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

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

  • Omid Beyraghdar Kashkooli, 
  • Joachim Gröger, 
  • Ismael Núñez-Riboni
PLOS
x

Abstract

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.

Introduction

Perceiving how ecosystem components and interactions are impacted by anthropogenic activities (e.g. fishing) as well as considering the surrounding environment is required for a reasonable ecosystem-based management [1]. The principle is that—apart from the influences of exploitation on fish populations—the main mechanisms controlling fish population dynamics are alterations in ecosystem forces, such as “climate–ocean impacts and trophic level interactions” [2]. Global fisheries processes and activities are inevitably under climate variability regimes [3] and the fisheries productivity, fish population dispersal and variability, intensively rely on the environment status [4]and climate dynamics [5]. Climate forces are exogenous, superintendent and latent drivers which induce profound large-scale alterations in marine ecosystems by impacting their environmental state. Environmental variability is a critical and significant component for marine ecosystem production and recovery capacity [6].

Identifying, characterizing and evaluating complex behavior of ecosystem components in response to external physical drivers–particularly those related to ongoing environmental changes–is considered to be fundamental towards fostering an Ecosystem-based Approach to Management (EAM) to sustainably exploit living resources. Debates and discourses about climate change and sustainability of marine resources are rising [7] and there are plenty of evidences which indicate the effects of climate variations on various ecosystems [5]. Climate change is impacting many natural populations such as fish and therefore marine and estuarine fisheries [8, 9]. For management purposes, perception of nature, extent and spatial distribution of present and upcoming climate change effects is necessary [9].

When looking for complex climate-driven impacts in marine resources, two major modalities can be recognized. On the one hand, marine population dynamics may be affected by gradual trends, continuous cycles and temporal variability of climate forcing factors (e.g. [1013]. On the other hand, sudden and high amplitude climatic perturbations may induce dramatic alterations in marine ecological regimes. Generally, the first type of impacts can be investigated by quantitative correlational analysis of climate-biological datasets [14]. However, the intricate reactions of ecosystem components due to abrupt climate alterations (which is known as climate-induced “regime shifts”) are qualitatively studied by comparing successive ecological states. Regime shift is being accounted as a global issue in aquatic ecosystems context with widely-documented and still growing evidences in several marine systems [11, 1523]. By combining various sets of aspects, [24] suggested a comprehensive, multi-criterial and “standard” definition for marine ecosystem regime shifts involving the occurrence of sudden, high-amplitude and low-frequency changes over a large geographical area that are perceptible in several features of the ecological components (biotic and abiotic).

Climate-induced regime shifts have profound implications for sustainability of marine resources and fisheries production [20, 21, 23, 24]. The worldwide occurrence of complex climate-induced ecological shifts in marine systems (which has been documented substantially) is one of the major challenges in bio-resources management [2227]. Identifying potential shift patterns (as well as relevant causes) in ecosystem components, structures and processes might lead to important implications for ecosystem-centered strategies aiming to achieve a sustainable long-term fisheries governance agenda.

During recent decades the unique ecosystem and biodiversity of the Caspian Sea has been under enormous pressure of numerous anthropogenic stressors and is encountering a multitude of ecological challenges such as industrial and biological contamination [2831], sea level fluctuation [3234], fisheries overexploitation, management failure and collapse of commercial fish stocks [3537], illegal fishing and poaching [35, 38, 39], invasion of Mnemiopsis leidyi [3942], eutrophication [43, 44], loss of biodiversity [45, 46], environmental mismanagement [47, 48], etc. On top of these pressures, the Caspian Sea environment and bio-resources have been—and will be—inevitably exposed to on-going global climatic changes (like other marine ecosystems); nevertheless, climate is still a largely-ignored issue in marine resources management in this region.

Some previous studies found that the thermal conditions and sea level in the Caspian Sea were associated to large scale climatic phenomenon such as the Southern Oscillation (SO) [32, 49], the North Atlantic Oscillation (NAO) [32, 50] and the Siberian High (SH) system [49, 51]. The proposed mechanism is generally related to the role of global atmospheric circulation forces on regulating moisture and heat fluxes over the Caspian region. However, most of those studies (based on correlation analysis) did not consider the possibility that the relation between large-scale climate variables and local conditions of the Caspian Sea could be through triggering of regime shifts. Moreover, a further influence of such climatic or local environmental changes in the Caspian Sea and local fisheries has been, so far, not yet documented. In the present study, we try to fulfill these gaps with a statistical method that identifies various types of regime changes.

Considering the importance of ecological shifts for fisheries sustainability in the context of EAM, this research for the first time attempted to provide a qualitative perspective on the occurrence of potential ecological climate-driven shifts in the Caspian Sea. This study was mainly focused on the regional investigation of potentially-visible shift patterns, dynamics and causalities by applying a qualitative-statistical approach called “shiftogram” [20]on a range of fisheries-related ecological proxies and climatic variables in the southern Caspian Sea. The fundamental working hypothesis was that obvious shifts in ecological proxies are in compliance with environmental factors shifts.

Material and methodology

Study area and fish species

The Caspian Sea, the largest enclosed water body of the earth, is located between Asia and Europe. The Caspian Sea is generally divided into three parts (considerably differing in physico-geographical features) namely, the northern, middle and southern part. Our study focuses on the deeper southern part(Fig 1).

Among 115 fish species and sub-species inhabiting this ecosystem, demersal Chondrichthyes sturgeons, and bentho-pelagic bony fish including kutum, mullets, breams, carps, barbus, salmons, as well as pelagic kilka species have been historically important in commercial fisheries of the Caspian Sea [46].

