Local expertise anchors biodiversity documentation, but geopolitical power drives parachute discovery

Local expertise anchors biodiversity documentation, but geopolitical power drives parachute discovery | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Brief Communication Local expertise anchors biodiversity documentation, but geopolitical power drives parachute discovery Mario Moura, Raquel Carvalho, Karoline Ceron, Jhonny Guedes, Matheus Moroti, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7724270/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Global biodiversity documentation is hampered by several factors, including the inequitable distribution of holotypes—critical reference specimens for the description of new species. While 95% of post-1990 mammal discoveries originated in the Global South, 60% of their holotypes are housed abroad, primarily in Global North institutions. Wealthier nations extract holotype specimens disproportionately, especially from biodiverse regions with weak environmental policies, despite stronger local academic capacity improving retention. Addressing these disparities requires changes in structures, rules, and functioning of institutions to empower biodiverse nations to document their own species, alongside frameworks for more equitable knowledge exchange. Biological sciences/Ecology Biological sciences/Zoology Scientific community and society/Scientific community Figures Figure 1 Figure 2 Figure 3 Main text The discovery of new species typically involves comparison with type specimens of known taxa, the reference material that ties scientific names to biological entities. Among these, a single specimen holds particular taxonomic significance as the name-bearing of a species—the holotype. These specimens are primarily stored in museums and natural history collections, yet such institutions are unevenly distributed worldwide, with their accessibility, resources, and functionality often shaped by socioeconomic context 1,2 . Consequently, biodiversity is better documented in the economically developed Global North than in the Global South, where many species remain understudied or entirely unknown 3,4 . This imbalance may encourage researchers from wealthier nations to conduct fieldwork in biodiverse regions but deposit name-bearing specimens abroad 5 . This disconnection between local researchers and name-bearing specimens limits access to critical material, hindering efforts to efficiently document global biodiversity and achieve goals under the Post-2020 Global Biodiversity Framework. Beyond geographic and socioeconomic factors, taxonomic preferences further distort biodiversity documentation. Charismatic taxa such as mammals attract more research than less conspicuous groups like insects, fungi, or small vertebrates 6,7 . Although this preference has generated relatively rich data for mammals, it can mask inequalities in global knowledge production 8 . If efforts to promote decolonial science align with taxonomic trends in research preference, colonial legacies should be less pronounced in well-studied and charismatic taxa. To test this, we analyzed 1116 mammal species descriptions published over the past 35 years 9 , examining the origin and destination of name-bearing specimens of mammals, and the geopolitical and socioeconomic relationships shaping their movement. Despite the Global South accounting for 95% of recent mammal discoveries, nearly 60% of their holotypes are housed abroad (Fig. 1). This contrasts with Global North discoveries, where only 22% of holotypes are deposited in foreign institutions, all those in the Global North. This knowledge split between holotype housing location and resident researchers is higher than that observed for less charismatic groups, such as freshwater fishes (43% of holotypes described post-1950 and 30% of those described post-2000 are housed in foreign institutions; Nakamura et al. 2024) and reptiles (50% of holotypes described post-1990 1,10 ). Notably, 92.6% of specimens housed in Global South collections are native, while 92.3% of holotypes deposited in Global North institutions originate elsewhere. These trends underscore a pronounced North–South divide in holotype retention and appropriation. Among 105 countries where new mammals were described, 68 (including 66 in the Global South) retained no holotypes. Of the 53 repository countries, only 20 housed exclusively native specimens (17 in the Global South). In contrast, 17 other countries, largely in Europe, hosted only foreign holotypes (Extended Data 1). The United States held the largest collection of holotypes at 324, accounting for 28.6% of the total; 98.5% of these (319) were imported from other countries. Network analyses based on the multidirectionality and intensity of holotype flows between countries further reveal that all of the top-15 countries with most-exploited holotypes are in the Global South—especially Sub-Saharan Africa and Southeast Asia—whereas 14 of the 15 most-extractive nations are located in the Global North, with China as the only exception (Fig. 2). Countries with stronger academic capacity, as reflected in a higher Human Capital Index, retain more holotypes and are less constrained by outflow networks (i.e., the degree of holotype extraction by different foreign institutions, Fig. 3). Susceptibility to these pathways of holotype extraction is amplified by weak environmental policies (proxied by low scores on the Biodiversity and Habitat Issue), a challenge that often overlaps with limited enforcement capacity in megadiverse countries 11 . Historical sampling and colonial legacies further reinforce these extractive pathways, with the labor of scientists and collectors in former colonies systematically exploited to export biodiversity specimens 5 , a process apparently disconnected from research investment. In contrast, both holotype appropriation and inflow (i.e., the country’s extractive influence) increase in countries with stronger conservation policies and especially in colonialist nations, indicating that destinations of mammal holotypes are driven mostly by political dominance 12 . Furthermore, holotype inflow increases with availability of research institutions and historical sampling, suggesting that established