Tree Estimation Research Articles

Short-term effect of thinning on inter- and intra-annual radial increment in Mediterranean Scots pine-oak mixed forests

Thinning treatments along with the establishment of mixed forest stands have been put forward as possible adaptation strategies to cope with climate change, although the effectiveness of combining these two measures has scarcely been studied and may vary depending on stand conditions and the thinning regime employed. The aim of this study was to better understand the effect of commercial thinning and of the different growth behavior of two coexisting species on their inter- and intra-annual cumulative radial increment patterns. For this purpose, we studied radial increment in a Scots pine-Pyrenean oak (Pinus sylvestris L.-Quercus pyrenaica Willd.) Mediterranean mixed forest in north-west Spain over two climatically contrasting years (2016–2017). The data came from a thinning trial consisting of a randomized latin square design with a control and two commercial thinning treatments from below; one moderate and the other heavy, removing 25% and 50 % of initial basal area, respectively, of both species. The radial increment was analyzed based on bi-weekly readings from band dendrometers installed on 90 oak and pine trees. A non-linear mixed model based on double-Richards curve was fitted to explore the differences between thinning treatments and species response in the intra-annual cumulative radial increment patterns. Inter-annual basal area increments for each species at stand level were quantified by aggregating the tree estimates obtained from the model fitted in the first step. Tree and stand level growth were greater in Scots pine, which also showed a greater growth response to early spring droughts than the Pyrenean oak. Heavy thinning increased radial increment in trees of both species at the expense of decreased total stand basal area. At species level, basal area growth in Scots pine decreased through thinning, whereas for Pyrenean oak, the heavy thinning intensity resulted in the same basal area growth as the control. Thus, heavy thinning induced a trade-off between total stand growth and tree-level response to climatic conditions for Scots pine but with no loss in productivity in the case of the Pyrenean oak. Hence, heavy thinning may be an appropriate measure to attain productive stability of the oak coppice in the studied mixed forest as well as to adapt tree growth to future droughts associated with climate change.

New aboveground biomass equations by components for small black spruce in peatland ecosystems of Western Canada

Black spruce is the most common tree species in Canada, dominating treed peatlands where they are usually stunted. We used 495 destructively sampled trees from 56 plots to develop allometric models of aboveground biomass by components (stem, branches, and needles) for small (<5 m tall) black spruce from peatlands in the Taiga Plains and Boreal Plains Ecozones of Western Canada, for which there were no specific models available of biomass by components. We used leave-one-plot-out cross-validation to assess transferability and compare our models with existing national and ecozone-specific equations. Our models predicted total tree biomass with better accuracy and less biased estimates than the national model (relative RMSE: 30% versus 35% national; relative bias: +1% versus –12% national). Similar results were obtained in other external datasets. Existing ecozone equations performed worse than either our models or the national ones. When we applied the models at the plot level to predict aboveground biomass density (Mg·ha−1), our models outperformed the national model again (relative RMSE: 15.9% versus 18.6% national, relative bias: +3.5% versus –13.6% national). These results indicate that at least for peatlands of Western Canada, our models provide better aboveground biomass estimates for small black spruce trees than existing models.

Precise aboveground biomass estimation of plantation forest trees using the novel allometric model and UAV-borne LiDAR

IntroductionPlantation forest is an important component of global forest resources. The accurate estimation of tree aboveground biomass (AGB) in plantation forest is of great significance for evaluating the carbon sequestration capacity. In recent years, UAV-borne LiDAR has been increasingly applied to forest survey, but the traditional allometric model for AGB estimation cannot be directly used without the diameter at breast height (DBH) of individual trees. Therefore, it is practicable to construct a novel allometric model incorporating the crown structure parameters, which can be precisely extracted from UAV LiDAR data. Additionally, the reduction effect of adjacent trees on crown area (Ac) should be taken into account.MethodsIn this study, we proposed an allometric model depending on the predictor variables of Ac and trunk height (H). The UAV-borne LiDAR was utilized to scan the sample plot of dawn redwood (DR) trees in the test site. The raw point cloud was first normalized and segmented into individual trees, whose Acs and Hs were sequentially extracted. To mitigate the effects of adjacent trees, the initial Acs were corrected to refer to the potential maximum Acs under undisturbed growth conditions. Finally, the corrected Acs (Acc) and Hs were input into the constructed allometric model to achieve the AGBs of DR trees.Results and discussionAccording to accuracy assessment, coefficient of determination (R2) and root mean square error (RMSE) of extracted Hs were 0.9688 and 0.51 m; R2 and RMSE of calculated AGBs were 0.9432 and 10.91 kg. The unrestricted growth parts of the tree crowns at the edge of a plantation forest could be used to derive the potential maximum Ac. Compared with the allometric models for AGB estimation relying only on trunk H or on initial Ac and H, the novel allometric model demonstrated superior performance in estimating the AGBs of trees in a plantation forest.

