Ecology

Co-culture dynamicsThis study was designed to enhance understanding of metabolite release and utilization across bloom stages in a simple community of phytoplankton and heterotrophic bacteria. The synthetic community was established with the diatom T. pseudonana and the bacterial strains R. pomeroyi DSS-3, Stenotrophomonas sp. SKA14, and P. dokdonensis MED152. These bacterial strains have high genetic similarity to isolates from phytoplankton cultures [14] and represent taxa that are common in phytoplankton blooms. Metabolites derived from the diatom were the sole source of carbon available for the bacteria, since no organic substrates were added. In addition, none of the bacteria can assimilate nitrate, and usable nitrogen was only available as diatom or bacterial extracellular products. The diatom had its highest specific growth rate of 1.65 d−1 during days 0–3, after which the rate declined (Fig. 1A). The total abundance of heterotrophic bacteria increased steadily but there was a succession that favored P. dokdonensis through day 15, and then R. pomeroyi by day 20; Stenotrophomonas disappeared from the model system by day 3 (Fig. 1B). The presence of bacteria did not affect the growth of diatoms based on comparisons of abundance in co-cultures versus axenic cultures at day 15 (Fig. 1A), as has been found previously [14, 26]. Inorganic nutrients were not limiting ( >5 μM at day 15; Table S1).Fig. 1: Time course of microbial abundances.A Cell abundance based on flow cytometric analysis for co-cultures (5 time points) and axenic cultures (day 15 only) (n = 3). The intensive sampling dates for the early and late bloom comparisons are marked with gray boxes. B Mean relative abundance of bacterial species is based on CFUs (n = 3). The day 0 samples were collected 8 h after inoculation.Full size imageDiatom endometabolite shiftsAnalyses focused on the day 3 (early bloom) and day 15 (late bloom) co-culture time points, for which a complete set of metabolomic and transcriptomic data were collected. Twenty-two diatom endometabolites that were annotated with high confidence by NMR analysis (Table S2) and quantified after normalizing to diatom cell number revealed that endometabolome composition differed substantially between bloom stages. Metabolites with significantly different cellular concentrations included nine compounds that were higher in intracellular concentration during the late bloom; these were arginine, valine, lysine, DHPS, glycerol-3-phosphate, phosphorylcholine, DMSP, glycine betaine, and homarine (T-test; P  More

Viral-like particle abundanceThe 10 sampling sites were equidistantly (average distance: 1.6 km) distributed between 1689 and 717 m above sea level in a 95.7 km2, pristine catchment and covered a flow-connected distance of 14.3 km (Fig. 1, Methods).Fig. 1: No evidence for a downstream accumulation of VLPs.Viral-like particles (VLP) were purified from 10 sites sampled during four seasons along an altitudinal gradient in an alpine stream (Vièze, Switzerland) (a). Neither VLP abundance (b) nor Virus-to-Prokaryote Ratios (VPR; (c)) showed pronounced spatial or temporal trends.Full size imageViral-like particle (VLP) counts normalized to areal coverage of the stream biofilm ranged from 2.8 × 109 to 3.4 × 1010 VLP m−2. On average, VLP abundance was highest in summer with 1.87 ± 0.75 × 1010 VLP m−2; however, there were no statistically significant seasonal differences in VLP abundance (repeated-measures ANOVA, F = 0.87, p = 0.47). VLP numbers did not exhibit a continuous spatial tendency, except during fall when VLP numbers increased significantly with downstream distance (r = 0.81, p 0.7 and/or pident >0.4). Indeed, 90 of the 203 putative viral depolymerases showed significant sequence similarity with 198 vOTU sequences (i.e., 6% of the overall vOTU diversity). We were able to obtain taxonomic classification for 80 of these 198 vOTUs, and found that all large Caudovirales families were represented (i.e., Myoviridae, n = 31, Siphoviridae, n = 17, Podoviridae, n = 15, Autographiviridae, n = 13, Ackermannviridae, n = 2, and Herelleviridae, n = 1). This suggests that depolymerase activity may be widespread among viruses infecting bacteria in stream biofilms. Although both the number of potential depolymerases included in our database and the number of classified vOTUs was limited, we observed that depolymerase-harboring Myoviridae vOTUs corresponded the expectation based on the overall relative abundance of Myoviridae, pointing toward the importance of dispersal for this important viral family. Siphoviridae, in contrast, were relatively underrepresented among depolymerase-harboring vOTUs. In combination with neutral model predictions, this may point towards a fundamental difference between Siphoviridae and Myoviridae in infecting stream biofilm bacteria. While Myoviridae may rather rely on efficiently spreading across distant biofilm patches facilitated by an ability to decompose the EPS matrix, many members of Siphoviridae seem to lack this ability.To investigate our second hypothesis, that lysogeny might be a successful viral life cycle strategy to spread locally within biofilm patches, we used BACPHLIP [36]. BACPHLIP predicted with high probability ( >75%) a lysogenic life cycle for 58 out of 256 complete viral genomes and a lytic life cycle for 177 viral genomes. For the remaining 21 complete viral genomes in our dataset, BACPHLIP did not result in sufficiently high prediction probability (i.e., More

