Karenia brevis blooms in the Gulf of Mexico
Item Type Article
Authors Guirhem, Gency L.; Baker, Laurie; Moraga, Paula
Citation Guirhem, G. L., Baker, L., & Moraga, P. (2023). Modeling the spatio-temporal distribution of Karenia brevis blooms in the Gulf of Mexico. F1000Research, 12, 633. https://doi.org/10.12688/
f1000research.133753.1 Eprint version Publisher's Version/PDF
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RESEARCH ARTICLE
Modeling the spatio-temporal distribution of Karenia brevis blooms in the Gulf of Mexico [version 1; peer review: awaiting peer review]
Gency L. Guirhem
1,2, Laurie Baker
3, Paula Moraga
11Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, 23955-6900, Saudi Arabia
2Institute of Marine Fisheries and Oceanology, College of Fisheries and Ocean Sciences, University of the Philippines Visayas, Miagao, Western Visayas, Philippines
3College of the Atlantic, Bar Harbor, Maine, USA
First published: 08 Jun 2023, 12:633
https://doi.org/10.12688/f1000research.133753.1 Latest published: 08 Jun 2023, 12:633
https://doi.org/10.12688/f1000research.133753.1
v1
Abstract
Background: Harmful algal blooms (HABs) of the toxic dinoflagellate Karenia brevis impact the overall ecosystem health.
Methods: K. brevis cell counts were extracted from Harmful Algal BloomS Observing System (HABSOS) in situ data and matched with 0.25º resolution environmental information from the Copernicus database to generate spatio-temporal maps of HABs in the Gulf of Mexico (GoM) between 2010 and 2020. The data was used to analyze the relationship between spatial and temporal variability in the presence/absence of K. brevis blooms (≥100,000 cells/L) and biotic and abiotic variables using Generalized Additive Models (GAM).
Results: The variability of blooms was strongly linked to geographic location (latitude and salinity), and temporal variables (month and year). A higher probability of K. brevis blooms presence was predicted in areas with negative sea surface height (SSH) values, silicate concentration (0, 30-35 mmol. m-3), sea surface temperature of 22-28 oC, and water currents moving south-westward (225º). The smooth effect of each environmental variable shows a bimodal pattern common in semi-enclosed basins such as GoM. The spatial predictions from the model identified an important permanent area in (1)
Southwest Florida (25.8-27.4o latitude), and four seasonally important areas, (2) North Central Florida (3) Central West Florida, (4) Alabama on Gulf Shores and (5) Mississippi with higher bloom probabilities during the fall to winter season (November-January). Results also suggest that HABs can extend until ≥ 300 km offshore; starting to form in March and reaching a peak in September, and were swept to the coastal area during fall and winter. This suggests the role of upwelling and water circulation in GoM for the accumulation of cells and HABs. Information on the spatio-temporal dynamics of K. brevis blooms and understanding the environmental drivers are crucial to
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Approval Status AWAITING PEER REVIEW Any reports and responses or comments on the article can be found at the end of the article.
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Corresponding author: Paula Moraga ([email protected])
Author roles: Guirhem GL: Conceptualization, Data Curation, Formal Analysis, Writing – Original Draft Preparation, Writing – Review &
Editing; Baker L: Supervision, Writing – Review & Editing; Moraga P: Conceptualization, Data Curation, Funding Acquisition, Supervision, Writing – Review & Editing
Competing interests: No competing interests were disclosed.
Grant information: GLG was supported by King Abdullah University of Science and Technology during her internship as part of the visiting student program.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Copyright: © 2023 Guirhem GL et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
How to cite this article: Guirhem GL, Baker L and Moraga P. Modeling the spatio-temporal distribution of Karenia brevis blooms in the Gulf of Mexico [version 1; peer review: awaiting peer review] F1000Research 2023, 12:633
https://doi.org/10.12688/f1000research.133753.1
First published: 08 Jun 2023, 12:633 https://doi.org/10.12688/f1000research.133753.1 support more holistic spatial management to decrease K. brevis
blooms incidence in bodies of water.
Keywords
HABs, Karenia brevis, GAMs, Gulf of Mexico, water current, HABSOS, upwelling, spatio-temporal modeling
This article is included in the Ecology and Global Change gateway.
Introduction
Harmful algal blooms (HABs) are caused by microscopic algae that accumulate in bodies of water and produce toxic substances that endanger marine and terrestrial life.1Shellfish poisoning, marine animals (mammals, fish, and waterfowl) mortality, tourism losses, and severe economic damage in coastal communities are caused by HABs.1–6These events are estimated to cost millions of dollars. For example, the United States lost $100 million on an annual basis from 2002-2018 because of HABs.1,3This cost does not include the monitoring and post-management of the effects of these events.1,3,7 K. brevisblooms, also referred to as red tides, cause the death of fish, birds, and marine mammals due to the production of brevetoxin, which alters the sodium channels of vertebrate nervous systems.8Moreover, brevetoxin can cause respiratory problems in humans and marine mammals when they become aerosolized.9This toxin can cause behavioral changes in mammals such as bottlenose dolphins (Tursiops truncatus) by altering animal activity budget, heightening disease susceptibility, and increasing socialization and expanded ranging behavior due to changing resource availability and distribution.10
In the Gulf of Mexico (GoM), an estimated 75 species of freshwater, estuarine, coastal, and marine toxic microalgae proliferate and are routinely monitored by state agencies.8,11,12Among them,K. brevis(syn.Gymnodinium breveand Ptychodiscus brevis) cause the most common bloom.8,12,13Though HAB events have been caused by different species of toxic micro algae, for simplicity, in this study we use HAB to refer to algal blooms ofK. brevis.
HABs in GoM occur naturally at concentrations of 103cells L-1and can also generate large, dense blooms (≥105cells L-1) in different Gulf states.14,15HABs have been documented along the coastal waters of every state bordering the GoM.
For example, recurrent intense blooms have been observed in Florida and Mississippi.9,14,16In 1996, red tides occurred in all five Gulf states’coastal waters which persisted for over a year along the coast of Florida, killing 150 endangered manatees.17In the last 50 years, the Florida red tide blooms have become more common, frequent, and intense, especially along the West Florida Shelf (WFS) on the Southwest Florida coast during late summer and early spring.18–20The Southwest coast of Florida, from Manatee to Collier County, has repeatedly experienced prolonged HABs since 2011 and 2017, which lasted for about 17 months and caused both hypoxic and anoxic events.18In Alabama, algal cells of of K. breviswas found in Dauphin Island and within the state coastal zone, in areas with low salinity levels.21
Monitoring HABs of K. brevisinvolvesin situquantification and microscopic examination of algae cells in water samples. However, this method is susceptible to spatial and temporal biases due to HABs being highly dependent on environmental conditions such as nutrient input and water circulation.22 Bio-optics advances have contributed to counteracting the biases duringin situdata collection by providing a means of non-intrusive data collection at more frequent spatial and temporal scales in identifying potential incidence zones of HABs.23,24
There have been several agencies monitoring and forecastingK. brevisblooms for selected areas in the Gulf of Mexico, such as The National Center for Coastal Oceans (NCCOS) and Southwest Florida (NOAA Coastal Science), the Collaboration for Prediction of Red tides (CPR) Florida Fish and Wildlife Conservation Commission’s Fish and Wildlife Research Institute (FWC- FWRI) and the University of South Florida’s College of Marine Science (USF-CMS) in coastal waters of the southeastern United States.
