Forest fragmentation, the decline of an endangered primate, and changes in host–parasite interactions relative to an unfragmented forest
Bibliographic record
Abstract
Forest fragmentation may alter host-parasite interactions in ways that contribute to host population declines. We tested this prediction by examining parasite infections and the abundance of infective helminths in 20 forest fragments and in unfragmented forest in Kibale National Park, Uganda. Over 4 years, the endangered red colobus (Procolobus rufomitratus) declined by 20% in fragments, whereas the black-and-white colobus (Colobus guereza) in fragments and populations of both colobines in unfragmented forest remained relatively stable. Seven nematodes (Strongyloides fulleborni, Strongyloides stercoralis, Oesophagostomum sp., an unidentified strongyle, Trichuris sp., Ascaris sp., and Colobenterobius sp.), one cestode (Bertiella sp.), and three protozoans (Entamoeba coli, Entamoeba histolytica/dispar, and Giardia sp.) were detected. Infection prevalence and the magnitude of multiple infections were greater for red colobus in fragmented than in unfragmented forest, but these parameters did not differ between forests for black-and-white colobus. Infective-stage colobus parasites occurred at higher densities in fragmented compared with unfragmented forest, demonstrating greater infection risk for fragmented populations. There was little evidence that the nature of the infection was related to the size of the fragment, the density of the host, or the nature of the infection in the other colobine, despite the fact that many of the parasites are considered generalists. This study suggests that forest fragmentation can alter host-parasite dynamics and demonstrates that such changes can correspond with changes in host population size in forest fragments.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".