in

Quantifying research waste in ecology

  • Ioannidis, J. P. A., Fanelli, D., Dunne, D. D. & Goodman, S. N. Meta-research: evaluation and improvement of research methods and practices. PLoS Biol. 13, e1002264 (2015).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • Hampton, S. E. et al. The Tao of open science for ecology. Ecosphere 6, art120 (2015).

    Article 

    Google Scholar 

  • Rothstein, H. R., Sutton, A. J. & Borenstein, M. Publication Bias in Meta-Analysis: Prevention, Assessment and Adjustments (John Wiley & Sons, 2005).

  • Sutton, A. J. in The Handbook of Research Synthesis and Meta-Analysis (eds Cooper, H. et al.) 435–452 (Russell Sage Foundation, 2009).

  • Nakagawa, S., Koricheva, J., Macleod, M. & Viechtbauer, W. Introducing our series: research synthesis and meta-research in biology. BMC Biol. 18, 20 (2020).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Nakagawa, S. et al. A new ecosystem for evidence synthesis. Nat. Ecol. Evol. 4, 498–501 (2020).

    PubMed 
    Article 

    Google Scholar 

  • Coolidge, H. J. & Lord, R. H. in Archibald Cary Coolidge: Life and Letters 308 (Houghton Mifflin Harcourt, 1932).

  • Nickerson, R. S. Confirmation bias: a ubiquitous phenomenon in many guises. Rev. Gen. Psychol. 2, 175–220 (1998).

    Article 

    Google Scholar 

  • Touchon, J. C. & McCoy, M. W. The mismatch between current statistical practice and doctoral training in ecology. Ecosphere 7, e01394 (2016).

    Article 

    Google Scholar 

  • Begley, C. G. & Ellis, L. M. Raise standards for preclinical cancer research. Nature 483, 531–533 (2012).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Aarts, A. A. et al. Estimating the reproducibility of psychological science. Science 349, aac4716 (2015).

    Article 
    CAS 

    Google Scholar 

  • Camerer, C. F. et al. Evaluating the replicability of social science experiments in Nature and Science between 2010 and 2015. Nat. Hum. Behav. 2, 637–644 (2018).

    PubMed 
    Article 

    Google Scholar 

  • Fraser, H., Parker, T., Nakagawa, S., Barnett, A. & Fidler, F. Questionable research practices in ecology and evolution. PLoS ONE 13, e0200303 (2018).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • Culina, A., van den Berg, I., Evans, S. & Sánchez-Tójar, A. Low availability of code in ecology: a call for urgent action. PLoS Biol. 18, e3000763 (2020).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Jennions, M. D. & Møller, A. P. Publication bias in ecology and evolution: an empirical assessment using the ‘trim and fill’ method. Biol. Rev. Camb. Philos. Soc. 77, 211–222 (2002).

    PubMed 
    Article 

    Google Scholar 

  • Jennions, M. D. & Møller, A. P. A survey of the statistical power of research in behavioral ecology and animal behavior. Behav. Ecol. 14, 438–445 (2003).

    Article 

    Google Scholar 

  • Cassey, P., Ewen, J. G., Blackburn, T. M. & Møller, A. P. A survey of publication bias within evolutionary ecology. Proc. Biol. Sci. 271, S451–S454 (2004).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Kardish, M. R. et al. Blind trust in unblinded observation in ecology, evolution, and behavior. Front. Ecol. Evol. 3, 51 (2015).

    Article 

    Google Scholar 

  • Jennions, M. D. & Møller, A. P. Relationships fade with time: a meta-analysis of temporal trends in publication in ecology and evolution. Proc. Biol. Sci. 269, 43–48 (2002).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Baker, M. 1,500 scientists lift the lid on reproducibility. Nature 533, 452–454 (2016).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Chalmers, I. & Glasziou, P. Avoidable waste in the production and reporting of research evidence. Lancet 374, 86–89 (2009).

    PubMed 
    Article 

    Google Scholar 

  • Altman, D. G. The scandal of poor medical research. BMJ 308, 283–284 (1994).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Glasziou, P. & Chalmers, I. Is 85% of health research really ‘wasted’? BMJ Opinion (14 January 2016).

  • Glasziou, P. & Chalmers, I. Research waste is still a scandal. BMJ 363, k4645 (2018).

    Article 

    Google Scholar 

  • Chalmers, I. et al. How to increase value and reduce waste when research priorities are set. Lancet 383, 156–165 (2014).

    PubMed 
    Article 

    Google Scholar 

  • Kunin, W. E. Robust evidence of declines in insect abundance and biodiversity. Nature 574, 641–642 (2019).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Christie, A. P. et al. Quantifying and addressing the prevalence and bias of study designs in the environmental and social sciences. Nat. Commun. 11, 6377 (2020).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Campbell, H. A. et al. Finding our way: on the sharing and reuse of animal telemetry data in Australasia. Sci. Total Environ. 534, 79–84 (2015).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Koricheva, J. Non-significant results in ecology: a burden or a blessing in disguise? Oikos 102, 397–401 (2003).

