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The discharge of hydrothermal vents on the seafloor provides energy sources for dynamic and productive ecosystems, which are supported by chemosynthetic microbial populations. These populations use the energy gained by oxidizing the reduced chemicals contained within the vent fluids to fix carbon and support multiple trophic levels. Hydrothermal discharge is ephemeral and chemical composition of such fluids varies over space and time, which can result in geographically distinct microbial communities. To investigate the foundational members of the community, microbial growth chambers were placed within the hydrothermal discharge at Axial Seamount (Juan de Fuca Ridge), Magic Mountain Seamount (Explorer Ridge), and Kamaʻehuakanaloa Seamount (Hawai'i hotspot). Campylobacteria were identified within the nascent communities, but different amplicon sequence variants were present at Axial and Kamaʻehuakanaloa Seamounts, indicating that geography in addition to the composition of the vent effluent influences microbial community development. Across these vent locations, dissolved iron concentration was the strongest driver of community structure. These results provide insights into nascent microbial community structure and shed light on the development of diverse lithotrophic communities at hydrothermal vents.

 

Natural hazards such as hurricanes, floods, and wildfires cause devastating socio-economic impacts on communities. In South Florida, most of these hazards are becoming increasingly frequent and severe because of the warming climate, and changes in vulnerability and exposure, resulting in significant damage to infrastructure, homes, and businesses. To better understand the drivers of these impacts, we developed a bottom-up impact-based methodology that takes into account all relevant drivers for different types of hazards. We identify the specific drivers that co-occurred with socio-economic impacts and determine whether these extreme events were caused by single or multiple hydrometeorological drivers (i.e., compound events). We consider six types of natural hazards: hurricanes, severe storm/thunderstorms, floods, heatwaves, wildfire, and winter weather. Using historical, socio-economic loss data along with observations and reanalysis data for hydrometeorological drivers, we analyze how often these drivers contributed to the impacts of natural hazards in South Florida. We find that for each type of hazard, the relative importance of the drivers varies depending on the severity of the event. For example, wind speed is a key driver of the socio-economic impacts of hurricanes, while precipitation is a key driver of the impacts of flooding. We find that most of the high-impact events in South Florida were compound events, where multiple drivers contributed to the occurrences and impacts of the events. For example, more than 50% of the recorded flooding events were compound events and these contributed to 99% of total property damages and 98% of total crop damages associated with flooding in Miami-Dade County. Our results provide valuable insights into the drivers of natural hazard impacts in South Florida and can inform the development of more effective risk reduction strategies for improving the preparedness and resilience of the region against extreme events. Our bottom-up impact-based methodology can be applied to other regions and hazard types, allowing for more comprehensive and accurate assessments of the impacts of compound hazards. 

Abstract. To improve our understanding of the influence of tropicalcyclones (TCs) on coastal flooding, the relationships between storm surgeand TC characteristics are analyzed for 12 sites along the east coast of theUnited States. This analysis offers a unique perspective by first examiningthe relationship between the characteristics of TCs and their resultingstorm surge and then determining the probabilities of storm surge associatedwith TCs based on exceeding certain TC characteristic thresholds. Usingobservational data, the statistical dependencies of storm surge on TCs areexamined for these characteristics: TC proximity, intensity, path angle, andpropagation speed, by applying both exponential and linear fits to the data.At each tide gauge along the east coast of the United States, storm surge isinfluenced differently by these TC characteristics, with some locations morestrongly influenced by TC intensity and others by TC proximity. Thecorrelation for individual and combined TC characteristics increases whenconditional sorting is applied to isolate strong TCs close to a location.The probabilities of TCs generating surge exceeding specific return levels(RLs) are then analyzed for TCs passing within 500 km of a tide gauge, wherebetween 6 % and 28 % of TCs were found to cause surge exceeding the1-year RL. If only the closest and strongest TCs are considered, thepercentage of TCs that generate surge exceeding the 1-year RL is between 30 % and 70 % at sites north of Sewell's Point, VA, and over 65 % atalmost all sites south of Charleston, SC. When examining storm surgeproduced by TCs, single-variable regression provides a good fit, whilemulti-variable regression improves the fit, particularly when focusing on TCproximity and intensity, which are, probabilistically, the two mostinfluential TC characteristics on storm surge.