2016
DOI: 10.1016/j.ecolmodel.2015.06.002
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The effects of model and data complexity on predictions from species distributions models

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Cited by 70 publications
(52 citation statements)
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“…However, they also indicate a much more systemic problem for the SDM literature: decades of methodological work in this field have resulted in a set of widely-adopted 'best practices', but a great majority of these studies have focused on optimizing models' discrimination accuracy on withheld occurrence data from real species distributions Domisch et al, 2013;Garcia-Callejas & Araujo, 2016;Guisan et al, 2007;Huang & Frimpong, 2016;Kuebler et al, 2016;Lopatin, Dolos, Hernandez, Galleguillos, & Fassnacht, 2016;Moreno-Amat et al, 2015;Radosavljevic & Anderson, 2014;Rovzar, Gillespie, & Kawelo, 2016;Soley-Guardia et al, 2016;Wisz et al, 2008). However, they also indicate a much more systemic problem for the SDM literature: decades of methodological work in this field have resulted in a set of widely-adopted 'best practices', but a great majority of these studies have focused on optimizing models' discrimination accuracy on withheld occurrence data from real species distributions Domisch et al, 2013;Garcia-Callejas & Araujo, 2016;Guisan et al, 2007;Huang & Frimpong, 2016;Kuebler et al, 2016;Lopatin, Dolos, Hernandez, Galleguillos, & Fassnacht, 2016;Moreno-Amat et al, 2015;Radosavljevic & Anderson, 2014;Rovzar, Gillespie, & Kawelo, 2016;Soley-Guardia et al, 2016;Wisz et al, 2008).…”
Section: Implications Of the Inability Of Discrimination Capacity Tmentioning
confidence: 99%
See 1 more Smart Citation
“…However, they also indicate a much more systemic problem for the SDM literature: decades of methodological work in this field have resulted in a set of widely-adopted 'best practices', but a great majority of these studies have focused on optimizing models' discrimination accuracy on withheld occurrence data from real species distributions Domisch et al, 2013;Garcia-Callejas & Araujo, 2016;Guisan et al, 2007;Huang & Frimpong, 2016;Kuebler et al, 2016;Lopatin, Dolos, Hernandez, Galleguillos, & Fassnacht, 2016;Moreno-Amat et al, 2015;Radosavljevic & Anderson, 2014;Rovzar, Gillespie, & Kawelo, 2016;Soley-Guardia et al, 2016;Wisz et al, 2008). However, they also indicate a much more systemic problem for the SDM literature: decades of methodological work in this field have resulted in a set of widely-adopted 'best practices', but a great majority of these studies have focused on optimizing models' discrimination accuracy on withheld occurrence data from real species distributions Domisch et al, 2013;Garcia-Callejas & Araujo, 2016;Guisan et al, 2007;Huang & Frimpong, 2016;Kuebler et al, 2016;Lopatin, Dolos, Hernandez, Galleguillos, & Fassnacht, 2016;Moreno-Amat et al, 2015;Radosavljevic & Anderson, 2014;Rovzar, Gillespie, & Kawelo, 2016;Soley-Guardia et al, 2016;Wisz et al, 2008).…”
Section: Implications Of the Inability Of Discrimination Capacity Tmentioning
confidence: 99%
“…These include choice of modelling algorithm, required sample size, optimal model complexity, choice of study area from which data are drawn, the exclusion of outliers and selection of environmental predictors, among others (Acevedo, Jimenez-Valverde, Lobo, & Real, 2012;Boria, Olson, Goodman, & Anderson, 2014;Domisch, Kuemmerlen, Jahnig, & Haase, 2013;Garcia-Callejas & Araujo, 2016;Guisan, Graham, Elith, Huettmann, & Distri, 2007;van Proosdij, Sosef, Wieringa, & Raes, 2016;Soley-Guardia et al, 2016;Varela, Anderson, Garcia-Valdes, & Fernandez-Gonzalez, 2014;Wisz et al, 2008). These include choice of modelling algorithm, required sample size, optimal model complexity, choice of study area from which data are drawn, the exclusion of outliers and selection of environmental predictors, among others (Acevedo, Jimenez-Valverde, Lobo, & Real, 2012;Boria, Olson, Goodman, & Anderson, 2014;Domisch, Kuemmerlen, Jahnig, & Haase, 2013;Garcia-Callejas & Araujo, 2016;Guisan, Graham, Elith, Huettmann, & Distri, 2007;van Proosdij, Sosef, Wieringa, & Raes, 2016;Soley-Guardia et al, 2016;Varela, Anderson, Garcia-Valdes, & Fernandez-Gonzalez, 2014;Wisz et al, 2008).…”
Section: Introductionmentioning
confidence: 99%
“…Species' life-history traits, physiology, or behavior can also influence complexity, such that choosing an optimally complex model requires identifying the most sensible predictors and datasets relative to a given study objective. Novel indices of complexity that emphasize the structural properties of the input data might help [43], as could standardized metrics of predictive performance.…”
Section: Why Transfer Models In the First Place?mentioning
confidence: 99%
“…Species' life-history traits, physiology, or behavior can also influence complexity, such that choosing an optimally complex model requires identifying the most sensible predictors and datasets relative to a given study objective. Novel indices of complexity that emphasize the structural properties of the input data might help [43], as could standardized metrics of predictive performance.Are There Spatial and Temporal Limits to Extrapolation in Model Transfers? While prediction error is expected to increase with 'distance' (e.g., km, days) from reference conditions [1], model transferability appears little related to geographic (and temporal) separation between reference systems and target systems (Figure 1).…”
mentioning
confidence: 99%
“…The operations can be easily implemented in the R package ENMeval [66]. Given the good performance of the Maxent with the AICc-selected parameter set (Maxent-AICc) model in balancing model goodness-of-fit against complexity that was reported in the context of species distribution modelling [63,[66][67][68], we wanted to see if the performance can be improved when the Maxent-AICc model is used in remotely-sensed mapping.…”
mentioning
confidence: 99%