Copepod community growth rates in relation to body size, temperature, and food availability in the East China Sea: a test of metabolic theory of ecology
Bibliographic record
Abstract
Abstract. Zooplankton play an essential role in marine food webs, and understanding how community-level growth rates of zooplankton vary in the field is critical for predicting how marine ecosystem function may vary in the face of environmental changes. Here, we used the artificial cohort method to examine the effects of temperature, body size, and chlorophyll concentration (a proxy for food) on weight-specific growth rates for copepod communities in the East China Sea. Specifically, we tested the hypothesis that copepod community growth rates can be described by the metabolic theory of ecology (MTE), linking spatio-temporal variation of copepod growth rate with temperature and their body size. Our results generally agree with predictions made by the MTE and demonstrate that weight-specific growth rates of copepod communities in our study area are positively related with temperature and negatively related to body size. However, the regression coefficients of body size do not approach the theoretical predictions. Furthermore, we find that the deviation from the MTE predictions may be partly attributed to the effect of food availability (which is not explicitly accounted for by the MTE). In addition, significant difference in the coefficients of temperature and body size exists among taxonomic groups. Our results suggest that considering the effects of food limitation and taxonomy is necessary to better understand copepod growth rates under in situ conditions, and such effects on the MTE-based predictions need further investigation.
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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 | 0.001 |
| 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) | 0.000 | 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".