Heterogeneity in shortgrass prairie vegetation: the role of playa lakes

Abstract. We examined the role of playa lakes in promoting regional heterogeneity on the southern High Plains. The goals of this paper were to: (1) describe vegetation types and zonation patterns within playas, (2) evaluate patterns of species distribution and abundance within and among playas, and (3) assess patterns of heterogeneity within and among playas on a portion of the southern High Plains. Perennial grasses were the most abundant species in playa vegetation. Playa vegetation exhibited distinct and repeatable vegetation zones at the majority of sites, but the number of distinct zones varied from site to site. Agropyron smithii, Buchloë dactyloides, and Panicum obtusum were the most important species of playa interior zones, and Bouteloua gracilis, Buchloë dactyloides, and Schedonnardus paniculatus were important upland species outside of playas. Species distribution and abundance were positively correlated at 38 of 40 sites. The distributions of species occurrences among sites were unimodal both locally and regionally. The degree of heterogeneity varied from playa to playa. Local heterogeneity within playas was found to increase regional heterogeneity; therefore, playas increase both local and regional heterogeneity of vegetation. Long-term monitoring will be necessary to understand the spatial and temporal response of vegetation within and among playas to stochastic climatic factors on the southern High Plains of North America.     

Creating robust, reliable, clinically relevant classifiers from spectroscopic data

I describe in detail the intimately connected feature extraction and classifier development stages of the data-driven Statistical Classification Strategy (SCS) and compare them with current practice used in MR spectroscopy. We initially created the SCS for the analysis of MR and IR spectra of biofluids and tissues, and subsequently extended it to analyze biomedical data in general. I focus on explaining how to extract discriminatory spectral features and create robust classifiers that can reliably discriminate diseases and disease states. I discuss our approach to identifying features that retain spectral identity and provisionally relate these features, averaged subregions of the spectra, to specific chemical entities (“metabolites”). Particular emphasis is placed on describing the steps required to help create classifiers whose accuracy doesn’t deteriorate significantly when presented with new, unknown samples. A simple but powerful extension of the discovered features to detect metabolite-metabolite (feature-feature) interactions is also sketched. I contrast the advantages and disadvantages of using either spectral signatures or explicit metabolite concentrations derived from the spectra as sets of discriminatory features. At present, no clear-cut preference is obvious and more objective comparisons will be needed. Finally, I argue that clinical requirements and exigencies strongly suggest adopting a two-phase approach to diagnosis/prognosis. In the first phase the emphasis ought to be on providing as accurate a diagnosis as possible, without any attempt to identify “biomarkers.” That should be the goal of the second, research phase, with a view of providing prognosis on disease progression.     

Quantifying successional changes in response to forest disturbances

Question: Can dissimilarity measures of individual plots be used to forecast the driving factors among various anthropogenic disturbances influencing understorey successional changes? Location: Yambulla State Forest, south‐eastern Australia (37°14'S, 149°38'E). Methods: Assessments of understorey vegetation communities were taken prior to anthropogenic disturbances and at three subsequent time periods representing a period of 15 years post‐disturbance. Dissimilarities were calculated from the original assessment and modelled in a Bayesian framework to examine the influence of logging, number of prescribed burns and time. Results: All sites underwent significant changes over time independently of the imposed management regimes. Logging resulted in an immediate change in vegetation assemblage which decreased in the subsequent assessments. The number of prescribed fires brought greater change in the shrub vegetation assemblages, but less change in the ground species vegetation assemblages. Conclusions: The anthropogenic disturbances did have some role in the changes of vegetation assemblages but these were minimal. The ongoing changes appear to be a natural response to the last wildfire, which passed through the study area in 1973 (13 years prior to the study). Forest management practices should consider the influence of wildfire succession when planning for the conservation of biodiversity.     

From dissimilarities among species to dissimilarities among communities: a double principal coordinate analysis

This paper proposes a new method to reverse engineer gene regulatory networks from experimental data. The modeling framework used is time-discrete deterministic dynamical systems, with a finite set of states for each of the variables. The simplest examples of such models are Boolean networks, in which variables have only two possible states. The use of a larger number of possible states allows a finer discretization of experimental data and more than one possible mode of action for the variables, depending on threshold values. Furthermore, with a suitable choice of state set, one can employ powerful tools from computational algebra, that underlie the reverse-engineering algorithm, avoiding costly enumeration strategies. To perform well, the algorithm requires wildtype together with perturbation time courses. This makes it suitable for small to meso-scale networks rather than networks on a genome-wide scale. An analysis of the complexity of the algorithm is performed. The algorithm is validated on a recently published Boolean network model of segment polarity development in Drosophila melanogaster.     

CVTree: A Parallel Alignment-free Phylogeny and Taxonomy Tool Based on Composition Vectors of Genomes

Composition Vector Tree (CVTree) is an alignment-free algorithm to infer phylogenetic relationships from genome sequences. It has been successfully applied to study phylogeny and taxonomy of viruses, prokaryotes, and fungi based on the whole genomes, as well as chloroplast genomes, mitochondrial genomes, and metagenomes. Here we presented the standalone software for the CVTree algorithm. In the software, an extensible parallel workflow for the CVTree algorithm was designed. Based on the workflow, new alignment-free methods were also implemented. And by examining the phylogeny and taxonomy of 13,903 prokaryotes based on 16S rRNA sequences, we showed that CVTree software is an efficient and effective tool for studying phylogeny and taxonomy based on genome sequences. The code of CVTree software can be available at https://github.com/ghzuo/cvtree.     

