Environmental effects on sprat (Sprattus sprattus) physiology and growth at the distribution frontier: A bioenergetic modelling approach

Frisk, Christina; Andersen, Ken Haste; Temming, Axel; Herrmann, Jens-Peter; Madsen, Kristine S.; Kraus, Gerd

doi:10.1016/j.ecolmodel.2014.11.026

Cited by 12 publications

(18 citation statements)

References 50 publications

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“…Fish growth, indicated by condition (Smith et al 1986, Froese 2006, can be affected by changes in the physical and biological environment, such as temperature, salinity, prey abundance, interspecific competition, and/or predation (Flinkman et al 1998, Casini et al 2006, Brunel & Dickey-Collas 2010, Frisk et al 2015. In this study, model results were contrary to some of our predictions; for example, age-0 herring condition increased when most adult herring spawned closer to the peak spring phytoplankton bloom date (rather than prior to the bloom, as predicted).…”

Section: Discussioncontrasting

confidence: 92%

“…Cushing's (1969) match−mismatch hypothesis states that the temporal overlap of fish larvae and their prey determines year class strength. Fish that acquire enough energy stores and are in good condition can also survive periods of low food availability such as their first winter (Paul et al 1998, Foy & Paul 1999, Frisk et al 2015. Top-down (predator-driven) processes have been identified as important in other hypotheses.…”

Section: Introductionmentioning

confidence: 99%

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Bottom-up and top-down control of small pelagic forage fish: factors affecting age-0 herring in the Strait of Georgia, British Columbia

Boldt¹,

Thompson²,

Rooper³

et al. 2019

Mar. Ecol. Prog. Ser.

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Small pelagic fish are key planktivores and prey in marine ecosystems, and their population abundances undergo strong temporal and spatial variability. Top-down (predator controlled) and bottom-up (prey-driven) processes during early life history are important for determining forage fish survival and recruitment. We examined biological and environmental factors hypothesized to influence age-0 Pacific herring Clupea pallasi in the Strait of Georgia (SOG), British Columbia, Canada. Primarily bottom-up processes affected interannual variability in age-0 herring abundance and condition, with some evidence of top-down effects on condition. Age-0 herring abundance increased with increasing adult spawning biomass and peaked when most adults spawned about 20 d prior to the peak spring primary production bloom. This timeline would temporally align first-feeding herring larvae with their prey, such as small copepods. Age-0 herring abundance also increased with increasing juvenile salmon abundance, indicating that conditions favourable for herring were also favourable for their predators and competitors. Age-0 herring condition decreased with increasing spawning biomass, increased when most adults spawned closer to the peak spring bloom, increased with increasing temperatures above 8.2°C, and increased then stabilized with increasing prey zooplankton density. Age-0 herring condition had a dome-shaped relationship with predator abundance, indicating that high predator abundances negatively affected fish condition. Study results suggest that density-dependent processes, such as intraspecific competition, may be important in the SOG. A positive correlation between age-0 herring abundance and subsequent age-3 recruit abundance may provide a leading indicator of low recruitment years.

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Section: Discussioncontrasting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Bottom-up and top-down control of small pelagic forage fish: factors affecting age-0 herring in the Strait of Georgia, British Columbia

Boldt¹,

Thompson²,

Rooper³

et al. 2019

Mar. Ecol. Prog. Ser.

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“…; Frisk et al. ), the assumption that active metabolic rate is a fixed proportion of standard metabolic rate may lead to miscalculations of consumption rates and should be explicitly tested for individual species (Mathot and Dingemanse ) and treated conservatively (Meskendahl et al. ).…”

Section: Discussionmentioning

confidence: 99%

“…The high mean consumption rate estimate of the physiological method is partly due to the activity multiplier. Despite the common use of the activity multiplier (Meskendahl et al 2010;Hughes et al 2014;Frisk et al 2015), the assumption that active metabolic rate is a fixed proportion of standard metabolic rate may lead to miscalculations of consumption rates and should be explicitly tested for individual species (Mathot and Dingemanse 2015) and treated conservatively (Meskendahl et al 2010). The activity multiplier can be estimated from laboratory feeding trials (Madenjian et al 2012;Cerino et al 2013); however, the estimated value still remains inflexible in response to changes in environmentally induced fish behavior (Whiterod et al 2013).…”

Section: Evaluating the Metabolic Rate Equationsmentioning

confidence: 99%

Improving consumption rate estimates by incorporating wild activity into a bioenergetics model

