“dendRoAnalyst”: A tool for processing and analysing dendrometer data

Dendrometers are vital tools for studying the response of trees to intra-annual environmental changes in different temporal resolutions, ranging from hourly, daily to weekly time resolution. Dendrometers are increasingly used in forest management and tree physiological studies. Besides the data analysis, data processing is also challenging, time-consuming and potentially error-prone due to the immense number of measurements generated by self-registering electronic dendrometers. We present the package ‘dendRoAnalyst’ based on R statistical software to process and analyse dendrometer data using various approaches. This package offers algorithms for handling and pre-cleaning of dendrometer data before the application of subsequent data analytical steps. This includes identifying and erasing artefacts in dendrometer datasets not related to actual stem circumference change, identifying data gaps within records, and the possibility of change(s) in temporal resolution. Furthermore, the package can calculate different daily statistics of dendrometer data, including the daily amplitude of tree growth. The package dendRoAnalyst is therefore intended to facilitate researchers with a collection of functions for handling and analysing dendrometer data.     

dendroTools: R package for studying linear and nonlinear responses between tree-rings and daily environmental data

We introduce in this paper the dendroTools R package for studying the statistical relationships between tree-ring parameters and daily environmental data. The core function of the package is daily_response(), which works by sliding a moving window through daily environmental data and calculating statistical metrics with one or more tree ring proxies. Possible metrics are correlation coefficient, coefficient of determination and adjusted coefficient of determination. In addition to linear regression, it is possible to use a nonlinear artificial neural network with the Bayesian regularization training algorithm (brnn). dendroTools provides the opportunity to use daily climate data and robust nonlinear functions for the analysis of climate-growth relationships. Models should thus be better adapted to the real (continuous) growth of trees and should gain in predictive capabilities. The dendroTools R package is freely available in the CRAN repository. The functionality of the package is demonstrated on two examples, one using a mean vessel area (MVA) chronology and one a traditional tree-ring width (TRW).     

New features in the dendroTools R package: Bootstrapped and partial correlation coefficients for monthly and daily climate data

Climate-growth relationships are usually analysed using monthly climate data. The dendroTools R package also provides methodological approaches that enable climate-growth analysis for daily climate data. Such analysis reveals more complete climate signal patterns. In this article, new functions of the dendroTools R package are presented. Partial correlation coefficients are now implemented and can be used to calculate the strength of a linear relationship between two variables, while controlling for a third variable. Bootstrapped correlations can then be used to provide insights into the confidence intervals of statistical estimates. The calculation of partial and bootstrapped correlations is available for daily and monthly data. Finally, data transformation, S3 generic plotting and summary functions are also presented here.     

Two phases of seasonal stem radius variations of Sabina przewalskii Kom. in northwestern China inferred from sub-diurnal shrinkage and expansion patterns

In the National Natural Reserve of the Qilian Mountains, northwest China, automated dendrometers were installed on Qilian juniper (Sabina przewalskii Kom.) in 2010. Here, we present a complete 1-year data series of dendrometer measurements and synchronous meteorological records. The mean daily radius change index curve obtained by first-difference standardization was analyzed. The results indicate strong similarities of stem radius variations among the studied trees. By graphical inspection of sub-diurnal shrinkage and expansion patterns, seasonal stem radius variations could be divided into two general phases. During early winter to spring, daily amplitudes show strong fluctuations, and meteorological data indicate that both air and soil temperatures are positively related to stem radius swelling. From late March to early November 2011, stem radius expansion is negatively related to both air and soil temperatures, as well as to vapor pressure deficit, and positively related to precipitation and soil moisture. We found that the dividing lines between the two phases were strongly linked to the occurrence of a 0?°C daily mean air temperature, indicating that air temperature played an important effect on stem radius variations in this cold and arid environment. During the growing season in 2011, May and June are the most productive months.     

