniture industry. Different types of trees are utilized in this manner and the massive amounts of sawdust produced as a result of these activities are polluting the environment causing health risks for humans and animals. Cellulose, a glucose bio-polymer is a major structural component of sawdust and could be developed as a renewable energy resource should the cellulose be degraded into glucose, a fermentable sugar. This saccharification was done with Aspergillus niger cellulase and to make the cellulose more susceptible for cellulase action the sawdust was delignified with hydrogen peroxide. Both delignified and non-delignified sawdust were treated with the cellulase enzyme at incubation temperatures of 30°C, 40°C, 50°C, and 60°C. Delignification proved to be effective as an increased amount of sugar was released from all delignified sawdust materials relative to the non-delignified materials when saccharified with A. niger cellulase. Most of the materials were degraded at an incubation temperature of 40°C and 50°C and the highest percentage saccharification of 58% was obtained during the degradation of delignifed cellulose from the tree, Ricindendron heudelotti

Tree-PointNet: A Novel Neural Network for Dormant Apple Trees Using Semi-Circle-Based Point Cloud for Robotic Pruning

HighlightsTree-PointNet, a novel neural network, integrates geometric down-sampling for efficient pruning.A Semi-Circle-Based point cloud dataset of apple trees was collected to aid in automated pruning.Enhanced algorithm performance was investigated, showing significant improvements in model accuracy. Abstract. Pruning during the dormant season is one of the most costly and labor-intensive operations in the production of specialty crops. In winter, many trained seasonal workers have to carefully remove branches from hundreds of trees according to pre-defined rules. The aim of automated pruning is to reduce this reliance on the large amount of labor currently required for the operation. The first step in the automatic application of pruning rules is the acquisition of point cloud data from trees. To match the optical vision system of an ideal automatic pruning robot, a brand new and challenging apple tree dataset was collected in which the point clouds were all based on semicircular shapes, rather than the carefully crafted point clouds of complete apple trees. 1000 Semi-Circle-Based point clouds of apple trees were used. For the brand-new structure of point cloud part segmentation, a neural network model (namely Tree-PointNet) was proposed. A Semi-Circle-Based point cloud of apple tree was segmented by the Tree-PointNet with the segmentation result consisting of a trunk and primary branches. With a weight size of only 7.9% of PointStack, the best OA of 95.6% was obtained by the Tree-PointNet network proposed in this paper. The GDS was also used, which could obtain more key points in the Branch-to-Trunk connection region as input to the proposed network. On the other hand, the OA and mIoU of the model improved by 10.2% and 7.98%, respectively, compared to the PointNet++. Keywords: Dormant apple trees, Part segmentation, PointNet++, Semi-circle-based point cloud.

Scaling up uncertainties in allometric models: How to see the forest, not the trees

Quantifying uncertainty in forest assessments is challenging because of the number of sources of error and the many possible approaches to quantify and propagate them. The uncertainty in allometric equations has sometimes been represented by propagating uncertainty only in the prediction of individuals, but at large scales with large numbers of trees uncertainty in model fit is more important than uncertainty in individuals. We compared four different approaches to representing model uncertainty: a formula for the confidence interval, Monte Carlo sampling of the slope and intercept of the regression, bootstrap resampling of the allometric data, and a Bayesian approach. We applied these approaches to propagating model uncertainty at four different scales of tree inventory (10 to 10,000 trees) for four study sites with varying allometry and model fit statistics, ranging from a monocultural plantation to a multi-species shrubland with multi-stemmed trees. We found that the four approaches to quantifying uncertainty in model fit were in good agreement, except that bootstrapping resulted in higher uncertainty at the site with the fewest trees in the allometric data set (48), because outliers could be represented multiple times or not at all in each sample. The uncertainty in model fit did not vary with the number of trees in the inventory to which it was applied. In contrast, the uncertainty in predicting individuals was higher than model fit uncertainty when applied to small numbers of trees, but became negligible with 10,000 trees. The importance of this uncertainty source varied with the forest type, being largest for the shrubland, where the model fit was most poor. Low uncertainties were observed where model fit was high, as was the case in the monoculture plantation and in the subtropical jungle where hundreds of trees contributed to the allometric model. In all cases, propagating uncertainty only in the prediction of individuals would underestimate allometric uncertainty. It will always be most correct to include both uncertainty in predicting individuals and uncertainty in model fit, but when large numbers of individuals are involved, as in the case of national forest inventories, the contribution of uncertainty in predicting individuals can be ignored. When the number of trees is small, as may be the case in forest manipulation studies, both sources of allometric uncertainty are likely important and should be accounted for.

