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18 results for “Marine ecosystem models”
Surrogate-based optimization using an artificial neural network for a parameter identification in a 3D marine ecosystem model
<p><strong>Abstract:</strong></p> <p>Parameter identification for marine ecosystem models is important for the assessment and validation of marine ecosystem models against observational data. The surrogate-based optimization (SBO) is a computationally efficient method to optimize complex models. SBO replaces the computationally expensive (high-fidelity) model by a surrogate constructed from a less accurate but computationally cheaper (low-fidelity) model in combination with an appropriate correction approach, which improves the accuracy of the low-fidelity model. To construct a computationally cheap low-fidelity model, we tested three different approaches to compute an approximation of the annually periodic solution (i.e., a steady annual cycle) of a marine ecosystem model: firstly, a reduced number of spin-up iterations (several decades instead of millennia), secondly, an artificial neural network (ANN) approximating the steady annual cycle and, finally, a combination of the both approaches. Except for the low-fidelity model using only the ANN, the SBO yielded a solution close to the target and reduced the computational effort significantly. If an ANN approximating appropriately a marine ecosystem model is available, the SBO using this ANN as low-fidelity model presents a promising and computational efficient method for the validation.</p> <p> </p> <p><strong>Content:</strong></p> <ul> <li>SQLite database including the data of the different optimization runs</li> <li>Structure and weights of the used artificial neural network</li> <li>Tracer concentrations obtain from the high-fidelity model for the different optimization runs</li> </ul>
Automatic time step adjustment for shortening the runtime of the simulation of marine ecosystem models
<p><strong>Abstract:</strong></p> <p>In investigating the global carbon cycle, shortening the runtime of the simulation of marine ecosystem models is an important issue. More specifically, steady annual cycles mostly are used to assess and validate the models against<br> observational data and to identify relevant biogeochemical processes. Offline simulations based on the transport matrix method already reduce the high computational effort significantly. Furthermore, they facilitate the application<br> of larger time steps in a simple way. In this paper, we present two different methods that automatically adjust the time step during the simulation of a steady state using transport matrices. The algorithms use either an adaptive<br> step size control or decreasing time steps. Their aim is to apply always the time step as large as possible but without any manual selection. We applied the methods for a variety of ecosystem models of different complexity, using Latin<br> hypercube samples of size 100 for the model parameters of each model. We showed that both methods computed an approximation of the steady annual cycle that was of the same accuracy as solutions obtained with a fixed time step. Both algorithms lowered the runtime of the steady annual cycle computation significantly. The performance gain depended on the complexity of the models. Moreover, the adaptive method has a certain overhead that might lead to higher computational cost in special cases.</p> <p><strong>Content:</strong></p> <ul> <li>Tracer concentrations of a reference solution for all parameter vectors and biogeochemical models</li> <li>SQLite database including the results using the decreasing time steps algorithm</li> <li>Tracer concentrations of the results using the decreasing time steps algorithm</li> <li>SQLite database including the results using the step size control algorithm</li> <li>Tracer concentrations of the results using the step size control algorithm</li> </ul>
MOM6-COBALTv2 model result for ecosystem response to marine heat waves
<p>0.5 × 0.5 resolution, Northeast Pacific ocean (184.8-120.2W, 28.06-64.97N). Monthly results from 1958-2019, all detrended with linear trend over full period removed. </p>
