Robust Tree Research Articles

A knowledge-based approach for discriminating multi-crop scenarios using multi-temporal polarimetric SAR parameters

ABSTRACTIn this article, we evaluate a series of POLSAR (Polarimetric Synthetic Aperture Radar) parameters and devise a robust, multi-date, and hierarchical decision tree algorithm for crop discrimination. The study area is a farmland in North India having relatively large patches of winter crops with medium heterogeneity. Ten POLSAR parameters are evaluated for this work including polarimetric entropy (), polarimetric alpha angle (), Yamaguchi decomposition components, radar vegetation index (RVI), volume scattering index, canopy scattering index, biomass index, and pedestal height (PH). We choose polarimetric RVI, polarimetric entropy, and polarimetric alpha angle parameter, after sensitivity analysis, to be incorporated into building the multi-temporal POLSAR decision tree. The proposed algorithm accepts, as input, a precisely co-registered multi-date stack of fully POLSAR imagery. The algorithm makes use of the temporal profiles of the selected POLSAR parameters in achieving the crop discrimination. We provide a quantitative assessment of classification accuracy of various classes based on extensive ground truth data. The multi-temporal algorithm is compared with the well-established, single-date, supervised Wishart classifier. Statistically significant improvement of accuracies is observed across various classes as compared to single-date methodology. Our study suggests that whenever ground truth data are extensively available, a supervised classifier based on carefully chosen multi-temporal POLSAR parameters yield very compelling crop discrimination capabilities.

Identifying the essential flood conditioning factors for flood prone area mapping using machine learning techniques

River flooding can be a highly destructive natural hazard. Numerous approaches have been used to study the phenomenon; however, insufficient knowledge regarding flood conditioning factors continues to hinder prevention and control measures. This research examines the hypothesis that by adding further conditioning factors to a dataset used in river flood modeling, increases the accuracy of the final susceptibility mapping result. Additionally, this study assesses the impact of individual conditioning factors on flood susceptibility mapping and their importance in the construction of precise mapping of potential flood regions. Two robust machine learning approaches, Decision Tree (DT) and Support Vector Machine (SVM), were utilized to evaluate spatial correlations between flood conditioning factors and rate their level of importance for mapping the flood prone areas. For this purpose, two datasets were used; dataset 1 (DS1): Light Detection and Ranging (LiDAR) derived factors of altitude, slope, aspect, curvature, Stream Power Index (SPI), Topographic Wetness Index (TWI), Topographic Roughness Index (TRI), and Sediment Transport Index (STI) and dataset 2 (DS2): a combination of LiDAR derived factors supplemented by geology, soil, landuse/cover (LULC), distance from roads and distance from rivers parameters. An extreme flood event in 2011 in Brisbane, Australia was used as a case study, in which DT and SVM techniques were both applied, using both datasets. In addition, multi-collinearity, variance inflation factors (VIF), Pearson's correlation coefficients and Cohen's kappa analysis provided useful information regarding the inter-relationships of factors, as well as the influence of each factor on the precision of the final map. The area under curve (AUC) method was used for accuracy assessment. SVM and DT produced the highest accuracies of prediction, with rates of 85.52% and 88.47% respectively, using DS1 (the LiDAR dataset). Altitude, SPI and TRI were found to have a significant impact on the precision of the outcomes. It was concluded that the inclusion of additional factors in the modeling, does not necessarily guarantee the achievement of greater accuracy. However, the modeling method, can significantly alter outcomes.

Factors Affecting the Response of Pomegranate Fruit to Alternaria alternata, the Causal Agent of Heart Rot.

