Harmful Algae 131 (2024) 102561
Contents lists available at ScienceDirect
Harmful Algae
journal homepage: www.elsevier.com/locate/hal
CiguaMOD I: A conceptual model of ciguatoxin loading in the Greater
Caribbean Region
Michael L. Parsons a, *, Mindy L. Richlen b, Tyler B. Smith c, Donald M. Anderson b,
Ashley L. Abram a, Deana L. Erdner d, Alison Robertson e, f
a
The Water School, Florida Gulf Coast University, 10501 FGCU Blvd South, Fort Myers, FL 33965, USA
Biology Department, MS #32, Woods Hole Oceanographic Institution, Woods Hole, MA 02543, USA
Center for Marine and Environmental Studies, University of the Virgin Islands, St. Thomas, US Virgin Islands 00802, USA
d
University of Texas Marine Science Institute, Port Aransas, TX 78373, USA
e
School of Marine and Environmental Sciences, University of South Alabama, Mobile, AL 36688, USA
f
Dauphin Island Sea Lab, Dauphin Island, AL 36528, USA
b
c
A R T I C L E I N F O
A B S T R A C T
Edited by: Dr. C. Gobler
Ciguatera poisoning (CP) is the most common form of phycotoxin-borne seafood poisoning globally, affecting
thousands of people on an annual basis. It most commonly occurs in residential fish of coral reefs, which consume
toxin-laden algae, detritus, and reef animals. The class of toxins that cause CP, ciguatoxins (CTXs), originate in
benthic, epiphytic dinoflagellates of the genera, Gambierdiscus and Fukuyoa, which are consumed by herbivores
and detritivores that facilitate food web transfer. A number of factors have hindered adequate environmental
monitoring and seafood surveillance for ciguatera including the low concentrations in which the toxins are found
in seafood causing illness (sub-ppb), a lack of knowledge on the toxicity equivalence of other CTXs and
contribution of other benthic algal toxins to the disease, and the limited availability of quantified toxin standards
and reference materials. While progress has been made on the identification of the dinoflagellate taxa and toxins
responsible for CP, more effort is needed to better understand the dynamics of toxin transfer into reef food webs
in order to implement a practical monitoring program for CP. Here, we present a conceptual model that utilizes
empirical field data (temperature, Gambierdiscus cell densities, macrophyte cover) in concert with other pub­
lished studies (grazing rates and preference) to produce modeling outputs that suggest approaches that may be
beneficial to developing monitoring programs: 1) targeting specific macrophytes for Gambierdiscus and toxin
measurements to monitor toxin levels at the base of the food web (i.e., toxin loading); and 2) adjusting these
targets across sites and over seasons. Coupling this approach with other methodologies being incorporated into
monitoring programs (artificial substrates; FISH probes; toxin screening) may provide an “early warning” system
to develop strategic responses to potential CP flare ups in the future.
Keywords:
Harmful algal blooms
BHAB, HAB
Ciguatera poisoning
CP
Ciguatoxin
Food web
1. Introduction
Ciguatera poisoning (CP) is the most common phycotoxin-derived
seafood illness experienced by people and is thought to afflict tens of
thousands of people around the globe on an annual basis (Chinain et al.,
2021). Ciguatoxins (CTXs) cause CP and are known to originate from
benthic dinoflagellates of the genera, Gambierdiscus and Fukuyoa, with
some evidence of other phycotoxins also being involved (e.g., toxic
compounds produced by Ostreopsis and Prorocentrum spp.; Holmes et al.,
2021). People are primarily exposed to CTXs through the consumption
of reef-associated fish that have accumulated and biotransformed the
toxins through their diet (FAO and WHO, 2020). The toxins enter the
reef food web through herbivorous and detritivorous fish and in­
vertebrates which are then preyed upon by secondary and tertiary
consumers. People have contracted CP by eating a variety of fish (and
invertebrates) from multiple trophic levels, demonstrating that the
CP-related toxins can move throughout food webs and bioaccumulate to
dangerous levels (0.01 µg CTX1B eq. kg− 1 fish flesh; FAO and WHO
2020). Additionally, a recent study found the potency of algal-derived
CTXs to be of the same order of magnitude of those found in apex
predatory reef fish (Mudge et al., 2023), indicating that potent CTXs –
and the subsequent risk of CP – may be present throughout the reef food
* Corresponding author.
E-mail address: mparsons@fgcu.edu (M.L. Parsons).
https://doi.org/10.1016/j.hal.2023.102561
Received 31 July 2023; Received in revised form 1 November 2023; Accepted 18 December 2023
Available online 19 December 2023
1568-9883/© 2023 Elsevier B.V. All rights reserved.
M.L. Parsons et al.
Harmful Algae 131 (2024) 102561
web.
Where endemic, CP is a known risk, yet it can be challenging to
prevent exposure when people may make risky choices in their seafood
consumption (FAO and WHO, 2020). For example, Sphyraena barracuda
(great barracuda) are considered to be one of the riskiest fishes for CP
world-wide (Chinain et al., 2021), yet people regularly target and
consume barracuda due to its large size and availability. Other con­
tributors to this risk-taking behavior is because barracuda is viewed in
some regions of the world as a prized delicacy worth the risk (Denny,
2022), and secondly because there is no reliable and readily-available
screening method to assess the risk prior to consumption. The latter
issue also results in unsuspecting consumers falling ill to CP in
non-endemic regions; e.g., barracuda consumed in New York City
(Graber et al., 2013), where locals may not be aware of the origin and
risk posed by barracuda and reef-associated fish from CP-endemic areas.
The lack of a widely-available, robust, and cost-effective screening tool
is due in part to the nature of CP. Ciguatoxins are cyclic polyether
molecules derived from some Gambierdiscus and Fukuyoa taxa (e.g.,
P-CTX-4A and C-CTX-5) that are thought to be modified by oxidation
and/or reduction (Ikehara et al., 2017; Mudge et al., 2023), and glu­
curonication (Gwinn et al., 2021) which could aid in elimination. The
concentration of CTXs in fish is quite low (i.e., sub-ppb levels), making
them hard to detect using instrumental analysis, but have high potency
and can therefore cause significant illness even when present at low
levels in the edible portions of the fish. Additionally, the limited avail­
ability of CTX standards and quantified reference materials has hindered
the development and implementation of standardized methodologies
(FAO and WHO, 2020; Pottier et al., 2023). These materials are neces­
sary for reliable estimation of toxin levels and for the determination of
toxin equivalency factors needed to interpret complex toxin profiles in
implicated seafood.
In order to mitigate the lack of effective screening tools to safeguard
people from toxic seafood, much research has focused on the source of
the toxins, Gambierdiscus and Fukuyoa. Toxigenic taxa have been iden­
tified (Chinain et al., 2021) and a few progenitor molecules have been
identified in some regions (e.g., C-CTX-5; Mudge et al., 2023). Many
ecological studies of these and other benthic harmful algal bloom
(BHAB) taxa have been done (reviewed in Chinain et al., 2021) and
BHAB monitoring tools are being developed to track when and where
the CTX toxin sources are being produced in specific areas/regions (FAO
and WHO, 2020). There have been difficulties in distinguishing the
cryptic members of Gambierdiscus in particular (requiring molecular
methods such as PCR), and enumeration of individual species has been
hindered by matrix interferences using such methods (Díaz-Asencio
et al., 2019). FISH probes (Pitz et al., 2021) and other methods (e.g.,
biosensors; Gaiani et al., 2020) have been developed to rectify this sit­
uation, although these are not yet validated, commercially-available, or
in widespread use.
