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    Substantial loss of isoprene in the surface ocean due to chemical and biological consumption

    Evidence for biological and chemical isoprene consumption in coastal seawaterThe time course of isoprene concentration in coastal seawater samples incubated in closed glass bottles at the in situ temperature and in the dark demonstrated sustained loss for at least 45 h (Fig. 1a). Enclosure without headspace prevented isoprene loss by ventilation, and darkness was assumed to arrest all or most of the biological production25 and any photochemical production15 or degradation. Thus, the measured loss was considered the result of microbial degradation and chemical oxidation. In most cases an exponential function fitted better the decay than a linear function (Supplementary Table 1), indicating first-order (concentration-dependent) kinetics for isoprene loss.Fig. 1: Isoprene loss in dark incubations of coastal seawater.a Time course of isoprene concentration in 2 L dark incubations of non-filtered seawater samples from the back-reef lagoon of Mo’orea in April (blue) and the coastal Mediterranean in March (red) and May (green). Filled and open symbols correspond to duplicate incubations. Exponential fits to the data are shown by lines. See Supplementary Table 1 for fit equations and metrics, water temperatures and chlorophyll a concentrations. b Time course of isoprene concentration in series of 30 mL dark incubations of coastal Mediterranean seawater. Dark blue: non-filtered; red: filtered through 0.2 µm; green: filtered + 10 µmol L−1 H2O2; purple: filtered + 0.0025 units mL−1 bromoperoxidase (BrPO); light blue: filtered + H2O2 + BrPO. Exponential fit results in Supplementary Table 2.Full size imageIncubation of microorganism-devoid (filtered through 0.2 µm) coastal seawater sampled next to seaweeds showed an isoprene loss (0.12 d−1) that was half the loss in non-filtered water (0.20 d−1; Fig. 1b and Supplementary Table 2), implying that chemical oxidation accounted for half the total loss. Oxidation by OH·, the fastest amongst isoprene reactions with oxidative transients for which reaction rate data exist19, could account for the observed chemical loss. However, the possibility of oxidation by hitherto overlooked, pervasive oxidants like H2O2 deserved consideration. The addition of unrealistically high concentrations of either H2O2 or the enzyme bromoperoxidase (BrPO), substantially speeded up the chemical loss (0.91 d−1 with 10 µmol H2O2 L−1, 0.31 d−1 with 0.0025 units BrPO mL−1; Fig. 1b and Supplementary Table 2). Isoprene could have reacted with H2O2 in seawater as it does in acidic aerosols26. Besides, should dissolved27 BrPOs from seaweeds or outer-membrane-bound28 BrPOs from phytoplankton occur, they would have reacted with added H2O2 to produce hypobromous acid (HOBr), a strong oxidant29 that would further remove isoprene. Indeed, the addition of BrPO consumed isoprene because it produced HOBr by reaction with the naturally occurring H2O2. Confirming this interpretation, large HOBr production by simultaneous addition of BrPO and H2O2 caused complete isoprene removal in less than 4 h (Fig. 1b). Therefore, the results shown in Fig. 1b indicate that isoprene is reactive to pervasive H2O2 either directly or through the formation of enzymatically derived HOBr. All in all, first-order total isoprene loss (Fig. 1a) is expected to depend on photochemically-produced oxidants30 like H2O2, OH· and 1O2 as well as on microbiota through (a) microbial uptake and catabolism11 and (b) reaction with biologically produced oxidants26,31,32 like HOBr, H2O2 or superoxide.Variability of isoprene loss rate constants in the open oceanTen of the eleven offshore experimental sites were located in the open ocean, and one was located on the Southwestern Atlantic Shelf. Altogether they covered wide ranges of latitude (40°N–61°S), sea surface temperature (−0.8–28.6 °C), daily-averaged wind speed (3–12 m s−1), fluorometric chlorophyll-a (chla) concentration (0.1–5.8 mg m−3), and isoprene concentration (4–104 nmol m−3) (Fig. 2, Table 1 and Supplementary Table 3). Unfiltered seawater samples from the surface ocean were incubated in glass bottles for 24 h, at the in situ temperature and in the dark, and first-order loss rate constants were determined from initial and final isoprene concentrations (see Methods). Note that loss was determined under the assumption that isoprene production was arrested in the dark25. There is published evidence that residual isoprene production may occur in the dark33, but in our incubations, it was insufficient to counteract loss. Thus, isoprene losses caused by processes other than ventilation may have been underestimated.Fig. 2: Geographical distribution of the offshore experiments.Location of the sampling and incubation sites are shown by circles, coloured for isoprene concentration.Full size imageTable 1 Measured biological variables and isoprene process rate constants.Full size tableLoss rate constants (kloss = kbio + kchem) varied over an order of magnitude, ranging 0.03–0.64 d−1 with a median of 0.08 d−1 (Table 1). They did not show any significant relationship to sea surface temperature (SST) (Supplementary Fig. 1) but showed proportionality to the chla concentration (Fig. 3a) that was best described by the following linear regression equation:$${k}_{{{{{{rm{loss}}}}}}}=0.10; (pm 0.01),{{{{{rm{x}}}}}}, [{{{{{rm{chl}}}}}}a]+0.05; (pm 0.01)$$