RuleCOSI+: Rule extraction for interpreting classification tree ensembles

Despite the advent of novel neural network architectures, tree-based ensemble algorithms such as random forests and gradient boosting machines still prevail in many practical machine learning problems in manufacturing, financial, and medical domains. However, tree ensembles have the limitation that the internal decision mechanisms of complex models are difficult to understand. Therefore, we present a post-hoc interpretation approach for classification tree ensembles. The proposed method, RuleCOSI+, extracts simple rules from tree ensembles by greedily combining and simplifying their base trees. Compared with its previous version, RuleCOSI, this new version can be applied to both bagging (e.g., random forest, RF) and boosting ensembles (e.g., gradient boosting machines, GBM) and run much faster for ensembles with hundreds of trees. To assess the performance and applicability of the method, empirical experiments were conducted using two bagging algorithms and four gradient boosting algorithms over 33 datasets. RuleCOSI+ could generate the best classification rulesets in terms of F-measure together with RuleFit for RF and GBM models of the datasets among five ensemble simplification algorithms, but the rulesets of RuleCOSI+ had, on average, less than half the size of those of RuleFit. Moreover, RuleCOSI+ had the best antecedent uniqueness rate (“uniq”) among the five algorithms, and had also ranked high in the number of rules (“Nrules”) and the rule reduction rate (“redu”). In addition, the proposed method could reduce generalization errors in the simplified rulesets to obtain, on average, slightly better classification errors than original models of two bagging and three gradient boosting algorithms except CATBoost.

Le territoire sauvage de l'âme by Jean-François Létourneau

Reviewed by: Le territoire sauvage de l'âme by Jean-François Létourneau Joseph A. Reiter LÉtourneau, Jean-FranÇois. Le territoire sauvage de l'âme. Boréal, 2021. ISBN 978-2-7646-2675-7. Pp. 144. This homage to the Inuit people and culture also celebrates the wilds of the province of Québec, family, fatherhood, storytelling, and (perhaps not surprisingly) hockey. Largely autobiographical, the author successfully employs and alternates two modes of narration—the singular tu, with an effective use of the present tense, to chronicle alter ego Guillaume's first years of teaching in Nunavik, and the third-person il, to recount family life with his wife and three children a decade or so later in their forest home in southern Québec. Readers might google maps to follow Guillaume's movements. Air travel was and remains the sole manner to reach Nunavik's towns, since no connecting roads exist, and once landed it is difficult for an outsider not to feel isolated or exiled. Add to this language—the Inuit of Kuujjuaq, old and young alike, communicate in Inuktitut, which Guillaume discovers during his initial flight from Montréal with a large group of teenagers. He earnestly wants to understand the conversation—"Tu aimerais leur parler, ne peux que leur sourire" (11)—but he can only guess the meaning, and over his four years in Kuujjuaq he will many times misinterpret what he hears, at times even when spoken in French. This of course will make for humorous tales he relates to his children in the prospector's tent erected in the woods behind his home. Guillaume was raised by a widowed father in the last house on a rang in a village in les Cantons de l'Est, now referred to as l'Estrie. The father told stories, sang—"un air traditionnel pour chaque moment du quotidien" (14)—chopped wood, worked the land, loved nature, and imparted that ethos to his son. Guillaume, to pay his way through college, planted hundreds of trees during summers in northern Ontario, where his yearning for the far North took hold: "Tu as voulu le Nord comme un vide à combler" (35). He does not make much headway with his students nor feel at ease for months after arriving. One Saturday night he heads to the hockey arena (Jean-François Létourneau was a champion hockey player) with skates and equipment and manages to be invited to play. His prowess stuns the players and crowd and opens the door to friendships and acceptance by the community. He will experience life with extended families, hunt caribou, camp on the tundra, learn legends, read the stars, and intensify his love for the environment and for traditional ways. Guillaume realizes he has learned much more than he was able to impart in his courses, perhaps expressing what many teachers feel: "J'ai l'impression de vous avoir pris quelque chose sans rien laisser en retour" (131). His decision to return to l'Estrie is viewed as a betrayal by the town and at departure he cannot adequately express his gratitude nor say farewell. Létourneau's poetic and poignant novel beautifully accomplishes that task: "Votre pays est magnifique, vous êtes magnifiques […] vous m'avez appris à rire. Vous m'avez appris à vivre" (131). [End Page 251] Joseph A. Reiter Phillips Exeter Academy (NH), emeritus Copyright © 2022 American Association of Teachers of French

