Marine biodiversity monitoring requires scalability and reliability across complex underwater environments to support conservation and invasive-species management. Yet existing detection solutions often exhibit a pronounced deployment gap, with performance degrading sharply when transferred to new sites. This work establishes the foundational detection layer for a multi-year invasive species monitoring initiative targeting Arctic and Atlantic marine ecosystems. We address this challenge by developing a Unified Information Pipeline that standardises heterogeneous datasets into a comparable information flow and evaluates a fixed, deployment-relevant detector under controlled cross-domain protocols. Across multiple domains, we find that structural factors, such as scene composition, object density, and contextual redundancy, explain cross-domain performance loss more strongly than visual degradation such as turbidity, with sparse scenes inducing a characteristic "Context Collapse" failure mode. We further validate operational feasibility by benchmarking inference on low-cost edge hardware, showing that runtime optimisation enables practical sampling rates for remote monitoring. The results shift emphasis from image enhancement toward structure-aware reliability, providing a democratised tool for consistent marine ecosystem assessment.

翻译：海洋生物多样性监测需要跨复杂水下环境实现可扩展性与可靠性，以支持保护及入侵物种管理工作。然而现有检测方案常表现出显著的部署差距，当迁移至新站点时性能急剧下降。本研究为一项针对北极与大西洋海洋生态系统的多年入侵物种监测计划建立了基础检测层。我们通过开发统一信息管道应对这一挑战，该管道将异构数据集标准化为可比信息流，并在受控跨领域协议下评估固定的部署相关检测器。跨多个领域的研究发现，结构因素（如场景构成、目标密度和上下文冗余）比视觉退化（如浑浊度）更能解释跨领域性能损失，稀疏场景会引发典型的"上下文坍缩"失效模式。我们进一步通过在低成本边缘硬件上进行推理基准测试验证了操作可行性，表明运行时优化能为远程监测实现实用采样率。这些结果将重点从图像增强转向结构感知的可靠性，为持续的海洋生态系统评估提供了普适化工具。