GIS-based modeling to assess ecological niche differentiation in four species of sympatric lichens at risk in Nova Scotia, Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Species distribution models (SDMs) rely on the concept of niche theory that suggests that individual species live within definable ranges of environmental conditions. Comparisons of SDMs between species can help further the understanding of competitive exclusion and niche differentiation. SDMs were created for Erioderma pedicellatum, E. mollissimum, Pectenia plumbea and Pannaria lurida, four sympatric species that occur in Nova Scotia, Canada. Logistic regression was used to create SDMs using nine environmental explanatory variables and presence of the modeled species as the response variable. There was significant overlap in environmental space between species, but each species tended to occupy a unique combination of environmental attributes. The Erioderma pedicellatum model from this study suggests this species occurs in cooler wet climate at mid-elevations in older closed canopy coniferous forest. Results from this study indicate Erioderma mollissimum occurs in old to mature deciduous forests at low to mid-elevation in warm, moderately wet climates. Pectenia plumbea tended to be found at low to mid-elevations in areas with moderately cool temperature with mid to high mean annual precipitation. Pannaria lurida tended to occupy mature to old forests occurring in areas with mid-range mean annual precipitation at higher elevations. Since this study examined a relatively small number of environmental variables, further study at different scales and with more extensive datasets would likely reveal further insights into competitive exclusion among these four cyanolichens.
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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 | 0.001 |
| 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) | 0.014 | 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 it