Dataset Information

Quantifying the responses of biological indices to rare macroinvertebrate taxa exclusion: Does excluding more rare taxa cause more error?

ABSTRACT: Including or excluding rare taxa in bioassessment is a controversial topic, which essentially affects the reliability and accuracy of the result. In the present paper, we hypothesize that biological indices such as Shannon-Wiener index, Simpson's index, Margalef index, evenness, BMWP (biological monitoring working party), and ASPT (Average Score Per Taxon) respond differently to rare taxa exclusion. To test this hypothesis, a benthic macroinvertebrate data set based on recent fifteen-year studies in China was built for suppositional plot analyses. A field research was conducted in the Nansi Lake to perform related analyses. The results of suppositional plot simulations showed that Simpson's index placed more weight on common taxa than any other studied indices, followed by Shannon-Wiener index which remained a high value with the exclusion of rare taxa. The results indicated that there was not much of effect on Simpson's index and Shannon-Wiener index when rare taxa were excluded. Rare taxa played an important role in Margalef index and BMWP than in other indices. Evenness showed an increase trend, while ASPT varied inconsistently with the exclusion of rare taxa. Results of the field study also indicated that rare taxa had few impacts on the Shannon-Wiener index. By examining the relationships between the rare taxa and biological indices in our study, it is suggested that including the rare taxa when using BMWP and excluding them in the proposed way (e.g., fixed-count subsampling) to calculate Shannon-Wiener index and Simpson's index could raise the efficiency and reduce the biases in the bioassessment of freshwater ecosystems.

SUBMITTER: Yu Z

PROVIDER: S-EPMC5330898 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Quantifying the responses of biological indices to rare macroinvertebrate taxa exclusion: Does excluding more rare taxa cause more error?

Yu Zhengda Z Wang Hui H Meng Jiao J Miao Mingsheng M Kong Qiang Q Wang Renqing R Liu Jian J

Ecology and evolution 20170208 5

Including or excluding rare taxa in bioassessment is a controversial topic, which essentially affects the reliability and accuracy of the result. In the present paper, we hypothesize that biological indices such as Shannon-Wiener index, Simpson's index, Margalef index, evenness, BMWP (biological monitoring working party), and ASPT (Average Score Per Taxon) respond differently to rare taxa exclusion. To test this hypothesis, a benthic macroinvertebrate data set based on recent fifteen-year studie ...[more]

PMID: 28261467

Similar Datasets

Project description:Assessing the importance of different taxa for inferring evolutionary history is a critical, but underutilized, aspect of systematics. Quantifying the importance of all taxa within a dataset provides an empirical measurement that can establish a ranking of extant taxa for ecological study and/or quantify the relative importance of newly announced or redescribed specimens to enable the disentangling of novelty and inferential influence. Here, we illustrate the use of taxon influence indices through analysis of both molecular and morphological datasets, introducing a modified Bayesian approach to the taxon influence index that accounts for model and topological uncertainty. Quantification of taxon influence using the Bayesian approach produced clear rankings for both dataset types. Bayesian taxon rankings differed from maximum likelihood (ML)-derived rankings from a mitogenomic dataset, and the highest ranking taxa exhibited the largest interquartile range in influence estimate, suggesting variance in the estimate must be taken into account when the ranking of taxa is the feature of interest. Application of the Bayesian taxon influence index to a recent morphological analysis of the Tully Monster (Tullimonstrum) reveals that it exhibits consistently low inferential importance across two recent treatments of the taxon with alternative character codings. These results lend support to the idea that taxon influence indices may be robust to character coding and therefore effective for morphological analyses. These results underscore a need for the development of approaches to, and application of, taxon influence analyses both for the purpose of establishing robust rankings for future inquiry and for explicitly quantifying the importance of individual taxa. Quantifying the importance of individual taxa refocuses debates in morphological studies from questions of character choice/significance and taxon sampling to explicitly analytical techniques, and guides discussion of the context of new discoveries.