Presently, the critical areas for HAB research focus on identifying and quantifying the environmental conditions that contribute to the proliferation of HABs in both the coastal and oceanic environments.25,26 Factors linked to HAB abundance are depth, water column stability, temperature, and decreasing wind.27There is a need for a more robust satellite-derived HAB index. Thus, a technique for detecting K. brevisblooms in the Gulf of Mexico that utilizes chlorophyll a (Chl-a) anomalies was introduced.28The ability of satellite ocean color technology to provide near-daily, synoptic-scale imagery in near-real time is unmatched by any other detection technique despite limitations and challenges.29
One of the variables that were previously used to describe blooms is the wind pattern; however, in this study, we used water currents as it has a direct impact on ocean circulation. While wind plays a major role in shaping the water currents, it is not the main factor driving the overall water circulation.30In the summer, there is an east and southeast water flow bringing water and nutrients from the Mississippi River plume onto the west Florida shelf at depths of 20–50 m. This water mass supplies utilizable inorganic and organic forms of nitrogen that promote the growth ofK. brevisto pre-bloom concentrations in sub-surface waters in the mid-shelf region. In the fall, there is an onshore current transport leading to the physical accumulation ofK. brevisblooms near the coast, resulting in fall blooms. Strong thermal fronts during the winter provide a mechanism for re-intensification of the blooms resulting in the winter blooms.31Jacksonet al.(2022) pointed out that there is a need to validate the possible westward migration ofK. brevisblooms along the northcentral GOM into AL and MS, and the possible reduction in cell density as the blooms migrate.32
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There have been several studies explaining the mechanisms ofK. brevisblooms in Florida and Mississippi, but none for Alabama; however, most of these studies are patchy and are concentrated in small spatial scales.16,31,33Thus, this study used all HABs reports comprising all three states (Florida, Alabama, and Mississippi) and compared bloom events in these three states. This study will contribute to the increasing knowledge of the factors that can describe HABs incidences for monitoring and detection systems. This will enable local and national agencies to work together to provide accurate forecasts on bloom presence, development, and transport. The monitoring and detection system is necessary to have realistic mitigation strategies that minimize the risks caused by HABs to human health and the economy.34Forecasting is necessary to enable an alert before a bloom, to allow the development of management options and approaches, and to minimize the risk to living marine resources and humans to HAB or biotoxin exposure.35Available information on HABs environmental covariates can enable the development of predictive models for the northern GoM.
Knowledge of planktonic HABs over Florida has been increasing but is specific to an area and is patchy, for example, in Charlotte Harbor, West Florida Shelf, and Caloosahatchee River.12,36–39The prediction in bigger spatial scales for complex coastal domains such as the GoM is still limited. Predicting the spatial and temporal presence of HABs requires knowledge of the spatial and temporal dynamics of a species and the associated environmental conditions. There is already an operational forecast system forK. brevisin the GoM which uses bloom extent and predicts the initiation and accumulation of cells along the coast; however, this model cannot be resolved at scales finer than 30 km.40In addition, the University of South Florida has another forecast model based on physical transport, but mostly concentrated in Florida and freshwater lakes (USF).
A Generalized Additive Model (GAM) was used to model HABSOS cell count data collected between 2010 and 2020 to analyze the environmental conditions influencing the relationship between the presence (≥100,000 cells/L) of HABs in the GoM in response to biotic and abiotic environmental variables and the spatial and temporal variability of locations of HABs presence. Since HABs are naturally and historically affected by water transport and in highly productive and nutrient-loaded regions, this study hypothesizes that the seasonal changes in the spatio-temporal distribution of HABs are linked to current circulation and upwelling areas in GoM. The resulting predictions of the probability of HABs presence were used to identify areas of potential high HAB proliferation, which may inform more holistic spatial management strategies to decrease the proliferation of HABs species and reduce their biological and economic impact in the GoM.
Methods Study area
This study covered three US states that border the northern GoM, namely the state of Florida, Alabama, and Mississippi (24° to 31°N and 80° to 89°W; see Supplementary Figure 1,Extended data). Supplementary Figure 1 shows study regions in the Gulf of Mexico ofK. brevisblooms in the period between 2010-2020 plotted inGoogle Earth. The identified areas are (1) Southwest Florida, (2) North Central, (3) Central west Florida, (4) Alabama, and (5) Mississippi.
In GoM, the circulation and water inputs are dominated by the Straights of Yucatán, the Mississippi-Atchafalaya system, the Usumacinta River, and the Mobile River.33,41The Loop Current, shaped by freshwater inflow from rivers and altered through water density differences and bathymetry, influences the general circulation transport near the Louisiana, Mississippi, and Alabama coastlines.41Mesoscale and sub-mesoscale features associated with coastal topography and coastline irregularities can promote primary productivity. These features include upwelling filaments, fronts, shelf and oceanic cyclonic and anticyclonic eddies, and coastal countercurrents.42,43West Florida in GoM is subjected to seasonal coastal upwelling, usually intensifying during the early spring to late summer. Along the meridional (southwest) coast, summer northerly winds force offshore transport of near-surface waters, promoting upwelling-favorable conditions.44,45 During fall, this regime is shifted to southerly winds, favoring downwelling conditions. Along the zonal (south) coast, upwelling conditions are associated with strong westerly winds, but the upwelling intensity is lower. During upwelling relaxation, there is connectivity between the east and west sectors in the Gulf.42 Precipitation exhibits a general decreasing trend from the east (Florida) to the west (Texas), while surface evaporation rates generally increase from east to west.46
The monthly mean tidal range is low in the winter and high in the late summer, with the highest range exhibited at stations in the central northern regions attributed to monthly changes in seawater density and atmospheric pressure changes.47The salinity in the northern GoM estuaries is influenced by water exchange between the estuarine entrance and the coastal zone and local forcing (tidal advection, river discharge, precipitation) within the estuary.43River discharge and storm conditions influence the salinity and temperature conditions of offshore waters, making the thermocline deeper and affecting circulation throughout the GoM.48
HAB data
HAB cell count was collected from the National Oceanographic and Atmospheric Administration (NOAA) National Centers for Environmental Information’s (NCEI) Harmful Algal BloomS Observing System.19,49HABSOS is a system that gathers and distributes data that caters to and provides tracking and observing present and historical harmful algal bloom (HAB), mainly ofK. brevisevents in the GoM. Data are gathered from the US states such as Florida, Mississippi, Alabama, and other countries being served by NOAA. Florida’s cell counts are monitored and reported by monitoring entities networks; for example, the Florida Fish and Wildlife Research Institute (FWRI), Mote Marine Laboratory (MML), Sarasota County Health Department (SCHD), and Collier County Pollution Control and Prevention Department (CCPCPD). The Mississippi Department of Marine Resources (MDMR) collects monthly phytoplankton samples for routine and responsive (emergency) sampling in 15 stations in the Mississippi Sound. The samples undergo qualitative analysis followed by a quantitative analysis if a toxic bloom is present. Site-specific sampling is conducted by MDCR upon receipt of the bloom report, in addition to trained field technicians’observations during sample collection upon completion of sample processing. The states submit the data to HABSOS at NCEI, where the data is archived and updated annually.32In Alabama, HABs are collected and monitored by the Alabama Department of Public Health -Seafood Branch (ADPH) with the Alabama Department of Environmental Management (ADEM) under the regulation and guidance of the United States Food and Drug Administration (FDA) (HABSOS).
