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).     

Comparison of an optimal regression method for climate reconstruction with the compare_methods() function from the dendroTools R package

The selection of a regression technique for climate reconstruction may have an important impact on reconstructed values. In this paper, we introduce the compare_methods() function from the dendroTools R package. This function compares different regression algorithms and returns validation results for each. In addition to mean validation metrics and ranks derived from these, transfer functions should have a key role in the evaluation of different regression algorithms. These are also returned as the output of compare_methods(). Our methodology is introduced on two case studies, one using a mean vessel area (MVA) chronology and one using a standardised tree-ring width (TRW) chronology. The nonlinear machine learning methods compared in our study provided relatively small (if any) improvements in terms of explaining climatic variance. However, they do offer different treatments of extreme values, and if providing more plausible climate reconstructions, this could make them a useful tool for climate reconstruction. We propose the use of the compare_methods() function as a standard methodological check before performing climate reconstruction.     

dendrometeR: Analyzing the pulse of trees in R

Dendrometers are measurement devices proven to be useful to analyze tree water relations and growth responses in relation to environmental variability. To analyze dendrometer data, two analytical methods prevail: (1) daily approaches that calculate or extract single values per day, and (2) stem-cycle approaches that separate high-resolution dendrometer records into distinct phases of contraction, expansion and stem-radius increment. Especially the stem-cycle approach requires complex algorithms to disentangle cyclic phases. Here, we present an R package, named dendrometeR, that facilitates the analysis of dendrometer data using both analytical methods. By making the package freely available, we make a first step towards comparable and reproducible methods to analyze dendrometer data. The package contains customizable functions to prepare, verify, process and plot dendrometer series, as well as functions that facilitate the analysis of dendrometer data (i.e. daily statistics or extracted phases) in relation to environmental data. The functionality of dendrometeR is illustrated in this note.     

Quantitative set analysis for gene expression: a method to quantify gene set differential expression including gene-gene correlations

Enrichment analysis of gene sets is a popular approach that provides a functional interpretation of genome-wide expression data. Existing tests are affected by inter-gene correlations, resulting in a high Type I error. The most widely used test, Gene Set Enrichment Analysis, relies on computationally intensive permutations of sample labels to generate a null distribution that preserves gene–gene correlations. A more recent approach, CAMERA, attempts to correct for these correlations by estimating a variance inflation factor directly from the data. Although these methods generate P-values for detecting gene set activity, they are unable to produce confidence intervals or allow for post hoc comparisons. We have developed a new computational framework for Quantitative Set Analysis of Gene Expression (QuSAGE). QuSAGE accounts for inter-gene correlations, improves the estimation of the variance inflation factor and, rather than evaluating the deviation from a null hypothesis with a P-value, it quantifies gene-set activity with a complete probability density function. From this probability density function, P-values and confidence intervals can be extracted and post hoc analysis can be carried out while maintaining statistical traceability. Compared with Gene Set Enrichment Analysis and CAMERA, QuSAGE exhibits better sensitivity and specificity on real data profiling the response to interferon therapy (in chronic Hepatitis C virus patients) and Influenza A virus infection. QuSAGE is available as an R package, which includes the core functions for the method as well as functions to plot and visualize the results.     

Individual and time-varying tree-ring growth to climate sensitivity of Pinus tabuliformis Carr. and Sabina przewalskii Kom. in the eastern Qilian Mountains, China

Individual tree-ring width chronologies and mean chronologies from Pinus tabuliformis Carr. (Chinese pine) and Sabina przewalskii Kom. (Qilian juniper) tree cores were collected and analyzed from two sites in the eastern Qilian Mountains of China. The chronologies were used to analyze individual and time-varying tree-ring growth to climate sensitivity with monthly mean air temperature and total precipitation data for the period 1958–2008. Climate–growth relationships were assessed with correlation functions and their stationarity and consistency over time were measured using moving correlation analysis. Individuals’ growth–climate correlations suggested increased percentages of individuals are correlated with certain variables (e.g., current June temperature at the P. tabuliformis site; previous June, December and current May temperature and May precipitation at the S. przewalskii site). These same climatic variables also correspond to the mean chronology correlations. A decreased percentage of individuals correlated with these climatic variables indicates a reduced sensitivity of the mean chronology. Moving correlation analysis indicated a significant change over time in the sensitivity of trees to climatic variability. Our results suggested: (1) that individual tree analysis might be a worthwhile tool to improve the quality and reliability of the climate signal from tree-ring series for dendroclimatology research; and (2) time-dependent fluctuations of climate growth relationships should be taken into account when assessing the quality and reliability of reconstructed climate signals.     

