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Grace V. DiRenzo, PhD

Assistant Unit Leader - Massachusetts Cooperative Fish and Wildlife Research Units

Dr. DiRenzo joined the MA Cooperative Research Unit in Nov 2020, where she is the Assistant Unit Leader of Wildlife and adjunct faculty in the department of Environmental Conservation at the University of Massachusetts, Amherst. Dr. DiRenzo received her PhD at the University of Maryland, College Park, and her Bachelor’s degree from the University of Connecticut, Storrs. Her research emphasizes disease dynamics, community and population ecology, and species conservation. To mimic natural hierarchical systems, she develops hierarchical Bayesian models, and she uses data collected over space and time to separate ecological and observational processes to answer ecological questions.

Dr. DiRenzo's research program focuses on unifying ecological and evolutionary theory to address fundamental questions in ecology using field, experimental, and quantitative approaches.

She enjoys teaching graduate courses related to quantitative ecology, disease ecology, and population/community ecology.

Professional Experience

  • Assistant Unit Leader, Massachusetts Cooperative Fish and Wildlife Research Unit, 2020-

Education and Certifications

  • PhD, University of Maryland, College Park, 2011

  • BS, University of Connecticut, 2007

Science and Products

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Farmington River in Collinsville, Connecticut

Framework for Protecting Aquatic Biodiversity in the Northeast Under Changing Climates

Aquatic ecosystems provide habitat and migration corridors to a myriad of species, including plants, fishes, amphibians, birds, mammals, and insects. These ecosystems typically contain relatively higher biodiversity than their terrestrial counterparts; yet, aquatic biodiversity loss in North America is occurring at a rate five times faster than in terrestrial ecosystems. One of the major causes of
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Climate Adaptation Science Centers
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Framework for Protecting Aquatic Biodiversity in the Northeast Under Changing Climates

Aquatic ecosystems provide habitat and migration corridors to a myriad of species, including plants, fishes, amphibians, birds, mammals, and insects. These ecosystems typically contain relatively higher biodiversity than their terrestrial counterparts; yet, aquatic biodiversity loss in North America is occurring at a rate five times faster than in terrestrial ecosystems. One of the major causes of
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Abiotic and biotic factors reduce viability of a high-elevation salamander in its native range

Includes data used to estimate population demographic parameters for an exemplary high-elevation amphibian species, the federally endangered Shenandoah salamander (Plethodon shenandoah). These parameters were entered into a Markov projection model which we used to forecast the future population status of the Shenandoah salamander.
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Eastern Ecological Science Center

Inferring pathogen presence when sample misclassification and partial observation occur

Surveillance programmes are essential for detecting emerging pathogens and often rely on molecular methods to make inference about the presence of a target disease agent. However, molecular methods rarely detect target DNA perfectly. For example, molecular pathogen detection methods can result in misclassification (i.e. false positives and false negatives) or partial detection errors (i.e. detecti
Authors
Evan H. Campbell Grant, Riley O. Mummah, Brittany A. Mosher, Jonah Evans, Graziella Vittoria Direnzo
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Ecosystems Mission Area, Eastern Ecological Science Center

A practical guide to understanding and validating complex models using data simulations

Biologists routinely fit novel and complex statistical models to push the limits of our understanding. Examples include, but are not limited to, flexible Bayesian approaches (e.g. BUGS, stan), frequentist and likelihood-based approaches (e.g. packages lme4) and machine learning methods.These software and programs afford the user greater control and flexibility in tailoring complex hierarchical mod
Authors
Graziella Vittoria Direnzo, Ephraim Hanks, David A. W. Miller
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Ecosystems Mission Area, Cooperative Research Units, Eastern Ecological Science Center

Optimizing survey design for shasta salamanders (Hydromantes spp.) to estimate occurrence in little-studied portions of their range

Shasta salamanders (collectively, Hydromantes samweli, H. shastae, and H. wintu; hereafter, Shasta salamander) are endemic to northern California in the general vicinity of Shasta Lake reservoir. Although generally associated with limestone, they have repeatedly been found in association with other habitats, calling into question the distribution of the species complex. Further limiting our knowle
Authors
Brian J. Halstead, Patrick M. Kleeman, Graziella Vittoria Direnzo, Jonathan P. Rose
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Ecosystems Mission Area, Eastern Ecological Science Center, Western Ecological Research Center (WERC)

Accommodating the role of site memory in dynamic species distribution models

First-order dynamic occupancy models (FODOMs) are a class of state-space model in which the true state (occurrence) is observed imperfectly. An important assumption of FODOMs is that site dynamics only depend on the current state and that variations in dynamic processes are adequately captured with covariates or random effects. However, it is often difficult to understand and/or measure the covari
Authors
Graziella Vittoria Direnzo, David A. W. Miller, Blake R. Hossack, Brent H. Sigafus, Paige E. Howell, Erin L. Muths, Evan H. Campbell Grant
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Ecosystems Mission Area, Eastern Ecological Science Center, Northern Rocky Mountain Science Center

Principles and mechanisms of wildlife population persistence in the face of disease

Emerging infectious diseases can result in species declines and hamper recovery efforts for at-risk populations. Generalizing considerations for reducing the risk of pathogen introduction and mitigating the effects of disease remains challenging and inhibits our ability to provide guidance for species recovery planning. Given the growing rates of emerging pathogens globally, we identify key princi
Authors
Robin E. Russell, Graziella Vittoria Direnzo, J. Szymanski, Katrina E. Alger, Evan H. Campbell Grant
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Ecosystems Mission Area, Biological Threats and Invasive Species Research Program, Eastern Ecological Science Center, National Wildlife Health Center

Code for: Small enzootic survival costs mask the potential for long-term population size suppression

This repository contains all of the scripts to reproduce the analyses, figures, and tables associated with the manuscript Glorioso et al. in review. The scripts are organized into folders, and the folders are numbered in the order in which they should be executed. Briefly, there are six folders that do the following: (1) format the data, (2) fit the model, (3) run the population projections, (4) r
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Cooperative Research Units

whitetailedSIRS: A package to project SARS-CoV-2 outbreak dynamics in white-tailed deer

This software release contains several R scripts that generate epidemic projections of SARS-CoV-2 in white tailed deer populations using a Susceptible-Infected-Recovered-Susceptible (SIRS) modeling framework. We provide a workflow of vignettes used in Rosenblatt et al. In Prep and Cook et al. In Prep. Users are able to specify transmission parameters for human-deer and deer-deer transmission to qu
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Cooperative Research Units

Code for: Leveraging local efforts to solve regional-scale ecological questions: using multiple sources of data and a multi-species occupancy model to explore bee-plant interactions

This repository contains all of the scripts to reproduce the analyses, figures, and tables associated with the manuscript Lee et al. in prep. The scripts are organized in the order in which they should be run. Briefly, the files do the following: format the data, fit the model, create the figures, and create the tables.
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Cooperative Research Units

Inferring pathogen presence when sample misclassification and partial observation occur

This software contains four separate R scripts and one Matlab script that comprise an analysis to estimate the posterior probability of pathogen presence when sample misclassification and partial observations occur. We develop a Bayesian hierarchal framework that accommodates false negative, false positive, and uncertain detections and apply this framework to a case study of the fungal pathogen Ps
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Cooperative Research Units

Simulations to understand and validate models

This software release contains one R script file that allows the user to download data and reproduce all of the figures and analyses associated with DiRenzo et al. In the R script, we provide a guide to understanding and validating complex models using data simulations. In an effort to facilitate the implementation of these methods, we provide a running example throughout DiRenzo et al. 2022 with
By
Cooperative Research Units

Science and Products