Invasion risk of the South American tomato pinworm Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae) in India: predictions based on MaxEnt ecological niche modelling

Fand, Babasaheb B.; Shashank, P. R.; Suroshe, Sachin S.; Chandrashekar, K.; Meshram, Naresh M.; Timmanna, H. N.

doi:10.1007/s42690-020-00103-0

Cited by 26 publications

(24 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Some of these variables were also found to have a significant contribution to predicting the occurrence of other invasive species such as the peach fruit fly, Bactrocera zonata (Saunders), 31,85 fall armyworm, Spodoptera frugiperda (J E Smith) 86 and tomato pinworm, Tuta absoluta (Meyrick). 87 Our results agreed with previous studies that showed temperature, rainfall, and relative humidity to have a significant effect on the population abundance of A. tubercularis under field conditions. 7,21,51,88 For example, Bautista-Rosales et al, 88 reported that temperature ranges between 18 and 28°C are the most favourable conditions in which a high population abundance occurs.…”

Section: Discussionsupporting

confidence: 93%

Predicting the habitat suitability of the invasive white mango scale,Aulacaspis tubercularis; Newstead, 1906 (Hemiptera: Diaspididae) using bioclimatic variables

Azrag

Mohamed

Ndlela

et al. 2022

Pest Management Science

View full text Add to dashboard Cite

BACKGROUND: The white mango scale, Aulacaspis tubercularis (Hemiptera: Diaspididae), is an invasive pest that threatens the production of several crops of commercial value including mango. Though it is an important pest, little is known about its biology and ecology. Specifically, information on habitat suitability of A. tubercularis occurrence and potential distribution under climate change is largely unknown. In this study, we used four ecological niche models, namely maximum entropy, random forest, generalized additive models, and classification and regression trees to predict the habitat suitability of A. tubercularis under current and future [representative concentration pathways (RCPs): RCP4.5 and RCP8.5 of the year 2070] climatic scenarios, using bioclimatic variables. Models' performance was evaluated using the true skill statistic (TSS), the area under the curve (AUC), correlation (COR), and the deviance. RESULTS: All models sufficiently predicted the occurrence of A. tubercularis with high accuracy (AUC ≥ 0.93, TSS ≥ 0.81 and COR ≥ 0.77). The random forest algorithm had the highest accuracy among the four models (AUC = 0.99, TSS = 0.93, COR = 0.90, deviance = 0.26). Temperature seasonality (Bio4), mean temperature of the driest quarter (Bio9), and precipitation seasonality (Bio15) were the most important variables influencing A. tubercularis occurrence. Models' predictions showed that countries in east, south, and west Africa are highly suitable for A. tubercularis establishment under current conditions. Similarly,

show abstract

Section: Discussionsupporting

confidence: 93%

Predicting the habitat suitability of the invasive white mango scale,Aulacaspis tubercularis; Newstead, 1906 (Hemiptera: Diaspididae) using bioclimatic variables

Azrag

Mohamed

Ndlela

et al. 2022

Pest Management Science

View full text Add to dashboard Cite

show abstract

“…In the era of climate change, forecasting of occurrence and spread of potential invasive pests is of considerable interest for better handling and management of invasive species ( Kumar et al, 2015 ; Fand et al, 2020 ). Ecological niche modelling (ENM) is a rapidly evolving field for estimating the distribution of species under different climate change scenarios ( Peterson, 2011 ).…”

Section: Introductionmentioning

confidence: 99%

Diagnosis and potential invasion risk of Thrips parvispinus under current and future climate change scenarios

Hulagappa

Baradevanal

Surpur

et al. 2022

PeerJ

View full text Add to dashboard Cite

Background and Objective Invasive thrips, Thrips parvispinus Karny recently reported in India, causing a widespread severe infestation in more than 0.4 million ha of chilli (Capsicum annum L.) growing areas. This species is native to Thailand and most prevalent in other South East Asian countries. Large scale cultivation of the major host plants (chilli and papaya), and favourable climatic conditions in India and other countries similar to native range of Thrips parvispinus expected to favour its further spread and establishment to new areas. Materials and Methods The present study was undertaken to confirm invasive thrips species identity through both morphological and molecular approaches and predict its potential invasion using the maximum entropy (MaxEnt) algorithm. Results The model predicted species range in respect of discrimination of suitable and unsuitable areas for its occurrence both in current and future climatic scenarios. The model provided a good fit for species distribution with a high value of area under the curve (0.957). The jackknife test indicated annual mean temperature and precipitation were found to be the most important bioclimatic variable in determining the distribution of T. parvispinus. High suitability areas were predicted in the countries wherever its occurrence was reported with high discrimination ability of suitable and unsuitable areas. Key distinguishing morphological characters of T. parvispinus were illustrated through high-resolution scanning electron microscopic images. Conclusion The identity of the thrips causing wide spread damage in chilli confirmed through morphological and molecular approaches. Key identifying characters were described through high resolution scanning electron microscopic images for accurate identification of the species. MaxEnt model identified high suitability regions for the potential establishment of T. parvispinus in India and other parts of the world. This study facilitates forecasting of further spread and also suggests imposing strict domestic quarantine measures to curtail its establishment in the new areas.

show abstract

“…Nilai AUC yang digunakan untuk menentukan kinerja model berada pada rentang nilai 0,5 -1, dimana pada nilai 0,5 -0,7 model diklasifikasikan sebagai kurang bagus, nilai 0,7 -0,8 diklasifikasikan sebagai bagus dan model yang memiliki nilai di atas 0,9 diklasifikasikan sebagai sangat bagus (Fand et al, 2020) dan (Kumbula et al, 2019).…”

Section: Metode Penelitianunclassified

Pemanfaatan Citra Sentinel 2 untuk Memprediksi Sebaran Hama Wereng Cokelat di Sebagian Wilayah Kabupaten Pangandaran

Perlambang

Suharyadi

Jatmiko

2021

SOSTECH

View full text Add to dashboard Cite

Hama wereng coklat merupakan salah satu hama yang paling sering menyerang tanaman padi, pendeteksian hama ini secara akan sangat membantu dalam pengendalian hama yang efektif. Penginderaan jauh merupakan suatu sistem yang dapat merekam perubahan yang terjadi di permukaan bumi, termasuk kerusakan yang disebabkan hama. Maxent merupakan salah satu pemodelan yang dapat menggambarkan sebaran suatu spesies, dengan pemodelan ini diharapkan mampu memprediksi sebaran hama mengunakan satelit penginderaan jauh sebagai input. Dari model yang dihasilkan, probabilitas tertinggi yang didapatkan model untuk memprediksi sebaran hama adalah sebesar 0,694 dengan kinerja model yang dipresentasikan sebagai area under curve (AUC) yang memiliki nilai 0,514

show abstract

Invasion risk of the South American tomato pinworm Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae) in India: predictions based on MaxEnt ecological niche modelling

Cited by 26 publications

References 35 publications

Predicting the habitat suitability of the invasive white mango scale,Aulacaspis tubercularis; Newstead, 1906 (Hemiptera: Diaspididae) using bioclimatic variables

Predicting the habitat suitability of the invasive white mango scale,Aulacaspis tubercularis; Newstead, 1906 (Hemiptera: Diaspididae) using bioclimatic variables

Diagnosis and potential invasion risk of Thrips parvispinus under current and future climate change scenarios

Pemanfaatan Citra Sentinel 2 untuk Memprediksi Sebaran Hama Wereng Cokelat di Sebagian Wilayah Kabupaten Pangandaran

Contact Info

Product

Resources

About