High productivity of tree species planted outside their current geographic range indicates large regions of unrealized niche space
Bibliographic record
Abstract
Introduction: Reforestation efforts that utilize assisted range expansion in response to climate change require an enhanced understanding of tree species' fundamental niches as well as potential interactions with local species when planted in novel environments. Methods: Using height-age modeling and dendrochronological approaches, we analyzed height and radial growth data from 25 disparate experimental plantings of three temperate zone conifer species (Douglas-fir, western larch and ponderosa pine) in environments north of, and colder than, their current geographic range in British Columbia, Canada, to explore how these types of trials can provide important insights into tree species' fundamental niches in regions that lack comprehensive, replicated experiments. Results and discussion: Height growth of the three species exceeded or equaled that of populations growing within their realized niche, suggesting that from a growth perspective, the fundamental niches of these species have historically included environments colder and further north than their current ranges. The productivity of Douglas-fir and western larch also exceeded that of a local tree species, lodgepole pine, across a range of sites and climates within the study region, indicating that these species may be successful competitors with other species outside their range. Across all species and locations, environmental constraints on tree productivity appear to be more consistently related to available soil moisture than temperature. This study suggests that temperate conifers may have unrealized niche space far outside their current geographic range, and their potential for range expansion may be larger than suggested by species distribution models.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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 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".