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Journal of Animal Ecology 2006 75, 575–583

Relationship between host abundance and parasite distribution: inferring regulating mechanisms from census data

Blackwell Publishing Ltd

MICHAL STANKO *, BORIS R. KRASNOV† and SERGE MORAND‡ *Institute of Zoology, Slovak Academy of Sciences, Lofflerova 10, SK-04001 Kosice, Slovakia; †Ramon Science Center and Mitrani Department of Desert Ecology, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, PO Box 194, Mizpe Ramon 80600, Israel; ‡Center for Biology and Management of Populations, Campus International de Baillarguet, CS 30016 34988 Montferrier-sur-Lez cedex, France

Summary 1. We studied the effect of host abundance on parasite abundance and prevalence using data on 57 associations of fleas (Siphonaptera) and their mammalian hosts from Slovakia. 2. We assumed that flea-induced host mortality could be inferred from the relationship between flea aggregation and flea abundance, whereas host-induced flea mortality could be inferred from the relationship between flea abundance or aggregation and host abundance. 3. Relationships between flea abundance or prevalence and host abundance were either negative (in 23 flea–host associations) or absent (in 34 flea–host associations). Negative relationships between flea abundance and host abundance were always accompanied by negative relationships between flea prevalence and host abundance. 4. The link between flea abundance/prevalence and host abundance was evaluated as the coefficient of determination of the respective regressions. Across flea–host associations, this link decreased with an increase in the degree of flea aggregation (measured as a parameter b of Taylor’s power law). 5. Mean crowding of fleas decreased with an increase of host abundance in eight flea– host associations, being asymptotic in four of them. On the other hand, mean crowding of fleas increased with an increase in flea abundance in 49 flea–host associations, being asymptotic in 15 of them. 6. Results of this study suggest that different flea–host associations are governed by different regulating mechanisms, but different regulation mechanisms may act simultaneously within the same flea–host associations. Key-words: aggregation, host abundance, parasite abundance, prevalence. Journal of Animal Ecology (2006) 75, 575–583 doi: 10.1111/j.1365-2656.2006.01080.x

Introduction Spatial distribution of parasitic organisms is represented by a set of more or less uniform inhabited ‘islands’ or patches (their host organisms), whereas the environment between these patches is absolutely unfavourable. Thus, abundance of the hosts is an important factor affecting the distribution and abundance of parasites (Arneberg et al. 1998). © 2006 The Authors. Journal compilation © 2006 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]

A general characteristic of the parasite–host relationship is the aggregation of a parasite population in a small proportion of the host population (Anderson & May 1978). Models implying the aggregated distribution of parasites predict that mean parasite abundance increases in a curvilinear fashion to a plateau with increasing host abundance (Anderson & May 1978; May & Anderson 1978). The increase in parasite abundance is expected because of increased probability of a parasite transmission stage to meet a host, whereas the plateau is expected due to regulatory mechanisms such as parasite-induced host mortality or density-dependent reductions in parasite fecundity and survival (Grenfell

576 M. Stanko, B. R. Krasnov & S. Morand

© 2006 The Authors. Journal compilation © 2006 British Ecological Society, Journal of Animal Ecology, 75, 575–583