Heart rot, caused by Alternaria alternata, is a major pomegranate disease that impacts production worldwide; most fruits in orchards are colonized by A. alternata but, nevertheless, symptoms are apparent on only a small proportion of the colonized fruits. During the years of our previous research it was noticed that within individual orchards, the incidence of pomegranate fruits exhibiting heart rot symptoms was related to the visual appearance of the trees: trees that appeared visually frail bore more diseased fruits than robust trees. Furthermore, it was noticed that the disease responses of different pomegranate accessions and possibly of different variants of the same cultivar varied markedly. The specific objectives of the present study were: (i) to characterize the relationship between the visual appearance of pomegranate plants or individual stems and the incidence of heart rot and their vulnerability to heart rot; and (ii) to elucidate factors affecting the response of pomegranate fruit to A. alternata. Analysis of heart rot incidence in four orchards in 2014 revealed large differences in heart rot incidence among trees growing side by side in the same orchard; these differences were related to the visual appearance of the pomegranate trees. There were significant differences among germination rates of A. alternata spores in juice prepared from asymptomatic fruits originating from these trees, and comparable differences were found among the acidity levels (pH) of the juices. These differences may reflect differences among the physiological responses of pomegranate trees to heart rot. Fruits collected from the pomegranate collection located in Newe Ya'ar, which comprised 95 accessions in 2015 and 110 accessions in 2016, were also examined. There were differences among the acidity levels (pH) of the juices produced from these fruits and among the germination rates of A. alternata spores in the juices. These differences may reflect variances among the genetic responses of pomegranate accessions to heart rot. Results of studying the relationship between the acidity levels of pomegranate juice and the germination rates of A. alternata spores supported the hypothesis that, apart from pH, a compound(s) present in the juice regulates the germination of A. alternata spores in the juice.

Tracking annual changes of coastal tidal flats in China during 1986–2016 through analyses of Landsat images with Google Earth Engine

Tidal flats (non-vegetated area), along with coastal vegetation area, constitute the coastal wetlands (intertidal zone) between high and low water lines, and play an important role in wildlife, biodiversity and biogeochemical cycles. However, accurate annual maps of coastal tidal flats over the last few decades are unavailable and their spatio-temporal changes in China are unknown. In this study, we analyzed all the available Landsat TM/ETM+/OLI imagery (~44,528 images) using the Google Earth Engine (GEE) cloud computing platform and a robust decision tree algorithm to generate annual frequency maps of open surface water body and vegetation to produce annual maps of coastal tidal flats in eastern China from 1986 to 2016 at 30-m spatial resolution. The resulting map of coastal tidal flats in 2016 was evaluated using very high-resolution images available in Google Earth. The total area of coastal tidal flats in China in 2016 was about 731,170 ha, mostly distributed in the provinces around Yellow River Delta and Pearl River Delta. The interannual dynamics of coastal tidal flats area in China over the last three decades can be divided into three periods: a stable period during 1986–1992, an increasing period during 1993–2001 and a decreasing period during 2002–2016. The resulting annual coastal tidal flats maps could be used to support sustainable coastal zone management policies that preserve coastal ecosystem services and biodiversity in China.

Phylogenomics, life history and morphological evolution of ophiocomid brittlestars

Brittlestars in the family Ophiocomidae are large and colourful inhabitants of tropical shallow water habitats across the globe. Here we use targeted capture and next-generation sequencing to generate robust phylogenomic trees for 39 of the 43 species in order to test the monophyly of existing genera. The large genus Ophiocoma, as currently constituted, is paraphyletic on our trees and required revision. Four genera are recognised herein: an expanded Ophiomastix (now including Ophiocoma wendtii, O. occidentalis, O. endeani, O. macroplaca, and Ophiarthrum spp), Ophiocomella (now including the non-fissiparous Ophiocoma pumila, aethiops and valenciae) and Breviturma (now including Ophiocoma pica, O. pusilla, O. paucigranulata and O. longispina) and a restricted Ophiocoma. The resulting junior homonym Ophiomastix elegans is renamed O. brocki. The genus Ophiomastix exhibits relatively high rates of morphological disparity compared to other lineages. Ophiomastix flaccida and O. (formerly Ophiarthrum) pictum have divergent mitochondrial genomes, characterised by gene-order rearrangements, strand recoding, enriched GT base composition, and a corresponding divergence of nuclear mitochondrial protein genes. The new phylogeny indicates that larval and developmental transitions occurred rarely. Larval culture trials show that species with abbreviated lecithotrophic larval development occur only within Ophiomastix, although the possible monophyly of these species is obscured by the rapid early radiation within this genus. Asexual reproduction by fission is limited to one species-complex within Ophiocomella, also characterised by elevated levels of allelic heterozygosity, and which has achieved a relatively rapid global distribution. The crown ages of the new genera considerably predate the closure of the Tethyan seaway and all four are distributed in both the Atlantic and Indo-Pacific Oceans. Two species pairs appear to reflect the closure of the Panama Seaway, although their fossil-calibrated node ages (12–14 ± 6 my), derived from both concatenated sequence and multispecies coalescent analyses, considerably predate the terminal closure event. Ophiocoma erinaceus has crossed the East Pacific barrier and is recorded from Clipperton Island, SW of Mexico.

