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Marine Science
ICES Journal of Marine Science (2015), 72(Supplement 1), i139 –i146. doi:10.1093/icesjms/fsu209
Contribution to the Supplement: ‘Lobsters in a Changing Climate’
Original Article
Modelling the spread and connectivity of waterborne marine
pathogens: the case of PaV1 in the Caribbean
Andrew S. Kough 1*, Claire B. Paris 1, Donald C. Behringer 2,3, and Mark J. Butler, IV4
1
Rosenstiel School of Marine and Atmospheric Sciences, University of Miami, Miami, FL 33149, USA
Fisheries and Aquatic Sciences Program, University of Florida, Gainesville, FL 32653, USA
3
Emerging Pathogens Institute, University of Florida, Gainesville, FL 32611, USA
4
Department of Biological Sciences, Old Dominion University, Norfolk, VA 23529, USA
2
*Corresponding author: tel: +1 240 447 0426; e-mail: [email protected]
Kough, A. S., Paris, C. B., Behringer, D. C., and Butler, M. J.IV Modelling the spread and connectivity of waterborne marine
pathogens: the case of PaV1 in the Caribbean. – ICES Journal of Marine Science, 72: i139 – i146.
Received 25 August 2014; revised 6 October 2014; accepted 28 October 2014; advance access publication 21 November 2014.
The PaV1 virus infects spiny lobsters (Panulirus argus) throughout most of the Caribbean, where its prevalence in adult lobsters can reach 17% and
where it poses a significant risk of mortality for juveniles. Recent studies indicate that vertical transmission of the virus is unlikely and PaV1 has not
been identified in the phyllosoma larval stages. Yet, the pathogen appears subclinically in post-larvae collected near the coast, suggesting that
lobster post-larvae may harbour the virus and perhaps have aided in the dispersal of the pathogen. Laboratory and field experiments also
confirm the waterborne transmission of the virus to post-larval and early benthic juvenile stages, but its viability in the water column may be
limited to a few days. Here, we coupled Lagrangian modelling with a flexible matrix model of waterborne and post-larval-based pathogen dispersal
in the Caribbean to investigate how a large area with complex hydrology influences the theoretical spread of disease. Our results indicate that if the
virus is waterborne and only viable for a few days, then it is unlikely to impact both the Eastern and Northwestern Caribbean, which are separated by
dispersal barriers. However, if PaV1 can be transported between locations by infected post-larvae, then the entire Caribbean becomes linked by
pathogen dispersal with higher viral prevalence in the North. We identify possible regions from which pathogens are most likely to spread, and
highlight Caribbean locations that function as dispersal “gateways” that could facilitate the rapid spread of pathogens into otherwise isolated areas.
Keywords: disease, lobster, Panulirus argus, physical oceanography, virus.
Introduction
Within the dense and stable medium of seawater, many animals
have evolved complex life cycles that include a distinctive planktonic
larval phase that facilitates population dispersal. Marine larvae can
harness powerful currents and potentially travel long distances
which led to the once widely accepted paradigm that marine populations are “open” (Roughgarden et al., 1985). However, we now know
that many larvae have specializations and behaviours that limit their
dispersal (Paris et al., 2007; Butler et al., 2011; Staaterman et al., 2012).
These adaptations enhance the local retention of larvae (Jones et al.,
1999; Paris and Cowen, 2004; Almany et al., 2007), resulting in a
continuum of dispersal potentials. Although this theory was promulgated on meroplankton, the dispersal of other important ecosystem
components, such as marine pathogens, may be similar but remain
understudied (Harvell et al., 2004).
