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Quantitative Genetics in the Wild$
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Anne Charmantier, Dany Garant, and Loeske E. B. Kruuk

Print publication date: 2014

Print ISBN-13: 9780199674237

Published to Oxford Scholarship Online: August 2014

DOI: 10.1093/acprof:oso/9780199674237.001.0001

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Quantitative genetics of wild populations of arthropods

Quantitative genetics of wild populations of arthropods

(p.147) Chapter 9 Quantitative genetics of wild populations of arthropods
Quantitative Genetics in the Wild

Felix Zajitschek

Russell Bonduriansky

Oxford University Press

This chapter discusses how genetic variation in fitness-related traits can be assessed in wild populations of arthropods and how the state of this research has developed in the recent past, including the use of genomic data. Understanding genetic variation in fitness within natural populations of invertebrates is of considerable theoretical importance, yet pure field estimates of quantitative genetic parameters of invertebrate species are almost non-existent. The reasons why many insect species are established model species for lab studies are identical to the problems researchers face in their attempts to study them in their natural habitat: they are small, short-lived and often can have a large number of offspring. There is a large literature on field-lab estimates (using data from laboratory reared offspring of field-derived parents), but this approach is not ideal in a number of ways. This chapter suggests potential ways to ameliorate these problems, and discusses some examples of suitable model systems. Systems such as these, and new approaches to tracking individuals and estimating relatedness, will ultimately enable researchers to estimate quantitative genetic parameters for natural populations of invertebrates. Such studies will allow for comparison with the enormous literature on captive invertebrate populations, as well as extend knowledge of the quantitative genetics of natural populations to a much broader range of biological diversity.

Keywords:   invertebrates, natural populations, tracking individuals, model systems, genomic data, field-lab estimates

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