Transforming ecosystems: When, where, and how to restore contaminated sites
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».