Not just the Big Five: African ecotourists prefer parks brimming with bird diversity
Bibliographic record
Abstract
Abstract Ecotourism helps sustain protected areas (PAs) that in turn conserve Africa's declining fauna. Identifying ecotourist preferences and which species and landscapes benefit from ecotourism could therefore support African biodiversity conservation efforts. Due to historic associations with trophy hunting and subsequent ecotourism marketing efforts, ecotourist preferences have been thought to traditionally center around the ‘Big Five’: elephant, lion, buffalo, leopard, and rhinoceros. But these preferences may be evolving. Here, we ask two questions, one about the drivers and one about the consequences of ecotourism: (1) Which species and landscapes do ecotourists most prefer based on realized visitation data? And (2), differently, which species and landscapes benefit most from ecotourism? We gathered data on average annual tourist visits, the occurrence of nine mammals, bird species richness, forest cover, national wealth, local human population and accessibility for 164 Sub‐Saharan African PAs. To address our first question, we used a Bayesian multivariable model to identify whether bird and megafaunal diversity explain visits to PAs while controlling for other factors. To address our second question, we used Bayesian univariate models to analyze the relationships between park visitation and each species/landscape. We found that tourist preferences extend beyond the Big Five to include bird diversity. We also observed that ecotourism may be well suited to conserve bird diversity, lion, cheetah, black and white rhinoceros, African wild dog and giraffe species. Collectively, our results may help inform how to leverage ecotourism to conserve African fauna.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".