1
Effects of anthropogenic and demographic factors on patterns
of parasitism in African small mammal communities
JOHANNA S. SALZER 1,2,3 , DARIN S. CARROLL 2,3 ,
AMANDA JO WILLIAMS-NEWKIRK 1,2,4 , STEFANIE LANG 2 ,
JULIAN KERBIS PETERHANS 5,6 , INNOCENT B. RWEGO 2,7,8 , SANDRA OCKERS 7
and THOMAS R. GILLESPIE 1,2,7 *
1
Program in Population Biology, Ecology, and Evolution, Emory University, 1462 Clifton Road, Atlanta, Georgia 30322,
USA
2
Department of Environmental Sciences, Emory University, 400 Dowman Drive, Atlanta, Georgia 30322, USA
3
Poxvirus and Rabies Branch, Division of High-Consequence Pathogens and Pathology, Centers for Disease Control and
Prevention, 1600 Clifton Road, Atlanta, Georgia 30333, USA
4
Rickettsial Zoonoses Branch, Division of Vector-borne Diseases, Centers for Disease Control and Prevention,
1600 Clifton Road, Atlanta, Georgia 30333, USA
5
College of Professional Studies, Roosevelt University, 430 South Michigan Avenue, Chicago, Illinois 60605, USA
6
Field Museum of Natural History, 1400 South Lake Shore Drive, Chicago, Illinois 60605, USA
7
Department of Environmental Health, Rollins School of Public Health, Emory University, 1518 Clifton Road, Atlanta,
Georgia 30322, USA
8
Department of Biological Sciences, Makerere University, Kampala, Uganda
SUMMARY
Habitat disturbance often results in alterations in community structure of small mammals. Additionally, the parasites
harboured by these small mammals may be impacted by environmental changes or indirectly affected by changes in available
hosts. To improve our understanding of this interplay, we examined the patterns of parasitism in small mammal
communities from a variety of habitats in forested Uganda. Small mammals were collected from areas experiencing variable
habitat disturbance, host density and species richness. The analysis focused on 3 most abundant rodent species, Lophuromys
aquilus, Praomys jacksoni and Hylomyscus stella, and a diverse group of parasites they harbour. The impact of various habitat
and host community factors on parasite prevalence was examined using linear regression and Spearman’s rank-order
correlation. We further investigated the parasite communities associated with each individual using correspondence
analysis. We determined that, parasite prevalence and richness may be occasionally influenced by community and habitat
factors, but taxonomy is a driving force in influencing the parasite community harboured by an individual host. Ultimately,
applying general principles across a broad range of disturbance levels and diverse host communities needs to be approached
with caution in complex communities.
Key words: ectoparasites, fleas, Giardia, Kibale National Park, lice, mites, Praomys, rodent, ticks, trypanosome.
INTRODUCTION
Habitat quality, host community assemblage, host
susceptibility/resistance and pathogen–pathogen
interactions are interconnected in complex and
dynamic ways (Woolhouse et al. 1997; Ostfeld et al.
2008; Beldomenico and Begon, 2010; Johnson et al.
2013). Disease dynamics can rarely be explained
by examining one component of a complex natural
system although general patterns are often sought.
Understanding the host community components and
interactions associated with disease emergence and
persistence may provide valuable information
* Corresponding author. Program in Population Biology,
Ecology, and Evolution, Emory University, 1462 Clifton
Road, Atlanta, Georgia 30322, USA; Department of
Environmental Sciences, Emory University, 400
Dowman Drive, Atlanta, Georgia 30322, USA; and
Department of Environmental Health, Rollins School of
Public Health, Emory University, 1518 Clifton Road,
Atlanta, Georgia 30322, USA. E-mail: Thomas.
Gillespie@emory.edu
Parasitology, Page 1 of 11. © Cambridge University Press 2014
doi:10.1017/S0031182014001450
for unlocking mechanisms driving disease dynamics
in the natural environment (Dobson, 2004; Lafferty,
2010; Telfer et al. 2010).
