Forecasting oak decline caused by Phytophthora cinnamomi in Andalusia: Identification of priority areas for intervention

Duque-Lazo, Joaquín; Navarro-Cerrillo, R.M.; Gils, Hein van; Groen, T.A.

doi:10.1016/j.foreco.2018.02.045

Cited by 50 publications

(37 citation statements)

References 51 publications

(84 reference statements)

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“…The link of oomycetes to differences in solar radiation is generally in line with previous findings (Joaquin Duque‐Lazo et al, ). However, in our dataset, higher solar radiation was detected in site 1, corresponding with higher levels of G. irregulare (OTU 1) but not P. cinnamomi (OTU 10).…”

Section: Discussionsupporting

confidence: 91%

“…Fungal endophytes also display clear altitudinal patterns for the major species detected (Goldmann et al, ; Osono & Hirose, ; Siddique & Unterseher, ). Similarly, altitude and oak cover were identified as the strongest predictor variables for the distribution of P. cinnamomi in Andalusia (Duque‐Lazo, Navarro‐Cerrillo, van Gils, & Groen, ) demonstrating the interplay between abiotic and biotic factors for oomycete dispersal.…”

Section: Discussionmentioning

confidence: 96%

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Site‐specific distribution of oak rhizosphere‐associated oomycetes revealed by cytochrome c oxidase subunit II metabarcoding

Sapp

Tyborski

Linstädter

et al. 2019

Ecology and Evolution

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The phylum Oomycota comprises important tree pathogens like Phytophthora quercina, involved in central European oak decline, and Phytophthora cinnamomi shown to affect holm oaks among many other hosts. Despite the importance to study the distribution, dispersal and niche partitioning of this phylum, metabarcoding surveys, and studies considering environmental factors that could explain oomycete community patterns are still rare. We investigated oomycetes in the rhizosphere of evergreen oaks in a Spanish oak woodland using metabarcoding based on Illumina sequencing of the taxonomic marker cytochrome c oxidase subunit II (cox2). We developed an approach amplifying a 333 bp long fragment using the forward primer Hud‐F (Mycologia, 2000) and a reverse primer found using DegePrime (Applied and Environmental Microbiology, 2014). Factors reflecting topo‐edaphic conditions and tree health were linked to oomycete community patterns. The majority of detected OTUs belonged to the Peronosporales. Most taxa were relatives of the Pythiaceae, but relatives of the Peronosporaceae and members of the Saprolegniales were also found. The most abundant OTUs were related to Globisporangium irregulare and P. cinnamomi, both displaying strong site‐specific patterns. Oomycete communities were strongly correlated with the environmental factors: altitude, crown foliation, slope and soil skeleton and soil nitrogen. Our findings illustrate the significance of small scale variation in habitat conditions for the distribution of oomycetes and highlight the importance to study oomycete communities in relation to such ecological patterns.

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Section: Discussionsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 96%

Site‐specific distribution of oak rhizosphere‐associated oomycetes revealed by cytochrome c oxidase subunit II metabarcoding

Sapp

Tyborski

Linstädter

et al. 2019

Ecology and Evolution

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“…Our ASF and wild boar models, each with two predictors show a high AUC value. This corroborates that predictor variables appropriately filtered from a large set of variables may produce good predictions (Duque‐Lazo et al., ; van Gils, Conti, Ciaschetti, & Westinga, ; Varela et al., ). We pioneered combining non‐spatial (PCA), spatial (backwards stepwise elimination and SD of the response curve) filtering methods of environmental variables with success, suggesting that both may be used to complement each other.…”

Section: Discussionsupporting

confidence: 75%

“…The response curves for the ASF and wild boar models are smoothly bell‐shaped with low SD suggesting a good transferability of the model (Duque‐Lazo, ). Our setting of the MaxEnt algorithm for linear and quadratic features only, instead of the default of five, may have contributed to the smooth curves (Elith et al., ), but smooth curves may be also achieved at the default setting (Duque‐Lazo et al., ).…”

