Lessons from meta-analysis in ecology and evolution: the need for trans-disciplinary evidence synthesis methodologies

Abstract

Lessons from meta-analysis in ecology and evolution: the need for trans-disciplinary evidence synthesis methodologies Evidence synthesis in ecology and evolution has a long and distinguished history dating back to the seminal works of Jessica Gurevitch and Larry Hedges (Gurevitch and Hedges, 1993;Gurevitch and Hedges, 1999), which popularised methods originally developed in social science and medicine. Currently, the field is seeing increasing numbers of meta-analyses, publication of methods textbooks such as the Handbook of Meta-analysis in Ecology and Evolution (Koricheva et al. 2013) and the further extension of evidence-based methods to other environmental fields such as food and chemical safety. Whilst acknowledging that the methodologies of evidence-based disciplines are generic and transferable, it must also be recognised that different fields of application hold different methodological challenges. This special issue focuses on three of the problematic areas for evidence synthesis in ecology, namely, acquisition of information, assessing strength of evidence and supporting decision-making in complex and uncertain systems. Searching for ecological literature is challenging for three reasons. First, the scope of ecological questions is usually very broad -encompassing multiple species, habitats and outcomes. Adopting the reductionist rationale of clinical medicine makes little sense because the primary reason for synthesis is generally understanding heterogeneity rather than increasing power. Whilst the need to explore heterogeneity is recognised in medicine (Lau et al. 1998), questions tend to focus on a restricted and specific population, set of comparators, outcomes and study design in comparison to ecology (Stewart 2010). Second, with the exception of evidence-based conservation, interventions are usually absent; instead, questions focus on impact or relationships and are thus more akin to medical reviews of adverse events or prognostic studies. Given the dearth of robust guidance on searching prognostic literature, the medical community may have as much to learn from ecologists (and social scientists) in this regard as vice versa. Third, the search infra-structure available to researchers out with health disciplines is woefully inadequate for the task of efficiently identifying pertinent information. Whilst accepting that the integrated MeSH headings of PubMed would be challenging to develop and apply to the wider science domain, the failure of publishers to utilise machine learning algorithms and sophisticated classification architectures to a greater extent is a major impediment to robust rapid evidence synthesis. The article by Bayliss & Beyer (Bayliss and Beyer, 2015) provides ecologists with pragmatic guidance on searching literature. It augments Chapter 4 of the Handbook of Meta-analysis in Ecology and Evolution (Côté et al. 2013). Much guidance will be familiar to information scientists in other free-text domains such as public health and social science, but searching across phylogenies offers n...

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