I am a postdoctoral fellow investigating climate drivers of mosquito-borne diseases with Erin Mordecai (Biology), Desiree LaBeaud (School of Medicine) and Eric Lambin (Woods Institute for the Environment). I am interested in the ecology and spatial dynamics of infectious diseases in humans and marine and terrestrial wildlife. A major component of my postdoctoral research is to develop climate-driven predictive models of dengue and chikungunya in Kenya and Ecuador.
Bachelor of Science, Pennsylvania State University (2009)
Doctor of Philosophy, University of Hawaii Manoa (2017)
Seasonal temperature variation influences climate suitability for dengue, chikungunya, and Zika transmission
PLOS NEGLECTED TROPICAL DISEASES
2018; 12 (5): e0006451
Dengue, chikungunya, and Zika virus epidemics transmitted by Aedes aegypti mosquitoes have recently (re)emerged and spread throughout the Americas, Southeast Asia, the Pacific Islands, and elsewhere. Understanding how environmental conditions affect epidemic dynamics is critical for predicting and responding to the geographic and seasonal spread of disease. Specifically, we lack a mechanistic understanding of how seasonal variation in temperature affects epidemic magnitude and duration. Here, we develop a dynamic disease transmission model for dengue virus and Aedes aegypti mosquitoes that integrates mechanistic, empirically parameterized, and independently validated mosquito and virus trait thermal responses under seasonally varying temperatures. We examine the influence of seasonal temperature mean, variation, and temperature at the start of the epidemic on disease dynamics. We find that at both constant and seasonally varying temperatures, warmer temperatures at the start of epidemics promote more rapid epidemics due to faster burnout of the susceptible population. By contrast, intermediate temperatures (24-25°C) at epidemic onset produced the largest epidemics in both constant and seasonally varying temperature regimes. When seasonal temperature variation was low, 25-35°C annual average temperatures produced the largest epidemics, but this range shifted to cooler temperatures as seasonal temperature variation increased (analogous to previous results for diurnal temperature variation). Tropical and sub-tropical cities such as Rio de Janeiro, Fortaleza, and Salvador, Brazil; Cali, Cartagena, and Barranquilla, Colombia; Delhi, India; Guangzhou, China; and Manila, Philippines have mean annual temperatures and seasonal temperature ranges that produced the largest epidemics. However, more temperate cities like Shanghai, China had high epidemic suitability because large seasonal variation offset moderate annual average temperatures. By accounting for seasonal variation in temperature, the model provides a baseline for mechanistically understanding environmental suitability for virus transmission by Aedes aegypti. Overlaying the impact of human activities and socioeconomic factors onto this mechanistic temperature-dependent framework is critical for understanding likelihood and magnitude of outbreaks.
View details for DOI 10.1371/journal.pntd.0006451
View details for Web of Science ID 000434021900021
View details for PubMedID 29746468
View details for PubMedCentralID PMC5963813
Host size and proximity to diseased neighbours drive the spread of a coral disease outbreak in Hawai'i.
Proceedings. Biological sciences
2018; 285 (1870)
Understanding how disease risk varies over time and across heterogeneous populations is critical for managing disease outbreaks, but this information is rarely known for wildlife diseases. Here, we demonstrate that variation in host and pathogen factors drive the direction, duration and intensity of a coral disease outbreak. We collected longitudinal health data for 200 coral colonies, and found that disease risk increased with host size and severity of diseased neighbours, and disease spread was highest among individuals between 5 and 20 m apart. Disease risk increased by 2% with every 10 cm increase in host size. Healthy colonies with severely diseased neighbours (greater than 75% affected tissue) were 1.6 times more likely to develop disease signs compared with colonies with moderately diseased neighbours (25-75% affected tissue). Force of infection ranged from 7 to 20 disease cases per 1000 colonies (mean = 15 cases per 1000 colonies). The effective reproductive ratio, or average number of secondary infections per infectious individual, ranged from 0.16 to 1.22. Probability of transmission depended strongly on proximity to diseased neighbours, which demonstrates that marine disease spread can be highly constrained within patch reefs.
View details for DOI 10.1098/rspb.2017.2265
View details for PubMedID 29321299
Intra-colony disease progression induces fragmentation of coral fluorescent pigments
2017; 7: 14596
As disease spreads through living coral, it can induce changes in the distribution of coral's naturally fluorescent pigments, making fluorescence a potentially powerful non-invasive intrinsic marker of coral disease. Here, we show the usefulness of live-imaging laser scanning confocal microscopy to investigate coral health state. We demonstrate that the Hawaiian coral Montipora capitata consistently emits cyan and red fluorescence across a depth gradient in reef habitats, but the micro-scale spatial distribution of those pigments differ between healthy coral and coral affected by a tissue loss disease. Naturally diseased and laboratory infected coral systematically exhibited fragmented fluorescent pigments adjacent to the disease front as indicated by several measures of landscape structure (e.g., number of patches) relative to healthy coral. Histology results supported these findings. Pigment fragmentation indicates a disruption in coral tissue that likely impedes translocation of energy within a colony. The area of fragmented fluorescent pigments in diseased coral extended 3.03 mm ± 1.80 mm adjacent to the disease front, indicating pathogenesis was highly localized rather than systemic. Our study demonstrates that coral fluorescence can be used as a proxy for coral health state, and, such patterns may help refine hypotheses about modes of pathogenesis.
