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Schizotetranychus hindustanicus Hirst (Acari: Tetranychidae) known as the Hindustan citrus mite, is a quarantine pest present in Roraima, Brazil. In 1924 this pest was described in India. It was reported in 2002 in Venezuela and in Roraima in 2008. In 2010, the Hindustan citrus mite was reported in Colombia. It is possible that it will be introduced in other areas of Brazil, resulting in a threat to Brazilian citrus industry. Our objective was to determine the most suitable regions of Brazil for S. hindustanicus using a maximum entropy (Maxent) algorithm, based on native and invasive updated occurrence records from published research, field surveys and online databases. To avoid overfitting and improving transferability, we chose parameter settings of Maxent to construct and validate models by searching for the best combination of feature classes and regularization multipliers. The model obtained showed excellent performance according to all evaluation metrics used. A high potential for the establishment of S. hindustanicus was identified in large areas of Roraima, the extreme west of Amazonas, the entire north of the State of Pará, also in northeast, south, east, and north of the State of Amapá, and in a small portion northwest of the State of Maranhão (all states belonging to the northern region of Brazil). Our results provide information for policy making and quarantine measures, especially where S. hindustanicus is still absent in Brazil., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Background: Raoiella indica Hirst (Acari: Tenuipalpidae) is the most critical coconut and banana pest recently introduced in Brazil. Once the mite pests are introduced, it is essential to understand their dynamics in important crops under open-field climatic conditions to implement strategies for their management and determine the periods when species populations may increase in the field. Modelling tools have been used to determine the potential distribution of species and implications for the management of invasive species. Thus, our aim in this study was to determine the seasonal variation in R. indica and the influence of the monthly climate using CLIMEX modelling. We adjusted the CLIMEX model for R. indica based on distribution data, additional biological characteristics, and fluctuations in the R. indica population in a commercial coconut plantation., Results: The model for the current climate shows a good match between the ecoclimatic index and the global distribution of R. indica. The model results demonstrate that most states of Brazil and several regions worldwide include areas with highly suitable climatic conditions for R. indica. We observed variations in the density of R. indica in commercial coconut crops, with the highest incidence occurring during the first months of the year., Conclusion: Our results showed different alterations in seasonal suitability for R. indica that may provide information for the implementation of methods for time management, such as strategies for sampling and control during periods with a high degree of suitability for R. indica. © 2020 Society of Chemical Industry., (© 2020 Society of Chemical Industry.)

Among pests that have recently been introduced into the Americas, the red palm mite, Raoiella indica Hirst (Prostigmata: Tenuipalpidae), is the most invasive. This mite has spread rapidly to several Caribbean countries, United States of America, Mexico, Venezuela, Colombia and Brazil. The potential dispersion of R. indica to other regions of South America could seriously impact the cultivation of coconuts, bananas, exotic and native palms and tropical flowers such as the Heliconiaceae. To facilitate the development of efficacious R. indica management techniques such as the adoption of phytosanitary measures to prevent or delay the dispersion of this pest, the objective of this paper was to estimate the potential geographical distribution of R. indica in South America using a maximum entropy model. The R. indica occurrence data used in this model were obtained from extant literature, online databases and field sampling data. The model predicted potential suitable areas for R. indica in northern Colombia, central and northern Venezuela, Guyana, Suriname, east French Guiana and many parts of Brazil, including Roraima, the eastern Amazonas, northern Pará, Amapá and the coastal zones, from Pará to north of Rio de Janeiro. These results indicate the potential for significant R. indica related economic and social impacts in all of these countries, particularly in Brazil, because the suitable habitat regions overlap with agricultural areas for R. indica host plants such as coconuts and bananas.

