Articles | Volume 14, issue 4
https://doi.org/10.5194/gmd-14-1949-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-1949-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
13 Apr 2021
Model description paper |
| 13 Apr 2021
| 13 Apr 2021
SPEAD 1.0 – Simulating Plankton Evolution with Adaptive Dynamics in a two-trait continuous fitness landscape applied to the Sargasso Sea
Department of Marine Biology and Oceanography, Institute of Marine Sciences (ICM – CSIC), Barcelona, Spain
Sergio M. Vallina
Spanish Institute of Oceanography (IEO), Ave Principe de Asturias 70 bis, 33212 Gijón, Spain
S. Lan Smith
Earth SURFACE Research Center, Research Institute for Global Change, JAMSTEC, Yokosuka, Japan
Pedro Cermeño
Department of Marine Biology and Oceanography, Institute of Marine Sciences (ICM – CSIC), Barcelona, Spain
Related authors
Guillaume Le Gland, Laurent Mémery, Olivier Aumont, and Laure Resplandy
Biogeosciences, 14, 3171–3189, https://doi.org/10.5194/bg-14-3171-2017, https://doi.org/10.5194/bg-14-3171-2017, 2017
Short summary
Short summary
In this study, we computed the fluxes of radium-228 from the continental shelf to the open ocean by fitting a numerical model to observations. After determining appropriate model parameters (cost function and number of source regions), we found a lower and more precise global flux than previous estimates: 8.01–8.49×1023 atoms yr−1. This result can be used to assess nutrient and trace element fluxes to the open ocean, but we cannot identify specific pathways like submarine groundwater discharge.
Onur Kerimoglu, Markus Pahlow, Prima Anugerahanti, and Sherwood Lan Smith
Geosci. Model Dev., 16, 95–108, https://doi.org/10.5194/gmd-16-95-2023, https://doi.org/10.5194/gmd-16-95-2023, 2023
Short summary
Short summary
In classical models that track the changes in the elemental composition of phytoplankton, additional state variables are required for each element resolved. In this study, we show how the behavior of such an explicit model can be approximated using an
instantaneous acclimationapproach, in which the elemental composition of the phytoplankton is assumed to adjust to an optimal value instantaneously. Through rigorous tests, we evaluate the consistency of this scheme.
Yoshikazu Sasai, Sherwood Lan Smith, Eko Siswanto, Hideharu Sasaki, and Masami Nonaka
Biogeosciences, 19, 4865–4882, https://doi.org/10.5194/bg-19-4865-2022, https://doi.org/10.5194/bg-19-4865-2022, 2022
Short summary
Short summary
We have investigated the adaptive response of phytoplankton growth to changing light, nutrients, and temperature over the North Pacific using two physical-biological models. We compare modeled chlorophyll and primary production from an inflexible control model (InFlexPFT), which assumes fixed carbon (C):nitrogen (N):chlorophyll (Chl) ratios, to a recently developed flexible phytoplankton functional type model (FlexPFT), which incorporates photoacclimation and variable C:N:Chl ratios.
Onur Kerimoglu, Prima Anugerahanti, and Sherwood Lan Smith
Geosci. Model Dev., 14, 6025–6047, https://doi.org/10.5194/gmd-14-6025-2021, https://doi.org/10.5194/gmd-14-6025-2021, 2021
Short summary
Short summary
In large-scale models, variations in cellular composition of phytoplankton are often insufficiently represented. Detailed modeling approaches exist, but they require additional state variables that increase the computational costs. In this study, we test an instantaneous acclimation model in a spatially explicit setup and show that its behavior is mostly similar to that of a variant with an additional state variable but different from that of a fixed composition variant.
Stephanie Dutkiewicz, Pedro Cermeno, Oliver Jahn, Michael J. Follows, Anna E. Hickman, Darcy A. A. Taniguchi, and Ben A. Ward
Biogeosciences, 17, 609–634, https://doi.org/10.5194/bg-17-609-2020, https://doi.org/10.5194/bg-17-609-2020, 2020
Short summary
Short summary
Phytoplankton are an essential component of the marine food web and earth's carbon cycle. We use observations, ecological theory and a unique trait-based ecosystem model to explain controls on patterns of marine phytoplankton biodiversity. We find that different dimensions of diversity (size classes, biogeochemical functional groups, thermal norms) are controlled by a disparate combination of mechanisms. This may explain why previous studies of phytoplankton diversity had conflicting results.
Jose Luis Otero-Ferrer, Pedro Cermeño, Antonio Bode, Bieito Fernández-Castro, Josep M. Gasol, Xosé Anxelu G. Morán, Emilio Marañon, Victor Moreira-Coello, Marta M. Varela, Marina Villamaña, and Beatriz Mouriño-Carballido
Biogeosciences, 15, 6199–6220, https://doi.org/10.5194/bg-15-6199-2018, https://doi.org/10.5194/bg-15-6199-2018, 2018
Short summary
Short summary
The effect of inorganic nutrients on planktonic assemblages has been traditionally assessed by looking at concentrations rather than fluxes of nutrient supply. However, in near-steady-state systems such as subtropical gyres, nitrate concentrations are kept close to the detection limit due to phytoplankton uptake. Our results, based on direct measurements of nitrate diffusive fluxes, support the key role of nitrate supply in controlling the structure of marine picoplankton communities.
Chris J. Daniels, Alex J. Poulton, William M. Balch, Emilio Marañón, Tim Adey, Bruce C. Bowler, Pedro Cermeño, Anastasia Charalampopoulou, David W. Crawford, Dave Drapeau, Yuanyuan Feng, Ana Fernández, Emilio Fernández, Glaucia M. Fragoso, Natalia González, Lisa M. Graziano, Rachel Heslop, Patrick M. Holligan, Jason Hopkins, María Huete-Ortega, David A. Hutchins, Phoebe J. Lam, Michael S. Lipsen, Daffne C. López-Sandoval, Socratis Loucaides, Adrian Marchetti, Kyle M. J. Mayers, Andrew P. Rees, Cristina Sobrino, Eithne Tynan, and Toby Tyrrell
Earth Syst. Sci. Data, 10, 1859–1876, https://doi.org/10.5194/essd-10-1859-2018, https://doi.org/10.5194/essd-10-1859-2018, 2018
Short summary
Short summary
Calcifying marine algae (coccolithophores) are key to oceanic biogeochemical processes, such as calcium carbonate production and export. We compile a global database of calcium carbonate production from field samples (n = 2756), alongside primary production rates and coccolithophore abundance. Basic statistical analysis highlights global distribution, average surface and integrated rates, patterns with depth and the importance of considering cell-normalised rates as a simple physiological index.
Bingzhang Chen and Sherwood Lan Smith
Geosci. Model Dev., 11, 467–495, https://doi.org/10.5194/gmd-11-467-2018, https://doi.org/10.5194/gmd-11-467-2018, 2018
Short summary
Short summary
Marine phytoplankton accounts for half of global primary production. Phytoplankton size is an important trait affecting its fitness and ecosystem functioning. We have developed a plankton model with continuous size distribution for phytoplankton and applied it in the North Pacific. This model is able to capture the general patterns of phytoplankton size distribution in the real ocean and can be used for understanding the mechanisms controlling phytoplankton size structure and diversity.
Guillaume Le Gland, Laurent Mémery, Olivier Aumont, and Laure Resplandy
Biogeosciences, 14, 3171–3189, https://doi.org/10.5194/bg-14-3171-2017, https://doi.org/10.5194/bg-14-3171-2017, 2017
Short summary
Short summary
In this study, we computed the fluxes of radium-228 from the continental shelf to the open ocean by fitting a numerical model to observations. After determining appropriate model parameters (cost function and number of source regions), we found a lower and more precise global flux than previous estimates: 8.01–8.49×1023 atoms yr−1. This result can be used to assess nutrient and trace element fluxes to the open ocean, but we cannot identify specific pathways like submarine groundwater discharge.
Virginia García-Bernal, Óscar Paz, and Pedro Cermeño
Biogeosciences Discuss., https://doi.org/10.5194/bg-2017-4, https://doi.org/10.5194/bg-2017-4, 2017
Manuscript not accepted for further review
Short summary
Short summary
Marine diatoms are responsible for roughly 40 % of modern ocean primary production and contribute disproportionately to the drawdown of atmospheric carbon dioxide through the export of organic carbon into the deep sea and sediments. Over the past 40 Myr their rise to ecological prominence and consequential decline of coccolithophores is linked to the silicon to phosphorus weathering ratio, which controls the oceanic nutrient inventories and hence the competitive ability of diatoms.
Esteban Acevedo-Trejos, Gunnar Brandt, S. Lan Smith, and Agostino Merico
Geosci. Model Dev., 9, 4071–4085, https://doi.org/10.5194/gmd-9-4071-2016, https://doi.org/10.5194/gmd-9-4071-2016, 2016
Short summary
Short summary
Marine phytoplankton plays a prominent role in regulating Earth’s climate. Numerical models are important tools that help us investigate the interactions between these microbes and their environment. We proposed PhytoSFDM as an open-source model to quantify size structure and functional diversity of marine phytoplankton communities. This tool allows us, in a manageable and computationally efficient way, to study patterns in planktonic ecosystems and their feedbacks with a changing environment.
Fatima Abrantes, Pedro Cermeno, Cristina Lopes, Oscar Romero, Lélia Matos, Jolanda Van Iperen, Marta Rufino, and Vitor Magalhães
Biogeosciences, 13, 4099–4109, https://doi.org/10.5194/bg-13-4099-2016, https://doi.org/10.5194/bg-13-4099-2016, 2016
Short summary
Short summary
Diatoms are the dominant primary producers of the most productive and best fishing areas of the modern ocean, the coastal upwelling systems. This turns them into important contributors to the biological pump and climate change. To help untangle their response to warming climate, we compare the worldwide diatom sedimentary abundance (SDA) to environmental variables and find that the capacity of diatoms to take up silicic acid sets an upper limit on global export production in these ocean regions.
