Literature DB >> 36261485

Determining the potential distribution of Oryctes monoceros and Oryctes rhinoceros by combining machine-learning with high-dimensional multidisciplinary environmental variables.

Owusu Fordjour Aidoo1, Fangyu Ding2,3, Tian Ma2,3, Dong Jiang2,3, Di Wang4,5, Mengmeng Hao6,7, Elizabeth Tettey8, Sebastian Andoh-Mensah8, Kodwo Dadzie Ninsin1, Christian Borgemeister9.   

Abstract

The African coconut beetle Oryctes monoceros and Asiatic rhinoceros beetle O. rhinoceros have been associated with economic losses to plantations worldwide. Despite the amount of effort put in determining the potential geographic extent of these pests, their environmental suitability maps have not yet been well established. Using MaxEnt model, the potential distribution of the pests has been defined on a global scale. The results show that large areas of the globe, important for production of palms, are suitable for and potentially susceptible to these pests. The main determinants for O. monoceros distribution were; temperature annual range, followed by land cover, and precipitation seasonality. The major determinants for O. rhinoceros were; temperature annual range, followed by precipitation of wettest month, and elevation. The area under the curve values of 0.976 and 0.975, and True skill statistic values of 0.90 and 0.88, were obtained for O. monoceros and O. rhinoceros, respectively. The global simulated areas for O. rhinoceros (1279.00 × 104 km2) were more than that of O. monoceros (610.72 × 104 km2). Our findings inform decision-making and the development of quarantine measures against the two most important pests of palms.
© 2022. The Author(s).

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Year:  2022        PMID: 36261485      PMCID: PMC9581929          DOI: 10.1038/s41598-022-21367-1

Source DB:  PubMed          Journal:  Sci Rep        ISSN: 2045-2322            Impact factor:   4.996


  27 in total

1.  Predicting the current and future distribution of the edible long-horned grasshopper Ruspolia differens (Serville) using temperature-dependent phenology models.

Authors:  Alfonce Leonard; James P Egonyu; Chrysantus M Tanga; Samuel Kyamanywa; Henri Z E Tonnang; Abdelmutalab G A Azrag; Fathiya M Khamis; Sunday Ekesi; Sevgan Subramanian
Journal:  J Therm Biol       Date:  2020-11-17       Impact factor: 2.902

2.  On estimating probability of presence from use-availability or presence-background data.

Authors:  Steven J Phillips; Jane Elith
Journal:  Ecology       Date:  2013-06       Impact factor: 5.499

3.  Modelling the effect of temperature on the biology and demographic parameters of the African coffee white stem borer, Monochamus leuconotus (Pascoe) (Coleoptera: Cerambycidae).

Authors:  Abdelmutalab G A Azrag; Abdullahi A Yusuf; Christian W W Pirk; Saliou Niassy; Ephantus K Guandaru; Guillaume David; Régis Babin
Journal:  J Therm Biol       Date:  2020-02-14       Impact factor: 2.902

4.  Assessing the impact of climate change on the worldwide distribution of Dalbulus maidis (DeLong) using MaxEnt.

Authors:  Paulo A Santana; Lalit Kumar; Ricardo S Da Silva; Jardel L Pereira; Marcelo C Picanço
Journal:  Pest Manag Sci       Date:  2019-04-16       Impact factor: 4.845

5.  Species-specific ecological niche modelling predicts different range contractions for Lutzomyia intermedia and a related vector of Leishmania braziliensis following climate change in South America.

Authors:  Shannon McIntyre; Elizabeth F Rangel; Paul D Ready; Bruno M Carvalho
Journal:  Parasit Vectors       Date:  2017-03-24       Impact factor: 3.876

6.  Predicting the potential distribution of the Asian citrus psyllid, Diaphorina citri (Kuwayama), in China using the MaxEnt model.

Authors:  Rulin Wang; Hua Yang; Wei Luo; Mingtian Wang; Xingli Lu; Tingting Huang; Jinpeng Zhao; Qing Li
Journal:  PeerJ       Date:  2019-07-15       Impact factor: 2.984

7.  Direct and indirect effects of environmental factors, spatial constraints, and functional traits on shaping the plant diversity of montane forests.

Authors:  Ting Li; Qinli Xiong; Peng Luo; Yubo Zhang; Xiaodong Gu; Bo Lin
Journal:  Ecol Evol       Date:  2019-12-15       Impact factor: 2.912

Review 8.  Can Biological Control Overcome the Threat From Newly Invasive Coconut Rhinoceros Beetle Populations (Coleoptera: Scarabaeidae)? A Review.

Authors:  Sulav Paudel; Sarah Mansfield; Laura F Villamizar; Trevor A Jackson; Sean D G Marshall
Journal:  Ann Entomol Soc Am       Date:  2021-02-03       Impact factor: 2.099

9.  Ecological niche modeling as an effective tool to predict the distribution of freshwater organisms: The case of the Sabaleta Brycon henni (Eigenmann, 1913).

Authors:  Daniel Valencia-Rodríguez; Luz Jiménez-Segura; Carlos A Rogéliz; Juan L Parra
Journal:  PLoS One       Date:  2021-03-03       Impact factor: 3.240

10.  Predictions of potential geographical distribution of Diaphorina citri (Kuwayama) in China under climate change scenarios.

Authors:  Rulin Wang; Hua Yang; Mingtian Wang; Zhe Zhang; Tingting Huang; Gang Wen; Qing Li
Journal:  Sci Rep       Date:  2020-06-08       Impact factor: 4.379

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