EVALUATION AND MONITORING OF ASH (FRAXINUS SPP.) TOLERANT TO LONG-TERM EMERALD ASH BORER (AGRILUS PLANIPENNIS [COLEOPTERA: BUPRESTIDAE]) EXPOSURE

Invasive insects that successfully establish in introduced areas can significantly alter natural communities. These pests require specific establishment criteria (e.g. host suitability) that, when known, can help quantify potential damage to infested areas. Emerald ash borer (Agrilus planipennis [Coleoptera: Buprestidae]) is an invasive phloem-feeding pest which is responsible for the death of millions of ash trees (Fraxinus spp. L.). Over 200 surviving ash trees were previously identified in the Huron-Clinton Metroparks located in southeast Michigan. Trees were assessed over a four year period and a hierarchical cluster analysis was performed on dieback, vigor, and presence of signs and symptoms, in order to place trees into one of three tolerance groups. The clustering of trees with different responses to emerald ash borer attack suggests that there are different tolerance levels in North American ash trees in southeastern Michigan, and these groups were designated as apparently tolerant, not tolerant and intermediate tolerance. Adult landing rates and evidence of adult emergence were significantly lower in the apparently tolerant group compared with the not tolerant group, but larval survival from eggs placed on trees did not differ between tolerance groups. Therefore, it appears that apparently tolerant trees survive because they are less attractive to adult beetles which results in fewer eggs being laid on them. Trees in the apparently tolerant group remained of higher vigor over the four years of the study. North American ash may survive the emerald ash borer epidemic due to natural variation and inherent resistance regardless of the lack of co-evolutionary history with emerald ash borer.

DATA DRIVEN INTELLIGENT AGENT NETWORKS FOR ADAPTIVE MONITORING AND CONTROL

To analyze the characteristics and predict the dynamic behaviors of complex systems over time, comprehensive research to enable the development of systems that can intelligently adapt to the evolving conditions and infer new knowledge with algorithms that are not predesigned is crucially needed. This dissertation research studies the integration of the techniques and methodologies resulted from the fields of pattern recognition, intelligent agents, artificial immune systems, and distributed computing platforms, to create technologies that can more accurately describe and control the dynamics of real-world complex systems. The need for such technologies is emerging in manufacturing, transportation, hazard mitigation, weather and climate prediction, homeland security, and emergency response. Motivated by the ability of mobile agents to dynamically incorporate additional computational and control algorithms into executing applications, mobile agent technology is employed in this research for the adaptive sensing and monitoring in a wireless sensor network. Mobile agents are software components that can travel from one computing platform to another in a network and carry programs and data states that are needed for performing the assigned tasks. To support the generation, migration, communication, and management of mobile monitoring agents, an embeddable mobile agent system (Mobile-C) is integrated with sensor nodes. Mobile monitoring agents visit distributed sensor nodes, read real-time sensor data, and perform anomaly detection using the equipped pattern recognition algorithms. The optimal control of agents is achieved by mimicking the adaptive immune response and the application of multi-objective optimization algorithms. The mobile agent approach provides potential to reduce the communication load and energy consumption in monitoring networks. The major research work of this dissertation project includes: (1) studying effective feature extraction methods for time series measurement data; (2) investigating the impact of the feature extraction methods and dissimilarity measures on the performance of pattern recognition; (3) researching the effects of environmental factors on the performance of pattern recognition; (4) integrating an embeddable mobile agent system with wireless sensor nodes; (5) optimizing agent generation and distribution using artificial immune system concept and multi-objective algorithms; (6) applying mobile agent technology and pattern recognition algorithms for adaptive structural health monitoring and driving cycle pattern recognition; (7) developing a web-based monitoring network to enable the visualization and analysis of real-time sensor data remotely. Techniques and algorithms developed in this dissertation project will contribute to research advances in networked distributed systems operating under changing environments.

