Ecological gradients driving the distribution of four Ericaceae in boreal Quebec, Canada
Bibliographic record
Abstract
Understory species play a significant role in forest ecosystem dynamics. As such, species of the Ericaceae family have a major effect on the regeneration of tree species in boreal ecosystems. It is thus imperative to understand the ecological gradients controlling their distribution and abundance, so that their impacts can be taken into account in sustainable forest management. Using innovative analytical techniques from landscape ecology, we aimed to position, along ecological gradients, four Ericaceae found in the boreal forest of Quebec (Canada) (Rhododendron groenlandicum, Kalmia angustifolia, Chamaedaphne calyculata, and Vaccinium spp), to regionalize these species into landscape units relevant to forest management, and to estimate the relative importance of several ecological drivers (climate, disturbances, stand attributes, and physical environment) that control the species distribution and abundance. We conducted our study in boreal Quebec, over a study area covering 535,355 km(2). We used data from 15,339 ecological survey plots and forest maps to characterize 1422 ecological districts covering the study region. We evaluated the relative proportion of each ericaceous species and explanatory variables at the district level. Vegetation and explanatory variables matrices were used to conduct redundancy, cluster, and variation partitioning analyses. We observed that ericaceous species are mainly distributed in the western part of the study area and each species has a distinct latitudinal and longitudinal gradient distribution. On the basis of these gradients, we delimited 10 homogeneous landscape units distinct in terms of ericaceous species abundance and environmental drivers. The distribution of the ericaceous species along ecological gradients is closely related to the overlaps between the four sets of explanatory variables considered. We conclude that the studied Ericaceae occupy specific positions along ecological gradients and possess a specific abundance and distribution controlled by the integration of multiple explanatory variables.
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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 | 0.000 | 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 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".