Special feature: 5<sup>th</sup> anniversary of <i>Methods in Ecology and Evolution</i>
Bibliographic record
Abstract
Methods in Ecology and Evolution was launched in 2010 with the ambition that we could transform the development and uptake of new methods. As the first journal in our field that specialized in publishing methodology papers, we aspired to give those researchers who develop new methods a place to publish and gain recognition for their work. We also aimed to improve the presentation of methodology to as wide an audience as possible by emphasizing the need for accessibility in published papers, as well as taking advantage of online support such as videos and podcasts. We have now published over 500 papers, together with 90 videos and podcasts as well as over 300 blog posts. Among the published papers, we have had 100 applications papers that describe new computer packages – allowing developers to gain credit for their work through publication and citation. Although an imperfect measure of quality or real impact, by 2015, Methods in Ecology and Evolution had an impact factor of 6·554 and was number 9 in the ISI ranking for ecology. If nothing else, the impact factor shows that we have definitely established ourselves in the mainstream of ecology journals. To celebrate the 5th anniversary of Methods in Ecology and Evolution, we held a symposium in 2015, jointly hosted between London and Calgary. Streamed live, and with talks recorded and hosted here (http://bit.ly/1dG4Bmz), the symposium showcased the range of methods that we had published across our discipline and involved many of our authors and members of our editorial board. A selection of the authors who gave talks at the symposium have contributed articles to this special feature. The first paper in this special feature is, fittingly, by the authors of the first paper published in Methods in Ecology and Evolution in 2010 – indeed the first paper submitted to the journal. In that first paper, Zuur, Ieno & Elphick (2010) provided practical advice on exploratory data analysis, and this has been exceptionally well cited. In their new paper, Zuur & Ieno (2016) focus on regression analysis, providing advice and guidance on performing analysis and reporting results. New methods are important because they often allow us to address novel and important questions that were previously intractable. In a very applied context, Redding et al. (2016) look at the impact of new methodology on the prediction of the emergence of diseases. Similarly, Pettorelli, Owen & Duncan (2016) review the application of remote sensing in conservation and biodiversity research, emphasizing the new opportunities that have been opened up by this technology. Annually, we award a prize (named after Lord May of Oxford) to an early career author of an exceptional paper published in the journal. Iain Stott was the first recipient of this prize (Stott et al. 2010). In his new paper, Iain reviews methods for analysing transient dynamics in population models using perturbation methods. The development of new statistical methods is a significant component of what we regularly publish, and Brewer, Butler & Cooksley (2016) review an important area of modern statistics, namely the use of information criteria. These have been extremely influential in the practice of statistics in ecology during the past decade, and Brewer et al. provide insights into the relative performance of the different indices. The final paper reflects the evolutionary content of the journal. Including two of the journal's Associate Editors, Cooper, Thomas & FitzJohn (2016) review the use of comparative methods in ecological and evolutionary research. They stress the need to bridge the gap between the technical developers of methods and those applying them – nicely rounding off this special feature by really emphasizing one of the key aspirations of the journal. What do we want from the next 5 years? Innovation in publishing will hopefully continue and a huge challenge will be to adapt to the unique publishing challenges for methods papers. For example, how do we review the tools we publish in a rigorous way? Code and software applications are regularly published, and we need to think about how we make the review of these as thorough as possible and also ensure that tools are accessible for as long as possible. This is the subject of ongoing journal development and an area where we hope to lead in terms of setting standards for ecology and evolution. But most of all, we hope to continue to publish a wide range of papers on as diverse a range of topics as possible, exemplified by the diversity of the papers in this feature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.536 | 0.393 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".