scholarly journals Methods for testing for texture convergence using abundance data: a randomisation test and a method for comparing the shape of distributions

2001 ◽  
Vol 2 (1) ◽  
pp. 57-66 ◽  
Author(s):  
J. B. Wilson ◽  
B. Smith
Keyword(s):  
Abundance Data ◽  
Randomisation Test

scholarly journals Application of the CPCAT to Assess Ecotoxicological Field Studies: Comparative Analysis Using Earthworm Field Test Abundance Data

2021 ◽  
Author(s):  
Benjamin Daniels ◽  
Martina Roß‐Nickoll ◽  
Stephan Jänsch ◽  
Silvia Pieper ◽  
Jörg Römbke ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Field Test ◽  
Field Studies ◽  
Abundance Data

First comparison of quantitative estimates of termite biomass and abundance reveals strong intercontinental differences

2014 ◽  
Vol 30 (2) ◽  
pp. 143-152 ◽  
Author(s):  
Cecilia A.L. Dahlsjö ◽  
Catherine L. Parr ◽  
Yadvinder Malhi ◽  
Homathevi Rahman ◽  
Patrick Meir ◽  
...  
Keyword(s):  
Functional Groups ◽  
Functional Diversity ◽  
Ecosystem Processes ◽  
Global Scale ◽  
Species Density ◽  
Abundance Data ◽  
Standardized Protocol ◽  
Biogeographical Distribution ◽  
Belt Transect

Abstract:Termite species and functional groups differ among regions globally (the functional-diversity anomaly). Here we investigate whether similar differences in biomass and abundance of termites occur among continents. Biomass and abundance data were collected with standardized sampling in Cameroon, Malaysia and Peru. Data from Peru were original to this study, while data from Cameroon and Malaysia were compiled from other sources. Species density data were sampled using a standardized belt transect (100 × 2 m) while the biomass and abundance measurements were sampled using a standardized protocol based on 2 × 2-m quadrats. Biomass and abundance data confirmed patterns found for species density and thus the existence of the functional diversity anomaly: highest estimates for biomass and abundance were found in Cameroon (14.5 ± 7.90 g m−2 and 1234 ± 437 ind m−2) followed by Malaysia (0.719 ± 0.193 g m−2 and 327 ± 72 ind m−2) and then Peru (0.345 ± 0.103 g m−2 and 130 ± 39 ind m−2). The biomass and abundance for each functional group were significantly different across sites for most termite functional groups. Biogeographical distribution of lineages was the primary cause for the functional diversity anomaly with true soil-feeding termites dominating in Cameroon and the absence of fungus-growing termites from Peru. These findings are important as the biomass and abundance of functional groups may be linked to ecosystem processes. Although this study allowed for comparisons between data from different regions further comparable data are needed to enhance the understanding of the role of termites in ecosystem processes on a global scale.

Randomisation test for the number of dimensions of the group average space in generalised Procrustes analysis

2002 ◽  
Vol 13 (3) ◽  
pp. 191-200 ◽  
Author(s):  
W. Wu ◽  
Q. Guo ◽  
S. de Jong ◽  
D.L. Massart
Keyword(s):  
Procrustes Analysis ◽  
Randomisation Test

Using Time Series to Estimate Rates of Population Change from Abundance Data

2007 ◽  
Vol 71 (1) ◽  
pp. 202-207 ◽  
Author(s):  
KRISTEN E. RYDING ◽  
JOSHUA J. MILLSPAUGH ◽  
JOHN R. SKALSKI
Keyword(s):  
Time Series ◽  
Population Change ◽  
Abundance Data

scholarly journals Detecting Selective Expression of Genes and Proteins

1999 ◽  
Vol 9 (3) ◽  
pp. 282-296 ◽  
Author(s):  
Larry D. Greller ◽  
Frank L. Tobin
Keyword(s):  
Noisy Data ◽  
Distribution Patterns ◽  
Statistical Test ◽  
Computational Method ◽  
Computational Results ◽  
Abundance Data ◽  
Expression Of Genes ◽  
Selective Expression ◽  
Gene Expression Analyses ◽  
General Method

Selective expression of a gene product (mRNA or protein) is a pattern in which the expression is markedly high, or markedly low, in one particular tissue compared with its level in other tissues or sources. We present a computational method for the identification of such patterns. The method combines assessments of the reliability of expression quantitation with a statistical test of expression distribution patterns. The method is applicable to small studies or to data mining of abundance data from expression databases, whether mRNA or protein. Though the method was developed originally for gene-expression analyses, the computational method is, in fact, rather general. It is well suited for the identification of exceptional values in many sorts of intensity data, even noisy data, for which assessments of confidences in the sources of the intensities are available. Moreover, the method is indifferent as to whether the intensities are experimentally or computationally derived. We show details of the general method and examples of computational results on gene abundance data.

scholarly journals A phylogenetic transform enhances analysis of compositional microbiota data

2016 ◽  
Author(s):  
Justin D Silverman ◽  
Alex Washburne ◽  
Sayan Mukherjee ◽  
Lawrence A David
Keyword(s):  
Microbial Communities ◽  
Relative Abundance ◽  
Dominant Role ◽  
Phylogenetic Information ◽  
Abundance Data ◽  
Sequencing Technologies ◽  
Environmental Selection ◽  
Health And Disease ◽  
High Throughput Dna Sequencing ◽  
Spurious Results

ABSTRACTHigh-throughput DNA sequencing technologies have revolutionized the study of microbial communities (microbiota) and have revealed their importance in both human health and disease. However, due to technical limitations, data from microbiota surveys reflect the relative abundance of bacterial taxa and not their absolute levels. It is well known that applying common statistical methods, such as correlation or hypothesis testing, to relative abundance data can lead to spurious results. Here, we introduce the PhILR transform, a data transform that utilizes microbial phylogenetic information. This transform enables off-the-shelf statistical tools to be applied to microbiota surveys free from artifacts usually associated with analysis of relative abundance data. Using environmental and human-associated microbial community datasets as benchmarks, we find that the PhILR transform significantly improves the performance of distance-based and machine learning-based statistics, boosting the accuracy of widely used algorithms on reference benchmarks by 90%. Because the PhILR transform relies on bacterial phylogenies, statistics applied in the PhILR coordinate system are also framed within an evolutionary perspective. Regression on PhILR transformed human microbiota data identified evolutionarily neighboring bacterial clades that may have differentiated to adapt to distinct body sites. Variance statistics showed that the degree of covariation of bacterial clades across human body sites tended to increase with phylogenetic relatedness between clades. These findings support the hypothesis that environmental selection, not competition between bacteria, plays a dominant role in structuring human-associated microbial communities.

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