& Dobson 1995). These mechanisms are difficult to demonstrate in the field, because dead hosts are rarely found and if they are, the cause of death can be rarely attributed unequivocally to the parasite (McCallum & Dobson 1995). However, parasite-induced host mortality or host-induced parasite mortality can be inferred from the pattern of parasite distribution and aggregation (Anderson & Gordon 1982; Rousset et al. 1996). The prevalence of parasite infestation (proportion of infested hosts) is also expected to increase with increasing host abundance, attaining a plateau under high host abundance. The argument for this can be the same as that for parasite abundance. Another explanation follows the metapopulation theory: parasites infesting different host individuals are analogous to free-living organisms inhabiting discrete patches, and the percentage of occupation of the latter increases with the decrease of patch isolation (Thomas & Hanski 1997). In spite of growing interest in the link between parasite and host population dynamics (see Tompkins et al. 2001), only a few empirical studies of the effect of host abundance on parasite abundance and distribution have been conducted (Haukisalmi & Henttonen 1990; Arneberg et al. 1998; Krasnov, Khokhlova & Shenbrot 2002). In general, the predictions of epidemiological theory concerning the relationships between host abundance and parasite abundance have been supported (Arneberg et al. 1998). However, several field studies have demonstrated patterns that contradict the above-mentioned theoretical predictions (Schwan 1986; Sorci, Defraipont & Clobert 1997; Stanko et al. 2002). These studies explained these contradictions as implying density-dependent changes in the host’s behaviour (territoriality, spatial host aggregation and increase in grooming effort) that are not usually taken into account in simple epidemiological models. Furthermore, parasite abundance in some of these studies (Sorci et al. 1997; Stanko et al. 2002) was calculated as the pooled abundance of several parasite species. This can mask the true pattern of the link between parasite and host abundances, because the pattern may differ depending on a particular type of relationship between a particular parasite and a particular host. Indeed, even a highly host–opportunistic parasite varies in its abundance among different host species (Marshall 1981). If the difference in the abundance of a parasite in different hosts stems from differential fitness rewards (Krasnov et al. 2004a), then different hosts play different roles in the long-term persistence of a parasite population. In such cases the parasite population would thus depend mainly on one or a few key host species. Consequently, a tight link between host abundance and abundance and distribution of a particular parasite should be expected for only some hosts from the entire host spectrum of this parasite. In this work, we studied the effect of host abundance on parasite abundance and distribution using data on fleas (Siphonaptera) parasitic on small mammals (rodents and insectivores) in central and eastern Slovakia. First, we asked (a) if and how flea abundance and pre-

valence and host abundance are related in a particular flea host association and (b) if this relationship for the same flea species varies between different host species. Secondly, we asked if the relationship between parasite and host abundances is regulated by parasite-induced host mortality or host-induced parasite mortality. We used an exponent of the power relationship between mean parasite abundance and its variance (Taylor 1961) which has been suggested to be an inverse indicator of parasite-induced host mortality (Anderson & Gordon 1982). We also used an approach similar to that of Anderson & Gordon (1982) and Rousset et al. (1996). Assuming that host mortality is induced by parasite accumulation, they observed that when the rate of parasite acquisition varies among hosts, the degree of parasite aggregation is dependent on the age of the host and declines in older host individuals. We assumed that flea-induced host mortality can be inferred from the relationship between flea aggregation and flea abundance, whereas host-induced flea mortality can be inferred from the relationship between flea abundance and aggregation and host abundance. If flea-induced host mortality plays a regulating role in host populations and imposes an upper limit on the number of fleas that a host is able to endure and not to die, then a degree of aggregation will approach an asymptote with an increase in flea abundance (because of the loss of heavily infested hosts at high flea abundances). If host-induced flea mortality plays the main regulating role and imposes an upper limit on the number of fleas that a host is able to tolerate and not to mount defence mechanisms, then (a) flea abundance will decrease or, at least, will not increase with an increase of host abundance and (b) a degree of flea aggregation will approach an asymptote with an increase in host abundance (because of the lack of heavily infested hosts despite an increase in transmission rate).

Materials and methods  ,      Mammals were sampled and fleas collected between 1983 and 2001 in 18 regions across Slovakia (see details in Stanko 1987, 1988, 1994 and Stanko et al. 2002). Trapping sessions (on average, 700 traps per session) in the same region were conducted at different locations and were at least 6 months apart, which allowed the avoidance of pseudoreplications. We included in the analyses (a) flea and host species for which at least 100 adult individuals were collected (Table 1) and (b) flea–host associations that were observed in at least six, not necessarily consecutive, trapping sessions (for the minimum number of data points necessary for the subsequent regression analyses, see below). Trapping resulted in 57 flea–host associations represented by 13 flea species (in total, 16 633 individuals) and nine host species (in total, 13 775 individuals).

