Occurrence and dominance of six Pacific Northwest conifer species
Bibliographic record
Abstract
Questions: Can probability of occurrence and dominance be accurately estimated for six important conifer species with varying range sizes? Does range size impact the accuracy of species probability of occurrence models? Is species predicted probability of occurrence significantly related to observed dominance? Location: Pacific Northwest region, North America (60°–40°N, 140°–110°W). Methods: This study develops near range-wide predictive distribution maps for six important conifer species (Pseudotsuga menziesii, Tsuga heterophylla, Pinus contorta, Thuja plicata, Larix occidentalis, and Picea glauca) using forest inventory data collected across the United States and Canada. Species model accuracies are compared with range size using a rank scoring system. A suite of climate and topographic predictor variables are used to investigate environmental constraints that limit species range and quantify relationships between species predicted probability of occurrence and dominance at both plot and landscape scales. Results: Evaluation statistics revealed significant and accurate probability of occurrence models were developed for all six species. Based on ranked evaluation statistics, Tsuga heterophylla had highest overall model accuracy (statistic rank score=5) and Pinus contorta the lowest (statistic rank score=17). Across species, ranked evaluation statistics also revealed a pattern of decreasing model accuracy with increasing range size. At plot level, correlations between dominance and probability of occurrence were weakly positive for all species with only half of the species having statistically significant correlations. Pseudotsuga menziesii had the highest correlation (r=0.36, P<0.001) and Thuja plicata lowest (r=0.038, P=0.799). At the 50-km scale, correlations between dominance and probability of occurrence improved for all species except Pinus contorta. Pseudotsuga menziesii displayed the highest correlation (r=0.68, P<0.001) and Thuja plicata the lowest (r=0.07, P>0.709). Conclusions: Species probability of occurrence model accuracy decreased with increasing range size. The strength and significance of correlations between probability of occurrence and dominance varied considerably by species and across spatial scales. Apart from Pseudotsuga menziesii and L. occidentalis, the results suggest that probability of occurrence is not a consistently reliable surrogate for species dominance in Pacific Northwest forests. We demonstrate how the degree of correlation between species occurrence and dominance can be used as an indicator of how well predictions of occurrence characterize the optimal niche of a species.
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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 | 0.001 | 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 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".