A monograph of Hydriastele (Areceae, Arecaceae) in New Guinea and Australia

A taxonomic revision of the genus Hydriastele H.Wendl. & Drude in New Guinea, Australia and immediately adjacent islands is presented. We recognise 25 species from this area including three new species of robust tree palms from New Guinea: Hydriastele calcicola, H. lanata and H. wosimiensis. All species are described in full in the taxonomic treatment, with accompanying botanical illustrations, photographs, distribution maps and preliminary IUCN Red List Assessments. In addition, a key to the species in New Guinea and Australia is provided as well as a systematic conspectus placing all 39 accepted species of Hydriastele in an informal infrageneric framework.

Long-Branch Attraction in Species Tree Estimation: Inconsistency of Partitioned Likelihood and Topology-Based Summary Methods.

With advances in sequencing technologies, there are now massive amounts of genomic data from across all life, leading to the possibility that a robust Tree of Life can be constructed. However, "gene tree heterogeneity", which is when different genomic regions can evolve differently, is a common phenomenon in multi-locus data sets, and reduces the accuracy of standard methods for species tree estimation that do not take this heterogeneity into account. New methods have been developed for species tree estimation that specifically address gene tree heterogeneity, and that have been proven to converge to the true species tree when the number of loci and number of sites per locus both increase (i.e., the methods are said to be "statistically consistent"). Yet, little is known about the biologically realistic condition where the number of sites per locus is bounded. We show that when the sequence length of each locus is bounded (by any arbitrarily chosen value), the most common approaches to species tree estimation that take heterogeneity into account (i.e., traditional fully partitioned concatenated maximum likelihood and newer approaches, called summary methods, that estimate the species tree by combining estimated gene trees) are not statistically consistent, even when the heterogeneity is extremely constrained. The main challenge is the presence of conditions such as long branch attraction that create biased tree estimation when the number of sites is restricted. Hence, our study uncovers a fundamental challenge to species tree estimation using both traditional and new methods.

Decision tree, a learning tool for the prediction of beef tenderness using rearing factors and carcass characteristics.

The present study explored the potential use of decision trees on rearing factors (q = 10) and carcass characteristics (q = 12) for the development of prediction model rules of beef tenderness prediction/categorization. Accordingly, 308 young bulls were used by a sensory panel to evaluate the tenderness potential of ribeye steaks grilled at 55 °C. A classification and regression tree method was implemented and allowed the prediction of tenderness using (i) rearing factors, (ii) carcass characteristics or (iii) both. The resultant tree models yielded predictive accuracies of 70.78% (with four rearing factors: concentrate percentage; fattening duration; initial body weight and dry matter intake); 67.21% (with four carcass characteristics: fatness carcass score; carcass weight; dressing percentage and muscle carcass percentage) and 84.41% (with six rearing factors and carcass characteristics) compared to the k-means clustering of tenderness. In the final and robust regression tree, from the 22 attribute information, two carcass characteristics (fatness carcass score and muscle carcass percentage) and four rearing factors (fattening duration; concentrate percentage; dry matter intake and initial body weight) were retained as predictors. The first splitter of the 308 ribeye steaks in accordance with their tenderness scores was fatness carcass score, followed by fattening duration and concentrate percentage. The trial in the preset study highlights the importance of thresholding approach for efficiently classifying ribeye steaks in accordance with their tenderness potential. The overall prediction model rule was: IF (fatness carcass score ≥ 2.88) AND (concentrate ≥ 82%) [AND (muscle carcass ≥ 71%)] THEN meat was [very] tender. © 2018 Society of Chemical Industry.

Genome sequence of Jatropha curcas L., a non-edible biodiesel plant, provides a resource to improve seed-related traits.

