Host–parasite interactions in a fragmented landscape

International Journal for Parasitology 43 (2013) 27–35

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Host–parasite interactions in a fragmented landscape A.R. Renwick ⇑, X. Lambin School of Biological Sciences, University of Aberdeen, Tillydrone Avenue, AB24 2TZ Aberdeen, United Kingdom

a r t i c l e

i n f o

Article history: Received 31 July 2012 Received in revised form 9 October 2012 Accepted 9 October 2012 Available online 15 November 2012 Keywords: Alternative hosts Dilution effect Fleas Habitat loss Thresholds Ticks

a b s t r a c t Theory suggests that habitat fragmentation should reduce the risk of being parasitised due to reduced size and increased isolation of the host population. It is predicted that a threshold host population size exists, below which parasites will not be able to persist. Small mammals were trapped and their ectoparasites removed in 14 field margins of varying widths over 2 years in a highly fragmented agro-ecosystem. No evidence to suggest the presence of a threshold in parasite prevalence was found, which may be due to the high rate of host movement and transiency within the system. Contrary to expectation, the probability of infestation decreased with host abundance and the abundance of alternative hosts, suggesting a dilution effect. The relatively long life cycle of small mammal specialist tick and flea species present under the prevailing environmental conditions may have left the parasites unable to keep up with the rate of reproduction and dispersal of the host. It is important to consider changes in the behaviour of the host and the presence of alternative hosts when predicting the effects of habitat fragmentation on disease spread. Ó 2012 Australian Society for Parasitology Inc. Published by Elsevier Ltd. All rights reserved.

1. Introduction The effects of habitat fragmentation on host–parasite interactions has received relatively little attention (but see Hess, 1996; Gog et al., 2002; McCallum and Dobson, 2002; Greer and Collins, 2008). Fragmentation should reduce the risk of a host species being parasitised due to the host population size falling below a threshold required for parasites to persist, the prevention of a parasite spreading into other subpopulations or a reduction in the dispersal rate of hosts between social groups in which the parasite has evolved, thereby slowing down the spread of parasites (McCallum and Dobson, 2002). However, the presence of alternative or reservoir hosts, the type of within-patch transmission (Begon et al., 2002) and indirect effects of fragmentation, such as host stress, which may increase susceptibility of an individual being infested (Zuk and McKean, 1996), also need to be considered. Although commonly assumed in theoretical models (Swinton et al., 2002; Lloyd-Smith et al., 2005), empirical support for a threshold in host abundance for parasite invasion and persistence in wildlife is rare (Begon et al., 2003) and, where present, rather weak (Davis et al., 2004). It is expected that rather than an abrupt threshold, a gradual change in parasite dynamics and population size occurs, termed a ‘soft threshold’ (Lloyd-Smith et al., 2005). The pattern of transmission and, consequently, the nature of the ⇑ Corresponding author. Present address: Environmental Decisions Group, School of Biological Sciences, University of Queensland, St Lucia, Queensland 4072, Australia. Tel.: +61 7 3365 1697. E-mail address: [email protected] (A.R. Renwick).

transmission term used in host–parasite models are central to determining the effect of fragmentation on parasite dynamics and the existence of thresholds for parasite persistence. Classical disease models assume susceptible and infective hosts mix at random, and that transmission between individuals is densitydependent, with the contact rate between hosts increasing with host density (Anderson and May, 1979, 1981). Under this model, habitat fragmentation may reduce the rate of contact between infected and susceptible hosts until a threshold in the host population size is reached, below which the disease is unable to persist. Alternatively, the proportion of hosts infected and contact rates between hosts may be constant, irrespective of the host density (Begon et al., 1999; De Jong et al., 1995, 2002). With frequency dependent transmission, habitat fragmentation has no direct effect on within-patch transmission and hence there is no threshold population size for parasite persistence (Begon et al., 2003). In reality, the pattern of transmission is likely to lie somewhere between the two extremes (Smith et al., 2009). Rather than the positive relationship expected under density dependence transmission/amplification (Krasnov et al., 2002a), negative relationships between parasite abundance or prevalence and host abundance and diversity have been explained as a consequence of the dilution effect, whereby a parasite population is spread over a larger number of hosts. The term ‘‘dilution effect’’ was first used in host–parasite relationships by Ostfeld and Keesing (2000a,b) to describe the situation where high host diversity reduced disease prevalence. It has since also been used to describe cases where high host abundance reduces the prevalence of a disease vector (Krasnov et al., 2007; Telfer et al., 2007). A reduction in