Anthropogenic disturbance and subsequent loss of
both biodiversity and community structure have
been associated with increases in disease emergence
(Keesing et al. 2010; Roche et al. 2012), as well as a
reduction in disease occurrence (Lafferty, 2012; Bush
et al. 2013; Young et al. 2013). There is evidence
supporting both positive and negative general linear
relationships among disease and factors such as
diversity, density and relative abundance (Randolph
and Dobson, 2012). Additionally, empirical evidence
is mounting that a host species’ relative abundance
and density are also linearly associated with parasite
prevalence and/or richness (Arneberg, 2002;
Froeschke et al. 2013). Regardless of shape or direction and the indirect or direct influences, anthropogenic disturbance can affect disease dynamics in
natural systems (Randolph and Dobson, 2012;
Salkeld et al. 2013).
2
Johanna S. Salzer and others
To improve our understanding of the general
patterns between host and parasite communities
in relation to anthropogenic disturbance, we investigated parasite dynamics in terrestrial small mammal
communities in and around Kibale National Park
(KNP), Western Uganda. Specifically, we focused
on 3 common forest dwelling rodent species –
Lophuromys aquilus, Praomys jacksoni and
Hylomyscus stella (Delany, 1975; Struhsaker, 1997).
We examined parasites of these host species and
investigate correlations between parasite prevalence
and habitat disturbance, host density and host species
richness. We further evaluated the parasite communities harboured by each of these host species.
These small mammals (or hosts) were collected from
habitats experiencing variable intensities of habitat
disturbance. The specific parasites investigated in our
study included: gastrointestinal protozoans (Giardia
spp. and Cryptosporidium spp.), blood-borne parasite
(Trypanosoma spp.) and ectoparasites from the
taxonomic orders Ixodida (ticks), Acarina (mites),
Siphonaptera (fleas) and Phthiraptera (lice). This
study investigates broad patterns pertaining to the
relationships among parasite prevalence and community structure in habitats that vary by species
richness, host density and disturbance intensity.
MATERIALS AND METHODS
Study area
KNP is a mid-elevation tropical moist forest located
in the foothills of the Rwenzori Mountains in
Western Uganda (0 13′–0 41′N, 30′19′–30′22′E)
(Struhsaker, 1997; Chapman and Lambert, 2000;
Hartter, 2009). The park and surrounding areas
represent a mosaic of habitats that have undergone
various types and frequency of habitat disturbance
(Hartter, 2009). Portions of KNP were logged at
varied intensities in the 1960s resulting in a series
of contiguous forest compartments of lightly logged,
heavily logged and unlogged status within KNP
(Struhsaker, 1997). Over the last four decades KNP
and the surrounding forest fragments have supported
research on the influence of habitat disturbance on
a variety of forest dwelling species (Kasenene, 1984;
Isabirye-Basuta and Kasenene, 1987; Lwanga, 1994;
Dranzoa, 1998; Chapman et al. 2000; Seavy and
Apodaca, 2002; Gillespie and Chapman, 2008;
Hartter et al. 2011). Our sampling sites represent a
broad gradient of anthropogenic disturbance in the
region, which was historically a contiguous forested
area. Sampling sites included, from least to most disturbed: (1) relatively pristine forest (known as CC),
(2) low-intensity selectively logged forest (known as
K14), (3) high-intensity selectively logged forest
(known as K15), (4) forest–agricultural interface
(known as forest edge), (5) forest fragments (referred
to as Fragments 1 and 2) and (6) human settlements
(Fig. 1). All sites were sampled twice except for K15
and the forest edge. Previous studies within KNP
(Chapman et al. 2000) and these forest fragments
(Gillespie and Chapman, 2006) have extensively
evaluated the gradient of habitat disturbance occurring in the areas studied in this investigation. These
previous studies allow our specific study sites to be
categorically placed along a gradient of disturbance
(Gillespie et al. 2005; Gillespie and Chapman, 2006,
2008).
Animal collection
Trapping webs were used in all habitats except within
village homes to accurately estimate the abundance,
density and structure of the small mammal community within each habitat (Anderson et al. 1983).
