Towards a more reproducible ecology

of research papers. But this development also poses some important challenges. The large amount of project-specific software being generated for analytical studies means that analytical standards are harder to establish, potentially limiting the reproducibility of much of recently published science. Also, analytical and coding errors may escape detection, with potentially highly problematic results, such as when Geoffrey Chang and colleagues had to retract three Science papers after discovering an error in a homemade data-analysis program (Miller 2006). Substantial progress in our understanding of ecology rests on trustworthy, reproducible and transparent data analysis. Reproducibility is the very hallmark of the scientific method. However, there is an increasing concern that many studies today might not be reproducible. The focus on novelty in ‘high-impact’ journals means that there is little incentive for researchers to directly replicate published studies, and this lack of replication of studies has come under increasing scrutiny (Iqbal et al. 2016, The Economist 2016). What is more worrying, efforts to systematically replicate published studies have often failed (Open Science Collaboration 2015). This is so concerning that it has lead to the founding of a new journal dedicated explicitly to the replication of published results (the Preclinical Reproducibility and Robustness channel of F1000 opened 4 February 2016). A recent comment in Nature magazine (Allison et al. 2016) reported on the widespread problem of reproducibility in the natural sciences, and gave a sobering account of the obstacles to overcoming it, which includes the pressure on researchers to publish, the lack of established pathways for dealing with non-reproducible articles, and consistent issues with the statistical treatment of data (Krumholz 2016, The Economist 2016). Ecology faces particular challenges in reproducibility because data collection is often context dependent (Ellison 2010), and because there are few established standards for storing metadata and facilitating study replication. The keys to a greater level of reproducibility in ecology are to establish analytical protocols that are robust and transparent, to faithfully document the analytical process including any failed attempts, and to ensure that the storage and acquisition of data is documented and includes the appropriate metadata. Fortunately, recent technological developments Ecography 39: 349–353, 2016 doi: 10.1111/ecog.02493 © 2016 The Authors. Ecography © 2016 Nordic Society Oikos