Influence of climatic factors on the potential distribution of pest Heortia vitessoides Moore in China

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

“…Due to the influence of the East Asian monsoon and the westerly circulation, drought and rainfall in China also change with the season and region (Qian et al., 2009). Moreover, environmental humidity has a very important influence on the occurrence period, amount and range of agricultural pests (Xu et al., 2020). This is consistent with the result that bio13 and prec9 are the environmental variables with one of the greatest influences on the distribution of O. rhinoceros .…”

Niche Modeling May Explain the Historical Population Failure of Phytoseiulus persimilis in Taiwan: Implications of Biocontrol Strategies

“…These variables were derived from monthly temperature and rainfall values and represented annual trends, seasonality, and extreme or restrictive environmental factors ( Table S2 ). Bioclimatic variables are related to the distribution and survival of small arthropods and have been widely used in global studies concerning the prediction of species distribution [ 14 , 40 ]. We extracted data from the WorldClim database using RStudio (version 2.1) with R language ‘raster’ [ 41 ] and ‘rgdal’ [ 42 ] packages.…”

“…The maximum entropy method (MaxEnt), a machine learning approach, makes predictions of species distributions by analyzing species–environment relationships through the use of presence-only data and environmental variables [ 10 ]. As only presence data are required, this method has multiple applications, including the determination of suitable habitats for conservative species [ 10 ], predicting the distribution of invasive species [ 13 ], and modeling distribution shifts caused by climatic changes [ 14 ]. In these studies, the distribution of invasive pests in non-invaded areas was predicted using global presence-only data [ 15 , 16 ].…”

Origin and Potential Expansion of the Invasive Longan Lanternfly, Pyrops candelaria (Hemiptera: Fulgoridae) in Taiwan

“…A total of 19 environmental variables (period: 1970-2000) were collected from World-Clim (https://www.worldclim.org, accessed on 1 April 2021) [34,35] at a spatial resolution of 30 arc-seconds (1 km 2 ). These bioclimatic variables were derived from temperature and precipitation (Table S3), which are considered to be related to the distribution and survival of small arthropods; therefore, they have been widely used in the prediction of species distribution [13,36]. Data were retrieved from the WorldClim database using RStudio (version 2.1) with R language 'raster' [37] and 'rgdal' [38] packages.…”

“…Moreover, the maximum entropy method (MaxEnt), a machine learning approach, has been widely used to explore species niches by analyzing species-environment relationships using presence-only data and environmental variables [10]. As only presence data are required, this method has multiple applications, including determining suitable habitats for protected species [10], predicting the distribution of invasive species [11], studying the establishment of invasive natural enemies [12] and modeling distribution shifts caused by climatic changes [13].…”