In this paper,K. breviscell count expressed in cells per liter (cells/L) were modeled. Cell count information was collected in the states of Florida, Alabama, and Mississippi, which are the areas in the GoM with the highest presence of HABs in the HABSOS database.
Predictor variables
The environmental variables considered to be potential predictor variables for the habitat modeling were nitrate (NO3), phosphate (PO4), silicate (Si), salinity (Salinity), sea surface height (SSH), mixed layer depth (MLD), sea surface temperature (SST), current velocity (Vel), eddy kinetic energy (Ke), and heading of the current (Heading) (Table 1).
Table 1shows a summary of the environmental variables used in the species distribution models forK. brevisblooms presence (≥100,000 cells/L). All variables were extracted with a 1/4° spatial and daily temporal resolution in the period 2010-2020 from the Copernicus Marine Environment Monitoring Service (CMEMS). Eddy kinetic energy (Ke), current velocity (Vel), and heading (Heading) of the current were derived using the eastward (Uo) and northward (Vo) current vectors. The resolution of the spatial and temporal variables is 0.25° and daily, respectively.
Different environmental covariates were chosen as a proxy of biological, chemical, and physical accumulation in the GoM that can be linked with the presence of HABs. Nitrate, silicate, and phosphate are essential nutrients for the photosynthesis of phytoplankton.12,39,50,51
These are limiting nutrients, especially in oligotrophic water.12,39,50,51
Salinity reflects the biological preference of organisms and freshwater input from land; however, in this study, it was used as a proxy for longitude37,52–54because of the high correlation between the two (Supplementary Figure 2,Extended data).
SSH is associated with mesoscale processes and upwelling.13,38,51,54,55
Colder temperatures and MLD are signs of upwelling events that bring nutrients to the surface layer.56Sea surface current flow (current velocity, eddy kinetic
Table 1.Environmental variables used in model building of HABs in GoM, 2010-2020.
Variable Acronym Units Mean Min Max Copernicus marine
products
Nitrate NO3 mmol m-3 0.004 1.354 41.884 NRT 001_028, RAN
001_029 Phosphate PO4 mmol m-3 2.783e-05 1.391e-03 1.715e-01
Silicate Si mmol m-3 1.732 9.414 47.471
Salinity Sal psu 8.565 35.240 37.411 NRT 001_024, RAN
001_030
Sea Surface Height SSH m -0.623 -0.106 0.511
Mixed Layer Depth MLD m 2.594 10.896 116.886
Sea Surface Temperature
SST oC 8.314 25.282 33.119
Current Velocity Vel m/s 0.000 0.119 1.463 ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
u2þv2 p *
Eddy Kinetic energy Ke m/s 0.000 0.011 1.070 u2þv22*
Heading of the current Heading degrees 0.000 229.700 359.800 atanuv3602π*
*uis the eastward current velocity;vis the northward current velocity.
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energy, and current heading) could also be linked to wind and physical accumulation and transport of HAB cells from the offshore environment to the coastal area.27,37,52
Environmental variables were extracted from the marine ocean product of the EUCopernicusMarine Environment Monitoring Service (CMEMS). Globally, the Copernicus platform compiles information regarding the physical state, variability, and dynamics of the ocean and marine ecosystems.55,57Environmental variables derived from models were selected overin situobservation and satellite products because they offered more spatial and temporal coverage and were less affected by cloud cover. This study used Near Real Time (NRT) and the past reanalysis (RAN) data. Since RAN only covers 2010–2018, NRT data was used for 2019–2020. All environmental variables matched the sampling data (latitude and longitude, observation date).
Modeling HABs presence
A univariate GAM was used to explore the relationship between the presence/absence of HABs (≥100,000 cells/L) in the GoM and different abiotic and biotic variables. The presence/absence of HABS was modeled as follows:
gð Þ ¼μi αþf1ð Þ þx1i f2ð Þ þx2i f3ð Þ…:x3i þfnð Þxni
Wheregis the link function, which is logit for binomial family,μiis the probability of presence,αis the intercept,fnare the smooth functions, andxnare the covariates.58
The covariates modeled were the environmental variables (Table 1), spatial variables (Longitude and Latitude), and temporal variables (Month and Year). Month and Year variable were included to account for the seasonal and inter-annual variability inK. brevisblooms, respectively.