Comparisons between OMI SO2 data and ground-based SO2 measurements at Turrialba volcano

Turrialba is one of the largest and most active stratovolcanoes in the Central Cordillera of Costa Rica and an excellent target for validation of satellite data using ground based measurements due to its high elevation, relative ease of access, and persistent elevated SO2 degassing. The Ozone Monitoring Instrument (OMI) aboard the Aura satellite makes daily global observations of atmospheric trace gases and it is used in this investigation to obtain volcanic SO2 retrievals in the Turrialba volcanic plume. We present and evaluate the relative accuracy of two OMI SO2 data analysis procedures, the automatic Band Residual Index (BRI) technique and the manual Normalized Cloud-mass (NCM) method. We find a linear correlation and good quantitative agreement between SO2 burdens derived from the BRI and NCM techniques, with an improved correlation when wet season data are excluded. We also present the first comparisons between volcanic SO2 emission rates obtained from ground-based mini-DOAS measurements at Turrialba and three new OMI SO2 data analysis techniques: the MODIS smoke estimation, OMI SO2 lifetime, and OMI SO2 transect techniques. A robust validation of OMI SO2 retrievals was made, with both qualitative and quantitative agreements under specific atmospheric conditions, proving the utility of satellite measurements for estimating accurate SO2 emission rates and monitoring passively degassing volcanoes.

STATE-AND-TRANSITION MODELING AND ADAPTIVE MANAGEMENT: TOOLS FOR FUTURE CO-DEVELOPMENT OF MADAGASCAR’S NATURAL RESOURCES AND PEOPLE

Madagascar’s terrestrial and aquatic ecosystems have long supported a unique set of ecological communities, many of whom are endemic to the tropical island. Those same ecosystems have been a source of valuable natural resources to some of the poorest people in the world. Nevertheless, with pride, ingenuity and resourcefulness, the Malagasy people of the southwest coast, being of Vezo identity, subsist with low development fishing techniques aimed at an increasingly threatened host of aquatic seascapes. Mangroves, sea grass bed, and coral reefs of the region are under increased pressure from the general populace for both food provisions and support of economic opportunity. Besides purveyors and extractors, the coastal waters are also subject to a number of natural stressors, including cyclones and invasive, predator species of both flora and fauna. In addition, the aquatic ecosystems of the region are undergoing increased nutrient and sediment runoff due, in part, to Madagascar’s heavy reliance on land for agricultural purposes (Scales, 2011). Moreover, its coastal waters, like so many throughout the world, have been proven to be warming at an alarming rate over the past few decades. In recognizing the intimate interconnectedness of the both the social and ecological systems, conservation organizations have invoked a host of complimentary conservation and social development efforts with the dual aim of preserving or restoring the health of both the coastal ecosystems and the people of the region. This paper provides a way of thinking more holistically about the social-ecological system within a resiliency frame of understanding. Secondly, it applies a platform known as state-and-transition modeling to give form to the process. State-and-transition modeling is an iterative investigation into the physical makeup of a system of study as well as the boundaries and influences on that state, and has been used in restorative ecology for more than a decade. Lastly, that model is sited within an adaptive management scheme that provides a structured, cyclical, objective-oriented process for testing stakeholders cognitive understanding of the ecosystem through a pragmatic implementation and monitoring a host of small-scale interventions developed as part of the adaptive management process. Throughout, evidence of the application of the theories and frameworks are offered, with every effort made to retool conservation-minded development practitioners with a comprehensive strategy for addressing the increasingly fragile social-ecological systems of southwest Madagascar. It is offered, in conclusion, that the seascapes of the region would be an excellent case study worthy of future application of state-and-transition modeling and adaptive management as frameworks for conservation-minded development practitioners whose multiple projects, each with its own objective, have been implemented with a single goal in mind: preserve and protect the state of the supporting environment while providing for the basic needs of the local Malagasy people.

SPRING DISCHARGE MONITORING IN LOW-RESOURCE SETTINGS: A CASE STUDY OF CONCEPCIÓN CHIQUIRICHAPA, GUATEMALA

Water springs are the principal source of water for many localities in Central America, including the municipality of Concepción Chiquirichapa in the Western Highlands of Guatemala. Long-term monitoring records are critical for informed water management as well as resource forecasting, though data are scarce and monitoring in low-resource settings presents special challenges. Spring discharge was monitored monthly in six municipal springs during the author’s Peace Corps assignment, from May 2011 to March 2012, and water level height was monitored in two spring boxes over the same time period using automated water-level loggers. The intention of this approach was to circumvent the need for frequent and time-intensive manual measurement by identifying a fixed relationship between discharge and water level. No such relationship was identified, but the water level record reveals that spring yield increased for four months following Tropical Depression 12E in October 2011. This suggests that the relationship between extreme precipitation events and long-term water spring yields in Concepción should be examined further. These limited discharge data also indicate that aquifer baseflow recession and catchment water balance could be successfully characterized if a long-term discharge record were established. This study also presents technical and social considerations for selecting a methodology for spring discharge measurement and highlights the importance of local interest in conducting successful community-based research in intercultural low-resource settings.