Each web was 200 m in diameter and covered 3·14 ha
with 12 radii each containing 12 Sherman traps
(3 × 3·5 × 9″, H.B. Sherman Traps, Inc, Tallahassee,
FL), with the first 4 of these traps set at 5 m intervals
and the 8 distal traps were set at 10 m intervals
(Mills et al. 1999). The centre of the web contained
4 Sherman traps and 1 Tomahawk trap (19 × 6 × 6″,
Tomahawk Live Trap Co., Tomahawk, WI). An
additional 4 Tomahawk traps were each set 50 m from
the centre in the cardinal directions. In total, 153
traps were used in each web. Trapping webs were
operated for three consecutive nights on each trapping occasion for a total of 5049 trap nights at 7 sites.
All sites were sampled on 2 occasions except the forest
edge and heavily logged forest sites, which were
sampled once. Traps were baited in evenings and
animals were collected at sunrise the following morning to prevent trap-associated deaths. All traps were
baited consistently with peanut butter and millet. All
trapping was conducted in the dry season between
May and July of 2009. Sites that had repeated sampling had at minimum a 6-week rest period between
sampling efforts.
Terrestrial small mammal collection was approved
by the Uganda Wildlife Authority, the Uganda
National Council for Science and Technology, local
authorities and homeowners at trap sites. Animal
handling protocols were approved by Institutional
Animal Care and Use Committees (IACUC) from
Emory University (#062-2009) and the Centers for
Disease Control and Prevention (CDC) (#1768).
Standard field methods for small mammal handling, necropsy and tissue collection techniques
were followed (Mills et al. 1995). Necropsies were
performed and tissue samples were collected for
molecular identification of mammalian species and
Trypanosoma spp. Feces were collected from the descending colon and preserved in 10% buffered formalin for detecting Giardia spp. and Cryptosporidium
spp.. Skulls from a subset of small mammals
sampled (n = 137) were prepared using standard
procedures and identified to species using established
Factors influencing small mammal parasitism
3
Fig. 1. Map of KNP (contrast enhanced as darker areas of image) and surrounding areas outside the national park. The
area in the map was all once associated with a contiguous mid-elevation tropical moist forest. Specific locations of
trapping webs (200 m in diameter) are identified as: CC, pristine primary forest; K14, lightly logged forest; K15, heavily
logged forest; Edge, forest edge that overlaps agriculture fields; Fragment 1, located near the village of Bugembe and
surrounded by small scale agriculture and human dwellings; Fragment 2, located near the village of Kiko and surround
by small-scale agriculture, trading centre, and monoculture in the form of tea plantations. Small mammals were also
collected from human dwellings located in areas around KNP and the forest fragments.
4
Johanna S. Salzer and others
mammalian guides (Delany, 1975; Nagorsen and
Peterson, 1980; Thorn and Kerbis Peterhans, 2009).
Specimens were catalogued at the Field Museum in
Chicago, IL, USA (Reference no. 210384–210540).
Molecular identification using cytochrome B gene
analysis was necessary for species indistinguishable
by morphometrics (i.e. P. jacksoni and P. misonnei)
(Peppers et al. 2002).
Immediately following humane euthanasia, each
small mammal was placed in an individual plastic bag.
The plastic bags containing euthanized small mammals were then opened and contents were placed in a
clean white plastic tub (approximately 8 × 40 × 30 cm)
(Bush, 2009). Each individual and the contents of
their plastic bag were processed within the tub for
easy visualization and collection of ectoparasites.
Ectoparasites were dislodged and collected by vigorous brushing of euthanized small mammals. Attached
ectoparasites (i.e. ixodid ticks) were collected by
parting the fur of each individual animal with forceps
and visually inspecting the skin. Small mammals were
processed until no additional ectoparasites were
collected or for a maximum of 20 min in the case of
heavily infested animals and all ectoparasites were
placed into 70% ethanol. This process was conducted
to maintain consistency in collection methods.
Parasite detection
All parasites were identified to the level of order.
DNA was extracted from the spleen tissue (stored at
−80 °C) of all the animals collected (n = 327) using
the DNA EZ1 tissue kit (Qiagen, Hilden, Germany).