A methodological framework to optimize models predicting critical dates of xylem phenology based on dendrometer data

Dendrometer measurements are a frequently used alternative to the laborious and time consuming microcoring to investigate intra-annual growth dynamics of trees. However, since dendrometer data not only comprise cambial growth, but also hydrological fluctuations, both signals need to be disentangled to derive critical dates of xylem phenology from dendrometer data. For this purpose, various approaches can be found in the literature, however a systematic comparison of the different options is still missing. In this study we present a methodological framework to evaluate the accuracy of different mathematical fittings to derive tree-ring phenology from dendrometer data and apply this approach to a data set comprised of three conifer species where high-resolution band dendrometer measurements and microcore sampling have been done in parallel. Based on our study we provide evidence that the most common approaches to derive onset and cessation of xylem cell enlargement from dendrometer data, i.e. applying absolute and relative thresholds to deterministic models fitted to dendrometer data, caused systematic deviations from the reference of xylogenesis observations, but could be improved by small adaptations. The most precise and unbiased predictions of xylem growth phenology were obtained by fitting the three-parameter Gompertz model to the dendrometer data and applying a relative threshold of 3.5% of annual increment to the model predictions for growth onset and an absolute threshold of 4.5 µm day^-1 based on the first model derivative for growth cessation. Our framework enables an improved usage of dendrometer data for the prediction of the onset and cessation of xylem cell enlargement, which are important ecological indicators to quantify the effects environmental changes on forest growth and the terrestrial carbon cycle.     

Growth in stem diameter of Larix principis-rupprechtii and its response to meteorological factors in the south of Liupan Mountain, China

Research related to daily and seasonal pattern of stem growth of Larix principis-rupprechtii was carried out with the help of a dendrometer from June to September in 2005 in the Liupan Mountain, Ningxia, Northwestern China. The results indicated that daily fluctuation of stem diameter was rhythmic and it could be divided into three continuous phases: contraction, expansion and stem diameter growth when daily rainfall < 10 mm during the measurement period. Comparatively, it showed a different pattern compared with the former when daily rainfall ≥10 mm and in the subsequent days. Based on the work done by Deslauriers et al. a modified method was designed to calculate daily and cumulative stem growth, and it showed that seasonal pattern of cumulative stem growth was similar among five sample trees. From June and July, stem growth rate was quick and the values were in the range 27.0–44.2 μm per day. Relatively, they took on a slow growth rate from August to September, and the values were under 10 μm per day. And it also showed that there existed a significant difference in stem growth among sample trees, which could be as a result of the difference in tree domain and their positions in the stand. The relationship between daily stem growth and meteorological factors was studied by principle component analysis and partial correlation analysis, and the result indicated that the daily rainfall, daily minimum temperature, daily average solar radiation and vapor pressure deficit were four significant factors which determined the daily stem growth.     

occAssess: An R package for assessing potential biases in species occurrence data

Species occurrence records from a variety of sources are increasingly aggregated into heterogeneous databases and made available to ecologists for immediate analytical use. However, these data are typically biased, i.e. they are not a probability sample of the target population of interest, meaning that the information they provide may not be an accurate reflection of reality. It is therefore crucial that species occurrence data are properly scrutinised before they are used for research. In this article, we introduce occAssess, an R package that enables straightforward screening of species occurrence data for potential biases. The package contains a number of discrete functions, each of which returns a measure of the potential for bias in one or more of the taxonomic, temporal, spatial, and environmental dimensions. Users can opt to provide a set of time periods into which the data will be split; in this case separate outputs will be provided for each period, making the package particularly useful for assessing the suitability of a dataset for estimating temporal trends in species'' distributions. The outputs are provided visually (as ggplot2 objects) and do not include a formal recommendation as to whether data are of sufficient quality for any given inferential use. Instead, they should be used as ancillary information and viewed in the context of the question that is being asked, and the methods that are being used to answer it. We demonstrate the utility of occAssess by applying it to data on two key pollinator taxa in South America: leaf‐nosed bats (Phyllostomidae) and hoverflies (Syrphidae). In this worked example, we briefly assess the degree to which various aspects of data coverage appear to have changed over time. We then discuss additional applications of the package, highlight its limitations, and point to future development opportunities.     

Analysis of interval‐grouped data in weed science: The binnednp Rcpp package

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plusTipTracker: Quantitative image analysis software for the measurement of microtubule dynamics