scientific infrastructure—rather than domestic academic capacity—facilitates these extractive collections. While museums with robust infrastructure may attract foreign deposits 13 , our findings reveal a pronounced power imbalance: only 3.4% of the exported mammal holotypes were described solely by domestic authors. Indeed, the vast majority (60.5%) of exported holotypes involved foreign-led research without local collaboration, underscoring a pattern of extraction where foreign scientists remove holotypes from their countries of origin. Our findings reveal that while charismatic taxa attract disproportionate research attention 6,7 , this has failed to spur decolonial science practices. Mammal holotype outflow remains tied to political leverage and research infrastructure in wealthier nations. This sustains a dependence cycle, where biodiversity-rich countries have their biodiversity mostly accessed and documented by foreign nations, without benefit-sharing. For example, of 278 patents derived from native Brazilian plants, over 94% are foreign-owned 14 . Mechanisms to counteract neocolonial extraction are available within international agreements like the Convention on Biological Diversity. Yet, the effectiveness of such mechanisms is systematically undermined by non-ratification from key economies like the USA. Efforts to comprehensively document global biodiversity are essential, but must confront the colonial structures still embedded in scientific practice 15 . We have shown that Global South nations with strong academic systems and well-established environmental policies retain more holotypes, whereas colonial history largely explains extractive pipelines. Without systemic change, the scientific marginalization of megadiverse nations will persist, undermining global efforts to mitigate biodiversity knowledge gaps. Online Methods Species Data: We examined 1116 descriptions of mammal species published from 1990 to early 2025 (as informed by Mammal Diversity Database v. 2.1 9 ). For each species in our database, we obtained information on: type-locality’s (1) latitude and (2) longitude, and the housing collection’s (3) latitude and (4) longitude. For cases where only the name of the locality was provided (e.g., names of protected areas or cities), approximate coordinates were sought using gazetteers or tools like Google Earth (https://earth.google.com). Two species, the Northern Talapoin Monkey ( Miopithecus ogouensis ) and the Giant Muntjac ( Muntiacus vuquangensis ), were excluded from the dataset due to missing information on either their type-locality or housing collection. Country Data: Using coordinates of type-localities and housing collections of name-bearer mammals, we extracted the geopolitical classification adopted by the United Nations 16 to obtain the following: (1) the ISO-alpha3 code, a three-character abbreviation representing each country’s name; (2) the geopolitical location of the country in the Global North or Global South; and (3) the geopolitical region to which the country belongs (Sub-Saharan Africa; Latin America and the Caribbean; North America; Australia and New Zealand; Central, Eastern, and Southern Asia; Europe; Near East and Northern Africa; Southeast Asia and Pacific Islands). Japan, South Korea, Israel, and French Guiana are classified as part of the Global North, although they are typically included in geopolitical regions dominated by countries in the Global South. For greater geographic detail, we removed the Russian Federation from Europe to represent it as an independent geopolitical region. All operations were conducted in R v. 4.5.0 17 . We computed 8 country-level socioeconomic and research effort variables. First, we extracted (1) the number of biodiversity institutions (i.e., museums, herbaria, and universities) based on the dataset available in the R package CoordinateCleaner 18 . We used the Open Alex database (https://api.openalex.org/) to retrieve the number of available biodiversity institutions for 11 countries lacking data on the biodiversity institutions counts (Antigua and Barbuda, Bonaire, Republic of the Congo, Grenada, Kyrgyzstan, Mali, Eswatini, Samoa, Seychelles, Saint Vincent and the Grenadines, and Timor-Leste). Next, we extracted the (2) number of preserved specimens collected in each country from the Global Biodiversity Information Facility (GBIF), downloading all available mammal records with coordinates 19 , yielding 3,366,092 records after removing duplicates. Using information on the locality of housing collections, we also computed the number of preserved specimens housed in each country, but we refrained from using it in the analysis due to its high data missingness. We used the wb_data function from the wbstats R package 20 to retrieve World Bank Open Data indicators on: (3) percentage of GDP Expenditure in Research and Development. Since this indicator is provided annually in the World Bank Open Data platform, we averaged their respective values between 1990-2025. Importantly, socioeconomic data is not free from geopolitical biases, and Global South nations present missing data more frequently than those in the Global North. For countries with missing data in one or two socioeconomic factors, we imputed values using relevant regional averages. Specifically, we applied the Least Developed Countries (LDC) average of percentage of GDP Expenditure in Research and Development to 11 nations (Benin, Cameroon, Central African Republic, Eswatini, Guinea, Liberia, Malawi, Samoa, Sao Tome and Principe, Timor-Leste) and the Latin America and Caribbean average to five non-LDC countries (Antigua and Barbuda, Barbados, Grenada, Guyana, and Suriname). We sourced additional country-level indicators from the Quality of Government (QoG) database 21 , including: (4) biodiversity and habitat issue category, measures national efforts to protect ecosystems and prevent species loss; (5) Global Peace Index, assesses national peacefulness based on conflict, safety, and militarization; (6) Human Capital Index, evaluates how well countries develop education and health for productivity; (7) Human Development Index, summarizes achievements in health, education, and living standards; and (8) colonial origin, indicates a country’s historical colonization