Upon the discovery of Gambierdiscus toxicus as the source of CTX as
reported by Yasumoto et al. (1977), subsequent studies focused on
relationship between cell abundance and toxicity of the biodetritus
layers (e.g., Bagnis et al., 1980, Chinain et al., 1999) and the diet of
herbivorous/omnivorous fish to determine if they had consumed Gam­
bierdiscus cells (e.g., Campbell et al., 1987, Magnelia et al., 1992). The
general consensus of these studies was that biodetritus toxicity was
correlated with Gambierdiscus cell abundance (Bagnis et al., 1980), but
not always (Chinain et al., 1999), and fish containing Gambierdiscus cells
in their guts were generally toxic (Campbell et al., 1987). More recent
studies have demonstrated that cell densities are often asynchronous
with toxin loading on macroalgae (Dictyota), where cell densities
generally were highest in the warmest months and toxin levels were
highest in the cooler months (St. Thomas, US Virgin Islands; Liefer et al.,
2021). These findings demonstrate that unlike pelagic HABs, cell den­
sities may not be the best (or sole) method for monitoring, and that
Gambierdiscus abundance and toxin loads can be expected to oscillate
with changing substrate, storm events, and other climatic factors.
The secondary focus on CP management and monitoring has been in
the avoidance of high risk seafood species from endemic areas (e.g.,
leeward side of an island with a history of CP) and the suggested sur­
veillance of CTXs in select vector species. For instance, one of the rec­
ommendations culminating from the 2018 FAO WHO Expert meeting
and subsequent report on ciguatera was that further actions be taken to
monitor how the CTX moves into the food web (FAO and WHO, 2020).
For example, sentinel fish (and/or invertebrates) should be selected for
biomonitoring in areas that are extensively fished and/or known to
produce seafood that causes CP. Unfortunately, without a
commercially-available or reliable, inexpensive, rapid, and validated
strategy for CTX surveillance in seafood, this remains a challenge to
achieve, and more efforts are needed before this becomes a reality.
An alternative and supplemental strategy is modeling, which could
provide some predictive capacity on CP trends in algae and/or fish.
While many models exist for population dynamics of phytoplankton
(Andersen and Visser 2023) and various food web constructions (Borga
et al. 2022), few focus on CP. Chateau-Degat et al. (2005) reported a
statistically significant relationship between sea surface temperatures
(SST), Gambierdiscus growth, and subsequent cases of CP, providing a
potential means for predictive modeling. Similarly, Kibler et al. (2017)
used sea surface temperature, bathymetric data, and light penetration
estimates to develop growth projections for Gambierdiscus in the Greater
Caribbean Region. Parsons et al. (2010) produced a simple model based
on Gambierdiscus physiology driven by changing environmental condi­
tions (Big Island, Hawaii). While these models were useful in predicting
Gambierdiscus abundance, they overlooked the food web dynamics that
exist in situ.
Recently, progress has been made on unraveling some of the dy­
namics of CTX trophic transfer through experimental studies that may
allow integration of the source to the vectors that ultimately cause
illness; a major limitation of prior models focused on changes in Gam­
bierdiscus alone. Clausing et al. (2018) conducted a laboratory-based
experiment in which juvenile unicornfish (Naso brevirostris) were fed
gelatin-based food containing toxigenic Gambierdiscus polynesiensis cells
over 16 weeks. Ciguatoxins were measured above estimated threshold
levels of human intoxication (1.2 µg CTX3C eq. kg− 1 fish) after two
weeks, and while tissue CTX concentrations stabilized after 8 weeks, the
muscle toxin burden continued to increase through the end of the
experiment at 16 weeks. Growth dilution was suspected to play a role in
the results but was not tested. Bennett and Robertson (2021) carried out
sub-lethal C-CTX-1 dietary exposure experiments using pinfish (Lagodon
rhomboides) fed C-CTX-1-laden feed over 20 days followed by a
non-toxic diet for 14 weeks to measure changes in C-CTX tissue distri­
bution, body burden, and depuration. By applying growth dilution
correction models, they found that although toxin loading increased in
muscle tissue over time, body burden did not significantly change
overall due to CTX redistribution between other tissues and organs. The
growth-dilution corrected half-life of CTX was calculated to be 143–148
days, which the authors relate to field findings of increased prevalence
of CTXs in site-attached reef fishes in endemic areas. In both cases,
important data were produced demonstrating how toxin exposure
translates into body burden, providing quantification on the trophic
transfer of CTX into the food web.
A triad of studies was recently published (Holmes et al., 2021;
Holmes and Lewis, 2022; Holmes and Lewis, 2023) that utilized a con­
ceptual food web approach. Holmes et al. (2021) devised a conceptual
model that incorporated the transfer of CTX across trophic levels and the
growth dilution and depuration of CTX in predatory fishes. They re­
ported that Cladophora macroalgae were the likely vector for ciguatoxins
into food webs in Platypus Bay (Australia), whereas turf algae were the
vector on the Great Barrier Reef. They also surmised that growth could
not significantly dilute fish CTX toxicity, particularly for larger,
commercially-targeted fishes; e.g., Spanish mackerel (Scomberomorus
commerson) and coral trout (Plectropomus leopardus). Holmes and Lewis
(2002) used a “top down” approach, where they started with a toxic
2
M.L. Parsons et al.
Harmful Algae 131 (2024) 102561
Spanish mackerel (0.01 µg P-CTX-1 kg− 1 fish) and worked backwards to
determine how many toxic Gambierdiscus cells (of a known, static
toxicity) would be needed to produce that toxic fish, including alpheid
shrimp at trophic level (TL) 2 and javelin fish (Pomadasys maculatus) at
TL3. Holmes and Lewis (2023) developed a similar model for the Great
Barrier Reef and coral grouper, utilizing toxigenic Gambierdiscus cells,
turf algae, and surgeonfishes (Ctenochaetus striatus) feeding on the turf
algae as toxin vectors into the grouper. These latter two models incor­
porated published data (e.g., ingestion rates, biotransformation, depu­
ration, and growth dilution) to track and quantify the toxin through the
different trophic levels. These modeling exercises produced a platform
to develop risk assessments and mitigation strategies for CP that could
be incorporated into developing monitoring programs in Australia and
elsewhere.
Here, we take a “bottom up” approach, expanding on work first
presented by Parsons et al. (2016). This model complements the above
studies by adding a seasonal component, variable grazing (by substrate
preference and season) and a suite of Gambierdiscus taxa of varying
toxicity. The goal of this conceptual model was to utilize empirical data
collected in the field (temperature, Gambierdiscus cell densities, macro­
phyte cover) in concert with other published studies (grazing rates and
preference) to produce modeling outputs that may merit consideration
in developing monitoring programs: 1) targeting macrophytes for
Gambierdiscus and toxin measurements to monitor CTX levels at the base
of the food web (i.e., toxin loading); and 2) adjusting these targets across
sites and over seasons. Coupling this approach with other methodologies
being incorporated into monitoring programs (use of artificial sub­
strates; FISH probes; toxin screening) may provide an “early warning”
system to develop strategic responses to potential CP outbreaks in the
future.
