What do worms want? Geographic variation and
habitat correlations of trematode parasites in the
gastropod Lavigeria nassa
Student: Ranjan Muthukrishnan
Mentor: Ellinor Michel
Introduction
Parasites can exert strong control on their hosts, influencing
behavior, fertility, and mortality (Jokela & Lively 1994;
Levri & Lively 1996). In areas where parasitism is common,
parasites are likely to be important agents of natural selection
and significantly affect host distributions. If the selective
pressure is strong enough and if there are environmental
variables that produce patterns in parasite distributions, these
should also control host distributions but with an inverse
pattern. But what determines parasite distributions is poorly
understood. Two major factors for parasite distributions are
availability of hosts and the ease of transmission between
hosts. Digenean trematodes have complex life cycles that
involve multiple hosts with at least one intermediate molluscan
host (Briers 2003). The definitive hosts for digenean parasites
are most often vertebrate species (Mouritsen & Poulin 2002).
In Lake Tanganyika the Lavigeria gastropod species flock
has been studied extensively and multiple parasites have been
found (McIntyre, et al. 2005, Faloon 2002) but the definitive
hosts have not yet been identified. Significant differences in
intensity of parasitism between sites have been indicated in
past studies (McIntyre, et al. 2005, Faloon 2002), but the
extent and ecological correlates of geographic variation were
not explored. Lavigeria species also show highly patchy
distributions (Michel, et al. 2003, Barrett, et al., 2003) but
the factors structuring communities are unknown (see papers
by Huntington and Meyer, this volume). I aimed to document
patterns of parasite variation on a wider scale than had been
attempted in the past, focusing on Lavigeria nassa which is
found at most sites around the lake, and to correlate parasite
prevalence with habitat variables.
Materials and methods
Site selection and environmental quantification
Ten sites were selected along the eastern shore of Lake
Tanganyika in the Kigoma region of Tanzania, with selection
criteria based on similarity in rocky substrates and other
variables discussed below (GPS coordinates and names in
Table 1). Field work was largely a team effort, with each team
member responsible for specific analyses.
Species diversity (Fisher’s alpha, which combines species
richness and eveness) and L. nassa densities were calculated
for all ten sites (J. Meyer this volume). Shoreline aspect and
benthic algal biomass were recorded at seven sites and rugosity
at nine sites (B. Huntington this volume). Benthic gross
primary productivity was measured at six sites (T. Thoms this
volume). Phytoplankton biomass after an upwelling event was
recorded at seven sites (J. Corman this volume).
Data collection
For this study, a minimum of 100 snails were collected at each
site by divers from a depth of 5m (+/- 1m). When possible,
individuals of larger sizes (18mm+) were preferred to increase
the proportion of sexually mature individuals. After collection,
samples were brought back to lab and maintained for up to
three days before processing. Each individual was digitally
photographed (D. Athuman this volume), and measurements
were taken of shell height, distance from the aperture to top of
snail, and distance from each scar to top of snail (measured from
the point where the scar meets the suture). Snails were assigned
as either juvenile, sub-adult, or adult based on lip thickness and
angle of shell accretion at the aperture (adult modifications,
sensu Papadopoulos et al. 2003). The shells of individuals
were then cracked open using a hammer and the soft tissue was
examined under a dissecting microscope. Sex and reproductive
status was determined by presence/absence of a brood and
examination of the texture of the gonad, and parasite infections
were recorded if present. If a parasite infection was not obvious
from examination of the intact gonad, it was dissected out,
squashed between two glass plates, and reexamined under the
dissecting microscope. The gonads of brooding females were
also not dissected due to the unlikelihood of infection (pers.
comm. P. McIntyre). All parasites were identified as digenean
trematodes and were further classified by morphotype. Seven
different morphotypes of parasite cercaria were identified and
sporocysts unaccompanied by cercaria were grouped together.
Parasite prevalence was then calculated for each site as the
number of infected hosts divided by the number of individuals
examined (Bush et al. 1997).
Data was analyzed using JMP IN v4.0.4 using standard least
squares regressions to compare single habitat factors against
the continuous response variable of parasite prevalence.
