Browsing by Author "Roberts, David W."

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Cigarette smoking is associated with an altered vaginal tract metabolomic profile
(2018-01) Nelson, Tiffanie M.; Borgogna, Joanna-Lynn C.; Michalek, R. D.; Roberts, David W.; Rath, J. M.; Glover, E. D.; Ravel, Jacques; Shardell, M. D.; Yeoman, Carl J.; Brotman, Rebecca M.
Cigarette smoking has been associated with both the diagnosis of bacterial vaginosis (BV) and a vaginal microbiota lacking protective Lactobacillus spp. As the mechanism linking smoking with vaginal microbiota and BV is unclear, we sought to compare the vaginal metabolomes of smokers and non-smokers (17 smokers/19 non-smokers). Metabolomic profiles were determined by gas and liquid chromatography mass spectrometry in a cross-sectional study. Analysis of the 16S rRNA gene populations revealed samples clustered into three community state types (CSTs) ---- CST-I (L. crispatus-dominated), CST-III (L. iners-dominated) or CST-IV (low-Lactobacillus). We identified 607 metabolites, including 12 that differed significantly (q-value < 0.05) between smokers and non-smokers. Nicotine, and the breakdown metabolites cotinine and hydroxycotinine were substantially higher in smokers, as expected. Among women categorized to CST-IV, biogenic amines, including agmatine, cadaverine, putrescine, tryptamine and tyramine were substantially higher in smokers, while dipeptides were lower in smokers. These biogenic amines are known to affect the virulence of infective pathogens and contribute to vaginal malodor. Our data suggest that cigarette smoking is associated with differences in important vaginal metabolites, and women who smoke, and particularly women who are also depauperate for Lactobacillus spp., may have increased susceptibilities to urogenital infections and increased malodor.
Comparison of silhouette‐based reallocation methods for vegetation classification
(Wiley, 2021-01) Lengyel, Attila; Roberts, David W.; Botta-Dukát, Zoltán
Aims: Vegetation classification seeks to partition the variability of vegetation into relatively homogeneous but distinct types. There are many ways to evaluate, and potentially improve, such a partitioning. One effective approach involves calculating silhouette widths which measure the goodness-of-fit of plots to their cluster. We introduce a new iterative reallocation clustering method — Reallocation of Misclassified Objects based on Silhouette width (REMOS) — and compare its performance with an existing algorithm — OPTimizing SILhouette widths (OPTSIL). REMOS reallocates misclassified objects to their nearest-neighbour cluster iteratively. Of its two variants, REMOS1 reallocates only the object with the lowest silhouette width, while REMOS2 reallocates all objects with negative silhouette width in each iteration. We test how REMOS1, REMOS2 and OPTSIL perform in terms of: (a) cluster homogeneity and separation; (b) the number of diagnostic species; and (c) runtime. Methods: We classified simulated data with the flexible-beta algorithm for values of beta from −1 to 0. These classifications were subsequently optimized by REMOS1, REMOS2 and OPTSIL and compared for mean silhouette widths, misclassification rate, and runtime. We classified three vegetation data sets from two to ten clusters, optimized all outcomes with the three reallocation methods, and compared their mean silhouette widths, misclassification rate, and number of diagnostic species. Results: OPTSIL achieved the highest mean silhouette width across the majority of the data sets. REMOS achieved zero or negligible misclassifications, outperforming OPTSIL on this criterion. REMOS algorithms were typically more than an order of magnitude faster to calculate than OPTSIL. There was no clear difference between REMOS and OPTSIL in the number of diagnostic species. Conclusions:REMOS algorithms may be preferable to OPTSIL when: (a) the primary objective is to reduce the number of negative silhouette widths in a classification, as opposed to maximizing mean silhouette width; or (b) when the time efficiency of the algorithm is important.
Evaluating and presenting uncertainty in model‐based unconstrained ordination
(2019-12) Hoegh, Andrew; Roberts, David W.
Variability in ecological community composition is often analyzed by recording the presence or abundance of taxa in sample units, calculating a symmetric matrix of pairwise distances or dissimilarities among sample units and then mapping the resulting matrix to a low‐dimensional representation through methods collectively called ordination. Unconstrained ordination only uses taxon composition data, without any environmental or experimental covariates, to infer latent compositional gradients associated with the sampling units. Commonly, such distance‐based methods have been used for ordination, but recently there has been a shift toward model‐based approaches. Model‐based unconstrained ordinations are commonly formulated using a Bayesian latent factor model that permits uncertainty assessment for parameters, including the latent factors that correspond to gradients in community composition. While model‐based methods have the additional benefit of addressing uncertainty in the estimated gradients, typically the current practice is to report point estimates without summarizing uncertainty. To demonstrate the uncertainty present in model‐based unconstrained ordination, the well‐known spider and dune data sets were analyzed and shown to have large uncertainty in the ordination projections. Hence to understand the factors that contribute to the uncertainty, simulation studies were conducted to assess the impact of additional sampling units or species to help inform future ordination studies that seek to minimize variability in the latent factors. Accurate reporting of uncertainty is an important part of transparency in the scientific process; thus, a model‐based approach that accounts for uncertainty is valuable. An R package, UncertainOrd, contains visualization tools that accurately represent estimates of the gradients in community composition in the presence of uncertainty.
