|Document Type:||Journal Article|
|Title:||Population coherence and environmental impacts across spatial scales: a case study of Chinook salmon|
|Author:||Jan Ohlberger, M. D. Scheuerell, D. E. Schindler|
|Keywords:||global change,time series,dynamic factor analysis,coherence,Chinook salmon|
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 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 remarkable dataset 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 across spatial scales, and to distinguish local, regional and global environmental drivers. Our findings show that productivity dynamics of Chinook salmon populations strongly covary at the regional scale, and less at the global scale. In addition, the timing of river ice break–up in spring was identified as an important driver of regional productivity dynamics, whereas broad–scale variability in population productivity was linked to the North Pacific Gyre Oscillation (NPGO), 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 scales, but that might be filtered by local habitat conditions.
|Theme:||Ecosystem approach to improve management of marine resources|
Understand how climate influences ecosystem variability.
Assess ecosystem status and trends.