Prediction of Parasite Infection Dynamics in Primate Metapopulations Based on Attributes of Forest Fragmentation
Bibliographic record
Abstract
Although the effects of forest fragmentation on species and ecological processes have been the focus of considerable research in conservation biology, our capacity to predict how processes will be altered and which taxonomic or functional groups will be most affected by fragmentation is still poor. This problem is exacerbated by inherent temporal and spatial variability in fragment attributes. To improve our understanding of this interplay, we examined how various fragment attributes affect one potentially important ecological process, parasite infection dynamics, and considered how changes in this process affect host metapopulations. From August 1999 to July 2003 we surveyed red colobus (Piliocolobus tephrosceles) metapopulations inhabiting nine fragments (1.2 to 8.7 ha) in western Uganda to determine the prevalence and richness of strongyle and rhabditoid nematodes, a group of potentially pathogenic gastrointestinal parasites. We used noninvasive fecal flotation and sedimentation (n = 536) to detect parasite eggs, cysts, and larvae in colobus fecal samples. To obtain an index of infection risk, we determined environmental contamination with Oesophagostomum sp., a representative strongyle nematode, in canopy (n = 30) and ground vegetation plots (n = 30). Concurrently, physical (i.e., size, location, and topography) and biological (i.e., tree diversity, tree density, stump density, and colobine density) attributes were quantified for each fragment. Interfragment comparisons of nine potential factors demonstrated that an index of degradation and human presence (tree stump density) strongly influenced the prevalence of parasitic nematodes. Infection risk was also higher in the fragment with the highest stump density than in the fragment with the lowest stump density. These results demonstrate that host-parasite dynamics can be altered in complex ways by forest fragmentation and that intensity of extraction (e.g., stump density) best explains these changes.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".