Ensemble algorithms for modeling forest live fuel loads and multivariate probability proportional to size sampling in Kunming, Yunnan, China

Forest live fuel load (LFL) models of tree, shrub, and herbaceous layers were established using linear function (LF), power function (PF), K-nearest neighbor (KNN), gradient boosting regression tree (GBRT), and random forest (RF) algorithms to improve the fit and prediction accuracy and reduce the bias of forest LFL models. Multivariate probability proportional to size sampling (MPPS), probability proportional to size sampling (PPS), and probability proportional to frequency sampling (PPF) were used to obtain mean estimates of the forest LFLs, and the sampling accuracies were assessed. We utilized forest stock and biomass survey data (forest fire risk data and forest resource planning and design survey data) from Kunming, Yunnan, China. The results indicated that the RF model had the highest fit and prediction accuracy. The R2 values of the fitted models for the LFLs of Pinus yunnanensis, Quercus spp., Pinus armandi, Eucalyptus spp., other soft broadleaf species, other hard broadleaf species, other conifer species, shrubs, and herbaceous species were 0.79, 0.89, 0.86, 0.88, 0.91, 0.83, 0.88 0.88, and 0.92, respectively. The F-values were 4.652, 2.582, 2.565, 2.551, 2.738, 3.158, 16.156, 3.479, and 8.468, respectively. The sampling results of forest LFLs were much better for MPPS than for PPF and PPS. Moreover, the two-stage MPPS was the most efficient method for estimating tree, shrub, and herbaceous LFLs. The mean estimates of tree fuel loads, shrub fuel loads, and herbaceous fuel loads were 144.7760, 3.8094, and 0.6442 t/hm2, respectively. There were 555 overlapping subunits for the three types of forest LFLs. The proposed method can significantly reduce the cost of LFL surveys and improve estimation accuracy.

PROBABILISTIC LEARNING OF TREATMENT TREES IN CANCER.

Accurate identification of synergistic treatment combinations and their underlying biological mechanisms is critical across many disease domains, especially cancer. In translational oncology research, preclinical systems such as patient-derived xenografts (PDX) have emerged as a unique study design evaluating multiple treatments administered to samples from the same human tumor implanted into genetically identical mice. In this paper, we propose a novel Bayesian probabilistic tree-based framework for PDX data to investigate the hierarchical relationships between treatments by inferring treatment cluster trees, referred to as treatment trees (Rx-tree). The framework motivates a new metric of mechanistic similarity between two or more treatments accounting for inherent uncertainty in tree estimation; treatments with a high estimated similarity have potentially high mechanistic synergy. Building upon Dirichlet Diffusion Trees, we derive a closed-form marginal likelihood encoding the tree structure, which facilitates computationally efficient posterior inference via a new two-stage algorithm. Simulation studies demonstrate superior performance of the proposed method in recovering the tree structure and treatment similarities. Our analyses of a recently collated PDX dataset produce treatment similarity estimates that show a high degree of concordance with known biological mechanisms across treatments in five different cancers. More importantly, we uncover new and potentially effective combination therapies that confer synergistic regulation of specific downstream biological pathways for future clinical investigations. Our accompanying code, data, and shiny application for visualization of results are available at: https://github.com/bayesrx/RxTree.