Fig. 1. Map of the Florida Keys and St. Thomas showing the locations of the
eight sites sampled in this study. For the Florida Keys sites, HGB = Heine
Grassbed; TPH = Tomato Patch Hardbottom, LKH = Long Key Hardbottom;
TRL = Tennessee Reef Lighthouse. For the St. Thomas sites, BP = Black Point,
CRK = Coculus Rock, FC = Flat Cay, and SH = Seahorse Cottage Shoal.
2. Methods
(Harvey 1853), L. intricata (J.V. Lamouroux 1813), and Thalassia testu­
dinum (K.D. Koenig 1805). Turf algae was also collected at two of the
Florida Keys sites (TRL and LKH). Triplicate samples of each macrophyte
(when common) were collected at least 5 m apart via SCUBA into
screw-capped polypropylene 50 mL centrifuge tubes. The samples were
then shaken and filtered through 200 and 20 μm sieves (PVC; Nitex
mesh; 6.3 cm diameter), re-filled with 20 μm-filtered ambient seawater,
and shaken and filtered an additional four times. Epiphytic material
collected on the 20 μm sieve was backwashed into a 15 mL centrifuge
tube using ambient filtered seawater and brought to a volume of 14 mL.
One mL of 50 % glutaraldehyde (CAS 111–20–8; Fisher Catalog
#BP25471 or equivalent) was then added to the sample to formulate a
2.5 % final solution. Back at the laboratory, macrophyte samples were
removed from the centrifuge tubes, blotted dry, and weighed (g wet
weight) on a Mettler Toledo AL204 balance or equivalent. The macro­
phytes were then identified, using keys as necessary (Littler and Littler,
2000; Dawes and Mathieson, 2008), and included microscopy and
thallus cross-sectioning as needed.
While many environmental parameters were measured in the field
(e.g., temperature, light intensity, nutrient concentrations, water
depth), we focused on temperature as a driver for this conceptual model.
The primary reasons for this were: 1) temperature was most consistently
recorded in the field; 2) temperature-based growth curves were avail­
able for the Gambierdiscus taxa known to occur in the region (Xu et al.,
2016); and 3) temperature-based grazing rates could be calculated for
the model (see Section 2.6.3 below). Temperature data were obtained
from Hobo® temperature data loggers that were deployed at each site
(Onset®; U22 loggers at STT; UA-002-64 loggers at FLK) and recorded
temperature at 15 min intervals. The data were downloaded on a
monthly to quarterly basis and then averaged in 30d increments (30
days prior to sample collection). These values were then used in the
model as described below.
2.1. Site descriptions
Monthly samples were collected at four sites south of St. Thomas
(STT; Fig. 1a) and at four sites in the vicinity of Long Key in the Florida
Keys (FLK; Fig. 1b) from January 2011 to December 2014 (STT) and
December 2011 to November 2015 (FLK). The STT sites included Black
Point (BP; 18.3445 N, 64.98595 W; a nearshore fringing coral reef; 7–16
m depth); Coculus Rock (CRK; 18.31257 N, 64.86058 W; scattered stony
corals on bedrock; 6–7 m depth); Flat Cay (FC; 18.31822 N, 64.99104 W;
a fringing coral reef; 11–16 m depth); and Seahorse Cottage Shoal (SH;
18.29467 N, 64.8675 W; a deep patch reef 2 km offshore of St. Thomas;
19–22 m depth). The algal communities at the STT sites are typically
dominated by Dictyota and Lobophora spp., in addition to turf algae. The
FLK sites included two located on the Gulf of Mexico (north) side of the
Keys; Heine Grass Bed (HGB) and Tomato Patch Hardbottom (TPH); and
two located on the Atlantic Ocean (south) side of the Keys; Long Key
Hardbottom (LKH) and Tennessee Reef Lighthouse (TRL). HGB is a
nearshore Thalassia seagrass bed in approximately 2 m water depth,
whereas TPH is a nearshore hardbottom site (approx. 1.5 m depth)
consisting of soft corals, sponges, and macroalgae. LKH is an offshore
hardbottom site (approx. 5 m depth) consisting of soft corals, sponges,
and macroalgae, while TRL is a reef flat/crest site (approx. 7 m depth)
consisting of hard and soft corals, sponges, and macroalgae.
2.2. Sample collection and processing
Macrophyte samples (algae and seagrass) were chosen and collected
based on their common abundance. Detailed methods for macroalgal
sample collection and processing are summarized in Parsons et al.
(2017) and Pitz et al. (2021). The targeted species included Dictyota
cervicornis (Kützing 1859), D. menstrualis ((Hoyt) Schnetter, Hörning &
Weber-Peukert 1987), Halimeda gracilis (Harvey ex J. Agardh 1887),
H. incrassata ((J. Ellis) J.V. Lamouroux 1816), Laurencia gemmifera
3
Harmful Algae 131 (2024) 102561
M.L. Parsons et al.
2.3. Benthic characterization
reliable and repeatable manner. However, as turf algae are suspected to
be an important vector for CTX transference into reef food webs via
herbivory, we thought it necessary to include turf algae in this con­
ceptual model. Gambierdiscus cell densities were therefore estimated
from the ratio of cell densities on Dictyota versus turf algae (22.86 to 1)
determined from samples collected at LKH and TRL in 2014 and 2015,
and then applying this ratio to the gaps where turf algae were not
collected (all STT sites; TPH (FLK); pre-2014 samples at LKH and TRL)
by dividing the Gambierdiscus cell density values on Dictyota by 22.86 to
estimate cell densities on turf algae were needed. Note that no turf algae
was observed at HGB so this site did not require this action.
Benthic characterization methods used at the FLK sites are described
in Parsons et al. (2017). Briefly, transect lines were deployed at each FLK
site along each of three pairs of (semi)permanent pins located approxi­
mately 20 m apart, each pair separated by 10 m. Forty photoquadrats
(approximately 0.5 m2) were taken both sides of each transect line using
an underwater digital camera. A subset of photoquadrats (n = 15) was
randomly selected from each transect line using a random number
generator (MS Excel RANDBETWEEN function) and examined using
Coral Point Count (CPCe v4.0). Twenty points were randomly placed on
each photoquadrat in CPC and the substrate/taxa located under each
point was identified and recorded.
Benthic characterization procedures for the STT sites are described in
Smith et al. (2013). Briefly, six permanent transects (10 m in length; 3–5
m apart) were videoed at each site and images were captured to repre­
sent each total transect length (0.5 m x 10 m for each transect). Ten
random points were placed on the images and the images were analyzed
using CPC as described above. The percent abundance (cover) of each
benthic category was determined from the CPC data and averaged across
the replicate transects, and data cover for the macrophytes (Halimeda,
Dictyota, Laurencia, Lobophora, Thalassia, and turf algae) were then used
in the model as described below.
