International Journal for Parasitology 37 (2007) 919–925 www.elsevier.com/locate/ijpara
Host defence versus intraspecific competition in the regulation of infrapopulations of the flea Xenopsylla conformis on its rodent host Meriones crassus Hadas Hawlena b
a,b,c,*
, Zvika Abramsky a, Boris R. Krasnov
b,c
, David Saltz
b
a Department of Life Sciences, Ben-Gurion University of the Negev, 84105 Beer-Sheva, Israel Mitrani Department of Desert Ecology, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boqer Campus, 84490 Midreshet Ben-Gurion, Israel c Ramon Science Center, P.O. Box 194, 80600 Mizpe Ramon, Israel
Received 6 December 2006; received in revised form 22 January 2007; accepted 28 January 2007
Abstract Mechanisms that regulate parasite populations may influence the evolution of hosts and parasites, as well as the stability of host-parasite dynamics but are still poorly understood. A manipulation experiment on the grooming ability of rodent hosts (Meriones crassus) and flea (Xenopsylla conformis) densities on these hosts successfully disentangled two possible regulating mechanisms: (i) behavioural defence of the host and (ii) intraspecific competition among parasites, and revealed their importance in suppressing the feeding of fleas. Moreover, the results suggest that flea competition is direct and is not mediated by host grooming, immune response, or parasite-induced damage to the host. These mechanisms, together with interspecific competition and density-dependent parasite-induced host damage, may limit the parasite burden on an individual host and may prevent parasites from overexploiting their host population. 2007 Australian Society for Parasitology Inc. Published by Elsevier Ltd. All rights reserved. Keywords: Regulation mechanisms; Host-parasite coexistence; Competition; Grooming; Feeding success; Ectoparasites; Xenopsylla conformis; Meriones crassus
1. Introduction Although extremely common in nature, parasites rarely overexploit their host population, suggesting some form of parasite- or host-mediated regulation (Anderson, 1991). Nevertheless, despite comprehensive empirical evidence for the existence of parasite regulation, the mechanisms driving it are poorly understood (Quinnell et al., 1990; Lowrie et al., 2004; see below). Experimental evidence from various host-parasite systems suggests that regulation of parasite populations is *
Corresponding author. Address: Department of Life Sciences and Ramon Science Center, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, 84105 Beer-Sheva, Israel. Tel.: +972 8 6472633; fax: +972 8 6472631. E-mail address:
[email protected] (H. Hawlena).
mostly density-dependent (Quinnell et al., 1990; Kelly et al., 1996; Tripet and Richner, 1999; Ebert et al., 2000; Tyre et al., 2003; Lowrie et al., 2004). Density-dependent processes may reduce reproductive success of parasites by either suppressing their somatic growth, decreasing their fecundity and/or increasing their mortality (Kelly et al., 1996; Levin and Fish, 1998; Lowrie et al., 2004; Tinsley, 2004). These three negative effects are most likely to arise via reduction in feeding success of the parasite. There are several possible direct and indirect density-dependent mechanisms that may constrain feeding success. These include intra- and interspecific competition of parasites for limited resources (Patrick, 1991), density-dependent behavioural responses (Edman et al., 1972; Murray, 1987), immune response of the host (Paterson and Viney, 2002), and density-dependent parasite-induced host damage (i.e., overexploitation of the host), which in certain
0020-7519/$30.00 2007 Australian Society for Parasitology Inc. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.ijpara.2007.01.015
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H. Hawlena et al. / International Journal for Parasitology 37 (2007) 919–925
cases may even cause host mortality, thus increasing the probability of mortality of parasites themselves due to starvation (Jaenike et al., 1995; Ebert et al., 2000; Lowrie et al., 2004). Most studies do not differentiate between the various density-dependent mechanisms mentioned above (e.g., Gregory et al., 1990; Patrick, 1991; Quinnell, 1992; Tompkins and Hudson, 1999; Moller, 2000; Tyre et al., 2003; Stanko et al., 2006, but see Paterson and Viney, 2002). Moreover, while most of the evidence of parasite regulation has come from studies of helminths (see reviews by Paterson and Viney, 2002; Tinsley and Jackson, 2002; Tinsley, 2004, but see Levin and Fish, 1998), less attention has been paid to arthropod parasites. Arthropod parasites such as ticks, mites, and fleas depend not only on their host but also on the host’s environment, and may be substantially threatened by the defensive behaviour of the host (Marshall, 1981; Murray, 1987). Patterns of parasite regulation should be further studied in host-ectoparasite systems, and the exact mechanisms of parasite regulation should be established. Such regulation mechanisms allow host and parasite populations to coexist at some kind of a stable equilibrium, and may influence the evolution of hosts and parasites. Knowledge of