Human‐induced habitat fragmentation effects on connectivity, diversity, and population persistence of an endemic fish, <i>Percilia irwini</i>, in the Biobío River basin (Chile)
Bibliographic record
Abstract
Abstract An understanding of how genetic variability is distributed in space is fundamental for the conservation and maintenance of diversity in spatially fragmented and vulnerable populations. While fragmentation can occur from natural barriers, it can also be exacerbated by anthropogenic activities such as hydroelectric power plant development. Whatever the source, fragmentation can have significant ecological effects, including disruptions of migratory processes and gene flow among populations. In Chile, the Biobío River basin exhibits a high degree of habitat fragmentation due to the numerous hydroelectric power plants in operation, the number of which is expected to increase following new renewable energy use strategies. Here, we assessed the effects of different kinds of barriers on the genetic structure of the endemic freshwater fish Percilia irwini, knowledge that is critically needed to inform conservation strategies in light of current and anticipated further fragmentation initiatives in the system. We identified eight genetic units throughout the entire Biobío system with high effective sizes. A reduced effective size estimate was, however, observed in a single population located between two impassable barriers. Both natural waterfalls and human‐made dams were important drivers of population differentiation in this system; however, dams affect genetic diversity differentially depending on their mode of operation. Evidence of population extirpation was found in two river stretches limited by upstream and downstream dams. Significant gene flow in both directions was found among populations not separated by natural or anthropogenic barriers. Our results suggest a significant vulnerability of P. irwini populations to future dam development and demonstrate the importance of studying basin‐wide data sets with genetic metrics to understand the strength and direction of anthropogenic impacts on fish 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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".