2018
DOI: 10.1002/ps.4906
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Potential global distribution of Diabrotica species and the risks for agricultural production

Abstract: Most of the suitable areas for Diabrotica species overlap with highly productive agricultural areas, suggesting that a potential spread of these species may cause economic loss. Our study provides a valuable contribution to the development of tools aiming to predict the potential spread of these species throughout the world. © 2018 Society of Chemical Industry.

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Cited by 35 publications
(30 citation statements)
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References 71 publications
(150 reference statements)
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“…Although some studies claim that the use of more algorithms improves model accuracy (Araújo & New ), others argue that consensus forecasting will not always outperform models built with single algorithm (Grenouillet et al ; Crimmins et al ). For the future climate projections, we employed an ensemble of five different GCMs as used in recent studies (Marchioro & Krechemer ; Gomes et al ).…”
Section: Discussionmentioning
confidence: 99%
“…Although some studies claim that the use of more algorithms improves model accuracy (Araújo & New ), others argue that consensus forecasting will not always outperform models built with single algorithm (Grenouillet et al ; Crimmins et al ). For the future climate projections, we employed an ensemble of five different GCMs as used in recent studies (Marchioro & Krechemer ; Gomes et al ).…”
Section: Discussionmentioning
confidence: 99%
“…Various software packages, such as CLIMEX, BIOCLIM, DOMAIN, GARP, GLMs and GAMs have been used to simulate the potential distribution for invasive species . However, MaxEnt (maximum entropy) was one of the mostly frequently used for present‐only SDMs for modeling and projecting current and future distributions of invasive species . Compared with other software (GARP, Mahalanobis typicalities, and random forests), MaxEnt has outperformed other methods for estimating potential species distributions, regardless of the number or geographical range of species records .…”
Section: Methodsmentioning
confidence: 99%
“…The interplay between the conservatism and divergence of niches in shaping lineage differentiation is far from completely understood (Pyron and Burbrink 2009, Peterson 2011, Hu et al 2015, despite its relevance for forecasting changes in biodiversity under changing environmental conditions or invasion risks (Hadly et al 2009, Hortal et al 2011, Lavergne et al 2013, Torres et al 2018. The use of ENMs has become instrumental in recent years (Lobo et al 2010) and is applied in a wide range of fields, such as those pertaining to geographic distributions (Ramoni-Perazzi et al 2012, 2017, past and potential future distributions in response to climate change (Dyderski et al 2018, Simpson et al 2018, Warren et al 2018, species invasions (Lins et al 2018, Oliveira et al 2018, diseases and agricultural pest organisms (Carmona-Castro et al 2018, Carvajal et al 2019, Marchioro and Krechemer 2018, biodiversity conservation priorities (Bonfim et al 2018), and even archaeology (Banks 2017, d'Errico et al 2017. The use of ENMs has become instrumental in recent years (Lobo et al 2010) and is applied in a wide range of fields, such as those pertaining to geographic distributions (Ramoni-Perazzi et al 2012, 2017, past and potential future distributions in response to climate change (Dyderski et al 2018, Simpson et al 2018, Warren et al 2018, species invasions (Lins et al 2018, Oliveira et al 2018, diseases and agricultural pest organisms (Carmona-Castro et al 2018, Carvajal et al 2019…”
Section: Introductionmentioning
confidence: 99%
“…Environmental niche models (hereafter ENMs) are predictions of species distributions in geographic space (hereafter G-space) that use computer algorithms and mathematical representations of the species' known distribution in environmental space (hereafter E-space; Leathwick 2009, Peterson 2011). The use of ENMs has become instrumental in recent years (Lobo et al 2010) and is applied in a wide range of fields, such as those pertaining to geographic distributions (Ramoni-Perazzi et al 2012, 2017, past and potential future distributions in response to climate change (Dyderski et al 2018, Simpson et al 2018, Warren et al 2018, species invasions (Lins et al 2018, Oliveira et al 2018, diseases and agricultural pest organisms (Carmona-Castro et al 2018, Carvajal et al 2019, Marchioro and Krechemer 2018, biodiversity conservation priorities (Bonfim et al 2018), and even archaeology (Banks 2017, d'Errico et al 2017. ENMs have been combined with multivariate analyses of the E-space (Broennimann et al 2012), reviving the interest in ecological niches (Kozak et al 2006, Warren et al 2008, McCormack et al 2010, Peterson 2011.…”
Section: Introductionmentioning
confidence: 99%