Halloween windstorm and tornadoes in England, 31 October 2021

During the morning of Sunday 31 October 2021, damaging winds affected a corridor from Dorset through the south and east Midlands to Lincolnshire, associated with a mesolow that developed along a cold front. Hundreds of trees were damaged or blown over, bus and train services were significantly disrupted and numerous buildings were also impacted. Provisional analysis by TORRO suggests at least eight confirmed tornadoes, and an additional six probable tornadoes, occurred within the much larger field of wind damage.

Fruit detection and load estimation of an orange orchard using the YOLO models through simple approaches in different imaging and illumination conditions

Fruit load estimation is an essential step toward Precision Agriculture (PA) as it helps growers more accurately predict market planning, worker planning, purchase of appropriate equipment and so on. Reliable and accurate estimation of fruit yield in an orchard with hundreds of trees needs automatic methods. In recent years, Deep Learning (DL) has been studied widely and applied in various fields of agriculture. Accordingly, the YOLO detection models were applied to detect and count ripe Dezful native orange in an orchard in southwestern Iran. The models were adapted through transfer learning and trained by Google Colaboratory in the RGB images to detect and count orange fruits. Models performance and accuracy of yield estimation for an orchard with 1115 trees were examined. The process was conducted in 3 steps, including training and testing the different versions of the YOLO models by creating an image dataset of orange trees in different illumination conditions, evaluating the models on 100 sample trees, and finally extracting the yield variation map of the orchard after detecting and counting the oranges on images taken from all the trees in the orchard. The precision, recall, F1-score and mAP of the YOLO-V4 as the best model for orange detection over the test images were 91.23%, 92.8%, 92%, and 90.8%, respectively. The overall performance of the models in nighttime and daytime imaging was not significantly different. The YOLO-V4 model was chosen to use for yield estimation in the orchard. The promising results show that the YOLO models can effectively provide researchers and agricultural activists with a simple and practical method for detecting and estimating the yield of orange fruits in an orchard. Significant differences were observed in yield estimation for two-side and four-side imaging. Accordingly, a combined imaging method including two-side and four-side imaging was proposed for thin and dense canopy, respectively. The map of fruit yield changes showed the spatial distribution of tree yield with a +9.19% error.

Isolation and Functional Analysis of a PISTILLATA-like MADS-Box Gene from Argan Tree (Argania spinosa).

Argan trees (Argania spinosa) belong to a species native to southwestern Morocco, playing an important role in the environment and local economy. Argan oil extracted from kernels has a unique composition and properties. Argan trees were introduced in Tunisia, where hundreds of trees can be found nowadays. In this study, we examined reproductive development in Argan trees from four sites in Tunisia and carried out the functional characterization of a floral homeotic gene in this non-model species. Despite the importance of reproductive development, nothing is known about the genetic network controlling flower development in Argania spinosa. Results obtained in several plant species established that floral organ development is mostly controlled by MADS-box genes and, in particular, APETALA3 (AP3) and PISTILLATA (PI) homologs are required for proper petal and stamen identity. Here, we describe the isolation and functional characterization of a MADS-box gene from Argania spinosa. Phylogenetic analyses showed strong homology with PI-like proteins, and the expression of the gene was found to be restricted to the second and third whorls. Functional homology with Arabidopsis PI was demonstrated by the ability of AsPI to confer petal and stamen identity when overexpressed in a pi-1 mutant background. The identification and characterization of this gene support the strong conservation of PI homologs among distant angiosperm plants.