Summary Jatropha curcas (physic nut), a non‐edible oilseed crop, represents one of the most promising alternative energy sources due to its high seed oil content, rapid growth and adaptability to various environments. We report ~339 Mbp draft whole genome sequence of J. curcas var. Chai Nat using both the PacBio and Illumina sequencing platforms. We identified and categorized differentially expressed genes related to biosynthesis of lipid and toxic compound among four stages of seed development. Triacylglycerol (TAG), the major component of seed storage oil, is mainly synthesized by phospholipid:diacylglycerol acyltransferase in Jatropha, and continuous high expression of homologs of oleosin over seed development contributes to accumulation of high level of oil in kernels by preventing the breakdown of TAG. A physical cluster of genes for diterpenoid biosynthetic enzymes, including casbene synthases highly responsible for a toxic compound, phorbol ester, in seed cake, was syntenically highly conserved between Jatropha and castor bean. Transcriptomic analysis of female and male flowers revealed the up‐regulation of a dozen family of TFs in female flower. Additionally, we constructed a robust species tree enabling estimation of divergence times among nine Jatropha species and five commercial crops in Malpighiales order. Our results will help researchers and breeders increase energy efficiency of this important oil seed crop by improving yield and oil content, and eliminating toxic compound in seed cake for animal feed.

Upsampling for Improved Multidimensional Attribute Space Clustering of Multifield Data

Clustering algorithms in the high-dimensional space require many data to perform reliably and robustly. For multivariate volume data, it is possible to interpolate between the data points in the high-dimensional attribute space based on their spatial relationship in the volumetric domain (or physical space). Thus, sufficiently high number of data points can be generated, overcoming the curse of dimensionality for this particular type of multidimensional data. We applies this idea to a histogram-based clustering algorithm. We created a uniform partition of the attribute space in multidimensional bins and computed a histogram indicating the number of data samples belonging to each bin. Without interpolation, the analysis was highly sensitive to the histogram cell sizes, yielding inaccurate clustering for improper choices: Large histogram cells result in no cluster separation, while clusters fall apart for small cells. Using an interpolation in physical space, we could refine the data by generating additional samples. The depth of the refinement scheme was chosen according to the local data point distribution in attribute space and the histogram’s bin size. In the case of field discontinuities representing sharp material boundaries in the volume data, the interpolation can be adapted to locally make use of a nearest-neighbor interpolation scheme that avoids averaging values across the sharp boundary. Consequently, we could generate a density computation, where clusters stay connected even when using very small bin sizes. We exploited this result to create a robust hierarchical cluster tree, apply our technique to several datasets, and compare the cluster trees before and after interpolation.

Anchored phylogenomics illuminates the skipper butterfly tree of life

BackgroundButterflies (Papilionoidea) are perhaps the most charismatic insect lineage, yet phylogenetic relationships among them remain incompletely studied and controversial. This is especially true for skippers (Hesperiidae), one of the most species-rich and poorly studied butterfly families.MethodsTo infer a robust phylogenomic hypothesis for Hesperiidae, we sequenced nearly 400 loci using Anchored Hybrid Enrichment and sampled all tribes and more than 120 genera of skippers. Molecular datasets were analyzed using maximum-likelihood, parsimony and coalescent multi-species phylogenetic methods.ResultsAll analyses converged on a novel, robust phylogenetic hypothesis for skippers. Different optimality criteria and methodologies recovered almost identical phylogenetic trees with strong nodal support at nearly all nodes and all taxonomic levels. Our results support Coeliadinae as the sister group to the remaining skippers, the monotypic Euschemoninae as the sister group to all other subfamilies but Coeliadinae, and the monophyly of Eudaminae plus Pyrginae. Within Pyrginae, Celaenorrhinini and Tagiadini are sister groups, the Neotropical firetips, Pyrrhopygini, are sister to all other tribes but Celaenorrhinini and Tagiadini. Achlyodini is recovered as the sister group to Carcharodini, and Erynnini as sister group to Pyrgini. Within the grass skippers (Hesperiinae), there is strong support for the monophyly of Aeromachini plus remaining Hesperiinae. The giant skippers (Agathymus and Megathymus) once classified as a subfamily, are recovered as monophyletic with strong support, but are deeply nested within Hesperiinae.ConclusionsAnchored Hybrid Enrichment sequencing resulted in a large amount of data that built the foundation for a new, robust evolutionary tree of skippers. The newly inferred phylogenetic tree resolves long-standing systematic issues and changes our understanding of the skipper tree of life. These resultsenhance understanding of the evolution of one of the most species-rich butterfly families.

