Challenges in modeling the abundance of 105 tree species in eastern North America for climate change research
Bibliographic record
Abstract
Trees are expected to modify their distribution and abundance in response to climate change with important consequences on forestry management practices and forest diversity.Whereas Species Distribution Models have been commonly used to relate known occurrence of species to the current climate as a first step to project future suitable environmental space, modeling abundance patterns using Species Abundance Models (SAMs) remains a challenge.This research aimed to:1) evaluate the predictive performance of SAMs in predicting the current abundance of 105 tree species in eastern North America in response to climatic, topographic and edaphic predictors, and 2) explain the variation in SAMs" performance among species.The relative importance values of 105 tree species were first related to environmental predictors using Random Forest.The predictive performance of SAMs for each tree species was then assessed using the coefficient of determination (R).Finally, multiple linear regression was performed to explain the variation in SAMs" performance among species (R) using biogeographical and spatial attributes of species as explanatory variables.Predicting the current relative abundance of tree species using a combination of climatic, topographic, and edaphic variables was only partially successful.The coefficients of determination (R) for all SAMs ranged from 0.000 to 0.857 with a mean of 0.258 and a standard deviation of 0.18.Black spruce (Picea mariana) had the best predictive model and Florida maple (Acer barbatum) the worst.Forty-one species out of 105 (39 %) had R 0.3.These species had climate as the best and/or second best environmental predictor, except for Quercus macrocarpa, Pinus rigida, Pinus resinosa, and Ulmus alata, which were best predicted by non-climatic variables.The variation in the performance of SAMs among species was best explained by the range of relative abundance values and the spatial aggregation of species.This study highlighted the challenge in accurately predicting the relative abundance of trees in relation to current and therefore future climate, and identified species for which modeling approach ii worked best and for which abundance patterns would likely respond to climate change.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".