Statistical Ecology and Forest Science Lab

The Statistical Ecology and Forest Science Lab is led by Jeff Doser in the Department of Forestry and Environmental Resources at North Carolina State University. The lab develops state-of-the-art statistical models and open-source software tools to inform forest and wildlife management and conservation objectives.

Research

Past and ongoing research themes in the lab

Developing statistical models to understand ecological processes across macroscales

Understanding the drivers of species distributions, forest resources, 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 the Statistical Ecology and Forest Science Lab, we account for these complexities to provide a more complete understanding of macroscale ecological processes and inform effective monitoring and conservation approaches across spatial scales.

Small area estimation of forest parameters

Forest management and production increasingly require estimates of forest variables in small domains. Classical design-based approaches are limited in their ability to provide unbiased estimates at small spatial scales using national forest inventory data. We develop Bayesian model-based approaches for improved estimation of forest parameters across user-defined small areas.

Statistical ecology software development

Effective forest and wildlife management requires user-friendly software that makes state-of-the-art statistical tools accessible to natural resource managers, foresters, wildlife professionals, and conservation practitioners. A key pillar of the lab’s 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 forest and wildlife management

Autonomous monitoring systems such as LiDAR, acoustic recording units, camera traps, and other remote sensing methods can provide massive amounts of data to inform forest and wildlife management, yet analyzing these data presents novel computational complexities. We develop quantitative approaches to leverage these complex data to inform a variety of forest and wildlife management objectives.

Hierarchical modeling applications

The Statistical Ecology and Forest Science Lab works closely with colleagues across the world to apply our modeling developments to different management, conservation, and ecological questions.

News

New discussion paper on spatial occupancy models

We recently wrote a short discussion paper on an exciting new development in spatial occupancy models by Wilson Wright and Mevin Hooten. Find our discussion and the paper by Wright and Hooten here.

New paper published in Current Biology

A paper led by Chris Dean at University College London was recently published in Ecology Letters. Using spOccupancy, we applied occupancy models to better understand sampling biases in the dinosaur fossil record.

New preprint on small area estimation

Check out our new preprint on multivariate spatial models for small area estimation of species-specific forest parameters. We apply our modeling framework to estimate species-level biomass for the 20 most common tree species across the Southern US, which resulted in large precision improvements relative to classical approaches.

New paper published in Ecology Letters

A new paper led by Casey Youngflesh at Clemson University was recently published in Ecology Letters on the role of environmental variation in shaping life history of birds across the globe.

New paper published in Ecology

A new paper led by Ken Kellner at Michigan State University was recently published in Ecology. We assessed the analytic reproducibility of nearly 500 papers from 2018-2022, finding that only 7% of papers had R code that ran without any errors.

New paper published in Science of the Total Environment

A fun new paper led by Gabriela Quinlan at Penn State University was just published in Science of the Total Environment. Using a dataset from Maryland and joint species distribution models implemented in spAbundance, we provide the largest scale, most phylogenetically resolved assessment of non-native honey bee density effects on wild bee abundance to date.

The Statistical Ecology and Forest Science Lab begins at NC State!

I am beyond excited to announce that I will be starting as an Assistant Professor in the Department of Forestry and Environmental Resources at North Carolina State University in Fall 2024!

Meet the team →