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Marine sediments play important roles in global biogeochemical processes such as carbon degradation and nutrient cycles. To determine the metabolic potential and structure of marine sediment microbial communities, marine sediments were collected from two collection stations (GMT and GMS) in the Gulf of Mexico and analyzed with a functional gene array containing >2000 probes from the genes involved in carbon fixation, organic carbon degradation, contaminant degradation, metal resistance, and nitrogen, sulfur, and phosphorous cycling. Several environmental variables were also measured, including ammonium, nitrate, sulfate, phosphorus, and sulfate reduction. A total of 1343 genes or 67% of the probes showed significant hybridization with at least one of the 12 samples. A total of 722 genes were detected at GMT and 844 were detected at GMS. Two times more genes on the array hybridized in the deep sediments (GMT-11 and GMS-11) than those in the top sediments (GMT-1, GMS-1). Simpson s reciprocal diversity index (1/D) indicated much higher levels of genetic diversity in the deep sediment samples (204.9 for GMT-11 and 201.6 for GMS-11) than the top sediment samples (e.g., 118.4 for GMT-1, and 121.9 for GMS-1). We found no overall correlation among the measured environmental variables and the functional gene community. However, specific gene groups showed significant relationships with specific environmental variables. Our results indicate that while geochemistry may not explain changes in community structure, overall; geochemistry may be related to specific functional guilds. Additionally, distance plays a large role in community structure for these sediment cores where near-neighbor shows the greatest similarity.

Computer simulations have been used by us since the early 1970s to gain an understanding of the spacing and movement patterns of confined animals. The work has progressed from the early stages, in which we used randomly positioned points, to current investigations of animats (computer-simulated animals), which show low levels of learning via artificial neural networks. We have determined that 1) pens of equal floor area but of different shape result in different spatial and movement patterns for randomly positioned and moving animats; 2) when group size increases under constant density, freedom of movement approaches an asymptote at approximately six animats; 3) matching the number of animats with the number of corners results in optimal freedom of movement for small groups of animats; and 4) perimeter positioning occurs in groups of animats that maximize their distance to first- and second-nearest neighbors. Recently, we developed animats that move, compete for social dominance, and are motivated to obtain resources (food, resting sites, etc.). We are currently developing an animat that learns its behavior from the spatial and movement data collected on live pigs. The animat model is then used to pretest pen designs, followed by new pig spatial data fed into the animat model, resulting in a new pen design to be tested, and the steps are repeated. We believe that methodologies from artificial-life and artificial intelligence can contribute to the understanding of basic animal behavior principles, as well as to the solving of problems in production agriculture in areas such as animal housing design. Key Words: Animal Housing, Group Behavior, Computer Simulation