Infectious disease in fish: global risk of viral ...

Rev Fish Biol Fisheries https://doi.org/10.1007/s11160-018-9524-3

RESEARCH PAPER

Infectious disease in fish: global risk of viral hemorrhagic septicemia virus Luis E. Escobar

. Joaquin Escobar-Dodero . Nicholas B. D. Phelps

Received: 27 November 2017 / Accepted: 15 June 2018 Ó Springer International Publishing AG, part of Springer Nature 2018

Abstract As the global human population continues to increase and become more industrialized, the need for safe, secure, and sustainable protein production is critical. One sector of particular importance is seafood production, where capture fishery and aquaculture industries provide 15–20% of the global protein supply. However, fish production can be severely affected by diseases. Notably, viral hemorrhagic septicemia, caused by the viral hemorrhagic septicemia virus (VHSv; Rhabdoviridae), may be one of the most devastating viral diseases of fishes

Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11160-018-9524-3) contains supplementary material, which is available to authorized users. L. E. Escobar (&) Department of Fish and Wildlife Conservation, Virginia Tech, 310 West Campus Drive, Cheatham Hall, Room 101, Blacksburg, VA 24061, USA e-mail: [email protected] J. Escobar-Dodero Facultad de Ciencias de la Vida, Universidad Andres Bello, Santiago, Chile N. B. D. Phelps Minnesota Aquatic Invasive Species Research Center, University of Minnesota, St. Paul, MN, USA

worldwide. We explored the ecology and epidemiology of VHSv using an ecological niche modeling approach to identify vulnerable disease-free regions. Results showed an impressive ecological plasticity of VHSv. The virus was found in [ 140 fish species in marine and freshwater ecosystems, with high diversity of lineages in Eurasia. Sub-genotypes from marine and fresh waters were ecologically similar, suggesting broad ecological niches, rather than rapid evolutive adaptation to novel environments. Ecological niche models predicted that VHSv may have favorable physical (e.g., temperature, runoff), chemical (e.g., salinity, pH, phosphate), and biotic (i.e., chlorophyll) conditions for establishing into areas with important fish industries that, so far, are believed to be diseasefree (i.e., freshwater and marine ecosystems of Africa, Latin America, Australia, and inland China). The model and our review suggest fish species from the Perciformes, Salmoniformes, and Gadiformes orders are likely to be infected with VHSv in novel regions as the virus expands its range to areas predicted to be at risk. In conclusion, VHSv remains an emerging disease threat to global food security and aquatic biodiversity. Keywords Disease Ecological niche model VHS Viral hemorrhagic septicemia

N. B. D. Phelps Department of Fisheries, Wildlife and Conservation Biology, University of Minnesota, St. Paul, MN, USA

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Introduction The sustainability of capture fisheries and aquaculture industries are vital to meeting the growing global demand for protein. Aquatic food represents 15–20% of the world’s population protein intake, with fish production growth annually surpassing that of terrestrial livestock including poultry, beef, and swine (Lucas 2012; FAO 2014). According to the Food and Agriculture Organization of the United Nations (FAO), global production of fish has increased consistently during the last 100 years, reaching approximately 167 million tons of fish products in 2014 (FAO 2016a). However, over the last 30 years, production from global capture fisheries have remained stable, while the aquaculture industry has been rising by 8.6% annually (FAO 2014, 2016a). The growth can be seen in terms of total production and number of species produced in both freshwater and marine systems (FAO 2016a). Today, approximately 44% of worldwide fish products are generated from aquaculture facilities in marine (16%) and freshwater (28%) systems and are valued at approximately US $137.7 billion (FAO 2016a). This shift towards aquaculture is largely in response to the human need for fish products and declining wild stocks (Penning et al. 2009). The intensification of aquaculture has increased infectious disease outbreaks in both farmed and wild fish populations (Lafferty and Hofmann 2016). High densities of fish can result in increased host stress and modern fish trade promotes the geographic movement of fish and byproducts, potentially driving disease emergence and spread (Walker and Winton 2010; Crane and Hyatt 2011; Owens 2012). Examples of this include the geographic translocation of Infectious Salmon Anemia virus from Europe to Chile (Kibenge et al. 2009), causing catastrophic losses to the salmon industry (Asche et al. 2009), wild fish infestations with sea lice (Lepeophtheirus salmonis) amplified by aquaculture facilities in Canada (Krkosek et al. 2005), and opportunistic infections with Flavobacteria spp. in catfish (Shoemaker et al. 2003), to name a few. First reported from freshwater Rainbow trout (Oncorhynchus mykiss, Salmonidae) in Europe in the 1930’s, viral hemorrhagic septicemia, caused by the viral hemorrhagic septicemia virus (VHSv), is a devastating fish disease (Wolf 1988; Kim and Faisal 2011). The VHSv is an RNA virus belonging to the

