Department of Fisheries and Wildlife, Michigan St. University, 480 Wilson Rd, East Lansing, MI, 48824, USA.
U.S. Geological Survey, Pennsylvania Cooperative Fish & Wildlife Research Unit, Pennsylvania State University, 402 Forest Resources Building, University Park, PA, 16802, USA.
Ecol Lett. 2019 Oct;22(10):1587-1598. doi: 10.1111/ele.13346. Epub 2019 Jul 25.
Although spatial and temporal variation in ecological properties has been well-studied, crucial knowledge gaps remain for studies conducted at macroscales and for ecosystem properties related to material and energy. We test four propositions of spatial and temporal variation in ecosystem properties within a macroscale (1000 km's) extent. We fit Bayesian hierarchical models to thousands of observations from over two decades to quantify four components of variation - spatial (local and regional) and temporal (local and coherent); and to model their drivers. We found strong support for three propositions: (1) spatial variation at local and regional scales are large and roughly equal, (2) annual temporal variation is mostly local rather than coherent, and, (3) spatial variation exceeds temporal variation. Our findings imply that predicting ecosystem responses to environmental changes at macroscales requires consideration of the dominant spatial signals at both local and regional scales that may overwhelm temporal signals.
尽管对生态属性的时空变化已经进行了充分的研究,但在宏观尺度上进行的研究以及与物质和能量相关的生态系统属性仍然存在关键的知识空白。我们在宏观尺度(1000 公里)范围内检验了生态属性时空变化的四个命题。我们利用贝叶斯层次模型拟合了二十多年来数千次观测结果,以量化四个变化组成部分——空间(局部和区域)和时间(局部和一致);并对其驱动因素进行建模。我们发现有强有力的证据支持三个命题:(1)局部和区域尺度的空间变化很大且大致相等,(2)年度时间变化主要是局部的而不是一致的,以及,(3)空间变化超过时间变化。我们的研究结果表明,预测宏观尺度上生态系统对环境变化的响应需要考虑局部和区域尺度上占主导地位的空间信号,这些信号可能会超过时间信号。