Hybrid Machine Learning Approach for Evapotranspiration Estimation of Fruit Tree in Agricultural Cyber-Physical Systems.

The flourish of the Internet of Things (IoT) and data-driven techniques provide new ideas for enhancing agricultural production, where evapotranspiration estimation is a crucial issue in crop irrigation systems. However, tremendous and unsynchronized data from agricultural cyber-physical systems bring large computational costs as well as complicate performing conventional machine learning methods. To precisely estimate evapotranspiration with acceptable computational costs under the background of IoT, we combine time granulation computing techniques and gradient boosting decision tree (GBDT) with Bayesian optimization (BO) to propose a hybrid machine learning approach. In the combination, a fuzzy granulation method and a time calibration technique are introduced to break voluminous and unsynchronized data into small-scale and synchronized granules with high representativeness. Subsequently, GBDT is implemented to predict evapotranspiration, and BO is utilized to find the optimal hyperparameter values from the reduced granules. IoT data from Xi'an Fruit Technology Promotion Center in Shaanxi Province, China, verify that the proposed granular-GBDT-BO is effective for cherry tree evapotranspiration estimation with reduced computational time, and acceptable and robust predictive accuracy. Consequently, the precise estimation of crop evapotranspiration could provide operational guidance for plant irrigation, plant conservations, and pest control in the agricultural greenhouse.

Individual tree volume estimation with terrestrial laser scanning: Evaluating reconstructive and allometric approaches

Accurate estimates of above-ground tree biomass within forest inventories are essential for calibration and validation of biomass mapping products based on Earth observation data. Terrestrial laser scanning (TLS) enables detailed and non-destructive volume estimation of individual trees, which can be converted to biomass with wood basic density. Existing TLS-based approaches range from simple geometrical features to virtual 3D reconstruction of entire trees. Validating such approaches with weight measurements is a key step before the integration of TLS or other close-range technologies into operational applications such as forest inventories. In this study, we firstly evaluate individual tree volume estimation approaches based on 3D reconstruction through quantitative structure models (QSM) against destructive reference data of 60 trees and compare them to operational allometric scaling models (ASM). Secondly, we determine the explanatory power of TLS-derived geometric parameters regarding total wood, stem, coarse wood and fine branch volume. We observe similar accuracy in merchantable (¿7 cm) wood compartments for ASMs (NRMSE = 25 %) and QSMs (NRMSE = 29 %), with QSMs showing better results for broadleaves than conifers and generally overestimating fine branch volume. Feature selection shows that a combination of stem diameters and volume of convex hulls around tree crowns has the most potential to model the entire tree volume including branches, especially for conifers. In cases where the quality of available point clouds is insufficient for QSMs, 3D information can thus still be utilised by deriving geometric parameters. The integration of crown dimension parameters into new allometric models could substantially improve the estimation of branch wood volume.

Weighted ASTRID: fast and accurate species trees from weighted internode distances

BackgroundSpecies tree estimation is a basic step in many biological research projects, but is complicated by the fact that gene trees can differ from the species tree due to processes such as incomplete lineage sorting (ILS), gene duplication and loss (GDL), and horizontal gene transfer (HGT), which can cause different regions within the genome to have different evolutionary histories (i.e., “gene tree heterogeneity”). One approach to estimating species trees in the presence of gene tree heterogeneity resulting from ILS operates by computing trees on each genomic region (i.e., computing “gene trees”) and then using these gene trees to define a matrix of average internode distances, where the internode distance in a tree T between two species x and y is the number of nodes in T between the leaves corresponding to x and y. Given such a matrix, a tree can then be computed using methods such as neighbor joining. Methods such as ASTRID and NJst (which use this basic approach) are provably statistically consistent, very fast (low degree polynomial time) and have had high accuracy under many conditions that makes them competitive with other popular species tree estimation methods. In this study, inspired by the very recent work of weighted ASTRAL, we present weighted ASTRID, a variant of ASTRID that takes the branch uncertainty on the gene trees into account in the internode distance.ResultsOur experimental study evaluating weighted ASTRID typically shows improvements in accuracy compared to the original (unweighted) ASTRID, and shows competitive accuracy against weighted ASTRAL, the state of the art. Our re-implementation of ASTRID also improves the runtime, with marked improvements on large datasets.ConclusionsWeighted ASTRID is a new and very fast method for species tree estimation that typically improves upon ASTRID and has comparable accuracy to weighted ASTRAL, while remaining much faster. Weighted ASTRID is available at https://github.com/RuneBlaze/internode.