    (1)
    Fig. 3: Isoprene processes and their main drivers.a Rate constant of isoprene loss in dark incubations (kloss, considered to be microbial and chemical consumption) vs. chlorophyll-a concentration. The linear regression equation is kloss = 0.10 × [chla] + 0.05 (R2 = 0.96, p = 10−7, n = 11). The standard error of the slope is 0.01 L mg−1 d−1, and the standard error of the intercept is 0.01 d−1. Error bars represent the experimentally determined standard error of kloss. The colour scale of the circles indicates bacterial abundances. b Specific (chla-normalised) rate of isoprene production vs seawater temperature (SST) across the sample series. The dashed line is the general smoothed trend. The blue line is the exponential adjustment at SST , 1000)$$
    (2)
    Substitution in Eq. (1) results in:$${k}_{{{{{{rm{loss}}}}}}}=0.14,{{{{{rm{x}}}}}}, {[{{{{{rm{chl}}}}}}{a}_{{{{{{rm{sat}}}}}}}]}^{1.28}+0.05$$
    (3)
    which is our recommended equation for kloss prediction from satellite chla. Note that only the variable term (kbio) changes from Eq. (1), while the intercept (kchem) is maintained at 0.05 d−1.Comparison of isoprene sinks and total turnover timeThe change of isoprene concentration ([iso]) in the surface mixed layer over time can be described as the budget of sources and sinks:$$varDelta [{{{{{rm{iso}}}}}}]/varDelta {{{{{rm{t}}}}}}=[{{{{{rm{iso}}}}}}]cdot ({k}_{{{{{{rm{prod}}}}}}} – {k}_{{{{{{rm{loss}}}}}}} – {k}_{{{{{{rm{vent}}}}}}} – {k}_{{{{{{rm{mix}}}}}}})$$
    (4)
    where kprod, kvent and kmix are the rate constants of isoprene production, ventilation to the atmosphere and vertical downward mixing by turbulent diffusion, respectively.We calculated kvent from our sampling sites over a period of 24 h (Table 1). Ventilation has been considered the main isoprene sink from the upper mixed layer of the ocean18. In our sampling sites, kloss was 0.4 to 10 times the kvent (median factor: 1.2). That is, loss through microbial + chemical consumption was of the same order as ventilation, sometimes considerably faster. Vertical mixing, kmix, was estimated to be one order of magnitude lower than the other process rates (Table 1), and in all cases but one it was calculated or assumed not to be a loss term but an import term into the mixed layer, because vertical profiles generally show maximum isoprene concentrations below the mixed layer and turbulent diffusion causes upward transport14,17. Altogether, the microbial, chemical, ventilation, and, where relevant, mixing losses resulted in total turnover times (1/(kloss + kvent + kmix)) of isoprene between 1.4 and 16 days, median 5 days (Table 1).Isoprene productionAssuming steady-state for isoprene concentrations over 24 h (Supplementary Fig. 2), i.e. Δ[iso]/Δt = 0 in Eq. (4), the sum of the daily rate constants of all sinks (kloss + kvent) equals the rate constant of isoprene production (kprod), with kmix adding to either side depending on whether it is an import to or an export from the mixed layer (Table 1). Note that kprod was the highest coinciding with higher [chla]. This is consistent with a recent study44 where measurement of the net biological isoprene production (i.e. production — consumption rates) across seasons in the open ocean was attempted; net production rates increased in May, coinciding with a large increase in [chla] and phytoplankton cell abundance.The product of kprod by the isoprene concentration gives the daily isoprene production rate, which can be normalised by dividing it by the chla concentration. In our study, this specific isoprene production rate varied between 1 and 38 nmol (mg chla)−1 d−1 (Table 1), median 8 nmol (mg chla)−1 d−1. These values are within the broad range reported across phytoplankton taxa from laboratory studies with monocultures41,45 (0.3–32, median 3 nmol (mg chla)−1 d−1, n = 124). Five of the eleven sites gave values >13 nmol (mg chla)−1 d−1, i.e. in the higher end of the laboratory data range. This is not unexpected, since measurements in monoculture experiments are typically conducted before reaching nutrient limitation, below light saturation and in the absence of UV radiation, to mention three stressors commonly occurring in the surface open ocean. If isoprene biosynthesis and release is enhanced by any of these stressors, as is the case in vascular plants7,10, then monoculture-derived results will easily render underestimates of isoprene production in the open ocean. Production by heterotrophic bacteria46 could