Investigating cumulative effects across ecological scales
Bibliographic record
Abstract
Species, habitats, and ecosystems are increasingly exposed to multiple anthropogenic stressors, fueling a rapidly expanding research program to understand the cumulative impacts of these environmental modifications. Since the 1970s, a growing set of methods has been developed through two parallel, sometimes connected, streams of research within the applied and academic realms to assess cumulative effects. Past reviews of cumulative effects assessment (CEA) methods focused on approaches used by practitioners. Academic research has developed several distinct and novel approaches to conducting CEA. Understanding the suite of methods that exist will help practitioners and academics better address various ecological foci (physiological responses, population impacts, ecosystem impacts) and ecological complexities (synergistic effects, impacts across space and time). We reviewed 6 categories of methods (experimental, meta-analysis, single-species modeling, mapping, qualitative modeling, and multispecies modeling) and examined the ability of those methods to address different levels of complexity. We focused on research gaps and emerging priorities. We found that no single method assessed impacts across the 4 ecological foci and 6 ecological complexities considered. We propose that methods can be used in combination to improve understanding such that multimodel inference can provide a suite of comparable outputs, mapping methods can help prioritize localized models or experimental gaps, and future experiments can be paired from the outset with models they will inform.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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; both teacher heads agree on what is shown here.
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".