Modeling vital rates improves estimation of population projection matrices
Abstract Population projection matrices are commonly used by ecologists and managers to analyze the dynamics of stage‐structured populations. Building projection matrices from data requires estimating transition rates among stages, a task that often entails estimating many parameters with few data....
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crwiley:10.1007/s10144-005-0238-8 2024-06-02T08:14:25+00:00 Modeling vital rates improves estimation of population projection matrices Gross, Kevin Morris, William F. Wolosin, Michael S. Doak, Daniel F. National Science Foundation 2005 http://dx.doi.org/10.1007/s10144-005-0238-8 https://onlinelibrary.wiley.com/doi/pdf/10.1007/s10144-005-0238-8 https://onlinelibrary.wiley.com/doi/full-xml/10.1007/s10144-005-0238-8 en eng Wiley http://onlinelibrary.wiley.com/termsAndConditions#vor Population Ecology volume 48, issue 1, page 79-89 ISSN 1438-3896 1438-390X journal-article 2005 crwiley https://doi.org/10.1007/s10144-005-0238-8 2024-05-03T10:44:45Z Abstract Population projection matrices are commonly used by ecologists and managers to analyze the dynamics of stage‐structured populations. Building projection matrices from data requires estimating transition rates among stages, a task that often entails estimating many parameters with few data. Consequently, large sampling variability in the estimated transition rates increases the uncertainty in the estimated matrix and quantities derived from it, such as the population multiplication rate and sensitivities of matrix elements. Here, we propose a strategy to avoid overparameterized matrix models. This strategy involves fitting models to the vital rates that determine matrix elements, evaluating both these models and ones that estimate matrix elements individually with model selection via information criteria, and averaging competing models with multimodel averaging. We illustrate this idea with data from a population of Silene acaulis (Caryophyllaceae), and conduct a simulation to investigate the statistical properties of the matrices estimated in this way. The simulation shows that compared with estimating matrix elements individually, building population projection matrices by fitting and averaging models of vital‐rate estimates can reduce the statistical error in the population projection matrix and quantities derived from it. Article in Journal/Newspaper Silene acaulis Wiley Online Library Population Ecology 48 1 79 89 |
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Wiley Online Library |
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English |
description |
Abstract Population projection matrices are commonly used by ecologists and managers to analyze the dynamics of stage‐structured populations. Building projection matrices from data requires estimating transition rates among stages, a task that often entails estimating many parameters with few data. Consequently, large sampling variability in the estimated transition rates increases the uncertainty in the estimated matrix and quantities derived from it, such as the population multiplication rate and sensitivities of matrix elements. Here, we propose a strategy to avoid overparameterized matrix models. This strategy involves fitting models to the vital rates that determine matrix elements, evaluating both these models and ones that estimate matrix elements individually with model selection via information criteria, and averaging competing models with multimodel averaging. We illustrate this idea with data from a population of Silene acaulis (Caryophyllaceae), and conduct a simulation to investigate the statistical properties of the matrices estimated in this way. The simulation shows that compared with estimating matrix elements individually, building population projection matrices by fitting and averaging models of vital‐rate estimates can reduce the statistical error in the population projection matrix and quantities derived from it. |
author2 |
National Science Foundation |
format |
Article in Journal/Newspaper |
author |
Gross, Kevin Morris, William F. Wolosin, Michael S. Doak, Daniel F. |
spellingShingle |
Gross, Kevin Morris, William F. Wolosin, Michael S. Doak, Daniel F. Modeling vital rates improves estimation of population projection matrices |
author_facet |
Gross, Kevin Morris, William F. Wolosin, Michael S. Doak, Daniel F. |
author_sort |
Gross, Kevin |
title |
Modeling vital rates improves estimation of population projection matrices |
title_short |
Modeling vital rates improves estimation of population projection matrices |
title_full |
Modeling vital rates improves estimation of population projection matrices |
title_fullStr |
Modeling vital rates improves estimation of population projection matrices |
title_full_unstemmed |
Modeling vital rates improves estimation of population projection matrices |
title_sort |
modeling vital rates improves estimation of population projection matrices |
publisher |
Wiley |
publishDate |
2005 |
url |
http://dx.doi.org/10.1007/s10144-005-0238-8 https://onlinelibrary.wiley.com/doi/pdf/10.1007/s10144-005-0238-8 https://onlinelibrary.wiley.com/doi/full-xml/10.1007/s10144-005-0238-8 |
genre |
Silene acaulis |
genre_facet |
Silene acaulis |
op_source |
Population Ecology volume 48, issue 1, page 79-89 ISSN 1438-3896 1438-390X |
op_rights |
http://onlinelibrary.wiley.com/termsAndConditions#vor |
op_doi |
https://doi.org/10.1007/s10144-005-0238-8 |
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Population Ecology |
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48 |
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1 |
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79 |
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89 |
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1800738245537955840 |