The geography of climate and the global patterns of species diversity
(Springer Science and Business Media LLC, 2023-09) Coelho, Marco Túlio P.; Barreto, Marco Túlio P.; Barreto, Elisa; Rangel, Thiago F.; Diniz-Filho, José Alexandre F.; Wüest, Rafael O.; Bach, Wilhelmine; Skeels, Alexander; McFadden, Ian R.; Roberts, David W.; Pellissier, Loïc; Zimmermann, Niklaus E.; Graham, Catherine H.
Climate’s effect on global biodiversity is typically viewed through the lens of temperature, humidity and resulting ecosystem productivity1,2,3,4,5,6. However, it is not known whether biodiversity depends solely on these climate conditions, or whether the size and fragmentation of these climates are also crucial. Here we shift the common perspective in global biodiversity studies, transitioning from geographic space to a climate-defined multidimensional space. Our findings suggest that larger and more isolated climate conditions tend to harbour higher diversity and species turnover among terrestrial tetrapods, encompassing more than 30,000 species. By considering both the characteristics of climate itself and its geographic attributes, we can explain almost 90% of the variation in global species richness. Half of the explanatory power (45%) may be attributed either to climate itself or to the geography of climate, suggesting a nuanced interplay between them. Our work evolves the conventional idea that larger climate regions, such as the tropics, host more species primarily because of their size7,8. Instead, we underscore the integral roles of both the geographic extent and degree of isolation of climates. This refined understanding presents a more intricate picture of biodiversity distribution, which can guide our approach to biodiversity conservation in an ever-changing world.
Measuring Understory Fire Effects from Space: Canopy Change in Response to Tropical Understory Fire and What This Means for Applications of GEDI to Tropical Forest Fire
(MDPI, 2023-01) East, Alyson; Hansen, Andrew; Armenteras, Dolors; Jantz, Patrick; Roberts, David W.
The ability to measure the ecological effects of understory fire in the Amazon on a landscape scale remains a frontier in remote sensing. The Global Ecosystem Dynamics Investigation’s (GEDI) LiDAR data have been widely suggested as a critical new tool in this field. In this paper, we use the GEDI Simulator to quantify the nuanced effects of understory fire in the Amazon, and assess the ability of on-orbit GEDI data to do the same. While numerous ecological studies have used simulated GEDI data, on-orbit constraint may limit ecological inference. This is the first study that we are aware of that directly compares methods using simulated and on-orbit GEDI data. Simulated GEDI data showed that fire effects varied nonlinearly through the canopy and then moved upward with time since burn. Given that fire effects peaked in the mid-canopy and were often on the scale of 2 to 3 m in height difference, it is unlikely that on-orbit GEDI data will have the sensitivity to detect these same changes.
Natural Ecosystems I. The Rocky Mountains
(2003-10) Reiners, William A.; Baker, William L.; Baron, Jill S.; Debinski, Diane M.; Elias, Scott A.; Fagre, Daniel B.; Findley, James S.; Mearns, Linda O.; Roberts, David W.; Seastedt, Timothy R.; Stohlgren, Thomas J.; Veblen, Thomas T.; Wagner, Frederic H.
This assessment of climate-change effects on Rocky Mountain terrestrial ecosystems is prepare from information generated by a workshop focused on terrestrial systems of the Rocky Mountains, and held in Boulder, CO, on 29-30 September 2000 at the National Center for Atmospheric Research. It is a compilation of this workshop's discussion along with material from earlier workshops.
Statistical Analysis of Maximally Similar Sets in Ecological Research
(2018-12) Roberts, David W.
Maximally similar sets (MSSs) are sets of elements that share a neighborhood in a high-dimensional space defined by a symmetric, reflexive similarity relation. Each element of the universe is employed as the kernel of a neighborhood of a given size (number of members), and elements are added to the neighborhood in order of similarity to the current members of the set until the desired neighborhood size is achieved. The set of neighborhoods is then reduced to the set of unique, maximally similar sets by eliminating all sets that are permutations of an existing set. Subsequently, the within-MSS variability of candidate explanatory variables associated with the elements is compared to random sets of the same size to estimate the probability of obtaining variability as low as was observed. Explanatory variables can be compared for effect size by the rank order of within-MSS variability and random set variability, correcting for statistical power as necessary. The analyses performed identify constraints, as opposed to determinants, in the triangular distribution of pair-wise element similarity. In the example given here, the variability in spring temperature, summer temperature, and the growing degree days of forest vegetation sample units shows the greatest constraint on forest composition of a large set of candidate environmental variables.