Tree species diversity and composition relationship to biomass, understory community, and crown architecture in intensively managed plantations of the coastal Pacific Northwest, USA
Bibliographic record
Abstract
Trends in land cover and the demand for ecosystem services suggest that plantation forests will be expected to provide a larger quantity and diversity of ecosystem services. We identified three measures indicative of diverse ecosystem services (aboveground biomass, understory biodiversity, and crown length) and compared their relationships to tree species composition in intensively managed forest plantations of the Coast Range mountains of the Pacific Northwest, United States. This study was conducted in stands of western hemlock (Tsuga heterophylla (Raf.) Sarg.), Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco), and red alder (Alnus rubra Bong.), as well as in mixtures of the three species that were 35–39 years old. In this operational setting, we did not observe the positive relationship between species diversity and productivity observed in other studies, which we attributed to management practices that minimize interspecific interaction during most of the rotation. Crown length and understory species diversity were greater in mixtures of tree species than in (monospecific) monocultures. When multiple ecosystem components were considered simultaneously, mixtures of tree species outperformed monocultures. The observed relationships of the three responses to tree species composition and diversity are likely explained by differences in tree phenology, shade tolerance, disease susceptibility, and management interventions. Based on the results, management that is solely fixated on wood production homogeneously throughout the plantation may miss opportunities to provide other ecosystem services.
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 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 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".