Tampa Bay Estuary Program

Abstract Extreme weather events increasingly threaten coastal water quality, yet the mechanisms by which tropical cyclones impair microbial water quality remain poorly quantified. We developed a Large-Language-Model-Assisted Microbial Source Tracking (LAMST) framework to trace the origins of microbial threats and applied it to coastal waters impacted by Hurricane Milton along Florida’s Gulf Coast. LAMST integrates 16S rRNA sequencing with species-level metadata from the NCBI BioSample database to classify taxa as marine, terrestrial, or wastewater in origin. Across 30 sites and three time points (1 week to 7 months post-storm), twelve microbial analytes, including fecal indicator bacteria (FIB), pathogens, and antimicrobial resistance genes (ARGs), were quantified. Colored dissolved organic matter as well as carbon and nitrogen stable isotopes in particulate material were characterized for the same samples and independently corroborated LAMST’s terrestrial and marine microbial classifications. Increases in terrestrial and marine bacterial counts in coastal waters indicated concurrent mobilization of land-derived inputs and marine sources following the hurricane. Site fixed-effects regressions showed terrestrial and wastewater bacteria were strongly associated with enterococci and two ARGs (i.e., sul2 and tetA ), whereas marine bacteria correlated with Vibrio parahaemolyticus . LAMST provides a quantitative, generalizable framework for source-resolved microbial tracking without reliance on regional reference sequences.

Abstract Continuous measurements of dissolved oxygen (DO) are useful for quantifying ecosystem metabolism, which is critical for understanding estuarine biogeochemistry and ecology, but current methods applied to these data may lead to estimates that are physically impossible and poorly constrained errors. Here, we present a new approach for estimating estuarine metabolism: Estuarine BAyesian Single‐station Estimation (EBASE). EBASE applies a Bayesian framework to a simple process‐based model and DO observations, allowing the estimation of critical model parameters, specifically light efficiency and respiration, as informed by a set of prior distributions. EBASE improves upon the stream‐based model from which it was derived by accommodating missing DO data and allowing the user to set the time period over which parameters are estimated. We demonstrate that EBASE can recover known metabolic parameters from a synthetic time series, even in the presence of noise (e.g., due to tidal advection) and when prior distributions are uninformed. Optimization periods of 7 and 30 d are more preferable than 1 d. A comparison with the more‐conventional method of Odum reveals the ability of EBASE to avoid unphysical results (such as negative photosynthesis and respiration) and improves when the DO data are detided. EBASE is available using open‐source software (R) and can be readily applied to multiple years of long‐term monitoring data that are available in many estuaries. Overall, EBASE provides an accessible method to parameterize a simple metabolic model appropriate for estuarine systems and will provide additional understanding of processes that influence ecosystem status and condition.

Dental-skeletal-retinal-anomaly (DSRA) is a newly described collagenopathy in Cane Corso dogs. The causative mutation has been linked with splice defects within the melanoma inhibitory activity member 3 (MIA/3) gene that codes for the TANGO1 protein. This case series presents the first dental-related radiographic and histopathological abnormalities in two dogs with genetically confirmed DSRA. The clinical, radiological, and histological features are similar to those reported for MIA3/TANGO1 splice defects previously reported in humans and knockout mice. Common clinical features of these patients include generalized opalescent discoloration of the permanent dentition (intrinsic dyschromia), enamel defects, fractured teeth, vision loss, shortened physical stature, and orthopedic abnormalities that resulted in chronic, early-onset lameness. Intraoral radiography revealed delayed dentin deposition, evidence of endodontic disease, and dental hard tissue loss in both cases. Histopathologic findings for both cases were consistent with dentinogenesis imperfecta (DGI). DSRA exhibits autosomal recessive heritability and commercial diagnostic tests are now available. Clinicians should be aware of the etiopathogenesis, genetic inheritance and associated comorbidities in order to treat and counsel clients on the management of this condition. It is recommended that all breeding individuals be tested, and carriers be sterilized or omitted from the breeding population. This case study describes intraoral diagnoses, treatments, and follow-up of two DSRA-positive dogs.

The long-term protection and restoration of aquatic resources depends on robust monitoring data; data that require systematic quality control and analysis tools. The MassWateR R package facilitates quality control, analysis, and data sharing for discrete surface water quality data collected by monitoring programs of various size and technical capacity. The tools were developed to address regional needs for programs in Massachusetts, USA, but the principles and outputs can be applicable to monitoring data collected anywhere. Users can create quality control reports, perform outlier analyses, and assess trends by season, date, and site for more than 40 parameters. Users can also prepare data for submission to the United States Environmental Protection Agency Water Quality Exchange, thus sharing data to the largest water quality database in the United States. The automated and reproducible workflow offered by MassWateR is expected to increase the quantity and quality of publicly available data to support the management of aquatic resources.