An Approximate Bayesian Reinforcement Learning Approach Using Robust Control Policy and Tree Search

For autonomous robots, we propose an approximate model-based Bayesian reinforcement learning (MB-BRL) approach that reduces real-world samples within feasible computational efforts. Firstly, to find an approximate solution of an original undiscounted infinite horizon MB-BRL problem with a cost-free termination, we consider a finite horizon (FH) MB-BRL problem in which terminal costs are given by robust control policies. The resulting performance is better than or equal to the performance obtained with a robust method, while the resulting policy may choose an explorative behavior to get useful information about parametric model uncertainty for reducing real-world samples. Secondly, to obtain a feasible solution of the FH MB-BRL problem using simulation samples, we propose a combination of robust RL, Monte Carlo tree search (MCTS), and Bayesian inference. We show an idea of reusing previous MCTS samples for Bayesian inference at a leaf node. The proposed approach allows an agent to choose from multiple robust policies at a leaf node. Numerical experiments of a two-dimensional peg-in-hole task demonstrate the effectiveness of the proposed approach.

Comparative Genomics of the Herbivore Gut Symbiont Lactobacillus reuteri Reveals Genetic Diversity and Lifestyle Adaptation.

Lactobacillus reuteri is a catalase-negative, Gram-positive, non-motile, obligately heterofermentative bacterial species that has been used as a model to describe the ecology and evolution of vertebrate gut symbionts. However, the genetic features and evolutionary strategies of L. reuteri from the gastrointestinal tract of herbivores remain unknown. Therefore, 16 L. reuteri strains isolated from goat, sheep, cow, and horse in Inner Mongolia, China were sequenced in this study. A comparative genomic approach was used to assess genetic diversity and gain insight into the distinguishing features related to the different hosts based on 21 published genomic sequences. Genome size, G + C content, and average nucleotide identity values of the L. reuteri strains from different hosts indicated that the strains have broad genetic diversity. The pan-genome of 37 L. reuteri strains contained 8,680 gene families, and the core genome contained 726 gene families. A total of 92,270 nucleotide mutation sites were discovered among 37 L. reuteri strains, and all core genes displayed a Ka/Ks ratio much lower than 1, suggesting strong purifying selective pressure (negative selection). A highly robust maximum likelihood tree based on the core genes shown in the herbivore isolates were divided into three clades; clades A and B contained most of the herbivore isolates and were more closely related to human isolates and vastly distinct from clade C. Some functional genes may be attributable to host-specific of the herbivore, omnivore, and sourdough groups. Moreover, the numbers of genes encoding cell surface proteins and active carbohydrate enzymes were host-specific. This study provides new insight into the adaptation of L. reuteri to the intestinal habitat of herbivores, suggesting that the genomic diversity of L. reuteri from different ecological origins is closely associated with their living environment.

Pity beetle algorithm – A new metaheuristic inspired by the behavior of bark beetles

In the past years a great variety of nature-inspired algorithms have proven their ability to efficiently handle combinatorial optimization problems ranging from design and form finding problems to mainstream economic theory and medical diagnosis. In this study, a new metaheuristic algorithm called Pity Beetle Algorithm (PBA) is presented and its efficiency against state-of-the-art algorithms is assessed. The proposed algorithm was inspired by the aggregation behavior, searching for nest and food, of the beetle named Pityogenes chalcographus, also known as six-toothed spruce bark beetle. This beetle has the ability to locate and harvest on the bark of weakened trees into a forest, while when its population exceeds a specific threshold it can infest healthy and robust trees as well. As it was proved in this study, PBA can be applied to NP-hard optimization problems regardless of the scale, since PBA has the ability to search for possible solutions into large spaces and to find the global optimum solution overcoming local optima. In this work, PBA was applied to well-known benchmark uni-modal and multi-modal, separable and non-separable unconstrained test functions while it was also compared to other well established metaheuristic algorithms implementing also the CEC 2014 benchmark and complexity evaluation tests.

Robust MST-Based Clustering Algorithm.

