Skip to main content
Open Access Publications from the University of California

Game of Tenure: the role of “hidden” citations on researchers’ ranking in Ecology

  • Author(s): Benítez-López, Ana
  • Santini, Luca
  • et al.

Field ecologists and macroecologists often compete for the same grants and academic positions, with the former producing primary data that the latter generally use for model parameterization. Primary data are usually cited only in the supplementary materials, thereby not counting formally as citations, creating a system where field ecologists are routinely under-acknowledged and possibly disadvantaged in the race for funding and positions. Here, we explored how the performance of authors producing novel ecological data would change if all the citations to their work would be accounted for by bibliometric indicators. We collected the track record of >2300 authors from Google Scholar and citation data from 600 papers published in 40 ecology journals, including field-based, conservation, general ecology, and macroecology studies. Then we parameterized a simulation that mimics the current publishing system for ecologists and assessed author rankings based on number of citations, H-Index, Impact Factor, and number of publications under a scenario where supplementary citations count. We found weak evidence for field ecologists being lower ranked than macroecologists or general ecologists, with publication rate being the main predictor of author performance. Current ranking dynamics were largely unaffected by supplementary citations as they are 10 times less than the number of main text citations. This is further exacerbated by the common practice of citing datasets assembled by previous research or data papers instead of the original articles. While accounting for supplementary citations does not appear to offer a solution, researcher performance evaluations should include criteria that better capture authors’ contribution of new, publicly available data. This could encourage field ecologists to collect and store new data in a systematic manner, thereby mitigating the data patchiness and bias in macroecology studies, and further accelerating the advancement of ecology and related areas of biogeography.

Main Content
Current View