Crown structure and wood properties: Influence on tree sway and response to high winds

Wind can alter plant growth and cause extensive, irreversible damage in forested areas. To better understand how to mitigate the effects of wind action, we investigated the sensitivity of tree aerodynamic behavior to the material and geometrical factors characterizing the aerial system. The mechanical response of a 35-yr-old maritime pine (Pinus pinaster, Pinaceae) submitted to static and dynamic wind loads is simulated with a finite element model. The branching structure is represented in three dimensions. Factor effects are evaluated using a fractional experimental design. Results show that material properties play only a limited role in tree dynamics. In contrast, small morphological variations can produce extreme behaviors such as either very little or nearly critical dissipation of stem oscillations. Slender trees are shown to be relatively more vulnerable to stem breakage than uprooting. Dynamic loading leads to deflections and forces up to 20% higher near the base of the tree than those calculated for a static loading of similar magnitude. Effects of branch geometry on dynamic amplification are substantial yet not linear. The flexibility of the aerial system is found to be critical to reducing the resistance to the airflow and thus to minimizing the risk of failure.     

广义模型及分类回归树在物种分布模拟中的应用与比较

比较3个应用较广的模拟物种地理分布模型:广义线性模型(GLM)、广义加法模型(GAM)与分类回归树(CART)对中国树种地理分布模拟的优劣,以提出更为合适的模拟物种地理分布模型,并用于预测气候变化对物种地理分布的影响。3个模型对中国15种树种地理分布的模拟研究表明:除对油松、辽东栎分布的模拟精度稍差外,对其余树种分布的模拟精度均较高,其中以GAM模型最好。结合地理信息系统(GIS),比较分析了这3个模型对青冈、木荷、红松和油松4种树种的地理分布模拟效果,结果亦表明:这3个模型均能很好模拟青冈和木荷的地理分布,而GLM模型对红松分布的模拟结果不太理想,3个模型对油松分布的模拟结果均不甚理想,其中以GLM模型最差。基于3个模型对未来气候变化下青冈与蒙古栎地理分布的预测表明:GLM模型与GAM模型对青冈分布的预测结果较为接近,青冈在未来气候变化情景下向西和向北扩展,而CART模型预测青冈在未来气候变化情景下除有向西、向北扩展趋势外,广东和广西南部的青冈分布区将消失;3个模型均预测蒙古栎在未来气候变化情景下向西扩展,扩展面积的大小为:模型的模拟面积>模型>模型。     

森林的风/雪灾害研究综述

风／雪灾害不仪极大影响木材生产,同时对森林生态系统的稳定性也造成很大影响。森林风／雪危害的主要类型有树干弯曲、干(冠)折、掘根以及后续危害等;其发生主要依赖于气象条件、立地因子、树木和林分特征及其之间的相互作用。其中．林木尖削度(胸径／树高)和林分结构特征(树种、组成、密度等)是控制树木和林分对风／雪荷载抵抗的主要特征量。因此．通过造林、调整林分结构．加强林分管理如间伐、施肥等措施一直是用来减少林木的风／雪灾害的主要措施。另外．林木或林分发生风／雪害的模型分析研究也取得了很大进展,但由于森林风／雪害受诸如地形、天气等多种因素影响、目前所建立的模型系统在实际应用中普适性较芹。通过综述以往研究结果认为：在气象和立地条件难以控制的情况下．通过改变可控因子林分结构来减少森林风／雪害是可行的。因此．研究如何加强森林经营管理,尤其是不同形式的间伐技术和不同处理的造林措施与风／雪灾害发生的关系、如何增加林木和林分抵抗风／雪灾害的能力等是今后该研究领域的重点和难点。与此同时．应加强风／雪灾害危险率评估研究．进而对森林进行风／雪灾害危险率管理;并注重对受灾前后林地内生态效应的研究,以便为灾后的森林经营管理和调控提供坚实的理论依据。     

Analysing the mechanical performance and growth adaptation of Norway spruce using a non-linear finite-element model and experimental data