# International
Pathogens play an important role in shaping marine communities (McCallum et al., 2001), where their dispersal and spread are
quite different from that of the terrestrial environment. Marine
pathogens tend to spread more rapidly than in terrestrial systems
(McCallum et al., 2003, 2004) and can fundamentally alter ecosystems (Harvell et al., 1999). For example, an unknown pathogen
that rapidly spread around the Caribbean in the 1980s killed longspined sea urchins (Diadema antillarium) en masse, removed an
important herbivore from the system, and contributed to a shift
towards macroalgae-dominated reefs (Lessios et al., 1984; Lessios,
1988). Emerging diseases that afflict habitat engineers, such as
corals or seagrasses, may become more prevalent as the climate
shifts (Harvell et al., 1999; Ward and Lafferty, 2004) and could
alter their range in tandem with their hosts. Unfortunately, the dispersal, epidemiology, and population dynamics of most marine
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diseases are unknown, and many diseases are never detected
(Harvell et al., 2004). Many pathogens affect both aquacultured
and wild species of interest to fisheries, so understanding the dynamics of disease in both is crucial (Daszak et al., 2000).
One such pathogen, the virus Panulirus argus Virus 1 (PaV1),
infects the Caribbean spiny lobster P. argus (Shields and
Behringer, 2004) and is found throughout much of the wider
Caribbean (Moss et al., 2013). PaV1 is a large, icosahedral,
non-enveloped, DNA virus, and is primarily pathogenic to juvenile
lobsters (Shields and Behringer, 2004; Butler et al., 2008; Behringer
et al., 2012a, b). The high mortality that juveniles suffer adds another
challenge to managing this fully exploited fishery (Chavez, 2009;
Ehrhardt et al., 2010). Caribbean spiny lobsters have phyllosoma
larvae which have a long pelagic larval duration of 6 months or
more (Goldstein et al., 2008) and thus great dispersal potential.
Research using genetics (Silberman et al., 1994; Naro-Maciel et al.,
2011) and biophysical modelling (Briones-Fourzan et al., 2008;
Butler et al., 2011; Kough et al., 2013) suggest that the international
exchange of larvae is common so that populations around the
Caribbean are linked through larval transport. Likewise, strains of
PaV1 from disparate countries share phenotypes (Moss et al.,
2013) and some post-larvae that arrive onshore in Florida are
infected with PaV1 (Moss et al., 2012), making infected larvae a potential dispersal pathway for the pathogen.
Experiments testing vertical transmission from female to larvae
have proven negative (Behringer and Butler, unpublished data),
so the mechanism or vector of dispersal for PaV1 among distant
populations remains elusive. Thus far, no PaV1-infected phyllosoma
larvae have been detected in field collections (Lozano-Alvarez et al.,
unpublished data). However, some post-larvae collected near the
Florida coast (Moss et al., 2012) and others many kilometres off of
the continental shelf of the Yucatan peninsula (Lozano-Alvarez
et al., unpublished data) were found subclinically infected with
PaV1. Post-larval lobsters are incapable of feeding (Wolfe and
Felgenhauer, 1991) and live off concentrated lipids stored during
the long larval phase (Lemmens, 1994; Lemmens and Knott, 1994),
so ingestion of the PaV1 pathogen by post-larvae is not a viable transmission pathway. Whether late-stage phyllosoma larvae acquire
the virus through ingestion of infected prey or marine aggregates and
then retain the virus after metamorphosis into post-larvae, or if only
post-larvae are susceptible to infection and the virus is acquired
offshore is as yet unknown. Regardless, the presence of PaV1-infected
post-larvae means that the virus can potentially spread over long
distances within infected, planktonic hosts.
Our goal was to compare the genetic relatedness of PaV1 strains
in the Caribbean (Moss et al., 2013) with modelled patterns
of pathogen spread. We used biophysical Lagrangian stochastic
modelling coupled to a stepping-stone model of pathogen spread
to describe the probable dispersal of a waterborne pathogen
compared with behaviourally adroit P. argus post-larvae in the
Caribbean Sea. We tested two hypotheses for how PaV1 may be
spread, as: (i) a passive waterborne particle within the top 10 m of
the ocean, or (ii) a lobster post-larvae infected with PaV1. Our
approach evaluates how hydrology could affect the potential
spread of a marine pathogen throughout the Caribbean.
