Tina Heger, Carlos Aguilar, Isabelle Bartram, Raul Rennó Braga, Gregory P. Dietl, Martin Enders, David J. Gibson, Lorena Gómez-Aparicio, Pierre Gras, Kurt Jax

In the current era of Big Data, existing synthesis tools (e.g. formal meta-analysis) are useful for handling the deluge of data and information. However, there is a need for complementary tools that help to (i) structure data and information, (ii) closely connect evidence to theory and (iii) further develop 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 as an evidence-driven, bottom-up approach, it can (i) show connections between empirical results, even when derived through diverse approaches; and (ii) indicate under which circumstances hypotheses are applicable. Used as a theory-driven, top-down method, it helps uncover mechanistic components of hypotheses. We offer guidance on how to build an HoH, provide examples from population and evolutionary biology and propose terminological clarifications.