Population coherence and environmental impacts across spatial scales: a case study of Chinook salmon

作者：Ohlberger Jan^*; Scheuerell Mark D; Schindler Daniel E

来源：Ecosphere, 2016, 7(4): e01333.

DOI：10.1002/ecs2.1333

• coherence
• environment
• multivariate analysis
• productivity dynamics
• spatial scale

A central problem in understanding how species respond to global change is in parsing the effects of local drivers of population dynamics from regional and global drivers that are shared among populations. Management and conservation efforts that typically focus on a particular population would benefit greatly from being able to separate the effects of environmental processes at local, regional, and global scales. One way of addressing this challenge is to integrate data across multiple populations and use multivariate time series approaches to estimate shared and independent components of dynamics among neighboring populations. Here, we use a data set of 15 populations of Chinook salmon (Oncorhynchus tshawytscha) covering a broad geographical range in the eastern North Pacific Ocean to show how Dynamic Factor Analysis (DFA) can be used to estimate temporal coherence in population dynamics and to detect environmental drivers across spatial scales. Our results show that productivity dynamics of Chinook salmon populations strongly covary at the regional scale, but to a lesser degree at larger spatial scales. The timing of river ice break-up in spring was identified as an important driver of regional productivity dynamics. In addition, broad-scale variability in population productivity was linked to the North Pacific Gyre Oscillation, a dominant pattern of sea surface height variability. These broad-scale patterns in productivity dynamics may be associated with recent regime shifts in the Northeast Pacific Ocean. However, our results also demonstrate that populations within regions do not always respond consistently to the same environmental drivers, thus suggesting location-specific impacts. Overall, this study illustrates the use of DFA for quantifying the spatial and temporal complexity of multiple population responses to environmental change, thereby providing insights to processes that affect populations across large geographic areas, but that might be filtered by local habitat conditions.

• 出版日期2016-4