Brodie

Taylor

Smith

et al. 2016

Ecology and Evolution

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Consumption is the basis of metabolic and trophic ecology and is used to assess an animal's trophic impact. The contribution of activity to an animal's energy budget is an important parameter when estimating consumption, yet activity is usually measured in captive animals. Developments in telemetry have allowed the energetic costs of activity to be measured for wild animals; however, wild activity is seldom incorporated into estimates of consumption rates. We calculated the consumption rate of a free‐ranging marine predator (yellowtail kingfish, Seriola lalandi) by integrating the energetic cost of free‐ranging activity into a bioenergetics model. Accelerometry transmitters were used in conjunction with laboratory respirometry trials to estimate kingfish active metabolic rate in the wild. These field‐derived consumption rate estimates were compared with those estimated by two traditional bioenergetics methods. The first method derived routine swimming speed from fish morphology as an index of activity (a “morphometric” method), and the second considered activity as a fixed proportion of standard metabolic rate (a “physiological” method). The mean consumption rate for free‐ranging kingfish measured by accelerometry was 152 J·g−1·day−1, which lay between the estimates from the morphometric method (μ = 134 J·g−1·day−1) and the physiological method (μ = 181 J·g−1·day−1). Incorporating field‐derived activity values resulted in the smallest variance in log‐normally distributed consumption rates (σ = 0.31), compared with the morphometric (σ = 0.57) and physiological (σ = 0.78) methods. Incorporating field‐derived activity into bioenergetics models probably provided more realistic estimates of consumption rate compared with the traditional methods, which may further our understanding of trophic interactions that underpin ecosystem‐based fisheries management. The general methods used to estimate active metabolic rates of free‐ranging fish could be extended to examine ecological energetics and trophic interactions across aquatic and terrestrial ecosystems.

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“…BRPs estimated in the runs for specified periods (division based on the dynamics of the biological and fishery variables) relative to BRPs estimated in the basic runs (with full available data series) for cod, herring, and sprat; estimated by using B&H (left) and Ricker (right) S-R relations an interspecific density-dependent control caused by an increase above the threshold biomass value for sprat, which is the main food competitor of herring (Casini et al 2010). Sprat growth is dependent on temperature, and is based on the model of Frisk et al (2015); the maximum body size is reduced with increasing temperature, because it affects respiration and activity costs. Pelagic fish production and high water temperature in autumn stimulated somatic growth of cod in 1980 (Uzars et al 2000).…”

Section: Disscusionmentioning

confidence: 99%

Change in Biological Reference Points Under Different Biological, Fishery, and Environmental Factors

Luzenczyk¹

2017

Acta Ichthyol. Piscat.

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Luzeńczyk A. 2017. Change in biological reference points under different biological, fishery, and environmental factors. Acta Ichthyol. Piscat. 47 (1): 41-51.Background. In this study, the relation between the biological reference points (BRPs) associated with Maximum Sustainable Yield (MSY) and the relevant biological, fisheries, and environmental factors were investigated. This knowledge is crucial to build the capacity for timely adaptation of management to the changes in an ecosystem. The research highlights the considerations that need to be taken into account when estimating and using BRPs in practice, and can thus lead to avoidance of overfishing. The BRPs were represented by MSY and the related spawning stock biomass (B MSY ), fishing mortality (F MSY ), and F MSY proxies. Materials and methods. To obtain the BRPs, the method of Horbowy and Luzeńczyk (2012)-that combines yield-per-recruit and spawning stock-per-recruit analyses with stock-recruitment relation models (Beverton and Holt (B&H) 1957, Ricker 1975)-was used. Data from the three biggest Baltic Sea stocks were used to test the sensitivity of BRPs to the model input data, and the influence of regime shifts or dynamics of the biological and fisheries variables on the BRPs. Results. The analyses show that an increase in maturity and weight at age generally led to an increase in BRPs. The opposite effect is observed in the case of natural mortality (stronger when the Ricker stock-recruitment (S-R) relation was used) and selectivity (proportion of the F of partially recruited age groups to the mean F of fully recruited age groups). An increase in the steepness of S-R models increases F MSY and its proxies as well as MSY, and had a decreasing influence on B MSY . Furthermore, the BRPs are very sensitive to the different data range divisions of the input data. Conclusion. When estimating BRPs, the time period of the input data should be selected with caution, and the appropriate time period should be based on strong biological, ecological, and environmental knowledge as, according to this study, all of these factors influence estimates of BRPs.

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Environmental effects on sprat (Sprattus sprattus) physiology and growth at the distribution frontier: A bioenergetic modelling approach

Cited by 12 publications

References 50 publications

Bottom-up and top-down control of small pelagic forage fish: factors affecting age-0 herring in the Strait of Georgia, British Columbia

Bottom-up and top-down control of small pelagic forage fish: factors affecting age-0 herring in the Strait of Georgia, British Columbia

Improving consumption rate estimates by incorporating wild activity into a bioenergetics model

Change in Biological Reference Points Under Different Biological, Fishery, and Environmental Factors

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