To detect Trypanosoma spp. DNA we amplified
the highly variable region of the 18S ribosomal
RNA gene using previously described nested PCR
methods (Noyes et al. 1999) and Platinum Taq
polymerase (Invitrogen, Grand Island, New York,
USA). We used external primers TRY927F and
TRY927R, and internal primers SSU561F and
SSU561R to confirm Trypanosome-positive individuals (Noyes et al. 2002).
Formalin-preserved feces were screened for
Giardia spp. cysts and Cryptosporidium spp. oocysts
using the MERIFLUOR immuno-fluorescent assay
(Meridian BioScience Inc. Cincinnati, Ohio, USA).
Fecal samples were concentrated into fecal pellets and
resuspended in 1 g/mL concentrations and scored for
presence/absence (Salzer et al. 2007).
All ectoparasites were shipped to Emory
University in Atlanta, Georgia, USA where they
were examined by light microscopy. Ectoparasites
from each animal were quantified and categorized according to taxonomic order – Ixodida (ticks), Acarina
(mites), Siphonaptera (fleas) or Phthiraptera (lice)
(Lane and Crosskey, 1993). Only motile stages were
identified for each ectoparasite, including larval,
nymphal and adult stages for ticks and mites; nymphal
and adult stages for lice; and adult stage for fleas.
Data analysis
Relative small mammal density for each habitat was
calculated and measured by trapping success divided
by trapping effort for each habitat where trapping
webs were used for animal collection (omitting human settlements from this analysis). Additionally,
relative species abundance was measured by the
number of total L. aquilus, H. stella and P. jacksoni
divided by the total animals collected at each site.
Using individual-based rarefaction, species richness
was estimated for each habitat. A rarefaction curve
was generated to determine adequate sampling effort
and species richness to correct for varied sample sized
across habitats.
Parasite point prevalence (referred to as prevalence) is the proportion of infected/infested individuals at the time of sample collection and was
calculated for each habitat and the most abundant
rodent species (L. aquilus, H. stella and P. jacksoni).
Correlations between parasite prevalence and species
richness, small mammal density and relative species
abundance were measured using linear regression
models with the prevalence of each parasite as outcome variable. Additionally, correlations between
habitat disturbance and parasite prevalence were
evaluated using Spearman’s rank-order correlation,
since habitat disturbance is a qualitative and ordinal
variable. Statistical analyses were performed using
the stats package on the R version 3.0.1 (R Core
Team, 2013).
To determine if parasite assemblages were more
influenced by habitat or by taxonomic category of
host (i.e. species), we considered each individual
small mammal collected as a patch or community of
parasites. Each patch was associated with parasite
presence/absence in addition to a variable considered
‘parasite free’ to account for small mammals free of
parasites. Using constrained correspondence analysis
(a.k.a. canonical correspondence analysis) (CCA) we
investigated the influence of the taxonomic classification of each individual and the habitat the individual
was from to understand what serves as the best
predictor for the parasites of each individual animal
captured. CCA was performed for the habitat while
conditioning for species and vice versa. The results of
each CCA were analysed using an ANOVA-like
permutation test for CCA to assess the significance
of constraints. All analyses were performed using the
Vegan package on the R statistical platform (version
3.0.1) (R Core Team, 2013) following established
methods (Bellier, 2012).
RESULTS
From May to July 2009, 327 small terrestrial mammals were collected in and around KNP (Fig. 1).
Small mammal density, species richness (rarefied
with standard error) and relative abundance of all 3
Factors influencing small mammal parasitism
host species were calculated for each habitat, although density was not calculated for human settlements (Table 1). Mammals collected represented
23 species and parasites varied in prevalence across
habitat and host species (Fig. 1). All parasites
examined were found to be harboured by P. jacksoni
and L. aquilus, while H. stella harboured all but
cryptosporidium.
