Seasonal reproduction patterns are typically observed in small ruminants and are a major limitation for production efficiency in most meat- and dairy-type production systems. Indeed, selection for reduced seasonality could be an appealing strategy for the small ruminant industry worldwide, although its genetic background has been poorly analyzed. One of the main limitations relied on the availability of appropriate analytical tools to cope with the circular (i.e. year-round) pattern of lambing and kidding data. The recent development of a heteroskedastic circular mixed model provided the statistical tool to go deeply into the knowledge of seasonality in small ruminants. In this study, 26 005 lambing distribution records from 4764 Ripollesa ewes collected in 20 purebred flocks were analyzed. The model accounted for systematic (lambing interval and ewe age), permanent environmental (flock-year-season and ewe) and additive genetic sources of variation influencing both mean and dispersion pattern (i.e. heteroskedasticity). Systematic effects suggested that first-lambing ewes and short lambing intervals delayed lambing date (~30 days) and increased dispersion of the lambing period. Nevertheless, this was partially compensated by ewe age, given that youngest females tended to concentrate the lambing peak. Flock-year-season, permanent ewe and additive genetic sources of variation reached moderate variance components for direct (and residual) effects on lambing distribution, they being 0.119 (0.156), 0.092 (0.132) and 0.195 (0.170) radians2, respectively. Moreover, all 95% credibility intervals were placed far from the null estimate. Covariances between direct and residual effects where high and positive for additive genetic (posterior mean, 0.814) and permanent ewe effects (posterior mean, 0.917), whereas it was not relevant for flock-year-season. Selection for direct additive genetic effects should be able to advance or delay the lambing peak, whereas selection applied on residual additive genetic effects should increase or reduce seasonality (i.e. concentrate or flatten the lambing peak). Moreover, the positive and relevant genetic covariance between direct and residual effects also suggested correlated genetic responses. As example, genetic selection for earlier lambing peaks must also reduce seasonality, whereas selection for narrower lambing seasons may originate a delay in the lambing peak. These results must be viewed as the first attempt to analyze systematic, environmental and genetic sources of variation of lambing distribution within the circular paradigm, they providing a reliable characterization of these effects within the context of an heteroskedastic model.