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John Bunge

Publications and source records attributed to John Bunge.

5 recordsLinked to original sources

Comment on "Computational improvements reveal great bacterial diversity and high metal toxicity in soil".

Gans et al. (Reports, 26 August 2005, p. 1387) provided an estimate of soil bacterial species richness two orders of magnitude greater than previously reported values. Using a re-derived mathematical model, we reanalyzed the data and found that the statistical error exceeds the estimate by a factor of 26. We also note two potential sources of error in the experimental data collection and measurement procedures.

Bacteria↗

Diversity estimates of microeukaryotes below the chemocline of the anoxic Mariager Fjord, Denmark.

Microbial communities of extreme environments have often been assumed to have low species richness. We analysed 18S rRNA gene signatures in a sample collected below the chemocline of the anoxic Mariager Fjord in Denmark, and from these data we computed novel parametric and standard nonparametric estimates of protistan phylotype richness. Our results indicate unexpectedly high richness in this environment: at the 99.5% phylotype definition, our most conservative estimate was 568 phylotypes (+/-114, standard error). Phylogenetic analyses revealed that the sequences collected cover the majority of described lineages in the eukaryotic domain. Out of 384 sequences analysed, 307 were identified as protistan targets, none of which was identical to known sequences. However, based on what is known about species that are phylogenetically related to the Mariager sequences, most of the latter seem to belong to strictly or facultative anaerobe organisms. We also found signatures that together with other environmental 18S rRNA gene sequences represent environmental clades of possibly high taxonomic levels (class to kingdom level). One of these clades, consisting exclusively of sequences from anoxic sampling sites, branches at the base of the eukaryotic evolutionary tree among the earliest eukaryotic lineages. Assuming eukaryotic evolution under oxygen-depleted conditions, these sequences may represent immediate descendants of early eukaryotic ancestors.

Colony Count, Microbial↗

Microeukaryote community patterns along an O2/H2S gradient in a supersulfidic anoxic fjord (Framvaren, Norway).

To resolve the fine-scale architecture of anoxic protistan communities, we conducted a cultivation-independent 18S rRNA survey in the superanoxic Framvaren Fjord in Norway. We generated three clone libraries along the steep O(2)/H(2)S gradient, using the multiple-primer approach. Of 1,100 clones analyzed, 753 proved to be high-quality protistan target sequences. These sequences were grouped into 92 phylotypes, which displayed high protistan diversity in the fjord (17 major eukaryotic phyla). Only a few were closely related to known taxa. Several sequences were dissimilar to all previously described sequences and occupied a basal position in the inferred phylogenies, suggesting that the sequences recovered were derived from novel, deeply divergent eukaryotes. We detected sequence clades with evolutionary importance (for example, clades in the euglenozoa) and clades that seem to be specifically adapted to anoxic environments, challenging the hypothesis that the global dispersal of protists is uniform. Moreover, with the detection of clones affiliated with jakobid flagellates, we present evidence that primitive descendants of early eukaryotes are present in this anoxic environment. To estimate sample coverage and phylotype richness, we used parametric and nonparametric statistical methods. The results show that although our data set is one of the largest published inventories, our sample missed a substantial proportion of the protistan diversity. Nevertheless, statistical and phylogenetic analyses of the three libraries revealed the fine-scale architecture of anoxic protistan communities, which may exhibit adaptation to different environmental conditions along the O(2)/H(2)S gradient.

Anaerobiosis↗

Predicting microbial species richness.

Microorganisms are spectacularly diverse phylogenetically, but available estimates of their species richness are vague and problematic. For example, for comparable environments, the estimated numbers of species range from a few dozen or hundreds to tens of thousands and even half a million. Such estimates provide no baseline information on either local or global microbial species richness. We argue that this uncertainty is due in large part to the way statistical tools are used, if not indeed misused, in biodiversity research. Here we develop a powerful synthetic statistical approach to quantify biodiversity. It provides statistically sound estimates of microbial richness at any level of taxonomic hierarchy. We apply this approach to a large original 16S rRNA dataset on marine bacterial diversity and show that the number of bacterial species in a sample from marine sediments is (2.4 +/- 0.5 SE) x 10(3). We argue that our methodology provides estimates of microbial richness that are reliable and general, have biologically meaningful SEs, and meet other fundamental statistical standards. This approach can be an essential tool in biodiversity research, and the estimates of microbial richness presented here can serve as a baseline in microbial diversity studies.

Bacteria↗

Estimating the number of species in a stochastic abundance model.

Consider a stochastic abundance model in which the species arrive in the sample according to independent Poisson processes, where the abundance parameters of the processes follow a gamma distribution. We propose a new estimator of the number of species for this model. The estimator takes the form of the number of duplicated species (i.e., species represented by two or more individuals) divided by an estimated duplication fraction. The duplication fraction is estimated from all frequencies including singleton information. The new estimator is closely related to the sample coverage estimator presented by Chao and Lee (1992, Journal of the American Statistical Association 87, 210-217). We illustrate the procedure using the Malayan butterfly data discussed by Fisher, Corbet, and Williams (1943, Journal of Animal Ecology 12, 42-58) and a 1989 Christmas Bird Count dataset collected in Florida, U.S.A. Simulation studies show that this estimator compares well with maximum likelihood estimators (i.e., empirical Bayes estimators from the Bayesian viewpoint) for which an iterative numerical procedure is needed and may be infeasible.

Animals↗