Logistic regressions were used to compare single factors
against a nominal response variable. Linear regressions were
then conducted between sites comparing parasite prevalence as
a response to other site wide characters. A multiple regression
was used in cases of multiple factors predicting a continuous
response variable. At the individual level, logistic regressions
were performed using parasite presence/absence as a response
to other individual level characters. Temporal trends in parasite
prevalence were examined by comparing data from 1998
(McIntyre et al. 2005) and 2002 (Falloon 2002) in a logistic
regression.
Results
Logistic regression revealed significant differences in parasite
prevalence between sites (Wald test df=9, x 2=73.860, p<.0001).
Linear regressions demonstrated significant correlations
of parasitism and average shell height (Fig. 1, F=5.7621,
p=0.0437) and average scar prevalence (Fig 2, F=7.1285,
p=0.0284) such that sites with larger and/or more frequently
scarred individuals had higher parasite prevalence. There
was no significant correlation for habitat rugosity (F=0.1102,
p=0.7496), species richness and evenness (quantified with
Fisher’s alpha, F=2.6365, p=0.1431), L. nassa density
(F=2.694, p=0.1392), species richness (simple species
presence/absence counts, F=0.0404, p=0.8487), benthic gross
primary productivity (F=3.5514, p=0.1326), algal biomass
(F=0.0031, p=0.9574), phytoplankton biomass in response to
upwelling events (F=4.9536, p=0.0677) or shoreline aspect
(F=2.8925, p=0.1497). Phytoplankton biomass was further
analyzed in a multiple regression with average shell height to
normalize for size differences between sites and resulted in an
increased significance for both factors (F=11.4136, p=0.0137).
Logistic regressions at the individual level revealed significant
correlations of parasite presence with later life stage (Wald test
df=2, x 2=6.592, p=0.0370), greater individual height (Wald test
df=1, x 2=20.196, p<.0001), and scar presence (Wald test df=1,
x 2=13.194, p=0.0002).
Temporal comparisons showed an overall increase in parasite
prevalence over time. With year nested within sites as the
independent variable and parasite presence/absence as the
response there was a significant correlation (Fig. 3, Wald test
df=4, x 2=29.451, p=0.0002)
Discussion
I looked at predictors of parasitism at two different scales: the
individual and the site. At the individual level there were good
predictors and there was considerable site to site variability but
the cause of the inter-site patterns is uncertain. At the individual
level, shell height is the best predictor of parasitism which is a
commonly found and accepted result (Krist & Lively 1998).
Age and size are strongly correlated so larger individuals are
likely to have had more time to encounter parasites. Large
individuals also have a greater probability of randomly
encountering parasites by virtue of their bodies providing larger
targets. The second and possibly more interesting predictor of
parasitism is shell scars. Shell scars are the result of failed
predation attempts by crabs and can be interpreted as a proxy
for crab/snail interaction levels.
The data suggests two possible interpretations which are
neither mutually exclusive nor all-encompassing. Firstly, there
is the possibility that parasitism in some way “weakens” the
snail making it more susceptible to predation which appears as
higher scarring rates. The second possibility uses scarring rates
Figure 1: Significant correlation of average shell height
with parasite prevalence (F=5.7621, p=0.0437)
Figure 2: Significant correlation of scar prevalence with
parasite prevalence (F=7.1285, p=0.0284)
only as a measure of crab-snail interaction and thus assumes
that high scarring occurs when interactions are common. Under
such conditions higher parasitism should correlate with greater
scarring if predatory crabs are another intermediate host of the
parasite because common encounters would assist transmission
between hosts. Trematodes must move to a vertebrate as
their definitive host, and are obliged to find a mollusc for
their first intermediate host, but crustacean secondary hosts
have also been documented (Mouritsen and Poulin 2002, G.
Nkwengulila, pers. comm.), but always between the mollusc
and vertebrate hosts. Thus, since the direction of transmission
is almost certainly from the mollusc to a crab host , and not
vice versa, this correlation suggests that parasite presence in a
snail increases the likelihood of crab encounters. It is possible
that in this system parasites induce aberrant, predator attracting
behaviors in the snails. This has been documented in other
systems, and would be of interest as an indication of fairly
highly co-evolved interactions.