A GAM was used in the modeling process to model the presence and absence ofK. brevisblooms (≥100,000 cells/L) in GoM to provide a probability of the presence of HABs, irrespective of abundance.58The binomial distribution was chosen because some areas have no presence of HABs, while others have more than 100,000 cells/L. Moreover, areas with abundance equal or higher than the threshold (100,000 cells/L) can have detrimental effects on the biological life in the region and significant socio-economic impacts on people.59–61
The smooth function degrees of freedom for each explanatory variable were restricted to avoid overfitting. The basis dimension of smoothers was set to k = 6 for the main effect and k = 20 for first-order interaction effects.62,63GAMs were fitted using (i) a cyclic cubic regression spline for the variable month, and heading of the current to account for cyclical effects, (ii) a Duchon spline to account for spatial effects (latitude and salinity) considering Euclidean distance, and (iii) thin plate regression splines for all other covariates.64GAMs were fitted using the R package mgcv v1.8.58,65 A univariate GAMs was done for each predictor variable to give information on the potential functional shape of each predictor variable and their contribution to the deviance explained (percentage of deviance).57,66
Correlation and collinearity of predictor variables
Two approaches to reduced correlation and collinearity between predictor variables were considered. First, Pearson’s rank correlation was used to examine all predictor covariates to identify highly correlated variables (Supplementary Figure 2A,Extended data). Only one of the correlated variables (Pearson correlation |r| > 0.6) was subjected to the variable selection process at a time and thus produced different model combinations.55Second, the Variance Inflation Factor (VIF) (Supplementary Figure 2B, Extended data) was used to assess multicollinearity among the predictor variables with a threshold value of 367using the function of from usdm package v 1.1-18.68
Model selection
A forward stepwise approach was used for variable selection to select the final model covariates.58Variables were selected if they improved the model’s Akaike’s Information Criterion (AIC) by a value of 2.0 in the selection process.62 AIC (AIC =2l + 2(p + 1)) prefers models that fit the data well and have few parameters.69Moreover, as an additional measure to avoid overfitting in the selection process, only significant variables (p < 0.05) were kept, and non-significant variables were sequentially omitted.55This approach was followed until all variables in the model were significant. The AICs of all best-fitting models were compared, and the lowest AICs were subjected to model validation (Supplementary Table 1,Extended data). Partial effect plots were used to assess the relative contribution of each predictor variable to the HABs presence for the final model with the lowest AICs.
Model validation
The final models were validated using the k-fold cross-validation (k = 5) method to split the training (80%) and testing (20%) data using the k-fold function from the dismo package in R software.70A five-fold cross-validation technique was
used and was repeated five times over different random sets to determine the predictive performance of the model.71 A five-fold cross-validation technique was used to deal with the time-dependence structure of the data.
The model performance of the predicted and observed values were evaluated using a confusion matrix calculated from the PresenceAbsence package v1.1.9 in R.72 The area under the receiver-operating curve (AUC), the ‘specificity, the‘sensitivity’and the mean True Skill Statistic (TSS) indices were calculated from the confusion matrix.71The AUC is threshold-independent and ranges from 0 to 1, an overall measure of accuracy. Values around 0.5 indicate that the prediction is as good as random, and values close to 1 indicate perfect prediction. This index measures the ability of the model to correctly predict where a species is present or absent.55,73Specificity is an index depicting the proportion of observed absences predicted correctly, and sensitivity illustrates the proportion of observed presences correctly predicted. The TSS is an alternative measure of accuracy calculated as the sensitivity plus specificity minus 1. TSS values can range from1 to +1. A TSS of 0 indicates no predictive skill, while a TSS value of +1 indicates perfect agreement and a value below zero (0) indicates no better than random performance.74As opposed to AUC, a threshold-independent approach for model performance, approaches such as sensitivity, specificity, and TSS indices which are threshold- dependent approaches, were also used to determine model performance. To select the threshold, two methods were explored to transform the probabilities into binary predictions (presence/absence)75; (i)“sensitivity is equal to specificity” and (ii)“maximization of sensitivity plus specificity.”Ultimately, the“sensitivity is equal to specificity”threshold was used as it gives the most accurate predictions in cases where the dataset has a low prevalence and avoids omissions (false negative) errors.41,75The performance scores from the four indices in combination were used to evaluate the predictive performance of the SDMs.
Model predictions
The probability of the presence of HABs in the final model was predicted with a spatial resolution of 0.25 latitude0.25 longitude grid cell and monthly temporal resolution between 2010-2020 using the prediction.gam function of the mgcv package v 1.8-36.58The predictions were made using the environmental conditions (significant variables selected in the final model) present in each time step (month between 2010-2020) at a 0.250.25 spatial resolution. For the model prediction, the monthly predictions between 2010-2020 were averaged to calculate an overall mean prediction map and the standard deviation across the monthly prediction over the 11 years to capture uncertainty. Finally, yearly predictions between 2010-2020 were calculated by averaging the monthly predictions within each year for the probability of the presence of HABs. The ranges for the monthly environmental values for the prediction for the whole study period were checked to ensure that the values of environmental variables were within the environmental envelope used to calibrate the SDMs. About 1.50% of the environmental data used in the model prediction were outside the range of environmental values used to build the species distribution models for HABs.
Model building, evaluation, and predictions were performed using R version 4.0.2.76 Results
Spatio-temporal pattern of HABs
This paper studied the presence of HABs (≥100,000 cells/L) in Florida, Alabama, and Mississippi. Because the presence of HABs was mostly in parts of Florida, Florida was divided into three regions1Southwest Florida,2North Central Florida, and3Central West Florida (Supplementary Figure 1,Extended data).
Figure 1shows the monthly spatial distribution of the presence (≥100,000 cells/L) and absence (<100,000 cells/L) of K. brevisblooms in the Gulf of Mexico from 20102020 (shaded 0.25 0.25 grids) from the Harmful Algal BloomS Observing System (HABSOS). The white cells mean no sample was collected in the grid cells. Time step 1–12 corresponds to a month in a year such as 1-January, 2-February, 3-March, 4-April, 5-May, 6-June, 7-July, 8-August, 9-September, 10-October, 11-November, 12-December.
Most of the presence of HABs was concentrated in Southwest Florida from -81 to -82.5 longitude and 26 to 27.5 latitude.
During January, February and December (winter), the highest HAB presence was observed in Southwest Florida (Figure 1). During March-May (spring), the recorded HABs presence was generally the same in all the sampling areas.
Spring is the season with the lowest HAB presence with HABs more offshore in May. In June-August (summer), the highest number of HABs presence has been observed in Southwest and North Central Florida, with HAB presence in North Central Florida more offshore than the rest of the season. From September to November (fall), HABs presence recorded was highest in all areas compared to the other season with the highest recorded HAB presence in Southwest Florida. During this time, HABs started to move shoreward in November.
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Figure 2shows yearly spatial distribution of the presence (≥100,000 cells/L) and absence (<100,000 cells/L) ofK. brevis blooms in the Gulf of Mexico from 20102020 with 0.250.25 degree grids, fromHABSOS. The white cells mean that no sample was collected in that grid cells.Figure 3shows temporal patterns of the total number of observed presences (≥100,000 cells/L) ofK. brevisblooms in the Gulf of Mexico from 2010-2020;Figure 3Ashows the total number of presences (≥100,000 cells/L) of HABs sampling by month between 2010-2020 whileFigure 3Bshows the total number of observed sets with the presence of HABs by month between 2010–2020.
In terms of the year, HAB blooms were irregular and restricted to one state or could occur in two to three states (Figure 2).
