Geographic scale and disturbance influence intraspecific trait variability in leaves and roots of North American understorey plants
Bibliographic record
Abstract
Abstract Considering intraspecific trait variability (ITV) in ecological studies has improved our understanding of species persistence and coexistence. These advances are based on the growing number of leaf ITV studies over local gradients, but logistical constraints have prevented a solid examination of ITV in root traits or at scales reflecting species’ geographic ranges. We compared the magnitude of ITV in above‐ and below‐ground plant organs across three spatial scales (biophysical region, locality and plot). We focused on six understorey species (four herbs and two shrubs) that occur both in disturbed and undisturbed habitats across boreal and temperate Canadian forests. We aimed to document ITV structure over broad ecological and geographical scales by asking: (a) What is the breadth of ITV across species range‐scale? (b) What proportion of ITV is captured at different spatial scales, particularly when local scale disturbances are considered? and (c) Is the variance structure consistent between analogous leaf and root traits, and between morphological and chemical traits? Following standardized methods, we sampled 818 populations across 79 forest plots simultaneously, including disturbed and undisturbed stands, spanning four biophysical regions (~5,200 km). Traits measured included specific leaf area (SLA), specific root length (SRL) and leaf and root nutrient concentrations (N, P, K, Mg, Ca). We used variance decomposition techniques to characterize ITV structure across scales. Our results show that an important proportion of ITV occurred at the local scale when sampling included contrasting environmental conditions resulting from local disturbance. A certain proportion of the variability in both leaf and root traits remained unaccounted for by the three sampling scales included in the design (36% on average), with the largest amount for SRL (54%). Substantial differences in magnitude of ITV were found among the six species, and between analogous traits, suggesting that trait distribution was influenced by species strategy and reflects the extent of understorey environment heterogeneity. Even for species with broad geographical distributions, a large proportion of within‐species trait variability can be captured by sampling locally across ecological gradients. This has practical implications for sampling design and trait selection for both local studies and continental‐scale modelling. A free Plain Language Summary can be found within the Supporting Information of this article.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".