background. The latter variable was binarized (0 = no colonial origin, 1 = colonial origin). Each country’s value was averaged across the available QoG time-series from 1990 to 2025. The Global Peace Index for seven countries was imputed using regional averages, including three LDC nations (Samoa, Sao Tome and Principe, and Solomon Islands) and for Seychelles (acknowledging that our data collection preceded its 2014 graduation from the LDC category), while the Latin America and the Caribbean average was used for Antigua and Barbuda, Suriname, and Saint Vincent and the Grenadines. Metrics of retention and appropriation of name-bearers: To avoid biases from country area effects, we calculated the proportion of native name-bearing specimens retained per country, relative to the total number of native mammal name-bearing specimens discovered in each country. As a metric of name-bearing specimen appropriation, we computed the proportion of imported name-bearing specimens relative to the total housed in each country. Metrics of extractive flows in holotype networks: We constructed a bipartite network to analyze the holotypes’ global flows between countries based on their type localities and the housing collections of name-bearing mammals. Weights represented the number of occurrences between country pairs. At the node level, we computed Katz centrality to analyze the influence of a node (i.e., country) within a network, based on its number of immediate neighbors and the direct and indirect paths to other countries in the network 22 . The attenuation factor (α) was set to 0.1, penalizing contributions from more distant connections (range: 0–0.2). This calculation was performed using the katzcent function from the centiserve R package 23 . Preliminary inspections showed no significant area effect on inflow and outflow metrics. Data Analysis: Some predictor variables (number of institutions, number of collected specimens, and GDP expenditure in research and development) vary over orders of magnitude and were log10-transformed to reduce skewness. Due to a lack of socioeconomic data for some countries, the analyses of holotype retention and outflow were based on 52 nations with at least three post-1990 mammal discoveries in their territory. These selected countries included the sourcing localities for 92% of all holotypes, and housed 97.3% of holotypes that remained in their country of origin. Similarly, the assessment of holotype appropriation and inflow included 32 nations that housed at least three holotypes, accounting for 97.6% of all housing localities, and 98% of all imported holotypes. All predictors were centered and standardized to allow comparability of model coefficients. We examined multicollinearity in predictor variables in each dataset separately (52-nations and 32-nations datasets), using the Variance Inflation Factor (VIF) computed via the usdm package 24 . Predictors with VIF values >5 were considered to exhibit strong multicollinearity and were removed from the models 25 (Table S1). For both country-level datasets, analyses included the following predictors: Global Peace Index, Human Capital Index, colonial origin registered as binary, Biodiversity and Habitat Issue, number of collected specimens. For the 53-nation dataset, which modeled holotype retention and outflow as response variables, we also included the percentage of GDP expenditure on research and development. For the 32-nation dataset, which modeled holotype appropriation and inflow, we additionally included the number of biodiversity institutions. The final predictor sets were selected on a per-dataset basis to minimize multicollinearity, and the variables differing between datasets (i.e., number of biodiversity institutions and percentage of GDP expenditure on research and development) are strongly correlated (r = 0.39, p < 0.001). To evaluate the effect of socioeconomic factors and research effort, we built four generalized linear models, one for each response variable (success in the retention of holotypes, success in the importation of foreign holotypes, and two network variables of inflow and outflow centrality for holotype transfers). Success in the retention/importation of holotypes was modeled with a beta-binomial error distribution, whereas holotype outflow/inflow centrality were modeled with a gamma error. All models were built using the glmmTMB package 26 . Pseudo-R² were calculated with the performance package 27 . Tjur’s method was used to compute pseudo-R² for Beta-binomial models, whereas Nagelkerke’s method was used to obtain R² for models with Gamma error distribution. Model diagnostics were performed using the DHARMa package 28 (see Supplementary Information and Data Availability 29 ). Declarations Data availability The data to reproduce the results of this study are available via Zenodo Digital Repository 29 . Code availability The code for reproducing the results of this study is available at Zenodo Digital Repository 29 . Acknowledgements Our sincere gratitude is extended to the global community of taxonomists, field naturalists, and scientists, past and present, whose dedicated work in discovering and describing new mammal species has made this study possible. Author contributions MRM conceived the study, MRM, JJMG, MTM compiled the data, MRM, GN, RLC, KC analyzed the data, MRM and GN developed the figures, MRM, RLC, and GN wrote the text. All authors provided critical feedback and helped shape the research, analysis, and manuscript. Funding RLC thanks the USP Programa de Apoio aos Novos Docentes USP – 2025 (PRPI) for their support. KC thanks Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) for research fellowship (#444240/2024-1). JJMG thanks Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) (proc. 88887.478942/2020-00) and the São Paulo Research Foundation (FAPESP), Brazil (proc. 2024/18469-0) for research grants. MTM thanks the São Paulo Research Foundation (FAPESP), Brazil (proc. 2023/14506-5 and 2024/22798-9) for research grants. Competing Interests The authors declare no competing interests. Extended Data Extended Data 1 to 5 are available online for this study. Additional information Supplementary Information is available for this paper, including Supplementary Tables S1 to S5. References Uetz, P. et al. A global catalog of primary reptile type specimens. 