Consulting the best available evidence is key to successful conservation decision-making. While much scientific evidence on conservation continues to be published in non-English languages, a poor understanding of how non-English-language science contributes to conservation decision-making is causing global assessments and studies to practically ignore non-English-language literature. By investigating the use of scientific literature in biodiversity assessment reports across 37 countries/territories, we have uncovered the established role of non-English-language literature as a major source of information locally. On average, non-English-language literature constituted 65% of the references cited, and these were recognized as relevant knowledge sources by 75% of report authors. This means that by ignoring non-English-language science, international assessments may overlook important information on local and/or regional biodiversity. Furthermore, a quarter of the authors acknowledged the struggles of understanding English-language literature. This points to the need to aid the use of English-language literature in domestic decision-making, for example, by providing non-English-language abstracts or improving and/or implementing machine translation. (This abstract is also avaialble in 21 other languages in Supplementary Data 4).
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The data in the 333 biodiversity assessment reports identified in 37 countries/territories, the 37 reports used for the analysis and the 130 reports on 11 countries used for the sensitivity analysis are available as Supplementary Data 1, 2 and 3, respectively. We are unable to make the data on the report authors’ responses to the survey questions publicly available, as per our agreement with the University of Queensland Ethics office and due to the confidentiality of the data.
All codes used in the analysis are available at https://doi.org/10.17605/OSF.IO/Y94ZT.
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We thank all authors and editors of the reports who participated in our survey, V. V. Natykanets for his help in data collection and M. Amano for all her support. This work was funded by an Australian Research Council Future Fellowship (FT180100354), University of Queensland strategic funding (T.A.), an Australian Research Council DECRA Fellowship (DE180100202, S.M.D.), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brazil (CAPES, Finance Code 001, J.P.N.-G.) and the Swiss National Science Foundation (G.W.P.).
The authors declare no competing interests.
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Extended Data Fig. 1 Geographic variations in the proportion of non-English-language references cited in national biodiversity assessments.
(a) The proportion of non-English language references (academic and grey literature combined) and (b) the proportion of non-English-language academic literature in each country. Countries without any records are shown in grey. Basemaps from Natural Earth (http://www.naturalearthdata.com/).
Extended Data Fig. 2 Factors associated with the proportion of non-English-language academic literature cited in national biodiversity assessments.
The relationship between the proportion of non-English-language academic literature and (a) the English Proficiency Index (see Methods for more details) and (b) gross domestic product (GDP) per capita (based on purchasing power parity (PPP), current international $) of each country. The size of each dot indicates the total number of academic literature cited in the report. The colours indicate regions (subregions defined by the IPBES11). The regression curve (shown as a black solid line, and 95% confidence interval as a shaded area) in (b) is based on the fitted generalised linear model with a binomial distribution (see Extended Data Table 1).
Extended Data Fig. 3 The number of years the report authors have been involved in conservation (either in on-ground management, research, or policy advice, or any combination).
Data were collected from 51 report authors in 35 countries/territories.
Extended Data Fig. 4 Self-reported English proficiency of the 51 report authors in 35 countries/territories.
The report authors were asked to answer how easy it is for them to read and understand the full text of an English-language peer-reviewed paper on biodiversity conservation, based on five options: ‘Very easy’, ‘Easy’, ‘Neutral’, ‘Difficult’ and ‘Very difficult’. Note that no authors selected ‘Very difficult’, which therefore is excluded from this figure. Orange indicates answers by academics (that is, those who chose ‘Academic institution or university’ in Question 1 of Supplementary Text 1) and blue by all others. Numbers above bars are the percentage of non-academic survey respondents in each category of English proficiency.
Extended Data Fig. 5 English-language barriers encountered by report authors across their self-reported English proficiency levels.
