Identifying consumer–resource population dynamics using paleoecological data
Bibliographic record
Abstract
Ecologists have long been fascinated by cyclic population fluctuations, because they suggest strong interactions between exploiter and victim species. Nonetheless, even for populations showing high-amplitude fluctuations, it is often hard to identify which species are the key drivers of the dynamics, because data are generally only available for a single species. Here, we use a paleoecological approach to investigate fluctuations in the midge population in Lake Mývatn, Iceland, which ranges over several orders of magnitude in irregular, multigeneration cycles. Previous circumstantial evidence points to consumer-resource interactions between midges and their primary food, diatoms, as the cause of these high-amplitude fluctuations. Using a pair of sediment cores from the lake, we reconstructed 26 years of dynamics of midges using egg remains and of algal groups using diagnostic pigments. We analyzed these data using statistical methods that account for both the autocorrelated nature of paleoecological data and measurement error caused by the mixing of sediment layers. The analyses revealed a signature of consumer-resource interactions in the fluctuations of midges and diatoms: diatom abundance (as inferred from biomarker pigment diatoxanthin) increased when midge abundance was low, and midge abundance (inferred from egg capsules) decreased when diatom abundance was low. Similar patterns were not found for pigments characterizing the other dominant primary producer group in the lake (cyanobacteria), subdominant algae (cryptophytes), or ubiquitous but chemically unstable biomarkers of total algal abundance (chlorophyll a); however, a significant but weaker pattern was found for the chemically stable indicator of total algal populations (β-carotene) to which diatoms are the dominant contributor. These analyses provide the first paleoecological evaluation of specific trophic interactions underlying high amplitude population fluctuations in lakes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 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".