Temporal dynamics of ecological networks: deciphering changes in cladoceran assemblages over the past ~ 150 years in response to land-use development
Bibliographic record
Abstract
Ecological networks offer a comprehensive view of communities by capturing potential species interactions. While valuable for studying ecological change in the Anthropocene, many studies lack data across expansive temporal and spatial gradients. We addressed this gap by applying network approaches to paleolimnological records capturing strong land-use changes. We analyzed cladoceran assemblages, key aquatic organisms with identifiable subfossils, using two paleolimnological methods: (i) top-bottom comparisons of sediment records from 101 Canadian lakes with varying land-use intensity, and (ii) high-resolution core records from two impacted lakes in eastern Canada. We used correlation matrices of taxon relative abundances to calculate network metrics across land-use types and time periods. We found that lake communities currently experiencing high human impact changed through time, showing a decrease in connectance (proportion of realized to potential links) and an increase in modularity (measure of network subcommunities); these patterns were also observed in our full core analyses as well as in our randomized simulation exercise. Overall, this first pan-Canadian study of zooplankton paleo-networks provides new insights into how lake food webs have changed over a period of accelerated anthropogenic change.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".