Here we introduce plusTipTracker, a Matlab-based open source software package that combines automated tracking, data analysis, and visualization tools for movies of fluorescently-labeled microtubule (MT) plus end binding proteins (+TIPs). Although +TIPs mark only phases of MT growth, the plusTipTracker software allows inference of additional MT dynamics, including phases of pause and shrinkage, by linking collinear, sequential growth tracks. The algorithm underlying the reconstruction of full MT trajectories relies on the spatially and temporally global tracking framework described in Jaqaman et al. (2008). Post-processing of track populations yields a wealth of quantitative phenotypic information about MT network architecture that can be explored using several visualization modalities and bioinformatics tools included in plusTipTracker. Graphical user interfaces enable novice Matlab users to track thousands of MTs in minutes. In this paper, we describe the algorithms used by plusTipTracker and show how the package can be used to study regional differences in the relative proportion of MT subpopulations within a single cell. The strategy of grouping +TIP growth tracks for the analysis of MT dynamics has been introduced before (Matov et al., 2010). The numerical methods and analytical functionality incorporated in plusTipTracker substantially advance this previous work in terms of flexibility and robustness. To illustrate the enhanced performance of the new software we thus compare computer-assembled +TIP-marked trajectories to manually-traced MT trajectories from the same movie used in Matov et al. (2010).     

The forestecology R package for fitting and assessing neighborhood models of the effect of interspecific competition on the growth of trees

1. Neighborhood competition models are powerful tools to measure the effect of interspecific competition. Statistical methods to ease the application of these models are currently lacking.
2. We present the forestecology package providing methods to (a) specify neighborhood competition models, (b) evaluate the effect of competitor species identity using permutation tests, and (cs) measure model performance using spatial cross‐validation. Following Allen and Kim (PLoS One, 15, 2020, e0229930), we implement a Bayesian linear regression neighborhood competition model.
3. We demonstrate the package''s functionality using data from the Smithsonian Conservation Biology Institute''s large forest dynamics plot, part of the ForestGEO global network of research sites. Given ForestGEO’s data collection protocols and data formatting standards, the package was designed with cross‐site compatibility in mind. We highlight the importance of spatial cross‐validation when interpreting model results.
4. The package features (a) tidyverse‐like structure whereby verb‐named functions can be modularly “piped” in sequence, (b) functions with standardized inputs/outputs of simple features sf package class, and (c) an S3 object‐oriented implementation of the Bayesian linear regression model. These three facts allow for clear articulation of all the steps in the sequence of analysis and easy wrangling and visualization of the geospatial data. Furthermore, while the package only has Bayesian linear regression implemented, the package was designed with extensibility to other methods in mind.

     

popsicleR: A R Package for Pre-processing and Quality Control Analysis of Single Cell RNA-seq Data

The advent of single-cell sequencing is providing unprecedented opportunities to disentangle tissue complexity and investigate cell identities and functions. However, the analysis of single cell data is a challenging, multi-step process that requires both advanced computational skills and biological sensibility. When dealing with single cell RNA-seq (scRNA-seq) data, the presence of technical artifacts, noise, and biological biases imposes to first identify, and eventually remove, unreliable signals from low-quality cells and unwanted sources of variation that might affect the efficacy of subsequent downstream modules. Pre-processing and quality control (QC) of scRNA-seq data is a laborious process consisting in the manual combination of different computational strategies to quantify QC-metrics and define optimal sets of pre-processing parameters.Here we present popsicleR, a R package to interactively guide skilled and unskilled command line-users in the pre-processing and QC analysis of scRNA-seq data. The package integrates, into several main wrapper functions, methods derived from widely used pipelines for the estimation of quality-control metrics, filtering of low-quality cells, data normalization, removal of technical and biological biases, and for cell clustering and annotation. popsicleR starts from either the output files of the Cell Ranger pipeline from 10X Genomics or from a feature-barcode matrix of raw counts generated from any scRNA-seq technology. Open-source code, installation instructions, and a case study tutorial are freely available at https://github.com/bicciatolab/popsicleR.     

六盘山半湿润区华北落叶松树干半径变化特征及其影响因素

在2016年生长季,利用树干径向生长测量仪监测了六盘山半湿润区华北落叶松的树干半径变化,研究其昼夜和生长季的变化格局,确定主要生长期并分析期间环境因素对半径变化的影响,以期准确理解短时环境变化对树木生长的影响。结果表明: 在昼夜时间尺度,树干半径呈白天收缩、夜间恢复并增长的变化格局;在生长季时间尺度,树径变化呈相对稳定、持续增大、涨缩波动3个阶段的格局。在本研究特定的气象、土壤湿度和地形条件下,华北落叶松树干半径的主要生长期为5月14日—7月31日,最大半径生长速率出现在6月8日。影响树干半径日收缩量的环境因子在不同时段(收缩阶段、日、循环周期)基本一致,主要为温度(空气/土壤温度)、太阳辐射强度、饱和水汽压差和土壤含水量,且温度的贡献率最大(50.3%～71.0%)。而影响树干半径日增长量的因子存在时段差异,在增长阶段是降水量(贡献率86.9%)和最高气温(13.1%),在循环周期是降水量(50.3%)、饱和水汽压差(29.9%)、相对空气湿度(12.7%)和太阳辐射强度(7.1%),在整日阶段仅是降水量和太阳辐射强度。树径增量的环境响应在循环周期比在日时段更敏感。     