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Sci. 122 , (2025). Moreira, A. C., Müller, A. C. A., Pereira, N. & Antunes, A. M. D. S. Pharmaceutical patents on plant derived materials in Brazil: Policy, law and statistics. World Pat. Inf. 28 , 34–42 (2006). Faxon, H. & Chapman, M. Beyond spatial bias: understanding the colonial legacies and contemporary social forces shaping biodiversity data. Environ. Res. Lett. 20 , 064053 (2025). United Nations Statistics Division. Standard country or area codes for statistical use (M49). Available at (2024). R Core Team. R: A Language and Environment for Statistical Computing. v. 4.5.0 at http://www.r-project.org/ (2025). Zizka, A. et al. CoordinateCleaner: Standardized cleaning of occurrence records from biological collection databases. Methods Ecol. Evol. 10 , 744–751 (2019). GBIF. Global Biodiversity Information Facility - Free and Open Access to Biodiversity Data. Mammal species occurrences (accessed 11 May 2025) at https://doi.org/10.15468/dl.32rfs8 (2025). Piburn, J. wbstats: Programmatic Access to the World Bank API. at (2020). Teorell, J. et al. The Quality Of Government Standard Dataset, Version Jan25 . University of Gothenburg: The Quality of Government Institute https://www.gu.se/en/quality-government/qog-data/data-downloads (2025) doi:10.18157/qogstdjan25. Katz, L. A New Status Index Derived from Sociometric Analysis. Psychometrika 18 , 39–43 (1953). Jalili, M. et al. CentiServer: A Comprehensive Resource, Web-Based Application and R Package for Centrality Analysis. PLoS One 10 , e0143111 (2015). Naimi, B. usdm: Uncertainty Analysis for Species Distribution Models. https://cran.r-project.org/package=usdm at https://cran.r-project.org/package=usdm (2017). Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1 , 3–14 (2010). Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. (2017). Lüdecke, D., Ben-Shachar, M., Patil, I., Waggoner, P. & Makowski, D. performance: An R Package for Assessment, Comparison and Testing of Statistical Models. J. Open Source Softw. 6 , 3139 (2021). Hartig, F. DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models. R package version 0.4.6. at http://florianhartig.github.io/DHARMa (2022). Moura, M. R. et al. Data from: Local expertise anchors biodiversity documentation, but geopolitical power drives parachute discovery. at https://doi.org/10.5281/zenodo.17187892 (2025). Additional Declarations There is NO Competing Interest. Supplementary Files SuppMaterial.docx SUPPLEMENTARY INFORMATION Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7724270","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Brief Communication","associatedPublications":[],"authors":[{"id":522093788,"identity":"d20337d0-cfb4-4d6d-be53-092d980e30a1","order_by":0,"name":"Mario Moura","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYJCCA1Ca8QGQ4OEjRQuzAUgLGym2sUmASULKdNvPPjzwcc82OYPbh59Vfs2xk2FjYH746AYeLWZn0g0Oznh229jgXJrZbdltyUCHsRkb5+DTciCN4TDPgduJG84wmN2W3MYM1MLDJo1Xy/lnMC3s34olt9UToeUG3BYeM8aP2w4To+UZw8EZB24bS57hKZZm3Hach42ZkF/OpzF/+HDgthzfGfaNH39uq7bnZ29++BifFhTAzAMmiVUOAow/SFE9CkbBKBgFIwYAAKuySzrjVyQiAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-7369-7502","institution":"Universidade Federal da Paraiba","correspondingAuthor":true,"prefix":"","firstName":"Mario","middleName":"","lastName":"Moura","suffix":""},{"id":522093789,"identity":"d5663cb6-b941-4aa7-8969-036accd62e4b","order_by":1,"name":"Raquel Carvalho","email":"","orcid":"","institution":"Universidade de São Paulo","correspondingAuthor":false,"prefix":"","firstName":"Raquel","middleName":"","lastName":"Carvalho","suffix":""},{"id":522093790,"identity":"f6c37d4e-822d-43ec-a9d0-450141117150","order_by":2,"name":"Karoline Ceron","email":"","orcid":"https://orcid.org/0000-0003-2354-3756","institution":"Universidade Federal do Ceará","correspondingAuthor":false,"prefix":"","firstName":"Karoline","middleName":"","lastName":"Ceron","suffix":""},{"id":522093791,"identity":"9efde619-1995-4f2a-aaf7-d4681caf0355","order_by":3,"name":"Jhonny Guedes","email":"","orcid":"https://orcid.org/0000-0003-0485-3994","institution":"Universidade Federal de Goiás","correspondingAuthor":false,"prefix":"","firstName":"Jhonny","middleName":"","lastName":"Guedes","suffix":""},{"id":522093792,"identity":"6f9b98d0-006e-4cb0-9bf7-e59c9f285810","order_by":4,"name":"Matheus Moroti","email":"","orcid":"","institution":"Universidade Estadual de Campinas","correspondingAuthor":false,"prefix":"","firstName":"Matheus","middleName":"","lastName":"Moroti","suffix":""},{"id":522093793,"identity":"a677b368-bd3c-4a06-b1fe-d3f0e912845a","order_by":5,"name":"Gabriel Nakamura","email":"","orcid":"","institution":"São Paulo University and National Institute of Science and Technology – Ecology, Evolution and Conservation Biology, INCT EECBio","correspondingAuthor":false,"prefix":"","firstName":"Gabriel","middleName":"","lastName":"Nakamura","suffix":""}],"badges":[],"createdAt":"2025-09-26 18:50:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7724270/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7724270/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92489078,"identity":"6d9cb531-dfff-42f8-9d80-ca4108a9d6a7","added_by":"auto","created_at":"2025-09-30 09:15:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":385830,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProportion of name-bearing mammal specimens retained or imported across geopolitical regions. \u003c/strong\u003eBlack points indicate type-localities of mammal species described between 1990 and 2025, and white squares represent mastozoological collections housing the holotypes, with their size scaled to the number of holotypes per collection. Barplots show the proportion of holotypes that were retained (housed in their country of origin) or imported (housed in a foreign country). Numbers adjacent to bars are holotype counts. Reddish and bluish colors distinguish geopolitical regions associated with the Global South and Global North, respectively. The map uses an equal-area projection. See Extended Data 1 for country-level spatial pattern.