Re-integrating ecology into integrated landscape approaches
Bibliographic record
Abstract
Abstract Context Integrated landscape approaches (ILAs) that aim to balance conservation and development targets are increasingly promoted through science, policy, and the donor community. Advocates suggest that ILAs are viable implementing pathways for addressing global challenges such as biodiversity loss, poverty alleviation, and climate change mitigation and adaptation. However, we argue that recent advances in ILA research and discourse have tended to emphasize the social and governance dimensions, while overlooking ecological factors and inadequately considering potential trade-offs between the two fields. Objectives By raising the issue of inadequate integration of ecology in ILAs and providing some general design suggestions, we aim to support and incentivise better design and practice of ILAs, supplementing existing design principles. Methods In this perspective we draw on the recent literature and our collective experience to highlight the need, and the means, to re-integrate ecology into landscape approaches. Results We suggest that better incorporation of the ecological dimension requires the integration of two approaches: one focusing on conventional scientific studies of biodiversity and biophysical parameters; and the other focusing on the engagement of relevant stakeholders using various participatory methods. We provide some general guidelines for how these approaches can be incorporated within ILA design and implementation. Conclusion Re-integrating ecology into ILAs will not only improve ecological understanding (and related objectives, plans and monitoring), but will also generate insights into local and traditional knowledge, encourage transdisciplinary enquiry and reveal important conservation-development trade-offs and synergies.
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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) | 0.021 | 0.002 |
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; both teacher heads agree on what is shown here.
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".