Predicting the potential distribution of geographically-limited species, Apsylla cistellata Buckton (Psyllidae: Hemiptera) on mango (Mangifera indica) under different climate change scenarios

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

doi:10.1007/s42690-020-00198-5

Cited by 3 publications

(1 citation statement)

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“…MaxEnt has proved to provide models with acceptable predictive ability even when few presence records are available (Peterson 2011;Elith et al 2006;Hernandez et al 2006). Species distribution models have many applications, such as mapping and monitoring of rare and endangered species (Edvardsen et al 2011), assessment of the impact of climate change on the niche expansion of the geographically limited species (Baradevanal et al 2020), and suitability assessment and risk zoning of quarantine pest (Choudhary et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Assessing the risk of mango quarantine pest Deanolis sublimbalis Snellen under different climate change scenarios

et al. 2021

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Considering the quarantine importance of the red-banded mango caterpillar, Deanolis sublimbalis (Crambidae: Lepidoptera), studies were carried out to predict the impact of climate change on its geographical distribution using the ecological niche modeling. Predictions were made based on the analysis of the relationship between occurrence points of D. sublimbalis and the corresponding current and future climate data of the study area, which was retrieved from the worldclim database. Spatial analysis software DIVA-GIS was used for visualization of the maps. The maximum entropy algorithm provided reasonable estimates of the species range in respect of discrimination of suitable and unsuitable areas for its occurrence in both present and future climatic conditions. The model provided a good fit for species distribution with a high value of area under the curve (0.971). Jackknife test indicated temperature seasonality to be the most important bioclimatic variable determining the potential geographical distribution of D. sublimbalis. The model predicted higher suitability areas for the pest occurrence in eastern parts of Andhra Pradesh, coastal regions of Orissa, southern parts of West Bengal, and some parts of Tripura. In future climate scenarios of 2030, 2050, 2070, and 2080, model-predicted relative increase in its distribution. Prediction of likely changes in the pest distribution with climate change will be useful in formulating effective management strategies against mango fruit borer.

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