The Hierarchy-of-Hypotheses Approach: A Synthesis Method for Enhancing Theory Development in Ecology and Evolution

Abstract In the current era of Big Data, existing synthesis tools such as formal meta-analyses are critical means to handle the deluge of information. However, there is a need for complementary tools that help to (a) organize evidence, (b) organize theory, and (c) closely connect evidence to theory....

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Published in:Bioscience 2021-04, Vol.71 (4), p.337-349
Main Authors: Heger, Tina, Aguilar-Trigueros, Carlos A, Bartram, Isabelle, Braga, Raul Rennó, Dietl, Gregory P, Enders, Martin, Gibson, David J, Gómez-Aparicio, Lorena, Gras, Pierre, Jax, Kurt, Lokatis, Sophie, Lortie, Christopher J, Mupepele, Anne-Christine, Schindler, Stefan, Starrfelt, Jostein, Synodinos, Alexis D, Jeschke, Jonathan M
Format: Article
Language:English
Subjects:
Developmental biology
Empirical analysis
Environmental Sciences
Hypotheses
Overview
Synthesis
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Summary:Abstract In the current era of Big Data, existing synthesis tools such as formal meta-analyses are critical means to handle the deluge of information. However, there is a need for complementary tools that help to (a) organize evidence, (b) organize theory, and (c) closely connect evidence to theory. We present the hierarchy-of-hypotheses (HoH) approach to address these issues. In an HoH, hypotheses are conceptually and visually structured in a hierarchically nested way where the lower branches can be directly connected to empirical results. Used for organizing evidence, this tool allows researchers to conceptually connect empirical results derived through diverse approaches and to reveal under which circumstances hypotheses are applicable. Used for organizing theory, it allows researchers to uncover mechanistic components of hypotheses and previously neglected conceptual connections. In the present article, we offer guidance on how to build an HoH, provide examples from population and evolutionary biology and propose terminological clarifications.
ISSN:0006-3568
1525-3244
DOI:10.1093/biosci/biaa130