Initial wood trait variation overwhelms endophyte community effects for explaining decay trajectories
Bibliographic record
Abstract
Abstract Microbial organisms, environmental conditions and their interactions govern many ecosystem processes. Recent studies have highlighted the importance of priority effects, that is, the identity of potential decomposers present early in community assembly, in determining resulting decay rates especially for wood. In diverse forests, available woody substrates differ chemically and structurally with implications for their role as both habitats and resources for microbes. Both wood traits and microbial communities at the start of the decay process affect subsequent decay rates, but the relative magnitude of effects is not known. In this work, we sought to ask a simple question: what are the relative effects of microbial communities and wood traits? We characterized fungal and oomycete endophytes with amplicon sequencing from stems of 22 woody species growing in woodlands near Richmond, NSW, Australia, and measured 11 traits to capture variation in the physical and chemical wood substrates. To evaluate the consequence of endophyte diversity and wood traits on the trajectory of decay, stem samples were sequentially harvested over 5 years to quantify the decay rate, its consistency and how it varies through time. We did not find evidence to support particular initial endophyte compositions leading to faster decay. Instead, initial wood attributes were much more powerful in explaining decay trajectories with smaller, less dense stems with high water, low N and low lignin concentrations decomposing consistently faster. These data show that initial wood traits have long‐lasting consequences on decay unlike natural variation in endophyte communities, supporting the idea that community member functions are highly redundant. Wood substrate‐driven environmental filtering, rather than endophyte‐driven priority effects, had a stronger effect on decay when real‐world levels of diversity in wood traits were considered. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.003 | 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".