Many aquaculture sectors internationally, most notably for the cultivation of bi valves, rely almost completely on wild-caught juveniles (‘spat’) to stock farms, with poor ‘catches’ representing one the biggest constraints on global production. An example of this practice is green-lipped mussel Perna canaliculus aquaculture in New Zealand, where the industry in the main growing region has been monitoring P. canaliculus settlement for almost 40 yr. This practice involves deploying settlement arrays across the region to guide the places and times to place spatcatching rope. Using a subset of these data spanning 25 yr (1993−2018), we identified regional spatio-temporal patterns of P. canaliculus spat settlement. Boosted regression tree (BRT) models were used to forecast settlement at 2 different sub-regions with consistent high catch yields. BRT models confirmed a strong seasonal influence on settlement, with highest predicted settlement levels coinciding with the main P. canaliculus spawning period (late summer to autumn). Positive relationships were detected between settlement and the occurrence of positive temperature anomalies, easterly winds, periods of large tidal range and Southern Ocean Oscillation Index values associated with La Niña episodes. The models were able to forecast P. canaliculus settlement with excellent prediction accuracy based on time of year and environmental conditions 1 mo prior to collection. This study highlights the benefit of undertaking long-term monitoring of spat settlement and the related environmental factors that affect this ecological process. In combination with advance modelling techniques that enable forecasting of settlement densities, such knowledge can help to overcome challenges in spat supply and enable production upscaling.

中文翻译：

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使用历史数据和增强回归树预测贻贝定居点

国际上的许多水产养殖部门，尤其是双瓣阀的养殖，几乎完全依赖野生捕捞的幼鱼（“卵”）来养殖养殖场，而“捕获量”不足是全球生产的最大制约因素之一。这种做法的一个例子是新西兰的绿唇贻贝 Perna canaliculus 水产养殖，主要种植区的行业近 40 年来一直在监测 P. canaliculus 的定居情况。这种做法涉及在整个地区部署定居点阵列，以指导放置溅水绳的地点和时间。使用跨越 25 年（1993-2018 年）的这些数据的一个子集，我们确定了 P. canaliculus 卵沉降的区域时空模式。增强回归树 (BRT) 模型用于预测 2 个不同子区域的定居点，并具有一致的高产量。BRT 模型证实了对沉降的强烈季节性影响，预测的最高沉降水平与主要的 P. canaliculus 产卵期（夏末至秋季）一致。在沉降与正温度异常、东风、大潮差周期和与拉尼娜事件相关的南大洋涛动指数值之间检测到正相关。这些模型能够根据一年中的时间和收集前 1 个月的环境条件，以出色的预测精度预测 P. canaliculus 沉降。本研究强调了对卵子沉降和影响这一生态过程的相关环境因素进行长期监测的好处。结合能够预测沉降密度的先进建模技术，