Plasmodium spp

Charpentier, M. J. E., Boundenga, L., Beaulieu, M., Dibakou, S. E., Arnathau, C., Sidobre, C., Willaume, E., Mercier-Delarue, S., Simon, F., Rougeron, V. & Prugnolle, F., 2019, A longitudinal molecular study of the ecology of malaria infections in freeranging mandrills, International Journal for Parasitology: Parasites and Wildlife 10, pp. 241-251 : 244

2.9.2. Physiological consequences of Plasmodium spp . infections

In these last analyses, Plasmodium occurrences and parasitaemia were considered as four independent variables. Both occurrences (presence/absence) were considered altogether in the same linear models but each parasitaemia was considered in two different models because individuals infected by one species may have not been infected by the other.

2.9.2.1. Skin temperature. We used LMM to study the relationship between skin temperature (i.e. the difference between the average daily individual's temperatures minus the temperatures averaged over all collars per day) that followed a Gaussian distribution and either Plasmodium occurrences (one model) or parasitaemia (two models). We took into account individual age and sex as explanatory variables but considered a nested random effect between the session of trapping and the individual's identity as both captures were performed at different times of the year.

2.9.2.2. N/L ratio and oxidative markers. We used LMM and GLM to study the relationship between anti-oxidant defenses (OXY), oxidative damage (ROM) and N/L ratio and either Plasmodium occurrences (one model; LMM) or parasitaemia (two models; GLM). OXY and ROM both followed Gaussian distributions and we ln-transformed N/L ratio to fit to such a distribution. We took into account the same explanatory variables and the same random effect (LMM) as above.