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nlme : Linear and nonlinear mixed effects models
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The article was published on 2006-01-01 and is currently open access. It has received 9437 citations till now.read more
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Gamma and Beta Oscillations Define a Sequence of Neurocognitive Modes Present in Odor Processing.
TL;DR: It is shown that gamma and beta oscillations occur in stereotyped sequence during odor sampling in associative tasks, with local gamma dominating the first 250 ms of odor sniffing, followed by systemwide beta as behavioral responses are prepared, showing that task features can dramatically adjust the dynamics of a cortical sensory system, which changes state every ∼250 ms.
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Evaluating 318 continental-scale species distribution models over a 60-year prediction horizon: what factors influence the reliability of predictions?
TL;DR: The authors used generalized linear mixed models to model variation in the predictive performance of SDMs over time in relation to a variety of factors, including the length of time between fitting and evaluation, species traits, taxonomic group and attributes of the dataset used to fit models.
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Wind field and sex constrain the flight speeds of central-place foraging albatrosses
Ewan D. Wakefield,Richard A. Phillips,Jason Matthiopoulos,Akira Fukuda,Hiroyoshi Higuchi,Gareth J. Marshall,Philip N. Trathan +6 more
TL;DR: In this paper, the authors used satellite telemetry and immersion logger data to quantify the effects of relative wind speed, sex, breeding stage, and trip stage on the ground speeds of four species of Southern Ocean albatrosses breeding at South Georgia.
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Trait-dependent responses of flower-visiting insects to distance to semi-natural grasslands and landscape heterogeneity
TL;DR: It is concluded that local species assemblages of flower-visiting insects in linear habitat elements were mainly affected by the occurrence of nearby semi-natural grasslands.
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Desert grassland responses to climate and soil moisture suggest divergent vulnerabilities across the southwestern United States.
TL;DR: This work combines climate and soil properties with a mechanistic soil water model to explain temporal fluctuations in perennial grass cover, and concludes that incorporating multiple aspects of climate and accounting for soil variability can improve ability to understand patterns, identify areas of vulnerability, and predict the future of desert grasslands.