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Organoarsenic compounds and trace elements are added to poultry feed for disease control and improved productivity. These elements are excreted into poultry litter, which is land applied as fertilizer. This study utilized field and laboratory experiments to address the mobility of arsenic (As) and other litter-derived elements within the Broadkill River watershed, DE, a region of intense poultry production. For the first project, a litter application experiment was conducted at an instrumented field site in Sussex Co., DE. After establishing baseline geochemistry of soil water, ground water, and surface water at the site, poultry litter was then applied, followed by post litter application monitoring. Results showed low concentrations of As and other litter-derived elements in soil water; little impact was measured on ground or surface water. Mass balance calculations suggest that the majority of mass of leached elements was accounted for by uptake in underlying soils. The second project examined the impact of long-term litter application on chemical signatures of As, copper (Cu), zinc (Zn) and phosphorous (P) in stream sediments within the Broadkill River watershed. Sediment samples were collected from both upgradient and downgradient reaches within the watershed. Using GIS to overlay hydrology and land use, statistical relationships between As, Cu, Zn and P enrichment factors and land use were examined. Results did not show a relationship between these elements and agricultural land use, but did show a correlation between some of the elements and residential land use. The third project examined the influence of dissolved organic carbon (DOC) on the mobility of As, Cu, Zn and P using laboratory column experiments with soil from the field site. Two influent solutions were used: poultry litter leachate and a simulated solution of similar element concentrations, pH, and ionic strength but without DOC. Results showed that DOC enhanced the mobility of all four elements, but that even with DOC, 60-70% of the Zn, As and P mass was retained within the soil. Cu was fully mobilized by DOC. Patterns of breakthrough curves (BTCs) and mass calculations suggest that the behavior of these litter-derived elements in the column is controlled by both adsorption (including competitive) to soils and complexation with DOC. / Ph. D.

Habitat modeling can assist in managing potentially widespread but poorly known biological resources such as the federally endangered Delmarva fox squirrel (DFS; Sciurus niger cinereus). The ability to predict or identify suitable habitat is a necessary component of this species' recovery. Habitat identification is also an important consideration when evaluating impacts of land development on this species distribution, which is limited to the Delmarva Peninsula. The goal of this study was to build a predictive model of DFS occurrence that can be used towards the effective management of this species. I developed 5 a'priori global models to predict DFS occurrence based on literature review, past models, and professional experience. I used infrared photomonitors to document habitat use of Delmarva fox squirrels at 27 of 86 sites in the southern Maryland portion of the Delmarva Peninsula. All data were collected on the U.S. Fish and Wildlife Service Chesapeake Marshlands National Wildlife Refuge in Dorchester County, Maryland. Preliminary analyses of 27 DFS present (P) and 59 DFS absent (A) sites suggested that DFS use in my study area was significantly (Wilcoxon Mann-Whitney, P < 0.10) correlated with tree stems > 50 cm dbh/ha (Pmean = 16 + 3.8, Amean = 8+ 2.2), tree stems > 40 cm dbh/ha (Pmean = 49 + 8.1, Amean = 33 + 5.5), understory height (Pmean = 11 m + 0.8, Amean = 9 m + 0.5), overstory canopy height (Pmean = 31 m + 0.6, Amean = 28 m + 0.6), percent overstory cover (Pmean = 82 + 3.9, Amean = 73 + 3.1), shrub stems/ha (Pmean = 8068 + 3218, Amean = 11,119 + 2189), and distance from agricultural fields (Pmean = 964 m + 10, Amean = 1308 m + 103). Chi-square analysis indicated a correlation with shrub evenness (observed on 7% of DFS present sites and 21% of DFS absent sites). Using logistic regression and the Information Theoretic approach, I developed 7 model sets (5 a priori and 2 post hoc) to predict the probability of Delmarva fox squirrel habitat use as a function of micro- and macro-habitat characteristics. Of over 200 total model arrays tested, the model that fit the statistical, biological, and pragmatic criteria postulated was a post hoc integrated model: DFS use = percent overstory cover + shrub evenness + overstory canopy height. This model was determined to be the best of its subset (wi = 0.54), had a high percent concordance (>75%), a significant likelihood ratio (P = 0.0015), and the lowest AICc value (98.3) observed. Employing this predictive model of Delmarva fox squirrel occurrence can benefit recovery and consultation processes by facilitating systematic rangewide survey efforts and simplifying site screenings. / Master of Science