Exploitation of bony fish (started since 1927) in Iranian waters of the Caspian Sea has always played a prominent role in fisheries economy of the southern region [46, 52, 53]. The target bentho-pelagic species Caspian kutum (Rutilus frissi kutum (Kamensky, 1901)) and the golden grey mullet, (Liza aurata (Risso, 1810)) constitute the major commercial fisheries catches in Iranian coastal waters of the Caspian Sea [46, 5355].

The Caspian kutum (Rutilus frisii kutum (Kamensky, 1901)), is a medium size endemic fish with a life span of 9–10 years in the southern Caspian Sea. Due to the nutritional value and palatable taste for consumers, as well as its profitability for fishermen, kutum is considered as the most important bony fish of the Caspian sea [46], covering more than 70% of the annual Iranian costal catches [56]. After kutum, L. aurata is the second most dominant fish in commercial catch composition of bony fish in the southern Caspian Sea coastal zones [57]. Also with regards to the increasing market demands, it is considered as one of the most valuable economic resources for local fishermen [57]. With regards to the legal restrictions for commercial catch of sturgeon species (due to the risk of extinction) during the last two decades [5861] as well as the recent collapse of kilka stocks in the Caspian Sea [3537], both kutum and mullet are currently considered as the main target species for the entire fisheries in the southern coasts.

Data description

The datasets used in this study belong to three categories: regional fisheries-derived ecological datasets, large-scale climatic indices and local satellite-based environmental time series. Their properties will be described in the subsequent sections, while their time scopes and data providers are shown in Table 1.

Regional fisheries-derived ecological data.

Multi-aspect regional data-provision constraints and improprieties in less studied areas such as the southern Caspian Sea always impose many limitations in long-term ecological dynamics research (particularly in climate-driven ecological dynamics studies). Hence, only the long-term, regular and reliable fisheries-based time series of two major commercial bentho-pelagic species were used: the Caspian Kutum and the golden grey mullet.

For both species, the most important population components (to serve as ecological proxies) including spawning (parental) stock biomass (SSB) and the recruit (R) numbers (complete series over a period of 1991–2011) were collected. The relevant data series were obtained and compiled from the final reports of the Iranian Fisheries Research Organization (IFRO) on the stock assessment of bony fishes (based on the biomass cohort analysis method, [62]) in Iranian waters of the Caspian Sea [63]. Recruitment numbers were estimated by dividing the biomass of the youngest fish observed in the catch composition (in both species age 2) by the average individual weight of this age (calculated from von Bertalanffy length-weight equation for each year; based on the IFRO reports). SSB estimates (metric tons) were extracted from the biomass through multiplying the biomass by the maturity index (based on the IFRO reports; Dr. Hasan Fazli, personal communication) for each age. Logarithmic-transformed data series were then used for the shift analysis.

Large-scale climatic indices.

Large-scale climatic indices are diagnostic quantities reflecting major modes of climatic variability. The following global climate indices were selected and examined as potential indicators of climatic fluctuations and variability (i.e. capture at least a fraction of variability) over the study area: North Atlantic Oscillation, the Southern Oscillation, East Atlantic-West Russia pattern and Siberian High.

The North Atlantic Oscillation (NAO) is one important global atmospheric variability pattern with inter-annual to inter-decadal fluctuations, exhibiting a wide range of impacts on ecological systems dynamics and processes [64, 65]. The NAO strongly influences the atmospheric circulations and weather conditions, especially during the winter in the northern hemisphere [66]. In this study, we use an NAO index based on principal component analysis (PCA) obtained from the NCAR/UCAR (U.S. National Center for Atmospheric Research/University Corporation for Atmospheric Research) website (https://www.climatedataguide.ucar.edu/climate-data/hurrell-north-atlantic-oscillation-nao-index-pc-based). The annual average index was utilized as first broad-scale climate proxy to investigate the potential climate-driven ecological shifts.

The Southern Oscillation (SO) is the major inter-annual climate variability mode of the world. The Southern Oscillation index (SOI) represents the strength of the so called El-Niño and La-Niña phases of the Southern Oscillation [6769]. Annual averages of SOI were obtained for the period 1951-2011from the CPC/NCEP/NOAA (Climate Prediction Center / National Centers for Environmental Prediction / U.S. National Oceanic and Atmospheric Administration) website (http://www.cpc.ncep.noaa.gov/data/indices/soi).

In the northern hemisphere, the Siberian High (SH) is considered one of the main air pressure centers and atmospheric-circulation systems during winter [70]. We computed the SHI from sea level pressure data from the NCEP/NCAR (U.S. National Centers for Environmental Prediction /the National Center for Atmospheric Research) reanalysis from the NOAA Earth System Research Laboratory website (http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.derived.surface.html)based on the analysis of [71]. By comparing our index with the one calculated by [71](which is based on the climatology of [72]) for the period of 1900–2001, an offset of 0.6 was added to our index so that both indices matched for the years 1996–2009.

The East Atlantic-West Russia (EA-WR) pattern is a teleconnection pattern affecting the climatic state and weather condition of the Eurasian region and consists of two major pressure/height anomaly cores centered on the Caspian Sea and western regions of Europe [73, 74]. We used annual averages of the EA-WR index obtained from the CPC/NCEP/NOAA website (http://www.cpc.ncep.noaa.gov/data/teledoc/eawruss.shtml).

Local satellite-based environmental data.