Thirteen Norway spruce [Picea abies (L.) Karst.] trees of different size, age, and social status, and grown under varying conditions, were investigated to see how they react to complex natural static loading under summer and winter conditions, and how they have adapted their growth to such combinations of load and tree state. For this purpose a non-linear finite-element model and an extensive experimental data set were used, as well as a new formulation describing the degree to which the exploitation of the bending stress capacity is uniform. The three main findings were: material and geometric non-linearities play important roles when analysing tree deflections and critical loads; the strengths of the stem and the anchorage mutually adapt to the local wind acting on the tree crown in the forest canopy; and the radial stem growth follows a mechanically high-performance path because it adapts to prevailing as well as acute seasonal combinations of the tree state (e.g. frozen or unfrozen stem and anchorage) and load (e.g. wind and vertical and lateral snow pressure). Young trees appeared to adapt to such combinations in a more differentiated way than older trees. In conclusion, the mechanical performance of the Norway spruce studied was mostly very high, indicating that their overall growth had been clearly influenced by the external site- and tree-specific mechanical stress.     

phastSim: Efficient simulation of sequence evolution for pandemic-scale datasets

Sequence simulators are fundamental tools in bioinformatics, as they allow us to test data processing and inference tools, and are an essential component of some inference methods. The ongoing surge in available sequence data is however testing the limits of our bioinformatics software. One example is the large number of SARS-CoV-2 genomes available, which are beyond the processing power of many methods, and simulating such large datasets is also proving difficult. Here, we present a new algorithm and software for efficiently simulating sequence evolution along extremely large trees (e.g. > 100, 000 tips) when the branches of the tree are short, as is typical in genomic epidemiology. Our algorithm is based on the Gillespie approach, and it implements an efficient multi-layered search tree structure that provides high computational efficiency by taking advantage of the fact that only a small proportion of the genome is likely to mutate at each branch of the considered phylogeny. Our open source software allows easy integration with other Python packages as well as a variety of evolutionary models, including indel models and new hypermutability models that we developed to more realistically represent SARS-CoV-2 genome evolution.     

Mechanistic models of seed dispersal by wind

Over the past century, various mechanistic models have been developed to estimate the magnitude of seed dispersal by wind, and to elucidate the relative importance of physical and biological factors affecting this passive transport process. The conceptual development has progressed from ballistic models, through models incorporating vertically variable mean horizontal windspeed and turbulent excursions, to models accounting for discrepancies between airflow and seed motion. Over hourly timescales, accounting for turbulent fluctuations in the vertical velocity component generally leads to a power-law dispersal kernel that is censored by an exponential cutoff far from the seed source. The parameters of this kernel vary with the flow field inside the canopy and the seed terminal velocity. Over the timescale of a dispersal season, with mean wind statistics derived from an “extreme-value” distribution, these distribution-tail effects are compounded by turbulent diffusion to yield seed dispersal distances that are two to three orders of magnitude longer than the corresponding ballistic models. These findings from analytic models engendered explicit simulations of the effects of turbulence on seed dispersal using computationally intensive fluid dynamics tools. This development marks a bifurcation in the approaches to wind dispersal, seeking either finer resolution of the dispersal mechanism at the scale of a single dispersal event, or mechanistically derived analytical dispersal kernels needed to resolve long-term and large-scale processes such as meta-population dynamics and range expansion. Because seed dispersal by wind is molded by processes operating over multiple scales, new insights will require novel theoretical tactics that blend these two approaches while preserving the key interactions across scales.     

Effect of plant interaction on wind-induced crop motion

Plant motion due to wind affects plant growth, a phenomenon called thigmomorphogenesis. Despite intensive studies of the turbulence over plant canopies, the study of plant motion induced by wind has often been limited to individual trees or cereal plants. Few models of global canopy motions are available. Moreover the numerical analysis of models that are based on individual stems becomes time consuming when dealing with crops. A model of motion within the canopies is proposed here using a wave propagation equation within a homogenized continuous medium, and a forcing function representing turbulent gusts advected over the canopy. This model is derived from a discrete model of a set of plant shoots represented as individual oscillators, including elastic contacts between shoots. Such contacts induce nonlinearities into the wave equation. A new experimental method to measure stem dynamical properties and elastic collision properties is presented with an illustration on alfalfa stems. Results obtained modeling plant motions in an alfalfa crop are presented.     

A Neuro-Muscular Elasto-Dynamic Model of the Human Arm Part 1: Model Development

In this paper we develop an elasto-dynamic model of the human arm for use in neuro-muscular control and dynamic interactionstudies.The motivation for this work is to present a case for developing and using non-quasistatic models of humanmusculo-skeletal biomechanics.The model is based on hybrid parameter multiple body system(HPMBS)variational projectionprinciples.In this paper,we present an overview of the HPMBS variational principle applied to the full elasto-dynamic model ofthe arm.The generality of the model allows one to incorporate muscle effects as either loads transmitted through the tendon atpoints of origin and insertion or as an effective torque at a joint.Though the technique is suitable for detailed bone and jointmodeling,we present in this initial effort only simple geometry with the bones discretized as Rayleigh beams with elongation,while allowing for large deflections.Simulations demonstrate the viability of the mcthod for use in the companion paper and infuture studies.     

