The Caspian Sea (CS), the largest lake in the world, is a unique ecosystem known for its rich and diverse aquatic fauna (Karpinsky et al. 2005). Since the 1990s, intensive fishing and invasive species are major direct or indirect threats to the structural and functional organization of this unique ecosystem. Direct effects are manifested as a spatial and temporal gradient in the abundance of target species, and habitat destruction (Roohi et al. 2010; Pourang et al. 2016; Tavakoli et al. 2019; Fazli et al. 2013Fazli et al. 2020Fazli et al. 2021), while indirect effects as changes in community structure or differential effects on functional groups (Roohi et al. 2010; Pourang et al. 2016) in the CS. On the other hand, one of the main sources of impact on marine resources is climate variability and regime shifts (Johnson & Welch 2009). Beyraghdar Kashkooli et al. (2017) documented that the benthopelagic fish stocks respond to climatic events and it seems that the CS confronts the regime shift, emphasizing the need for more research and further supporting evidence.
Knowledge of fish habitat requirements and the interactions between habitat and population dynamics is essential for the management and conservation of aquatic resources (Mouton et al. 2007; Hermosilla et al. 2011). Since single-species management strategies do not fully account for ecological interactions and environmental factors (Garcia 2003), ecosystem-based fishery management is therefore a practical management approach to the conservation and restoration of fish stocks (Thrush & Dayton 2010).
Kutum (
This study focuses on the deeper southern part of the sea, i.e. the southern waters of the Caspian Sea, where more than 121 beach seine fishing cooperatives target bony fishes (Fig. 1). Bony fishes were sampled by using beach seines 1200 m long and 10–20 m high, with a codend bag of 30–33 mm mesh size. Input data, including catch data by species and fishing effort (beach seine hauling) were collected from 1996 to 2017 (Iran Fisheries Organization IFO, 2018). The fishing season starts in October when the mean SST is 22.6 ± 0.84°C and ends in mid-April when SST is 14.1 ± 1.21°C (Fig. 2).
Data on 13 species were collected randomly from 121 beach seine fishing cooperatives; fork length (FL) was measured to the nearest 0.1 cm, and body weight was determined to the nearest 0.1 g. CPUE, catch (weight, kg) per unit effort and NPUE catch (number) per unit effort of fish were estimated for each hauling, respectively (Sparre & Venema 1998). According to the catch data, about 375.8 million individuals belonging to 13 species were caught by beach seines between 1996 and 2017.
Global variables with large-scale influence – the global temperature anomaly (GTA), the East Atlantic-West Russian pattern (EAWR), the North Atlantic Oscillation (NAO), and two regional environmental variables – sea surface temperature (SST) and sea surface level (SSL) were investigated to assess the potential effects on fish species assemblages between 1996 and 2017 (Table 1). Monthly averaged regional satellite-based environmental variables, i.e. SST with 1°×1° resolution from the NOAA website (
Environmental parameters in the Caspian Sea from 1996 to 2017.
Variable | Mean | SD | Minimum | Maximum |
---|---|---|---|---|
1Precipitation, cm | 125.8 | 20.27 | 85.3 | 153.9 |
1Evaporation, cm | 42.9 | 3.38 | 34.9 | 48.8 |
2SST_Mar, °C | 11.4 | 0.77 | 10.2 | 12.6 |
2SST_Apr, °C | 14.2 | 1.19 | 12.1 | 17.6 |
2SST_Oct, °C | 22.7 | 0.79 | 21.6 | 24.2 |
2SST_Aug, °C | 28.2 | 0.99 | 25.3 | 29.7 |
2SST, °C | 19.2 | 0.42 | 18.3 | 20.2 |
3SSL, m | −26.4 | 0.34 | −27.1 | −26.1 |
3Volga discharge, km3 | 233.3 | 33.96 | 176.2 | 283.5 |
3Iran's rivers discharge, km3 | 3.79 | 1.58 | 1.98 | 7.50 |
2GTA | 1.14 | 0.82 | −0.88 | 2.42 |
4EAWR | −0.20 | 0.94 | −2.12 | 2.11 |
4NAO | −0.13 | 0.38 | −1.29 | 0.40 |
Babolsar meteorological station (Meteorological Organization of Mazandaran);
Caspian Sea National Research Center;
The annual Volga discharge (VD) and five Iranian river discharges (IRD) were used in this study. In the Caspian basin, about 80% of the river discharge (with an average flow of 237 km3/year) is from the Volga River, from the north of the sea (Arpe et al. 2000). In the Iranian basin, the average inflow of the Sefidrood, Polrood, Haraz, Chaloos, and Baborood rivers is 5.33 km3/year (Ataei et al. 2019).
