River dolphins as indicators of ecosystem degradation in large tropical rivers
Bibliographic record
Abstract
Freshwater ecosystem degradation in the Amazon and Orinoco river basins is increasing due to rising human population numbers, and large water development projects. Therefore, monitoring ecosystem condition in these rivers of high biodiversity is of global conservation importance. This dissertation evaluated the potential of using river dolphins as indicators of ecosystem condition in large tropical rivers of South America. First, population estimates of river dolphins were obtained by line-strip transect surveys and mark-recapture methods on photo-identifications. Using this information, I identified critical habitat, hotspots, and areas of concern for river dolphins, as well as the relationship between dolphin density and group size estimates with ecological features. Second, I evaluated the relationship between dolphin estimates and human stressors. Higher densities of dolphins occurred in rivers with low indices of overall freshwater degradation, such as rivers with high water quality and the lowest human population numbers. Thus, dolphin density estimates seem to be good indicators of freshwater ecosystem degradation in the Amazon and Orinoco basins. These top predators not only are indicator species, but also have the potential to act as flagship and sentinel species, indicating freshwater ecosystem degradation and stimulating conservation action. This dissertation highlights the large changes in the Amazon and Orinoco that are approaching fast. Indicator, flagship and sentinel species can become science-based conservation tools not to only document freshwater ecosystem degradation, but to raise awareness about broader implications of human stressors on biodiversity and river systems.
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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".