scholarly journals Using the Biological Condition Gradient Model as a Bioassessment Framework to Support Rehabilitation and Restoration of the Upper Tana River Watershed in Kenya

2021 ◽  
Vol 9 ◽  
Author(s):  
George G. Ndiritu ◽  
Taita Terer ◽  
Peter Njoroge ◽  
Veronica M. Muiruri ◽  
Edward L. Njagi ◽  
...  

The biological condition gradient (BCG), a scientific framework that describes the change in ecosystem characteristics in response to human-induced levels of stressors, was modified and used to characterize watershed habitats in the Upper Tana River watershed, Kenya. The inbuilt utilities of BCG, including its simplicity, versatility, and its robust nature, allowed its use by seven taxonomic groups of macroinvertebrates, diatoms, fish, herpetofauna (amphibians and reptiles), plants, macrofungi, and birds to assess and monitor landscape conditions in both terrestrial and aquatic habitats. The biological data were described using taxa abundance distribution measures followed by multivariate analyses to determine their relationship with water or soil quality and thereafter assessment of taxa tolerant levels in response to environmental stress and disturbances. Preliminary findings reported that the taxonomic groups complemented each other, with each taxonomic group reliably assessing ecological conditions to a certain degree that supported assigning all 36 sampled sites into BCG tiers. The BCG models developed for all taxonomic groups assisted in the identification and selection of taxa indicating varying levels of landscape conditions. These taxa, referred to as flagship or indicator taxa, assist in simplifying the BCG model and, hence, are possible for use by parataxonomists or ordinary citizens to assess and monitor the ecological health of habitats under consideration. Furthermore, the capability of BCG models to assess landscape conditions shows how they can be used to identify important habitats for conservation, direct investment for restoration, and track progress.

2021 ◽  

Abstract R is an open-source statistical environment modelled after the previously widely used commercial programs S and S-Plus, but in addition to powerful statistical analysis tools, it also provides powerful graphics outputs. In addition to its statistical and graphical capabilities, R is a programming language suitable for medium-sized projects. This book presents a set of studies that collectively represent almost all the R operations that beginners, analysing their own data up to perhaps the early years of doing a PhD, need. Although the chapters are organized around topics such as graphing, classical statistical tests, statistical modelling, mapping and text parsing, examples have been chosen based largely on real scientific studies at the appropriate level and within each the use of more R functions is nearly always covered than are simply necessary just to get a p-value or a graph. R comes with around a thousand base functions which are automatically installed when R is downloaded. This book covers the use of those of most relevance to biological data analysis, modelling and graphics. Throughout each chapter, the functions introduced and used in that chapter are summarized in Tool Boxes. The book also shows the user how to adapt and write their own code and functions. A selection of base functions relevant to graphics that are not necessarily covered in the main text are described in Appendix 1, and additional housekeeping functions in Appendix 2.


1969 ◽  
Vol 57 (3) ◽  
pp. 673 ◽  
Author(s):  
R. B. Ivimey-Cook ◽  
M. C. F. Proctor ◽  
D. L. Wigston

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Francesc López-Giráldez ◽  
Andrew H. Moeller ◽  
Jeffrey P. Townsend

Phylogenetic research is often stymied by selection of a marker that leads to poor phylogenetic resolution despite considerable cost and effort. Profiles of phylogenetic informativeness provide a quantitative measure for prioritizing gene sampling to resolve branching order in a particular epoch. To evaluate the utility of these profiles, we analyzed phylogenomic data sets from metazoans, fungi, and mammals, thus encompassing diverse time scales and taxonomic groups. We also evaluated the utility of profiles created based on simulated data sets. We found that genes selected via their informativeness dramatically outperformed haphazard sampling of markers. Furthermore, our analyses demonstrate that the original phylogenetic informativeness method can be extended to trees with more than four taxa. Thus, although the method currently predicts phylogenetic signal without specifically accounting for the misleading effects of stochastic noise, it is robust to the effects of homoplasy. The phylogenetic informativeness rankings obtained will allow other researchers to select advantageous genes for future studies within these clades, maximizing return on effort and investment. Genes identified might also yield efficient experimental designs for phylogenetic inference for many sister clades and outgroup taxa that are closely related to the diverse groups of organisms analyzed.


