2016
DOI: 10.1007/s10530-016-1118-1
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Shedding light on the effects of climate change on the potential distribution of Xylella fastidiosa in the Mediterranean basin

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Cited by 130 publications
(116 citation statements)
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“…Nodes with limited spreading influence are predominantly in the northeast of Puglia, where orchards are sparser and less connected to the rest of the network. Coincidentally, climatic conditions in this area might also be less favourable for the disease 18–20 .…”
Section: Introductionmentioning
confidence: 99%
“…Nodes with limited spreading influence are predominantly in the northeast of Puglia, where orchards are sparser and less connected to the rest of the network. Coincidentally, climatic conditions in this area might also be less favourable for the disease 18–20 .…”
Section: Introductionmentioning
confidence: 99%
“…The suitable and unsuitable habitats or binary presence/absence maps for the two invasive species were defined by a 10th percentile training presence logistic threshold. This threshold has been widely applied in species distribution modeling, especially when data were collected by different collectors . In order to explain the reasonable simulated results of two invasive species, the potential distribution map was reclassified into four levels: < threshold, unsuitable habitat (no risk); threshold‐0.4, low habitat suitability (low risk); 0.4–0.6, moderate habitat suitability (medium risk); and 0.6–1, high habitat suitability (high risk).…”
Section: Methodsmentioning
confidence: 99%
“…This threshold has been widely applied in species distribution modeling, especially when data were collected by different collectors. 55 In order to explain the reasonable simulated results of two invasive species, the potential distribution map was reclassified into four levels: < threshold, unsuitable habitat (no risk); threshold-0.4, low habitat suitability (low risk); 0.4-0.6, moderate habitat suitability (medium risk); and 0.6-1, high habitat suitability (high risk). Finally, 10-fold cross-validation was used to run MaxEnt to prevent random errors from predicted samples.…”
Section: Modeling Approachmentioning
confidence: 99%
“…X. fastidiosa is a devastating factor for oil production in an Italian region (Apulia) in which it represents a major agricultural activity and income. Moreover, this economic threat could interest the whole Mediterranean area [5,6], in which the olive tree is among the predominant cultivated species. However, X. fastidiosa is not only a production-related menace, as is the case with P. infestans or many other pathogens.…”
Section: Olive Quick Decline Syndrome An Agroecosystem Threatmentioning
confidence: 99%
“…multiplex and fastidiosa to olive at low efficiency [14]. Predictive models of X. fastidiosa distribution in the Mediterranean basin were also proposed, suggesting that the pest may spread up to Central Italy [5] and overcome the current boundaries outside Italy [6].…”
Section: Disease Monitoring and Widespread Distribution Of Pathogensmentioning
confidence: 99%