Biology subjects

Patterson, E. M.

Publications and source records attributed to Patterson, E. M..

3 recordsLinked to original sources

bioRxiv2024

Seasonal contact and migration structure mass epidemics and inform outbreak preparedness in bottlenose dolphins

Infectious respiratory diseases have detrimental impacts across wildlife taxa, particularly in marine species. Despite this vulnerability, we lack information on the complex spatial and contact structures of marine populations which reduces our ability to understand disease spread and our preparedness for epidemic response. We leveraged a collated dataset to establish the first data-driven epidemiological model on a cetacean species, the Tamanends bottlenose dolphin (Tursiops erebennus), whose populations are periodically impacted by deadly respiratory disease in the northwest Atlantic. We found their spatial distribution and contact is heterogeneous along the coastline and varies by ecotype, which explains differences in infection burdens observed in past outbreaks. We also determined that outbreaks beginning in northern parts of their habitat during migratory seasons have the highest epidemic risk and that dolphins in North Carolina estuaries would be the best sentinels for disease surveillance. Our mathematical model provides a generalizable, non-invasive tool that takes advantage of routinely collected marine mammal data to mechanistically understand disease transmission and inform disease surveillance tactics for marine sentinels. Our findings highlight the heterogeneities that play a crucial role in shaping the impacts of infectious diseases in wildlife, and how a data-driven understanding of these mechanisms can enhance epidemic preparedness.

ecology↗

bioRxiv2024

Automated Skin Lesion Detection and Prevalence Estimation in Tamanend's Bottlenose Dolphins

Anthropogenic global change is occurring at alarming rates, leading to increased urgency in the ability to monitor wildlife health in real time. Monitoring sentinel marine species, such as bottlenose dolphins, is particularly important due to extensive anthropogenic modifications to their habitats. The most common non-invasive method of monitoring cetacean health is documentation of skin lesions, often associated with poor health or disease, but the current methodology is inefficient and imprecise. Recent advancements in technology, such as machine learning, can provide researchers with more efficient ecological monitoring methods to address health questions at both the population and the individual levels. Our work develops a machine learning model to classify skin lesions on the understudied Tamanends bottlenose dolphins (Tursiops erebennus) of the Chesapeake Bay, using manual estimates of lesion presence in photographs. We assess the models performance and find that our best model performs with a high mean average precision (65.6%-86.8%), and generally increased accuracy with improved photo quality. We also demonstrate the models ability to address ecological questions across scales by generating model-based estimates of lesion prevalence and testing the effect of gregariousness on health status. At the population level, our model accurately estimates a prevalence of 72.1% spot and 27.3% fringe ring lesions, with a slight underprediction compared to manual estimates (82.2% and 32.1%). On the other hand, we find that individual-level analyses from the model predictions may be more sensitive to data quality, and thus, some individual scale questions may not be feasible to address if data quality is inconsistent. Manually, we do find that lesion presence in individuals suggests a positive relationship between lesion presence and gregariousness. This work demonstrates that object detection models on photographic data are reasonably successful, highly efficient, and provide initial estimates on the health status of understudied populations of bottlenose dolphins.

ecology↗

bioRxiv2023

Breathing in sync: how a social behavior structures respiratory epidemic risk in bottlenose dolphins

Dolphin morbillivirus has caused mass mortalities in dolphin populations globally. Given their role as ecosystem sentinels, mass mortalities among these populations can be detrimental. Morbillivirus is transmitted through respiratory droplets and occurs when dolphins breathe synchronously, a variable social behavior. To assess the role of variable social behavior on disease risk empirically, we collected behavioral data from two wild bottlenose dolphins populations (Tursiops spp.), developed network models that synthesize transmission contacts, and used an epidemiological model to predict disease consequences. We find that juveniles have more contacts than adults, adult males have more contacts than adult females, and that individuals preferentially contact others in their own demographic group. These patterns translate to higher infection risk for juveniles and adult males, which we support using data from a morbillivirus outbreak. Our work characterizes the impact of bottlenose dolphin social dynamics on infectious disease risk and informs the structure of vulnerability for future epizootics.

ecology↗