have also contributed to increase apparent specific isoprene production rates, but the occurrence and importance of this process in the marine environment is unknown.When plotted against the SST, which was also the temperature of the incubations, specific isoprene production rates increased exponentially between −0.8 and 23 °C and dropped drastically at higher SST (Fig. 3b). Several studies with phytoplankton monocultures have reported positive dependence of specific isoprene production rates on temperature45,47,48,49,50. One of these studies45 described that the increase with temperature reaches an optimum for production that varies among phytoplankton strains and with light intensity, but falls around 23–26 °C. The most detailed study47 was conducted with a Prochlorococcus strain; remarkably, the shape of the specific production rate vs. temperature curve for this cyanobacterium strain was almost identical to that of Fig. 3b, with an exponential increase until 23 °C and a drop thereafter. This is the canonical curve type of enzymatic activities, but the thermal behaviour of the enzymes for isoprene synthesis in marine unicellular algae has not yet been characterised12.Revising the magnitude and players of the marine isoprene cycleOur results allow redrawing the isoprene cycle in the surface mixed layer of the ocean. Figure 4 sketches the magnitude of the rate constants for production and sinks presented in Table 1, averaged according to a chla concentration threshold: the blue and green arrows correspond to the experiments in waters with [chla] lower and higher than 0.4 mg m−3, respectively. Isoprene production in productive (chla-richer) waters is faster than in oligotrophic (chla-poorer) waters. Vertical mixing is assumed to majorly constitute an input into the mixed layer, yet very small. Photochemical production and emission from surfactants15 in the surface microlayer of productive waters is depicted as uncertain. Among sinks, the microbiota-dependent consumption is much faster in productive waters; actually, the statistical uncertainty of Eq. (1) and the uneven distribution of incubation results along the [chla] axis hamper resolving kbio in phytoplankton-poor waters ( More

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    High-throughput SNPs dataset reveal restricted population connectivity of marine gastropod within the narrow distribution range of peripheral oceanic islands

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    Extensive oceanic mesopelagic habitat use of a migratory continental shark species

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    Diversity and dynamics of bacteria at the Chrysomya megacephala pupal stage revealed by third-generation sequencing

    The microbiomes associated with insects are important in mediating host health and fitness. In recent years, numerous studies have explored the microbial diversity and variations across different developmental stages in insects, particularly for pests, including Bactrocera dorsalis16, Monochamus alternatus17, and Zeugodacus tau18. Previously, bacterial communities were investigated using inefficient, low-throughput culture-based or conventional molecular methods19,20, inevitably underestimating the microbial abundance. The advancements in sequencing technology have inspired more research on insect microbial communities, thereby enriching the information on the microbiome of insects. However, a comprehensive understanding of the C. megacephala pupal stage microbiome remains unclear. Therefore, this paper presents a study of the diversity and dynamics of bacteria in the pupal stage of C. megacephala using third-generation sequencing of bacterial 16S rRNA. The results provide a better understanding of the C. megacephala microbiome.This annotation results demonstrate that the bacteria in the pupal stage of C. megacephala are rich and diverse, but the diversity is indiscrete. At the phylum level, Proteobacteria, Firmicutes, and Bacteroidetes were the three predominant phyla, similar to the observation from the housefly Musca domestica21, possibly owing to a semblable ecological niche. The bacterial community analysis identified Clostridia and Gammaproteobacteria as the two predominant bacterial classes in the pupal stage of C. megacephala with ~ 30% relative abundances. However, another study of the gut bacteria across the lifecycle of C. megacephala showed Gammaproteobacteria as the dominant class with over 60% relative abundance. These results suggest that Clostridia may be from other C. megacephala tissues apart from the gut.Compared with the previous results about C. megacephala bacterial communities that were determined using culture-based or conventional molecular methods, the microbial diversity was much higher in this study using third-generation sequencing technology22. However, we cannot identify some bacteria to the species level, such as Klebsiella pneumoniae and Aeromonas hydrophila23, so culture-based