An end-to-end coupled model ROMS-N2P2Z2D2-OSMOSE of the southern Benguela foodweb: parameterisation, calibration and pattern-oriented validation

Travers-Trolet, Morgane; Shin, Yun Ji; Field, J. G.

doi:10.2989/1814232x.2014.883326

Cited by 47 publications

(51 citation statements)

References 60 publications

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“…A full life-cycle model of European anchovy in the northern Aegean Sea successfully reproduced observed characteristics of the stock, including migration (Politikos et al 2015). Retention and habitat, including future pCO 2 , have been indicated to be important in the Benguela Current System (Brochier et al 2013, Travers-Trolet et al 2014. The effect of El Niño on the anchoveta in the Humboldt Current System has been modeled (Xu et al 2015).…”

Section: Modelsmentioning

confidence: 87%

Climate, Anchovy, and Sardine

Checkley

Asch

Rykaczewski

2017

Annu. Rev. Mar. Sci.

180

129

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Anchovy and sardine populated productive ocean regions over hundreds of thousands of years under a naturally varying climate, and are now subject to climate change of equal or greater magnitude occurring over decades to centuries. We hypothesize that anchovy and sardine populations are limited in size by the supply of nitrogen from outside their habitats originating from upwelling, mixing, and rivers. Projections of the responses of anchovy and sardine to climate change rely on a range of model types and consideration of the effects of climate on lower trophic levels, the effects of fishing on higher trophic levels, and the traits of these two types of fish. Distribution, phenology, nutrient supply, plankton composition and production, habitat compression, fishing, and acclimation and adaptation may be affected by ocean warming, acidification, deoxygenation, and altered hydrology. Observations of populations and evaluation of model skill are essential to resolve the effects of climate change on these fish.

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Section: Modelsmentioning

confidence: 87%

Climate, Anchovy, and Sardine

Checkley

Asch

Rykaczewski

2017

Annu. Rev. Mar. Sci.

180

129

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“…Spatially-explicit ecosystem models, especially highly sophisticated models such as applications of the Atlantis modeling platforms, rely on numerous data inputs, many of which can have a significant impact on ecosystem model predictions (Fulton et al, 2007;Shin et al, 2010;Walters et al, 2010;Steele et al, 2013). Many spatially-explicit ecosystem models have been designed worldwide (Fulton, 2010;EspinozaTenorio et al, 2012;Steele et al, 2013), and significant attention has been devoted to enhance the structure and assumptions of ecosystem modeling platforms (e.g., Shin et al, 2010;Steenbeek et al, 2013Steenbeek et al, , 2016Travers-Trolet et al, 2014;Grüss et al, 2016b,c). Therefore, it is now high time to develop and employ methodologies such as ours for ensuring that the spatially-explicit ecosystem models available around the world are provided with reliable inputs generated using the best available data and information.…”

Section: Discussionmentioning

confidence: 99%

Producing Distribution Maps for a Spatially-Explicit Ecosystem Model Using Large Monitoring and Environmental Databases and a Combination of Interpolation and Extrapolation

et al. 2018

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To be able to simulate spatial patterns of predator-prey interactions, many spatially-explicit ecosystem modeling platforms, including Atlantis, need to be provided with distribution maps defining the annual or seasonal spatial distributions of functional groups and life stages. We developed a methodology combining extrapolation and interpolation of the predictions made by statistical habitat models to produce distribution maps for the fish and invertebrates represented in the Atlantis model of the Gulf of Mexico (GOM) Large Marine Ecosystem (LME) ("Atlantis-GOM"). This methodology consists of: (1) compiling a large monitoring database, gathering all the fisheries-independent and fisheries-dependent data collected in the northern (U.S.) GOM since 2000; (2) compiling a large environmental database, storing all the environmental parameters known to influence the spatial distribution patterns of fish and invertebrates of the GOM; (3) fitting binomial generalized additive models (GAMs) to the large monitoring and environmental databases, and geostatistical binomial generalized linear mixed models (GLMMs) to the large monitoring database; and (4) employing GAM predictions to infer spatial distributions in the southern GOM, and GLMM predictions to infer spatial distributions in the U.S. GOM. Thus, our methodology allows for reasonable extrapolation in the southern GOM based on a large amount of monitoring and environmental data, and for interpolation in the U.S. GOM accurately reflecting the probability of encountering fish and invertebrates in that region. We used an iterative cross-validation procedure to validate GAMs. When a GAM did not pass the validation test, we employed a GAM for a related functional group/life stage to generate distribution maps for the southern GOM. In addition, no geostatistical GLMMs were fit for the functional groups and life stages whose depth, longitudinal and latitudinal ranges within the U.S. GOM are not entirely covered by the data from the large monitoring database; for those, only GAM predictions were employed to obtain distribution maps for Atlantis-GOM. Pearson residuals were Grüss et al.Maps for an Ecosystem Model computed to validate geostatistical binomial GLMMs. Ultimately, 53 annual maps and 64 seasonal maps (for 32 different functional groups/life stages) were produced for Atlantis-GOM. Our methodology could serve other world's regions characterized by a large surface area, particularly LMEs bordered by several countries.