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Novirhabdovirus genus within the Rhabdoviridae family (Dietzgen et al. 2011), with a broad distribution of lineages across continents (He et al. 2014), ecosystems (i.e., marine and freshwater) (Smail and Snow 2011), and host species, infecting cool and cold water fish (Einer-Jensen et al. 2004; Snow et al. 2004). Given the wide range of fish species affected by the virus, broad geographic distribution, pathogenicity, disease course, mortality rates, and high dispersal potential, VHSv could indeed be considered one of the most serious viral pathogens of wild and farm-raised fish worldwide (see Skall et al. 2005a, b; Kim and Faisal 2011). Waterborne transmission is the natural and dominant route of VHSv infection (Hershberger et al. 2011). Oral transmission has been associated with VHSv infection but in lesser magnitude; nevertheless, predation of infected fish cannot be excluded as a potential transmission route (Scho¨nherz et al. 2012; Getchell et al. 2013). Once infected, fish can develop a series of symptoms including the hemorrhagic signs characteristic of VHS (Wolf 1988) with internal lesions that include edema (liquid in cavities of the body and tissues) and petechiae (minor bleeding) in visceral organs, muscle, and brain, and external lesions including exophthalmia (protrusion of the eyeball), skin darkening, and pale gills. Behavioral alterations can appear, including anorexia, lethargy, and erratic swimming (Skall et al. 2005b; Emmenegger et al. 2013; Lovy et al. 2013; Cornwell et al. 2014; Munro et al. 2015). Based on the structural composition of the nucleoprotein and glycoprotein, four VHSv genotypes have been identified (I, II, III and IV) which are also divided in sub-genotypes (i.e., Ia–Ie and IVa–IVc) (EinerJensen et al. 2004; Snow et al. 2004). In general, research on VHSv has largely focused on understanding the distribution of the virus from the microscopic to local scale (Estepa and Coll 1997; Gaudin et al. 1999; Isshiki et al. 2002; Arkush et al. 2006; Vo et al. 2015) [but see (King et al. 2001b; Cornwell et al. 2015)]. There have been limited explorations on the global distribution of this pathogen, the biogeographic factors limiting its distribution, or the potential areas at risk for future epidemic (VHSV Expert Panel and Working Group 2010). Given the critical importance of aquaculture to global food security and the continued risk of VHSv emergence in new geographic areas around the world,


we reviewed the ecology and epidemiology of this virus across its entire distribution. This study is the first comprehensive review of VHSv cases and species affected around the world coupled with ecological data. We defined two main goals: (1) describe the biogeographic patterns of VHSv lineages (i.e., their geographic and environmental distribution), and, based on this knowledge, (2) forecast potential distribution of VHSv spread in marine and freshwater ecosystems. Accomplishing these goals provided information to understand the ecology and geography of VHSv at a global scale, and also provided the tools to identify high risk areas to improve disease monitoring and surveillance where, according to our models, aquaculture industries could be impacted in the future.

Methods First, an assessment of susceptible species was conducted based on a literature review of infected species. Then, we used ecological niche modeling methods based on a type of logistic-regression linking VHSv cases with environmental variables. Models were developed for the entire VHSv range at a coarse scale and complementary models at fine resolution were developed for inland and marine regions. Cases of VHSv lineages were represented in geographic coordinates, while environmental variables were summarized in global grids. VHSv distribution A comprehensive scoping study of worldwide VHSv cases was conducted using repositories and peerreview literature (Arksey and O’Malley 2005; Levac et al. 2010). The retrieved information included genotype, geographic location, and host species infected by region by year. We grouped records by region, order, family, and species. To identify susceptible fish species, we searched for reports of fish infections including evaluation of natural outbreaks, and those identified as susceptible in laboratory challenge studies. Occurrence records of VHSv from around the globe, comprising mainly Europe and Asia, were collected from the FishPathogens repository (Jonstrup et al. 2009). Most VHSv cases from North America were collected primarily from Escobar and

colleagues (Escobar et al. 2017) who in turn collected the data from the Molecular Epidemiology of Aquatic Pathogens viral hemorrhagic septicemia virus repository (USGS 2013). These two online repositories were consulted to include data up to February 2016. Complementary VHSv cases were gathered from scientific literature (see (Meyers et al. 1994; Takano et al. 2001; Dopazo et al. 2002; Hedrick et al. 2003; Kim et al. 2003, 2009, 2011; Dixon et al. 2003; Gagne´ et al. 2007; Lee et al. 2007; Faisal and Schulz 2009; Altuntas and Ogut 2010; Faisal and Winters 2011; Frattini et al. 2011; Millard and Faisal 2012; Faisal et al. 2012; Garver et al. 2013; Gadd 2013; Minamoto et al. 2014; Cornwell et al. 2014; Moreno et al. 2014; Ogut and Altuntas 2014a; Ahmadivand et al. 2016)). Geographic coordinates of VHSv cases were grouped according to genotype information, when available (Fig. 1; Supporting Information S1). Reports without coordinates were georeferenced using Google Earth, and VHSv locations in forms of maps without detailed geographic coordinates were orthorectified to extract coordinates using the Georeferencer tool in QGIS Pisa (QGIS Development Team 2015). In all, 1095 geographic coordinates were collected for wild and farmed fishes infected with VHSv. After removing duplicates, 598 VHSv locations remained for freshwater and marine models (Ia = 102, Ib = 37, Ic = 5, Id = 24, Ie = 9, II = 13, III = 27, IVa = 95, IVb = 103, IVc = 4, not genotype identified = 233). Ecological niches Biogeographic explorations to determine the geographic and environmental distribution of VHS lineages were done using ecological niche modeling theory and methods. An ecological niche is defined as the set of environmental conditions in a region, necessary for a species to persist without the need of immigration (Peterson et al. 2011). According to Hutchinson (Hutchinson 1957), ecological niches are demarcated first in environmental dimension and then expressed in the geographic space (Colwell and Rangel 2009) (for a deeper explanation see Supporting Information S2). Ecological niche models by sub-genotype Ecological niche models by sub-genotype were developed in the model calibration area defined based on