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7724270/v1/3015782b072672c5d5eaab29.png"},{"id":92488259,"identity":"24b86f57-b818-4214-81e1-0674a16b421e","added_by":"auto","created_at":"2025-09-30 09:07:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":894846,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGlobal outflow of name-bearing mammal specimens across countries (1990-2025).\u003c/strong\u003e Lateral barplots show top-15 countries (ISO-alpha3 code) in housing (top-left), sourcing (top-right), outflow (bottom-left), and inflow (bottom-right) of name-bearers of mammals. High inflow centrality indicates countries exerting extractive influence in parachute science networks, while high outflow centrality reflects greatly exploited countries. Only countries with at least three holotypes in foreign collections are shown (colors match Fig. 1). Arrows represent source-to-repository pathways between countries (self-links omitted). See Extended Data 2-3 for complete country-rankings and Extended Data 4 for a representation of including holotype retentions.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7724270/v1/c3800dd73bb39528d9be2b79.png"},{"id":92489079,"identity":"805064bc-12e1-476a-bdf2-a34e749ddcd6","added_by":"auto","created_at":"2025-09-30 09:15:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":134796,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSocioeconomic drivers of global holotype dynamics\u003c/strong\u003e. Effect sizes and 95% confidence intervals from models examining the influence of socioeconomic factors on per-country metrics. Panel (a) shows factors affecting the retention of native holotypes and the importation of foreign holotypes. Panel (b) shows factors affecting outflow centrality (the degree of holotype extraction by different foreign institutions) and inflow centrality (the country’s extractive influence). See Supplementary Information for coefficient estimates, standard errors, and significance values.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7724270/v1/70f138eec6648b1f2eea396f.png"},{"id":92646243,"identity":"20864610-a3b3-4a68-8be7-d8b577e83b1e","added_by":"auto","created_at":"2025-10-02 09:59:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1723509,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7724270/v1/15148a7d-584e-4cb4-9bc1-833830bc919a.pdf"},{"id":92488261,"identity":"cb53221f-78f1-4b53-b024-ae6ea3935e63","added_by":"auto","created_at":"2025-09-30 09:07:35","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":294128,"visible":true,"origin":"","legend":"SUPPLEMENTARY INFORMATION","description":"","filename":"SuppMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7724270/v1/d155301eaa33aade5e1d0e27.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Local expertise anchors biodiversity documentation, but geopolitical power drives parachute discovery","fulltext":[{"header":"Main text","content":"\u003cp\u003eThe discovery of new species typically involves comparison with type specimens of known taxa, the reference material that ties scientific names to biological entities. Among these, a single specimen holds particular taxonomic significance as the name-bearing of a species\u0026mdash;the holotype. These specimens are primarily stored in museums and natural history collections, yet such institutions are unevenly distributed worldwide, with their accessibility, resources, and functionality often shaped by socioeconomic context\u003csup\u003e1,2\u003c/sup\u003e. Consequently, biodiversity is better documented in the economically developed Global North than in the Global South, where many species remain understudied or entirely unknown\u003csup\u003e3,4\u003c/sup\u003e. This imbalance may encourage researchers from wealthier nations to conduct fieldwork in biodiverse regions but deposit name-bearing specimens abroad\u003csup\u003e5\u003c/sup\u003e. This disconnection between local researchers and name-bearing specimens limits access to critical material, hindering efforts to efficiently document global biodiversity and achieve goals under the Post-2020 Global Biodiversity Framework.\u003c/p\u003e\n\u003cp\u003eBeyond geographic and socioeconomic factors, taxonomic preferences further distort biodiversity documentation. Charismatic taxa such as mammals attract more research than less conspicuous groups like insects, fungi, or small vertebrates\u003csup\u003e6,7\u003c/sup\u003e. Although this preference has generated relatively rich data for mammals, it can mask inequalities in global knowledge production\u003csup\u003e8\u003c/sup\u003e. If efforts to promote decolonial science align with taxonomic trends in research preference, colonial legacies should be less pronounced in well-studied and charismatic taxa. To test this, we analyzed 1116 mammal species descriptions published over the past 35 years\u003csup\u003e9\u003c/sup\u003e, examining the origin and destination of name-bearing specimens of mammals, and the geopolitical and socioeconomic relationships shaping their movement.\u003c/p\u003e\n\u003cp\u003eDespite the Global South accounting for 95% of recent mammal discoveries, nearly 60% of their holotypes are housed abroad (Fig. 1). This contrasts with Global North discoveries, where only 22% of holotypes are deposited in foreign institutions, all those in the Global North. This knowledge split between holotype housing location and resident researchers is higher than that observed for less charismatic groups, such as freshwater fishes (43% of holotypes described post-1950 and 30% of those described post-2000 are housed in foreign institutions; Nakamura et al. 2024) and reptiles (50% of holotypes described post-1990\u003csup\u003e1,10\u003c/sup\u003e). Notably, 92.6% of specimens housed in Global South collections are native, while 92.3% of holotypes deposited in Global North institutions originate elsewhere.