Influence of Invasive Species, Climate Change and Population Density on Life Histories and Mercury Dynamics of Two Coregonus Species
Bibliographic record
Abstract
Non-indigenous species can profoundly alter the ecosystems they invade and impact local economies. Growth and body condition declines of commercially fished Great Lakes lake whitefish coincide with the establishment of non-native dreissenid mussels and the cladoceran Bythotrephes longimanus. Declines in lake herring abundance—a key prey item for other commercially important species—have also been reported. Though additional stressors such as climate change may have contributed to changes in coregonid populations, they have not been thoroughly evaluated. Here, I present data that condition and contaminant declines in coregonids are associated with increasing density or warming climate, but growth declines in lake whitefish are likely due to ecosystem changes associated with dreissenids and Bythotrephes. In South Bay, Lake Huron, changes in lake whitefish diet composition and stable isotope signatures were consistent with increased reliance on nearshore resources after dreissenid establishment; lake whitefish occupied shallower habitats and experienced declines in mean diet energy densities post-dreissenid invasion. Growth of South Bay lake whitefish declined after environmental effects were statistically removed, whereas condition declines were explained best by changes in lake whitefish density. Among four lake whitefish populations, growth declined after dreissenids established, but not in uninvaded reference populations. Growth also declined among four lake whitefish populations after the establishment of Bythotrephes relative to reference populations. In contrast with growth, condition of lake whitefish did not change as a result of dreissenid or Bythotrephes invasion. Bioenergetic models revealed that activity rates increased and conversion efficiencies decreased in lake whitefish populations exposed to dreissenids, despite higher consumption rates in populations with dreissenids present. Condition declines among many lake whitefish and lake herring populations (and declines in mercury among herring populations) reflected regional differences and were not related to the presence of Bythotrephes or Mysis relicta. Declines in condition were more pronounced in northwest Ontario populations where climate has changed more dramatically than in southern Ontario. This work suggests that projected range expansions of dreissenid mussels and Bythotrephes will likely affect native fisheries, and their effect on these fisheries may be exacerbated by declining fish condition associated with climate change.
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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 | 0.000 | 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 | 0.000 |
| 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 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".