0020-7519/$36.00 Ó 2012 Australian Society for Parasitology Inc. Published by Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ijpara.2012.10.012

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disease transmission as host diversity or abundance increases may be explained by a decrease in the abundance of the competent host, that the parasite population is of fixed size and is spread over a larger number of host individuals (Krasnov et al., 2007) or differences in composition of the host community, for example the presence of a low-competency host (Johnson et al., 2008). The existence of alternative hosts is one of several factors that makes determining the effect of fragmentation and the presence of thresholds in the wild more complicated (Lloyd-Smith et al., 2005). Despite their potential impact on host–parasite interactions, the contribution of alternative hosts has often been neglected in studies of wildlife disease (e.g. Begon et al., 1999; Telfer et al., 2005; Brinkerhoff et al., 2008; Lembo et al., 2008) and to date the existence of alternative hosts has not been incorporated into empirical studies determining the effect of habitat fragmentation on disease dynamics. This may have non-intuitive consequences for disease spread due to the varying degrees of habitat specificity among the alternative hosts. Intensification of agriculture has resulted in highly fragmented landscapes. In Europe, field voles (Microtus agrestis) inhabit grassy areas and within intensively farmed landscapes are found predominately in field margins, together with other more generalist small mammals which can also be found within the cropped field. They are commonly parasitised by both flea and tick species that are specific to small mammals, and may act as vectors for many diseases. Although ticks and fleas can be found within the cropped fields, it is unlikely they are able to survive the annual ploughing of the fields, making their lifespan in such crops transitory. The effects of habitat fragmentation on host–parasite interactions were investigated, while accounting for the presence of alternative hosts. We hypothesised (i) no threshold (abrupt change) in parasite infestation in relation to host population size would be present due to the presence of alternative hosts and (ii) the probability of infestation would increase with increasing host abundance, margin width and the abundance of alternative hosts. 2. Materials and methods 2.1. Study sites The study was carried out in a lowland arable agricultural landscape in north eastern Scotland, approximately 50 km south of Aberdeen (56°80 N, 02°30 W). The area is predominately arable with the remainder grazed by cattle and very little semi-natural habitat. Most hedges have been removed and fields (5–20 ha) are separated by wire fences. Crops grown were primarily cereals (mainly winter sown crops) with a small amount of oil seed rape, root vegetables and daffodils. Field voles were trapped within 14 separate selected margins on seven farms, with two unconnected margins selected from neighbouring fields within each farm. Margins were chosen to border arable fields on both sides (unless one side of the margin bordered a road), be a minimum of 300 m in length and provide a gradient in width ranging from <1 to 14 m. The seven farms were the main arable farms in the area. The average distance between margins on separate farms was 6.3 km (range: 0.2–13.3 km, median: 4.2 km) ensuring independence while maintaining the same topographical characteristics. The distance between the selected margins within each farm was not sufficient to prevent dispersal of voles, however no movement of marked individuals between the margins was observed. All margins 3 m and over had been implemented as part of an agri-environment scheme and had been planted with a mixture of grass seeds in 2003. 2.2. Small mammal trapping Within each margin, voles were trapped along a single 280 m transect of 40 trap stations at 7 m intervals. At each trap station,