Methods
We used an advanced multi-scale open-source Lagrangian biophysical model of dispersal, the Connectivity Modeling System (CMS;
Paris et al., 2013), to probabilistically describe Caribbean-wide connections between reef sites for 5 years, from the start of 2004 through
A. S. Kough et al.
the end of 2008. We then used the resulting matrices to randomly
and repeatedly seed an ensemble of simulations with pathogen
emergence and subsequent spread.
Habitat sites
We chose habitat sites containing coral reefs from those identified in
the Millennium Coral Reef Mapping Project (Andréfouët et al.,
2006). We overlaid an 8 × 8 km grid across the Caribbean and
created 3202 sites intersecting the reef extent, following Holstein
et al. (2014). Real-world spatial data do not always translate into operational hydrodynamic models, and can rest on boundaries
between land and water in the model, rendering them ineffective.
We removed 806 sites that were impacted by the structure of the
modelling framework. Further, we restricted our simulations to
sites that both exchanged and received larvae (removing another
714 sites) so that we were working with a theoretical network with
no disconnected sites. The resulting 1682 habitat sites stretched
across the Caribbean, from the Barbados to the northern edge of
the Florida Keys (USA) reef track (Figure 1).
Lagrangian tracking
We coupled and nested offline operational hydrodynamic models
with the CMS. More specifically, we used the 1/128 Hybrid
Coordinate Ocean Model (HyCOM) with NCODA assimilated
and the 1/248 HyCOM Gulf of Mexico models (Bleck, 2002) that
are publicly available and cover our study location. Both models
yield daily output, resolve mesoscale eddy features, and have been
widely applied to many different ocean transport scenarios. The
habitat seascape and hydrodynamic models were integrated
within the CMS for simulations of particle transport. We used highfrequency daily particle releases to model connectivity, corresponding to the daily windforcing of the HyCOM models (Bleck, 2002). A
sensitivity analysis indicated that 100 particles released daily from
each reef habitat site achieved probabilistic saturation at particle
tracking times longer than those used in this study (Kough, 2014).
Simulations
We tested two different hypotheses on how PaV1 virions may be
transported around the Caribbean: in seawater or in infected postlarvae. Our first hypothesis was that the pathogen is passively
advected as a waterborne particle among habitat locations in the
surface (,10 m) water layer (Waterborne simulation: WB). In the
WB scenario, we used a pelagic duration of 5 d, which corresponds
to laboratory estimates of how long PaV1 can be infectious in seawater (Butler and Behringer, unpublished data). Considering
PaV1 virions lack a structural envelope (Shields and Behringer,
2004), they are apt to be more environmentally stable (see
Satheesh Kumar et al., 2013 and references therein), which supports
the possibility that PaV1 spreads in the water column itself. Unlike
non-enveloped viruses such as PaV1, the lipoprotein layer that typically covers enveloped viruses and functions in host cell recognition
and penetration, is fragile, and can limit their persistence. The 5 d
duration in seawater that we modelled for PaV1 also fits well
within the range of viabilities known for several other marine
viruses. The viability of all viruses depends on environmental characteristics, particularly salinity and temperature, but the examples
that follow all come from experiments that were conducted in seawater (36 psu) and under temperatures likely to be experienced
by a virus in the tropical Caribbean Sea (20–338C). Some of these
viruses have envelopes, such as white spot syndrome virus, a large
DNA virus of decapod crustaceans that can remain viable in the
Marine pathogen dispersal
i141
Figure 1. Habitat map and mean pathogen spread times around the Caribbean. The habitat map (a) shows the centroids of the 8 × 8 km square
coral reef habitat locations (n ¼ 1682) used in the simulations. The mean number of steps in the model (equivalent to time) for PaV1 to spread to
each habitat location from random origins is shown for two scenarios: waterborne (WB; b) and post-larvae (PL; c). Colour is used to identify locations
on the map and links the position of the habitat on the x-axis in the panels (b and c) of the mean pathogen spread time to a spatial location (a). The
position of the habitat identified here is consistent throughout the paper.
seawater for 12 d (Satheesh Kumar et al., 2013), or the viral haemorrhagic septicaemia virus, an enveloped RNA virus of fish that can
remain viable in seawater for 4 d (Hawley and Garver, 2008).