577 Host abundance and flea distribution

Table 1. Data on fleas and their mammalian hosts used in the analyses (abbreviations of species name in parentheses)

Flea

Sample size

Host

Sample size

Amalareaus penicilliger Grube (APEN) Ctenophthalmus agyrtes Heller (CAGY) Ctenophthalmus assimilis Taschenberg (CASS) Ctenophthalmus solutus Jordan et Rothschild (CSOL) Ctenophthalmus uncinatus Wagner (CUNC) Doratopsylla dasycnema Rothschild (DDAS)

792 6845 2351 1185 241 307

Apodemus agrarius Pallas (AAGR) Apodemus flavicollis Melchior (AFLA) Apodemus sylvaticus Linnaeus (ASYL) Apodemus uralensis Pallas (AURA) Clethrionomys glareolus Schreber (CGLA) Microtus arvalis Pallas (MARV)

3463 4975 117 1220 2327 1071

Hystrichopsylla orientalis Smit (HORI) Megabothris turbidus Rothschild (MTUR) Nosopsyllus fasciatus Bosc (NFAS) Peromyscopsylla bidentata Kolenati (PBID) Palaeopsylla similis Dampf (PSIM) Palaeopsylla soricis Dale (PSOR) Rhadinopsylla integella Jordan et Rothschild (RINT)

180 2762 926 315 100 480 149

Microtus subterraneus de Selys-Longchamps (MSUB)Neomys fodiens Pennant (NFOD)Sorex araneus Linnaeus (SARA)

 


For each flea–host association in each trapping session, we calculated (a) mean abundance and variance of abundance of a flea; (b) prevalence (proportion of infested hosts); and (c) Lloyd’s (1967) index of mean crowding (m*). This index 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 flea within a host as m* = M + [V(M)/M − 1], where M is the mean and V(M) is the variance of the number of fleas on an average host. We used the number of captures of a host species per 100 traps and per night as an estimate of host abundance for each trapping session. Body mass of individual adult hosts did not affect flea abundance (r 2 = 0·01– 0·07, P > 0·3 for all). Consequently, we did not control for the effect of body mass when calculating parasitological parameters. Mean flea abundance and prevalence were log- or arcsine-transformed, respectively, prior to analyses. These transformations provided distributions that did not differ significantly from normal (Shapiro– Wilks tests, NS). To test for the relationship between flea abundance and host density we regressed logtransformed mean abundance or arcsine-transformed prevalence of a flea against log-transformed host abundance within flea–host associations across trapping sessions. The resulting coefficient of determination (r 2) was then used as an indicator of the strength of association between host and flea abundances. Mean abundance (M ) and its variance [V(M)] of an organism’s distribution are related as V(M) = aMb (Taylor 1961). This empirical relationship, known as Taylor’s power law, has been supported by numerous data on various taxa of both free-living and parasitic organisms (Taylor & Taylor 1977; Shaw & Dobson 1995). The exponent (parameter b or slope of Taylor’s relationship) of this power function usually varies among species as 1 < b < 2. For parasites, it has been shown to be an inverse indicator of parasite-induced host mor-

100 143 359

tality (Anderson & Gordon 1982). We obtained values of the b parameter for each flea–host association by regression of the log-transformed variance of flea abundance against log-transformed mean of flea abundance across trapping sessions. Then, we regressed the values of the coefficient of determination (r 2 ) of the link between flea abundance or prevalence and host abundance against values of the Taylor’s b parameter across flea host associations in log–log space. This was performed using both conventional statistics and the method of independent contrasts (Felsenstein 1985), which controls for the confounding effect of phylogeny. A phylogenetic tree for flea–host associations was constructed using various sources (see Krasnov et al. 2004b, 2004c for details). Basal branch topology was based on flea phylogeny, whereas associations of the same flea species with different host species were considered as derived branches for each flea species. The topology of these branches was based on host phylogeny. To compute independent contrasts, we used the : program (Midford, Garland & Maddison 2004) implemented in the Mesquite modular system for evolutionary analysis (Maddison & Maddison 2004). Procedure of the analysis followed Garland, Harvey & Ives (1992). In addition, we tested for the relationship between the coefficient of determination of the link between flea and host abundances and the size of the flea community encountered by each flea species when exploiting each host species. Flea community size was characterized by mean species richness of a flea infracommunity (mean number of flea species within a host individual) or component community (mean number of flea species across host individuals within a trapping session). We regressed the log-transformed values of the coefficient of determination against log-transformed values of flea community size using both conventional statistics and method of independent contrasts. To test for the asymptote in the relationship between the pattern of flea distribution across hosts and host or flea abundance, we performed a linear regression of


m* against host or flea abundance on logarithmically transformed values. In a regression using log-transformed data, an absolute value of slope = 1 indicates a linear relationship between the degree of flea aggregation with either host or flea abundance, whereas an absolute value of slope < 1 indicates curvilinearity with an asymptote; i.e. the rate of change of the degree of aggregation decreases with an increase of either host or flea abundance. We avoided a Type I error for analyses of the relationship between parasitological parameters and host and flea abundances by performing Bonferroni adjustments of α.