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Fig. 1 Global distribution of viral hemorrhagic septicemia virus (VHSv) reports. Global distribution of VHSv genotypes (green dots). a The geographic location of VHSv sub-genotype IVa in the Pacific coast of North America (red points); b subgenotypes IVb and IVc in the Great Lakes region (red squares) and Atlantic coast of Canada (red triangles), respectively, and

genotype III (green triangle); c sub-genotypes Ia (blue points), Ib (yellow squares), Ic (orange points), Id (purple triangles), Ie (gray squares), II (light blue points), and III (green triangle) in Eurasia; d sub-genotypes IVa (red points) and Ib (yellow square) in Japan and South Korea. Point location without a reported VHSv genotype are also displayed (pink crosses)

our hypothesis of VHSv dispersal potential. To the best of our knowledge, the VHSv reports in inland North America represent a new introduction of the virus into the Great Lakes region, thus, the dispersal of VHSv across inland North America may be a proxy of dispersal potential for VHSv. We measured the maximum distance between VHSv reports in this area, * 2100 km (details in Supporting Information S2), and used this distance as a buffer surrounding all the occurrence reports of VHSv to establish our model calibration area used in posterior analyses (Fig. 2a). In the model calibration area, we first explored patterns of VHSv distribution based on climatic information covering freshwater and marine ecosystems. Climate explains biomes worldwide and is a good approximation to understand biogeographic patterns of organism distribution (Martıńez-Meyer et al. 2004).

Specifically, we used 19 bioclimatic variables developed based on long-term values of temperature and precipitation from ecoClimate (Table 1). ecoClimate is an open access repository of global climate data covering freshwater and marine regions at 0.5° spatial resolution for the period 1950–1999 (Lima-Ribeiro et al. 2015). Using ArcGIS 10.3 (ESRI 2017), we first limited the original ecoClimate variables to the model calibration area (Fig. 2a), then, we developed a principal components analysis (PCA) for standardization of variables, reduction of correlation, and dimensionality reduction, retaining the new components summarizing C 99.9% of the information from the original variables (Peterson et al. 2011). The retained components were used to calibrate the ecological niche models by VHSv sub-genotype using Maxent 3.3.3k (Phillips et al. 2006), parameterizing each

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Fig. 2 Model calibration area and areas of model transference. a Model calibration area based on a proxy of VHSv dispersal, used for ecological niche modeling. Model selection, similarity tests, and final calibration were developed in this region (dark

gray). b Area used for ecological niche model transference to inland freshwaters, based on regions reporting Rainbow trout industries (dark gray; see methods). c Area used for ecological niche model transference to marine coastal regions (dark gray)

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Rev Fish Biol Fisheries Table 1 Variables used for the VHSv ecological niche models Models by sub-genotype Abbr.

Variable

Bio1

Models in inland Unit

Abbr.

Variable

Annual mean temperature

°C

Bio1

Bio2

Mean diurnal range

°C

Bio3

Isothermality

Bio4

Temperature seasonality

Bio5

Maximum temperature of warmest month Minimum temperature of coldest month

Bio7

Models in marine areas Unit

Abbr.

Variable

Unit

Annual mean temperature

°C

Calcite

Mean calcite (CaCO3) concentration

mol/m3

Bio2

Mean diurnal range

°C

Chlomean

mg/m3

%

Bio4

Temperature seasonality

Mean chlorophyll A concentration Mean cloud fraction

%

Bio7

Temperature annual range

°C

pH

pH values in the ocean

–

°C

Bio8

°C

Phosphate

Bio12

Phosphate concentration Dissolved salt content

lmol/l

°C

Mean temperature of wettest quarter Annual precipitation

Temperature annual range

°C

Bio15

Bio8

Mean temperature of wettest quarter

°C

Bio17

Bio9

Mean temperature of driest quarter

°C

Bio10

Mean temperature of warmest quarter

°C

Bio11

Mean temperature of coldest quarter

°C

Bio12

Annual precipitation

mm/ m2

Bio13

Precipitation of wettest month

mm/ m2

Bio14

Precipitation of driest month

mm/ m2

Bio15

Precipitation seasonality

mm/ m2

Bio16

Precipitation of wettest quarter

mm/ m2

Bio17

Precipitation of driest quarter

mm/ m2

Bio18

Precipitation of warmest quarter

mm/ m2

Bio19

Precipitation of coldest quarter

mm/ m2

Bio6

%

Cloudmean

%

mm

Salinity

Practical salinity scale (PSS)

