Improving quantitative synthesis to achieve generality in ecology

Spake, Rebecca; O’Dea, Rose E.; Nakagawa, Shinichi; Doncaster, C. Patrick; Ryo, Masahiro; Callaghan, Corey T.; Bullock, James M. ORCID: 2022 Improving quantitative synthesis to achieve generality in ecology. Nature Ecology & Evolution, 6 (12). 1818-1828.

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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.

Item Type: Publication - Article
Digital Object Identifier (DOI):
UKCEH and CEH Sections/Science Areas: Biodiversity (Science Area 2017-)
ISSN: 2397-334X
Additional Information. Not used in RCUK Gateway to Research.: Publisher link (see Related URLs) provides a read-only full-text copy of the published paper.
Additional Keywords: biodiversity, conservation biology, ecological modelling
NORA Subject Terms: Ecology and Environment
Related URLs:
Date made live: 30 Dec 2022 19:19 +0 (UTC)

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