Results Results of the regression analyses of flea abundance and prevalence and host abundance are presented in Table 2. In general, relationships between flea and host abundance were either negative (in 23 of 57 flea–host associations; see Fig. 1a for the illustrative example) or statistically non-significant (in the remaining 34 flea– host associations). The same was true for the relationship between flea prevalence and host density. It was negative in 34 (see Fig. 1b for the illustrative example) and absent in the remaining 23 flea–host associations. Negative relationships between flea and host abundance were always accompanied with negative relationships between flea prevalence and host density. However,


Fig. 1. Relationships between the mean abundance (a) and prevalence (b) of C. agyrtes on A. flavicollis.

Fig. 2. Relationships between the coefficient of determination of the regressions of flea vs. abundance and parameter b of Taylor’s power law across 57 flea–host associations using conventional statistics (a) and the method of independent contrasts (b).

in eight flea–host associations, negative relationships between host abundance and flea prevalence, but not abundance, were found. Thus, in general, flea abundance or prevalence or both decreased significantly with an increase of host abundance in 34 of 57 flea–host associations. The values of the coefficient of determination of the regressions of flea abundance or prevalence and host abundance decreased with an increase in parameter b of Taylor’s power law (see Fig. 2 for an example with flea abundance). This was true for both conventional statistics (r 2 = 0·25, F1,55 = 18·7 for abundance and r 2 = 0·17, F1,55 = 11·1 for prevalence; P < 0·001 for both) and the method of independent contrasts (r = − 0·39 for abundance and r = − 0·36 for prevalence; P < 0·005 for both). The link between flea abundance or prevalence and host abundance, evaluated as the coefficient of determination of the respective regressions, was not related to mean species richness of either infra- or component communities (r 2 = 0·0005 – 0·04, F1,55 = 0·03–2·4 for conventional statistics and r = − 0·13–0·06 for method of independent contrasts; P > 0·3 for all). The index of mean crowding of fleas decreased significantly with an increase of host abundance in eight of 57 flea–host associations, whereas no relation between these parameters was found in the remaining associations (Table 2). Furthermore, the absolute value of the


Table 2. Summary of regression analyses of the relationships between (A) mean flea and host abundance, (B) flea prevalence and host abundance, (C) mean flea crowding and mean flea abundance and (D) mean flea crowding and host abundance. See Table 1 for the abbreviations of species names. Presence of the slope value in a cell designates significance of the regression ( P < 0·0002) A Analysis Flea Host


d.f.

r2

B Slope

r2

0·13 0·07 11·0

– – − 0·86

F

D

Slope

r2

0·0004 0·1 0·14 0·7 0·63 14·0

– – − 0·84

0·01 0·12 0·34

0·4 – 0·22 – 4·5 –

0·5 0·86 0·74

25·9 24·0 24·0

1·21 1·55 1·08

– – – – – – – – –

0·46 71·3 0·69 113·2 0·61 71·5 0·74 22·8 0·63 69·8 0·67 34·6 0·61 6·0 0·66 28·7 0·69 26·5

