Species Status Assessments (SSAs) are a recent development intended to increase scientific rigor as well as streamline and improve efficiency and effectiveness in listing and management of endangered species under the Endangered Species Act. A fundamental component of a successful SSA is a comprehensive understanding of the current and predicted future distribution of a species considered for ESA listing. An empirical species distribution modeling (SDM) approach provides a transparent and repeatable method for incorporating existing species occurrence data and predicting its present distribution based on presumed associations with habitat and landscape features.
Approaches to developing empirically‐derived SDMs include generalized linear (GLM), generalized additive (GAM), generalized boosting (GBM), maximum entropy (MaxEnt) models and more. Ensemble modeling, a technique that aggregates many models to account for uncertainties associated with different modeling approaches, can alleviate potential bias and inaccuracy related to variation in modeling approaches. In this project we will work with federal and state partners to identify a suite of at-risk species, coalesce available data, and develop and validate ensemble SDM’s to predict present and future distributions of species in the Southeastern U.S. These data will be used to guide SSA monitoring efforts and inform listing decisions made under the U.S. Endangered Species Act.
SUMMARY OF POSITION
The Post-Doctoral Associate will work closely with co-investigators at the Mississippi State University Quantitative Ecology and Spatial Technologies (QuEST) lab, scientists from the U.S. Fish and Wildlife Service (USFWS), and southeastern state wildlife agencies to carry out the project. The successful candidate for this position will compile distribution data for At-risk Species from various sources (e.g., State Heritage Programs, NatureServe, iNaturalist), develop high-resolution distribution maps for species and species-guilds on the Service’s At-risk Species list for the Southeastern U.S. This information will also inform an assessment of species sustainability based on habitat condition, species presence, and future potential system stressors (e.g., urbanization). The successful candidate will also be responsible for developing relationships and communicating with SSA leads for each target species representing U.S. Fish and Wildlife Service, State Wildlife Diversity coordinators, State Heritage Program coordinators and database managers, and other key conservation partners. The candidate will also be required to publish 2 or more peer-reviewed publications from this work.
In addition to project duties the candidate will be expected to take a leadership role in the MSU QuEST lab, which includes but is not limited to mentorship of graduate and undergraduate researchers, involvement in lab research projects, coordination of lab meetings, leadership on lab-generated publications, as well as development and curation of lab website content.
The 2-year term limited position will be funded from 2 January 2019 through 31 December 2020. Salary will be $45,900 per year, including a full benefits package, with the possibility of extension pending acquisition of additional extramural funding. The Post-Doctoral Associate will be based out of the Quantitative Ecology and Spatial Technologies Lab in the Department of Wildlife, Fisheries and Aquaculture at Mississippi State University in Starkville, Mississippi.
Minimum Qualifications. The Postdoctoral Associate must hold a Ph.D. in wildlife and fisheries science, natural resource management, biological science, environmental science/management, or other related fields. The candidate must have some combination of the following skills: spatially explicit species distribution modeling, landscape conservation, and computer programming. Expertise with geographic information systems (GIS) and other related software applications and technologies is also required, and a general background in quantitative ecology and biostatistics will be viewed favorably. The candidate must also have excellent oral and written communication skills, be self-motivated, and able to work effectively both independently and as part of a cross-agency team.
Preferred Qualifications. A Ph.D. in wildlife and fisheries science, natural resource management, biological science, environmental science/management, or other related fields. The preferred candidate will also have experience compiling and conducting QA/QC on state-level wildlife and fisheries datasets, working with sensitive species data (T&E species or Species at Risk), developing large-scale species and ecosystem models, and extrapolating models to watershed-level spatial datasets. The preferred candidate will have an understanding of the U.S. Fish and Wildlife Service At-Risk Species efforts, state wildlife diversity programs, and state natural heritage programs. Additional expertise in other species modeling (e.g., Maxent, GAM, GBM, GLM), urbanization and climate change predictive modeling, and GIS software applications (e.g., Google Earth Engine, ERDAS Imagine, ENVI, GRASS, QGIS, etc.) is considered a valuable asset for this position.
Applications must include: 1) cover letter, 2) resume or Curriculum Vitae, 3) one-page statement of interest and expertise, 4) contact information for three references, and 5) academic transcripts, Applicants must complete an application through the MSU HRM website, but should also email a copy of the cover letter and resume/CV directly to firstname.lastname@example.org.
Application review will begin October 22, 2018 and will continue until the position is filled.
ANTICIPATED START DATE
January 2, 2019
This position may require some travel to stakeholder meetings and to present research findings at regional and/or national conferences (approximately 2-4 overnight trips during the year).
Applicants may contact Kristine Evans [email@example.com] with any questions prior to application submission.
MSU is an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, ethnicity, sex (including pregnancy and gender identity), national origin, disability status, age, sexual orientation, genetic information, protected veteran status, or any other characteristic protected by law. We always welcome nominations and applications from women, members of any minority group, and others who share our passion for building a diverse community that reflects the diversity in our student population.