Appropriate Level Of Complexity Research Articles

An operationalized post-normal science framework for assisting in the development of complex science policy solutions: the case of nanotechnology governance

Scientists, engineers, and policy analysts commonly suggest governance regimes for technol- ogy to maximize societal benefits and minimize negative societal and environmental impacts of inno- vation processes. Yet innovation is a complex socio- technical process that does not respond predictably to modification. Our human propensity to exclude com- plexity when attempting to manage systems often results in insufficient, one-dimensional solutions. The tendency to exclude complexity (1) reinforces itself by diminishing experience and capacity in the design of simple solutions to complex problems, and (2) leads to solutions that do not address the identified problem. To address the question of how to avoid a complexity- exclusion trap, this article operationalizes a post- normal science framework to assist in the enhance- ment or design of science policy proposals. A literature review of technological fixes, policy pana- ceas, and knowledge-to-action gaps is conducted to survey examples of post-normal science frameworks. Next, an operational framework is used to assess the case of a proposed international nanotechnology advisory board. The framework reveals that the board addresses a slice of the broader, more complex problem of nanotechnology governance. We argue that while the formation of an international advisory board is not problematic in-and-of-itself, it is symp- tomatic of and plays into a complexity-exclusion trap. We offer researchers, policy analysts, and decision- makers three recommendations that incorporate a more appropriate level of complexity into governance proposals.

Guidelines for thermodynamic sorption modelling in the context of radioactive waste disposal

Thermodynamic sorption models (TSMs) offer the potential to improve the incorporation of sorption in environmental modelling of contaminant migration. One specific application is safety cases for radioactive waste repositories, in which radionuclide sorption on mineral surfaces is usually described using distribution coefficients (Kd values). TSMs can be utilised to provide a scientific basis for the range of Kd values included in the repository safety case, and for assessing the response of Kd to changes in chemical conditions. The development of a TSM involves a series of decisions on model features such as numbers and types of surface sites, sorption reactions and electrostatic correction factors. There has been a lack of consensus on the best ways to develop such models, and on the methods of determination of associated parameter values. The present paper therefore presents recommendations on a number of aspects of model development, which are applicable both to radioactive waste disposal and broader environmental applications.The TSM should be calibrated using a comprehensive sorption data set for the contaminant of interest, showing the impact of major geochemical parameters including pH, ionic strength, contaminant concentration, the effect of ligands, and major competing ions. Complex natural materials should be thoroughly characterised in terms of mineralogy, surface area, cation exchange capacity, and presence of impurities. During the application of numerical optimisation programs to simulate sorption data, it is often preferable that the TSM should be fitted to the experimentally determined Kd parameter, rather than to the frequently used percentage sorbed. Two different modelling approaches, the component additivity and generalised composite, can be used for modelling sorption data for complex materials such as soils. Both approaches may be coupled to the same critically reviewed aqueous thermodynamic data sets, and may incorporate the same, or similar, surface reactions and surface species. The quality of the final sorption model can be assessed against the following characteristics: an appropriate level of complexity, documented and traceable decisions, internal consistency, limitations on the number of adjustable parameter values, an adequate fit to a comprehensive calibration data set, and capability of simulating independent data sets. Key recommendations for the process of TSM development include: definition of modelling objectives, identification of major decision points, a clear decision-making rationale with reference to experimental or theoretical evidence, utilisation of a suitable consultative and iterative model development process, testing to the maximum practicable extent, and thorough documentation of key decisions. These recommendations are consistent with international benchmarks for environmental modelling.

RUNX1: A microRNA hub in normal and malignant hematopoiesis.

Hematopoietic development is orchestrated by gene regulatory networks that progressively induce lineage-specific transcriptional programs. To guarantee the appropriate level of complexity, flexibility, and robustness, these networks rely on transcriptional and post-transcriptional circuits involving both transcription factors (TFs) and microRNAs (miRNAs). The focus of this review is on RUNX1 (AML1), a master hematopoietic transcription factor which is at the center of miRNA circuits necessary for both embryonic and post-natal hematopoiesis. Interference with components of these circuits can perturb RUNX1-controlled coding and non-coding transcriptional programs in leukemia.

