ecology

Jasper Ridge Biological Preserve, Stanford University’s biological field station, is pleased to announce a new fellowship program to support a visiting scholar, focused on the emerging science of restoration ecology. Pervasive impacts of human actions now affect all parts of the Earth, degrading its ability to provide critical goods and services. These impacts underscore theneed for science-based approaches to sustaining or restoring ecosystems. Increasingly, the scientific challenges faced by ecologists extend beyond understanding the basic mechanisms that control the responses of unmanaged ecosystems. The fundamental science of ecology must develop the foundations to support thoughtful, sophisticated, and effective interventions, operating at a range of different scales. To support this development, the Jasper Ridge restoration fellowship will support visits, up to a year in length, by researchersranging in seniority from post-docs to senior faculty. Applications will be assessed based on an individual’s past accomplishments and on potential to take advantage of the ecosystems and past research at Jasper Ridge, as well as the intellectual community at Stanford. Field-based research at Jasper Ridge, collaborations with Stanford faculty, and an interest in interacting with students will all be assets.

Applications are invited for a full-time Senior Research Specialist position in Dave Breshears' group in the School of Natural Resources at the University of Arizona (annual salary range $39,173 - $44,364, Annually (DOE); hard money position). The successful candidate will interact with collaborators in ecosystems ecology, hydrology, physiological ecology, soil science, landscape ecology, remote sensing and ecosystem modeling, including University of Arizona activities related to national environmental research networks and experimental facilities. Research emphasizes interrelationships between ecology and hydrology in dryland ecosystems, interactions between woody and herbaceous plants, carbon and water fluxes, wind vs. water erosion and new technology for measuring soil carbon (Laser-Induced Breakdown Spectroscopy). Research in progress or planned for the near future will include studies of tree mortality in the field and in an experimental facility and field and lab studies relating soil erosion to decomposition rates in drylands. Regular multi-day field trips to research sites off campus and out of state for data and sample collection are required. Must be physically fit and capable of working outdoors under rigorous climatic conditions at remote sites. Supervision and coordination of activities of undergraduate student workers, technicians and graduate students in the processing of plant, soil, and water samples for physical and chemical analysis are an important component of the position. Statistical analysis, management of databases and the preparation of manuscripts will also be important responsibilities. The position can be tailored to the expertise/interests of the selected applicant. Candidate is expected to actively participate in the writing of manuscripts and grant proposals. For more details on activities see http://ag.arizona.edu/research/breshears/ and http://snr.arizona.edu/. Minimum Qualifications: MS in botany, plant ecophysiology, plant ecology, or related field of environmental science and 3 years of research experience; or Bachelor's degree related to environmental science and 4 years of research experience. Starting Date: 10 September 2008 (or as soon thereafter as possible) Application Procedure: Applications for this position (Job No. 40904) must be made on-line via the University of Arizona Human Resources web page

Landscape physical and biological processes are coupled to, and generally primary determinants of, in-stream physical and biological processes. In many cases we have a thorough mechanistic understanding of these relationships, though perhaps only for small spatial scales or single process domains; while in other cases we may have correlated patterns of physical and biological processes across the landscape. Nonetheless, this knowledge can be aggregated to form the basis of larger scale predictive models. This project seeks to develop landscape-scale process models to predict large-scale patterns of productivity and life-history diversity in Pacific Northwest salmonid fishes. These process models are intended to relate upslope conditions (e.g., vegetation, geology, topography) and processes (e.g., erosion, heat-transport) to in-stream biological and physical conditions and processes to explore the extent to which landscape scale physical and biological processes predict patterns of life history and species diversity. The end product of this project would be a spatially explicit prediction of in-stream habitat conditions that support salmonid ecological and evolutionary processes. Data and information for this project will be drawn from ongoing research and monitoring efforts across the Pacific Northwest; however, where locally specific information is lacking, the candidate will be expected to develop the necessary data from the literature. The candidate for this position will be an active member of an existing research team, and will need to interact with a broad range of researchers to develop model inputs. The position will require the ability to take a diverse set of inputs (data, expert opinion, off-the shelf models) to generate the necessary predictive model components, and as a result, the ability to develop complex computer programs for data reduction and model construction is necessary. These Post-doctoral or Senior Associate research position are being offered through the NRC's Research Associateship Program (http://www7.nationalacademies.org/rap/). For further information please contact: Dr. Chris Jordan NOAA-National Marine Fisheries – Northwest Fisheries Science Center c/o US EPA Western Ecology Division Corvallis, OR chris.jordan@noaa.gov

In an ongoing research project funded by NOAA-Fisheries, Bonneville Power Administration and the US Bureau of Reclamation, we are developing approached to data collection, management and analysis to support the conservation of ESA listed salmonid fish populations and their freshwater habitats. The analysis component of this project has the following goals: (i) understanding the role monitoring methods play in determining data quality and utility; (ii) developing recommendations on monitoring methods; (iii) developing process models to guide resource management decision making; and (iv) developing a data analysis framework that will form the basis of future data collection and management efforts. The project currently has an opening for a post-doctoral researcher in the data analysis group. This position will work closely with the Principal Investigator, project coordinators and contract technical staff, but would have free reign to design and conduct data analysis projects. The data analysis component of the project will require significant data reduction, description and summarization through standard univariate modeling approaches. However, the novel aspects of the work are expected to come from pattern and process exploration through multivariate approaches to ordination and classification, as well as model based methods such as, Structural Equation Modeling and MAR-1. The successful candidate will be a productive, self-directed and motivated individual interested in exploring multiple large, complex data sets through statistically based modeling approaches. Familiarity with aquatic biological processes, in particular anadromous salmonid life-cycles, is a strong plus, but not an absolute requirement. The ability to develop novel, single-use computer models, as well as make use of existing data analysis software packages, is a requirement. Formal training in model-based statistical data analysis and a demonstrated record of productivity is necessary. For further information please contact: Dr. Chris Jordan NOAA-National Marine Fisheries – Northwest Fisheries Science Center c/o US EPA Western Ecology Division Corvallis, OR chris.jordan@noaa.gov