COPASutils: An R Package for Reading,Processing, and Visualizing Data from COPAS Large-Particle Flow Cytometers

The R package COPASutils provides a logical workflow for the reading, processing, and visualization of data obtained from the Union Biometrica Complex Object Parametric Analyzer and Sorter (COPAS) or the BioSorter large-particle flow cytometers. Data obtained from these powerful experimental platforms can be unwieldy, leading to difficulties in the ability to process and visualize the data using existing tools. Researchers studying small organisms, such as Caenorhabditis elegans, Anopheles gambiae, and Danio rerio, and using these devices will benefit from this streamlined and extensible R package. COPASutils offers a powerful suite of functions for the rapid processing and analysis of large high-throughput screening data sets.     

fullfact: an R package for the analysis of genetic and maternal variance components from full factorial mating designs

Full factorial breeding designs are useful for quantifying the amount of additive genetic, nonadditive genetic, and maternal variance that explain phenotypic traits. Such variance estimates are important for examining evolutionary potential. Traditionally, full factorial mating designs have been analyzed using a two‐way analysis of variance, which may produce negative variance values and is not suited for unbalanced designs. Mixed‐effects models do not produce negative variance values and are suited for unbalanced designs. However, extracting the variance components, calculating significance values, and estimating confidence intervals and/or power values for the components are not straightforward using traditional analytic methods. We introduce fullfact – an R package that addresses these issues and facilitates the analysis of full factorial mating designs with mixed‐effects models. Here, we summarize the functions of the fullfact package. The observed data functions extract the variance explained by random and fixed effects and provide their significance. We then calculate the additive genetic, nonadditive genetic, and maternal variance components explaining the phenotype. In particular, we integrate nonnormal error structures for estimating these components for nonnormal data types. The resampled data functions are used to produce bootstrap‐t confidence intervals, which can then be plotted using a simple function. We explore the fullfact package through a worked example. This package will facilitate the analyses of full factorial mating designs in R, especially for the analysis of binary, proportion, and/or count data types and for the ability to incorporate additional random and fixed effects and power analyses.     

Dendrochronological potential of the Azorean endemic gymnosperm Juniperus brevifolia (Seub.) Antoine

Tree-ring inter-annual pattern variation is crucial in dendrochronology, allowing the identification of possible limiting factors on growth. Thus, trees exposed to subtropical or tropical climates without a marked seasonality may show a low degree of interannual variation, impeding a straightforward dendroclimatological approach. Meanwhile, subtropical regions, and areas in transitional climates such as the Azores archipelago, are widely unexplored in terms of dendroclimatology, providing opportunities to work with endemic trees, including the dominant Azorean tree Juniperus brevifolia (Seub.) Antoine. To evaluate the dendrochronological potential of J. brevifolia, we analyzed tree-ring patterns, crossdating capabilities, and correlation with climate parameters. We sampled 48 individual trees from two natural populations (São Miguel and Terceira islands) using an increment borer. Besides, a Trephor tool was used to obtain wood microcores for micro-anatomical analysis. Although the transition between early and latewood was evident, partially indistinct ring boundaries and wedging rings were present in some cases, affecting the crossdating process, but not impeding the establishment of reliable ring-width chronologies. Following detrending, master chronologies were built and correlated with monthly temperature and precipitation data using the treeclim R package. The climate-growth relationships indicated negative correlations with late summer temperature in both populations. Considering our results and the importance of J. brevifolia as a dominant tree in the Azores natural forests, we conclude that it shows an acceptable potential for dendrochronological research. Thus, this study provides baseline information to help fill the knowledge gap regarding the climate-growth relationship of Azorean trees.     

