Predicting the potential global distribution of &lt;i&gt;Pseudomonas syringae&lt;/i&gt; pv &lt;i&gt;actinidiae&lt;/i&gt; (Psa)

Khandan, H.A. Narouei; Worner, Susan P.; Jones, E. Eirian; Villjanen-Rollinson, S.L.H.; Gallipoli, L.; Mazzaglia, A.; Balestra, Giorgio Mariano

doi:10.30843/nzpp.2013.66.5601

Cited by 11 publications

(11 citation statements)

References 30 publications

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“…Meanwhile, suitability detected in southern provinces (e.g. Yunnan, Guangxi) in this study is also consistent with reports of Khandan et al (). However, the present findings of the overall contraction of suitable habitats for Psa and their spatial shift are not completely consistent with the MaxEnt predictions of Wang et al ().…”

Section: Discussionsupporting

confidence: 93%

“…The detected suitability for Psa in most parts of Yunnan and patches in westernmost Tibet, where infection of this pathogen has been recently reported as a potential threat to local kiwifruit production (Yan et al , ), further confirms the high reliability of the model. When using this ensemble model for future climate projections, the findings of unsuitability in the Jing‐jin‐Ji area agree with results of Khandan et al (), for which Hebei and Beijing were projected as unsuitable for Psa by CLIMEX and MaxEnt models. Meanwhile, suitability detected in southern provinces (e.g.…”

Section: Discussionsupporting

confidence: 86%

“…These models use known species distributions in conjunction with a set of environmental variables to develop a correlative model of the environmental conditions that meet a species’ ecological requirements (Phillips & Dudík, ). The most well‐known modelling approaches, for instance, maximum entropy (MaxEnt) and CLIMEX, have been successfully used for assessing the potential distribution of some plant pathogens (Khandan et al , ; Wang et al , ).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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.

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Section: Discussionsupporting

confidence: 93%

Section: Discussionsupporting

confidence: 86%

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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“…CLIMEX is considered a semimechanistic model, with moderate complexity, that estimates the potential geographical distribution of a species and its response to climate based on long‐term climate data (Sutherst et al , ; Khandan et al , ; Kriticos et al , ). The process involves matching the species distribution and climate pattern using a calibration method rather than a statistical fitting process.…”

Section: Methodsmentioning

confidence: 99%

Projecting the suitability of global and local habitats for myrtle rust (Austropuccinia psidii) using model consensus

et al. 2019

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Myrtle rust (caused by Austropuccinia psidii) affects more than 500 known host species in the Myrtaceae family. Three different modelling approaches (CLIMEX, MaxEnt and Multi‐Model Framework) were used to project the habitat suitability for myrtle rust at both global and local scales. Current data on the global occurrence of myrtle rust were collected from online literature and expert solicitation. Long‐term averages of climate data (1960–1990) were sourced from WorldClim and CliMond websites. Recent reports of myrtle rust in New Zealand were used for validation of model outputs but not in model training and testing. The model outputs were combined into a consensus model to identify localities projected to be suitable for myrtle rust according to two or three models (hotspots). In addition to the locations where the pathogen is currently present, all models successfully projected independent occurrence data in New Zealand suitable for establishment of the pathogen. Climate suitability for the pathogen was primarily related to temperature followed by rainfall in MaxEnt and the CLIMEX model. The results confirmed the optimum temperature range of this pathogen in the literature (15–25 °C). Additional analysis of the precipitation variables indicated that excessive rain (more than 2000 mm in warmest quarter of the year) combined with high temperatures (>30 °C) constrain pathogen establishment. The results of the current study can be useful for countries such as New Zealand, China, South Africa and Singapore where the pathogen has not fully spread or established.

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“…actinidae (Psa), responsible for bacterial canker of kiwifruit, has compromised the viability of this production on a global scale [1][2][3][4][5][6]. The pathogen is highly virulent, spreading quickly between plants of different cultivars and vines of different ages [5][6][7][8]. For example, in Italy, between 2010 and 2012, more than 2000 ha of kiwifruit orchards have been either destroyed by the disease or cut down, causing severe economic losses for growers, satellite industries, and other stakeholders of the kiwifruit production chain [9].…”

Section: Introductionmentioning

confidence: 99%

Pseudomonas syringae pv. actinidiae: Ecology, Infection Dynamics and Disease Epidemiology

et al. 2020

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Since 2008, the kiwifruit industry has been devastated by a pandemic outbreak of Pseudomonas syringae pv. actinidiae (Psa), the causal agent of bacterial canker. This disease has become the most significant limiting factor in kiwifruit production. Psa colonizes different organs of the host plant, causing a specific symptomatology on each of them. In addition, the systemic invasion of the plant may quickly lead to plant death. Despite the massive risk that this disease poses to the kiwifruit industry, studies focusing on Psa ecology have been sporadic, and a comprehensive description of the disease epidemiology is still missing. Optimal environmental conditions for infection, dispersal and survival in the environment, or the mechanisms of penetration and colonization of host tissues have not been fully elucidated yet. The present work aims to provide a synthesis of the current knowledge, and a deeper understanding of the epidemiology of kiwifruit bacterial canker based on new experimental data. The pathogen may survive in the environment or overwinter in dormant tissues and be dispersed by wind or rain. Psa was observed in association with several plant structures (stomata, trichomes, lenticels) and wounds, which could represent entry points for apoplast infection. Environmental conditions also affect the bacterial colonization, with lower optimum values of temperature and humidity for epiphytic than for endophytic growth, and disease incidence requiring a combination of mild temperature and leaf wetness. By providing information on Psa ecology, these data sets may contribute to plan efficient control strategies for kiwifruit bacterial canker. Keywords Actinidia chinensis Planch. var. chinensis. Actinidia chinensis var. deliciosa (A.Chev.). Temperature. Relative humidity. Confocal laser scanning microscopy (CLSM). Overwintering

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Predicting the potential global distribution of <i>Pseudomonas syringae</i> pv <i>actinidiae</i> (Psa)

Cited by 11 publications

References 30 publications

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

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

Projecting the suitability of global and local habitats for myrtle rust (Austropuccinia psidii) using model consensus

Pseudomonas syringae pv. actinidiae: Ecology, Infection Dynamics and Disease Epidemiology

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