Response diversity, functional redundancy, and post‐logging productivity in northern temperate and boreal forests
Bibliographic record
Abstract
The development of efficient ecosystem resilience indicators was identified as one of the key research priorities in the improvement of existing sustainable forest management frameworks. Two indicators of tree diversity associated with ecosystem functioning have recently received particular attention in the literature: functional redundancy (FR) and response diversity (RD). We examined how these indicators could be used to predict post-logging productivity in forests of Québec, Canada. We analysed the relationships between pre-logging FR and RD, as measured with sample plots, and post-logging productivity, measured as seasonal variation in enhanced vegetation index obtained from MODIS satellite imagery. The effects of the deciduous and coniferous tree components in our pre-disturbance diversity assessments were isolated in order to examine the hypothesis that they have different impacts on post-disturbance productivity. We also examined the role of tree species richness and species identity effects. Our analysis revealed the complementary nature of traditional biodiversity indicators and trait-based approaches in the study of biodiversity-ecosystem-functioning relationships in dynamic ecosystems. We report a significant and positive relationship between pre-disturbance deciduous RD and post-disturbance productivity, as well as an unexpected significant negative effect of coniferous RD on productivity. This negative relationship with post-logging productivity likely results from slower coniferous regeneration speeds and from the relatively short temporal scale examined. Negative black-spruce-mediated identity effects were likely associated with increased stand vulnerability to paludification and invasion by ericaceous shrubs that slow down forest regeneration. Response diversity outperformed functional redundancy as a measure of post-disturbance productivity most likely due to the stand-replacing nature of the disturbance considered. To the best of our knowledge, this is among the first studies to report a negative significant relationship between a component of RD and ecosystem functioning, namely coniferous RD and forest ecosystem productivity after a stand-replacing disturbance.
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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".