Characterization of Bundaberg Bee Virus 4 Genome Identified in Hawaii

To identify genetic markers of different cultivars of Stevia rebaudiana (stevia), leaf samples from twenty-three genetically unique stevia plants were subjected to RNA sequencing. Unexpectedly, transcriptome annotation revealed a ‘viral polyprotein’ contig, which was identified as the Bundaberg Bee Virus 4 (BBV4), a virus that has not been found in Hawaii before. BBV4 expression was found in twelve samples, and the expression levels ranged from 0.08-339.18 transcripts per million (TPM). A ‘BBV4-Hawaii’ consensus sequence was generated from 227,427 reads and shares 99.7% identity with the BBV4 reference sample from Australia. A phylogenetic tree estimation constructed using three viral domains placed BBV4 in a monophyletic clade with other members of Iflaviridae, supporting an Iflaviridae classification. A BBV4 intergenic region (IGR) was found to contain a ‘slippery sequence’ within a region that is predicted to contain the unique structure required for Programmed Ribosomal Shifting (PRS). A homological comparison of the BBV4 IGR to two other Picornavirales that utilize alternative translation revealed an area of overlap in the analogous regions. These results suggest that BBV4 may utilize alternative translation and that its IGR is involved in the process. This study also demonstrates the utility of repurposing large Next Generation Sequence (NGS) datasets with sufficiently deep coverage for viral genome identification, quantification, and characterization. The results herein represent the first full capture of the BBV4 genomic sequence and the second reported observation of the virus worldwide.

Taxonomic Status and Phylogenetic Relationship of the Charadriidae Family Based on Complete Mitogenomes.

The Charadriiformes provide a good source for researching evolution owing to their diverse distribution, behavior, morphology, and ecology. However, in the Charadrii, family-level relationships remain understudied, and the monophyly of Charadriidae is also a subject of controversy. In the present study, we generated complete mitogenomes for two species, Charadrius leschenaultii and Charadrius mongolus, which were found to be 16,905 bp and 16,844 bp in length, respectively. Among the 13 protein codon genes, we observed variation in the rate of non-synonymous substitution rates, with the slowest rate found in COI and the fastest rate observed in ATP8. The Ka/Ks ratio for all Charadriidae species was significantly lower than one, which inferred that the protein-coding genes underwent purifying selection. Phylogenetic analysis based on the genes of Cyt b, 12S and ND2 revealed that the genus Pluvialis is the sister group of three families (Haematopodidae, Ibidorhynchidae, Recurvirostridae). However, the phylogenetic analysis based on complete mitogenomes indicated that the genus Pluvialis is within the Charadriidae family. This study highlights the importance of carefully selecting the number of genes used to obtain accurate estimates of the species tree. It also suggests that relying on partial mtDNA genes with fast-evolving rates may lead to misleading results when resolving the Pluvialis sister group. Future research should focus on sequencing more mitogenomes at different taxonomic levels to gain a better understanding of the features and phylogenetic relationships within the Charadriiformes order.

Estimating tree volume based on crown mapping by UAV pictures in the Amazon Forest

The use of remote sensing images obtained by unmanned aerial vehicle (UAV) systems enables measuring the morphometry of the tree canopy to estimate the volume stock in the Amazon Forest. In this study, we used RGB images from a low-cost UAV to map tree species and extract volumetric stock estimates in an Amazonian Forest. Individual tree crowns (ITC) were outlined in the UAV images and identified to the species level using forest inventory data. The average diameter and crown area of the trees were measured to estimate the volume, basal area and DBH per diameter class for 260 ha of tropical forest. The RMSE volume adjustment for the separate field inventory dataset was 19.31% with an R2 of 0.967. The UAV system images has the potential to map tree species and estimate tree biometry in the Amazon Forest, providing valuable insights for forest management and conservation.