Linear regression was used to investigate parasite
prevalence among the various species (Table 2). We
identified a significant positive association between
the overall small mammal density of a community
and the prevalence of trypanosomes (R2 = 0·586,
P = 0·047) and lice (R2 = 0·781, P = 0·012) among
P. jacksoni. Additionally, there was a positive association among the relative abundance of H. stella
and the prevalence of trypanosomes (R2 = 0·708,
P = 0·011) and mites (R2 = 0·717, P = 0·010). Therefore, the more dominant H. stella become in the
community of small mammals, the more individual
H. stella are harbouring trypanosomes and mites.
Parasite prevalence among L. aquilus was more
closelyassociated with small mammal species richness
with an association with flea infestation (R2 = 0·755,
P = 0·016) and trypanosome (R2 = 0·759, P = 0·014).
Relationships between habitat disturbance and parasite prevalence were examined using Spearman’s
rank-order correlation. There was a negative association between habitat disturbance and trypanosome
prevalence among H. stella (rs = − 0·880, P = 0·021).
A Bonferroni correction was not applied to these
analyses because of the increased criticism of its validity in ecological cases and the increased risk of a
Type II error (Krasnov et al. 2008). But we cautiously interpreted our data in consideration of Type I
error and the consideration that with Bonferroni
correction these results would be considered insignificant despite the biological associations.
Correspondence analysis was performed to consider the parasite communities (presence/absence)
harboured by each individual. Using the parasite communities associated with each individual within the
populations of P. jacksoni, L. aquilus and H. stella, we
investigated the influence of habitat and/or taxonomic classification on the parasite community.
We found that a host’s parasite community was
more closely associated with other individuals of that
same host species than to other hosts within their
same habitat but of various species. When the species
was controlled and the habitat type was examined, no
significant associations were identified (P = 0·784)
(Fig. 2A). Alternatively, when habitat was controlled,
the species was significantly clustered (P < 0·0001)
(Fig. 2B).
DISCUSSION
Generalized laws and theories, which are broadly
applicable and repeatable under various conditions,
5
were used to understand ecological interactions
(Lawton, 1999; Lange, 2005; Poulin, 2007). We
investigated a broad range of habitat types that
harbour diverse communities of host species as
well as parasites. We focused our study on a single
point in time and identified relatively a few patterns
of parasitism among host species. In general, we
did not identify any universal relationships or
patterns. Despite the preliminary nature of our
study, this work does provide additional empirical
evidence for the importance of understanding host
taxonomy and parasite specificity (Froeschke et al.
2013).
Several patterns among the different species did
emerge in this study, although at this point we can
only speculate as to the dynamic drivers. Among the
P. jacksoni population in forested Uganda, we found
an association between trypanosomes and lice prevalence and increases in small mammal density. It is
accepted that both lice and trypanosomes are moderately host-specific, usually infecting multiple hosts
of restricted phylogenies or taxonomic classifications
(Dobigny et al. 2011; Froeschke et al. 2013).
Therefore, the association we found between parasite
prevalence and total small mammal density (as
opposed to relative abundance of P. jacksoni) may
be associated with spill-over of trypanosomes and lice
from other dominant host species. This pattern
may also be indicative of a decline in host resistance
in competitive environments (Johnson et al. 2013).
Interestingly, trypanosome prevalence among
H. stella was associated with increases in relative
abundance and habitat disturbance which may
indicate H. stella as a potential primary native host
for trypanosomes in addition to possible spill-over
from other invasive host species in the disturbed
habitats. One of the most common species of
trypanosome-infecting rodents is Trypanosoma lewisi
which is a globally invasive parasite of rodents and
transmitted from invasive Rattus rattus into wild
populations (Dobigny et al. 2011). Lophuromys
aquilus experienced a positive association of both
trypanosomes and fleas and overall small mammal
richness, which may compete with hypotheses that
predict an increase in host species richness leads to
declines in host-specific parasite prevalence. This
relationship between trypanosomes and fleas among
L. aquilus and overall small mammal richness may
also be explained by spill-over if these parasites are
less host-specific and have the ability to infect a wide
range of hosts. Additionally, we found an association
between mite prevalence on H. stella and relative
abundance of H. stella, which is not a surprising
association since mites are recognized as being
predominately host-specific and we would suspect
their infestation rate to increase as the available
hosts increase (Fain, 1994). We did not find any
linear associations among ticks, Giardia or
Cryptosporidium.
