Predicting the site productivity of forest tree species using climate niche models
Bibliographic record
Abstract
Species occurrence-based climate niche models (CNMs) serve as valuable tools for predicting the future ranges of species’ suitable habitats, aiding the development of climate change adaptation strategies. However, these models do not address an essential aspect - productivity, which holds economic significance for timber production and ecological importance for carbon sequestration and ecosystem services. In this study, we investigated the potential to extend the CNMs to predict species productivity under various climate conditions. Lodgepole pine (Pinus contorta Dougl. ex Loud.) and Douglas-fir (Pseudotsuga menziesii Franco.) were selected as our model species due to their comprehensive range-wide occurrence data and measurement of site productivity. To achieve this, we compared and optimized the performance of four individual modeling algorithms (Random Forest (RF), Maxent, Generalized Boosted Models (GBM), and Generalized Additive Model (GAM)) in reflecting site productivity by evaluating the effect of spatial filtering, and the ratio of presence to absence (p/a ratio) observations. Additionally, we applied a binning process to capture the overarching trend of climatic effects while minimizing the impact of other factors. We observed consistency in optimal performance across both species when using the unfiltered data and a 1:1.5 p/a ratio, which could potentially be extended to other species. Among the modeling algorithms explored, we selected the ensemble model combining RF and Maxent as the final model to predict the range-wide site productivity for both species. The predicted range-wide site productivity was validated with an independent dataset for each species and yielded promising results (R2 above 0.7), affirming our model’s credibility. Our model introduced an innovative approach for predicting species productivity with high accuracy using only species occurrence data, and significantly advanced the application of CNMs. It provided crucial tools and insights for evaluating climate change's impact on productivity and holds a better potential for informed forest management and conservation decisions.
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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) | 0.001 | 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 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".