Knowledge from non-English-language studies broadens contributions to conservation policy and helps to tackle bias in biodiversity data

Local ecological evidence is key to informing conservation. However, many
global biodiversity indicators often neglect local ecological evidence
published in languages other than English, potentially biassing our
understanding of biodiversity trends in areas where English is not the
dominant language. Brazil is a megadiverse country with a thriving
national scientific publishing landscape. Here, using Brazil and a species
abundance indicator as examples, we assess how well bilingual literature
searches can both improve data coverage for a country where English is not
the primary language and help tackle biases in biodiversity datasets. We
conducted a comprehensive screening of articles containing abundance data
for vertebrates published in 59 Brazilian journals (articles in Portuguese
or English) and 79 international English-only journals. These were grouped
into three datasets according to journal origin and article language
(Brazilian-Portuguese, Brazilian-English and International). We analysed
the taxonomic, spatial and temporal coverage of the datasets, compared
their average abundance trends and investigated predictors of such trends
with a modelling approach. Our results showed that including data
published in Brazilian journals, especially those in Portuguese, strongly
increased representation of Brazilian vertebrate species (by 10.1 times)
and populations (by 7.6 times) in the dataset. Meanwhile, international
journals featured a higher proportion of threatened species. There were no
marked differences in spatial or temporal coverage between datasets, in
spite of different bias towards infrastructures. Overall, while
country-level trends in relative abundance did not substantially change
with the addition of data from Brazilian journals, uncertainty
considerably decreased. We found that population trends in international
journals showed stronger and more frequent decreases in average abundance
than those in national journals, regardless of whether the latter were
published in Portuguese or English. Policy implications. Collecting data
from local sources markedly further strengthens global biodiversity
databases by adding species not previously included in international
datasets. Furthermore, the addition of these data helps to understand
spatial and temporal biases that potentially influence abundance trends at
both national and global level. We show how incorporating
non-English-language studies in global databases and indicators could
provide a more complete understanding of biodiversity trends and therefore
better inform global conservation policy.