Oikos 000: 001–011, 2010
doi: 10.1111/j.1600-0706.2009.18292.x
© 2009 The Authors. Journal compilation © 2009 Oikos
Subject Editor: Christopher Lortie. Accepted 10 December 2009
Tropics, trophics and taxonomy: the determinants of
parasite-associated host mortality
Nicholas Robar, Gary Burness and Dennis L. Murray
N. Robar, G. Burness and D. L. Murray ([email protected]), Dept of Biology, Trent Univ., 1600 West Bank Drive, Peterborough,
ON, K9J 7B8, Canada.
Empirical studies often reveal deleterious effects of parasites on host survival, but the ecological and environmental processes modulating parasite-associated host mortality are not well understood. We conducted meta-analysis of experimental
studies assessing parasite-associated mortality (n52) to evaluate broad-scale patterns in host mortality risk relative to host
or parasite taxon, parasite life cycle, or local environmental conditions. Overall, likelihood of host mortality was ~2.6 times
higher among infected individuals when compared with hosts that either lacked parasites or had experimentally-reduced
parasite burdens. Parasites with complex life cycles reliant on predation-mediated transmission generally were associated
with higher mortality risk than those exploiting other transmission strategies. We also detected a negative relationship
between parasite-associated host mortality and latitude; host mortality risk declined by ~2.6% with each degree increase in
latitude. This result indicated the likely importance of abiotic factors in determining parasite effects. Host taxonomy further influenced parasite-associated mortality risk, with amphibian, fish, and mollusc hosts generally having higher hazard
than arthropod, mammal, and bird hosts. Our results suggest patterns that conform to the predicted link between host
mortality and parasite transmissibility, and pathogenicity. The relationship between host mortality and latitude in particular may portend marked shifts in host–parasite relationships pursuant to ongoing and projected global climate change.
Recent decades have seen several paradigm shifts in evolutionary ecology and population biology, including changes
in our understanding of the role of parasitism in host mortality. Previously, most authorities viewed parasites as being
largely benign to hosts, based on the assumption that adverse
effects on host survival are deleterious to parasite fitness
(Gulland 1995, Tompkins et al. 2002). Yet, mounting empirical evidence highlights the marked adverse effects of many
parasites on host fitness (Tompkins et al. 2002), including
increased risk from other causes of death such as starvation
or predation (Murray et al. 1997, Seppälä et al. 2004). Current theory acknowledges that competition among parasites
within hosts may lead to more intense exploitation of host
resources, thereby increasing severity of direct or indirect
adverse effects (Ewald 1995, Frank 1996). On its face, a
tendency toward harming hosts appears detrimental to parasite viability. However, the recognized strong correlation
between virulence (severity of resource exploitation by parasites; Poulin and Combes 1999) and transmissibility (rate at
which the parasite is spread to uninfected animals; Galvani
2003) leads to the inference that virulent parasites can maintain high fitness by maximizing transmission efficacy rather
than by mitigating impacts on hosts (Anderson and May
1982, Ebert and Herre 1996).
Notwithstanding the above generalizations, it is understood that parasite effects on host survival can be markedly
variable across host–parasite systems depending on intrinsic and extrinsic features of the host–parasite relationship
(Bull 1994, Ewald 1995, Galvani 2003). Indeed, in some
cases parasitic infection can cause mass die-off of hosts
­(Cleaveland et al. 2002, Daszak et al. 2003), whereas in
others parasitic infection has little impact on host survival
(Haemig et al. 1998, Kelly et al. 2003). Accordingly, ecologists are charged with determining the broader factors
influencing variability in parasite virulence and, ultimately,
pathogenicity (effects of infection on hosts; Poulin and
Combes 1999). For example, it is held that parasite transmission strategies are critical determinants of virulence (Galvani
2003), with parasites having complex life cycles benefiting
from high exploitation of intermediate hosts if a higherorder host mediates transmission (Ewald 1995). Parasites
often rely on ­predation events to facilitate transmission from
one trophic level to another. This strategy, hereafter referred
to as predation-mediated transmission, is prevalent across a
wide range of parasite groups (e.g. acanthocephalans, cestodes, nematodes, trematodes and protozoa; Brown et al.
