Ecology of flea populations - Flea Species - Ecology Center (2024)

Charles Krebs defined ecology as 'the scientific study of the interactions that determine the distribution and abundance of organisms' (Krebs, 1994: 3). In other words, the main unit of ecological interest is not the individual organism but rather an assemblage of individuals belonging to the same species and coexisting in time and space. Contrary to that of most free-living species, spatial distribution of parasites is not continuous but consists of a set of more or less uniform inhabited 'islands' or patches represented by the host organisms, while the environment between these patches is decidedly unfavourable. In the majority of fleas, a 'habitat patch' also includes the host burrow or nest. This, however, does not negate the fragmented character of spatial distribution of an ensemble of conspecific fleas. This ensemble is fragmented amongst (a) host individuals; (b) host species within a location; and (c) locations. Strict terminology is required in order to distinguish between these different levels of fragmentation.

The scale involving host individuals does not represent a problem. An assemblage of parasites of a particular species inhabiting a particular individual host of a particular species is commonly defined as an infrapopulation (Margolis et al., 1982; Sousa, 1994; Combes, 2001; Poulin, 2007a). In contrast, there is no agreement regarding the terminology related to the host species and location (spatial) scales. Margolis et al. (1982) suggested referring to the assemblage of conspe-cific parasites inhabiting a particular host species in a particular location as a suprapopulation, while referring to that inhabiting an assemblage of host species in a particular location as a metapopulation. Combes (2001), however, argued that the term 'suprapopulation' in fact relabels what ecologists call 'population' and is thus redundant and confusing. In addition, he also opposed the term 'metapopulation' sensu Margolis et al. (1982) because (a) the initial definition of the term involves genetic exchange between fragments of a metapopulation

(Hanski, 1998); and therefore (b) 'metapopulations' of parasites should not differ from those of free-living species; and, thus, (c) it is problematic to use the term 'metapopulation' for a level of fragmentation below that of the population. Instead, Combes (2001) suggested defining (a) an assemblage of conspecific parasites infesting an assemblage of hosts of a particular species in a particular location as a xenopopulation; (b) an assemblage of conspecific parasites infesting an assemblage of sympatric host species as a population; and (c) an assemblage of all interconnected ensembles of conspecific parasites infesting all host species as a metapopulation.

Throughout this chapter, I use Combes's (2001) version of this terminology. I start with basic information on how to measure abundance and distribution of fleas. Then I consider variation in patterns of flea abundance among flea species as well as among host species, between host genders, and among host age cohorts. Next, I discuss the aggregative pattern of flea distribution, its causes and its consequences. Finally, I focus on host- and environment-related factors affecting flea abundance and distribution.

15.1 Measuring abundance and distribution

The fragmented pattern of distribution of a parasite among host individuals prevents us from characterizing the abundance of this parasite by a single value. This pattern stems mainly from the fact that the distribution of a parasite population across a host population is usually aggregated. In other words, most parasite individuals occur in a few host individuals, while most host individuals have only a few, if any, parasites (Anderson & May, 1978; Poulin, 1993; Shaw & Dobson, 1995; Wilson et al., 2001). Furthermore, aggregation of parasites is an almost universal phenomenon (Anderson & May, 1978; May & Anderson, 1978; Anderson & Gordon, 1982; Shaw & Dobson, 1995; Shaw et al., 1998; Poulin, 2007a, b). This ubiquity suggests that similar processes may be involved in generating the same pattern in different host—parasite systems. The aggregated distribution of parasite individuals among hosts is caused by a variety of factors (Poulin, 2007a) and can have important consequences for different aspects of the evolutionary ecology of parasites (e.g. Morand et al., 1993). As a result, the fraction of uninfested hosts should also be taken into account when the abundance of a parasite is considered. In other words, given the aggregated distribution of a parasite across hosts, a parasite's abundance should be considered in conjunction with its distribution.

Common measures of parasite abundance and distribution are mean abundance, intensity of infestation and prevalence. Mean abundance is simply the mean number of parasites per host individual and is calculated by summing both infested and uninfested hosts. Intensity of infestation (sometimes called parasite burden or parasite load) is the mean abundance of parasites per infested host individual, whereas prevalence is the proportion of infested hosts. Obviously, intensity of infestation is a product of mean abundance and prevalence. These measures are straightforward and simple to understand, and may be easily calculated.

