Data management plan for the grant, "NSFDEB-NERC: Collaborative Research: Wildlife corridors: do they work and who benefits?" Research on the impact of wildlife corridors using genetics as the measure of effectiveness. The study will use 20 independent landscapes to quantify how corridor traits affect gene flow, and will use non-flying mammals as focal species because they are strongly affected by fragmentation. The research team hypothesizes (1) a strong non-linear decline in success (gene flow) with corridor length, reflecting the skewed distribution of dispersal distances within species; (2) success will drop steeply as corridor width falls below a threshold, with the threshold determined by species traits; and (3) species that are bigger, are habitat specialists, or have greater dispersal abilities (relative to brain size or reproductive rate) will benefit more from corridors. Testing these hypotheses will allow generalization to a wide range of mammal species not included in this project. It will use highly flexible Random Forest models to answer the overarching question: What landscape traits (e.g., corridor width, degree of human disturbance) and species traits (mobility, affinity to particular land cover types) are associated with effective corridors?

Data management plan for the research grant "Generating pathogen- / pest-resistant non-GMO cotton through targeted genome editing of oxylipin signaling pathways."

Data management plan for the grant, "Collaborative Research: LTREB Renewal - River ecosystem responses to floodplain restoration." This project centers on researching the metabolic transformations of the metal-contaminated Upper Clark Fork River in western Montana, USA, that are shifting with environmental restoration efforts. Specifically, the work conducted at UNT includes dissolved organic matter (DOM) chemical characterization of river samples along a 200 km transect from the Upper Clark Fork River, metal concentration analysis, and short-term incubation experiments on the river water (30 days of microbial processes) using multiple analytical chemistry techniques such as absorbance and fluorescence spectroscopy and atomic absorption spectroscopy or inductively coupled plasma mass spectrometry. Data will be analyzed to identify chemical and biological signals of restoration and assess ecosystem health.