Spatial and temporal changes in occupancy frequency distribution patterns of freshwater macrophytes in Finland

A useful method for characterizing biological numerous assemblages at regional scales is the species occupancy frequency distribution (SOFD). An SOFD shows the number or proportion of study sites each species occurred. Species that occur at only a few sites are termed satellite species, while species that occur at many sites are termed core species.This study is the first to document and assess SOFD patterns in aquatic macrophytes. It characterizes SOFD patterns of freshwater macrophyte assemblages in Finland at two spatial and two temporal scales. For this, I analyzed three published datasets on freshwater macrophyte distributions: two from studies conducted at a local scale and the third from large national surveys. One local study and the national study also included data on temporal variation in species occupancy frequencies.In the national study, the number of core and satellite species varied slightly between the older and the newer survey, respectively. Among the 113 waterbodies surveyed as part of the national study, the SOFD followed a unimodal satellite pattern. However, for the older dataset (from the 1930s), a bimodal symmetric pattern also fit the SOFD data well. At the local scale, I observed geographical variation in SOFD patterns. The dataset from southern Finland followed a unimodal satellite SOFD pattern; data from central Finland instead displayed a bimodal symmetric SOFD pattern, although they also fit equally well with a bimodal truncated pattern. Moreover, temporal patterns in central Finland seemed to demonstrate a shift from a bimodal symmetric to a bimodal asymmetric SOFD probably.Geographical variation in the SOFD pattern may be due to variation in the regional species pool. The temporal changes in SOFD pattern may be due to lake eutrophication and anthropogenic disturbance around waterbodies, which may increase number of macrophyte species.     

Genes@Work: an efficient algorithm for pattern discovery and multivariate feature selection in gene expression data

MOTIVATION: Despite the growing literature devoted to finding differentially expressed genes in assays probing different tissues types, little attention has been paid to the combinatorial nature of feature selection inherent to large, high-dimensional gene expression datasets. New flexible data analysis approaches capable of searching relevant subgroups of genes and experiments are needed to understand multivariate associations of gene expression patterns with observed phenotypes. RESULTS: We present in detail a deterministic algorithm to discover patterns of multivariate gene associations in gene expression data. The patterns discovered are differential with respect to a control dataset. The algorithm is exhaustive and efficient, reporting all existent patterns that fit a given input parameter set while avoiding enumeration of the entire pattern space. The value of the pattern discovery approach is demonstrated by finding a set of genes that differentiate between two types of lymphoma. Moreover, these genes are found to behave consistently in an independent dataset produced in a different laboratory using different arrays, thus validating the genes selected using our algorithm. We show that the genes deemed significant in terms of their multivariate statistics will be missed using other methods. AVAILABILITY: Our set of pattern discovery algorithms including a user interface is distributed as a package called Genes@Work. This package is freely available to non-commercial users and can be downloaded from our website (http://www.research.ibm.com/FunGen).     

Application of a disposable transparent filtration membrane to the infrared spectroscopic discrimination among bacterial species

This study describes the application of filtration, infrared spectroscopy, and multivariate analysis to the identification of 10 foodborne bacterial species. The bacteria were applied by filtration to a disposable optical membrane that is transparent to infrared radiation. The filtration step was rapid (2 min). Observed cellular infrared spectra were unique and were used to discriminate among the different species. A dataset for the 10 bacterial species investigated was successfully used to correctly identify unknowns included in the dataset.     

Body mass and foraging ecology predict evolutionary patterns of skeletal pneumaticity in the diverse "waterbird" clade

Extensive skeletal pneumaticity (air-filled bone) is a distinguishing feature of birds. The proportion of the skeleton that is pneumatized varies considerably among the >10,000 living species, with notable patterns including increases in larger bodied forms, and reductions in birds employing underwater pursuit diving as a foraging strategy. I assess the relationship between skeletal pneumaticity and body mass and foraging ecology, using a dataset of the diverse "waterbird" clade that encompasses a broad range of trait variation. Inferred changes in pneumaticity and body mass are congruent across different estimates of phylogeny, whereas pursuit diving has evolved independently between two and five times. Phylogenetic regressions detected positive relationships between body mass and pneumaticity, and negative relationships between pursuit diving and pneumaticity, whether independent variables are considered in isolation or jointly. Results are generally consistent across different estimates of topology and branch lengths. "Predictive" analyses reveal that several pursuit divers (loons, penguins, cormorants, darters) are significantly apneumatic compared to their relatives, and provide an example of how phylogenetic information can increase the statistical power to detect taxa that depart from established trait correlations. These findings provide the strongest quantitative comparative support yet for classical hypotheses regarding the evolution of avian skeletal pneumaticity.     