2.5. Data preparation for modeling
For all sites, macrophyte abundance, Gambierdiscus cell densities,
and 30d temperature averages were averaged for each month across
years; i.e., all of the January values across the four-year interval were
averaged, then the February values, etc. The result was an average
“typical” year that was used in the modeling effort in a similar manner to
Parsons et al. (2010).
2.6. Model parameter calculations
2.6.1. Macrophyte abundance and Gambierdiscus cell densities
The abundance of individual Gambierdiscus species was estimated
using the average relative abundance of each of the five Gambierdiscus
taxa known to occur in STT and FLK waters based on data presented in
Richlen et al., 2023 (this issue): G. belizeanus, G. caribaeus, G. caro­
linianus, G. carpenteri, and G. silvae (Table 1). The hypothetical seasonal
changes in relative abundance for each taxon were simulated by
2.4. Gambierdiscus cell enumeration
For the FLK samples, the abundance of Gambierdiscus cells was
determined by transferring 3 mL of the epiphyte sample into each of
three wells in a six well flat-bottomed tissue culture plate (Corning™
Costar™), stained with Uvitex® (similar to calcofluor; Polysciences,
Ltd., cat. #19517-10), and analyzed on an Olympus IX71 inverted mi­
croscope at powers of 200x and 400x using a DAPI filter. For the STT
samples, 0.5–1.0 mL of sample was loaded into a Sedgewick rafter slide
and samples were counted on a Zeiss Axioscope microscope at 100x
magnification under transmitted light. Cell abundances in each 15 mL
sample were determined for the FLK samples by multiplying the sum­
med cell counts from the three wells by a subsample proportion factor (i.
e., 9 mL out of the 15 mL washed off of the 20 µm sieve = 9/15). Cell
abundances for the STT 15 mL samples were determined by multiplying
the summed cells counted by a subsample proportion factor (i.e., pro­
portion of Sedgewick Rafter slide counted; volume of aliquots counted
out of 15 mL sample collected). Cell densities from both regions were
then calculated by dividing the cell abundance values by the macro­
phyte wet weight (ww) to provide values for Gambierdiscus cells g ww− 1
for each macrophyte sample.
While the generally low abundance of Halimeda and Lobophora at the
STT sites did not merit routine collection, both genera were recorded in
CPC analysis and were therefore included in the model. Gambierdiscus
cell abundances on Halimeda were estimated by multiplying Gambier­
discus cell densities on Dictyota by a factor of 0.28, which was derived
from a zero-intercept regression equation using Gambierdiscus cell den­
sities on Halimeda versus Dictyota cell densities on Dictyota from the LKH
(FLK) site (n =; r2 = 0.76; p < 0.0001). This conversion was also used for
TPH (FLK) for four missing data points in Gambierdiscus cell densities.
Cell densities on Lobophora were assumed to be similar to those on
Dictyota, as more recent sampling efforts at the STT sites have demon­
strated (Richlen et al., 2023 this issue). In a similar manner, Thalassia was
not routinely collected at TPH (FLK). The cell densities of Gambierdiscus
on Thalassia at TPH were estimated by multiplying Gambierdiscus cell
densities on Halimeda by a factor of 0.52, which was derived from a
zero-intercept regression equation using Gambierdiscus cell densities on
Halimeda versus Gambierdiscus cell densities on Thalassia from the HGB
(FLK) site (n = 48; slope = 0.52; r2 = 0.765; p = 0.0001).
Turf algae samples were not collected prior to 2014 at the FLK sites
and were not collected at all at the STT sites, due to methodological
difficulties in assessing Gambierdiscus abundance on turf algae in a
Table 1
Average relative abundance values for the five Gambierdiscus taxa, temperaturebased (temp) growth curves for each Gambierdiscus taxa, and temperature-based
(temp) grazing functions utilized in the model. FLK = Florida Keys sites. STT =
St. Thomas (USVI) sites. HGB = Heine Grassbed (FLK). n.a. = not applicable.
Parameter
FLK
STT
relative abundance of
Gambierdiscus
belizeanus
relative abundance of
Gambierdiscus
caribaeus
relative abundance of
Gambierdiscus
carolinianus
relative abundance of
Gambierdiscus
carpenteri
relative abundance of
Gambierdiscus silvae
growth curve for
Gambierdiscus
belizeanus
growth curve for
Gambierdiscus
caribaeus
growth curve for
Gambierdiscus
carolinianus
growth curve for
Gambierdiscus
carpenteri
growth curve for
Gambierdiscus silvae
1%
2%
38 %
42 %
53 %
43 %
9%
1%
1%
12 %
)
temp − 26.8279 2
− 0.5(
3.711
0.3532e
same as FLK
− 0.5(
)
temp − 26.3167 2
4.8486
same as FLK
− 0.5(
)
temp − 25.3522 2
3.7105
same as FLK
− 0.5(
)
temp − 26.1818 2
5.2149
same as FLK
− 0.5(
)
temp − 24.4156 2
2.8061
same as FLK
grazing function (g ww
m− 2 month− 1) sans
HGB
grazing function (g ww
m− 2 month− 1) HGB
4
0.3497e
0.2441e
0.1798e
0.2252e
312*(1-(0.2*(COS(12*(temp +
6.51)))))
60*(0.2473e(0.028*temp))
736.8*(1-(0.2*
(COS(12*(temp +
6.51)))))
n.a.
M.L. Parsons et al.
Harmful Algae 131 (2024) 102561
multiplying the relative abundance values by the% change in growth in
response to seasonal changes in temperature at each site using poly­
nomial equations derived from data presented in Xu et al. (2016) as
shown in Table 1. The resultant proportions were then multiplied by the
overall Gambierdiscus cell densities to estimate the Gambierdiscus species
composition on each macrophyte host at each site, each month.
sinusoidal equation (Table 1) derived from data presented in Table 9 of
Carpenter (1986). This study took place on St. Croix reefs (USVI) and
was the most relevant found for our study (i.e., Caribbean-based; sea­
sonal data over the year). The STT sinusoidal function was based on an
average grazing value of 6.14 g dry wt algae m− 2 day − 1 (derived from
Carpenter, 1986). The FLK function was based on an annual average
grazing rate of 2.6 g dry wt algae m− 2 day − 1 estimated using data
presented in Fig. 2c in Paddack et al. (2006). For HGB, pinfish are the
predominant herbivorous fish, so grazing rates (0.11 g dry wt algae
fish− 1 day − 1 at 20 ◦ C) were based on data derived from Heck et al.
(2015)
and
adjusted
for
temperature
effects
using
metabolic-temperature relationships derived from Hoss (1974). Grazing
rates were converted from macrophyte dry weights to wet weights by
multiplying by 4 (representing average of 75 % water content; Gessner,
1971; Multer, 1988; Targett and Targett, 1990). Daily rates were con­
verted to monthly rates by multiplying by 30. The resultant weights in
each sinusoidal equation (Table 1) were therefore 4 × 30×2.6 = 312
(FLK) and 4 × 30×6.14 = 736.8 (STT). HGB rates per fish were con­
verted to rates per m2 by multiplying by 2 (estimated 2 pinfish per m2;
2.6.2. Macrophyte toxin loading
Average cell toxicity for each of the five species was determined from
culture-based studies on isolates from the STT and FLK sites by Rob­
ertson et al. (2018). The average values were as follows (pg C-CTX-1 eq.
cell− 1): G. belizeanus (0.46), G. caribaeus (0.003), G. carolinianus (0.007),
G. carpenteri (0), and G. silvae (3.27). These cellular toxicity values were
multiplied by the cell abundances of each species on each macrophyte to
provide an estimate of CTX content (pg C-CTX-1 eq.) g ww− 1 host
macrophyte.
