Protected land enhances the survival of native aquatic macrophytes and limits invasive species spread in the Panama Canal
Bibliographic record
Abstract
Abstract This study examined whether protected land in a tropical reservoir's catchment can promote the survival of native aquatic plants (macrophytes) and limit the spread of invasive alien plant species (macrophyte IAS), which can threaten native wildlife and require expensive remediation. As the number of tropical river dams is expected to increase in the coming decades to meet societal demands, it is crucial to explore solutions for preserving aquatic biodiversity. The study used a before–after–control–intervention design, based on monitoring data and long‐term sedimentological, climatic and ecological records from both lake and river zones adjacent to protected and unprotected lands around the 100‐year‐old Gatun Lake in the Panama Canal, Panama. The research examined the impact of impoundment and the invasion of Pontederia crassipes (water hyacinth) and Hydrilla verticillata (water thyme) on native macrophyte communities and environmental variables. Lake zones adjacent to protected lands had lower nutrient concentrations, greater variations in water depth profiles and reduced fluctuations in water chemistry than lake zones outside areas of land protection. In addition, the results showed that whereas zones adjacent to unprotected land became dominated by macrophyte IAS, lake zones adjacent to protected areas were more resilient to the spread of macrophyte IAS and were able to maintain viable populations of native pre‐dam species for >100 years. This study indicates that protecting land adjacent to tropical reservoirs could be a cost‐effective solution for preserving aquatic macrophyte biodiversity by retaining nutrients, stabilizing water chemistry, providing habitat heterogeneity and protecting native vegetation, while still supporting terrestrial conservation goals. These findings could aid in planning measures for the hundreds of proposed dam projects across lowland tropical areas and provide new insights into best practices for enhancing river ecosystem resilience.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".