Exploring factors affecting smallmouth bass nest success and reproductive behavior
Bibliographic record
Abstract
Herein, I describe research that explores factors affecting smallmouth bass (Micropterus dolomieu) reproductive behavior and success.Smallmouth bass are a model species to explore reproductive behavior because they invest large amounts of energy in parental care, spawn multiple times, and exhibit indeterminate growth.Smallmouth bass must balance current and future fitness when deciding how much care to provide.Ultimately, reproductive success and behavior may influence recruitment.By conducting a series of observational studies and manipulative experiments, I examined how smallmouth bass reproductive success and behavior differed by environment (Lake Erie, Ohio, USA, and Lake Opeongo, Ontario, Canada).In Lake Erie, exotic nest predators (round goby, Neogobius melanostomus) consumed more than 800 smallmouth bass embryos from nests every time an angler caught and released the nest-guarding male (Chapter 2).In addition, high round goby densities necessitated vigorous nest defense by parents in Lake Erie, thus causing parents to expend twice the energy on care than Lake Opeongo, where round gobies were absent (Chapter 3).Smallmouth bass nest success in Lake Erie was negatively influenced by angling and storms, but not by round goby consumption of offspring (Chapter 4).Simulations of nest-guarding smallmouth bass, making decisions that maximized their expected lifetime fitness, defended smaller broods in Lake Erie than in Lake Opeongo (Chapter 5).Mean adult survival rate, a function of age and care cost, was most important in determining optimal behavior (Chapter 5).By decreasing offspring survival and increasing cost of care, round goby caused optimal parents to abandon larger broods than when round gobies were absent (Chapter 5).Parental behavior, in turn, affected abandonment of offspring by simulated smallmouth bass populations; however, offspring production was not influenced by behavior (Chapter 6).Simulations of spawning smallmouth bass in lakes Erie and Opeongo demonstrated that the success of management strategies depended on parental behavior and the environmental conditions underlying optimal decisions (Chapter 6).While round gobies, as predators, may reduce smallmouth bass offspring production, round gobies, as prey, provided a new food source for young-ofthe-year smallmouth bass, which are growing faster since the round goby arrived (Chapter 7).
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 | 0.000 | 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 | 0.014 |
| 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".