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Make Him a Monogamy Junkie

The Monogamy Method

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Female

Male

Monogamy

Polygyny

Variance in male reproductive success

Fig. 4.9 Schematic diagram (a) and results (b) of an individual-based simulation model used to investigate the spread of an STD in males and females within the context of variance in male mating success, female dispersal between groups, and mortality. (a) Each male was assigned an attractiveness score from 0 to 1, and more attractive males were assigned greater numbers of females. The number of males was equal to the number of females. (b) Model results showing the change in population-wide STD prevalence in males and females separately, in relation to overall variance in male mating success. Note that the left side of this figure reflects a more monogamous situation, in which males tend to have single mates, and the right reflects extreme polygyny, in which a few males monopolized all the females in the population. Bottom figure redrawn from Thrall, P. H., J. Antonovics, and A. P. Dobson. Sexually transmitted diseases in polygynous mating systems: prevalence and impact on reproductive success. Proceedings of the Royal Society London B., 267, 1555-1563. Fig 1(a), Copyright

(2000) The Royal Society.

non-monogamous species. (see Fig. 3.11). Although these analyses were consistent with the model predictions, alternative explanations are possible, including the possibility that females are more susceptible to STDs. Higher STD prevalence among females has also been reported among captive breeding primate colonies, including sooty mangabeys and baboons (Levin et al. 1988; Fultz et al. 1990).

Differences in STD prevalence between males and females are more striking because theory predicts the opposite pattern for OIDs, with higher prevalence in males due to energetic costs associated with competition for mates or the deleterious effects of testosterone on immunocompetence (see Chapter 3). Other observations show that males might not only account for more infections, but also contribute disproportionately to the transmission of macro- and microparasites (Perkins et al. 2003a; Ferrari et al. 2004a). Further studies are needed to determine the mechanisms and consequences of sex-biased susceptibility, including its role in the evolution of mate choice and traits that signal parasite infection (see Chapters 5 and 6).

An individual-based simulation model was recently used to investigate how mating group size, group composition, and dispersal rates influenced the ability of a highly pathogenic disease to spread through a susceptible population of primates (Nunn et al. in review). In this model, females were assumed to disperse to new groups when the number of males in their group dropped to zero, which could happen during disease epidemics when the harem-holding male dies. The results from this simulation model revealed that introduced pathogens such as Ebola virus spread the fastest in host systems characterized by highly polygynous groups (Fig. 4.10).

Social network theory represents a third approach that allows researchers to focus directly on how interactions among individuals influence the spread of disease (Moore and Newman 2000). This strategy is borrowed from sociological methods in which researchers investigate relations and connectedness among individuals (Wasserman and Faust 1994), and this basic approach could be applied to model contagious agents in primate social groups. In the most basic sense, network data can be captured by a square array of values, where both rows and columns are the same individuals or subjects, and each cell of the array defines the relationship between two individuals. In this case, each animal or person becomes a point (or node) in a network, and lines (or edges) represent relationships between subjects (Fig. 4.11). In these networks, some animals might have few connections whereas others have many, essentially representing hubs of activity or potential "super-spreaders" of infectious disease. Network models can simulate realistic social and sexual interactions (Jones and Handcock 2003; Cross et al. 2004; Eubank et al. 2004). This approach has been used to evaluate strategies for limiting the spread of human pathogens, including emerging respiratory infections, HIV/AIDS, and potential bioterror agents such as smallpox (Ancel Meyers et al. 2003; Jones and Handcock 2003; Eubank et al. 2004). Information on pairwise relationships between individuals in nonhuman primates could be used to explore disease spread in the context of social interactions (e.g. using grooming matrices, Hemelrijk and Lutejin 1998).

4.4.3 Multi-host dynamics

The majority of parasites examined to date, including many emerging diseases and over 60% of pathogens infecting humans and nonhuman primates, are capable of infecting more than one host species (Murphy 1998; Cleaveland et al. 2001; Dobson and Foufopoulos 2001; Pedersen et al. 2005). These multi-host parasites tend to pose problems for a wide array of wildlife species, as evidenced by population declines or high mortality in African carnivores caused by rabies and canine distemper virus, sea otters infected with Toxoplasma, and black-footed ferrets infected by canine distemper (Roelke-Parker et al. 1996; Harvell et al. 1999; Daszak et al. 2000; Jensen et al. 2002; Miller et al. 2002). In some cases, outbreaks originate from livestock or animals kept as pets, from recently introduced exotic hosts, or from pathogen exchanges following contact between wild host species that do not normally interact with one another. Despite their apparent importance, however, the dynamics of multi-host parasites in wild animal populations are not well understood (Desdevises et al. 2002), in part because conventional studies focus mainly on single host-pathogen systems (Anderson and May 1991; Bull 1994; Day 2001).

