DOI: 10.6084/m9.figshare.29996155.v1
title: <b>Unveiling the patterns and drivers of scientific and societal interest in Brazilian lepidopterans</b>

description: There are 5 files associated with this manuscript:<br>(<i>1</i>) taxon_data.Rdata (10 columns with species-level information):"speciesName": currently valid species name based on the Taxonomic Catalogue of the Fauna of Brazil."authorship": species authorship."year": year of description."subfamily": taxonomic subfamily each species belong to."family": taxonomic family each species belong to."genus" taxonomic genus each species belong to."synonyms": unique synonyms associated with each valid species."subspecies": subspecies associated with each valid species."Npapers": Number of papers recovered from the Scopus database for each species."N_wiki_views": Number of Wikipedia pageviews for each species.<br>(<i>2</i>) genus_data.Rdata (10 columns with genus-level information):"genus": Taxonomic genus."family": Taxonomic family each genus belong to."Total_wiki_views": Total Wikipedia pageviews aggregated from species-level data."Total_n_papers": Total number of publications aggregated from species-level data."Desc_year_med": Median description years based on all species within a genus."N_spp": Total number of species per genus."range_size": Range size as countries where the genus is found (see main text for specific details)."N_preserved_spec": Total number of preserved specimens per genus, derived from GBIF."iNat_records": Total number of iNaturalist records per genus. # "Group": Inform whether the genus represent moths or butterflies<br>(<i>3</i>) Rcode_lepidoptera.R: this R code is used to perform all analyses of our work as well as for creating the images on the main text and supplementary material. The script is commented by the authors and intuitively structured, including needed packages and their respective versions.<br>(<i>4</i>) 01_get_societal_interest.R: this R code is used to obtain the response variable for societal interest (number of Wikipedia pageviews).<br>(<i>5</i>) 02_get_research_effort.R: this R code is used to obtain the response variable for scientific interest (number of publications on Scopus).<br><b>Code/Software</b>All analyses were performed using the software R version 4.3.1.<br><b>Usage notes</b>Software R is required to open the R codes and the associated Rdata files containing the datasets.<br><b>Methods</b><b><i>Research effort</i></b>We quantified research effort as the number of scientific publications available for each lepidopteran species using the Scopus database. Scopus is one of the largest abstract and citation indexing platforms, offering broader journal coverage than other well-curated sources such as Web of Science (Mongeon & Paul-Hus, 2016). Although Google Scholar theoretically aggregates a wider range of electronic resources, including grey literature, its frequent changes in content, database structure, and search algorithms pose significant challenges for bibliometric analyses (Giustini & Boulos, 2013).For each of the 13,362 lepidopteran species listed in the Taxonomic Catalogue of the Fauna of Brazil (TCFB) ver. 1.23 (Brazilian Zoology Group, 2023), we retrieved the number of publications indexed in Scopus from 1960 to 2024. To ensure that only studies where a species was a focal taxon were counted, searches were restricted to article titles, abstracts, and keywords, thereby excluding studies where a species was mentioned only incidentally for comparison. Given the dynamic nature of taxonomy, species names often change over time. Searching only by the currently accepted name risks missing publications backed by an old species name (i.e. synonyms). Similarly, ignoring subspecies may also lead to the same problem, that is, underestimation of research efforts. To address this, we compiled all unique names and subspecies associated with each lepidopteran species, using data from the TCFB and the Catalogue of Life (CoL) Checklist ver. 2024.11. Unique names correspond to a single valid species, whereas ambiguous names are linked to multiple valid species. We conducted the searches in December 2024, querying the accepted name, subspecies, and all unique synonyms using the rscopus package ver. 0.6.6 (Muschelli, 2019) in the software R ver. 4.3.1 (R Core Team, 2023).<b><i>Societal interest</i></b>We quantified societal interest for each species (including their subspecies and unique synonyms) as the number of Wikipedia pageviews. For such, we quantified the total number of pageviews across all Wikipedia language editions where the species has a dedicated page. To obtain these data, we first retrieved each species’ unique identification number from the Wikidata knowledge base using the R package WikidataQueryServiceR ver. 1.0.0 (Popov, 2020). Using these identifiers, we compiled a list of all available Wikipedia pages for each species in any language. Once the full list of Wikipedia pages was identified, we extracted monthly user pageviews (excluding bot traffic) using the R package pageviews ver. 0.5.0 (Keyes & Lewis, 2024). Pageview data were collected for the period spanning July 1, 2015, to December 31, 2023.