Mapping the Potential Global Codling Moth (Cydia pomonella L.) Distribution Based on a Machine Learning Method

scientific article published in Scientific Reports

Mapping the Potential Global Codling Moth (Cydia pomonella L.) Distribution Based on a Machine Learning Method is …
instance of (P31):
scholarly articleQ13442814

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P356DOI10.1038/S41598-018-31478-3
P932PMC publication ID6117298
P698PubMed publication ID30166625

P50authorDong JiangQ56863133
P2093author name stringShuai Chen
Jingying Fu
Mengmeng Hao
Fangyu Ding
P2860cites workBIOTIC INVASIONS: CAUSES, EPIDEMIOLOGY, GLOBAL CONSEQUENCES, AND CONTROLQ28315407
Trade, transport and trouble: managing invasive species pathways in an era of globalizationQ28342453
Mapping spatial pattern in biodiversity for regional conservation planning: where to from here?Q31061670
The effects of sampling bias and model complexity on the predictive performance of MaxEnt species distribution modelsQ34606307
Assessing the potential for establishment of western cherry fruit fly using ecological niche modeling.Q35207189
Assessing the Global Risk of Establishment of Cydia pomonella (Lepidoptera: Tortricidae) using CLIMEX and MaxEnt Niche Models.Q35809018
Molecular phylogeny and population structure of the codling moth (Cydia pomonella) in Central Europe: II. AFLP analysis reflects human-aided local adaptation of a global pest speciesQ42028617
Correction: The Effects of Sampling Bias and Model Complexity on the Predictive Performance of MaxEnt Species Distribution Models.Q45895750
Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria.Q51172584
Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000Q56168542
WorldClim 2: new 1-km spatial resolution climate surfaces for global land areasQ56210311
Novel methods improve prediction of species’ distributions from occurrence dataQ57014231
Avoiding Pitfalls of Using Species Distribution Models in Conservation PlanningQ57016500
A statistical explanation of MaxEnt for ecologistsQ57062660
Response of Cydia pomonella to selection on mobility: laboratory evaluation and field verificationQ57308616
Flash flood susceptibility assessment in Jeddah city (Kingdom of Saudi Arabia) using bivariate and multivariate statistical modelsQ57598112
Evaluating predictive models of species’ distributions: criteria for selecting optimal modelsQ58006385
P275copyright licenseCreative Commons Attribution 4.0 InternationalQ20007257
P6216copyright statuscopyrightedQ50423863
P4510describes a project that usesArcGISQ513297
P433issue1
P407language of work or nameEnglishQ1860
P921main subjectmachine learningQ2539
Cydia pomonellaQ45262
P304page(s)13093
P577publication date2018-08-30
P1433published inScientific ReportsQ2261792
P1476titleMapping the Potential Global Codling Moth (Cydia pomonella L.) Distribution Based on a Machine Learning Method
P478volume8

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Q96230384Predictions of potential geographical distribution of Diaphorina citri (Kuwayama) in China under climate change scenarioscites workP2860

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