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Record W2947015137

Plant evolution, soil ecosystems, and feedback

2018· dissertation· en· W2947015137 on OpenAlexfundno aff
Connor R. Fitzpatrick

Bibliographic record

VenueTSpace (University of Toronto) · 2018
Typedissertation
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsEcosystemPlant evolutionEnvironmental scienceEcologyEnvironmental resource managementEarth scienceBiologyGeology
DOInot available

Abstract

fetched live from OpenAlex

Plants are inextricably linked to their environment. They can alter biotic and abiotic properties of soil ecosystems, which in turn can feed back to affect plant performance. My thesis explores this dynamic link between plants and soil at microevolutionary (within plant species) and macroevolutionary (between plant species) scales. In the microevolutionary half of my thesis I first test whether genetic variation, range-wide geographic trait clines and contemporary evolution in the focal plant species Oenothera biennis (Onagraceae) influence soil ecosystems. I found strong effects of plant genetic variation and evolution, but not geographic origin, on the structure of soil invertebrate communities and ecosystem processes such as soil respiration, litter decay and N mineralization rates. Finally, to understand how soil may be influencing plant evolution, I test whether variation in soil microbial communities can alter plant evolution across two common environmental stressors, competition and drought. I found that soil microbes drastically modify plant fitness, the expression of, and natural selection on flowering time. In the macroevolutionary half of my thesis I use a set of co-occurring plant species to investigate how evolutionary divergence over longer timescales influences plant-soil interactions. First, I conducted a multi-generational experiment to understand the drivers of plant–soil feedback (PSF) across 50 plant species. I found that evolutionary divergence and overall phenotypic similarity were poor predictors of soil feedback, however individual plant traits were strongly related to PSF. Next, I characterized the assembly and ecological function of the root microbiome across 30 plant species. Close plant relative exhibited high similarity in the diversity and composition of their root microbiota. Furthermore, patterns of root microbial recruitment among host plant species were related to PSF and plant drought tolerance. My thesis provides a unique evolutionary perspective on the reciprocal interactions between plants and soil.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.006
GPT teacher head0.203
Teacher spread0.197 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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