Precipitation seasonality

%

Sstmean

Mean sea surface temperature

°C

Precipitation of driest quarter

mm

Sstrange

Sea surface temperature range

°C

Models by sub-genotype ‘‘ecoClimate’’ variables at coarse scale (i.e., 0.5° spatial resolution) (Lima-Ribeiro et al. 2015). Models in inland areas ‘‘WorldClim’’ variables at fine scale (i.e., 0.05° spatial resolution) (Hijmans et al. 2005). Models in marine areas ‘‘BioORACLE’’ environmental layers (0.09° spatial resolution) (Tyberghein et al. 2012)

model based on the data available for each subgenotype (details in Supporting Information S2). Visualization and posterior analysis of inland and

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marine data in a multidimensional environmental scenarios were conducted using NicheA software (Qiao et al. 2016).


Finally, we explored ecological niche similarities among VHSv sub-genotypes in environmental and geographic dimensions. We used the Jaccard index (Jaccard 1912) ranging from 0 to 1 to measure the overlap of convex polyhedrons constructed with the binary Maxent models for each sub-genotype in the multidimensional environmental space estimated using NicheA (Qiao et al. 2016). We also used the Schoener’s D index (Warren et al. 2010) ranging from 0 to 1 to measure the overlap of the binary ecological niche models by sub-genotype developed in Maxent and expressed in the form of a geographic raster of suitable (i.e., 1) and unsuitable conditions (i.e., 0). Both indices were plotted to identify if patterns of niche similarity remained consistent among both environmental and geographic dimensions. Ecological niche models in freshwater and marine ecosystems Additionally, more detailed ecological niche models of VHSv were developed for freshwater and marine ecosystems using the protocol describe above, but with environmental variables at finer spatial resolution. These models forecasted specific areas at risk in terms of environmental suitability for VHSv. First, all cases were pooled for those occurring in freshwater or marine ecosystems. Location in brackish zones were included in both ecosystems when occurrences overlapped environmental rasters. Freshwater models were calibrated using a subset of the 19 bioclimatic variables from the Worldclim repository (Hijmans et al. 2005), including information of temperature and precipitation at 0.05° for the period 1970–2000, the latter being a proxy of water accumulation. Marine models were calibrated using a subset of 23 satellitebased geophysical, biotic, and climatic variables from the Bio-ORACLE (Tyberghein et al. 2012), repository, including sea surface temperature, oxygen, chlorophyll, salinity, pH, nitrate, phosphate, silicate, and cloud cover at 0.09° for the period 2005–2010. The number and correlation of original variables in each dataset (i.e., freshwater or marine) were reduced by removing variables with correlation C 0.7, retaining those with higher biological association to the virus (Escobar et al. 2017). Freshwater and marine models were calibrated in the model calibration area (Fig. 2a; Supporting Information S2) for a posterior model transference to regions of interest, but also

outside the calibration area, including countries with significant aquaculture industries producing susceptible species, such as Rainbow trout (Fig. 2b) (Neukirch and Glass 1984; Skall et al. 2004; FAO 2016b). We also considered neighbor regions to VHSv endemic countries assuming that the closeness to infected countries may be of special risk for VHSv spread. Marine variables were transferred to the coastal areas of these countries based on a 370 km buffer from the shoreline (i.e., exclusive economic zone as area of potential aquaculture activity; Fig. 2c). Standard deviations were estimated from 1000 permutations to account for variability in the final models. The most important environmental variables for model calibration were identified using Maxent, interpreting them as the most likely variables that explain the presence of VHSv across its distribution. Continuous models were converted to binary using a threshold value based on our tolerance of omission error (i.e., E = 5%), which represents the removal of 5% of the occurrence points with lowest suitability values predicted by the model (Peterson et al. 2008). This removal was assumed to represent sink populations found in the less suitable conditions where VHSv has been detected to date (Peterson 2014).

Results The review of VHSv cases resulted in 144 fish species reported infected with the virus and 4 genera not identified at species level (Supporting Information S3). Annual reports of novel fish species confirmed VHSv-positive, ranged from 0 to 17 with an average of * 3 new fish species detected annually since 1962 (Fig. 3). The fish orders with the more species reported infected with VHSv were Perciformes (n = 49), Salmoniformes (n = 16), and Gadiformes (n = 14). Genotype IV had the widest documented host range (n = 70 fish species), followed by I (n = 31), III (n = 20), and II (n = 5). Genotype was not reported in 33 fish species infected with VHSv (Supporting Information S3). Genotype I was most frequent in orders Perciformes (6), Gadiformes (6) and Pleuronectiformes (6), while VHSv genotype II was only found in Cupleiformes (2), Cypriformes (1), Gadiformes (1), and Petromyzontiformes (1). Genotype III occurred with highest frequency in orders Perciformes (5) and Gadiformes (5), while VHSv genotype IV

