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Improving quantitative synthesis to achieve generality in ecology

Improving quantitative synthesis to achieve generality in ecology
Improving quantitative synthesis to achieve generality in ecology

Synthesis of primary ecological data is often assumed to achieve a notion of ‘generality’, through the quantification of overall effect sizes and consistency among studies, and has become a dominant research approach in ecology. Unfortunately, ecologists rarely define either the generality of their findings, their estimand (the target of estimation) or the population of interest. Given that generality is fundamental to science, and the urgent need for scientific understanding to curb global scale ecological breakdown, loose usage of the term ‘generality’ is problematic. In other disciplines, generality is defined as comprising both generalizability—extending an inference about an estimand from the sample to the population—and transferability—the validity of estimand predictions in a different sampling unit or population. We review current practice in ecological synthesis and demonstrate that, when researchers fail to define the assumptions underpinning generalizations and transfers of effect sizes, generality often misses its target. We provide guidance for communicating nuanced inferences and maximizing the impact of syntheses both within and beyond academia. We propose pathways to generality applicable to ecological syntheses, including the development of quantitative and qualitative criteria with which to license the transfer of estimands from both primary and synthetic studies.

2397-334X
1818-1828
Spake, Rebecca
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O’dea, Rose E.
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Nakagawa, Shinichi
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Doncaster, C. Patrick
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Ryo, Masahiro
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Callaghan, Corey T.
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Bullock, James M.
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Spake, Rebecca
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O’dea, Rose E.
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Nakagawa, Shinichi
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Doncaster, C. Patrick
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Ryo, Masahiro
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Callaghan, Corey T.
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Bullock, James M.
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Spake, Rebecca, O’dea, Rose E., Nakagawa, Shinichi, Doncaster, C. Patrick, Ryo, Masahiro, Callaghan, Corey T. and Bullock, James M. (2022) Improving quantitative synthesis to achieve generality in ecology. Nature Ecology & Evolution, 6 (12), 1818-1828. (doi:10.1038/s41559-022-01891-z).

Record type: Review

Abstract

Synthesis of primary ecological data is often assumed to achieve a notion of ‘generality’, through the quantification of overall effect sizes and consistency among studies, and has become a dominant research approach in ecology. Unfortunately, ecologists rarely define either the generality of their findings, their estimand (the target of estimation) or the population of interest. Given that generality is fundamental to science, and the urgent need for scientific understanding to curb global scale ecological breakdown, loose usage of the term ‘generality’ is problematic. In other disciplines, generality is defined as comprising both generalizability—extending an inference about an estimand from the sample to the population—and transferability—the validity of estimand predictions in a different sampling unit or population. We review current practice in ecological synthesis and demonstrate that, when researchers fail to define the assumptions underpinning generalizations and transfers of effect sizes, generality often misses its target. We provide guidance for communicating nuanced inferences and maximizing the impact of syntheses both within and beyond academia. We propose pathways to generality applicable to ecological syntheses, including the development of quantitative and qualitative criteria with which to license the transfer of estimands from both primary and synthetic studies.

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Accepted/In Press date: 26 August 2022
Published date: December 2022
Additional Information: Funding Information: We thank J. Chase for informative discussion of concepts. R.S. is grateful for funding from the German Centre for Integrative Biodiversity Research – iDiv - Halle-Jena-Leipzig. J.M.B. was funded under UKCEH National Capability project 06895. C.T.C. was supported by a Marie Skłodowska-Curie Individual Fellowship (no. 891052). Publisher Copyright: © 2022, Springer Nature Limited.

Identifiers

Local EPrints ID: 473115
URI: http://eprints.soton.ac.uk/id/eprint/473115
ISSN: 2397-334X
PURE UUID: 32d4fe72-0dc2-4e84-a317-8131959a06f0
ORCID for C. Patrick Doncaster: ORCID iD orcid.org/0000-0001-9406-0693

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Date deposited: 10 Jan 2023 18:18
Last modified: 17 Mar 2024 07:36

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Contributors

Author: Rebecca Spake
Author: Rose E. O’dea
Author: Shinichi Nakagawa
Author: Masahiro Ryo
Author: Corey T. Callaghan
Author: James M. Bullock

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