Mapping the landscape pool of ardeid species to landscape structure associated with Japanese encephalitis virus in Australia
Walsh, M.; Webb, C. E.; Brookes, V.
Show abstract
Japanese encephalitis virus (JEV), a zoonotic, mosquito-borne virus, has broad circulation across the Central Indo-Pacific biogeographical region (CIPBR), which recently expanded dramatically within this region across southeastern Australia over the summer of 2021-2022. Preliminary investigation of the landscape epidemiology of the outbreaks of JEV in Australian piggeries found associations with particular landscape structure as well as ardeid species richness. The ways in which waterbird species from diverse taxonomic pools with substantial functional variation might couple with JEV-associated landscape structure was not explored, and therefore, key questions regarding the landscape epidemiology and infection ecology of JEV remain unanswered. Moreover, given the established presence of JEV within the CIBPR, the extent to which waterbird species pools in JEV-associated landscapes in Australia reflect broader regional patterns in functional biogeography presents a further knowledge gap particularly with respect to potential virus dispersal via maintenance hosts. The current study investigated waterbird species presence, ecological traits, and functional diversity distribution at landscape scale, and how these aligned with confirmed JEV detections in eastern Australia and the wider CIPBR. The results showed that waterbird habitat associated with JEV detection in Australia in 2022 and more widely across the CIPBR over the last 20 years reflects a range of species representing 8 families in 4 orders (ardeids, anatids, rallids, phalacrocoracids, threskiornithids, gruids, and pelecanids). Increasing waterbird functional diversity (trait-based mean pairwise dissimilarity) was associated with landscapes delineating JEV occurrence, while only one individual trait, high hand-wing index, was consistently associated with species presence in these JEV-associated landscapes in both Australia and the broader CIPBR. This suggests that dispersal capacity among the waterbird species pools that dominate JEV-associated landscapes might be important. By taking an agnostic approach to JEV maintenance host status, this study indicates a relatively large, CIPBR-wide pool of waterbird families associated with JEV landscapes, challenging the narrow view that JEV maintenance is limited to ardeid birds. In addition, these findings highlight the potential for leveraging functional biogeography in high-risk landscapes across broad geographic extent to guide landscape-specific selection of species for JEV surveillance.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- A typology of Australian terrestrial bird communities 94%
- Identifying conservation priorities in a defaunated tropical biodiversity hotspot 93%
- Metrics for conservation success: using the 'Bird-Friendliness Index' to evaluate grassland and aridland bird community resilience across the Northern Great Plains ecosystem 93%
Similar papers in this journal
Similar papers in this journal
- Predicting fine-scale distributions and emergent spatiotemporal patterns from temporally dynamic step selection simulations 93%
- Bayesian species distribution models integrate presence-only and presence-absence data to predict deer distribution and relative abundance. 93%
- Inferring wildlife poaching in Southeast Asia with multispecies dynamic site-occupancy models 92%
Similar papers in this journal
- Diverging effects of global change on future invasion risks of Agama picticauda between invaded regions: same problem, different solutions 92%
- Mapping differences in mammalian distributions and diversity using environmental DNA from rivers 92%
- Wildlife is imperiled in peri-urban landscapes: threats to arboreal mammals 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.