Optical model for the Baltic Sea with an explicit CDOM state variable: a case study with Model ERGOM (version 1.2)

Neumann, Thomas; Koponen, Sampsa; Attila, Jenni; Brockmann, Carsten; Kallio, Kari; Kervinen, Mikko; Mazeran, Constant; Müller, Dagmar; Philipson, Petra; Thulin, Susanne; Väkevä, Sakari; Ylöstalo, Pasi

doi:10.5194/gmd-14-5049-2021

Cited by 18 publications

(19 citation statements)

References 29 publications

(46 reference statements)

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“…Pairs of predictors were tested for collinearity, and other highly correlated independent variables (those that indicated within any pair Pearson correlation r > 0.90, p level 0.05, and had the lower predictive power) were omitted from the analysis to ovoid overfitting (for variables and correlation matrices, see Supplementary Table S2 ). A larger set of other predictors was also tested, but did not enter the final model, including ice thickness as well as near-bottom salinity, temperature and oxygen modelled as described in Neumann et al [ 36 ].…”

Section: Methodsmentioning

confidence: 99%

Benthic Macrofauna Community Bioirrigation Potential (BIPc): Regional Map and Utility Validation for the South-Western Baltic Sea

Gogina

Renz

Förster

et al. 2022

Biology

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Benthic community bioirrigation potential (BIPc), an index developed to quantify the anticipated capacity of macrofauna to influence the solute exchange at the sediment–water interface, was calculated for the south-western Baltic Sea. This index can be regarded as an effect trait that is useful for predicting ecosystem processes impacted by animal burrow ventilation. The special feature, and presumably an advantage, of BIPc, compared to alternative recently developed benthic macrofauna-based bioirrigation indices, lies in its ability to distinguish the taxa-specific score values between diffusion- and advection-dominated sediment systems. The usefulness of the BIPc index was compared against the estimates of the well-established community bioturbation potential index (BPc). The BIPc index displayed a moderately but significantly stronger correlation with estimates of irrigation rates derived from tracer experiments. Using a random forest machine learning approach and a number of available relevant environmental predictor layers, we have modelled and mapped the spatial differences in this ecosystem functioning expression. The key species contributing to bioirrigation potential in the study area were identified. The interannual variation in BIPc was assessed on a small exemplary dataset. The scores required to calculate the index, that were assigned to 120 taxa dominating abundance and biomass in the region, are provided for reuse. The utility, temporal variability and uncertainty of the distribution estimate are discussed.

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Section: Methodsmentioning

confidence: 99%

Benthic Macrofauna Community Bioirrigation Potential (BIPc): Regional Map and Utility Validation for the South-Western Baltic Sea

Gogina

Renz

Förster

et al. 2022

Biology

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“…Depth raster files were made available by the EMODnet Bathymetry project, https://www.emodnet.eu/en/bathymetry, funded by the European Commission Directorate General for Maritime Affairs and Fisheries. Biomass density of S. entomon was extracted from a habitat distribution model coupled with modelled hydrographical data from the regional coupled ocean biogeochemical model ERGOM (Gogina et al ., 2020; Neumann et al ., 2021). We used predicted densities of cod and flounder (kg/km 2 ) from GLMMs (described above) as covariates, since not all hauls in the CPUE (density) data could be standardized and joined with the condition data.…”

Section: Methodsmentioning

confidence: 99%

Evaluating drivers of spatiotemporal individual condition of a bottom-associated marine fish

