Anthropogenic landscape alteration promotes higher disease risk in wild New Zealand avian communities
Bibliographic record
Abstract
Anthropogenic changes can have dramatic effects on wild populations. Moreover, by promoting the emergence of vector-borne diseases in many ecosystems, those changes can lead to local extinction of native wildlife. One of those diseases, avian malaria, has been shown to be on the rise in New Zealand, threatening native bird species that are among the most extinction-prone in the world. It is thus of prime importance to better understand the potential cascading effects that anthropogenic modifications have on those fragile species. Here, we aim to test how long-lasting modification to regional environmental filters can subsequently alter local biotic filters, in turn promoting the emergence of avian malaria in New Zealand avian communities. To this end, we used Bayesian structural equation modelling to unravel the drivers of disease emergence within the complex interplay between landscape and local species pools. We show that altered landscape, quantified through a lower enhanced vegetation index, leads to more infections in Turdus spp. and modification in avian community composition, potentially raising the probability of infection for other species in the community. In addition, we show that climatic variables associated with the presence of vectors play a predominant role in shaping the regional pattern of avian malaria occurrence. Our results suggest long-lasting impacts of anthropogenic changes on regional environmental filters and demonstrate that conservation efforts should align toward restoring the landscape to prevent further emergence of infectious diseases in wild ecosystems.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".