included as a delineator of the adult stock's centre of abundance (Jung & Houde 2004a):
where R y = recruitment level, S = estimated baywide A. mitchilli spawning stock biomass, ΔL = centre of latitudinal location of the A. mitchilli spawning stock. A high percentage of the annual variability in recruitment was explained by the modified Ricker model.
Including water temperature as an environmental variable in a Ricker S‐R model effectively described age‐1 recruitment of a Baltic Sea percid Sander lucioperca in the Archipelago Sea near Finland (Figure 3.19) (Heikinheimo et al. 2014). Although recruitment of S. lucioperca exhibited a moderate degree of density dependence related to adult stock abundance, temperature was the major driving force determining recruitment level.
3.4.2.3 Predicting and forecasting recruitment
Predicting and forecasting recruitment has obvious value for stock assessments and management of estuary‐dependent and ‐associated fishes. Beyond the importance for fishery management, forecasting recruitment has ecological value to predict short‐ (annual) and long‐term trends in stock abundances. The search for environmental predictors of recruitment is at least a century old (Cushing 1982), and despite substantial efforts by scientists to include environment–recruitment correlations in stock assessments and forecasts, success has remained rather poor in practice (Walters & Collie 1988, Myers 1998, Houde 2008, 2016, Sharma et al. 2019). Nevertheless, forecasts that explore and analyse relationships between the environment (broadly), adult stock and recruitment, i.e. ecological forecasting, have value even if failing to meet the rigour required by fisheries managers for stock assessments.
Figure 3.19 Recruitment time series for Sander lucioperca in the Archipelago Sea, Finland. Recruitment expressed as loge number of recruits (R) per unit spawning biomass (S). Modified Ricker model, with July–August water temperature included in the stock–recruitment model, is fitted to the data
(modified from Heikinheimo et al. (2014, their figure 7)).
The complexity of estuaries and the life cycles of fishes that reproduce in and recruit to estuaries add to the challenge of successful forecasting. The complexity itself suggests that forecasting based on adult abundance alone is insufficient and that environmental variables must be incorporated into analyses and models to account for factors driving recruitment variability. For many estuary‐dependent and ‐associated species in which egg, larval and juvenile stages occupy different habitats, identifying variables and life stages most linked to recruitment variability is particularly challenging. Incorporating freshwater flow and temperature variables into stock‐recruitment models for estuarine‐associated fishes has gained considerable success in predicting recruitments in the past three decades. Two notable examples include the clupeid Brevoortia patronus in the Gulf of Mexico, in which freshwater discharge from the Mississippi River has predictive power (Vaughan et al. 2011) and the sciaenid Micropogonias undulatus along the coast of the northeast USA in which winter temperatures are directly related to recruitment success (Figure 3.17a) (Hare et al. 2010). Predictive models presented in Figures 3.16 and 3.17 for two estuary‐dependent species are good examples of how S‐R models that include adult biomass and environmental variables can be effective for hindcasting but are not necessarily used or effective for forecasting (Haltuch et al. 2019).
Climate variability in marine ecosystems often is expressed in patterns that dominate for periods of a few years to decades and thus has the potential for predicting and forecasting trends in fish recruitment. Both oscillating climate regimes and directional climate shifts may affect reproduction and recruitment in marine and estuarine fishes (Nye et al. 2014, Gillanders et al. 2022). Using a synoptic climatology approach, inter‐annual recruitment variability in several offshore‐spawning, estuary‐dependent and anadromous fishes was investigated (Wood 2000, Wood & Austin 2009). These analyses indicated considerable potential and ability to hindcast recruitment and to predict probable trends for these species.
In many fishery management programs, it is important to have an ability to forecast or predict abundances of exploitable stages earlier than at the age individuals are first fished. Surveys of young‐of‐year juveniles are commonly conducted in estuarine nurseries to provide ‘juvenile indices’ that have utility in forecasting future recruitment (see Figure 3.15). Identifying a life stage that is strongly linked to recruitment variability is critical for forecasting. It is axiomatic that the life stage closest to the recruited stage will be the stage most likely to succeed in forecasting recruitment (e.g. Bradford 1992), but dynamics occurring in that life stage may not have determined the fate of a year class or cohort.
Although forecasting recruitment is accomplished most confidently based on abundances of late‐stage larvae and juveniles (Bradford & Cabana 1997), success in forecasting using those abundances does not necessarily mean that recruitment levels were set in the late‐larval or juvenile stages because dynamics in egg and early‐larval stages could have driven the outcome. This is apparently the case for the pleuronectid Pleuronectes platessa whose offshore survival during egg and early‐larval stages coarsely controls recruitment level. However, fine‐tuning recruitment estimates and acquiring predictive capability are greatly enhanced during the juvenile stage in estuaries and coastal nurseries where regulation occurs (Beverton & Iles 1992, Iles 1994, Van der Veer et al. 1994, 2015). In a paralichthyid Paralichthys lethostigma, meteorological forcing and river discharge at the time of larval ingress were successful in hindcasting recruitment levels of age‐0+ juveniles (Figure 3.20) in North Carolina (USA) estuaries (Taylor et al. 2010). In this example, relative abundances of age‐1–3 individuals could be predicted based upon age‐0+ abundances that were modelled from larval ingress and environmental forcing variables. The inclusion of river discharge in the P. lethostigma model may be especially appropriate because age‐0+ individuals settle and concentrate in the fresher waters of estuaries (Lowe et al. 2011).
In the Baltic Sea, progress in predicting recruitments of clupeid fishes has evolved by considering climate, hydrographic and other environmental factors (e.g. temperatures, ice cover, larval prey abundance) in modelling the recruitment process. For the Gulf of Riga Clupea harengus, year‐class abundance was predicted from the mean water temperature of the 0–20 m depth layer in May and the biomass of a key larval prey, the copepod Eurytemora affinis (ICES 2009). Also, research on environmental variables was conducted to understand and predict recruitment of Sprattus sprattus, focusing on important factors such as spring temperatures, ice coverage and the North Atlantic Oscillation climate variable (MacKenzie & Köster 2004, MacKenzie et al. 2008). While recruitment levels of clupeid species and stocks in the Baltic may be influenced by numerous factors, climate indicators, especially temperature, and spawning stock biomass (for C. harengus) were important variables for predicting recruitment (Margonski et al. 2010).