Machine Learning: Volume and Biomass Estimates of Commercial Trees in the Amazon Forest

Accurate estimation of the volume and above-ground biomass of exploitable trees by the practice of selective logging is essential for the elaboration of a sustainable management plan. The objective of this study is to develop machine learning models capable of estimating the volume and biomass of commercial trees in the Southwestern Amazon, based on dendrometric, climatic and topographic characteristics. The study was carried out in the municipality of Porto Acre, Acre state, Brazil. The volume and biomass of sample trees were determined using dendrometric, climatic and topographic variables. The Boruta algorithm was applied to select the best set of variables. Support Vector Machines (SVM), Artificial Neural Networks (ANN), Random Forests (RF) and the Generalized Linear Model (GLM) were the machine learning methods evaluated. In general, the evaluated methods showed a satisfactory generalization power. The results showed that the volume and biomass predictions of commercial trees in the Amazon rainforest differed between the techniques (p < 0.05). ANNs showed the best performance in predicting the volume and biomass of commercial trees, with the highest ryŷ and the lowest RSME and MAE. Thus, machine learning methods such as SVM, ANN, RF and GLM are shown to be useful and efficient tools for estimating the volume and biomass of commercial trees in the Amazon rainforest. These methods can be useful tools to improve the accuracy of estimates in forest management plans.

Characterization of DC corona discharge current pulses using high-frequency measurement techniques

This paper presents corona discharge current pulse characterization under positive and negative DC voltage excitations. Two electrode systems with three different gap spacings generated corona discharges. Different corona modes in atmospheric air were observed with voltage level changes. Commercial shunt resistors and a high-frequency current transformer (HFCT) captured corona current pulses. The appropriate resistor type and value were determined for the shunt resistor measurement. BNC terminator was proposed as a shunt resistor to detect corona discharge current pulses, and the availability of the shunt resistors and HFCTs for corona pulse detection was discussed. After gathering corona discharge pulse data, features from the pulses were extracted. The best features describing corona discharge types were specified using a recursive feature elimination method based on a decision tree estimator. The corona discharges were discriminated with an accuracy rate of 0.994.

Effect of Different Types of Sequence Data on Palaeognath Phylogeny.

Palaeognathae consists of five groups of extant species: flighted tinamous (1) and four flightless groups: kiwi (2), cassowaries and emu (3), rheas (4), and ostriches (5). Molecular studies supported the groupings of extinct moas with tinamous and elephant birds with kiwi as well as ostriches as the group that diverged first among the five groups. However, phylogenetic relationships among the five groups are still controversial. Previous studies showed extensive heterogeneity in estimated gene tree topologies from conserved nonexonic elements, introns, and ultraconserved elements. Using the noncoding loci together with protein-coding loci, this study investigated the factors that affected gene tree estimation error and the relationships among the five groups. Using closely related ostrich rather than distantly related chicken as the outgroup, concatenated and gene tree-based approaches supported rheas as the group that diverged first among groups (1)-(4). Whereas gene tree estimation error increased using loci with low sequence divergence and short length, topological bias in estimated trees occurred using loci with high sequence divergence and/or nucleotide composition bias and heterogeneity, which more occurred in trees estimated from coding loci than noncoding loci. Regarding the relationships of (1)-(4), the site patterns by parsimony criterion appeared less susceptible to the bias than tree construction assuming stationary time-homogeneous model and suggested the clustering of kiwi and cassowaries and emu the most likely with ∼40% support rather than the clustering of kiwi and rheas and that of kiwi and tinamous with 30% support each.

