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How to fit nonlinear plant growth models and calculate growth rates: an update for ecologists


Paine, C E T; Marthews, T R; Vogt, D R; Purves, D; Rees, M; Hector, A; Turnbull, L A (2012). How to fit nonlinear plant growth models and calculate growth rates: an update for ecologists. Methods in Ecology and Evolution, 3(2):245-256.

Abstract

1. Plant growth is a fundamental ecological process, integrating across scales from physiology to com- munity dynamics and ecosystem properties. Recent improvements in plant growth modeling have al- lowed deeper understanding and more accurate predictions for a wide range of ecological issues, includ- ing competition among plants, plant-herbivore interactions and ecosystem functioning.
2. One challenge in modeling plant growth is that, for a variety of reasons, relative growth rate (RGR) almost universally decreases with increasing size, though traditional calculations assume that RGR is constant. Nonlinear growth models are flexible enough to account for varying growth rates.
3. We demonstrate a variety of nonlinear models that are appropriate for modeling plant growth and, for each, show how to calculate function-derived growth rates, which allow unbiased comparisons among species at a common time or size. We show how to propagate uncertainty in estimated parameters to ex- press uncertainty in growth rates. Fitting nonlinear models can be challenging, so we present extensive worked examples and practical recommendations, all implemented in R.
4. The use of nonlinear models coupled with function-derived growth rates can facilitate the testing of novel hypotheses in population and community ecology. For example, the use of such techniques has allowed better understanding of the components of RGR, the costs of rapid growth, and the linkage be- tween host and parasite growth rates. We hope this contribution will demystify nonlinear modeling and persuade more ecologists to use these techniques.

1. Plant growth is a fundamental ecological process, integrating across scales from physiology to com- munity dynamics and ecosystem properties. Recent improvements in plant growth modeling have al- lowed deeper understanding and more accurate predictions for a wide range of ecological issues, includ- ing competition among plants, plant-herbivore interactions and ecosystem functioning.
2. One challenge in modeling plant growth is that, for a variety of reasons, relative growth rate (RGR) almost universally decreases with increasing size, though traditional calculations assume that RGR is constant. Nonlinear growth models are flexible enough to account for varying growth rates.
3. We demonstrate a variety of nonlinear models that are appropriate for modeling plant growth and, for each, show how to calculate function-derived growth rates, which allow unbiased comparisons among species at a common time or size. We show how to propagate uncertainty in estimated parameters to ex- press uncertainty in growth rates. Fitting nonlinear models can be challenging, so we present extensive worked examples and practical recommendations, all implemented in R.
4. The use of nonlinear models coupled with function-derived growth rates can facilitate the testing of novel hypotheses in population and community ecology. For example, the use of such techniques has allowed better understanding of the components of RGR, the costs of rapid growth, and the linkage be- tween host and parasite growth rates. We hope this contribution will demystify nonlinear modeling and persuade more ecologists to use these techniques.

Citations

63 citations in Web of Science®
66 citations in Scopus®
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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Evolutionary Biology and Environmental Studies
Dewey Decimal Classification:570 Life sciences; biology
590 Animals (Zoology)
Uncontrolled Keywords:Mixed-effects models, Nonlinear regression, Relative growth rate, R language
Language:English
Date:2012
Deposited On:20 Oct 2011 12:44
Last Modified:05 Apr 2016 15:02
Publisher:Wiley-Blackwell
ISSN:2041-2096
Publisher DOI:10.1111/j.2041-210X.2011.00155.x
Permanent URL: http://doi.org/10.5167/uzh-50033

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