and conventional molecular methods are also important.Ignatzschineria indica and Wolbachia endosymbiont were the two predominant species in the bacterial communities in the C. megacephala pupal stage. Ignatzschineria indica is a Gram-negative bacterium commonly associated with maggot infestation and myiasis, a probable marker for myiasis diagnosis24,25. Wolbachia are intracellular symbiotic bacteria widely distributed in the reproductive tissues of arthropods. They cause reproductive alterations in their hosts, such as cytoplasmic incompatibility (CI)26, feminization27, killing males28, and inducing parthenogenesis (PI)29. Wolbachia increases the resistance to arbovirus infection, resulting in decreased virus transmission. The reproductive regulation of Wolbachia on target organisms may be important in future biological prevention and pest control. Since Wolbachia causes CI, Wolbachia-infected populations can be established and released to reduce to the environment to reduce the reproductive potential of harmful target insect populations. Modified Wolbachia that harbor anti-parasitic or anti-viral genes can be adopted to control virus transmission in insects carrying viruses30.However, few studies have reported that Ignatzschineria and Wolbachia can coexist in an individual insect, despite their status as common bacterial genera. Several possibilities may explain this analytical discrepancy. Firstly, in this study, Spearman’s rank correlation between Wolbachia and Ignatzschineria showed a negative correlation, suggesting a competitive relationship between Wolbachia and Ignatzschineria. Secondly, the previous investigations of bacterial communities applied inefficient, low throughput culture-based or conventional molecular methods, potentially generating incomplete results. Finally, numerous studies have established that microbial communities differ between insect populations because of different sampling techniques and procedures31. This study analyzed C. megacephala sampled from a laboratory population reared with pork for five years. Nevertheless, the significant decrease in the relative abundance of Wolbachia observed at the end of the pupal development is unsolved, thus, required further studies.Traditionally, the most common method for pest control is by chemical pesticides. However, the excessive use of chemical pesticides causes the rapid build-up of pesticide resistance and environmental pollution. Therefore, it is urgent to develop biological control methods for pests. Nasonia vitripennis (Walker), is an important parasitoid whose female wasp stings, injects venom, and lays eggs in different fly pupae, where parasitoid eggs, larvae, pupae, and early-stage adults develop. N. vitripennis lives in species of the family Calliphoridae, Sarcophagidae, and Muscidae, where their larvae feed on fly pupae, allowing N. vitripennis to function as a biological agent to control the flies.The microbial communities of fly species and N. vitripennis live in an enclosed environment, providing more opportunities for the N. vitripennis-fly communication. Therefore, the impacts of micro-communities of the fly hosts on N. vitripennis are worth studying, precisely at the pupal stage. Studies of different fly hosts and their corresponding N. vitripennis showed diverse core microbiota, and so other fly hosts shaped the bacterial diversity of their parasitic wasps32. In addition, parasitic wasps infected with Wolbachia produced more female offspring than uninfected ones, further emphasizing the need to improve biological prevention and control efficiency33. Therefore, a deliberate focus to study the micro-communities of different fly species at the pupal stage and the interaction between the fly species and N. vitripennis will guide the development and utilization of N. vitripennis as biological agents for the prevention and control of flies.Approximately half of the bacteria identified at the species level in this study are pathogens or conditional pathogens (Supplementary Table S2), Escherichia coli, Providencia burhodogranariea, and Morganella morganii, among others. Another uncommon pathogenic bacterium, Erysipelothrix rhusiopathiae was also identified at the species level. E. rhusiopathiae is the etiological agent of swine erysipelas and causes economically important chicken, duck, and sheep diseases. Although E. rhusiopathiae primarily infects pigs, it also infects various domestic and wild mammals, including marine mammals, birds, and humans. Humans infected with E. rhusiopathiae develop large areas of red spots on their body. Severe E. rhusiopathiae infection causes endocarditis and septicemia, which have a 38% mortality rate34.However, very few studies have focused on the insects that transmit E. rhusiopathiae35. Considering that the C. megacephala samples in this study were obtained from a laboratory population reared for five years, it is likely that the E. rhusiopathiae originated from infected pork and were transmitted to C. megacephala through feeding. Thus, disease-vector insects can infect and spread pathogens beyond their feeding activities, and disease-vector insects require more comprehensive prevention and control methods (“Supplementary information”).In conclusion, this study comprehensively investigated the pupal stage microbiome of C. megacephala using third-generation sequencing to deepen the understanding of C. megacephala microbial communities on the whole. The study provides a basis for subsequent studies of biological control and the comprehensive utilization of C. megacephala. Future studies should focus on the transmission patterns and biological functions of these microbial species. More

