Literature DB >> 32348931

Rethinking discretization to advance limnology amid the ongoing information explosion.

B M Kraemer1.   

Abstract

Limnologists often adhere to a discretized view of waterbodies-they classify them, divide them into zones, promote discrete management targets, and use research tools, experimental designs, and statistical analyses focused on discretization. By offering useful shortcuts, this approach to limnology has profoundly benefited the way we understand, manage, and communicate about waterbodies. But the research questions and the research tools in limnology are changing rapidly in the era of big data, with consequences for the relevance of our current discretization schemes. Here, I examine how and why we discretize and argue that selectively rethinking the extent to which we must discretize gives us an exceptional chance to advance limnology in new ways. To help us decide when to discretize, I offer a framework (discretization evaluation framework) that can be used to compare the usefulness of various discretization approaches to an alternative which relies less on discretization. This framework, together with a keen awareness of discretization's advantages and disadvantages, may help limnologists benefit from the ongoing information explosion.
Copyright © 2020 Elsevier Ltd. All rights reserved.

Keywords:  Big data; Classification; Computing; Discretization evaluation framework; Management; Statistics; Trophic state; Zonation

Mesh:

Year:  2020        PMID: 32348931     DOI: 10.1016/j.watres.2020.115801

Source DB:  PubMed          Journal:  Water Res        ISSN: 0043-1354            Impact factor:   11.236


  1 in total

1.  A functional definition to distinguish ponds from lakes and wetlands.

Authors:  David C Richardson; Meredith A Holgerson; Matthew J Farragher; Kathryn K Hoffman; Katelyn B S King; María B Alfonso; Mikkel R Andersen; Kendra Spence Cheruveil; Kristen A Coleman; Mary Jade Farruggia; Rocio Luz Fernandez; Kelly L Hondula; Gregorio A López Moreira Mazacotte; Katherine Paul; Benjamin L Peierls; Joseph S Rabaey; Steven Sadro; María Laura Sánchez; Robyn L Smyth; Jon N Sweetman
Journal:  Sci Rep       Date:  2022-06-21       Impact factor: 4.996

  1 in total

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