Combined effects of maternal traits and spring warming patterns on spawning success of walleye
Bibliographic record
Abstract
Factors influencing survival through the earliest life stages are believed to be strong drivers of recruitment variability in many fishes. Previous research on walleye (Sander vitreus) has suggested that early life survival and subsequent year-class strength may be related to the age composition of spawners through maternal influences on egg quality, as well as environmental conditions during spawning and early rearing periods. My objective was to examine survival and developmental rates for embryo batches of individual female walleye of Lake Nipissing, Ontario, in relation to the combined influences of maternal effects and incubation temperature. Female walleye were spawned on multiple dates in each of three consecutive years (2017 – 2019) and their embryos incubated under three spring warming regimes (slow, seasonal, and rapid warming; approximately 4 °C range in mean daily temperature) in a controlled laboratory setting. Embryo survival to hatch and thermal units to hatch (TU50; cumulative growing degree-days) were determined for each egg batch. Maternal traits measured included female age and length, ovum (egg) size and total lipid content, and relative abundances of essential fatty acids in ova lipids. For each year, embryo survival and TU50 were modelled as functions of spawning date, incubation temperature treatment and maternal and ova traits using standard frequentist statistics and a model selection approach based on AICc ranking. Embryo survival varied among spawning dates in 2017, the year with the earliest spawning period, but not in 2018 or 2019. Embryo survival did not vary with respect to incubation warming rate in any year. Relationships between embryo survival and maternal and ova traits varied among spawning years but did not appear to be related to incubation temperature treatments within years. Contrary to my predictions, embryo ivsurvival was more consistently related to ova characteristics than to female age or size, and relationships between embryo survival and indices of ova quality were not always positive. Mean TU50 was higher in 2017, the year with the earliest spawning period, than in 2018 and 2019. Spawning date and incubation warming rate had interactive effects on TU50; there was generally a positive relationship between TU50 and spawning date in all years in the seasonal and slow-warming incubation treatments but not in the rapidwarming treatment. Similarly, relationships between TU50 and maternal and ova traits were strongest in the slow-warming treatment and weakest in the rapid-warming treatment. TU50 tended to decline with increasing female age and size, and egg size. Time to 50% hatch ranged from 13 to 33 days across all years, spawn dates, treatments and females in my experiments. Based on my laboratory results and temperature measurements at natural spawning sites I estimated that walleye would hatch 3 to 5 days earlier at river inflow sites than at shoreline spawning sites in Lake Nipissing. My results provide new and unique information on reproduction and the recruitment process in walleye and have implications for size-selective harvest guidelines used in fisheries management. It appears that the older and larger females of spawning stocks may not always produce the eggs with highest survival. Also, it appears that temperature could influence early life survival more indirectly, through its effects on hatch timing, this is an aspect that merits further investigation.
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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 | 0.000 | 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) | 0.001 | 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".