Oecologia https://doi.org/10.1007/s00442-019-04564-0 COMMUNITY ECOLOGY – ORIGINAL RESEARCH 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 Abstract 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 Introduction 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] 1 Instituto de Investigación e Ingeniería Ambiental, Universidad Nacional de San Martín, Campus Miguelete, 25 de Mayo y Francia, 1650, San Martín, Buenos Aires, Argentina 2 Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina 3 Departamento de Ecología, Genética y Evolución, IEGEBA (CONICET-UBA), Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina 4 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 13 Vol.:(0123456789) Oecologia 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). 13 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 Oecologia 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). Results 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). Discussion 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 13 Oecologia 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 13 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 Oecologia Table 1 Hantavirus genotypes included in the meta-analysis Hantavirus n Mean SE Min. Max. Andes (South) Bayou Blue River/New York El Moro Canyon Jabora and Ape Aime-Itapua Lechiguanas Limestone Canyon Maciel Muleshoe Necocli Pergamino Sin Nombre 9 1 2 1 3 16 4 6 1 1 13 28 0.08 0.16 0.21 0.11 0.11 0.07 0.15 0.05 0.2 0.14 0.17 0.13 0.03 – 0.06 – 0.05 0.02 0.04 0.02 – – 0.04 0.02 0 – 0.14 – 0.01 0 0.05 0 – – 0.02 0 0.24 – 0.27 – 0.19 0.31 0.27 0.11 – – 0.41 0.38 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 shown Table 2 Parameter estimates of the models that best explain hantavirus antibody prevalence in the Americas Variable Model 1 Intercept Reservoir abundance Model 2 Intercept Prop reservoir Estimate Std. error p value − 1.947 0.100 0.029 0.005 < 0.000 < 0.000 − 2.060 1.722 0.157 0.516 < 0.000 0.002 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 13 Oecologia 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 13 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 Oecologia 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 (CONICET). 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