Simultaneous evolution of multiple dispersal components and kernel
Bibliographic record
Abstract
Global climate is changing rapidly and is accompanied by large‐scale fragmentation and destruction of habitats. Since dispersal is the first line of defense for mobile organisms to cope with such adversities in their environment, it is important to understand the causes and consequences of evolution of dispersal. Although dispersal is a complex phenomenon involving multiple dispersal‐components like propensity (tendency to leave the natal patch) and ability (to travel long distances), the relationship between these traits is not always straight‐forward, it is not clear whether these traits can evolve simultaneously or not, and how their interactions affect the overall dispersal profile. To investigate these issues, we subjected four large (n ∼ 2400) outbred populations of Drosophila melanogaster to artificial selection for increased dispersal, in a setup that mimicked increasing habitat fragmentation over 33 generations. The propensity and ability of the selected populations were significantly greater than the non‐selected controls and the difference persisted even in the absence of proximate drivers for dispersal. The dispersal kernel evolved to have significantly greater standard deviation and reduced values of skew and kurtosis, which ultimately translated into the evolution of a greater frequency of long‐distance dispersers (LDDs). We also found that although sex‐biased dispersal exists in D. melanogaster , its expression can vary depending on which dispersal component is being measured and the environmental condition under which dispersal takes place. Interestingly though, there was no difference between the two sexes in terms of dispersal evolution. We discuss possible reasons for why some of our results do not agree with previous laboratory and field studies. The rapid evolution of multiple components of dispersal and the kernel, expressed even in the absence of stress, indicates that dispersal evolution cannot be ignored while investigating eco‐evolutionary phenomena like speed of range expansion, disease spread, evolution of invasive species and destabilization of metapopulation dynamics.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".