1·1 1·26 1·33 1·04 1·31 0·7 – 0·9 1·37

– − 0·6 – – – – – – –

0·48 0·6 0·8 0·56 0·7 0·67 0·7 0·9 0·73

30·2 53·9 16·4 21·4 66·4 50·4 17·2 45·7 16·8

1·09 1·22 1·11 1·28 0·95 0·97 0·74 1·21 0·75

– − 0·31 −1·0 – – –

0·75 0·74 0·9 0·26 0·73 0·84

44·6 61·8 28·5 3·8 39·0 8·2

1·4 0·61 0·84 – 0·98 –

0·3 0·65

4·5 30·4

– 1·08

F

F

APEN

CGLA MARV PSUB

CAGY

AAGR 84 0·27 AFLA 103 0·24 AMIC 47 0·06 ASYL 11 0·05 CGLA 86 0·02 MARV 38 0·37 NFOD 11 0·11 PSUB 35 0·07 SARA 14 0·1

30·3 32·1 3·1 0·5 1·83 20·9 1·2 2·7 1·4

− 0·48 − 0·41 – – – − 0·64 – – –

0·38 0·45 0·15 0·55 0·19 0·54 0·11 0·25 0·24

51·2 84·1 8·3 10·9 19·8 43·2 1·1 11·1 3·8

− 0·4 − 0·38 – − 0·59 − 0·26 − 0·62 – − 0·75 –

0·02 0·02 0·001 0·06 0·001 0·04 0·04 0·49 0·009

1·9 2·1 0·3 0·2 0·05 1·1 0·2 6·7 0·05

CASS

AAGR AFLA ASYL AURA CGLA MARV MSUB NFOD SARA

70 73 7 36 61 55 21 8 9

0·4 0·46 0·62 0·17 0·33 0·04 0·18 0·07 0·32

45·4 59·8 8·1 7·2 29·4 2·04 4·4 0·49 3·4

− 0·64 − 0·71 – – − 0·62 – – – –

0·27 0·49 0·75 0·24 0·44 0·29 0·19 0·38 0·83

25·7 67·8 14·8 11·1 46·6 21·7 4·5 3·7 36·5

− 0·3 − 0·31 −1·18 − 0·21 − − 0·44 – – − 0·89

0·11 0·13 0·67 0·04 0·15 0·02 0·11 0·09 0·3

7·9 10·7 8·2 1·4 5·0 1·4 2·2 0·3 0·07

CSOL

AAGR AFLA ASYL AURA CGLA MARV

32 46 6 8 16 6

0·02 0·3 0·84 0·01 0·35 0·67

0·32 19·2 22·0 0·6 7·7 8·1

– − 0·84 −1·3 – – –

0·03 0·33 0·77 0·01 0·41 0·87

0·43 – 22·0 − 0·38 13·4 −1·34 0·4 – 9·7 – 26·3 −1·08

CUNC

AFLA CGLA

14 0·09 21 0·12

1·3 2·8

– –

0·1 0·05

1·4 1·1

DDAS

AFLA NFOD SARA

6 0·61 12 0·82 19 0·09

7·5 161·7 1·8

−1·61 −1·23 –

0·82 0·62 0·11

HORI

AAGR AFLA CGLA

28 0·54 15 0·22 7 0·2

30·3 3·8 1·3

− 0·9 – –

MTUR

AAGR AFLA ASYL AURA CGLA MARV MSUB

69 86 9 41 85 38 13

0·27 0·37 0·33 0·23 0·16 0·13 0·15

24·8 22·9 3·4 11·5 15·7 12·1 1·9

NFAS

AAGR AFLA AURA CGLA MARV

45 50 31 9 9

0·35 0·29 0·14 0·08 0·68

PBID

AAGR AFLA CGLA MARV

7 7 15 7

0·77 0·88 0·004 0·33

PSIM

NFOD

6 0·63

PSOR

AFLA NFOD SARA

9 0·02 13 0·09 41 0·24

AFLA CGLA

9 0·37 14 0·0009

RINT

28 0·004 6 0·02 10 0·58

C

0·009 0·14 0·1 24·5 0·95 22·6 0·21 0·53 0·11 1·9 0·66 6·5

Slope

Slope

F

– –

0·03 0·03

19·1 56·9 2·2

− 0·2 − 0·48 –

0·9 30·1 −1·38 0·07 0·73 – 0·005 0·09 –

0·99 132·2 0·8 41·0 0·71 40·1

0·72 0·93 1·18

0·45 0·09 0·16

25·9 1·4 1·0

− 0·16 – –

0·22 0·08 0·54

7·5 1·2 6·2

– – –

0·61 0·46 0·53

41·5 11·2 5·7

1·19 0·32 –

− 0·45 − 0·57 – − 0·59 − 0·34 − 0·3 –

0·31 0·47 0·46 0·24 0·3 0·22 0·38

29·7 35·2 6·0 12·4 34·4 24·1 6·7

0·31 − 0·24 – − 0·24 − 0·36 − 0·29 –

0·04 0·08 0·42 0·06 0·04 0·02 0·02

3·3 7·5 5·2 2·8 3·8 1·0 0·23

– – – – – – –

0·47 58·7 0·57 110·0 0·54 7·1 0·57 22·9 0·58 113·4 0·44 28·6 0·64 14·3

1·27 1·25 – 1·29 1·18 1·12 1·11

22·9 19·7 4·6 0·6 15·1

− 0·51 − 0·57 – – − 0·72

0·38 0·37 0·13 0·05 0·73

26·5 28·6 4·2 0·4 19·5

− 0·38 − 0·33 – – − 0·16

