Root and biomass allocation traits predict changes in plant species and communities over four decades of global change
Bibliographic record
Abstract
Global change is affecting the distribution and population dynamics of plant species across the planet, leading to trends such as shifts in distribution toward the poles and to higher elevations. Yet, we poorly understand why individual species respond differently to warming and other environmental changes, or how the trait composition of communities responds. Here we ask two questions regarding plant species and community changes over 42 years of global change in a temperate montane forest in Québec, Canada: (1) How did the trait composition, alpha diversity, and beta diversity of understory vascular plant communities change between 1970 and 2010, a period over which the region experienced 1.5°C of warming and changes in nitrogen deposition? (2) Can traits predict shifts in species elevation and abundance over this time period? For 46 understory vascular species, we locally measured six aboveground traits, and for 36 of those (not including shrubs), we also measured five belowground traits. Collectively, they capture leading dimensions of phenotypic variation that are associated with climatic and resource niches. At the community level, the trait composition of high-elevation plots shifted, primarily for two root traits: specific root length decreased and rooting depth increased. The mean trait values of high-elevation plots shifted over time toward values initially associated with low-elevation plots. These changes led to trait homogenization across elevations. The community-level shifts in traits mirrored the taxonomic shifts reported elsewhere for this site. At the species level, two of the three traits predicting changes in species elevation and abundance were belowground traits (low mycorrhizal fraction and shallow rooting). These findings highlight the importance of root traits, which, along with leaf mass fraction, were associated with shifts in distribution and abundance over four decades. Community-level trait changes were largely similar across the elevational and temporal gradients. In contrast, traits typically associated with lower elevations at the community level did not predict differences among species in their shift in abundance or distribution, indicating a decoupling between species- and community-level responses. Overall, changes were consistent with some influence of both climate warming and increased nitrogen availability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".