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Fig. 3 Cumulative number of fish species reported positive to VHSv between 1962 and 2015

occurred mainly in Perciformes (23), Cypriniformes (9), and fish species from the order Salmoniformes (8). Ten fish species were reported positive to more than one VHSv genotype (see more details in table of Supporting Information S3). Reports were distributed only across the Northern Hemisphere with no reports from tropical or Southern Hemisphere regions (Figs. 1, 2a). However, based on the presence of susceptible hosts and potential translocation through aquaculture activities, areas of interest for model projection were included in all the Southern Hemisphere as well (Fig. 2). Climatic variables from ecoClimate were highly correlated (e.g., bio1 had a q [ 0.9 with bio6, bio11, and bio10, similar to bio16 vs. bio12 and bio 13; Supporting Information S4), thus, the first three principal components summarized 98.39% of the overall variability, and eight components included [ 99.9% of all the information and were used for generating models of sub-genotypes. Sub-genotype models required regularization coefficients ranging from 0.5 to 1.5 to obtain the best model fit (Supporting Information S5). Explorations of niche similarities

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resulted in an agreement of estimations between Schoener’s D and Jaccard indices for most comparisons, especially for sub-genotypes Ia, Id, IVb, and IVc (Fig. 4). Ecological niche models of sub-genotypes Ia, Id, IVb, and IVc have low similarity with models of other sub-genotypes. Niche similarities were higher for comparisons between Ib versus Ic, Ic versus Ib, Ic versus Ie, Ie versus Ic, III versus IVa, III versus Ib, and IVa versus III due to the broad niche breath for some sub-genotypes. For example, when sub-genotypes III and IVa were displayed in environmental dimensions we observed that both populations occupied a similar environmental space; however, sub-genotype IVa had a broader niche entirely containing sub-genotype III (Fig. 5). The final models from the calibration area were projected to inland areas in 77 countries (Fig. 2b) and coastal areas in 19 countries (Fig. 2c). Inland predictions included eight uncorrelated variables (i.e., bio1, bio2, bio4, bio7, bio8, bio12, bio15, bio17; Table 1; Supporting Information S6) and a regularization coefficient of 1 provided the best model fit in Maxent (Supporting Information S5). The environmental


Fig. 4 Niche similarity tests. Pairwise comparisons of Schoener’s D (y axis) and Jaccard indices (x axis) between one Viral Hemorrhagic Septicemia virus sub-genotype versus all the other sub-genotypes. E.g., the first scatterplot denotes

sub-genotype Ia versus sub-genotypes Ib (blue), Ic (light green), Id (yellow), Ie (black), II (light blue), III (pink), IVa (brown), IVb (orange), IVc (green)

variables of inland models with highest percent contribution included annual mean temperature (bio1, 42.8%), precipitation of driest quarter (bio17, 24.8%), and mean diurnal range (bio2, 12.7%). Eight uncorrelated environmental variables were used for ecological niche models developed in coastal areas

(i.e., calcite, mean chlorophyll, mean cloud fraction, phosphate, salinity, and mean and range of sea surface temperature; Table 1; Supporting Information S7). The final Maxent ecological niche model for coastal areas was calibrated with a regularization coefficient of 1.5 (Supporting Information S5). The

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Rev Fish Biol Fisheries Fig. 5 Ecological niche modeling comparisons in environmental space. a Example of two VHSv sub-genotypes, III (green) and IVa (red), occurring in regions geographically distant. b Ecological niche models of two VHSv subgenotypes, III (green) and IVa (red), overlap in environmental dimensions, suggesting similar environmental conditions occupied by both subgenotypes. Environmental space created based on values (gray points) of the first three principal components of 19 bioclimatic variables (X = PC1, Y = PC2, Z = PC3)

environmental variables of marine models with highest percent contribution included mean chlorophylla concentration (chlomean, 60.3%), mean sea surface temperature (Sstmean, 27.3%), and sea surface temperature range (Sstrange, 4.5%). Freshwater and marine ecological niche models allowed predictions at a finer resolution and uncertainty estimations by pixel cell (Supporting Information S8 and S9). According to Maxent, the two most important variables for inland and marine regions were annual mean temperature (42.8%) and precipitation of driest quarter (24.8%), and mean chlorophyll-a concentration (60.3%) and mean sea surface temperature (27.3%), respectively. Freshwater forecasts found suitability for VHSv in Asia, Europe, Africa, the Americas and, Australia (Fig. 6). Continuous suitable areas were found across the Great Lakes region of North America and in Europe including coastal areas of the Black Sea in Turkey. Scattered areas were found in Central America, southern parts of Chile, Ecuador, Colombia,

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Argentina, and Venezuela. The models predicted environmental suitability for VHSv across the Himalayas, Australia, and inland China. VHSv’s suitable regions were also predicted in broad coastal areas in the Pacific coast of North America from northern Mexico to Alaska, the Atlantic coast of North America from Florida in the United States to Nova Scotia province in Canada, southern coasts of Iceland, coastal regions of northern Europe including the North Sea, Blatic Sea, English Channel, the coast of Western Sahara, the Gulf of Cadiz, and small portions the Alboran Sea. Additionally, suitability was found in the Yellow Sea in southern China, coastal zones surrounding South Korea, and Japan (Fig. 6).