2.6.3. Grazing rate simulations
For all sites except HGB, grazing rates were simulated using a
Fig. 2. Monthly plots of (a & b) Gambierdiscus taxa relative abundance (%); (c & d) Gambierdiscus cell densities (cells g− 1 ww); (e & f) ciguatoxin loading (C–CTX-1
eq.) on macrophytes (pg g− 1 ww); and (g & h) macrophyte consumption (g ww m− 2 month− 1). The legend for figures a – d is as follows: brown (G. caribaeus), yellow
(G. belizeanus), green (G. carolinianus), red (G. silvae), and blue (G. carpenteri). The legend for figures e – h is as follows: brown (Dictyota spp.), green (Halimeda spp.),
yellow (turf algae), and red (Laurencia spp. (e & g) or Lobophora (f & h)). Figures a, c, e, and g are for LKH (Florida Keys). Figures b, d, f, and h are for SH (St. Thomas).
5
M.L. Parsons et al.
Harmful Algae 131 (2024) 102561
Parsons pers. obs.). In all cases, therefore, grazing rates were ultimately
simulated as g ww macrophyte consumed m− 2 month− 1.
Tables include data from all sites and the toxin vector outputs are plotted
for all sites. Supplemental graphs are also provided for additional
context.
2.6.4. Grazing preference
Grazing preferences were estimated to be turf > Halimeda > Tha­
lassia > Dictyota = Lobophora = Laurencia. An examination of data
presented in Fig. 3 of Kopp et al. (2010) indicates that 72 % of observed
fish bites occurred on turf algae (including rubble), 21 % on Halimeda;
and 6 % on Dictyota in their study of herbivorous fishes at sites around
Guadeloupe. These percentages were applied to the STT sites with the
caveat that Lobophora is also present and equally palatable to Dictyota
(Hanmer et al., 2017). For the FLK sites, turf-covered rubble was not
common, so only the turf, Halimeda, and Dictyota categories from Kopp
et al. (2010) were utilized (52 %, 37.5 %, and 10.5 %, respectively).
Halimeda was considered to be slightly more palatable than Thalassia
(53 % versus 47 % when converted to wet weight equivalents; Targett
and Targett, 1990). These values were then multiplied by the percent
cover for each macrophyte to reflect that grazers are more likely to eat
the macrophyte if it is more common (given its preference) and less
likely to eat it if it is rare. The resultant proportional values were then
multiplied by the overall monthly grazing rate to simulate how much of
each macrophyte was eaten each month by the hypothetical grazers.
3.1. Relative abundance of Gambierdiscus
As reported in Richlen et al., 2023 (this issue), Gambierdiscus caribaeus
and G. carolinianus are the two most common taxa of Gambierdiscus
observed at the STT and FLK sites (Fig. 2a & b). Gambierdiscus carpenteri
is more abundant at the FLK sites (e.g., LKH), whereas G. belizeanus and
G. silvae are more common at the STT sites (e.g., SH). Seasonal dynamics
exhibited by G. caribaeus reflects the warming and cooling across sea­
sons, which is more pronounced in the subtropical FLK sites. Gambier­
discus silvae exhibits an opposite trend, being more abundant in the
cooler winter months versus summer (SH; Fig. 2b).
3.2. Gambierdiscus cell densities
The seasonal signal is also evident in the Gambierdiscus cell densities,
with a spring and fall peak displayed at LKH (Fig. 2c) and a fall peak
evident at SH (Fig. 2d). The highest cell densities were observed at the
FLK Gulf of Mexico sites (HGB and TPH), reaching densities >500 cells
g− 1 ww at several time points (Fig. S1). Cell densities were generally
<200 cells g− 1 ww on each macrophyte at all other sites (Table 2). At
LKH, cell densities were generally higher on Dictyota in the spring and
summer months and higher on Laurencia in the late fall months. This
seasonal alternation of primary hosts was also evident at other sites
(Fig. S1). Note that cell densities on Lobophora and Halimeda at SH are a
function of Dictyota (Section 2.4), so the observed seasonal trends
(Fig. 2d) are proportional to Dictyota cell densities and are hypothetical.
2.6.5. Toxin vector
The final step in this conceptual model was to calculate the CTX
exposure (vector) to hypothetically estimate how much CTX was
entering the grazing fish community at each site (pg C-CTX-1 eq. m− 2
month− 1). The toxin loading values (pg C-CTX-1 eq. g ww− 1 host
macrophyte) for each macrophyte (from each site, each month) were
multiplied by the consumption estimates (g ww macrophyte consumed
m− 2 month− 1) to provide a hypothetical CTX exposure (vector) rate for
grazers consuming macrophytes hosting toxigenic Gambierdiscus cells
(pg C-CTX-1 eq. m− 2 month− 1).
3.3. Toxin loading
Modeled toxin concentrations were higher on the macroalgae from
the STT sites versus FLK (Table 3; Fig. 2e & f). At the FLK sites, toxin
concentrations were highest on Thalassia (HGB; Table 3) and Dictyota
(other FLK sites; Table 3) with a few monthly exceptions (e.g.,
November for LKH where Laurencia exhibited the highest CTX load;
Fig. 2e). Toxin concentrations mirrored cell densities in most cases and
were significantly correlated in 10 out of 16 comparisons (Table S1).
3. Results
The majority of the model outputs presented here focus on two sites:
LKH (FLK) and SH (STT). These two sites are representative of the others
while also displaying many of the dynamic characteristics of model
outputs that will be identified below and discussed in the next section.
Fig. 3. Log monthly ciguatoxin (C–CTX-1 eq.) consumption rates (pg m− 2 month− 1) for the eight sites modeled in this study: (a) HGB; (b) TPH; (c) LKH; (d) TRL; (e)
BP; (f) CRK; (g) FC; and (h) SH. Site names are provided in Fig. 1. The legend is as follows: dark green (Thalassia testudinum), brown (Dictyota spp.), light green
(Halimeda spp.), yellow (turf algae), and red (Laurencia spp. (a – d) or Lobophora (e – h)).
6
M.L. Parsons et al.
Harmful Algae 131 (2024) 102561
two regions (6.14 versus 2.6 g dry wt algae m− 2 day− 1; Section 2.6.3).
Thalassia was the primary macrophyte consumed at HGB (Fig. S1),
whereas turf algae was most consumed at TRL (data not shown) and the
four STT sites (e.g., SH; Fig. 2h). Halimeda was primarily consumed at
LKH, with turf consumption being higher in April and May (Fig. 2g).
Table 2
Average Gambierdiscus cell densities (cells g− 1 ww) on macrophytes across sites
(± 1 standard deviation). Laurencia applies to the Florida Keys sites (FLK).
