Anthropogenic modifications to the landscape have altered several ecological processes worldwide, creating new ecological boundaries at the human/wildlife interface. Outbreaks of zoonotic pathogens often occur at these ecological boundaries, but the mechanisms behind new emergences remain drastically understudied. Here, we test for the influence of two types of ecosystem boundaries on spillover risk: (1) biotic transition zones such as species range edges and transitions between ecoregions and (2) land use transition zones where wild landscapes occur in close proximity to heavily impacted areas of high human population density. Using ebolavirus as a model system and an ensemble machine learning modeling framework, we investigated the role of likely reservoir (bats) and accidental host (primates) range edges and patterns of land use (defined using SEDAC categories) on past spillover events. Our results show that overlapping species range edges and heightened habitat diversity increase ebolavirus outbreaks risk. Moreover, we show that gradual transition zones, represent by high proportion of rangelands, acts as a buffer to reduces outbreak risks. With increasing landscape changes worldwide, we provide novel ecological and evolutionary insights into our understanding of zoonotic pathogen emergence and highlight the risk of aggressively developing ecological boundaries.
Using ebolavirus as a model system and an ensemble machine learning modeling framework, we investigated the role of likely reservoir (bats) and accidental host (primates) range edges and patterns of land use on past spillover events. Our results show that overlapping species range edges and heightened habitat diversity increase ebolavirus outbreak risk.
Method
ensemble machine learning modeling
Reservoir EcologyExtraction confidence 0.90
Key finding
Gradual transition zones with high proportions of rangelands reduced ebolavirus outbreak risk, acting as an ecological buffer.
Our results show that overlapping species range edges and heightened habitat diversity increase ebolavirus outbreak risk. Moreover, we show that gradual transition zones, represent by high proportion of rangelands, acts as a buffer to reduces outbreak risks.
Method
ensemble machine learning modeling
Zoonotic Surveillance2 records
Zoonotic SurveillanceExtraction confidence 0.60
Key finding
Modeling of bats and primates range edges revealed ecological boundary effects influencing ebolavirus spillover risk.
Using ebolavirus as a model system and an ensemble machine learning modeling framework, we investigated the role of likely reservoir (bats) and accidental host (primates) range edges and patterns of land use on past spillover events.
Method
machine learning modeling
Zoonotic SurveillanceExtraction confidence 0.60
Key finding
Primate range edges were included as accidental host boundaries potentially influencing ebolavirus spillover occurrence.
Using ebolavirus as a model system and an ensemble machine learning modeling framework, we investigated the role of likely reservoir (bats) and accidental host (primates) range edges and patterns of land use on past spillover events.
Method
machine learning modeling
Spillover Event1 records
Spillover EventExtraction confidence 0.95
Key finding
Ebolavirus spillover events have occurred from bat reservoirs and accidental primate hosts to humans at ecological boundaries.
Using ebolavirus as a model system and an ensemble machine learning modeling framework, we investigated the role of likely reservoir (bats) and accidental host (primates) range edges and patterns of land use on past spillover events.
Method
ensemble machine learning modeling
Study design
modeling study
Transmission direction
animal-to-human
Citation context
References
66 references
Reference network
Force-directed citation graph. OmniVira-indexed references are prioritized and recursively expanded up to three steps.
Where boundaries become bridges: Mosquito community composition, key vectors, and environmental associations at forest edges in the central Brazilian Amazon.