Related subject area
Biogeosciences
DeepPhenoMem V1.0: deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology
Impacts of land-use change on biospheric carbon: an oriented benchmark using the ORCHIDEE land surface model
Implementing the iCORAL (version 1.0) coral reef CaCO3 production module in the iLOVECLIM climate model
Assimilation of carbonyl sulfide (COS) fluxes within the adjoint-based data assimilation system – Nanjing University Carbon Assimilation System (NUCAS v1.0)
Quantifying the role of ozone-caused damage to vegetation in the Earth system: a new parameterization scheme for photosynthetic and stomatal responses
Radiocarbon analysis reveals underestimation of soil organic carbon persistence in new-generation soil models
Exploring the potential of history matching for land surface model calibration
EAT v1.0.0: a 1D test bed for physical–biogeochemical data assimilation in natural waters
Using deep learning to integrate paleoclimate and global biogeochemistry over the Phanerozoic Eon
Modelling boreal forest's mineral soil and peat C dynamics with the Yasso07 model coupled with the Ricker moisture modifier
Dynamic ecosystem assembly and escaping the “fire trap” in the tropics: insights from FATES_15.0.0
In silico calculation of soil pH by SCEPTER v1.0
Learning from conceptual models – a study of emergence of cooperation towards resource protection in a social-ecological system
Simple process-led algorithms for simulating habitats (SPLASH v.2.0): robust calculations of water and energy fluxes
A global behavioural model of human fire use and management: WHAM! v1.0
Terrestrial Ecosystem Model in R (TEMIR) version 1.0: simulating ecophysiological responses of vegetation to atmospheric chemical and meteorological changes
BOATSv2: New ecological and economic features improve simulations of High Seas catch and effort
biospheremetrics v1.0.2: an R package to calculate two complementary terrestrial biosphere integrity indicators – human colonization of the biosphere (BioCol) and risk of ecosystem destabilization (EcoRisk)
Modeling boreal forest soil dynamics with the microbially explicit soil model MIMICS+ (v1.0)
Biogeochemical model Biome-BGCMuSo v6.2 provides plausible and accurate simulations of carbon cycle in Central European beech forests
Optimal enzyme allocation leads to the constrained enzyme hypothesis: the Soil Enzyme Steady Allocation Model (SESAM; v3.1)
Implementing a dynamic representation of fire and harvest including subgrid-scale heterogeneity in the tile-based land surface model CLASSIC v1.45
Inferring the tree regeneration niche from inventory data using a dynamic forest model
A dynamical process-based model AMmonia–CLIMate v1.0 (AMCLIM v1.0) for quantifying global agricultural ammonia emissions – Part 1: Land module for simulating emissions from synthetic fertilizer use
Optimising CH4 simulations from the LPJ-GUESS model v4.1 using an adaptive Markov chain Monte Carlo algorithm
Biological nitrogen fixation of natural and agricultural vegetation simulated with LPJmL 5.7.9
The XSO framework (v0.1) and Phydra library (v0.1) for a flexible, reproducible, and integrated plankton community modeling environment in Python
AgriCarbon-EO v1.0.1: large-scale and high-resolution simulation of carbon fluxes by assimilation of Sentinel-2 and Landsat-8 reflectances using a Bayesian approach
SAMM version 1.0: a numerical model for microbial- mediated soil aggregate formation
A model of the within-population variability of budburst in forest trees
Computationally efficient parameter estimation for high-dimensional ocean biogeochemical models
The community-centered freshwater biogeochemistry model unified RIVE v1.0: a unified version for water column
Observation-based sowing dates and cultivars significantly affect yield and irrigation for some crops in the Community Land Model (CLM5)
The statistical emulators of GGCMI phase 2: responses of year-to-year variation of crop yield to CO2, temperature, water, and nitrogen perturbations
A novel Eulerian model based on central moments to simulate age and reactivity continua interacting with mixing processes
AdaScape 1.0: a coupled modelling tool to investigate the links between tectonics, climate, and biodiversity
An along-track Biogeochemical Argo modelling framework: a case study of model improvements for the Nordic seas
Peatland-VU-NUCOM (PVN 1.0): using dynamic plant functional types to model peatland vegetation, CH4, and CO2 emissions
Quantification of hydraulic trait control on plant hydrodynamics and risk of hydraulic failure within a demographic structured vegetation model in a tropical forest (FATES–HYDRO V1.0)
SedTrace 1.0: a Julia-based framework for generating and running reactive-transport models of marine sediment diagenesis specializing in trace elements and isotopes
A high-resolution marine mercury model MITgcm-ECCO2-Hg with online biogeochemistry
Improving nitrogen cycling in a land surface model (CLM5) to quantify soil N2O, NO, and NH3 emissions from enhanced rock weathering with croplands
Ocean biogeochemistry in the coupled ocean–sea ice–biogeochemistry model FESOM2.1–REcoM3
Forcing the Global Fire Emissions Database burned-area dataset into the Community Land Model version 5.0: impacts on carbon and water fluxes at high latitudes
Modeling of non-structural carbohydrate dynamics by the spatially explicit individual-based dynamic global vegetation model SEIB-DGVM (SEIB-DGVM-NSC version 1.0)
Simulating Bark Beetle Outbreak Dynamics and their Influence on Carbon Balance Estimates with ORCHIDEE r7791
MEDFATE 2.9.3: a trait-enabled model to simulate Mediterranean forest function and dynamics at regional scales
Modelling the role of livestock grazing in C and N cycling in grasslands with LPJmL5.0-grazing
Implementation of trait-based ozone plant sensitivity in the Yale Interactive terrestrial Biosphere model v1.0 to assess global vegetation damage
The Permafrost and Organic LayEr module for Forest Models (POLE-FM) 1.0
Guohua Liu, Mirco Migliavacca, Christian Reimers, Basil Kraft, Markus Reichstein, Andrew D. Richardson, Lisa Wingate, Nicolas Delpierre, Hui Yang, and Alexander J. Winkler
Geosci. Model Dev., 17, 6683–6701, https://doi.org/10.5194/gmd-17-6683-2024, https://doi.org/10.5194/gmd-17-6683-2024, 2024
Short summary
Short summary
Our study employs long short-term memory (LSTM) networks to model canopy greenness and phenology, integrating meteorological memory effects. The LSTM model outperforms traditional methods, enhancing accuracy in predicting greenness dynamics and phenological transitions across plant functional types. Highlighting the importance of multi-variate meteorological memory effects, our research pioneers unlock the secrets of vegetation phenology responses to climate change with deep learning techniques.
Thi Lan Anh Dinh, Daniel Goll, Philippe Ciais, and Ronny Lauerwald
Geosci. Model Dev., 17, 6725–6744, https://doi.org/10.5194/gmd-17-6725-2024, https://doi.org/10.5194/gmd-17-6725-2024, 2024
Short summary
Short summary
The study assesses the performance of the dynamic global vegetation model (DGVM) ORCHIDEE in capturing the impact of land-use change on carbon stocks across Europe. Comparisons with observations reveal that the model accurately represents carbon fluxes and stocks. Despite the underestimations in certain land-use conversions, the model describes general trends in soil carbon response to land-use change, aligning with the site observations.
Nathaelle Bouttes, Lester Kwiatkowski, Manon Berger, Victor Brovkin, and Guy Munhoven
Geosci. Model Dev., 17, 6513–6528, https://doi.org/10.5194/gmd-17-6513-2024, https://doi.org/10.5194/gmd-17-6513-2024, 2024
Short summary
Short summary
Coral reefs are crucial for biodiversity, but they also play a role in the carbon cycle on long time scales of a few thousand years. To better simulate the future and past evolution of coral reefs and their effect on the global carbon cycle, hence on atmospheric CO2 concentration, it is necessary to include coral reefs within a climate model. Here we describe the inclusion of coral reef carbonate production in a carbon–climate model and its validation in comparison to existing modern data.
Huajie Zhu, Mousong Wu, Fei Jiang, Michael Vossbeck, Thomas Kaminski, Xiuli Xing, Jun Wang, Weimin Ju, and Jing M. Chen
Geosci. Model Dev., 17, 6337–6363, https://doi.org/10.5194/gmd-17-6337-2024, https://doi.org/10.5194/gmd-17-6337-2024, 2024
Short summary
Short summary
In this work, we developed the Nanjing University Carbon Assimilation System (NUCAS v1.0). Data assimilation experiments were conducted to demonstrate the robustness and investigate the feasibility and applicability of NUCAS. The assimilation of ecosystem carbonyl sulfide (COS) fluxes improved the model performance in gross primary productivity, evapotranspiration, and sensible heat, showing that COS provides constraints on parameters relevant to carbon-, water-, and energy-related processes.
Fang Li, Zhimin Zhou, Samuel Levis, Stephen Sitch, Felicity Hayes, Zhaozhong Feng, Peter B. Reich, Zhiyi Zhao, and Yanqing Zhou
Geosci. Model Dev., 17, 6173–6193, https://doi.org/10.5194/gmd-17-6173-2024, https://doi.org/10.5194/gmd-17-6173-2024, 2024
Short summary
Short summary
A new scheme is developed to model the surface ozone damage to vegetation in regional and global process-based models. Based on 4210 data points from ozone experiments, it accurately reproduces statistically significant linear or nonlinear photosynthetic and stomatal responses to ozone in observations for all vegetation types. It also enables models to implicitly capture the variability in plant ozone tolerance and the shift among species within a vegetation type.
Alexander S. Brunmayr, Frank Hagedorn, Margaux Moreno Duborgel, Luisa I. Minich, and Heather D. Graven
Geosci. Model Dev., 17, 5961–5985, https://doi.org/10.5194/gmd-17-5961-2024, https://doi.org/10.5194/gmd-17-5961-2024, 2024
Short summary
Short summary
A new generation of soil models promises to more accurately predict the carbon cycle in soils under climate change. However, measurements of 14C (the radioactive carbon isotope) in soils reveal that the new soil models face similar problems to the traditional models: they underestimate the residence time of carbon in soils and may therefore overestimate the net uptake of CO2 by the land ecosystem. Proposed solutions include restructuring the models and calibrating model parameters with 14C data.
Nina Raoult, Simon Beylat, James M. Salter, Frédéric Hourdin, Vladislav Bastrikov, Catherine Ottlé, and Philippe Peylin
Geosci. Model Dev., 17, 5779–5801, https://doi.org/10.5194/gmd-17-5779-2024, https://doi.org/10.5194/gmd-17-5779-2024, 2024
Short summary
Short summary
We use computer models to predict how the land surface will respond to climate change. However, these complex models do not always simulate what we observe in real life, limiting their effectiveness. To improve their accuracy, we use sophisticated statistical and computational techniques. We test a technique called history matching against more common approaches. This method adapts well to these models, helping us better understand how they work and therefore how to make them more realistic.
Jorn Bruggeman, Karsten Bolding, Lars Nerger, Anna Teruzzi, Simone Spada, Jozef Skákala, and Stefano Ciavatta
Geosci. Model Dev., 17, 5619–5639, https://doi.org/10.5194/gmd-17-5619-2024, https://doi.org/10.5194/gmd-17-5619-2024, 2024
Short summary
Short summary
To understand and predict the ocean’s capacity for carbon sequestration, its ability to supply food, and its response to climate change, we need the best possible estimate of its physical and biogeochemical properties. This is obtained through data assimilation which blends numerical models and observations. We present the Ensemble and Assimilation Tool (EAT), a flexible and efficient test bed that allows any scientist to explore and further develop the state of the art in data assimilation.
