The effects of oil spill dispersant use on marine birds: a review of scientific literature and identification of information gaps
Bibliographic record
Abstract
Dispersants, a class of chemical spill-treating agents used to treat oil spills, are commonly used globally as an alternative response measure. Applying dispersants to an oil slick, shortly after the spill has occurred, can protect shoreline environments and sea surface-dwelling animals, such as some marine bird species, limiting individuals or local populations from the consequences of coming into contact with large quantities of oil. However, this benefit comes with the cost of increasing oil exposure risk to marine biota that spend time in the water column. It is generally believed that the benefits of dispersant use outweigh the costs under most circumstances. However, it is rarely acknowledged that the use of dispersants may have negative impacts on marine biota at the individual or local population level, including marine birds. In Canada, Corexit EC9500A, a regulated dispersant, is being proposed for expanded use beyond treating spills from an offshore oil and gas facility. To understand what the potential impacts from dispersant use are to marine birds, we conducted a literature review to identify the direct and indirect effects of their use. We also provide oil spill responders with a Pathway of Effects (POE) conceptual model, a tool for understanding the interactions between dispersants, marine birds, and their environment to support a holistic consideration as part of the oil spill response decision-making process. Fundamental uncertainties remain, however, and if left unaccounted for in the decision-making process, they may compromise the appropriateness of spill response approaches and outcomes. We recommend that oil spill responders incorporate the known benefits and costs of dispersant use on marine birds into a decision-making framework such as a Net Environmental Benefit Analysis (NEBA) and with consideration of the POE concept models provided. These recommendations are particularly relevant where a decision-making framework such as NEBA is becoming a more standardized component of the response process. Additionally, greater investment in lab and field-based research, and field observations through monitoring, is required to address existing decision-making uncertainties and provide information gap closure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".