Lindmark

Anderson

Gogina

et al. 2022

Preprint

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A fish’s body condition is described by its weight given its length and is often positively associated with fitness. Atlantic cod (Gadus morhua) in the south-eastern Baltic Sea has experienced a drastic deterioration of its physiological status since the early 1990s to levels that compromise the growth or even survival of the population. Several hypotheses have been proposed (e.g., competition, hypoxia, lack of prey) and evaluated temporally using averages over large spatial scales (basin or population level). However, as these variables operate at local spatial scales and are heterogenous in space, it is important to evaluate the link between body condition and covariates on local scales as well. By applying a geostatistical model that includes spatially and spatiotemporally correlated random effects using Gaussian Markov random fields, we analyze the body condition of cod in the autumn (main feeding season) in relation to biotic and abiotic covariates at different spatial scales and their spatiotemporal dynamics. We find that body condition declined over the whole domain until 2008, after which a plateau was reached. Oxygen, sprat biomass (at the subdivision level), haul-level temperature, and the biomass of the benthic isopod Saduria entomon (to a lesser extent) where positively related to condition, whereas the haul-level density of cod and depth were negatively associated with condition. However, the effect sizes of these variables were small, such that even though cod are now found in deeper and less-oxygenated waters, this could not alone explain the steep decline that occurred between 1993–2008. In fact, latent spatial and spatiotemporal variation was several times larger in magnitude than any single covariate’s coefficient, and spatial and spatiotemporal random effects explained 6.5 times more variation than all fixed effects. However, body condition is a complicated trait to analyze as it builds up over a long time period. Hence, observational analysis of condition data should be complemented with e.g., tagging studies or otoliths analyses, before mechanistic links between condition and covariates can be determined. Understanding the drivers of spatiotemporal variation in body condition is critical for identifying impacts of environmental change and for the management of marine fish and fisheries.

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“…Primary production, forced by photosynthetically active radiation (PAR), is provided by three functional phytoplankton groups (large cells, small cells, and cyanobacteria). The chlorophyll concentration used in the optical model is estimated from the phytoplankton groups (Neumann et al, 2021). Dead particles accumulate in the detritus state variable.…”

Section: Biogeochemical Modelmentioning

confidence: 99%

“…For model testing, we use a coupled system of circulation and biogeochemical model similar to that in Neumann et al (2021). The circulation model is MOM5.1 (Griffies, 2004) adapted for the Baltic Sea.…”

Section: Model Setup and Simulationsmentioning

confidence: 99%

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Non-Redfieldian carbon model for the Baltic Sea (ERGOM version 1.2) – implementation and budget estimates

et al. 2022

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Abstract. Marine biogeochemical models based on Redfield stoichiometry suffer from underestimating carbon fixation by primary production. The most pronounced indication of this is the overestimation of the dissolved inorganic carbon (DIC) concentration and, consequently, the partial pressure of carbon dioxide in surface waters. The reduced production of organic carbon will impact most biogeochemical processes. We propose a marine biogeochemical model allowing for a non-Redfieldian carbon fixation. The updated model is able to reproduce observed partial pressure of carbon dioxide and other variables of the ecosystem, like nutrients and oxygen, reasonably well. The additional carbon uptake is realized in the model by an extracellular release (ER) of dissolved organic matter (DOM) from phytoplankton. Dissolved organic matter is subject to flocculation and the sinking particles remove carbon from surface waters. This approach is mechanistically different from existing non-Redfieldian models which allow for flexible elemental ratios for the living cells of the phytoplankton itself. The performance of the model is demonstrated as an example for the Baltic Sea. We have chosen this approach because of a reduced computational effort which is beneficial for large-scale and long-term model simulations. Budget estimates for carbon illustrate that the Baltic Sea acts as a carbon sink. For alkalinity, the Baltic Sea is a source due to internal alkalinity generation by denitrification. Owing to the underestimated model alkalinity, an unknown alkalinity source or underestimated land-based fluxes still exist.

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Optical model for the Baltic Sea with an explicit CDOM state variable: a case study with Model ERGOM (version 1.2)

Cited by 18 publications

References 29 publications

Benthic Macrofauna Community Bioirrigation Potential (BIPc): Regional Map and Utility Validation for the South-Western Baltic Sea

Benthic Macrofauna Community Bioirrigation Potential (BIPc): Regional Map and Utility Validation for the South-Western Baltic Sea

Evaluating drivers of spatiotemporal individual condition of a bottom-associated marine fish

Non-Redfieldian carbon model for the Baltic Sea (ERGOM version 1.2) – implementation and budget estimates

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