Characterizing Driver Intention via Hierarchical Perception–Action Modeling

We seek a mechanism for the classification of the intentional behavior of a cognitive agent, specifically a driver, in terms of a psychological Perception-Action (P-A) model, such that the resulting system would be potentially suitable for use in intelligent driver assistance. P-A models of human intentionality assume that a cognitive agent's perceptual domain is learned in response to the outcome of the agent's actions rather than vice versa. In this way, the perceptual domain is maintained at an appropriate level of complexity in relation to the agent's embodied motor capabilities, greatly simplifying visual processing. A subsumptive P-A model further captures the hierarchical nature of the subtask structure implicit in human actions and assumes that a parallel hierarchical structuring exists within the perceptual domain. Adopting this model enables us to characterize intentions at each level of the P-A hierarchy in terms of a range of descriptors derived from the U.K. Highway Code by examining their correlation with driver gaze behavior. The problem of classifying intentions thus becomes one of reconciling high-level protocols (i.e., Highway Code rules) with low-level perceptual features. We perform a “proof-of-concept” assessment of the model by comparative evaluation of a number of logic-based methods (both stochastic and deductive) for carrying out this classification utilizing the control, signal, and motor inputs of an instrumented vehicle driven by a single driver, and find that a deductive model gives superior intentional classification performance due to the strongly protocol-governed nature of the driving environment.

Implications of the World Report on Disability for responding to communication disorders in Brazil

The World Report on Disability has resulted in major shifts in healthcare policy in different national scenarios. The current paper seeks to elucidate some of the changes regarding communication sciences and disorders with the ultimate goal of improved service delivery for persons with communication disabilities in Brazil. The inherent national diversity presents major challenges to both planning and service delivery. The task of identifying under-served populations and the specific barriers to access to services and resources is not straightforward, particularly given that 200 million people reside in an area of over 8.5 million km2. To address this need, changes have already been implemented, namely increased participation of professional and scientific associations related to communication sciences and disorders. Wylie, McAllister, Davidson, and Marshall (2013) offered a provocative analysis in their recent paper and their positions are further discussed within the current document. The bio-psycho-social model of disability should be the foundation for both public agencies and the academy to enhance this area of concern in research, professional training, and service delivery. The real challenge seems to be providing services with the appropriate level of complexity and specialization required for each unique scenario.

Evolutionary kernel machines

This special issue on evolutionary kernel machines is dedicated to the latest developments in combining kernelbased algorithms and evolutionary computation. Kernelbased methods are among the state-of-the-art approaches in machine learning and data mining (e.g., [3, 4]). The idea of kernel machines is to change the representation of the input data by mapping the input space to some metric feature space. The kernel is a real-valued function of two input space elements that corresponds to a scalar product of its arguments mapped to the feature space [1]. In algorithms using the ‘kernel trick’, all operations in the feature space can be expressed by scalar products—and the kernel replaces all these operations. What are the advantages of using a kernel? The kernel renders an explicit mapping of input data to the feature space unnecessary, and a kernel function can usually be computed much more efficiently than mapping two data points to the feature space and computing their scalar product in the feature space representation. Thus a kernel allows to work in very highdimensional feature spaces. Further, kernel-based algorithms are very modular and flexible. Apart from hyperparameters (such as learning rates and regularization parameters) only the kernel function has to be changed when the problem domain changes. The kernel defines the feature space and the algorithm’s notion of similarity between input objects. The clear interface makes handling of non-standard data (e.g., biological sequences) comparatively easy. Finally, the convenient mathematical structure of the kernelinduced feature and hypothesis spaces supports design and analysis of the adaptive system. The basic optimization problems arising when applying kernel methods—for example, training a support vector machine—are usually solved using mathematical programming. However, in some scenarios, there are good reasons to employ stochastic optimization techniques due to the need to solve problems with local optima, high dimensionality, discontinuities, multiple conflicting objectives, and noise. Examples are the fundamental task of finding a proper kernel function for an application (e.g., [2]), feature selection, or label assignment in semi-supervised learning. Here, evolutionary algorithms come into play, which are applicable to objective functions suffering from the aforementioned characteristics. In addition, they are embarrassingly parallelizable. This special issue comprises three original research papers. The article by Koch, Bischl, Flasch, Bartz-Beielstein and Konen studies the problem of automated tuning of support vector machines. It shows how kernels can be evolved by genetic programming, and how sophisticated parameter tuning methods can help to find appropriate hyperparameters. Glasmachers, Koutnik and Schmidhuber introduce a kernel-based evolutionary framework for learning continuous functions. Within this framework, nested function spaces of increasing complexity are used to incrementally determine an appropriate level of complexity. Finally, Gieseke, Kramer, Airola and Pahikkala employ stochastic local search schemes to address the combinatorial problem of assigning labels to unlabeled data in semiand unsupervised kernel-based classification. O. Kramer (&) Oldenburg, Germany e-mail: oliver.kramer@uni-oldenburg.de

Does increased model complexity improve description of phosphorus dynamics in a large treatment wetland?

