Data underlying the publication: Boosting efficiency of mussel seed collection for ecological sustainability: identifying critical drivers and informing management

In this study, our objective is to examine the variability of SMCs (suspended mussel spat collectors) efficiency under different habitat conditions and management options. Specifically, we dedicated to address the following key research questions (KRQ):1)   Is SMCs efficiency consistent across time and space?2)   Does SMCs efficiency rely on biotic drivers in response to mussel life cycle, such as larval abundance and spat settlement?3)   Are there critical drivers that would improve the predictability of SMCs efficiency?4)   How does SMCs efficiency change with potential management strategies targeting identified critical drivers?Adopting the Dutch Wadden Sea as a model system, we first addressed KRQ_1 by investigating the deployment and harvesting of SMCs in this region over an 11-year period. Secondly, KRQ_2 was validated through a four-year experiment at four representative sites. Thirdly, we utilized machine learning algorithms on an integrated 11-year dataset to identify dominant factors affecting SMCs efficiency and develop a predictive model, addressing KRQ_3. Finally, KRQ_4 was addressed by conducting model experiments that evaluated the sensitivity of SMCs efficiency to critical factors. These files include the data used to create each figure in the manuscript, organized as follows:1. The 11-year dataset 2. Field experiments a)       Larval abundance b)       Spat settlement For a complete description, see 'Data description.docx'<br>

本研究旨在探究悬浮贻贝稚贝采集器（suspended mussel spat collectors, SMCs）在不同生境条件与管理方案下的效能变异性。具体而言，本研究致力于解答如下核心研究问题（key research questions, KRQs）：
1. 悬浮贻贝稚贝采集器的效能在时间与空间维度上是否保持一致？
2. 悬浮贻贝稚贝采集器的效能是否依赖于响应贻贝生活史的生物驱动因子，例如幼虫丰度与稚贝沉降量？
3. 是否存在关键驱动因子可提升悬浮贻贝稚贝采集器效能的可预测性？
4. 针对已识别的关键驱动因子制定的潜在管理策略，会如何影响悬浮贻贝稚贝采集器的效能？

本研究以荷兰瓦登海（Dutch Wadden Sea）为模式系统，首先通过分析该区域11年间悬浮贻贝稚贝采集器的布设与回收数据，解答核心研究问题1；其次，在4个代表性站点开展为期4年的野外实验，以此验证核心研究问题2；第三，基于整合的11年数据集运用机器学习算法，识别影响悬浮贻贝稚贝采集器效能的主导因子并构建预测模型，以解决核心研究问题3；最后，通过开展模型敏感性实验，评估悬浮贻贝稚贝采集器效能对关键因子的响应，完成核心研究问题4的探究。

本研究附带的文件包含了论文中每张图表所使用的原始数据，具体组织形式如下：
1. 11年整合数据集
2. 野外实验数据
a) 幼虫丰度数据
b) 稚贝沉降数据
完整的数据说明请参见Data description.docx文件。