    Article 

    Google Scholar 

  • Bennett, L. T. & Adams, M. A. Assessment of ecological effects due to forest harvesting: approaches and statistical issues. J. Appl. Ecol. 41, 585–598 (2004).

    Article 

    Google Scholar 

  • Duval, S. & Tweedie, R. A nonparametric ‘trim and fill’ method of accounting for publication bias in meta-analysis. J. Am. Stat. Assoc. 95, 89–98 (2012).

    Google Scholar 

  • Brlík, V. et al. Weak effects of geolocators on small birds: a meta-analysis controlled for phylogeny and publication bias. J. Anim. Ecol. 89, 207–220 (2020).

    PubMed 
    Article 

    Google Scholar 

  • Hurlbert, S. H. Pseudoreplication and the design of ecological field experiments. Ecol. Monogr. 54, 187–211 (1984).

    Article 

    Google Scholar 

  • Jennions, M. D. & Møller, A. P. A survey of the statistical power of research in behavioral ecology and animal behaviour. Behav. Ecol. 14, 438–445 (2003).

    Article 

    Google Scholar 

  • Culina, A., Purgar, M. & Klanjscek, T. Datasets and codes for Purgar et al. 2022: quantifying research waste in ecology. Zenodo https://zenodo.org/record/6566100#.YrLWB-zMIqs (2022).

  • Ferguson, C. et al. Europe PMC in 2020. Nucleic Acids Res. 49, D1507–D1514 (2021).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Huang, C.-K. et al. Meta-Research: Evaluating the impact of open access policies on research institutions. eLife 9, e57067 (2020).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Ross-Hellauer, T. Open science, done wrong, will compound inequities. Nature 603, 363 (2022).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Smith, A. C. et al. Assessing the effect of article processing charges on the geographic diversity of authors using Elsevier’s ‘Mirror journal’ system. Quant. Sci. Stud. 2, 1123–1143 (2021).

    Article 

    Google Scholar 

  • Christie, A. P. et al. Reducing publication delay to improve the efficiency and impact of conservation science. PeerJ 9, e12245 (2021).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Desjardins-Proulx, P. et al. The case for open preprints in biology. PLoS Biol. 11, e1001563 (2013).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Munafò, M. R. et al. A manifesto for reproducible science. Nat. Hum. Behav. 1, 0021 (2017).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • O’Dea, R. E. et al. Towards open, reliable, and transparent ecology and evolutionary biology. BMC Biol. 19, 68 (2021).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Culina, A. et al. Navigating the unfolding open data landscape in ecology and evolution. Nat. Ecol. Evol. 2, 420–426 (2018).

    PubMed 
    Article 

    Google Scholar 

  • Culina, A., Crowther, T. W., Ramakers, J. J. C., Gienapp, P. & Visser, M. E. How to do meta-analysis of open datasets. Nat. Ecol. Evol. 2, 1053–1056 (2018).

    PubMed 
    Article 

    Google Scholar 

  • Roche, D. G., Kruuk, L. E. B., Lanfear, R. & Binning, S. A. Public data archiving in ecology and evolution: how well are we doing? PLoS Biol. 13, e1002295 (2015).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • Grainger, M. J., Bolam, F. C., Stewart, G. B. & Nilsen, E. B. Evidence synthesis for tackling research waste. Nat. Ecol. Evol. 4, 495–497 (2020).

    PubMed 
    Article 

    Google Scholar 

  • Nørgaard, B. et al. Systematic reviews are rarely used to inform study design—a systematic review and meta-analysis. J. Clin. Epidemiol. 145, 1–13 (2022).

    PubMed 
    Article 

    Google Scholar 

  • Webb, J. A. et al. Weaving common threads in environmental causal assessment methods: toward an ideal method for rapid evidence synthesis. Freshw. Sci. 36, 250–256 (2017).

    Article 

    Google Scholar 

  • Collins, A., Coughlin, D., Miller, J. & Kirk, S. The Production of Quick Scoping Reviews and Rapid Evidence Assessments: A How to Guide (Joint Water Evidence Group, 2015).

  • Carrick, J. et al. Is planting trees the solution to reducing flood risks? J. Flood Risk Manag. 12, e12484 (2019).

    Article 

    Google Scholar 

  • Nuñez, M. A. & Amano, T. Monolingual searches can limit and bias results in global literature reviews. Nat. Ecol. Evol. 5, 264 (2021).

    PubMed 
    Article 

    Google Scholar 

  • Morrison, A. et al. The effect of English-language restriction on systematic review-based meta-analyses: a systematic review of empirical studies. Int. J. Technol. Assess. Health Care 28, 138–144 (2012).

