Quantifying ecological and social drivers of ecological surprise
Bibliographic record
Abstract
Abstract A key challenge facing ecologists and ecosystem managers is understanding what drives unexpected shifts in ecosystems and limits the effectiveness of human interventions. Research that integrates and analyses data from natural and social systems can provide important insight for unravelling the complexity of these dynamics. It is, therefore, a critical step towards the development of evidence‐based, whole‐system management approaches. To examine our ability to influence ecosystems that are behaving in unexpected ways, we explore three prominent cases of “ecological surprise.” We captured the social‐ecological systems (SES) using key variables and interactions from Ostrom’s SES framework, which integrates broader ecosystem processes (e.g. climate, connectivity), management variables (e.g. quotas, restrictions, monitoring), resource use behaviours (e.g. harvesting) and the resource unit (e.g. trees, fish, clean water) being managed. Structural equation modelling revealed that management interventions often influenced resource use behaviours (e.g. rules and limits strongly affected harvest or pollution), but they did not have a significant effect on the abundance of the managed resource. Instead, most resource variability was related to ecological processes and feedbacks operating at broader spatial or temporal scales than management interventions, which locked the resource system into the degraded state. Synthesis and applications. Mismatch between the influence of management systems and ecosystem processes can limit the effectiveness of human interventions during periods of ecological surprise. Management strategies should shift from a conventional focus on removal or addition of a single resource towards solutions that influence the broader ecosystem. Operationalizing Ostrom’s framework to quantitatively analyse social‐ecological systems using structural equation models shows promise for testing solutions to navigate these events.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".