Understory vegetation diversity and composition in intensively managed plantations compared to extensively managed forests
Bibliographic record
Abstract
The benefits of plantations for wood production are well known, but it is unclear whether they have a clear negative effect on biodiversity, compared to natural forests. It is also unclear how overstory species identity (i.e., exotic, compared to native) would interact with the simple plantation effects on understory vegetation biodiversity. Here, we assessed bryophyte, lichen, and vascular plant diversity and composition in mono-specific and mixed plantations of exotic and native hybrid poplars ( Populus spp.) and exotic and native spruces ( Picea spp.). We compared these plantations to reference forests with similar overstory types (coniferous, deciduous, mixed) to those of the plantations in the southern boreal forest of Quebec. We also assessed whether the overstory species identity in plantations influenced understory vegetation biodiversity compared to reference forests. Our results show that plantations were not biological deserts, since mixed plantations contained similar bryophyte and vascular plant diversity as reference mixed native forests. The presence of native spruces and exotic poplars in mixed plantations increased bryophyte and vascular plant diversity respectively. This suggests that the influence of plantations on understory species diversity, compared to reference forests, was linked to the overstory species identity. Plantations were associated with ruderal plant species, while reference forests were composed of forest species. Bryophyte composition was similar in plantations and reference forests, while lichen species were only present in plantations. Our findings highlight that plantations, especially mixtures of white spruce and exotic poplar, are a promising option for planting strategies to enhance biodiversity.
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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) | 0.000 | 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".