The proportion of report authors who (a) experienced difficulties in searching (n = 51) and (b) understanding (n = 51) English-language literature for their report because the source was written in English, and its association with their self-reported English proficiency (based on five options: ‘Very easy’, ‘Easy’, ‘Neutral’, ‘Difficult’ and ‘Very difficult’ to read and understand the full text of an English-language peer-reviewed paper on biodiversity conservation: note that no authors selected ‘Very difficult’, which therefore is excluded from this figure). Numbers above bars are the number of survey respondents in each category of English proficiency.
Extended Data Fig. 6 Reasons why machine translation does not help report authors (a) search or (b) understand English-language literature.
Answers were collected from (a) 38 and (b) 26 report authors who answered either ‘No’ or ‘Unsure’ to Questions 23 (Do you think that machine translation helps you search relevant English-language literature for your report?) and 25 (Do you think that machine translation helps you understand relevant English-language literature for your report?) in Supplementary Text 1, respectively (shown in Fig. 4c).
Extended Data Fig. 7 The difference in the proportion of non-English-language literature cited between the reports used in the analysis and other eligible reports in each of the 11 countries of focus.
The comparison of proportions of (a) non-English-language references (academic and grey literature combined) and (b) non-English-language academic literature. Grey dots represent values in each report and red diamonds represent the mean value in each country.
Supplementary Discussion and Text 1.
Supplementary Data 1
List of 333 biodiversity assessment reports identified in 37 countries/territories. The explanations of column names are as follows: Country/territory: country/territory where the report was published; Non-English title: report title in the non-English language; English title: report title in English (if available); Publication language: the language of publication; Used in analysis: YES for the 37 reports used in the analysis; Organization(s) that edited/published the report: organizations that edited or published the report; Publication year: publication year; Topic: broad topic covered by the report; Citing non-English language references: whether the report cited at least one non-English-language reference; Citing at least 15 references: whether the report cited at least 15 references in total; URL: link to the report.
Supplementary Data 2
List of 37 biodiversity assessment reports used for the analysis, with the numbers of references by category and language. The explanations of column names are as follows: Country/territory: country/territory where the report was published; Language: the language of publication; Report name: report title in the non-English language; English-language academic literature: the number of English-language academic literature cited; English-language grey literature: the number of English-language grey literature cited; Non-English-language academic literature: the number of non-English-language academic literature cited; Non-English-language grey literature: the number of non-English-language grey literature cited; EPI: English Proficiency Index; GDPpercapita: gross domestic product per capita (based on purchasing power parity, current international dollars); Region: regions defined by the IPBES11; Subregion: subregions defined by the IPBES11; Number of authors/editors contacted: the number of the report authors/editors contacted; Number of authors/editors who participated: the number of the report authors/editors who participated in the survey.
Supplementary Data 3
List of 130 eligible reports in 11 countries, used for the sensitivity analysis. Details of column names are as follows: Country/territory: country/territory where the report was published; Used in analysis: YES for the 11 reports used in the analysis; Publication language: the language of publication; Publication year: publication year; Non-English title: report title in the non-English language; English title: report title in English (if available); Topic: broad topic covered by the report; Organization(s) that edited/published the report: organizations that edited or published the report; English_academiclit: the number of English-language academic literature cited; English_greylit: the number of English-language grey literature cited; NonEnglish_academiclit: the number of non-English-language academic literature cited; NonEnglish_greylit: the number of non-English-language grey literature cited; URL: link to the report.
Supplementary Data 4
Non-English-language abstract in 21 languages (Bahasa Indonesia, Catalan, Czech, Finnish, French, German, Greek, Hungarian, Italian, Japanese, Korean, Norwegian, Polish, Portuguese, Romanian, Simplified Chinese, Slovak, Spanish, Turkish, Ukrainian and Vietnamese).
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Amano, T., Berdejo-Espinola, V., Akasaka, M. et al. The role of non-English-language science in informing national biodiversity assessments. Nat Sustain (2023). https://doi.org/10.1038/s41893-023-01087-8