The roles of adaptation and plasticity in populations persistence in the face of multiple stressors
Bibliographic record
Abstract
Global environmental changes are rapidly transforming ecosystems. As a result, populations will face changes in environmental conditions over short timescales that can threaten their persistence. When dispersal to more suitable habitats is not possible, the threat of extinction can be ameliorated through distinct, but non-exclusive biological mechanisms, including phenotypic plasticity, which refers to changes in traits mostly common due to shifts in gene expression, and genetic adaptation by rapid evolutionary shifts in gene frequencies. In my dissertation research, I studied the phenotypic and molecular population responses of a freshwater zooplankton (Daphnia) from Sierra Nevada mountain lakes of California to anthropogenic environmental variation caused by climate change and introduced predators. Hence, populations with different environmental histories may diverge in response to changing selective pressures, such as those caused by such as warming and introduced predators, because they might differ on the level of plasticity and adaptation they show to current environmental conditions. In Chapter 1, I show that different Daphnia populations were equally able to evolve plasticity in response to selection by different temperature regimes for two years in field experiment. This results highlights the importance of evolution of plasticity in response to temperature regime, thus in Chapter 2, I explore whether or not a population of Daphnia exhibits genetic variation that confers shifting plasticity in response to temperature throughout the annual cycle, since there is strong seasonal variability in temperature in most lakes located in temperate climates Studying Daphnia population from Blue Lake I found a marked distinction in plastic responses between Daphnia collected in the beginning and end of the growing season, indicating that seasonal temperature variability has distinct effects on plasticity for different traits. In Chapter 3 I studied the possible molecular mechanisms behind phenotypic response to temperature and how it is affected by predation. I studied two Daphnia genotypes collected from Blue and Gardisky Lakes. Overall our results suggest that both genotypes can reach similar fitness through distinct underlying molecular mechanisms, but also that they have distinct environmental sensitivities to stressors. Together these findings illustrate the variation and the importance of plasticity in addition to the potential for its evolution in maintaining Daphnia populations as key species in freshwater ecosystems, helping to mitigate the effects of climate change on food webs and ecosystem processes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".