\u003c/p\u003e\n\u003cp\u003eThese trends underscore a pronounced North\u0026ndash;South divide in holotype retention and appropriation. Among 105 countries where new mammals were described, 68 (including 66 in the Global South) retained no holotypes. Of the 53 repository countries, only 20 housed exclusively native specimens (17 in the Global South). In contrast, 17 other countries, largely in Europe, hosted only foreign holotypes (Extended Data 1). The United States held the largest collection of holotypes at 324, accounting for 28.6% of the total; 98.5% of these (319) were imported from other countries. Network analyses based on the multidirectionality and intensity of holotype flows between countries further reveal that all of the top-15 countries with most-exploited holotypes are in the Global South\u0026mdash;especially Sub-Saharan Africa and Southeast Asia\u0026mdash;whereas 14 of the 15 most-extractive nations are located in the Global North, with China as the only exception (Fig. 2).\u003c/p\u003e\n\u003cp\u003eCountries with stronger academic capacity, as reflected in a higher Human Capital Index, retain more holotypes and are less constrained by outflow networks (i.e., the degree of holotype extraction by different foreign institutions, Fig. 3). Susceptibility to these pathways of holotype extraction is amplified by weak environmental policies (proxied by low scores on the Biodiversity and Habitat Issue), a challenge that often overlaps with limited enforcement capacity in megadiverse countries\u003csup\u003e11\u003c/sup\u003e. Historical sampling and colonial legacies further reinforce these extractive pathways, with the labor of scientists and collectors in former colonies systematically exploited to export biodiversity specimens\u003csup\u003e5\u003c/sup\u003e, a process apparently disconnected from research investment.\u003c/p\u003e\n\u003cp\u003eIn contrast, both holotype appropriation and inflow (i.e., the country\u0026rsquo;s extractive influence) increase in countries with stronger conservation policies and especially in colonialist nations, indicating that destinations of mammal holotypes are driven mostly by political dominance\u003csup\u003e12\u003c/sup\u003e. Furthermore, holotype inflow increases with availability of research institutions and historical sampling, suggesting that established scientific infrastructure\u0026mdash;rather than domestic academic capacity\u0026mdash;facilitates these extractive collections. While museums with robust infrastructure may attract foreign deposits\u003csup\u003e13\u003c/sup\u003e, our findings reveal a pronounced power imbalance: only 3.4% of the exported mammal holotypes were described solely by domestic authors. Indeed, the vast majority (60.5%) of exported holotypes involved foreign-led research without local collaboration, underscoring a pattern of extraction where foreign scientists remove holotypes from their countries of origin.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur findings reveal that while charismatic taxa attract disproportionate research attention\u003csup\u003e6,7\u003c/sup\u003e, this has failed to spur decolonial science practices. Mammal holotype outflow remains tied to political leverage and research infrastructure in wealthier nations. This sustains a dependence cycle, where biodiversity-rich countries have their biodiversity mostly accessed and documented by foreign nations, without benefit-sharing. For example, of 278 patents derived from native Brazilian plants, over 94% are foreign-owned\u003csup\u003e14\u003c/sup\u003e. Mechanisms to counteract neocolonial extraction are available within international agreements like the Convention on Biological Diversity. Yet, the effectiveness of such mechanisms is systematically undermined by non-ratification from key economies like the USA. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEfforts to comprehensively document global biodiversity are essential, but must confront the colonial structures still embedded in scientific practice\u003csup\u003e15\u003c/sup\u003e. We have shown that Global South nations with strong academic systems and well-established environmental policies retain more holotypes, whereas colonial history largely explains extractive pipelines. Without systemic change, the scientific marginalization of megadiverse nations will persist, undermining global efforts to mitigate biodiversity knowledge gaps.\u003c/p\u003e"},{"header":"Online Methods","content":"\u003cp\u003e\u003cstrong\u003eSpecies Data:\u003c/strong\u003e We examined 1116 descriptions of mammal species published from 1990 to early 2025 (as informed by Mammal Diversity Database v. 2.1\u003csup\u003e9\u003c/sup\u003e). For each species in our database, we obtained information on: type-locality\u0026rsquo;s (1) latitude and (2) longitude, and the housing collection\u0026rsquo;s (3) latitude and (4) longitude. For cases where only the name of the locality was provided (e.g., names of protected areas or cities), approximate coordinates were sought using gazetteers or tools like Google Earth (https://earth.google.com). Two species, the Northern Talapoin Monkey (\u003cem\u003eMiopithecus ogouensis\u003c/em\u003e) and the Giant Muntjac (\u003cem\u003eMuntiacus vuquangensis\u003c/em\u003e), were excluded from the dataset due to missing information on either their type-locality or housing collection.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCountry Data:\u003c/strong\u003e Using coordinates of type-localities and housing collections of name-bearer mammals, we extracted the geopolitical classification adopted by the United Nations\u003csup\u003e16\u003c/sup\u003e to obtain the following: (1) the ISO-alpha3 code, a three-character abbreviation representing each country\u0026rsquo;s name; (2) the geopolitical location of the country in the Global North or Global South; and (3) the geopolitical region to which the country belongs (Sub-Saharan Africa; Latin America and the Caribbean; North America; Australia and New Zealand; Central, Eastern, and Southern Asia; Europe; Near East and Northern Africa; Southeast Asia and Pacific Islands). Japan, South Korea, Israel, and French Guiana are classified as part of the Global North, although they are typically included in geopolitical regions dominated by countries in the Global South. For greater geographic detail, we removed the Russian Federation from Europe to represent it as an independent geopolitical region. All operations were conducted in R v. 4.5.0\u003csup\u003e17\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eWe computed 8 country-level socioeconomic and research effort variables. First, we extracted (1) the number of biodiversity institutions (i.e., museums, herbaria, and universities) based on the