BACKGROUND Raoiella indica Hirst (Acari: Tenuipalpidae) is the most critical coconut and banana pest recently introduced in Brazil. Once the mite pests are introduced, it is essential to understand their dynamics in important crops under open‐field climatic conditions to implement strategies for their management and determine the periods when species populations may increase in the field. Modelling tools have been used to determine the potential distribution of species and implications for the management of invasive species. Thus, our aim in this study was to determine the seasonal variation in R. indica and the influence of the monthly climate using CLIMEX modelling. We adjusted the CLIMEX model for R. indica based on distribution data, additional biological characteristics, and fluctuations in the R. indica population in a commercial coconut plantation. RESULTS: The model for the current climate shows a good match between the ecoclimatic index and the global distribution of R. indica. The model results demonstrate that most states of Brazil and several regions worldwide include areas with highly suitable climatic conditions for R. indica. We observed variations in the density of R. indica in commercial coconut crops, with the highest incidence occurring during the first months of the year. CONCLUSION: Our results showed different alterations in seasonal suitability for R. indica that may provide information for the implementation of methods for time management, such as strategies for sampling and control during periods with a high degree of suitability for R. indica. © 2020 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

• We assessed how the size of the study area impacts the distribution of Raoiella indica. • The size of the area used during model training significantly influences the model outcomes. • Models developed using smaller study areas exhibited a higher number of false-negative predictions. • The extent of the study area is crucial when developing models for invasive species. Ecological niche models are used to quantify the relationships between known occurrence records of a given species and environmental variables at these locations. Maxent is among the most widely used algorithms for modeling species distribution and has demonstrated better performance compared to other methods. However, the extent of the study area is a critical issue in the development of presence-only species distribution models because it encompasses the region used to extract the background points employed to characterize the environments accessible to the species. Thus, this study evaluated the effect of the extension of the study area on the species distribution modeling with the Maxent algorithm and occurrence data from the invasive species Raoiella indica Hirst (Acari: Tenuipalpidae). The increase in the study area extent inflated most of the threshold-dependent and -independent metrics used to assess model performance. The selection of the study area also affected the predicted suitable areas for the species (its potential distribution). The analysis shows that models developed with smaller study areas resulted in model overfitting and an increase in false-negative predictions. The extent of the area used during model training has a strong influence on the model outputs, with significant consequences for predicting the potential distribution of invasive species and thus for the areas under risk of invasion. [ABSTRACT FROM AUTHOR]

The red palm mite, Raoiella indica Hirst, is the most invasive pest, having spread quickly to several countries. In Brazil it is already present in several regions and has a high potential for dispersion to other regions, which could severely affect the cultivation of coconuts, bananas, native and exotic palm trees, and tropical flowers, such as those of the Heliconiaceae family. Thus, we aimed to determine the regions of Brazil most prone to the occurrence of R. indica using Maxent with updated data on its occurrence and a selection of bioclimatic variables that may influence its establishment. The occurrence data used were obtained through a literature search, online databases, and surveys carried out in the field. The model indicated a high potential for the establishment of R. indica in the following locations: a large part of the State of Roraima, the eastern part of the State of Amazonas, the northern and central-western parts of the State of Pará, and parts of the states of Acre and Amapá (North Region); the coast of Brazil from Rio Grande do Norte to the states of Paraíba, Pernanbuco, Alagoas, Sergipe and Bahia (Northeast Region); the states of Rio de Janeiro and Espírito Santo, the far east of Minas Gerais, and almost the entire state of São Paulo, except the northern part (Southeast Region); most of the State of Mato Grosso do Sul and the most southern portion of the State of Mato Grosso (Midwest Region); and the northern part of the State of Paraná and small parts of the states of Santa Catarina and Rio Grande do Sul (South Region). The bioclimatic variables that most influenced the potential geographical distribution of R. indica were precipitation of driest month (Bio14), precipitation of wettest month (Bio13), mean diurnal range (Bio2), and isothermality (Bio3). The parameterization for the Maxent model provides an updated model to prevention invasion of R. indica due updated data on its occurrence used. Thus, potential geographic distribution of R. indica in Brazil should be consider from this study. [ABSTRACT FROM AUTHOR]