Bending of apricot tree branches under the weight of axillary growth: test of a mechanical model with experimental data

Stem orientation is an important factor for fruit tree growth and branching habit since it influences fruit production as well as training practices. A mechanical model of the bending of a stem under axillary load was written and evaluated using experimental data on apricot trees (Prunus armeniaca L.). A set of 15 1-year-old stems of various shapes was observed during the early stage of the growing season when radial growth is still negligible and the loading of the stem increases considerably. The structural modulus of elasticity (MOE) of the stems was estimated through in situ bending tests assuming homogeneous material behaviour. The effect of viscoelasticity was observed through creep tests performed on similar stems during winter. Inputs of the model are initial shape, initial diameter, and final load, defined at various positions along the stem. The final shape was simulated based on different mechanical assumptions, and compared to observations. Assuming small deflections resulted in an underestimate of the mean slope variation of 48%, accounting for large displacements reduced this underestimate to 29% and accounting for viscoelasticity reduced it further to 14%. An adjustment of the structural MOE to fit the final shape led to an excellent fit of the data in most cases, the residual errors for some axes being attributed to material heterogeneity. The use of biomechanical models to predict the shape of fruit trees based on growth parameters, provided adequate assumptions are made, is discussed.     

Using models of pollen dispersal and deposition in hilly landscapes: Some possible approaches

Recent developments in the modelling of pollen dispersal and deposition have led to the development of user-friendly computer software for simulating vegetation mosaics and pollen assemblages at specified points within those mosaics. In this paper we discuss the possible application of these approaches to modelling the pollen deposition in mountain areas. First, we demonstrate the use of the Multiple Scenario Approach by reconstructing mid-Holocene tree line position in the southern Dark Peak area of the Peak District in northern England from a published pollen record. However, the underlying model of pollen dispersal and deposition assumes a flat landscape, which makes extending the approach to mountainous areas problematic. Therefore we also present simulation experiments exploring the effects of modifying aspects of the existing model to better simulate the montane situation (ecotone structure, changes in the wind rose to mimic the effects of topography on aerial pollen transport). We suggest that, as a first approximation, topographic effects can be incorporated in the models by varying the wind rose at the sampling point to reflect the impact of topography on air flow.     

Plant propagation fronts and wind dispersal: an analytical model to upscale from seconds to decades using superstatistics

Scale separation crossing many orders of magnitude is a consistent challenge in the ecological sciences. Wind dispersal of seed that generates plant propagation fronts is a typical case where timescales range from less than a second for fast turbulent processes to interannual timescales governing plant growth and climatic forcing. We show that the scale separation can be overcome by developing mechanistic and statistical links between processes at the different timescales. A mechanistic model is used to scale up from the turbulent regime to hourly timescales, while a superstatistical approach is used to relate the half-hourly timescales to annual vegetation migration speeds. We derive a semianalytical model to predict vegetation front movement as a function of wind-forcing statistics and characteristics of the species being dispersed. This model achieves better than order-of-magnitude agreement in a case study of tree dispersal from the early Holocene, a marked improvement over diffusion models. Plant migration is shown to depend nonlinearly on the wind environment forcing the movement but linearly on most physiological parameters. Applications of these analytical results to parameterizing models of plant dispersion and the implications of the superstatistical approach for addressing other ecological problems plagued by similar "dimensionality curses" are outlined.     

