ecosystem models
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2022 ◽  
Vol 176 ◽  
pp. 106546
Author(s):  
Ram Swaroop Meena ◽  
Ashutosh Yadav ◽  
Sandeep Kumar ◽  
Manoj Kumar Jhariya ◽  
Surendra Singh Jatav

2022 ◽  
Vol 464 ◽  
pp. 109837
Author(s):  
Chen Zhang ◽  
Zixuan Zhu ◽  
Maria Špoljar ◽  
Natalia Kuczyńska-Kippen ◽  
Tvrtko Dražina ◽  
...  

2022 ◽  
Author(s):  
Markus Pfeil ◽  
Thomas Slawig

Abstract. The reduction of the computational effort is desirable for the simulation of marine ecosystem models. Using a marine ecosystem model, the assessment and the validation of annual periodic solutions (i.e., steady annual cycles) against observational data are crucial to identify biogeochemical processes, which, for example, influence the global carbon cycle. For marine ecosystem models, the transport matrix method (TMM) already lowers the runtime of the simulation significantly and enables the application of larger time steps straightforwardly. However, the selection of an appropriate time step is a challenging compromise between accuracy and shortening the runtime. Using an automatic time step adjustment during the computation of a steady annual cycle with the TMM, we present in this paper different algorithms applying either an adaptive step size control or decreasing time steps in order to use the time step always as large as possible without any manual selection. For these methods and a variety of marine ecosystem models of different complexity, the accuracy of the computed steady annual cycle achieved the same accuracy as solutions obtained with a fixed time step. Depending on the complexity of the marine ecosystem model, the application of the methods shortened the runtime significantly. Due to the certain overhead of the adaptive method, the computational effort may be higher in special cases using the adaptive step size control. The presented methods represent computational efficient methods for the simulation of marine ecosystem models using the TMM but without any manual selection of the time step.


Land ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 87
Author(s):  
Huimin Zou ◽  
Jiquan Chen ◽  
Changliang Shao ◽  
Gang Dong ◽  
Meihui Duan ◽  
...  

Selecting an appropriate model for simulating ecosystem respiration is critical in modeling the carbon cycle of terrestrial ecosystems due to their magnitude and high variations in time and space. There is no consensus on the ideal model for estimating ecosystem respiration in different ecosystems. We evaluated the performances of six respiration models, including Arrhenius, logistic, Gamma, Martin, Concilio, and time series model, against measured ecosystem respiration during 2014–2018 in four grassland ecosystems on the Mongolian Plateau: shrubland, dry steppe, temperate steppe, and meadow ecosystems. Ecosystem respiration increased exponentially with soil temperature within an apparent threshold of ~19.62 °C at shrubland, ~16.05 °C at dry steppe, ~16.92 °C at temperate steppe, and ~15.03 °C at meadow. The six models explained approximately 50–80% of the variabilities of ecosystem respiration during the study period. Both soil temperature and soil moisture played considerable roles in simulating ecosystem respiration with R square, ranging from 0.5 to 0.8. The Martin model performed better than the other models, with a relatively high R square, i.e., R2 = 0.68 at shrubland, R2 = 0.57 at dry steppe, R2 = 0.74 at temperate steppe, and R2 = 0.81 at meadow. These models achieved good performance for around 50–80% of the simulations. No single model performs best for all four grassland types, while each model appears suitable for at least one type of ecosystem. Models that oil moisture include models, especially the Martin model, are more suitable for the accurate prediction of ecosystem respiration than Ts-only models for the four grassland ecosystems.


