Plant genetic diversity yields increased plant productivity and herbivore performance
Bibliographic record
Abstract
Summary 1. Plant genotypic diversity has important consequences for a variety of ecosystem processes, yet empirical evidence for its effects on productivity, one of the most fundamental of these processes, is lacking. In addition, the performance of insect herbivores in response to high genotypic diversity is unknown, despite previous work demonstrating differential herbivore performance among plant genotypes. 2. We manipulated genotypic diversity of the annual plant Arabidopsis thaliana in both the presence and absence of the generalist herbivore Trichoplusia ni under semi‐natural growth conditions. We used nine genotypes (eight ecotypes and one mutant) of A. thaliana known to differ widely in functional traits. Productivity and insect biomass were measured in monocultures and mixtures of all nine genotypes grown at multiple fertilization levels and planting densities. 3. In both the absence and presence of herbivores, genotypic diversity increased plant productivity and survival. This effect was, for the most part, independent of fertility or density. Sampling or selection effects did not appear to be wholly responsible for these results as all genotypes were maintained in equal proportion and no single genotype became dominant for the duration of the experiment. 4. High diversity increased T. ni biomass and survival in all treatments. Insect biomass was positively, but not tightly, correlated to plant biomass, indicating that the higher herbivore performance observed in genotypic mixtures was only partially due to higher productivity. 5. Synthesis. Our data support the idea that even within a single plant species, genotypic diversity can exert strong influences on both the producer and herbivore communities. The exact mechanisms responsible for these effects and the relative importance of genotypic diversity in natural communities warrant further investigation.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".