<b><i>Predictor variables</i></b>Significant gaps remain in the natural history, ecology, and distribution of lepidopteran species, particularly in tropical regions (Girardello et al., 2019). These knowledge gaps hinder species-level analyses across broad spatial or taxonomic scales as the majority of species would have to be discarded from analyses due to missing data in predictor variables. Similarly, commonly used traits are not readily available for most species, such as body size and habitat use. However, to investigate how biological and cultural factors shape research and societal interest, we gathered predictor variables at either the species or genus level and aggregated all variables (predictors and responses) to the genus level. Thus, genus was used as the modelling unit in this study (n = 2953), and each genus constituted a single independent observation in the analyses.We retrieved the number of preserved specimens per genus from biological collections listed in the Global Biodiversity Information Facility (GBIF) using the occ_count function from the rgbif R package (Chamberlain et al., 2023). Search queries included genus names, with the basisOfRecord argument set to preserved specimen. To estimate range size, we downloaded all available lepidopteran occurrence records from the Americas on GBIF (as of December 16, 2024; https://doi.org/10.15468/dl.bsqm5u), retaining only those with geographic coordinates and no suspicious flags (i.e., potentially erroneous records). Initially, we obtained 13,254,907 records, but after data cleaning procedures, such as removing duplicates with the CoordinateCleaner R package (Zizka et al., 2019), this number was reduced to 3,658,936 observations. We then aggregated data by genera and used the number of countries each genus occurs in as a proxy for range size. For Brazil, the USA, and Canada, we counted occurrences at the state level, as these countries are among the largest in the world, with individual states comparable in area to many countries. We did not have range size data for 641 genera, which reduced the total number of genera modelled to 2,312.We extracted the year of description for each species from authority information available in the TCFB, then computed the median species description year per genus. Additionally, we extracted species-level observation data from iNaturalist, obtained via GBIF in August 19, 2025 (https://doi.org/10.15468/dl.wzecjd). Specifically, we downloaded 7,671,217 research-grade observations (relatively high-confidence records) for all lepidopteran species from the Americas. After removing genera that do not occur in Brazil and applying data cleaning procedures, 3,830,635 records remained. We then counted the number of observations per genus. Finally, we also obtained per-genus species richness and identified whether a given genus belongs to either moths or butterflies based on their taxonomic family.<b><i>Statistical analyses</i></b>We fitted mixed-effects generalized linear models (GLMMs; Zuur et al., 2009) with a negative binomial distribution to both of our response variables: the number of Wikipedia pageviews and the number of papers. To account for taxonomic relatedness among genera, we included taxonomic family as a random effect (Revell, 2010). We tested whether the response variables were influenced by the year of description, range size (log10-transformed), species richness (log10-transformed), number of iNaturalist records (log10-transformed), and number of preserved specimens (log10-transformed; included only in the number of papers model) (Supplementary Figure S1). Additionally, we included the taxonomic group (moth vs. butterflies) as a predictor in our models to account for biological and ecological differences between them. Continuous predictors were z-transformed to enable direct comparisons of effect sizes. We assessed multicollinearity among predictors using Variation Inflation Factors (VIFs; Mansfield & Helms, 1982). Strong multicollinearity is typically associated with VIFs &gt; 5 or 10 (Kutner et al., 2005), but all predictors had VIFs &lt; 3 and were therefore retained in subsequent analyses (Supplementary Table S1). Models were fitted using the R package glmmTMB (Brooks et al., 2017).Given the high frequency of zeros in the number of papers variable (71.5%), we used the R package DHARMa (Hartig, 2022) to assess whether zero-inflated models would be more appropriate (for the number of Wikipedia pageviews this was not a concern, as there was only 7% of zeros in the data). However, there was no need to use zero-inflated models for this response variable (Supplementary Figure S2). Model diagnostics, performed with DHARMa, revealed no major issues with the fitted GLMM models (Supplementary Figure S3–4).Finally, we quantified the evenness of both scientific and research attention within genera to understand whether observed patterns were driven by many species or by a few highly influential taxa. For each genus, we calculated Pielou’s evenness index (Jost, 2010; Smith & Wilson, 1996), which measures how evenly research publications and Wikipedia pageviews are distributed among species. Values close to 1 indicate that attention is spread relatively evenly across species, whereas values near 0 reflect strong concentration in one or a few species. We then plotted this metric against per-genus species richness. Pielou’s evenness was obtained using the R package vegan (Oksanen et al., 2021). All analyses were conducted in the R software version 4.3.1 (R Core Team, 2023). See the Data Availability section for raw data and R code.<i>Note</i><i> (references mentioned above can be verified in the main text)</i>