2001). Under such circumstances parasites should intensify
host exploitation if it leads to increased transmission, in certain cases by increasing risk of host predation (Poulin et al.
2005). In contrast, where parasites undergo a direct life cycle
consisting of a single host, increased host mortality may be
maladaptive. However, results of recent theoretical models
1
(Day 2002) have contradicted the aforementioned generalizations and raised questions regarding their veracity. Thus,
additional investigation involving a range of parasite species with variable life histories is necessary to better understand the complex interplay between parasite fitness and
pathogenicity.
Although parasite-mediated transmission is a primary
aspect of the host–parasite relationship potentially influencing host mortality, other factors acting at the interface
between parasites and the environment also may be influential. For example, abiotic factors such as temperature
and seasonality limit parasite productivity, thereby curbing
transmission efficacy (Marcogliese 2001, Harvell et al. 2002,
Poulin and Mouritsen 2006). It seems plausible that constraints imposed by these factors may be particularly limiting
to parasites found in harsh climates; this may be illustrated
by a relationship between parasite productivity and latitude (Harvell et al. 2002, Poulin and Mouritsen 2006)
and pathogenicity (Møller 1998, Marcogliese 2001). However, although some authors have suggested an influence of
latitude on adverse effects of parasitism (Møller 1998,
Marcogliese 2001), to date this has not been confirmed in
the context of host mortality. Thus, an appreciation for the
interaction between abiotic factors and parasite effects cannot be properly assessed without considering impacts of parasites on hosts across a broad range of climatic conditions.
Deleterious effects of parasites on hosts are highly variable among taxonomically diverse host–parasite systems.
Identifying broad-scale patterns in parasite pathogenicity
requires rigorous contrast and comparison across a variety
of parasite–host systems, and such an approach is currently
absent from the literature. Formal meta-analysis allowed us
to quantify determinants of parasite-associated mortality
and thereby test the influence of a number of factors on the
costs of parasitism to hosts. We predicted that parasites utilizing predation-mediated transmission to higher-order hosts
would be associated with higher host mortality than those
that employed other transmission strategies. In addition, we
sought to characterize a relationship between effects of parasitism and environmental factors by linking risk of parasiteinduced host mortality with latitude. Using meta-analysis of
studies involving a broad range of host and parasite taxa, our
study evaluates potential determinants of parasite-associated
host mortality and advances our understanding of the ecological role of parasitism.
Methods
Data acquisition
We conducted searches for studies quantifying parasiteassociated host mortality through ISI Web of Science and
Google Scholar. Initial searches were conducted using combinations of the terms ‘host’, ‘parasites’, ‘parasitism’, ‘survival’ and ‘mortality’. Works citing or cited by manuscripts
retrieved in this manner were also considered for inclusion
in the meta-analysis. The search was restricted to literature
published during 1980–2007, and excluded studies lacking either the required descriptive statistics (e.g. mean survival, variance, sample sizes, latitude) or experimental design
2
conducive to robust meta-analysis (e.g. no replication, pseudoreplication). Candidate studies had to include extractable
survival data for both parasitized and non-parasitized (control) groups, where parasitized groups were derived either
from natural or experimental infection; in the latter case,
infection was achieved either through direct inoculation or
exposure to infected animals. Prospective hosts that were
known to be uninfected or were subject to experimental load
reduction through anti-parasitic treatment were considered
controls. Recognizing that parasite-reduction tends not to
achieve complete parasite removal, we consider estimates of
parasite-associated mortality based on this approach to be
conservative. Because we sought to explore ultimate effects
of parasitism on host survival, we did not consider proximate
cause of death of experimental animals. We estimate that,
due to our strict inclusion criteria, fewer than 25% of studies
considered were ultimately used in the meta-analysis.
