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- Cardinale, M, et al.
(författare)
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CES. 2009. Report of the Workshop on Combining Climatic Scenarios and Medium‐Term Predictions for Baltic Herring and Sprat stocks (WKCSMPB), 13–16 October 2009, Ponza, Italy. ICES CM 2009/BCC:03. : ICES WKCSMPB REPORT 2009
- 2009
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The direct and indirect effects of density dependence (i.e. parental stock effects) and climate‐induced hydrographic change on five different Baltic herring stocks and on Baltic sprat, investigated by WKHRPB and WKSSRB (ICES 2007b and 2008) to develop stock specific recruitment‐environment relationships, were revisited and updated. The predictors to be included in the final recruitment models were selected on the basis of the parsimonious principle, statistical significance of the predictors and the ecological criterion being fulfilled simultaneously (Cardinale et al. 2009). In previous work, temperature was detected to be an important predictor for several stocks of clupeids. Weight at age (WAA) was the major factor explaining recruitment for Main Basin herring (MBH) while spawning stock biomass (SSB) was important for Baltic sprat (BS) and Gulf of Riga herring (GRH). For MBH, food supply was also a significant predictor, suggesting that a part of the changes in climate and hydrographic conditions may affect herring indirectly via prey availability. The best recruitment model for Baltic sprat (BS) linked recruitment success to its spawning stock biomass (SSB), sea surface temperature in August (NASA8), predation mortality by cod (PM), and Bottom Depth Anomaly (BDA) which is a proxy of drift/retention of sprat larvae (ICES 2008). The final recruitment models provided by Cardinale et al. (2009) were tested with updated data series only for MBH, GRH and BS as no satisfactory final model was found for the other stocks (Cardinale et al. 2009). Further, as the main aim was to include climatic scenarios for recruitment predictions, number of recruits (thereafter referred also as recruitment) was used for all stocks instead of recruitment success. Thus, models developed for MBH, GRH and BS were re‐fitted with updated input data and with number of recruits as response variable using both a linear and a GAM model to allow for medium‐term recruitment predictions under different climatic scenarios. SSB time series were generated using the BALMAR food‐web model (Lindegren et al. 2009), a linear state‐space model based on a theoretical approach for predicting longterm responses of populations to environmental change (Ives 1995; Ives et al. 2003). SSB time series were generated assuming two different levels of fishing mortalities (Fmed, Fmsy or Fmp). Predictions of SST were generated using higher resolution Regional Climate Models (RCMs). The results show that in the next 30 years recruitment of herring stocks will generally increase or stay relatively stable at values observed in the last decade for any of the scenarios considered. This is likely to be mainly an effect of a predicted increase in SST. On the other hand, the effect of SSB is small for MBH mainly due to the narrow range of predicted SSB values over the next 3 decades. In the case of GRH, a similar pattern was observed although the increase in recruitment is not as large as for MBH. For sprat, a satisfactory model was not found for predicting recruitment and further analysis are needed.
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