As the structure of ecological models grows more complex, it becomes increasingly important to identify the appropriate level of complexity for reliable process description and prediction. Here, a suite of mechanistic biogeochemical models with different levels of complexity for representing phosphorus cycling processes was developed and tested against observations from a large treatment wetland. The study site was the 147-ha Cell 4 of Stormwater Treatment Area 1 West, which was designed to help protect the greater Everglades, FL, USA, from nutrient over-enrichment. Six biogeochemical models of differing complexity were coupled with a pre-calibrated two-dimensional hydrodynamic model of Cell 4 and tested against field data. We provide guidance for evaluating a set of models with varying level of complexity using key model attributes that influence the suitability of a model or a set of models. Considerations of model accuracy, complexity, and explanatory depth are combined into a single indicator of model effectiveness. Results revealed that the most complex model structure may not necessarily be the most effective in simulating the dynamics of total phosphorus (TP) concentrations in the wetland. The rate of improvement in the model performance decreased as model complexity increased. Although the most complex model reproduced the field observations best, the marginal improvement in model performance compared to simpler models was outweighed by the higher costs of increased complexity. Highly detailed representations of system structures may not be useful to simulate TP dynamics in treatment wetlands if comprehensive data sets are not available to constrain each pathway. It is crucial for model developers and users to evaluate model structures of differing complexity to identify the appropriate level of complexity for given data and questions of interest.

A Guided Internship For High School Students Using iRobot Create

AbstractWe present an internship program focused on project-based learning designed to introduce students to robotics engineering utilizing the iRobot Create platform. Our aim is to provide instructors and mentors with a set of projects with an appropriate level of complexity so as to stimulate the interest of the students while providing them a sense of accomplishment. We provide a detailed description of the student projects, an overview of the iRobot Create hardware, and insights gained from our experience and student feedback.

Active Modeling: A Method for Creating and Simulating Variable-Complexity Models

Effective and efficient simulation-based design is facilitated by models of appropriate complexity. A single model will not have the most appropriate level of complexity throughout all phases of a simulation maneuver if inputs or parameters vary. Ideally, a model for which complexity can be varied as necessary will achieve the best possible trade-off between accuracy and computational efficiency. A method is presented for switching system model elements on and off as their importance changes, and predicting the response of the resulting variable-complexity model. A modified transformer element removes the dynamic output of a model element to the rest of the system when the moving average of its absolute power falls below a user-defined threshold. When the element is “off,” the input from the system to the element is still passed through the transformer so that an estimate of element power and importance can continue to be calculated and the element switched back “on” if necessary. The bond graph formalism is used to facilitate implementation. Switch configurations are defined for both causally weak and causally strong energy storage and dissipative elements. The method is applicable to linear or nonlinear systems that can be modeled with lumped parameter elements. The approach is demonstrated for quarter- and half-car vehicle models subject to a road profile of varying frequency. The appropriate model complexity at all stages is determined and implemented continuously without prior knowledge of input or parameter changes.

Understanding differences in predictions of HPV vaccine effectiveness: A comparative model-based analysis

Mathematical models of HPV vaccine effectiveness and cost-effectiveness have produced conflicting results. The aim of this study was to use mathematical models to compare and isolate the impact of the assumptions most commonly made when modeling the effectiveness of HPV vaccines. Our results clearly show that differences in how we model natural immunity, herd immunity, partnership duration, HPV types, and waning of vaccine protection lead to important differences in the predicted effectiveness of HPV vaccines. These results are important and useful to assist modelers/health economists in choosing the appropriate level of complexity to include in their models, provide epidemiologists with insight on key data necessary to increase the robustness of model predictions, and help decision makers better understand the reasons underlying conflicting results from HPV models.

The identification of informative genes from multiple datasets with increasing complexity.