Exhaustive data are often regarded as essential components
of high-quality meta-analyses (Lipsey and Wilson 2001), but
results derived from non-exhaustive data sets should be considered robust if 1) analyses are statistically-rigorous, 2) data
sets lack significant bias and 3) included studies are randomly
sampled from the broader literature. Meta-analyses based on
non-exhaustive data sets meeting these criteria are analogous
to empirical studies that use data from a random sample of
individuals to make inferences about a broader population.
Thus, while some studies that meet the inclusion criteria
for our analysis were presumably overlooked, we employ
statistically-sound techniques and contend that our database
constitutes a random and non-biased (Results) sample of the
available literature. As such, our conclusions should constitute
a robust and representative assessment of parasite-associated
mortality across a broad range of host–parasite systems.
Studies included in the analysis expressed crude mortality (Anderson et al. 1980) as either counts or proportions
of living and dead individuals at the end of a given monitoring period. Mortality data that were only available from
graphs were extracted using ImageJ ver. 1.40g (Rasband
2008). In studies where a range of infection levels was used,
we included only treatment levels reflecting natural parasite
burdens. Because we used proportions of surviving animals
to quantify parasite effects, our approach assumed that mortality rates were constant during the study and that any lost
animals were censored randomly (Murray 2006). We assessed
the potential relationship between parasite-associated mortality and study duration to determine if crude mortality was
an appropriate unit of measure (as opposed to ‘time-to-failure’
methods, which consider survival time explicitly).
We considered several covariates as potential determinants of parasite-mediated mortality. We recorded host taxon
(amphibian, arthropod, bird, fish, mammal, mollusc), host
sex and age, parasite taxon (arthropod, helminth, microparasite), study type (field or laboratory), and latitude of study.
Host and parasite taxonomic categories were defined based
on a need to develop parsimonious classes for model selection. In addition, we recorded whether or not studies involved
intermediate hosts of parasites that relied on predationmediated transmission to their next target host. This determination was based on life cycle of a given parasite provided
in the literature. Note that ‘predation-mediated’ studies
assessed parasite-associated mortality in intermediate hosts
only, whereas ‘non-predation-mediated’ studies could quantify
parasite effects on either intermediate or definitive hosts. We
excluded studies of systems where the higher-order host is
a vector. For laboratory studies, we assumed that observed
parasite-associated mortality was representative of that occurring naturally (this was confirmed statistically; Results). Consequently, when evaluating the impact of latitude on host
survival, we used latitude corresponding with the location
from which animals were collected from the field. Adverse
effects of parasites are generally dose-dependent (Gulland
1995, Day 2002), therefore some variability in host mortality
may be attributable to among-study differences in infection
intensity. However, because parasite loads are not comparable
among parasite taxa (e.g. protozoa vs arthropods), a standardized covariate describing intensity was not generated. Given
the relationship between dose and parasite effects, it is conceivable that studies using experimental inoculation may yield
biased estimates of effect size. Accordingly, we included the
variable ‘load type’, wherein studies were categorized based
on whether baseline parasite loads were derived from natural
or experimentally-induced study protocols.
Data analysis
We explored factors influencing parasite-associated mortality using random effects meta-regression (Berkey et al. 1995,
Thompson and Higgins 2002, Harbord and Higgins 2008).
Meta-regression uses a linear regression approach to explore
relationships between likelihood of parasite-associated mortality and study-level covariates of interest (Lau et al. 1998).
Its capacity for multivariate analysis allows for assessment
of each covariate’s relative importance through model selection and multi-model inference techniques (Burnham and
Anderson 2002, 2004). Contrary to a fixed effects approach,
random effects meta-regression recognizes inherent heterogeneity in effect sizes among studies that cannot be attributed
to covariates (DerSimonian and Laird 1986, Lau et al. 1998,
­Thompson and Higgins 2002). This method is less prone to
type I statistical error than alternative approaches and is therefore more robust (DerSimonian and Laird 1986, Thompson
and Higgins 2002). Allowing for among-study variation in
effect size was critical to our study given the large variability in
experimental conditions and taxonomic groups examined.
We generated all meta-regression models using the
updated ‘metareg’ command (Harbord and Higgins 2008) in
STATA version 10.0 (Statacorp 2007). Study-specific effect
size of parasitism on host mortality was expressed quantitatively as the natural logarithm of the odds ratio, which is
the appropriate unit for comparing binary data between two
groups (Harbord and Higgins 2008, Sutton and Higgins
2008). The log-odds ratio for data set i (log(OR)i) is derived
from mortality (m) and survival (s) frequencies of parasitized
(P) versus non-parasitized (N) hosts, and is calculated as
 Pm N s 
 Ps N m 
log(OR)i  log e 
(Sharp 1998, Lipsey and Wilson 2001). Note that in our
study the odds ratio compares the odds of mortality among
parasitized individuals to that of controls. Values originally expressed as proportions were multiplied by their
corresponding sample sizes to yield frequencies, and for
datasets where one or more of the frequencies had a value
of zero, 0.5 was added to all four values (Lipsey and Wilson
2001). ‘Metareg’ estimates the within-study variance of i (vi)
using the standard error of the log-odds ratio (SE), calculated from the literature as:
1
1
1
1
SE 
1
1 1
i
Pm N s Ps N m
(Sharp 1998, Lipsey and Wilson 2001).
The generalized linear model (GLM) employed by
‘metareg’ is based on a random effects model proposed by
Berkey et al. (1995). Least squares estimation of the parameter coefficient corresponding with each covariate is weighted
by (vi 1 τ2)–1, where τ2 is the among-study variance (Berkey
et al. 1995, Harbord and Higgins 2008). This allows for heterogeneity of effect sizes and assigns relative importance to
each study according to its statistical power (Berkey et al.
1995). We used a restricted maximum likelihood (REML)
method to estimate τ2 (DerSimonian and Laird 1986,
­Harbord and Higgins 2008), as is recommended for models
that include one or more covariates (Thompson and Sharp
1999). ‘Metareg’ indicates a model’s capacity for explaining among-study variance as an I2 value. This estimates the
proportion of variance in the data that cannot be attributed
to covariates or residual error (Higgins et al. 2003). Where
applicable, results were back-transformed from log-odds
ratios (and associated confidence intervals) to more interpretable odds ratios. An odds ratio is considered statistically
significant if its 95% confidence interval does not overlap
1.00 (Lipsey and Wilson 2001), and an odds ratio of one
indicates equal odds of mortality in both treatment groups.
Categorical covariates were coded as dummy variables to
facilitate inclusion in regression models. Preliminary analysis based on univariate meta-regression models indicated
covariates of potential importance to explaining amongstudy variance in parasite-associated mortality. We then used
standard model selection and multi-model inference procedures to evaluate the relative importance of these covariates
(Burnham and Anderson 2002, 2004). We checked for twoway interactions using meta-regression models that included
the interaction term of interest and associated main effects as
the only covariates. None of the tested interactions were significant (p0.05), and therefore they were not considered in
the model selection exercise. Multicollinearity was examined
by perturbation analysis using STATA’s ‘perturb’ command.
Large fluctuations in coefficients following strategic misclassification of data points may be attributed to multicollinearity (Zar 1999, Hendrickx and Pelzer 2004). As this can lead
to spurious conclusions (Zar 1999), models containing collinear variables were excluded. Multicollinearity was detected
between ‘host taxon’ and ‘study type’, preventing the generation of models that included these two covariates. With this
exception, all combinations of covariates were considered in
developing multivariate models.
Cumulative Akaike weights (Σw) were calculated for each
covariate by summing the weights of all models containing that variable (Burnham and Anderson 2002) and serve
as indicators of support for particular variables as determinants of parasite-associated mortality. Model-averaged
3
coefficients and unconditional standard errors were calculated
from high-ranking models (wi0.01) to quantify the relative
importance of each covariate (Anderson et al. 2000).
Publication bias
We tested for publication bias in our study sample (i.e.
the file drawer problem, Rothstein et al. 2005) by evaluating funnel shape in a comparison of within-study standard
errors versus associated log-odds ratios (Sterne and Egger
2005). Funnel plot asymmetry was assessed through Egger’s
regression technique (Egger et al. 1997), using the metabias
command in STATA. When funnel asymmetry was detected,
we modeled the impact of the absence of ‘missing’ data on
our results using Duval and Tweedie’s (2000a, 2000b) trim
and fill method. This was executed using STATA’s metatrim
command based on a random-effects meta-analysis model
(Duval and Tweedie 2000b, Thompson and Higgins 2002).
Results
We retrieved 59 separate datasets from 46 papers that met
our inclusion criteria (Supplementary material Appendix
1 and 2). Of the included studies, 17% corresponded specifically to female hosts and 10% to male hosts, while the
remainder was not identified by sex. Thirty-four percent of
studies were specific to adult hosts, 43% to juveniles, and
the remainder was not identified by age. Univariate metaregression indicated that likelihood of parasite-associated
mortality did not differ among sex (F2,561.29, p0.28)
nor age groups (F2,560.37, p0.70), thereby allowing
pooling of datasets from a single paper that differed only by
subject sex or age. In doing so, we reduced the total number
of datasets to 52.
Duration of the studies under consideration averaged 210
days ( 48 SE, range: 0.04–1825 days). Univariate metaregression suggested no significant relationship between
study duration and likelihood of parasite-associated mortality (F1,500.00, p0.50), implying that our crude estimate
of mortality was an appropriate surrogate for standard timeto-failure methods.
molluscs than among arthropods, birds, and mammals. Parasite taxon had an effect on the likelihood of host survival (univariate meta-regression: F2,493.31, p0.05), with helminths
having the highest (odds ratio  SE: 3.86  1.28, n23),
and microparasites the lowest (1.36  1.49, n8) effects on
survival (arthropods: 2.32  1.28, n21). Notably, parasiteassociated mortality was 2.4 times more likely among intermediate hosts of predation-mediated parasites (5.26  1.40,
n13) than among other hosts (2.18  1.19, n39; univariate meta-regression: F1,505.49, p0.02). Studies where
parasitized groups were established through experimental inoculation revealed marginally higher (univariate meta-regression:
F1,503.84, p0.06) parasite-associated mortality (4.48 
1.37, n16) than those that observed natural parasite loads
(2.20  1.20, n36). Parasite-associated mortality was higher
among laboratory studies than among field studies (univariate meta-regression: F1,504.18, p0.05; lab studies: 3.93 
1.29, n23; field studies: 2.03  7.54, n29).
Studies included in our analysis ranged from 2.5° to 62.6°
latitude, and univariate meta-regression suggested an average increase of ~4% (univariate meta-regression: β–0.04 
0.01, p0.01; β is the slope of the univariate meta-regression
model) in log-odds ratio for each one-degree decrease in
latitude of study sites (Fig. 1). Based on this result, parasiteassociated mortality was 8.4 times more likely at the lowest
versus highest latitude study area (odds ratios: 10.90 1.62
and 1.30 1.32, respectively). The negative relationship
between parasite-associated mortality and latitude was marginally significant when restricted to studies conducted specifically in the field (n23, β–0.02  0.01, p0.06).