Blooms in a specific place can occur in consecutive years or every other year. In 2010, HABs occurred in all identified areas, with no area more prominent than others (Figures 2and3). In 2011, HABs presence was observed to increase in Southwest Florida offshore the Naples beach. In 2012 and 2013, HABs’presence expanded from Naples’s beach up to Tampa. In 2014, HABs presence started to increase in North Central Florida in Apalachee Bay, which had the highest HAB presence in the said year. During these years (2010-2014) HABs were observed in Florida and Alabama while no HAB presence nor sampling was done Mississippi. In 2015, there was an increase in HAB presence in Southwest Florida, Central West Florida, Alabama, and Mississippi, with a noticeable decrease in North Central Florida with highest HABs in Tampa and Panama City beach in the Florida Panhandle. In 2016 and 2017, HABs covered only Florida and Alabama, while no presence was recorded in Mississippi. In 2018, HAB presence was highest in all areas. In 2019, only Southwest Florida had a noticeable HAB presence. In 2020, there was a noticeably low number of sampling and presence of HABs in the study area.
The lowest sampling of HABs was found to occur in 2020 (Figures 2Aand3A). The highest HAB monitoring was done from August to November, with increasing monitoring from 2016-2019. The highest level of monitoring regarding the number of samples collected was from September to November, which coincides with the fall season and the months of January and December, corresponding to the winter season. The presence of HABs was found to be highest in January, and October to December (Figures 2Band3B) and from 2016-2019. Blooms majorly occurred in fall to winter regardless of year or state (Figure 1).
Figure 1.Monthly spatial distribution of the presence (≥100,000 cells/L) of HABs.
Generalized additive models
After analyzing all possible combinations of covariates (10 candidate models), the final model was chosen based on the lowest AIC values (Supplementary Table 1,Extended data). Because of high correlation and collinearity among several variables, only one variable from the following five groups of covariates was included in the selection process and SDMs Figure 2.Yearly spatial distribution of the presence (≥100,000 cells/L) of HABs.
Figure 3.Temporal patterns of the total number of (A) sampling and (B) HABs presence (≥100,000 cells/L).
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at a time (Supplementary Figure 2,Extended data): 1) latitude and longitude; 2) salinity and longitude; 3) nitrate and longitude; 4) nitrate and silicate; 5) nitrate and salinity; 6) nitrate and latitude; and 7) current velocity and eddy kinetic energy. Since latitude and longitude are correlated, we used salinity to replace longitude since salinity is correlated to longitude but not to latitude (Supplementary Figure 2A,Extended data). Therefore, the interaction of latitude and salinity was used to locate grids with a high probability of HABs and answer problems on spatial autocorrelation.
Figure 4shows the partial effect of the interaction of latitude and salinity in predicting the probability presence (≥100,000 cells/L) ofK. brevisblooms in the Gulf of Mexico in the period 2010-2020 in 0.250.25 grids. White grids mean that there was no sample collected in that area. The interaction between latitude and salinity highlighted three potential areas (1) Southwest Florida, (2) North Central, and (3) Central West Florida, with a higher probability of HABs (Figure 4).
The results of the final model forK. breviswere summarized and presented (Table 2). The final model for HABs had 23.2% of the total deviance (adjusted r2= 0.123). This model included as predictor variables: the interaction between latitude and salinity, the environmental variables of SSH, silicate, SST, MLD, and heading of the current (Figure 5, Supplementary Animation 1,Extended data), and the temporal variability of month and year (Figure 6).
Figure 5shows the partial effects of each individual covariate in the final model for predicting the probability ofK. brevis HABs presence (≥100,000 cells/L) in the GoM from 20102020. The selected environmental variables were: A) SSH (m), B) Silicate (mmol m-3), C) SST, D) MLD (m), E) heading of the current (degree). The shaded regions correspond to t standard errors, above and below the estimate of the smooth terms. Short vertical lines located on the x-axis indicate the values at which observations were made.Figure 6shows temporal variables selected in the final model ofK. brevisHABs (≥100,000 cells/L) in the Gulf of Mexico from 20102020; The figure shows the partial effects of (A) month and B) year as categorical predictor variables in the final model for predicting the probability of occurrence ofK. brevisblooms (≥100,000 cells/L). The dashed lines indicate the 95% confidence interval for the parametric terms, and the black boxes in the x-axis show the distribution of the data.
The Year variable was modeled as a categorical variable that also contributed to explaining the presence ofK. brevis blooms. The Year variable was used to account for other variables that were not included in building the candidate models (Figure 6). Lower probabilities of HABs presence were predicted at the beginning of the study period (the year 2010), followed by an increase from 2011-2013 and a gradual decrease in 2014. There was an increase from 2015 to 2016. In 2017 there was an observed decrease in the probability of presence, followed by an increase in 2018. From 2019 there was an observed decrease until 2020. The piecewise construction of each variable for modeling HABs is provided in Supplementary Table 2 (Extended data), and the individual contribution to the percent deviance (Table 3). The time series of HAB probability of presence (Supplementary Animation 2,Extended data) and associated environmental variables used in building the candidate model is shown in Supplementary Figure 3 (Extended data).
Figure 4.Partial effect of the interaction of latitude and salinity for predicting the probability of HABs.
Model performance
The predictive performance of the final model for HABs (Table 4) was high, with AUC≥0.8, despite the very low presence of HABs with (≥100,000 cells/L) (6.82%) than absence or HABs with <100,000 cells/L in the data set used.77 AUC values of≥0.75 are considered to have good predictive power and can be used for conservation planning.66,78The final model had a model sensitivity value of 0.76 which means that the model can identify areas where HABs are present 76% of the time. The model’s specificity was 0.75, which suggests that the model can identify areas where HABs are absent 75% of the time. The TSS score for the HAB species distribution model was 0.51.
Predicted presence of Harmful Algal Blooms
The overall averaged predictions across 2010-2020 identified one important permanent area, (1) Southwest Florida, with higher probabilities of the presence of HABs (Figure 7A). However, the areas in (2) North Central Florida, (3) Central West Florida in the Gulf of Mexico, (4) Alabama, and (5) Mississippi had relatively lower probabilities of HAB presence and high variability based on the standard deviation (Figure 7B). For more detailed daily probabilities of HAB presence with cells≥100,000 cells/L please see Supplementary Animation 2 (Extended data).
The monthly (Figure 8) and yearly (Figure 9) predictions also confirmed the five areas with higher probabilities of the presence of HABs, yet some areas persist over the year, and others are more seasonal. The probability of the presence of HABs is highest during the fall-winter period (November, December-February) in the five identified areas, suggesting a higher probability of presence during this period. In January all three states are prominent, giving Southwest Florida a higher probability of blooms relative to other states. There is also a formation of bands of blooms near the coast and approximately 120 km away from the shore which continue to be present until March. In February, algal growth is more Table 2.Summary results for parametric coefficients and smooth terms of the final GAM for HABs.