NEW TOOLS FOR STUDYING INTEGRATION AND MODULARITY

Abstract The study of phenotypic integration concerns the modular nature of organismal phenotypes. The concept provides a rationale for why certain subsets of phenotypic traits show particularly high levels of association over development and/or evolution. The techniques detailed in this report facilitate the generation and testing of hypotheses of phenotypic integration and trait interaction. The approach advocated for exploring patterns of interaction among traits is based on the statistical notion of conditional independence, incorporated in a technique known as graphical modeling. The use of graphical models is illustrated with an application to a well-known biological dataset of fowl skeletal measurements, previously analyzed by Sewall Wright. A definition of phenotypic modularity is given, based on a notion of mutual information, which provides a consistent criterion for recognizing and delimiting integrated subsets of traits and which can be related to standard models of multivariate selection.     

Estimating Urban Traffic Patterns through Probabilistic Interconnectivity of Road Network Junctions

The emergence of large, fine-grained mobility datasets offers significant opportunities for the development and application of new methodologies for transportation analysis. In this paper, the link between routing behaviour and traffic patterns in urban areas is examined, introducing a method to derive estimates of traffic patterns from a large collection of fine-grained routing data. Using this dataset, the interconnectivity between road network junctions is extracted in the form of a Markov chain. This representation encodes the probability of the successive usage of adjacent road junctions, encoding routes as flows between decision points rather than flows along road segments. This network of functional interactions is then integrated within a modified Markov chain Monte Carlo (MCMC) framework, adapted for the estimation of urban traffic patterns. As part of this approach, the data-derived links between major junctions influence the movement of directed random walks executed across the network to model origin-destination journeys. The simulation process yields estimates of traffic distribution across the road network. The paper presents an implementation of the modified MCMC approach for London, United Kingdom, building an MCMC model based on a dataset of nearly 700000 minicab routes. Validation of the approach clarifies how each element of the MCMC framework contributes to junction prediction performance, and finds promising results in relation to the estimation of junction choice and minicab traffic distribution. The paper concludes by summarising the potential for the development and extension of this approach to the wider urban modelling domain.     

Hotelling's T2 multivariate profiling for detecting differential expression in microarrays

The most widely used statistical methods for finding differentially expressed genes (DEGs) are essentially univariate. In this study, we present a new T(2) statistic for analyzing microarray data. We implemented our method using a multiple forward search (MFS) algorithm that is designed for selecting a subset of feature vectors in high-dimensional microarray datasets. The proposed T2 statistic is a corollary to that originally developed for multivariate analyses and possesses two prominent statistical properties. First, our method takes into account multidimensional structure of microarray data. The utilization of the information hidden in gene interactions allows for finding genes whose differential expressions are not marginally detectable in univariate testing methods. Second, the statistic has a close relationship to discriminant analyses for classification of gene expression patterns. Our search algorithm sequentially maximizes gene expression difference/distance between two groups of genes. Including such a set of DEGs into initial feature variables may increase the power of classification rules. We validated our method by using a spike-in HGU95 dataset from Affymetrix. The utility of the new method was demonstrated by application to the analyses of gene expression patterns in human liver cancers and breast cancers. Extensive bioinformatics analyses and cross-validation of DEGs identified in the application datasets showed the significant advantages of our new algorithm.     

Mantel test in population genetics

The comparison of genetic divergence or genetic distances, estimated by pairwise F_ST and related statistics, with geographical distances by Mantel test is one of the most popular approaches to evaluate spatial processes driving population structure. There have been, however, recent criticisms and discussions on the statistical performance of the Mantel test. Simultaneously, alternative frameworks for data analyses are being proposed. Here, we review the Mantel test and its variations, including Mantel correlograms and partial correlations and regressions. For illustrative purposes, we studied spatial genetic divergence among 25 populations of Dipteryx alata (“Baru”), a tree species endemic to the Cerrado, the Brazilian savannas, based on 8 microsatellite loci. We also applied alternative methods to analyze spatial patterns in this dataset, especially a multivariate generalization of Spatial Eigenfunction Analysis based on redundancy analysis. The different approaches resulted in similar estimates of the magnitude of spatial structure in the genetic data. Furthermore, the results were expected based on previous knowledge of the ecological and evolutionary processes underlying genetic variation in this species. Our review shows that a careful application and interpretation of Mantel tests, especially Mantel correlograms, can overcome some potential statistical problems and provide a simple and useful tool for multivariate analysis of spatial patterns of genetic divergence.     

