Modeling the occurrence of 15 coniferous tree species throughout the Pacific Northwest of North America using a hybrid approach of a generic process-based growth model and decision tree analysis
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
Question: Can we interpret how climatic variation limits photosynthesis and growth for one widely distributed species, and then relate these responses to model the geographic distributions of other species? Location: The forested region of the Pacific Northwest, United States and Canada. Methods: We first mapped monthly climatic data, averaged for the period 1950 to 1975 at 1 km resolution across the region. The recorded presence and absence of 15 native tree species were next mapped at 1 km resolution from data acquired on 22 771 field survey plots. To establish seasonal limits on photosynthesis and water use, a process-based growth model (3-PG, Physiological Processes to Predict Growth) was parameterized for Douglas-fir (Pseudotsuga menziesii), one of the most widely distributed species in the region. Automated decision tree analyses were used to predict the distribution of different species by creating a suite of rules associated with the relative constraints that soil drought, atmospheric humidity deficits, suboptimal and subfreezing temperatures would impose on the growth of Douglas-fir. Results: The 3-PG process-based modeling approach, combined with automated decision tree analyses, predicted presence and absence of 15 conifers on field survey plots with an average accuracy of 82±12%. Predictive models of current distribution for each species differed in the number of, order in, and physiological thresholds selected. A deficit in the soil water balance, followed by departures from optimum temperatures in the summer were the two most important variables selected in predicting species distributions. Conclusions: Although empirical models using different sampling techniques and statistical analyses may be more accurate in predicting current distribution of species, the hybrid approach presented in this paper provides a greater mechanistic understanding of the limits to growth and tree distributions. These attributes of process-based models make them particularly useful in designing mitigating strategies to projected changes in climate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it