The birds and the trees: Avian ecosystem (dis)service perspectives and farmers' willingness to plant native trees in the agricultural landscape of the Galapagos Islands
Bibliographic record
Abstract
Abstract Agricultural landscapes hold great potential for biodiversity conservation; however, this will require finding solutions that work for both people and nature. Increasingly, the conservation community is calling for more cross‐disciplinary research integrating ecological questions with social and behavioural sciences for a more complete and successful approach to conservation. Here, we used a mixed methods approach, including 53 in‐person interviews, to examine how small‐scale farmers in the Galapagos Islands perceive landbirds and their ecosystem (dis)services. We also studied farmers' motivations and hesitations in planting native trees on their farms, an action that was identified to be critical for landbird conservation in Galapagos in previous ecological studies. We found that all native landbirds provided important cultural ecosystem services (CES) to farmers, with some variation between species. We also found that perceptions around pest control and pollination services were more variable and that landbirds were generally highly liked, even those who provided disservices such as crop damage. Most farmers were willing to plant native and endemic trees on their farms, although typically only in small quantities. Farmers' main motivations for planting trees were instrumental (e.g. shade/freshness), while their primary hesitation was a perceived lack of space on their farms. The main predictor of willingness was the prior presence of native trees on their farm. Synthesis and applications : Our results suggest tree‐planting conservation efforts should be adaptive, context‐dependent, and leverage incentives and community events for farmers. As a starting point, we recommend working with larger farms and with farmers interested in agrotourism for farmland restoration, recognizing that birdwatching has untapped potential in Galapagos. We further suggest focusing conservation messaging for the local community on the CES we identify for specific Galapagos landbirds, since CES are linked to people's motivations to care for nature. Finally, more research is needed to explore what ‘Galapagos identity’ means since we found it to be associated with farmers' landbird perspectives and willingness to plant trees, and self‐identity can drive pro‐environmental behaviours. We also recommend more research to understand the pest control services landbirds may be providing, if any, in the agricultural zone. 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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".