An integrative method for characterizing and monitoring animal

An integrative approach for characterizing and monitoring animal distributions at
hydrothermal vents
Elizabeth L. Podowski1,*, George W. Luther III2, and Charles R. Fisher1
Pennsylvania State University, Biology Department, 208 Mueller Building, University Park, PA
University of Delaware, College of Marine and Earth Studies, Lewes, DE
*[email protected]; Tel.: 001 814 863 8360; Fax: 001 814 865 9131
The feasibility and economic interest of mining deep-sea polymetallic sulfides at
hydrothermal vents has recently escalated, particularly at back-arc spreading centers in the
Western Pacific where the biological communities associated with these hydrothermal vents are
poorly understood. With the advent of deep-sea mining, understanding the processes mediating
animal distribution and natural change in vent communities will be integral in devising apt and
effective environmental regulations and in assessing the potential environmental impacts
anthropogenic disturbance may incur.
We have developed and employed a non-invasive technique that combines highresolution imagery, photo-mosaicking, and spatially resolved in-situ electrochemical
measurements to characterize relationships among the distributions of animals and abiotic factors
and to follow changes in communities over time at diffuse flow areas and on chimneys. We
applied this technique along the Eastern Lau Spreading Center (ELSC) to characterize 7 diffuse
flow (2005 and 2006) and 12 chimney sites (2006), and we will continue to track change in these
communities during an upcoming research cruise in May 2009.
We will present data from two different diffuse flow sites along the ELSC in order to
demonstrate the resolution and efficiency of our technique and describe the occurrence of
different species and their habitat characteristics. As an example of our ability to monitor
temporal change, we will present data from one site that was imaged and chemically surveyed in
2005 and again in 2006. These temporal datasets will establish an important baseline for
distinguishing between natural and human-induced change.