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Fig. 3.5 Calculation of phylogenetically independent contrasts. Independent contrasts are calculated as differences between species values or higher nodes. In this diagram, species are indicated by Latin numbers and contrasts between species sets are labeled with lower case letters. The values of two traits to be compared are indicated by X and Y. In this example, a pair of contrasts can be calculated as the difference between species i and ii: AX = 38 — 28 = 10, and AY = 21 — 15 = 6, with other contract calculated as shown. Ancestral states at higher nodes can be reconstructed, and contrasts calculated at these branching points as well. These contrasts are independent of one another and as species differences, they represent evolutionary change since two species last shared a common ancestor. Thus, a contrasts plot (such as Fig 3.6 and other figures in this volume) can be interpreted as evolutionary change in X in relation to evolutionary change in Y. See Nunn and Barton (2001) for further details.

explanations for patterns of parasite diversity (Nunn and Barton 2000, 2001). For example, body mass is a surrogate variable that covers a large number of potential mechanisms that might increase disease risk or parasite diversity, such as niches for parasite colonization and increased energy needs of large-bodied hosts (Nunn et al. 2003a). Greater body mass is also correlated with greater host defenses or immunity, with larger-bodied primates and carnivores exhibiting higher leukocyte counts (Nunn et al. 2000, 2003b; Nunn 2002a) and larger spleens (Nunn 2002b). Similarly, larger bodied species are often more dimorphic in body mass (Mitani et al. 1996b; Smith and Cheverud 2002), and competition among males might make these individuals more susceptible to parasites through the immunosuppressive effects of testosterone. And from an epidemiological view, body mass is correlated negatively with population density in mammals (Damuth 1981), even though both variables are usually hypothesized to be positively associated with parasitism, suggesting conflicting associations (see Nunn et al. 2003a for discussion of these and other issues). Thus, differences between phylogenetic and non-phylogenetic tests—when such differences exist—can often point to biologically relevant factors for investigation in future studies.

reproduce later in life, and have lower average birth rates (Harvey and Clutton-Brock 1985; Ross and Jones 1999). Among individuals, older individuals should harbor greater parasite diversity because they encounter more parasite species and are exposed to a larger number of infectious stages throughout their lifetimes (Pacala and Dobson 1988; Bell and Burt 1991). This principle is perhaps most obvious for STDs, where prevalence should be higher in sexually active adults than in younger, sexually naïve animals (Nunn and Altizer 2004). Mathematical models further predict that host life history traits should interact with key epidemiological processes because high host mortality will limit parasite establishment (Chapter 4 and Anderson and May 1979; Thrall et al. 1993a; De Leo and Dobson 1996; Altizer and Augustine 1997). Thus, across species, those taxa in which hosts have longer lifespans should encounter more parasites. These effects of age and life history mainly involve encounter probabilities, but age-related effects on infection probability can also be important. Thus, for many species, individual immune defenses are weakest at the beginning and end of life (Lloyd 1995), with the latter association possibly leading to increased parasite susceptibility among older animals or species with slower life histories (Morand and Harvey 2000).

Based on these considerations, a positive association should exist between measures of disease risk and body mass, and between disease risk and age or longevity, both at the individual and species levels (Table 3.1). Separating the correlated effects of age and body mass is challenging but crucial for explaining comparative and within-species patterns, as is controlling for other variables frequently correlated with these two variables, including dominance rank, sex, age at first reproduction, interbirth interval, population density, and habitat use. In what follows, we present evidence bearing on these predictions from primates and other mammals.

3.3.1.1 Body mass

In a phylogenetic comparative study of white blood cell counts in primates, body mass was positively correlated with neutrophil counts (Fig. 3.6), suggesting that larger-bodied species experience greater disease risk. In cross-species comparisons of primates and other mammals, body mass also correlated positively with parasite species richness and prevalence of infection, but mainly in tests that did not control for host phylogeny (Table 3.2). For example, in a cross-species comparative study of Amazonian primates, Davies et al. (1991) showed that malaria infection rates increased with (sleeping) group size and body mass, possibly because larger-bodied primates emit more cues used by mosquitoes to locate hosts. The effect of body mass on malaria prevalence became non-significant once phylogeny was taken into account, possibly because group size is a better estimate of the area over which a group is spread, as compared to mean body mass of individual hosts in that species (Nunn and Heymann 2005). Another recent study of mammals found that the

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