Toward Sustainable Biocultural Ecotourism: An Integrated Spatial Analysis of Cultural and Biodiversity Richness in Colombia
Bibliographic record
Abstract
Abstract Ecotourism plays a vital role in both economic development and depending on the scale, it can also aid environmental conservation. Ecotourism planning often considers culture‐based and nature‐based tourism separately, failing to recognize the synergies between them, with the potential to market locations as biocultural destinations. Using Colombia as a case study, we created metrics of taxonomic biological diversity as measured by vertebrate species richness (including birds, mammals, freshwater fishes, reptiles and amphibians) and institutionalized cultural richness (by counting the number of UNESCO World Heritage Sites, UNESCO Intangible Cultural Heritage sites, museums, endemic music festivals, Afro‐Colombian Territories and Indigenous Reserves) and evaluated the spatial correlations between them. To determine biocultural ecotourism potential, we evaluated whether biocultural richness was accessible and mapped potential biocultural ecotourism supply. By mapping areas of sports fisheries, birdwatching destinations, national park annual visitors and airport arrivals we also estimated spatial demand. We also analysed the difference between biocultural ecotourism supply and demand to assess the realized and untapped potential for biocultural destinations. While biocultural richness is high in the Amazon, Pacific and Caribbean regions, we found that there are no win‐win‐win locations where culture, species richness and accessibility are all high. Areas with great potential for biocultural ecotourism development largely coincide with designated Indigenous Reserves and Afro‐Colombian Territories. Our paper suggests that Colombia is currently safeguarding its biocultural capital and that it remains inaccessible to tourists. This study underscores the power of integrating cultural and biological variables to reshape the ecotourism sector. Read the free Plain Language Summary for this article on the Journal blog.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".