CSS-Inc., Fairfax, Virginia, United States of America.
NOAA National Centers for Coastal Ocean Science, Beaufort, North Carolina, United States of America.
PLoS One. 2021 May 14;16(5):e0251818. doi: 10.1371/journal.pone.0251818. eCollection 2021.
As species distribution models, and similar techniques, have emerged in marine ecology, a vast array of predictor variables have been created and diverse methodologies have been applied. Marine fish are vital food resources worldwide, yet identifying the most suitable methodology and predictors to characterize spatial habitat associations, and the subsequent distributions, often remains ambiguous. Our objectives were to identify knowledge gaps in fish guilds, identify research themes, and to determine how data sources, statistics, and predictor variables differ among fish guilds. Data were obtained from an international literature search of peer-reviewed articles (2007-2018; n = 225) and research themes were determined based on abstracts. We tested for differences in data sources and modeling techniques using multinomial regressions and used a linear discriminant analysis to distinguish differences in predictors among fish guilds. Our results show predictive studies increased over time, but studies of forage fish, sharks, coral reef fish, and other fish guilds remain sparse. Research themes emphasized habitat suitability and distribution shifts, but also addressed abundance, occurrence, stock assessment, and biomass. Methodologies differed by fish guilds based on data limitations and research theme. The most frequent predictors overall were depth and temperature, but most fish guilds were distinguished by their own set of predictors that focused on their specific life history and ecology. A one-size-fits-all approach is not suitable for predicting marine fish distributions. However, given the paucity of studies for some fish guilds, researchers would benefit from utilizing predictors and methods derived from more commonly studied fish when similar habitat requirements are expected. Overall, the findings provide a guide for determining predictor variables to test and identifies novel opportunities to apply non-spatial knowledge and mechanisms to models.
随着物种分布模型和类似技术在海洋生态学中的出现，已经创建了大量的预测变量，并应用了各种方法。海洋鱼类是全球重要的食物资源，但确定最合适的方法和预测因子来描述空间栖息地关联以及随后的分布，通常仍然不明确。我们的目标是确定鱼类群的知识空白，确定研究主题，并确定鱼类群之间在数据源、统计数据和预测变量方面的差异。数据来自对同行评议文章（2007-2018 年；n=225）的国际文献检索，并根据摘要确定了研究主题。我们使用多项回归检验了数据源和建模技术的差异，并使用线性判别分析来区分鱼类群之间的预测变量差异。我们的研究结果表明，预测性研究随着时间的推移而增加，但关于饲料鱼类、鲨鱼、珊瑚礁鱼类和其他鱼类群的研究仍然很少。研究主题强调了栖息地适宜性和分布变化，但也涉及到丰度、出现、种群评估和生物量。基于数据限制和研究主题，不同鱼类群的方法学也存在差异。总体而言，最常见的预测因子是深度和温度，但大多数鱼类群都有自己的一套预测因子，这些预测因子侧重于其特定的生活史和生态学。一刀切的方法并不适合预测海洋鱼类的分布。然而，鉴于一些鱼类群的研究较少，当预期具有类似栖息地需求时，研究人员将受益于利用更常研究的鱼类的预测因子和方法。总的来说，这些发现为确定要测试的预测变量提供了指导，并为应用非空间知识和机制提供了新的机会。
