Bibliographic record
Abstract
According to life-history theory, growth rates are subject to strong directional selection due to reproductive and survival advantages associated with large adult body size. Yet, growth is commonly observed to occur at rates lower than the maximum that is physiologically possible and intrinsic growth rates often vary among populations. This implies that slower growth is favoured under certain conditions. Realized growth rate is thus the result of a compromise between the costs and advantages of growing rapidly, and the optimal rate of growth is not equivalent to the fundamental maximum rate. The ecological and evolutionary factors influencing growth rate are reviewed, with particular emphasis on how growth might be constrained by direct fitness costs. Costs of accelerating growth might contribute to the variance in fitness that is not attributable to age or size at maturity, as well as to the variation in life-history strategies observed within and among species. Two main approaches have been taken to study the fitness trade-offs relating to growth rate. First, environmental manipulations can be used to produce treatment groups with different rates of growth. Second, common garden experiments can be used to compare fitness correlates among populations with different intrinsic growth rates. Data from these studies reveal a number of potential costs for growth over both the short and long term. In order to acquire the energy needed for faster growth, animals must increase food intake. Accordingly, in many taxa, the major constraint on growth rate appears to arise from the trade-off between predation risk and foraging effort. However, growth rates are also frequently observed to be submaximal in the absence of predation, suggesting that growth trajectories also impact fitness via other channels, such as the reallocation of finite resources between growth and other traits and functions. Despite the prevalence of submaximal growth, even when predators are absent, there is surprisingly little evidence to date demonstrating predator-independent costs of growth acceleration. Evidence that does exist indicates that such costs may be most apparent under stressful conditions. Future studies should examine more closely the link between patterns of resource allocation to traits in the adult organism and lifetime fitness. Changes in body composition at maturation, for example, may determine the outcome of trade-offs between reproduction and survival or between early and late reproduction. A number of design issues for studies investigating costs of growth that are imposed over the long term are discussed, along with suggestions for alternative approaches. Despite these issues, identifying costs of growth acceleration may fill a gap in our understanding of life-history evolution: the relationships between growth rate, the environment, and fitness may contribute substantially to the diversification of life histories in nature.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".