Aside from the global-scale climatic indices, we were also interested on investigating the potential impacts of local-scale hydrographic variables on the species dynamics. Therefore, we used satellite-derived Sea Surface Temperature (SST) and surface chlorophyll-a concentration (Chl-a) to represent potential local hydrographic variations in our analyses. SST is one of the most crucial and straightforward-in-touch ambient properties for all aquatic organisms. The upper-layer chlorophyll-a concentration can reflect the potential basal-trophic productivity in marine ecological webs which could be assumed to be sequentially transitioned through the ecological chain influencing higher-trophic level organisms–such as fish.

We use the weekly averaged NOAA Earth System Research Laboratory’s SST with resolution of 1°×1° from the NOAA website (http://www.esrl.noaa.gov/psd/data/gridded/data.noaa.oisst.v2.highres.html). We calculated annual averages of the southern part of the Caspian Sea (south of 40° N) from 1982 to 2011.

Surface chlorophyll-a concentration, as a qualitative indicator of phytoplankton biomass, was obtained from the global satellite products of ACRI-ST GlobColour service supported by EU FP7 MyOcean and SA GlobColour Projects (downloaded from http://hermes.acri.fr). Annual averages of the surface chlorophyll-a concentrations for the Southern Caspian Sea (spatially-enclosed to 50° to 52°E and 38° to 40°N) were calculated (from 1998 to 2011).

Methodology

In this study we adopt an algorithm for the identification of shiftsin time series developed by [20], known as “shiftogram” approach being inspired from studying structural breaks in econometric time series models. Compared to other shift detection methods, this approach offers several technical preferences and some benefits: powerful hypothetical context, objective and flexible selection/localization of different break types, accurate and multi-criteria statistical diagnostics testing, dealing with rather short time series and with robust manifold properties. It has also been applied successfully in several studies as an explorative “screening device” to characterize the types and locations of potential shifts in some marine studies, in both cases of univariate and complex multivariate set-ups, as well as in different levels of ecological organization and time scales [11, 19, 20, 75].

This approach is based on modeling structural breaks in time series by inserting external shift (indicator) variables as additional predictors in a linear regression model for a given response variable yt over time. A pulse (at t0) and a step at (t0+1) intervention variable are appended to a basic time regression model (deterministic time trend model) for each input variable: (1) Where the α’s and β’s are regression parameters while εt is a white noise error term. and are defined as: and

A potential breakpoint at t0 iteratively passes along a given time series by consecutively incrementing t0 by one year each time step. In each iteration, Ordinary Least Square (OLS) is used to solve the extended regression equation. Pertinent statistical proof criteria, like model-fit-measure and a series of marginal p-values, are recorded during each iteration. In order to simplify the investigation and interpretation of shift analysis, the original data series and the iterations outputs are represented graphically in a set of a 10-plots template (entitled as “shiftogram”) comprising the following elements presented in Table 2:

While the procedures behind panel plots 1 to 7 use the entire time series, for the procedures behind panel plots 8 to 10 a symmetric window before and after t0may be specified to reflect the “small-scale behavior” of a time series in the vicinity of t0 [20]. For specifying this window, a rule of thumb is using 20% of the length of time series (by considering the sample size and potential cyclic element in the time series [20]).

According to [11] plots 2 (AICc), 3 (p-joint) and 4 (power) are the major reference indicators for recognizing the locality of the shift (temporal positioning), while all other plots are informative about the changes in properties of a time series and are beneficial for distinguishing the shift type. The shiftogram panels 2, 3 and 4 (i.e. AICc, p-joint statistics and power surrounded by red rectangles) are considered as the key-statistical indicators for recognizing the year of the shift.

The occurrence of climate-induced shifts was evaluated by comparing the timing of potential shifts in each ecological and climatic series. Since an occurrence of climate shifts in the past may also influence the species, the shift inspection process for the climatic time series was initialized 4 years (20% length of time series) before the beginning of the species population time series.

All data analysis has been performed here using SAS 9.4 and the “shiftogram” module (SAS macro, by Prof. Dr. J. Gröger) was adopted for this study.

Results

Environmental data series

Fig 2 illustrates the corresponding shiftogram of environmental time series (A-E) and chlorophyll-a time series(F), where the black vertical-dashed lines indicate the years of potential structural breaks.

  1. NAO. The center of a major structural break could be identified in year 2010 with a gradual decrease in the level and a sudden increase thereafter: this shock signal is well indicated by the lowest measures of AICc and p-joint and at the same time by the maximum recorded values of power (plots 2, 3 and 4, respectively). P-joint and AICc also indicate that the initial gradual decrease began around 2006. Furthermore, one less strong and rather gradual shift is centered somewhere around (perhaps between) 1989 and 1990.
  2. SOI. A rather strong shift could be observed during the period of the study, being centered around 2007. Beside this, the diagram indicates a prominent (but not significant) shift-like signal around 1999.
  3. SHI. Local minima of AICc plus local maxima of power indicate a strong shift during the late 1980’s (especially in 1989). Moreover, after 1989 time series shows an obvious gradual upward trend in the AICc and auto-regression.
  4. EA-WR. Despite the shiftogram shows repeating bumpy changes along the time series of EA-WR (especially around 1989, 1992, 1997 and 2010), these break-like signals are not confirmed by the AICc, p-joint and power panels.
  5. SST. The shiftogram indicates a shift centered at year 1994, with an asymmetric gradual transition period with a shorter initial phase when compared with the outgoing phase. The observed shift has a clear level changing pattern. The main changes in time series characteristics represent a long-transitioning period with rather slow slope changes within a considerable width (8 years). This obvious shift is characterized by an initial gradual decrease in AICc and p-joint began around 1991, reached to the deepest point (local minima) in 1994 and followed by a gradual increase until 2000.
  6. Chl-a. During period 1998 to 2011 the chlorophyll shiftogram (Chl-a log-transformed) indicates a rather prominent (but non-significant) break-like signal centered at year 2001. The signal represents a weak indication of an impulse-like (rather flat but not sharp) pattern. This break-like signal is also characterized by an obvious change in auto-regression behavior of the time series. After reaching to the local minima in 2001, the auto-regression initially increased and then reached to a rather plateau-like level by the end of the time series.
thumbnail
Fig 2. Shiftograms of (A) NAO, (B) SOI, (C) SHI, (D) EA-WR, (E) SST and (F) Chl-a.

