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Hufnagl, M., and Peck, M. A. 2011. Physiological individual-based modelling of larval Atlantic herring (Clupea harengus) foraging and growth: insights on climate-driven life-history scheduling. – ICES Journal of Marine Science, 68: 1170–1188. A physiological individual-based model for the foraging and growth of Atlantic herring (Clupea harengus) larvae was constructed, validated using laboratory and field data, tested for parameter sensitivity, and used to examine climate-driven constraints on life-history scheduling. Model scenarios examined how natural (phenological and magnitude) changes in key environmental factors (temperature, prey, and photoperiod/daylength) affected the estimates of survival and growth of spring- and autumn-spawned larvae. The most suitable hatching seasons agreed well with the periods of larval abundance in Northeast Atlantic waters. Modelled survival is unlikely in June, July, and November. Mean annual temperature, prey concentration, and composition significantly influenced larval growth of both autumn and spring spawners. The model suggested that climate-driven changes in bottom-up factors will affect spring- and autumn-spawned larvae in different ways. It is unlikely that autumn-spawning herring will be able to avoid unfavourable conditions by delaying their spawning time or by utilizing more northern spawning grounds because of limitations in daylength to larval growth and survival. Conversely, earlier spawning in spring, or later, midsummer spawning will be tightly constrained by match–mismatch dynamics between larvae and zooplankton production.

We review and compare four broad categories of spatially-explicit modelling approaches currently used to understand and project changes in the distribution and productivity of living marine resources including: 1) statistical species distribution models, 2) physiology-based, biophysical models of single life stages or the whole life cycle of species, 3) food web models, and 4) end-to-end models. Single pressures are rare and, in the future, models must be able to examine multiple factors affecting living marine resources such as interactions between: i) climate-driven changes in temperature regimes and acidification, ii) reductions in water quality due to eutrophication, iii) the introduction of alien invasive species, and/or iv) (over-)exploitation by fisheries. Statistical (correlative) approaches can be used to detect historical patterns which may not be relevant in the future. Advancing predictive capacity of changes in distribution and productivity of living marine resources requires explicit modelling of biological and physical mechanisms. New formulations are needed which (depending on the question) will need to strive for more realism in ecophysiology and behaviour of individuals, life history strategies of species, as well as trophodynamic interactions occurring at different spatial scales. Coupling existing models (e.g. physical, biological, economic) is one avenue that has proven successful. However, fundamental advancements are needed to address key issues such as the adaptive capacity of species/groups and ecosystems. The continued development of end-to-end models (e.g., physics to fish to human sectors) will be critical if we hope to assess how multiple pressures may interact to cause changes in living marine resources including the ecological and economic costs and trade-offs of different spatial management strategies. Given the strengths and weaknesses of the various types of models reviewed here, confidence in projections of changes in the distribution and productivity of living marine resources will be increased by assessing model structural uncertainty through biological ensemble modelling.

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Existing laboratory and field data on growth were combined, reanalyzed and discussed to generate a holistic temperature-, length-and gender-dependent growth rate (G, mm d -1 ) model for North Sea region brown shrimp Crangon crangon (L.). Length (L, mm) and temperature (T, °C) dependent growth rates of Crangon crangon are highly variable within and among studies but decrease with L and increase with T. Applying general nonlinear regression, mean growth was derived as G = 0.02421·T -0.00115·e 0.08492·T · L (r² = 0.860). Applying quantile regression (75th percentile), a growth model describing growth of the fastest growing fraction of the population was derived as G max = 0.03054·T -0.00104 · e 0.09984·T · L (r² = 0.857). Female growth rates were higher than male growth rates and were similar to G max . In a simulation, G and G max were used with seasonally varying temperature to generate monthly length trajectories (cohorts). Further, length-based mortality was included and the fraction of each cohort attaining minimal commercial size was calculated. May cohorts (5 mm initial length), representing spring recruitment, grew to 50 mm by November if G was used. Application of the fast growth model (G max ) allowed for the same length to be reached 2 mo earlier. We conclude that the autumnal peak in adult abundance in the North Sea is most probably due to recruitment from the spring cohort of the same year. Our results suggest that the previous year's summer cohort contributes little to this autumnal peak because of high cumulative and overwintering mortality.KEY WORDS: Growth rates · Life cycle · Recruitment · Commercial fishing · Size-at-age · Moult Resale or republication not permitted without written consent of the publisherMar Ecol Prog Ser 435: [155][156][157][158][159][160][161][162][163][164][165][166][167][168][169][170][171][172] 2011 shrimps (> 50 mm) are adult females (Tiews 1954, Martens & Redant 1986.While Temming & Damm (2002) have shown that the spring recruitment most likely originates from the winter egg production of the previous winter, it is still not clear whether this spring recruitment generates the autumn peak of adult density. This uncertainty mainly originates from differences in reported growth rates from both laboratory and field studies. The different hypotheses assign the autumn peak in adult density either to shrimps from the same year's spring recruitment (Kuipers & Dapper 1984), the same year's summer recruitment (Boddeke & Becker 1979) or the previous year's summer recruitment (Campos et al. 2009a). Boddeke & Becker's (1979) hypothesis obviously requires the highest growth rates followed by the life cycle model of Kuipers & Dapper (1984). The latter concept was based on laboratory growth data obtained by M. Fonds (unpubl.), but these rates were too low to connect the maximum juvenile abundance in spring with the maximum landings per unit effort (LPUE) in autumn, as discussed by Beukema (1992). Berghahn (1991) calculated that higher temperatures experienced by juv...

Hydrodynamic Ocean Circulation Models and Lagrangian particle tracking models are valuable tools e.g. in coastal ecology to identify the connectivity between offshore spawning and coastal nursery areas of commercially important fish, for risk assessment and more for defining or evaluating marine protected areas. Most studies are based on only one model and do not provide levels of uncertainty. Here this uncertainty was addressed by applying a suite of 11 North Sea models to test what variability can be expected concerning connectivity. Different notional test cases were calculated related to three important and well-studied North Sea fish species: herring (Clupea harengus), and the flatfishes sole (Solea solea) and plaice (Pleuronectes platessa). For sole and plaice we determined which fraction of particles released in the respective spawning areas would reach a coastal marine protected area. For herring we determined the fraction located in a wind park after a predefined time span. As temperature is more and more a focus especially in biological and global change studies, furthermore inter-model variability in temperatures experienced by the virtual particles was determined. The main focus was on the transport variability originating from the physical models and thus biological behavior was not included. Depending on the scenario, median experienced temperatures differed by 3. °C between years. The range between the different models in one year was comparable to this temperature range observed between modelled years. Connectivity between flatfish spawning areas and the coastal protected area was highly dependent on the release location and spawning time. No particles released in the English Channel in the sole scenario reached the protected area while up to 20 of the particles released in the plaice scenario did. Interannual trends in transport directions and connectivity rates were comparable between models but absolute values displayed high variations. Most models showed systematic biases during all years in comparison to the ensemble median, indicating that in general interannual variation was represented but absolute values varied. In conclusion: variability between models is generally high and management decisions or scientific analysis using absolute values from only one single model might be biased and results or conclusions drawn from such studies need to be treated with caution. We further concluded that more true validation data for particle modelling are required. © 2017 Elsevier B.V

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