Species
4·00 (0·00)
4·88 (0·95)
5·59 (0·55)
6·67 (0·92)
Trapping
effort
918
918
459
459
Density/
trap success
0·029
0·072
0·0828
0·085
Total number
collected
11
1
11
4
27
1
14
1
2
1
29
18
66
3
13
3
2
16
1
38
1
1
2
1
1
4
6
23
39
1
1
1
1
5
1
8
3
5
6
Relative
abundance (%)
41
4
41
15
2
21
2
3
2
44
27
8
34
8
5
42
3
3
3
5
3
3
10
15
59
2
2
2
2
11
2
17
6
11
13
Trypanosoma
2
0
1
1
4
0
2
0
0
0
11
1
14
2
2
0
0
7
0
11
0
0
0
0
0
0
0
12
12
0
0
0
0
0
0
0
3
1
0
Giardia
0
0
0
0
0
0
0
0
0
0
2
0
2
0
0
3
0
0
0
3
0
0
0
0
0
2
0
1
3
0
0
0
0
0
0
0
0
2
1
Cryptosporidium
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
1
0
0
0
0
0
1
0
0
1
0
0
0
0
0
0
0
0
0
0
Ticks
Mites
6
0
4
0
10
0
8
0
0
1
8
4
21
1
4
0
0
5
0
10
0
0
2
0
1
3
2
9
17
1
1
0
0
1
0
2
1
1
1
11
1
11
4
27
1
14
1
2
1
29
18
66
3
13
1
2
16
1
36
1
0
1
1
1
4
4
21
33
0
1
1
1
5
0
7
3
5
6
Fleas
0
0
3
0
3
0
4
0
1
1
9
5
20
1
5
0
1
9
0
16
0
0
2
0
0
1
0
5
8
1
0
1
0
1
0
1
2
3
2
Lice
0
0
0
2
2
0
1
1
1
0
11
6
20
2
1
1
0
11
1
16
0
0
1
0
0
3
0
14
18
0
0
0
0
2
0
0
0
2
2
6
Hylomyscus stella
Malacomys longipes
Praomys jacksoni
Praomys misonnei
Total in habitat
Hybomys lunaris
Hylomyscus stella
Lophuromys aquilus
Malacomys longipes
Mastomys natalensis
Praomys jacksoni
Praomys misonnei
Total in habitat
Hybomys lunaris
Hylomyscus stella
Lophuromys aquilus
Mus bufo
Praomys jacksoni
Praomys misonnei
Total in habitat
Crocidura dolichura
Crocidura fuscomurina
Crocidura nigrofusca
Dasymys incomtus
Hylomyscus stella
Lophuromys aquilus
Mus grata
Praomys jacksoni
Total in habitat
Crocidura littoralis
Crocidura maurisca
Crocidura olivieri
Dendromus mystacalis
Gerbilliscus kempi ruwenzorii
Hybomys lunaris
Hylomyscus stella
Lemniscomys striatus
Lophuromys aquilus
Mus grata
Species richness
(standard error)
Johanna S. Salzer and others
Table 1. In total, 327 small mammals representing 23 distinct species were collected from 7 habitats in and around Kibale National Park. Each habitat
experienced varied levels of host density, species richness (rarefied) and disturbance level. Each individual small mammal was screened for a variety of parasites –
Giardia spp., Cryptosporidium spp., Trypanosoma spp., mites, lice, fleas and ticks. The numbers in each parasite column (Giardia, Cryptosporidium, etc.) indicate
the number of host from each individual species infected with each parasite group
9·35 (1·16)
6·71 (1·09)
5·92 (0·80)
918
918
na
4590
0·0512
0·0545
na
1
14
47
1
1
2
31
7
2
4
1
1
50
4
1
3
5
4
3
40
60
327
2
30
0
1
5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
0
11
14
60
2
2
4
62
14
4
8
2
2
7
2
5
8
7
5
67
0
0
3
0
0
0
21
0
0
0
0
0
21
0
0
2
0
0
0
0
2
34
0
0
0
0
0
0
3
0
0
0
0
0
3
0
0
1
0
0
0
1
2
7
0
2
10
1
1
2
15
5
2
1
0
1
28
0
1
2
3
2
1
2
11
107
1
13
43
0
0
2
30
6
1
4
0
0
43
0
1
3
3
3
2
4
16
264
0
6
17
0
1
1
6
1
0
2
0
1
12
1
0
1
0
0
1
11
14
90
0
3
9
1
0
1
18
3
2
2
0
0
27
3
0
3
0
3
0
3
12
104
Factors influencing small mammal parasitism
Oenomys hypoxanthus
Praomys jacksoni
Total in habitat
Crocidura nigrofusca
Crocidura olivieri
Gerbilliscus kempi ruwenzorii
Lophuromys aquilus
Mus grata
Oenomys hypoxanthus
Praomys jacksoni
Rattus rattus