The black vertical-continuous lines indicate location of strong structural breaks (years of shift occurrence); the black vertical-dashed lines indicate location of prominent(less strong and minor) shift-like signals; the open surrounding rectangle with red-broken lines shows the triple key-statistical indicators for recognizing the breaks (i.e. AICc, p-joint statistics and power).

https://doi.org/10.1371/journal.pone.0176892.g002

Bentho-pelagic species population components data series

Fig 3 illustrates the corresponding shift screening diagrams for the population components (i.e. recruitment Ln (R) and parental stock size Ln (SSB)) related to Caspian kutum (A and B) and golden grey mullet (C and D). The logarithmic-transformed values of population indices were used as inputs for the shift detection algorithm. The results of the shift analysis (Fig 2(A)–2(D)) are described below:

  1. Caspian kutum recruitment. Inspection of the shiftogram suggests a strong break in year 2000. This shift is well characterized by the local maxima of the power along with the lowest measures of AICc and p-joint. The changes in the main statistical break features are rather symmetric and short-lasting (around two years before and after shift center). Also inspection of the AICc shows a rather valley-like pattern. Moreover, the auto-regression represents two obvious and separated trends before and after the shift location (downward and upward trends, respectively).
  2. Caspian kutum SSB. The shiftogram revealed an evident major structural break around 2003–2004. The major characteristics of this shift are rather similar to those of kutum recruits. However, the change in auto-regression before and after the shift location was a little steeper and more sudden.
  3. Golden grey mullet recruitment. The shiftogram suggests a strong shift around 1996–1997 similar to an inflection point. Inspection of AICc shows a rather valley-like pattern initially began about two years before the shift occurrence. The most obvious pattern change in statistical features of the time series is related to the auto-regression behavior: the plateau-like auto-regression pattern appears to be interrupted at years 1996 and 1997 by a reverse valley-type layout.
  4. Golden grey mullet SSB. Based on the corresponding key-statistics, the shiftogram indicates two significant shifts, one in 1995 reflecting the steepest (inflection) point of increase, a second less strong one around 2005–2006 reflecting the steepest (inflection) point of decrease. Inspection of the AICc shows a rather symmetric valley-like pattern for both shifts developments. Furthermore, the entire time series shows a very small auto-regression changing pattern.
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Fig 3. Shiftograms of recruitment and SSB of the Caspian kutum (A and B, respectively) and of golden grey mullet (C and D, respectively).

The black vertical-continuous lines indicate location of strong structural breaks (years of shift occurrence); the open surrounding rectangle with red-broken lines shows the triple key-statistical indicators for recognizing the breaks (i.e. AICc, p-joint statistics and power).

https://doi.org/10.1371/journal.pone.0176892.g003

Discussion

Reactions of ecosystem components confronting sudden shifts in external driving forces have a quite different character and conceptual nature (qualitative). Such regime changes are related to variations of the mean or variance of a variable and are, thus, not necessarily assessable with conventional correlational statistics and quantitative causal dependency analysis (i.e. the correspondence could not be expressed on metric scale).

The observed structural changes (Fig 2) in global-scale atmospheric forces might potentially act as one of the key-factors triggering the contemporary wide-range alterations in the Caspian ecosystem components, dynamics and functions.

Analyzing data series for detecting the development and timing of shift signals demonstrates the following pattern (summarized in Table 3): regime changes in the atmospheric processes mostly occur from the late 1980’s to the 1990’s, regime shifts in the local temperature conditions (i.e. SST) in the Caspian Sea occur during 1991–2000 and ecological time series exhibit evidences of major shifts during a period between mid-1990’s and mid-2000ies. This might indicate a linkage (a cascade of influences) between external forces and dynamics of ecosystem components. From the shift patterns, it could be hypothesized that external atmospheric processes may be partly involved in (and triggered) manifestation of the regional ecological shifts in the following order: global-scale climate forces imply changes on local environmental processes, which in turn imply biological components dynamics.

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Table 3. Years of the identified shift patterns and break-like changes of all variables considered in this study.

https://doi.org/10.1371/journal.pone.0176892.t003

The NAO and SH show a regime shift in the late 1980’s (near 1989). This regime shift could be considered as the reason for the regime shift in general patterns of physical environment of the Caspian Sea (here SST as the most coherent variable), which starts to occur one or two years later (approximately in 1991). The EA-WR shows no regime shifts and the SOI has two regime shifts in the late 1990’s and around mid-2000ies, i.e., after the regime shift of SST. Therefore, only the SH and the NAO regime shifts seem to be candidates for triggering the SST regime shift.

