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

2.9.1. Determinants of Plasmodium spp . infections

2.9.1.1. Intrinsic host characteristics and seasonality. Considering all studied animals, we first used Generalized Linear Mixed Models (GLMM; SAS studio, “glimmix” procedure) with a binary distribution to study the relationships between Plasmodium occurrences (presence/ absence of either P. mandrilli or P. gonderi ) as two dependent variables and different determinants. Second, considering infected animals only, we used General Linear Models (GLM; SAS studio, “glm” procedure) with a Gaussian distribution to study the impact of the same determinants on either P. mandrilli or P. gonderi parasitaemia (two dependent variables). Parasitaemia were ln-transformed to fit to normal distributions.

In these first four models, we considered as explanatory variables the individual age and its quadratic term (continuous variable) and sex (categorical variable with two modalities). In addition, we took into account the ecological season (categorical variable with three modalities). Gabon is characterized by four climatic seasons with a pronounced long rainy season (Feb–May) and a long dry season (Jun–Sept), in addition to a short rainy season (Oct–Nov), and a short dry season (Dec–Jan; for details, see: (Nsi Akoué et al., 2017)). These two short seasons were combined in our models because two blood samples only were collected during the short rainy season (excluding these two samples did not change the results).

Restricting the dataset to females aged ≥4 yrs, we then studied whether females' reproductive status (categorical variable with three modalities) impacted Plasmodium infection. In particular, we distinguished pregnancy and lactation because these two periods of a female's life are probably the most energetically demanding ones, possibly resulting in higher parasite susceptibility (e.g. ( De Nys et al., 2014)). All other females (neither pregnant nor lactating) were pooled together in a third category. In these additional models, we considered female's age as quadratic term but did not take into account the season of sampling because it was highly correlated to female's reproductive status.

2.9.1.2. Co-infections. We first tested whether being infected by a given Plasmodium species impacted the probability of infection by the other Plasmodium species using Spearman's rank correlation tests for both occurrences and parasitaemia.

Second, in males aged ≥6 yrs, we studied the relationship between P. mandrilli occurrence (dependent variable) and SIV and STLV infection statuses using GLMM. Almost all males that were infected by P. gonderi were also STLV-positive. Consequently, when studying P. gonderi occurrence , we considered males' SIV status only (GLMM). We studied the relationship between P. gonderi parasitaemia and SIV and STLV statuses using a GLM as above. In this particular case, we re-ran, however, an additional General Linear Mixed Model (LMM; SAS studio, “glimmix” procedure) with a random effect of male's identity to make sure that the marginal effect of SIV we found was not due to any confounding effect due to repeated sampling on males. For these four linear models, we considered male's age (in a quadratic form) as a confounding factor but we neither took into account the season of sampling, because this variable never influenced former models, nor interaction terms because of limited sample sizes. Finally, we studied the relationship between P. mandrilli parasitaemia and either SIV or STLV statuses using two non-parametric analyses of variance (SAS studio, “npar1way” procedure) because of limited sample sizes.