Using social network analysis and non-invasive antibody detection to explore pathogen exposure in wildlife communities
Bibliographic record
Abstract
Abstract Understanding the dynamics of infectious diseases in ecosystems shared with wildlife is a priority for preventing future threats to human, animal health and conservation. However, the collection of ecological and epidemiological data on wild populations has relied on invasive and costly methods which limited the capacity of investigation. Recent technological developments have changed this trend. In Hwange national park and its surrounding area in Zimbabwe, we combined camera trap-based ecological monitoring over a period of 12 months, covering 14 water holes with a fecal-based antibody survey in 16 large herbivore species. A survey involving 52,829 pictures as well as 629 faecal samples collected every 15 days, has shown Through the modelling of multispecies contact networks, the risk of infection exposure at species-level is predicted. Coupled with foot-and-mouth disease virus (FMDV) antibody information, the role of each species in the dynamics of infectious diseases is explored. This study highlights how community networks can provide valuable insights into the functional epidemiological role of wildlife populations. Rather than establishing transmission routes, our aim is to propose a scalable and non-invasive surveillance framework that identifies priority species and areas for epidemiological monitoring in complex ecological systems. Significance Statement Monitoring disease circulation in wildlife is often hindered by the difficulty of collecting epidemiological data. We propose a novel non-invasive approach that combines species interaction networks derived from camera-trap data with non-invasive antibody detection to explore the exposure patterns of large herbivore communities to foot-and-mouth disease virus (FMDV). By linking the position of species in contact networks with their immunological status, we demonstrate the potential of using ecological centrality as a proxy for identifying key hosts in transmission or indicator species for pathogen circulation. Beyond FMDV, this framework can be adapted to other pathogens for which non-invasive immunological assays are currently being developed, making it broadly relevant for wildlife disease surveillance in remote or protected areas.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".