Scutisorex somereni1
Total in habitat
Crocidura olivieri
Hylomyscus stella
Lophuromys aquilus
Mus grata
Mus triton
Praomys jacksoni
Rattus rattus
Total in habitat
Total
Table 2. Linear regression was used to investigate relationships between parasite prevalence and small mammal density, species richness and relative species
abundance of the 3 most abundant rodent species (P. jacksoni, L. aquilus and H. stella). Additionally, correlation between habitat disturbance and parasite
prevalence among these 3 host species was examined using Spearman’s rank-order correlation test. Human settlements were omitted from the analysis of small
host density.
Total small mammal density
Parasite
Praomys
jacksoni
Relative species abundance
Lophuromys Hylomyscus Praomys Lophuromys Hylomyscus
aquilus
stella
jacksoni aquilus
stella
R2 = 0·586
P = 0·047
Giardia spp.
ns
Cryptosporidium spp. ns
Ticks
ns
Mites
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
Fleas
ns
ns
ns
ns
Lice
R2 = 0·781
P = 0·012
ns
ns
ns
Trypanosoma spp.
Species richness
Praomys Lophuromys
jacksoni aquilus
ns
ns
R2 = 0·708
P = 0·011
ns
ns
ns
R2 = 0·717
P = 0·010
ns
na
na
ns
ns
ns
ns
na
ns
Habitat disturbance
Hylomyscus Praomys Lophuromys Hylomyscus
stella
jacksoni aquilus
stella
R2 = 0·759
P = 0·016
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
rs = − 0·880
P = 0·021
ns
ns
ns
ns
R2 = 0·755
P = 0·016
ns
ns
ns
ns
ns
ns
ns
ns
ns
7
Johanna S. Salzer and others
8
Fig. 2. (A) Correspondence analysis of habitats associated with KNP using parasite communities within each habitat
harboured the most abundant rodent species – P. jacksoni, L. aquilus and H. stella. The 7 habitats are shown in the
ordination plot represent a gradient of habitat disturbance (least to most disturbed is listed from top to bottom in the
figure key) with each habitat represented by a specific colour indicated in the figure key and each point an individual.
Some points are superimposed on each other. The correspondence analysis determined parasite communities on each
individual small mammal were not associated with the habitat the host occupied. (B) Correspondence analysis of the
parasite communities harboured by the 3 most abundant rodent hosts – P. jacksoni, L. aquilus and H. stella sampled
from all habitats. Each species is represented by a specific colour indicated in the figure key and each point represents an
individual. Ellipses represent 95% confidence interval of the species centroid, and non-overlapping ellipses are
interpreted as significant differences between the species at α = 0·05. Some points are superimposed on each other. All 3
rodent species show distinct parasite community differences. The correspondence analysis determined parasite
communities were significantly associated with taxonomic classification of their host species (P = 0·0001).
9
Factors influencing small mammal parasitism
Our study highlights the importance of taxonomic
classification of host species to understand their parasite communities, with environmental factors being a
secondary influence on a host’s parasite community.
