Modifiers of impacts on marine ecosystems: disturbance regimes, multiple stressors and receiving environments
Bibliographic record
Abstract
Introduction Effective management and the maintenance of marine ecosystem services rely on a capacity to predict the ecological consequences of environmental change and potential management interventions (Chapter 1). Making these predictions is difficult because anthropogenic stressors do not produce uniform or consistent impacts on biodiversity and ecosystem functioning. Rather, their effects can be modified by a variety of factors that cause them to vary among locations and different points in time. Thus, the effectiveness of actions taken to manage environmental problems is likely to vary in a similar way: interventions that are sufficient to mitigate a stressor's impacts in one situation might be inadequate or excessive in others. Both sound science and efficient management require us to recognise that spatial and temporal variability are inherent to natural systems, and that the ecosystem complexity places inherent limits on our ability to predict future ecological conditions. However, many of the causes of this variability have been identified. Careful consideration of these factors will enhance scientific understanding, improve ecological prediction and enhance our efforts to optimise marine policy and management by reducing the uncertainty associated with the effects of stressors. In this chapter, we examine three factors that cause anthropogenic activities to have inconsistent impacts on marine ecosystems. Both individual organisms and entire ecosystems tend to respond nonlinearly to gradients in stressor severity. Thus, we begin by discussing how differences in stressor intensity, as well as spatial and temporal characteristics of stressors' disturbance regimes can influence their impacts. Next, we discuss how characteristics of receiving ecosystems influence their sensitivity to stressors' impacts. These include biological characteristics such as the genetic structure of populations and community composition, as well as physical and chemical variables that may alter stressors' effects. Finally, we will discuss an issue of increasing concern among scientists and policy makers: the impacts of multiple stressors and the ways in which stressors can modify one another's effects.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".