Minimax similarity stresses the connectedness of points via mediating elements rather than favoring high mutual similarity. The grouping principle yields superior clustering results when mining arbitrarily-shaped clusters in data. However, it is not robust against noises and outliers in the data. There are two main problems with the grouping principle: first, a single object that is far away from all other objects defines a separate cluster, and second, two connected clusters would be regarded as two parts of one cluster. In order to solve such problems, we propose robust minimum spanning tree (MST)-based clustering algorithm in this letter. First, we separate the connected objects by applying a density-based coarsening phase, resulting in a low-rank matrix in which the element denotes the supernode by combining a set of nodes. Then a greedy method is presented to partition those supernodes through working on the low-rank matrix. Instead of removing the longest edges from MST, our algorithm groups the data set based on the minimax similarity. Finally, the assignment of all data points can be achieved through their corresponding supernodes. Experimental results on many synthetic and real-world data sets show that our algorithm consistently outperforms compared clustering algorithms.

Phylogenic inference using alignment-free methods for applications in microbial community surveys using 16s rRNA gene.

The diversity of microbiota is best explored by understanding the phylogenetic structure of the microbial communities. Traditionally, sequence alignment has been used for phylogenetic inference. However, alignment-based approaches come with significant challenges and limitations when massive amounts of data are analyzed. In the recent decade, alignment-free approaches have enabled genome-scale phylogenetic inference. Here we evaluate three alignment-free methods: ACS, CVTree, and Kr for phylogenetic inference with 16s rRNA gene data. We use a taxonomic gold standard to compare the accuracy of alignment-free phylogenetic inference with that of common microbiome-wide phylogenetic inference pipelines based on PyNAST and MUSCLE alignments with FastTree and RAxML. We re-simulate fecal communities from Human Microbiome Project data to evaluate the performance of the methods on datasets with properties of real data. Our comparisons show that alignment-free methods are not inferior to alignment-based methods in giving accurate and robust phylogenic trees. Moreover, consensus ensembles of alignment-free phylogenies are superior to those built from alignment-based methods in their ability to highlight community differences in low power settings. In addition, the overall running times of alignment-based and alignment-free phylogenetic inference are comparable. Taken together our empirical results suggest that alignment-free methods provide a viable approach for microbiome-wide phylogenetic inference.

Development and evaluation of robust tree biomass equations for rubber tree (Hevea brasiliensis) plantations in India

BackgroundIn India, rubber (Hevea brasiliensis) plantations cover ~0.8 million ha of land, emphasizing its significant role in the Earth’s carbon dynamics. Therefore, it is important to estimate the biomass stocks of plantations precisely in the context of carbon management. Previous studies in India have focused on development of allometric equations for estimating aboveground biomass (AGB) through harvesting younger trees (up to 14 yr) only or on studies with small sample sizes without assessing model bias. The objective of this study was to develop biomass estimation models for different tree components in rubber plantations and assess model predictive performance at the stand level.MethodsA total of 67 trees were harvested from plantations of different ages (6, 15, 27 and 34 yr) in North East India and their diameter at 200 cm (D), height and dry weights of different tree components were recorded. The data were used for evaluation of H-D and biomass estimation models at the stand level.ResultsThe Michaelis-Menten function was found to be the most appropriate model for estimating tree height among 10 commonly used H-D models. For estimation of AGB and coarse root biomass, a model that involves tree volume (i.e. D2H) was found to provide better prediction than either D or H alone or a model that combines H, D and stand density. The estimated AGB varied from 28 Mg·ha−1 in 6 yr. old plantation to 169 Mg·ha−1 in 34 yr. old plantations. The coarse root biomass was estimated at 4 Mg·ha−1 for 6 yr. old plantation and 12 Mg·ha−1 for 34 yr. old stands.ConclusionsIt is concluded that models involving tree volume are more appropriate for regional level biomass estimation than simple power-law models for individual stands. We recommend that the power-law model should not be used for estimation of AGB in plantations at different growth stages because power-law parameters can be biased due to data truncation.

