Population-Dynamic Consequences of Predator-Induced Life History Variation in the Guppy (Poecilia reticulata)
Bibliographic record
Abstract
A fundamental goal of population ecology is to quantify how spatial and temporal variation in life history traits translates into variation in population-level parameters such as vital rates and projected growth rate (λ). Further emphasis has been on the sensitivity of λ to absolute variation (sensitivities) and relative variation (elasticities) in vital rates, because λ can be related to the evolutionary fitness of a life history phenotype (r = lnλ). Furthermore, because density regulation can affect the expression and evolution of life history traits, which in turn can feed back on population dynamics, correctly incorporating the relationship between the two is critical to understanding the evolution and evolvability of life history strategies. Here we ask how life history variation affects projected population growth and evolutionary fitness dynamics in the long-term study system of the Trinidadian guppy (Poecilia reticulata). There are striking differences in the life history traits of guppies that co-occur with two different assemblages of predators and so these populations are a model natural system for examining the fitness consequences of, and selective pressures on, traits that covary predictably. We found that populations experiencing high rates of predation had lower estimates of growth and fitness than did those experiencing low rates of predation (pooled r = 0.02 and 0.17, respectively). But when we incorporated density regulation in low predation sites, observed with field manipulations and modeled with simulations, this difference became negligible. Furthermore, for all populations irrespective of predators, growth rate was most sensitive to changes in neonatal growth and was contributed to maximally by early adult survival. Particularly in light of growing concerns on correctly identifying sensitive life stages in overharvested fauna, addressing these questions in a long-term natural experimental system may provide guidance.
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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 | 0.001 |
| 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".