2010 ◽  
Vol 14 (06) ◽  
pp. 1129-1147 ◽  
Author(s):  
MARTINA N. MANSFELD ◽  
KATHARINA HÖLZLE ◽  
HANS GEORG GEMÜNDEN

First and foremost, innovation is driven by people. These people have specific personal characteristics and fulfil designated roles in innovation management. We have conducted an empirical study among 190 employees in R&D departments of mature international firms from four different countries (Germany, U.S.A., Great Britain and Switzerland) currently working on innovation projects. Using multivariate analyses, we could identify personal characteristics associated with different roles people can take over the course of an innovation project. These roles are called expert, power, process or relationship promotor as well as champion. The identified personal characteristics exhibit a distinctive pattern in their combined occurrence for each role. Our results show a detailed picture of specific personal characteristics of individuals, so called innovators, necessary for successful innovation. Based on these findings, we derive recommendations for a targeted human resource acquisition and a better selection of employees for successful innovation teams.


PLoS ONE ◽  
2010 ◽  
Vol 5 (10) ◽  
pp. e13518 ◽  
Author(s):  
Paul Chuchana ◽  
Philippe Holzmuller ◽  
Frederic Vezilier ◽  
David Berthier ◽  
Isabelle Chantal ◽  
...  

2016 ◽  
Vol 90 ◽  
pp. 194-204 ◽  
Author(s):  
Carla M. Stinco ◽  
María Luisa Escudero-Gilete ◽  
Francisco J. Heredia ◽  
Isabel M. Vicario ◽  
Antonio J. Meléndez-Martínez

2019 ◽  
Vol 23 (3) ◽  
pp. 312-319
Author(s):  
P. S. Demenkov ◽  
O. V. Saik ◽  
T. V. Ivanisenko ◽  
N. A. Kolchanov ◽  
A. V. Kochetov ◽  
...  

The development of highly efficient technologies in genomics, transcriptomics, proteomics and metabolomics, as well as new technologies in agriculture has led to an “information explosion” in plant biology and crop production, including potato production. Only a small part of the information reaches formalized databases (for example, Uniprot, NCBI Gene, BioGRID, IntAct, etc.). One of the main sources of reliable biological data is the scientific literature. The well-known PubMed database contains more than 18 thousand abstracts of articles on potato. The effective use of knowledge presented in such a number of non-formalized documents in natural language requires the use of modern intellectual methods of analysis. However, in the literature, there is no evidence of a widespread use of intelligent methods for automatically extracting knowledge from scientific publications on cultures such as potatoes. Earlier we developed the SOLANUM TUBEROSUM knowledge base (http://www-bionet.sysbio.cytogen. ru/and/plant/). Integrated into the knowledge base information about the molecular genetic mechanisms underlying the selection of significant traits helps to accelerate the identification of candidate genes for the breeding characteristics of potatoes and the development of diagnostic markers for breeding. The article searches for new potential participants of the molecular genetic mechanisms of resistance to adverse factors in plants. Prioritizing candidate genes has shown that the PHYA, GF14, CNIH1, RCI1A, ABI5, CPK1, RGS1, NHL3, GRF8, and CYP21-4 genes are the most promising for further testing of their relationships with resistance to adverse factors. As a result of the analysis, it was shown that the molecular genetic relationships responsible for the formation of significant agricultural traits are complex and include many direct and indirect interactions. The construction of associative gene networks and their analysis using the SOLANUM TUBEROSUM knowledge base is the basis for searching for target genes for targeted mutagenesis and marker-oriented selection of potato varieties with valuable agricultural characteristics.


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