Modelling the influence of environment on juvenile modulus of elasticity in Pinus radiata grown in Chile

Measurements of modulus of elasticity (E) were taken from Pinus radiata D. Don growing at 88 sites that encompass the range of environmental conditions over which forest plantations have been established in Chile. Using this dataset the objectives of this research were to (i) identify the key determinants of E across sites and (ii) develop a robust tree level model to predict E across the environmental range over which P. radiata is grown in Chile.Variance components analysis showed variation between sites to account for almost twofold more variation in E than variation within sites. Across the studied region, mean site values of E varied widely ranging fourfold from 3.41 to 14.1GPa, with a mean of 8.52GPa. The mean within site variation at each site was 4.50GPa, ranging from 1.50 to 14.6GPa.Of the climatic and stand level variables used in analyses stem slenderness, S, (tree height/diameter at breast height) exhibited the strongest correlation with E accounting for 43% of the variance in the property. The final model of E at the site level included S and minimum air temperature during May, both of which were positively related to E. This model was extended to the tree level through inclusion of a term that accounted for the random intercept between S and E. This model accounted for 75% of the variation in the data and exhibited little apparent bias.

Fossils matter: improved estimates of divergence times in Pinus reveal older diversification

BackgroundThe taxonomy of pines (genus Pinus) is widely accepted and a robust gene tree based on entire plastome sequences exists. However, there is a large discrepancy in estimated divergence times of major pine clades among existing studies, mainly due to differences in fossil placement and dating methods used. We currently lack a dated molecular phylogeny that makes use of the rich pine fossil record, and this study is the first to estimate the divergence dates of pines based on a large number of fossils (21) evenly distributed across all major clades, in combination with applying both node and tip dating methods.ResultsWe present a range of molecular phylogenetic trees of Pinus generated within a Bayesian framework. We find the origin of crown Pinus is likely up to 30 Myr older (Early Cretaceous) than inferred in most previous studies (Late Cretaceous) and propose generally older divergence times for major clades within Pinus than previously thought. Our age estimates vary significantly between the different dating approaches, but the results generally agree on older divergence times. We present a revised list of 21 fossils that are suitable to use in dating or comparative analyses of pines.ConclusionsReliable estimates of divergence times in pines are essential if we are to link diversification processes and functional adaptation of this genus to geological events or to changing climates. In addition to older divergence times in Pinus, our results also indicate that node age estimates in pines depend on dating approaches and the specific fossil sets used, reflecting inherent differences in various dating approaches. The sets of dated phylogenetic trees of pines presented here provide a way to account for uncertainties in age estimations when applying comparative phylogenetic methods.

Enriching the ant tree of life: enhanced UCE bait set for genome‐scale phylogenetics of ants and other Hymenoptera

Summary Targeted enrichment of conserved genomic regions (e.g. ultraconserved elements or UCEs) has emerged as a promising tool for inferring evolutionary history in many organismal groups. Because the UCE approach is still relatively new, much remains to be learned about how best to identify UCE loci and design baits to enrich them. We test an updated UCE identification and bait design workflow for the insect order Hymenoptera, with a particular focus on ants. The new strategy augments a previous bait design for Hymenoptera by (i) changing the parameters by which conserved genomic regions are identified and retained, and (ii) increasing the number of genomes used for locus identification and bait design. We perform in vitro validation of the approach in ants by synthesizing an ant‐specific bait set that targets UCE loci and a set of ‘legacy’ phylogenetic markers. Using this bait set, we generate new data for 84 taxa (16/17 ant subfamilies) and extract loci from an additional 17 genome‐enabled taxa. We then use these data to examine UCE capture success and phylogenetic performance across ants. We also test the workability of extracting legacy markers from enriched samples and combining the data with published datasets. The updated bait design (hym‐v2) contained a total of 2590‐targeted UCE loci for Hymenoptera, significantly increasing the number of loci relative to the original bait set (hym‐v1; 1510 loci). Across 38 genome‐enabled Hymenoptera and 84 enriched samples, experiments demonstrated a high and unbiased capture success rate, with the mean locus enrichment rate being 2214 loci per sample. Phylogenomic analyses of ants produced a robust tree that included strong support for previously uncertain relationships. Complementing the UCE results, we successfully enriched legacy markers, combined the data with published Sanger datasets and generated a comprehensive ant phylogeny containing 1060 terminals. Overall, the new UCE bait design strategy resulted in an enhanced bait set for genome‐scale phylogenetics in ants and other Hymenoptera. Our in vitro tests demonstrate the utility of the updated design workflow, providing evidence that this approach could be applied to any organismal group with available genomic information.