Many applications of ocular biometrics require long-term stability, yet only limited data on the effects of disease and aging on the error rates of ocular biometrics is currently available. Based on pathologies simulated using image manipulation validated by opthalmology and optometry specialists, the present paper reports on the effects that selected common ocular diseases and age-related pathologies have on the recognition performance of two widely used iris and retina recognition algorithms, finding the algorithms to be robust against many even highly visible pathologies, permitting acceptable re-enrolment intervals for most disease progressions.