Measuring the level of aggregation is a much greater challenge. There are several methods for this (Southwood, 1966; Elliott, 1977; Wilson et al., 2001). The most popular ones are calculation of the variance-to-mean ratio and parameter k of the negative binomial distribution. A variance-to-mean ratio of greater than 1 points to a departure from randomness and a tendency towards aggregation, while an increase in the value of the ratio indicates an increase in the aggregation level. Fitting the negative binomial distribution to an observed distribution is also a common practice to evaluate aggregation. Aggregation increases as k gets smaller until it converges on the logarithmic series with k close to zero. At large k, the distribution approaches the Poisson. The value of k can also be calculated in other ways besides fitting the negative binomial distribution, for example, by using the moment estimate of Elliot (1977), corrected for sample size:

where M is mean abundance, V(M) is variance of abundance and n is host sample size.

Another method is estimation of k using Taylor's power law (Taylor, 1961). According to this law, mean abundance (M) and variance of abundance [V(M)] of an organism's distribution are related as:

This pattern of abundance and distribution is astonishingly similar in both free-living and parasitic organisms and is supported by numerous data (Taylor & Taylor, 1977; Taylor & Woiwod, 1980; Anderson & Gordon, 1982; Perry & Taylor, 1986; Shaw & Dobson, 1995; Morand & Guegan, 2000; Simkova et al.,

2002). The exponent (parameter b or slope of Taylor's relationship, i.e. slope of linear regression of variance of abundance on mean abundance in the log—log space) of this power function usually varies among species as 1 < b <2, but the causes of the variation in this parameter between species (e.g. Kilpatrick & Ives,

2003) as well as within species (at spatial or temporal scale) are poorly understood. For parasites, it has been thought to be an inverse indicator of parasite-induced host mortality (Anderson & Gordon, 1982), as an increase in b suggests that at least some of the hosts are infected with heavy burdens of parasites. In addition, the value of the exponent b has been suggested to be an indicator n of a tendency of organisms to be mutually attracted (Perry, 1988). According to Taylor et al. (1979), parameters a and b of Taylor's power law are related to k as:

Two less popular measures of aggregation are Lloyd's (1967) index of mean crowding (m*) and the index of intraspecific aggregation, J, proposed by Ives (1988a, 1991). Index of mean crowding (m*) is useful when studying aggregation from the parasite point of view (Wilson et al., 2001). It quantifies the degree of crowding experienced by an average parasite within a host by the following expression:

where M is the mean and V(M) is the variance of the number of parasites on an average host. It is therefore a measure based on individual counts.

A measure of intraspecific aggregation, J, represents the proportional increase in the number of conspecific competitors experienced by a random individual of species k, relative to a random distribution:

where nki is the number of parasite species k on host individual i, and mk and Vk are the mean number and the variance in number of parasite species k, respectively. A zero value of J indicates random distribution of individuals, whereas J = 0.5 indicates an increase of 50% in the number of conspecific competitors expected in a patch (=host) compared to a random distribution.

15.2 Is abundance a flea species character?

It is commonly accepted that the density (abundance per unit area) of a species in a location results from the interplay between the intrinsic properties of that species and the extrinsic properties of the local habitat, both biotic and abiotic. For example, the density of a species has been shown to depend on intrinsic characters such as body size and associated metabolic rate (Blackburn & Gaston, 2001), fecundity (Hughes et al., 2000) and social structure (Lopez-Sepulcre & Kokko, 2005). On the other hand, the density of a species is undoubtedly determined by characteristics of the habitat it occupies such as the identity and composition of coexisting competitors (Rosenzweig, 1981), the amount of resources available (Newton, 1998) and the pattern of resource acquisition (Morris, 1987a).

Consequently, because it results from interactions among a variety of factors, the predictability of the density level of any given species is often low, causing problems for conservationists and pest managers (e.g. Beissinger & Westphal, 1998; Ludwig, 1999; Fieberg & Ellner, 2000). One of the probable reasons for this low predictability is the fact that the density of a species is determined simultaneously by extrinsic factors generating variation among populations of this species, and by intrinsic factors promoting between-population stability (i.e. repeatability) in density.

