Temperature effects on vital rates of different life stages and implications for population growth of Baltic sprat

Abstract

Baltic sprat (Sprattus sprattus balticus S.) is a key species in the pelagic ecosystem of the Baltic Sea. Most stocks of small pelagic species are characterized by natural, fishery-independent fluctuations, which make it difficult to predict stock development. Baltic sprat recruitment is highly variable, which can partly be related to climate-driven variability in hydrographic conditions. Results from experimental studies and field observations demonstrate that a number of important life history traits of sprat are affected by temperature, especially the survival and growth of early life stages. Projected climate-driven warming may impact important processes affecting various life stages of sprat, from survival and development during the egg and larval phases to the reproductive output of adults. This study presents a stage-based matrix model approach to simulate sprat population dynamics in relation to different climate change scenarios. Data obtained from experimental studies and field observations were used to estimate and incorporate stage-specific growth and survival rates into the model. Model-based estimates of population growth rate were affected most by changes in the transition probability of the feeding larval stage at all temperatures (?0, ?2, ?4, ?6°C). The maximum increase in population growth rate was expected when ambient temperature was elevated by 4°C. Coupling our stage-based model and more complex, biophysical individual-based models may reveal the processes driving these expected climate-driven changes in Baltic Sea sprat population dynamics.

Seasonal weather patterns drive population vital rates and persistence in a stream fish

“…Second, animal populations are structured by age, size or life stage, and individuals require different resources through ontogeny (Werner & Gilliam, ). Different individuals are unlikely to respond uniformly to a specific climatic condition, with potentially large differences among life stages (Haslob et al ., ; Diamond et al ., ; Dybala et al ., ). Finally, animal populations are additionally regulated by density‐dependent factors, and population density has been shown to influence recruitment and survival across many taxa (Nicola et al ., ; Diamond et al ., ; Lok et al ., ).…”

Integrated ecosystem impacts of climate change and eutrophication on main Baltic fishery resources

“…Other studies also showed that, the stronger the climate changes (e.g. warmer temperatures and lower salinities in the Baltic Sea), the less cod (see also MacKenzie et al, 2011), but the more sprat (see also Haslob et al, 2012;MacKenzie & Köster, 2004;MacKenzie et al, 2012) and herring (see also Bartolino et al, 2014;Cardinale et al, 2009;Hakala et al, 2003). So the cumulative effect of high nutrients and stronger climate change is even more pronounced with less cod.…”

“…Some research has also explored these pressures in a future climate condition. Haslob et al (2012) for instance simulated sprat population dynamics in relation to different climate change scenarios, Bartolino et al (2014) looked at the combined effects of fisheries exploitation and climate change on the temporal dynamics of the central Baltic herring stock, while Margonski et al (2010) modelled the effect of climate on the stock-recruitment relationships for cod, sprat and herring. Saraiva et al (2019a) investigated the effects of climate change on eutrophication, while Wåhlström et al (2020) investigated the combined effect of climate change and nutrient loads on the future cod reproductive volume.…”

Uncertainty propagation in matrix population models: Gaps, importance and guidelines

“…Bjørkvoll et al., 2016), others consolidate their own estimates with estimates derived from other sources. These other sources are highly variable: Estimates may be from the same population at a different time (Haslob et al., 2012), from the same species at a different location/time (Stratford et al., 2016), from closely related species, or even ‘guestimates’ from experts (Beissinger, 2014). Some data sources, particularly those relying on expert opinion or literature estimates, can challenge uncertainty quantification and cause omissions (e.g.…”