Annals of Applied Statistics

Phenotypic evolution studied by layered stochastic differential equations

Trond Reitan, Tore Schweder, and Jorijntje Henderiks

Full-text: Open access

Abstract

Time series of cell size evolution in unicellular marine algae (division Haptophyta; Coccolithus lineage), covering 57 million years, are studied by a system of linear stochastic differential equations of hierarchical structure. The data consists of size measurements of fossilized calcite platelets (coccoliths) that cover the living cell, found in deep-sea sediment cores from six sites in the world oceans and dated to irregular points in time. To accommodate biological theory of populations tracking their fitness optima, and to allow potentially interpretable correlations in time and space, the model framework allows for an upper layer of partially observed site-specific population means, a layer of site-specific theoretical fitness optima and a bottom layer representing environmental and ecological processes. While the modeled process has many components, it is Gaussian and analytically tractable. A total of 710 model specifications within this framework are compared and inference is drawn with respect to model structure, evolutionary speed and the effect of global temperature.

Article information

Source
Ann. Appl. Stat., Volume 6, Number 4 (2012), 1531-1551.

Dates
First available in Project Euclid: 27 December 2012

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1356629050

Digital Object Identifier
doi:10.1214/12-AOAS559

Mathematical Reviews number (MathSciNet)
MR3058674

Zentralblatt MATH identifier
1263.92036

Keywords
Causal model coccolith fossil data latent processes time series Ornstein–Uhlenbeck process

Citation

Reitan, Trond; Schweder, Tore; Henderiks, Jorijntje. Phenotypic evolution studied by layered stochastic differential equations. Ann. Appl. Stat. 6 (2012), no. 4, 1531--1551. doi:10.1214/12-AOAS559. https://projecteuclid.org/euclid.aoas/1356629050


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Supplemental materials

  • Supplementary material: Phenotypic evolution studied by layered stochastic differential equations—supplementary material. Supplementary material: Mathematical details, description of the prior distribution, Kalman filtering, practical restrictions, numerical methods, data issues, extra material on simulation studies and model selection results, and robustness analysis.