Summary of key findings Analyses suggest that VHSv may have favorable conditions for spread and establishment beyond the current areas affected in the Northern Hemisphere. For


Fig. 6 Ecological niche models of VHSv in freshwater and marine ecosystems. The potential geographic distribution of VHSv based on environmentally suitable conditions (red) were identified in a Europe; b The Great Lakes region and the east coast of North America; c The west coast of northern North

America; d Central America and northern South America; e southern South America; f northwestern coast of Africa; g Kenya; h Himalaya Mountains, China, and Japan; and i southern Australia

example, freshwater and marine ecosystems of Africa, Latin America, Australia, and inland China were found suitable for VHSv in terms of environmental conditions. Strikingly, areas suitable have fish taxa potentially susceptible of VHSv infection as suggested

by the phylogenetic relationship with fishes known to be affected by VHSv. That is, our study suggests that fish species from the Perciformes, Salmoniformes, and Gadiformes orders are likely to be infected with VHSv

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in novel regions as the virus expands its range to areas predicted to be at risk.

Discussion Susceptible fishes Across all regions of the world, in freshwater and marine environments, we found VHSv-susceptible fish species of ecological or economical importance. The order Perciformes had the highest number of fish species susceptible, mainly in wild fishes in Europe (Skall et al. 2005a; Moreno et al. 2014; Munro et al. 2015) (Supporting Information S3), while Salmoniformes was the second (Meyers et al. 1994; Mortensen et al. 1999; Skall et al. 2005a; Gadd et al. 2011; Garver et al. 2013; Sandlund et al. 2014). Historically, Salmoniformes production has been significantly impacted by VHSv epidemics. For example, Rainbow trout, a Salmoniforme, is highly susceptible to genotypes I (Skall et al. 2004) and genotype III (Dale et al. 2009). Indeed, mass mortalities of Rainbow trout infected with VHSv and the subsequent management interventions has had a major impact on the European aquaculture industry (Jimenez de la Fuente et al. 1988; Skall et al. 2005b). However, in the Great Lakes region of North America, native Salmoniformes have been shown to be susceptible to VHSv with minimal mortality rates (Kim and Faisal 2010, 2011; Weeks et al. 2011; Emmenegger et al. 2013; Garver et al. 2013). In this region, besides Salmoniformes fishes, VHSv has affected native wild Perciformes fishes, causing massive fish kills (Groocock et al. 2007; Lumsden et al. 2007; Faisal et al. 2012). We argue that Perciformes could play a key role in the ecology and epidemiology of VHSv and their role as a carrier group should not be overlooked considering the broad range of host species found infected. The order Pleuronectiformes has been associated with VHSv outbreaks in economically important fish species in Asia and Europe (Schlotfeldt et al. 1991; Ross et al. 1994; Takano et al. 2000; Isshiki et al. 2001; Kim et al. 2009). This shows that the virus can infect distant regions and taxa (Lo´pez-Va´zquez et al. 2011). Additionally, several wild fish belonging to the order Gadiformes have been infected with VHSv across Eurasian coasts (Ogut and Altuntas 2014b), Atlantic and Pacific Oceans (Meyers et al. 1992; Mortensen

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et al. 1999; Smail 2000; King et al. 2001b; Dixon et al. 2003; Skall et al. 2005a; Sandlund et al. 2014; Wallace et al. 2014), and in the Great Lakes region in North America (Thompson et al. 2011). Fish from the order Clupeiformes are also susceptible and carriers of VHSv in different regions including Europe (Mortensen et al. 1999; King et al. 2001a; Skall et al. 2005a; Moreno et al. 2014; Ogut and Altuntas 2014a, b; Wallace et al. 2014) and North America (Kocan et al. 1997; Hedrick et al. 2003; Cornwell et al. 2012; Faisal et al. 2012; Garver et al. 2013) from fish kills and asymptomatic individuals. Finally, fish from the Esociformes order are known to be susceptible to VHSv with mortality reports in farmed and wild fish in Europe and North America (Meier and Jørgensen 1980; Enzmann et al. 1993; Millard and Faisal 2012) including the Great Lakes region (Elsayed et al. 2006). Many other fishes from several orders are susceptible to VHSv as several surveys have shown (Mortensen et al. 1999; Hedrick et al. 2003; Moreno et al. 2014; Ogut and Altuntas 2014b) (Supporting Information S3), occasionally from single, sometimes asymptomatic, reports. VHSv genotypes The distribution of VHSv sub-genotypes was sitespecific (Fig. 1 and Supporting Information S3), supporting previous reports from limited data (Mortensen et al. 1999; King et al. 2001b; Skall et al. 2005a; Ogut and Altuntas 2014b). Genotype IV had the greatest host species diversity, thus, this genotype would be a candidate for plausible spillover into novel species as it successfully invades freshwater and marine systems in North America. Conversely, the low similarity estimated from the narrow niches of Ia and Id suggests that these sub-genotypes are specialized or restricted to specific environmental conditions (Fig. 4). Sub-genotypes with high values of overlap and high similarity with other genotypes suggest broad niches associated with generalist VHSv sub-genotypes. Genotypes of broad niches would then be highly adaptable and with high potential for spillover to other fish species or regions. We note that geographic distance is not necessarily indicative of environmental difference. For example, we found VHS genotype III in areas geographically isolated with one population in northern Europe and another restricted off eastern Newfoundland, in an area known as the Grand Banks