    PubMed 
    Article 

    Google Scholar 

  • Wu, T., Li, Y., Bian, Z., Liu, G. & Moher, D. Randomized trials published in some Chinese journals: how many are randomized? Trials 10, 46 (2009).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Vorobeichik, E. L. & Kozlov, M. V. Impact of point polluters on terrestrial ecosystems: methodology of research, experimental design, and typical errors. Russ. J. Ecol. 43, 89–96 (2012).

    Article 

    Google Scholar 

  • Simmons, J. P., Nelson, L. D. & Simonsohn, U. False-positive psychology: undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychol. Sci. 22, 1359–1366 (2011).

    PubMed 
    Article 

    Google Scholar 

  • Transforming Our World: the 2030 Agenda for Sustainable Development (United Nations, 2015).

  • MacCoun, R. & Perlmutter, S. Blind analysis: hide results to seek the truth. Nature 526, 187–189 (2015).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Parker, T. H. et al. Transparency in ecology and evolution: real problems, real solutions. Trends Ecol. Evol. 31, 711–719 (2016).

    PubMed 
    Article 

    Google Scholar 

  • Announcement: reducing our irreproducibility. Nature 496, 398 (2013).

  • Moher, D. et al. Increasing value and reducing waste in biomedical research: who’s listening? Lancet 387, 1573–1586 (2016).

    PubMed 
    Article 

    Google Scholar 

  • Smaldino, P. E. & McElreath, R. The natural selection of bad science. R. Soc. Open Sci. 3, 160384 (2016).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Vrieze, J. Landmark research integrity survey finds questionable practices are surprisingly common. ScienceInsider https://www.sciencemag.org/news/2021/07/landmark-research-integrity-survey-finds-questionable-practices-are-surprisingly-common (2021).

  • Woolston, C. Impact factor abandoned by Dutch university in hiring and promotion decisions. Nature 595, 462 (2021).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Directorate-General for Research and Innovation (European Commission). Towards a Reform of the Research Assessment System. Scoping Report (Publications Office, 2021).

  • Athena Research & Innovation Center, Directorate-General for Research and Innovation (European Commission), PPMI, UNU-MERIT. Monitoring the Open Access Policy of Horizon 2020. Final report (European Commission, 2021).

  • Kwon, D. University of California and Elsevier forge open-access deal. TheScientist https://www.the-scientist.com/news-opinion/university-of-california-and-elsevier-forge-open-access-deal–68557 (2021).

  • Vines, T. H. et al. Mandated data archiving greatly improves access to research data. FASEB J. 27, 1304–1308 (2013).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • NPQIP Collaborative Group. Did a change in Nature journals’ editorial policy for life sciences research improve reporting? BMJ Open Sci. 3, e000035 (2019).

    Article 

    Google Scholar 

  • Glasziou, P. et al. Reducing waste from incomplete or unusable reports of biomedical research. Lancet 383, 267–276 (2014).

    PubMed 
    Article 

    Google Scholar 

  • Fecher, B. & Friesike, S. in Opening Science: the Evolving Guide on How the Internet is Changing Research, Collaboration and Scholarly Publishing (eds Bartling, S. & Friesike, S.) 17–47 (Springer International Publishing, 2014).

  • Hardwicke, T. E. et al. Calibrating the scientific ecosystem through meta-research. Annu. Rev. Stat. Appl. 7, 11–37 (2020).

    Article 

    Google Scholar 

  • McKiernan, E. C. et al. How open science helps researchers succeed. eLife 5, e16800 (2016).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Fidler, F. et al. Metaresearch for evaluating reproducibility in ecology and evolution. Bioscience 67, 282–289 (2017).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Cornwall, C. E. & Hurd, C. L. Experimental design in ocean acidification research: problems and solutions. ICES J. Mar. Sci. 73, 572–581 (2016).

    Article 

    Google Scholar 

  • Fidler, F., Burgman, M. A., Cumming, G., Buttrose, R. & Thomason, N. Impact of criticism of null-hypothesis significance testing on statistical reporting practices in conservation biology. Conserv. Biol. 20, 1539–1544 (2006).

    PubMed 
    Article 

    Google Scholar 

  • Forstmeier, W. & Schielzeth, H. Cryptic multiple hypotheses testing in linear models: overestimated effect sizes and the winner’s curse. Behav. Ecol. Sociobiol. 65, 47–55 (2011).

    PubMed 
    Article 

    Google Scholar 

  • Gillespie, B. R., Desmet, S., Kay, P., Tillotson, M. R. & Brown, L. E. A critical analysis of regulated river ecosystem responses to managed environmental flows from reservoirs. Freshw. Biol. 60, 410–425 (2015).

