Comparisons of macrofungi in plantations of Sitka spruce (<i>Picea sitchensis</i>) in its native range (British Columbia, Canada) versus non-native range (Ireland and Britain) show similar richness but different species composition
Bibliographic record
Abstract
In the absence of native forests, non-native plantation forests have been identified as having an important function in conserving native biodiversity world-wide, including fungal biodiversity. The non-native tree species Sitka spruce (Picea sitchensis (Bong.) Carrière) is now the most abundant tree species in forests in Ireland and Britain, and these forests have been the focus of recent research into their ability to conserve native biodiversity. We conducted an analysis using data from macrofungal surveys from Sitka spruce forests in its native (Vancouver Island, Canada) and non-native (Ireland and Britain) range. Also included in all analyses were data for macrofungal diversity from other native tree species forests in each of the three regions. A total of 630 macrofungal species from seven forest types were analyzed, including 122, 247, and 70 species from Irish, British, and Vancouver Island Sitka spruce forests, respectively. In all three regions, notwithstanding differences in the ages of the sites surveyed in each region, the Sitka spruce forests were found to have species richness similar to that of the other forests types investigated. The communities of the Sitka spruce forests were clearly different in each of the regions, with only 17 species shared among Sitka spruce forests in all three regions. Overall, we found that Sitka spruce plantations in Ireland and Britain could provide a complementary ecosystem for native macrofungi, acting as a suitable forest type for many macrofungi in the absence of native forests. By encouraging the development of old-growth conditions in some plantations, along with the conservation of already existing seminatural forests in Britain and Ireland, we believe the best situation for macrofungal conservation can be achieved.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".