Spatial variation in herbivory, climate and isolation predicts plant height and fruit phenotype in <i>Plectritis congesta</i> populations on islands
Bibliographic record
Abstract
Abstract Climate and herbivory can each drive natural selection on plant traits but may interact to give rise to different patterns in trait distributions when surveyed across island populations. These different patterns may arise because the occurrence of ungulate herbivores often varies across archipelagos, potentially leading to strong and abrupt spatial heterogeneity in the direction or intensity of natural selection. In contrast, climate tends to vary gradually and thus is more likely to lead to gradual clines in trait values. Population isolation may also affect trait values, given that random genetic drift may fix alleles or traits in the absence of gene flow or because gene flow between populations with similar or opposing selection pressures may augment or swamp the effects of selection. Here, we estimate the independent and interactive effects of deer, climate and isolation on fruit phenotype and plant height in 285 Plectritis congesta populations at 77 island and 44 mainland sites in western North America. Plectritis congesta is a palatable winter annual with two fruit phenotypes based on a simple Mendelian locus with clear dominance and heritable variation in height as a polygenic trait. Fruit phenotype and plant height were well predicted by deer occurrence; plants in populations with resident deer were short (15.0 ± 1.1 cm) and mainly expressed wingless fruits (73.0 ± 4.0%), whereas plants in populations without deer were 2.6 times taller (38.9 ± 5.3 cm) and only 9.0 ± 1.6% expressed wingless fruits. Wingless fruits were less common in populations experiencing wetter conditions with more seasonal variability in temperatures, particularly in the absence of deer. In contrast, population isolation was unrelated to fruit phenotype, except in populations rarely exposed to deer, where plants expressed phenotypes more like those in populations without deer as isolation increased. Our results are consistent with the hypothesis that spatial variation in browsing by deer or other factors correlated with it contributes to population‐level variation in fruit phenotype and plant height in P. congesta and that climate leads to a modest spatial gradient in plant height. Synthesis . The occurrence of deer at 285 island and mainland populations of Plectritis congesta predicted plant height and fruit phenotype, supporting the hypothesis that spatial variation in herbivory is a key factor affecting population‐level variation in the distribution of these heritable traits.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".