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Novel use of statistics to determine precise relationship assignment to estimate breeding output of a threatened amphibian with tadpole genotypes
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  • Chad Beranek,
  • Kim Colyvas,
  • John Clulow,
  • Michael Mahony
Chad Beranek
The University of Newcastle
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Kim Colyvas
The University of Newcastle
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John Clulow
The University of Newcastle
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Michael Mahony
The University of Newcastle
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Abstract

Relatedness (rxy) measures are useful in molecular ecology studies as they can provide a means to answer biological hypotheses where pedigree information is valuable. Our understanding of the reproductive ecology of the threatened amphibian Litoria aurea is not complete where applying rxy measurements may provide further elucidation. Here we use SNPs to identify which rxy estimators (or combination of) most precisely assign relationships in L. aurea tadpoles to determine how many breeding pairs contribute to producing propagules in explosive breeding events. We aimed to (1) use simulated L. aurea genotype data to determine the precision of six rxy estimators, (2) compare the precision of relationship assignment thresholds between rxy estimators computed by discriminant function analysis (DFA) and test if using multiple estimators improved precision, and (3) Apply the best performing DFA model to assign relationships in wild tadpoles to quantify how many mating pairs reproduced in explosive breeding events. We hypothesised that each tadpole cohort produced during explosive breeding events would be conceived by >|2| mating pairs. The triadic maximum likelihood estimator had the highest Pearson’s R2 (0.92) and the lowest amount of misclassifications in the DFA (16.00%). A multi-variate DFA that included three rxy estimators further reduced the misclassifications down to 11.88%. There was evidence that more than one mating pair contributed to each explosive breeding event (n = 11). We show that the multi-variate DFA enabled precise relationship classification of free-living tadpoles which improved knowledge on amphibian reproductive ecology. We recommend this method for relationship assignments in SNP genotyped datasets.