Mote Marine Laboratory

Karenia brevis regularly forms harmful blooms along the West Florida Shelf that negatively affect marine and terrestrial organisms through toxin production. These blooms impose economic and environmental hardship, driving the need for research to understand the factors influencing their dynamics and to mitigate their impacts. A mostly unresolved issue is the potential role of viruses in bloom termination. We conducted an experiment incubating K. brevis cultures with size-fractionated bloom water samples. Flow cytometry revealed giant virus-like populations (VLPs) in replicate cultures with <1 μm-filtered and <0.2 μm-filtered bloom water. The VLPs’ abundance was paralleled by declines in photoefficiency and culture lysis. Metagenomic analyses of the lysates revealed 11 giant virus genomes (35%–100% complete) representing 7 viral operational taxonomic units (vOTUs) within the order Imitervirales (Nucleocytoviricota). Ten of these vOTUs were more abundant in the incubations with <0.2 μm-filtered bloom water, coinciding with the absence or low abundance of algicidal bacteria. The vOTUs and K. brevis cell abundances showed a positive correlation at a coastal site during bloom and nonbloom periods. The most apparent association was to vOTU6, which may owe its competitive advantage to the presence of the auxiliary metabolic genes bacteriorhodopsin, carbonic anhydrase, and dinoflagellate viral nucleoprotein. The metagenomes also contained polinton-like virus (PLV) genomes. Since many PLVs are hypothesized to depend on co-infection with Nucleocytoviricota viruses for their propagation, our results suggest complex viral interactions within K. brevis blooms. Future research to elucidate virus–bacteria–K. brevis interaction mechanisms may be key to understanding bloom dynamics and developing management tools.

ABSTRACT Cognition is an animal's real‐time adaptation system for responding to change. Rapid environmental change, often anthropogenic, is expanding the range and severity of challenges confronting wild animals. Effective conservation requires a multifaceted approach that includes animals' capacities. Large‐brained, long‐lived animals such as marine mammals often have extensive capability to adaptively modify their behavior due to their cognition, which comprises the mechanisms of information acquisition, processing, and flexible action. Consequently, current behavior need not be a final predictor of future behavior for these animals. This flexibility provides an underutilized and under examined point of leverage for humans interested in improving life outcomes for wild animals. In this team‐written, interdisciplinary paper, we argue that application of cognitive approaches may facilitate many conservation efforts directed toward marine mammals. Starting with a workshop on this topic at the 24th Biennial Conference on the Biology of Marine Mammals, scientists representing a wide range of disciplinary expertise addressed eight different conservation concerns for six marine mammal species and provided potential cognitive explanations of and interventions aimed at related behavior. Our treatment highlights the value of integrated laboratory and field research, and the importance of tight lines of communication between scientists and conservation managers.

Abstract IntroductionSeagrass restoration has historically been difficult and expensive. Recently, there has been interest in co‐restoring seagrasses with Hard clams (Mercenaria spp.) to improve seagrass restoration through nutrient enrichment and water filtration.ObjectivesTo examine the effects of clam co‐restoration and transplantation techniques on seagrass (Halodule wrightii) growth, two experiments were conducted in the Indian River Lagoon (IRL), Florida, from May through November 2023. One experiment compared transplantation techniques (non‐anchored, staples, burlap mats, and jute mats) with and without juvenile clams, while another experiment investigated the effects of juvenile versus adult clams to determine size effects of clams on planting success.MethodsTreatments were monitored for seagrass cover, shoot count, and canopy height. Clam measurements and seagrass nutrients were measured to determine differences driven by treatments.ResultsJuvenile clams in both experiments died weeks after deployment, possibly from predation and transplantation stress. In the transplantation experiment, clam presence had no significant effect on seagrasses due to mortality. Plots planted with burlap had higher cover versus all other techniques (p ≤ 0.0133) and higher shoots versus all techniques except jute (p ≤ 0.0048). The clam maturity experiment found that no clam and adult clam plots had higher cover (p ≤ 0.0462) and shoots (p ≤ 0.0358) versus juvenile clams.ConclusionWhile no relationship between clams and seagrass growth was found due to clam mortality (and possibly algae bloom‐related stress for adult clams), the study determined burlap as the optimal restoration technique of the methods tested in the IRL.

