Does variation in movement tactics and trophic interactions among American alligators create habitat linkages?

1. Highly mobile top predators are hypothesized to spatially and/or temporally link disparate habitats through the combination of their movement and feeding patterns, but recent studies suggest that individual specialization in habitat use and feeding could keep habitats compartmentalized. 2.  We used passive acoustic telemetry and stable isotope analysis to investigate whether specialization in movement and feeding patterns of American alligators (Alligator mississippiensis) in an oligotrophic subtropical estuary created habitat linkages between marine and estuarine/freshwater food webs. 3.  Individual alligators adopted one of the three relatively distinct movement tactics that were linked to variation in diets. Fifty-six per cent of alligators regularly travelled from the upstream (freshwater/mid-estuary) areas into the downstream (marine-influenced) areas where salinities exceed those typically tolerated by alligators. Thirty-one per cent of the alligators made regular trips from the mid-estuarine habitat into the upstream habitat; 13% remained in the mid-estuary zone year-round. 4.  Stable isotopic analysis indicated that, unlike individuals remaining in the mid-estuary and upstream zones, alligators that used the downstream zone fed at least partially from marine food webs and likely moved to access higher prey abundance at the expense of salt stress. Therefore, ‘commuting’ alligators may link marine food webs with those of the estuary and marshes in the coastal Everglades and create an upstream vector for allochthonous nutrient inputs into the estuary. 5.  This study lends further support to the hypothesis that large-bodied highly mobile predators faced with trade-offs are likely to exhibit individual specialization leading to habitat linkages, rather than compartmentalization. However, the conditions under which this scenario occurs require further investigation.

Modeling the role of investigator bias, interrogation techniques, and suspect decision -making on the likelihood of confession

The purpose of the current study was to attempt to model various cognitive and social processes that are believed to lead to false confessions. More specifically, this study manipulated the variables of experimenter expectancy, guilt-innocence of the suspect, and interrogation techniques using the Russano et al. (2005) paradigm. The primary measure of interest was the likelihood of the participant signing the confession statement. By manipulating experimenter expectancy, the current study sought to further explore the social interactions that may occur in the interrogation room. In addition, in past experiments, the interrogator has typically been restricted to the use of one or two interrogation techniques. In the present study, interrogators were permitted to select from 15 different interrogation techniques when attempting to solicit a confession from participants. ^ Consistent with Rusanno et al. (2005), guilty participants (94%) were more likely to confess to the act of cheating than innocent participants (31%). The variable of experimenter expectancy did not effect confessions rates, length of interrogation, or the type of interrogation techniques used. Path analysis revealed feelings of pressure and the weighing of consequences on the part of the participant were associated with the signing of the confession statement. The findings suggest the guilt/innocence of the participant, the participant's perceptions of the interrogation situation, and length of interrogation play a pivotal role in the signing of the confession statement. Further examination of these variables may provide researchers with a better understanding of the relationship between interrogations and confessions. ^

Modeling, simulation, and characterization of space debris in low-Earth orbit

Every space launch increases the overall amount of space debris. Satellites have limited awareness of nearby objects that might pose a collision hazard. Astrometric, radiometric, and thermal models for the study of space debris in low-Earth orbit have been developed. This modeled approach proposes analysis methods that provide increased Local Area Awareness for satellites in low-Earth and geostationary orbit. Local Area Awareness is defined as the ability to detect, characterize, and extract useful information regarding resident space objects as they move through the space environment surrounding a spacecraft. The study of space debris is of critical importance to all space-faring nations. Characterization efforts are proposed using long-wave infrared sensors for space-based observations of debris objects in low-Earth orbit. Long-wave infrared sensors are commercially available and do not require solar illumination to be observed, as their received signal is temperature dependent. The characterization of debris objects through means of passive imaging techniques allows for further studies into the origination, specifications, and future trajectory of debris objects. Conclusions are made regarding the aforementioned thermal analysis as a function of debris orbit, geometry, orientation with respect to time, and material properties. Development of a thermal model permits the characterization of debris objects based upon their received long-wave infrared signals. Information regarding the material type, size, and tumble-rate of the observed debris objects are extracted. This investigation proposes the utilization of long-wave infrared radiometric models of typical debris to develop techniques for the detection and characterization of debris objects via signal analysis of unresolved imagery. Knowledge regarding the orbital type and semi-major axis of the observed debris object are extracted via astrometric analysis. This knowledge may aid in the constraint of the admissible region for the initial orbit determination process. The resultant orbital information is then fused with the radiometric characterization analysis enabling further characterization efforts of the observed debris object. This fused analysis, yielding orbital, material, and thermal properties, significantly increases a satellite's Local Area Awareness via an intimate understanding of the debris environment surrounding the spacecraft.

