Acclimating to degraded environments: The social rationale for swift action on restoration
Bibliographic record
Abstract
As environmental degradation progresses, economies and societies adapt to the loss of ecosystem services and public attention to degradation subsides. In systems experiencing such societal acclimation to degradation, net incentives for stakeholder mitigation peak during early degradation phases and subside over time. Using harmful algae blooms in western Lake Erie as a case study, we illustrate how declines in public attention and societal reliance on lake recreation (i.e., finding recreation alternatives) reduce the incentives for stakeholders to reduce pollution runoff (i.e., mitigation efforts throughout the watershed). We then analyze how acclimation can affect a broad array of conservation challenges by developing a general socio-ecological model of societal response to degradation. We find that delays in initiating stakeholder-driven mitigation efforts can exponentially prolong restoration projects. Furthermore, when alleviating intense degradation relies upon voluntary commitments by many individuals, windows of opportunity for mitigation can be very limited because feedback loops of societal adaptation doom late restoration efforts to failure and lock human-environment systems into degraded states. These windows of opportunity can be particularly narrow when a) stakeholder mitigation requires supportive public opinion or b) even modestly valuable alternative services are available in degraded ecosystems. In such cases, maintaining undegraded human-environment regimes may hinge on quickly initiating stakeholder mitigation movements and allocating limited government conservation funds soon after degradation begins instead of spreading mitigation efforts out over decades. Such initiatives, regardless of whether acclimation is slow or rapid in a given system, also greatly accelerate the pace of environmental restoration. Significance Statement As societies acclimate to degraded environments, mitigation efforts that hinge on action by many stakeholders can erode. Developing a socio-ecological model of acclimation, we reveal how social and environmental processes intertwine to create alternative stable socio-ecological regimes, with either: 1) undegraded ecosystem states sustained by widespread mitigation adoption, or 2) degraded states where societies neither maintain nor continue relying on traditional, local ecosystem services. This dynamic places a premium on prompt mitigation efforts, which may face narrow opportunity windows to get started and avert degraded regimes in systems that rely on stakeholder-driven mitigation. Moreover, in any system requiring stakeholder action, societal acclimation will increase the importance of early action because decaying mitigation incentives exponentially lengthen restoration efforts.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.030 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".