Data from: Warming speeds up range expansion in an experimental model system
Bibliographic record
Abstract
Dispersal is becoming increasingly critical to understand as climate change is forcing species to shift their ranges to track changing environments. While we know that warmer temperatures can prompt range shifts, there is little known about how temperature influences the capacity for organisms to move by changing rates of range expansion. Warmer temperatures could affect the range expansion rate through two pathways: by increasing random, density-independent movement via increased metabolic rates, and/or by increasing population growth rates and driving density-dependent movement. Surprisingly, there have been no experimental tests of the effect of temperature on range expansion rate. We used red flour beetles, Tribolium castaneum, to test the overall effect of temperature on the rate of range expansion and to determine if random movement, population density, or both could be driving this effect. We grew beetle populations in linear connected landscapes at three temperatures (27.5 °C, 30 °C, and 32.5 °C) and tracked their expansion across these landscapes for 18 weeks. We then conducted separate assays to isolate the effects temperature on movement (density-independent dispersal probability) and on population growth rates. We found a positive effect of temperature on range expansion rate, with beetles in the 32.5 °C exhibiting the fastest range expansion. We also found positive effects of temperature on both dispersal probability and on population growth rate, indicating that both processes are likely contributing to this overall effect. Our findings highlight the importance of integrating the effects of temperature on range expansion speed in order to fully understand how quickly, species’ ranges could shift under climate change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".