2021 ◽  
Vol 680 ◽  
pp. 1-6
Author(s):  
SS Hjøllo ◽  
SM van Leeuwen ◽  
M Maar

The earth’s oceans and ecosystems face climatic changes and multiple anthropogenic stressors. In the face of this, managers of the marine environment are increasingly adopting the ecosystem approach to underpin their decision making. Process-based ecosystem models (frequently referred to as dynamic models) synthesize existing observational and experimental knowledge into a numerical framework, but an obstacle to the incorporation of these models in management is the lack of credibility due to limited control of uncertainty in the results. The 13 papers in this Theme Section highlight how ecosystem models are, or can be, applied as management tools, and the various ways in which they quantify uncertainty and evaluate the skill. The papers span all levels of biological organization from individuals to populations and ecosystems, and cover a wide selection of anthropogenic pressures. Bearing in mind that the interpretation of observations is in fact also a model with representativeness error, we advocate a closer combination of observations and models to bring both methods forward. With the current challenges to the marine ecosystem and our uses of it, the more tools marine managers have in their ‘toolbox’, the better; dynamic modelling is one such very important tool, and its inclusion in ecosystem management should be continuously assessed.


2021 ◽  
Author(s):  
◽  
Vidette McGregor

<p>The Chatham Rise is a highly productive deep-sea ecosystem that supports numerous substantial commercial fisheries, and is therefore a likely candidate for an ecosystem based approach to fisheries management in New Zealand. This thesis describes model construction, calibration and validation, for the first end-to-end ecosystem model of the Chatham Rise, New Zealand. The work extends beyond what has previously been done for validating such models, and explores uncertainty analyses through bootstrapping the oceanographic variables, perturbing the model's initial conditions, and analysing species interaction effects, with the results further analysed with respect to known data gaps. This enables the inclusion of uncertainty in simulated scenarios using the Chatham Rise Atlantis model, thus providing an envelope of results with which to analyse and understand the likely responses of the Chatham Rise ecosystem. The model was designed with 24 dynamic polygons, 5 water column depth bins, 55 species functional groups, and used 12-hour timesteps. The transfer of energy was tracked throughout the system using nitrogen as the model's main currency. The model simulated the system from 1900–2015, preceded by a 35 year burn-in period. The model produced very similar biomass trajectories in response to historical fishing to corresponding fisheries stock assessment models for key fisheries species. Population dynamics and system interactions were considered realistic with respect to growth rates, mortality rates, diets and species group interactions. The model was found to be generally stable under perturbations to the initial conditions, with lower trophic level species groups having the most variability. The specification of the Spawning Stock Recruitment curve was explored, as it relates to the multi-species and ecosystem models within which it is now applied. Close attention needs to be given to population dynamics specific to multi-species interactions such as predation-release, in particular the Spawning Stock Recruitment curve. Potentially misleading dynamics under predation-release were identified, and the simple solution of applying a cap to recruitment when biomass exceeds virgin levels was explored. The population dynamics of myctophids under fishing induced predation release were analysed with and without limiting recruitment to virgin levels. The effects were evident in several ecosystem indicators, suggesting unintentional mis-specification could lead to erroneous model results. It raises several questions around the specification of the Spawning Stock Recruitment relationship for multispecies models, and more generally, whether the concept of ‘virgin’ (or ‘unfished’) biomass should be reconsidered to reflect dynamic natural mortality and potentially changing unfished states. The model components that had knowledge gaps and were found to most likely to influence model results were the initial conditions, oceanographic variables, and the aggregate species groups ‘seabird’ and ‘cetacean other’. It is recommended that applications of the model, such as forecasting biomasses under various fishing regimes, should include alternatives that vary these components, and present appropriate levels of uncertainty in results. Initial conditions should be perturbed, with greater variability applied to species groups modelled as biomass-pools, and age-structured species groups that have little data available from the literature.</p>


