Understanding Sudden Unusual Mortality Syndrome (SUMS) in oysters

Abstract

The research team will investigate the causes of SUMS in Eastern oysters by combining field monitoring at six commercial farms with laboratory analyses and advanced hydrodynamic and machine-learning models. Researchers will track water quality, oyster physiology, parasite loads and mortality in both diploid and triploid oysters to identify the environmental and biological factors that trigger SUMS events. The team will then analyze its data to determine what conditions make oyster die-offs more likely and to identify signs that an event is going to occur. Oyster farmers, resource managers and scientists will be able to use these results to help prevent losses and keep Gulf Coast oyster farms healthy and sustainable.

Objectives

The objective of this proposal is to identify the causes of Sudden Unusual Mortality Syndrome (SUMS) in Eastern oysters, a recurring but poorly understood phenomenon affecting commercial oyster farms in Mississippi and Alabama. The project takes an integrated approach combining field monitoring, laboratory analysis and advanced modeling to uncover the environmental and biological mechanisms that drive SUMS-related mortality.

Five core objectives guide the work:
  1.    Collaborate with growers to establish a reporting and sampling framework for SUMS events.
  2.    Evaluate water quality parameters — including temperature, salinity, dissolved oxygen and turbidity — and their relationship to mortality events.
  3.    Compare mortality rates between diploid and triploid oysters under natural conditions to assess genetic susceptibility.
  4.    Analyze oyster physiological condition, focusing on energy reserves and condition index, to determine their role in vulnerability to SUMS.
  5.    Quantify parasite burden, particularly Perkinsus marinus and Polydora spp., to assess their contribution to mortality events.

Field data will be collected at six commercial farms and integrated with outputs from a calibrated hydrodynamic-water quality model. This data will support machine learning analysis to identify complex environmental interactions and predictive indicators of SUMS. The findings will fill critical knowledge gaps, enable the development of early warning tools, and support science-based management strategies to enhance the resilience and sustainability of oyster aquaculture in the Gulf Coast.

Methodology

This project employs a multi-faceted methodology combining field data collection, laboratory analysis and advanced modeling to investigate the causes of Sudden Unusual Mortality Syndrome (SUMS) in oysters.

Six commercial oyster farms in Alabama and Mississippi will participate by reporting SUMS events, collecting water quality data, and hosting field trials with both diploid and triploid oysters. Each farm will receive standardized oyster cohorts reared at the Auburn University Shellfish Lab (AUSL), allowing for controlled comparisons across sites. The participating farms have been identified and letters of support from each one accompanies this proposal.

Continuous water quality monitoring — including temperature, salinity, dissolved oxygen and turbidity — will be conducted using existing sensors and newly deployed HOBO loggers. Quarterly oyster sampling will assess mortality, physiological condition (energy reserves, condition index), and parasite burden (e.g., Perkinsus marinus, Polydora spp.) using established protocols. During SUMS events, rapid-response sampling will also be initiated.

Field data will be statistically analyzed using generalized linear mixed models and survival analysis to evaluate relationships between environmental and biological variables and mortality. Concurrently, a validated EFDC+ hydrodynamic-water quality model will simulate water conditions at farm sites across years.

Time-series outputs from the model will be combined with mortality reports to train machine learning models (Random Forest, XGBoost, LightGBM) that identify environmental conditions preceding SUMS events. SHAP values and Generalized Additive Models (GAMs) will be used for model interpretability and to pinpoint risk thresholds.

This integrated methodology will generate actionable insights into the causes of SUMS and support the development of predictive tools to inform oyster farm management and mitigate future losses.

Rationale

Sudden Unusual Mortality Syndrome (SUMS) is an emerging and poorly understood condition that causes rapid, high mortality in Eastern oysters, particularly those nearing market size. Since first being reported in 2012, SUMS has led to significant economic losses across the Gulf and Atlantic coasts, including recent widespread events in Alabama and Mississippi. Despite its impact, SUMS remains under-researched, with minimal representation in the scientific literature and no formal tracking system.

Preliminary evidence suggests that SUMS is not caused by a single pathogen but likely results from a combination of environmental stressors — such as extreme or fluctuating salinity, temperature and dissolved oxygen — along with biological factors like ploidy, spawning condition, energy reserves and parasite load. However, the mechanisms remain unclear, and farmers currently lack the information and tools needed to predict or mitigate these mortality events.

This project addresses a critical knowledge gap by investigating the environmental and physiological drivers of SUMS in real-world aquaculture settings. By partnering with oyster growers, collecting standardized field and lab data, and applying advanced hydrodynamic and machine learning models, the project will uncover how different stressors interact to cause mortality.

The rationale for this integrated approach is to generate actionable knowledge that improves understanding of SUMS, enables early warning of high-risk conditions, and supports science-based strategies for reducing oyster losses. Ultimately, the project aims to enhance the sustainability and resilience of the Gulf Coast oyster aquaculture industry, protect farm profitability and strengthen coastal economies.