Predicting the potential distribution of Pseudomonas syringae pv. actinidiae in China using ensemble models

Pseudomonas syringae pv. actinidiae (Psa) is a causal agent of kiwifruit bacterial canker worldwide, which has affected kiwifruit vines in China since 1996 and has subsequently spread to the main cultivation areas. Based on occurrence of Psa and pseudo‐absences randomly generated in China, the conse...

Full description

Saved in:
Bibliographic Details
Published in:Plant pathology 2020-01, Vol.69 (1), p.120-131
Main Authors: Qin, Z., Zhang, J. E., Jiang, Y. P., Wang, R. L., Wu, R. S.
Format: Article
Language:English
Subjects:
Actinidia
Algorithms
BIOMOD2
Canker
China
climate change
Climatic conditions
Cultivation
Epidemics
General circulation models
Kiwifruit
kiwifruit host
Limiting factors
Model accuracy
Modelling
Pseudomonas
Pseudomonas syringae
Pseudomonas syringae pv. actinidiae
Shrinkage
Statistical analysis
Vines
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Pseudomonas syringae pv. actinidiae (Psa) is a causal agent of kiwifruit bacterial canker worldwide, which has affected kiwifruit vines in China since 1996 and has subsequently spread to the main cultivation areas. Based on occurrence of Psa and pseudo‐absences randomly generated in China, the consensus‐based modelling technique was used to estimate the spatial spread of Psa epidemics within China. Environmental variables that related to Psa development were identified, and their contributions to Psa development were evaluated. Three modelling algorithms, namely generalized boosting models (GBM), random forests (RF) and classification tree analysis (CTA) within the BIOMOD2 framework, were employed to construct the model. The ensemble models weighted by the true skill statistic (TSS) value were used to predict the current habitat suitability of Psa, and were projected using the four general circulation models (GCMs) to assess range shifts under two types of representative concentration pathways (RCP 4.5 and RCP 8.5) by 2050. The results indicated that precipitation in March and mean temperature of warmest quarter were the most important limiting factors for distribution of Psa. The predictive accuracy of the ensemble model showed acceptable predictive powers (TSS = 0.852). Under future climate conditions, substantial net loss of suitability for Psa was estimated to be 3.03–12.5% under RCP 4.5 (except one GCM), and 2.46–9.89% under RCP 8.5. Shrinkage of suitable habitats was detected mainly in the areas currently infected by Psa. Special attention should be given to recent infectious regions in south and southwest China, considering the locally expanding kiwifruit commercial plantations.
ISSN:0032-0862
1365-3059
DOI:10.1111/ppa.13112