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.
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
Framework for Protecting Aquatic Biodiversity in the Northeast Under Changing Climates
Optimizing survey design for shasta salamanders (Hydromantes spp.) to estimate occurrence in little-studied portions of their range
Accommodating the role of site memory in dynamic species distribution models
Principles and mechanisms of wildlife population persistence in the face of disease
Inferring pathogen presence when sample misclassification and partial observation occur
Simulations to understand and validate models
Science and Products
Framework for Protecting Aquatic Biodiversity in the Northeast Under Changing ClimatesAquatic 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
Optimizing survey design for shasta salamanders (Hydromantes spp.) to estimate occurrence in little-studied portions of their rangeShasta 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
Accommodating the role of site memory in dynamic species distribution modelsFirst-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
Principles and mechanisms of wildlife population persistence in the face of diseaseEmerging 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
Inferring pathogen presence when sample misclassification and partial observation occurThis 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
Simulations to understand and validate modelsThis 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