Niche models do not predict experimental demography but both suggest dispersal limitation across the northern range limit of the scarlet monkeyflower (<i>Erythranthe cardinalis</i>)
Bibliographic record
Abstract
Abstract Aim Geographical ranges largely reflect the projection of species’ environmental niches onto the landscape, but dispersal limitation can cause ranges to fall short of niche limits. Understanding the prevalence of niche and dispersal limitation is a fundamental problem in ecology and biogeography, and it is relevant in predicting climate‐driven range shifts. Dispersal limitation could also cause widely used ecological niche models (ENM), which relate records of occurrence to environmental predictors, to underestimate properties of the ecological niche. Using a combination of experimental transplants and ENM, we tested for (a) dispersal and niche limitation across a species’ range boundary and (b) associations between ENM predictions and population performance. Location Oregon, USA. Taxon Scarlet monkeyflower ( Erythranthe cardinalis ). Methods We created experimental populations within and beyond the northern range edge and used integral projection models (IPM) to infer potential population growth trajectories. We also built two classes of ecological niche models (ENM), one using climatic predictors and the other using fine‐scale stream habitat variables, to estimate habitat suitability across the northern range edge. Finally, we tested whether higher ENM suitability scores predict greater demographic performance. Results Consistent with dispersal limitation, experimental populations beyond the range were projected to persist or spread in three of four sites (compared to two of four sites within the range) and stream habitat ENM projected abundant suitable stream microhabitat within the species’ thermal envelope beyond the range edge. In contrast, climatic ENM suggested decreasing habitat suitability and availability at the northern range edge. Unexpectedly, higher climatic ENM scores were associated with negative population growth rates, while higher stream habitat ENM scores were unrelated to population growth. Main conclusions The northern range edge falls short of the species niche limit and is instead limited by dispersal into suitable habitat. Dispersal limitation caused correlative niche models to underestimate the climatic niche and to poorly predict demographic performance in a short‐term field study. These results highlight key challenges to applying predictions from correlative ENM to understanding range and niche limits.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".