Different climate response of three tree ring proxies of Pinus sylvestris from the Eastern Carpathians,Romania

The aim of this study was to compare the climatic responses of three tree rings proxies: tree ring width (TRW), maximum latewood density (MXD), and blue intensity (BI). For this study, 20 cores of Pinus sylvestris covering the period 1886–2015 were extracted from living non-damaged trees from the Eastern Carpathian Mountains (Romania). Each chronology was compared to monthly and daily climate data. All tree ring proxies had a stronger correlation with the daily climate data compared to monthly data. The highest correlation coefficient was obtained between the MXD chronology and daily maximum temperature over the period beginning with the end of July and ending in the middle of September (r = 0.64). The optimal intervals for the temperature signature were 01 Aug – 24 Sept for the MXD chronology, 05 Aug – 25 Aug for the BI chronology, and both 16 Nov of the previous year – 16 March of the current year and 15 Apr – 05 May for the TRW chronology. The results from our study indicate that MXD can be used as a proxy indicator for summer maximum temperature, while TRW can be used as a proxy indicator for just March maximum temperature. The weak and unstable relationship between BI and maximum temperature indicates that BI is not a good proxy indicator for climate reconstructions over the analysed region.     

“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.     

Applying climwin to dendrochronology: A breakthrough in the analyses of tree responses to environmental variability

Observational, correlative approaches are one of the backbones of dendrochronology. For instance, climate-growth relationships are usually quantified by calculating Pearson correlations. However, the ability to detect these relationships and the probability of declaring significant correlations by chance pose multiple challenges to such correlative framework. The R climwin package, developed a few years ago within the discipline of animal ecology, overcomes these limitations. In this paper we apply climwin to study relationships between climate and tree-ring widths and anatomy to show the advantages of using this package in the field of dendrochronology. This package allows calculating several models considering multiple windows relating a response variable to the climatic factors at different time resolutions. Then, the most parsimonious model is selected through an information-theoretic approach and randomization tests are computed to establish the significance of the selected model. We compare analyses based on Pearson correlations with climwin results using several environmental drivers (climate variables, drought indices, river flow), response variables (tree-ring width, tracheid lumen area and cell-wall thickness), and tree species from ecologically contrasting sites (cold- and water-limited conifers, Mediterranean riparian ash forests). Analyses of climate-growth/anatomy relationships based on the use of climwin showed several advantages over simple Pearson correlations: (i) they did not depend on the use of arbitrary time intervals of fixed duration, (ii) they allowed reducing probabilities associated with type I and II errors, (iii) they resulted in more consistent findings, (iv) they increased the capacity to detect differences between sites or periods in a time series, and (v) they provided more explanatory power.     

Influence of climate on multiple tree-ring parameters of Pinus kesiya from Manipur,Northeast India

Tree-rings of Pinus kesiya from southern region of Manipur, Northeast India were used to develop chronologies of multiple tree-ring parameters that are: total-ring width (TRW), earlywood width (EW), latewood width (LW) and adjusted latewood (LWadj). The time span of these chronologies is 39 years (1980–2018 C.E.) and we compared their growth responses with monthly and daily climatic records. The comparison revealed a broadly consistent pattern of climate sensitivity with daily climate exhibiting higher correlation. The climate signals during pre-monsoon (March–May) were recorded in TRW and EW, whereas late-monsoon to post-monsoon climate signals were recorded in LW and LWadj. The spatial correlation analysis of tree-ring parameters and global sea surface temperature (SST) showed a positive relationship between tree growth with tropical Pacific Ocean and Indian Ocean during winter (December–February) and pre-monsoon (March–May) seasons. The LW and LWadj were also correlated with peak summer monsoon (July–August) SST over the tropical Pacific Ocean. IADFs observed in EW (E-IADF) were caused by dry and warm conditions during March–April. IADFs in LW (L-IADF) occurred due to a combination of enhanced rainfall and temperature during post-monsoon (October–November) season. Evidence of stand-specific micro-climatic conditions in the formation IADFs in this species was also found. This study showed that multiple parameters of P. kesiya provides a lucid understanding of climate response on its growth and can be considered as a proxy for studying sub-seasonal changes in past environmental conditions in longer records.     