种子的长距离风传播模型研究进展

 植物种子的长距离传播在物种迁移、生物入侵、保护生物学等领域有重要的生态和进化意义。种子传播有很多方式,开阔草原等地区的草本植物和许多热带和温带的树木都是通过风传播种子的。风传播的方式最适合进行种子长距离传播现象的模拟研究。种子的风传播模型是传播生态研究的一个重要领域,尤其是种子的长距离风传播模型,对于外来入侵植物的扩散和破碎化景观中植物种群的基因交流等生态过程研究举足轻重,然而国内鲜见这方面的研究成果。本文综述了种子长距离风传播现象研究的背景和意义,分析了风传播种子模型的基本形式和构成原理,并分别就现象模型和机理模型的相关研究进展进行了总结,同时指出了未来发展的几个重要方向。种子的风传播模型可以分为现象模型和机理模型两类,现象模型按种子传播核心的形式包括短尾模型、偏峰长尾模型和混合传播核心模型,后两者对于长距离传播数据的模拟可以取得很好的效果。机理模型按照模拟机制可分为欧拉对流扩散模型和拉格郎日随机模型两类。本文重点介绍了种子的长距离风传播现象的形成机理和两类机理模型的参数构成和处理方式。适合种子脱落的天气和适合传播的天气的同步性可能是形成种子长距离风传播的一个重要前提,林缘和地表存在的上升气流及大风和暴风中形成的速度梯度都可能对于种子的长距离传播有重要的作用。机理模型的操作因子主要包括生物方面的因子、气象方面的因子和地形方面的因子。同时对目前几个应用比较成功的机理模型进行了简要的介绍和评价,包括倾斜羽毛模型、对流-扩散-下降模型、无掩蔽模型、背景模型、WINDISPER及其改进模型和PAPPUS模型。最后指出,目前在风传播种子的长距离模型研究中,对草本植物种子的传播模拟的投入明显不如树木种子的长距离传播模拟,对于破碎化景观中种子长距离的风传播的研究还存在很大的差距,而对提高机理模型预测能力的高分辨率物理环境数据输入技术的需求则为多学科交叉提供了很好的机会。     

Environmental variability and the initiation of dispersal: turbulence strongly increases seed release

Dispersal is a critical process in ecology. It is an important biological driver of, for example, invasions, metapopulation dynamics, spatial pattern formation and pathogen movement. Much is known about the effect of environmental variability, including turbulence, on dispersal of diaspores. Here, we document experimentally the strong but under-explored influence of turbulence on the initiation of dispersal. Flower heads of two thistle species (Carduus nutans and Carduus acanthoides) with ripe seeds were exposed to series of laminar and turbulent air flows of increasing velocity in a wind tunnel. Seed release increased with wind speeds for both laminar and turbulent flows for both species. However, far more seeds were released, at significantly lower wind speeds, during turbulent flows. These results strongly suggest a need for more quantitative studies of abscission in the field, as well as dispersal models that incorporate variability in the diaspore release phase.     

GenNon-h: Generating multiple sequence alignments on nonhomogeneous phylogenetic trees

ABSTRACT: BACKGROUND: A number of software packages are available to generate DNA multiple sequence alignments (MSAs) evolved under continuous-time Markov processes on phylogenetic trees. On the other hand, methods of simulating the DNA MSA directly from the transition matrices do not exist. Moreover, existing software restricts to the time-reversible models and it is not optimized to generate nonhomogeneous data (i.e. placing distinct substitution rates at different lineages). RESULTS: We present the first package designed to generate MSAs evolving under discrete-time Markov processes on phylogenetic trees, directly from probability substitution matrices. Based on the input model and a phylogenetic tree in the Newick format (with branch lengths measured as the expected number of substitutions per site), the algorithm produces DNA alignments of desired length. GenNon-h is publicly available for download. CONCLUSION: The software presented here is an efficient tool to generate DNA MSAs on a given phylogenetic tree. GenNon-h provides the user with the nonstationary or nonhomogeneous phylogenetic data that is well suited for testing complex biological hypotheses, exploring the limits of the reconstruction algorithms and their robustness to such models.     

Bayesian Approach to MSD-Based Analysis of Particle Motion in Live Cells

Quantitative tracking of particle motion using live-cell imaging is a powerful approach to understanding the mechanism of transport of biological molecules, organelles, and cells. However, inferring complex stochastic motion models from single-particle trajectories in an objective manner is nontrivial due to noise from sampling limitations and biological heterogeneity. Here, we present a systematic Bayesian approach to multiple-hypothesis testing of a general set of competing motion models based on particle mean-square displacements that automatically classifies particle motion, properly accounting for sampling limitations and correlated noise while appropriately penalizing model complexity according to Occam's Razor to avoid over-fitting. We test the procedure rigorously using simulated trajectories for which the underlying physical process is known, demonstrating that it chooses the simplest physical model that explains the observed data. Further, we show that computed model probabilities provide a reliability test for the downstream biological interpretation of associated parameter values. We subsequently illustrate the broad utility of the approach by applying it to disparate biological systems including experimental particle trajectories from chromosomes, kinetochores, and membrane receptors undergoing a variety of complex motions. This automated and objective Bayesian framework easily scales to large numbers of particle trajectories, making it ideal for classifying the complex motion of large numbers of single molecules and cells from high-throughput screens, as well as single-cell-, tissue-, and organism-level studies.     