Dongyu Zheng, Andrew S. Merdith, Yves Goddéris, Yannick Donnadieu, Khushboo Gurung, and Benjamin J. W. Mills
Geosci. Model Dev., 17, 5413–5429, https://doi.org/10.5194/gmd-17-5413-2024, https://doi.org/10.5194/gmd-17-5413-2024, 2024
Short summary
Short summary
This study uses a deep learning method to upscale the time resolution of paleoclimate simulations to 1 million years. This improved resolution allows a climate-biogeochemical model to more accurately predict climate shifts. The method may be critical in developing new fully continuous methods that are able to be applied over a moving continental surface in deep time with high resolution at reasonable computational expense.
Boris Ťupek, Aleksi Lehtonen, Alla Yurova, Rose Abramoff, Bertrand Guenet, Elisa Bruni, Samuli Launiainen, Mikko Peltoniemi, Shoji Hashimoto, Xianglin Tian, Juha Heikkinen, Kari Minkkinen, and Raisa Mäkipää
Geosci. Model Dev., 17, 5349–5367, https://doi.org/10.5194/gmd-17-5349-2024, https://doi.org/10.5194/gmd-17-5349-2024, 2024
Short summary
Short summary
Updating the Yasso07 soil C model's dependency on decomposition with a hump-shaped Ricker moisture function improved modelled soil organic C (SOC) stocks in a catena of mineral and organic soils in boreal forest. The Ricker function, set to peak at a rate of 1 and calibrated against SOC and CO2 data using a Bayesian approach, showed a maximum in well-drained soils. Using SOC and CO2 data together with the moisture only from the topsoil humus was crucial for accurate model estimates.
Jacquelyn K. Shuman, Rosie A. Fisher, Charles Koven, Ryan Knox, Lara Kueppers, and Chonggang Xu
Geosci. Model Dev., 17, 4643–4671, https://doi.org/10.5194/gmd-17-4643-2024, https://doi.org/10.5194/gmd-17-4643-2024, 2024
Short summary
Short summary
We adapt a fire behavior and effects module for use in a size-structured vegetation demographic model to test how climate, fire regime, and fire-tolerance plant traits interact to determine the distribution of tropical forests and grasslands. Our model captures the connection between fire disturbance and plant fire-tolerance strategies in determining plant distribution and provides a useful tool for understanding the vulnerability of these areas under changing conditions across the tropics.
Yoshiki Kanzaki, Isabella Chiaravalloti, Shuang Zhang, Noah J. Planavsky, and Christopher T. Reinhard
Geosci. Model Dev., 17, 4515–4532, https://doi.org/10.5194/gmd-17-4515-2024, https://doi.org/10.5194/gmd-17-4515-2024, 2024
Short summary
Short summary
Soil pH is one of the most commonly measured agronomical and biogeochemical indices, mostly reflecting exchangeable acidity. Explicit simulation of both porewater and bulk soil pH is thus crucial to the accurate evaluation of alkalinity required to counteract soil acidification and the resulting capture of anthropogenic carbon dioxide through the enhanced weathering technique. This has been enabled by the updated reactive–transport SCEPTER code and newly developed framework to simulate soil pH.
Saeed Harati-Asl, Liliana Perez, and Roberto Molowny-Horas
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-57, https://doi.org/10.5194/gmd-2024-57, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Social-ecological systems are the subject of many sustainability problems. Because of the complexity of these systems we must be careful when intervening in them, otherwise we may cause irreversible damage. Using computer models, we can gain insight about these complex systems without harming them. In this paper we describe how we connected an ecological model of forest insect infestation with a social model of cooperation, and simulated an intervention measure to save a forest from infestation.
David Sandoval, Iain Colin Prentice, and Rodolfo L. B. Nóbrega
Geosci. Model Dev., 17, 4229–4309, https://doi.org/10.5194/gmd-17-4229-2024, https://doi.org/10.5194/gmd-17-4229-2024, 2024
Short summary
Short summary
Numerous estimates of water and energy balances depend on empirical equations requiring site-specific calibration, posing risks of "the right answers for the wrong reasons". We introduce novel first-principles formulations to calculate key quantities without requiring local calibration, matching predictions from complex land surface models.
Oliver Perkins, Matthew Kasoar, Apostolos Voulgarakis, Cathy Smith, Jay Mistry, and James D. A. Millington
Geosci. Model Dev., 17, 3993–4016, https://doi.org/10.5194/gmd-17-3993-2024, https://doi.org/10.5194/gmd-17-3993-2024, 2024
Short summary
Short summary
Wildfire is often presented in the media as a danger to human life. Yet globally, millions of people’s livelihoods depend on using fire as a tool. So, patterns of fire emerge from interactions between humans, land use, and climate. This complexity means scientists cannot yet reliably say how fire will be impacted by climate change. So, we developed a new model that represents globally how people use and manage fire. The model reveals the extent and diversity of how humans live with and use fire.
Amos P. K. Tai, David H. Y. Yung, and Timothy Lam
Geosci. Model Dev., 17, 3733–3764, https://doi.org/10.5194/gmd-17-3733-2024, https://doi.org/10.5194/gmd-17-3733-2024, 2024
Short summary
Short summary
We have developed the Terrestrial Ecosystem Model in R (TEMIR), which simulates plant carbon and pollutant uptake and predicts their response to varying atmospheric conditions. This model is designed to couple with an atmospheric chemistry model so that questions related to plant–atmosphere interactions, such as the effects of climate change, rising CO2, and ozone pollution on forest carbon uptake, can be addressed. The model has been well validated with both ground and satellite observations.
Jerome Guiet, Daniele Bianchi, Kim J. N. Scherrer, Ryan F. Heneghan, and Eric D. Galbraith
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-26, https://doi.org/10.5194/gmd-2024-26, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Numerical models that capture key features of the global dynamics of fish communities play a crucial role in addressing the impacts of climate change and industrial fishing on ecosystems and societies. Here, we detail an update of the BiOeconomic marine Trophic Size-spectrum model that corrects the model representation of the dynamic of fisheries in the High Seas. This update also allows a better representation of biodiversity to improve future global and regional fisheries studies.
Fabian Stenzel, Johanna Braun, Jannes Breier, Karlheinz Erb, Dieter Gerten, Jens Heinke, Sarah Matej, Sebastian Ostberg, Sibyll Schaphoff, and Wolfgang Lucht
Geosci. Model Dev., 17, 3235–3258, https://doi.org/10.5194/gmd-17-3235-2024, https://doi.org/10.5194/gmd-17-3235-2024, 2024
Short summary
Short summary
We provide an R package to compute two biosphere integrity metrics that can be applied to simulations of vegetation growth from the dynamic global vegetation model LPJmL. The pressure metric BioCol indicates that we humans modify and extract > 20 % of the potential preindustrial natural biomass production. The ecosystems state metric EcoRisk shows a high risk of ecosystem destabilization in many regions as a result of climate change and land, water, and fertilizer use.
Elin Ristorp Aas, Heleen A. de Wit, and Terje K. Berntsen
Geosci. Model Dev., 17, 2929–2959, https://doi.org/10.5194/gmd-17-2929-2024, https://doi.org/10.5194/gmd-17-2929-2024, 2024
Short summary
Short summary
By including microbial processes in soil models, we learn how the soil system interacts with its environment and responds to climate change. We present a soil process model, MIMICS+, which is able to reproduce carbon stocks found in boreal forest soils better than a conventional land model. With the model we also find that when adding nitrogen, the relationship between soil microbes changes notably. Coupling the model to a vegetation model will allow for further study of these mechanisms.
Katarína Merganičová, Ján Merganič, Laura Dobor, Roland Hollós, Zoltán Barcza, Dóra Hidy, Zuzana Sitková, Pavel Pavlenda, Hrvoje Marjanovic, Daniel Kurjak, Michal Bošeľa, Doroteja Bitunjac, Masa Zorana Ostrogovic Sever, Jiří Novák, Peter Fleischer, and Tomáš Hlásny
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-45, https://doi.org/10.5194/gmd-2024-45, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
We developed a multi-objective calibration approach leading to robust parameter values, aiming to strike a balance between their local precision and broad applicability. Using Biome-BGCMuSo model, we tested the calibrated parameter sets for simulating European beech forest dynamics across large environmental gradients. Leveraging data from 87 plots and five European countries, the results demonstrated reasonable local accuracy and plausible large-scale productivity responses.
Thomas Wutzler, Christian Reimers, Bernhard Ahrens, and Marion Schrumpf
Geosci. Model Dev., 17, 2705–2725, https://doi.org/10.5194/gmd-17-2705-2024, https://doi.org/10.5194/gmd-17-2705-2024, 2024
Short summary
Short summary
Soil microbes provide a strong link for elemental fluxes in the earth system. The SESAM model applies an optimality assumption to model those linkages and their adaptation. We found that a previous heuristic description was a special case of a newly developed more rigorous description. The finding of new behaviour at low microbial biomass led us to formulate the constrained enzyme hypothesis. We now can better describe how microbially mediated linkages of elemental fluxes adapt across decades.
Salvatore R. Curasi, Joe R. Melton, Elyn R. Humphreys, Txomin Hermosilla, and Michael A. Wulder
Geosci. Model Dev., 17, 2683–2704, https://doi.org/10.5194/gmd-17-2683-2024, https://doi.org/10.5194/gmd-17-2683-2024, 2024
Short summary
Short summary
Canadian forests are responding to fire, harvest, and climate change. Models need to quantify these processes and their carbon and energy cycling impacts. We develop a scheme that, based on satellite records, represents fire, harvest, and the sparsely vegetated areas that these processes generate. We evaluate model performance and demonstrate the impacts of disturbance on carbon and energy cycling. This work has implications for land surface modeling and assessing Canada’s terrestrial C cycle.
Yannek Käber, Florian Hartig, and Harald Bugmann
Geosci. Model Dev., 17, 2727–2753, https://doi.org/10.5194/gmd-17-2727-2024, https://doi.org/10.5194/gmd-17-2727-2024, 2024
Short summary
Short summary
Many forest models include detailed mechanisms of forest growth and mortality, but regeneration is often simplified. Testing and improving forest regeneration models is challenging. We address this issue by exploring how forest inventories from unmanaged European forests can be used to improve such models. We find that competition for light among trees is captured by the model, unknown model components can be informed by forest inventory data, and climatic effects are challenging to capture.