Multivariate analysis
The candidate set contained 48 multivariate models, of which
10 were considered high-ranking (wi 0.01; Supplementary
material Table A1) and used to calculate model-averaged coefficients and unconditional standard errors (Table 1). Ultimately, reliance on predation-mediated transmission, host
taxon, and latitude were the most important determinants of
parasite-associated mortality (Table 1). Parasites with indirect
life cycles that were reliant on predation to reach their next
Univariate analysis
Parasitized individuals had higher overall odds of mortality
than did controls in 83% of comparisons, whereas in 15%
of cases odds of mortality were qualitatively higher among
controls (remaining studies documented equivalent mortality between treatment groups). Within-study odds ratio had
an unweighted average of 2.84 (1.19 SE), implying that
parasitized individuals were substantially more likely to die
than those that were non-parasitized.
Overall mortality was significantly higher in parasitized than
non-parasitized animals, according to the reduced meta-regression model (odds ratio2.65, one-tailed, 95% CI2.02).
Odds of mortality were 2.7 times higher among parasitized
animals compared to controls. Host taxon was a significant
determinant of parasite effects (univariate meta-regression:
F5,463.68, p0.01), with the likelihood of parasite-associated
mortality being markedly higher among amphibians, fish, and
4
Figure 1. Relationship between likelihood of parasite-associated
mortality and latitude. Circles are proportionate to the SE of each
data point, thus smaller points have more weight. The solid line
indicates the linear relationship estimated by the univariate metaregression model.
Table 1. Model-averaged coefficients, unconditional 95% confidence intervals, and cumulative weights (∑w) for meta-regression covariates
of parasite-associated mortality. These were the main criteria considered in assessing importance of each covariate as a determinant of
prevalence of parasite-associated mortality.
Covariate
Predation-mediated?
no
yes
Host taxon
amphibian
arthropod
bird
fish
mammal
mollusc
Latitude
Load type
inoculated
natural
Study type
field
laboratory
Parasite taxon
arthropod
helminth
microparasite
Constant
n
Weighted parameter estimate ( unconditional CI)
39
13
4
12
22
3
7
4
52
16
36
29
23
21
23
8
52
1.174 (0.324)*
∑w
0.926
0.887
1.673 (0.356–2.989)*
‡
0.749 (–0.331–1.830)
1.968 (0.451–3.486)*
0.561 (–0.587–1.709)
1.622 (0.126–3.117)*
–0.026 (–0.006)*
0.051 (–0.075)
0.886
0.163
0.541 (–0.310–1.392)
0.047
‡
0.260 (–0.753–1.273)
0.052 (–1.155–1.259)
1.030 (–0.758–2.817)†
0.046
*denotes statistical significance (α0.05). †The coefficient and unconditional CI values provided for the constant represent these two
variables. ‡Indicates variables that were excluded due to multicollinearity.
target host were associated with significantly higher mortality
than those transmitted by other means (Fig. 2). In terms of
host taxa, amphibians, fish, and molluscs exhibited more
prevalent parasite effects on survival than did arthropods,
birds, or mammals (Fig. 3). Latitude was negatively associated with likelihood of parasite-associated mortality. Other
variables (load type, study type, and parasite taxon) were of
negligible importance. Among-study heterogeneity due to
unknown factors remained high (reduced model: I20.80,
top model: I20.68), however, this was not surprising given
the broad array of host–parasite systems under consideration
within a meta-regression framework (Higgins et al. 2003).
Our sampling diagnostics revealed that studies reporting
non-significant results were slightly under-represented in our
sample (β01.07, p0.01), with an additional 7% more
data points needed to achieve funnel symmetry (Fig. 4). Correcting for asymmetry did not influence results qualitatively,
although overall odds ratio of parasite-associated mortality
declined from 2.65 to 2.23 (one-tailed CI1.84). We also
detected a slight bias toward significant results among studies involving helminth parasites (β01.79, p0.04, n22),
with absence of an estimated 8% of points needed to achieve
symmetry about an estimated log-odds ratio of 1.12 (odds
ratio3.06, two-tailed CI1.92–4.88). This is slightly lower
Figure 2. Predicted likelihood of helminth-associated amphibian
mortality according to mode of transmission to the next target host.