a minimum of two Longworth traps was used. Prior to trapping, the area was surveyed for signs of vole activity and, in areas where there appeared to be high vole activity (runs and grass clipping), three traps were used at each trap station to avoid trap saturation. Traps were baited with bruised oats, chopped carrot and blowfly (Calliphoridae) larvae, and left locked open for 2 days before each trapping session to allow the animals to become familiar with the traps (the larvae was not added for these 2 days). Traps were set in the evening and checked twice daily in the morning and evening for five checks. Each site was trapped at 8 week intervals, twice between June and September 2006 and four times between April and October in 2007 (due to a change in farm management one margin was only trapped in April and one was only trapped in April and June of 2007). Primary sessions are defined as the overall bimonthly trap period per margin and secondary sessions as the individual checks within a primary session. All field voles trapped were marked with a pair of uniquely numbered ear tags, and all other small mammals (bank vole, Myodes glareolus, wood mouse, Apodemus sylvaticus, house mouse, Mus domesticus, common shrew, Sorex araneus, pygmy shrew, Sorex minutus and water shrew, Neomys fodiens) were marked with a fur clip. The mass, sex and reproductive status of field voles were recorded at the time of first capture in each primary session. Every animal was released at the point of capture. Field voles were classified as adult, young breeder or juvenile based on the following criteria: adults: individuals weighing over 15 g; young breeders: individuals weighing between 10 and 15 g and showing signs of sexual maturity; juveniles: individuals weighing 15 g or less and showing no signs of sexual maturity. All fleas and ticks detected on each individual at first capture (apart from shrews) were collected. Fleas were removed by gently blowing the animal’s fur over a small bath of water and stored in 70% ethanol. Ticks were removed using small tweezers and stored in 70% ethanol. All ecto-parasites collected were later identified and classified to stage for ticks in the laboratory using keys (Smit, 1957; Snow, 1978; Hillyard, 1996). 2.3. Statistical analyses To account for extra data in April and October 2007, the data were analysed using two subsets. The first subset contained all of the primary sessions in 2007 (referred to as ‘2007 data’ throughout) which allowed a seasonal component to be included in the analyses, and the second subset contained data from June and August from both years (referred to as ‘summer data’ throughout) which allowed a year effect to be investigated. Both ticks and fleas were grouped by species and analysed using generalised linear mixed models (GLMMs) with a logit link and binomial errors to investigate the effect of margin width, vole abundance, abundance of alternative hosts (mice, bank voles and shrews) and month of sampling on the probability that an individual vole was infested by a tick or flea (parasite presence or absence was used as a binary response variable). Due to the extended life cycle of ticks (Randolph, 1975b), and possibly of fleas (Osacar et al., 2001; Krasnov et al., 2002b), vole density in the previous year was also added together with year as fixed effects in the summer data analyses. Vole abundance was calculated using closed mark-recapture models and density was estimated by using the area bounded by the total width of the margin multiplied by the transect length plus a buffer of half an inter-trap distance at each end of the transect as the effective trap area (Renwick and Lambin, 2011). The abundance of alternative hosts was calculated as the number of animals trapped. Insufficient data prevented analysing each tick stage (larvae, nymph and adult) separately and analysing the relationships with parasite burden. Models were fitted using maximum likelihood (ML) estimates with ‘margin nested within farm’ (n = 14) as

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collected, all of which were on field voles. Ixodes ricinus was therefore not included in the analyses due to their low numbers. In 2007, the abundance of I. trianguliceps larvae peaked in October, whereas nymphs showed a small peak in August and adults peaked in April. In 2007 only 6.6% of field voles were infested by I. trianguliceps. Similarly, only 6.1% of field voles were infested by I. trianguliceps in the summer of both years (summer data), indicating that the probability of being infested was low. The tick burden per individual ranged from 0 to 7 (median = 0). The majority of the flea species collected were Ctenophthalmus nobilis, Peromyscopsylla spectabilis spectabilis and Megabothris rectangulatus. Hystrichopsylla talpae talpae, Rhadinopsylla pentacantha and Amalaraeus penicilliger mustelae were found less frequently. Ctenophthalmus nobilis was the most abundant flea species collected, occurring at highest numbers at the beginning of the year. The less frequently collected P. spectabilis was most abundant towards the end of the year. In 2007, 23.9% of voles were infested with fleas: 12.8% were infested with C. nobilis, 4.2% were infested with P. spectabilis and 4.0% were infested with M. rectangulatus. In the summer, 31.9% of voles were infested with fleas: 15.7% were infested with C. nobilis, 8.2% were infested with P. spectabilis and 6.3% were infested with M. rectangulatus. The flea intensity ranged from 0 to 17 in 2007 and 0 to 13 in the summer data (both had a median = 0).

a random factor. Analyses of the GLMMs were performed using the ‘lme4’ package (Bates et al., 2008) in R statistical and programming environment v2.7.2 (R Core Development Team, Vienna, Austria, 2008).