Jize Jiang, David S. Stevenson, and Mark A. Sutton
EGUsphere, https://doi.org/10.5194/egusphere-2024-962, https://doi.org/10.5194/egusphere-2024-962, 2024
Short summary
Short summary
A special model called AMmonia–CLIMate (AMCLIM) has been developed to understand and calculate NH3 emissions from fertilizer use, whilst taking into account how the environment influences these NH3 emissions. It is estimated that about 17 % of applied N in fertilizers were lost due to NH3 emissions. Hot and dry conditions and regions with high pH soils can expect higher NH3 emissions.
Jalisha T. Kallingal, Johan Lindström, Paul A. Miller, Janne Rinne, Maarit Raivonen, and Marko Scholze
Geosci. Model Dev., 17, 2299–2324, https://doi.org/10.5194/gmd-17-2299-2024, https://doi.org/10.5194/gmd-17-2299-2024, 2024
Short summary
Short summary
By unlocking the mysteries of CH4 emissions from wetlands, our work improved the accuracy of the LPJ-GUESS vegetation model using Bayesian statistics. Via assimilation of long-term real data from a wetland, we significantly enhanced CH4 emission predictions. This advancement helps us better understand wetland contributions to atmospheric CH4, which are crucial for addressing climate change. Our method offers a promising tool for refining global climate models and guiding conservation efforts
Stephen Björn Wirth, Johanna Braun, Jens Heinke, Sebastian Ostberg, Susanne Rolinski, Sibyll Schaphoff, Fabian Stenzel, Werner von Bloh, and Christoph Müller
EGUsphere, https://doi.org/10.5194/egusphere-2023-2946, https://doi.org/10.5194/egusphere-2023-2946, 2024
Short summary
Short summary
We present a new approach to model biological nitrogen fixation (BNF) in the Lund Potsdam Jena managed Land dynamic global vegetation model. While in the original approach BNF depended on actual evapotranspiration, the new approach considers soil water content and temperature, the nitrogen (N) deficit and carbon (C) costs. The new approach improved global sums and spatial patterns of BNF compared to the scientific literature and the models’ ability to project future C and N cycle dynamics.
Benjamin Post, Esteban Acevedo-Trejos, Andrew D. Barton, and Agostino Merico
Geosci. Model Dev., 17, 1175–1195, https://doi.org/10.5194/gmd-17-1175-2024, https://doi.org/10.5194/gmd-17-1175-2024, 2024
Short summary
Short summary
Creating computational models of how phytoplankton grows in the ocean is a technical challenge. We developed a new tool set (Xarray-simlab-ODE) for building such models using the programming language Python. We demonstrate the tool set in a library of plankton models (Phydra). Our goal was to allow scientists to develop models quickly, while also allowing the model structures to be changed easily. This allows us to test many different structures of our models to find the most appropriate one.
Taeken Wijmer, Ahmad Al Bitar, Ludovic Arnaud, Remy Fieuzal, and Eric Ceschia
Geosci. Model Dev., 17, 997–1021, https://doi.org/10.5194/gmd-17-997-2024, https://doi.org/10.5194/gmd-17-997-2024, 2024
Short summary
Short summary
Quantification of carbon fluxes of crops is an essential building block for the construction of a monitoring, reporting, and verification approach. We developed an end-to-end platform (AgriCarbon-EO) that assimilates, through a Bayesian approach, high-resolution (10 m) optical remote sensing data into radiative transfer and crop modelling at regional scale (100 x 100 km). Large-scale estimates of carbon flux are validated against in situ flux towers and yield maps and analysed at regional scale.
Moritz Laub, Sergey Blagodatsky, Marijn Van de Broek, Samuel Schlichenmaier, Benjapon Kunlanit, Johan Six, Patma Vityakon, and Georg Cadisch
Geosci. Model Dev., 17, 931–956, https://doi.org/10.5194/gmd-17-931-2024, https://doi.org/10.5194/gmd-17-931-2024, 2024
Short summary
Short summary
To manage soil organic matter (SOM) sustainably, we need a better understanding of the role that soil microbes play in aggregate protection. Here, we propose the SAMM model, which connects soil aggregate formation to microbial growth. We tested it against data from a tropical long-term experiment and show that SAMM effectively represents the microbial growth, SOM, and aggregate dynamics and that it can be used to explore the importance of aggregate formation in SOM stabilization.
Jianhong Lin, Daniel Berveiller, Christophe François, Heikki Hänninen, Alexandre Morfin, Gaëlle Vincent, Rui Zhang, Cyrille Rathgeber, and Nicolas Delpierre
Geosci. Model Dev., 17, 865–879, https://doi.org/10.5194/gmd-17-865-2024, https://doi.org/10.5194/gmd-17-865-2024, 2024
Short summary
Short summary
Currently, the high variability of budburst between individual trees is overlooked. The consequences of this neglect when projecting the dynamics and functioning of tree communities are unknown. Here we develop the first process-oriented model to describe the difference in budburst dates between individual trees in plant populations. Beyond budburst, the model framework provides a basis for studying the dynamics of phenological traits under climate change, from the individual to the community.
Skyler Kern, Mary E. McGuinn, Katherine M. Smith, Nadia Pinardi, Kyle E. Niemeyer, Nicole S. Lovenduski, and Peter E. Hamlington
Geosci. Model Dev., 17, 621–649, https://doi.org/10.5194/gmd-17-621-2024, https://doi.org/10.5194/gmd-17-621-2024, 2024
Short summary
Short summary
Computational models are used to simulate the behavior of marine ecosystems. The models often have unknown parameters that need to be calibrated to accurately represent observational data. Here, we propose a novel approach to simultaneously determine a large set of parameters for a one-dimensional model of a marine ecosystem in the surface ocean at two contrasting sites. By utilizing global and local optimization techniques, we estimate many parameters in a computationally efficient manner.
Shuaitao Wang, Vincent Thieu, Gilles Billen, Josette Garnier, Marie Silvestre, Audrey Marescaux, Xingcheng Yan, and Nicolas Flipo
Geosci. Model Dev., 17, 449–476, https://doi.org/10.5194/gmd-17-449-2024, https://doi.org/10.5194/gmd-17-449-2024, 2024
Short summary
Short summary
This paper presents unified RIVE v1.0, a unified version of the freshwater biogeochemistry model RIVE. It harmonizes different RIVE implementations, providing the referenced formalisms for microorganism activities to describe full biogeochemical cycles in the water column (e.g., carbon, nutrients, oxygen). Implemented as open-source projects in Python 3 (pyRIVE 1.0) and ANSI C (C-RIVE 0.32), unified RIVE v1.0 promotes and enhances collaboration among research teams and public services.
Sam S. Rabin, William J. Sacks, Danica L. Lombardozzi, Lili Xia, and Alan Robock
Geosci. Model Dev., 16, 7253–7273, https://doi.org/10.5194/gmd-16-7253-2023, https://doi.org/10.5194/gmd-16-7253-2023, 2023
Short summary
Short summary
Climate models can help us simulate how the agricultural system will be affected by and respond to environmental change, but to be trustworthy they must realistically reproduce historical patterns. When farmers plant their crops and what varieties they choose will be important aspects of future adaptation. Here, we improve the crop component of a global model to better simulate observed growing seasons and examine the impacts on simulated crop yields and irrigation demand.
Weihang Liu, Tao Ye, Christoph Müller, Jonas Jägermeyr, James A. Franke, Haynes Stephens, and Shuo Chen
Geosci. Model Dev., 16, 7203–7221, https://doi.org/10.5194/gmd-16-7203-2023, https://doi.org/10.5194/gmd-16-7203-2023, 2023
Short summary
Short summary
We develop a machine-learning-based crop model emulator with the inputs and outputs of multiple global gridded crop model ensemble simulations to capture the year-to-year variation of crop yield under future climate change. The emulator can reproduce the year-to-year variation of simulated yield given by the crop models under CO2, temperature, water, and nitrogen perturbations. Developing this emulator can provide a tool to project future climate change impact in a simple way.
Jurjen Rooze, Heewon Jung, and Hagen Radtke
Geosci. Model Dev., 16, 7107–7121, https://doi.org/10.5194/gmd-16-7107-2023, https://doi.org/10.5194/gmd-16-7107-2023, 2023
Short summary
Short summary
Chemical particles in nature have properties such as age or reactivity. Distributions can describe the properties of chemical concentrations. In nature, they are affected by mixing processes, such as chemical diffusion, burrowing animals, and bottom trawling. We derive equations for simulating the effect of mixing on central moments that describe the distributions. We then demonstrate applications in which these equations are used to model continua in disturbed natural environments.
Esteban Acevedo-Trejos, Jean Braun, Katherine Kravitz, N. Alexia Raharinirina, and Benoît Bovy
Geosci. Model Dev., 16, 6921–6941, https://doi.org/10.5194/gmd-16-6921-2023, https://doi.org/10.5194/gmd-16-6921-2023, 2023
Short summary
Short summary
The interplay of tectonics and climate influences the evolution of life and the patterns of biodiversity we observe on earth's surface. Here we present an adaptive speciation component coupled with a landscape evolution model that captures the essential earth-surface, ecological, and evolutionary processes that lead to the diversification of taxa. We can illustrate with our tool how life and landforms co-evolve to produce distinct biodiversity patterns on geological timescales.
Veli Çağlar Yumruktepe, Erik Askov Mousing, Jerry Tjiputra, and Annette Samuelsen
Geosci. Model Dev., 16, 6875–6897, https://doi.org/10.5194/gmd-16-6875-2023, https://doi.org/10.5194/gmd-16-6875-2023, 2023
Short summary
Short summary
We present an along BGC-Argo track 1D modelling framework. The model physics is constrained by the BGC-Argo temperature and salinity profiles to reduce the uncertainties related to mixed layer dynamics, allowing the evaluation of the biogeochemical formulation and parameterization. We objectively analyse the model with BGC-Argo and satellite data and improve the model biogeochemical dynamics. We present the framework, example cases and routines for model improvement and implementations.
Tanya J. R. Lippmann, Ype van der Velde, Monique M. P. D. Heijmans, Han Dolman, Dimmie M. D. Hendriks, and Ko van Huissteden
Geosci. Model Dev., 16, 6773–6804, https://doi.org/10.5194/gmd-16-6773-2023, https://doi.org/10.5194/gmd-16-6773-2023, 2023
Short summary
Short summary
Vegetation is a critical component of carbon storage in peatlands but an often-overlooked concept in many peatland models. We developed a new model capable of simulating the response of vegetation to changing environments and management regimes. We evaluated the model against observed chamber data collected at two peatland sites. We found that daily air temperature, water level, harvest frequency and height, and vegetation composition drive methane and carbon dioxide emissions.