Canine oral lymphoma is an infrequently diagnosed malignancy in dogs and reports in the literature are lacking. Most cases are due to epitheliotropic lymphoma, in which lesions are often multifocal and involve the skin as well as the oral cavity. Epitheliotropic lymphoma is an uncommon form of lymphoma that is characterized by neoplastic T-lymphocyte tropism for epithelial tissues. However, not all cases of oral lymphoma are due to epitheliotropic lymphoma, and B-cell disease is also possible. This article describes very different cases of oral lymphoma in dogs including the history at presentation, examination and radiographic findings, treatments, and outcomes. The cases highlight the variability in signalment, examination findings, and clinical courses that are found with oral lymphoma.

Effective stewardship of ecosystems to sustain current ecological status or mitigate impacts requires nuanced understanding of how conditions have changed over time in response to anthropogenic pressures and natural variability. Detecting and appropriately characterizing changes requires accurate and flexible trend assessment methods that can be readily applied to environmental monitoring datasets. A key requirement is complete propagation of uncertainty through the analysis. However, this is difficult when there are mismatches between sampling frequency, period of record, and trends of interest. Here, we propose a novel application of generalized additive models (GAMs) for characterizing multi-decadal changes in water quality indicators and demonstrate its utility by analyzing a 30-year record of biweekly-to-monthly chlorophyll-a concentrations in the San Francisco Estuary. GAMs have shown promise in water quality trend analysis to separate long-term (i.e., annual or decadal) trends from seasonal variation. Our proposed methods estimate seasonal averages in a response variable with GAMs, extract uncertainty measures for the seasonal estimates, and then use the uncertainty measures with mixed-effects meta-analysis regression to quantify inter-annual trends that account for full propagation of error across methods. We first demonstrate that nearly identical descriptions of temporal changes can be obtained using different smoothing spline formulations of the original time series. We then extract seasonal averages and their standard errors for an a priori time period within each year from the GAM results. Finally, we demonstrate how across-year trends in seasonal averages can be modeled with mixed-effects meta-analysis regression that propagates uncertainties from the GAM fits to the across-year analysis. Overall, this approach leverages GAMs to smooth data with missing observations or varying sample effort across years to estimate seasonal averages and meta-analysis to estimate trends across years. Methods are provided in the wqtrends R package.

Abstract Biological invasions can greatly alter ecological communities, affecting not only the diversity and abundance but also composition of invaded assemblages. This is because invaders’ impacts are mediated by characteristics of resident species: some may be highly sensitive to invader impacts while others are unaffected or even facilitated. In some cases, this can result in invasive species promoting further invasions; in particular, herbivory by introduced animals has been shown to disproportionately harm native plants, which can indirectly benefit non‐native plants. Here, we investigated whether such patterns emerged through the effects of an invasive fish species on lake plant communities. Specifically, we tested whether invasion of Minnesota (U.S.A.) lakes by Cyprinus carpio (common carp), an omnivorous, benthivorous fish known to reduce abundance and richness of aquatic plants, differentially affected native versus non‐native plant species. We applied statistical models to a large, long‐term monitoring dataset (206 macrophyte taxa recorded in 913 lakes over a 20‐year time period) to test whether carp altered community composition, to identify which macrophyte species were most sensitive to carp and determine whether species characteristics predicted carp sensitivity, and to characterise consequences of carp invasion on lake‐level vegetation attributes. We found that carp exerted strong selective pressure on community composition. Native macrophytes, those with a more aquatic growth form, and those considered less tolerant of disturbance (i.e. higher coefficients of conservatism) were more sensitive to carp. Conversely, no introduced macrophytes exhibited sensitivity to carp and all had higher probabilities of occurrence as carp abundance increased. The net effect of carp invasion was a shift toward less species‐rich plant communities characterised by more non‐native and disturbance‐tolerant species. These results have several implications for conservation and management. First, they reinforce the need to prevent further spread of carp outside of their native range. Where carp have already established, their control should be incorporated into efforts to restore aquatic vegetation; this may be an essential step for recovering particular plant species of high conservation importance. Furthermore, reducing carp abundance could have ancillary benefits of reducing dominance by invasive plant species. Lastly, where carp cannot be eliminated, managers should target native macrophytes that are relatively tolerant of carp in shoreline plantings and other revegetation efforts.