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Journal of Animal Ecology 2004 73, 1121–1128

Flea species richness and parameters of host body, host geography and host ‘milieu’ Blackwell Publishing, Ltd.

BORIS R. KRASNOV*, GEORGY I. SHENBROT*, IRINA S. KHOKHLOVA† and A. ALLAN DEGEN† *Ramon Science Center and Mitrani Department of Desert Ecology, Jacob Blaustein Institute for Desert Research, Ben-Gurion University of the Negev, PO Box 194, Mizpe Ramon 80600, Israel; and †Desert Animals Adaptation and Husbandry, The Wyler Department of Dryland Agriculture, Jacob Blaustein Institute for Desert Research, BenGurion University of the Negev, Beer-Sheva 84105, Israel

Summary 1. We have assessed how different host parameters affect species richness of flea assemblages using the independent contrasts method. Three groups of host parameters were examined. The first group included host body parameters (body size, basal and average daily metabolic rates), the second group included parameters of geographical range size and position of this range in relation to the equator (latitude) and the third group comprised parameters related to the number of sympatric closely related species. 2. None of the host body parameters correlated with species richness of flea assemblages. 3. Flea species richness increased with an increase in latitude of the geographical range centre of a host as well as with an increase in a composite variable that described the size of the geographical range. 4. The number of sympatric closely related species both across the entire geographical range and locally was correlated positively with species richness of fleas. 5. Our results show that species richness of ectoparasites is affected little by parameters of the host body and to a greater extent by parameters related to the host environment. Key-words: body size, flea species richness, geographical range, latitude, mammals, metabolic rate. Journal of Animal Ecology (2004) 73, 1121–1128

Introduction Parasites play important roles in the regulation of populations and communities of their hosts (e.g. Poulin 1998). Consequently, the number of attempts to explain patterns of parasite species richness among host species has increased greatly recently. Different host species harbour different number of parasite species (e.g. Caro, Combes & Euset 1997). It is highly improbable that parasite species are distributed randomly among their hosts but rather parasite species richness results from multiple factors (see Combes 2001). In fact, Combes (2001) listed as many as 16 different (not necessarily alternative) hypotheses related to correlates of parasite species richness. However, most of these hypotheses have never been tested, whereas testing others provided contradictory results. For example, Price & Clancy (1983) demonstrated that parasite

© 2004 British Ecological Society

Correspondence: Dr Boris Krasnov, Ramon Science Center, PO Box 194, Mizpe Ramon 80600, Israel. Fax: +972 8 6586369; E-mail: [email protected]

assemblages are richer in hosts that have larger geographical ranges. Guegan & Kennedy (1993) re-analysed Price & Clancy’s data and showed that the observed correlation was due to recently introduced hosts that both occupy limited areas and harbour poor parasite assemblages. Other host parameters that have been tested as correlates of parasite richness include body size (e.g. Morand & Poulin 1998), basal metabolic rate (Morand & Harvey 2000) and co-occurrence of closely related species (e.g. Caro et al. 1997). Most studies aimed at finding a correlation between parasite species richness and some host parameters were done using helminth parasites (Morand & Poulin 1998; Rohde & Heap 1998; Morand & Harvey 2000) or ectoparasites of marine animals (Poulin & Rohde 1997; Raibaut, Combes & Benuit 1998; Rohde & Heap 1998). Correlates of species richness in ectoparasite assemblages of terrestrial hosts are less known, although studies to describe these patterns in terrestrial ecosystems have been attempted (Kuris & Blaustein 1977; Cumming 2000; Clayton & Walther 2001; Stanko et al. 2002). However, ectoparasites must be influenced not only by