Lobophora applies to the St. Thomas sites (STT). n.a. = not applicable.
Region
Site
Dictyota
Halimeda
Laurencia or
Lobophora
Thalassia
turf
algae
FLK
HGB
n.a.
n.a.
73.0 ±
42.95
457.7 ±
211.88
412.18 ±
219.27
n.a.
n.a.
LKH
195.7 ±
121.33
17.0 ±
9.38
118.5 ±
55.08
91.4 ±
51.26
207.4 ±
217.00
48.9 ±
32.09
95.0 ±
79.37
n.a.
n.a.
122.3 ±
88.01
412.18 ±
219.27
101.4 ±
44.15
54.1 ±
25.00
101.2 ±
44.47
56.4 ±
28.13
78.3 ±
26.62
28.4 ±
12.35
15.1 ±
7.00
28.3 ±
12.44
15.8 ±
7.87
21.9 ±
7.45
101.4 ±
44.15
54.1 ±
25.00
101.2 ±
44.47
56.4 ±
28.13
78.3 ±
26.62
n.a.
TPH
TRL
Total
STT
BP
CRK
FC
SH
Total
60.1 ±
50.79
184.6 ±
81.58
n.a.
n.a.
n.a.
n.a.
n.a.
3.5. CTX consumption estimates
4.0 ±
2.29
20.0
±
9.27
6.2 ±
5.63
10.0
±
8.71
4.4 ±
1.93
2.4 ±
1.09
4.4 ±
1.95
2.5 ±
1.23
3.4 ±
1.16
Ciguatoxin (CTX) consumption estimates were a function of algal
cover, Gambierdiscus cell densities, toxin loading, and grazing activities.
As a result, the CTX consumption patterns (Fig. 3) do not necessarily
match up with the other parameters (Table 4). For example, CTX con­
sumption was highest on Thalassia in February and December at HGB
(Fig. 3a), matching toxin loading values to some degree (Fig. S1), but not
grazing activity (Fig. S1). CTX consumption was also highest at TPH in
February and December (Fig. 3b), with Laurencia being the primary
vector in this exercise (with Halimeda being second most important).
LKH exhibited peaks in March and November (Fig. 3c), with Halimeda
being the primary vector in March, and Laurencia in November. CTX
consumption was more even across an annual cycle at TRL (Fig. 3d),
being higher in January – March, lower April – July, and increasing from
August – December. Dictyota was generally the primary vector, although
Halimeda was the main source of CTX in March at TRL. The STT sites
(Fig. 3e-h) were similar to TRL, with peaks in spring (March - April) and
fall (October – November), with the exception of FC which showed little
seasonality (Fig. 3g). Turf algae was generally the primary vector at BP
and CRK (followed by Dictyota), with a more even distribution between
turf algae and Dictyota at FC (Fig. 3e – g). Dictyota was the primary
vector at SH followed by Lobophora.
Ciguatoxin consumption was generally lower at the FLK versus STT
sites (25 – 2000 versus 500 – 6000 pg m− 2 month− 1, respectively) and
exhibited a larger dynamic range (40x versus 12x), possibly reflecting a
stronger seasonal signal in the subtropical Florida Keys. TPH had the
highest monthly CTX consumption rates for FLK (up to 2000 pg m− 2
month− 1), whereas TRL had the lowest (<250 pg m− 2 month− 1). SH had
the highest monthly CTX consumption rates for STT (up to 6000 pg m− 2
month− 1), whereas BP had the lowest (<3000 pg m− 2 month− 1) (Fig. 3e
& h, respectively).
Table 3
Average toxin loading (pg C-CTX-1 eq. g− 1 wet wt algae) on macrophytes across
sites (± 1 standard deviation). Laurencia applies to the Florida Keys sites (FLK).
Lobophora applies to the St. Thomas sites (STT). n.a. = not applicable.
Region
Site
Dictyota
Halimeda
Laurencia or
Lobophora
Thalassia
turf
algae
FLK
HGB
n.a.
n.a.
1.8 ±
1.96
11.5 ±
10.47
1.7 ±
0.57
3.8 ±
5.21
35.5 ±
11.44
19.1 ±
7.20
36.9 ±
15.41
19.5 ±
5.43
27.7 ±
9.77
1.5 ± 1.68
10.8 ±
10.31
n.a.
n.a.
LKH
5.4 ±
5.31
0.4 ±
0.30
3.2 ±
2.83
1.0 ±
0.92
2.5 ±
2.27
9.9 ±
3.20
5.3 ±
2.01
10.3 ±
4.31
5.5 ±
1.52
7.8 ±
2.73
5.0 ± 4.00
n.a.
n.a.
n.a.
1.6 ± 2.34
10.8 ±
10.31
n.a.
TPH
TRL
Total
STT
BP
CRK
FC
SH
Total
35.5 ±
11.44
19.1 ± 7.20
n.a.
36.9 ±
15.41
19.5 ± 5.43
n.a.
27.7 ± 9.77
n.a.
n.a.
0.1 ±
0.08
0.5 ±
0.46
0.1 ±
0.05
0.2 ±
0.22
1.6 ±
0.50
0.8 ±
0.31
1.6 ±
0.67
0.9 ±
0.24
1.2 ±
0.43
4. Discussion
This conceptual model provides a framework to explore the dynamic
processes involved in the introduction of ciguatoxins into coral reef food
webs. While the model is not necessarily precise, the parameters are
based on published data, so we believe model outputs are reasonable
approximations of cell densities, toxin loadings on host macrophytes,
and toxin vectoring into herbivorous fishes. Acknowledging these limi­
tations, there are several interesting results meriting further discussion.
Perhaps the most interesting aspect of this exercise is the lack of
consistent synchronicity among the major parameters examined herein
(e.g., Gambierdiscus cell densities, host macrophyte toxin loading,
Toxin concentrations were not correlated with cell densities at LKH
(Dictyota, Halimeda, and turf), TRL (Dictyota), CRK (Dictyota), or FC
(Dictyota).
Table 4
Months where numerical maxima were observed for Gambierdiscus cell densities
(cells g− 1 wet wt algae), ciguatoxin (CTX) loading on host macrophytes (pg CCTX-1 eq. g− 1 wet wt algae), grazing (g wet wt m− 2 month− 1), and CTX
vectoring (pg C-CTX-1 eq. m− 2 month− 1).
3.4. Grazing estimates
Grazing rates generally followed a sinusoidal pattern (Fig. 2g & h),
peaking in the fall/autumn months (August for HGB (data not shown);
October for all other sites) with a late winter (HGB; February) or late
spring (May; all other sites) minimum. Grazing rates were much lower at
HGB (<35 ww m− 2 month− 1), likely due to the use of a different
formulation for grazing estimates versus the other sites (Section 2.6.3).
Grazing rates were generally lower at the other FLK sites (<400 ww m− 2
month− 1; Fig. 2g) versus the STT sites (600 – 900 ww m− 2 month− 1;
Fig. 2h) due to the different average grazing rate baselines applied to the
Region
Site
cell densities
CTX loading
grazing
CTX vectoring
FLK
HGB
LKH
TPH
TRL
BP
CRK
FC
SH
2
4
1
10
9
11
6
10
12
3
1
3
3
4
6
10
8
10
10
10
10
10
10
10
12
11
12
3
11
11
1
10
STT
7
M.L. Parsons et al.
