Eco-evolutionary rescue: an adaptive dynamic analysis
Bibliographic record
Abstract
Populations exposed to changing environments often decline in abundance and may therefore find themselves at risk of extinction. Safe abundances can be regained, and persistence secured, when populations can adapt fast enough. Here I ask how population and environmental factors determine a population's ability to persist in changing environments by adapting genetically. In Chapter 2 I investigate the response to a gradual, directional change in the environment and in Chapter 3 I investigate the response to a sudden, sustained shift. Chapter 3 also discusses the effects of interspecific competition. In both chapters I use the canonical equation of adaptive dynamics, which allows me to derive analytical expressions while including ecological process neglected in previous theory; in particular, I consider logistic population growth, frequency-dependent intraspecific competition, and interspecific competition. I use computer simulations to examine the accuracy of my analytical results when the simplifying assumptions of adaptive dynamics are relaxed. Chapter 2 derives the trait lag which maximizes the rate of evolution and also computes this maximum. Populations with higher mutational input experiencing stronger selection have the ability to adapt faster. Computer simulations show that the derived maximum rate of evolution is a good predictor of extinction across a wide range of parameter values, including those that deviate from the assumptions of adaptive dynamics. Chapter 3 first uncovers an expression for the population trait value across time and then uses this expression to calculate the time a population is below a threshold abundance, the 'time at risk'. The population trait value approaches the fitness peak quickly at first, and the rate declines exponentially. Populations with greater mutational input and maximum abundance, and those which are initially better adapted spend less time at risk. The time at risk is maximized at intermediate selection strengths, as strong selection lowers abundance and weak selection slows adaptation. Simulations show good alignment when mutations are rare and population sizes small. Interspecific competition lowers the abundance of the focal population, generally increasing the time at risk, but this can be compensated for under particular scenarios by increased selection pressure. Interspecific competition can sometimes speed adaptation and foster persistence. The ecological and evolutionary response of natural populations to environmental change depends on complex ecological interactions. Theory and experiments which include such interactions are needed for accurate descriptions of genetic adaptation to environmental change, in both the lab and in the wild.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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; both teacher heads agree on what is shown here.
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".