Life history constraints explain negative relationship between fish productivity and dissolved organic carbon in lakes
Bibliographic record
Abstract
Abstract Resource availability constrains the life history strategies available to organisms and may thereby limit population growth rates and productivity. We used this conceptual framework to explore the mechanisms driving recently reported negative relationships between fish productivity and dissolved organic carbon (DOC) concentrations in lakes. We studied populations of bluegill (Lepomis macrochirus) in a set of lakes withDOCconcentrations ranging from 3 to 24 mg/L; previous work has demonstrated that primary and secondary productivity of food webs is negatively related toDOCconcentration across this gradient. For each population, we quantified individual growth rate, age at maturity, age‐specific fecundity, maximum age, length‐weight and length‐egg size relationships, and other life history characteristics. We observed a strong negative relationship between maximum size andDOCconcentration; for instance, fish reached masses of 150 to 260 g in low‐DOClakes but <120 g in high‐DOClakes. Relationships between fecundity and length, and between egg size and length, were constant across theDOCgradient. Because fish in high‐DOClakes reached smaller sizes but had similar fecundity and egg size at a given size, their total lifetime fecundity was as much as two orders of magnitude lower than fish in low‐DOClakes. HighDOCconcentrations appeared to constrain the range of bluegill life history strategies available; populations in high‐DOClakes always had low initial growth rates and high ages at maturity, whereas populations in low‐DOCshowed higher variability in these traits. This was also the case for the intrinsic rates of natural increase of these populations, which were always low at the high end of theDOCgradient. The potentially lower capacity for fish populations in high‐DOClakes to recover from exploitation has clear implications for the sustainable management of recreational fisheries in the face of considerable spatial heterogeneity and ongoing temporal change in lakeDOCconcentrations.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".