MaxEnt prediction for potential risk of mango wilt caused by Ceratocystis fimbriata Ellis and Halst under different climate change scenarios in India

Shukla, Prabhat Kumar; Baradevanal, Gundappa; Rajan, Shailendra; Fatima, Tahseen

doi:10.1007/s42161-020-00502-9

Cited by 10 publications

(4 citation statements)

References 13 publications

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“…Sin embargo, pueden realizarse otros estudios con la base de datos disponibles en Worldclim. El programa Worldclim permite predecir la evolución de una enfermedad frente al cambio climático (Kumar Shukla et al, 2020). Este estudio podría realizarse para el TFC y la MPS, sin embargo, el sentido de este análisis es relativo y no sería representativo; en el futuro no se sabe cuál será la constitución genética de los cultivares de soja que se siembren en la Argentina.…”

Section: Discussionunclassified

Soybean (Glycine max) Purple Syndrome and its relationship with bioclimatic variables

Lavilla,

Ivancovich,

Díaz Paleo

2023

Agron. Mesoam.

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Introduction. Cercospora leaf blight (TFC) and purple seed stain (MPS) are two endemic soybean (Glycine max) diseases in Argentina. Objective. Select the bioclimatic variables (VB) related to severity values greater than or equal to 90 % of the TFC and/or incidence greater than or equal to 50 % of the MPS in Argentina. Materials and methods. In the soybean region of Argentina during the years 2015 and 2023, 45 VB available in Worldclim (temperatures, precipitations and radiation) were used for modeling with the MaxEnt program. From the maps obtained in the modeling, the probabilities of a severity (SEV) of TFC ≥ 90 % and/or an incidence (I) of MPS ≥ 50 % were extracted for each geographical point evaluated in this experience, to later use them in the analysis. generation of multiple linear regressions. Results. Of the 45 VB analyzed, the ones that were most related to both diseases were rainfall and temperatures. Radiation was the VB with the least association both with the SEV of the TFC and with the I of the MPS Conclusion. The bioclimatic variables temperatures (between 25 °C and 30 °C) and rainfall between the months of December to April had the greatest associations with severity values greater than or equal to 90 % of the TFC and/or incidence greater than or equal to 50 % of the MPS in Argentina.

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Section: Discussionunclassified

Soybean (Glycine max) Purple Syndrome and its relationship with bioclimatic variables

Lavilla,

Ivancovich,

Díaz Paleo

2023

Agron. Mesoam.

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“…A species distribution model mainly uses georeferenced occurrence records of species and the related environmental data to estimate the ecological position of species, and these positions are projected onto the landscape according to the specific algorithm. The modeling results can be explained as the probability of species occurrence, habitat suitability, or species richness [ 33 , 34 , 35 ]. The geographical distribution of species and species diversity and richness depend on whether people understand their niches.…”

Section: Methodsmentioning

confidence: 99%

Predicting the Suitable Geographical Distribution of Sinadoxa Corydalifolia under Different Climate Change Scenarios in the Three-River Region Using the MaxEnt Model

Huang

Chen

et al. 2020

Plants

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Sinadoxa corydalifolia is a perennial grass with considerable academic value as a rare species owing to habitat destruction and a narrow distribution. However, its distribution remains unclear. In this study, we predicted the distribution of Sinadoxa corydalifolia in the three-river region (the source of the Yangtze River, Yellow River, and Lancang River) under the context of climate change using the maximum entropy (MaxEnt) model. Under the current climate scenario, the suitable distribution mainly occurred in Yushu County and Nangqian County. The suitable distribution area of Sinadoxa corydalifolia covered 3107 km2, accounting for 0.57% of the three-river region. The mean diurnal air temperature range (Bio2), temperature seasonality (Bio4), and mean air temperature of the driest quarter (Bio9) contributed the most to the distribution model for Sinadoxa corydalifolia, with a cumulative contribution of 81.4%. The highest suitability occurred when air temperature seasonality (Bio4) ranged from 6500 to 6900. The highest suitable mean air temperature of the driest quarter ranged from −5 to 0 °C. The highest suitable mean diurnal temperature (Bio2) ranged from 8.9 to 9.7 °C. In future (2041–2060) scenarios, the suitable distribution areas of Sinadoxa corydalifolia from high to low are as follows: representative concentration pathway (RCP)26 (6171 km2) > RCP45 (6017 km2) > RCP80 (4238 km2) > RCP60 (2505 km2). In future (2061–2080) scenarios, the suitable distribution areas of Sinadoxa corydalifolia from high to low are as follows: RCP26 (18,299 km2) > RCP60 (11,977 km2) > RCP45 (10,354 km2) > RCP80 (7539 km2). In general, the suitable distribution will increase in the future. The distribution area of Sinadoxa corydalifolia will generally be larger under low CO2 concentrations than under high CO2 concentrations. This study will facilitate the development of appropriate conservation measures for Sinadoxa corydalifolia in the three-river region.

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“…The program integrates machine learning and maximum entropy principles to simulate the potential geographical distribution of species (Elith et al, 2011). Compared with other SDMs, MaxEnt uses presence‐only data, and has been shown to remain effective even when occurrences are low (Blank & Blaustein, 2012; Costa et al, 2010; Hernández‐Baz et al, 2016; Papeş & Gaubert, 2007); numerous studies have used it to model the distribution of various pathogens (Bebber, 2015; Bie et al, 2021; Shukla et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Potential distribution of the citrus Mal Secco disease in the Mediterranean basin under current and future climate conditions

et al. 2022

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Species distribution models are widely used to estimate the potential geographic distribution of species habitat. They are also used to predict the impact of climate change on species distributions. In this study, we examined the distribution of Mal Secco disease, an infective disease of citrus caused by the fungus Plenodomus tracheiphilus. To model the Mal Secco distribution under current and two future (2050 and 2070) climatic scenarios in the Mediterranean basin, eight climate variables were incorporated into the Maximum Entropy (MaxEnt) model. All three model distributions had high AUC values (0.97), indicating excellent performance. The precipitation during the wettest month and the minimum temperature during the coldest month contributed the most to the model. The suitable areas for the Mal Secco disease were predicted to decrease by up to 23% by the year 2070. For each of the three scenarios, the change in the proportion of suitable areas along the longitudinal (west–east) and latitudinal (south–north) geographical axes of the Mediterranean basin showed a similar trend. Our study highlights the supposition that while climate change is likely to reduce the extent of suitable areas, no range shift is expected to occur in the Mediterranean basin.

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MaxEnt prediction for potential risk of mango wilt caused by Ceratocystis fimbriata Ellis and Halst under different climate change scenarios in India

Cited by 10 publications

References 13 publications

Soybean (Glycine max) Purple Syndrome and its relationship with bioclimatic variables

Soybean (Glycine max) Purple Syndrome and its relationship with bioclimatic variables

Predicting the Suitable Geographical Distribution of Sinadoxa Corydalifolia under Different Climate Change Scenarios in the Three-River Region Using the MaxEnt Model

Potential distribution of the citrus Mal Secco disease in the Mediterranean basin under current and future climate conditions

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