SynergyFinder Plus: Toward Better Interpretation and Annotation of Drug Combination Screening Datasets

Combinatorial therapies have been recently proposed to improve the efficacy of anticancer treatment. The Synergy Finder R package is a software used to analyze pre-clinical drug combination datasets. Here, we report the major updates to the Synergy Finder R package for improved interpretation and annotation of drug combination screening results. Unlike the existing implementations, the updated Synergy Finder R package includes five main innovations. 1) We extend the mathematical models to higher...     

Kinetic modeling and fitting software for interconnected reaction schemes: VisKin

Reaction kinetics for complex, highly interconnected kinetic schemes are modeled using analytical solutions to a system of ordinary differential equations. The algorithm employs standard linear algebra methods that are implemented using MatLab functions in a Visual Basic interface. A graphical user interface for simple entry of reaction schemes facilitates comparison of a variety of reaction schemes. To ensure microscopic balance, graph theory algorithms are used to determine violations of thermodynamic cycle constraints. Analytical solutions based on linear differential equations result in fast comparisons of first order kinetic rates and amplitudes as a function of changing ligand concentrations. For analysis of higher order kinetics, we also implemented a solution using numerical integration. To determine rate constants from experimental data, fitting algorithms that adjust rate constants to fit the model to imported data were implemented using the Levenberg-Marquardt algorithm or using Broyden-Fletcher-Goldfarb-Shanno methods. We have included the ability to carry out global fitting of data sets obtained at varying ligand concentrations. These tools are combined in a single package, which we have dubbed VisKin, to guide and analyze kinetic experiments. The software is available online for use on PCs.     

热带季节雨林多花白头树年内径向生长动态及其对环境因子的响应

为了解热带地区树木的季节性生长动态和规律,在西双版纳热带季节雨林利用高精度生长仪和微树芯法对落叶树种多花白头树的径向生长季节动态进行监测。结合木质部非结构性碳水化合物和环境因子的监测,分析其形成层活动和径向季节动态的生理生态驱动因子。结果表明: 在2020年,生长仪的监测显示,多花白头树于5月底(儒略日DOY:149.3±7.2)开始生长,8月底(DOY:241.0±14.7)生长结束,年生长量为3.12 mm,最大生长速率为0.04 mm·d^-1。而微树芯法显示,扩大细胞3月9日(DOY:69.2±6.2)开始出现,9月19日(DOY:262.8±2.8)细胞加厚结束,木质部生长量为1.76 mm,最大生长速率为0.009 mm·d^-1。多花白头树径向日生长量与生长季的降水、相对湿度、日最低气温、深度为20 cm的土壤含水量和温度呈显著正相关,而与日最高气温、水汽压亏缺、最大风速和水汽压呈显著负相关。多花白头树边材淀粉含量和可溶性糖含量均在生长季开始之前保持较高水平,淀粉含量在3月底达到最低,而可溶性糖含量5月中旬达到最低,随着生长季的结束淀粉和可溶性糖的含量分别在10月中旬和12月底达到最高。     

Separating water‐potential induced swelling and shrinking from measured radial stem variations reveals a cambial growth and osmotic concentration signal

The quantification of cambial growth over short time periods has been hampered by problems to discern between growth and the swelling and shrinking of a tree stem. This paper presents a model, which separates cambial growth and reversible water‐potential induced diurnal changes from simultaneously measured whole stem and xylem radial variations, from field‐measured Scots pine trees in Finland. The modelled growth, which includes osmotic concentration changes, was compared with (direct) dendrometer measurements and microcore samples. In addition, the relationship of modelled growth and dendrometer measurements to environmental factors was analysed. The results showed that the water‐potential induced changes of tree radius were successfully separated from stem growth. Daily growth predicted by the model exhibited a high correlation with the modelled daily changes of osmotic concentration in phloem, and a temperature dependency in early summer. Late‐summer growth saw higher dependency on water availability and temperature. Evaluation of the model against dendrometer measurements showed that the latter masked a true environmental signal in stem growth due to water‐potential induced changes. The model provides better understanding of radial growth physiology and offers potential to examine growth dynamics and changes due to osmotic concentration, and how the environment affects growth.     