Project description:Water flow in river networks is frequently regulated by man-made in-stream barriers. These obstacles can hinder dispersal of aquatic organisms and isolate populations leading to the loss of genetic diversity. Although millions of small in-stream barriers exist worldwide, their impact on dispersal of macroinvertebrates remains unclear. Therefore, we, therefore, assessed the effects of such barriers on the population structure and effective dispersal of five macroinvertebrate species with strictly aquatic life cycles: the amphipod crustacean Gammarus fossarum (clade 11), three snail species of the Ancylus fluviatilis species complex and the flatworm Dugesia gonocephala. We studied populations at nine weirs and eight culverts (3 pipes, 5 tunnels), built 33-109 years ago, mainly in the heavily fragmented catchment of the river Ruhr (Sauerland, Germany). To assess fragmentation and barrier effects, we generated genome-wide SNP data using ddRAD sequencing and evaluated clustering, differentiation between populations up- and downstream of each barrier and effective migration rates among sites and across barriers. Additionally, we applied population genomic simulations to assess expected differentiation patterns under different gene flow scenarios. Our data show that populations of all species are highly isolated at regional and local scales within few kilometers. While the regional population structure likely results from historical processes, the strong local differentiation suggests that contemporary dispersal barriers exist. However, we identified significant barrier effects only for pipes (for A. fluviatilis II and III) and few larger weirs (>1.3 m; for D. gonocephala). Therefore, our data suggest that most small in-stream barriers can probably be overcome by all studied taxa frequently enough to prevent fragmentation. However, it remains to be tested if the strong local differentiation is a result of a cumulative effect of small barriers, or if larger in-stream barriers, land use, chemical pollution, urbanization, or a combination of these factors impede gene flow.

Project description:Background: The accuracy of microbial community detection in 16S rRNA marker-gene and metagenomic studies suffers from contamination and sequencing errors that lead to either falsely identifying microbial taxa that were not in the sample or misclassifying the taxa of DNA fragment reads. Removing contaminants and filtering rare features are two common approaches to deal with this problem. While contaminant detection methods use auxiliary sequencing process information to identify known contaminants, filtering methods remove taxa that are present in a small number of samples and have small counts in the samples where they are observed. The latter approach reduces the extreme sparsity of microbiome data and has been shown to correctly remove contaminant taxa in cultured "mock" datasets, where the true taxa compositions are known. Although filtering is frequently used, careful evaluation of its effect on the data analysis and scientific conclusions remains unreported. Here, we assess the effect of filtering on the alpha and beta diversity estimation as well as its impact on identifying taxa that discriminate between disease states. Results: The effect of filtering on microbiome data analysis is illustrated on four datasets: two mock quality control datasets where the same cultured samples with known microbial composition are processed at different labs and two disease study datasets. Results show that in microbiome quality control datasets, filtering reduces the magnitude of differences in alpha diversity and alleviates technical variability between labs while preserving the between samples similarity (beta diversity). In the disease study datasets, DESeq2 and linear discriminant analysis Effect Size (LEfSe) methods were used to identify taxa that are differentially abundant across groups of samples, and random forest models were used to rank features with the largest contribution toward disease classification. Results reveal that filtering retains significant taxa and preserves the model classification ability measured by the area under the receiver operating characteristic curve (AUC). The comparison between the filtering and the contaminant removal method shows that they have complementary effects and are advised to be used in conjunction. Conclusions: Filtering reduces the complexity of microbiome data while preserving their integrity in downstream analysis. This leads to mitigation of the classification methods' sensitivity and reduction of technical variability, allowing researchers to generate more reproducible and comparable results in microbiome data analysis.

Dataset Information

Quantifying the responses of biological indices to rare macroinvertebrate taxa exclusion: Does excluding more rare taxa cause more error?

Publications

Quantifying the responses of biological indices to rare macroinvertebrate taxa exclusion: Does excluding more rare taxa cause more error?

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets