Jeff Doser

Jeff Doser

Postdoctoral Research Associate

Michigan State University

Biography

I am a statistical ecologist interested in the development and application of hierarchical Bayesian models for wildlife and natural resource management and conservation. I currently work as a postdoctoral research associate in the Department of Integrative Biology at Michigan State University in the Zipkin Quantitative Ecology Lab. I received my PhD in Forestry and Ecology, Evolution, and Behavior at Michigan State University in the Geospatial Lab of Dr. Andrew Finley. My research interests lie in the development of Bayesian hierarchical models for environmental monitoring and decision making. More specifically, I am interested in the development and application of statistical models for methods of monitoring wildlife and plant populations across large spatio-temporal regions by leveraging a variety of data sources, including citizen science data and acoustic recordings.

Interests
  • Statistical Ecology
  • Wildlife Conservation
  • Bayesian Modeling and Software
  • Natural Resource Management
Education
  • PhD Forestry and Ecology, Evolution, and Behavior, 2022

    Michigan State University

  • MS Applied Statistics, 2021

    Michigan State University

  • BS Mathematics and Biology, 2018

    State University of New York at Geneseo

Skills

R Programming
Bayesian Statistics
Wildlife Ecology

Experience

 
 
 
 
 
Co-Instructor
Michigan State University
Jan 2021 – Sep 2022 East Lansing, Michigan

Courses taught:

  • FOR/STT 875: R Programming for Data Sciences (Summer 2021-2022)
  • IBIO 831: Statistical Methods in Ecology and Evolution (Spring 2022)
 
 
 
 
 
Statistical Consultant
Michigan State University College of Agriculture and Natural Resources
Sep 2019 – Aug 2020 East Lansing, Michigan
Provided statistical and programming advice to graduate students, technicians, and faculty members employed by the College of Agriculture and Natural Resources or with AgBioResearch.
 
 
 
 
 
Teaching Assistant
Michigan State University
Jan 2021 – Sep 2022 East Lansing, Michigan

Courses taught:

  • FOR 472: Ecological Monitoring and Data Analysis
  • FOR/STT 875: R Programming for Data Sciences

Responsibilities included:

  • Development of course textbook
  • Hands on student assistance
  • Teaching
 
 
 
 
 
Database Programmer
State University of New York at Geneseo
Aug 2016 – May 2018 Geneseo, New York
Developed, modified, and tested Banner applications using SQL, Groovy, SQR, and the Argos Enterprise Reporting System

Research Interests

Developing statistical models to understand biodiversity across macroscales

Developing statistical models to understand biodiversity across macroscales

Understanding the drivers of species distributions and biodiversity at macroscales is complicated by a variety of ecological and observational complexities, such as spatial autocorrelation, nonstationarity in species-environment relationships, and species interactions. In my work, I account for these complexities to provide a more complete understanding of macroscale biodiversity and inform effective monitoring and conservation approaches across spatial scales.

Statistical ecology software development

Statistical ecology software development

Effective wildlife and natural resource management requires user-friendly software that makes state-of-the-art statistical tools accessible to natural resource managers and conservation practitioners. A key pillar of my research is developing computationally-efficient and accessible software to understand the ecological and anthropogenic drivers of species distributions, population dynamics, and biodiversity patterns.

Using autonomous monitoring systems to inform wildlife and natural resource management

Using autonomous monitoring systems to inform wildlife and natural resource management

Autonomous monitoring systems such as acoustic recording units, camera traps, and remote sensing methods (e.g., LiDAR) can provide massive amounts of data to inform biodiversity conservation, yet analyzing these data presents novel computational complexities. In my work, I develop quantitative approaches to leverage these complex data to inform a variety of wildlife and natural resource management objectives.

Recent Publications

A century of statistical Ecology
Guidelines for the use of spatially-varying coefficients in species distribution models
A within-lake occupancy model for starry stonewort, Nitellopsis obtusa, to support early detection and monitoring