  Reproductive phenology, growth and mortality rates are key ecological
parameters that determine population dynamics and are therefore of vital
importance to stock assessment models for fisheries management. In many fish
species, the spawning phenology is sensitive to environmental factors that
modulate or trigger the spawning event, which differ between regions and
seasons. In addition, climate change may also alter patterns of reproductive
phenology at the community level. Usually, hatch-date distributions are
determined back-calculating the age estimated on calcified structures from the
capture date. However, these estimated distributions could be biased due to
mortality processes or time spaced samplings derived from fishery. Here, we
present a Bayesian approach that functions as a predictive model for the
hatching date of individuals from a fishery-dependent sampling with temporal
biases. We show that the shape and shift of the observed distribution is
corrected. This model can be applied in fisheries with multiple cohorts, for
species with a wide geographical distribution and living under contrasting
environmental regimes and individuals with different life histories such as
thermo-dependent growth, length-dependent mortality rates, etc.