Authors in [32]found at the inter-annual time scale a relation between local physical conditions of the Caspian Sea (in their case, sea level) and NAO changes. They proposed a mechanism of influence of the NAO on precipitation of the Volga catchment region, but without giving further details. [77](as quoted by [51]) suggested a major influence of the SH on air temperature and moisture. The mechanism proposed was that changes of the SH are related to changes in the atmospheric circulation pattern that favor or block intrusion of cyclonic systems from the Mediterranean region into the Caspian basin. The NAO regime shift being weak (the statistical power is around half of that of SH) in comparison to the SH shift, suggests that most probably the SH shift is the one triggering the SST regime shift.

Our lack of influence of the SO on SST of the Caspian Sea seems in contradiction with [32] and [49]who found a relation between ENSO changes and sea level [32] and the probability of drought in Iran [49]. A reason for this disagreement could be found on the different time scales of those studies: Authors in [32] who found lags of up to 10 years between SO changes and sea level changes, while [49]is related to inter-annual time scales. [49]is also related to meteorological conditions of the southern region of the Caspian Sea (the Caspian Sea local conditions could be more influenced, for instance, through the Volga, which is in the north, which is surely the case for sea level). We only found, in agreement with the notions of [32] and [49]an evidence of a potential relation between the SO and Chl-a: There is a relatively weak regime shift of SO in 1999, which is followed by a rather break-like signal in Chl-a data series in 2001.

It seems that the regime shift in the thermal condition (as one of the most striking abiotic factor) could provide a basic explanation for understanding of potential climate-induced shifts in bentho-pelagic subsystem in the Caspian Sea. All the prominent shift events in biological system components (a period between mid-1990’s and mid-2000ies; Fig 3 and Table 3) basically followed the long transitioning shift signal in local environmental processes (SST). Thus, the local temperature regime change (during 1991–2000) appears to be one of the driving forces behind occurrence of shift signals in both bentho-pelagic fish population proxies (both recruits and parental stocks) in the Caspian Sea.

Qualitative correspondence and comparable timing of biological responses of bentho-pelagic stocks (expressed by the population proxies) to environmental events (particularly here the regime change of SST) is one of the supporting evidences that overall Caspian ecosystem structures and functioning might have–at least partially–been impacted by climate-induced shifts. Furthermore, the concurrent shift-like signal in primary production (represented in Chl-a data series, covering period 1998–2011) could be assumed as a potential sign of coupled (interlinked) physical-ecological shifts influencing trophic structures/interactions and cascading intermediate food-web processes.

The explorative findings from the qualitative serial shift analyses in this study tend to correspond well with a series of parallel events and recent substantial ecosystem-wide changes in the southern Caspian Sea (i.e. overlap in temporal zone). In this context, a comprehensive review by [39] suggested several empirical evidences of significant biotic alterations in the southern Caspian environment (particularly between mid-1990’s and mid-2000’s) including:

  • Major change in primary and secondary trophic production of the ecosystem expressed by significant variations in demographical-phenological traits of local planktonic communities (both phyto- and zooplanktons structures) involving species diversity, abundance, biomass, size composition and seasonal blooming events.
  • Appearance, increase in biomass, spatial distribution and outburst of the invasive comb jellyfish (Mnemiopsis leidyi) population.
  • Structural changes in macrobenthic fauna including biodiversity, biomass, abundance and partial replacement of zoobenthic species.
  • Drastic variations in local commercial fisheries especially decrease in catch and collapse of pelagic Kilka species.

A rough simultaneity of the above-mentioned structural changes in various ecosystem compartments, biodiversity and trophic structures and interactions in the southern Caspian Sea, as well as the identified ecological shift signals appear to be (at least in part) driven by physical processes caused by integration of exogenously-forced climatic feedback mechanisms. Taken together, these evidences may support that the Caspian Sea has likely encountered regime shift by climate forcing under on-going environmental changes (such as global marine ecosystems [2127]). However, a robust statement on occurrence of an entire regime shift in the Caspian Sea requires a continuation of investigations on many other biotic and abiotic variables to provide further supporting evidences.

Acknowledgments

Sincere thanks are expressed to the Ministry of Science, Research and Technology (MSRT) of Iran for granting Omid Beyraghdar Kashkooli a PhD scholarship for studying in Germany. This work was achieved in partial fulfillment of the requirements of Omid Beyraghdar Kashkooli’s PhD degree submitted to the University of Rostock. Thünen Institute (TI) of Sea Fisheries in Hamburg and University of Rostock are acknowledged for providing all the scientific facilities and requirements during the course of this work. Authors would like to express their thanks to the Caspian Sea Ecology Research Center, Iranian Fisheries Research Organization for providing the regional fisheries datasets and information. Omid Beyraghdar Kashkooli would like thank his co-supervisor Dr. Hasan Fazli (Caspian Sea Ecology Research Center, Iranian Fisheries Research Organization) for providing the fisheries-derived data, his support and scientific guidance during his doctoral research. We are grateful to Dr. Boris Cisewski (TI) for his help with the chlorophyll-a data series. Dr. Maria Shahgedanova (University of Reading) is acknowledged for providing the relevant information regarding the calculation of the Siberian High Index.

Author Contributions

  1. Conceptualization: OBK JG.
  2. Formal analysis: OBK JG INR.
  3. Investigation: OBK JG INR.
  4. Methodology: JG OBK.
  5. Resources: OBK JG INR.
  6. Supervision: JG.
  7. Visualization: OBK JG.
  8. Writing – original draft: OBK.
  9. Writing – review & editing: OBK JG INR.

