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Hantavirus Infection

Hantavirus infection and biodiversity in the Americas
María Victoria Vadell1,2,4
· Isabel Elisa Gómez Villafañe2,3 · Aníbal Eduardo Carbajo1,2
Received: 29 October 2018 / Accepted: 22 November 2019
© Springer-Verlag GmbH Germany, part of Springer Nature 2019
Species diversity has been proposed to decrease prevalence of disease in a wide variety of host–pathogen systems, in a
phenomenon labeled the dilution effect. This phenomenon was first proposed and tested for vector-borne diseases but was
later extended to directly transmitted parasite systems such as hantavirus. Though there seems to be clear evidence for the
dilution effect in some hantavirus/rodent systems, the generality of this hypothesis remains debated. In the present metaanalysis, we examined the evidence supporting the dilution effect for hantavirus/rodent systems in the Americas. General
linear models employed on data from 56 field studies identified the abundance of the reservoir rodent species and its relative
proportion in the community as the only relevant variables explaining the prevalence of antibodies against hantavirus in the
reservoir. Thus, we found no clear support for the dilution effect hypothesis for hantavirus/rodent systems in the Americas.
Keywords Dilution effect · Biodiversity · Disease · Host · Pathogen
The effects of diversity on host-disease systems have been,
and still are, the subject of much research and controversy
(Pongsiri et al. 2009; Keesing et al. 2010; Ostfeld and Keesing 2012; Randolph and Dobson 2012; Ostfeld 2013; Salkeld
et al. 2013; Wood and Lafferty 2013; Civitello et al. 2015;
Pfäffle et al. 2015; Huang et al. 2016; Levi et al. 2016; Luis
Communicated by Herwig Leirs.
Electronic supplementary material The online version of this
article (https://doi.org/10.1007/s00442-019-04564-0) contains
supplementary material, which is available to authorized users.
* María Victoria Vadell
[email protected]
Instituto de Investigación e Ingeniería Ambiental,
Universidad Nacional de San Martín, Campus Miguelete,
25 de Mayo y Francia, 1650, San Martín, Buenos Aires,
Consejo Nacional de Investigaciones Científicas y Técnicas
(CONICET), Buenos Aires, Argentina
Departamento de Ecología, Genética y Evolución, IEGEBA
(CONICET-UBA), Facultad de Ciencias Exactas y Naturales,
Universidad de Buenos Aires, Buenos Aires, Argentina
Present Address: Instituto Nacional de Medicina Tropical
(INMeT)-ANLIS “Dr. Carlos G. Malbrán”, Puerto Iguazú,
Misiones, Argentina
et al. 2018). Species diversity has been proposed to decrease
prevalence of disease in a variety of host–pathogen systems,
in a phenomenon called the dilution effect (Keesing et al.
2006; Pfäffle et al. 2015). This phenomenon was first proposed for vector-borne diseases, whereby it was observed
that the presence of vertebrate hosts with a low capacity
to infect feeding vectors (incompetent reservoirs) diluted
the effect of infection of highly competent reservoirs, thus
reducing the disease risk (Dobson et al. 2006). The opposed
phenomena, termed the amplification effect, by which species diversity increases disease risk, has also been acknowledged (Keesing et al. 2006; Ogden and Tao 2009), though
this effect has been less observed and tested. The effect of
diversity on host-disease systems has primarily been studied
in vector-borne diseases such as Lyme disease (Ostfeld and
Keesing 2000a; Schmidt and Ostfeld 2001; LoGiudice et al.
2003; Wood and Lafferty 2013) and West Nile encephalitis
(Ezenwa et al. 2006; Swaddle and Calos 2008; Allan et al.
2009), but has also been tested on directly transmitted parasite systems such as hantavirus (Clay et al. 2009a, b; Dizney
and Ruedas 2009; Luis et al. 2018).
Hantaviruses are enveloped, single-stranded negativesense RNA viruses of the recently proposed Orthohantavirus genus (family Hantaviridae; Adams et al. 2017). More
than 90 hantavirus species have been described worldwide
(except Antarctica and Australia) from rodents, shrews and
bats (Milholland et al. 2018). Each hantavirus is associated
with one or a few closely related species, supporting the
idea that hantaviruses might have co-evolved with their
hosts, though increasing numbers of exceptions and host
switches have been reported recently (Forbes et al. 2018).
Hantaviruses are transmitted between their vertebrate
hosts through inhalation of aerosolized virus shed to the
environment in saliva, urine, and faeces of infected hosts,
and during intense contact such as biting, grooming and
sharing food resources (Forbes et al. 2018). More than 20
rodent-borne hantaviruses are known to cause disease in
humans, being the etiologic agents of hemorrhagic fever
with renal syndrome (HFRS) in Europe and Asia, and hantavirus pulmonary syndrome (HPS) in the Americas (Bi
et al. 2008; Milholland et al. 2018).
