Can avian functional traits predict cultural ecosystem services?
Bibliographic record
Abstract
Abstract The functional trait diversity of species assemblages can predict the provision of ecosystem services such as pollination and carbon sequestration, but it is unclear whether the same trait‐based framework can be applied to identify the factors that underpin cultural ecosystem services and disservices. To explore the relationship between traits and the contribution of species to cultural ecosystem services and disservices, we conducted 404 questionnaire surveys with birdwatchers and local residents in Guanacaste, Costa Rica. We used an information–theoretic approach to identify which of 20 functional traits for 199 Costa Rican bird species best predicted their cultural ecosystem service scores related to birdwatching, acoustic aesthetics, education and local identity, as well as disservices (e.g. harm to crops). We found that diet was the most important variable explaining perceptions of cultural ecosystem service and disservice providers. Aesthetic traits such as plumage colour and pattern were important in explaining birdwatching scores. We also found people have a high affinity for forest‐affiliated birds. The insight that functional traits can explain variation among cultural perspectives on values derived from birds offers a first step towards a trait‐based system for understanding the species attributes that underpin cultural ecosystem services and disservices.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".