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© 2004 British Ecological Society, Journal of Animal Ecology, 73, 1121–1128

host characteristics, but also by the characteristics of their off-host environment. Consequently, species richness of ectoparasites is expected to be correlated with the parameters of the host body, such as body size and metabolic rate, at least to the same extent as with the parameters related to the host environment such as geographical range and the number of sympatric species. Fleas (Siphonaptera) are characteristic mammalian ectoparasites and are most diverse on small and mediumsized species. They usually alternate between periods when they occur on the host body and periods when they occur in its burrow or nest. In most cases, preimaginal development is entirely off-host. The larvae are usually not parasitic and feed on organic debris in the burrow and /or nest of the host. The degree of association between a particular flea species and a particular host species varies from highly host-specific to host-opportunistic fleas with the majority of species being specific for a particular host genus or subfamily (Marshall 1981). The aim of this study was to assess which host parameters correlate with species richness of flea assemblages. We investigated three groups of host parameters. The first group characterized the host body and included body size, basal metabolic rate (BMR) and average daily metabolic rate (ADMR). Larger hosts are expected to sustain richer flea assemblages because they provide more space and a greater variety of niches and, thus, can provide different fleas with an opportunity of spatial niche diversification (e.g. Hsu, Hsu & Wu 2002). In addition, larger hosts presumably have a larger number of sites that need different defensive abilities against parasites. A host trade-off between host-defensive abilities on different sites and a parasite trade-off in parasite evasive abilities on different sites may lead to segregation of parasite site-specificities (Reiczigel & Rózsa 1998). This can be another reason for higher flea richness on larger hosts. BMR is expected to correlate positively with parasite species richness because hosts exposed to diverse infections should invest in a high BMR in order to compensate for a costly immune response (Morand & Harvey 2000), although others have argued that the cost of immune response is an energy cost above that of BMR (Degen 1997). Nevertheless, BMR in mammals has been shown to correlate positively with helminth species richness without compromising host longevity (Morand & Harvey 2000). If parasite species richness is expected to be correlated positively with BMR, correlation of parasite richness with ADMR is expected to be more pronounced. This is because ADMR includes the BMR, the heat increment of feeding for maintenance, locomotory and thermoregulatory costs and, consequently, is considered as a more appropriate measurement than BMR for evaluating the energy requirements and efficiency of energy utilization of an animal (Degen 1997). The second group of characteristics included parameters of geographical range size and position of this

range in relation to the equator. We hypothesized that the size of the geographical range will correlate positively with flea richness (Combes 2001), whereas the latitude will correlate negatively with flea richness according to the well-known pattern of latitudinal gradient of species richness. Finally, the third group of characteristics comprised parameters related to the number of sympatric closely related species. The richer taxonomic ‘milieu’ of a host increases the probability of lateral transfer of parasites and, thus, can increase parasite richness of a host species (Combes 2001).

Materials and methods Data on flea species richness were obtained from published studies that sampled fleas and reported data on number of flea species found on a particular rodent species in a particular location (see Appendix S1). We used only those sources where sampling effort (the number of hosts examined) was reported. Data on basal metabolic rate (BMR) and average daily metabolic rate (ADMR) (for some species) were obtained from various sources (Appendix S1). Metabolic rate in these sources was expressed either as O2 consumption per unit time or in energetic units. To ensure the consistency of the data we recalculated all data in energetic units, assuming 20·08 kJ per ml O2. Data on mean body mass were obtained either from original sources or from Silva & Downing (1995). In total, we used data on flea species richness and metabolic characteristics on 92 rodent species. Estimates of parasite species richness may be biased if some hosts are studied more intensively than others (Morand & Poulin 1998). Consequently, unequal between-host study effort may result in confounding variation in estimates of flea species richness. To ensure that variation in between-host sampling effort did not bias estimates of species richness, we regressed the number of flea species found against the number of hosts examined. Flea richness appeared to be affected strongly by sampling effort (r2 = 0·49, F = 90·0, P < 0·001). Each value of flea richness was then substituted by its residual deviation from a linear regression on the number of hosts examined. This provided a measure of flea richness that is independent on sampling effort. Both BMR and ADMR were correlated significantly with body mass (r2 = 0·18, F = 19·5 and r2 = 0·91, F = 322·5; P < 0·001 for both). Consequently, we controlled BMR and ADMR for body mass, using residuals from linear regression in a log-log space. Parameters that described the geographical range of each host species included the area of geographical range, north–south and west–east lengths of geographical range and the latitude of the geometric centre of the range. These variables were generated from distribution maps of each rodent species. Distribution range maps were composed as polygon maps using the  3·2 software based on maps from Hall (1981), Panteleev, Terekhina & Warshavsky (1990), Redford & Eisenberg