References

  1. 1. Deyle ER, Fogarty M, Hsieh C-h, Kaufman L, MacCall AD, Munch SB, et al. (2013) Predicting climate effects on Pacific sardine. Proc Natl AcadSci U S A 110: 6430–6435.
  2. 2. King J, McFarlane G (2006) A framework for incorporating climate regime shifts into the management of marine resources. Fish ManagEcol 13: 93–102.
  3. 3. Johnson JE, Welch DJ (2009) Marine fisheries management in a changing climate: a review of vulnerability and future options. Rev Fish Sci 18: 106–124.
  4. 4. MacKenzie BR, Gislason H, Möllmann C, Köster FW (2007) Impact of 21st century climate change on the Baltic Sea fish community and fisheries. Glob Chang Biol 13: 1348–1367.
  5. 5. Lehodey P, Alheit J, Barange M, Baumgartner T, Beaugrand G, Drinkwater K, et al. (2006) Climate variability, fish, and fisheries. J Cli 19: 5009–5030.
  6. 6. Sissenwine M, Murawski S (2004) Moving beyond 'intelligent tinkering': advancing an Ecosystem Approach to Fisheries: Perspectives on eco-system-based approaches to the management of marine resources. Marine ecology Progress series 274: 291–295.
  7. 7. Rice JC, Garcia SM (2011) Fisheries, food security, climate change, and biodiversity: characteristics of the sector and perspectives on emerging issues. ICES J Mar Sci 68: 1343–1353.
  8. 8. Roessig JM, Woodley CM, Cech JJ Jr, Hansen LJ (2004) Effects of global climate change on marine and estuarine fishes and fisheries. Rev Fish Biol Fish 14: 251–275.
  9. 9. Wilsey CB, Lawler JJ, Maurer EP, McKenzie D, Townsend PA, Gwozdz R, et al. (2013) Tools for assessing climate impacts on fish and wildlife. J Fish WildlManag 4: 220–241.
  10. 10. Gröger JP, Kruse GH, Rohlf N (2010) Slave to the rhythm: how large-scale climate cycles trigger herring (Clupea harengus) regeneration in the North Sea. ICES J Mar Sci: fsp 259.
  11. 11. Gröger JP, Hinrichsen H-H, Polte P (2014) Broad-scale climate influences on spring-spawning herring (Clupea harengus, L.) recruitment in the Western Baltic Sea. PLoSOne 9: e87525.
  12. 12. Ottersen G, Alheit J, Drinkwater K, Friedland K, Hagen E,Stenseth NC, et al. (2005) The responses of fish populations to ocean climate fluctuations. Marine Ecosystems and Climate Variation Oxford University Press, Oxford: 73–94.
  13. 13. Ottersen G, Kim S, Huse G, Polovina JJ, Stenseth NC (2010) Major pathways by which climate may force marine fish populations. J Mar Syst 79: 343–360.
  14. 14. Brander KM (2007) Global fish production and climate change. Proc Natl AcadSci U S A 104: 19709–19714.
  15. 15. Hare SR, Mantua NJ (2000) Empirical evidence for North Pacific regime shifts in 1977 and 1989. ProgOceanogr 47: 103–145.
  16. 16. Scheffer M, Carpenter S, Foley JA, Folke C, Walker B (2001) Catastrophic shifts in ecosystems. Nature 413: 591–596. pmid:11595939
  17. 17. Scheffer M, Carpenter SR (2003) Catastrophic regime shifts in ecosystems: linking theory to observation. Trends EcolEvol 18: 648–656.
  18. 18. Möllmann C, Diekmann R, Müller-Karulis B, Kornilovs G, Plikshs M, Axe P(2009) Reorganization of a large marine ecosystem due to atmospheric and anthropogenic pressure: a discontinuous regime shift in the Central Baltic Sea. Glob Chang Biol 15: 1377–1393.
  19. 19. Arula T, Gröger J, Ojaveer H, Simm M (2014) Shifts in the Spring Herring (Clupea harengus membras) Larvae and Related Environment in the Eastern Baltic Sea over the Past 50 Years. PLoS One 9: e91304. pmid:24637830
  20. 20. Gröger JP, Missong M, Rountree RA (2011) Analyses of interventions and structural breaks in marine and fisheries time series: Detection of shifts using iterative methods. Ecol Indic 11: 1084–1092.
  21. 21. Levin PS, Möllmann C (2015) Marine ecosystem regime shifts: challenges and opportunities for ecosystem-based management. Philos Trans R SocLond B BiolSci 370: 20130275.
  22. 22. Möllmann C, Diekmann R (2012) 4 Marine Ecosystem Regime Shifts Induced by Climate and Overfishing: A Review for the Northern Hemisphere. AdvEcol Res 47: 303.
  23. 23. Möllmann C, Folke C, Edwards M, Conversi A (2015) Marine regime shifts around the globe: theory, drivers and impacts. Philos Trans R SocLond B BiolSci 370.
  24. 24. Lees K, Pitois S, Scott C, Frid C, Mackinson S (2006) Characterizing regime shifts in the marine environment. Fish Fish 7: 104–127.
  25. 25. Dippner JW, Möller C, Hänninen J (2012) Regime shifts in North Sea and Baltic Sea: a comparison. J Mar Syst 105: 115–122.
  26. 26. deYoung B, Harris R, Alheit J, Beaugrand G, Mantua N, Shannon L (2004) Detecting regime shifts in the ocean: Data considerations. ProgOceanogr 60: 143–164.
