Modeling Oyster Reef Reproductive Sustainability: Analyzing Gamete Viability, Hydrodynamics, and Reef Structure to Facilitate Restoration of $\textit{Crassostrea virginica}$
The eastern oyster is a keystone species and ecosystem engineer. However, restoration efforts of wild oysters are often unsuccessful, in that they do not produce a robust population of oysters that are able to successfully reproduce. Furthermore, the dynamics of wild oyster fertilization is not yet well understood. Through conducting an experiment predicated on quantifying the influence of elementary aspects of fertilization kinetics--sperm concentration, gamete age, and success rate--we found that, as stochastic as the mating process may seem, there are correlations which fundamentally serve as the framework for assessing long-term sustainability, reef structure, and hydrodynamic parameters in relation to fertilization. We then focused on mathematically defining a procedure which simulated a concentration distribution of a single sperm and egg release where there existed conditions necessary for breeding to take place. We found a very significant impact of both gamete age and sperm concentration on fertilization rate ($p < 0.0001$). Our hydrodynamic model demonstrates that distance can also drastically influence broadcast spawning. This could be used as a foundation for developing a flexible model for wild oyster fertilization based on placement, initial seawater conditions, and size of the starting population. The results of this research could be implemented into a more user-friendly program which would accept multiple variables as inputs and output the probability of fertilization given arbitrary values. By accounting for environmental deviations, this generalization would increase its compatibility with the public and actualize the project's intended purpose: enhance the planning of oyster reef restoration projects.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Is AI currently capable of identifying wild oysters? A comparison of human annotators against the AI model, ODYSSEE
Oysters are ecologically and commercially important species that require frequent monitoring to track population demographics (e.g. abundance, growth, mortality). Current methods of monitoring oyster reefs often require …
OysterNet: Enhanced Oyster Detection Using Simulation
Oysters play a pivotal role in the bay living ecosystem and are considered the living filters for the ocean. In recent years, oyster reefs have undergone major devastation caused by commercial over-harvesting, requiring …
Contextual Multi-Task Reinforcement Learning for Autonomous Reef Monitoring
Although autonomous underwater vehicles promise the capability of marine ecosystem monitoring, their deployment is fundamentally limited by the difficulty of controlling vehicles under highly uncertain and non-stationary…
Reinforcement LearningPredictive Model for Gross Community Production Rate of Coral Reefs using Ensemble Learning Methodologies
Coral reefs play a vital role in maintaining the ecological balance of the marine ecosystem. Various marine organisms depend on coral reefs for their existence and their natural processes. Coral reefs provide the necessa…
Ensemble LearningDiscovering strategies for coastal resilience with AI-based prediction and optimization
Tropical storms cause extensive property damage and loss of life, making them one of the most destructive types of natural hazards. The development of predictive models that identify interventions effective at mitigating…