Ecosystem connectivity and configuration can mediate instability at a distance in metaecosystems
Bibliographic record
Abstract
Abstract Ecosystems are connected by flows of nutrients and organisms. Changes to connectivity and nutrient enrichment may destabilise ecosystem dynamics far from the nutrient source. We used gradostats to examine the effects of trophic connectivity (movement of consumers and producers) versus nutrient-only connectivity in different metaecosystem configurations (linear vs dendritic) on dynamics of Daphnia pulex (consumers) and an algae (resources). We found that peak population size and instability (coefficient of variation; CV) of Daphnia populations increased as distance from the nutrient input increased, but were lower in metaecosystems connected by all trophic levels compared to nutrient-only connected systems and in dendritic systems compared to linear ones. We examined the effects of trophic connectivity (i.e. how many trophic levels are moving rather than one or the other) using a generic model to qualitatively assess patterns of ecosystem dynamics. Our model predicted increased population sizes and fluctuations in ecosystems with nutrient connectivity, with this pattern being more pronounced in linear rather than dendritic systems. These results confirm that connectivity may propagate and even amplify instability over a metaecosystem to communities far from the source disturbance, and suggest a pathway for future experiments, that recreate conditions closer to those found in natural systems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".