Other viruses such as PaV1 are non-enveloped or “naked”, such as
the poliovirus, echovirus, and coxsackie virus; all are RNA mammal
viruses that remain viable in seawater for 1–12 d (Fujioka et al.,
1980; Labelle and Gerba, 1980; Block, 1983), 3 d (Labelle and
Gerba, 1980), and 30 d (Nasser et al., 2003), respectively.
Our second hypothesis was that after metamorphosis from the
phyllosoma larval phase, presumably seaward of the continental
shelf (McWilliam and Phillips, 2007; Phillips and McWilliam,
2009), P. argus post-larvae become infected with PaV1 and the
pathogen spreads among locations during the post-larval competency period (Postlarvae simulation: PL). In the PL scenario, we
used a variable pelagic duration of 5 –14 d based on estimates of
the average duration (Goldstein et al., 2008) and metabolic
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A. S. Kough et al.
constraints (Wilkin and Jeffs, 2011) of the post-larval stage. For the
PL, we included a daily vertical migration behaviour from the
surface layer at night to depths of at least 100 m during the day as
hypothesized by Phillips et al. (1978) for P. cygnus and Butler et al.
(2011) for P. argus.
Connectivity probability matrices M(i,j) were constructed for
the WB and PL scenarios using the modelled particles that successfully arrived at a habitat site ( j) from a habitat site (i), after the
pelagic duration.
Particlesij
Mij = .
Particlesi
measure of how important a habitat location is to the entire connectivity network. It was formulated by calculating how many times a
given habitat location is involved in the fastest pathway of pathogen
spread between any other two locations in the network. Thus, we
used the time to infection as an edge weight, to assess how rapidly
pathogens from one location spread throughout the system. In
this context, locations in the Caribbean with the highest betweenness centrality played crucial roles in rapidly sending pathogens to
other areas. Calculations were made in MATLABw using the BGL
toolbox developed by Gleich (2006).
Results
Waterborne
Stepping-stone pathogen spread model
We used the probability connectivity matrices from the Lagrangian
tracking simulations in a MATLABw model (Supplementary material) that seeded a randomly selected location in the Caribbean
with disease and then spread the pathogen elsewhere based on
either the WB or PL simulation scenario (Figure 2). The generality
of the model is improved by incorporating stochasticity in the
spatial origin and connectivity of the pathogen, which is pertinent
for PaV1 as its origin is unknown and the probability of exchange
between any two locations is likely to change from year to year
based on factors such as temporal variability in the prevalence of
PaV1 (Behringer et al., 2011) or changing atmospheric and
oceanic conditions (e.g. ENSO, current positions and meanders,
mesoscale eddy formation, etc.).
Gateway habitats
To evaluate how important any given habitat location is in the
disease network, we considered its potential for facilitating further
spread using betweenness centrality. Betweenness centrality is a
In the WB scenario, the Eastern and Central Caribbean were not
reached by pathogens originating in the North (Figure 3). Indeed,
many regions do not exchange disease with other regions in this
scenario and were isolated. This isolation delayed (i.e. more
model steps and longer mean time for pathogen spread) or prevented the spread of the pathogen to other locations. Spread-time
greatly increased near major oceanographic barriers: for example,
near the Turks and Caicos Islands and the island of Hispanola (see
Figures 1b and 3WB). The locations with the most betweenness centrality were clustered near the northwest coast of Cuba, and the
northeast coast of the Yucatan (Figure 4). The connections at
these sites represent a pathway for disease to spread from the
Yucatan into the Northern Caribbean and would expand the
network.