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Our results show that the average diel activity patterns of Ae. aegypti populations in both Miami, FL and in Brownsville, TX were very similar; they both had two peaks, one in the early morning and the other in the evening, and the average host-seeking peaks are between 7:00 and 8:00 and between 19:00 and 20:00 (Fig. 4). Similar observations were previously reported by several investigators3,4,10,11,12 and the bimodal diel activity pattern is the most frequently reported for Ae. aegypti populations worldwide. However, variations between peak activity have been detected between populations. In East Africa, for instance, Trpis et al.3 reported peak activity at 7:00 and at 19:00, whereas McClelland10 reported peak activity two or three hours after sunrise (9:00 or 10:00) and one or two hours before sunset (17:00 or 16:00). Similarly, in the United States, Smith et al.7 observed a bimodal diel activity pattern for Ae. aegypti, but the evening peak was earlier, between 17:00 and 19:00. Despite these variations, the spacing of the peaks is similar in all these studies despite the fact that these studies were conducted in ecologically and climatically diverse locations.The activity patterns observed at site 3 in Brownsville (Fig. 2) and at site 1 in Miami (Fig. 1) were trimodal. In Brownsville, the trimodal activity peaks were between 6:30 and 7:30, 9:30 and 10:30, and 18:30 and 19:30 (Fig. 2), and in Miami the trimodal peaks were between 7:00 and 8:00, 9:00 and 10:00 and between 19:00 and 20:00 (Fig. 1). Interestingly, the timing of the third peak was similar in both Brownsville site 3 and Miami site 1 suggesting similar underlying factors despite geographic distance, different ecology, and different climate. Brownsville, Texas, is in the Lower Rio Grande Alluvial Floodplain ecoregion. The climate is humid subtropical and urbanization has removed most of the indigenous palm trees and floodplain forests vegetation (https://www.epa.gov/sites/default/files/2018-05/documents/brownsvilletx.pdf). Miami is in the Tropical Florida Ecoregion. Similar to Brownsville, Texas, urbanization and agriculture has replaced most of the indigenous Pine Rockland vegetation. Trimodal biting patterns for Ae. aegypti have been observed before in Trinidad by Chadee and Martinez4, but the middle peak was observed at 11:00 which is half an hour to an hour later than what we observed in Miami and Brownsville, respectively (Figs. 1 and 2). While the morning and evening peaks coincide with human outdoor activity, the middle peak occurs during high heat conditions and the factors that lead to this peak or its importance in the epidemiology of Ae. aegypti-borne arboviral diseases are currently not known. The studies by McClelland13 observed multiple activity peaks in an East African population of Ae. aegypti. The significance of the different activity patterns to the epidemiology of Ae. aegypti-borne arboviral diseases are currently unknown and we think they need more investigation especially since Ae. aegypti-borne arboviral infections have been rising in the recent past14,15.We observed that the host-seeking activity peaks were consistent between 5:45 and 7:30 and between 18:00 and 20:45 (Figs. 1 and 2). These observations are important in planning and conducting control operations directed at the adult Ae. aegypti female populations. During the 2016 Zika outbreak, there was no specific information on the host-seeking activity patterns of Ae. aegypti in Miami Dade County and the adulticide treatment implemented as part of an integrated approach targeted the morning activity16. The integrated approach effectively reduced the vector population and interrupted the transmission of the Zika virus; however, it highlighted the need for site-specific information on the diel activity patterns of Ae. aegypti in Miami Dade County in particular and the CONUS in general. There have been sporadic Ae. aegypti-borne arboviral disease outbreaks in Miami Dade County, FL and the city of Brownsville, TX17,18,19,20,21, in the future we will be better prepared to conduct effective adulticide applications with the current knowledge of the diel activity patterns of Ae. aegypti in these areas. Furthermore, we are now better equipped to educate the public on how to minimize exposure to Ae. aegypti-borne arboviral diseases by avoiding outdoor activities during peak biting activity periods.In our studies, we used BG-Sentinel 2 traps and monitored them every hour, twenty-four hours a day over 96 h, a method with some similarities to that used by Smith et al.7. In the past, diel biting activity studies were carried out using human landing catches following the methods primarily established by Haddow22. To our knowledge, only two studies have previously used sampling procedures not based on human landing catches to study the biting activity patterns of Ae. aegypti; the study by Ortega-Lopez et al.6 used mosquito electrocuting traps, and the study by Smith et al.7 used a mechanical rotator mosquito trap. In the present study, the use of BG-Sentinel II traps had the advantage that it was specifically designed to capture female host-seeking Ae. aegypti8,9. In addition, attached BG-Counter devices can keep track of the number of mosquitoes captured per specified unit time and environmental conditions, and store the information in a cloud server. However, the BG-Sentinel 2 traps collected a wide variety of mosquito species, (Table 1), and to keep track of specific species captured each hour, we had to monitor them every hour.Overall, we present data on the diel activity of Ae. aegypti populations in two cities in the southern United States. In both cities the activity patterns were bimodal; there were peaks of activity in the mornings and the evenings. The significance of these observations is that these peaks can be targeted to improve the effectiveness of adulticide treatments aimed at controlling Ae. aegypti adult populations. Using BG-Sentinel 2 traps eliminates individual variations associated with human landing catches and the associated danger of infections from wild mosquitoes especially during ongoing outbreaks. More

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