Climate and leaf traits, not latitude, explain variation in plant–herbivore interactions across a species' range
Bibliographic record
Abstract
Abstract Spatial variation in abiotic and biotic factors creates local contexts that influence the intensity of plant–herbivore interactions. Some previous studies have accounted for the complexity of these interactions with latitudinal clines, while the absence of such clines in many other systems suggests other, often unknown, local community factors may instead explain the variation in herbivory across populations. We investigated plant–herbivore interactions across the entire range of a long‐lived tree ( Quercus garryana ), evaluating the relative importance of climate, latitude, population size, and insect feeding guilds in determining leaf phenotype and the extent and variation in insect herbivory. In this ecosystem, rain shadows create a nonlinear relationship between climate and latitude, allowing us to disentangle the effects of environmental factors. By performing similar analyses on trees grown in a common garden, we were able to assess the relative importance of environmental factors to leaf defence traits and herbivory. Total herbivory varied significantly among populations, and was best explained by variation in spring precipitation, leaf traits, and tree population size, but not latitude. The relative importance of each of these factors changed over the growing season and with insect feeding guild. Conversely, damage in the common garden did not vary among trees from different origins when grown in a constant environment, leading us to believe variation in damage in natural populations is more likely the result of the local environments. Leaf traits (trichome density and specific leaf area) varied significantly among populations, but neither showed an effect of latitude. Variation in both traits was best explained by tree size, and seasonal temperatures or precipitation. We found no variation in insect diversity among field populations, but abundance varied with mean summer precipitation and population size. Synthesis . Seasonal precipitation consistently explained the geographical variation in the extent of herbivory to Q. garryana , while latitude and winter temperatures, factors that are commonly associated with latitudinal gradients in the intensity of species interactions, did not. Our findings highlight the importance of local climates and functional traits in shaping biotic interactions and intraspecific variation in plant–insect interactions across large spatial scales.
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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) | 0.001 | 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 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".