Harmful Algae 131 (2024) 102561
grazing and toxin vectoring; Table 4). SH is the only site where all of
these parameters align, resulting in the highest CTX vectoring values
observed among the eight sites (Fig. 3h). Altogether, the outputs suggest
two scenarios: 1) toxin pulses (vectoring) into reef food webs are not
necessarily synchronous with Gambierdiscus cell densities (a scenario
supported by Liefer et al., 2021); and 2) when the major parameters do
align (SH; Table 4; Fig. 3h), a significant vectoring event is possible.
Overall, CTX loading on macrophytes was most frequently correlated
with CTX vectoring (13 out of 16 comparisons; 81 %), whereas Gam­
bierdiscus cell densities were least (9 out of 16; 56 %; Table S1). These
results suggest that cell densities are not the best indicator of the po­
tential for toxin flux (vectoring) into coral reef food webs. As consid­
erable effort has been spent on studying Gambierdiscus cell densities on
host macrophytes (and proxy artificial substrates), monitoring programs
(established or in planning) should acknowledge that density data may
be only a small part of the vectoring process (Holmes and Lewis, 2023)
and that other aspects should be studied (e.g., macrophyte abundance,
grazing). Limited studies have examined toxin loading on host macro­
algae (Bagnis et al., 1980; Chinain et al., 1999; Liefer et al., 2021), or
grazing and vectoring into reef food webs (Loeffler et al., 2015; Holmes
et al., 2021; Holmes and Lewis, 2022 and 2023). As this study demon­
strates (as well as the latter ones above), these aspects of ciguatoxin
trophic transfer are likely critical components that should be considered
beyond basic cell density estimates.
A second scenario of interest produced by the model is “quality
versus quantity”. Previous studies examined the relationship between
cell numbers and toxicity of the epiphytic biomass, which were corre­
lated in some cases (Bagnis et al., 1980), but not others (Chinain et al.,
1999) as our studies have also demonstrated (Liefer et al., 2021). These
findings support the presence of highly toxigenic strains of Gambierdis­
cus – i.e., the “superbug hypothesis” (Holmes et al., 1991). Several
studies have identified other “superbugs”, including G. polynesiensis in
the Pacific (Chinain et al., 2010) and G. excentricus in the Atlantic (Ca­
nary Islands; Pisapia et al., 2017), indicating that the decoupling of cell
densities and epiphyte toxicity may occur globally.
The influence of such superbugs on toxin loadings on host macro­
phytes is demonstrated at the STT sites. The higher relative abundance
of G. silvae and G. belizeanus observed at STT sites versus FLK (Richlen
et al., 2023, this issue) expresses itself in this model by the higher toxin
loading values (averaging 27.7 versus 4.8 pg CTX g− 1 ww) even though
cell densities are much lower (averaging 78.3 versus 207.4 cells g− 1 ww;
Table 2). In such cases, cell densities will not be a good predictor of toxin
loading, as reported by Liefer et al. (2021). Rather, it is the presence of
the significant toxin producers that is important (i.e., quality). Alter­
natively, there may be scenarios where a large number of less toxic cells
(e.g., G. caribaeus in FLK) may produce a significant toxin pulse (i.e.,
quantity), as exhibited at HGB (Fig. S1; Table 3). Therefore, monitoring
programs should consider if one or both scenarios (Highly toxigenic
species present? High cell densities observed?) are possible and devel­
op/adapt their monitoring programs accordingly. It should be noted,
however, that toxin content may be highly variable within a taxon (even
the “superbug” taxa). For example, toxin content in G. polynesiensis was
found to range between 0.7 – 20.9 pg CTX3C equivalents cell− 1 (Chinain
et al., 2010, Pawlowiez et al., 2013, Rhodes et al., 2014, Darius et al.,
2018, Darius et al., 2022). This variability may depend on culture con­
ditions, toxin detection methods, strain, and origin of the culture
(Mudge et al., 2023). Therefore, researchers and managers should screen
the Gambierdiscus in their region to accurately assess the toxin potential
of the taxa present.
The higher toxin loads modeled at STT (approximately 6x higher;
Table 3) are not the only factor leading to the much higher resultant
toxin vectoring (approximately 30x higher). Computed grazing rates
were twice as high at STT versus FLK (Fig. 2g & h), resulting in much
higher toxin fluxes at STT. As stated in Section 2.6.3, grazing rates
(except for HGB) were estimated from Carpenter (1986) and Paddack
et al. (2006) for STT and FLK, respectively. While these data may not be
entirely applicable to this model (i.e., rates are likely different at each
site and algae palatability had to be separately addressed as described in
Section 2.6.4), the results herein are relevant in terms of the important
influence grazing pressure has on the introduction of ciguatoxins into
the reef food web.
Grazing not only influences the abundance of algae on the reef, but
also the algal composition (Dell et al., 2020) and epiphyte densities
including Gambierdiscus. For example, Loeffler et al. (2015) found that
Gambierdiscus densities were significantly higher (138 %) on ungrazed
(caged) PVC tiles versus grazed (uncaged) tiles. Similarly, Sparrow and
Heimann (2016) noted that observed Gambierdiscus cell densities from
field samples are not gross values, but instantaneous measures incor­
porating losses due to grazing. Holmes and Lewis (2023) also pointed
out that Gambierdiscus growth rates and turnover rates of the grazed
algae play roles in resultant Gambierdiscus cell densities. Additionally,
Cruz-Rivera and Villareal (2006) conducted a thorough review of mac­
roalgal palatability and how palatable algae may be a vector (conduit)
for introducing ciguatoxins into the food web, whereas unpalatable
(defended) algae may be refugia for Gambierdiscus cells that may
“reseed” nearby cropped algae targeted by grazers.
The larger CTX vectors modeled at the STT sites may (partially)
explain the higher number of CP cases in STT versus FLK (120 versus
0.56 per 10,000, respectively; Radke et al., 2013, 2015). Other studies
support the role that grazing may play in CP, including Rongo and van
Woesik (2011) and Bagnis et al. (1988). In the former case, the authors
suggested that the decrease in CP cases reported in Rarotonga (southern
Cook Islands) from 2006 – 2009 coincided with a decrease in herbivo­
rous fish densities on surrounding reefs during the same time period.
Bagnis et al. (1988) suspected that an increase in algae on reefs around
the Gambier Islands in the early 1970s enhanced grazing activity,
thereby increasing the flux of ciguatoxins into the reef food web. In both
cases, the authors argue that grazing ultimately plays an important role
in CP.