3. Results A total of 380 individual field voles were examined for parasites in 2006 and 1,697 were examined in 2007 Across both years, 1,430 individual field voles were examined in June and August (summer data). Estimated vole abundance within each margin ranged from 2.00–161 (median = 31.1 in 2007 and 23.8 in the summer data set), which corresponds to an estimated density between 67.2– 1,477.8 voles/ha (median = 299.3 voles/ha) in 2007 and 43.7– 1,150.1 voles/ha (median 230 voles/ha) in the summer data set, if one assumed that voles exclusively use the margins and not the cropped areas. The abundance of alternative hosts ranged from 10–110 (median = 33.5) in 2007 and from 3 to 101 (median = 29) in the summer data set. Field voles were by far the most abundant species trapped, followed by common shrews and wood mice. The dominant tick species collected was Ixodes trianguliceps with only nine Ixodes ricinus

A

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290

0.6

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0.2

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340

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140 0.8

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14

Margin width Fig. 1. Predicted probability of Ixodes trianguliceps infestation when considering a year effect (summer data) with (A) vole abundance when other variables are held constant at mean values (margin width 6.75 m; abundance of alternative hosts 43.9), (B) abundance of alternative hosts when other variables are held constant at mean values (margin width 6.75 m; vole abundance 77.34) and (C) margin width (m) (n = 1,419 voles) when other variables are held constant at mean values (vole abundance = 77.34; alternative host abundance = 43.9). The histograms show the frequency of infested (top axis) and non-infested (bottom axis) voles for different values of the variable on the x axis.

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Table 1 Parameter estimates for the final model for predicting the probability of tick presence using generalised linear mixed models (GLMMs) with binomial errors and logit link (reference coefficients are sex (female), month (August)). The variance between farms was 0.07 and the variance between margins within farms was 0.47 when considering data from all seasons (2007 data). The variance between farms was 0.12 and the variance between margins within farms was 0.27 when considering a year effect (summer data). Coefficients

(Intercept) Sex (male) Month (April) Month(June) Month (Oct) Vole abundance Alternative hosts abundance Width Sex (male): month (April) Sex (male): month(June) Sex (male): month(Oct) Sex (male): vole abundance

Seasonal data (2007)

Summer data (both years)

Estimatee

SE

z-value

P-value

Estimate

SE

z-value

P-value

1.85 1.62 0.77 0.48 0.36 0.01

0.58 0.79 0.59 0.44 0.41 0.005

3.17 2.10 1.31 1.08 0.89 2.58

0.002 0.040 0.191 0.279 0.376 0.010

2.40

0.41

5.84

<0.001

0.01 0.02 0.14

0.003 0.01 0.05

3.40 2.73 2.64

<0.001 0.006 0.008

1.31 0.52 1.92 0.01

0.81 0.66 0.60 0.006

1.61 0.79 3.21 2.10

0.108 0.432 0.001 0.036

3.1. Thresholds As predicted, no threshold was detected in the prevalence of ticks and fleas (including all species together and each species separately) within each margin in relation to field vole abundance, estimated vole density, margin width, overall host abundance and estimated vole density in the previous year (Supplementary Figs. S1–S5). Interestingly, there was only one margin, on one sampling occasion, where there were no fleas collected and this occurred when only two voles were trapped in this margin on this sampling occasion. There was also only one site in which the vole density in the previous year was zero followed by zero prevalence the following year. However, no inference can be possibly drawn from this single observation, especially when there were other sites with higher past densities which also had zero tick prevalence in 2007. 3.2. Infestation probability 3.2.1. Host abundance The probability of being infested by ticks decreased as vole abundance increased. This was consistent for both sexes during the summer (Fig. 1A) but only within females when considering data from across all seasons (Table 1). The probability of being infested by fleas decreased with vole abundance in April and June (Fig. 2, Table 2) when considering all species together and C. nobilis separately (Table 3), however the influence of year was greater than the effect of season for P. spectabilis whereby the negative relationship was stronger in 2006 than 2007 (Table 4). Vole abundance had no effect on the probability of being infested with M. rectangulatus (Table 5). 3.2.2. Alternative host abundance In the model correcting for field vole abundance, the probability of being infested by ticks also decreased with increasing abundance of alternative hosts during the summer months (Fig. 1B) but not when only considering the 2007 data (Table 1). A similar relationship was found with fleas whereby the probability of infestation decreased with increasing abundance of alternative hosts during the summer months of 2006 only (Fig. 3, Table 2). No such effect was observed when considering the three main flea species separately. 3.2.3. Margin width The probability of being infested by ticks increased with margin width during the summer (Fig. 1C) but not when only considering the 2007 data (Table 1). The probability of flea infestation in-

