Dynamics of microcystins and saxitoxin in the Indian River Lagoon, Florida.

Harmful algal blooms that can produce toxins are common in the Indian River Lagoon (IRL), which covers ~250 km of Florida's east coast. The current study assessed the dynamics of microcystins and saxitoxin in six segments of the IRL: Banana River Lagoon (BRL), Mosquito Lagoon (ML), Northern IRL (NIRL), Central IRL (CIRL), Southern IRL (SIRL), and the St. Lucie Estuary (SLE). Surface water samples (n = 40) collected during the 2018 wet and 2019 dry season were analyzed to determine associations between toxins and temperature, salinity, pH, oxygen saturation, concentrations of dissolved nutrients and chlorophyll-a, presence of biosynthetic genes for toxins, relative abundance of planktonic species, and composition of the microbial community. The potential toxicity of samples was assessed using multiple mammalian cell lines. Enzyme-Linked Immunosorbent Assays were used to determine concentrations of microcystins and saxitoxin. Overall, the microcystins concentration ranged between 0.01-85.70 µg/L, and saxitoxin concentrations ranged between 0.01-2.43 µg/L across the IRL. Microcystins concentrations were 65% below the limit of quantification (0.05 µg/L), and saxitoxin concentrations were 85% below the limit of detection (0.02 µg/L). Microcystins concentrations were higher in the SLE, while saxitoxin was elevated in the NIRL and BRL. Cytotoxicity related to the presence of microcystins was seen in the SLE during the wet season. No significant patterns between cytotoxicity and saxitoxin were identified. Dissolved nutrients were identified as the most highly related parameters, explaining 53% of microcystin and 47% of saxitoxin variability. Multivariate models suggested cyanobacteria, flagellates, ciliates, and diatoms as the subset of microorganisms whose abundances were maximally correlated with saxitoxin and microcystins concentrations. Lastly, biosynthetic genes for microcystins were detected in the SLE and for saxitoxin in the BRL and NIRL. These results highlight the synergistic roles environmental and biological parameters play in influencing the dynamics of toxin production by harmful algae in the IRL.

The relationship between environmental parameters and microbial water quality at two Costa Rican beaches from 2002 to 2017.

Environmental conditions influence fecal indicator bacteria (FIB) levels, which are routinely used to characterize recreational water quality. This study examined 15 years of environmental and FIB data at Puntarenas and Jacó beach, Costa Rica. FIB relationships with sea level, wave height, precipitation, direct normal irradiance (DNI), wind, and turbidity were analyzed. Pearson's correlations identified lags between 24 and 96 h among environmental parameters and FIB. Multiple linear regression models composed of environmental parameters explained 24% and 27% of fecal coliforms and enterococci variability in Jacó, respectively. Puntarenas's models explained 17-26% of fecal coliforms and 12-18% enterococci variability. Precipitation, sea level anomalies, and wave height most frequently explained FIB variability. Hypothesis testing often identified significant differences in precipitation, wave height, daily sea level anomalies, and maximum sea level 24 h prior between days with and without FIB threshold exceedance. Unexpected FIB interactions with DNI, sea level, and turbidity highlight the importance of future investigations.

Predicting culturable enterococci exceedances at Escambron Beach, San Juan, Puerto Rico using satellite remote sensing and artificial neural networks.

Predicting recreational water quality is key to protecting public health from exposure to wastewater-associated pathogens. It is not feasible to monitor recreational waters for all pathogens; therefore, monitoring programs use fecal indicator bacteria (FIB), such as enterococci, to identify wastewater pollution. Artificial neural networks (ANNs) were used to predict when culturable enterococci concentrations exceeded the U.S. Environmental Protection Agency (U.S. EPA) Recreational Water Quality Criteria (RWQC) at Escambron Beach, San Juan, Puerto Rico. Ten years of culturable enterococci data were analyzed together with satellite-derived sea surface temperature (SST), direct normal irradiance (DNI), turbidity, and dew point, along with local observations of precipitation and mean sea level (MSL). The factors identified as the most relevant for enterococci exceedance predictions based on the U.S. EPA RWQC were DNI, turbidity, cumulative 48 h precipitation, MSL, and SST; they predicted culturable enterococci exceedances with an accuracy of 75% and power greater than 60% based on the Receiving Operating Characteristic curve and F-Measure metrics. Results show the applicability of satellite-derived data and ANNs to predict recreational water quality at Escambron Beach. Future work should incorporate local sanitary survey data to predict risky recreational water conditions and protect human health.

Application of Artificial Neural Networks for Dengue Fever Outbreak Predictions in the Northwest Coast of Yucatan, Mexico and San Juan, Puerto Rico.