POTENTIAL OF MOBILE MAPPING TO CREATE DIGITAL TWINS OF FORESTS

Abstract. Forests are irreplaceable and are being studied extensively. Better forest inventory and understanding necessitate effective mapping, modeling, and automatic analysis. As a result, considerable research effort is being devoted to digitizing forest environments. Recently, digital twins have come to the attention of the geospatial community as a virtual representation of the Earth’s surface linked to its corresponding physical asset. This concept is applicable to forests and has been studied in the literature. This requires initial input data obtained through reality capture. Among mapping techniques, laser scanning has emerged as a state-of-the-art technology for vegetation modeling. In this paper, we look into the potential of mobile laser scanning for forest digital twinning. While most studies concentrate on single tree detection, modeling, and estimation of dendrometric parameters, we also include lower vegetation in our investigations. To accomplish this, we first detect single trees and then investigate different vegetation densities and levels using geometric metrics. We also demonstrate how to model the underlying layers of vegetation in a digital twin. We perform the tests on data from mobile laser scanning (MLS) and compare the results to those from airborne laser scanning (ALS).We show that single tree detection based on crown separation using MLS data works similarly to or slightly better than ALS data. Furthermore, we demonstrate that MLS data allows for more detailed analysis of understory vegetation taking into account different height levels and a multi-level representation, whereas ALS data only allows for rough analysis of the lower parts of forest vegetation.

Improving quartet graph construction for scalable and accurate species tree estimation from gene trees.

methods are widely used to estimate species trees from genome-scale data. However, they can fail to produce accurate species trees when the input gene trees are highly discordant because of estimation error and biological processes, such as incomplete lineage sorting. Here, we introduce TREE-QMC, a new summary method that offers accuracy and scalability under these challenging scenarios. TREE-QMC builds upon weighted Quartet Max Cut, which takes weighted quartets as input and then constructs a species tree in a divide-and-conquer fashion, at each step forming a graph and seeking its max cut. The wQMC method has been successfully leveraged in the context of species tree estimation by weighting quartets by their frequencies in the gene trees; we improve upon this approach in two ways. First, we address accuracy by normalizing the quartet weights to account for "artificial taxa" introduced during the divide phase so subproblem solutions can be combined during the conquer phase. Second, we address scalability by introducing an algorithm to construct the graph directly from the gene trees; this gives TREE-QMC a time complexity of [Formula: see text], where n is the number of species and k is the number of gene trees, assuming the subproblem decomposition is perfectly balanced. These contributions enable TREE-QMC to be highly competitive in terms of species tree accuracy and empirical runtime with the leading quartet-based methods, even outperforming them on some model conditions explored in our simulation study. We also present the application of these methods to an avian phylogenomics data set.

Filtration of Gene Trees From 9,000 Exons, Introns, and UCEs Disentangles Conflicting Phylogenomic Relationships in Tree Frogs (Hylidae).

An emerging challenge in interpreting phylogenomic data sets is that concatenation and multi-species coalescent summary species tree approaches may produce conflicting results. Concatenation is problematic because it can strongly support an incorrect topology when incomplete lineage sorting (ILS) results in elevated gene-tree discordance. Conversely, summary species tree methods account for ILS to recover the correct topology, but these methods do not account for erroneous gene trees ("EGTs") resulting from gene tree estimation error (GTEE). Third, site-based and full-likelihood methods promise to alleviate GTEE as these methods use the sequence data from alignments. To understand the impact of GTEE on species tree estimation in Hylidae tree frogs, we use an expansive data set of ∼9,000 exons, introns, and ultra-conserved elements and initially found conflict between all three types of analytical methods. We filtered EGTs using alignment metrics that could lead to GTEE (length, parsimony-informative sites, and missing data) and found that removing shorter, less informative alignments reconciled the conflict between concatenation and summary species tree methods with increased gene concordance, with the filtered topologies matching expected results from past studies. Contrarily, site-based and full-likelihood methods were mixed where one method was consistent with past studies and the other varied markedly. Critical to other studies, these results suggest a widespread conflation of ILS and GTEE, where EGTs rather than ILS are driving discordance. Finally, we apply these recommendations to an R package named PhyloConfigR, which facilitates phylogenetic software setup, summarizes alignments, and provides tools for filtering alignments and gene trees.

MutaGAN: A sequence-to-sequence GAN framework to predict mutations of evolving protein populations.

