The Ethiopian wolf can act as a flagship and umbrella species to protect the Afroalpine ecosystem and foster sustainable development
Bibliographic record
Abstract
Summary Habitat alteration and climate change are important threats to terrestrial biodiversity in the tropics. Endorsing flagship or umbrella species can help conserve sympatric biodiversity, restore degraded ecosystems and achieve United Nations Sustainable Development Goals (UN SDGs). The Ethiopian wolf ( Canis simensis ) is a rare and endemic Ethiopian canid. It is Africa’s most endangered canid species and is restricted to several isolated patches of Afroalpine habitats. While its behavioural ecology and conservation biology have been well studied, studies of the Ethiopian wolf’s significance for the conservation of its habitat and sympatric species are lacking. Here we use geographical range overlap and geospatial modelling to evaluate the importance of the Ethiopian wolf as a flagship and/or umbrella species. We assess whether conservation interventions targeting the Ethiopian wolf could help to restore and protect Afroalpine habitat and conserve sympatric species whilst simultaneously providing a wide range of socioeconomic and environmental benefits. We found that Ethiopian wolves share their range with 73 endemic and/or threatened vertebrate species, 68 of which are Afroalpine ecosystem species, and at least 121 endemic and/or threatened plant species. Ethiopian wolves are taxonomically distinctive and charismatic species classified as Endangered on the International Union for Conservation of Nature (IUCN) Red List. Thus, they meet both the flagship and umbrella species criteria to restore Afroalpine habitats and conserve threatened sympatric species. A conservation strategy protecting and restoring Afroalpine habitat has the potential to contribute to achieving at least five of the 17 UN SDGs. The protection of flagship and umbrella species should be integrated into broader regional biodiversity and habitat conservation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".