Configuration files for model stations presented in the manuscript "Sensitivity of shelf sea marine ecosystems to temporal resolution meteorological forcing"
<p>This repository contains configuration files for running GOTM-FABM-ERSEM at stations L4 and CCS to produce results presented in the manuscript "Sensitivity of shelf sea marine ecosystems to meteorological forcing" in addition to meteorology files for running the sensitivity analysis presented in the manuscript. Ncfiles containing model results for all scenarios presented in the manuscript are also included within the zip files for both stations</p> <p><br> GOTM code is freely available from: <br> https://github.com/gotm-model/code</p> <p><br> FABM code is freely available from:<br> https://github.com/fabm-model/fabm.git</p> <p><br> ERSEM code is freely available from:</p> <p><a href="https://www.pml.ac.uk/Modelling_at_PML/Access_Code">https://www.pml.ac.uk/Modelling_at_PML/Access_Code</a><br> </p> <p>Instructions for compiling GOTM-FABM-ERSEM can be found in the ERSEM git repository after registering for the code using the link above. </p> <p>Versions/commits for the model code used to create results presented in this manuscript are:</p> <p>GOTM: commit 38e5d5b77adc7b3b5364aed7d7e4921b04b1781f </p> <p>FABM: commit 69da88c87ec59a51d1e2143c1f76111526ed6498 </p> <p>ERSEM: Version 19.04</p> <p> </p> <p> </p>
Terrain variables used for ensemble distribution modelling of vulnerable marine ecosystems indicator taxa on data-limited seamounts of Cabo Verde (NW Africa)
<p><em>Aim:</em> Seamounts are conspicuous geological features with an important ecological role and can be considered Vulnerable Marine Ecosystems (VMEs). Since many deep-sea regions remain largely unexplored, investigating the occurrence of VME taxa on seamounts is challenging. Our study aimed to predict the distribution of four cold-water coral (CWC) taxa, indicators for VMEs, in a region where occurrence data is scarce.</p> <p><em>Location: </em>Seamounts around the Cabo Verde Archipelago (NW Africa).</p> <p><em>Methods:</em> We used species presence-absence data obtained from Remotely Operated Vehicle (ROV) footage collected during two research expeditions. Terrain variables calculated using a multiscale approach from a 100 m resolution bathymetry grid, as well as physical oceanographical data from the VIKING20X model, at a native resolution of 1/20°, were used as environmental predictors. Two modelling techniques (Generalized Additive Model (GAM) and Random Forest (RF)) were employed and single-model predictions were combined into a final weighted-average ensemble model. Model performance was validated using different metrics through cross-validation.</p> <p><em>Results</em>: Terrain orientation, at broad-scale, presented one of the highest relative variable contributions to the distribution models of all CWC taxa, suggesting that hydrodynamic-topographic interactions on the seamounts could benefit CWCs by maximizing food supply. However, changes at finer scales in terrain morphology and bottom salinity were important for driving differences in the distribution of specific CWCs. The ensemble model predicted the presence of VME taxa on all seamounts and consistently achieved the highest performance metrics, outperforming individual models. Nonetheless, model extrapolation and uncertainty, measured as the coefficient of variation, were high, particularly, in least surveyed areas across seamounts, highlighting the need to collect more data in future surveys.</p> <p><em>Main conclusions:</em> Our study shows how data-poor areas may be assessed for the likelihood of VMEs and provides important information to guide future research in Cabo Verde, which is fundamental to advise ongoing conservation planning.</p>
Spread of the non-native anemone Anemonia alicemartinae Häussermann & Försterra, 2001 along the Humboldt-current large marine ecosystem: an ecological niche model approach
<p>Environmental variables and script</p>
Data and code for FishMIP global marine ecosystem model ensemble projections summarised by countries and territories and other selected marine spatial regions.
