Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
Contradictory Results

Decontextualizing Big Data for a Better Perception of Macroecology

View ORCID ProfileChristian Mulder, View ORCID ProfileGiorgi Mancinelli
doi: https://doi.org/10.1101/059915
Christian Mulder
1National Institute for Public Health and the Environment, Bilthoven, The Netherlands
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Christian Mulder
  • For correspondence: christian.mulder@rivm.nl
Giorgi Mancinelli
2ECOTEKNE, University of Salento, Lecce, Italy
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Giorgi Mancinelli
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

ABSTRACT

Fish species are charismatic subjects widely used for ecological assessment and modelling. We investigated the influence of electrofishing in an attempt to illuminate the extent to which datasets might be merged together. Three American Midwestern regions in Ohio were chosen as study area and the changes in the size-biomass spectra of more than 2000 fish assemblages were analysed. These communities behaved differently according to the sampling method in conjunction to the morphology of the investigated streams and rivers, as shown by decreasing predatory species and lowering of allometric slopes. There are here several lines of evidence indicating that the chosen sampling method, as determined by different habitats, acts as a pitfall and strongly influences the allometry of fish spectra. In the ongoing data-rich era, our results highlight that macroecological investigations, often performed by machine-learning systems without considering the different procedures adopted to collect original data, can easily produce artefactual allometric scalings.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
Back to top
PreviousNext
Posted June 22, 2016.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Decontextualizing Big Data for a Better Perception of Macroecology
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Decontextualizing Big Data for a Better Perception of Macroecology
Christian Mulder, Giorgi Mancinelli
bioRxiv 059915; doi: https://doi.org/10.1101/059915
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Decontextualizing Big Data for a Better Perception of Macroecology
Christian Mulder, Giorgi Mancinelli
bioRxiv 059915; doi: https://doi.org/10.1101/059915

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Ecology
Subject Areas
All Articles
  • Animal Behavior and Cognition (3514)
  • Biochemistry (7364)
  • Bioengineering (5341)
  • Bioinformatics (20316)
  • Biophysics (10038)
  • Cancer Biology (7769)
  • Cell Biology (11346)
  • Clinical Trials (138)
  • Developmental Biology (6445)
  • Ecology (9977)
  • Epidemiology (2065)
  • Evolutionary Biology (13351)
  • Genetics (9369)
  • Genomics (12603)
  • Immunology (7724)
  • Microbiology (19083)
  • Molecular Biology (7458)
  • Neuroscience (41125)
  • Paleontology (300)
  • Pathology (1235)
  • Pharmacology and Toxicology (2142)
  • Physiology (3174)
  • Plant Biology (6873)
  • Scientific Communication and Education (1276)
  • Synthetic Biology (1900)
  • Systems Biology (5324)
  • Zoology (1091)