NOAA Channel Islands National Marine Sanctuary, Santa Barbara, CA, United States of America.
Ecology Evolution and Marine Biology Department, University of California Santa Barbara, Santa Barbara, CA, United States of America.
PLoS One. 2021 Apr 28;16(4):e0250792. doi: 10.1371/journal.pone.0250792. eCollection 2021.
Global climate change increasingly contributes to large changes in ecosystem structure. Timely management of rapidly changing marine ecosystems must be matched with methods to rapidly quantify and assess climate driven impacts to ecological communities. Here we create a species-specific, classification system for fish thermal affinities, using three quantifiable datasets and expert opinion. Multiple sources of information limit potential data bias and avoid misclassification. Using a temperate kelp forest fish community in California, USA as a test case for this new methodology, we found the majority of species had high classification agreement across all four data sources (n = 78) but also a number of low agreement species (2 sources disagree from the others, n = 47). For species with low agreement, use of just one dataset to classify species, as is commonly done, would lead to high risk of misclassification. Differences in species classification between individual datasets and our composite classification were apparent. Applying different thermal classifications, lead to different conclusions when quantifying 'warm' and 'cool' species density responses to a marine heatwave. Managers can use this classification approach as a tool to generate accurate, timely and simple information for resource management.
全球气候变化导致生态系统结构发生巨大变化。必须及时管理快速变化的海洋生态系统，并采用快速量化和评估气候对生态群落影响的方法。在这里，我们使用三种可量化的数据集和专家意见，为鱼类的热亲和性创建了一种特定于物种的分类系统。多种信息来源限制了潜在的数据偏差并避免了错误分类。使用美国加利福尼亚州的温带海带林鱼类群落作为该新方法的测试案例，我们发现大多数物种在所有四个数据源（n = 78）中具有较高的分类一致性，但也有一些低一致性的物种（n = 47）。对于一致性低的物种，如果像通常那样仅使用一个数据集对物种进行分类，则可能会导致高错误分类风险。个别数据集和我们的综合分类之间的物种分类差异明显。应用不同的热分类方法，在量化海洋热浪对“暖”和“冷”物种密度的响应时，会得出不同的结论。管理者可以将这种分类方法用作生成准确，及时和简单信息的工具，以进行资源管理。
