Combined tracers reveal the multi‐dimensionality of resource partitioning among sympatric forage fish
Bibliographic record
Abstract
Abstract Quantifying the multi‐dimensional nature of resource partitioning in‐situ represents a fundamental challenge for understanding how environmental conditions contribute to the co‐existence of sympatric species. For aquatic poikilotherms, the availability of temperatures at or near optima generally define the ranges and limitations of species biology and ecology. Specifically, how poikilotherms partition this abiotic resource remains a broader unknown in aquatic ecology. Alewife ( Alosa pseudoharengus ), deepwater sculpin ( Myoxocephalus thompsonii ), rainbow smelt ( Osmerus mordax ), and round goby ( Neogobius melanostomus ) co‐occur throughout Lake Ontario and are known to share common prey resources. Here, we used a combination of trophic tracers (stable isotopes), pollutants (polychlorinated biphenyls, PCBs), and bioenergetic modelling to illustrate resource partitioning of diet items and thermal habitat among Lake Ontario prey fish species. We predicted that the combined use of δ 13 C, δ 15 N, and PCBs will capture the multi‐dimensional nature of resource partitioning among coexisting species inhabiting similar compartments within this ecosystem. The δ 13 C and δ 15 N values confirmed our understanding of trophic relationships among these species. A bioenergetic model predicted temperature occupancies for each species and for individuals during the open water growing season when prey consumption rates are likely to be maximised and the PCB congener profiles were highly correlated with these predictions. Three‐dimensional niche modelling demonstrated that ecological tracer assimilation is probably a direct result of species behavioural thermoregulation during the open water growing season when temperatures are at or near physiological optima. These results demonstrate that the ability of aquatic poikilotherms to track optimal temperatures and exploit habitat and food resources is captured by their PCB congener profiles. Consumer PCB congener profiles integrate not only the specific prey resources consumed, but also the predominant thermal conditions under which those resources were acquired and assimilated and that subsequently regulate individual and species growth. Given their global ubiquity and capacity for interpretation under the assumptions applied to ecological tracers, consumer PCB profiles could greatly advance our understanding of the mechanisms regulating resource partitioning, especially among aquatic poikilotherms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 teacher head, 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".