Post-larvae
The modelled pathogen spread faster throughout the Caribbean in
the PL scenario than in the WB scenario, regardless of origin (see
Figure 1c vs. b). The pathogen spread more widely between the
Northern and the Central and Eastern Caribbean (see Figure 3PL)
and spread more rapidly from the Central and Eastern Caribbean
into the Northwestern Caribbean than vice versa. In both the PL
and WB scenarios, the Lesser Antilles remained isolated, because
it took more model steps for pathogen to spread to them or it
never reached them. However, the Florida Keys took longer to
receive the pathogen in the PL scenario than it did in the WB scenario (see Figure 3; the leftmost columns are darker in WB than in
PL). The locations with the highest betweenness centrality in the
PL scenario were next to oceanographic features such as the
Windward, Mona, and Anegada passages, the Gulf of Colombia
and Gulfo de los Mosquitos, the Nicaraguan Rise, and the
Yucatan Channel (see Figure 4).
Discussion
Figure 2. A flowchart summarizing the steps in the pathogen
stepping-stone simulations. A pathogen was seeded 10 000 times from
a random starting location in the Caribbean. For each seeding event, the
pathogen could spread between locations for 300 model steps. During
each step, the probability of dispersal away from an infected location
was taken from a matrix of the 5-year mean modelled particle transport
specific to either a waterborne or post-larval scenario. This probability
was augmented by a stochastic addition to better capture
unpredictability between years.
Our findings support the expected pattern of linkages among areas
in the Caribbean based on well-studied oceanic circulation. The
Northern, Central, and Eastern Caribbean were isolated from each
other when the pathogen was waterborne and short-lived.
Particles have to travel against the prevailing flow to spread from
the Northern into the Central and Eastern Caribbean, so this was
expected. It is unlikely that the two regions would rapidly share a
disease outbreak that originated in the North, although some exchange is possible through pathways from the Greater into the
Lesser Antilles and on into the gyres off of Colombia and Panama.
Thus, even short-term waterborne transport could spread PaV1
through the Caribbean over long enough periods of time.
However, when we modelled the pathogen travelling within an
Marine pathogen dispersal
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Figure 3. Time matrices for the Waterborne (WB) pathogen simulation (left) and Post-larval (PL) pathogen simulation (right) that show how fast
the pathogen spread from one specific location (y-axis) to another specific location (x-axis). The position of habitat on both axes corresponds to
previously used colour (see Figure 1a) and x-axis position (see Figure 1b and c). The shade of grey represents how many model steps it took for the
pathogen to spread from one location to the next. There was no spread of disease among locations shown in white, slow spread between areas
shown in lighter shades of gray, and rapid spread between areas shown in black.
Figure 4. Map showing habitats that functioned as “gateways” from which the modelled pathogens spread most rapidly to other habitats.
Locations denoted with white stars (waterborne scenario) and grey squares (post-larval scenario) were the areas we defined as “gateways” based on
the betweenness centrality values of their pathogen spread time. These were the sites with the highest betweenness centrality (same order of
magnitude) in the networks of each scenario. The prevailing surface currents in the Caribbean are shown with the dotted line, and major
oceanographic features are labelled.
infected post-larval host, there was considerably more exchange
between the Northern and Southern Caribbean. Regardless of the
pathogen transport scenario, it took more model time-steps for
the pathogen to spread to the Lesser Antilles, South American
Coast, and Central America. Thus, a highly virulent disease that is
mainly limited by oceanographic dispersal should spread to
i144
these locations last. We also identified certain locations that acted as
“gateways” through which the pathogen spread rapidly among habitats. Gateways tended to be next to major currents and to other
oceanographic divides. These gateways facilitated connections
between localities that were otherwise isolated, and therefore are
important locations to focus potential management for future
epizootics.