Moving up to the scale of site-to-site differences I hoped to
find environmental characters that could be structuring parasite
distributions. The only places where patterns appeared were
again in the individual-based measures, scarring and shell
height, which suggests that there is some other factor about
the sites that causes parallel responses in multiple variables.
Possibly any of the three could be driving all of the characters
in concert, but without experimental evidence those causative
relationships cannot be teased out. Other measures (i.e.
productivity, shoreline aspect, Fisher’s alpha, etc.) were
considered to test for site-wide factors that could be influencing
the variation between sites but variation in response to upwelling
events in phytoplankton biomass (when normalized for height
differences) was the only factor that could significantly predict
parasitism, whereas phytoplankton seems to be a fairly removed
variable from snail parasite abundances it could quite possibly
be linked by indirect mechanisms. The upwelling response
supports a hypothesis that a second intermediate host of the
parasites is a planktivorous fish whose distribution is controlled
by phytoplankton abundances (pers. comm. P. McIntyre).
Sites that consistently respond more strongly to upwelling
should be able to support larger planktivore populations with
the upwelling subsidy than more poorly responding sites (J.
Corman this volume). Consequently, if a plankitivorous fish
environmental variable, be it biotic or abiotic. A likely variable
to be controlling parasite prevalence in snails is the distribution
of a second intermediate or definitive host of the parasite.
The identity of those other hosts is unknown but correlations
between parasite prevalence and physical variables may
provide clues to the identity of alternate hosts by predicting
host distributions. Finally, parasite prevalence may not be
controlled the distribution of alternate hosts, but directly by
the physical factors themselves and in such a case only direct
experimental evidence will be able to verify the causal link.
Figure 3: Changes in parasite prevalence over time at 4 sites.
Shows a significant increase in parasitism over time (Wald test
df=4, x 2=29.451, p=0.0002)
is the definitive host for the parasites, regions with higher local
abundances again should provide higher parasite encounters
and have higher infection rates. It is difficult to make definitive
statements about what is driving parasite distributions without
experimental evidence but the strong patterning between sites
suggests there is some factor, or composite of factors, affecting
them either directly or indirectly.
Finally, an analysis of three datasets collected over a period of
eight years shows a general trend of increasing parasitism over
time (Fig. 3). This conclusion contradicts earlier findings that
parasite prevalence was temporally stable (Faloon 2002) and
is perhaps questionable because the pattern is largely driven
by only two of the four sites which have multi-year data but it
does statistically exist. This could be a likely pattern if L. nassa
was a relatively long-lived species, trematode infections have
a relatively low impact on mortality and the parasite invasion
was relatively recent. Very little is known about L. nassa
lifespans but the second condition of low mortality is likely to
be the case. The parasites studied here were exclusively found
in snail gonads. While parasite presence has strong selective
implications for the snail because the individual becomes
“reproductively dead”, for the parasite does not necessarily
decrease survival and thus may not directly increase mortality
in the population (Mouritsen & Poulin 2002). This condition
would allow time for parasites to be passed through a population
and, all other things being equal, the infection rate should be a
function of the number of currently infected individuals that
can “donate” parasites. Although infection is not fatal, parasiteinduced sterility would eventually lead a local population to
extinction unless the population could equilibrate to a stable
proportion of parasitized to unparasitized individuals. This
could occur from a resistant phenotype arising in the population
or from a constant immigration of uninfected individuals. My
results, showing significant differences in parasite prevalence
both geographically and temporally, indicate that such an
equilibrium has not been reached, and the interactions between
snail hosts and trematode parasites are in dynamic flux.
Conclusion
Parasitism is not rare in the Kigoma Bay region and appears
to be on the rise, making it likely to be a significant selective
pressure on local species. Different sites in the region show
variability in trematode parasite prevalence in L. nassa. This
variability is likely to be controlled by some as-yet unknown
Acknowledgments
I would like to thank Ellinor Michel for her assistance at every
level of this project, Pete McIntyre for his insights on parasites
and their ecology, Daima Athuman for help prepping samples,
Brittany Huntington, Justin Meyer, Joe Sapp, and Cody
Helfrich for assistance in collections and the NSF for funding
of this work through the Nyanza Project.
