Journal
PROCEEDINGS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES
Volume 280, Issue 1753, Pages -Publisher
ROYAL SOC
DOI: 10.1098/rspb.2012.2506
Keywords
global factors; emerging diseases; Batrachochytrium dendrobatidis; species distribution models; propagule pressure; trade
Categories
Funding
- National Natural Science Foundation of China [31200416, 31172111]
- Chinese Academy of Sciences [kscx2-yw-z-1021]
- US Department of Agriculture [NRI 2006-01370, 2009-35102-05043]
- US Environmental Protection Agency [STAR R833835, CAREER 83518801]
- Direct For Biological Sciences
- Division Of Environmental Biology [1241889] Funding Source: National Science Foundation
- NIFA [2009-35102-05043, 583481] Funding Source: Federal RePORTER
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Global factors, such as climate change, international trade and introductions of exotic species are often elicited as contributors to the unprecedented rate of disease emergence, but few studies have partitioned these factors for global pandemics. Although contemporary correlative species distribution models (SDMs) can be useful for predicting the spatial patterns of emerging diseases, they focus mainly on the fundamental niche (FN) predictors (i.e. abiotic climate and habitat factors), neglecting dispersal and propagule pressure predictors (PP, number of non-native individuals released into a region). Using a validated, predictive and global SDM, we show that both FN and PP accounted for significant, unique variation to the distribution of the chytrid fungus Batrachochytrium dendrobatidis (Bd), a pathogen implicated in the declines and extinctions of over 200 amphibian species worldwide. Bd was associated positively with vegetation, total trade and introduced amphibian hosts, nonlinearly with annual temperature range and non-significantly with amphibian leg trade or amphibian species richness. These findings provide a rare example where both FN and PP factors are predictive of a global pandemic. Our model should help guide management of this deadly pathogen and the development of other globally predictive models for species invasions and pathogen emergence influenced by FN and PP factors.
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