Fish abundance indices (NPUE and CPUE) were fourth-root transformed prior to analysis. Non-metric multidimensional scaling (nMDS) based on the Bray–Curtis similarity measure was used to determine similarities in the species composition across the study years (Clarke & Warwick 2001).
A Distance-based Linear Model (DistLM) was employed to analyze the relationships between species composition and environmental variables. The analysis was carried out after normalization and removal of highly correlated environmental variables (draftsmans plot > 0.9). DistLM based on the best procedure, Akaike’s information criterion (AIC), and 9999 permutations were used to test significant relationships between diversity indices, single species (Euclidian distance), and environmental variables. AIC was employed to select the best model.
ABC (abundance biomass comparison) curves were created and W statistics were estimated for the years 1996 to 2017. Multivariate analysis was carried out using PRIMER V.6 and PERMANOVA + (Clark & Warwick 2001; Clarke & Gorley 2006; Anderson et al. 2008).
For each year, the Shannon–Wiener index (H′, using log base e) was calculated using the DIVERS routine in the PRIMER package (Clarck & Gorley 2006).
In the present study, Cyprinidae with eight species (61.5%), followed by Mugilidae with two species (15.4%) were the dominant families. Three families – Percidae (
Mean annual catch number per unit effort (NPUE) and catch per unit effort (kg/beach seine, CPUE, ± SE) of commercial bony fish in the Caspian Sea in 1996–2017.
Family | Species | NPUE (number) | CPUE (kg/beach seine) | ||||
---|---|---|---|---|---|---|---|
Mean | SE | % | Mean | SE | % | ||
Cyprinidae | 168.20 | 12.97 | 47.14 | 118.32 | 9.37 | 62.65 | |
16.01 | 7.12 | 4.49 | 9.38 | 3.35 | 4.97 | ||
4.45 | 1.21 | 1.25 | 0.70 | 0.20 | 0.37 | ||
3.22 | 0.81 | 0.90 | 0.44 | 0.11 | 0.23 | ||
1.05 | 0.26 | 0.29 | 0.21 | 0.05 | 0.11 | ||
0.06 | 0.02 | 0.02 | 0.04 | 0.01 | 0.02 | ||
0.60 | 0.14 | 0.17 | 0.12 | 0.03 | 0.06 | ||
0.14 | 0.03 | 0.04 | 0.10 | 0.02 | 0.06 | ||
Mugilidae | 145.61 | 11.79 | 40.80 | 55.41 | 4.67 | 29.34 | |
16.09 | 4.41 | 4.51 | 3.57 | 0.98 | 1.89 | ||
Percidae | 1.34 | 0.34 | 0.38 | 0.50 | 0.13 | 0.27 | |
Salmonidae | 0.02 | 0.01 | 0.01 | 0.05 | 0.01 | 0.03 | |
Siluridae | 0.05 | 0.02 | 0.01 | 0.02 | 0.01 | 0.01 |
The nMDS analysis based on the commercial bony fish composition from all the study years showed a trajectory from the years 1996 to 2017 for both NPUE and CPUE and a gradient from 1996 to 2017 (Fig. 3). In addition, based on the data for both indices, the result of clustering of the years using the Bray–Curtis index for similarities showed four groups (group a = 1996–2002, b = 2003–2005, c = 2006–2008, and d = 2009–2017; Fig. 3).
DisTLM showed that five of the environmental variables were significantly correlated with the fauna NPUE resemblance matrix (DisTLM,
Results of the DistLM model, containing the Sum of Square (SS), Pseudo-F statistic,
Variable | SS | Pseudo-F | P-value | Proportion % |
---|---|---|---|---|
Year | 1007.0 | 17.70 | 0.001 | 46.94 |
SSL | 730.1 | 10.32 | 0.001 | 34.04 |
VD | 321.5 | 3.53 | 0.013 | 14.99 |
IRD | 281.2 | 3.02 | 0.032 | 13.11 |
EAWR | 273.5 | 2.92 | 0.040 | 12.75 |
EV | 192.0 | 1.97 | 0.111 | 8.95 |
SST_Mar | 169.8 | 1.72 | 0.155 | 7.91 |
SST | 146.0 | 1.46 | 0.208 | 6.81 |
SST_Aug | 96.2 | 0.94 | 0.433 | 4.48 |
NAO | 96.1 | 0.94 | 0.430 | 4.48 |
PR | 86.8 | 0.84 | 0.486 | 4.04 |
SST_Apr | 85.4 | 0.83 | 0.491 | 3.98 |
SST_Oct | 67.9 | 0.65 | 0.602 | 3.16 |
GTA | 58.1 | 0.56 | 0.688 | 2.71 |
The DistLM results indicated that H′ was correlated with year, SSL, and IRD. For this index, the three factors explained 86.7% of the total variation. The DistLM results for the most abundant species (species with a percentage value higher than 1%) showed that the distribution of
Relationship between environmental variables and diversity index, most abundant fish species (species with a percentage value higher than 1%) in the Caspian Sea.
