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我们可以从10万个淡水预测中学到什么? 来自NEON生态预测挑战的综合

Freya Olsson^1,2, Cayelan C Carey^1,2, Carl Boettiger³

¹Department of Biological Sciences, Virginia Tech, Virginia, USA.

Ecological applications : a publication of the Ecological Society of America

|February 12, 2025

概括

生态预测模型对水生生态系统的管理有希望. 一个NEON挑战比较了水温和溶解氧的模型,揭示了影响预测准确性和可预测性的关键因素.

科学领域:

生态预测生态预测
淡水生态系统管理的管理
环境建模环境建模

背景情况:

代生态预测有助于生态系统的理解和管理.
由于保护需求和数据可用性,水生生态系统有更多的预测.
之前的研究表明,在个别站点有希望,但跨站点模型性能分析缺乏.

研究的目的:

综合分析多个地点的各种生态预测模型的性能.
评估模型结构,共变量和不确定性来源如何影响淡水变量预测的准确性.
通过模型相互比较,确定水生生态系统中预测技能的关键驱动因素.

主要方法:

分析了超过10万个对水温和七个湖泊溶解氧的概率预测.
利用国家生态观测网络 (NEON) 预测挑战 (2023) 的数据.
对基线无效模型进行评估预测性能,根据模型结构,共变量和不确定性而有所不同.

主要成果:

34%-40%的预测模型表现出对水温和溶解氧的技能.
纳入空气温度作为共变量和基于过程的模型的模型显示了更高的水温预测性能.
预测技能随着预测时间的延长而下降;熟练的预测经常占更多的不确定性来源.

结论:

NEON预测挑战为淡水生态系统可预测性提供了有价值的模型相互比较.
了解对预测性能的控制有助于我们管理受威胁的水生生态系统的能力.
性能最好的模型有多样性,突出了对多样化的建模方法和对淡水可预测性的进一步研究的需求.

关键词:

虹是什么意思虹生态预测生态预测预测挑战预测挑战淡水淡水水的情况.短期预测短期预测不确定性是一种不确定性.水的质量水的质量.

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