Several non-mutually exclusive mechanisms by which
species diversity may decrease hantavirus prevalence have
been proposed (Keesing et al. 2006; Suzán et al. 2009).
For example, it has been proposed that the presence of
other species in a community may reduce intraspecific
encounters among hantavirus hosts, resulting in fewer
opportunities for pathogen transmission (Clay et al. 2009a;
Dizney and Ruedas 2009; Suzán et al. 2009; Dearing et al.
2015). Alternatively, it has been proposed that the duration of each encounter could be reduced by the presence
of other species, though, to our knowledge, no evidence
has been found in support of this hypothesis (Clay et al.
2009a). Species diversity may also reduce prevalence by
reducing host survival and persistence (and therefore, density), via predation or exploitative competition (Peixoto
and Abramson 2006; Clay et al. 2009b; Luis et al. 2018).
Using data from ten field studies conducted in the
southwestern United States, James Mills (2006) showed
a clear negative association between hantavirus antibody
prevalence in the dominant host species and small mammal diversity. Also, in an experimental study carried out
in Panamá, Suzán et al. (2009) found that both hantavirus
antibody prevalence in wild reservoir rodents and reservoir population density increased where small mammal
species diversity was reduced. In another study, Yahnke
et al. (2001) observed a general increase in hantavirus
antibody prevalence as the relative proportion of the reservoir increased in the community, but suggested that the
observed reduction in antibody prevalence resulted from
the presence of non-host individuals in large numbers, and
not from biodiversity itself. Using long-term data from
studies carried out in four US states, Luis et al. (2018)
found evidence supporting the co-occurrence of dilution
and amplification effects in some of their sites. They suggest that the observed dilution effect is a result of increasing small mammal diversity leading to decreased reservoir
population density, while the amplification effect is related
to an increase in the transmission rate at sites with higher
diversity (Luis et al. 2018).
In spite of the fact there is evidence in favor of the dilution effect in certain hantavirus/rodent systems (Ruedas et al.
2004; Suzán et al. 2008, 2009; Mills 2006; Carver et al.
2011; Dizney and Ruedas 2009; Luis et al. 2018), its generality and mechanisms are still not clear. Moreover, several
studies suggest that the dilution effect may be idiosyncratic,
resulting from the unconscious pressure to find extra reasons
to stop and reverse the alarming biodiversity loss (Randolph
and Dobson 2012; Salkeld et al. 2013).
The aim of our research was to study the association
between hantavirus infection and rodent community characteristics in different habitats throughout the Americas, in
an attempt to elucidate whether the dilution effect is generally supported for hantavirus/rodent systems in the region.
Materials and methods
Data collection
We collected all field studies conducted in the Americas
published before 2017 that provided measures of hantavirus
antibody seroprevalence (as an estimate of infection) in natural rodent populations, and the data necessary to estimate
small rodent diversity. We used the Google Scholar web
search engine to track original research articles using the
following key words: “hantavirus”, “prevalence”, “rodent”,
“antibody”, and the species name of known hantavirus reservoirs in the Americas. We identified additional papers by
searching the reference sections of these articles, as well
as those of reviews of related topics. We included papers
on all currently known New World hantavirus reservoirs
and their genotypes as described by de Carvalho Oliveira
et al. (2014b), except for Zygodontomys cherriei which was
included due to the high hantavirus prevalence reported for
this species by Londoño et al. (2011). We used all studies
with trapping periods which encompassed at least two seasons and a minimum of 250 trap nights per site to have reasonably reliable estimates of hantavirus infection and rodent
diversity. We did not include studies where data from different sites were combined because the effect of biodiversity
on hantavirus infection that we wanted to investigate operates on a local scale. Unfortunately, many studies presented
results by habitat type, combining results from different
sites and, consequently, had to be excluded (see Table 1 in
Electronic Supplementary Material). Including these studies
with combined data in our analysis would have been inappropriate because the combination of different communities
would confound the effects of biodiversity itself on hantavirus infection.
Using the data provided in each article, we calculated
a diversity index (H), an evenness index (E), and the species richness of the small rodent community (see Table 2
in ESM). Fossorial or strictly arboreal species were not
included in these indexes because such species are not
expected to interact with the reservoir species. We also
calculated the relative proportion of rodents in the community belonging to the hantavirus reservoir species, the
total abundance of small rodents, and the abundance of the
reservoir species (see Table 2 in ESM). The habitat of the
studied community was characterized as natural or rural, and
according to its main vegetation type (see Table 2 in ESM).