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“…This releases Maury, 2010;Dueri et al, 2014;Lefort et al, 2015;Le Mezo et al, 2016 OSMOSE Impacts of fishing and climate change on higher trophic levels in marine ecosystems 2 groups (small, large). Predators of phytoplankton and food for higher trophic levels Shin and Cury, 2004;Travers-Trolet et al, 2014;Grüss et al, 2016 Discrete size class Impacts of fishing on marine ecosystems, and the effect of parameter uncertainty Background food source for fish species, but not explicitly resolved Hall et al, 2006;Pope et al, 2006;Thorpe et al, 2015 Static size continuum Establishing baseline, unperturbed abundance of marine ecosystems…”

Section: Current Zooplankton Representation In Models Biogeochemical mentioning

confidence: 99%

Modeling What We Sample and Sampling What We Model: Challenges for Zooplankton Model Assessment

et al. 2017

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Zooplankton are the intermediate trophic level between phytoplankton and fish, and are an important component of carbon and nutrient cycles, accounting for a large proportion of the energy transfer to pelagic fishes and the deep ocean. Given zooplankton's importance, models need to adequately represent zooplankton dynamics. A major obstacle, though, is the lack of model assessment. Here we try and stimulate the assessment of zooplankton in models by filling three gaps. The first is that many zooplankton observationalists are unfamiliar with the biogeochemical, ecosystem, size-based and individual-based models that have zooplankton functional groups, so we describe their primary uses and how each typically represents zooplankton. The second gap is that many modelers are unaware of the zooplankton data that are available, and are unaccustomed to the different zooplankton sampling systems, so we describe the main sampling platforms and discuss their strengths and weaknesses for model assessment. Filling these gaps in our understanding of models and observations provides the necessary context to address the last gap-a blueprint for model assessment of zooplankton. We detail two ways that zooplankton biomass/abundance observations can be used to assess models: data wrangling that transforms observations to be more similar to model output; and observation models that transform model outputs to be more like observations. We hope that this review will encourage greater assessment of zooplankton in models and ultimately improve the representation of their dynamics.Keywords: plankton net, bioacoustics, optical plankton counter, Continuous Plankton Recorder, size-spectra, ecosystem model, observation model, model assessment Everett et al. Challenges for Zooplankton Model Assessment THE IMPORTANCE OF ZOOPLANKTONAll marine phyla are part of the zooplankton-either permanently as holoplankton (e.g., copepods or arrow worms) or temporarily as meroplankton (e.g., crab or fish larvae). In this review we define zooplankton as all organisms drifting in the water whose locomotive abilities are insufficient to progress against ocean currents (Lenz, 2000). Their sizes range from flagellates (about 20 µm) to siphonophores up to 30 m long. Zooplankton are the intermediate trophic level between phytoplankton and fish and are an important component of carbon and nutrient cycles in the ocean. They account for a large proportion of the energy transfer to fish on continental shelves (Marquis et al., 2011), temperate reefs (Kingsford and MacDiarmid, 1988;Champion et al., 2015), seagrass meadows (Edgar and Shaw, 1995), and coral reefs (Hamner et al., 1988;Frisch et al., 2014). Zooplankton are also key in the transfer of energy between benthic and pelagic domains (Lassalle et al., 2013). Zooplankton are responsible for transferring energy to deep water through the sinking of fecal pellets and moribund carcases (Stemmann et al., 2000;Henschke et al., 2013Henschke et al., , 2016 or through diel vertical migration (Ariza et al., 2015) and can play...

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An end-to-end coupled model ROMS-N₂P₂Z₂D₂-OSMOSE of the southern Benguela foodweb: parameterisation, calibration and pattern-oriented validation

Cited by 47 publications

References 60 publications

Climate, Anchovy, and Sardine

Climate, Anchovy, and Sardine

Producing Distribution Maps for a Spatially-Explicit Ecosystem Model Using Large Monitoring and Environmental Databases and a Combination of Interpolation and Extrapolation

Modeling What We Sample and Sampling What We Model: Challenges for Zooplankton Model Assessment

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