Adoption of Clay Charcoal Briquetting to Curb Deforestation and the Impacts of Covid-19 Pandemic in Rural Areas, Kenya

Wood fuel is the principal source of energy in Kenya and contributes to more than 17% of the global deforestation. Saving on the amount of charcoal used at home will not only help save on cost that can be used to offset the economic burden caused by the impacts of COVID-19 pandemic but also control deforestation. This will foster the realization of more than10% tree cover in Kenya by 2022 as per the presidential directive of 2019, Kenya Vision 2030 and the Constitution of Kenya 2010. This study aimed at promoting this goal by encouraging adoption of charcoal briquettes made from clay soil as a binder, charcoal dust and charcoal pellets as raw materials and ignited by pure charcoal (PC) to produce energy to form Clay Charcoal Blend Briquettes (CCBB). The study compared the amount of energy consumed by a household of five people using CCBB and PC. The results were: adoption of CCBB reduced the amount of charcoal used by 1.025kg per day, 21.325kg per week, 92.25kg per month and 1,122.375kg per year. This would save hundreds of trees from destruction. Additionally, using CCBB saved a household Ksh.45 (0.41USD) a day, Ksh. 945 (8.59 USD) a week, Ksh. 4,050 (36.82 USD) a month and Ksh. 49,275 (447.95 USD) a year as compared to using PC. The study therefore encouraged for adoption of CCBB concept.

BarcodeTree: Scalable Comparison of Multiple Hierarchies.

We propose BarcodeTree (BCT), a novel visualization technique for comparing topological structures and node attribute values of multiple trees. BCT can provide an overview of one hundred shallow and stable trees simultaneously, without aggregating individual nodes. Each BCT is shown within a single row using a style similar to a barcode, allowing trees to be stacked vertically with matching nodes aligned horizontally to ease comparison and maintain space efficiency. We design several visual cues and interactive techniques to help users understand the topological structure and compare trees. In an experiment comparing two variants of BCT with icicle plots, the results suggest that BCTs make it easier to visually compare trees by reducing the vertical distance between different trees. We also present two case studies involving a dataset of hundreds of trees to demonstrate BCT's utility.

Adaptation of Argania spinosa L. in Northern Tunisia: Soil analysis and morphological traits variability

The argan tree (Argania spinosa L. Skeels) originates from Morocco, is the most remarkable species from North Africa, both by its botanical and bio-ecological interest also by its social value. It’s rare to find argan trees outside of Morocco and Algeria; however, hundreds of trees exist in Tunisia. Phonological, morphological, and pomological characteristics were evaluated for 30 different trees from two different locations in Tunisia. 33 morphometric measurement data taken on traits related to vegetative aspect were used. The aim of this work was the phenotypic characterization of argan to begin a program of conservation and improvement of this species in Tunisia. The principal component analysis (PCA), the multiple Correspondence Analysis (MCA), and the Ward dendrogram were constructed. Analysis of the results shows the presence of a large morphological variability within and between the two sites which could be dependent on the geographical origin of the studied collections of argan tree. The introduction and exploitation of this multipurpose tree in Tunisian agriculture will lead to many benefits in different fields.

Protecting monarch butterflies' winter home could mean moving hundreds of trees

Researchers are trying to shift Mexico's oyamel firs to higher elevations to help them weather warming temperatures. Researchers are trying to shift Mexico's oyamel firs to higher elevations to help them weather warming temperatures.