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    Passive acoustic monitoring of sperm whales and anthropogenic noise using stereophonic recordings in the Mediterranean Sea, North West Pelagos Sanctuary

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    Changes in the acoustic activity of beaked whales and sperm whales recorded during a naval training exercise off eastern Canada

    We observed a clear reduction in the acoustic activity of sperm whales and beaked whales during the period when sonar signals were recorded at Station 5, indicating that whales ceased foraging in this area while military sonars were in use. The acoustic detection rate of sperm whales returned to pre-exercise baseline levels within the days following the CF16 exercise, while the observed reduction in beaked whale acoustic activity was more prolonged. Detection rates of Cuvier’s beaked whale clicks remained low throughout the 8-day period immediately following the exercise, and UMBW clicks were largely absent during this period. This study is observational and limited to showing correlation rather than cause and effect; nonetheless, these results are consistent with previous experimental research on the responses of beaked whales to simulated and real military sonars and suggest that whales were disturbed from normal foraging behaviour and likely displaced from the affected area during the CF16 exercise.The scale and duration of sonar use recorded during this study provides important context for the observed results. Much of the experimental work conducted to date on the responses of beaked whales and other odontocetes to sonar has involved controlled exposure experiments using animal-borne tags to record the fine-scale movements and acoustic behavior of individuals, allowing responses to be examined on the scale of minutes to hours e.g.,7,8,10. Experimental exposures to simulated sonar signals lasting approximately 15–30 min have elicited pronounced avoidance responses in Blainville’s beaked whales7, Cuvier’s beaked whales8, Baird’s beaked whales16, and northern bottlenose whales9,10. Generally, these studies were focused on the onset of the response and did not always assess the duration over which altered behaviour continued. However, the absence of foraging behaviour for several hours following exposure was noted in some cases, and focal animals performed sustained directed movement away from the exposure location during this time, covering distances of up to tens of kilometers10. In broader-scale studies examining responses of Blainville’s beaked whales to real multi-ship naval training operations on the Atlantic Undersea Test and Evaluation Center (AUTEC) in the Bahamas, displacements of up to 68 km were observed, lasting 2–4 days before whales returned to foraging in the area where they were exposed7,28. In the present study, the duration of naval sonar activity recorded during the CF16 exercise was considerably more prolonged, with bouts of sonar continuing for up to 13 consecutive hours and occurring repeatedly over an 8-day period. Although we can only make inference on species-level rather than individual-level responses based on the absence of clicks in our recordings, it is plausible that military sonar activity at this scale led to wide spatial avoidance of the affected area over an extended period.The absence of sperm whale click detections in the Station 5 recordings for 6 consecutive days during the CF16 exercise is notable; few prior studies have demonstrated sustained changes in foraging behaviour or substantial displacement of sperm whales following sonar exposure. Behavioural response studies conducted in northern Norway using controlled experimental exposures showed varying responses by sperm whales, which included changes in orientation and direction of horizontal movement, changes in acoustic behaviour, and altered dive profiles23. Exposure to lower frequency sonar signals in the 1–2 kHz range generally prompted stronger responses, including a reduction in foraging effort or transition from a foraging to non-foraging state, while exposure to higher frequency sonar signals in the range of 6–7 kHz did not appear to trigger changes in foraging behaviour21,29. More recently, Isojunno et al.30 quantified the responses of sperm whales to continuous and pulsed active sonars, and found that sound exposure level was more important than amplitude in predicting a change in foraging effort. We were not able to investigate differential responses to frequency or other sonar characteristics in this study, due to the observational nature of the study and the absence of sperm whale clicks throughout most of the exercise period. Likewise, we cannot exclude the physical presence of ships, aircraft, and submarines in the area or additional types of noise produced during maneuvers as potential factors contributing to the cessation of sperm whale and beaked whale click production and foraging behaviour.The observed changes in acoustic activity were more easily quantified for sperm whales than for beaked whales, due to higher baseline hourly presence of sperm whale clicks in the recordings. Sperm whales produce powerful echolocation clicks throughout their foraging dives, which can be recorded at ranges of 16 km or more31, and a single individual foraging in the vicinity of a hydrophone may be detected continuously throughout multiple dive cycles. Our analysis was based on sperm whale click detections that met a threshold signal-to-noise ratio (SNR), and the results therefore provide a minimum estimate of sperm whale presence in the vicinity of the recorder. Reporting results at the level of hourly presence rather than the number of individual click detections largely mitigated the effects of excluding low-SNR clicks recorded at greater distances from the hydrophone or during higher ambient noise conditions. Likewise, the presence of sperm whales on an hourly time scale is not likely to be substantially underestimated when recordings are collected using a low duty cycle32. By contrast, beaked whales produce echolocation clicks at higher frequencies and lower source levels, with highly directional beam patterns33. These clicks are likely only detected at ranges of up to approximately 4 km when the whale is oriented toward the hydrophone, and at lesser distances when clicks are received off-axis34. As a result, there is greater variability and lower baseline detection rates of beaked whale clicks on fixed passive acoustic recorders, which reduces statistical power to assess temporal changes in acoustic activity. Moreover, the duty-cycled recording schedule used at Station 5 provided only 65 s of high-frequency data 3 times per hour, and the presence of beaked whales is likely to be underestimated by this duty cycle, with potentially greater underestimation of Mesoplodont species compared to Cuvier’s beaked whales35.Continuous recordings were collected at the East Gully and Central Gully recording sites, but included only partial temporal coverage of the exercise period and no pre-exercise baseline data. No comparable recordings were available from these locations in a prior or subsequent year to form a control dataset. As a result, we were not able to use these datasets to assess changes in acoustic activity associated with the CF16 exercise. A slight decrease in hourly presence of northern bottlenose whale clicks in the Central Gully recordings occurred on September 19th–20th, 2016; however, we are aware that an oceanographic research vessel was coincidentally in the area deploying scientific instrumentation in close proximity to the Central Gully recording site on these dates, creating an additional source of potential disturbance. Despite these limitations, we included an analysis of the recordings collected at the East and Central Gully sites for two reasons: first, to provide perspective on the geographic extent over which activities associated with the CF16 exercise occurred; and second, to illustrate the diversity in beaked whale species composition at different locations across the region. Analysis of the recordings for sonar signals revealed that higher levels of sonar activity occurred near the Station 5 recording site than near the East or Central Gully locations. Due to the distance between recording sites and the timing of the sonar signals recorded, it appears that the recorded sonar signals came from multiple source locations over the duration of the exercise. Recordings from Central Gully contained the fewest sonar signals and lowest measured received levels, likely due to the deliberate avoidance of the Gully MPA and surrounding area by exercise participants during CF16. The Gully was established as an MPA in 2004, and is one of three adjacent canyons on the eastern Scotian Shelf currently designated as critical habitat areas for the endangered Scotian Shelf population of northern bottlenose whales36. The Station 5 recording site was located approximately 300 km to the southwest, and experienced higher levels of naval sonar activity during CF16. However, none of the locations were chosen specifically to monitor CF16, and we do not have access to information on the general exercise areas used, specific locations of naval vessels, submarines, or aircraft participating in the CF16 exercise, or the source levels of transmitted sonar signals. Due to the opportunistic nature of the recordings, the received levels of sonar signals measured at Station 5 likely do not represent the highest sound levels introduced into the marine environment during the CF16 exercise.Unlike many areas where behavioural responses to sonar are commonly studied, there are no instrumented naval training ranges off eastern Canada, and cetaceans inhabiting this region are unlikely to be accustomed to regularly hearing naval active sonars. Other than during the CF16 exercise, sonar signals were not noted during a large-scale analysis of cetacean call occurrence and soundscape characterization in 2 years of recordings collected at Station 5 and numerous other passive acoustic monitoring sites off eastern Canada26. Exposure context and familiarity with a signal may be important factors influencing an individual’s response to acoustic disturbance15. Experimental research on Cuvier’s beaked whales near a U.S. naval training range located off southern California demonstrated possible distance-mediated effects of sonar exposure, with more pronounced behavioural responses occurring with closer source proximity, even when received levels from the closer source were likely lower than those from more distant, high-powered sonar transmissions, which did not elicit as strong a response15. The movement and predictability of the sound source as well as the timing and duration of sonar transmissions may also be important factors influencing the behavioural response15. Whales inhabiting waters off southern California are likely habituated to hearing distant sonar due to routine naval training activities occurring on the range. Conversely, Wensveen et al.10 found that northern bottlenose whales in the eastern North Atlantic exhibited similar responses to simulated sonar signals played at various distances up to 28 km, suggesting that they perceived this novel stimuli as a potential threat even from a distance and at relatively low received levels. Bernaldo de Quiros et al.5 hypothesized that beaked whales not regularly exposed to active sonar signals may respond more strongly, both physiologically and behaviourally, which poses a concern for a region where military training activities involving the use of sonar are relatively infrequent, but occur periodically in the form of large-scale exercises involving the extensive use of active sonars and creating significant potential for acoustic disturbance.Behavioural disturbance due to anthropogenic noise may have energetic, health, and fitness consequences for deep-diving odontocete species. Disruption of normal diving patterns creates energetic costs due to the significant investment in each dive and the reduction of time available for prey intake when foraging dives are interrupted. Recent studies on the functional relationship between beaked whales and deep-sea prey resources suggest that certain characteristics of prey, including minimum size and density thresholds, are required for beaked whales to successfully meet their energetic needs12,37. While the distribution and characteristics of deep-sea prey are challenging to study and largely unknown in most regions, considerable environmental heterogeneity may be present, causing the quality of foraging habitat to vary significantly over even small horizontal scales12,37. This patchiness in habitat quality has important implications for behavioural disturbance, as even short-term displacement from high-quality habitat areas can affect the fitness of individuals and potentially lead to population-level consequences13.In addition to the consequences of sublethal disturbance, it is important to note that the likelihood of observing more acute impacts of exposure to naval active sonar, including injuries or fatalities, is extremely low in offshore regions. Individual and mass strandings of beaked whales and other cetaceans associated with military activities have typically been documented on oceanic islands with populated coastlines1,3,6. Factors affecting the probability that cetacean carcasses will wash ashore include buoyancy and decomposition rates in local water conditions, oceanic surface currents, the topography of coastlines, and the location of habitat relative to shore6. Off Nova Scotia, potential beaked whale and sperm whale habitat (consisting of water depths greater than 500 m) is located more than 100 km from the coastline, and injuries or fatalities occurring in deep water habitat in this region are unlikely to result in observed strandings. Stranding incidents involving sperm whales and beaked whales have been reported in Nova Scotia, but the cause of mortality is usually unknown38. Cetacean mortality is highly underestimated even in the aftermath of catastrophic events such as large oil spills39, and a lack of observed injuries or mortalities following offshore