HABITAT SPECIALIZATION AND THE EXPLOITATION OF ALLOCHTHONOUS CARBON BY ZOOPLANKTON
Bibliographic record
Abstract
The significance of spatial subsidies depends on consumer resource interactions in the recipient habitat. Lakes are subsidized by terrestrial carbon sources, but the pathways of allochthonous carbon through lake food webs are complex and not well understood. Zooplankton vertically partition resources within stratified lakes in response to life history trade-offs that are governed by predators, the quantity and quality of food, and abiotic conditions (e.g., UV, temperature, and viscosity). We measured habitat specialization of zooplankton in an oligotrophic lake where allochthonous and autochthonous resources varied with depth. During stratification, the quantity and quality of zooplankton food was highest in the hypolimnion. We used a yearlong time series of the delta13C of zooplankton and particulate organic matter (POM) to determine which zooplankton species exploited hypolimnetic rather than epilimnetic resources. Because the delta13C of POM decreased with depth, we used the delta13C of zooplankton to detect inter- and intraspecific variation in habitat selection. We incubated Daphnia pulex at discrete depths in the water column to confirm that the delta13C of zooplankton can indicate habitat specialization. Zooplankton that specialized in the epilimnion relied more on allochthonous carbon sources than those that specialized in the hypolimnion. Therefore, the fate of allochthonous carbon subsidies to lakes depends on spatially explicit consumer-resource interactions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".