Raw data graphing: an informative but under‐utilized tool for the analysis of multivariate abundances

Abstract In exploring the relationship between multivariate abundance data and environmental variables, a rarely used approach is to graph raw data separately for each different taxon. It is proposed that such raw data graphs become part of the standard toolset for graphing and analysing multivariate abundances. The key advantage of this approach is that axis scales have quantitative interpretations, enabling quantitative interpretation of patterns in abundance. In contrast, ordinations only present qualitative information. Ordinations are useful for inferring overall, qualitative patterns and raw data graphing is a complementary tool of greater use for answering more specific questions, aimed at a deeper understanding the ecology of a community. It is demonstrated using some well‐known examples that our understanding of the nature of associations can be considerably improved by using raw data graphs, even when only plotting a subset of variables. One example describes how an often‐cited dataset has been misinterpreted in key methodological papers, because data were interpreted from ordinations alone, with no consideration of plots of the raw data.     

DNA barcodes identify Chinese medicinal plants and detect geographical patterns of Sinosenecio (Asteraceae)

DNA barcodes have proved to be efficient for plants species discrimination and identification using short and standardized genomic regions. The genus Sinosenecio(Asteraceae) is used for traditional medicinal purposes in China. Most species of the genus occur in restricted geographical regions and exhibit a wide range of morphological variations within species, making them difficult to differentiate in the field. Previously, taxonomic revisions have been made based on morphological and cytological evidence. In the present study, barcoding analysis was performed on 107 individuals representing 38 species in this genus to evaluate the performance of four candidate barcoding loci (matK, rbcL, trnH-psbA and internal transcribed spacer [ITS]) and detect geographical patterns. Three different methods based on genetic distance, sequence similarity, and the phylogenetic tree were used. Comparably high species discrimination power was detected in species-level taxonomic process by the ITS dataset alone or combined with other loci, which was suggested to be the most suitable barcode for Sinosenecio. Our results are congruent with previous taxonomic studies concerning the monophyly of the S. oldhamianus group. The present study provides an empirical paradigm for the identification of medicinal plant species and their geographical patterns, ascertaining the congruence between taxonomical studies and barcoding analysis inSinosenecio.     

A hierarchical spatiotemporal analog forecasting model for count data

Analog forecasting is a mechanism‐free nonlinear method that forecasts a system forward in time by examining how past states deemed similar to the current state moved forward. Previous applications of analog forecasting has been successful at producing robust forecasts for a variety of ecological and physical processes, but it has typically been presented in an empirical or heuristic procedure, rather than as a formal statistical model. The methodology presented here extends the model‐based analog method of McDermott and Wikle (Environmetrics, 27, 2016, 70) by placing analog forecasting within a fully hierarchical statistical framework that can accommodate count observations. Using a Bayesian approach, the hierarchical analog model is able to quantify rigorously the uncertainty associated with forecasts. Forecasting waterfowl settling patterns in the northwestern United States and Canada is conducted by applying the hierarchical analog model to a breeding population survey dataset. Sea surface temperature (SST) in the Pacific Ocean is used to help identify potential analogs for the waterfowl settling patterns.     

森林-农业景观格局变化的社会-生态系统模拟模型方法进展

社会-生态系统（SES）模拟模型是景观格局分析和决策的有效工具,能表征景观格局变化的社会-生态效应及景观决策的复杂反馈机制。文献综述了森林-农业景观格局的SES模型方法进展发现：（1）多数模型对景观过程与社会经济决策的反馈关系分析不足;（2）应集成多种情景模拟和景观效应分析方法,完善现有SES模型的理论方法基础;（3）通过集成格局优化模型和自主体模型会有效改进SES模型功能,具体途径包括：集成情景-生态效应的景观格局模拟方法、完善景观决策的理论基础、加强集成模型的不确定性分析、降低模型复杂性和综合定性-定量数据等。研究结果有助于理解多尺度森林-农业景观格局在社会-生态系统中的重要作用,能更好地支持跨学科集成模型开发与应用。     