1123 Species richness of flea assemblages


(1992), Zhang et al. (1997) and Skinner & Smithers (1990) with corrections of Bates (1994), Smith (1998), Mezhzherin (1997) and Kefelioglu & Krystufek (1999). Because the first three geographical variables were correlated strongly with each another (r = 0·59–0·93, P < 0·001 for all), we substituted them with the scores calculated from principal component analysis of these three variable after log-transformation. The resulting ‘geographical range factor’ explained 83·5% of the variance, its eigenvalue was 2·5 and factor loadings were 0·97, 0·92 and 0·83, respectively. The size of the geographical range also correlated, albeit weakly, with the latitude (r = 0·32, P < 0·05). However, when we included the latitude into principal component analysis with the other three geographical variables, its loading into the resulting principal component (‘geographical range factor’) was not significant (0·35). Consequently, the latitude variable was omitted from the construction of the ‘geographical range factor’ and analysed separately. The ‘milieu’ of each host species was characterized by the number of sympatric species belonging to the same subfamily both across its entire geographical range and locally where flea species richness was studied. Because of a correlation between these variables (r = 0·69, P < 0·05), we substituted them with the principal component scores calculated as described above and obtained the ‘milieu factor’ (percentage of variance explained was 84·3, eigenvalue was 1·7 and factor loadings were 0·92 and 0·91, respectively). To control for the effects of phylogeny, we used the method of independent contrasts (Felsenstein 1985). We used a phylogeny of rodents derived from various sources. Sources for the construction of phylogenetic tree were Gromov et al. (1965), Giboulet et al. (1997) and Piaggio & Spicer (2001) for Sciuridae; Best (1993) and Riddle (1995) for Heteromyidae; Smith (1998) for Geomyidae; Rossolimo et al. (2001) for Myoxidae; Shenbrot (1992) for Dipodidae; Gromov & Polyakov (1977), Conroy & Cook (2000) and Haring, HerzigStraschil & Spitzenberger (2000) for Arvicolinae; Pavlinov et al. (1990) for Gerbillinae; Martin et al. (2000) and Ducroz, Volobuev & Ganjon (2001) for Murinae; Smith (1999), Riddle, Hafner & Alexander (2000), Tiemann-Boege et al. (2000), Bonvicino & Moreira (2001), Edwards & Bradley (2002), D’Elia (2003) and Weksler (2003) for Sigmodontinae and Peromyscinae. Family and subfamily relations were based on Michaux & Catzeflis (2000) and Montgelard, Tirard & Verneau (2002). The branch length was set to 1·0. The phylogenetic tree of rodents is given in Fig. 1. To compute independent contrasts, we used the : program (Garland et al. 1993; Midford, Garland & Maddison 2003) implemented in Mesquite modular system for evolutionary analysis (Maddison & Maddison 2004). Pairs of sister branches that diverged before many years are likely to return greater contrasts than pairs that diverged recently. To avoid this, we standardized each contrast by dividing it by its

standard deviation (Garland, Harvey & Ives 1992). To verify that contrasts were properly standardized, we plotted the absolute values of standardized contrasts against their standard deviation (Garland et al. 1992). No significant linear or non-linear trend was found in these plots, suggesting that the contrasts were adequately standardized. We obtained 91 independent contrasts for comparison of flea richness with each of host variables except for ADMR. The list of species for which information on both ADMR and richness of flea assemblages was available included only 32 species and, thus, 31 independent contrasts were produced by the analysis. To test for the correlation between flea richness and body mass, metabolic, geographical and milieu characteristics of host species, we regressed standardized contrasts of number of flea species on standardized contrasts of each of these variables using major axis regression forced through the origin (Garland et al. 1992; Pagel 1992). We avoided an inflated Type I error by performing Bonferroni adjustments of α. All quantitative data were log-transformed.

