Wednesday, April 11 - 2:00 pm to 3:30 pm

North Ballroom of the Student Union Memorial Center

 Download Plenary Speakers Abstracts

Department of Soil Water and Environmental Science

Hany M. Almotairy 

Accumulation of Heavy Metals in Aquaponic system and Effects on Bacterial Antibiotic Resistance

Aquaponics is the combined culture of fish (aquaculture) and plants (hydroponic) in a recirculating water system. It is a technology that holds promise to enhance global food production. However, aquaponics can present potential food safety hazards. For example, heavy metal (HMs) accumulation can be a problem if metal concentrations exceed the maximum contaminant level goal (MCLG). In addition, several studies report that water with elevated levels of HMs correlate to high levels of bacterial antibiotic resistance. This experiment evaluated the presence, diversity, distribution, and accumulation of HMs, and the development bacterial antibiotic resistance in a small scale aquaponic system. Six replicates: 3 control, and 3 spiked with HMs (Cadmium (Cd), Lead (Pb), Mercury (Hg), and Arsenic (As)) were stocked with 25 Tilapia (Oreochromis niloticus) fingerling fish and six plants (Butterhead lettuce (Lactuca sativa)). Accumulation of HMs were determined in water samples (collected every week); sediment samples (collected after week 2); and fish and plant samples collected on day (0) and at the last day of the experiment. In addition, weekly water samples were collected for culturing bacteria to evaluate levels of antibiotic resistance. This presentation, will present results and lessons learned to date. Because many developing countries rely on water that may contain HMs, studies such as this will be of value in maintaining aquaculture systems that produce a safe and sustainable food supply.

Laboratory of Tree-Ring Research

Rebecca R. Renteria

Many ways of knowing: Community-based participatory research in archaeology and STEM-related fields

Archaeological and other scientific projects often take place in communities that may be directly affected by the final outcomes of these endeavors. It is also often the case that these communities are not included or considered in the work being done. Community-based participatory research (CBPR) creates potential to not only involve communities in the work being done, but also gives control to the community to shape, form, and ask research questions that pertain to their wants and needs. This approach has the effect of supporting marginalized and vulnerable communities through partnerships with academic and other community resources. Further, CBPR provides a sustainable model in which projects can be undertaken. Involving and giving agency to communities inherently considers the historical processes that have shaped their presence, existence, resiliency—their sustainability. Over the course of the past three years, I have worked specifically with local high school students from marginalized and vulnerable communities to provide them with exposure to STEM and cultural and heritage preservation opportunities through an archaeological lens. This program, Linking Southwestern Heritage through Archaeology (a partner program of the University of Arizona, the National Park Service, and a local nonprofit), and others like it, if shaped by the needs and wants of the communities in which they are offered, can have the effect of supporting the youth in these communities. These youths then might pursue related fields in their futures that support and empower their communities. This prioritizes and respects the traditional knowledge that has sustained these communities.

Department of Geosciences

Jessie Pearl

Ghost Forest Stories 

(How coastal trees tell us about shoreline change and past hurricanes)

Extreme hydrologic events pose a present and future threat to cities and infrastructure in the densely populated coastal corridor of the northeastern United States (NE).  An understanding of the potential range and return interval of storms, floods, and droughts is important for improving coastal management and hazard planning, as well as the detection and attribution of trends in regional climate phenomena. Here, we examine a suite of evidence for hydrologic events over the past 2000 years in the NE. Our study analyzes a network of hydroclimate sensitive trees, subfossil 'ghost' forests and co-located sediment records, using both classical and isotope dendrochronology, radiocarbon analyses, and sediment stratigraphy. Atlantic White cedar (AWC) forests grow along the NE coast and are exposed to severe coastal weather, as they are typically most successful in near-shore, glacially formed depressions. Many coastal AWC sites are ombrotrophic and contain a precipitation or drought signal in their ring widths. Sub-fossil AWC forests are found where near-shore swamps were drowned and exposed to the ocean. Additionally, the rings of coastal AWC may contain the geochemical signature of landfalling tropical cyclones, which bring with them a large influx of precipitation with distinct oxygen isotopes, which can be used to identify these large storms. Dendrochronology, radiocarbon dating, and analysis of sediment cores are used here to identify and date the occurrence of large overwash events along the coastline of the northeastern United States associated with extreme storms.

School of Natural Resources and the Environment

Mallory Barnes 

Upscaling semi-arid ecosystem carbon flux measurements using spaceborne imagery: a machine learning approach

Remote sensing observations and eddy covariance measurements are both widely used in ecology to improve understanding of biosphere-atmosphere-hydrosphere interactions across scales and in various ecosystems. Continuous measurements from flux towers facilitate exploration of the exchange of carbon dioxide, water and energy between the land surface and the atmosphere at fine temporal and spatial scales, while satellite observations can fill in the large spatial gaps of in-situ measurements and provide long-term temporal continuity. Here we demonstrate a machine learning approach to upscale ecosystem-scale carbon flux estimates to the Southwest (SW United States and NW Mexico) regional scale using remotely sensed and gridded meteorological inputs. Our upscaling method leverages the strengths of both the satellite and flux data, producing spatially and temporally continuous high-resolution estimates of Gross Primary Productivity (GPP). We focus here on water-limited ecosystems, which have been shown to disproportionately impact variability in the global terrestrial carbon sink. Existing upscaled flux products are sparsely informed by water-limited ecosystem measurements. Our machine learning approach was designed specifically for semi-arid ecosystems: with explicit consideration for the impacts of the water balance and drought on carbon dynamics, and validation procedures that assess both interannual and seasonal variability in vegetation carbon uptake. Our spatially and temporally continuous upscaled GPP product help us understand linkages between the carbon and water cycles in semi-arid ecosystems and informs predictions of vegetation response to future climate conditions. By including a multi-scalar drought index (SPEI; Standardized Precipitation Evapotranspiration Index) at multiple timescales as a predictor in our machine learning models, we captured the response of vegetation to short-term drought, seasonal water availability, and interannual precipitation variability. We found that our 1 km spatial resolution was necessary to accurately quantify drought impacts on carbon uptake in the Southwest due to spatially heterogeneity in vegetation and topography. Our product improves on existing globally upscaled products, which do not generally perform well in semi-arid regions. Our machine-learning approach using moderate-resolution (i.e. 1km) satellite and meteorological inputs combines ground measurements of carbon fluxes and spaceborne estimates of vegetation productivity to produce continuous estimates of GPP through space and time that reflect semi-arid ecosystem dynamics. Machine learning approaches can bridge ground and spaceborne observations, with potential applications to improve estimates of ecosystem processes across spatial and temporal scales. 

Department of Hydrology and Atmospheric Sciences
Jack Anderson 
Bioswales: Benefit or Burden?


Green infrastructure has been a tool used by cities to mitigate runoff and improve water quality. However there are dozens of guides, both official manuals and informal neighborhood workshops, leading to a wide variety of design choices. This lack of uniformity can be seen in Tucson with the recent advent of bioswales: vegetated basins intended to capture and clean street runoff during precipitation events. With the lack of comprehensive studies, it is unclear if the hundreds of bioswales, representing thousands of dollars and many of hours of maintenance each year, are performing as their original designers envisioned. In order to test performance, bioswales were subjected to a combination of qualitative and quantitative tests. Based on preliminary results there is evidence that supports the use of large boulders and coarse soil for maximizing infiltration, while basins with high organic matter content may actually reduce overall infiltration. When attempting to balance runoff mitigation with street aesthetics in the form of mature vegetation, there is no single “best” bioswale, but standardization of a few designs can lead to better use of city and neighborhood resources.