2021 ◽  
Author(s):  
◽  
Vidette McGregor

<p>The Chatham Rise is a highly productive deep-sea ecosystem that supports numerous substantial commercial fisheries, and is therefore a likely candidate for an ecosystem based approach to fisheries management in New Zealand. This thesis describes model construction, calibration and validation, for the first end-to-end ecosystem model of the Chatham Rise, New Zealand. The work extends beyond what has previously been done for validating such models, and explores uncertainty analyses through bootstrapping the oceanographic variables, perturbing the model's initial conditions, and analysing species interaction effects, with the results further analysed with respect to known data gaps. This enables the inclusion of uncertainty in simulated scenarios using the Chatham Rise Atlantis model, thus providing an envelope of results with which to analyse and understand the likely responses of the Chatham Rise ecosystem. The model was designed with 24 dynamic polygons, 5 water column depth bins, 55 species functional groups, and used 12-hour timesteps. The transfer of energy was tracked throughout the system using nitrogen as the model's main currency. The model simulated the system from 1900–2015, preceded by a 35 year burn-in period. The model produced very similar biomass trajectories in response to historical fishing to corresponding fisheries stock assessment models for key fisheries species. Population dynamics and system interactions were considered realistic with respect to growth rates, mortality rates, diets and species group interactions. The model was found to be generally stable under perturbations to the initial conditions, with lower trophic level species groups having the most variability. The specification of the Spawning Stock Recruitment curve was explored, as it relates to the multi-species and ecosystem models within which it is now applied. Close attention needs to be given to population dynamics specific to multi-species interactions such as predation-release, in particular the Spawning Stock Recruitment curve. Potentially misleading dynamics under predation-release were identified, and the simple solution of applying a cap to recruitment when biomass exceeds virgin levels was explored. The population dynamics of myctophids under fishing induced predation release were analysed with and without limiting recruitment to virgin levels. The effects were evident in several ecosystem indicators, suggesting unintentional mis-specification could lead to erroneous model results. It raises several questions around the specification of the Spawning Stock Recruitment relationship for multispecies models, and more generally, whether the concept of ‘virgin’ (or ‘unfished’) biomass should be reconsidered to reflect dynamic natural mortality and potentially changing unfished states. The model components that had knowledge gaps and were found to most likely to influence model results were the initial conditions, oceanographic variables, and the aggregate species groups ‘seabird’ and ‘cetacean other’. It is recommended that applications of the model, such as forecasting biomasses under various fishing regimes, should include alternatives that vary these components, and present appropriate levels of uncertainty in results. Initial conditions should be perturbed, with greater variability applied to species groups modelled as biomass-pools, and age-structured species groups that have little data available from the literature.</p>


2021 ◽  
Vol 2131 (3) ◽  
pp. 032079
Author(s):  
S Golosov ◽  
I Zverev ◽  
A Terzhevik ◽  
N Palshin ◽  
G Zdorovennova ◽  
...  

Abstract Parametrization of the formation of organic matter in ecological models is traditionally carried out by using the dependence of the Michaelis – Menten – Monod type [Monod, 1942], which describes the growth rate of algal biomass depending on the factor limiting their development. One of the biggest drawbacks of these dependences is the presence of empirical parameters in them, which in a complex way depend on environmental factors and are an individual characteristic of various types of algae. These parameters in the models actually become fitting coefficients that provide the best fit between observational data and modeling results, which does not allow for effective diagnostics and forecasting of the state of aquatic ecosystems. In this work, on the basis of dimensional analysis, a parametrization was obtained that describes the photosynthesis of algae depending on the parameters relatively easily measured in natural conditions - total solar radiation, phytoplankton biomass, and water transparency. Parametrization has been verified according to observations on more than 30 different types of lakes located in different regions of the world. The calculated data are in satisfactory agreement with the data of field observations, both qualitatively and quantitatively. Discrepancies in field and calculated data may be due to the fact that the species composition of algae in lakes of different trophic status is not taken into account, which can lead to errors in assessing the efficiency of using solar radiation. Discrepancies may also be related to the total solar radiation, rather than photosynthetic active radiation, which varies in different geographic and atmospheric conditions. The proposed parametrization can be used in the development of mathematical models of lake ecosystems, as well as to determine the trophic status of poorly studied water bodies.


2021 ◽  
Vol 168 ◽  
pp. 104157
Author(s):  
M. Prodana ◽  
A.C. Bastos ◽  
A.R.R. Silva ◽  
R.G. Morgado ◽  
S. Frankenbach ◽  
...  

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