Species–area curve and distance–decay relationships indicate habitat thresholds of ectomycorrhizal fungi in an old‐growth <i>Pseudotsuga menziesii</i> landscape
Bibliographic record
Abstract
Abstract Aim Ectomycorrhizal fungi ( EMF ) are a diverse and essential biota of forests that are vulnerable to species loss through reductions in late‐seral habitat. We examined how the spatial ecology of this biota, particularly distance–decay and species–area relationships, could better inform habitat thresholds for EMF conservation planning. Location Southeast Vancouver Island near Victoria, British Columbia, Canada. Methods Using a stratified sampling design, 11 plots (0.15 ha in size) were established at 0.05–17.5 km apart across 2,800 ha of mesic old‐growth Pseudotsuga menziesii var. menziesii and Tsuga heterophylla forests. EMF communities were compiled through molecular analysis of root tips and sporocarps. Results The EMF community was comprised of many Cortinarius , Piloderma , Russula and Tricholoma species typical of mesotrophic habitat. A total of 238 EMF species were observed, of which 86 species were detected only once. The ratio of average species richness per plot (84 taxa) to total richness was low at 0.35, and inherent stochasticity of the EMF community was estimated to be 31% community dissimilarity for species incidence. Distance decay of EMF communities was nonlinear, with an estimated slope break at 2.6 km, followed by a largely unchanging trend in β‐diversity. Accumulated species–area curves were fitted best by the cumulative Weibull sigmoid model, and the asymptote (367 species) at approx. 50 ha was consistent with nonparametric estimates of γ‐diversity (342–362 spp.). Main conclusions Old‐growth forests host an impressive amount of EMF diversity, and many of the Ramaria , Inocybe and Russula species are likely to be endemic to the Pacific Northwest. Both niche‐ and neutral‐based processes influenced EMF community composition, resulting in a minimum threshold of 50 ha (1.8% of the sample area) for capturing γ‐diversity. These spatial patterns will help design and evaluate conservation efforts, such as retention forestry, to sustain fully diverse EMF communities over managed landscapes.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 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 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".