Potential changes to the biology and challenges to the management of invasive sea lamprey<i>Petromyzon marinus</i>in the Laurentian Great Lakes due to climate change
Bibliographic record
Abstract
Control programs are implemented to mitigate the damage caused by invasive species worldwide. In the highly invaded Great Lakes, the climate is expected to become warmer with more extreme weather and variable precipitation, resulting in shorter iced-over periods and variable tributary flows as well as changes to pH and river hydrology and hydrogeomorphology. We review how climate change influences physiology, behavior, and demography of a damaging invasive species, sea lamprey (Petromyzon marinus), in the Great Lakes, and the consequences for sea lamprey control efforts. Sea lamprey control relies on surveys to monitor abundance of larval sea lamprey in Great Lakes tributaries. The abundance of parasitic, juvenile sea lampreys in the lakes is calculated by surveying wounding rates on lake trout (Salvelinus namaycush), and trap surveys are used to enumerate adult spawning runs. Chemical control using lampricides (i.e., lamprey pesticides) to target larval sea lamprey and barriers to prevent adult lamprey from reaching spawning grounds are the most important tools used for sea lamprey population control. We describe how climate change could affect larval survival in rivers, growth and maturation in lakes, phenology and the spawning migration as adults return to rivers, and the overall abundance and distribution of sea lamprey in the Great Lakes. Our review suggests that Great Lakes sea lamprey may benefit from climate change with longer growing seasons, more rapid growth, and greater access to spawning habitat, but uncertainties remain about the future availability and suitability of larval habitats. Consideration of the biology of invasive species and adaptation of the timing, intensity, and frequency of control efforts is critical to the management of biological invasions in a changing world, such as sea lamprey in the Great Lakes.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".