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Record W4410734021 · doi:10.1016/j.ecolind.2025.113616

Assessing Biocultural Diversity Across Scales Using Ecological Indicators

2025· article· en· W4410734021 on OpenAlexafffund
Bryam Mateus-Aguilar, Andrés Felipe Díaz-Salazar, Federico Andrade‐Rivas, Natasha Batista, Anaid Cárdenas‐Navarrete, Armando Dávila Arenas, Katherine Victoria Hernandez, Guido A. Herrera‐R, Kelley E. Langhans, Dallas Levey, Andrew Neill, Oliver T. Nguyen, Natalia Ocampo‐Peñuela, Andrés Felipe Suárez‐Castro, Felipe Zapata, Alejandra Echeverri

Bibliographic record

VenueEcological Indicators · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Cultural Studies in Latin America and Beyond
Canadian institutionsUniversity of British Columbia
FundersCanadian Institute for Advanced ResearchUniversity of TennesseeStanford University
KeywordsEcologyDiversity (politics)Ecological indicatorGeographyBeta diversityScale (ratio)Environmental scienceBiodiversityEcosystemBiologyCartographyAnthropology

Abstract

fetched live from OpenAlex

• We propose an indicator of biocultural diversity combining vertebrate richness and cultural data like UNESCO Heritage Sites. • Ecological indices like Shannon and Simpson can be adapted to quantify biocultural diversity. • Correlations between biodiversity and cultural diversity are found at national but not ecoregional scales. • Orinoquia and Amazonia are Colombia’s most biodiverse ecoregions, while the Caribbean ranks highest in cultural diversity. • Valledupar emerged as the most biocultural diverse. municipality in Colombia. Despite considerable efforts to promote biocultural diversity as a conservation objective, the spatial correlation between cultural and biological diversities, as well as its variation across geographic scales, remains unclear. In this study, we used Colombia as a case study to examine the relationships between biodiversity and cultural diversity at both national and ecoregional scales. Using municipality as the unit of analysis, we gathered data on a range of biological and cultural variables. We quantified six biodiversity indicators–species richness of freshwater fishes, mammals, birds, amphibians, reptiles, and number of ecosystems. For cultural diversity, we used seven indicators: music festivals, Indigenous reserves, Afro-Colombian lands, UNESCO World Heritage sites, UNESCO Intangible Cultural Heritage sites, museums, and native languages, as proxies for institutionalized cultural diversity. Diversity metrics adapted from ecology, including the Shannon Diversity Index (SDI) and the Inverse-Simpson Diversity Index (InvSDI), were used to calculate both biodiversity and cultural diversity at national and ecoregional scales. Our results suggest that biodiversity and cultural diversity are partially positively correlated at the national scale, as indicated by the InvSDI, which incorporates cultural data that the SDI omits. However, at the ecoregional scale, we found no consistent correlation, though both positive and negative trends emerged. This study presents a methodological innovation for quantifying biocultural diversity and raises important questions for future research on the connections between nature and culture.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.002
Scholarly communication0.0000.000
Open science0.0010.004
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.302
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2025
Admission routes2
Has abstractyes

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