The role of heritability, priority effects, and climate change in plant microbiomes
Bibliographic record
Abstract
Microbes and plants shape each other's fitness, meaning we cannot understand or predict the ecology and evolution of plants and microbes without also understanding their interactions. In my thesis, I examine how the patterns and processes that underlie plant-microbe symbioses affect plant performance, with a focus on the role of host genotype (Chapter 2), priority effects (Chapter 3), and abiotic stressors (Chapters 4 and 5) in the community assembly of plant microbiomes.Determining which plant traits matter to microbes is important for understanding how and why plants and microbes interact. I explored the genetic architecture of a vine’s leaf microbial phenotype by comparing the relative importance of quantitative genetic loci and a Mendelian locus of large effect on microbe abundance and heritability (Chapter 2). I found that both types of genetic variation mattered to microbes, with roughly equal magnitudes of effect, but that host age and environment also played a large role. My results suggest there is scope for eco-evolutionary feedbacks between plants and heritable microbes. Given that plants do not always associate with the most beneficial microbes, factors other than partner choice must play a large role in mutualistic symbioses. I investigated the role of historical contingency and priority effects using the legume-rhizobium symbiosis (Chapter 3). The first rhizobium strain to colonize the legume primarily determined plant performance and the root rhizobia community. My results demonstrate that mutualism outcomes can be influenced not just by partner identity, but by partner interaction order, and that priority effects could help lower-quality mutualists persist. Heat and drought alter soil microbiomes, and the persistence of these changes and their subsequent impacts on plant performance and symbiosis are important for predicting plant responses to climate change. I found that mutualistic and parasitic symbiotic soil microbes are affected by heat and drought long after treatment (Chapter 4) and that while drought-treated microbes harm plant performance, the addition of a mutualist erased plant performance differences between climate-treated soil microbiomes (Chapter 5). Strong microbially-mediated indirect effects of climate change may impose additional ecological pressures on plants who must already respond to the direct effects of climate.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".