creased with margin width. This was consistent across all seasons and both years (Fig. 4, Table 2) even when only considering P. spectabilis (Table 4) but was not evident when only considering C. nobilis (Table 3) or M. rectangulatus separately (Table 5). 4. Discussion Despite sampling a wide range of host abundances, there was no evidence of a threshold host abundance below which either fleas or ticks would not be able to persist. This may have been either because no such threshold exists, or because the total abundance of all hosts in each of the margins sampled was above any hypothetical threshold level. In a review of host population thresholds for the invasion or persistence of wildlife diseases, LloydSmith et al. (2005) found only two cases where thresholds were unambiguously identified (Begon et al., 2003; Davis et al., 2004). More recently, Begon et al. (2009a) found no evidence of a threshold response in cowpox virus seroprevalence in field voles, despite a wide range of host abundances. Consequently, it is recognised that the presence of clearly defined thresholds in wildlife disease are not readily detectable (Lloyd-Smith et al., 2005; Begon et al., 2009a,b) and there is little empirical evidence as yet to support theoretical models. In theoretical models host density, not host population size, is considered important in parasite transmission (density-dependent transmission) and population size is not related to the prevalence of parasites (Anderson and May, 1979, 1981). However, there was no evidence of a threshold density for parasite prevalence. In one of the few cases where thresholds have been detected in nature, Begon et al. (2003) found a threshold in rodent host abundance for cowpox virus, rather than host density, providing little support for pure density-dependent transmission. However, the alternative mode of transmission, pure frequency-dependent transmission, is also unlikely as the host-finding behaviour of the parasites will be affected by both host density and parasite mobility (Antonovics et al., 1995). Although both of these terms are useful when determining transmission dynamics, it is recognised that, in practise, transmission is likely to lie somewhere between the two (Antonovics et al., 1995; De Jong et al., 1995; Begon et al., 1998) and maybe even change seasonally (Smith et al., 2009). Within this study, both the tick and flea species were parasites of small mammals and infested all seven of the small mammal species trapped. Therefore, it is incorrect to consider thresholds for field voles alone, as the parasites were shared by all small mammal species. However, when the total abundance of all hosts trapped in the margin was considered, there was still no evidence of a threshold. It is likely that the high level of transiency and movement

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A

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50

0.4 50 0.2 25 0.0 10

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120

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C

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Vole abundance

Fig. 2. Predicted probability of flea infestation with the abundance of voles in 2007 during (A) April (n = 252 voles), (B) June (n = 453 voles), (C) August (n = 597 voles) and D) October (n = 395 voles) when other variables are held constant at sex = female and mean margin width = 6.86 m. The histograms show the frequency of infested (top axis) and non-infested (bottom axis) voles for different vole abundances.

Table 2 Parameter estimates for the final model for predicting the probability of flea presence using generalised linear mixed models (GLMMs) with binomial errors and logit link (reference coefficients are sex (female), month (August), year (2006)). The variance between farms was 0.08 and the variance between margins within farms was <0.001 when considering data from all seasons (2007 data). The variance between farms was 0.06 and the variance between margins within farms was <0.001 when considering a year effect (summer data). Coefficients

(Intercept) Sex (male) Width Month (April) Month (June) Month (Oct) Vole abundance Abundance alternative hosts^0.5 Year (2007) Month (April): vole abundance Month (June): vole abundance Month(Oct): vole abundance Abundance alternative host^0.5: year (2007)

Seasonal data (2007)

Summer data (both years)