Variables | AIC | RSS | R2 | |
---|---|---|---|---|
Community | Year, PR, SST_Apr, SSL, SST, GTA, IRD, VD | 83.0 | 422.7 | 0.803 |
Shannon diversity | Year, SSL, IRD | −86.3 | 0.302 | 0.867 |
Year, PR, SST_Apr, SSL, SST, VD | −64.0 | 0.634 | 0.666 | |
Year, PR, SSL, SST, SST_Aug, IRD, VD | −30.9 | 2.615 | 0.770 | |
Year, PR, SST_Mar, SST_Apr, SST_Oct, SSL, SST, SST_Aug, EAWR, VD, NAO | −95.1 | 0.098 | 0.975 | |
Year, SST_Oct, EV, SSL, SST, GTA, EAWR, VD, NAO | −66.8 | 0.388 | 0.832 | |
Year, PR, SST_Oct, SSL, SST, GTA, EAWR, VD, NAO | −57.9 | 0.638 | 0.912 |
Figure 5 shows ABC curves for the study period. There is a gentle gradient of changes in the abundance and biomass dominance. For the period 1996–2003, the abundance curve lies above the biomass curve and has a negative W statistic, while in the later period, the biomass curve lies above the abundance curve, and the W statistic is positive, except for 2014 (Fig. 5).
Figure 6 shows the temporal trends in SSL, CPUE, SST, W statistic, and H′ for the period from 1996 to 2017. SST and H′ showed linear relationships, while other variables showed polynomial relationships across the years. SSL and H′ showed a decreasing trend during this period. CPUE and W statistics showed an increasing trend for the period from 1996 to 2006, while in the later period these variables showed a decreasing trend (Fig. 6).
The ABC curves and W statistics suggest that commercial bony fish communities were environmentally stressed in the southern CS from 1996 to 2003 (negative scores of the W index). The ABC variations were mainly due to changes in the relative abundance and biomass of benthic communities such as polychaete species (Warwick & Clarke 1994). Most of the species involved in the present study, such as
The decreasing trend of H′ suggests that the bony fish assemblage has been exposed to increasing stress over the last 22 years (Fig. 6). The ABC curves and the W index were helpful in determining the environmental stress (Mise et al. 2018), however, more conclusions were derived from analyzing the correlations between fish diversity and environmental conditions and anthropogenic activities.
In recent decades, due to the increasing impact of anthropogenic activities on marine biota, the United Nations has intensified efforts to ensure the conservation of marine biodiversity (Escobar-Toledo et al. 2015). As a result, it has been suggested that strategies for marine resources management should be based on the ecosystem context, including protection of habitats, non-target species, and populations of commercially valuable species (Pikitch et al. 2004). In the case of the CS, commercially most important fish stocks, such as two main pelagic species i.e. kilka (Fazli et al. 2020) and sturgeons (Khodorevskaya et al. 2009Khodorevskaya et al. 2014; Qiwei 2010; Tavakoli et al. 2019; Fazli et al. 2021), collapsed due to overfishing and ecosystem destruction.
The present study provides baseline information on the commercial bony fish populations in the CS. Of the 13 commercial bony fish species, only two species
NPUE and CPUE of bony fish showed a decreasing trend from 1996 to 2017 (Fig. 3), and their community structure was strongly correlated with year, PR, SSL, SST, GTA, and freshwater discharges. These environmental variables significantly correlated with the distribution of the three most migratory anadromous fish species (
Based on the ABC curves and W statistics obtained in the present study, it can be concluded that the fish assemblage in the southern CS was environmentally stressed between 1996 and 2003. The abundance indices of bony fish show a gradient, and their community structure is strongly correlated with environmental variability. The environmental parameters, namely PR, SST, SSL, and freshwater discharge, significantly affect the multispecies community, H′, and individual fish species. H′ shows a decreasing trend, suggesting that Iranian commercial fish species have been increasingly stressed over the past 22 years. Considering the current environmental conditions (decreasing trend in SSL river discharges and increasing SST) and anthropogenic activities (spawning ground deterioration and overfishing), it appears that the decreasing trend in H′ will continue in the future.