The ability to predict the evolution of a pathogen would significantly improve the ability to control, prevent, and treat disease. Machine learning, however, is yet to be used to predict the evolutionary progeny of a virus. To address this gap, we developed a novel machine learning framework, named MutaGAN, using generative adversarial networks with sequence-to-sequence, recurrent neural networks generator to accurately predict genetic mutations and evolution of future biological populations. MutaGAN was trained using a generalized time-reversible phylogenetic model of protein evolution with maximum likelihood tree estimation. MutaGAN was applied to influenza virus sequences because influenza evolves quickly and there is a large amount of publicly available data from the National Center for Biotechnology Information's Influenza Virus Resource. MutaGAN generated 'child' sequences from a given 'parent' protein sequence with a median Levenshtein distance of 4.00 amino acids. Additionally, the generator was able to generate sequences that contained at least one known mutation identified within the global influenza virus population for 72.8 per cent of parent sequences. These results demonstrate the power of the MutaGAN framework to aid in pathogen forecasting with implications for broad utility in evolutionary prediction for any protein population.

Circulation of Human Immunodeficiency Virus 1 A6 Variant in the Eastern Border of the European Union-Dynamics of the Virus Transmissions Between Poland and Ukraine.

The human immunodeficiency virus (HIV) type 1 A6 variant is dominating in high-prevalence Eastern European countries, with increasing prevalence over the remaining regions of Europe. The recent war in Ukraine may contribute to further introductions of this A6 lineage. Our aim was to model the transmission dynamics of the HIV-1 A6 variant between Poland and Ukraine. HIV-1 A6 partial pol sequences originating from Poland (n = 1185) and Ukraine (n = 653) were combined with publicly available sequences (n = 7675) from 37 other countries. We used maximum likelihood-based tree estimation followed by a bayesian inference strategy to characterize the putative transmission clades. Asymmetric discrete phylogeographic analysis was used to identify the best-supported virus migration events across administrative regions of Poland and Ukraine. We identified 206 clades (n = 1362 sequences) circulating in Poland or Ukraine (63 binational clades, 79 exclusively Polish, and 64 exclusively Ukrainian). Cross-border migrations were almost exclusively unidirectional (from Ukraine to Poland, 99.4%), mainly from Eastern and Southern Ukraine (Donetsk, 49.7%; Odesa, 17.6% regions) to the Central (Masovian, 67.3%; Lodz, 18.2%) and West Pomeranian (10.1%) districts of Poland. The primary sources of viral dispersal were the Eastern regions of Ukraine, long affected by armed conflict, and large population centers in Poland. The Polish outbreak of the A6 epidemic was fueled by complex viral migration patterns across the country, together with cross-border transmissions from Ukraine. There is an urgent need to include war-displaced people in the national HIV prevention and treatment programs to reduce the further spread of transmission networks.

Allometric Growth of Common Urban Tree Species in Qingdao City of Eastern China

Allometric growth equations help to describe the correlation between the variables of tree biological characteristics (e.g., diameter and height, diameter and canopy width) and estimate tree dynamics at a given tree dimension. Allometric models of common tree species within urban forests are also important to relate ecosystem services to common urban tree measurements such as stem diameter. In this study, allometric growth models were developed for common tree species used for urban greening on the streets of seven municipal districts in Qingdao city of eastern China. A sampling survey was constructed on an urban–rural gradient to obtain the data of tree diameter, crown width, height to live crown base, and tree height. From these measurements, the crown volume and crown projection area of tree species were calculated. The allometric relationship between two variables was established using quantile regression models (95%, 50%, and 5%) to explore its correlation and the influence of tree species characteristics on urban tree allometry. Our results showed that the correlation of allometric growth of different tree species varied largely due to their own characteristics, and the allometric growth of the same tree species was significantly different as well under the fitting of different quantiles. We found that the relationship between crown width and diameter was stronger than that between tree height and diameter. Moreover, the allometric relationship between crown projection area–diameter and crown width–diameter showed similar fitting. The allometric relationship analysis provide theoretical models and a way to use common field data to support urban forestry management and for the subsequent evaluation and prediction of tree functions and ecosystem services.