Modelling Seasonal Vertical Migration in Marine Zooplankton

海洋中浮游动物的季节性垂直迁移（SVM）在季节性显著的高纬度地区可能对决定初级和出口产量起关键作用。支持向量机是高纬度地区许多海洋中浮游动物群落行为的重要组成部分，使这些生物能够有效地利用春季水华期间产生的生物量。支持向量机上升的时间选择在早春，下降的时间选择在夏季，这对于最大限度地提高食物的可得性和最大限度地降低被大型动物捕食的风险至关重要：上升得太早或太晚可能导致饥饿，而下降得太晚可能增加被吃掉的可能性（匹配不匹配假说）。支持向量机在季节性较弱的低纬度海洋区域不重要或不存在。由于这些复杂性，大多数生物地球化学模型只通过它们的摄食来定义浮游动物的行为，而忽略了它们的迁徙行为。支持向量机已被用于研究桡足类动物的季节发育和区域分布及其生命阶段的个体模型（IBMs）中。然而，对于3D全局模型的应用来说，ibm的计算成本过于昂贵，特别是对于长时间尺度的模拟。在之前的生物地球化学建模项目中，我们发现在高纬度地区观测到的和预测的二次产量之间存在显著的不匹配，这可能与模型中缺乏支持向量机行为有关。我们建议开发更简单的、基于特征和最优性的模型，以便在能够在几十年到几个世纪的时间尺度上进行长期模拟的大规模模型中表示支持向量机。我们将使用我们以前开发的建模技术来探索如何特征，如上升日，或度日，控制支持向量机行为及其演变。我们将开发基于特征的支持向量机描述，以分析支持向量机行为的驱动力，首先在1D中，然后在3D生物地球化学模型中。因此，本项目的主要目标是了解哪些环境因素决定了局部支持向量机行为的演变，以及它们如何塑造支持向量机行为的全球模式及其对浮游生物生态学和生物地球化学的影响。然后，我们将分析在全球模型中包括支持向量机行为的潜力，以提高它们描述观测到的生物地球化学场的整体能力，例如，营养浓度分布以及初级和出口生产。最后，我们将评估考虑SVM行为对过去和未来气候情景长期模拟的影响。我们的项目需要DynaTrait和其他大型研究项目之间的紧密联系，从dfg资助的SFB 754氧气最小带和bmbf资助的PalMod长期气候模拟项目中受益并做出贡献。因此，我们的工作将显著提高基于特征的建模在全球建模社区的可见性和相关性。

Seasonal vertical migration (SVM) of marine mesozooplankton potentially plays a key role in determining primary and export production in higher latitudes with pronounced seasonality. SVM is an important part of the behaviour of many marine mesozooplankton communities at high latitudes, enabling these organisms to exploit the biomass produced during spring blooms efficiently. The timing of the SVM ascent in early spring and descent in summer are essential for maximising food availability and minimising the risk of predation by larger animals: ascending too early or too late can result in starvation and descending too late can increase the likelihood of being eaten (match-mismatch hypothesis). SVM is less important or absent in low-latitude ocean regions with little seasonality. Owing to these complications, most biogeochemical models define zooplankton behaviour only through their feeding, and ignore any migratory behaviour.SVM has been considered in individual-based models (IBMs) geared towards the seasonal development and regional distribution of copepods and their life stages in several ocean regions. However, IBMs are computationally too expensive for applications in 3D global models, particularly for simulations over long time scales. In previous biogeochemical modelling projects, we have found significant mismatches between observed and predicted secondary production in high-latitude locations, likely related to the lack of SVM behaviour in the model. We propose to develop simpler, trait- and optimality-based models amenable for representing SVM in large-scale models capable of long-term simulations on time scales of decades to centuries. We will use modelling techniques we have developed previously to explore how traits, such as the day of ascent, or degree-days, control SVM behaviour and its evolution.We will develop trait-based descriptions of SVM for analysing the driving forces of SVM behaviour, first in 1D and then also in 3D biogeochemical models. The main goal of this project is thereby an understanding of which environmental factors determine the evolution of SVM behaviour locally and how they shape global patterns in SVM behaviour and its effects on plankton ecology and biogeochemistry. We will then analyse the potential of including SVM behaviour in global models for improving their overall ability to describe observed biogeochemical fields, e.g., distributions of nutrient concentrations and primary and export production. Finally, we will evaluate the effect of considering SVM behaviour on long-term simulations of past and future climate scenarios.Our project entails strong links between DynaTrait and other large research projects, benefitting from and contributing to the DFG-funded SFB 754 on oxygen minimum zones and the BMBF-funded PalMod project on long-term climate simulations. Thus, our work will significantly improve the visibility and relevance of trait-based modelling in the global-modelling community.