Parasitism by metacercariae modulates the morphological, organic and mechanical responses of the shell of an intertidal bivalve to environmental drivers.

Environmental variation alters biological interactions and their ecological and evolutionary consequences. In coastal systems, trematode parasites affect their hosts by disrupting their life-history traits. However, the effects of parasitism could be variable and dependent on the prevailing environmental conditions where the host-parasite interaction occurs. This study compared the effect of a trematode parasite in the family Renicolidae (metacercariae) on the body size and the shell organic and mechanical characteristics of the intertidal mussels Perumytilus purpuratus, inhabiting two environmentally contrasting localities in northern and central Chile (ca. 1600 km apart). Congruent with the environmental gradient along the Chilean coast, higher levels of temperature, salinity and pCO, and a lower pH characterise the northern locality compared to that of central Chile. In the north, parasitised individuals showed lower body size and shell resistance than non-parasitised individuals, while in central Chile, the opposite pattern was observed. Protein level in the organic matter of the shell was lower in the parasitised hosts than in the non-parasitised ones regardless of the locality. However, an increase in polysaccharide levels was observed in the parasitised individuals from central Chile. These results evidence that body size and shell properties of P. purpuratus vary between local populations and that they respond differently when confronting the parasitism impacts. Considering that the parasite prevalence reaches around 50% in both populations, if parasitism is not included in the analysis, the true response of the host species would be masked by the effects of the parasite, skewing our understanding of how environmental variables will affect marine species. Considering parasitism and identifying its effects on host species faced with environmental drivers is essential to understand and accurately predict the ecological consequences of climate change.