How do the two scenarios that we modelled (e.g. WB vs. PL)
compare with the geographic structure of relatedness between
strains of PaV1 around the Caribbean (Moss et al., 2013)? The
WB scenario better captured the isolation and absence of PaV1 in
the Lesser Antilles, and in the South and Central Caribbean. Yet,
only the PL scenario was consistent with genetic linkages between
the PaV1 virus in Panama and the Northern Caribbean (Moss
et al., 2013), as it created increased exchange of the pathogen from
Central and South America into the Central and Northern
Caribbean. However, the number of infected lobsters found in
Panama and the Southern Caribbean was low and the authors
noted that they had the most diversity where they had the highest
sample size (Moss et al., 2013). Still, the presence of shared variants
between Central America and the Greater Antilles indicates that
the dispersal method for PaV1 requires a waterborne or vectored
pathogen that is viable in seawater for more than a few days.
From the perspective of the pathogen, post-larvae represent an
idealized method of dispersal. Post-larvae use chemical cues
(Herrnkind and Butler, 1986; Goldstein and Butler, 2009) and
tidal signals (Kough et al., 2014) to reach nursery habitat—an
optimal location for a pathogen to reach susceptible juvenile
hosts. Still, how do the non-feeding post-larvae acquire the pathogen, if they are not first infected as larvae? Early benthic juvenile
lobsters are highly susceptible to PaV1 and can be readily infected
through viral particles in the water column (Butler et al., 2008).
Spiny lobster post-larvae come onshore during the dark phase of
the lunar month with nocturnal flood tides (Acosta et al., 1997;
Yeung et al., 2001), navigating from offshore using chemical, tidal,
and probably other cues (Goldstein and Butler, 2009; Kough et al.,
2014). Post-larvae orient towards coastal water masses, which may
expose them to viral particles or other post-larvae already infected
within the nursery habitat. Indeed, the model results suggest that
the pathogen could spread across the Caribbean travelling within
post-larvae if they remain subclinically infected and offshore for
an extended time. In addition, stronger linkages between the
North and South would exist and be maintained if the pathogen
had origins in South or Central America.
The transport of pathogens within marine aggregates is perhaps
another way that coastal communities around the Caribbean may be
linked. Our results in the WB scenario demonstrate that passive particle dispersal connects otherwise disparate areas, even over short
time-scales. Many organisms, chemicals, and gradients are transported throughout the water column (Maddox, 2006) and drifting
debris can harbour infectious pathogens (Lizarraga-Partida et al.,
2011) and a diverse microbial community (Lyons et al., 2010).
Marine microbial communities can transition into opportunistic
pathogens that afflict plants and animals, especially in a changing
ocean (Burge et al., 2013). The floating assemblages of biological
materials such as mucilaginous aggregates and algae offshore of
coastal communities can play an important role for larval recruitment as shelter and perhaps as a signal of nearby benthic habitat
(Wells and Rooker, 2004; Goldstein and Butler, 2009). If such aggregates are used by meroplankton transitioning from the pelagic into
benthic habitats, these could be locations where concentrations of
A. S. Kough et al.
susceptible animals come into contact with pathogens such as
PaV1. Marine aggregates may also function as vessels for keeping
the virus protected (e.g. from UV light) and viable (Bongiorni
et al., 2007), and thus aid in pathogen transport for longer times
and potentially larger distances.
Our discovery of key gateways that facilitate pathogen connectivity among localities that would otherwise be isolated is meaningful
for marine pathogen epidemiology and potential management of
the spread of disease. The Greater Antilles have the highest PaV1
genetic diversity (Moss et al., 2013) and also contained the highest
number of gateway habitats in the PL simulation scenario, supporting the contention that such locations acted as points connecting a
large number of origins and destinations. In the WB scenario, the
Caribbean network was split into two relatively separate areas:
northwest and southeast of the prevailing current. The major gateways in the WB scenario straddled the primary pathway for linkage
between these two areas and occurred at the northeastern tip of
Mexico and on the west coast of Cuba. Very few gateway locations
were shared between the two scenarios, although there were overlaps
at the Cay Sal Bank (western Bahamas) and southeastern Cuba.
