Land use changes in an afrotropical biodiversity hotspot affect stream alpha and beta diversity
Bibliographic record
Abstract
Abstract Land use changes such as deforestation and agricultural expansion strongly affect stream biodiversity, with several studies demonstrating negative impacts on stream alpha diversity. Effects of forest conversion on stream beta diversity are much harder to predict, both because empirical studies are few and because competing theories suggest opposite responses. Moreover, almost no data exist for tropical Africa, a region that is paradoxically a hotspot of both current deforestation and freshwater biodiversity. Here, we compared environmental variables, invertebrate community composition, and alpha and beta diversity of forested and deforested (agricultural) streams in and around Kibale National Park, Uganda. We found that forest conversion strongly influenced stream environmental variables and invertebrate community composition, and that agricultural land use reduced stream alpha diversity. However, among‐stream beta diversity was greater across the agricultural landscape than inside the forest. Decomposing beta diversity into taxa replacement and richness differences demonstrated that replacement contributed a similar proportion to total beta diversity in both land use classes. Because of this greater beta diversity, the agricultural landscape had similar gamma diversity as the forested landscape despite its lower alpha diversity. We discuss conservation implications of these land use‐associated biodiversity changes in a highly diverse yet little‐studied deforestation hotspot.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".