Testing the Skill of a Species Distribution Model Using a 21st Century Virtual Ecosystem
Testing the Skill of a Species Distribution Model Using a 21st Century Virtual Ecosystem
复制标题
用21世纪虚拟生态系统测试物种分布模型的能力
DOI：
10.1029/2021gl093455
复制
发表时间：
2021-11-28
影响因子：
5.2
通讯作者：
Cael, B. B.
中科院分区：
文献类型：
--
作者：
Bardon, L. R.;Ward, B. A.;Cael, B. B.
浮游生物群落在海洋食物网、生物地球化学循环和地球气候中发挥着重要作用；然而，观测很少，对它们如何应对气候变化的预测也各不相同。相关物种分布模型（SDM's）已被应用于基于观测环境变量关系的生物地理预测。为了研究不确定性的来源，我们使用相关SDM来预测21世纪海洋生态系统模型（达尔文）的浮游生物地理。对达尔文输出进行采样以模拟历史海洋观测，并使用广义加性模型训练SDM。我们发现，预测技能在不同的测试用例和功能组之间存在差异，其误差更多地归因于时空抽样偏差，而不是样本量。本世纪末的预测很差，受限于目标-预测者关系随时间的变化。我们的发现说明了经验模型在使用有限的观测数据来预测复杂的动态系统时所面临的基本挑战。
Plankton communities play an important role in marine food webs, in biogeochemical cycling, and in Earth's climate; yet observations are sparse, and predictions of how they might respond to climate change vary. Correlative species distribution models (SDM's) have been applied to predicting biogeography based on relationships to observed environmental variables. To investigate sources of uncertainty, we use a correlative SDM to predict the plankton biogeography of a 21st century marine ecosystem model (Darwin). Darwin output is sampled to mimic historical ocean observations, and the SDM is trained using generalized additive models. We find that predictive skill varies across test cases, and between functional groups, with errors that are more attributable to spatiotemporal sampling bias than sample size. End-of-century predictions are poor, limited by changes in target-predictor relationships over time. Our findings illustrate the fundamental challenges faced by empirical models in using limited observational data to predict complex, dynamic systems.