Transforming ecosystems: When, where, and how to restore contaminated sites
Bibliographic record
Abstract
Abstract Chemical contamination has impaired ecosystems, reducing biodiversity and the provisioning of functions and services. This has spurred a movement to restore contaminated ecosystems and develop and implement national and international regulations that require it. Nevertheless, ecological restoration remains a young and rapidly growing discipline and its intersection with toxicology is even more nascent and underdeveloped. Consequently, we provide guidance to scientists and practitioners on when, where, and how to restore contaminated ecosystems. Although restoration has many benefits, it also can be expensive, and in many cases systems can recover without human intervention. Hence, the first question we address is: “When should we restore contaminated ecosystems?” Second, we provide suggestions on what to restore—biodiversity, functions, services, all 3, or something else—and where to restore given expected changes to habitats driven by global climate change. Finally, we provide guidance on how to restore contaminated ecosystems. To do this, we analyze critical aspects of the literature dealing with the ecology of restoring contaminated ecosystems. Additionally, we review approaches for translating the science of restoration to on-the-ground actions, which includes discussions of market incentives and the finances of restoration, stakeholder outreach and governance models for ecosystem restoration, and working with contractors to implement restoration plans. By explicitly considering the mechanisms and strategies that maximize the success of the restoration of contaminated sites, we hope that our synthesis serves to increase and improve collaborations between restoration ecologists and ecotoxicologists and set a roadmap for the restoration of contaminated ecosystems. Integr Environ Assess Manag 2016;12:273–283. © 2015 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of SETAC Key Points We merge insights from ecological and economic theory and on-the-ground restoration activities to provide guidance on what endpoints should be targeted for restoration in contaminated ecosystems and when, where, and how to restore ecosystems degraded by contaminants. We encourage practitioners to consider restoration as early as possible (i.e., before injury or before remediation) and to restore both structural and functional endpoints. We also promote consideration of broader landscape and seascape contexts and new ideas and approaches that can overcome the scientific and financial limitations of restoration. We urge more reciprocal transfer of knowledge among theorist and practitioners and academics, industry, government, tribal organizations, NGOs and the public to improve the science of restoration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".