Mate Preference and Larval Growth of Great Lakes Sea Lamprey (Petromyzon Marinus) in a Warming Climate
Bibliographic record
Abstract
Sea lamprey (Petromyzon marinus) are parasitic pests in the Great Lakes. Once sea lamprey started to have a negative impact on important game fish populations, management efforts began. More information on how sea lamprey choose mates and how larval sea lamprey grow could give more insight on how to better manage their populations. Increased temperatures due to global climate change may result in increased growth of individuals, higher count of eggs, higher quality of eggs, and higher sperm production. I presented an average-sized ovulating female with the choice of a small or large spermiating male in a two-way mate preference experiment. Trials were conducted and investigated whether stream side bias, male or female activity, or the presence of male odor upstream affected the female’s preference. Results showed the female sea lamprey had a mesocosm side bias and females preferred to be in front of the small male when male odor was released. Improving the accuracy of larval sea lamprey growth models would benefit management strategies by providing better predictions as to when metamorphosis could occur. The primary technique used to establish growth of sea lamprey within the control program is the use of an incomplete growing degree day (GDD) metric, where average daily growth across a latitudinal gradient during the warmer months is used to predict time of metamorphosis. I tested a complete GDD metric in which the number of year-round growing degree days for each age-class of sea lamprey population tested was calculated. Water temperatures were obtained as much as possible during the larval growth time frame for each stream examined. For streams in which I did not have water temperature, I placed data loggers in streams to record the temperature every hour for one year. Air temperatures were then obtained from weather station locations closest to the mouth of the river for the same year. A relationship between the air and water temperature for each stream was established from this year’s data. Air temperature were then obtained from weather stations closest to each stream during the periods of larval growth, and air temperature was used to predict water temperature larval sea lamprey experienced. A generalized linear model was used to determine the relationship between the response variable, lamprey length-at-age, and one or more predictors, which included log-transformed GDD, log-transformed calendar days, stream, and lake. The best fit model, which used basin wide data, was log-transformed calendar days and lake. The results show that GDD was the best predictor for Lake Ontario and calendar days were the best predictor for Lakes Huron and Michigan to determine growth of sea lamprey. Calendar days and GDD both predicted length-at-age for Lake Superior populations equally well.
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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".