Annual mean temperature, maximum temperature of
warmest month, minimum temperature of coldest month,
annual precipitation, and precipitation of wettest and driest
months (see Table 2 in ESM) were obtained from Worldclim
online database (Hijmans et al. 2005).
Model building
General linear models (GLMs; Crawley 1993) were used to
explore the relationship between hantavirus antibody seroprevalence in reservoir populations, as the response variable,
and characteristics of the small rodent community, habitat,
and climate, as explanatory variables (see Table 2 in ESM).
Quadratic terms, interactions, geographic coordinates, and
hemisphere (north/south) were also tested during the model
building process. Briefly, GLMs consist of three elements:
a probability distribution from the exponential family, a linear predictor (LP) that relates the response variables to the
explanatory variables, and a link function that provides the
relationship between the linear predictor and the mean of the
distribution function (McCullagh and Nelder 1989).
Hantavirus antibody seroprevalence in the reservoir species (of each of the reservoir species if more than one was
reported) at each site was modeled with a quasi-binomial
error distribution (to account for over-dispersion) and a logit
link so that μ = exp (LP)/[1 + exp (LP)]. A manual upward
stepwise multiple regression procedure was performed to
find the best models. First, explanatory variables were centered, squared and fitted individually. Continuous explanatory variables (see Table 2 in ESM) were examined for collinearity prior to model building. One variable of each pair
presenting a Spearman’s correlation coefficient |r| ≥ 0.8 was
removed. Because H and E indexes were strongly correlated
(see Fig. 1 in ESM), we kept only E index for the analyses. Also, Temp and MinT were highly correlated and we
selected Temp, while MaxRain and MinRain were removed
because of their high correlation with Rain. This was done
because Temp and Rain variables are more frequently used
in the literature and have easier interpretation than MinT,
MaxRain and MinRain.
We evaluated significance for each term addition with a
significant F test on the change in deviance, according to the
methodology applied with over-dispersed data (Burnham and
Anderson 2002). All variables with a significant change in
deviance were used in turn as start up. Subsequent variables
were added one at a time provided that they were not collinear
with any variable already included, which was evaluated using
variance inflation factor (VIF) analysis (Zuur et al. 2007).
Model validity was verified with the bootstrap procedure and
residuals plots (Crawley 2012). The explanatory power of the
model was estimated by 1 minus the ratio of the residual to null
deviance (equivalent to R2 in least-square models). Modeling
was performed in R Core Team (2016) using Car (Prüss-Ustün
et al. 2016), Boot (Dye et al. 2013), and MuMIn packages (Barton 2013).
Our database included studies on 85 rodent communities
published in 22 publications with quantitative information
that satisfied our criteria for the meta-analysis. These studies
were conducted in four countries (Argentina, Colombia, Paraguay and United States of America) on 14 different hantavirus rodent reservoir species (eight genera) associated with
13 different hantavirus genotypes (Fig. 1; Table 1). Most
publications reported longitudinal data collected in more
than one site (i.e.; different rodent communities). Among
the 85 rodent communities studied, 27 inhabited forests, 16
steppes, 40 grasslands and croplands, and two peridomestic
habitats. Fifty-eight sites were natural environments while
27 were rural ones.
Antibody seroprevalence ranged between 0 and 0.41, with
maximum values reported for Pergamino and Sin Nombre
viruses (Table 1). Species richness varied considerably
among the communities, ranging from 1 to 19 species, with
the maximum value reported for a grassland site in Colorado, USA.
Models were built on 56 field studies. We used only data
for Peromyscus, Oligoryzomys, and Akodon due to the low
number of samples of other genera. We also excluded studies
where less than eight individuals were tested for hantavirus
antibody. Two final models were obtained after bootstrapping and residual analysis (Table 2; see all tested models in
Table 3 in ESM). The best model explained 35.8% of the
variance in the seroprevalence of hantavirus antibodies in
the Americas as a function of the abundance of the reservoir
species (model 1, Table 2; Fig. 2). The other final model
explained 20% of the variance in seroprevalence as a function of the relative proportion of the reservoir species in the
community (model 2, Table 2; Fig. 3).