Family Binomial
Link function Logit
Adjusted r2 0.123
Deviance explained 23.2%
Parametric coefficients
Estimate Std. Error z value Pr(>|z|)
Intercept - 2010 -7.2016 0.5046 -14.272 < 2e-16***
2011 3.3879 0.5103 6.639 3.16e-11***
2012 3.6099 0.5070 7.121 1.07e-12***
2013 3.5101 0.5069 6.925 4.35e-12***
2014 2.7862 0.5146 5.414 6.15e-08***
2015 3.2767 0.5119 6.401 1.54e-10***
2016 4.0511 0.5084 7.968 1.62e-15***
2017 2.7459 0.5127 5.356 8.50e-08***
2018 4.7801 0.5066 9.435 < 2e-16***
2019 3.1393 0.5080 6.179 6.43e-10***
2020 2.0355 0.5320 3.826 0.00013***
Smooth terms
e.d.f Ref. df Chi.sq p-value
Latitude * Salinity 16.915 18.068 607.43 < 2e-16***
Sea Surface Height 4.470 4.861 166.48 < 2e-16***
Silicate 4.992 5.000 237.76 < 2e-16***
Mix Layer Depth 4.842 4.975 138.24 < 2e-16***
Sea Surface Height 4.896 4.994 46.81 < 2e-16***
Heading 3.513 4.000 22.48 4.92e-05***
Month 3.966 4.000 873.49 < 2e-16***
***p-value < 0.001.
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Figure 5.Partial effects of individual covariate in the final model of HABs in GoM, 20102020.(A) Sea surface height [m]; (B) Silicate [mg/m3]; (C) Sea surface temperature [oC]; (D) Mix layer depth [m]; (E) Current heading [degrees].
Figure 6.Partial effects of temporal variables (A) Month, (B) Year in the final model of HABs.
prominent in Southwest Florida while in March, the probability of HABs decreases and there is no area that is more prominent than the other. In April the formation of more offshore blooms starts off the coast of Florida (-81.5 to -86 degrees longitude) and continues to develop and reach its peak in September. In October the offshore bloom was replaced by a more coastal bloom and Southwest Florida became prominent again. In November, the highest probability in all three states was observed. In December, the probability decreases slightly. The seasonal variability of the environmental variable used in building the candidate model is in Supplementary Figure 4 (Extended data).
The yearly prediction mainly shows only the permanent areas identified in Florida (Figure 9). Because the central West Florida, Mississippi, and Alabama HAB areas occur only in the fall-winter season, these areas were not reflected in the Table 3.Summary results of the final model and associated variable % deviance in modelling HABs.
Variables % Deviance
Latitude * Salinity 6.62%
Sea Surface Height 2.30%
Silicate 2.03%
Sea Surface Temperature 1.86%
Mix Layer Depth 1.53%
Heading 0.53%
Month 8.13%
Year 6.46%
Table 4.Accuracy indices to evaluate the predictive performance of the final model of HABs in GoM.
AUC Sensitivity Specificity TSS
1 0.84 0.73 0.78 0.51
2 0.82 0.73 0.75 0.48
3 0.82 0.75 0.75 0.50
4 0.83 0.78 0.75 0.53
5 0.83 0.78 0.75 0.53
Mean 0.83 0.76 0.75 0.51
Figure 7.Overall (A) mean prediction of the probability HABs and (B) standard deviation.
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predicted yearly probabilities of the presence of HABs since it only occurs briefly, and yearly maps take the average of monthly predictions in a year. The interannual changes in the probability of the presence of HABs are driven by both the interannual variability and the changes in the final model environmental covariates (Figure 9). The higher probabilities of the presence of HABs were observed to peak in each area in different years and were more variable in Southwest Florida.
Higher probabilities of the presence of HABs occurred in Southwest Florida in 2012, 2013, 2015, 2016, 2018, and 2019.
The highest probability of HABs was observed in 2016 and 2018. The interannual variability of the environmental variable used in building the candidate model is in Supplementary Figure 5 (Extended data).
Discussion
We modeled the spatio-temporal distributions ofK. brevisblooms in the GoM from HABSOS data collected by different US states between 2010 and 2020, using a GAM. The spatial, temporal, and environmental conditions associated with HABs presence showed some seasonal and interannual changes in response to the oceanographic dynamics in GoM. The model identified and highlighted the importance of five areas in the GoM: (1) Southwest Florida and (2) North Central Florida, (3) Central West Florida, (4) Alabama, and (5) Mississippi. The modeling of HABs in the GoM suggested that the higher probability of HABs presence can be associated with negative SSH, high silicate, sea surface temperature, low MLD and southwestward movement of the current, suggesting that HABs are affected by the upwelling and currents in GOM. This study also suggests a potential offshore source of HAB cells extending to more than 16-64 km (10 to 40 miles) which is more than what was previously reported.11
K. brevisblooms or HABs were associated with low SSH associated with upwelling regions. SSH was chosen as one of the explanatory variables. Previous studies had related this variable to the position of the Loop Current, which is associated with changes in temperature and degree of upwelling.50The area with the negative sea surface height mainly Figure 8.Overall monthly mean prediction of the probability of HABs (≥100,000 cells/L) in GoM, 2010-2020.
exhibited the cyclonic event and represented the upwelling event.79For example, HABs in the fall of 2020 were preceded by anomalies of SSH and signs of cyclonic eddies where upwelling occurs.80HABs generally occur after upwelling events.81Thus, increasing upwelling events are likely to increase future incidences of HABs. Moreover, the oceanog- raphy of the GoM, such as the changes in Loop Current dynamics, can affect HABs proliferation in GoM. In the western Florida shelf, altimetry-derived offshore forcing was linked to the occurrence and severity of coastal blooms ofKarenia brevis.Years without major blooms tend not to have prolonged offshore forcing, suggesting that the offshore areas can be a potential source of cells. Moreover, the initiation of a more uniform SSH that started in the summer and continued in the fall (Supplementary Figure 4.1B,Extended data) could have been a precursor of HABs, bringing cells offshore to the coastal areas of the GoM states. As such, this study suggests that offshore current forcing and uniform SSH can trigger blooms and demonstrates the importance of physical oceanography in shelf ecology.82
High silicate values are often associated with blooms of diatoms. However,K. brevisdon’t use silicate as an energy source; yet this study associated blooms with both low and high silicate concentration, due to the bimodal nature of the response of the variable to the presence of harmful levels ofK. brevisblooms common in gulf studies. A previous study concluded an inverse correlation between dissolved inorganic nitrogen (DIN): Si (OH)4 andKarenia spp.blooms.83 Silicate is not a physiological requirement ofKarenia, and was primarily an artifact of higher Si (OH)4 concentrations common in coastal areas in GoM.83In addition, its association to high silicate can be explained byK. brevis’s competitive interactions with silicate-utilizing phytoplankton. High silicate areas suggest that there are no silicate-utilizing micro- algae present in the area, which allowsK. brevisto dominate pelagic communities.84Thus, this study associatesK. brevis blooms with higher levels of silicate due to its allelopathic mechanisms and due to its incapacity to utilize silicate as a food source.