Adding multiple host species to an infectious disease system introduces another level of heterogeneity that can have major impacts on pathogen spread and evolution, as researchers must account for transmission within and between host species and differential effects of parasites on each host (Frank 1993; Begon et al. 1999; Woolhouse et al. 2001; Antonovics et al. 2002; Gandon 2002, 2004; Holt 2003). Theoretical studies point out several key dynamical properties of multi-host pathogens (Dobson 2004; Fenton and Pedersen 2005). First, the presence of reservoir hosts can lead to periodic pathogen resurgence following long durations of disease-free periods in highly susceptible host species (Cleaveland and Dye 1995; Keeling and Gilligan 2000; Haydon et al. 2002a; Swinton et al. 2002). Second, parasites in multiple host systems can intensify disease impacts on sensitive wildlife species (Greenman and Hudson 2000). This effect arises because a pathogen restricted to a rare species is unlikely, by itself, to drive the species to extinction; on the other hand, if the pathogen can infect a common host species, then infections to a less common species can remain high even if that species is declining toward extinction (McCallum and Dobson 1995). Third, host-parasite interactions involving more than two host species can yield complex dynamical outcomes, and often support the proverb that "my enemy's enemy is also my friend" (Dobson and Crawley 1994). For example, parasites can reverse the outcome of competition between host species sharing the same resource if the dominant competitor is more susceptible to infection. Apparent competition is a related phenomenon whereby two or more hosts not directly competing for resources are affected by the same parasite, but to different degrees (Holt and Pickering 1985; Greenman and Hudson 1999; Gilbert et al. 2001). In this case, generalist parasites that are relatively benign in one host species may depress the density of other hosts for which they are more pathogenic. These general insights indicate that many threatened species, including a number of

Fig. 4.10 Disease emergence following the introduction of a novel parasite into a susceptible host population defined by different average numbers of males and females per group, and with female dispersal among groups. Plots show (a) average number of infections and (b) average number of groups infected at the end of the simulation, relative to variation in the number of males and females. The parasite establishes more readily in single-male systems (darker bars) due to dispersal of females from groups following the death of the male. Groups were formed and the infection was initiated in one randomly chosen individual, with a user-defined incubation period, disease-induced host mortality rate (virulence), and within-group transmission rate. In this spatially explicit simulation model, dispersing individuals were assumed to move in a random walk through the population until they encountered another group with one or more opposite-sexed individuals. (Nunn et al. in review).

Fig. 4.10 Disease emergence following the introduction of a novel parasite into a susceptible host population defined by different average numbers of males and females per group, and with female dispersal among groups. Plots show (a) average number of infections and (b) average number of groups infected at the end of the simulation, relative to variation in the number of males and females. The parasite establishes more readily in single-male systems (darker bars) due to dispersal of females from groups following the death of the male. Groups were formed and the infection was initiated in one randomly chosen individual, with a user-defined incubation period, disease-induced host mortality rate (virulence), and within-group transmission rate. In this spatially explicit simulation model, dispersing individuals were assumed to move in a random walk through the population until they encountered another group with one or more opposite-sexed individuals. (Nunn et al. in review).

Fig. 4.11 Example of a social (in this case, sexual) network for modeling the spread of directly transmitted infections. In this diagram, the solid circles are males and open circles are females, and the lines connecting individuals indicate sexual relationships. Note that in this network, the typical individual has relatively few partners per year, but there are a few individuals that connect many of the nodes, including the male in the dotted circle who has had mating contacts with nine other partners and represents a major link among these individuals. Modified from Jones, J. H. and Handcock, M. S. "An assessment of preferential attachment as a mechanism of human sexual network formation. Proceedings: Biological Sciences. 270: 1123-1128.

Copyright (2003) by The Royal Society.