Chonggang Xu, Bradley Christoffersen, Zachary Robbins, Ryan Knox, Rosie A. Fisher, Rutuja Chitra-Tarak, Martijn Slot, Kurt Solander, Lara Kueppers, Charles Koven, and Nate McDowell
Geosci. Model Dev., 16, 6267–6283, https://doi.org/10.5194/gmd-16-6267-2023, https://doi.org/10.5194/gmd-16-6267-2023, 2023
Short summary
Short summary
We introduce a plant hydrodynamic model for the U.S. Department of Energy (DOE)-sponsored model, the Functionally Assembled Terrestrial Ecosystem Simulator (FATES). To better understand this new model system and its functionality in tropical forest ecosystems, we conducted a global parameter sensitivity analysis at Barro Colorado Island, Panama. We identified the key parameters that affect the simulated plant hydrodynamics to guide both modeling and field campaign studies.
Jianghui Du
Geosci. Model Dev., 16, 5865–5894, https://doi.org/10.5194/gmd-16-5865-2023, https://doi.org/10.5194/gmd-16-5865-2023, 2023
Short summary
Short summary
Trace elements and isotopes (TEIs) are important tools to study the changes in the ocean environment both today and in the past. However, the behaviors of TEIs in marine sediments are poorly known, limiting our ability to use them in oceanography. Here we present a modeling framework that can be used to generate and run models of the sedimentary cycling of TEIs assisted with advanced numerical tools in the Julia language, lowering the coding barrier for the general user to study marine TEIs.
Siyu Zhu, Peipei Wu, Siyi Zhang, Oliver Jahn, Shu Li, and Yanxu Zhang
Geosci. Model Dev., 16, 5915–5929, https://doi.org/10.5194/gmd-16-5915-2023, https://doi.org/10.5194/gmd-16-5915-2023, 2023
Short summary
Short summary
In this study, we estimate the global biogeochemical cycling of Hg in a state-of-the-art physical-ecosystem ocean model (high-resolution-MITgcm/Hg), providing a more accurate portrayal of surface Hg concentrations in estuarine and coastal areas, strong western boundary flow and upwelling areas, and concentration diffusion as vortex shapes. The high-resolution model can help us better predict the transport and fate of Hg in the ocean and its impact on the global Hg cycle.
Maria Val Martin, Elena Blanc-Betes, Ka Ming Fung, Euripides P. Kantzas, Ilsa B. Kantola, Isabella Chiaravalloti, Lyla L. Taylor, Louisa K. Emmons, William R. Wieder, Noah J. Planavsky, Michael D. Masters, Evan H. DeLucia, Amos P. K. Tai, and David J. Beerling
Geosci. Model Dev., 16, 5783–5801, https://doi.org/10.5194/gmd-16-5783-2023, https://doi.org/10.5194/gmd-16-5783-2023, 2023
Short summary
Short summary
Enhanced rock weathering (ERW) is a CO2 removal strategy that involves applying crushed rocks (e.g., basalt) to agricultural soils. However, unintended processes within the N cycle due to soil pH changes may affect the climate benefits of C sequestration. ERW could drive changes in soil emissions of non-CO2 GHGs (N2O) and trace gases (NO and NH3) that may affect air quality. We present a new improved N cycling scheme for the land model (CLM5) to evaluate ERW effects on soil gas N emissions.
Özgür Gürses, Laurent Oziel, Onur Karakuş, Dmitry Sidorenko, Christoph Völker, Ying Ye, Moritz Zeising, Martin Butzin, and Judith Hauck
Geosci. Model Dev., 16, 4883–4936, https://doi.org/10.5194/gmd-16-4883-2023, https://doi.org/10.5194/gmd-16-4883-2023, 2023
Short summary
Short summary
This paper assesses the biogeochemical model REcoM3 coupled to the ocean–sea ice model FESOM2.1. The model can be used to simulate the carbon uptake or release of the ocean on timescales of several hundred years. A detailed analysis of the nutrients, ocean productivity, and ecosystem is followed by the carbon cycle. The main conclusion is that the model performs well when simulating the observed mean biogeochemical state and variability and is comparable to other ocean–biogeochemical models.
Hocheol Seo and Yeonjoo Kim
Geosci. Model Dev., 16, 4699–4713, https://doi.org/10.5194/gmd-16-4699-2023, https://doi.org/10.5194/gmd-16-4699-2023, 2023
Short summary
Short summary
Wildfire is a crucial factor in carbon and water fluxes on the Earth system. About 2.1 Pg of carbon is released into the atmosphere by wildfires annually. Because the fire processes are still limitedly represented in land surface models, we forced the daily GFED4 burned area into the land surface model over Alaska and Siberia. The results with the GFED4 burned area significantly improved the simulated carbon emissions and net ecosystem exchange compared to the default simulation.
Hideki Ninomiya, Tomomichi Kato, Lea Végh, and Lan Wu
Geosci. Model Dev., 16, 4155–4170, https://doi.org/10.5194/gmd-16-4155-2023, https://doi.org/10.5194/gmd-16-4155-2023, 2023
Short summary
Short summary
Non-structural carbohydrates (NSCs) play a crucial role in plants to counteract the effects of climate change. We added a new NSC module into the SEIB-DGVM, an individual-based ecosystem model. The simulated NSC levels and their seasonal patterns show a strong agreement with observed NSC data at both point and global scales. The model can be used to simulate the biotic effects resulting from insufficient NSCs, which are otherwise difficult to measure in terrestrial ecosystems globally.
Guillaume Marie, Jina Jeong, Hervé Jactel, Gunnar Petter, Maxime Cailleret, Matthew McGrath, Vladislav Bastrikov, Josefine Ghattas, Bertrand Guenet, Anne-Sofie Lansø, Kim Naudts, Aude Valade, Chao Yue, and Sebastiaan Luyssaert
EGUsphere, https://doi.org/10.5194/egusphere-2023-1216, https://doi.org/10.5194/egusphere-2023-1216, 2023
Short summary
Short summary
This research looks at how climate change influences forests, particularly how altered wind and insect activities could make forests emit, instead of absorb, carbon. We've updated a land surface model called ORCHIDEE to better examine the effect of bark beetles on forest health. Our findings suggest that sudden events, like insect outbreaks, can dramatically affect carbon storage, offering crucial insights for tackling climate change.
Miquel De Cáceres, Roberto Molowny-Horas, Antoine Cabon, Jordi Martínez-Vilalta, Maurizio Mencuccini, Raúl García-Valdés, Daniel Nadal-Sala, Santiago Sabaté, Nicolas Martin-StPaul, Xavier Morin, Francesco D'Adamo, Enric Batllori, and Aitor Améztegui
Geosci. Model Dev., 16, 3165–3201, https://doi.org/10.5194/gmd-16-3165-2023, https://doi.org/10.5194/gmd-16-3165-2023, 2023
Short summary
Short summary
Regional-level applications of dynamic vegetation models are challenging because they need to accommodate the variation in plant functional diversity. This can be done by estimating parameters from available plant trait databases while adopting alternative solutions for missing data. Here we present the design, parameterization and evaluation of MEDFATE (version 2.9.3), a novel model of forest dynamics for its application over a region in the western Mediterranean Basin.
Jens Heinke, Susanne Rolinski, and Christoph Müller
Geosci. Model Dev., 16, 2455–2475, https://doi.org/10.5194/gmd-16-2455-2023, https://doi.org/10.5194/gmd-16-2455-2023, 2023
Short summary
Short summary
We develop a livestock module for the global vegetation model LPJmL5.0 to simulate the impact of grazing dairy cattle on carbon and nitrogen cycles in grasslands. A novelty of the approach is that it accounts for the effect of feed quality on feed uptake and feed utilization by animals. The portioning of dietary nitrogen into milk, feces, and urine shows very good agreement with estimates obtained from animal trials.
Yimian Ma, Xu Yue, Stephen Sitch, Nadine Unger, Johan Uddling, Lina M. Mercado, Cheng Gong, Zhaozhong Feng, Huiyi Yang, Hao Zhou, Chenguang Tian, Yang Cao, Yadong Lei, Alexander W. Cheesman, Yansen Xu, and Maria Carolina Duran Rojas
Geosci. Model Dev., 16, 2261–2276, https://doi.org/10.5194/gmd-16-2261-2023, https://doi.org/10.5194/gmd-16-2261-2023, 2023
Short summary
Short summary
Plants have been found to respond differently to O3, but the variations in the sensitivities have rarely been explained nor fully implemented in large-scale assessment. This study proposes a new O3 damage scheme with leaf mass per area to unify varied sensitivities for all plant species. Our assessment reveals an O3-induced reduction of 4.8 % in global GPP, with the highest reduction of >10 % for cropland, suggesting an emerging risk of crop yield loss under the threat of O3 pollution.
Winslow D. Hansen, Adrianna Foster, Benjamin Gaglioti, Rupert Seidl, and Werner Rammer
Geosci. Model Dev., 16, 2011–2036, https://doi.org/10.5194/gmd-16-2011-2023, https://doi.org/10.5194/gmd-16-2011-2023, 2023
Short summary
Short summary
Permafrost and the thick soil-surface organic layers that insulate permafrost are important controls of boreal forest dynamics and carbon cycling. However, both are rarely included in process-based vegetation models used to simulate future ecosystem trajectories. To address this challenge, we developed a computationally efficient permafrost and soil organic layer module that operates at fine spatial (1 ha) and temporal (daily) resolutions.
