The spectral color of natural and anthropogenic time series and its impact on the statistical significance of cross correlation.

The cross-correlation between time series is a common tool to study and quantify the impact of climatic and anthropogenic changes on ecosystems. The traditional method for estimating the statistical significance of correlation relies on the assumption that the data are independent, but time series found in nature are often strongly auto-correlated because of low-frequency environmental variability and ecosystem inertia. Previous authors have used Monte Carlo simulations to study the impact of serial auto-correlation on the significance of cross-correlations. Most studies have used random time series that are often a poor representation of those found in nature, e.g., low-order auto-regressive models with normally distributed noise. Moreover, we are not aware of any tests of the applicability of those methods to anthropogenic time series. Here, we study the effect of serial auto-correlation on the performance of two methods for estimating the significance of cross-correlations determined from Monte Carlo simulations with time series that are generated synthetically based on power-law specification of spectral characteristics. Such time series have an auto-correlation structure defined by a single parameter, their spectral "color", and are generally more convenient representations of natural time series than the autoregressive models. Our results show that one of the two methods considered here accurately reproduces prescribed error rates for the wide range of spectral colors representative of climatic, ecological and anthropogenic time series. For this, we characterized roughly 1800 observational records in different categories of spectral colors, including climate variability, abundance of vertebrate species, and pollution. We specifically focus on time series with annual sampling over data records of at least 40 years, which are particularly relevant for climate studies. The methodology advocated in this study provides a simple and realistic assessment of the significance of sample estimates of cross correlation for time series with any sample interval and record length.

Deep demersal fish communities respond rapidly to warming in a frontal region between Arctic and Atlantic waters.

The assessment of climate impact on marine communities dwelling deeper than the well-studied shelf seas has been hampered by the lack of long-term data. For a long time, the prevailing expectation has been that thermal stability in deep ocean layers will delay ecosystem responses to warming. Few observational studies have challenged this view and indicated that deep organisms can respond exceptionally fast to physical change at the sea surface. To address the depth-specific impact of climate change, we investigated spatio-temporal changes in fish community structure along a bathymetry gradient of 150-1500 m between 1998 and 2016 in East Greenland. Here, the Arctic East Greenland Current and the Atlantic Irminger Current meet and mix, representing a sub-Arctic transition zone. We found the strongest signals of community reorganizations at depths between 350 and 1000 m and only weak responses in the shallowest and deepest regions. Changes were in synchrony with atmospheric warming, loss in sea ice and variability in physical sea surface conditions both within our study region and North of the Denmark Strait. These results suggest that interannual variability and long-term climate trends of the larger ecoregion can rapidly affect fish communities down to 1000-m depth through atmospheric ocean coupling and food web interactions.

Qualitative assessment of climate-driven ecological shifts in the Caspian Sea.

The worldwide occurrence of complex climate-induced ecological shifts in marine systems is one of the major challenges in sustainable bio-resources management. The occurrence of ecological environment-driven shifts was studied in the Southern Caspian Sea using the "shiftogram" method on available fisheries-related (i.e. commercially important bentho-pelagic fish stocks) ecological and climatic variables. As indicators of potential environmentally driven shift patterns we used indices for the North Atlantic Oscillation, the Southern Oscillation, the Siberian High, the East Atlantic-West Russia pattern, as well as Sea Surface Temperature and surface chlorophyll-a concentration. Given the explorative findings from the serial shift analyses, the cascading and serial order of multiple shift events in climatic-ecologic conditions of the southern Caspian Sea suggested a linkage between external forces and dynamics of ecosystem components and structures in the following order: global-scale climate forces lead to local environmental processes, which in turn lead to biological components dynamics. For the first time, this study indicates that ecological shifts are an integral component of bentho-pelagic subsystem regulatory processes and dynamics. Qualitative correspondence of biological responses of bentho-pelagic stocks to climatic events is one of the supporting evidences that overall Caspian ecosystem structures and functioning might have-at least partially-been impacted by global-scale climatic or local environmental shifts. These findings may help to foster a regional Ecosystem-based Approach to Management (EAM) as an integral part of bentho-pelagic fisheries management plans.

Spatially-Resolved Influence of Temperature and Salinity on Stock and Recruitment Variability of Commercially Important Fishes in the North Sea.

Understanding of the processes affecting recruitment of commercially important fish species is one of the major challenges in fisheries science. Towards this aim, we investigated the relation between North Sea hydrography (temperature and salinity) and fish stock variables (recruitment, spawning stock biomass and pre-recruitment survival index) for 9 commercially important fishes using spatially-resolved cross-correlation analysis. We used high-resolution (0.2° × 0.2°) hydrographic data fields matching the maximal temporal extent of the fish population assessments (1948-2013). Our approach allowed for the identification of regions in the North Sea where environmental variables seem to be more influential on the fish stocks, as well as the regions of a lesser or nil influence. Our results confirmed previously demonstrated negative correlations between temperature and recruitment of cod and plaice and identified regions of the strongest correlations (German Bight for plaice and north-western North Sea for cod). We also revealed a positive correlation between herring spawning stock biomass and temperature in the Orkney-Shetland area, as well as a negative correlation between sole pre-recruitment survival index and temperature in the German Bight. A strong positive correlation between sprat stock variables and salinity in the central North Sea was also found. To our knowledge the results concerning correlations between North Sea hydrography and stocks' dynamics of herring, sole and sprat are novel. The new information about spatial distribution of the correlation provides an additional help to identify mechanisms underlying these correlations. As an illustration of the utility of these results for fishery management, an example is provided that incorporates the identified environmental covariates in stock-recruitment models.