Results Contrasts in independent variables were not correlated with each another (r = −0·14–0·18, P > 0·05 for all) except for contrasts in ‘geographical range factor’ and latitude (r = 0·26, P < 0·05). Consequently, the two latter variables were analysed both separately and together using stepwise regression. None of the parameters of the host body correlated with species richness of flea assemblages (r = 0·05 for body mass, r = 0·10 for BMR, and r = 0·02 for ADMR, P > 0·3 for all). In contrast, both geographical variables correlated positively with flea species richness. Species richness of flea assemblages increased with an increase in a composite variable that described overall size of geographical range and two dimensions of the length of geographical range (r = 0·34, P < 0·001; Fig. 2a). In addition, flea species richness increased with the latitude of the centre of the geographical range (r = 0·35, P < 0·001; Fig. 2b). Finally, the number of sympatric closely related species both across the entire geographical range and locally was positively correlated with species richness of fleas (r = 0·29, P < 0·001; Fig. 3).

Discussion This study failed to find a relationship between species richness and mammalian metabolic rate, which is unlike the findings of Morand & Harvey (2000), who reported a positive correlation between parasite species richness and mass-independent BMR. They stated that BMR in species harbouring rich assemblages of parasites increases owing to a higher allocation to the immune system. This is because activation of an immune response, and even maintenance of a competent immune system,


Fig. 1. Phylogeny of rodent species used in the data set.


is an energetically demanding process that necessitates trade-off decisions among competing energy demands for growth, reproduction, thermoregulation, work and immunity (Sheldon & Verhulst 1996; but see Klaising 1998). The absence of correlation between any of the metabolic parameters and flea species richness found in our study suggests that either flea parasitism does not affect a rodent host negatively or does not trigger an immune response, or else the immune response to a particular flea species is equally effective for other flea species (cross-resistance). However, flea parasitism has been shown to have an energetic cost for a host. Energy requirements for maintenance in gerbil Gerbillus dasyurus (Wagner) increased under parasitism by the flea Xenopsylla ramesis (Rothsch.), although the amount of

blood consumed by the parasite was extremely small (Khokhlova et al. 2002). This suggests that the major effects of the parasites on the energy expenditure of the host could be through means other than blood depletion, such as stimulation of an immune response by gerbils to derived molecules from the salivary glands of the fleas. However, the similarity of salivary components within a parasite taxon can lead to cross-resistance (= heterospecific resistance) of a host against closely related parasites (McTier, George & Bennet 1981). The occurrence of cross-reactions of the immune response to different fleas can be responsible for the absence of correlation between host metabolic parameters and flea species richness. In addition, an increase in energy requirements for maintenance in G. dasyurus under flea


Fig. 3. Relationship between flea species richness (corrected for host sampling effort) and the principal component of the number of sympatric members of the same subfamily across the entire geographical range and in the location of flea richness study using independent contrasts.

Fig. 2. Relationship between flea species richness (corrected for host sampling effort) and (a) the first principal component of size and north–south and west–east lengths of host geographical range and (b) latitude using independent contrasts.


parasitism in spite of the relatively low lost of blood indicated that an energetic cost of an immune response was above the ADMR of the rodent (Khokhlova et al. 2002), which suggests that a better parameter of flea richness on a host to be tested may be the ability of the host to increase its metabolic rate above requirements and not ADMR itself. We did not find a correlation between host body mass and flea species richness in spite of relatively large range in body mass in our data set (range = 7·4 –1500 g, median = 42·8 g). A positive correlation between parasite richness and body size of mammalian hosts has been reported previously (Gregory, Keymer & Harvey 1996). However, no relationship between mammalian body size and helminth parasite species richness was found in other studies (Poulin 1995; Feliu et al. 1997; Morand & Poulin 1998). The same was true for bird body size and species richness of lice (Clayton & Walther 2001). Furthermore, Arneberg (2002) found a positive relationship between strongylid nematode richness and mammalian body mass, but the effect of host population densities had to be controlled for to see this. However, in rodents the density can vary greatly on a temporal scale, with 10-fold fluctuations often found. Consequently, consideration of mean rodent density in the present context is not feasible. Our results suggest that the conclusions of Poulin (1995) and