dataset available in the R package \u003cem\u003eCoordinateCleaner\u003c/em\u003e\u003csup\u003e18\u003c/sup\u003e. We used the Open Alex database (https://api.openalex.org/) to retrieve the number of available biodiversity institutions for 11 countries lacking data on the biodiversity institutions counts (Antigua and Barbuda, Bonaire, Republic of the Congo, Grenada, Kyrgyzstan, Mali, Eswatini, Samoa, Seychelles, Saint Vincent and the Grenadines, and Timor-Leste). Next, we extracted the (2) number of preserved specimens collected in each country from the Global Biodiversity Information Facility (GBIF), downloading all available mammal records with coordinates\u003csup\u003e19\u003c/sup\u003e, yielding 3,366,092 records after removing duplicates. Using information on the locality of housing collections, we also computed the number of preserved specimens housed in each country, but we refrained from using it in the analysis due to its high data missingness.\u003c/p\u003e\n\u003cp\u003eWe used the \u003cem\u003ewb_data\u003c/em\u003e function from the \u003cem\u003ewbstats \u003c/em\u003eR package\u003csup\u003e20\u003c/sup\u003e to retrieve World Bank Open Data indicators on: (3) percentage of GDP Expenditure in Research and Development. Since this indicator is provided annually in the World Bank Open Data platform, we averaged their respective values between 1990-2025. Importantly, socioeconomic data is not free from geopolitical biases, and Global South nations present missing data more frequently than those in the Global North. For countries with missing data in one or two socioeconomic factors, we imputed values using relevant regional averages. Specifically, we applied the Least Developed Countries (LDC) average of percentage of GDP Expenditure in Research and Development to 11 nations (Benin, Cameroon, Central African Republic, Eswatini, Guinea, Liberia, Malawi, Samoa, Sao Tome and Principe, Timor-Leste) and the Latin America and Caribbean average to five non-LDC countries (Antigua and Barbuda, Barbados, Grenada, Guyana, and Suriname).\u003c/p\u003e\n\u003cp\u003eWe sourced additional country-level indicators from the Quality of Government (QoG) database\u003csup\u003e21\u003c/sup\u003e, including: (4) biodiversity and habitat issue category, measures national efforts to protect ecosystems and prevent species loss; (5) Global Peace Index, assesses national peacefulness based on conflict, safety, and militarization; (6) Human Capital Index, evaluates how well countries develop education and health for productivity; (7) Human Development Index, summarizes achievements in health, education, and living standards; and (8) colonial origin, indicates a country\u0026rsquo;s historical colonization background. The latter variable was binarized (0 = no colonial origin, 1 = colonial origin). Each country\u0026rsquo;s value was averaged across the available QoG time-series from 1990 to 2025. The Global Peace Index for seven countries was imputed using regional averages, including three LDC nations (Samoa, Sao Tome and Principe, and Solomon Islands) and for Seychelles (acknowledging that our data collection preceded its 2014 graduation from the LDC category), while the Latin America and the Caribbean average was used for Antigua and Barbuda, Suriname, and Saint Vincent and the Grenadines.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eMetrics of retention and appropriation of name-bearers: \u003c/strong\u003eTo avoid biases from country area effects, we calculated the proportion of native name-bearing specimens retained per country, relative to the total number of native mammal name-bearing specimens discovered in each country. As a metric of name-bearing specimen appropriation, we computed the proportion of imported name-bearing specimens relative to the total housed in each country.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eMetrics of extractive flows in holotype networks:\u003c/strong\u003e We constructed a bipartite network to analyze the holotypes\u0026rsquo; global flows between countries based on their type localities and the housing collections of name-bearing mammals. Weights represented the number of occurrences between country pairs. At the node level, we computed Katz centrality to analyze the influence of a node (i.e., country) within a network, based on its number of immediate neighbors and the direct and indirect paths to other countries in the network\u003csup\u003e22\u003c/sup\u003e. The attenuation factor (\u0026alpha;) was set to 0.1, penalizing contributions from more distant connections (range: 0\u0026ndash;0.2). This calculation was performed using the \u003cem\u003ekatzcent \u003c/em\u003efunction from the \u003cem\u003ecentiserve \u003c/em\u003eR package\u003csup\u003e23\u003c/sup\u003e. Preliminary inspections showed no significant area effect on inflow and outflow metrics.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eData Analysis:\u003c/strong\u003e Some predictor variables (number of institutions, number of collected specimens, and GDP expenditure in research and development) vary over orders of magnitude and were log10-transformed to reduce skewness. Due to a lack of socioeconomic data for some countries, the analyses of holotype retention and outflow were based on 52 nations with at least three post-1990 mammal discoveries in their territory. These selected countries included the sourcing localities for 92% of all holotypes, and housed 97.3% of holotypes that remained in their country of origin. Similarly, the assessment of holotype appropriation and inflow included 32 nations that housed at least three holotypes, accounting for 97.6% of all housing localities, and 98% of all imported holotypes. All predictors were centered and standardized to allow comparability of model coefficients.