Anthropogenic disturbance drives dispersal syndromes, demography, and gene flow in amphibian populations
Bibliographic record
Abstract
There is growing evidence that anthropogenic landscapes can strongly influence the evolution of dispersal, particularly through fragmentation, and may drive organisms into an evolutionary trap by suppressing dispersal. However, the influence on dispersal evolution of anthropogenic variation in habitat patch turnover has so far been largely overlooked. In this study, we examined how human-driven variation in patch persistence affects dispersal rates and distances, determines dispersal-related phenotypic specialization, and drives neutral genetic structure in spatially structured populations. We addressed this issue in an amphibian, Bombina variegata, using an integrative approach combining capture-recapture modeling, demographic simulation, common garden experiments, and population genetics. B. variegata reproduces in small ponds that occur either in habitat patches that are persistent (i.e. several decades or more), located in riverine environments with negligible human activity, or in patches that are highly temporary (i.e. a few years), created by logging operations in intensively harvested woodland. Our capture-recapture models revealed that natal and breeding dispersal rates and distances were drastically higher in spatially structured populations (SSPs) in logging environments than in riverine SSPs. Population simulations additionally showed that dispersal costs and benefits drive the fate of logging SSPs, which cannot persist without dispersal. The common garden experiments revealed that toadlets reared in laboratory conditions have morphological and behavioral specialization that depends on their habitat of origin. Toadlets from logging SSPs were found to have higher boldness and exploration propensity than those from riverine SSPs, indicating transgenerationally transmitted dispersal syndromes. We also found contrasting patterns of neutral genetic diversity and gene flow in riverine and logging SSPs, with genetic diversity and effective population size considerably higher in logging than in riverine SSPs. In parallel, intra-patch inbreeding and relatedness levels were lower in logging SSPs. Controlling for the effect of genetic drift and landscape connectivity, gene flow was found to be higher in logging than in riverine SSPs. Taken together, these results indicate that anthropogenic variation in habitat patch turnover may have an effect at least as important as landscape fragmentation on dispersal evolution and the long-term viability and genetic structure of wild populations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".