About

Dr. Cherie Briggs is a professor in the Department of Ecology, Evolution, and Marine Biology at UC Santa Barbara, holding a joint appointment in EEMB and BMSE, and serving as a Mellichamp Chair in Systems Biology. She received B.S. degrees in Electrical Engineering and Biological Sciences from Michigan Technological University, an M.S. in Electrical Engineering from Princeton University while working at Bell Labs, and a Ph.D. in Biology from UCSB, where she studied host-parasitoid population dynamics. Her postdoctoral research at Imperial College, Silwood Park in England, involved developing models of insect-pathogen interactions. Her research combines modeling and experiments to understand the factors affecting the dynamics of animal populations. Her lab investigates disease-host and parasitoid-host interactions, including the persistence of mountain yellow-legged frog populations infected with the chytrid fungus Batrachochytrium dendrobatidis through field surveys, experiments, genetics, molecular techniques, and modeling. Additionally, her work on Lyme disease in Southern California explores the dynamics of ticks, bacteria, and vertebrate hosts to understand the factors contributing to the low prevalence of the pathogen in this region.

Research topics

• Ecology
• Biology

Selected publications

• Appendix from Dynamic mean and variance of microparasite load give key insights into population dynamics and underlying mechanisms

  Figshare · 2026-01-01

  articleOpen access

  This file contains useful information, figures, derivations, etc. referenced in the main text.

  PublisherDOI

• Dynamic mean and variance of microparasite load give key insights into population dynamics and underlying mechanisms

  Journal of The Royal Society Interface · 2026-02-11 · 1 citations

  articleOpen access

  Individual heterogeneity, in number of parasites, size, etc., interacts critically with population dynamics. We tease this out in a model case study of microparasite load with empirically supported assumptions to investigate how variance in load interacts with population dynamics, We show how the mean and variance of load vary throughout an epidemic. Further, we show how mean and variance have mutual negative feedbacks on each other mediated by high death rates at high loads. Helpfully, we find that mean and variance provide information into underlying processes as well. Population trends in the mean and variance reveal underlying trends in within-host processes, e.g. differentiating host evolution of defence that manifests as tolerance, constitutive resistance, inducible resistance or acquired resistance. Our findings apply to many microparasites, including fungal pathogens which show large variance in infection load. As a case study, we consider endangered frog populations recovering from fungal epidemics and find that the mean and variance guide management actions. Lastly, we demonstrate the impact of load variance on host fitness, pathogen fitness and host population suppression. Our results demonstrate the importance of trait heterogeneity and the insights available from relatively simple models, both for microparasite load and possibly other traits.

  PublisherOA PDFDOI

• Appendix from Dynamic mean and variance of microparasite load give key insights into population dynamics and underlying mechanisms

  Open MIND · 2026-01-01

  article

  This file contains useful information, figures, derivations, etc. referenced in the main text.

  DOI

• Integrating infection intensity and aggregation into the dynamics of pathogens with within‐host replication

  Journal of Animal Ecology · 2026-05-22

  articleOpen accessSenior author

  Disease outcomes depend heavily on infection intensity which is often heterogeneous across and within host populations. Most individuals carry low pathogen loads and a few carry high loads, a pattern known as aggregation. Although well characterized in macroparasite systems, aggregation and infection intensity are rarely incorporated into microparasite models. This raises key questions: Do similar mechanisms underlie aggregation in macro- and microparasite systems? Moreover, how do aggregation and load-dependent effects shape outcomes such as host suppression and virulence-transmission trade-offs? To address these questions, we developed a series of differential equation models that allow the pathogen load distribution across hosts to evolve dynamically, shaped by both within- and between-host processes. We applied this framework to the amphibian chytrid fungus system caused by Batrachochytrium dendrobatidis (Bd), a fungal pathogen threatening amphibian populations worldwide. Our results show that both stronger load-dependent mortality and faster within-host replication reduce aggregation. Aggregation, in turn, weakens host suppression and flattens virulence-transmission trade-off, shifting peak transmission to higher replication rates. Overall, our models show that similar mechanisms of infection intensity and aggregation influence host-pathogen dynamics in microparasites as in macroparasites. This work offers a framework for advancing theoretical and data-driven understanding of how within-host processes scale to population-level disease dynamics, advocating for a unified approach to disease modelling that bridges the macro- and microparasites.

