Local adaptation in island populations of <i>Plectritis congesta</i> that differ in historic exposure to ungulate browsers
Bibliographic record
Abstract
Spatial variation in the occurrence of browsing ungulates can drive local adaptation in plant traits but also lead to trade-offs among traits potentially enhancing competitive ability versus resistance or tolerance to browsing. Plectritis congesta populations co-occurring on islands with and without ungulates offer striking examples of population-level variation in traits, such as plant height and fruit morphology, which may also affect fitness. We monitored split-plot common gardens exposed to and protected from browsing ungulates for 5 yr to test for local adaptation (local vs. foreign comparison) in P. congesta by comparing the survival and fecundity of 4,392 sown fruits from six island populations where ungulates were present ("historically exposed") and six where they were absent ("historically naïve"). Our results indicate that local adaptation to browsing in P. congesta favored rosette formation, delayed flowering, reduced height, and the production of wingless fruits, all of which appeared to enhance survival, fecundity, and population growth in plants from populations historically exposed to ungulate browsers, as compared to plants from historically naïve populations. In contrast, plants from historically naïve populations displayed higher relative fitness in the absence of ungulates, increased in height, flowered earlier, and produced fewer but larger, winged fruits, often in large terminal inflorescences. Our results support the hypothesis that variation in the occurrence of ungulate browsers has led to (1) spatial heterogeneity in natural selection and rapid adaptation in P. congesta populations on islands, and (2) context-dependent trade-offs in the fitness value of traits linked to the resistance or tolerance of browsing versus success in competition for light, pollinators, or other resources. Because patterns of selection in plant communities will vary with the introduction or extirpation of top predators or browsers, we suggest historical context, local adaptation, and the capacity for rapid adaptation should be a focal concern of those aiming to maximize or predict population persistence under environmental change in conservation plans.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".