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

Abstract

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.

Predicting disease occurrence of cabbage Verticillium wilt in monoculture using species distribution modeling

“…It appeared that many grids where disease occurrence was recorded had a high probability. However, the AUC, CCI, sensitivity, specificity and TSS obtained from our analysis were moderate compared with other studies (Wang et al, 2018;Narouei-Khandan et al, 2020). The sensitivity of the model should be regarded as the most important for our objective because we hope use it to monitor grids with a high disease occurrence probability.…”

“…Recently, MaxEnt has been used to predict plant disease distribution, such as Fusarium dry root rot in common beans, Phomopsis vaccinii in Vaccinium species, myrtle rust in Myrtaceae family and Pseudomonas syringae pv. actinidiae in kiwifruit (Cunniffe et al, 2016;Macedo et al, 2017;Narouei-Khandan et al, 2017;Berthon et al, 2018;Wang et al, 2018;Narouei-Khandan et al, 2020). In the present study, we used MaxEnt to predict the occurrence probability of Verticillium wilt, a soil-borne disease, as a case study on cabbage field in Japan.…”

The Climate Change Alliance: botanic garden horticulturists as agents for change

“…The warming and drying effects resulting from climate change can increase the frequency and severity of fires (Jones et al, 2020), and so in turn increase the risk they present to plant diversity. These intensifying conditions can also impact recruitment cycles and vegetation succession even in fire-adapted vegetation (Bowman et al, 2014).…”

“…It is not clear whether climate change will increase the severity of this disease in Victoria as it requires suitable conditions of leaf wetness, hours of darkness and optimum temperatures to infect (Makinson, 2018). However, the optimum temperature range of 15-25 °C for development of myrtle rust and some climate niche models (Berthon et al, 2018;Makinson, 2018;Narouei-Khandan et al, 2020) suggest that global warming may provide new environments or seasonal shifts that support the proliferation of this disease in Victoria.…”

Peer Review #3 of "Predicting disease occurrence of cabbage Verticillium wilt in monoculture using species distribution modeling (v0.1)"

“…Recently, MaxEnt has been used to predict plant disease distribution, such as Fusarium dry root rot in common beans, Phomopsis vaccinii in Vaccinium species, myrtle rust in Myrtaceae family and Pseudomonas syringae pv. actinidiae in kiwifruit (Cunniffe et al 2016;Macedo et al 2017;Narouei-Khandan et al 2017;Berthon et al 2018;Wang et al 2018;Narouei-Khandan et al 2020). In the present study, we used MaxEnt to predict the occurrence probability of Verticillium wilt, a soil-borne disease, as a case study on cabbage field in Japan.…”