Our analysis using published literature on hantavirus infection in rodents as represented by antibody prevalence provides no clear support for the dilution effect hypothesis. We
Fig. 1 Geographical distribution of the studies used in our research. The hantavirus host species and the hantavirus genotypes reported in each
publication are shown
found a positive association between hantavirus antibody
seroprevalence and the relative proportion of the reservoir
species, but no significant association between antibody
seroprevalence and any diversity estimate, suggesting that
rodent diversity per se does not have a general effect on
hantavirus transmission and persistence. However, we can
also consider the possibility that rodent diversity could be
operating under different mechanisms with opposed results,
as observed by Luis et al. (2018). These authors proposed
that dilution and amplification effects can occur at the same
time in the same system, at the same scale, and that the overall effect would be determined by which effect is stronger
(Luis et al. 2018). If this is the case, these opposite effects of
diversity could be cancelling each other in the various systems of our study which could explain our lack of observed
general effect.
It is worth mentioning that in our data, rodent species
richness and reservoir abundance are slightly associated (see
Table 1 Hantavirus genotypes included in the meta-analysis
Andes (South)
Blue River/New York
El Moro Canyon
Jabora and Ape Aime-Itapua
Limestone Canyon
Sin Nombre
The number of rodent communities in which the genotype was present (n), mean hantavirus antibody seroprevalence (mean), standard
error (SE) and, minimum (min.) and maximum (max.) values are
Table 2 Parameter estimates of the models that best explain hantavirus antibody prevalence in the Americas
Model 1
Reservoir abundance
Model 2
Prop reservoir
Std. error
p value
− 1.947
< 0.000
< 0.000
− 2.060
< 0.000
Fig. 1 in ESM). Because they were not strongly correlated,
both variables were used in model building, with reservoir
abundance being the only one which explained some of
the variations in seroprevalence of hantavirus. Though it
is expected for a community highly dominated by a certain
species (a hantavirus reservoir in this case) not to be very
rich, not much can be concluded concerning richness in
communities not strongly dominated. High levels of dominance of the reservoir may increase competent hantavirus
transmission via increased intraspecific encounters, independently of species richness. Clay et al. (2009a) observed
a reduction in the rate of intraspecific encounters of P. maniculatus with an increase in rodent biodiversity. However,
density of P. maniculatus was correlated with the absolute
rate of intraspecific encounters, suggesting that host density
could also be involved in the effect on hantavirus transmission. In a similar but experimental study, Rubio et al. (2017)
did not find differences in the frequency of intraspecific
interactions of P. maniculatus among assemblages of 1, 2
and 3 species, and suggest that the observed differences in
interspecific encounters are due to the identity of one of the
non-host species included. Studying Lyme disease, LoGiudice et al. (2008) observed that low diversity communities
were more likely dominated by P. leucopus, (a ubiquitous
mouse which has a particularly strong effect on pathogen
prevalence) and concluded that infection depends on the
identities and frequencies of the host species rather than on
conventional metrics of biodiversity.
Several valuable attempts to elucidate whether the dilution effect is generally supported across disease systems are
found in the literature (Ostfeld and Keesing 2000b; Pongsiri et al. 2009; Keesing et al. 2010; Randolph and Dobson 2012; Salkeld et al. 2013; Wood and Lafferty 2013;
Fig. 2 Hantavirus seroprevalence in Akodon (circles),
Peromyscus (plus signs) and
Oligoryzomys (triangles) as a
function of reservoir abundance
in the community according to
model 1 (line). Note that the
explanatory variable is centered
Fig. 3 Hantavirus seroprevalence in Akodon (circles),
Peromyscus (plus signs) and
Oligoryzomys (triangles) as a
function of the relative proportion of the reservoir species in
the community according to
model 2 (line). Note that the
explanatory variable is centered
Civitello et al. 2015), several of which found evidence in
support of this hypothesis. However, given the wide variety
and diversity of disease/host systems, such general attempts
could be misleading because the effect of biodiversity will
depend on the characteristics of pathogen transmission that
vary among disease systems. For example, when testing the
dilution effect in a particular disease system, it is important
to consider which definition of biodiversity should be used
(Johnson et al. 2015). If the mechanism tested involves the
presence of incompetent reservoirs in a vector-borne disease, then the only species that should be considered are
those chosen as a meal by the feeding vector, not that of
all the species present; while if the mechanism proposed is
that of reducing host survival, then even predators should
be included. It is also important to consider which metric of
disease risk will be used, whether it is the density of infected
reservoir hosts, the prevalence of infection in reservoir hosts,
the density of infected vectors, the infection prevalence in
vectors, or any other, because the relevance for epidemiology and the sensitivity to species diversity may vary according to the type of disease system under study (Keesing et al.
2006; Huang et al. 2016).
Among several criticisms of the dilution effect hypothesis, it has been proposed that pathogen transmission could
be observed to increase in high-diversity communities
due to the increased chance of including a particular species that has a positive effect on pathogen transmission, an
effect recently termed as identity effect (Huang et al. 2016).