(Fig. 5). While the presence of this genotype near Newfoundland appears odd by itself—considering the abrupt changes in topography in the area and the high volume of fresh water flowing from the Artic current (i.e., Labrador Current and sea ice) that generates shallow, cold, low salinity sea water (Fratantoni and McCartney 2010). This supports the idea of environmental similarity among populations of this genotype across its entire geographic distribution. Europe presents the highest diversity of VHSv lineages and is here proposed as the nucleus of VHSv emergence and diversification. Thus, this region may be of special interest to explore the potential effects of co-infections (presence of more than one VHSv genotype in the same individual host) in wild fish. That is, future research may include assessing the effects of co-infections on VHSv virulence, immune response, and virus evolution. Understanding the effects of co-infections would help to understand how co-infections impact individuals and populations, and will facilitate to identify areas with co-circulation of genotypes to quantify the propensity of these areas for VHSv epidemics. VHSv risk areas Our ecological niche model predicts VHSv suitable areas across the world, beyond the areas where the disease is currently endemic. Some novel areas predicted at risk include Argentina, Australia, Chile, Mexico and Portugal (Fig. 6). Tropical countries like Colombia and Ecuador showed suitability in highlands, especially in freshwaters of the Andes region, which could support VHSv replication due to the cold temperatures in these areas. Canada, China, France, Germany, Italy, Spain, Turkey, and United Kingdom showed suitable conditions for VHSV and had reported VHSv outbreaks in the past; however, we predicted suitable areas for VHSv in these countries beyond the sites of previous records (Figs. 1, 6). On the other hand, Norway and Denmark have been free of VHSv for a decade (Dale et al. 2009; Kahns et al. 2012), but were predicted as areas of high risk by our models. Indeed, Norway experienced a VHSv outbreak in 2007 (Dale et al. 2009), supporting the status of a high risk area. Of the areas predicted VHSv suitable, United States, Chile, Denmark, France, Italy, Norway, and Turkey are of particular concern due to the importance of farmed Rainbow trout, posing a risk

for their respective aquaculture industries. The models also predicted some surprising patterns in freshwaters, with suitable conditions predicted in the Great Lakes region but only partial suitability to the northern areas of Lake Superior. Likewise, a similar pattern was observed in marine ecosystems in eastern Sweden in the Gulf of Bothnia (Fig. 6). Such inconsistencies could be related to the particularly low temperatures at the latitudes where these lakes occur. Laboratory assessments found that VHSv tolerates temperatures ranging between 10 and 20 °C (Winton et al. 2007; Goodwin and Merry 2011). Thus, temperatures below this range may limit the presence of the virus in the host or can be associated to fish species non susceptible to VHSv. Using reports from wild and farmed individuals could be a limitation of the model to characterize suitable landscape conditions where outbreaks could occur (Peterson 2014); however, we found that locations with VHSv-positive farms generally reported wild fishes VHSv-positive (Einer-Jensen et al. 2004; Dale et al. 2009), thus, suggesting that the occurrences used provided the ecological signal to identify the conditions environmentally suitable for VHSv. Considering the high number of fish species VHSv positive, our modeling framework was focused on the abiotic environmental conditions suitable for VHSv (Supporting Information S2), neglecting the presence of susceptible fish. However, a more detailed exploration at a local scale should consider the presence and density of fish species from the orders found highly susceptible to the virus. Furthermore, correlative ecological niche models are impacted by the areas selected for calibration (Barve et al. 2011). Here, models were calibrated using a hypothesis of dispersal potential estimated base on the spread of VHSv in the last decade in the Great Lakes region. This scenario, however, may be an overestimation of viral translocation facilitated by human intervention given other possible pathways of dispersal. Regardless of the limitations, the information obtained from the ecological niche models provide a signal of plausible areas at risk that can be employed to justify VHSv prevention and control methods including movement restriction to reduce the potential spread of the virus to naı¨ve areas and species. We noted that human population densities are overall heaviest in some areas of highest potential infection in Europe and North America (Fig. 6). This