Values were generated using the composite model, assuming freeranging, naturally-occurring systems at 45° latitude. Error bars
denote back-transformed unconditional SE.
Figure 3. Predicted likelihood of parasite-associated mortality
according to host taxon. Values were generated using the composite
model, assuming free-ranging, naturally-occurring systems at 45°
latitude where transmission is non-predation-mediated. Error bars
denote back-transformed unconditional SE.
5
Figure 4. Funnel plot addressing sampling bias in a meta-analysis of
parasite-associated mortality corrected for asymmetry (filled
dotsincluded data, unfilled dotsmissing data). Note that applying the correction resulted in a slight reduction of the overall logodds ratio (solid linecorrected effect size, broken lineuncorrected
effect size).
than the original estimate of helminth-associated mortality
(log-odds ratio1.38; odds ratio3.97, two-tailed CI2.12–
7.22). We failed to detect evidence of publication bias among
remaining covariates, implying that overall conclusions based
on the uncorrected dataset were robust. Further, the lack of
substantial asymmetry suggests that our data set comprises a
representative sample of the relevant literature.
Discussion
Overall, we found a significant increase in mortality among
parasitized hosts, and the magnitude of such effects was
related to parasite life cycle, latitude, and host taxon. Ours is
the most comprehensive and statistically rigorous assessment
of parasite-associated host mortality to date, and we believe
that our representative sample leads to conclusions about the
ecological role of parasites that are applicable across a broad
range of host–parasite systems.
The link between intensity of parasite effects and transmissibility is highlighted by the increased mortality risk
among intermediate hosts infected with predation-mediated
parasites. Increasing host predation risk should be adaptive
among parasites that rely on predation for transmission to
their next target host (Poulin 1998, Poulin et al. 2005), and
such transmission can be augmented by parasite manipulation of host behaviour (Seppälä et al. 2004, Poulin et al.
2005) or morphology (Jensen et al. 1998, Fuller et al. 2003).
Ewald’s (1995) qualitative review suggested more severe
disease among intermediate hosts of predation-mediated
parasites than among other hosts, regardless of host taxon,
but our analysis actually quantifies such effects in a statistically rigorous manner. For example, our model predicts
a three-fold difference in likelihood of parasite-associated
mortality between parasites employing predation-mediated
versus non-predation-mediated transmission strategies in
­amphibian–helminth host–parasite systems (Fig. 3). This
6
difference also serves to highlight the strength of selection
likely acting on traits such as host behavioural modification
among predation-mediated parasites.
The variability in mortality risk according to host taxon
was anticipated, but the observed pattern failed to follow that
predicted by host immunity. For example, vertebrate hosts
were expected to have more proficient immunity to parasites
compared to invertebrate hosts (Pancer and Cooper 2006),
thus the high mortality risk among amphibians and fish as
well as the relatively low mortality among arthropod hosts
was difficult to explain from an immunological perspective
alone. However, given the importance of host response in
determining pathogenicity (Poulin 1998, Casadevall and
Pirofski 1999), it would be imprudent to discount potential
roles of acquired or innate defenses in regulating parasiteassociated mortality without further investigation. Our
failure to detect clear differences according to host immune
response might imply that hosts lacking adaptive immunity
apply other means (e.g. reproductive tradeoffs) to limit parasite effects, or that our study was too coarse to properly assess
host-specific differences in parasite impacts. Recent developments in phylogenetic meta-analysis (Adams 2008) may
provide a more precise means of evaluating the presumed
role of host taxon on host mortality.