    Article 

    Google Scholar 

  • Haddaway, N. R., Styles, D. & Pullin, A. S. Evidence on the environmental impacts of farm land abandonment in high altitude/mountain regions: a systematic map. Environ. Evid. 3, 17 (2014).

    Article 

    Google Scholar 

  • Heffner, R. A., Butler, M. J. & Reilly, C. K. Pseudoreplication revisited. Ecology 77, 2558–2562 (1996).

    Article 

    Google Scholar 

  • Holman, L., Head, M. L., Lanfear, R. & Jennions, M. D. Evidence of experimental bias in the life sciences: why we need blind data recording. PLoS Biol. 13, e1002190 (2015).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • Hurlbert, S. H. & White, M. D. Experiments with freshwater invertebrate zooplanktivores: quality of statistical analyses. Bull. Mar. Sci. 53, 128–153 (1993).

    Google Scholar 

  • Johnson, W. T.3rd & Freeberg, T. M. Pseudoreplication in use of predator stimuli in experiments on antipredator responses. Anim. Behav. 119, 161–164 (2016).

    Article 

    Google Scholar 

  • Kozlov, M. V. Pseudoreplication in ecological research: the problem overlooked by Russian scientists. Zh. Obshch. Biol. 64, 292–307 (2003).

    CAS 
    PubMed 

    Google Scholar 

  • Kozlov, M. V. Plant studies on fluctuating asymmetry in Russia: mythology and methodology. Russ. J. Ecol. 48, 1–9 (2017).

    Article 

    Google Scholar 

  • McDonald, S., Cresswell, T., Hassell, K. & Keough, M. Experimental design and statistical analysis in aquatic live animal radiotracing studies: a systematic review. Crit. Rev. Environ. Sci. Technol. 52, 2772–2801 (2021).

    Article 

    Google Scholar 

  • Møller, A. P., Thornhill, R. & Gangestad, S. W. Direct and indirect tests for publication bias: asymmetry and sexual selection. Anim. Behav. 70, 497–506 (2005).

    Article 

    Google Scholar 

  • Mrosovsky, N. & Godfrey, M. H. The path from grey literature to Red Lists. Endang. Species Res. 6, 185–191 (2008).

    Google Scholar 

  • O’Brien, C., van Riper, C.3rd & Myers, D. E. Making reliable decisions in the study of wildlife diseases: using hypothesis tests, statistical power, and observed effects. J. Wildl. Dis. 45, 700–712 (2009).

    PubMed 
    Article 

    Google Scholar 

  • Parker, T. H. What do we really know about the signalling role of plumage colour in blue tits? A case study of impediments to progress in evolutionary biology. Biol. Rev. Camb. Philos. Soc. 88, 511–536 (2013).

    PubMed 
    Article 

    Google Scholar 

  • Ramage, B. S. et al. Pseudoreplication in tropical forests and the resulting effects on biodiversity conservation. Conserv. Biol. 27, 364–372 (2013).

    PubMed 
    Article 

    Google Scholar 

  • Sallabanks, R., Arnett, E. B. & Marzluff, J. M. An evaluation of research on the effects of timber harvest on bird populations. Wildl. Soc. Bull. 28, 1144–1155 (2000).

    Google Scholar 

  • Sánchez-Tójar, A. et al. Meta-analysis challenges a textbook example of status signalling and demonstrates publication bias. eLife 7, e37385 (2018).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Waller, B., Warmelink, L., Liebal, K., Micheletta, J. & Slocombe, K. Pseudoreplication: a widespread problem in primate communication research. Anim. Behav. 86, 483–488 (2013).

    Article 

    Google Scholar 

  • Van Wilgenburg, E. & Elgar, M. A. Confirmation bias in studies of nestmate recognition: a cautionary note for research into the behaviour of animals. PLoS ONE 8, e53548 (2013).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Yoccoz, N. G. Use, overuse, and misuse of significance tests in evolutionary biology and ecology. Bull. Ecol. Soc. Am. 72, 106–111 (1991).

    Google Scholar 

  • Zaitsev, A. S., Gongalsky, K. B., Malmström, A., Persson, T. & Bengtsson, J. Why are forest fires generally neglected in soil fauna research? A mini-review. Appl. Soil Ecol. 98, 261–271 (2016).

    Article 

    Google Scholar 

  • Zvereva, E. L. & Kozlov, M. V. Biases in studies of spatial patterns in insect herbivory. Ecol. Monogr. 89, e01361 (2019).

    Article 

    Google Scholar 

  • RStudio Team. RStudio: Integrated Development for R (RStudio, 2020).

  • Viechtbauer, W. Conducting meta-analyses in R with the metafor package. J. Stat. Softw. 36, 1–48 (2010).

    Article 

    Google Scholar 


  • Source: Ecology - nature.com

    Silk offers an alternative to some microplastics

    Thermodynamic basis for the demarcation of Arctic and alpine treelines