Sampling from commercial vessel routes can capture marine biodiversity distributions effectively

Collecting fine-scale occurrence data for marine species across large spatial scales is logistically challenging but is important to determine species distributions and for conservation planning. Inaccurate descriptions of species ranges could result in designating protected areas with inappropriate locations or boundaries. Optimising sampling strategies, therefore, is a priority for scaling up survey approaches using tools such as environmental DNA (eDNA) to capture species distributions. In a marine context, commercial vessels, such as ferries, could provide sampling platforms allowing access to under-sampled areas and repeatable sampling over time to track community changes. However, sample collection from commercial vessels could be biased and may not represent biological and environmental variability. Here, we evaluate whether sampling along Mediterranean ferry routes can yield unbiased biodiversity survey outcomes, based on perfect knowledge from a stacked species distribution model (SSDM) of marine megafauna from online data repositories. Simulations to allocate sampling point locations were carried out representing different sampling strategies (random vs regular), frames (ferry routes vs unconstrained) and number of sampling points. SSDMs were remade from different sampling simulations and compared to the ‘perfect knowledge’ SSDM to quantify the bias associated with different sampling strategies. Ferry routes detected more species and were able to recover known patterns in species richness at smaller sample sizes better than unconstrained sampling points. However, to minimise potential bias, ferry routes should be chosen to cover the variability in species composition and its environmental predictors in the SSDMs. The workflow presented here can be used to design effective sampling strategies using commercial vessel routes globally, including for eDNA analyses. This approach has potential to provide a cost-effective method to access remote oceanic areas on a regular basis and can recover meaningful data on spatiotemporal biodiversity patterns.

Methods
This dataset includes binary species distribution models for 43 species of marine predators (9 mammals, 13 elasmobranchs, 20 fishes, and one turtle) from the Mediterranean Sea, and a binary stacked species distribution model showing the species richness of all marine predators. Models are available at 0.083° x 0.083° resolution in a WGS84 projection. Species distribution models were made with occurrence data collated from publically available data sources GBIF, OBIS, EurOBIS, and ACCOBAMS, and the Medlem database which is available upon request from its authors, as well as environmental predictors from Bio-Oracle and Marspec. Quality checking of occurrence records prior to modelling has been carried out including removal of records with GPS coordinates with fewer than three decimal places and duplicates between records based on the species, coordinates, year and month. Records were manually filtered further to identify records with the same species, year and month but different coordinates as a result of potential rounding between the different datasets.

在大空间尺度上收集海洋物种的精细发生数据，虽在后勤层面颇具挑战，但对于明确物种分布格局、开展保护规划而言至关重要。对物种分布范围的不准确描述，可能导致保护区选址或边界划定失当。因此，优化采样策略成为利用环境DNA（environmental DNA, eDNA）等工具拓展调查方法、精准捕捉物种分布的优先事项。在海洋研究场景中，轮渡等商业船舶可作为采样平台，得以进入此前采样不足的区域，并可开展长期重复采样以追踪群落动态变化。不过，从商业船舶开展的样本采集可能存在偏倚，无法完整反映生物与环境的异质性。本研究基于在线数据仓库中海洋巨型动物的堆叠物种分布模型（stacked species distribution model, SSDM）所提供的完美先验知识，评估沿地中海轮渡航线开展采样能否获得无偏的生物多样性调查结果。研究通过模拟分配采样点位，设置了不同的采样策略（随机采样与规则采样）、采样框架（轮渡航线与无约束采样）以及采样点数梯度。基于不同采样模拟的结果重新构建SSDM，并将其与"完美先验知识"SSDM进行对比，以量化不同采样策略带来的偏倚程度。相较于无约束采样点位，轮渡航线可检测到更多物种，且在较小采样规模下就能更好地还原物种丰富度的已知格局。不过，为尽可能降低潜在偏倚，所选轮渡航线应覆盖SSDM中物种组成及其环境预测因子的变异范围。本研究提出的工作流程可用于全球范围内依托商业船舶航线设计高效采样策略，其中也包括环境DNA（eDNA）分析相关的采样设计。该方法有望提供一种兼具成本效益的途径，实现对偏远大洋区域的常态化采样，并可获取具有科学价值的时空生物多样性格局数据。

方法
本数据集包含地中海海域43种海洋捕食者的二元物种分布模型（其中哺乳类9种、软骨鱼类13种、鱼类20种、海龟1种），以及表征所有海洋捕食者物种丰富度的二元堆叠物种分布模型。模型采用WGS84坐标系，分辨率为0.083°×0.083°。物种分布模型的构建采用了公开数据源全球生物多样性信息设施（Global Biodiversity Information Facility, GBIF）、海洋生物地理信息系统（Ocean Biogeographic Information System, OBIS）、欧洲海洋生物地理信息系统（European Ocean Biogeographic Information System, EurOBIS）、黑海、地中海及毗邻大西洋海域鲸类保护协定（Agreement on the Conservation of Cetaceans of the Black Sea, Mediterranean Sea and Contiguous Atlantic Area, ACCOBAMS），以及可通过作者申请获取的Medlem数据库中的整合发生数据，同时辅以Bio-Oracle与Marspec提供的环境预测因子。建模前已对发生记录开展质量校验，包括剔除GPS坐标精度不足三位小数的记录，以及基于物种、坐标、年份和月份识别并移除重复记录。此外还开展了人工筛选，以识别因不同数据集间潜在的坐标舍入问题导致的、物种、年份和月份一致但坐标存在差异的记录。