BackgroundIn microarray data analysis, factors such as data quality, biological variation, and the increasingly multi-layered nature of more complex biological systems complicates the modelling of regulatory networks that can represent and capture the interactions among genes. We believe that the use of multiple datasets derived from related biological systems leads to more robust models. Therefore, we developed a novel framework for modelling regulatory networks that involves training and evaluation on independent datasets. Our approach includes the following steps: (1) ordering the datasets based on their level of noise and informativeness; (2) selection of a Bayesian classifier with an appropriate level of complexity by evaluation of predictive performance on independent data sets; (3) comparing the different gene selections and the influence of increasing the model complexity; (4) functional analysis of the informative genes.ResultsIn this paper, we identify the most appropriate model complexity using cross-validation and independent test set validation for predicting gene expression in three published datasets related to myogenesis and muscle differentiation. Furthermore, we demonstrate that models trained on simpler datasets can be used to identify interactions among genes and select the most informative. We also show that these models can explain the myogenesis-related genes (genes of interest) significantly better than others (P < 0.004) since the improvement in their rankings is much more pronounced. Finally, after further evaluating our results on synthetic datasets, we show that our approach outperforms a concordance method by Lai et al. in identifying informative genes from multiple datasets with increasing complexity whilst additionally modelling the interaction between genes.ConclusionsWe show that Bayesian networks derived from simpler controlled systems have better performance than those trained on datasets from more complex biological systems. Further, we present that highly predictive and consistent genes, from the pool of differentially expressed genes, across independent datasets are more likely to be fundamentally involved in the biological process under study. We conclude that networks trained on simpler controlled systems, such as in vitro experiments, can be used to model and capture interactions among genes in more complex datasets, such as in vivo experiments, where these interactions would otherwise be concealed by a multitude of other ongoing events.

Sensitivity Approach for Modeling Stochastic Field of Keulegan–Carpenter and Reynolds Numbers Through a Matrix Response Surface

The actual challenge for requalification of existing offshore structures through a rational process of reassessment indicates the importance of employing a response surface methodology. At different steps in the quantitative analysis, quite a lot of approximations are performed as a surrogate for the original model in subsequent uncertainty and sensitivity studies. This paper proposes to employ a geometrical description of the nth order Stokes model in the form of a random linear combination of deterministic vectors. These vectors are obtained by rotation transformations of the wave directional vector. This facilitates introduction of an appropriate level of complexity in stochastic modeling of the wave velocity and of the Reynolds and Keulegan–Carpenter numbers for probabilistic mechanics analysis of offshore structures. In situ measurements are used to assess suitable ranges and distributions of basic variables.

A critical evaluation of the curriculum development strategy of the LIS education programs in Sri Lanka

PurposeA comprehensive analysis of the LIS education system was carried out covering the 2004‐2007 period and it was established that currently 24 LIS education programs are conducted in Sri Lanka by eight educational institutions. The purpose of this paper is to present a critical evaluation of the curriculum development strategy of the LIS education programs in Sri Lanka based on the major research.Design/methodology/approachThe research used both quantitative as well as qualitative research methods to determine the present status of affairs and the study was based on primary and secondary data collected by means of questionnaires, semi‐structured interviews, participatory observations and review of documents. Primary data were collected from the LIS course co‐ordinators, teachers and policy makers. Researcher's own reflections were also used.FindingsThe empirical survey identified that six main subjects are taught across most of the LIS programs; that most programs provide a general knowledge in LIS but not opportunities for specialisation; appropriate levels of complexity were not evident across the different levels of programs; and there is no national core in LIS so that all programs cover a set of common subjects at appropriate depths and breadths suitable for different levels of programs. Findings with regard to the curriculum development strategies identified that most of the curricula documents are not comprehensive, and that the Sri Lankan LIS curriculum developers do not use any formal curriculum development model. Analysis of the curriculum development teams indicated that most members have no training in curriculum design, and the teams lack contributions by other stakeholders except LIS professionals. Several recommendations are presented to eliminate the weaknesses of the curriculum development strategy in the LIS programs.Research limitations/implicationsThis paper is based on a broader empirical study of the LIS education system of Sri Lanka and throws some light on specific research on identifying the most appropriate curriculum development model for Sri Lanka.Practical implicationsThe findings of the research are directly applicable to the tasks of the curriculum developers and the LIS education policy makers in Sri Lanka and they can use the findings for the improvement of the LIS education programs.Originality/valueContents or the curriculum development strategy of the current Sri Lankan LIS education programs have not been critically analysed before and this paper presents the original research findings relevant to all the programs conducted in 2007.

