University of Arizona School of Earth and Environmental Sciences (SEES)

EarthWeek 2018

Plenary Session Wednesday April 11th

2:00 PM – 3:30 PM Student Union - North Ballroom

Presented by:

Department of Hydrology and Atmospheric Sciences Department of Soil, Water and Environmental Science

Department of Geosciences School of Natural Resources and the Environment

Laboratory of Tree-Ring Research

Plenary Speakers

Hany M. Almotairy Graduate Student, SWES

Rebecca Renteria Graduate Student, LTRR

Jessie Pearl Graduate Student, GEO

Mallory Barnes Graduate Student, SNRE

TBD Graduate Student, HAS

SWES

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

Hany M. Almotairy1,

Co-authors: Kevin M. Fitzsimmons1, and Jean E. McLain1

1Department of Soil Water and Environmental Science, The University of Arizona,

Tucson, Arizona

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. Keywords: (Aquaponic, Hydroponic, Bacterial Antibiotic-Resistance, Heavy Metals, Accumulation)

LTRR

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

Rebecca R. Renteria1

1Laboratory of Tree-Ring Research and the School of Anthropology

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.

GEO

Ghost Forest Stories (How coastal trees tell us about shoreline change and past

hurricanes)

Jessie Pearl1

1Department of Geosciences, The University of Arizona, Tucson, Arizona

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

SNRE

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

Mallory Barnes1

Co-authors: Russell L. Scott, David J.P. Moore, Guillermo Ponce Campos, Joel A. Biederman, David D. Breshears

1School of Natural Resources and the Environment, The University of Arizona, Tucson,

Arizona 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.

HAS

TBD