military activities should not be construed as evidence that no direct or immediate harm was caused.This study offered a unique opportunity to use existing passive acoustic monitoring (PAM) data to assess disturbance of poorly-known odontocete species during a real-world, large-scale military sonar exercise in a region where military sonar use at this scale is relatively uncommon. Ideally, a PAM study designed to examine disturbance in this context would collect continuous rather than duty-cycled recordings, and include ample baseline data surrounding the period of interest as well as in prior and subsequent years. Additionally, multiple acoustic sensors arranged in a dense array surrounding exercise locations would provide further insight into the spatial context of exposure and patterns of disturbance. Despite the data limitations in the present study, our results demonstrate that changes in odontocete foraging behaviour associated with acute, large-scale disturbance may be evident in PAM data even at low duty cycles. The nature of the observed effect (e.g., temporary disruption of foraging, spatial displacement, or more acute injury or distress) remains unknown, as do the number of individuals affected and the longer-term health and fitness implications. Broader baseline data on species occurrence and an improved understanding of species’ ecology and habitat use in the region are necessary for making informed mitigation decisions, allowing key habitat areas to be avoided, and understanding the impacts of naval active sonar exposure in this region on individuals and populations. More

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    Airborne microalgal and cyanobacterial diversity and composition during rain events in the southern Baltic Sea region

    This research focuses on the quantitative and qualitative analyses of cyanobacteria and microalgae present in rainfall during the summer phytoplankton bloom season of August–September 2019. In addition, a continuous episode of rainfall over several days was selected to demonstrate the washout process of microorganisms from the air with rain.Quantity of cyanobacteria and microalgae washed out with rain during the growing seasonCurrently, there is a growing number of scientific articles on cyanobacteria and microalgae in the atmosphere8. Unfortunately, there is a reference methodology for efficiently counting the microorganisms present in the air or in rainfall. A popular method for quantifying cyanobacteria and microalgae in the air is to show the number of taxa found in the collected samples after growth6,31,42,43,44,45,46. In this study, a total of 16 taxa of airborne cyanobacteria and microalgae were found in the samples. In the rainwater samples obtained during the summer of 2019, 11 taxa of cyanobacteria and microalgae were distinguished. The green algae in the rainwater samples included Bracteacoccus sp., Oocystis sp., Coenochloris sp., Chlorella sp., and Chlorococcum sp., while the cyanobacteria included Leptolyngbya sp., Pseudanabaena sp., Synechococcus sp., and Synechocystis sp. In addition, Chrysochromulina sp., which belongs to Haptophyta, was observed.Other studies recorded the presence of several to several dozen taxa in the air6,31,42,43,44,45,46. Certainly, a number of factors, starting with atmospheric conditions and ending with physical and chemical parameters of the surrounding waters, influence the diversity of cyanobacteria and microalgae in the atmospheric air. Analyzing global trends, only cyanobacteria have been found in the atmosphere of every region of the world31. However, according to Dillon et al.47, cyanobacteria have been detected in clouds at variable abundances between ~ 1% and 50% of the total microbial community. Xu et al.48 found that cyanobacteria constituted only 1.1% of the total bacterial community in clouds. It needs to be highlighted that there is still a lack of research available to provide this type of information for rainfall samples.For the period from July to September 2019, the results showed that the number of cyanobacteria and microalgae cells present in rainfall varied over time (Fig. 1) and ranged between 100 cells L–1 and 342.2 × 103 cells L–1. From July to the end of August, the cell number was relatively low, ranging from 100 cells L–1 to 28.6 × 103 cells L–1. This variability was related to the change in the biomass of blue green algae in the Gulf of Gdańsk (Table S2; Fig. 1). Therefore, this research also shows the close relationship between the processes taking place in the Baltic Sea and the presence of cyanobacteria and microalgae in the atmosphere. As the biomass of cyanobacteria in the Baltic Sea increased, the number of cyanobacteria and microalgae cells in the rainfall samples also increased (***p  More

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    Higher temperature extremes exacerbate negative disease effects in a social mammal

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