A social-ecological approach to characterize ecosystem services in the Ecuadorian Amazon
Bibliographic record
Abstract
Social-ecological approaches are necessary to understand complex systems in which humans are dependent on ecosystem services to support their livelihoods. We implemented structured interviews (n=89) to characterize the social-ecological interactions between colonists and ecosystem services in four social-ecological systems located in two southern Amazonian provinces of Ecuador. This characterization allowed us to describe the subsistence activities, ecological knowledge, and local institutions present in the studied social-ecological systems. Cattle ranching, agriculture, and hunting provide safety nets to generate moderate levels of cash for colonists to face unpredictable events. However, these subsistence activities, as well as ecological knowledge and local institutions are not adapted to the local dynamic of the Amazonian ecosystems. Through this characterization of the colonists’ social-ecological interactions, we also identify the ecosystem services and disservices obtained. Thirteen ecosystem services were identified, six of which were generated within protected areas. Seven ecosystem disservices were also identified, none of them produced within protected areas. Our study shows the separation prevailing between humans and ecosystems in the social-ecological interactions of the colonists, and, at the same time, the key role of these maladapted interactions in their subsistence activities. This research contributes a qualitative strategy to assessing social-ecological interactions and illustrates the importance of the ecosystem services provided by the Amazon ecosystems to colonists.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".