Integrating ecosystem services and resilience toward sustainability
Bibliographic record
Abstract
In the Anthropocene era, humanity has considerably altered the functioning of Earth, resulting in global and inter-related social, economic and environmental crises. In response, resilience, ecosystem services (ES) and sustainability have gained tremendous popularity in the scientific, policy and management arenas. However, less attention has been paid to the relationships between ES and resilience and how these concepts interact with sustainability. We, therefore, analyzed the concepts of ES and resilience, their relationships, strengths and weaknesses to determine how resilience and ES could be together operationalized for sustainable forest management. This analysis, based on a literature review and on interviews with experts, shows that resilience and ES are closely intertwined. They meet in the social-ecological system perspective where resilience determines the capacity of the system to face disturbances and thus to provide ES and is influenced, in turn, by human actions taken to response to changes in ES. In a narrower sense, resilience is defined as the ability to maintain ES. Finally, in some ES classifications, resilience is treated as an ES among others. The resilience approach contributes to improve the ES approach and vice versa: resilience introduces the temporal dimension in ES while ES help integrating the multiple dimensions, scales, methods and points of views as well as their interactions in resilience. Resilience may be mandatory to ES and vice versa as a loss of resilience/ES could jeopardize ES/resilience. In conclusion, pairing ES and resilience is essential to promote policies toward sustainable forest management. However, caution should be exercised to avoid traps of one concept overriding the other.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".