Immediate and longer-term impact of the COVID-19 pandemic on scientific productivity in ecology and evolution

Abstract

While the subject of much speculation, most quantitative assessments of the effect of the COVID-19 pandemic on scientific productivity i) are based on self-reported survey data, ii) cover only a short period of time, iii) may be biased by an increase in COVID-19-based research, iv) cover a limited range of publishers or publishing outlets, and/or v) cannot distinguish between changes in submission versus acceptance rates. Here we analyse submission and acceptance data from 2012 to 2023 for 25 journals in ecology and evolution, a field that has produced relatively few COVID-19-related articles. We show that although submission rates spiked when the pandemic began, they have been plummeting since. While there is variation in these patterns among countries and journals, the latter is unrelated to journal impact factor. The absence of a coinciding change in acceptance rates suggests that journals have not changed their quality standards to buffer these trends in productivity. Together, this demonstrates dynamic but long-term consequences of the COVID-19 pandemic on scientific productivity, suggestive of fundamental changes to scientific practise and communication. A profitable direction for future research would be to build upon our results by targeting topic-, method- and system-related variation in productivity within and across journals. 

Data accessibility

Data and R scripts that allow for the reproduction of results and figures are provided. We are unable to provide the raw –  manuscript-level – data as they include personally identifiable information. Instead, we provide the aggregated number of submissions and acceptences per month and per journal and/or country. Furthermore, we have anonymised journal names, which was a condition of our data-sharing agreement with the journals that participated in this study. The latter also means that we are unable to share the anonymized journal IDs and their impact factor. Although these data are required to replicate our analyses of the role of impact factor in shaping changes in submission and acceptance rates, sharing these data would break anonymity.