  27. 27. Jiao Y (2009) Regime shift in marine ecosystems and implications for fisheries management, a review. Rev Fish Biol Fish 19: 177–191.
  28. 28. de Mora S, Villeneuve J-P, Sheikholeslami MR, Cattini C, Tolosa I (2004) Organochlorinated compounds in Caspian Sea sediments. Mar Pollut Bull 48: 30–43. pmid:14725874
  29. 29. Nezlin NP (2005) Patterns of seasonal and interannual variability of remotely sensed chlorophyll. The Caspian Sea Environment: Springer. pp. 143–157.
  30. 30. Stolberg F, Borysova O, Mitrofanov I, Barannik V, Eghtesadi P (2006) Caspian Sea GIWA regional assessment 23. University of Kalmar on behalf of United Nations Environment Programme, Kalmar, Sweden.
  31. 31. Askarova MA, Mussagaliyeva AN (2014) The Ecological Situation in Contaminated Areas of Oil and Gas Exploration in Atyrau Region. Procedia SocBehavSci 120: 455–459.
  32. 32. Arpe K, Bengtsson L, Golitsyn G, Mokhov I, Semenov V, Sporyshev PV(2000) Connection between Caspian Sea level variability and ENSO. Geophys Res Lett 27: 2693–2696.
  33. 33. Renssen H, Lougheed B, Aerts J, De Moel H, Ward P, Kwadijk JCJ. (2007) Simulating long-term Caspian Sea level changes: the impact of Holocene and future climate conditions. Earth Planet Sci Lett 261: 685–693.
  34. 34. Ibrayev R, Özsoy E, Schrum C, Sur H (2010) Seasonal variability of the Caspian Sea three-dimensional circulation, sea level and air-sea interaction. Ocean Sci 6: 311–329.
  35. 35. Daskalov GM, Mamedov EV (2007) Integrated fisheries assessment and possible causes for the collapse of anchovy kilka in the Caspian Sea. ICES J Mar Sci 64: 503–511.
  36. 36. Fazli H, Zhang CI, Hay DE, Lee CW, Janbaz AA, Borani MS(2007) Population ecological parameters and biomass of anchovy kilka Clupeonella engrauliformis in the Caspian Sea. Fish Sci 73: 285–294.
  37. 37. Ustarbekova D (2014) Morpho-ecological features of anchovy kilka under the changing conditions of the Caspian Sea. Arid ecosystems 4: 44–48.
  38. 38. Abdolmalaki S, Psuty I (2007) The effects of stock enhancement of pikeperch (Sander lucioperca) in Iranian coastal waters of the Caspian Sea. ICES J Mar Sci 64: 973–980.
  39. 39. Roohi A, Kideys AE, Sajjadi A, Hashemian A, Pourgholam R,Fazli H, et al. (2010) Changes in biodiversity of phytoplankton, zooplankton, fishes and macrobenthos in the Southern Caspian Sea after the invasion of the ctenophore Mnemiopsis leidyi. Biol Invasions 12: 2343–2361.
  40. 40. Finenko GA, Kideys AE, Anninsky BE, Shiganova TA, Roohi A,Tabari MR, et al. (2006) Invasive ctenophore Mnemiopsis leidyi in the Caspian Sea: feeding, respiration, reproduction and predatory impact on the zooplankton community. Mar EcolProgSer 314: 171–185.
  41. 41. Kideys A, Moghim M (2003) Distribution of the alien ctenophore Mnemiopsisleidyi in the Caspian Sea in August 2001. Mar Biol 142: 163–171.
  42. 42. Kideys A, Roohi A, Bagheri S, Finenko G, Kamburska L (2005) Impacts of invasive ctenophores on the fisheries of the Black Sea and Caspian Sea. Oceanography 18: 76–85.
  43. 43. Leonov A, Stygar O (2001) Mathematical modeling of organogenic material biotransformation processes for studying the conditions of water eutrophication in the Caspian Sea surface layer. Water Resour 28: 535–552.
  44. 44. Nasrollahzadeh HS, Din ZB, Foong SY, Makhlough A (2008) Trophic status of the Iranian Caspian Sea based on water quality parameters and phytoplankton diversity. Cont Shelf Res 28: 1153–1165.
  45. 45. Karpinsky MG, Katunin DN, Goryunova VB, Shiganova TA (2005) Biological features and resources. The Caspian Sea Environment: Springer. pp. 191–210.
  46. 46. Valipour A, Khanipour A (2006) Kutum, Jewel of the Caspian Sea. Caspian Environment Program.
  47. 47. Barale V (2008) The European marginal and enclosed seas: an overview. Remote Sensing of the European Seas: Springer. pp. 3–22.
  48. 48. Fendereski F, Vogt M, Payne M, Lachkar Z, Gruber N, Salmanmahiny A,et al. (2014) Biogeographic classification of the Caspian Sea. Biogeosciences 11: 6451–6470.
  49. 49. Nazemosadat M, Ghasemi A (2004) Quantifying the ENSO-related shifts in the intensity and probability of drought and wet periods in Iran. J Cli 17: 4005–4018.
  50. 50. Panin G, Diansky N (2014) On the correlation between oscillations of the Caspian Sea level and the North Atlantic climate. IzAOP 50: 266–277.
  51. 51. Elguindi N, Giorgi F (2006) Simulating multi-decadal variability of Caspian Sea level changes using regional climate model outputs. ClDy 26: 167–181.
