How Does Climate Change Affect Emergent Properties of Aquatic Ecosystems?
Bibliographic record
Abstract
Abstract Emergent properties of ecosystems are community attributes, such as structure and function, that arise from connections and interactions (e.g., predator–prey, competition) among populations, species, or assemblages that, when viewed together, provide a holistic representation that is more than the sum of its individual parts. Climate change is altering emergent properties of aquatic ecosystems through component responses, a combination of shifts in species range, phenology, distribution, and productivity, which lead to novel ecosystems that have no historical analog. The reshuffling, restructuring, and rewiring of aquatic ecosystems due to climate impacts are of high concern for natural resource management and conservation as these changes can lead to species extinctions and reductions in ecosystem services. Overall, we found that substantial progress has been made to advance our understanding of how climate change is affecting emergent properties of aquatic ecosystems. However, responses are incredibly complex, and high uncertainty remains for how systems will reorganize and function over the coming decades. This cross-system perspective summarizes the state of knowledge of climate-driven emergent properties in aquatic habitats with case studies that highlight mechanisms of change, observed or anticipated outcomes, as well as insights into confounding non-climate effects, research tools, and management approaches to advance the field.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".