Application of biomarkers and compound specific stable isotopes for the assessment of hydrology as a driver of organic matter dynamics in the Everglades ecosystem

The Everglades is a sub-tropical coastal wetland characterized among others by its hydrological features and deposits of peat. Formation and preservation of organic matter in soils and sediments in this wetland ecosystem is critical for its sustainability and hydrological processes are important divers in the origin, transport and fate of organic matter. With this in mind, organic matter dynamics in the greater Florida Everglades was studied though various organic geochemistry techniques, especially biomarkers, bulk and compound specific δ13C and δD isotope analysis. The main objectives were focused on how different hydrological regimes in this ecosystem control organic matter dynamics, such as the mobilization of particulate organic matter (POM) in freshwater marshes and estuaries, and how organic geochemistry techniques can be applied to reconstruct Everglades paleo-hydrology. For this purpose organic matter in typical vegetation, floc, surface soils, soil cores, and estuarine suspended particulates were characterized in samples selected along hydrological gradients in the Water Conservation Area 3, Shark River Slough and Taylor Slough. ^ This research focused on three general themes: (1) Assessment of the environmental dynamics and source-specific particulate organic carbon export in a mangrove-dominated estuary. (2) Assessment of the origin, transport and fate of organic matter in freshwater marsh. (3) Assessment of historical changes in hydrological conditions in the Everglades (paleo-hydrology) though biomarkes and compound specific isotope analyses. This study reports the first estimate of particulate organic carbon loss from mangrove ecosystems in the Everglades, provides evidence for particulate organic matter transport with regards to the formation of ridge and slough landscapes in the Everglades, and demonstrates the applicability of the combined biomarker and compound-specific stable isotope approach as a means to generate paleohydrological data in wetlands. The data suggests that: (1) Carbon loss from mangrove estuaries is roughly split 50/50 between dissolved and particulate carbon; (2) hydrological remobilization of particulate organic matter from slough to ridge environments may play an important role in the maintenance of the Everglades freshwater landscape; and (3) Historical changes in hydrology have resulted in significant vegetation shifts from historical slough type vegetation to present ridge type vegetation. ^

Formal modeling and analysis techniques for high level Petri nets

Petri Nets are a formal, graphical and executable modeling technique for the specification and analysis of concurrent and distributed systems and have been widely applied in computer science and many other engineering disciplines. Low level Petri nets are simple and useful for modeling control flows but not powerful enough to define data and system functionality. High level Petri nets (HLPNs) have been developed to support data and functionality definitions, such as using complex structured data as tokens and algebraic expressions as transition formulas. Compared to low level Petri nets, HLPNs result in compact system models that are easier to be understood. Therefore, HLPNs are more useful in modeling complex systems. ^ There are two issues in using HLPNs—modeling and analysis. Modeling concerns the abstracting and representing the systems under consideration using HLPNs, and analysis deals with effective ways study the behaviors and properties of the resulting HLPN models. In this dissertation, several modeling and analysis techniques for HLPNs are studied, which are integrated into a framework that is supported by a tool. ^ For modeling, this framework integrates two formal languages: a type of HLPNs called Predicate Transition Net (PrT Net) is used to model a system's behavior and a first-order linear time temporal logic (FOLTL) to specify the system's properties. The main contribution of this dissertation with regard to modeling is to develop a software tool to support the formal modeling capabilities in this framework. ^ For analysis, this framework combines three complementary techniques, simulation, explicit state model checking and bounded model checking (BMC). Simulation is a straightforward and speedy method, but only covers some execution paths in a HLPN model. Explicit state model checking covers all the execution paths but suffers from the state explosion problem. BMC is a tradeoff as it provides a certain level of coverage while more efficient than explicit state model checking. The main contribution of this dissertation with regard to analysis is adapting BMC to analyze HLPN models and integrating the three complementary analysis techniques in a software tool to support the formal analysis capabilities in this framework. ^ The SAMTools developed for this framework in this dissertation integrates three tools: PIPE+ for HLPNs behavioral modeling and simulation, SAMAT for hierarchical structural modeling and property specification, and PIPE+Verifier for behavioral verification.^

Formal Modeling and Analysis Techniques for High Level Petri Nets

Petri Nets are a formal, graphical and executable modeling technique for the specification and analysis of concurrent and distributed systems and have been widely applied in computer science and many other engineering disciplines. Low level Petri nets are simple and useful for modeling control flows but not powerful enough to define data and system functionality. High level Petri nets (HLPNs) have been developed to support data and functionality definitions, such as using complex structured data as tokens and algebraic expressions as transition formulas. Compared to low level Petri nets, HLPNs result in compact system models that are easier to be understood. Therefore, HLPNs are more useful in modeling complex systems. There are two issues in using HLPNs - modeling and analysis. Modeling concerns the abstracting and representing the systems under consideration using HLPNs, and analysis deals with effective ways study the behaviors and properties of the resulting HLPN models. In this dissertation, several modeling and analysis techniques for HLPNs are studied, which are integrated into a framework that is supported by a tool. For modeling, this framework integrates two formal languages: a type of HLPNs called Predicate Transition Net (PrT Net) is used to model a system's behavior and a first-order linear time temporal logic (FOLTL) to specify the system's properties. The main contribution of this dissertation with regard to modeling is to develop a software tool to support the formal modeling capabilities in this framework. For analysis, this framework combines three complementary techniques, simulation, explicit state model checking and bounded model checking (BMC). Simulation is a straightforward and speedy method, but only covers some execution paths in a HLPN model. Explicit state model checking covers all the execution paths but suffers from the state explosion problem. BMC is a tradeoff as it provides a certain level of coverage while more efficient than explicit state model checking. The main contribution of this dissertation with regard to analysis is adapting BMC to analyze HLPN models and integrating the three complementary analysis techniques in a software tool to support the formal analysis capabilities in this framework. The SAMTools developed for this framework in this dissertation integrates three tools: PIPE+ for HLPNs behavioral modeling and simulation, SAMAT for hierarchical structural modeling and property specification, and PIPE+Verifier for behavioral verification.