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Record W3090225052 · doi:10.1002/fee.2256

It is time to rebalance the risk equation

2020· review· en· W3090225052 on OpenAlexaboutno aff
Michelle Marvier, Peter Kareiva

Bibliographic record

VenueFrontiers in Ecology and the Environment · 2020
Typereview
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsnot available
Fundersnot available
KeywordsUnintended consequencesWildlifePsychological interventionEnvironmental ethicsClimate changeEnvironmental planningPrecautionary principleEnvironmental resource managementEcologyNatural resource economicsPolitical scienceGeographyBiologyPsychologyEnvironmental scienceLawEconomics

Abstract

fetched live from OpenAlex

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://reviv​erest​ore.org/inten​ded-conse​quences), 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

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 imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.014
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.033
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.005
Scholarly communication0.0050.014
Open science0.0020.003
Research integrity0.0050.020
Insufficient payload (model declined to judge)0.0140.002

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.

Opus teacher head0.017
GPT teacher head0.231
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations6
Published2020
Admission routes1
Has abstractyes

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