Spatial and temporal variation of forest floor, throughfall, and stemflow properties associated with Bigleaf Maple in a mixed conifer forest of coastal British Columbia
Bibliographic record
Abstract
Bigleaf maple (Acer macrophyllum Pursh) is a large deciduous tree that is abundant in western North America. This study addressed whether the predicted increase in abundance of bigleaf maple because of climate change could influence forest hydrology and site fertility due to species-specific effects on incident rainfall distribution and nutrient cycling. The study examined the spatial and temporal variation of forest floor, throughfall, and stemflow properties associated with bigleaf maple in a forest dominated by conifers. In bigleaf maple plots, the throughfall enrichment ratio of major chemicals was highest for NO3 during the leafed period and for P, K, Ca, and Mg during the leaf senescence period. The fluxes of DOC, total-N, DON, P, K, Ca, Mg, S, and SO4 in throughfall were higher in the leafless period than the leafed period. Compared to conifer trees, throughfall was larger for bigleaf maple and had a higher pH, and concentrations and fluxes of P and K. Similarly, stemflow of bigleaf maple had higher pH, and K concentration. The under-canopy and near trunk forest floor associated with bigleaf maple trees showed higher pH, total exchangeable bases, cation exchange capacity and concentrations of exchangeable Ca and Mg compared to Douglas-fir. The application of the PCNM method enabled us to segregate the broad and fine spatial patterns and to extract the main factors impacting forest floor pH and possibly other soil properties at multiple scales. It revealed that topography mainly acts broadly and canopy cover, canopy density and moisture content act at a finer scale. A random design has a higher accuracy in mapping the spatial distribution of forest floor pH and NH4 compared to stratified random and systematic cluster designs, and a sample size of 50 seems to be adequate for mapping the spatial structure of both variables. This study contributes to the understanding of species-specific impacts on soil properties in a mixed forest and suggests that bigleaf maple has a positive effect on site quality. This would enable it to have legacy effects on soil fertility, enhance overall ecosystem resilience, and promote conifer productivity later in succession following mortality of bigleaf maple.
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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 | 0.001 | 0.002 |
| 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".