High intraspecific variation in the population parameters of parasites, such as their intensity of infestation, abundance and prevalence, is well documented. For example, the abundance of parasites is strongly dependent on the abundance of their host (Anderson & May 1978; see below), which, in turn, is spatially and temporally variable. Moreover, the relationship between parasite and host abundance varies from being positive (e.g. Krasnov et al., 2002e) to being negative (e.g. Stanko et al., 2006) among different species of the same parasite taxa depending on species-specific reproductive rate and seasonality (see also below). In addition, the dependence of survival and, consequently, abundance of parasites on spatially variable abiotic factors (e.g. microclimate) has been reported for both endoparasites (via effects on transmission: Galaktionov, 1996) and ectoparasites (direct effect: Metzger & Rust, 1997). However, in spite of the strong dependence of parasite population parameters on extrinsic factors and, therefore, the expected spatial and temporal variation of these parameters, species-specific features of parasites such as body size and egg production could constrain this variation (Poulin, 1999). Indeed, Arneberg et al. (1997), studying nematodes parasitic on mammals, demonstrated that intensity of infection as well as abundance were repeatable within nematode species, i.e. were less variable within than between species. The conclusion from their study is, therefore, that the levels of intensity of infection and abundance are 'true' attributes of a nema-tode species. Another study of intraspecific variability versus stability of parasite population parameters was carried out on different taxa of metazoan parasite species of Canadian freshwater fish (Poulin, 2006). Again, prevalence, intensity of infection and abundance values from different populations of the same parasite species were more similar to each other, and more different from those of other species, than expected by chance alone. These results suggest that intensity of infestation and abundance are real characters of parasite species, supporting the view that the biological features of parasite species can potentially override local environmental conditions in driving parasite population dynamics.

Fleas are much more strongly influenced by their off-host environment than either the endoparasites or permanent ectoparasites studied by Arneberg et al. (1997) and Poulin (2006). This suggests that the reported patterns may not be valid for them. However, low variation of within-flea species density on a temporal scale (e.g. Oropsylla silantiewi and Rhadinopsylla li in Central Asia: Berendyaeva & Kudryavtseva, 1969; Citellophilus tesquorum: Tchumakova et al., 2002) or a spatial scale (e.g. Xenopsylla conformis, Nosopsyllus laeviceps, Stenoponia tripectinata and Rhadinopsylla ucrainica in the Apsheron Peninsula: Kadatskaya & Kadatsky, 1983) has been reported. Launay (1989) noted that temporal density fluctuations of Xenopsylla cunicularis in Spain are less expressed than those of its host, the European rabbit Oryctolagus cuniculus.

To compare within- and between-species variation in flea abundance, Krasnov et al. (2006d) used data on fleas parasitic on small mammals for samples of 548 flea populations, representing 145 flea species and obtained from 48 different geographical regions. First, a strong positive correlation was found between the lowest observed abundances and all other observed abundance values across flea species. In other words, different fleas demonstrated a relatively narrow range of abundances when exploiting the same host species in different regions (Fig. 15.1a). Second, the results of the repeatability analysis (Arneberg et al., 1997) showed that abundances of the same flea species on the same host species but in different regions were more similar to each other than expected by chance, and varied significantly among flea species, with 46% of the variation among samples accounted for by differences between flea species (Fig. 15.1b). Thus, estimates of abundance are repeatable within the same flea species. The same repeatability was also observed, but to a lesser extent, across flea genera, tribes and subfamilies, but not families.

The above analysis demonstrated that patterns found for mammalian endoparasites (Arneberg et al., 1997) and parasites of fish (Poulin, 2006) are also valid for fleas despite their greater sensitivity to external factors. Flea abundance can thus be considered as a true flea species character. Furthermore, abundance can also be considered an attribute characteristic of a flea genus, tribe or subfamily, but not family. This implies that some flea species-specific life-history traits determine the limits of abundance.

Lower limits of flea abundance can be affected by species-specific mating systems and/or the relationship between mating and blood-feeding, whereas upper limits of abundance can be determined by species-specific reproductive outputs, generation times, preferences for blood-sucking on a specific body part of a host and/or the ability of both imagoes and larvae to withstand crowding. For example, site-specific fleas are more prone to crowding and thus may achieve lower abundance than non-site-specific species. Numerous examples of variation in these parameters among flea species can be found in the previous chapter of this book.

Figure 15.1 (a) Relationship between the lowest abundance and other abundance values on the same host species for 145 flea species recorded in at least two regions; (b) rank plot of flea abundance (see Fig. 14.4 for explanations). Redrawn after Krasnov et al. (2006d) (reprinted with permission from Springer Science and Business Media).

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