The dynamical Matryoshka model: 1. Incoherent neutron scattering functions for lipid dynamics in bilayers

Fluid lipid bilayers are the building blocks of biological membranes. Although there is a large amount of experimental data using incoherent quasi-elastic neutron scattering (QENS) techniques to study membranes, very little theoretical works have been developed to study the local dynamics of membranes. The main objective of this work is to build a theoretical framework to study and describe the local dynamics of lipids and derive analytical expressions of intermediate scattering functions (ISF) for QENS. As results, we developed the dynamical Matryoshka model which describes the local dynamics of lipid molecules in membrane layers as a nested hierarchical convolution of three motional processes: (i) individual motions described by the vibrational motions of H-atoms; (ii) internal motions including movements of the lipid backbone, head groups and tails, and (iii) molecule movements of the lipid molecule as a whole. The analytical expressions of the ISF associated with these movements are all derived. For use in analyzing the QENS experimental data, we also derived an analytical expression for the aggregate ISF of the Matryoshka model which involves an elastic term plus three inelastic terms of well-separated time scales and whose amplitudes and rates are functions of the lipid motions. And as an illustrative application, we used the aggregated ISF to analyze the experimental QENS data on a lipid sample of multilamellar bilayers of DMPC (1,2-dimyristoyl-sn-glycero-3-phosphocholine). It is clear from this analysis that the dynamical Matryoshka model describes very well the experimental data and allow extracting the dynamical parameters of the studied system.     

samc: an R package for connectivity modeling with spatial absorbing Markov chains

Quantifying landscape connectivity is fundamental to better understand and predict how populations respond to environmental change. Currently, popular methods to quantify landscape connectivity emphasize how landscape features provide resistance to movement. While many tools are available to quantify landscape resistance, these do not discern between two fundamentally different sources of resistance: movement behavior and mortality. To address this issue, we developed the samc R package that quantifies landscape connectivity using absorbing Markov chain theory. Within this mathematical framework, movements are represented as transient states in the Markov chain, while mortality is represented by transitions to absorbing states. Not only does this framework explicitly account for these different issues, it provides a probabilistic approach that can incorporate both short-term and long-term dynamics, as well as species distribution and abundance. The package includes functions to quantify life expectancy, long-term visitation rates, and various spatially and temporally explicit measures of mortality and movement at the local and landscape scales. These functions in samc have been optimized to find computationally practical solutions in landscapes comprised of > 2 × 10⁶ cells. Here, we illustrate the workflow of the samc package with publicly available movement and mortality data on the endangered Florida panther Puma concolor coryi. This analysis showed that movement and mortality are generally correlated except for locations near roads (areas of high mortality risk) that are within the dispersal range of source locations. This pattern would have been undetectable with current methods that quantify movement resistance. Overall, the samc package provides a means for implementing spatial absorbing Markov chains that can distinguish between movement behavior and mortality resulting in more reliable landscape connectivity measures.     

signatureSearch: environment for gene expression signature searching and functional interpretation

signatureSearch is an R/Bioconductor package that integrates a suite of existing and novel algorithms into an analysis environment for gene expression signature (GES) searching combined with functional enrichment analysis (FEA) and visualization methods to facilitate the interpretation of the search results. In a typical GES search (GESS), a query GES is searched against a database of GESs obtained from large numbers of measurements, such as different genetic backgrounds, disease states and drug perturbations. Database matches sharing correlated signatures with the query indicate related cellular responses frequently governed by connected mechanisms, such as drugs mimicking the expression responses of a disease. To identify which processes are predominantly modulated in the GESS results, we developed specialized FEA methods combined with drug-target network visualization tools. The provided analysis tools are useful for studying the effects of genetic, chemical and environmental perturbations on biological systems, as well as searching single cell GES databases to identify novel network connections or cell types. The signatureSearch software is unique in that it provides access to an integrated environment for GESS/FEA routines that includes several novel search and enrichment methods, efficient data structures, and access to pre-built GES databases, and allowing users to work with custom databases.