Passive acoustic monitoring is a cost‐effective, minimally invasive technology commonly used to study behavior and population dynamics of soniferous fish species. To understand the strengths and limitations of acoustic monitoring for this purpose at fish spawning aggregations (FSA) requires an assessment of the variability in aggregation‐associated sounds (AAS) as a function of time, space, and proximity for spawning fishes of interest. Here, we evaluate temporal and spatial trends in the detection of AAS by Nassau Grouper (Epinephelus striatus) using an array of six hydrophones deployed across a large Nassau Grouper FSA at Little Cayman, Cayman Islands. We collected continuous data for nine days during a winter spawning season and subsequently used an automatic classifier to extract the embedded Nassau Grouper AAS. Using these data, we analyzed variability in spatiotemporal AAS detection rates across the array with a Bayesian mixed effects model. We found high variability in the detection of AAS across the spawning site, with positive correlations among neighboring hydrophone pairs trending toward negative correlations with distances exceeding 350 m. Indeed, temporal trends in AAS rates at the spawning site were approximately inverted at the two most distant hydrophones (~600 m). Across the hydrophone network, our model predicted strong positive effects of fish proximity, spawning behavior, and crepuscular periods on detected AAS. Our findings suggest hydrophone placement can strongly influence AAS detection rates and even basic temporal patterns in AAS across the spawning season. Given both the vagaries of movement and behavior of aggregating fish at spawning sites and the limits of AAS detection using standard monitoring tools, we suggest spawning site acoustic monitoring programs deploy hydrophone arrays of sufficient size to capture the site‐wide trends in AAS rates if possible; this is particularly true if researchers hope to compare/contrast AAS rates between spawning sites or across seasons for the purpose of population assessment.

Abstract Toothed whales, despite being one of the most acoustically specialized lineages of vertebrates, lack detailed vocal repertoire characterizations comparable to those of songbirds and primates. Current descriptions often lack standardization, limiting interspecies and intraspecies comparisons critical for understanding the contribution of phylogenetic, social and environmental drivers of vocal repertoire evolution. This study reviews six bioacoustics software packages (Luscinia, Beluga, ARTwarp, DeepSqueak, PAMGuard and SASLab) using machine learning for toothed whale whistle detection, extraction of whistle fundamental frequency contours and categorization. We applied these tools to acoustic recordings from four dolphin species to create a primer with guidelines and scripts for those interested in replicating the analyses. The results show that manual (Luscinia) and semi‐automated (Beluga, DeepSqueak) detection and extraction of whistles yielded a greater number of usable contours than the fully automated approaches (PAMGuard and SASLab). The integration of hard and fuzzy unsupervised machine learning analyses provided a multifaceted characterization of whistle repertoires based on identified patterns and structures. The hard approaches from ARTwarp and DeepSqueak were used to categorize signals based on contour similarity. Both packages produced similar numbers of categories. DeepSqueak was faster in categorizing contours, but ARTwarp is more flexible in how similarity thresholds are set. We used these categories to demonstrate estimating relative whistle repertoire size using three methods—Coupon Collector, Capture–Recapture and Hill Numbers. While the methods differ in their assumptions, there was consensus in the ranking of species based on repertoire size. We favour the application of Hill numbers to measure repertoire size and compositional similarity, as this method addresses uneven sampling. The fuzzy approach used the frequency and temporal features extracted from the traced contours in Luscinia to evaluate repertoire similarity in acoustic structure using the package's built‐in multivariate tools. This primer equips researchers to generate more detailed toothed whale repertoire characterizations by integrating machine learning tools with mathematical and community ecology approaches. Supplementary material is provided to support researchers with varying levels of experience in implementing these techniques and indices in their studies.

