A mixed method evaluation of ecosystem services and services-to-ecosystems illuminates culturally important trees in a settled landscape
Bibliographic record
Abstract
Biodiversity loss can lead to losses in ecological functions, ecosystem services, and cultural values. Yet, literature that empirically relates species’ ecological and cultural importance is scarce. We investigate tree species’ cultural importance in a settled landscape in Canada by combining biophysical plot sampling (n = 122) and semi-structured interviews (n = 31). We examined relationships between cultural importance, ecosystem services (benefits from trees), services-to-ecosystems (actions toward trees), and the abundance, mortality, and modification of trees. We found that tree abundance generally correlated with cultural emphasis. Maple was the most biophysically abundant and culturally important tree, associated with the greatest diversity of ecosystem services. However, some trees were mentioned disproportionately more frequently than their biophysical abundance, likely due to high mortality in the case of ash and cultural importance in the case of apples. Apple trees were culturally important and linked to the most diverse services-to-ecosystems. Our findings suggest that cultural importance relates to provisioning ecosystem services and services-to-ecosystems, which may, in turn, influence the abundance of these culturally important genera on the landscape. This connection between cultural importance and services-to-ecosystems could be a potential lever for wider biocultural conservation. This study contributes to ongoing discussions about cultural ecosystem services and highlights the importance of services-to-ecosystems in understanding human-nature relationships. The results have implications for protected area management, suggesting that reduced human-tree interactions could negatively impact biocultural values.
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 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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".