<p>R code to extract and create data tables and summary plots of FishMIP mean ensemble projections provided are for percentage change in "exploitable fish biomass", which is a proxy for the biomass available to fisheries, consisting of marine animals spanning the size range 10 g to 100 kg: this is typically dominated by fish, but is also inclusive of other animals such as crustaceans and cephalopods.</p> <p>This release contains scripts and summary data for producing figures in Part A of the following report:</p> <p>Blanchard, J.L., Novaglio, C., eds. (2024). Climate change risks to marine ecosystems and fisheries: Future projections from the Fisheries and Marine Ecosystems Model Intercomparison Project. FAO Fisheries and Aquaculture Technical Paper No. 707. Rome, FAO.</p> <p>Please refer to the above report to cite and for more information.</p> <p>The summary data are here:</p> <p>https://github.com/Fish-MIP/FAO_Report/blob/main/data/table_stats_formatted_admin_full.csv</p> <p>Where the column 'spatial_scale' refers to the type of aggregation:</p> <p>FAO_area = High Sea areas grouped by FAO Major Fishing Areas</p> <p>countries = Exclusive Economic Zones</p> <p>countries_admin = Exclusive Economic Zones results aggregated into Administrative Countries</p> <p>Please note that these results can also be visualised and downloaded from our shiny app: https://rstudio.global-ecosystem-model.cloud.edu.au/shiny/FAO_report_shiny/</p> <p> </p>
Terrain variables used for ensemble distribution modelling of vulnerable marine ecosystems indicator taxa on data-limited seamounts of Cabo Verde (NW Africa)
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Data from: Combining mesocosms with models to unravel the effects of global warming and ocean acidification on a temperate marine ecosystem
<p><span>Ocean warming and species exploitation have already caused large-scale reorganization of biological communities across the world. Accurate projections of future biodiversity change require a comprehensive understanding of how entire communities respond to global change. We combined a time-dynamic integrated food web modelling approach (Ecosim) with previous data from community-level mesocosm experiments to determine the independent and combined effects of ocean warming and acidification, and fisheries exploitation, on a well-managed temperate coastal ecosystem. The mesocosm parameters enabled important physiological and behavioural responses to climate stressors to be projected for trophic levels ranging from primary producers to top predators, including sharks. Through model simulations, we show that under sustainable rates of exploitation, near-future warming or ocean acidification in isolation could benefit species biomass at higher trophic levels (e.g., mammals, birds, and demersal finfish) in their current climate ranges, with the exception of small pelagic fish. However, under warming and acidification combined biomass-increases at higher trophic levels will be lower or absent, whilst in the longer term reduced productivity of prey species is unlikely to support the increased biomass at the top of the food web. We also show that increases in exploitation will suppress any positive effects of human-driven climate change, causing individual species biomass to decrease at higher trophic levels. Nevertheless, total future potential biomass of some fisheries species in temperate areas might remain high, particularly under acidification, because unharvested opportunistic species will likely benefit from decreased competition and show an increase in biomass. Ecological indicators of species composition such as the Shannon diversity index declined under all climate change scenarios, suggesting a trade-off between biomass gain and functional diversity. By coupling parameters from multi-level mesocosm food web experiments with dynamic food web models, we were able to simulate the generative mechanisms that drive complex responses of temperate marine ecosystems to global change. This approach, which blends theory with experimental data, provides new prospects for forecasting climate-driven biodiversity change and its effects on ecosystem processes.</span></p>
Data from: Combining mesocosms with models to unravel the effects of global warming and ocean acidification on a temperate marine ecosystem
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Approximation of a marine ecosystem model by artificial neural networks designed using a genetic algorithm