Modelling dengue fever in endemic areas is important to mitigate and improve vector-borne disease control to reduce outbreaks. This study applied artificial neural networks (ANNs) to predict dengue fever outbreak occurrences in San Juan, Puerto Rico (USA), and in several coastal municipalities of the state of Yucatan, Mexico, based on specific thresholds. The models were trained with 19 years of dengue fever data for Puerto Rico and six years for Mexico. Environmental and demographic data included in the predictive models were sea surface temperature (SST), precipitation, air temperature (i.e., minimum, maximum, and average), humidity, previous dengue cases, and population size. Two models were applied for each study area. One predicted dengue incidence rates based on population at risk (i.e., numbers of people younger than 24 years), and the other on the size of the vulnerable population (i.e., number of people younger than five years and older than 65 years). The predictive power was above 70% for all four model runs. The ANNs were able to successfully model dengue fever outbreak occurrences in both study areas. The variables with the most influence on predicting dengue fever outbreak occurrences for San Juan, Puerto Rico, included population size, previous dengue cases, maximum air temperature, and date. In Yucatan, Mexico, the most important variables were population size, previous dengue cases, minimum air temperature, and date. These models have predictive skills and should help dengue fever mitigation and management to aid specific population segments in the Caribbean region and around the Gulf of Mexico.

Environmental Factors Correlated with Culturable Enterococci Concentrations in Tropical Recreational Waters: A Case Study in Escambron Beach, San Juan, Puerto Rico.

Enterococci concentration variability at Escambron Beach, San Juan, Puerto Rico, was examined in the context of environmental conditions observed during 2005-2015. Satellite-derived sea surface temperature (SST), turbidity, direct normal irradiance, and dew point were combined with local precipitation, winds, and mean sea level (MSL) observations in a stepwise multiple regression analyses (Akaike Information Criteria model selection). Precipitation, MSL, irradiance, SST, and turbidity explained 20% of the variation in observed enterococci concentrations based upon these analyses. Changes in these parameters preceded increases in enterococci concentrations by 24 h up to 11 days, particularly during positive anomalies of turbidity, SST, and 480-960 mm of accumulated (4 days) precipitation, which relates to bacterial ecology. Weaker, yet still significant, increases in enterococci concentrations were also observed during positive dew point anomalies. Enterococci concentrations decreased with elevated irradiance and MSL anomalies. Unsafe enterococci concentrations per US EPA recreational water quality guidelines occurred when 4-day cumulative precipitation ranged 481-960 mm; irradiance < 667 W·m-2; daily average turbidity anomaly >0.005 sr-1; SST anomaly >0.8 °C; and 3-day average MSL anomaly <-18.8 cm. This case study shows that satellite-derived environmental data can be used to inform future water quality studies and protect human health.

Satellite Remote Sensing for Coastal Management: A Review of Successful Applications.

Management of coastal and marine natural resources presents a number of challenges as a growing global population and a changing climate require us to find better strategies to conserve the resources on which our health, economy, and overall well-being depend. To evaluate the status and trends in changing coastal resources over larger areas, managers in government agencies and private stakeholders around the world have increasingly turned to remote sensing technologies. A surge in collaborative and innovative efforts between resource managers, academic researchers, and industry partners is becoming increasingly vital to keep pace with evolving changes of our natural resources. Synoptic capabilities of remote sensing techniques allow assessments that are impossible to do with traditional methods. Sixty years of remote sensing research have paved the way for resource management applications, but uncertainties regarding the use of this technology have hampered its use in management fields. Here we review examples of remote sensing applications in the sectors of coral reefs, wetlands, water quality, public health, and fisheries and aquaculture that have successfully contributed to management and decision-making goals.

Modelling dengue fever risk in the State of Yucatan, Mexico using regional-scale satellite-derived sea surface temperature.

Accurately predicting vector-borne diseases, such as dengue fever, is essential for communities worldwide. Changes in environmental parameters such as precipitation, air temperature, and humidity are known to influence dengue fever dynamics. Furthermore, previous studies have shown how oceanographic variables, such as El Niño Southern Oscillation (ENSO)-related sea surface temperature from the Pacific Ocean, influences dengue fever in the Americas. However, literature is lacking on the use of regional-scale satellite-derived sea surface temperature (SST) to assess its relationship with dengue fever in coastal areas. Data on confirmed dengue cases, demographics, precipitation, and air temperature were collected. Incidence of weekly dengue cases was examined. Stepwise multiple regression analyses (AIC model selection) were used to assess which environmental variables best explained increased dengue incidence rates. SST, minimum air temperature, precipitation, and humidity substantially explained 42% of the observed variation (r2=0.42). Infectious diseases are characterized by the influence of past cases on current cases and results show that previous dengue cases alone explained 89% of the variation. Ordinary least-squares analyses showed a positive trend of 0.20±0.03°C in SST from 2006 to 2015. An important element of this study is to help develop strategic recommendations for public health officials in Mexico by providing a simple early warning capability for dengue incidence.