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Benjamin M. Bolker
Researcher at McMaster University
Publications - 159
Citations - 90175
Benjamin M. Bolker is an academic researcher from McMaster University. The author has contributed to research in topics: Population & Generalized linear mixed model. The author has an hindex of 57, co-authored 150 publications receiving 60042 citations. Previous affiliations of Benjamin M. Bolker include Princeton University & University of Cambridge.
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Modelling long‐distance seed dispersal in heterogeneous landscapes
TL;DR: The results suggest that long-distance dispersal events can be predicted using spatially explicit modelling to scale-up local movements, placing them in a landscape context.
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Estimating Initial Epidemic Growth Rates
TL;DR: This work compares the performance of four commonly used phenomenological models (exponential, Richards, logistic, and delayed logistic) in estimating initial epidemic growth rates by maximum likelihood, by fitting them to simulated epidemics with known parameters.
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Strategies for fitting nonlinear ecological models in R, AD Model Builder, and BUGS
Benjamin M. Bolker,Beth Gardner,Mark N. Maunder,Casper Willestofte Berg,Mollie Elizabeth Brooks,Liza S. Comita,Elizabeth E. Crone,Sarah Cubaynes,Trevor D. Davies,Perry de Valpine,Jessica Ford,Olivier Gimenez,Marc Kéry,Eun Jung Kim,Cleridy E. Lennert-Cody,Arni Magnusson,Steve Martell,John C. Nash,Anders Nielsen,Jim Regetz,Hans J. Skaug,Elise F. Zipkin +21 more
TL;DR: This paper compares three open‐source model fitting tools and discusses general strategies for defining and fitting models; R is convenient and (relatively) easy to learn, AD Model Builder is fast and robust but comes with a steep learning curve, while BUGS provides the greatest flexibility at the price of speed.
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Linear analysis of soil decomposition: insights from the century model
TL;DR: In this paper, the authors simplify the Century decomposition model to a set of differential equations with little loss of generality and propose a framework for making decomposition models less complex and producing parsimonious, statistically defensible models in terrestrial ecosystems.
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Reconciling early-outbreak estimates of the basic reproductive number and its uncertainty: framework and applications to the novel coronavirus (SARS-CoV-2) outbreak.
Sang Woo Park,Benjamin M. Bolker,David Champredon,David J. D. Earn,Michael Li,Joshua S. Weitz,Bryan T. Grenfell,Bryan T. Grenfell,Jonathan Dushoff +8 more
TL;DR: This work presents a statistical framework for comparing and combining different estimates of R0 across a wide range of models by decomposing the basic reproductive number into three key quantities: the exponential growth rate, the mean generation interval and the generation-interval dispersion.