  52. 52. Bandpei A, Mansor M, Abdolmalaki S, Keymaram F, Isa MM, Janbaz AA (2010) Age and growth of kutum (Rutilus frisii kutum, Kamensky, 1901) in southern Caspian Sea. IntAqu Res 2: 25–33.
  53. 53. Fazli H, Daryanabard G, Salmanmahiny A, Abdolmaleki S, Bandani G, Bandpei MA (2012) Fingerling release program, biomass trend and evolution of the condition factor of Caspian Kutum during the 1991–2011 period. Cybium 36: 545–550.
  54. 54. Fazli H, Janbaz A, Taleshian H, Bagherzadeh F (2008) Maturity and fecundity of golden grey mullet (Liza aurata Risso, 1810) in Iranian waters of the Caspian Sea. J ApplIchthyol 24: 610–613.
  55. 55. Ghaninejad D, Abdolmalaki S, Kuliyev Z (2010) Reproductive biology of the golden grey mullet, Liza aurata in the Iranian coastal waters of the Caspian Sea. Iran J Fish Sci 9: 402–411.
  56. 56. Yousefian M, Mosavi H (2008) Spawning of South Caspian Kutum (Rutilus frisii kutum) in Most Migratory River of South Caspian Sea. Asian J Anim Vet Adv 3: 437–442.
  57. 57. Zorriehzahra MJ, Nazari A, Ghasemi M, Ghiasi M, Karsidani SH, Bovo G,et al. (2014) Vacuolating encephalopathy and retinopathy associated with a nodavirus-like agent: a probable cause of mass mortality of wild Golden grey mullet (Liza aurata) and Sharpnose grey mullet (Liza saliens) in Iranian waters of the Caspian Sea. Virus Dis 25: 430–436.
  58. 58. Khodorevskaya R, Krasikov YV (1999) Sturgeon abundance and distribution in the Caspian Sea. J ApplIchthyol 15: 106–113.
  59. 59. Mathews C, Peacock N, Gilkolei R (2006) Theoretical scenarios for research and development needs to save dwindling sturgeon populations in the Caspian Sea. J ApplIchthyol 22: 132–138.
  60. 60. Ruban G, Khodorevskaya R (2011) Caspian Sea sturgeon fishery: a historic overview. J ApplIchthyol 27: 199–208.
  61. 61. Pourkazemi M (2006) Caspian Sea sturgeon conservation and fisheries: past present and future. J ApplIchthyol 22: 12–16.
  62. 62. Zhang CI, Sullivan PJ (1988) Biomass-based cohort analysis that incorporates growth. Trans Am Fish Soc 117: 180–189.
  63. 63. Fazli H (2012) Stock assessment reports of the bony fishes in Iranian coastal waters of the Caspian Sea. Iranian Fisheries Research Organization (IFRO).
  64. 64. Blenckner T, Hillebrand H (2002) North Atlantic Oscillation signatures in aquatic and terrestrial ecosystems—a meta-analysis. Glob Chang Biol 8: 203–212.
  65. 65. Hurrell JW, Kushnir Y, Ottersen G, Visbeck M (2003) An overview of the North Atlantic oscillation. Geophysical Monograph-American Geophysical Union 134: 1–36.
  66. 66. Hurrell JW, and Dickson R.R. (2004) Climate variability over the North Atlantic. In: Stenseth GO N.C., Hurrell J.W., and Belgrano A., editor. Marine Ecosystems and Climate Variation—the North Atlantic: Oxford University Press. pp. 15–32.
  67. 67. Rasmusson EM, Wallace JM (1983) Meteorological aspects of the El Nino/southern oscillation. Science 222: 1195–1202. pmid:17806710
  68. 68. Philander SG, Holton JR, Dmowska R (1989) El Nino, La Nina, and the Southern Oscillation: Elsevier Science.
  69. 69. Allan RJ, Lindesay J, Parker DE (1996) El Niño, southern oscillation, and climatic variability: CSIRO.
  70. 70. Jeong JH, Ou T, Linderholm HW, Kim BM, Kim SJ, Kug JS,et al. (2011) Recent recovery of the Siberian High intensity. J Geophys Res Atmos 116.
  71. 71. Panagiotopoulos F, Shahgedanova M, Hannachi A, Stephenson DB (2005) Observed trends and teleconnections of the Siberian high: A recently declining center of action. J Cli 18: 1411–1422.
  72. 72. Trenberth KE, Paolino DA Jr (1980) The Northern Hemisphere sea-level pressure data set: Trends, errors and discontinuities. MWRv 108: 855–872.
  73. 73. Krichak SO, Alpert P (2005) Decadal trends in the east Atlantic-west Russia pattern and Mediterranean precipitation. IJCli 25: 183–192.
  74. 74. Lim Y-K (2014) The East Atlantic/West Russia (EA/WR) teleconnection in the North Atlantic: climate impact and relation to Rossby wave propagation. ClDy 44: 3211–3222.
  75. 75. Lindegren M, Dakos V, Gröger JP, Gårdmark A, Kornilovs G,Otto SA, et al. (2012) Early detection of ecosystem regime shifts: a multiple method evaluation for management application. PLoS One 7: e38410–e38410. pmid:22808007
  76. 76. Hurvich CM, Tsai CL (1993) A corrected Akaike information criterion for vector autoregressive model selection. JTSA 14: 271–279.
  77. 77. Lydolph PE, Landsberg HE (1977) Climates of the Soviet Union: Elsevier Scientific Publishing Company.