Ground based hyperspectral imaging for extensive mango yield estimation

Fruit yield estimation in orchard blocks is an important objective in the context of precision agriculture, as it makes it easier for the farmer to plan ahead and efficiently use resources. Nevertheless, its implementation is labour-intensive and involves the manual counting of the fruit present in the trees. While colour (RGB) has been widely shown to be successful and arguably sufficient for yield estimation in orchards, hyperspectral imaging (HSI) shows promise for more nuanced tasks such as disease detection, cultivar classification and fruit maturity estimation. Therefore, it is important to ask how appropriate is HSI for the task of yield estimation, with a view to performing all of these tasks with just one sensor. This paper presents a novel mango yield estimation pipeline using ground based line-scan HSI acquired from an unmanned ground vehicle. Hyperspectral images were collected on a commercial mango orchard block in December 2017 and pre-processed for illumination compensation. After tree delimitation and mango pixel identification, an optimisation process was carried out to obtain the best models for fruit counting, using mango counts obtained by manually counting the fruit on-tree, and using state-of-the-art RGB techniques for yield estimation. Models were validated and tested on hundreds of trees, and subsequently mapped. In testing, determination coefficients reached values of up to 0.75 against field counts (predicting 18 trees) and 0.83 against RGB mango counts (predicting 216 trees). These results suggest that line-scan HSI can be used to accurately estimate yield in orchards, especially in scenarios in which this technology is already chosen for the determination of other traits.

Maturity estimation of mangoes using hyperspectral imaging from a ground based mobile platform

Monitoring the maturity of fruit in commercial orchards can help growers optimise the time of harvest. Dry matter content (DM) of fruit is used as an indicator of mango maturity, measured in-field with a hand-held spectrometer. This approach is labour intensive, limiting the extent to which DM variability can be measured across an orchard block, which would enable selective harvesting. This paper proposes an alternative approach that utilises a hyperspectral camera, LIDAR sensor and navigation system mounted to a ground vehicle to predict fruit DM individually for hundreds of trees in a mango orchard block. First, the challenges faced due to tree geometry and shadows in mango orchards are addressed. Then the ability to predict DM at a distance using hyperspectral imaging (411.3–867.0 nm) was demonstrated. Two regression methods, partial least squares (PLS) and a convolutional neural network (CNN) were compared and tested against DM results from a hand-held NIR spectrometer using harvested (n=468,σ=2.32%w/w) and on-tree fruit (n=662,σ=1.79%w/w). The CNN achieved a cross validation RCV2=0.64 and RMSECV=1.08%w/w in fruit on tree, while PLS achieved RCV2=0.58 and RMSECV=1.17%w/w. In order to discriminate mango and non-mango pixels, PLS discriminant analysis (PLS-DA) and a CNN were also compared, where both methods achieved good classification performance with a mean F1>0.97. Having established mango classification and DM prediction performance, hyperspectral data were processed for a full orchard block and projected to world coordinates using AGV position and orientation as provided by the navigation system. Trees were segmented using corresponding LIDAR data, which allowed association of projected DM predictions to individual trees. Repeated scans of the orchard block over two days allowed a measure of repeatability, which was achieved with an RMSE<0.29%w/w. The results provide strong evidence that predicting maturity at a distance for all trees in an orchard is feasible using a hyperspectral camera, which will be an important management tool for growers to optimise harvest timing and yield.

