Notice bibliographique
Résumé
The fear of unintended consequences is frequently used to argue against conservation interventions that range from climate engineering, to genetic editing of imperiled species, to actions as seemingly mundane as using seeds from non-local sources in restoration projects. There is no denying that unintended consequences are real and worthy of concern. Indeed, environmental textbooks are filled with descriptions of past interventions gone awry (such as the introduction of cane toads to Australia for biocontrol, the impacts of long-term wildfire suppression, or the use of DDT to control insect pests). However, there are also numerous counterexamples of interventions turning out as planned (for instance, barging salmon smolts around Snake River dams and human-assisted hybridization rescuing the depleted gene pool of Florida panthers). For actions under consideration, the question is how to weigh the possible unintended consequences versus the highly likely intended benefits. A June 2020 workshop (https://reviverestore.org/intended-consequences), organized by Revive & Restore, assembled an international group of conservationists (including wildlife biologists, restoration scientists, geneticists, ethicists, and social scientists) to re-examine the precautionary principle and its associated focus on unintended and unanticipated consequences. Two observations make apparent the need for this reassessment. First, accelerating anthropogenic climate change and the expanding human footprint create ever-greater urgency for actions that could avert disasters or prevent human-driven extinctions. With most rivers dammed, a nitrogen cycle dominated by human alterations, and such severe global warming that within 50 years as many as one in three humans could be forced to migrate in search of a habitable environment (https://nyti.ms/2E5a0Wi), conservationists do not have the luxury of “doing nothing” out of fear of unintended consequences. Caution is prudent, but paralysis is unconscionable. Second, the science of risk assessment has advanced so that, although it is impossible to eliminate uncertainty, the likelihood of horrific ecological surprises is much less now than in previous decades. Ecologists today better understand ecosystems and indirect effects than they did in the 1960s and 1970s when several well-intended, but ill-fated, introductions were conducted (eg introducing the American red squirrel to Newfoundland to augment the diet of pine martens; introducing the seed weevil Rhinocyllus conicus to control exotic thistles in North America). Lessons learned with each intervention reduce the chance for future errors. Not only is risk assessment improving, but some of the tools for intervention are becoming refined. For example, the genomic and phenotypic changes that result from gene editing are much more precise than those wrought by more widely accepted techniques like hybridization and mutagenesis. Obviously, the answer is not to blithely ignore unintended consequences and adopt an “anything goes” attitude. However, it is time to recalibrate our traditional cautionary approach to environmental decision making with fine-tuning in four dimensions. First, the intended consequences of proposed interventions must carry more weight in analyses. Consider the public debate over transgenic Bt crops. The intended benefit of reducing the application of broadly toxic insecticides has largely been overshadowed by fears of environmental harms, for which there is no convincing evidence. Second, scientists should avoid being overly influenced by examples of disaster from generations ago and give more credit to recently accumulated empirical evidence. From reintroductions to genetic rescues, the vast majority of contemporary environmental interventions have produced their intended positive outcomes, yet a few historical cases of problematic outcomes continue to dominate public perception. Moreover, all actions, including inaction, entail the potential for unintended consequences. For instance, growing evidence shows that protected area creation – an intervention largely embraced by conservationists – typically displaces, rather than curtails, environmental harms and can prove counterproductive if local communities are alienated. Third, risk assessment relies on tools ranging from controlled experiments and practical experience to models and simulations. All of these tools help characterize risk, but empirical data should be far more reassuring than theoretical, but untested, models. Thirty years of globally widespread Bt crops with no ill-effects ought to inspire confidence, whereas speculative interventions such as solar geoengineering warrant greater precaution. Lastly, and most importantly, scientists need to recognize that the worst unintended consequences may not be environmental or ecological, but rather social. A carbon tax might be a great way to reduce greenhouse-gas emissions and a protected area may secure a remnant population of a declining species, but such actions disproportionately impose costs on marginalized human communities. A wider variety of voices must be invited to weigh in on what intended outcomes are desired, and how best to achieve them. While risk reduction efforts now better recognize and minimize unintended environmental harms, much work remains to address unintended social and cultural consequences. MICHELLE MARVIER Santa Clara University, Santa Clara, CA PETER KAREIVA Aquarium of the Pacific, Long Beach, CA
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,033 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,005 |
| Communication savante | 0,005 | 0,014 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,005 | 0,020 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 0,002 |
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 source (Gemma direct ou Codex distillé), 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 ».