Tractable Bayes of skew-elliptical link models for correlated binary data

Correlated binary response data with covariates are ubiquitous in longitudinal or spatial studies. Among the existing statistical models, the most well-known one for this type of data is the multivariate probit model, which uses a Gaussian link to model dependence at the latent level. However, a symmetric link may not be appropriate if the data are highly imbalanced. Here, we propose a multivariate skew-elliptical link model for correlated binary responses, which includes the multivariate probit model as a special case. Furthermore, we perform Bayesian inference for this new model and prove that the regression coefficients have a closed-form unified skew-elliptical posterior with an elliptical prior. The new methodology is illustrated by an application to COVID-19 data from three different counties of the state of California, USA. By jointly modeling extreme spikes in weekly new cases, our results show that the spatial dependence cannot be neglected. Furthermore, the results also show that the skewed latent structure of our proposed model improves the flexibility of the multivariate probit model and provides a better fit to our highly imbalanced dataset.     

Impact of ecological redundancy on the performance of machine learning classifiers in vegetation mapping

Vegetation maps are models of the real vegetation patterns and are considered important tools in conservation and management planning. Maps created through traditional methods can be expensive and time‐consuming, thus, new more efficient approaches are needed. The prediction of vegetation patterns using machine learning shows promise, but many factors may impact on its performance. One important factor is the nature of the vegetation–environment relationship assessed and ecological redundancy. We used two datasets with known ecological redundancy levels (strength of the vegetation–environment relationship) to evaluate the performance of four machine learning (ML) classifiers (classification trees, random forests, support vector machines, and nearest neighbor). These models used climatic and soil variables as environmental predictors with pretreatment of the datasets (principal component analysis and feature selection) and involved three spatial scales. We show that the ML classifiers produced more reliable results in regions where the vegetation–environment relationship is stronger as opposed to regions characterized by redundant vegetation patterns. The pretreatment of datasets and reduction in prediction scale had a substantial influence on the predictive performance of the classifiers. The use of ML classifiers to create potential vegetation maps shows promise as a more efficient way of vegetation modeling. The difference in performance between areas with poorly versus well‐structured vegetation–environment relationships shows that some level of understanding of the ecology of the target region is required prior to their application. Even in areas with poorly structured vegetation–environment relationships, it is possible to improve classifier performance by either pretreating the dataset or reducing the spatial scale of the predictions.     

An approach to analyzing spatial patterns in annual dynamics of planktonic ciliate communities and their environmental drivers in marine ecosystems

The environmental drivers to shape the spatial patterns in annual dynamics of the planktonic ciliate communities were studied based on an annual dataset from a bay, northern Yellow Sea. Samples were biweekly collected at five stations with different environmental condition status during a 1-year period. The second-stage-analysis-based multivariate approaches were used to reveal the internal dynamics in annual patterns of the ciliate assemblages. Results showed that: (a) there was a clear spatial variability in annual dynamics among five stations; (b) the dominant species represented different succession dynamics among four samples stations during the 1-year cycle; and (c) the spatial variations in annual patterns of the ciliates were significantly correlated with nutrients, alone or in combination with salinity and dissolve oxygen (DO). Thus, it is suggested that the nutrients may be the main drivers to shape the spatial patterns in annual dynamics of planktonic ciliate communities in marine ecosystems.     

A method to incorporate the effect of taxonomic uncertainty on multivariate analyses of ecological data

Researchers in ecology commonly use multivariate analyses (e.g. redundancy analysis, canonical correspondence analysis, Mantel correlation, multivariate analysis of variance) to interpret patterns in biological data and relate these patterns to environmental predictors. There has been, however, little recognition of the errors associated with biological data and the influence that these may have on predictions derived from ecological hypotheses. We present a permutational method that assesses the effects of taxonomic uncertainty on the multivariate analyses typically used in the analysis of ecological data. The procedure is based on iterative randomizations that randomly re‐assign non identified species in each site to any of the other species found in the remaining sites. After each re‐assignment of species identities, the multivariate method at stake is run and a parameter of interest is calculated. Consequently, one can estimate a range of plausible values for the parameter of interest under different scenarios of re‐assigned species identities. We demonstrate the use of our approach in the calculation of two parameters with an example involving tropical tree species from western Amazonia: 1) the Mantel correlation between compositional similarity and environmental distances between pairs of sites, and; 2) the variance explained by environmental predictors in redundancy analysis (RDA). We also investigated the effects of increasing taxonomic uncertainty (i.e. number of unidentified species), and the taxonomic resolution at which morphospecies are determined (genus‐resolution, family‐resolution, or fully undetermined species) on the uncertainty range of these parameters. To achieve this, we performed simulations on a tree dataset from southern Mexico by randomly selecting a portion of the species contained in the dataset and classifying them as unidentified at each level of decreasing taxonomic resolution. An analysis of covariance showed that both taxonomic uncertainty and resolution significantly influence the uncertainty range of the resulting parameters. Increasing taxonomic uncertainty expands our uncertainty of the parameters estimated both in the Mantel test and RDA. The effects of increasing taxonomic resolution, however, are not as evident. The method presented in this study improves the traditional approaches to study compositional change in ecological communities by accounting for some of the uncertainty inherent to biological data. We hope that this approach can be routinely used to estimate any parameter of interest obtained from compositional data tables when faced with taxonomic uncertainty.     

