Drivers of understory species richness in reconstructed boreal ecosystems: a structural equation modeling analysis
Bibliographic record
Abstract
Understory vegetation accounts for the most diverse part of the plant community in boreal forests and plays a critical role in stand dynamics and ecosystem functions. However, the ecological processes that drive understory species diversity are poorly understood and largely unexplored for reconstructed boreal ecosystems. The current study explored the relationships between understory species richness and biotic and abiotic factors in sites reclaimed after oil sands mining in northern Alberta, Canada, three and six growing seasons post-reclamation. Reclaimed sites with two main surface soils, forest floor mineral soil mix (FFMM) and peat mineral soil mix (PMM), were used along with post-fire benchmarks. A number of soil physicochemical (including nutrients) and vegetation properties were measured and considered in the a-priori hypothesis framework. Structural equation models (SEM) were used to evaluate the multivariate relationships. In general, the FFMM sites had greater species richness than the PMM sites, even six growing seasons after reclamation. A maximum 254% increase in graminoid and shrub cover was observed on FFMM between year 3 and 6 post-reclamation, whereas a maximum 137% increase in forb and bryophyte cover was recorded on PMM. The post-fire sites showed a significant increase (70%) only in shrub cover. Major driving factors of understory species richness varied among soil types. The SEM revealed a strong interdependency between species richness and soil and vegetation factors on FFMM with a positive control from soil N on species richness. In contrast, on PMM soil nutrients had a negative effect on species richness. Temporal changes in the drivers of species richness were mostly observed on FFMM through a negative vegetation control on species richness. The models and significant causal paths can be used in monitoring changes in understory species relationships in reclaimed sites and in identifying future research priorities in similar systems.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".