Perspectives: The license to fail – Steps towards an adaptive paradigm for forest management in times of unprecedented uncertainty
Bibliographic record
Abstract
This article offers a new perspective on possible consequences of looking at uncertainty in forest ecosystem management as something irreducible. Management strategies suggested by forest science are often focused on minimizing uncertainty by using predictive or probabilistic models and risk management via diversification in order to enable the achievement of relatively rigid targets or desired ecosystem conditions. These approaches, however, do not fully capture the complex, dynamic nature of forest ecosystems and the challenges ecosystem managers face as societal needs and the world’s climate are changing in an unprecedented manner. The fact that a good proportion of future events is unpredictable and that forest ecosystem management has to find ways to act upon this uncertainty precisely because it is irreducible is often overlooked. The conceptual background for this publication was drawn from considerations on uncertainty and entrepreneurial action in both forest sciences and economics. Building on that, we argue that the understanding of forests as complex and adaptive socio-ecological systems has to be better represented in systematic and hierarchical structures. Central to sector-wide innovation and adaptation is a new management paradigm: A better understanding of ecosystem dynamics in real-time and a substitution of approaches that are designed to plan or steer ecosystems with experimental proceedings via trial and error. Therefore, we propose a theoretical workflow that bolsters the decisions of individual forest managers with the results of a landscape-wide experimental network. By promoting a culture of “not-knowing”, bottom-up exchange between hierarchical levels and continuous learning and experimentation, institutions can encourage individual forest managers to learn from their own management process and thus improve the way they navigate the complexities of ecosystem management in an uncertain future. • Global change amplifies irreducible uncertainty in forest management. • Recognizing uncertainty fosters shared responsibility and resilient strategies. • Experiments and improved monitoring help to understand current ecosystem dynamics. • We propose a workflow for adaptive management and structured experiments.
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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 | 0.026 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.071 |
| Scholarly communication | 0.019 | 0.035 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.016 | 0.021 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".