Cited articles
Ackley, S. F. and Sullivan, C. W.: Physical controls on the development and
characteristics of Antarctic sea ice biological communities: a review and
synthesis, Deep-Sea Res. Pt. I, 41, 1583–1604, https://doi.org/10.1016/0967-0637(94)90062-0, 1994. a
Allen, A. P., Gillooly, J. F., and Brown, J. H.: Linking the global carbon
cycle to individual metabolism, Funct. Ecol., 19, 202–213,
https://doi.org/10.1111/j.1365-2435.2005.00952.x, 2005. a
Álvarez, E., Nogueira, E., and López-Urrutia, Á.: In-vivo
single-cell fluorescence and the size-scaling of phytoplankton chlorophyll
content, Appl. Environ. Microb., 83, e03317-16,
https://doi.org/10.1128/AEM.03317-16, 2017. a
Aumont, O., Maier-Reimer, E., Blain, S., and Monfray, P.: An ecosystem model of the global ocean including Fe, Si, P colimitations,
Global Biogeochem. Cy., 17, 1060, https://doi.org/10.1029/2001GB001745, 2003. a
Aumont, O., Ethé, C., Tagliabue, A., Bopp, L., and Gehlen, M.: PISCES-v2: an ocean biogeochemical model for carbon and ecosystem studies, Geosci. Model Dev., 8, 2465–2513, https://doi.org/10.5194/gmd-8-2465-2015, 2015. a
Aumont, O., van Hulten, M., Roy-Barman, M., Dutay, J.-C., Éthé, C., and Gehlen, M.: Variable reactivity of particulate organic matter in a global ocean biogeochemical model, Biogeosciences, 14, 2321–2341, https://doi.org/10.5194/bg-14-2321-2017, 2017. a
Baretta, J. W., Ebenhöh, W., and Ruardij, P.: The European Regional Seas
Ecosystem Model, a complex marine ecosystem model, Neth. J. Sea Res., 33, 233–246, https://doi.org/10.1016/0077-7579(95)90047-0, 1995. a
Barton, A. D., Dutkiewicz, S., Flierl, G., Bragg, J., and Follows, M. J.:
Patterns of diversity in marine phytoplankton, Science, 327, 1509–1511,
https://doi.org/10.1126/science.1184961, 2010. a
Beardall, J., Allen, D., Bragg, J., Finkel, Z. V., Flynn, K. J., Quigg, A.,
Rees, T. A. V., Richardson, A., and Raven, J. A.: Allometry and stoichiometry
of unicellular, colonial and multicellular phytoplankton, New Phytol.,
181, 295–309, https://doi.org/10.1111/j.1469-8137.2008.02660.x, 2009. a
Beardmore, R., Gudelj, I., Lipson, D. A., and Hurst, L. D.: Metabolic
trade-offs and the maintenance of the fittest and the flattest, Nature, 472,
342–346, https://doi.org/10.1038/nature09905, 2011. a, b
Beckmann, A., Schaum, C.-E., and Hense, I.: Phytoplankton adaptation in
ecosystem models, J. Theor. Biol., 468, 60–71,
https://doi.org/10.1016/j.jtbi.2019.01.041, 2019. a, b, c, d
Bennett, A. F., Dao, K. M., and Lenski, R. E.: Rapid evolution in response to
high-temperature selection, Nature, 346, 79–81, https://doi.org/10.1038/346079a0,
1990. a
Biller, S. J., Berube, P. M., Lindell, D., and Chisholm, S. W.:
Prochlorococcus: the structure and function of collective
diversity, Nat. Rev. Microbiol., 13, 13–27, https://doi.org/10.1038/nrmicro3378, 2015. a, b
Bruggeman, J. and Bolding, K.: A general framework for aquatic biogeochemical
models, Environ. Modell. Softw., 61, 249–265,
https://doi.org/10.1016/j.envsoft.2014.04.002, 2014. a
Bruggeman, J. and Kooijman, A. L. M.: A biodiversity-inspired approach to
aquatic ecosystem modeling, Limnol. Oceanogr., 52, 1533–1544,
https://doi.org/10.4319/lo.2007.52.4.1533, 2007. a, b, c, d
Brzezinski, M. A.: Vertical distribution of ammonium in stratified oligotrophic waters, Limnol. Oceanogr., 33, 1176–1182, 1988. a
Button, D. K., Robertson, B., Gustafson, E., and Zhao, X.: Experimental and
theoretical bases of specific affinity, a cytoarchitecture-based formulation
of nutrient collection proposed to supercede the Michaelis-Menten paradigm of
microbial kinetics, Appl. Environ. Microb., 70, 5511–5521,
https://doi.org/10.1128/AEM.70.9.5511-5521.2004, 2004. a
Castenholz, R. W.: Thermophilic blue-green algae and the thermal environment,
Bacteriol. Rev., 33, 476–504, https://doi.org/10.1128/MMBR.33.4.476-504.1969, 1969. a
Cermeño, P. and Figueiras, F. G.: Species richness and cell-size
distribution: size structure of phytoplankton
communities, Mar. Ecol. Prog. Ser., 357, 75–85, https://doi.org/10.3354/meps07293, 2008. a, b, c
Cermeño, P., Lee, J.-B., Wyman, K., Schofield, O., and Falkowski, P. G.:
Competitive dynamics in two species of marine phytoplankton under
non-equilibrium conditions, Mar. Ecol. Prog. Ser., 429, 19–28,
https://doi.org/10.3354/meps09088, 2011. a
Champagnat, N., Ferrière, R., and Méléard, S.: Unifying
evolutionary dynamics: From individual stochastic processes to macroscopic
models, Theor. Popul. Biol., 69, 297–321, https://doi.org/10.1016/j.tpb.2005.10.004, 2006. a
Chen, B. and Laws, E. A.: Is there a difference of temperature sensitivity
between marine phytoplankton and heterotrophs, Limnol. Oceanogr.,
62, 806–817, https://doi.org/10.1002/lno.10462, 2017. a
Chen, B., Smith, S. L., and Wirtz, K. W.: Effect of phytoplankton size
diversity on primary productivity in the North Pacific: trait distributions
under environment variability, Ecol. Lett., 22, 56–66,
https://doi.org/10.1111/ele.13167, 2019. a, b, c
Chisholm, S. W., Olson, R. J., Zettler, E. R., Goericke, R., Waterbury, J. B., and Welschmeyer, N. A.: A novel free-living prochlorophyte abundant in the oceanic euphotic zone, Nature, 334, 340–343, https://doi.org/10.1038/334340a0, 1988. a
Coutinho, R. M., Klauschies, T., and Gaedke, U.: Bimodal trait distributions
with large variances question the reliability of trait-based aggregate
models, Theor. Ecol., 9, 389–408, https://doi.org/10.1007/s12080-016-0297-9, 2016. a, b, c, d
DeVries, T. and Primeau, F.: Dynamically and Observationally Constrained
Estimates of Water-Mass Distributions and Ages in the Global Ocean,
J. Phys. Oceanogr., 41, 2381–2401, https://doi.org/10.1175/jpo-d-10-05011.1, 2011. a
DeVries, T., Primeau, F., and Deutsch, C.: The sequestration efficiency of the biological pump, Geophys. Res. Lett., 39, L13601,
https://doi.org/10.1029/2012GL051963, 2012. a
Dieckmann, U. and Law, R.: The dynamical theory of coevolution: a derivation
from stochastic ecological processes, J. Math. Biol., 34, 579–612, https://doi.org/10.1007/bf02409751, 1996. a
Doebeli, M., Ispolatov, Y., and Simon, B.: Towards a mechanistic foundation of evolutionary Theory, Elife, 6, e23804, https://doi.org/10.7554/eLife.23804.001, 2017. a
Downing, A. S., Hajdu, S., Hjerne, O., Otto, S. A., Blenckner, T., Larsson, U., and Winder, M.: Zooming in on size distribution pattern under species
coexistence in Baltic Sea phytoplankton, Ecol. Lett., 17, 1219–1227,
https://doi.org/10.1111/ele.12327, 2014. a, b
Dutkiewicz, S., Follows, M. J., and Bragg, J. G.: Modeling the coupling of
ocean ecology and biogeochemistry, Global Biogeochem. Cy., 23, GB4017,
https://doi.org/10.1029/2008GB003405, 2009. a, b
Dutkiewicz, S., Scott, J. R., and Follows, M. J.: Winners and losers:
Ecological and biogeochemical changes in a warming ocean,
Global Biogeochem. Cy., 27, 463–477, https://doi.org/10.1002/gbc.20042, 2013. a
Edwards, K. F., Thomas, M. K., Klausmeier, C. A., and Litchman, E.: Allometric scaling and taxonomic variation in nutrient utilization traits and maximum growth rate of phytoplankton, Limnol. Oceanogr., 57, 554–566,
https://doi.org/10.4319/lo.2012.57.2.0554, 2012. a, b, c
Edwards, K. F., Thomas, M. K., Klausmeier, C. A., and Litchman, E.: Light and
growth in marine phytoplankton: allometric, taxonomic, and environmental
variation, Limnol. Oceanogr., 60, 540–552, https://doi.org/10.1002/lno.10033, 2015. a, b
Einstein, A.: On the Movement of Small Particles Suspended in Stationary
Liquids Required by the Molecular-Kinetic Theory of Heat, Ann. Phys.,
322, 549–560, https://doi.org/10.1002/andp.19053220806, 1905. a
Elbing, K., Larsson, C., Bill, R. M., Albers, E., Snoep, J. L., Boles, E.,
Hohmann, S., and Gustafson, L.: Role of hexose transport in control of
glycolytic flux in Saccharomyces cerevisiae,
Appl. Environ. Microb., 70, 5323–5330, https://doi.org/10.1128/AEM.70.9.5323-5330.2004, 2004. a
Falkowski, P.: The power of plankton, Nature, 483, 17–20,
https://doi.org/10.1038/483S17a, 2012. a
Falkowski, P. G., Barber, R. T., and Smetacek, V.: Biogeochemical Controls and Feedbacks on Ocean Primary Production, Science, 281, 200–206,
https://doi.org/10.1126/science.281.5374.200, 1998. a
Falkowski, P. G., Katz, M. E., Knoll, A. H., Quigg, A., Raven, J. A.,
Schofield, O., and Taylor, F. J. R.: The Evolution of Modern Eukaryotic
Phytoplankton, Science, 305, 354–360, https://doi.org/10.1126/science.1095964, 2004. a
Fasham, M. J. R., Ducklow, H. W., and McKelvie, S. M.: A nitrogen-based model
of plankton dynamics in the oceanic mixed layer, J. Mar. Res.,
48, 591–639, https://doi.org/10.1357/002224090784984678, 1990. a
Field, C. B., Behrenfield, M. J., Randerson, J. T., and Falkowski, P.: Primary production of the biosphere: integrating terrestrial and oceanic components, Science, 281, 237–240, https://doi.org/10.1126/science.281.5374.237, 1998. a
