2 new papers from NYMPHS project

Two new forensic palynology papers from our MURI-funded NYMPHS project were recently published.

Daoqin Tong (ASU) led the effort to develop GOFIND, a geocomputational network model to help identify “spatial footprints” or likely locations associated with an object.

Eddie Helderop (UT-Austin) was lead author on a that further refined the GOFIND model using modified social network analysis tools.

New paper on quantifying dynamic interactions

This paper explores combining point-based and path-based dynamic interaction metrics to explore whether movement differences associated with proximity can be detected. Differences in simulated trajectories for ovement parameters ranging from step length and relative angle to more complex parameters such as persistence index were tested. Movement was often different for simulated ‘leader’ and ‘follower’ trajectories, and different interaction-related movement behaviors were explored using data on black-backed jackals in Etosha National Park.

A spatial exploration of the ‘halo effect’ in the 2016 US presidential election

Was the ‘halo effect’ (proportion of immigrants in surrounding area) correlated with GOP vote proportion in the 2016 election? The halo effect has been studied in Europe, where it has often been correlated with far-right voting patterns. This paper examines the halo effect and uses geographically weighted regression to explore whether the effect varies spatially.

Post-doc opportunity in SDM

Opportunity: 1-3 years post-doctoral position using species distribution models to track movement of pollen. This opportunity will be in the Geography Department at UT-Austin but part of a collaborative project with the Biology Department at UT, Arizona State University, and Emory University.

Responsibilities: This (potentially) 5 year project is focused on forensic palynology (NYMPHS) The post-doc position will primarily be involved in developing models that incorporate ‘movement’ (dispersal, accessibility) and human impacts, in addition to biotic and abiotic factors (see figure 1) in order to model the distribution of pollen and pollinators. Ultimately, we are interested in developing a geocomputation toolbox that is capable of predicting the movement paths of forensic pollen samples over space and time.

Experience: Ideal candidates will have PhD in Geography, Ecology/Biology, or similar discipline with excellent statistical and data management skills. In particular, we are looking for candidates with extensive experience using R for species distribution models (preferably experience using BIOMOD2 or similar ensemble forecasting package). Additional experience and interest in pollen/pollinator networks, data science, and/or GIScience is preferred. Candidate should also have a strong publication record and ability to work in a collaborative research environment.

Location: The University of Texas at Austin, one of the biggest and best research universities in the world, is located in the dynamic capitol city of Austin, recently named the best place to live in America for the 2nd year in a row. The position will be in the Department of Geography and the Environment.

More information: Start date can be as soon as March 2020 (negotiable). To apply, please send a single .PDF document with your CV, cover letter outlining your relevant experience and when you could start, and names and contact information for three references.  For more information, contact Jennifer A Miller

New project on Forensic Palynology

Jennifer is one of six PIs on a new MURI-funded project on ‘Multi-layer Network Modeling of Plant and Pollen Distribution across Space’ (NYMPHS). The purpose of this research program is to make forensic palynology via metabarcoding a key operational contributor to national security by equipping the DoD with a set of reliable, globally validated, easy-to-use geocomputational tools, mathematical models, and SDMs for geolocating pollen samples.