\u003c/p\u003e\n\u003cp\u003eWe examined multicollinearity in predictor variables in each dataset separately (52-nations and 32-nations datasets), using the Variance Inflation Factor (VIF) computed via the \u003cem\u003eusdm \u003c/em\u003epackage\u003csup\u003e24\u003c/sup\u003e. Predictors with VIF values \u0026gt;5 were considered to exhibit strong multicollinearity and were removed from the models\u003csup\u003e25\u003c/sup\u003e (Table S1). For both country-level datasets, analyses included the following predictors: Global Peace Index, Human Capital Index, colonial origin registered as binary, Biodiversity and Habitat Issue, number of collected specimens. For the 53-nation dataset, which modeled holotype retention and outflow as response variables, we also included the percentage of GDP expenditure on research and development. For the 32-nation dataset, which modeled holotype appropriation and inflow, we additionally included the number of biodiversity institutions. The final predictor sets were selected on a per-dataset basis to minimize multicollinearity, and the variables differing between datasets (i.e., number of biodiversity institutions and percentage of GDP expenditure on research and development) are strongly correlated (r = 0.39, p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003eTo evaluate the effect of socioeconomic factors and research effort, we built four generalized linear models, one for each response variable (success in the retention of holotypes, success in the importation of foreign holotypes, and two network variables of inflow and outflow centrality for holotype transfers). Success in the retention/importation of holotypes was modeled with a beta-binomial error distribution, whereas holotype outflow/inflow centrality were modeled with a gamma error. All models were built using the \u003cem\u003eglmmTMB \u003c/em\u003epackage\u003csup\u003e26\u003c/sup\u003e. Pseudo-R\u0026sup2; were calculated with the \u003cem\u003eperformance \u003c/em\u003epackage\u003csup\u003e27\u003c/sup\u003e. Tjur\u0026rsquo;s method was used to compute pseudo-R\u0026sup2; for Beta-binomial models, whereas Nagelkerke\u0026rsquo;s method was used to obtain R\u0026sup2; for models with Gamma error distribution. Model diagnostics were performed using the \u003cem\u003eDHARMa\u003c/em\u003e package\u003csup\u003e28\u003c/sup\u003e (see Supplementary Information and Data Availability\u003csup\u003e29\u003c/sup\u003e).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data to reproduce the results of this study are available via Zenodo Digital Repository\u003csup\u003e29\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe code for reproducing the results of this study is available at Zenodo Digital Repository\u003csup\u003e29\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur sincere gratitude is extended to the global community of taxonomists, field naturalists, and scientists, past and present, whose dedicated work in discovering and describing new mammal species has made this study possible.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMRM conceived the study, MRM, JJMG, MTM compiled the data, MRM, GN, RLC, KC analyzed the data, MRM and GN developed the figures, MRM, RLC, and GN wrote the text. All authors provided critical feedback and helped shape the research, analysis, and manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRLC thanks the USP Programa de Apoio aos Novos Docentes USP \u0026ndash; 2025 (PRPI) for their support. KC thanks Conselho Nacional de Desenvolvimento Cient\u0026iacute;fico e Tecnol\u0026oacute;gico (CNPq) for research fellowship (#444240/2024-1). JJMG thanks Coordena\u0026ccedil;\u0026atilde;o de Aperfei\u0026ccedil;oamento de Pessoal de N\u0026iacute;vel Superior (CAPES) (proc. 88887.478942/2020-00) and the S\u0026atilde;o Paulo Research Foundation (FAPESP), Brazil (proc. 2024/18469-0) for research grants. MTM thanks the S\u0026atilde;o Paulo Research Foundation (FAPESP), Brazil (proc. 2023/14506-5 and 2024/22798-9) for research grants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExtended Data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExtended Data 1 to 5 are available online for this study.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary Information is available for this paper, including Supplementary Tables S1 to S5.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eUetz, P. \u003cem\u003eet al.\u003c/em\u003e A global catalog of primary reptile type specimens. \u003cem\u003eZootaxa\u003c/em\u003e \u003cstrong\u003e4695\u003c/strong\u003e, (2019).\u003c/li\u003e\n\u003cli\u003eMeyer, C., Kreft, H., Guralnick, R. \u0026amp; Jetz, W. 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R. \u003cem\u003eet al.\u003c/em\u003e Data from: Local expertise anchors biodiversity documentation, but geopolitical power drives parachute discovery. at https://doi.org/10.5281/zenodo.17187892 (2025).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7724270/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7724270/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Global biodiversity documentation is hampered by several factors, including the inequitable distribution of holotypes—critical reference specimens for the description of new species. While 95% of post-1990 mammal discoveries originated in the Global South, 60% of their holotypes are housed abroad, primarily in Global North institutions. Wealthier nations extract holotype specimens disproportionately, especially from biodiverse regions with weak environmental policies, despite stronger local academic capacity improving retention. Addressing these disparities requires changes in structures, rules, and functioning of institutions to empower biodiverse nations to document their own species, alongside frameworks for more equitable knowledge exchange.","manuscriptTitle":"Local expertise anchors biodiversity documentation, but geopolitical power drives parachute discovery","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-30 09:07:30","doi":"10.21203/rs.3.rs-7724270/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1f18b585-360e-44b7-974a-2658ee758e38","owner":[],"postedDate":"September 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55487757,"name":"Biological sciences/Ecology"},{"id":55487758,"name":"Biological sciences/Zoology"},{"id":55487759,"name":"Scientific community and society/Scientific community"}],"tags":[],"updatedAt":"2025-10-02T09:50:59+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-30 09:07:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7724270","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7724270","identity":"rs-7724270","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}