Estimate

SE

z-value

P-value

Estimate

SE

z-value

P-value

1.26 0.64 0.07 0.40 0.50 0.97 0.005

0.32 0.12 0.02 0.47 0.37 0.41 0.002

3.92 5.41 3.36 0.85 1.37 2.35 2.04

<0.001 <0.001 <0.001 0.394 0.170 0.019 0.042

1.17 0.68 0.07

0.69 0.12 0.02

1.69 5.51 2.83

0.092 <0.001 0.005

0.47

0.23

2.03

0.043

0.006 0.32 2.65

0.002 0.13 0.81

2.79 2.53 3.29

0.005 0.012 0.001

0.02 0.02 0.005

0.01 0.004 0.006

1.67 3.55 0.91

0.095 <0.001 0.366

0.02

0.004

3.77

<0.001

0.36

0.14

2.61

0.009

within this multi-host system (Renwick and Lambin, 2011) may have facilitated the spread of parasites between margins (Hess, 1994, 1996; Gudelj and White, 2004). The host species varied in

their degree of habitat specialisation and hence their response to habitat fragmentation. Whereas field voles are considered grassland habitat specialists, wood mice and shrew species (S. araneus,

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Table 3 Parameter estimates for the final model for predicting the probability of the presence of Ctenophthalmus nobilis using generalised linear mixed models (GLMMs) with binomial errors and logit link (reference coefficients are sex (female), month (August), year (2006)). The variance between farms was 0.06 and the variance between margins within farms was 0.06 when considering data from all seasons (2007 data). The variance between farms was 0.01 and the variance between margins within farms was 0.06 when considering a year effect (summer data). Coefficients

Seasonal data (2007)

Summer data (both years)

Estimate

SE

z-value

P-value

Estimate

SE

z-value

P-value

(Intercept) Sex (male) Month (April) Month (June) Month (Oct) Vole abundance Year (2007) Month (April): vole abundance

2.75 0.56 1.94 1.33 1.32 0.006

0.42 0.15 0.58 0.48 0.71 0.003

6.52 3.68 3.36 2.76 1.86 1.93

<0.001 <0.001 <0.001 0.006 0.063 0.053

1.96 0.59

0.23 0.15

8.59 3.85

<0.001 <0.001

0.93

0.27

3.46

<0.001

0.003 0.57

0.003 0.24

1.20 2.33

0.23 0.02

0.03

0.01

2.28

0.022 0.02

0.004

4.02

<0.001

Month (June):vole abundance Month (Oct): vole abundance

0.02 0.008

0.005 0.009

3.65 0.93

<0.001 0.355

Table 4 Parameter estimates for the final model for predicting the probability of the presence of Peromyscopsylla spectabilis using generalised linear mixed models (GLMMs) with binomial errors and logit link (reference coefficients are sex (female), month (August), year (2006)). The variance between farms was 0.03 and the variance between margins within farms was <0.001 when considering data from all seasons (2007 data). The variance between farms was 0.03 and the variance between margins within farms was <0.001 when considering a year effect (summer data). Coefficients

Seasonal data (2007)

(intercept) Sex (male) Width Month(April) Month(June) Month(Oct) Vole abundance Year (2007) Vole abundance: year (2007)

Summer data (both years)

Estimate

SE

z-value

P-value

Estimate

SE

z-value

P-value

2.85 0.57 0.15 2.26 1.17 0.25 0.02

0.45 0.25 0.03 0.59 0.27 0.32 0.004

6.41 2.26 4.45 3.80 3.16 0.78 3.74

<0.001 0.024 <0.001 <0.001 0.002 0.433 <0.001

1.53 0.43 0.17

0.41 0.22 0.03

3.72 1.95 5.08

<0.001 0.051 <0.001

0.63

0.24

2.62

0.009

0.05 1.80 0.04

0.02 0.53 0.02

3.14 3.41 2.42

0.002 <0.001 0.015

Table 5 Parameter estimates for the final model for predicting the probability of the presence of Megabothris rectangulatus using generalised linear mixed models (GLMMs) with binomial errors and logit link (reference coefficients are sex (female), month (August), year (2006)). The variance between farms was <0.001 and the variance between margins within farms was 0.18 when considering data from all seasons (2007 data). The variance between farms was 0.12 and the variance between margins within farms was 0.22 when considering a year effect (summer data). Coefficients