Comanche Marker Trees of Texas by Steve Houser, Linda Pelon, Jimmy W. Arterberry

Reviewed by: Comanche Marker Trees of Texas by Steve Houser, Linda Pelon, Jimmy W. Arterberry Daniel R. Uden Comanche Marker Trees of Texas. By Steve Houser, Linda Pelon, and Jimmy W. Arterberry. College Station: Texas A&M University Press, 2016. ix + 186 pp. Figures, references, contributors, index. $35.00 flexbound. Comanche Marker Trees of Texas is a collaborative work by Steve Houser (owner of Arborilogical Services, Inc.), Linda Pelon (Professor of Anthropology at McLennan Community College), and Jimmy W. Arterberry (Tribal Historic Preservation Officer for the Comanche Nation) that traces the continuing journey of the authors and their collaborators to identify and certify Comanche marker trees in the southern Great Plains. This book is divided into three parts. Part 1 gives necessary background information on Comanche marker trees—what they are, what they are for, and who they are for—as well as a detailed description of the marker tree identification process developed by the authors. Part 2 deals with the biology of Comanche marker trees, describing the indicators and effects of the human practice of tree-bending and how it differs from the natural (non-human) bending of trees. In Part 3, profiles (including photographs) of certified Comanche marker trees are presented. The first marker tree certification took place in 1997, and a total of six trees had been certified at the time of this book’s publication in 2016, with more since. Hundreds of trees have been submitted for consideration as Comanche marker trees, and the list continues to grow. The goal of the authors and fellow volunteers is to eventually confirm or rule out each tree candidate. Their process for doing so calls for a careful examination of the tree and its place, including past and present human activity. Multiple lines of evidence (e.g., biological, cultural, historical, geological, and spatial) are combined to determine the likelihood that a tree is in fact a Comanche marker tree. Ultimately, the decision to formally certify a marker tree lies with the Comanche. The authors emphasize the foundation of their collaborative tree evaluation process in respect for the trees, land, and people. Researching and affirming Comanche marker trees is not only a time-intensive endeavor but also a time-sensitive one. Given that the minimum qualifying age for a Comanche marker tree is 144 years, and given the harsh conditions that marker trees endure on the Southern Plains, fewer and fewer marker trees remain. However, the trees, like the Comanche, persevere and [End Page 218] maintain their significance in contemporary landscapes of the Southern Plains. This book will be understandable and interesting to a wide audience. Sufficient biological, cultural, and historical information is provided to accommodate readers from different backgrounds and disciplines. As the authors note, students of Comanche land-use patterns may find the book particularly useful. Beyond being a description of Comanche marker trees or the authors’ process for identifying them, this book is a call for volunteers to join in the efforts of Comanche marker tree identification, as well as a call for submitting additional tree candidates for future consideration. More information on volunteering and tree submission can be found on the website of the Texas Historic Tree Coalition (http://www.txhtc.org/). Daniel R. Uden Nebraska Cooperative Fish and Wildlife Research Unit, School of Natural Resources University of Nebraska–Lincoln Copyright © 2018 Center for Great Plains Studies, University of Nebraska–Lincoln

Quantifying pruning impacts on olive tree architecture and annual canopy growth by using UAV-based 3D modelling

BackgroundTree pruning is a costly practice with important implications for crop harvest and nutrition, pest and disease control, soil protection and irrigation strategies. Investigations on tree pruning usually involve tedious on-ground measurements of the primary tree crown dimensions, which also might generate inconsistent results due to the irregular geometry of the trees. As an alternative to intensive field-work, this study shows a innovative procedure based on combining unmanned aerial vehicle (UAV) technology and advanced object-based image analysis (OBIA) methodology for multi-temporal three-dimensional (3D) monitoring of hundreds of olive trees that were pruned with three different strategies (traditional, adapted and mechanical pruning). The UAV images were collected before pruning, after pruning and a year after pruning, and the impacts of each pruning treatment on the projected canopy area, tree height and crown volume of every tree were quantified and analyzed over time.ResultsThe full procedure described here automatically identified every olive tree on the orchard and computed their primary 3D dimensions on the three study dates with high accuracy in the most cases. Adapted pruning was generally the most aggressive treatment in terms of the area and volume (the trees decreased by 38.95 and 42.05% on average, respectively), followed by trees under traditional pruning (33.02 and 35.72% on average, respectively). Regarding the tree heights, mechanical pruning produced a greater decrease (12.15%), and these values were minimal for the other two treatments. The tree growth over one year was affected by the pruning severity and by the type of pruning treatment, i.e., the adapted-pruning trees experienced higher growth than the trees from the other two treatments when pruning intensity was low (<10%), similar to the traditionally pruned trees at moderate intensity (10–30%), and lower than the other trees when the pruning intensity was higher than 30% of the crown volume.ConclusionsCombining UAV-based images and an OBIA procedure allowed measuring tree dimensions and quantifying the impacts of three different pruning treatments on hundreds of trees with minimal field work. Tree foliage losses and annual canopy growth showed different trends as affected by the type and severity of the pruning treatments. Additionally, this technology offers valuable geo-spatial information for designing site-specific crop management strategies in the context of precision agriculture, with the consequent economic and environmental benefits.Graphical

Wild trees in the Amazon basin harbor a great diversity of beneficial endosymbiotic fungi: is this evidence of protective mutualism?