  PublisherDOI

• The myth of resilience: Necessary non-resilience and the role of variable choice in ecosystem recovery

  2025-11-03

  articleOpen access

  Resilience, the ability to resist or recover from disturbance, is ubiquitous in ecology but defined and measured in different ways. The evaluation of resilience depends on decisions made by the investigator(s), including the variables measured. Here we highlight an under-appreciated observation: there is no canonical definition of overall resilience and such a definition may be unattainable. Therefore, we make four key points. First, we highlight and categorize the diverse variables used to measure ecological resilience and place them in a conceptual model. Second, we argue that different relevant variables often respond very differently to disturbance and prove that no system can be completely resilient to a press disturbance (‘Necessary Non-resilience’). Third, we demonstrate with four examples how categorization of diverse resilience variables and a conceptual model can stimulate new research questions. Fourth, we apply our framework to four empirical case studies to demonstrate the biological relevance of such new directions. Overall, we argue that advancing resilience ecology will require a deeper consideration of variable choice, how different resilience variables interact, the inevitable failure of resilience in some variables, and how these ideas can foster new, general research directions.

  PublisherOA PDFDOI

• Author response for "A Busse Balloon in the Lagoon: Herbivore Behaviour Generates Spatial Patterns in Coral Reef Ecosystems"

  2025-02-20

  peer-review
  PublisherDOI

• Author response for "A Busse Balloon in the Lagoon: Herbivore Behaviour Generates Spatial Patterns in Coral Reef Ecosystems"

  2025-02-10

  peer-review
  PublisherDOI

• A continuous-time microparasite model incorporating infection intensity and parasite aggregation

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-06-20

  preprintOpen accessSenior author

  Abstract Disease outcomes depend heavily on infection intensity which is often heterogeneous across and within host populations. Most individuals carry low pathogen loads and a few carry high loads, a pattern known as aggregation. While well-characterized in macroparasite systems, aggregation and infection intensity are rarely incorporated into microparasite models. This raises key questions: Do similar mechanisms underlie aggregation in macro- and microparasite systems? And how do aggregation and load-dependent effects shape outcomes such as host suppression and virulence-transmission trade-offs? To address these questions, we developed a novel continuous time microparasite model that allows the pathogen load distribution across hosts to evolve dynamically, shaped by within- and between-host processes. We applied this framework to the amphibian chytrid fungus system involving Batrachochytrium den-drobatidis ( Bd ), a fungal pathogen threatening amphibian populations worldwide. Our results show that load-dependent mortality reduces aggregation, while faster within-host replication increases it. Aggregation, in turn, weakens host suppression and flat-tens virulence-transmission trade-off, shifting peak transmission to higher replication rates. Overall, our continuous-time microparasite model provides new insights into how infection intensity and aggregation influence host-pathogen dynamics and offers a valuable framework for advancing theoretical and data-driven understanding of how within-host processes scale to population-level disease dynamics for microparasites.

  PublisherDOI

• Wildfire disturbance and ecological cascades: Teasing apart the direct and indirect effects of fire on tick populations