Our results support recent findings that relationships
between biodiversity and pathogen transmission
are idiosyncratic and highly dependent on the
host species and parasites studied (Salkeld et al.
2013). It is possible that general patterns between
parasite prevalence and host dynamics/environment
would emerge in more simplistic community assemblages of small mammals experiencing less
extensive habitat disturbance. Additionally, there
are possible factors and interactions unaccounted for
in our work, which may be more influential on
parasite prevalence than those examined in our present study. Such factors in more disturbed habitats
could likely include pathogen spill-over from human
and domestic animal sources, the influence of
invasive species (and their parasites), and alterations
in individual host susceptibility to parasites (Daszak
et al. 2000; Torchin et al. 2003; Dobson et al. 2008).
General linear patterns associated with parasitism
among different host communities are likely more
easily identified when these communities examined
maintain a certain level of shared species and experience a more gradual gradient of habitat disturbance.
To more accurately investigate relationships between
host diversity and parasite prevalence/richness in
KNP, we would need to have a larger sampling
effort with substantial replications over multiple
sites and seasons. The sampling in this study was
from 7 distinct habitats experiencing distinct small
mammal communities with an absence of paired
samplings. Replicate sites would need to first be
identified and then sampled at various time points
to more accurately answer questions related to the
dilution effect and amplification of parasites in
forested Uganda.
The coarseness of the parasite data limits our
ability to identify the host species responsible for the
maintenance of specific parasites within these communities and parasite–host specificity. Future studies
could provide further taxonomic characterizations of
these parasites to the species level. This finer examination of parasites, particularly the ectoparasites,
would certainly identify even greater parasite richness and identify parasite–host dynamics specific
to KNP (Alvarado-Otegui et al. 2012; Salyer et al.
2012). Despite the coarseness of the data and the
innate limitations, host taxonomy is still significant
in determining the parasites an individual harbours.
Further identification of parasite species will only
strengthen this finding. Additionally, given recent
identification of viral anti-bodies circulating in small
mammals around KNP, future work using serologic
assays would broaden our knowledge of disease life
history for each individual small mammal (Salzer
et al. 2013).
Ecosystems are complex with an intricate network
of hosts and parasites. General patterns would be
expected to be less common as complexity increases
in these natural systems. In the absence of a larger
sampling effort, this impact of habitat disturbance,
host community density and species richness in
Western Ugandan small mammal communities appear less influential in determining parasite prevalence and parasite community. Our data provide
empirical evidence that within the small mammal
communities in Western Uganda, very few patterns
emerged to explain these ecological drivers of parasite
prevalence. However, we did find strong associations
between individual small mammal taxonomy and the
community of parasites they harbour. This study
provides further evidence that the parasite communities found in the mosaic landscape in Western
Uganda are strongly influenced by the taxonomy of
the dominant host species and the natural history of
the parasites they harbour.
ACKNOWLEDGEMENTS
We thank the Uganda Wildlife Authority, Uganda
National Council for Science and Technology, Makerere
University Biological Field Station and local authorities for
permission to conduct this study. IACUC committees
at Emory University (#062-2009) and the CDC (#1768)
approved all animal collection and handling protocols.
Additionally, animals from human dwellings were collected
with permission from local authorities (Local Chairman)
and homeowners. Thanks to R. R. Lash for cartographic
contributions and C. Akora and I. Mwesige who provided
assistance in the field and K. Cross for assistance in the
laboratory. We also thank R. Stevens, W. Stanley, J. Mills,
A. Peralta, I. Damon and C. Hughes for helpful comments,
logistical and/or analytical assistance. The views expressed
in this paper are solely those of the authors and do not
represent those of the CDC, US Government or any other
entity which the authors may be affiliates. Lastly, we are
very grateful to two anonymous reviewers for their helpful
comments and expertise.
FINANCIAL SUPPORT
This research was supported in part by the Emory Global
Health Institute, Emory University Environmental
Sciences Department and the appointment of J.S.S. to
the Research Participation Programme administered by
Oak Ridge Institute for Science and Education (ORISE)
through an interagency agreement with CDC.
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