Follows, M. J. and Dutkiewicz, S.: Modeling diverse communities in marine
microbes, Annu. Rev. Mar. Sci., 3, 427–451,
https://doi.org/10.1146/annurev-marine-120709-142848, 2011. a, b
Frederickson, A. G. and Stephanopoulos, G.: Microbial competition, Science,
213, 972–979, https://doi.org/10.1126/science.7268409, 1981. a
Fussmann, G. F., Loreau, M., and Abrams, P. A.: Eco-evolutionary dynamics of
communities and ecosystems, Funct. Ecol., 21, 465–477,
https://doi.org/10.1111/j.1365-2435.2007.01275.x, 2007. a, b, c
Gaedke, U. and Klauschies, T.: Analyzing the shape of observed trait
distributions enables a data-based moment closure of aggregate models,
Limnol. Oceanogr.-Meth., 15, 979–994, https://doi.org/10.1002/lom3.10218, 2017. a
Ghalambor, C. K., McKay, J. K., Carroll, S. P., and Reznick, D. N.: Adaptive
versus non-adaptive phenotypic plasticity and the potential for contemporary
adaptation in new environments, Funct. Ecol., 21, 394–407,
https://doi.org/10.1111/j.1365-2435.2007.01283.x, 2007. a
Gillooly, J., Brown, J., West, G., Savage, V., and Charnov, E.: Effects of size and temperature on metabolic rate, Science, 293, 2248–2251,
https://doi.org/10.1126/science.1061967, 2001. a
Goericke, R. and Welschmeyer, N. A.: Response of Sargasso Sea phytoplankton
biomass, growth rrate and primary production to seasonally varying physical
forcing, J. Plankton Res., 20, 2223–2249, https://doi.org/10.1093/plankt/20.12.2223, 1998. a
Hansen, P. J., Bjørnsen, P. K., and Hansen, B. W.: Zooplankton grazing and
growth: Scaling with the 2–2000-µm body size
range, Limnol. Oceanogr., 42, 687–704, https://doi.org/10.1016/0077-7579(95)90047-0, 1997. a
Hardin, G.: The competitive exclusion principle, Science, 131, 1292–1297,
https://doi.org/10.1126/science.131.3409.1292, 1960. a, b
Healey, F. P. and Hendzel, L. L.: Physiological indicators of nutrient
deficiency in lake phytoplankton,
Can. J. Fish. Aquat. Sci., 37, 442–453, https://doi.org/10.1139/f80-058, 1980. a
Holling, C. S.: The components of predation as revealed by a study of small
mammal predation of the European pine sawfly, Can. Entomol., 91,
293–320, https://doi.org/10.4039/Ent91293-5, 1959. a
Huete-Ortega, M., Cermeño, P., Calvo-Díaz, A., and Marañon, E.:
Isometric size-scaling of metabolic rate and the size abundance distribution
of phytoplankton, P. Roy. Soc. B-Biol. Sci.,
279, 1815–1823, https://doi.org/10.1098/rspb.2011.2257, 2012. a
Hutchinson, G. E.: The paradox of the plankton, Am. Nat., 95,
137–145, https://doi.org/10.1086/282171, 1961. a
Irwin, A. J., Nelles, A. M., and Finkel, Z. V.: Phytoplankton niches estimated from field data, Limnol. Oceanogr., 57, 787–797,
https://doi.org/10.4319/lo.2012.57.3.0787, 2012. a, b, c
Irwin, A. J., Finkel, Z. V., Müller-Karger, F. E., and Ghinaglia, L. T.:
Phytoplankton adapt to changing ocean environments,
P. Natl. Acad. Sci. USA, 112, 5762–5766, https://doi.org/10.1073/pnas.1414752112,
2015. a, b, c
Isserlis, L.: On certain probable errors and correlation coefficients of
multiple frequency distributions with skew regression, Biometrika, 11,
185–190, https://doi.org/10.1093/biomet/11.3.185, 1916. a
Jakobsen, H. H. and Markager, S.: Carbon-to-chlorophyll ratio for
phytoplanktoon in temperate coastal waters: Seasonal pattern and relationship
to nutrients, Limnol. Oceanogr., 61, 1853–1868,
https://doi.org/10.1002/lno.10338, 2016. a
Kinnison, M. T. and Hairston, N. G.: Eco-evolutionary conservation biology:
contemporary evolution and the dynamics of persistence, Funct. Ecol.,
21, 444–454, https://doi.org/10.1111/j.1365-2435.2007.01278.x, 2007. a, b
Kiørboe, T.: Turbulence, phytoplankton cell size, and the structure of
pelagic food webs, Adv. Mar. Biol., 29, 1–72,
https://doi.org/10.1016/S0065-2881(08)60129-7, 1993. a
Kiørboe, T., Saiz, E., and Viitasalo, M.: Prey switching behaviour in the
planktonic copepod Acartia tonsa, Mar. Ecol. Prog. Ser., 143,
65–75, https://doi.org/10.3354/meps143065, 1996. a
Klauschies, T., Coutinho, R. M., and Gaedke, U.: A beta distribution-based
moment closure enhances the reliability of trait-based aggregate models for
natural populations and communities, Ecol. Model., 381, 46–77,
https://doi.org/10.1016/j.ecolmodel.2018.02.001, 2018. a, b, c
Kremer, C. T. and Klausmeier, C. A.: Coexistence in a variable environment:
Eco-evolutionary perspectives, J. Theor. Biol., 339, 14–25,
https://doi.org/10.1016/j.jtbi.2013.05.005, 2013. a
Lännergren, C.: Buoyancy of natural populations of marine
phytoplankton, Mar. Biol., 54, 1–10, https://doi.org/10.1007/BF00387045, 1979. a
Le Gland, G. and Vallina, S. M.: SPEAD v1.1 (Version v1.1), Zenodo, https://doi.org/10.5281/zenodo.4673500, 2021. a
Le Quéré, C., Harrison, S. P., Prentice, I. C., Buitenhuis, E. T., Aumont, O., Bopp, L., Claustre, H., Cotrim Da Cunha, L., Geider, R., Giraud, X., Klaas, C., Kohfeld, K. E., Legendre, L., Manizza, M., Platt, T., Rivkin,
R. B., Sathyendranath, S., Uitz, J., Watson, A. J., and Wolf-Gradow, D.:
Ecosystem dynamics based on plankton functional types and for global ocean
biogeochemistry models, Global Change Biol., 11, 2016–2040,
https://doi.org/10.1111/j.1365-2486.2005.1004.x, 2005. a, b
Lefèvre, M., Vézina, A., Levasseur, M., and Dacey, J. W. H.: A model of
dimethylsulfide dynamics for the subtropical North Atlantic,
Deep-Sea Res. Pt. I, 49, 2221–2239, https://doi.org/10.1016/S0967-0637(02)00121-8, 2002. a, b
Lenski, R. E., Rose, M. R., Simpson, S. C., and Tadler, S. C.: Long-term
experimental evolution in Escherichia coli, I. Adaptation and
divergence during 2000 generations, Am. Nat., 138,
1315–1341, https://doi.org/10.1086/285289, 1991. a
Levins, R.: The strategy of model building in population
biology, Am. Sci., 54, 421–431, 1966. a
Li, W. K. W.: Cytometric diversity in marine ultraphytoplankton, Limnol. Oceanogr., 42, 874–880, https://doi.org/10.4319/lo.1997.42.5.0874, 1997. a
Litchman, E., Klausmeier, C. A., Schofield, O. M., and Falkowski, P. G.: The
role of functional traits and trade-offs in structuring phytoplankton
communities: scaling from cellular to ecosystem level, Ecol. Lett., 10,
1170–1181, https://doi.org/10.1111/j.1461-0248.2007.01117.x, 2007. a, b
López-Urrutia, A., San Martin, E., Harris, R. P., and Irigoien, X.: Scaling
the metabolic balance of the oceans, P. Natl. Acad. Sci. USA, 103, 8739–8744, https://doi.org/10.1073/pnas.0601137103, 2006. a
Maier-Reimer, E.: Geochemical cycles in an ocean general circulation model,
Preindustrial tracer distributions, Global Biogeochem. Cy., 7,
645–677, https://doi.org/10.1029/93GB01355, 1993. a
Marañon, E.: Cell size as a key determinant of phytoplankton metabolism and
community structure, Annu. Rev. Mar. Sci., 7, 241–264,
https://doi.org/10.1146/annurev-marine-010814-015955, 2015. a, b
Marañon, E., Cermeño, P., López-Sandoval, D. C., Rodríguez-Ramos, T.,
Sobrino, C., Huete-Ortega, M., Blanco, J. M., and Rodríguez, J.: Unimodal
size scaling of phytoplankton growth and the size dependence of nutrient
uptake and use, Ecol. Lett., 16, 371–379, https://doi.org/10.1111/ele.12052, 2013. a
Marshall, J., Hill, C., Perelman, L., and Adcroft, A.: Hydrostatic,
quasi-hydrostatic, and nonhydrostatic ocean modeling,
J. Geophys. Res., 102, 5733–5752, https://doi.org/10.1029/96JC02776, 1997. a
Menzel, D. W. and Spaeth, J. P.: Occurrence of ammonia in Sargasso Sea waters
and in rain water at Bermuda, Limnol. Oceanogr., 7, 159–162,
https://doi.org/10.4319/lo.1962.7.2.0159, 1962. a
Meyer, J. R., Gudelj, I., and Beardmore, R.: Biophysical mechanisms that
maintain biodiversity through trade-offs, Nat. Commun., 6, 6278,
https://doi.org/10.1038/ncomms7278, 2015. a
Monod, J.: The growth of bacterial cultures, Annu. Rev. Microbiol., 3, 371–395, https://doi.org/10.1146/annurev.mi.03.100149.002103, 1949. a
Murdoch, W. W.: Switching in general predators: experiments on predator
specificity and stability of prey populations, Ecol. Monogr., 39,
335–354, https://doi.org/10.2307/1942352, 1969. a
Norberg, J.: Biodiversity and ecosystem functioning: A complex adaptive systems approach, Limnol. Oceanogr., 49, 1269–1277,
https://doi.org/10.4319/lo.2004.49.4_part_2.1269, 2004. a
Norberg, J., Urban, M. C., Vellend, M., Klausmeier, C. A., and Loeuille, N.: Eco-evolutionary responses of biodiversity to climate change,
Nat. Clim. Change, 2, 747–751, https://doi.org/10.1038/NCLIMATE1588, 2012. a, b, c, d
Platt, T., Gallegos, C. L., and Harrison, W. G.: Photoinhibition of
photosynthesis in natural assemblages of marine phytoplankton, J. Mar. Res., 38, 687–701, 1980. a
Quintana, X. D., Brucet, S., Boix, D., López-Flores, R., Gascón, S.,
Badosa, A., Sala, J., Moreno-Amich, R., and Egozcue, J. J.: A nonparametric
method for the measurement of size diversity with emphasis on data
standardization, Limnol. Oceanogr.-Meth., 6, 75–86,
https://doi.org/10.4319/lom.2008.6.75, 2008. a, b
