scholarly journals LTRE Decomposition of the Stochastic Growth Rate

Author(s):  
Hal Caswell
2009 ◽  
Vol 220 (5) ◽  
pp. 605-610 ◽  
Author(s):  
Per Åberg ◽  
Carl Johan Svensson ◽  
Hal Caswell ◽  
Henrik Pavia

2011 ◽  
Vol 80 (1) ◽  
pp. 1-15 ◽  
Author(s):  
David Steinsaltz ◽  
Shripad Tuljapurkar ◽  
Carol Horvitz

2002 ◽  
Vol 59 (6) ◽  
pp. 1014-1023 ◽  
Author(s):  
Richard A Hinrichsen

The accuracies of four alternative estimators of stochastic growth rate for salmon populations are examined using bootstrapping. The first estimator is based on a stochastic Leslie matrix model that uses age-specific spawner counts. The other three estimators use spawner counts with limited age-structure information: a Botsford–Brittnacher model method and two diffusion approximation methods, namely, the least squares approach of Dennis and the robust approach of Holmes. Accuracy of the estimators was quantified using median bias and interquartile ranges of the stochastic growth rate estimates. The Botsford–Brittnacher estimator was found to be unreliable due to large bias. Of the remaining estimators, the stochastic Leslie approach tended to produce the most reliable estimates but had the greatest data demands. With severe lognormal measurement error, the Dennis estimators produced less biased estimates than the other methods, but precision of the stochastic growth rate was generally highest using the stochastic Leslie estimator.


2015 ◽  
Author(s):  
bahram houchmandzadeh

Abstract The Luria-Delbrück experiment is a cornerstone of evolutionary theory, demonstrating the randomness of mutations before selection. The distribution of the number of mutants in this experiment has been the subject of intense investigation during the last 70 years. Despite this considerable effort, most of the results have been obtained under the assumption of constant growth rate, which is far from the experimental condition. We derive here the properties of this distribution for arbitrary growth function, for both the deterministic and stochastic growth of the mutants. The derivation we propose is surprisingly simple and versatile, allowing many generalizations to be taken easily into account.


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