0·02 0·09 0·02 0·02 0·61

1·1 4·7 0·8 0·2 11·0

– – – – −1·03

0·45 0·66 0·71 0·77 0·8

34·6 91·8 73·5 24·1 28·0

1·28 1·36 1·45 1·42 1·35

17·1 38·4 0·05 2·5

−1·3 − 0·87 – –

0·75 0·76 0·003 0·72

15·4 − 0·15 15·6 − 0·04 0·04 – 13·3 −1·03

0·84 0·88 0·02 0·02

27·5 38·4 0·3 0·1

−2·05 − 0·88 – –

0·97 189·6 0·99 45·5 0·52 14·0 0·76 16·5

1·49 1·00 1·13 0·86

−1·03

0·74

11·3

−1·32

0·01

0·05 –

0·4

5·7

–

– – − 0·6

0·24 0·52 0·37

2·3 11·9 23·2

− 0·99 − 0·79

0·03 0·1 0·1

0·22 – 0·1 – 5·1 –

0·54 0·58 0·71

8·2 24·0 88·6

– 0·91 1·09

– –

0·35 0·01

4·8 0·1

–

0·62

11·6 − 0·75 0·55 0·01 – 0·51

88·7 12·5

0·37 1·77

– 0·11 1·1 12·7 5·1 0·1

0·001

0·44 – 0·78 –

r2

determined by populations of some but not other hosts. This consideration provides firmer grounds for a classification that distinguishes between principal and auxiliary hosts among the entire host spectrum of a parasite (Marshall 1981; Poulin 2005). Assigning a host species to one of these categories is usually performed according to abundance attained by a parasite in this host species (Krasnov et al. 2004d; Poulin 2005). However, a host in which a parasite attains the highest abundance and a host whose population dynamics is linked with that of a parasite are not always the same species. For example, Amalareaus penicilliger in the study area exploited mainly three vole species. However, the abundance and prevalence of this flea was heavily dependent on the abundance of Microtus subterraneus only, whereas it attained the highest abundance on Clethrionomys glareolus. In some cases, population dynamics of a flea was related to population dynamics of several host species and, thus, it was difficult to distinguish a single principal host. For example, abundance and prevalence of Doratopsylla dasycnema were linked almost equally with abundances of two of its three host species (Table 2). From this viewpoint, unequivocal assignment of a host species to a category of either principal or auxiliary hosts of a parasite, based on abundance attained by this parasite in this host, does not seem valid.


Fig. 3. Relationships between the mean crowding of C. solutus and (a) the abundance of A. flavicollis and (b) its own mean abundance (untransformed data).

slope in four of these eight associations was < 1, thus suggesting that the index of mean flea crowding decreased with an increase in host abundance to an asymptote (see Fig. 3a for an illustrative example). In contrast, the index of mean crowding increased significantly with an increase in flea abundance in 49 of 57 flea–host associations (Table 2). However, in only 15 associations the value of the slope was < 1. This suggests that the mean crowding of fleas in these hosts approached an asymptote at high flea abundances (see Fig. 3b for an illustrative example).