Figure 9.Overall yearly mean prediction of the probability of HABs (≥100,000 cells/L) in GoM, 2010-2020.
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SST influences phytoplankton productivity, allowing them to thrive in specific temperature regimes and biochemical materials availability needed for their growth and development.85,86Studies have shown a correlation between SST and algal bloom distributions globally.38,87–90In this study, the blooms ofK. brevisincreased in the fall and winter primarily because of the ideal temperature conditions for HABs during these times of the year, which ranges from 22-28°C.38,91 However, in several cases, increased SST was found to be conducive to HAB development in the coastal waters of Oman92and in the Gulf of Mexico.93A study on Kamchatka HABs has found that temperature anomalies can trigger blooms as it is often followed by intense ocean level variability and formation of vortices and upwelling events in the photic layer of the water.80Thus, in this study, we found that HABs were impacted by temperatures of 22-28°C and were found to decrease temperature >28 °C.
HABs are associated with a relatively shallow MLD, which signifies that the water column is stable and highly stratified.
There were two maxima for MLD (Figure 5E) that may reflect different conditions associated with high HABs presence in nearshore and offshore waters. The nearshore waters were generally characterized by shallower MLDs than the offshore waters (Supplementary Figures 4 and 5,Extended data). HABs are frequently located in the shallow mixed layer.94A shallow mixed layer is a result of the decline in the wind speed and an increase in the air temperature and, thus, an increase in stratification.95This increase in stratification is accompanied by a decrease in the amount of nutrients mixed into the surface layers. As such, this can make the bottom water depleted of oxygen, which can potentially develop into a dead zone because of the lack of mixing during HABs events.96,97In addition, HABs are also associated with higher MLD associated with deeper offshore water (Supplementary Figure 4.1D, Extended data). A previous study showed that K. brevisundergoes vertical migration.37This suggests that in a deeper MLD, in a more offshore environment, HABs start to proliferate but because of the oligotrophic characteristics of GoM, the cells are not able to form intense blooms. Thus, this study further illustrated the effects of shallow MLD on the proliferation of HABs in the Gulf of Mexico.
The heading of the current is also an important environmental variable for the HABs model. There is a high probability of HAB presence in currents moving southwestward. On the West Florida coast, the hydrographic features are influenced by the coastal current. The southwestward coastal current drives a subsequent offshore Ekman transport which might lead to coastal upwelling, transporting sub-surface water containing HABs cells closer to the coast.9Moreover, inshore water is more favorable to HAB proliferation than offshore waters due to both anthropogenic input, such as sewage waste, and natural processes, such as coastal upwelling in the inshore environment as what was observed in coastal blooms in Florida (Figures 7-9). Thus, cyst (cell) concentration was higher inshore than offshore.9,98Offshore water can be oligotrophic and change yearly depending on the interannual variability in the circulation in GoM.31,45,99However, factors such as disturbances brought by cyclones can temporarily disrupt the circulation patterns, thus allowing more intense blooms of K. brevisto appear along the coasts.99Thus, this study pointed out how currents can serve as a vector in transporting HABs from a non-bloom area to an area that is favorable for blooms, such as coastal waters.
Predictions from the SDMs identified five important areas with a higher probability of HABs presence. Probabilities in (1) Southwest Florida, remained relatively stable and persistent throughout the year, suggesting these areas may be highly suitable for HABs year-round. In contrast, in some areas, blooms were irregularly distributed in time. In addition, blooms could be ephemeral and restricted to one state; the (2) North Central Florida, (3) Central West Florida, (4) Alabama, and (5) Mississippi area HAB blooms were more pronounced during the fall-winter season and, and lesser extent in summer seasons, thus, indicating a more seasonal presence. Blooms from the Florida Panhandle region are advected westward towards the Mississippi-Alabama coast that persisted until December. In contrast, the blooms in the West Florida Shelf can be advected by the Loop Current into the Florida Straits and Gulf Stream Current along the East Coast of the United States.100Although circulation patterns favor westward transport during the fall season in the GoM, drifter data shows that there are no favorable westward currents from Florida to the Mississippi Sound during that time frame, except for a short time. This adds to several factors that explainwhy Mississippi blooms are more temporary.101This study also provided evidence that the mid-shelf serves as a growth and accumulation region of the blooms and contributes to supplying the coastal blooms. Local eddy circulation in the northeast affects the retention and coastal distribution of blooms.100This shows how the oceanography of GoM drives HABs and the connectivity of different regions in GoM.
The model identified strong seasonal patterns in HAB blooms in the GoM. Although Florida is the GoM state that most frequently experiencesK. brevisblooms, all other coastal states of the GoM have been affected to a lesser extent. It is also worth noting that the important permanent areas for HABs presence are found in Florida, where water is more stagnant than in the rest of GoM. In contrast, the seasonal areas in Alabama and Mississippi, acted upon by tributary rivers that carry freshwater and high nutrient input, have blooms that are more seasonal.43The authors hypothesized that in the seasonal areas identified, HABs could be triggered by more seasonal factors, such as an increase in nutrient load, and thus occur over a short time span. In contrast, for those identified permanent areas, HABs persist because of very slow flushing times and dead zones formed in those areas, as well as the currents acting upon it and its proximity to the potential offshore
HABs source. The calculated along-isobath geostrophic flows are southeastward from December to March and northwestward in June, August, and September in the Big Bend.102Such blooms are associated with the southwestward movement of the current (Figure 5) in fall/early winter (Figure 6) regardless of the year. In support of this, previous studies have shown thatK. brevisblooms occurred irregularly over the years in the western GoM,103and thatK. brevisblooms occur in fall/winter in southwest Florida.9,99Karenia brevis, the GoM’s‘red tide’organism, usually blooms on the southern/central West Florida Sound during late summer/early fall supported by the first minor peak in our Generalized Additive Model (GAM) model for months starting in August (Figure 6). It is likely that the northwestward along-isobath flow in June, August, and September is capable of transportingK. brevisblooms northward to the Big Bend shelf region during these months.102
Yearly variability in HABs was observed. There were also six years (Figure 9), 2012, 2013, 2015, 2016, 2018, and 2019, with high blooms. The blooms had a high probability of occurrence in Southwest Florida in those years. It is also worth noting that high bloom probability happens in consecutive years. Aside from Southwest Florida, the Mississippi area and Alabama experience less intense blooms and happen to be more seasonal. The Mississippi River delta has a presence of HABs but covers less area and is temporary.43In 2015, blooms were first reported in Florida in September, and in Alabama and Mississippi in November and October, respectively. The 2018 HABs were intense because of the cells from the preceding 2017 bloom and the newly formed bloom in 2018, until 2019 when the offshore conditions were again favorable for bloom development.43,104This is consistent with westward migration events ofK. brevisblooms from areas in the northeast GOM that have been reported in the past, where blooms that originated in central or northern Florida were transported westward.16,102Our results suggest that these migrating blooms may reach Alabama, such as the blooms in 2018, and Mississippi in 2015. Moreover, as the HABs are transported by water currents, the cell count can increase or decrease depending to how favorable the water is (Figure 5).
