Identification of Shared Spatial Dynamics in Temperature, Salinity, and Ichthyoplankton Community Diversity in the California Current System With Empirical Dynamic Modeling

Andrew R Thompson,Brice X Semmens,Peter T Kuriyama,George Sugihara

doi:10.3389/fmars.2020.557940

Abstract

Identifying spatially shared dynamics is a key component of community ecology studies as they provide evidence of common responses to environmental factors. We apply co-prediction, an empirical dynamic modeling method (EDM), where values in one time series are predicted from another to quantify shared dynamics in the California Cooperative Fishery Oceanographic Investigation (CalCOFI) dataset composed of spatially explicit physical and biological measurements. Co-prediction can arise in the absence of correlation between two time series. The survey dates to 1951 and consists of a semi-regular, fixed-station design off the west coast of the USA. While the California Current is a dynamic system with multiple identified regimes, we found evidence of coherence measured in terms of spatially shared dynamics in salinity, temperature, Shannon index of ichthyoplankton abundance, and single-species ichthyoplankton abundance throughout the CalCOFI survey area. Leave-one-out hindcast skill, without including any knowledge of shared dynamics was significant in 27 stations for salinity data, 36 for temperature data, and 33 for Shannon index (out of 81 total stations). We then constructed composite libraries, in which correlated or co-predicted time series are concatenated to produce denser attractors), to measure the hindcast skill when including knowledge of shared dynamics. While the number of correlated stations was generally higher than the number of co-predicted stations, hindcast skill from composite libraries of correlated stations did not improve much. On the other hand, composite libraries of co-predicted stations had significant leave-one-out hindcast skill in 60 stations for salinity data, 60 for temperature, and 72 for Shannon index. While there were high levels of correlation among stations, co-prediction proved a more robust method of identifying shared dynamics. Shared dynamics were largely concentrated south of Point Conception, considered an oceanographic and biological breakpoint, although in some cases shared dynamics spanned this boundary. Taken together, these results provide a view of the realized spatial structure occurring in the physical and biological dynamics of the California Current.

Highlights

One main objective of ecology is to understand how the environment influences biological organisms from individual to ecosphere scales, and identification of shared dynamics or synchrony across space and time is a valuable tool for achieving this goal (Hsieh et al, 2005)
We found evidence of synchrony between stations within all time series with both correlational and co-prediction analyses
We found a similar pattern for co-predicted pairs north of Point Conception: 11% for salinity, 18% for temperature, and 6% for Shannon index (Table 1)

Summary

Introduction

One main objective of ecology is to understand how the environment influences biological organisms from individual to ecosphere scales, and identification of shared dynamics or synchrony across space and time is a valuable tool for achieving this goal (Hsieh et al, 2005). Marine fish populations shift distributions in response to ocean warming (Pinsky et al, 2013), and this kind of knowledge is necessary to inform resilient fisheries management (Wilson et al, 2018) or ecosystem-based fisheries management (Pikitch et al, 2004). The abundance of species can fluctuate in response to combinations of abiotic and biotic factors, and these dynamics can be shared across species-specific populations in space and time. Ecological studies have focused on identifying patterns of synchrony, defined to be shared fluctuations between time series of population abundance. In terrestrial and marine systems, synchrony between two populations decreases as a function of distance (Ranta et al, 1995; Sutcliffe et al, 1996; Bjørnstad et al, 1999), and estimation of this decay is a key component of spatiotemporal models (Cressie and Wikle, 2015; Thorson et al, 2015)

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