Abstract It has been shown that the disappearance of, or drastic changes in, ancestral and indigenous (or native) endosymbiotic microbiota can lead to many adverse health consequences. However, the effects of changes in beneficial endosymbionts in plants are poorly known (except for mycorrhizal and rhizobial associations). We sampled and compared endophytes from hundreds of trees belonging to the economically important genus Hevea, the source of natural rubber, in their native range in the Amazon basin and in plantations. We also conducted antagonism tests to determine the potential effects that some of these endophytes may have on selected plant pathogenic fungi. The natural and indigenous endosymbiotic mycota of the rubber tree (Hevea) contains a high diversity of beneficial fungi that may protect against pathogens (protective mutualism). In contrast, plantation trees have a reduced and different diversity of these beneficial fungi. We propose that abundance, and not just presence, of competitive fungal strains and species (i.e., Trichoderma and Tolypocladium) create a protective effect against pathogens in wild trees. This study provides support for the importance of mutualistic endosymbionts in plant health and ecosystem resilience, and calls for awareness of their potential loss by human-related activities.

Interactive exploration of parameter space in data mining: Comprehending the predictive quality of large decision tree collections

Decision trees are an intuitive yet powerful tool for performing predictive data analysis in data mining. In order to generate an adequate predictive model from a data set, a data analyst has to assess the predictive quality of the decision trees derived from several combinations of working parameters. Except in very simple cases, this may be a tedious and error prone supervised task, since the parameter space is frequently huge. Analysts rely on their intuition and usually test just a few different parameter settings. In this work we present an interactive approach to facilitate the comprehension of the predictive power of large collections of decision trees by exploring large portions of the parameter space. For this, we developed novel views that allow us to visualize and analyze the predictive quality of hundreds of trees, working together with coordinated multiple views of tree representations (needed to understand the tree shapes and actual information herein), and aggregates of Receiver Operating Characteristic (ROC) and lift curves for assessing the predictive quality of the models. We developed a worked example using a data set from a Telecommunications company, showing how easy and natural it is to gain insight into the behavior of the data within our exploration tool, as compared with the traditional and widespread common practice of data analysts.

TreSpEx–-Detection of Misleading Signal in Phylogenetic Reconstructions Based on Tree Information

Phylogenies of species or genes are commonplace nowadays in many areas of comparative biological studies. However, for phylogenetic reconstructions one must refer to artificial signals such as paralogy, long-branch attraction, saturation, or conflict between different datasets. These signals might eventually mislead the reconstruction even in phylogenomic studies employing hundreds of genes. Unfortunately, there has been no program allowing the detection of such effects in combination with an implementation into automatic process pipelines. TreSpEx (Tree Space Explorer) now combines different approaches (including statistical tests), which utilize tree-based information like nodal support or patristic distances (PDs) to identify misleading signals. The program enables the parallel analysis of hundreds of trees and/or predefined gene partitions, and being command-line driven, it can be integrated into automatic process pipelines. TreSpEx is implemented in Perl and supported on Linux, Mac OS X, and MS Windows. Source code, binaries, and additional material are freely available at http://www.annelida.de/research/bioinformatics/software.html.

Bacterial molecular phylogeny using supertree approach.

It has been claimed that complete genome sequences would clarify phylogenetic relationships between organisms but, up to now, no satisfying approach has been proposed to use efficiently these data. For instance, if the coding of presence or absence of genes in complete genomes gives interesting results, it does not take into account the phylogenetic information contained in sequences and ignores hidden paralogy by using a similarity-based definition of orthology. Also, concatenation of sequences of different genes takes hardly in consideration the specific evolutionary rate of each gene. At last, building a consensus tree is strongly limited by the low number of genes shared among all organisms. Here, we use a new method based on supertree construction, which permits to cumulate in one supertree the information and statistical support of hundreds of trees from orthologous gene families and to build the phylogeny of 33 prokaryotes and four eukaryotes with completely sequenced genomes. This approach gives a robust supertree, which demonstrates that a phylogeny of prokaryotic species is conceivable and challenges the hypothesis of a thermophilic origin of bacteria and present-day life. The results are compatible with the hypothesis of a core of genes for which lateral transfers are rare but they raise doubts on the widely admitted "complexity hypothesis" which predicts that this core is mainly implicated in informational processes.