The gateways in the Cay Sal Bank facilitated connections into
the Bahamas and Florida, and were an important intermediary location next to the powerful Florida current. Active management of
fishery activities that potentially increase pathogen prevalence or
transmission (Behringer et al., 2012a, b) within gateway sites may
help to forestall the spread of pathogens among regions of the
Caribbean.
As with any study, there are certain caveats to our results that bear
mention. The time-scale of the Lagrangian modelling was 5 years,
which does not encompass the full length of time since PaV1 was
first detected. However, our aim was not to recreate the actual
pattern of the spread of PaV1 disease, which is unknown, but to
instead explore the potential way in which water- or vector-borne
pathogens may transition between locations. By tying a stochastic
stepping-stone model to the connectivity matrices generated by
the Lagrangian particle tracking model, we added variation to simulate periodic, but important events. This construct could miss rare
events that happen with a periodicity of ,5 years, but the mean connectivity measured over shorter periods is comparable to the connectivity over longer time-scales (Berglund et al., 2012). We also
simulated the spread of a waterborne pathogen using only a 5 d duration, a time frame believed relevant to the PaV1 virus. The viability
of many seawater-borne pathogens is poorly known, but their duration will clearly affect patterns of connectivity, as will other factors
that we did not simulate (e.g. alternate hosts, dormant stages, host
population dynamics, etc.). The CMS also used general ocean circulation models that best represented geostrophic flow (Bleck, 2002);
the nearshore tidal and local wind-driven dynamics were not well
represented in the general ocean circulation. However, we were
interested in the transport through pelagic oceans that could
advect a pathogen across greater distances than possible by nearshore processes. When the modelled pathogen originated in
Florida in the PL scenario, it spread in a countercurrent direction
similar to the well-documented invasion of the Caribbean by lionfish (Morris and Whitfield, 2009). Therefore, although the PL scenario used a shorter dispersal time than a lionfish larva, our model’s
results were comparable to a known pattern of Caribbean-wide invasion. In addition, using only open-access tools, such as HyCOM
and CMS, makes our work easily reproducible and accessible for
other researchers to build on while investigating dispersal of other
marine pathogens.
Marine pathogen dispersal
Although we were inspired to model pathogen dispersal by the
discovery and subsequent Caribbean-wide interest in PaV1, our
work is relevant to a myriad of different scenarios and organisms
affected by marine pathogens. Distant pathogen dispersal could be
tied into well-studied disease and ecosystem model systems
(Cáceres et al., 2014; Dolan et al., 2014), therefore linking the
oceanographic environment to population-based models and yielding a more comprehensive epidemiological model (McCallum et al.,
2004). Indeed, considering distant dispersal in tandem with disease
dynamics has better explained the outbreak of Aspergillosis in
seafans throughout the Caribbean (Bruno et al., 2011) and the persistence of PaV1 in lobster populations in Florida (Dolan et al.,
2014). Still, our understanding of the large-scale epidemiology of
marine diseases is in its infancy and will likely require analyses
that integrate physical dynamics, anthropogenic influence, and
disease history so as to identify potential hotspots of marine pathogens in the world’s oceans, similar to work in terrestrial environments (Jones et al., 2008). Benthic marine ecosystems receive new
recruits, and potentially pathogens, from ocean currents that
unite habitat over broad spatial and temporal scales, making
Lagrangian modelling an appropriate tool for examining the potential connectivity of marine diseases.
Supplementary data
Supplementary material is available at ICESJMS online version of
the manuscript.
Acknowledgements
This project greatly benefited from advice and direction given
by Michael Schmale, Tom Dolan, and two anonymous reviewers.
Funding was provided through the National Science Foundation
awards OCE-0928930 (CBP), OCE-0929086 (MJB), and OCE0928398 (DCB).
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