Tree-related microhabitat assemblages in boreal forests depend more on local environmental conditions and tree species than on tree size
Bibliographic record
Abstract
Tree-related microhabitats (TreMs) are crucial for biodiversity, supporting thousands of species. Most TreM research has focused on temperate forests, prompting us to evaluate the TreM typology in the boreal biome. We aimed to: (i) identify factors influencing TreM occurrence on living trees, (ii) assess redundancy and complementarity of TreM supply among Scots pine, birch, and aspen in Fennoscandia, (iii) compare TreM occurrence between Fennoscandian and Canadian boreal forests, and (iv) determine the minimum sample size for assessing TreM diversity in large-scale inventories. We inventoried 1515 trees in long unmanaged forests across Fennoscandia and compared results with a Canadian dataset. Findings show that Scots pine, birch, and aspen support distinct and complementary TreM assemblages, emphasizing the importance of mixed stands for biodiversity. Over larger geographic areas, local plot context – including climate, disturbance history, and biotic/abiotic factors – was the main driver of TreM occurrence. Unlike temperate forests, tree diameter was not a significant driver in boreal forests. The variability of TreM assemblages within the boreal biome underscores the need for large sample sizes to accurately assess TreM diversity. Sampling 1000 trees is sufficient to assess the seven TreM forms, while a larger sample is required to capture all fifteen TreM groups.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".