<p>Data from the Paper: Approximation of a marine ecosystem model by artificial neural networks designed using a genetic algorithm.</p> <p>Abstract: </p> <p>Marine ecosystem models are important to identify the processes that affects for example the global carbon cycle. Computation of an annually periodic solution (i.e., a steady annual cycle) for these models requires a high computational effort. To reduce this effort, we approximated an exemplary marine ecosystem model by different artificial neural networks. We used a fully connected network, then applied the sparse evolutionary training (SET) procedure, and finally applied a genetic algorithm (GA) to optimize both the network topology. With all three approaches, a direct approximation of the steady annual cycle was not sufficiently accurate. However, using the mass-corrected prediction of the ANN as initial concentration for additional model runs, the results were in very good agreement. In this way, we achieved a runtime reduction by about 15 \%. The result from the SET algorithm were comparable to those of the full network. Further application of the GA may lead to an even higher reduction.</p> <p>Content:</p> <p>Database sqlite <a href="https://zenodo.org/api/files/669d208b-7304-4d31-a6cc-3ad78e3544e9/ANN_Database.db">ANN_Database.db</a></p> <p>zip-files with data: </p> <p><a href="https://zenodo.org/api/files/669d208b-7304-4d31-a6cc-3ad78e3544e9/ANN-Data.zip">ANN-Data.zip</a> structure and weights of used networks</p> <p><a href="https://zenodo.org/api/files/669d208b-7304-4d31-a6cc-3ad78e3544e9/ANN-Results.zip">ANN-Results.zip</a> results obtained with networks</p> <p><a href="https://zenodo.org/api/files/669d208b-7304-4d31-a6cc-3ad78e3544e9/Reference-Results.zip">Reference-Results.zip</a> reference results and training data</p> <p> </p> <p> </p> <p> </p> <p> </p>
Shortening the runtime using larger time steps for the simulation of marine ecosystem models
<p><strong>Abstract:</strong></p> <p>The reduction of computational costs for marine ecosystem models is important for the investigation and detection of the relevant biogeochemical processes because such models are computationally expensive. In order to lower these computational costs by means of larger time steps we investigated the accuracy of steady annual cycles (i.e., an annual periodic solution) calculated with different time steps. We compared the accuracy for a hierarchy of biogeochemical models showing an increasing complexity and computed the steady annual cycles with offline simulations that are based on the transport matrix approach. For each of these biogeochemical models, we obtained practically the same solution even though larger time steps. This indicates that larger time steps shortened the runtime with an acceptable loss of accuracy.</p> <p><strong>Content:</strong></p> <ul> <li>SQLite database including the obtained results</li> <li>Tracer concentrations obtained with different time steps for all used models and parameter vectors</li> </ul>
Datasets for "A skill assessment framework for the Fisheries and Marine Ecosystem Model Intercomparison Project"
<p>CMIP6-forced EcoOcean data for "A skill assessment framework for the Fisheries and Marine Ecosystem Model Intercomparison Project"</p> <p> </p>
Data and Analysis Scripts for the Application of the Sprat Marine Ecosystem Model to the Eastern Scotian Shelf
<p>Data and Analysis Scripts for the Application of the Sprat Marine Ecosystem Model to the Eastern Scotian Shelf</p>
Data and Analysis Scripts for the Application of the Sprat Marine Ecosystem Model to the Eastern Scotian Shelf
<p>Data and Analysis Scripts for the Application of the Sprat Marine Ecosystem Model to the Eastern Scotian Shelf</p>
Unique steady annual cycle in marine ecosystem model simulations
<p><strong>Abstract:</strong></p> <p>Marine ecosystem models are important to assess the ocean biota's role in climate change and to identify relevant biogeochemical processes by validating the model outputs against observational data. For the assessment of the marine ecosystem models, the existence and uniqueness of an annually periodic solution (i.e., a steady annual cycle) is desirable. To analyze the uniqueness of a steady annual cycle, we performed a larger number of simulations starting from different initial concentrations for a hierarchy of biogeochemical models with an increasing complexity. The numerical results suggested that the simulations finished always with the same steady annual cycle regardless of the initial concentration. Due to numerical instabilities, inadmissible approximations of the steady annual cycle, however, occurred in some cases for the three most complex biogeochemical models. Our numerical results indicate an unique steady annual cycle for practical applications.</p> <p><strong>Content:</strong></p> <ul> <li>Files with the tracer concentrations of the reference solution</li> <li>Files including the different initial tracer concentrations which are not constant</li> <li>SQLite database including the results of the different simulations starting from various initial concentrations</li> <li>Files with the tracer concentrations of the different simulations starting from various initial concentrations</li> </ul>
Data from: How to predict biodiversity in space? An evaluation of modelling approaches in marine ecosystems
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model output used for Paper "Simulating ecosystem dynamics and marine biogeochemical cycles with multiple plankton functional types"
<p>This dataset contains the model output from CESM2.2-8p4z, used in Yu et al., 2024 in Journal of Advances in Modeling Earth Systems (JAMES). </p>
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