Crops and climate change: progress, trends, and challenges in simulating impacts and informing adaptation

Assessments of the relationships between crop productivity and climate change rely upon a combination of modelling and measurement. As part of this review, this relationship is discussed in the context of crop and climate simulation. Methods for linking these two types of models are reviewed, with a primary focus on large-area crop modelling techniques. Recent progress in simulating the impacts of climate change on crops is presented, and the application of these methods to the exploration of adaptation options is discussed. Specific advances include ensemble simulations and improved understanding of biophysical processes. Finally, the challenges associated with impacts and adaptation research are discussed. It is argued that the generation of knowledge for policy and adaptation should be based not only on syntheses of published studies, but also on a more synergistic and holistic research framework that includes: (i) reliable quantification of uncertainty; (ii) techniques for combining diverse modelling approaches and observations that focus on fundamental processes; and (iii) judicious choice and calibration of models, including simulation at appropriate levels of complexity that accounts for the principal drivers of crop productivity, which may well include both biophysical and socio-economic factors. It is argued that such a framework will lead to reliable methods for linking simulation to real-world adaptation options, thus making practical use of the huge global effort to understand and predict climate change.

The extrapolation problem and how population modeling can help

We argue that population modeling can add value to ecological risk assessment by reducing uncertainty when extrapolating from ecotoxicological observations to relevant ecological effects. We review other methods of extrapolation, ranging from application factors to species sensitivity distributions to suborganismal (biomarker and "-omics") responses to quantitative structure-activity relationships and model ecosystems, drawing attention to the limitations of each. We suggest a simple classification of population models and critically examine each model in an extrapolation context. We conclude that population models have the potential for adding value to ecological risk assessment by incorporating better understanding of the links between individual responses and population size and structure and by incorporating greater levels of ecological complexity. A number of issues, however, need to be addressed before such models are likely to become more widely used. In a science context, these involve challenges in parameterization, questions about appropriate levels of complexity, issues concerning how specific or general the models need to be, and the extent to which interactions through competition and trophic relationships can be easily incorporated.

Finding the appropriate level of complexity for a simulation model: An example with a forest growth model

The topic of model complexity is fundamental to model developers and model users. In this study, we investigate how over- and under-fitting of a driving function in a simulation model influences the predictive ability of the model. Secondly, we investigate whether model selection approaches succeed in selecting driving functions with the best predictive ability. We address these issues through an example with the forest simulator SORTIE-ND. Utilizing maximum likelihood methods and individual tree growth data we parameterize five growth functions of increasing complexity. We then incorporate each growth function into the simulation model SORTIE-ND and test predicted growth against independent data. Compared to the independent data, the simplest and the most complex growth functions had the poorest predictive ability while functions of intermediate complexity had the best predictive ability. The poor predictive ability of the simplest model is caused by poor approximation of the system while the poor predictive ability of the most complex model is caused by biased parameter estimates. A growth function of intermediate complexity was the most parsimonious model where error due to approximation and error due to estimation were simultaneously minimized. The model selection criteria AIC and BIC were found to select complex functions that were over-fitted according to the independent data comparison. BIC was closer to choosing the model that minimized prediction error than AIC. In this example, BIC is the more appropriate model selection criterion. It is important that both model developers and models users remember that more complex models do not always result in better predictive models.

The role of modelling in identifying drug targets for diseases of the cell cycle.

The cell cycle is implicated in diseases that are the leading cause of mortality and morbidity in the developed world. Until recently, the search for drug targets has focused on relatively small parts of the regulatory network under the assumption that key events can be controlled by targeting single pathways. This is valid provided the impact of couplings to the wider scale context of the network can be ignored. The resulting depth of study has revealed many new insights; however, these have been won at the expense of breadth and a proper understanding of the consequences of links between the different parts of the network. Since it is now becoming clear that these early assumptions may not hold and successful treatments are likely to employ drugs that simultaneously target a number of different sites in the regulatory network, it is timely to redress this imbalance. However, the substantial increase in complexity presents new challenges and necessitates parallel theoretical and experimental approaches. We review the current status of theoretical models for the cell cycle in light of these new challenges. Many of the existing approaches are not sufficiently comprehensive to simultaneously incorporate the required extent of couplings. Where more appropriate levels of complexity are incorporated, the models are difficult to link directly to currently available data. Further progress requires a better integration of experiment and theory. New kinds of data are required that are quantitative, have a higher temporal resolution and that allow simultaneous quantitative comparison of the concentration of larger numbers of different proteins. More comprehensive models are required and must accommodate not only substantial uncertainties in the structure and kinetic parameters of the networks, but also high levels of ignorance. The most recent results relating network complexity to robustness of the dynamics provide clues that suggest progress is possible.

