Habitat loss in the restricted range of the endemic Ghanaian cichlid <i>Limbochromis robertsi</i>
Bibliographic record
Abstract
ABSTRACT Remote sensing, through satellite image analysis has become an integral and invaluable tool to inform biodiversity conservation and monitoring of habitat degradation and restoration over time. Despite the disproportionately high levels of biodiversity loss in freshwater ecosystems worldwide, ichthyofauna are commonly overlooked in favor of other keystone species. Freshwater fish, as indicators of overall aquatic ecosystem health can also be indicators of larger scale problems within an ecosystem. If endemic and specialized fishes are at risk, the forest and landscape around their habitat is also undergoing change. As a case study demonstrating the utility of multi-temporal, multi-resolution satellite imagery, we examined deforestation and forest fragmentation around the Atewa Forest Reserve, south eastern Ghana. Within small creeks, Limbochromis robertsi , a unique freshwater cichlid with an extremely limited distribution range can be found. Historically, the land cover in the area has undergone substantial deforestation for agriculture and artisanal small-scale mining, primarily for gold. We found deforestation accelerated along with increased forest fragmentation in the 2014 – 2017 period with the majority of the forest loss along the river and creek banks due to small-scale mining operations and increased agriculture. Field visits indicate a decrease in the total population by approximately 90% from the early 1990s to 2018. We illustrate the benefits of determining landscape metrics from local scale remote sensing studies as proxies to assess the decline of endemic species with restricted ranges, whose habitat characteristics and the subsequent pressures they face require detailed analysis at fine temporal and spatial scales not captured by global or continental scale datasets.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".