Genetic Influences on Experimental Population Dynamics of the Least Killifish
Bibliographic record
Abstract
When natural populations differ in density or in the dynamic fluctuations of population size, some of those differences may result from their different ecological conditions, and some may originate from genetically based differences in life history expression. Natural populations of the live-bearing poeciliid fish, Heterandria formosa, vary considerably in their population dynamics, with densities that differ between populations by as much as sevenfold. This system offers an excellent opportunity to explore the potential role of genetically based differences in life history expression in creating different dynamic patterns in a common environment. We created five different genetic stocks of H. formosa by carrying out a series of crosses using fish from two North Florida populations (the Wacissa River and Trout Pond) and used them to initiate replicate experimental populations in artificial ponds. The five stocks consisted of two “controls,” which were pure Wacissa River and Trout Pond stocks, and three types of hybrid stocks. The hybrid stocks differed in a regular way in the proportion of genes from one population or the other. The crossing scheme was designed so that each hybrid stock would have the same proportion of heterozygous (or “heterodemic”) loci but would differ in the proportion and/or identity of homozygous (or “homodemic”) loci from the Wacissa River and Trout Pond populations. These populations were chosen because a previous study had found that population densities in the Wacissa River greatly exceeded those of Trout Pond and exhibited a higher range of population fluctuation during the breeding season. We addressed four questions in this experiment: (1) Are there genetically based differences in life history traits of fish from the two populations? (2) If so, do differences in life history expression produce differences in population dynamics in a common environment? (3) Which traits have the greatest influence on population dynamics? (4) How do changes in density affect the phenotypes of individual traits that govern the rates of birth and death in a population? We followed experimental populations of the five genetic stocks from their initiation at low density through 4–6 generations of population growth and decline. The mean offspring size differed among stocks by as much as 50%. At low densities, offspring size exhibited a trade-off with brood size: Trout Pond alleles were associated with more, smaller offspring. At higher densities, offspring sizes were similar among stocks, and the trade-off with offspring number was not evident. Stocks differed in realized population growth rate by as much as 70%; the rank order differences among stocks with respect to population growth rate appeared to match the genetic relatedness among stocks based on the expected percentage of Trout Pond alleles. Differences in population growth rate appeared to be due to differences in brood size among stocks at low density. Stocks did not differ in the equilibrium population size, which indicated the absence of a trade-off between population growth rate and carrying capacity in this environment. Adult survival and recruitment of juveniles into the adult population both declined linearly with increasing density; and stocks did not generally differ in those rates after the effects of density had been taken into account. The stocks differed in their response to the depressant effects of density on life history trait expression. The offspring size of the pure Wacissa River stock was much more sensitive to density than was the offspring size of the pure Trout Pond stock. However, the brood sizes of the Wacissa River stock were reduced much less than those of the Trout Pond stock when exposed to the same high density. These results suggest that life history distinctions among populations, both in the mean values and plasticity of traits, play a role in creating different dynamics. We discuss the ways in which phenotypic plasticity in reproductive traits potentially acts as a mechanism to stabilize population dynamics in this species.
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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".