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What life cycle graphs can tell about the evolution of life histories

Overview of attention for article published in Journal of Mathematical Biology, February 2012
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Title
What life cycle graphs can tell about the evolution of life histories
Published in
Journal of Mathematical Biology, February 2012
DOI 10.1007/s00285-012-0509-x
Pubmed ID
Authors

Claus Rueffler, Johan A. J. Metz, Tom J. M. Van Dooren

Abstract

We analyze long-term evolutionary dynamics in a large class of life history models. The model family is characterized by discrete-time population dynamics and a finite number of individual states such that the life cycle can be described in terms of a population projection matrix. We allow an arbitrary number of demographic parameters to be subject to density-dependent population regulation and two or more demographic parameters to be subject to evolutionary change. Our aim is to identify structural features of life cycles and modes of population regulation that correspond to specific evolutionary dynamics. Our derivations are based on a fitness proxy that is an algebraically simple function of loops within the life cycle. This allows us to phrase the results in terms of properties of such loops which are readily interpreted biologically. The following results could be obtained. First, we give sufficient conditions for the existence of optimisation principles in models with an arbitrary number of evolving traits. These models are then classified with respect to their appropriate optimisation principle. Second, under the assumption of just two evolving traits we identify structural features of the life cycle that determine whether equilibria of the monomorphic adaptive dynamics (evolutionarily singular points) correspond to fitness minima or maxima. Third, for one class of frequency-dependent models, where optimisation is not possible, we present sufficient conditions that allow classifying singular points in terms of the curvature of the trade-off curve. Throughout the article we illustrate the utility of our framework with a variety of examples.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 8%
France 2 5%
Unknown 33 87%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 29%
Student > Ph. D. Student 8 21%
Professor 3 8%
Professor > Associate Professor 3 8%
Student > Master 3 8%
Other 7 18%
Unknown 3 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 21 55%
Environmental Science 5 13%
Mathematics 3 8%
Computer Science 2 5%
Linguistics 1 3%
Other 3 8%
Unknown 3 8%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 July 2012.
All research outputs
#15,247,248
of 22,671,366 outputs
Outputs from Journal of Mathematical Biology
#328
of 654 outputs
Outputs of similar age
#163,853
of 247,817 outputs
Outputs of similar age from Journal of Mathematical Biology
#3
of 4 outputs
Altmetric has tracked 22,671,366 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 654 research outputs from this source. They receive a mean Attention Score of 3.6. This one is in the 36th percentile – i.e., 36% of its peers scored the same or lower than it.
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