Institute of Marine Research, Bergen, Norway.
CSIRO Oceans and Atmosphere, Hobart, Tasmania, Australia.
PLoS One. 2019 Feb 8;14(2):e0210419. doi: 10.1371/journal.pone.0210419. eCollection 2019.
Using end-to-end models for ecosystem-based management requires knowledge of the structure, uncertainty and sensitivity of the model. The Norwegian and Barents Seas (NoBa) Atlantis model was implemented for use in 'what if' scenarios, combining fisheries management strategies with the influences of climate change and climate variability. Before being used for this purpose, we wanted to evaluate and identify sensitive parameters and whether the species position in the foodweb influenced their sensitivity to parameter perturbation. Perturbing recruitment, mortality, prey consumption and growth by +/- 25% for nine biomass-dominating key species in the Barents Sea, while keeping the physical climate constant, proved the growth rate to be the most sensitive parameter in the model. Their trophic position in the ecosystem (lower trophic level, mid trophic level, top predators) influenced their responses to the perturbations. Top-predators, being generalists, responded mostly to perturbations on their individual life-history parameters. Mid-level species were the most vulnerable to perturbations, not only to their own individual life-history parameters, but also to perturbations on other trophic levels (higher or lower). Perturbations on the lower trophic levels had by far the strongest impact on the system, resulting in biomass changes for nearly all components in the system. Combined perturbations often resulted in non-additive model responses, including both dampened effects and increased impact of combined perturbations. Identifying sensitive parameters and species in end-to-end models will not only provide insights about the structure and functioning of the ecosystem in the model, but also highlight areas where more information and research would be useful-both for model parameterization, but also for constraining or quantifying model uncertainty.
使用端到端模型进行基于生态系统的管理需要了解模型的结构、不确定性和敏感性。挪威和巴伦支海（NoBa）的 Atlantis 模型已被实施用于“假设”情景，将渔业管理策略与气候变化和气候变异性的影响结合起来。在将其用于此目的之前，我们希望评估和确定敏感参数，以及物种在食物网中的位置是否会影响其对参数扰动的敏感性。对巴伦支海的 9 种生物量主导关键物种的繁殖、死亡率、猎物消耗和生长率进行了 +/-25%的扰动，同时保持物理气候不变，结果表明增长率是模型中最敏感的参数。它们在生态系统中的营养位置（低营养级、中营养级、顶级捕食者）影响了它们对扰动的响应。作为杂食动物的顶级捕食者，对其个体生活史参数的扰动反应最大。中层物种对扰动最脆弱，不仅对其自身的个体生活史参数，而且对其他营养级（更高或更低）的扰动也很敏感。对低营养级别的扰动对系统的影响最大，导致系统中几乎所有组成部分的生物量发生变化。联合扰动通常会导致模型响应的非加性，包括减弱的效应和联合扰动的影响增加。识别端到端模型中的敏感参数和物种，不仅可以深入了解模型中生态系统的结构和功能，还可以突出需要更多信息和研究的领域，这对模型参数化以及约束或量化模型不确定性都很有用。