Section: Discussionmentioning

confidence: 99%

“…Next, the least contributing predictors of the non‐collinear variables were eliminated stepwise until each variables contribute more than 10% to the model in MaxEnt (Elith & Leathwick, ; van Gils et al., ). Finally, we eliminated predictor variables with a high standard deviation (SD) (Duque‐Lazo, Navarro‐Cerrillo, van Gils, & Groen, ) based on visual observation of the response curves. Lastly, both prediction maps and the domestic pig distribution map were smoothed by taking the average probability value for each three‐by‐three pixel neighbourhood (van Gils et al., ).…”

Section: Methodsmentioning

confidence: 99%

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High probability areas for ASF infection in China along the Russian and Korean borders

Fekede

Gils

Huang³

et al. 2019

Transbound Emerg Dis

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Summary African swine fever (ASF) is a transcontinental, contagious, fatal virus disease of pig with devastating socioeconomic impacts. Interaction between infected wild boar and domestic pig may spread the virus. The disease is spreading fast from the west of Eurasia towards ASF‐free China. Consequently, prediction of the distribution of ASF along the Sino‐Russian‐Korean borders is urgent. Our area of interest is Northeast China. The reported ASF‐locations in 11 contiguous countries from the Baltic to the Russian Federation were extracted from the archive of the World Organization for Animal Health from July 19, 2007 to March 27, 2017. The locational records of the wild boar were obtained from literature. The environmental predictor variables were downloaded from the WorldClim website. Spatial rarefication and pair‐wise geographic distance comparison were applied to minimize spatial autocorrelation of presence points. Principal component analysis (PCA) was used to minimize multi‐collinearity among predictor variables. We selected the maximum entropy algorithm for spatial modelling of ASF and wild boar separately, combined the wild boar prediction with the domestic pig census in a single map of suids and overlaid the ASF with the suids map. The accuracy of the models was assessed by the AUC. PCA delivered five components accounting for 95.7% of the variance. Spatial autocorrelation was shown to be insignificant for both ASF and wild boar records. The spatial models showed high mean AUC (0.92 and 0.97) combined with low standard deviations (0.003 and 0.006) for ASF and wild boar, respectively. The overlay of the ASF and suids maps suggests that a relatively short sector of the Sino‐Russian border has a high probability entry point of ASF at current conditions. Two sectors of the Sino‐Korean border present an elevated risk.

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Spatial risk of Haemaphysalis longicornis borne Dabieshan tick virus (DBTV) in China

Han,

Gao,

Xin

et al. 2024

Journal of Medical Virology

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The uncertainty and unknowability of emerging infectious diseases have caused many major public health and security incidents in recent years. As a new tick‐borne disease, Dabieshan tick virus (DBTV) necessitate systematic epidemiological and spatial distribution analysis. In this study, tick samples from Liaoning Province were collected and used to evaluate distribution of DBTV in ticks. Outbreak points of DBTV and the records of the vector Haemaphysalis longicornis in China were collected and used to establish a prediction model using niche model combined with environmental factors. We found that H. longicornis and DBTV were widely distributed in Liaoning Province. The risk analysis results showed that the DBTV in the eastern provinces of China has a high risk, and the risk is greatly influenced by elevation, land cover, and meteorological factors. The risk geographical area predicted by the model is significantly larger than the detected positive areas, indicating that the etiological survey is seriously insufficient. This study provided molecular and important epidemiological evidence for etiological ecology of DBTV. The predicted high‐risk areas indicated the insufficient monitoring and risk evaluation and the necessity of future monitoring and control work.

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Forecasting oak decline caused by Phytophthora cinnamomi in Andalusia: Identification of priority areas for intervention

Cited by 50 publications

References 51 publications

Site‐specific distribution of oak rhizosphere‐associated oomycetes revealed by cytochrome c oxidase subunit II metabarcoding

Site‐specific distribution of oak rhizosphere‐associated oomycetes revealed by cytochrome c oxidase subunit II metabarcoding

High probability areas for ASF infection in China along the Russian and Korean borders

Spatial risk of Haemaphysalis longicornis borne Dabieshan tick virus (DBTV) in China

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