Bayesian Decision Tree for the Classification of the Mode of Motion in Single-Molecule Trajectories

Membrane proteins move in heterogeneous environments with spatially (sometimes temporally) varying friction and with biochemical interactions with various partners. It is important to reliably distinguish different modes of motion to improve our knowledge of the membrane architecture and to understand the nature of interactions between membrane proteins and their environments. Here, we present an analysis technique for single molecule tracking (SMT) trajectories that can determine the preferred model of motion that best matches observed trajectories. The method is based on Bayesian inference to calculate the posteriori probability of an observed trajectory according to a certain model. Information theory criteria, such as the Bayesian information criterion (BIC), the Akaike information criterion (AIC), and modified AIC (AICc), are used to select the preferred model. The considered group of models includes free Brownian motion, and confined motion in 2nd or 4th order potentials. We determine the best information criteria for classifying trajectories. We tested its limits through simulations matching large sets of experimental conditions and we built a decision tree. This decision tree first uses the BIC to distinguish between free Brownian motion and confined motion. In a second step, it classifies the confining potential further using the AIC. We apply the method to experimental Clostridium Perfingens -toxin (CPT) receptor trajectories to show that these receptors are confined by a spring-like potential. An adaptation of this technique was applied on a sliding window in the temporal dimension along the trajectory. We applied this adaptation to experimental CPT trajectories that lose confinement due to disaggregation of confining domains. This new technique adds another dimension to the discussion of SMT data. The mode of motion of a receptor might hold more biologically relevant information than the diffusion coefficient or domain size and may be a better tool to classify and compare different SMT experiments.     

Analysis and simulation of dynamic response behavior of Scots pine trees to wind loading

This paper presents an empirical approach for the decomposition, simulation, and reconstruction of wind-induced stem displacement of plantation-grown Scots pine trees. Results from singular spectrum analysis (SSA) allow a low-dimensional characterization of the complex and complicated tree motion patterns in response to non-destructive wind excitation. Since motion of the sample trees was dominated by sway in the first mode, the application of SSA on time series of sample trees’ stem displacement yielded characteristic and distinguishable non-oscillatory trend components, quasi-oscillatory sway, and noise, of which only the non-oscillatory components were correlated directly with wind characteristics. Although sway in the range of the dominant damped fundamental frequency dominated the measured stem displacement signals, it was almost decoupled from near-surface airflow. The ability to discriminate SSA-components is demonstrated based on correlation and spectral analysis. These SSA-components, as well as wind speed measured in the canopy space of the Scots pine forest, were used to train neural networks, which could then reasonably simulate tree response to wind excitation.     

From individuals to aggregations: the interplay between behavior and physics

This paper analyses the processes by which organisms form groups and how social forces interact with environmental variability and transport. For aquatic organisms, the latter is especially important-will sheared or turbulent flows disrupt organism groups? To analyse such problems, we use individual-based models to study the environmental and social forces leading to grouping. The models are then embedded in turbulent flow fields to gain an understanding of the interplay between the forces acting on the individuals and the transport induced by the fluid motion. Instead of disruption of groups, we find that flows often enhance grouping by increasing the encounter rate among groups and thereby promoting merger into larger groups; the effect breaks down for strong flows. We discuss the transformation of individual-based models into continuum models for the density of organisms. A number of subtle difficulties arise in this process; however, we find that a direct comparison between the individual model and the continuum model is quite favorable. Finally, we examine the dynamics of group statistics and give an example of building an equation for the spatial and temporal variations of the group-size distribution from the individual-based simulations. These studies lay the groundwork for incorporating the effects of grouping into models of the large scale distributions of organisms as well as for examining the evolutionary consequences of group formation.     

Disk-covering, a fast-converging method for phylogenetic tree reconstruction.

The evolutionary history of a set of species is represented by a phylogenetic tree, which is a rooted, leaf-labeled tree, where internal nodes represent ancestral species and the leaves represent modern day species. Accurate (or even boundedly inaccurate) topology reconstructions of large and divergent trees from realistic length sequences have long been considered one of the major challenges in systematic biology. In this paper, we present a simple method, the Disk-Covering Method (DCM), which boosts the performance of base phylogenetic methods under various Markov models of evolution. We analyze the performance of DCM-boosted distance methods under the Jukes-Cantor Markov model of biomolecular sequence evolution, and prove that for almost all trees, polylogarithmic length sequences suffice for complete accuracy with high probability, while polynomial length sequences always suffice. We also provide an experimental study based upon simulating sequence evolution on model trees. This study confirms substantial reductions in error rates at realistic sequence lengths.