The Practical Use of Simplicity in Developing Ground Water Models

The advantages of starting with simple models and building complexity slowly can be significant in the development of ground water models. In many circumstances, simpler models are characterized by fewer defined parameters and shorter execution times. In this work, the number of parameters is used as the primary measure of simplicity and complexity; the advantages of shorter execution times also are considered. The ideas are presented in the context of constructing ground water models but are applicable to many fields. Simplicity first is put in perspective as part of the entire modeling process using 14 guidelines for effective model calibration. It is noted that neither very simple nor very complex models generally produce the most accurate predictions and that determining the appropriate level of complexity is an ill-defined process. It is suggested that a thorough evaluation of observation errors is essential to model development. Finally, specific ways are discussed to design useful ground water models that have fewer parameters and shorter execution times.

Motivating OOP by blowing things up

For the introductory student, the process of learning to program is an arduous task. Not only does the student have to learn the syntax of a programming language, he or she also has to apply concepts of object-oriented design and software construction in service of the projects created within the classroom. Unfortunately, as students further their study of programming, they often feel disconnected from the examples and projects they construct as part of their coursework. Projects are often perceived as toy problems and do not match the graphics-rich, interactive notion of programming that students bring with them to class. In addition, projects and classroom exercises do not convey the appropriate level of complexity and fail in the task of challenging the student to critically think about what they are constructing. This paper discusses TankBrains, a cooperative and competitive programming project presented to students in the latter part of their introductory programming course sequence. Students are challenged to create simple tank artificial intelligences as teams, which are later tested against each other in head-to-head combat. Students create their TankBrains within a collaborative virtual environment, which enforces the physics and rules of the combat simulation. Students must also present their strategies and findings to each other. This paper also examines how the use of the TankBrain system influences the students' perception regarding achievements and learning in the classroom.

Modeling the primary and secondary productions of the southern Benguela upwelling system: A comparative study through two biogeochemical models

A three‐dimensional primitive equation model, the Regional Ocean Modeling Systems (ROMS), coupled to two biogeochemical configurations (NPZD and N2P2Z2D2) was used to study the dynamics of the first trophic levels of the pelagic food web in the southern Benguela upwelling system. The domain extends from the Agulhas Bank bordered by the Agulhas Current to 27°S on the west coast of South Africa. The circulation is driven by monthly climatologies of atmospheric forcing fields. The NPZD ecosystem model consists of four state variables: nutrient (nitrate), phytoplankton, zooplankton and detritus. In the N2P2Z2D2 model, ammonium has been added and the three other variables have been divided into small and large organisms or detritus. Both models are able to reproduce the spatio‐temporal phytoplankton distribution. Along the west coast, chlorophyll concentrations maxima are associated to surface waters. Westward dominating winds generate the lowest chlorophyll concentrations encountered in winter. The small phytoplankton organisms simulated by the N2P2Z2D2 model are responsible for a weaker chlorophyll inshore/offshore gradient, in closer agreement with observations. Transitions from a regime dominated by new production (high f ratio) to one dominated by regenerated production (low f ratio) happen to be abrupt, underlying the constant competition between small and large organisms with regard to upwelling induced nutrient inputs. On the Agulhas Bank, the summer enrichment is associated with subsurface maxima, while in winter, mixing by storms results in a homogeneous phytoplankton distribution in the water column. Regenerated production plays an important role in maintaining the total phytoplankton growth. Zooplankton biomass reflects the overall patterns of chlorophyll a concentrations with differences between the west coast and the Agulhas Bank, consistent with data, and its distribution exhibits a clear seasonal contrast. The seasonality of small and large zooplankton in the N2P2Z2D2 model is quite distinct, which allows, from the Agulhas Bank to St. Helena Bay, a food continuum for fish larvae. This was not achieved with the simpler NPZD model, emphasizing the importance of representing the appropriate level of complexity to characterize food availability for higher trophic levels.