Redfield, A. C.: On the proportions of organic derivatives in sea water and their relation to the composition of plankton, in: James Johnstone memorial volume, University Press of Liverpool, 176–192, 1934. a
Riley, G.: Factors controlling phytoplankton populations on Georges Bank, J. Mar. Res., 6, 54–73, 1946. a
Riley, G.: A mathematical model of regional variations in plankton, Limnol. Oceanogr., 10, 202–215, https://doi.org/10.4319/lo.1965.10.suppl2.r202, 1965. a
Rodríguez, J.: Some comments on the size-based structural analysis of the
pelagic ecosystem, Sci. Mar., 58, 1–10, 1994. a
Sauterey, B., Ward, B., Rault, J., Bowler, C., and Claessen, D.: The
implications of eco-evolutionary processes for the emergence of marine
plankton community biogeography, Am. Nat., 190, 116–130,
https://doi.org/10.1086/692067, 2017. a
Schartau, M., Landry, M. R., and Armstrong, R. A.: Density estimation of
plankton size spectra: a reanalysis of IronEx II data, J. Plankton Res., 32, 1167–1184, https://doi.org/10.1093/plankt/fbq072, 2010. a
Schlüter, L., Lohbeck, K. T., Gröger, J. P., Riebesell, U., and Reusch, T. B. H.: Long-term dynamics of adaptive evolution in a global important phytoplankton species to ocean acidification, Science Advances, 2,
e1501660, https://doi.org/10.1126/sciadv.1501660, 2016. a, b, c, d
Steele, J. H.: Plant production in the northern North Sea, H. M. Stationery
Office, Marine Res., Scot. Home Dept., 1958, 1–36, 1958. a
Steinberg, D. K., Carlson, C. A., Bates, N. R., Johnson, R. J., Michaels,
A. F., and Knap, A. H.: Overview of the US JGOFS Bermuda Atlantic Time-series
Study (BATS): a decade-scale look at ocean biology and biogeochemistry,
Deep-Sea Res. Pt. II, 48, 1405–1447, https://doi.org/10.1016/S0967-0645(00)00148-X, 2001. a, b
Strzepek, R. F. and Harrison, P. J.: Photosynthetic architecture differs in
coastal and oceanic diatoms, Nature, 431, 689–692, https://doi.org/10.1038/nature02954, 2004. a
Swift, E.: The marine diatom Ethmodiscus rex: Its morphology and occurrence in the plankton of the Sargasso Sea, J. Phycol., 9, 456–460,
https://doi.org/10.1111/j.1529-8817.1973.tb04121.x, 1973. a
Taguchi, S.: Relationship between photosynthesis and cell size of marine
diatoms, J. Phycol., 12, 185–189, https://doi.org/10.1111/j.1529-8817.1976.tb00499.x, 1976. a
Taylor, A. H., Geider, R., and Gilbert, F. J. H.: Seasonal and latitudinal
dependencies of phytoplankton carbon-to-chlorophyll a ratio: results of a
modeling study, Mar. Ecol. Prog. Ser., 152, 51–66,
https://doi.org/10.3354/meps152051, 1997. a
Terseleer, N., Bruggeman, J., Lancelot, C., and Gypens, N.: Trait-based
representation of diatom functional diversity in a plankton functional type
model of the eutrophied southern North Sea, Limnol. Oceanogr., 59,
1958–1972, https://doi.org/10.4319/lo.2014.59.6.1958, 2014. a, b
Thingstad, T. F.: Elements of a theory for the mechanisms controlling
abundance, diversity, and biogeochemical role of lytic bacterial viruses in
aquatic systems, Limnol. Oceanogr., 45, 1320–1328,
https://doi.org/10.4319/lo.2000.45.6.1320, 2000. a
Thingstad, T. F. and Lignell, R.: Theoretical models for the control of
bacterial growth rate, abundance, diversity and carbon
demand, Aquat. Microb. Ecol., 13, 19–27, https://doi.org/10.3354/ame013019, 1997. a
Thingstad, T. F., Øvreås, L., Egge, J. K., Løvdal, T., and Heldal,
M.: Use of non-limiting substrates to increase size, a genereic strategy to
simultaneously optimize uptake and minimize predation in pelagic osmotrophs?, Ecol. Lett., 8, 675–682, https://doi.org/10.1111/j.1461-0248.2005.00768.x, 2005. a
Thomas, M. K., Kremer, C. T., Klausmeier, C. A., and Litchman, E.: A global
pattern of thermal adaptation in marine phytoplankton, Science, 338,
1085–1088, https://doi.org/10.1126/science.1224836, 2012. a, b
Timofeeff-Ressovsky, N. W.: Mutations and geographical variation, in: The new systematics, Clarendon Press Oxford, 73–136, 1940. a
Travisano, M., Mongold, J. A., Bennett, A. F., and Lenski, R. E.: Experimental tests of the role of adaptation, chance, and history in evolution, Science, 267, 87–90, https://doi.org/10.1126/science.7809610, 1995. a
Vallina, S. M.: Reply to a comment by Larsen, S. H. on “Analysis of a potential “solar radiation dose-dimethylsulfide-cloud condensation nuclei” link from global mapped seasonal correlations”, Global Biogeochem. Cy., 22,
GB3006, https://doi.org/10.1029/2007GB003099, 2008. a
Vallina, S. M., Sim'o, R., Anderson, T. R., Gabric, A., and Cropp, R.: A
dynamic model of oceanic sulfure (DMOS) applied to the Sargasso Sea:
Simulating the dimethylsulfide (DMS) summer paradox, J. Geophys. Res., 113, G01009, https://doi.org/10.1029/2007JG000415, 2008. a, b, c
Vallina, S. M., Follows, M. J., Dutkiewicz, S., Montoya, J. M., Cermeño, P., and Loreau, M.: Global relationship between phytoplankton diversity and
productivity in the ocean, Nat. Commun., 5, 4299,
https://doi.org/10.1038/ncomms5299, 2014a. a, b
Vallina, S. M., Ward, B. A., Dutkiewicz, S., and Follows, M. J.: Maximum
feeding with active prey-switching: A kill-the-winner functional response ad
its effect on global diversity and biogeography, Prog. Oceanogr.,
120, 93–109, https://doi.org/10.1016/j.pocean.2013.08.001, 2014b. a, b, c
Vallina, S. M., Martinez-Garcia, R., Smith, S. L., and Bonachela, J. A.:
Models in microbial ecology, in: Encyclopedia of microbiology, Elsevier, Amsterdam, Netherlands, 211–246, https://doi.org/10.1016/B978-0-12-809633-8.20789-9, 2019.
a, b, c
Villareal, T. A.: Positive buoyancy in the oceanic diatom Rhizosolenia debyana H. Peragallo, Deep-Sea Res. Pt. I, 35, 1037–1045, https://doi.org/10.1016/0198-0149(88)90075-1, 1988. a
Villareal, T. A. and Carpenter, E. J.: Chemical composition and photosynthetic characteristics of Ethmodiscus rex (Bacillariophyceae): Evidence for vertical migration, J. Phycol., 30, 1–8, https://doi.org/10.1111/j.0022-3646.1994.00001.x, 1994. a
Volk, T. and Hoffert, M. I.: Ocean Carbon Pumps: Analysis of Relative Strengths and Efficiencies in Ocean-Driven Atmospheric CO2 Changes, in: The Carbon Cycle and Atmospheric CO2: Natural Variations Archean to Present, edited by: Sundquist, E. and Broecker, W., American Geophysical Union, Washington, D.C., United States, 99–110, https://doi.org/10.1029/GM032p0099, 1985. a
Walsh, J. J., Dieterle, D. A., and Lenes, J.: A numerical analysis of carbon
dynamics of the Southern Ocean phytoplankton community: the role of light and
grazing in effecting both sequestration of atmospheric CO2 and food
availability to larval krill, Deep-Sea Res. Pt. I, 48, 1–48, https://doi.org/10.1016/S0967-0637(00)00032-7, 2001. a
Ward, B. A. and Follows, M. J.: Marine mixotrophy increases trophic transfer
efficiency, mean organism size, and vertical carbon flux, P. Natl. Acad. Sci. USA, 113, 2958–2963, https://doi.org/10.1073/pnas.1517118113,
2016. a
Ward, B. A., Dutkiewicz, S., Jahn, O., and Follows, M. J.: A size-structured
food-web model for the global ocean, Limnol. Oceanogr., 57,
1877–1891, https://doi.org/10.4319/lo.2012.57.6.1877, 2012. a
Ward, B. A., Collins, S., Dutkiewicz, S., Gibbs, S., Bown, P., Ridgwell, A., Sauterey, B., Wilson, J. D., and Oschlies, A.: Considering the role of adaptive evolution in models of the ocean and climate system, J. Adv. Model. Earth Sy., 11, 3343–3361, 2019. a
Whitelam, G. C. and Codd, G. A.: Photoinhibition of photosynthesis in the
cyanobacterium Microcystis areruginosa, Planta, 157, 561–566,
https://doi.org/10.1007/BF00396889, 1983. a
Wickman, J., Diehl, S., and Bränström, Â.: Evolution of resource
specialisation in competitive metacommunities, Ecol. Lett., 22,
1746–1756, https://doi.org/10.1111/ele.13338, 2019. a
Wirtz, K. W.: A generic model for changes in microbial kinetic coefficients,
J. Biotechnol., 97, 147–162, https://doi.org/10.1016/S0168-1656(02)00064-0, 2002. a
Wirtz, K. W.: Mechanistic origins of variability in phytoplankton dynamics:
Part I: niche formation revealed by a size-based model, Mar. Biol., 160,
2319–2335, https://doi.org/10.1007/s00227-012-2163-7, 2013. a, b, c
Yoshida, T., Hairston, N. G., and Ellner, S. P.: Evolutionary trade-off between defence against grazing and competitive ability in a simple unicellular alga, Chlorella vulgaris, P. Roy. Soc. B-Biol. Sci., 271, 1947–1953, https://doi.org/10.1098/rspb.2004.2818, 2004. a
Short summary
We present an ecological model called SPEAD wherein various phytoplankton compete for nutrients. Phytoplankton in SPEAD are characterized by two continuously distributed traits: optimal temperature and nutrient half-saturation. Trait diversity is sustained by allowing the traits to mutate at each generation. We show that SPEAD agrees well with a more classical discrete model for only a fraction of the cost. We also identify realistic values for the mutation rates to be used in future models.
We present an ecological model called SPEAD wherein various phytoplankton compete for nutrients....
