Genetic Influences on Experimental Population Dynamics of the Least Killifish
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».