The Wildlife Habitat Ecology Lab at Montana State University in collaboration with the Montana/Dakotas Office of the Bureau of Land Management is searching for a Postdoctoral Research Associate to lead the development of rigorous spatially-explicit indices of conservation importance and a prioritization framework for conservation activity/policy in the Northern Great Plains (NGP). The candidate will 1) lead the development and testing of species distribution models for rangeland dependent wildlife species in the NGP which will be used to 2) quantify the relative importance of BLM lands for rangeland wildlife conservation, and 3) evaluate the degree to which areas of high wildlife importance overlap with currently identified conservation priority areas and identify priority areas threatened by habitat degradation/loss. Ultimately, the results of this effort will identify and rank new conservation priority areas for wildlife in the NGP, with special focus on BLM lands.

The primary objectives of the research are to:

• Build and evaluate distribution models for rangeland dependent native birds, mammals, and herpetofauna in the Northern Great Plains.
• Identify areas of highest relative importance for rangeland wildlife species and identify biodiversity hotspots within the NGP.
• Evaluate the degree to which areas of high importance:
  • intersect BLM lands (i.e., quantify the relative importance of BLM lands for rangeland wildlife conservation in the NGP),
  • overlap or do not overlap with currently identified conservation priority areas (e.g., US Federal Priority Areas for Conservation, state-designated priority areas) and protected areas (e.g., national monuments, wildlife refuges),
  • are threatened by rangeland loss to woody encroachment, energy development and other alterations to rangelands.
• Identify and rank new conservation priority areas for wildlife in the NGP.
• Identify areas for energy development with lowest relative conservation risk on BLM lands.

Qualifications

Applicants should have a Ph.D. in ecology, environmental sciences, quantitative ecology, or related field. The successful candidate will be a highly motivated and organized individual with quantitative skills in species distribution modeling, machine learning, programming in R / Python. Experience acquiring and handling large datasets of species occurrence, abundance, and remotely sensed habitat characteristics a must. Candidates should have 1) a commitment to producing high-quality research products, 2) excellent communication skills, 3) a demonstrated desire and proven history of publication in peer-reviewed journals.