While a potential inverse relationship between parasiteassociated mortality and latitude has been alluded to previously, our study provides the first evidence of such a trend
across host–parasite systems. Past authors speculated that
tropical hosts were at higher risk of severe infection than
those found in more temperate climates (Harvell et al. 2002,
Owen-Ashley et al. 2008), and that tropical climate factors
favouring transmission should lead to more virulent parasites
(Møller 1998, Marcogliese 2001). Our findings indicate that
increased rates of parasite infection and diversity translate
to increased host mortality. We predict a ~2.6% decrease in
the log-odds ratio associated with parasite-associated mortality for each degree of distance away from the equator. Thus,
our composite model predicts that an average host–parasite
system (e.g. avian–helminth) should exhibit a nearly fivefold difference in prevalence of parasite-associated mortality
over our observed study range (assuming a non-predation­mediated parasite in a natural system).
Given its influential role in parasite life history, we speculate that ambient temperature contributes to the latitudinal
gradient through its effects on parasite productivity. There
are general positive relationships between temperature and
parasite growth, developmental rate, fecundity, time to next
generation, and time of infectivity (Poulin 1998, Marcogliese
2001, Harvell et al. 2002). Thus, warmer temperatures at
lower latitudes may benefit both parasite abundance and
transmission success. It is important to note that the gradient of parasite-associated mortality was robust to the inclusion of laboratory studies of natural host–parasite systems
(i.e. model selection indicated that ‘study type’ was not
an important covariate; Table 1). Accordingly, latitudinal
changes in pathogenicity likely represent evolved adaptations of parasites to climate rather than plastic responses to
acute shifts in ambient conditions.
The reduced seasonality in tropical regions should further contribute to parasite-associated mortality in a manner
that is similar to temperature, by limiting periods of arrested
development and reduced parasite productivity (Harvell
et al. 2002). Modeling by Hochberg and van Baalen (1998)
suggests a possible positive relationship between parasite virulence and host productivity, which may be relevant to the
observed higher habitat productivity at low latitudes (Møller
1998). Further, high parasite abundance at low latitudes
could lead to enhanced incidence of multiple infections,
which tend to favor the evolution of more virulent parasite
strains (Nowak and May 1994, May and Nowak 1995).
Alternatively, increased parasite productivity at higher temperatures may directly increase parasite-associated mortality
through higher initial infection doses, rather than by enhancing virulence. Regardless, each of the scenarios listed above
would favor a host–parasite relationship with relatively high
pathogenicity. To more fully address the mechanisms underlying increased parasite pathogenicity at lower latitudes, we
recommend that future empirical studies focus on comparing mortality and infection within specific host taxa and
across a broad latitudinal gradient.
If the global gradient of parasite-associated mortality is
closely tied with climate, then shifts toward more parasitefavourable conditions in temperate regions due to climate
change may allow local parasites to evolve higher virulence among native hosts (Marcogliese 2001) or increase
their productivity (Kutz et al. 2005, Poulin and Mouritsen
2006). Both scenarios ultimately could lead to increased
parasitic infection and host mortality at high latitude,
which ultimately can threaten host population viability
(Jensen and Mouritsen 1992). However, impacts of climate change on parasite effects are difficult to forecast due
to an array of potential confounding factors, such as shifts
in hosts’ geographical distributions (Fischlin et al. 2007)
and coevolved resistance in hosts as infections become
more harmful. Regardless, potentially higher costs of parasitism at higher latitudes could portend increased threat
to long-term viability of many host populations. Future
research should be aimed at comparing parasite effects on
host survival in systems with and without ongoing climate
change, as well as developing models to explore changes
in host survival and population viability in the face of a
range of climate change scenarios. Such a system-specific
approach will be critical to anticipating and possibly mitigating future changes in host-parasite relationships consequent to climate change.
Acknowledgements – Funding was provided by an Ontario Graduate
Scholarship to NR, and NSERC Discovery grants to GB and
DLM. R. Harbord provided helpful ‘metareg’ advice. Thanks to
J. ­Arseneau, M. Forbes and the Murray and Burness labs for insightful suggestions.
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