Distribution models for Ascia monuste and the host Brassica oleracea var. capitata

Santos, Abraão Almeida; Hogendoorn, Katja; Ramos, Rodrigo Soares; Picanço, Marcelo Coutinho

doi:10.1111/jen.12675

Cited by 7 publications

(5 citation statements)

References 43 publications

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“…The model performance was evaluated using the area under the receiver‐operator curve (AUC). The AUC values vary from 0.5 to 1.0 with values closer to 1, indicating a higher accuracy of model prediction 39,40 . In addition to AUC, other evaluation indicators, true skill statistics (TSS) and the continuous Boyce index (CBI), were computed.…”

Section: Methodsmentioning

confidence: 99%

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Predicting the invasion risk of rugose spiraling whitefly, Aleurodicus rugioperculatus, in India based on CMIP6 projections by MaxEnt

Maruthadurai

Das

Ramesh

2022

Pest Management Science

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BACKGROUND Rugose spiraling whitefly (RSW), Aleurodicus rugioperculatus Martin, is a highly polyphagous invasive pest native to Central America. The occurrence of A. rugioperculatus in the Oriental region was first reported from Pollachi, Tamil Nadu, India in 2017. This pest is widely distributed in India, causing severe economic damage to coconut and other horticultural crops. It is a recent invasion in India and information on its potential distribution is lacking. Thus, in the present study we used the latest Coupled Model Intercomparison Project phase 6 (CMIP6) dataset through Maximum Entropy species distribution modelling (version 3.4.1, MaxEnt) to determine the potential distribution of RSW in present and future climate change scenarios in 2050 and 2070 under Shared Socioeconomic Pathway (SSP) 126 and SSP585 emission scenarios. The performance of the model was evaluated using the area under the curve (AUC), true skill statistics (TSS) and the continuous Boyce index (CBI). RESULTS The MaxEnt model performed well and predicted the potential distribution of A. rugioperculatus with high‐accuracy AUC values of 0.991 and 0.989, TSS values of 0.891 and 0.842, and CBI values of 0.972 and 0.934 for training and testing, respectively. Jackknife analysis revealed that A. rugioperculatus distribution was mostly influenced by temperature‐based bioclimatic variables contributing 62.1% of the suitability, with precipitation variables contributing the remainder. The most important bioclimatic variables for RSW distribution were annual mean temperature (Bio 1, 28.9%) followed by mean diurnal range (Bio 2, 19.5%) and annual precipitation (Bio 12, 19.1). Potential suitable areas for RSW establishment were mostly found in all coastal and southern states of India. A. rugioperculatus prefers a warm and humid climate, indicating that the tropics, subtropics and temperate regions are ideal for its spread and invasion. Our results highlighted that the suitable habitat area for A. rugioperculatus is predicted to increase and highest probability of invasion and spread in 2050 and 2070 under future climate change scenarios of SSP126 and SSP585 compared to present climatic conditions. CONCLUSIONS This is the first study to use the latest CMIP6 models and it predicts the potential distribution of RSW in India under present and future climate change scenarios. The implementation of strict domestic quarantine measures may prevent the spread and damage of RSW to noncoastal regions of India. The results of the current study should help in timely monitoring and surveillance of RSW and to formulate integrated pest management strategies at the national level to restrict its spread, invasion and damage to new areas. © 2022 Society of Chemical Industry.

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Section: Methodsmentioning

confidence: 99%

“…The AUC values vary from 0.5 to 1.0 with values closer to 1, indicating a higher accuracy of model prediction. 39,40 In addition to AUC, other evaluation indicators, true skill statistics (TSS) and the continuous Boyce index (CBI), were computed. Both TSS and CBI values vary between −1 and +1.…”

Section: Model Evaluation and Analysismentioning

confidence: 99%

Predicting the invasion risk of rugose spiraling whitefly, Aleurodicus rugioperculatus, in India based on CMIP6 projections by MaxEnt

Maruthadurai

Das

Ramesh

2022

Pest Management Science

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“…Theoretically, the value of the AUC ranges from 0.5 to 1. The closer the AUC value is to 1, the higher the accuracy of the model prediction (Santos et al., 2019). The AUC evaluation criteria are shown in Table 1.…”

Section: Methodsmentioning

confidence: 99%

Distribution and invasion risk assessment of Oryctes rhinoceros (L.) in China under changing climate

Zhuo

et al. 2022

J Applied Entomology

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“…2017; Santos et al . 2019). The species is considered an important pest of Brassica crops in the neotropics, responsible for losses of up to 100% (Alam 1992).…”

Section: Introductionmentioning

confidence: 99%

Season and weather affect the mortality of immature stages of Ascia monusteorseis (Lepidoptera: Pieridae) caused by natural factors

et al. 2020

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In this study, we evaluated the causes of mortality in immature stages of Ascia monuste orseis Godart 1918 (Lepidoptera: Pieridae) over each of four seasons, for 2 years, on an experimental cabbage plot using life tables. We addressed two key questions: (1) Do the mortality rates caused by a factor vary by season? And (2) how does the local weather contribute to such variation? We identified five causes of mortality (failure, pathogens, parasitism, predation and rainfall) that together tended to be higher during wet–warm conditions with values up to 94%. However, each cause and its mortality rate varied among seasons. Failure was observed in all stages and tended to increase during cold conditions with a negative relationship with air temperature and relative humidity and positive for precipitation. Larval and pupal predation increased during warm conditions and was positively associated with both air temperature and relative humidity. Conversely, in seasons with intense precipitation, pupal predation was reduced and had a negative relationship. Rainfall caused mortality of eggs and larvae, mostly during intense events, and egg mortality also showed a positive and significant association with precipitation. Larval parasitism was low, and pathogens were noted in larvae and pupae, but we did not find variation by season for these factors. Our results indicate that mortality caused by failure, predation and rainfall on immature stages of A. monuste orseis can vary among seasons and appear to be related to local weather. These findings improve our comprehension of how local weather affects the immature stages of a neotropical pest and the associated mortality factors.

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Distribution models for Ascia monuste and the host Brassica oleracea var. capitata

Cited by 7 publications

References 43 publications

Predicting the invasion risk of rugose spiraling whitefly, Aleurodicus rugioperculatus, in India based on CMIP6 projections by MaxEnt

Predicting the invasion risk of rugose spiraling whitefly, Aleurodicus rugioperculatus, in India based on CMIP6 projections by MaxEnt

Distribution and invasion risk assessment of Oryctes rhinoceros (L.) in China under changing climate

Season and weather affect the mortality of immature stages of Ascia monusteorseis (Lepidoptera: Pieridae) caused by natural factors

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