Grazing partitioning also plays a role in vectoring ciguatoxins into
the food web, particularly the “direction” of the vector. For example,
Rongo and van Woesik (2011) noted that certain macroalgae were more
abundant in the Rarotonga lagoon in the 1990s (Sargassum, Dictyota,
Jania, and Galaxaura) and may have been the vector for subsequent CP
cases in herbivorous fishes such as Ctenochaetus spp., Acanthurus spp.,
and Naso unicornis during that time. Turf algae was more predominant in
the 2000s, possibly explaining the shift in CP cases to benthic inver­
tivores such as goatfish and mullet that would have targeted in­
vertebrates living in/on the turf. Our conceptual model supports the
possibility of such a “vector shift”; e.g., Halimeda is projected to be
primary vector in March at LKH (Fig. 3c), whereas Laurencia is the pri­
mary vector in the fall. If different herbivores targeted Halimeda versus
Laurencia and/or Dictyota (similar to the scenario presented by Rongo
and van Woesik, 2011), there could be different vectors in the spring
versus fall/winter. It should be noted that the high grazing preference of
Halimeda in this model (second ranked) reflects the large focus on par­
rotfishes by Kopp et al. (2010). As (some) parrotfishes are among the few
grazing fishes that consume Halimeda, this vector will be highly
dependent on the presence of parrotfishes on the reef. Similar “shifts”
are modeled at the STT sites between turf algae and Dictyota (Fig. 3e–g).
Turf algae are thought to be a major vector for CTX into reef food webs
in both the Pacific and Caribbean (Randall, 1958; Parsons et al. 2021;
Holmes et al., 2021) and should be studied more thoroughly. Herbivory
selectivity should be considered in ciguatoxin flux models and moni­
toring programs as noted by Cruz-Rivera and Villareal (2006).
Another factor that must be considered is carrying capacity. The
calculated algal consumption rates (Fig. 3g & h) assume that the algae
turnover rate is high enough to support the estimated grazing rates. The
ample presence of algae (30–50 %) suggests that algae is not a limiting
resource, but the model (and supporting data from Carpenter (1986) and
Paddack et al. (2006)) suggests grazing pressures may vary considerably
between the two regions (approximately twice as high at STT). These
8
M.L. Parsons et al.
Harmful Algae 131 (2024) 102561
values are overall estimates (i.e., g ww macrophyte m− 2 m− 1) and do not
account for the number of herbivorous fishes grazing (nor any in­
vertebrates, which may be significant contributors), with the exception
of HGB, where consumption rates were based on two pinfish m− 2
(Parsons pers. obs.). Paddack et al. (2006) report 0.5 – 2 herbivorous fish
m− 2 (which could mean the consumption rates would be multiplied by
four (0.5 fish m− 2) or divided by two (2 fish m− 2) to obtain per fish
values (perhaps the next step in this modeling effort in a similar vein to
work done by Holmes et al., 2021 and Holmes and Lewis, 2022 and
2023). Carpenter (1986) offers no similar estimate of fish densities (he
relied on time-lapse photography at a single location and data were not
converted to fish m− 2). The USVI Territorial Coral Reef Monitoring
Program (TCRMP) has reported an average herbivorous fish density of
6.4 fish m− 2 across the four STT sites during the 2011 – 2014 time
period, however (Smith, pers. comm), indicating that the higher grazing
rates could reflect higher densities of herbivorous fish at STT versus FLK.
Fish census data are clearly needed to better assess the magnitude of the
CTX vector into reef food webs.
Assuming the abundance of algae overall in both regions is not a
limiting resource for grazers (and therefore algal consumption rates
would not be reduced due to carrying capacity limitations), another
caveat presents itself that is worth further consideration. The theory of
density-dependent habitat selection states that a population will utilize
the most optimal habitat (i.e., resulting in the highest fitness) possible.
As the population increases, it must expand and occupy less-desirable
habitats to accommodate the expansion (Rogers and Lorenzen, 2016).
As applied to our conceptual model, this theory dictates that herbivo­
rous fishes will eat the most palatable algae first, but will increasingly
eat less palatable algae as a function of the reduced availability (abun­
dance) of the former. For example, Cruz-Rivera and Villareal (2006)
note that periods of high recruitment of juvenile rabbitfishes (Siganus
spp.) in Guam can result not only in the complete consumption of
palatable algae, but those taxa considered to be non-palatable as well.
The primary focus of this model is the transfer of ciguatoxins into
trophic level 2 herbivorous fishes. How this toxin exposure is translated
into bioaccumulation, depuration and body burden is being studied by
our group (e.g., Bennett and Robertson, 2021) and others (e.g., Holmes
et al., 2021; Holmes and Lewis, 2022 and 2023). Ultimately, these ef­
forts point towards the development of food web-based trophodynamic
models where areal toxin loads (CTX m− 2) can be coupled with algal
cover and fish (and invertebrate) densities to quantify and partition the
“system toxin load” across trophic levels over time. Holmes and Lewis
(2022) provide the first such example of this through their conceptual
ciguatoxin flux model in Platypus Bay (Australia), but acknowledge that
some parameters may be unrealistic (i.e., 40,000 –160,000 alpheid
shrimp needed to consume Gambierdiscus cells). Therefore, such
modeling efforts will need a “mass balance” approach to align the
amounts of toxin (precursor) being produced by Gambierdiscus with the
proportions being vectors into herbivores and detritivores (including
invertebrates) and upwards to higher trophic levels, including accurate
measures of bioaccumulation, depuration and growth dilution as
mentioned above. Interestingly, this food web approach brings ciguatera
research full circle: prior to the discovery of Gambierdiscus as the pro­
genitor of the ciguatoxins leading to ciguatera poisoning, Randall (1958)
proposed the “new surface theory” in which he associated toxic fish
outbreaks with destructive forces on coral reefs, and postulated that
subsequent algal colonization of newly created surfaces, followed by
grazing, somehow injected poisons into the reef food web. As we learn
more about toxin production in various Gambierdiscus taxa and the flux
of these toxins into benthic food webs, we will build the food web-based
tools to better manage and mitigate this food poisoning threat to
vulnerable and unsuspecting populations.
in the field and laboratory: Samantha Blonder, Ashley Brandt, Adam
Catasus, Amanda Ellsworth, Alexander Leynse, and Shannon White
(Florida Gulf Coast University); Taylor Sehein, Danielle Whalen, Evan­
geline Fachon, Grace Di Cecco (Woods Hole Oceanographic Institution);
Katherine Baltzer, Clayton Bennett, Justin Liefer (University of South
Alabama); Katharine Baltzer, Robert Brewer, Rosmin Ennis, Sarah
Groves, Leslie Henderson, Colin Howe, Jonathan Jossart, Jennifer
Kisabeth, Tanya Ramseyer (University of the Virgin Islands). Funding
was provided to M.L.P., D.M.A., M.L.R., and T.B.S. by NOAA NOS
(Cooperative Agreements NA11NOS478-0060 and NA11NOS4780028).
Additional support to all authors was provided by the Greater Caribbean
Center for Ciguatera Research (NIEHS P01ES028949) and NSF (OCE1841811), and CiguaPIRE (NSF OISE-1743802). Funding to D.M.A. and
M.L.R. also provided by the National Science Foundation (OCE1840381) and National Institutes of Health (NIEHS 1P01-ES028938-01)
through the Woods Hole Center for Oceans and Human Health. This is
UVI Center for Marine and Environmental Studies publication number
285. [CG].
Declaration of Competing Interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Supplementary materials
Supplementary material associated with this article can be found, in
the online version, at doi:10.1016/j.hal.2023.102561.
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