Early warning indicators (EWIs) have been applied within the framework of dynamical systems theory to detect in advance tipping points in stressed or perturbed ecosystems, including the early detection of disease outbreaks. Early warning signals in epidemiology occur when an epidemic transition point is approached, marking the transition in the state of an epidemic. Here, EWIs were used to detect large outbreaks of sea lice (Caligus rogercresseyi) in advance using time series of total adult abundance from a highly infested salmon farming area in southern Chile. Generic early warning indicators successfully detected increases in C. rogercresseyi abundance several months in advance of two major transition points marked by: i) El Nino 2016, ii) abnormal climatic/ oceanographic conditions during the spring-summer of 2019-2020. The emergence of a trend in EWIs was interpreted as an early response of C. rogercresseyi to oceanographic disturbances. New signatures, characterized by a marked loss of long-term memory and predictability, and an increase in randomness in sea lice abundance time series were detected using the Hurst coefficient (H) and the weighted permutation entropy (WPE). An opposite pattern of variability between H and WPE was observed; and a segmented regression identified major breakpoints in the overall sea lice abundance dynamics as H decreased. Because H and WPE track the long-term dynamics of sea lice abundance well, we proposed them as complementary indicators to aid in the management and control of increasing sea lice epidemic outburst, as they appear to track the developed resistance of sea lice to anti-lice chemicals.