References
Barrett, M., J. Catron, and B. Bishobishiri. 2003. Why do Tanganyikan
gastropods have patchy distributions? Abundance, diversity
and species distribution in relation to abiotic and biotic
parameters. Nyanza Project 2003Annual Report. 113-129.
Briers, R. A. 2003. Range limits and parasite prevalence in a
freshwater snail. Proceedings of the Royal Society of
London B (Supplement), 270:S178-S180.
Bush A.O. , K.D. Lafferty, J.M. Lotz, A.W. Shostak . 1997.
Parasitology meets ecology on its own terms: Margolis et.
al. revisited. Journal of Parasitology. 83(4): 575-583.
Faloon, K. 2002. Trematode parasite prevalence in Lavageria: The
effects of sedimentation, depth, size and species.
Jokela, J. and C. M. Lively. 1994. Parasites, sex, and early
reproduction in a mixed population of freshwater snails.
Evolution. 49(6):1268-1271
Krist, A. C. and C. M. Lively. 1998. Experimental exposure of
juvenile snails (Potamopyrgus antipodarum) to infection by
trematode larvae (Microphallus sp.): infectivity, fecundity
compensation and growth. Oecologia. 116: 575-582.
Levri, E. P. and C. M. Lively. 1996. The effects of size, reproductive
condition and parasitism on foraging behavior in a
freshwater snail, Potamopyrgus antipodarum. Animal
Behavior. 51: 891-901.
Michel, E., Todd, T., Cleary, F.R., Kingma, I., Cohen, A., Genner, M.
(2003) Scales of endemism: Challenges for conservation
and incentives for evolutionary studies in a gastropod
species flock from Lake Tanganyika. Journal of Conchology
Special publication 3: 1-18
Mouritsen, K. N. and R. Poulin. 2002. Parasitism, community
structure and biodiversity in intertidal ecosystems.
Parasitology. 124: S101-S117
Papadopoulos, L. N., J. A. Todd and E. Michel. 2003. Adulthood and
phylogenetic analysis in gastropods: character recognition
and coding in shells of Lavigeria (Cerithioidea, Thiaridae)
from Lake Tanganyika. Zoological Journal of the Linnean
Society 140: 223-240.
Site Name
Jakobsen Outer
Jakobsen Inner
Maji Menge
Katabe South
Hilltop
Nondwa Point
Euphorbia
Kalalangabo South
Mitumba
Mwamgongo
Longitude
29.5961°E
29.59835 °E
29.5947°E
29.60062°E
29.61325 °E
29.60848 °E
29.60877°E
29.60742°E
29.63163°E
29.63817°E
Lattitude
4.916367°S
4.9134°S
4.902683°S
4.90005°S
4.887117°S
4.862817°S
4.849267°S
4.843833°S
4.63295°S
4.616°S
Table 1: Site names and GPS coordinates
Site
Jakobsen
Outer
Jakobsen
Inner
Maji Menge
Katabe
South
Hilltop
Nondwa
Point
Euphorbia
Kalalangabo
South
Mitumba
L. nassa
density
ind/m^2
Rugosity
Fisher
alpha
Parasite
prevalence
Scar
prevalence
Avg
height
(mm)
GPP mgC/
m^2h
shoreline
aspect
chl a
Species
Richness
8.6
6.3
1.65
0.15
0.41
20.06
91.974
90W
8.373
9
3.4
8
6.47
6.54
1.81
1.12
0.51
0.25
0.61
0.48
21.32
21.27
117.798
139.580
80W
95NW
8.878
9.387
7
6
12
12.2
6.26
5.28
0.85
0.83
0.1
0.08
0.16
0.2
18.87
16.45
42.746
-
180N
210NE
10.089
8.348
6
6
6.2
1.8
5.99
0.96
1.21
0.18
0.14
0.24
0.49
19.80
20.17
65.315
85W
15.315
7
8
5.6
7.41
7.64
1.76
0.93
0.13
0.18
0.39
0.43
19.21
21.06
60.451
-
145NW
-
4.526
-
10
-
Table 2: Site characteristics