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Multispecies Mortality Patterns of Commercial Bivalves in Relation to Estuarine Salinity Fluctuation

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Abstract

Fluctuations in salinity may cause huge economic losses in estuaries with exploited commercial bivalves owing to their effect on mortality of these species. However, the same decrease in salinity does not affect all species in the same way, so it is interesting to study the effect of salinity from a multispecies standpoint. In the management of exploited bivalve beds, it is important to know the tolerance thresholds of the species, not only in cases of extremely low salinities but also over prolonged periods when salinities are low but not extreme. An analysis of mortality episodes of commercial bivalves in the Ulla River estuary (Galicia, NW Spain) from 1977 to 2009 revealed two mortality patterns related to how greatly the different species were affected. A mathematical model was designed to estimate salinity in the estuary based on weather conditions and tidal amplitude. By applying this model, it was possible to deduce the intensity and duration of the salinity decrease in the days prior to each mortality episode with the goal of relating these factors to mortality patterns. The two parameters found to be sufficient to explain the mortality observed were the minimum salinity at high tide and the number of consecutive days below a specific salinity threshold.

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Acknowledgments

G. Martínez and P. Carreira took part in the collection of salinity data. The suggestions of D. Iglesias and J. Blanco greatly improved the original manuscript.

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Correspondence to José Manuel Parada.

Electronic Supplementary Material

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Online Resource 1

Recovery of salinity values in bottom waters at high tide during a period of heavy rainfall in which no mortalities were recorded. (DOC 841 kb)

Online Resource 2

Results of the correlation analyses of precipitation (pp) and net precipitation (ppN) data series in the stations of Corón (C) and Mouriscade (M) versus salinity in LU6 (S) for the selection of the most appropriate combination of lags and accumulation of values for the salinity model. In the correlations between precipitation and salinity (r² (pp vs S)), precipitation data were used in each of the two isolated stations, the sum of the two data series (C + M), and the sum of the data series from Corón plus that of Mouriscade with lags ranging from 1 (C + (M−1 day)) to 4 days (C + (M−4 day)). The correlations with net precipitation ( (ppN vs S)) used the sum of the precipitations in Corón plus Mouriscade with a lag of 1 day, accumulated between 1 (C + (M−1 day)1 day) and 7 days (C + (M−1 day)7 days). In all cases, the resulting correlations were highly significant (p < 0.01). (DOC 30 kb)

Online Resource 3

Correlation between wind speed at the meteorological station of Corón corrected in relation to wind direction. In (a), all directions were assumed to affect salinity. Black lines in (b) indicate directions for which the speed was not corrected as they were considered to favor the renovation of salt water in the estuary. Gray lines in (b) show the directions for which the speed was considered to have a negative value (x(−1)) as they were thought to favor the accumulation of fresh water in the estuary. In (b), the different combinations are shown, assuming that the wind coming from certain directions (white lines) did not exert any effect on salinity. The table in (b) highlights the combination presenting the highest correlation (0.576). (DOC 1,764 kb)

Online Resource 4

Regression model for salinity estimations based on net precipitation corrected with tide amplitude (P-T) and wind velocity corrected with direction (WM-D). (DOC 34 kb)

Online Resource 5

Model of salinity estimation. a Log correction of the multiple regression model, b, c distribution of residuals, d comparison of estimated and observed values. (DOC 1,626 kb)

Online Resource 6

Values of the log-adjusted curve of salinity estimated by the linear model (S_LM) to observed salinity (S_obs). Constants (A 1, A 2, x 0, and P), number of paired data used (n), determination coefficients (r² and year r²adj) and statistical significance (p) are shown. (DOC 28 kb)

Online Resource 7

Indices of fits obtained in the calibration of the linear regression model (S_LM) and in the calibration and validation of the log correction of the linear model (S*LM). R correlation coefficient, MBE mean bias error, MAE mean absolute error, RMSE root mean squared error, d index of agreement. (DOC 27 kb)

Online Resource 8

Groups (M, S, Sa, and Sb) resulting from the Ward cluster classification analyses (a) and UPGMA (b) of the mortality episodes related to the characterization of the low salinity events (Table 3). (DOC 3,367 kb)

Online Resource 9

Results of the Mann–Whitney U test for comparison of groups (M, S, Sa, and Sb) identified by classification analyses and PCA. (DOC 31 kb)

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Parada, J.M., Molares, J. & Otero, X. Multispecies Mortality Patterns of Commercial Bivalves in Relation to Estuarine Salinity Fluctuation. Estuaries and Coasts 35, 132–142 (2012). https://doi.org/10.1007/s12237-011-9426-2

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