View details for DOI 10.1038/s41598-017-15084-3
View details for Web of Science ID 000414416200002
View details for PubMedID 29097717
View details for PubMedCentralID PMC5668308
Satellite SST-Based Coral Disease Outbreak Predictions for the Hawaiian Archipelago
2016; 8 (2): 93
Predicting wildlife disease risk is essential for effective monitoring and management, especially for geographically expansive ecosystems such as coral reefs in the Hawaiian archipelago. Warming ocean temperature has increased coral disease outbreaks contributing to declines in coral cover worldwide. In this study we investigated seasonal effects of thermal stress on the prevalence of the three most widespread coral diseases in Hawai'i: Montipora white syndrome, Porites growth anomalies and Porites tissue loss syndrome. To predict outbreak likelihood we compared disease prevalence from surveys conducted between 2004 and 2015 from 18 Hawaiian Islands and atolls with biotic (e.g., coral density) and abiotic (satellite-derived sea surface temperature metrics) variables using boosted regression trees. To date, the only coral disease forecast models available were developed for Acropora white syndrome on the Great Barrier Reef (GBR). Given the complexities of disease etiology, differences in host demography and environmental conditions across reef regions, it is important to refine and adapt such models for different diseases and geographic regions of interest. Similar to the Acropora white syndrome models, anomalously warm conditions were important for predicting Montipora white syndrome, possibly due to a relationship between thermal stress and a compromised host immune system. However, coral density and winter conditions were the most important predictors of all three coral diseases in this study, enabling development of a forecasting system that can predict regions of elevated disease risk up to six months before an expected outbreak. Our research indicates satellite-derived systems for forecasting disease outbreaks can be appropriately adapted from the GBR tools and applied for a variety of diseases in a new region. These models can be used to enhance management capacity to prepare for and respond to emerging coral diseases throughout Hawai'i and can be modified for other diseases and regions around the world.
View details for DOI 10.3390/rs8020093
View details for Web of Science ID 000371898800046
View details for PubMedID 29071133
View details for PubMedCentralID PMC5651227
Suitable Days for Plant Growth Disappear under Projected Climate Change: Potential Human and Biotic Vulnerability
2015; 13 (6): e1002167
Ongoing climate change can alter conditions for plant growth, in turn affecting ecological and social systems. While there have been considerable advances in understanding the physical aspects of climate change, comprehensive analyses integrating climate, biological, and social sciences are less common. Here we use climate projections under alternative mitigation scenarios to show how changes in environmental variables that limit plant growth could impact ecosystems and people. We show that although the global mean number of days above freezing will increase by up to 7% by 2100 under "business as usual" (representative concentration pathway [RCP] 8.5), suitable growing days will actually decrease globally by up to 11% when other climatic variables that limit plant growth are considered (i.e., temperature, water availability, and solar radiation). Areas in Russia, China, and Canada are projected to gain suitable plant growing days, but the rest of the world will experience losses. Notably, tropical areas could lose up to 200 suitable plant growing days per year. These changes will impact most of the world's terrestrial ecosystems, potentially triggering climate feedbacks. Human populations will also be affected, with up to ~2,100 million of the poorest people in the world (~30% of the world's population) highly vulnerable to changes in the supply of plant-related goods and services. These impacts will be spatially variable, indicating regions where adaptations will be necessary. Changes in suitable plant growing days are projected to be less severe under strong and moderate mitigation scenarios (i.e., RCP 2.6 and RCP 4.5), underscoring the importance of reducing emissions to avoid such disproportionate impacts on ecosystems and people.
View details for DOI 10.1371/journal.pbio.1002167
View details for Web of Science ID 000357339600007
View details for PubMedID 26061091
View details for PubMedCentralID PMC4465630
Development of Genomic Resources for a thraustochytrid Pathogen and Investigation of Temperature Influences on Gene Expression
2013; 8 (9): e74196
Understanding how environmental changes influence the pathogenicity and virulence of infectious agents is critical for predicting epidemiological patterns of disease. Thraustochytrids, part of the larger taxonomic class Labyrinthulomycetes, contain several highly pathogenic species, including the hard clam pathogen quahog parasite unknown (QPX). QPX has been associated with large-scale mortality events along the northeastern coast of North America. Growth and physiology of QPX is temperature-dependent, and changes in local temperature profiles influence pathogenicity. In this study we characterize the partial genome of QPX and examine the influence of temperature on gene expression. Genes involved in several biological processes are differentially expressed upon temperature change, including those associated with altered growth and metabolism and virulence. The genomic and transcriptomic resources developed in this study provide a foundation for better understanding virulence, pathogenicity and life history of thraustochytrid pathogens.
View details for DOI 10.1371/journal.pone.0074196
View details for Web of Science ID 000324547300031
View details for PubMedID 24069279
View details for PubMedCentralID PMC3775781