The Spatial Signature of Biotic Interactions of a Clonal and a Non-clonal Palmetto in a Subtropical Plant Community

Spatial analyses of plant-distribution patterns can provide inferences about intra- and interspecific biotic interactions. Yet, such analyses are rare for clonal plants because effective tools (i.e., molecular markers) needed to map naturally occurring clonal individuals have only become available recently. Clonal plants are unique in that a single genotype has a potential to spatially place new individuals (i.e., ramets) in response to intra- and interspecific biotic interactions. Laboratory and greenhouse studies suggest that some clonal plants can avoid intra-genet, inter-genet, and inter-specific competition via rootplacement patterns. An intriguing and yet to be explored question is whether a spatial signature of such multi-level biotic interactions can be detected in natural plant communities. The facultatively clonal Serenoa repens and non-clonal Sabal etonia are ecologically similar and co-dominant palmettos that sympatrically occur in the Florida peninsula. We used amplified fragment length polymorphisms (AFLPs) to identify Serenoa genets and also to assign field-unidentifiable small individuals as Sabal seedlings, Serenoa seedlings, or Serenoa vegetative sprouts. Then, we conducted univariate and bivariate multi-distance spatial analyses to examine the spatial interactions of Serenoa (n=271) and Sabal (n=137) within a 20x20 m grid at three levels, intragenet, intergenet and interspecific. We found that spatial interactions were not random at all three levels of biotic interactions. Serenoa genets appear to spatially avoid self-competition as well as intergenet competition. Furthermore, Serenoa and Sabal were spatially negatively associated with each other. However, this negative association pattern was also evident in a spatial comparison between non-clonal Serenoa and Sabal, suggesting that Serenoa genets’ spatial avoidance of Sabal through placement of new ramets is not the explanation of the interspecific-level negative spatial pattern. Our results emphasize the importance of investigating spatial signatures of biotic as well as abiotic interactions at multiple levels in understanding spatial distribution patterns of clonal plants in natural plant communities.

Automated Portal Placement for Arthroscopic Hip Surgery

Placing portal incisions during arthroscopic hip surgery presents challenges for surgeons in terms of anatomic accessibility and patient safety. Based on key anatomic landmarks and portal placement information from recent literature, suggested portal incisions were determined. Guidance in the placement of the three most common portal incision locations (anterior, anterolateral, and posterolateral) for arthroscopic surgery; in addition to visual feedback on tool trajectory to the hip joint is provided in real time by a computer aided system for hip arthroscopy. By simplifying the portal placement process, one of the most challenging aspects of arthroscopic hip surgery, an increased use of this minimally invasive technique could be possible. In addition to portal information, improvements to an existing computer aided system for arthroscopic hip surgery, including a new hip model and redesigned mechanical tracking linkage, were completed.

Hierarchical Approach for Survivable Network Design

A central design challenge facing network planners is how to select a cost-effective network configuration that can provide uninterrupted service despite edge failures. In this paper, we study the Survivable Network Design (SND) problem, a core model underlying the design of such resilient networks that incorporates complex cost and connectivity trade-offs. Given an undirected graph with specified edge costs and (integer) connectivity requirements between pairs of nodes, the SND problem seeks the minimum cost set of edges that interconnects each node pair with at least as many edge-disjoint paths as the connectivity requirement of the nodes. We develop a hierarchical approach for solving the problem that integrates ideas from decomposition, tabu search, randomization, and optimization. The approach decomposes the SND problem into two subproblems, Backbone design and Access design, and uses an iterative multi-stage method for solving the SND problem in a hierarchical fashion. Since both subproblems are NP-hard, we develop effective optimization-based tabu search strategies that balance intensification and diversification to identify near-optimal solutions. To initiate this method, we develop two heuristic procedures that can yield good starting points. We test the combined approach on large-scale SND instances, and empirically assess the quality of the solutions vis-à-vis optimal values or lower bounds. On average, our hierarchical solution approach generates solutions within 2.7% of optimality even for very large problems (that cannot be solved using exact methods), and our results demonstrate that the performance of the method is robust for a variety of problems with different size and connectivity characteristics.