Discussion        


This study demonstrated that the occurrence and strength of the link between host and flea abundances differed among host species exploited by the same flea. A strong link between host and parasite population dynamics suggests close interactions between host and parasite demographic parameters (Anderson & May 1978). The lack of this link in a parasite–host system hints that demographies of a given parasite and a given host are unrelated. In other words, a host-opportunistic parasite is not equally dependent on all its host species, but rather the parasite’s population dynamics is

          The most surprising result of this study is that we did not find an increase in flea abundance or prevalence with host abundance growth in any flea–host association, as predicted by epidemiological models. Instead, flea abundance or prevalence either decreased with an increase in host abundance or was not correlated with it. Patterns of relationships between parasite distribution and host abundance contradicting the predictions of epidemiological models have been reported in other studies. For example, the relationship between the abundance of a flea Nosopsyllus iranus theodori Smit and density of a gerbil Gerbillus dasyurus Wagner was linearly positive, and there was no plateau under high gerbil density (Krasnov et al. 2002). Prevalence of a flea Xenopsylla bantorum Jordan increased with a decrease in density of its host, Arvicanthis niloticus Desmarest (Schwan 1986). Abundance of mesostigmatid mites was strongly negatively correlated with the density of its lizard host, Lacerta vivipara Von Jacquin, whereas there was no relationship between mite prevalence and lizard density (Sorci et al. 1997). A negative relationship or a lack of relationship between parasite abundance or prevalence with an increase in host abundance can arise due to a number of reasons. One of these reasons may be the lower rate of flea reproduction and transmission compared to the rate of reproduction and dispersal of hosts. In other words, the rate of establishment of new patches (newly born or dispersing young mammals) is faster than the


rate of their infestation. Consequently, under high host density a fraction of host individuals may remain ‘under-used’ by the fleas, merely because they cannot keep pace with host reproduction and dispersal. Data on temporal patterns of flea reproduction and development support the feasibility of this explanation. For example, the development time of many fleas (see Vatschenok 1988) is longer than the time of pregnancy and postnatal development (until dispersal) of many small mammals (see Bashenina 1977). Another explanation can be related to the age structure of host populations in periods of high vs. low density. An increase of density in populations of small mammals often results in a surplus of individuals that have no individual home ranges (Brandt 1992; Gliwicz 1992). These ‘homeless’ individuals should not be putative hosts for fleas, because they do not possess burrows that are necessary for flea reproduction and development of pre-imaginal stages. As a result, the flea abundance at high host abundance might decrease, because the resident individuals would compose an ever-decreasing fraction of the overall host population. Indeed, the proportion of young individuals of Apodemus agrarius in periods of high abundance attains as high as 95% (Stanko 1992). The abundance of this host was correlated negatively with abundance and prevalence of six flea species (Table 2). However, abundance and prevalence of other fleas (e.g. Ctenophthalmus solutus) was not affected by the abundance of A. agrarius, suggesting the role of some other factors.

  : -  


A negative parasite abundance/host abundance pattern or the lack of any pattern can be shaped by a number of regulating mechanisms. The most important of these mechanisms are parasite-induced host mortality, hostinduced parasite mortality and density-dependent interand intraspecific effects in parasite populations and communities. Parasites may cause the death of their hosts due to different reasons, both direct and indirect. Examples of indirect causes are increased susceptibility to predation (Kavaliers & Colwell 1994) and modification of the outcome of competitive interactions (Hudson & Greenman 1998). A correlation between the coefficient of determination of the regressions of flea abundance or prevalence against host abundance and the slope of Taylor’s mean /variance power law suggests some role of parasiteinduced host mortality in a relationship between flea and host population dynamics. Anderson & Gordon (1982) showed that when the rate of parasite-induced host mortality was high, the slope of the relationship between the logs of the variances and means was low while, conversely, when the rate of mortality was low, the slope was high. Thus, this slope can be used as an inverse indicator of parasite-induced host mortality. The strength of the link between flea and host popula-

