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Charles J Labuzzetta, PhD

Charlie is a Biologist at the Fort Collins Science Center who uses his training in applied statistics to develop mathematical models of ecological systems.

Charlie serves as a quantitative Biologist for the North American Bat Monitoring Program (NABat). NABat is a continental scale bat population monitoring program that assembles data and delivers information on the bat species the United States shares with Canada and Mexico. Through tremendous collaboration, NABat creates an economy of scale that reduces burden and expands capacity for hundreds of external partner organizations. The multi-disciplinary NABat team works collaboratively to provide the services, resources, digital infrastructure, and science that support the success of the broader NABat collective. In his role with NABat, Charlie is investigating the impact of wind energy on bat populations. Previously with the USGS, he modeled the movement of invasive carp in river systems. Charlie seeks to deliver tools that can inform external wildlife management partners based on the best available data and methods. His skillset includes expertise in computing and machine learning, with particular interest in Bayesian approaches, neural networks, and computer vision.

Professional Experience

  • Biologist, 2024 – present, Fort Collins Science Center, USGS, Fort Collins, Colorado

  • Mathematical Statistician, 2023 – present, Upper Midwest Environmental Sciences Center, USGS, La Crosse, Wisconsin

  • Postdoctoral Contractor, 2022 – 2023, Upper Midwest Environmental Sciences Center, USGS, La Crosse, Wisconsin

Education and Certifications

  • Iowa State University, PhD in Statistics and Environmental Science, 2022

  • Iowa State University, MS in Statistics, 2019

  • Iowa State University, BS in Mathematics, 2017

Science and Products

Comparing maximum likelihood and Bayesian methods for fitting hidden Markov models to multi-state capture-recapture data of invasive carp in the Illinois River

BackgroundHidden Markov Models (HMMs) are often used to model multi-state capture-recapture data in ecology. However, a variety of HMM modeling approaches and software exist, including both maximum likelihood and Bayesian methods. The diversity of these methods obscures the underlying HMM and can exaggerate minor differences in parameterization.MethodsIn this paper, we describe a general framework
Authors
Charles J. Labuzzetta, Alison A. Coulter, Richard A. Erickson
By
Ecosystems Mission Area, Upper Midwest Environmental Sciences Center

Non-USGS Publications**

Labuzzetta, C. J., & Zhu, Z. (2024). Mapping Conservation Practices via Deep Learning: Improving Performance via Hillshade Imagery, Sampling Design, and Centerline Dice Loss. Statistics and Data Science in Imaging, 1(1). https://doi.org/10.1080/29979676.2024.2401756
Labuzzetta, C., Zhu, Z., Chang, X., & Zhou, Y. (2021). A Submonthly Surface Water Classification Framework via Gap-Fill Imputation and Random Forest Classifiers of Landsat Imagery. Remote Sensing, 13(9), 1742. https://doi.org/10.3390/rs13091742

**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.

link
Bats benefit from maintaining a close-knit roosting group.

North American Bat Monitoring Program (NABat)

North American bats face unprecedented threats including habitat loss and fragmentation, white-nose syndrome, wind energy development, and climate change. However, it is difficult to evaluate the impacts of these threats due to a lack of basic information about the distribution and abundance of bats across the continent. Although bat monitoring has long been conducted in individual areas and for...
By
Biological Threats and Invasive Species Research Program, Species Management Research Program, Fort Collins Science Center, Northern Rocky Mountain Science Center
link

North American Bat Monitoring Program (NABat)

North American bats face unprecedented threats including habitat loss and fragmentation, white-nose syndrome, wind energy development, and climate change. However, it is difficult to evaluate the impacts of these threats due to a lack of basic information about the distribution and abundance of bats across the continent. Although bat monitoring has long been conducted in individual areas and for...
Learn More
link
A commercially caught wild black carp from the Mississippi River

Modeling to inform bighead and silver carp management

Bighead carp ( Hypophthalmichthys nobilis ) and silver carp ( Hypophthalmichthys molitrix ) are invasive carp species within the Mississippi River basin. In the early-1970s these species escaped from ponds where people introduced them to control algae (ICRCC). Invasive carp cause ecological and economic damage by outcompeting native species and disrupting native food webs. Stopping the spread of...
By
Upper Midwest Environmental Sciences Center
link

Modeling to inform bighead and silver carp management

Bighead carp ( Hypophthalmichthys nobilis ) and silver carp ( Hypophthalmichthys molitrix ) are invasive carp species within the Mississippi River basin. In the early-1970s these species escaped from ponds where people introduced them to control algae (ICRCC). Invasive carp cause ecological and economic damage by outcompeting native species and disrupting native food webs. Stopping the spread of...
Learn More

occstanhm: Hierarchical occupancy models with correlated error structure

This repository contains a R package with 2-level and 3-level occupancy models. The package also contains a set of tutorials designed to help users understand how to use and code occupancy models in Stan. The models include a statistical hierarchy that allows for multi-species modeling and estimating correlations among species. The models may also be used as a more general "random-effect" type occ
By
Upper Midwest Environmental Sciences Center

multistater: A MULTI-STATistical approach to fitting MULTI-STATE models with R

The multistater package can fit several types of multi-state models, including discrete-state discrete-time Hidden Markov Models using both maximum-likelihood and Bayesian approaches (HMMs) and discrete-state continuous-time state-space models (SSMs). The package was created to specify HMM and SSM designs that can be fit in both the frequentist and Bayesian paradigms within a single package. Our s
By
Upper Midwest Environmental Sciences Center