With the association of HABs with different environmental parameters, ocean warming, marine heatwaves, oxygen loss, eutrophication, pollution, and climate change are only a few of the different factors that contribute to the proliferation of HABs.105In particular, climate change may magnify temperature effects on HABs cell metabolism, range of prolifer- ation, and bloom duration. Hypoxia and anoxia, occurring as a result of elevated bloom biomass and/or the decomposition of HAB-related mortalities, are among the impacts of HABs that are of great concern.11As global warming intensifies, HABs can occur earlier and attain higher biomass levels.106As such, the coastal regions of the GoM, because of their relatively flat topography, land subsidence, and extensive coastal development, are particularly vulnerable to climate change and sea-level rise.107Moreover, climate change, which impacts the water current forcing, and coastal eutrophi- cation have likely increased the occurrence of HABs.11
Limitations of the study
This study provides insights into the spatio-temporal patterns and environmental preferences of K. brevisblooms.
However, the modeling results should be interpreted with caution as they explain a small proportion of the total deviance (23.3%). The low deviance might be explained by the low percentage of data points which has a cell count of≥100,000 cells/L (presence of HABs) in the HABSOS dataset. Areas below this cell count are considered a no-bloom area. Also, the use of relatively bigger spatial scales of environmental data contributed to the low percentage of deviance, however since this study aimed to observe spatio-temporal patterns in bigger scales and within a 11-year period, we used 0.250.25 degree resolution data. Despite the low deviance explained, it is typical for studies to model the spatio- temporal distribution to explain a small proportion of the total deviance (10–19 %).63,66,108These variables are usually sufficient to explain the target species’distribution and habitat suitability.109This high percent deviance may be due to the quality and quantity of data covering the whole distribution of the data-richer species and the spatio-temporal scales used to conduct the analysis.
The authors acknowledge the biases associated with this data set. Sampling is concentrated if and when there is a bloom, although there are or have been years with more regular monitoring. Moreover, we acknowledge that a 0.25-degree grid is very coarse for identifying small-scale patterns; however, since the study comprised three states, we used a 0.25-degree resolution. Moreover, the said spatial resolution was proven enough to correlate the presence ofK. brevisblooms (≥100,000cells/L) between physical/chemical influences on bloom dynamics, especially on the coast of the study areas.
Several ways were suggested to improve and validate the species distribution models for HABs in the GoM.
The information and types of data used for modeling species distributions are also growing and rapidly driven by technological improvements in data collection systems.110For example, satellite applications are increasingly being used to inform and validate species distribution models. There are emerging statistical approaches (e.g., integrated species distribution models) capable of integrating and modeling different types of data sets and getting the most from the data revolution.110–112Lastly, future modeling approaches could also explore the other environment and biological prefer- ences of HABs.
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In this study, the model residuals had both temporal and spatial autocorrelation due to the seasonal patterns observed in the sampling of HABs and the clustering of reported cell count in spaces. The authors thus suggest accounting for this autocorrelation in future studies.113
Conclusions
Knowledge of the temporal, spatial, and environmental factors influencing HABs presence is essential to identifying areas with high HAB presence and developing measures to minimize their proliferation in bodies of water. Using HABSOSin situdata matched with 0.25°-resolution environmental information from the Copernicus database, this study was able to explore changes in the spatio-temporal distribution of HABs in the Gulf of Mexico. There are five important areas identified to have high HABs presence, including important permanent areas: (1) Southwest Florida, which were characterized by higher probabilities of HABs presence, predicted throughout the year; and four seasonally important areas, (2) North Central Florida (3) Central West Florida, (4) Alabama and (5) Mississippi with higher HABs probabilities during the late fall to winter season (November-January) and a minor peak identified during the late summer season.
HABs are linked to SSH, silicate, SST, MLD and heading of the current. This study also provided a possible offshore source of cells that supplies HAB area transported by the currents and supported by the hydrodynamic characteristics of GoM. Identifying blooms in smaller areas needs information fromin situmeasurements of the environmental variables, in addition to the K. breviscell density data sets, and thus would require collaboration from different institutions.
Information on the spatio-temporal dynamics of HABs in the GoM and understanding the environmental drivers are crucial to support more holistic spatial management to decrease HABs incidence.
Author contributions
GLG and PM conceived the study and designed the methodology. GLG performed the analysis. GLG wrote the first draft of the manuscript. PM and LB provided feedback on the analysis and manuscript. All authors contributed to the manuscript revision and read and approved the submitted version.
Data availability Underlying data
Data used in this study were downloaded from the National Centers for Environmental Information (NCEI)- Harmful Algal BloomS Observation System (HABSOS) through this link:https://habsos.noaa.gov/. To download the dataset, you need to click the“data access and map”and then“access map”. Once on the interactive map, click“metadata”. You will be directed to the HABSOS layer information, click“cell count-metadata”. You can download the latest version of the data containing cell counts ofK. brevis.Data Source: NOAA National Centers for Environmental Information (2014), https://www.ncei.noaa.gov/archive/accession/0120767114
Extended data
Zenodo: Modeling the spatio-temporal distribution of Karenia brevis blooms in the Gulf of Mexico,https://doi.org/
10.5281/zenodo.7897979115
This project contains the following extended data:
- Supplementary Animation 1.mp4 - Supplementary Animation 2.mp4 - Supplementary Figure 1.docx - Supplementary Figure 2.docx - Supplementary Figure 3.docx - Supplementary Figure 4.docx - Supplementary Figure 5.docx - Supplementary Table 1.docx - Supplementary Table 2.docx
