Influence of Two Important Leguminous Trees on Their Soil Microbiomes and Nitrogen Cycle Activities in a Primary and Recovering Secondary Forest in the Northern Zone of Costa Rica
Bibliographic record
Abstract
Inga edulis and Pentaclethra macroloba are dominant N-fixing forest trees in Costa Rica, likely important for recovery of soil N and C after deforestation, yet little is known of their soil microbiomes nor how land use impacts them. Soils from both trees in a primary and secondary forest were assessed for N-cycle metrics and DNA sequence-based composition of total bacterial, potential N-fixing bacterial, and potential ammonium oxidizing bacterial genera. The compositions of the functional groups of bacteria, but not their total relative abundance of DNA, were different across the soils. The P. macroloba soils had greater NO3− levels and richness of both functional groups, while I. edulis soils had greater NH4+ levels, consistent with its NH4+ preference for root nodule development. The bacterial communities were different by habitat, as secondary forest I. edulis microbiomes were less rich, more dominant, possibly more affected by the disturbance, or reached equilibrium status quicker than the richer, less dominant P. macroloba microbiomes, which may be developing slower along with secondary forest succession, or were less affected by the disturbance. Functional redundancy and switching of 10 N-cycle bacterial genera was evident between the primary and secondary forest soils, likely to maintain stable levels of N-cycle activity following disturbance. In summary, the two tree soil microbiomes are different, land use differentially affects them, and, thus, both tree species should be used during forest regeneration strategies in this region.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".