2019
DOI: 10.1007/s10682-019-09968-1
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Integrating phylogeography and ecological niche modelling to test diversification hypotheses using a Neotropical rodent

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Cited by 22 publications
(15 citation statements)
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“…Similarly, the diversification of the Amazonian lineages was probably determined primarily by the formation and dynamics of the rivers of the Amazon basin, which form barriers to gene flow between L. coronata populations, as also observed in other groups of organisms, such as birds (Fernandes et al, ; Ribas et al, ), primates (Cortéz‐Ortiz et al, ), rodents (Machado et al, ) and reptiles (Geurgas & Rodrigues, ). The initial diversification of the Amazonian populations of L. coronata occurred at approximately 0.9 mya (species tree) through a second vicariant event, which resulted in the formation of lineage C and the ancestor of the sister lineages, A and B.…”
Section: Discussionmentioning
confidence: 86%
“…Similarly, the diversification of the Amazonian lineages was probably determined primarily by the formation and dynamics of the rivers of the Amazon basin, which form barriers to gene flow between L. coronata populations, as also observed in other groups of organisms, such as birds (Fernandes et al, ; Ribas et al, ), primates (Cortéz‐Ortiz et al, ), rodents (Machado et al, ) and reptiles (Geurgas & Rodrigues, ). The initial diversification of the Amazonian populations of L. coronata occurred at approximately 0.9 mya (species tree) through a second vicariant event, which resulted in the formation of lineage C and the ancestor of the sister lineages, A and B.…”
Section: Discussionmentioning
confidence: 86%
“…Many literatures have modeled the ecology of prehistoric species using pollen records (Meadows 2007, Qiu et al 2020, climate, and ecological niche modeling (Machado et al 2019). One of successful attempts can be seen from works by Ulker et al (2018) on pedunculate oak (Quercus robur) during…”
Section: Discussionmentioning
confidence: 99%
“…On the other hand, occurrence records are commonly grouped in areas with greater sampling effort, which may result in spatial autocorrelation. Numerous studies have used spatial filtering to avoid spatial autocorrelation by excluding records very close to each other (Boria, Olson, Goodman, & Anderson, ; Machado et al, ). Spatial filtering reduces overfitting effects, which occurs when the model is too tightly adjusted to calibration data and cannot predict data accuracy independently (Radosavljevic & Anderson, ).…”
Section: Methodsmentioning
confidence: 99%
“…On the other hand, occurrence records are commonly grouped in areas with greater sampling effort, which may result in spatial autocorrelation. Numerous studies have used spatial filtering to avoid spatial autocorrelation by excluding records very close to each other (Boria, Olson, Goodman, & Anderson, 2014;Machado et al, 2019).…”
Section: Data Treatmentmentioning
confidence: 99%