BackgroundAs coral diseases become more prevalent and frequent, the need for new intervention strategies also increases to counteract the rapid spread of disease. Recent advances in coral disease mitigation have resulted in increased use of antibiotics on reefs, as their application may halt disease lesion progression. Although efficacious, consequences of deliberate microbiome manipulation resulting from antibiotic administration are less well-understood– especially in non-diseased corals that appear visually healthy. Therefore, to understand how apparently healthy corals are affected by antibiotics, we investigated how three individual antibiotics, and a mixture of the three, impact the microbiome structure and diversity of a disease-resistant Caribbean staghorn coral (Acropora cervicornis) genotype. Over a 96-hour, aquarium-based antibiotic exposure experiment, we collected and processed coral tissue and water samples for 16S rRNA gene analysis.ResultsWe found that antibiotic type and dose distinctively impact microbiome alpha diversity, beta diversity, and community composition. In experimental controls, microbiome composition was dominated by an unclassified bacterial taxon from the order Campylobacterales, while each antibiotic treatment significantly reduced the relative abundance of this taxon. Those taxa that persisted following antibiotic treatment largely differed by antibiotic type and dose, thereby indicating that antibiotic treatment may result in varying potential for opportunist establishment.ConclusionTogether, these data suggest that antibiotics induce microbiome dysbiosis– hallmarked by the loss of a dominant bacterium and the increase in taxa associated with coral stress responses. Understanding the off-target consequences of antibiotic administration is critical not only for informed, long-term coral restoration practices, but also for highlighting the importance of responsible antibiotic dissemination into natural environments.

The scalloped hammerhead Sphyrna lewini and the great hammerhead S. mokarran are large, coastal to semi-oceanic shark species common to waters of the US east coast where they are regularly taken in commercial and recreational fisheries, particularly the bottom longline fishery. High rates of hooking mortality and low rates of population growth are believed to have caused severe declines in the US Atlantic populations of these species. The objective of this study was to determine the physiological stress induced by bottom longline capture in both S. lewini and S. mokarran. Physiological stress was quantified using the blood biochemical indicators glucose, lactate, pH, haematocrit, sodium, potassium, calcium, chloride and magnesium, which have been demonstrated to indicate physiological stress in elasmobranchs. Each shark captured was assigned a condition factor, which was compared with the stress parameters and time on hook to quantify stress induced by different longline hook times. In S. lewini, the physiological stress parameters lactate, pH, sodium and chloride scaled with hook time, whereas in S. mokarran, only lactate was affected by hook time. In both species, water temperature affected lactate and glucose levels, as well as sodium and pH levels in S. lewini and magnesium levels in S. mokarran. These data will be useful for estimating post-release mortality of S. lewini and S. mokarran from measurements taken at the time of capture, and quantifying the physiological stress response to longline capture in both species to the Atlantic bottom longline fishery.

Elasmobranchs often exhibit nocturnal or crepuscular activity, with shark diel patterns being better understood than ray diel patterns. We used accelerometry and hidden Markov models (HMMs) to classify female southern stingray ( Hypanus americanus Hildebrand &amp; Schroeder, 1928) activity states across diel periods and environmental conditions in Belize. Three state (low, medium, and high activity) HMMs were constructed for all individuals together and separately ( N = 9). Combined, stingrays were most likely to be highly active at night and in low and medium activity states in the morning. However, there was individual variation in diel activity. In contrast, all stingrays consistently used shallow water (&lt;4 m) during periods of high activity. Temperature had less influence on activity patterns, although three individuals exhibited high activity in cooler water. Generally, high stingray activity was driven by diel cycles and depth. Future research should link activity states to specific behaviours, include a larger sample size, extend tag deployment durations, and explore the impact of predator and prey dynamics on stingray activity.