(Intercept) Sex (male) Month(April) Month(June) Month(Oct) Year (2007)

Seasonal data (2007)

Summer data (both years)

Estimate

SE

z-value

P-value

Estimate

SE

z-value

P-value

3.60 0.77 0.75 0.38 0.59

0.30 0.26 0.33 0.35 0.41

11.82 2.95 2.30 1.06 1.44

<0.001 0.003 0.021 0.288 0.149

2.65 1.34

0.33 0.27

8.06 4.94

<0.001 <0.001

1.49

0.24

6.29

<0.001

S. minutus and N. fodiens) are habitat generalists and hence their foraging activities are not as strictly restricted to the field margins, although they are likely to use these as shelter (Todd et al., 2000). This may have further facilitated the spread of parasites throughout the landscape. It is therefore possible that a threshold may exist at a larger spatial scale not obvious at the scale of this study. A negative relationship between host abundance and the probability of parasite infestation occurred for both fleas and ticks. This relationship was also evident when considering the main flea species separately for both C. nobilis and P. spectabilis. The negative relationship suggests a type of dilution effect as described by Krasnov et al. (2007) and Telfer et al. (2007), whereby as the host abundance increases the parasites are divided between more potential hosts and hence individual hosts are less likely to be infested. Telfer et al. (2007) similarly found that field voles were less likely to be infested with fleas within high host density populations. There are a number of possible explanations for this negative

relationship. The first may be related to the proportion of juveniles or non-residents (individuals only caught once in the primary session) in the host population as host abundance increases. These individuals do not have nests and burrows which are necessary for small mammal tick and flea reproduction and development (Stanko et al., 2006). Consequently, as the proportion of these individuals increases, the relative proportion of individuals with burrows declines, and as a result parasite abundance may decline. A second explanation for the negative relationship is that the parasite reproduction and transmission rates may be slower than the rate of reproduction and dispersal of the host. Thus, the production of newborn or dispersing hosts would be faster than the rate of infestation and consequently a fraction of the host population would remain parasite free (Stanko et al., 2006). This scenario is quite feasible for I. trianguliceps ticks as their life cycle is approximately 2 years (Randolph, 1975b). At least some species of flea can develop from egg to imago in approximately 35 days in warm

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A

B 1.0

0

1.0

0

0.4

80

0.2

0.8

150

Frequency

0.6

Probability of infestation

75

80

Frequency

Probability of infestation

40 0.8

0.6

0.4

150

0.2

40 0.0

75

0 1.4

1.6

1.8

2.0

2.2

2.4

0.0

0

2.6

2.0

Alternative host abundance^0.5

2.5

3.0

Alternative host abundance^0.5

Fig. 3. Predicted probability of flea infestation with alternative host abundance when incorporating a year effect (summer data) during (A) 2006 (n = 372 voles) and (B) 2007 (n = 1,047 voles) when other variables are held constant at sex = female, month = June, mean vole abundance = 77.34 and mean margin width = 6.75 m. The histograms show the frequency of infested (top axis) and non-infested (bottom axis) voles for different alternative host abundances.

A

B 1.0

0

1.0

0 105

0.6

0.4

190

0.2

0.8

210

Frequency

190

Probability of infestation

0.8

Frequency

Probability of infestation

95

0.6

0.4

210

0.2

95 0.0

0 2

4

6

8

10

12

14

Margin width (m)

105 0.0

0 0

2

4

6

8

10

12

14

Margin width (m)

Fig. 4. Predicted probability of flea infestation with margin width (m) using (A) 2007 data (n = 1,697 voles) when other variables are held constant at sex = female, month = April and mean vole abundance = 78.2, and (B) summer data (n = 1,419 voles) when other variables are held constant at sex = female, year = 2006, mean vole abundance = 77.34 and mean abundance of alternative host^0.5 = 6.46. The histograms show the frequency of infested (top axis) and non-infested (bottom axis) voles at different margin widths.

