Projecting the Potential Global Distribution of Carpomya vesuviana (Diptera: Tephritidae), Considering Climate Change and Irrigation Patterns

Guo, Siwei; Ge, Xuezhen; Ya, Zou; Zhou, Yuting; Wang, Tao; Zong, Shixiang

doi:10.3390/f10040355

Cited by 13 publications

(11 citation statements)

References 15 publications

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“…Among several software packages such as BIOCLIM, CLIMEX and GARP [ 42 , 43 ], Maxent has been used to predict the current and future global distribution of the GWM. Meanwhile, Maxent’s artificial intelligence of maximum entropy is often regarded as the most widely used software for simulating species distributions using presence-only data [ 44 , 45 ].…”

Section: Methodsmentioning

confidence: 99%

Modeling the Potential Global Distribution of Honeybee Pest, Galleria mellonella under Changing Climate

Hosni

Al-Khalaf

Nasser

et al. 2022

Insects

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Beekeeping is essential for the global food supply, yet honeybee health and hive numbers are increasingly threatened by habitat alteration, climate change, agrochemical overuse, pathogens, diseases, and insect pests. However, pests and diseases that have unknown spatial distribution and influences are blamed for diminishing honeybee colonies over the world. The greater wax moth (GWM), Galleria mellonella, is a pervasive pest of the honeybee, Apis mellifera. It has an international distribution that causes severe loss to the beekeeping industry. The GWM larvae burrow into the edge of unsealed cells that have pollen, bee brood, and honey through to the midrib of the wax comb. Burrowing larvae leave behind masses of webs that cause honey to leak out and entangle emerging bees, resulting in death by starvation, a phenomenon called galleriasis. In this study, the maximum entropy algorithm implemented in (Maxent) model was used to predict the global spatial distribution of GWM throughout the world. Two representative concentration pathways (RCPs) 2.6 and 8.5 of three global climate models (GCMs), were used to forecast the global distribution of GWM in 2050 and 2070. The Maxent models for GWM provided a high value of the Area Under Curve equal to 0.8 ± 0.001, which was a satisfactory result. Furthermore, True Skilled Statistics assured the perfection of the resultant models with a value equal to 0.7. These values indicated a significant correlation between the models and the ecology of the pest species. The models also showed a very high habitat suitability for the GWM in hot-spot honey exporting and importing countries. Furthermore, we extrapolated the economic impact of such pests in both feral and wild honeybee populations and consequently the global market of the honeybee industry.

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Section: Methodsmentioning

confidence: 99%

Modeling the Potential Global Distribution of Honeybee Pest, Galleria mellonella under Changing Climate

Hosni

Al-Khalaf

Nasser

et al. 2022

Insects

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“…Several software packages, such as CLIMEX, GARP, BIOCLIM and MaxEnt, have been used to suggest the potential distribution of invasive species under different climatic scenarios [ 45 , 46 ]. Nevertheless, the artificial intelligence of maximum entropy implemented in Maxent is considered to be the most frequently used package performing species distribution modeling using presence-only data [ 47 , 48 ].…”

Section: Methodsmentioning

confidence: 99%

Modeling the Invasion of the Large Hive Beetle, Oplostomusfuligineus, into North Africa and South Europe under a Changing Climate

Abou‐Shaara

Al-Ashaal

Hosni

et al. 2021

Insects

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Some beetle species can attack honeybee colonies, causing severe damage to beekeeping. These pests include Oplostomus fuligineus, which is also known as the Large Hive Beetle (LHB). This beetle is native to Sub-Saharan Africa and has recently also been recorded in some parts of North Africa. It feeds mainly on young bee larvae and stored food within the colonies, causing severe damage to weak colonies. The present work sheds light on the current and future distribution (from 2050 to 2070) of this beetle in Africa and South Europe using species distribution modeling. Maxent was used to model the invasion of LHB. The Shared Socioeconomic Pathways (SSPs) 126 and 585 were used to model the future distribution of LHB. The Maxent models showed satisfactory results with a high Area Under Curve (AUC) value (0.85 ± 0.02). Furthermore, the True Skill Statistics (TSS) value was equal to 0.87. The current and future maps showed a high risk of invasion because of temperature variation in most of the parts of North Africa and South Europe. The maps also predicted the future invasion of LHB into other countries, mainly through southern Europe. These predictive risk maps will help quarantine authorities in highly relevant countries to prevent the expansion of this pest outside of its natural range.

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“…The CRU TS4.01 dataset is a monthly gridded field data based on monthly observational data calculated from daily or sub-daily data by National Meteorological Services and other external agents (Harris and Jones, 2017). This dataset was chosen for its wider application in various studies (Guo et al, 2019;Ahmed et al, 2019;Herrera-Soto et al, 2018;Shi et al, 2017;Los, 2015), and its spatial and temporal coverage. Accordingly, the climate data was collected on twelve systematically selected stations on a monthly basis and used for analyses of drought hazard.…”

Section: Climate Data: In This Research Gridded Climaticmentioning

confidence: 99%

Analyses of the Spatial Pattern of Drought Risk in Tigray Region, Northern Ethiopia

Tefera¹,

Ayoade²,

Bello³

2019

Journal of Applied Sciences and Environmental Management

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Drought risk index uses the meteorological drought hazard index and the socioeconomic drought vulnerability aspects to assess the level of drought risk in an area. This study examined the extent and spatial pattern of drought risk in, Tigray region, one of the recurrently drought-affected areas in Ethiopia. Monthly Gridded Climatic Research Unit (CRU) Time-series (TS) data version 4.01 data on twelve selected stations, was used to calculate the Standard Precipitation and Evapotranspiration Index (SPEI). Additionally, socioeconomic data collected from various sources were also used to assess drought vulnerability. Drought risk levels of each district were, then calculated based on the drought hazard and drought vulnerability indices. The results indicated that about 44.1% of districts were categorized under high to very high drought risk levels. However, the test of variation, using one-way ANOVA, has shown no significant variation in drought risk between districts at p < 0.05. The results of spatial autocorrelation test also indicated the presence of clustered pattern of drought risk. The study has also revealed high and very high drought risk levels largely clustered in the western, eastern and southern zones of the study area. Overall, the study has demonstrated the importance of integrating socioeconomic and climate data for comprehensive drought risk characterization, which can be used as an input for drought management planning.

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Projecting the Potential Global Distribution of Carpomya vesuviana (Diptera: Tephritidae), Considering Climate Change and Irrigation Patterns

Cited by 13 publications

References 15 publications

Modeling the Potential Global Distribution of Honeybee Pest, Galleria mellonella under Changing Climate

Modeling the Potential Global Distribution of Honeybee Pest, Galleria mellonella under Changing Climate

Modeling the Invasion of the Large Hive Beetle, Oplostomusfuligineus, into North Africa and South Europe under a Changing Climate

Analyses of the Spatial Pattern of Drought Risk in Tigray Region, Northern Ethiopia

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