School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia.
Australian Museum Research Institute, Sydney, Australia.
PLoS Biol. 2019 Jul 12;17(7):e3000380. doi: 10.1371/journal.pbio.3000380. eCollection 2019 Jul.
Larval dispersal is a critically important yet enigmatic process in marine ecology, evolution, and conservation. Determining the distance and direction that tiny larvae travel in the open ocean continues to be a challenge. Our current understanding of larval dispersal patterns at management-relevant scales is principally and separately informed by genetic parentage data and biological-oceanographic (biophysical) models. Parentage datasets provide clear evidence of individual larval dispersal events, but their findings are spatially and temporally limited. Biophysical models offer a more complete picture of dispersal patterns at regional scales but are of uncertain accuracy. Here, we develop statistical techniques that integrate these two important sources of information on larval dispersal. We then apply these methods to an extensive genetic parentage dataset to successfully validate a high-resolution biophysical model for the economically important reef fish species Plectropomus maculatus in the southern Great Barrier Reef. Our results demonstrate that biophysical models can provide accurate descriptions of larval dispersal at spatial and temporal scales that are relevant to management. They also show that genetic parentage datasets provide enough statistical power to exclude poor biophysical models. Biophysical models that included species-specific larval behaviour provided markedly better fits to the parentage data than assuming passive behaviour, but incorrect behavioural assumptions led to worse predictions than ignoring behaviour altogether. Our approach capitalises on the complementary strengths of genetic parentage datasets and high-resolution biophysical models to produce an accurate picture of larval dispersal patterns at regional scales. The results provide essential empirical support for the use of accurately parameterised biophysical larval dispersal models in marine spatial planning and management.
幼虫扩散是海洋生态学、进化和保护中一个至关重要但神秘的过程。确定微小幼虫在开阔海域中移动的距离和方向仍然是一个挑战。我们目前对管理相关尺度上幼虫扩散模式的理解主要分别由遗传亲子关系数据和生物海洋学（生物物理）模型提供信息。亲子数据集提供了个体幼虫扩散事件的明确证据，但它们的发现具有空间和时间上的局限性。生物物理模型提供了区域尺度上扩散模式的更完整图景，但准确性不确定。在这里，我们开发了统计技术，将这两个关于幼虫扩散的重要信息源整合在一起。然后，我们将这些方法应用于广泛的遗传亲子数据集，成功验证了大堡礁南部经济重要的珊瑚鱼物种 Plectropomus maculatus 的高分辨率生物物理模型。我们的结果表明，生物物理模型可以在与管理相关的空间和时间尺度上提供对幼虫扩散的准确描述。它们还表明，遗传亲子数据集提供了足够的统计能力来排除较差的生物物理模型。包含特定物种幼虫行为的生物物理模型比假设被动行为对亲子数据的拟合要好得多，但错误的行为假设会导致比完全忽略行为更差的预测。我们的方法利用了遗传亲子数据集和高分辨率生物物理模型的互补优势，在区域尺度上生成了准确的幼虫扩散模式图。结果为在海洋空间规划和管理中使用经过准确参数化的生物物理幼虫扩散模型提供了必要的经验支持。