The NUCOMBog R package for simulating vegetation,water, carbon and nitrogen dynamics in peatlands

Since peatlands store up to 30% of the global soil organic carbon, it is important to understand how these ecosystems will react to a change in climate and management. Process-based ecosystem models have emerged as important tools for predicting long-term peatland dynamics, but their application is often challenging because they require programming skills. In this paper, we present NUCOMBog, an R package of the NUCOM-Bog model (Heijmans et al. 2008), which simulates the vegetation, carbon, nitrogen and water dynamics of peatlands in monthly time steps. The package complements the model with appropriate functions, such as the calculation of net ecosystem exchange, as well as parallel functionality. As a result, the NUCOMBog R package provides a user-friendly tool for simulating vegetation and biogeochemical cycles/fluxes in peatlands over years/decades, under different management strategies and climate change scenarios, with the option to use all the in-built model analysis capabilities of R, such as plotting, sensitivity analysis or optimization.     

Growth ring response of two Atlantic Forest tree species pre- and post-dam operation in Southern Brazil

Hydropower plants are important sources of renewable energy, but the climatic impacts of their constructions remain poorly explored. Considering that tree growth analysis is a useful tool to identify environmental impacts, this study aimed at using climate records and tree-ring chronologies to understand possible local climate changes caused by the construction of a hydropower plant in the 1980s in the State of Paraná, Southern Brazil. Historical climatic data were obtained from the local meteorological station and surrounding municipalities and analyzed using ANOVA and means tests. The Pettitt test was additionally used to identify change-points in the meteorological data. Wood samples from a total of 60 trees from Araucaria angustifolia (Bertol.) Kuntze (Araucariaceae) and Cedrela fissilis Vell. (Meliaceae) were collected, and tree-ring chronologies were built using dendrochronological standard procedures. Chronologies for A. angustifolia and C. fissilis represented time periods from 1800 to 2016 and 1899–2015, respectively. Tree-ring growth responses to climatic variables were evaluated by adjusting generalized mixed linear models and the Spearman correlations. Our results evidenced that the hydropower plant altered the local climate, mostly influencing the hydrological cycle by increasing local rainfall, with monthly rain volumes being statistically higher than in other meteorological stations. Significant responses in the growth of A. angustifolia were found to be associated with the water level increase caused by the dam and of C. fissilis due to the increase in cloud cover.     

bioclim: An R package for bioclimatic classifications via adaptive water balance

bioclim is a software package in R language for bioclimatic classification based on the Type of Bioclimatic Regime approach, which combines climatic and soil properties to classify a region according to its suitability for plant vegetative activity. We present the software's operating modes, capabilities and limitations, including real-world usage examples. Using monthly temperature, precipitation, and field capacity as inputs, bioclim follows a straightforward workflow using three functions to compute: i) a comprehensive water balance describing water resource dynamics throughout the year; ii) a bioclimatic balance to estimate plant vegetative activity; and iii) a collection of bioclimatic intensities quantifying vegetative activity changes. The program uses the results of these functions to classify bioclimatic type at zonal, regional and local scales. The three functions' outputs can be calculated independently, strengthening the software's cross-disciplinary application potential, such as climatology and hydrology. bioclim uses numeric and raster formats as input data and contains highly flexible options for a wide range of purposes, from scientific research to end users' representations. The water and bioclimatic balance results can be presented in numerical, graphical, or cartographic forms.     