Morand & Poulin (1998) about the lack of relationship between body size and species richness of mammalian endoparasites are also valid for ectoparasites. It may differ in fish, however, as a correlation between host body size and parasite richness was reported for fish ectoparasites when the effect of phylogeny was removed (Guegan & Morand 1996). Another, but not necessarily alternative, explanation for the absence of correlation between body size and flea richness may be that the main habitat for fleas is not the body of a host but rather its burrow or nest. Consequently, flea richness might be related to the size and the degree of complexity of the host burrow rather than to its body size, although this has never been tested. That this may be the case found support in the great gerbil Rhombomys opimus (Lichtenstein). This rodent constructs highly complicated and deep burrows and has the richest flea assemblages (15 species) among gerbillines. A positive correlation between flea species richness and the size of the host geographical range suggests that hosts with larger geographical ranges encounter more parasite species. Indeed, this pattern was found in other parasite assemblages (e.g. Feliu et al. 1997), although it appeared not to be universal (e.g. Clayton & Walther 2001). Combes (2001) suggested that this relationship could be interpreted in the framework of the theory of island biogeography (species richness on islands correlates positively with the size of the island: MacArthur & Wilson 1967). The most common pattern of latitudinal gradient is that, in general, the inventory of species declines further from the equator as was repeatedly shown for free-living animals (e.g. Rohde 1992; Rosenzweig 1992). However, studies of this pattern in parasites provided contradictory results. For example, Rohde & Heap (1998) showed this pattern for monogeneans but not for endoparasites of marine fish, whereas Poulin (1995) failed to find any relationships between latitude and richness of gastrointestinal parasites of birds and



mammals. The absence of a latitudinal gradient pattern for endoparasites was explained by the relative stability of their environment (inside the host body) (Rohde & Heap 1998). Ectoparasites, in contrast, are exposed to environmental conditions that change with latitude. However, the trend that we found for flea assemblages is opposite to the main latitudinal gradient rule. One of the reasons for this can be that only few flea assemblages of both tropical and Arctic rodents have been studied. In our data set, the centre of geographical range was situated at latitudes lower than 20° in only two species and at latitudes higher than 60° in only three species. Another reason for the positive correlation between flea species richness and latitude can be the positive correlation, albeit relatively weak in our study set, between latitude and the size of the geographical range of a species (Rapoport’s rule; Rapoport 1982) and, thus, the observed trend could merely obscure the true relationship between flea richness and geographical range size. However, a stepwise multiple regression did not remove the effect of latitude when it was analysed together with ‘geographical range factor’, suggesting that latitude has some independent effect on flea richness. Perhaps this is due to the deeper burrows in rodents from temperate regions (Kucheruk 1983), as flea assemblages are likely to be richer in the deeper burrows (see above). A pattern opposed to the common latitudinal gradient expectation was reported by Poulin (2001) for helminth communities in temperate vs. tropical fish. Rohde (1996, 1999) questioned the generality of the latitudinal gradient rule and suggested that this rule is a ‘local’ phenomenon that is restricted to the Holarctic above latitude of 40°–50°N. However, when we limited the data set to 62 Holarctic species with geographical range centres higher than 40°N, positive correlation between latitude and flea richness remained (r = 0·34, P < 0·01). A positive correlation between flea species richness and the number of co-occurring close relatives of a host can be explained from both evolutionary and ecological perspectives. A high number of closely related hosts can allow some parasite speciations that can be transferred between hosts later, increasing parasite richness of host species (Combes 2001; Krasnov & Shenbrot 2002). This pattern was supported for ectoparasites of marine fish (Caro et al. 1997; Raibaut et al. 1998), but was not supported for parasitoids and their insect hosts (Hawkins & Lawton 1987). In conclusion, our results show that species richness of ectoparasites is affected to a greater extent by parameters related to the host environment than by parameters related to the host body, thus reflecting the important role of the environment in mediation of host– ectoparasite relationships.

Acknowledgements We thank Robert Poulin and Serge Morand for helpful comments on the earlier draft of the manuscript. This

study was supported partly by the Israel Science Foundation (grant no. 663/01–17·3 to B. R. K., G. I. S. and I. S. K). This is publication no. 158 of the Ramon Science Center and no. 408 of the Mitrani Department of Desert Ecology.

Supplementary material The following material is available from: http://www.blackwellpublishing.com/products/ fournals/suppmat/JAE/JAE883/JAE883sm.htm Appendix S1. Sources of data on species richness of flea assemblages and metabolic characteristics in 92 rodent species

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