Back

Extreme male reproductive skew limits effective population size in American Standardbred horses

Waples, R. S.; Khanshour, A. M.; Cothran, E. G.

2026-01-03 evolutionary biology
10.64898/2026.01.03.697495 bioRxiv
Show abstract

Standardbred horses are widely used for harness racing in North America, Europe, Australia, and New Zealand. In the U.S., two gaits (Pacers and Trotters) are genetically differentiated at a level typical for different horse breeds. We used the complete pedigree for all registered American Standardbred foals born 1970-2014 (>200K Trotters and >500K Pacers) to calculate annual and lifetime reproductive skew and its consequences for the effective number of breeders per year (Nb) and effective size per generation (Ne). With rare exceptions, females can only produce 0 or 1 foal per year, which limits annual and lifetime variance in offspring number. These constraints do not apply to males: with humans (rather than horses) deciding who breeds with whom, and how often, the most prolific stud regularly sires well over 100 foals each year and often over 1000 across its lifetime. Male reproductive skew increased over time in both gaits, with the most prolific studs producing 20% or more of all foals sired by members of their birth cohort. Although each gait in the U.S. is represented by tens of thousands of adult horses at any given time, male Ne is typically in the low hundreds, which drives the overall Ne/N ratio to or below 0.01--a value often considered to be "tiny." Ne/N ratios this low have rarely been reported for large mammals and generally are thought to apply primarily to some marine species with very high fecundity. When Ne/N is this low, even large populations can experience substantial erosion of genetic diversity.

Published in Journal of Heredity (predicted rank #2) · training set

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.