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pattern could suggest that VHSv spread and establishment may be facilitated by human intervention (e.g., recreational fishing and aquaculture). On the other hand, VHSv models may be revealing the influence of sampling bias—more reports in areas with more people may result in models overpredicting highly populated regions. Additionally, other organisms overlapping with the distribution of VHSv could influence its presence and detectability. For example, considering that fish species vary in their susceptibility to VHSv, future research could focus on the association of the community composition on the prevalence of VHSv, to determine if an increase in fish species diversity reduces the prevalence of VHSv—a.k.a. dilution effect (Schmidt and Ostfeld 2001). Additionally, co-infections of VHSv and other piscine rhabdoviruses may reduce the detectability and systemic distribution of one of the viruses, suggesting apparentcompetition at the cell level (Brudeseth et al. 2002). However, the use of ecological niche modeling to reconstruct biotic interactions among multiple species is still in its infancy (Anderson 2016), and more research is necessary to assess the abilities of ecological niche modeling to reconstruct complex biotic interaction in disease systems. While we believe the best available and most complete data were used for this analysis, there are nevertheless limitations given the types and quality of data available. For example, each dataset was assembled with a different methodology, including the use of atmosphere–ocean global climate models (i.e., ecoClimate), interpolation of data from climatic stations (i.e., Worldclim), and data interpolating variational analysis (Bio-Oracle), resulting in different values of uncertainty and assumptions. ecoClimate provides a realistic estimation of global processes associated with climate, covering a comprehensive period and providing data for inland and marine regions (Lima-Ribeiro et al. 2015); however, values are expressed at coarse resolution (50 km) and variables may be highly correlated requiring a reduction in number and collinearity. Worldclim provides information of temperature and precipitation from a comprehensive period from which most ecological niche modeling estimations are developed (Hijmans et al. 2005); however, data from countries with limited number of climatic stations are underrepresented and most values are simulated—i.e., \ 1% values in the climatic layers are real data (Peterson 2014). Bio-

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Oracle was produced using averages of satellitederived data with most values representing real data and providing information from environmental patterns in the ocean at fine spatial resolution (Tyberghein et al. 2012); however, these data cover a narrow period and are representative of the surface of the sea, neglecting the environmental conditions under the surface, which are known to be dynamic and complex. Finally, the models were based on static variables since we used environmental data that captured longterm patterns across the landscape. Future research to account for spatial and temporal variability, such as water currents, would create more dynamic models that can be used to determine potential sites of origin, paths of spread, and potential locations of future VHSv outbreaks. Final remarks Our detailed analyses focused on specific areas for model calibration, strict model transference into novel regions, detailed model parametrization, model fit evaluations, and fine resolution of uncorrelated environmental variables. This allowed us to develop detailed maps of potential VHSv establishment (Fig. 6), and also provide uncertainty estimations to better inform results’ interpretation (S7 and S8). We found that VHSv has the potential to affect a broad range of taxa, geographic areas, and environmental conditions. The general patterns suggest that the geographic distribution of VHSv is expected to increase if the virus is translocated to suitable areas across South America, Australia, and Asia. VHSv is also expected to infect novel fish species not reported in this study, with some generalist virus genotypes potentially more prone to spillover due to the broad environmental space currently occupied (e.g., genotype IV). The risk of continued VHSv emergence in wild and farm-raised fish is of considerable importance given the history of VHSv. The increasing number of VHSv reports, highlight the importance of proper surveillance not only in aquaculture facilities, but also in wild species. Our results should guide efforts to develop active epidemiological surveillance programs in areas where VHSv is not yet detected and reinforces the need for proactive regulatory and management intervention when fish and equipment translocation occurs from endemic regions into areas predicted suitable.


The risk maps generated in this study can also help to design future phylogeographic analysis (e.g., fullgenome sequence analysis) of VHSv across its distribution to determine the fish species acting as reservoirs and end-hosts unable to maintain the virus in the long term. This approach can also elucidate the role of water flow (e.g., rivers) and humans (e.g., recreational fishing) in the spread of the virus. Lastly, VHSv has potential for global translocation resulting in considerable risk to the fish industry, but also to local native fish communities. Thus, VHSv is an example of a pathogen requiring multidisciplinary and international collaborative efforts under the One Health approach, to facilitate the participation of professionals from animal health, economics, international policy, and epidemiology. The dramatic mortalities, potential for spill over novel fish species, and broad geographic distribution of VHSv, justify an integrated effort aiming to prevent and mitigate the impacts of this virus in the areas predicted at risk. Acknowledgements Authors thank Gael Kurath for her invaluable discussion on the ecology of VHSv. LEE thanks A. Townsend Peterson and Huijie Qiao for their crucial role in developing disease biogeography theory and methods employed here. Andres Perez provided comments in an early version. This study was supported by the Minnesota Environment and Natural Resources Trust Fund and the Minnesota Aquatic Invasive Species Research Center. LEE thanks the University of Minnesota Institute of the Environment for grant MiniGrants MF-0010-15 used to support the internship of JED in Minnesota. LEE had full access to all the data in the study and had final responsibility for the decision to submit for publication.

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