tion dynamics decreased with an increase of the slope of Taylor’s law, thus suggesting that parasite-induced host mortality is expected to be high in flea–host associations where flea abundance is affected strongly by host abundance. Also, parasite-induced host mortality can be inferred by comparing the degree of flea aggregation in host populations that are characterized by different levels of flea abundance. Indeed, in some flea– host associations, flea aggregation increased initially with an increase of flea abundance until a certain level and then did not change at high flea abundances. In other words, flea population growth beyond a certain level did not lead to extreme infestation of some host individuals. The most parsimonious explanation of the absence of heavily infested hosts at high flea abundances implying regulation is flea-induced (direct or indirect) host mortality. However, an asymptote in the relationship between mean flea crowding and flea abundance (slope < 1 in log–log space) was found in four flea–host associations only. This suggests that flea-induced host mortality might be important in shaping the pattern of the flea abundance/host density relationship in some cases but not in others. Alternatively, the reason for this can be simply that some fleas in our study area did not attain the level of abundance high enough for fleainduced host mortality to be detected. It should be noted, however, that parasite-induced host mortality is not the only mechanism that can produce an asymptote of flea aggregation level with an increase of flea abundance; other mechanisms may operate as well (Anderson & Gordon 1982; Wilson et al. 2001). For example, this asymptote can be associated with acquisition of immunity by hosts (Anderson & Gordon 1982). This acquired immunity (see below) can be either age-related (Hudson & Dobson 1995) or parasite intensity-related (e.g. de Lope, Møller & de la Cruz 1998; but see Schmid-Hempel & Ebert 2003) or both. Other mechanisms that might create the observed pattern of flea aggregation with changes in flea and/or host abundance are spatial and/or temporal variation in body condition and behaviour among host individuals (Wilson et al. 2001).

  : -   Another, not necessarily alternative, mechanism of the negative relationship between flea abundance or prevalence and host abundance can be host-induced parasite mortality. Ectoparasites can be controlled by grooming (Hart 1988) as well as by immune responses (Wikel 1996). Grooming activity of hosts has been hypothesized to increase with an increase in their density (Stanko et al. 2002), although this hypothesis has never been tested. Nevertheless, an increase in grooming under social stress (Mineur et al. 2003) which, in turn, increases under high density (Krebs 1996) can be a mechanism of increased host-induced flea mortality at high host abundances. This can lead to a negative flea abundance–host abundance


relationship. An increase in immune responses at high density has been suggested for social rodents, such as some Microtus species (Nelson et al. 1996 but see Pastoret et al. 1998). In addition, the production of immunosuppressive steroid hormones decreases at high density (Rogovin et al. 2003). This can also induce flea mortality at high host densities in at least some host species. Host-induced flea mortality is suggested by a negative relationship between the degree of flea aggregation and host abundance. In other words, the number of fleas on ‘heavily infested’ hosts could differ among periods of host abundance. ‘Heavily infested’ hosts at periods of high host abundance harboured fewer fleas than ‘heavily infested’ hosts at periods of low host abundances. The regulating role of hosts in flea mortality is strengthened by an asymptote in the relationship between mean flea crowding and host abundance (absolute value of slope < 1 in log–log space) observed in some flea–host associations. This means that the rate of decrease in the degree of flea aggregation decreased with an increase in host abundance until a certain level which seemed to be tolerable for a host, beyond which the host ceased to defend itself.

  :   


The abundance of a flea species can be affected by competitive interactions with other flea species. Competition can be both among imago (Day & Benton 1980) and larval (Krasnov et al. 2005) fleas and can lead to competitive exclusion (Krasnov et al. 2005). However, the lack of the effect of flea species richness on the coefficient of determination of the regression of flea abundance or prevalence against host abundance does not allow consideration of this explanation in the present context. Finally, density-dependent intraspecific processes in fleas can also mediate relationships between flea and host abundance (Hudson & Dobson 1997). For example, these processes can keep flea density at a certain level and, thus, be responsible for the lack of the relationship between flea and host abundance in some flea–host associations. Indeed, the reproductive success of fleas breeding in the nest of the blue tit, Parus caeruleus Linnaeus, was affected by the number of conspecifics in the same nest (Tripet & Richner 1999). In conclusion, whenever flea abundance and prevalence was affected by host abundance this effect was negative, thus countering the current assumptions of mathematical models. The pattern of the relationship between flea aggregation and their host abundances varied among flea–host associations. This suggests that various regulating mechanisms could be involved in the mediation of flea–host relationships and that different flea–host associations could be governed by different regulating mechanisms. However, this does not refute the possibility that different regulating mechanisms may act simultaneously within the same flea–host associations.

Acknowledgements We thank L. MoSansky and J. Fricová for their help in the field. A. Degen (Ben-Gurion University of the Negev) read an earlier version of the manuscript and made helpful comments. This study was supported partly by the Slovak Grant Committee VEGA (grant no. 2/5032/ 25 to Michal Stanko). The manipulations comply with the laws of the Slovak Republic. This is publication no. 195 of the Ramon Science Center and no. 502 of the Mitrani Center of Desert Ecology.

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