This effect could be particularly misleading in broad-scale
studies including communities with and without disease.
Though our meta-analysis is broad scale, we avoided the
identity effect by only including studies on communities
with hantavirus. In another criticism of the dilution effect
hypothesis, Salkeld et al. (2013) have suggested the existence of publication bias favoring significant negative relationships between biodiversity and disease risk. It is unlikely
for authors and editors to publish an article on the dilution
effect if the data shown do not support this hypothesis,
though they will probably publish these data focusing on
other aspects of the results. On the contrary, articles on the
dilution effect will certainly be published if the data support
this hypothesis, even if the data shown include few sites or
only certain habitats, and not the whole study area or the
whole distribution of the reservoir species. In the present
research, we avoided publication bias by including mainly
studies that did not directly address the dilution effect.
A challenge in assessing evidence in support of the dilution effect was related to the intrinsic differences in hantavirus/community systems. Although our research included
only New World hantaviruses (see Salkeld et al. 2013;
Civitello et al. 2015 for examples of meta-analyses including different pathogens), the ecological processes that act
in each hantavirus/rodent system might be heterogeneous
due to differences in hantavirus genotypes, behavior of the
different reservoir species, species assemblages, vegetation,
predation, and climate, among others. These differences
could account for the low proportion of variance explained
by our models. If this is the case, then the effect of the relative proportion and the abundance of the reservoir must be
stronger than what is reflected in the models to emerge from
the intrinsic variation of this system.
Another important challenge for our analysis is related to
the difference in length of the trapping period of the available studies in the literature. While some of the studies we
included encompass more than 5 years of monthly or seasonal trapping (Douglass et al. 2001; Calisher et al. 2005a,
b), some others lasted less than 1 year (Graham and Chomel
1997; Nisbett 2001; Owen et al. 2010). This high variability in the length of the studies could have implications in
the results because of the temporal variability that seems
characteristic of the New World’s hantavirus infections in
reservoir populations (Douglass et al. 2001; Calisher et al.
2005b; Mills et al. 2007, 2010; Polop et al. 2010; Luong
et al. 2011; de Carvalho Oliveira et al. 2014a; Douglass and
Vadell 2016). In fact, the inclusion of temporal variability in
the study of the dilution effect in hantavirus/rodent systems
as done, for example, by Luis et al. (2018) is ideal, but given
that our study included 85 different field studies from 22
published articles across the Americas, a temporal approach
was out of our scope and possibilities. In spite of our effort
to include as many studies as possible, the existence of many
publications that did not follow our criteria for inclusion
(publications with data from different sites grouped together,
or without data from non-reservoir species, among other reasons for exclusion) forced us to leave out long-term studies
carried out in a wide variety of countries and habitats that
would have otherwise highly enriched our analysis.
An important issue we thoroughly considered given that
we were statistically testing a hypothesis, based on very dissimilar data, is the degree of robustness and reliability of the
results. In this study, we applied a bootstrapping method to
calculate 95% confidence intervals of the estimated parameters to improve the quality of the inference. The basic idea
of bootstrapping is that inference about a population from
sample data can be modelled by resampling the sample data
(with replacement) a huge number of times, and performing
inference about a sample from resampled data (Efron and
Tibshirani 1994). Bootstrapping is particularly useful for
discarding models based on very influential observations,
which, if overlooked, may lead to false conclusions.
The ecology of hantavirus/rodent systems is complex and
dependent on a wide variety of meteorological, biological
and geological characteristics (Douglass and Vadell 2016)
and, therefore, it seems improbable that biodiversity alone
could have a strong (and general) effect on the rate of infection. Our results showed a relation between hantavirus infection and both the abundance and the relative proportion of
reservoirs present, suggesting these, and not biodiversity
per se are affecting virus transmission at the local community scale, as previously suggested by Yahnke et al. (2001).
It is, however, important to keep in mind that associations
between variables do not necessary mean causation and,
therefore, we have to be cautious when suggesting causes
and mechanisms based on observational studies.
To reveal new insights into the mechanisms underlying
hantavirus infection, future efforts should also focus on longterm studies on the ecology of hantavirus and the interactions
among hosts, non-host species, predators and abiotic factors
at a local scale, abandoning, at least for a while, our hope of
finding universal broad-scale explanations.
Acknowledgements We are grateful to Richard Douglass for his
valuable comments to the manuscript. This work was supported by
the National Scientific and Technical Research Council of Argentina
Author contribution statement MVV collected the bibliography, performed the meta-analysis and wrote the first draft of the manuscript;
IEGV and AEC contributed substantially to the design of the analysis
and to revisions of the manuscript.
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