Monitoring social–ecological networks for biodiversity and ecosystem services in human-dominated landscapes
Bibliographic record
Abstract
The demand the human population is placing on the environment has triggered accelerated rates of biodiversity change and created trade-offs among the ecosystem services we depend upon. Decisions designed to reverse these trends require the best possible information obtained by monitoring ecological and social dimensions of change. Here, we conceptualize a network framework to monitor change in social–ecological systems. We contextualize our framework within Ostrom’s social–ecological system framework and use it to discuss the challenges of monitoring biodiversity and ecosystem services across spatial and temporal scales. We propose that spatially explicit multilayer and multiscale monitoring can help estimate the range of variability seen in social–ecological systems with varying levels of human modification across the landscape. We illustrate our framework using a conceptual case study on the ecosystem service of maple syrup production. We argue for the use of analytical tools capable of integrating qualitative and quantitative knowledge of social–ecological systems to provide a causal understanding of change across a network. Altogether, our conceptual framework provides a foundation for establishing monitoring systems. Operationalizing our framework will allow for the detection of ecosystem service change and assessment of its drivers across several scales, informing the long-term sustainability of biodiversity and ecosystem services.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".