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)
 
© 2006 The Authors.
Journal compilation
© 2006 British
Ecological Society,
Journal of Animal
Ecology, 75,
575–583
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
578
M. Stanko,
B. R. Krasnov
& S. Morand
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,
© 2006 The Authors.
Journal compilation
© 2006 British
Ecological Society,
Journal of Animal
Ecology, 75,
575–583
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
579
Host abundance
and flea
distribution
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
© 2006 The Authors.
Journal compilation
© 2006 British
Ecological Society,
Journal of Animal
Ecology, 75,
575–583
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.
580
M. Stanko,
B. R. Krasnov
& S. Morand
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
    
  
© 2006 The Authors.
Journal compilation
© 2006 British
Ecological Society,
Journal of Animal
Ecology, 75,
575–583
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
581
Host abundance
and flea
distribution
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.
  :
-  
© 2006 The Authors.
Journal compilation
© 2006 British
Ecological Society,
Journal of Animal
Ecology, 75,
575–583
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
582
M. Stanko,
B. R. Krasnov
& S. Morand
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.
  :
  
© 2006 The Authors.
Journal compilation
© 2006 British
Ecological Society,
Journal of Animal
Ecology, 75,
575–583
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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Received 20 August 2005; accepted 20 December 2005