  Journal of Applied Ecology · 2025-10-24 · 1 citations

  articleOpen accessSenior author

  Abstract Wildfires are a significant ecological force in the western United States, reshaping landscapes and ecological communities. However, assessing wildfires' full impact is challenging due to the complexity of fire severity and its varied effects on ecological dynamics. Understanding species‐specific responses to disturbances within their environmental context is essential for predicting cascading ecological impacts. Arthropods, including ticks, are particularly sensitive to both abiotic and biotic changes, making them especially vulnerable to the impacts of wildfire. In this study, we tease apart the complex direct and indirect effects of wildfire on tick populations through a combination of field‐level measurements and remote sensing. We assessed tick densities across 88 plots within large, protected reserves in California following three wildfires in August 2020, using data on soil conditions, vegetation cover, tick densities and landscape‐level remotely sensed variables related to vegetation regeneration and vertebrate recolonization. To support a multi‐scalar approach, we applied piecewise structural equation models to incorporate factors across distinct spatial scales and assess how fire severity affects tick populations, with vegetation and habitat structure as mediating variables, thereby evaluating the relative importance of local drivers within a broader landscape context. Our results indicate that tick densities were consistently lower in burned plots across all vegetation types, with higher fire severity associated with the greatest reductions. This direct effect of fire severity outweighed indirect influences such as the presence of remaining woody debris, which can support tick populations by offering microhabitat for vertebrate hosts following a fire event. Landscape‐level characteristics—such as proximity to the fire perimeter and the percentage of the reserve burned—exerted stronger influences on tick densities than plot‐level fire severity. These broader spatial characteristics likely facilitate the movement of vertebrate hosts into unburned areas, promoting tick recolonization and recovery following wildfire disturbance. Our results suggest that simplified field assessments focusing on key habitat indicators may be effective for monitoring tick responses to wildfire. Synthesis and applications . This study highlights the importance of integrating multiple data sources and ecological scales to predict wildfire impacts on ecosystems and public health. By advancing our understanding of wildfire effects on ticks, the research offers valuable insights for ecosystem management and disease vector control. The use of advanced statistical tools, like piecewise structural equation models, combined with remotely sensed data, can facilitate rapid assessments and targeted monitoring efforts.

  PublisherOA PDFDOI

• Data Analysis in R to Gain Insights for Conservation: Examples From Long-Term Ecological Research

  Lessons in Conservation · 2025-01-01

  articleSenior author

  The R programming language is a powerful tool for analyzing ecological datasets and gaining valuable insights to inform conservation efforts. This module is designed in two parts to help develop foundational skills for working with ecological data in R. The first part introduces you to R and RStudio, providing a solid foundation for data analysis. The second part focuses on key techniques for data wrangling and visualization. Whether you’re new to R or looking to expand your skills from a fresh perspective, this module offers something for those early in their R journey. Throughout, we will use data from long-term ecological research sites to emphasize the critical role of continuous monitoring in understanding the conservation impacts of our changing world.

  PublisherDOI

Recent grants

• LTREB : Collaborative Research: Long-term dynamics of amphibian populations following disease-driven declines

  NSF · $248k · 2016–2023

• Dissertation Research: Associations between Symbiotic Bacterial Communities and Infection by an Emerging Fungal Pathogen: Distinguishing Cause from Correlation

  NSF · $15k · 2012–2014

• NIH Grant R01GM109499

  NIH · $1.7M · 2019

• NIH Grant R01ES012067

  NIH · $2.2M · 2008

• Collaborative Research: After the Crash: Factors Allowing Host Persistence Following Outbreaks of a Highly Virulent Disease

  NSF · $2.4M · 2007–2013

Frequent coauthors

• William W. Murdoch

  University of California, Santa Barbara

  38 shared

• Hamish McCallum

  Griffith University

  31 shared

• Maria Ribas

  Universitat Autònoma de Barcelona

  30 shared

• Laura A. Brannelly

  University of Pittsburgh

  27 shared

• Roland A. Knapp

  26 shared

• Bruce E. Kendall

  25 shared

• David Newell

  Southern Cross University

  25 shared

• Laura F. Grogan

  Griffith University

  25 shared

Labs

• Briggs LabPI

  The Briggs Lab at the University of California, Santa Barbara

Education

• B.S., Electrical Engineering and Biological Sciences

  Michigan Technological University

• M.S., Electrical Engineering

  Princeton University

• Ph.D., Biology

  UC Santa Barbara