Dietary patterns and changes in body weight in women

Objective: Our objective was to examine the association between adherence to dietary patterns and weight change in women. Research Methods and Procedures: Women (51,670, 26 to 46 years old) in the Nurses’ Health Study II were followed from 1991 to 1999. Dietary intake and body weight were ascertained in 1991, 1995, and 1999. A Western pattern, characterized by high intakes of red and processed meats, refined grains, sweets and desserts, and potatoes, and a prudent pattern, characterized by high intakes of fruits, vegetables, whole grains, fish, poultry, and salad dressing, were identified with principal component analysis, and associations between patterns and change in body weight were estimated. Results: Women who increased their Western pattern score had greater weight gain (multivariate adjusted means, 4.55 kg for 1991 to 1995 and 2.86 kg for 1995 to 1999) than women who decreased their Western pattern score (2.70 and 1.37 kg for the two time periods), adjusting for baseline lifestyle and dietary confounders and changes in confounders over time (p < 0.001 for both time periods). Furthermore, among women who increased their prudent pattern score, weight gain was smaller (multivariate‐adjusted means, 1.93 kg for 1991 to 1995 and 0.66 kg for 1995 to 1999) than among women who decreased their prudent pattern score (4.83 and 3.35 kg for the two time periods) (p < 0.001). The largest weight gain between 1991 and 1995 and between 1995 and 1999 was observed among women who decreased their prudent pattern score while increasing their Western pattern score (multivariate adjusted means, 6.80 and 4.99 kg), whereas it was smallest for the opposite change in patterns (0.87 and ?0.64 kg) (p < 0.001). Discussion: Adoption of a Western dietary pattern is associated with larger weight gain in women, whereas a prudent dietary pattern may facilitate weight maintenance.     

Continental-scale patterns of Cecropia reproductive phenology: evidence from herbarium specimens

Plant phenology is concerned with the timing of recurring biological events. Though phenology has traditionally been studied using intensive surveys of a local flora, results from such surveys are difficult to generalize to broader spatial scales. In this study, contrastingly, we assembled a continental-scale dataset of herbarium specimens for the emblematic genus of Neotropical pioneer trees, Cecropia, and applied Fourier spectral and cospectral analyses to investigate the reproductive phenology of 35 species. We detected significant annual, sub-annual and continuous patterns, and discuss the variation in patterns within and among climatic regions. Although previous studies have suggested that pioneer species generally produce flowers continually throughout the year, we found that at least one third of Cecropia species are characterized by clear annual flowering behaviour. We further investigated the relationships between phenology and climate seasonality, showing strong associations between phenology and seasonal variations in precipitation and temperature. We also verified our results against field survey data gathered from the literature. Our findings indicate that herbarium material is a reliable resource for use in the investigation of large-scale patterns in plant phenology, offering a promising complement to local intensive field studies.     

On measures of dissimilarity between point patterns: Classification based on prototypes and multidimensional scaling

This paper presents a collection of dissimilarity measures to describe and then classify spatial point patterns when multiple replicates of different types are available for analysis. In particular, we consider a range of distances including the spike‐time distance and its variants, as well as cluster‐based distances and dissimilarity measures based on classical statistical summaries of point patterns. We review and explore, in the form of a tutorial, their uses, and their pros and cons. These distances are then used to summarize and describe collections of repeated realizations of point patterns via prototypes and multidimensional scaling. We also show a simulation study to evaluate the performance of multidimensional scaling with two types of selected distances. Finally, a multivariate spatial point pattern of a natural plant community is analyzed through various of these measures of dissimilarity.