Comparing maximum likelihood and Bayesian methods for fitting Hidden Markov Models to multi-state capture recapture data of invasive Silver Carp in the Illinois River

This repository contains the data, code for modeling and analysis, and publication markdown documents for a comparison of various methods for fitting multi-state models, including maximum likelihood and Bayesian approaches via R.
By
Upper Midwest Environmental Sciences Center

Science and Products

Comparing maximum likelihood and Bayesian methods for fitting hidden Markov models to multi-state capture-recapture data of invasive carp in the Illinois River

BackgroundHidden Markov Models (HMMs) are often used to model multi-state capture-recapture data in ecology. However, a variety of HMM modeling approaches and software exist, including both maximum likelihood and Bayesian methods. The diversity of these methods obscures the underlying HMM and can exaggerate minor differences in parameterization.MethodsIn this paper, we describe a general framework
Authors
Charles J. Labuzzetta, Alison A. Coulter, Richard A. Erickson
By
Ecosystems Mission Area, Upper Midwest Environmental Sciences Center

Non-USGS Publications**

Labuzzetta, C. J., & Zhu, Z. (2024). Mapping Conservation Practices via Deep Learning: Improving Performance via Hillshade Imagery, Sampling Design, and Centerline Dice Loss. Statistics and Data Science in Imaging, 1(1). https://doi.org/10.1080/29979676.2024.2401756
Labuzzetta, C., Zhu, Z., Chang, X., & Zhou, Y. (2021). A Submonthly Surface Water Classification Framework via Gap-Fill Imputation and Random Forest Classifiers of Landsat Imagery. Remote Sensing, 13(9), 1742. https://doi.org/10.3390/rs13091742

**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.

link
Bats benefit from maintaining a close-knit roosting group.

North American Bat Monitoring Program (NABat)

North American bats face unprecedented threats including habitat loss and fragmentation, white-nose syndrome, wind energy development, and climate change. However, it is difficult to evaluate the impacts of these threats due to a lack of basic information about the distribution and abundance of bats across the continent. Although bat monitoring has long been conducted in individual areas and for...
By
Biological Threats and Invasive Species Research Program, Species Management Research Program, Fort Collins Science Center, Northern Rocky Mountain Science Center
link

North American Bat Monitoring Program (NABat)

North American bats face unprecedented threats including habitat loss and fragmentation, white-nose syndrome, wind energy development, and climate change. However, it is difficult to evaluate the impacts of these threats due to a lack of basic information about the distribution and abundance of bats across the continent. Although bat monitoring has long been conducted in individual areas and for...
Learn More
link
A commercially caught wild black carp from the Mississippi River

Modeling to inform bighead and silver carp management

Bighead carp ( Hypophthalmichthys nobilis ) and silver carp ( Hypophthalmichthys molitrix ) are invasive carp species within the Mississippi River basin. In the early-1970s these species escaped from ponds where people introduced them to control algae (ICRCC). Invasive carp cause ecological and economic damage by outcompeting native species and disrupting native food webs. Stopping the spread of...
By
Upper Midwest Environmental Sciences Center
link

Modeling to inform bighead and silver carp management

Bighead carp ( Hypophthalmichthys nobilis ) and silver carp ( Hypophthalmichthys molitrix ) are invasive carp species within the Mississippi River basin. In the early-1970s these species escaped from ponds where people introduced them to control algae (ICRCC). Invasive carp cause ecological and economic damage by outcompeting native species and disrupting native food webs. Stopping the spread of...
Learn More

occstanhm: Hierarchical occupancy models with correlated error structure

This repository contains a R package with 2-level and 3-level occupancy models. The package also contains a set of tutorials designed to help users understand how to use and code occupancy models in Stan. The models include a statistical hierarchy that allows for multi-species modeling and estimating correlations among species. The models may also be used as a more general "random-effect" type occ
By
Upper Midwest Environmental Sciences Center

multistater: A MULTI-STATistical approach to fitting MULTI-STATE models with R

The multistater package can fit several types of multi-state models, including discrete-state discrete-time Hidden Markov Models using both maximum-likelihood and Bayesian approaches (HMMs) and discrete-state continuous-time state-space models (SSMs). The package was created to specify HMM and SSM designs that can be fit in both the frequentist and Bayesian paradigms within a single package. Our s
By
Upper Midwest Environmental Sciences Center

Comparing maximum likelihood and Bayesian methods for fitting Hidden Markov Models to multi-state capture recapture data of invasive Silver Carp in the Illinois River

This repository contains the data, code for modeling and analysis, and publication markdown documents for a comparison of various methods for fitting multi-state models, including maximum likelihood and Bayesian approaches via R.
By
Upper Midwest Environmental Sciences Center
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