Effect of sampling effort on the regional chronology statistics and climate–growth relationships estimation

Regional chronology and climate–growth relationships assessment are known to be sensitive to sampling effort. To disentangle the respective benefits of increasing whether the number of plots or the number of trees per plot when investigating climate–growth relationships under temperate conditions, we propose to simulate samples from a set of 84 plots established in plantations of Corsican pine (Pinus nigra Arnold ssp. laricio Poiret var. Corsicana), within which 10 dominant trees were cored in 1992. The effect of sampling effort was investigated through 20 modalities of sampled plots (from 3 to 84) and 10 modalities of sampled trees per plot (from 1 to 10). Regional chronology was studied using the mean effective correlation and the expressed population signal, while climate–growth relationships were evaluated through correlation functions. The calculation of the correlation functions also allowed testing the effect of sampling effort on various climatic regressors presenting different climate–growth correlation strengths. The accuracy of the dendroecological investigations increased with increasing sample size: estimating climate sensitivity with a regional chronology built from a small sample led to a general under-estimation of the climate–growth correlations. Decreasing sample size also increased the risk of estimating (i) “false” non-significant correlations for the most influencing climatic regressors, and (ii) “false” significant correlations for the less influencing ones. Increasing the number of plots was found of a greater interest than increasing the number of trees per plot to improve the climate–growth relationships assessment. Finally, the analysis revealed that the improvement of the expressed population signal with increasing sampling effort did not linearly mirror the bettering in the climate–growth correlations assessment.     

稻麦轮作FACE系统平台Ⅱ.系统控制和数据分析软件

稻麦轮作水稻田建立开放式空气CO2增加即FACE系统（Free Air C02 Enrichment)的管理、系统所测定及系统控制过程中每天都产生大量数据,用汇编语言和Visual BASIC语言编写的FACE数据采集控制和分析处理软件包能够自动操作任务并利用OLE技术开发Office应用程序的功能,具有系统平台控制、数据采集、原始数据存储备份、日数据处理、月数据处理和任意时间段的FACE系统控制状态分析等功能,FACE系统控制人员可以及时了解FACE系统的控制状态和改善控制精度,研究人员借助软件包可以随时便利地获取所需数据。     

affy--analysis of Affymetrix GeneChip data at the probe level

MOTIVATION: The processing of the Affymetrix GeneChip data has been a recent focus for data analysts. Alternatives to the original procedure have been proposed and some of these new methods are widely used. RESULTS: The affy package is an R package of functions and classes for the analysis of oligonucleotide arrays manufactured by Affymetrix. The package is currently in its second release, affy provides the user with extreme flexibility when carrying out an analysis and make it possible to access and manipulate probe intensity data. In this paper, we present the main classes and functions in the package and demonstrate how they can be used to process probe-level data. We also demonstrate the importance of probe-level analysis when using the Affymetrix GeneChip platform.     

On the influence of tree size on the climate–growth relationship of New Zealand kauri (Agathis australis): insights from annual, monthly and daily growth patterns

Many tree-ring-based climate reconstructions are based on the assumption that the climate reaction of trees is independent of their size. Here, we test this assumption for New Zealand kauri (Agathis australis), one of the longest tree ring-based proxies for the El Niño-Southern Oscillation (ENSO). The most recent kauri chronology contains a large amount of archaeological material, e.g. timber for which the original tree size is often unknown. We analyzed the climate–growth relationship of different-sized kauri in a pristine forest using different temporal scales, i.e. annually, monthly and daily data on tree growth and climate conditions. Trees of different life stages exhibited approximately the same seasonal growth peaks during austral spring (October and November). The dormancy period overlaps with the period where weekly air temperature maxima are below ca. 17–18 °C, and where the corresponding daily minima are below ca. 8 °C. However, both correlation functions between annual growth and seasonal climate as well as Kalman filter regressions between daily growth and climate conditions suggest an influence of tree size on the climate–growth relationship for kauri. Smaller trees (DBH < 40 cm) contain weaker climate signals than larger trees. Therefore, the precautionary stripping of near-pith material (first 20 cm) from kauri chronologies may result in more uniform responses to climate forcing and thus enhance the reliability of long-term climate reconstructions.