INTEGRATED ASSESSMENT OF ANTHROPOGENIC, CLIMATE, AND POLICY INDUCED CHANGES OF PHOSPHORUS EXPORT IN THE UNITED STATES LAURENTIAN GREAT LAKES WATERSHEDS

Anthropogenic activities have increased phosphorus (P) loading in tributaries to the Laurentian Great Lakes resulting in eutrophication in small bays to most notably, Lake Erie. Changes to surface water quality from P loading have resulted in billions of dollars in damage and threaten the health of the world’s largest freshwater resource. To understand the factors affecting P delivery with projected increasing urban lands and biofuels expansion, two spatially explicit models were coupled. The coupled models predict that the majority of the basin will experience a significant increase in urban area P sources while the agriculture intensity and forest sources of P will decrease. Changes in P loading across the basin will be highly variable spatially. Additionally, the impacts of climate change on high precipitation events across the Great Lakes were examined. Using historical regression relationships on phosphorus concentrations, key Great Lakes tributaries were found to have future changes including decreasing total loads and increases to high-flow loading events. The urbanized Cuyahoga watersheds exhibits the most vulnerability to these climate-induced changes with increases in total loading and storm loading , while the forested Au Sable watershed exhibits greater resilience. Finally, the monitoring network currently in place for sampling the amount of phosphorus entering the U.S. Great Lakes was examined with a focus on the challenges to monitoring. Based on these interviews, the research identified three issues that policy makers interested in maintaining an effective phosphorus monitoring network in the Great Lakes should consider: first, that the policy objectives driving different monitoring programs vary, which results in different patterns of sampling design and frequency; second, that these differences complicate efforts to encourage collaboration; and third, that methods of funding sampling programs vary from agency to agency, further complicating efforts to generate sufficient long-term data to improve our understanding of phosphorus into the Great Lakes. The dissertation combines these three areas of research to present the potential future impacts of P loading in the Great Lakes as anthropogenic activities, climate and monitoring changes. These manuscripts report new experimental data for future sources, loading and climate impacts on phosphorus.

CHARACTERIZING THE FIRST HISTORIC ERUPTION OF NABRO, ERITREA: INSIGHTS FROM THERMAL AND UV REMOTE SENSING

June 2011 saw the first historic eruption of Nabro volcano, one of an ongoing sequence of eruptions in the Afar-Red Sea region since 2005. It halted air travel in northern Africa, contaminated food and water sources, and displaced thousands from their homes. Due to its remote location, little was known about this event in terms of the quantity of erupted products and the timing and mechanisms of their emplacement. Geographic isolation, previous quiescence and regional civil unrest meant that this volcano was effectively unmonitored at the time of eruption, and opportunities for field study are limited. Using free, publicly available satellite data, I examined rates of lava effusion and SO2 emission in order to quantify the amount of erupted products and understand the temporal evolution of the eruption, as well as explore what information can be gleaned about eruption mechanisms using remote sensing data. These data revealed a bimodal eruption, beginning with explosive activity marked by high SO2 emission totalling 1824 - 2299 KT, and extensive ash fall of 270 - 440 km2. This gave way to a period of rapid effusion, producing a ~17 km long lava flow, and a volume of ~22.1 x 106 m3. Mass balance between the SO2 and lava flows reveals no sulfur 'excess', suggesting that nearly all of the degassed magma was extruded. The 2011 eruption of Nabro lasted nearly 6 weeks, and may be considered the second largest historic eruption in Africa. Work such as this highlights the importance of satellite remote sensing for studying and monitoring volcanoes, particularly those in remote regions that may be otherwise inaccessible.