Phytoplankton phenology describes yearly algal growth cycles and characterises the timing, duration, and magnitude of bloom occurrences. This study used satellite chlorophyll-a data from 1998 to 2020 and the Hierarchical Agglomerative Clustering method to define phenoregions based on phytoplankton phenology spatial patterns over the British Columbia and Southeast Alaska coastal oceans. The defined phenoregions were used to simplify the spatial complexity of the heterogenous study region and thus better describe phytoplankton seasonality across the target area. The cluster analysis allowed the delineation of four coherent regions: two coastal regions and northern and southern shelf/offshore regions. Results showed that each phenoregion had distinguishable phytoplankton phenological characteristics, likely due to different physical forcings acting in these areas. Moreover, the interannual variability of the spring bloom initiation was evaluated considering interactions between sea surface temperature (SST) anomalies and the El Niño Southern Oscillation Index (ENSO). Early spring blooms were associated with positive SST anomalies and El Niño conditions; conversely, average or late spring blooms occurred in years with negative SST anomalies and La Niña conditions, with the strongest relationship occurring in the southern shelf/offshore phenoregion. This study provided new insights into the regionalisation of the British Columbia and Southeast Alaska coastal oceans based on phytoplankton phenology patterns. Given the critical role of phytoplankton as the base of the marine food web, such phenoregions have implications for regional zooplankton biomass and fish production. The link between phytoplankton phenology and climate drivers points to the importance of environmental change in phytoplankton bloom dynamics. Further research into the connection between phytoplankton bloom indices and zooplankton community structure and production would be an important step towards using these indices for ecosystem monitoring and fisheries management.

中文翻译：

---

通过基于卫星的方法绘制东北太平洋海洋沿海生态系统的物候区和浮游植物季节性


浮游植物物候学描述了每年的藻类生长周期，并描述了水华发生的时间、持续时间和程度。本研究使用 1998 年至 2020 年的卫星叶绿素-a 数据和分层聚集聚类方法，根据不列颠哥伦比亚省和阿拉斯加东南部沿海海洋的浮游植物物候空间模式来定义表型区。定义的表型区域用于简化异质研究区域的空间复杂性，从而更好地描述目标区域的浮游植物季节性。聚类分析可以划分出四个相关区域：两个沿海区域以及北部和南部陆架/近海区域。结果表明，每个表型区都具有明显的浮游植物物候特征，这可能是由于作用在这些区域的不同物理强迫所致。此外，考虑到海面温度（SST）异常与厄尔尼诺南方涛动指数（ENSO）之间的相互作用，评估了春季水华起始的年际变化。早春开花与正海温异常和厄尔尼诺现象有关；相反，平均或晚春水华发生在海温负异常和拉尼娜现象的年份，其中最强的关系发生在南部陆架/近海表象区。这项研究为基于浮游植物物候模式的不列颠哥伦比亚省和阿拉斯加东南部沿海海洋的区域化提供了新的见解。鉴于浮游植物作为海洋食物网基础的关键作用，此类表型区对区域浮游动物生物量和鱼类生产具有影响。 浮游植物物候与气候驱动因素之间的联系表明环境变化在浮游植物水华动态中的重要性。进一步研究浮游植物水华指数与浮游动物群落结构和生产之间的联系将是将这些指数用于生态系统监测和渔业管理的重要一步。