climes (Krasnov et al., 2002c), making this explanation less plausible for fleas. However, seasonal dynamics have been shown to occur in wild populations of fleas (Haukisalmi and Hanski, 2007; Lindsay and Galloway, 1997) as fleas may enter diapause under unsuitable conditions, thus prolonging their life cycle (Osacar et al., 2001; Krasnov et al., 2002b). A third explanation that has been proposed is that host-induced parasite mortality may contribute to the negative host–parasite relationship (Stanko et al., 2006). Both grooming activity and immune responses may increase at high host density (Stanko et al., 2002; Bailey et al., 2008) or under social stress (Mineur et al., 2003), thus increasing parasite mortality and causing a negative relationship between the presence of

parasites and host abundance. However, it is unlikely that they play a role in this system as the host–parasite burden was relatively low and probably not sufficient to generate these costly responses (Stanko et al., 2002). Further, Randolph (1975a) found no evidence that deticking was an important factor determining infestation levels of I. trianguliceps on small mammals. It was expected that the presence of alternative hosts would add to the overall host pool and thus each individual vole would be more likely to become infested with parasites when the abundance of alternative hosts was high. However, the probability of infestation decreased with increasing abundance of alternative hosts during the summer months but this was not evident when

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considering only data from 2007. This represents a further dilution effect during the summer as occurred with field vole abundance. In this study system, it seems highly likely that some degree of between-species transmission occurred, given than seven host species were present which often share burrows and runways (Randolph, 1975a) and may interact behaviourally (Flowerdew et al., 1985; Geuse, 1985; Geuse et al., 1985; Rajska-Jurgiel, 2001). Furthermore, the presence of alternative hosts is likely to have facilitated the spread of parasites, thus increasing the chance of a vole encountering a parasite within the burrows and runways. Previous studies investigating the effect of habitat size on host– parasite interactions have generally related observed effects to host density or abundance (Begon et al., 2003). However, the patterns have been inconsistent, suggesting the possible influence of landscape features. In this study, the probability of being infested by ticks or fleas increased with margin width when controlling for vole abundance. This relationship was consistent across all seasons in fleas but was only evident during the summer months in ticks. An overlap in individual home ranges, a proxy for contact rate, was greater in wider margins (Renwick, A.R., 2009. Abundance thresholds and ecological processes in a fragmented landscape: Field vole, parasites and predators. PhD thesis. University of Aberdeen, UK), possibly increasing the probability of being infested, at least for fleas where between-species transmission may occur. It is highly possible that the narrow margins in this study were adversely affected by edge effects, thus reducing their suitability for ticks (Vandergast and Roderick (2003). Ixodid ticks are particularly sensitive to desiccation (Hillyard, 1996) and wider margins may have provided them with a more buffered environment. This may be especially evident after crop spraying which killed much of the vegetation within the narrow margins compared with the wider margins that did not receive such spray drift. This study highlights the importance of accounting for the behaviour and biology of all the hosts and the nature of the surrounding landscape when predicting the effects of parasite spread in fragmented systems. The threshold concept, which was developed primarily for well-mixed host populations with density dependent transmission and no alternative hosts (Lloyd-Smith et al., 2005), may therefore not be applicable in this system. Acknowledgments ARR was supported by a studentship from the Natural Environmental Research Council, United Kingdom (NER/S/A/2004/12201). We thank K. Enright, J. McLean, E. Moore and C. Lutton for help with field work, R. George for help with flea identification and the farmers for allowing us to work in their fields. XL acknowledges support from a Leverhulme Trust Research, United Kingdom fellowship. The study conforms to UK regulations and a license from Scottish Natural Heritage was obtained in order to live trap shrews. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.ijpara.2012.10. 012. References Anderson, R.M., May, R.M., 1979. Population biology of infectious diseases: part I. Nature 280, 361–367. Anderson, R.M., May, R.M., 1981. The population-dynamics of micro-parasites and their invertebrate hosts. Philos. Trans. R. Soc. Lond. Ser. B-Biol. Sci. 291, 451– 524. Antonovics, J., Iwasa, Y., Hassell, M.P., 1995. A generalized-model of parasitoid, venereal, and vector-based transmission processes. Am. Nat. 145, 661–675. Bailey, N.W., Gray, B., Zuk, M., 2008. Does immunity vary with population density in wild populations of Mormon crickets? Evol. Ecol. Res. 10, 599–610.

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