Macrofungus ecology and diversity under different conifer monocultures on southern Vancouver Island
Bibliographic record
Abstract
There is concern that growing forest plantations in close rotation may adversely impact the rate of litter decomposition and thus soil productivity. The impact of conifer monocultures of Sitka spruce- Picea sitchensis (Bong.) Carr., Douglas-fir-Pseudotsuga menziesii (Mirb.) Franco, western red cedar-Thuja plicata Donn ex D. Don in Lamb., and western hemlock-Tsuga heterophylla (Raf.) Sarg., on the diversity and abundance of macrofungi was researched. Study sites were established at three locations on the west-coast of Vancouver Island, based on soil moisture and nutrient regimes, and a systematic survey of fungus species was conducted throughout the growing seasons in 1997 and 1998. A total of 277 taxa were identified, a large portion of them belonging to the genus Mycena (45 species). ANOVA analysis showed that conifer species as well as site differences affect composition, diversity, and abundance of macrofungus communities. Overall, the lowest diversity and abundance were noted in western red cedar and the highest in Douglas-fir stands. Western hemlock supported the highest number of ectomycorrhizal fungi, and Sitka spruce habitat is characterized by a unique abundance of certain Mycena species, e.g. M. tenax. The following fungi were most commonly observed in this study, in descending order of their abundance: Mycena amicta, Cantharellus formosus, Mycena metata group, Mycena rorida, Mycena aurantiidisca, Mycena galopus, and Clavulina cristata. The most frequent genera, from the total of 95, were: Mycena, Cortinarius, Inocybe, Lactarius, Russula, and Galerina. Species composition differed amongst the four conifer habitats, with even some non-mycorrhizal macrofungi showing preferences for a given conifer litter. There were considerably more saprobic than ectomycorrhizal species in each habitat, the ratio for the whole study being 7:3. In both years, a vast majority of all macrofungi fruited in September and October, with the least productive months being June, July, and August, due to insufficient precipitation. Ordination analyses suggest that in addition to conifer effects and some degree of spatial autocorrelation, site characteristics, such as soil moisture, nutrient availability, type of undergrowth, may have determined the observed differences in diversity and abundance of macrofungi.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".