How many fathers? Study design implications when inferring multiple paternity in crocodilians

Area of Science:

• * Genetics and Evolutionary Biology
• * Reproductive Biology
• * Conservation Genetics

Background:

• * Previous studies suggested multiple sires for crocodilian clutches, but methodological concerns about upward bias in sire number inference were raised.
• * Accurate determination of reproductive strategies, including paternity, is vital for understanding population dynamics and effective conservation.

Purpose of the Study:

• * To investigate the impact of varying microsatellite data and parentage analysis techniques on the accuracy of inferred sire numbers in crocodilians.
• * To test the premise that suboptimal study designs can lead to an overestimation of multiple paternity.

Main Methods:

• * Analysis of a published dataset of 16 confirmed single-sire saltwater crocodile pairs and their offspring.
• * Simulations involved varying the number of microsatellite loci, omitting parental genotypes, and employing different parentage analysis software (CERVUS, COLONY, GERUD).
• * Evaluation of how total allele number and loci count influenced sire accuracy under different analytical scenarios.

Main Results:

• * Total allele number, not just the number of loci, was a key determinant of inferred sire accuracy.
• * Methods requiring prior family knowledge (single-locus minimum, GERUD) accurately inferred single sires with fewer alleles.
• * Software like CERVUS and COLONY, when provided with limited data (fewer loci/genotypes), incorrectly inferred multiple sires (up to 6-7) from confirmed single-sire clutches.

Conclusions:

• * Inappropriate study designs, particularly using genotype data intended for population diversity studies to infer paternity, can significantly inflate the inferred number of sires.
• * The study highlights the critical importance of robust experimental design and sufficient genetic data for accurate multiple paternity inference in crocodilians.
• * Researchers must carefully consider data collection assumptions when using genetic markers for different analytical purposes.