Dynamic macroecology and the future for biodiversity

Reliable projections of climate‐change impacts on biodiversity are vital in formulating conservation and management strategies that best retain biodiversity into the future. While recent modelling has focussed largely on individual species, macroecology has the potential to add significant value to these efforts, by incorporating important community‐level constraints and processes. Here we show how a new dynamic macroecological approach can project climate‐change impacts collectively across all species in a diverse taxonomic group, overcoming shortfalls in our knowledge of biodiversity, while incorporating the key processes of dispersal and community assembly. Our approach applies a recently published technique (DynamicFOAM) to predict the present composition of every community, which form the initial conditions for a new metacommunity model (M‐SET) that projects changes in composition over time, under specified climate and habitat scenarios. Applying this approach at fine resolution to plant biodiversity in Tasmania (2,051 species; 1,157,587 communities), we project high average turnover in community composition from 2010 to 2100 (mean Sorensen's dissimilarity = 0.71 (±7.0 × 10^?5)), with major reductions in species richness (32.9 (±0.02) species lost per community) and no plant species benefitting from climate change in the long term. We also demonstrate how our modelling approach can identify habitat likely to be of high value for retaining rare and poorly reserved species under climate change. Our analyses highlight the potential value of this dynamic macroecological approach, that incorporates key ecological processes in projecting climate change impacts for all species simultaneously and uses simple macroecological inputs that can be derived even for highly diverse and poorly studied taxa.     

Statistical Measures of DNA Sequence Dissimilarity under Markov Chain Models of Base Composition

In molecular biology, the issue of quantifying the similarity between two biological sequences is very important. Past research has shown that word-based search tools are computationally efficient and can find some new functional similarities or dissimilarities invisible to other algorithms like FASTA. Recently, under the independent model of base composition, Wu, Burke, and Davison (1997, Biometrics 53, 1431 1439) characterized a family of word-based dissimilarity measures that defined distance between two sequences by simultaneously comparing the frequencies of all subsequences of n adjacent letters (i.e., n-words) in the two sequences. Specifically, they introduced the use of Mahalanobis distance and standardized Euclidean distance into the study of DNA sequence dissimilarity. They showed that both distances had better sensitivity and selectivity than the commonly used Euclidean distance. The purpose of this article is to extend Mahalanobis and standardized Euclidean distances to Markov chain models of base composition. In addition, a new dissimilarity measure based on Kullback-Leibler discrepancy between frequencies of all n-words in the two sequences is introduced. Applications to real data demonstrate that Kullback-Leibler discrepancy gives a better performance than Euclidean distance. Moreover, under a Markov chain model of order kQ for base composition, where kQ is the estimated order based on the query sequence, standardized Euclidean distance performs very well. Under such a model, it performs as well as Mahalanobis distance and better than Kullback-Leibler discrepancy and Euclidean distance. Since standardized Euclidean distance is drastically faster to compute than Mahalanobis distance, in a usual workstation/PC computing environment, the use of standardized Euclidean distance under the Markov chain model of order kQ of base composition is generally recommended. However, if the user is very concerned with computational efficiency, then the use of Kullback-Leibler discrepancy, which can be computed as fast as Euclidean distance, is recommended. This can significantly enhance the current technology in comparing large datasets of DNA sequences.     

Compositional dissimilarity as a robust measure of ecological distance

The robustness of quantitative measures of compositional dissimilarity between sites was evaluated using extensive computer simulations of species' abundance patterns over one and two dimensional configurations of sample sites in ecological space. Robustness was equated with the strength over a range of models, of the linear and monotonic (rank-order) relationship between the compositional dissimilarities and the corresponding Euclidean distances between sites measured in the ecological space. The range of models reflected different assumptions about species' response curve shape, sampling pattern of sites, noise level of the data, species' interactions, trends in total site abundance, and beta diversity of gradients.The Kulczynski, Bray-Curtis and Relativized Manhattan measures were found to have not only a robust monotonic relationship with ecological distance, but also a robust linear (proportional) relationship until ecological distances became large. Less robust measures included Chord distance, Kendall's coefficient, Chisquared distance, Manhattan distance, and Euclidean distance.A new ordination method, hybrid multidimensional scaling (HMDS), is introduced that combines metric and nonmetric criteria, and so takes advantage of the particular properties of robust dissimilarity measures such as the Kulczynski measure.We thank M. P. Austin for encouraging this study, and I. C. Prentice, E. Van der Maarel, and an anonymous reviewer for helpful comments. E. M. Adomeit provided technical assistance.     

Optimum-transformation of plant species cover-abundance values

Most of the methods used in the multivariate analysis of data on vegetation and environment, or transformations implied in such methods, put disproportionate emphasis on species with a relatively wide ecological amplitude occurring with relatively high cover-abundance values, and/or rare species. This problem can be overcome to some extent by reducing the cover-abundance values to presence-absence data, but this means a severe loss of information. A standardization of values by species maxima as is done automatically in some programs, may lead to an undesirable emphasis on species represented with low values only.In this paper a method is presented, by which relatively low cover-abundance values of species are upweighted to an arbitrarily chosen higher value, if these low values are considered to indicate an optimum response of that particular species. The method has been tested on a selection of 40 phytosociological relevés from dune slacks in the Voorne dunes, as well as on the Dune Meadow data set used in the textbook of Jongman et al. (1987). The cluster structure obtained with the optimum-transformation appears to be clearer and the contribution of typical dune slack species to the cluster structure increased significantly. Canonical correspondence analysis of the transformed data gave slightly more important main axes.Abbreviations CA = Correspondence Analysis - CCA = Canonical Correspondence Analysis - DOL = Detection of Optimality Level - SWOM = Standardized Weighted Optimality Measure - WPGMA = Weighted Pair Group Method Average linking clustering