Space‐for‐time inferences about range‐edge dynamics of tree species can be influenced by sampling biases
Bibliographic record
Abstract
Differences between the distributions of tree saplings and adults in geographic or niche space have been used to infer climate change effects on tree range dynamics. Previous studies have reported narrower latitudinal or climatic niche ranges of juvenile trees compared to adults, concluding that tree ranges are contracting, contradicting climate-based predictions. However, more comprehensive sampling of adult trees than juvenile trees in most regional forest inventories could potentially bias ontogenetic comparisons. Here we first report spatial simulations showing that reduced sampling intensity can result in underestimates of range and niche limits, but that resampling the same number of individuals of different life stages can eliminate this bias. We then reanalyzed the U.S. Forest Inventory and Analysis data, comparing the range and niche limits between adult trees and saplings of 92 tree species, both using the original data and two resampling procedures. Resampling aimed to reduce sampling biases by controlling for either sampling area or the number of individuals sampled. Overall, these resampling procedures had a major influence on the estimation of range limits, most often by reducing, eliminating, or even reversing the tendency in the original analyses for saplings to have broader distributions than adult trees. These results indicate that previous conclusions that the distributions of juvenile trees were contracting in response to climate change were potentially artifacts of sampling in the underlying data. More generally, sampling effects involved in the estimation of geographic ranges and environmental niche widths need to be taken into account in studies comparing different life stages, and also likely in other types of distribution comparisons.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.045 | 0.198 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".