The Evolutionary and Ecological Consequences of Partner Variation in the Mutualism between Legumes and Symbiotic Nitrogen Fixing Bacteria
Bibliographic record
Abstract
A fundamental goal in ecology and evolutionary biology has been to understand how microevolutionary forces affect the origin and maintenance of mutualisms over ecological and evolutionary time scales. Mutualistic partners vary in the reciprocal benefits they provide, yet the role of partner variation on microevolutionary forces that impact the maintenance of mutualisms is unclear. Using the mutualism between legumes and nitrogen fixing symbionts, my dissertation investigated the ecological and evolutionary consequences of variation in partner quality. In the first experiment, I demonstrate how insect herbivory can change the costs and benefits of associating with exploiters, and that some degree of exploitation from non-beneficial rhizobia can reduce insect herbivory, thus removing the fitness advantage of associating purely with beneficial rhizobia. In the second study, I examine how rhizobia genotype modifies competition between hosts grown in kin and non-kin groups. I show that lower fitness in plant kin groups can simply be a by-product of genetic variance in plant size and non-linear relationships between plant size and fitness. I further show that the symbiotic community can change difference in fitness between kin and non-kin groups independent of these by-product effects. In my last chapter, I provide the first empirical evidence that an important mechanism for mutualism stability-- the ability for hosts to preferentially associate with beneficial rhizobia-- is genetically variable and can evolve in response to exploitation. I also show that host preference for beneficial rhizobia can be maintained in legume populations, even in the absence of exploitation. My dissertation provides insight into the potential evolutionary dynamics of stabilizing mechanisms by suggesting that the agents of selection that affect the level of host exploitation can come from biotic factors other than the exploiters themselves. My dissertation has also shown that inclusion of other ecological interactions, such as herbivory, can provide valuable perspective on fitness effects of symbionts on their hosts, and can even change our fundamental assumptions about the effects of exploitation on host fitness, which has formed the backbone of mutualism theory.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".