Fig. 4.11 Example of a social (in this case, sexual) network for modeling the spread of directly transmitted infections. In this diagram, the solid circles are males and open circles are females, and the lines connecting individuals indicate sexual relationships. Note that in this network, the typical individual has relatively few partners per year, but there are a few individuals that connect many of the nodes, including the male in the dotted circle who has had mating contacts with nine other partners and represents a major link among these individuals. Modified from Jones, J. H. and Handcock, M. S. "An assessment of preferential attachment as a mechanism of human sexual network formation. Proceedings: Biological Sciences. 270: 1123-1128.

Copyright (2003) by The Royal Society.

primates, may be at risk from generalist parasites held in reservoir populations (addressed in Chapter 7).

Multi-host parasites are often transmitted by vectors or via long-lived infective stages that persist in the hosts' environment. Although many researchers assume that adding multiple host species to parasite transmission dynamics will have negative effects on vulnerable wildlife species, in the case of vector-borne diseases this effect could be reversed. Thus, Rudolf and Antonovics (2005) developed a general host-pathogen model to show that under the assumption of frequency-dependent transmission (which probably characterizes many vector-borne pathogens, see Box 4.3), adding a second host species to the system could actually prevent the pathogen-mediated extinction of a more vulnerable host. Their study emphasizes the need for empirical data on the role of host diversity in the dynamics and impacts of multi-host parasites.

For some vector-borne pathogens, such as Borrelia burgdorferi (a tick-borne bacterial pathogen and the causative agent of Lyme disease), a greater diversity of host species might reduce pathogen prevalence and impacts on humans or species of conservation concern (Schmidt et al. 2000; Logiudice 2003). This occurs in part through a mechanism termed the "dilution effect," whereby high host species diversity reduces parasite prevalence by limiting the effects of competent reservoir hosts.

In the Lyme disease system, for example, white-footed mice are the most competent host for Borrelia replication. As the number of non-mouse species increases, more contacts are likely to occur between the deer tick vectors and less competent reservoir hosts, thus tending to reduce prevalence in the ticks, the non-competent reservoirs, and the mice. Although host species diversity could play a similar role in reducing the transmission potential of other vector-borne diseases, including parasites that infect a range of primate hosts, its general importance in wild animal populations is largely unknown.

The presence of multiple host species could also impact the evolution of pathogen virulence (Woolhouse et al. 2001; Gandon 2004). For parasites infecting a single host species, theory predicts that they should evolve to optimum levels of virulence as determined by tradeoffs between virulence and transmission (or by different levels of within-host competition, see Chapter 2). On the other hand, the presence of multiple host species allows parasites with unusually high virulence to persist in some "dead end" hosts or those that contribute only weakly to parasite transmission, provided that they have weaker effects in a reservoir host. Indeed, this could explain die-offs caused by some multi-host pathogens in primates, including outbreaks of Ebola and related filoviruses in humans and apes (Sanchez et al. 1995; Leroy et al. 2004a), Sin Nombre Virus outbreaks in humans in the southwestern United States (Khan et al. 1996), and high mortality induced by yellow fever virus among monkeys and humans in Central and South America (Chapter 1). These and other consequences of parasite interactions with multiple host species remain largely unstudied at an empirical level.

Finally, it is important to keep in mind that the interplay between parasitism and multi-host systems can have major repercussions for biodiversity and stability of ecological communities (Holt and Pickering 1985; Begon and Bowers 1994). Thus, parasites could prevent any single species or group of species from dominating communities, allowing many species to coexist at relatively low densities. Several empirical observations illustrate the role of pathogens in determining plant and animal community structure and modifying ecosystems. Pathogens that attack key herbivores can have major effects on plant recruitment and abundance (Dobson and Crawley 1994), and can also impact the density of predators and other natural enemies (Dobson and Hudson 1986). One example is furnished by the myxoma virus epidemic in rabbits in southern England. Although a high abundance of rabbits in the mid-1900s prevented the regeneration of woody plants in grassland habitats, myxoma virus (introduced in the 1950s) led to a scarcity of rabbits for the next 15 years. Remarkably, in areas where rabbit grazing had previously prevented tree establishment, a cohort of oak seedlings grew into forests following the initial epidemic (Dobson and Crawley 1994). Similar cases can be found in East Africa, where rinderpest and bacterial pathogens caused changes in herbivore abundance and radically altered the structure of plant communities. Although these examples are cases where pathogens have generated striking changes in community structure, the vast majority of host-parasite interactions are likely to yield more subtle yet still substantial effects on the assembly of ecological communities.

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