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An analysis of mating biases in trees


Ismail, Sascha A; Kokko, Hanna (2020). An analysis of mating biases in trees. Molecular Ecology, 29(1):184-198.

Abstract

Assortative mating is a deviation from random mating based on phenotypic similarity. As it is much better studied in animals than in plants, we investigate for trees whether kinship of realized mating pairs deviates from what is expected from the set of potential mates and use this information to infer mating biases that may result from kin recognition and/or assortative mating. Our analysis covers twenty species of trees for which microsatellite data is available for adult populations (potential mates) as well as seed arrays. We test whether mean relatedness of observed mating pairs deviates from null expectations that only take pollen dispersal distances into account (estimated from the same dataset). This allows to identify elevated as well as reduced kinship among realized mating pairs, indicative of positive and negative assortative mating, respectively. The test is also able to distinguish elevated biparental inbreeding that occurs solely as a result of related pairs growing closer to each other from further assortativeness.
Assortative mating in trees appears potentially common but not ubiquitous: nine data sets show mating bias with elevated inbreeding, nine do not deviate significantly from the null expectation, and two show mating bias with reduced inbreeding.
While our datasets lack direct information on phenology, our investigation of the phenological literature for each species identifies flowering phenology as a potential driver of positive assortative mating (leading to elevated inbreeding) in trees. Since active kin recognition provides an alternative hypothesis for these patterns, we encourage further investigations on the processes and traits that influence mating patterns in trees.

Abstract

Assortative mating is a deviation from random mating based on phenotypic similarity. As it is much better studied in animals than in plants, we investigate for trees whether kinship of realized mating pairs deviates from what is expected from the set of potential mates and use this information to infer mating biases that may result from kin recognition and/or assortative mating. Our analysis covers twenty species of trees for which microsatellite data is available for adult populations (potential mates) as well as seed arrays. We test whether mean relatedness of observed mating pairs deviates from null expectations that only take pollen dispersal distances into account (estimated from the same dataset). This allows to identify elevated as well as reduced kinship among realized mating pairs, indicative of positive and negative assortative mating, respectively. The test is also able to distinguish elevated biparental inbreeding that occurs solely as a result of related pairs growing closer to each other from further assortativeness.
Assortative mating in trees appears potentially common but not ubiquitous: nine data sets show mating bias with elevated inbreeding, nine do not deviate significantly from the null expectation, and two show mating bias with reduced inbreeding.
While our datasets lack direct information on phenology, our investigation of the phenological literature for each species identifies flowering phenology as a potential driver of positive assortative mating (leading to elevated inbreeding) in trees. Since active kin recognition provides an alternative hypothesis for these patterns, we encourage further investigations on the processes and traits that influence mating patterns in trees.

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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)
Scopus Subject Areas:Life Sciences > Ecology, Evolution, Behavior and Systematics
Life Sciences > Genetics
Uncontrolled Keywords:Genetics, Ecology, Evolution, Behavior and Systematics
Language:English
Date:1 January 2020
Deposited On:04 Dec 2019 14:57
Last Modified:29 Jul 2020 12:04
Publisher:Wiley-Blackwell Publishing, Inc.
ISSN:0962-1083
OA Status:Hybrid
Publisher DOI:https://doi.org/10.1111/mec.15312

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