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Person

Don DeAngelis

Research Ecologist

Wetland and Aquatic Research Center

Email: don_deangelis@usgs.gov
Office Phone: 305-284-1690
Fax: 305-284-3039
ORCID: 0000-0002-1570-4057

Location
Dept. of Biology, Univ of Miami, P.O. Box 249118
Coral Gables , FL 33124-0421
USA

Supervisor: Deborah M Epperson
The data are population sizes of yeast Saccharaomyces cerevisiae growth in laboratory cultures over a period of several days with different levels of growth inhibitor cycloheximide.
This project directly addresses a recovery action in FWS’s recovery plan for the snail kite (Multi-species Recovery Plan for South Florida - Everglade Snail Kite, 1999), which recommends the development of modeling that can predict the response of snail kites to changes in hydropattern anticipated for specific water management proposals (recovery action H3.1). The proposed real-time models would be a significant first step in completing this recovery action, which specifies modeling that would include “...linkage to apple snail distribution and abundance, vegetation characteristics in the landscape influencing the snail kite’s successful foraging, and linkage of all these factors to hydrology.” In addition, this...
Old World Climbing Fern (Lygodium microphyllum (Cav.) R. Br.) is an invasive plant species threatening South Florida ecosystems. In 1989 L. microphyllum was observed in the Arthur R. Marshall Loxahatchee National Wildlife Refuge. L. microphyllum has attributes that allow it to thrive in South Florida. It produces a huge number of small spores that are easily suspended by wind and dispersed, have a high germination rate, and gametophytes have a high rate of sporophyte production. Aerial dispersal allows it to spread to suitable germination sites and its ability to self-fertilize at such high rates promotes successful colonization. The intense, abundant spore production also assists in saturating most suitable colonization...
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Natural resource planning at all scales demands methods for assessing the impacts of resource development and use, and in particular it requires standardized methods that yield robust and unbiased results. Building from existing probabilistic methods for assessing the volumes of energy and mineral resources, we provide an algorithm for consistent, reproducible, quantitative assessment of resource development impacts. The approach combines probabilistic input data with Monte Carlo statistical methods to determine probabilistic outputs that convey the uncertainties inherent in the data. For example, one can utilize our algorithm to combine data from a natural gas resource assessment with maps of sage grouse leks and...
Categories: Data, Publication; Types: Citation
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