Literature DB >> 30013132

Logarithmic scales in ecological data presentation may cause misinterpretation.

Duncan N L Menge1, Anna C MacPherson2, Thomas A Bytnerowicz3, Andrew W Quebbeman3, Naomi B Schwartz3, Benton N Taylor3, Amelia A Wolf3,4.   

Abstract

Scientific communication relies on clear presentation of data. Logarithmic scales are used frequently for data presentation in many scientific disciplines, including ecology, but the degree to which they are correctly interpreted by readers is unclear. Analysing the extent of log scales in the literature, we show that 22% of papers published in the journal Ecology in 2015 included at least one log-scaled axis, of which 21% were log-log displays. We conducted a survey that asked members of the Ecological Society of America (988 responses, and 623 completed surveys) to interpret graphs that were randomly displayed with linear-linear or log-log axes. Many more respondents interpreted graphs correctly when the graphs had linear-linear axes than when they had log-log axes: 93% versus 56% for our all-around metric, although some of the individual item comparisons were even more skewed (for example, 86% versus 9% and 88% versus 12%). These results suggest that misconceptions about log-scaled data are rampant. We recommend that ecology curricula include explicit instruction on how to interpret log-scaled axes and equations, and we also recommend that authors take the potential for misconceptions into account when deciding how to visualize data.

Mesh:

Year:  2018        PMID: 30013132     DOI: 10.1038/s41559-018-0610-7

Source DB:  PubMed          Journal:  Nat Ecol Evol        ISSN: 2397-334X            Impact factor:   15.460


  5 in total

1.  Physical and Functional Constraints on Viable Belowground Acquisition Strategies.

Authors:  M Luke McCormack; Colleen M Iversen
Journal:  Front Plant Sci       Date:  2019-10-11       Impact factor: 5.753

2.  Molybdenum threshold for ecosystem scale alternative vanadium nitrogenase activity in boreal forests.

Authors:  Romain Darnajoux; Nicolas Magain; Marie Renaudin; François Lutzoni; Jean-Philippe Bellenger; Xinning Zhang
Journal:  Proc Natl Acad Sci U S A       Date:  2019-11-14       Impact factor: 11.205

Review 3.  Research Trends in Crop-Livestock Systems: A Bibliometric Review.

Authors:  Guoting Yang; Jing Li; Zhen Liu; Yitao Zhang; Xiangbo Xu; Hong Zhang; Yan Xu
Journal:  Int J Environ Res Public Health       Date:  2022-07-13       Impact factor: 4.614

4.  How People Understand Risk Matrices, and How Matrix Design Can Improve their Use: Findings from Randomized Controlled Studies.

Authors:  Holly Sutherland; Gabriel Recchia; Sarah Dryhurst; Alexandra L J Freeman
Journal:  Risk Anal       Date:  2021-09-14       Impact factor: 4.302

5.  The scale of COVID-19 graphs affects understanding, attitudes, and policy preferences.

Authors:  Alessandro Romano; Chiara Sotis; Goran Dominioni; Sebastián Guidi
Journal:  Health Econ       Date:  2020-08-25       Impact factor: 2.395

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.