these mechanisms is thus important for the development of control strategies against parasites and the diseases that they cause/transmit (e.g., Paterson and Viney, 2002). Here, we studied feeding success of fleas (Xenopsylla conformis Rothschild, 1904) on rodent hosts (Meriones crassus Sundevall, 1842), attempting to disentangle two of the above-mentioned density-dependent regulating mechanisms, namely (i) behavioural defence of the host and (ii) intraspecific competition among parasites. Feeding success of a flea has been shown to be a good indicator of its fecundity (Krasnov et al., 2004). Another advantage of using feeding success as a fitness-related feature is that, unlike egg production and other fitness-correlative traits, it can be measured over a short time interval ( 0.43, P < 0.001 for all combinations). The first two measures were not correlated with the proportion of fleas remaining on the host’s body (Pearson correlation: r = 0.12, P > 0.20 for both). Consequently, in the following analyses, we used mean body mass change of each flea group as the main indicator of feeding success. We applied general linear models to explore the effect of grooming status, flea density and engorgement time (independent variables) on the amount of blood engorged and the proportion of fleas remaining on the host’s body (dependent variables). Initial flea attachment to the rodent was rapid, i.e., within 3 min of the shortest infestation period. Consequently, in the analysis we deducted 3 min from all exposure times, thus setting the shortest exposure time (3 min) to zero and the longest exposure time to 112 min. Within these boundaries the regression slope reflects engorgement rate and the zero-intercept reflects the initial response which is a combination of the initial attachment success and engorgement rate over the first 3 min (Fig. 1). We tested whether the slopes of the regressions of dependent variables against engorgement time between (i) grooming-restrained and grooming-allowed rodents and (ii) low and high flea densities were different, by including the interaction between engorgement time (covariate) and grooming status or flea density (categorical predictors) in the homogeneity of slopes model. To test the effect of grooming status and flea density on the intercept of the above-mentioned regression lines, we applied either a
b 0.14
0.14
Engorgement time
0.10
0.06
0.10
0.06
0.02
-0.02
Initial response
Engorgement time Initial response
Blood engorgement (mg)
a
921
0.02 0
20
40
60
80
100 120
-0.02
0
20
40
60
80 100 120
Time (min) Fig. 1. Relationships between the level of engorgement of an individual flea (calculated as the initial group mass divided by the number of fleas weighed, minus the final group mass divided by the number of fleas weighed) and engorgement time at low (dashed line, empty circles) and high (solid line, full circles) flea densities on grooming-restrained (a) and grooming-allowed (b) M. crassus. The figure is separated into the two periods of initial response (grey) and engorgement (white) of fleas.
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separate slopes model (when a significant interaction between these variables was found) or an analysis of covariance (ANCOVA) model (when the interaction term was not significant). To adjust deviations from normality, an arcsine transformation was applied to all dependent variables. All statistical tests were two-tailed. Data are presented as means ± SE prior to transformation. P values below 0.05 were considered significant. 3. Results A strong correlation between mean body mass change per flea and the proportion of fleas with a highly engorged midgut (Pearson correlation: r = 0.57, P < 0.001) suggests that the increase in body mass of fleas was due to engorgement and therefore in the Results and Discussion, we refer to the mean body mass changes as the ‘level of engorgement per flea’. A significant effect of the grooming · engorgement time interaction on the level of engorgement indicates that grooming decreased the engorgement rate of fleas (Table 1 and Fig. 1a and b). However, the non-significant effect of grooming on level of engorgement indicates that grooming did not affect the initial response of fleas (Table 1 and Fig. 1a and b). In contrast, the significant effect of flea density combined with the non-significant effect of the flea density · engorgement time interaction on level of engorgement (Table 1 and Fig. 1a and b) showed that flea density affected the initial response but not the engorgement rate of fleas. No significant effect of the grooming status · flea density interaction on either rate of engorgement or initial response of fleas was found (Table 1 and Fig. 1a and b). The mean proportion of fleas remaining on the host’s body at the end of the infestation period was Table 1 Summary of general linear models testing the effect of grooming status
Grooming (G) Flea density (FD) Engorgement time (ET) G · FD G · ET FD · ET G · FD · ET
Level of engorgement
Proportion of fleas on host
F
F
P
0.32 0.16 0.24 0.34 1.7 0.28 1.7
0.57c 0.69c 0.62a 0.56c 0.20a 0.60a 0.20a
0.0015 38 7.7 0.47 4.3 1.2 0.73
P b
0.97
