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19 April 2010 End-To-End Models for the Analysis of Marine Ecosystems: Challenges, Issues, and Next Steps
Kenneth A. Rose, J. Icarus Allen, Yuri Artioli, Manuel Barange, Jerry Blackford, François Carlotti, Roger Cropp, Ute Daewel, Karen Edwards, Kevin Flynn, Simeon L. Hill, Reinier HilleRisLambers, Geir Huse, Steven Mackinson, Bernard Megrey, Andreas Moll, Richard Rivkin, Baris Salihoglu, Corinna Schrum, Lynne Shannon, Yunne-Jai Shin, S. Lan Smith, Chris Smith, Cosimo Solidoro, Michael St. John, Meng Zhou
Author Affiliations +

There is growing interest in models of marine ecosystems that deal with the effects of climate change through the higher trophic levels. Such end-to-end models combine physicochemical oceanographic descriptors and organisms ranging from microbes to higher-trophic-level (HTL) organisms, including humans, in a single modeling framework. The demand for such approaches arises from the need for quantitative tools for ecosystem-based management, particularly models that can deal with bottom-up and top-down controls that operate simultaneously and vary in time and space and that are capable of handling the multiple impacts expected under climate change. End-to-end models are now feasible because of improvements in the component submodels and the availability of sufficient computing power. We discuss nine issues related to the development of end-to-end models. These issues relate to formulation of the zooplankton submodel, melding of multiple temporal and spatial scales, acclimation and adaptation, behavioral movement, software and technology, model coupling, skill assessment, and interdisciplinary challenges. We urge restraint in using end-to-end models in a true forecasting mode until we know more about their performance. End-to-end models will challenge the available data and our ability to analyze and interpret complicated models that generate complex behavior. End-to-end modeling is in its early developmental stages and thus presents an opportunity to establish an open-access, community-based approach supported by a suite of true interdisciplinary efforts.

There is growing interest in models of marine ecosystems that include descriptors of climate as it affects higher trophic levels (Travers et al. 2007; Cury et al. 2008). These so-called end-to-end models typically combine submodels of physicochemical oceanographic processes with descriptors of lower- and higher-trophic-level organisms into a single modeling framework (Travers et al. 2009). There are currently several established modeling efforts along this line, including OSMOSE (Shin and Cury 2001), Ecospace (part of Ecopath with Ecosim; Walters et al. 1999; in press), ATLANTIS (Fulton et al. 2004a, 2004b), and SEAPODYM (Lehodey et al. 2003). These models and others were recently reviewed in Plagányi (2007). Newer efforts are also under way, such as a multispecies fish extension to the NEMURO family of models. While none of these newer models yet constitutes a comprehensive end-to-end model, the great variety in their underlying approaches and details provides a promising foundation for the development of the next generation of end-to-end models.

Traditionally, in marine ecosystem modeling models of lower trophic levels (LTLs) and higher trophic levels (HTLs) have been developed quasi-independently (Jennings et al. 2001; Travers et al. 2007). The LTLs have been modeled in terms of single-element (typically nitrogen or carbon) dynamics among the various forms of nutrients and multiple functional groups of autotrophs (i.e., phytoplankton) and heterotrophs (i.e., bacteria and zooplankton), as typified by nutrient–phytoplankton–zooplankton (NPZ) models (Fasham et al. 1990; Fennel and Neumann 2004).1

Models of HTL organisms (e.g., fish) are typically based on biomass or age-, stage-, or size-classes (e.g., Tuljapurkar and Caswell 1997; Quinn and Deriso 1999) and embody simplified physical (environmental) conditions and LTL (ecological) representations (Latour et al. 2003). These models include single-species population dynamics models (Quinn and Deriso 1999) and multispecies models that represent the predator–prey and competitive interactions among the HTL species (Pauly et al. 2000; Rose and Sable 2009). When included, humans are typically represented in a highly simplified manner in such models. However, several models have begun to include more dynamic representations of human activities, mostly in terms of the dynamics of harvesting (Plagányi 2007). End-to-end models attempt to meld the physical–LTL and HTL modeling approaches and include important feedbacks among these three factors. Ultimately, end-to-end models should more broadly include humans as members of the HTL community that react and adjust to changing conditions (Liu et al. 2007; Perry et al. 2010).

The increasing interest in developing end-to-end models is the result of several developments: the worldwide movement toward ecosystem-based management, advances in ecosystem modeling, improvements in computing power, and the ever increasing appreciation of the complexity of the interacting factors that control ecosystem dynamics. A major driver is the demand for quantitative tools to support ecosystem-based management initiatives (Pikitch et al. 2004). It has long been recognized that there are strong interactions and feedbacks among climate, upper-ocean biogeochemistry, and the LTL components of marine food webs. Ecosystem approaches are now considered necessary because of the growing appreciation of the interconnectedness between climate and fish and other HTL organisms and because of the recognized deficiencies in the commonly used single-species approach to fisheries management (Latour et al. 2003). Climate scale variability and its impact on fish population dynamics have been documented in many ecosystems (e.g., Chavez et al. 2003; Beamish et al. 2004; Lehodey et al. 2006). Regional and global climate change can affect HTL organisms in a multitude of ways. For example, the growth, mortality, reproduction, and movement of fish are directly affected by changes in the physical and chemical properties of the ocean (temperature, salinity, mixing, advection, and pH) and directly and indirectly by changes affecting phytoplankton, zooplankton, benthos, and other fish in their roles as predators, prey, and competitors (Ito et al. 2010).

Another driver for the development of end-to-end models is concern about the overharvesting of many fish populations. Much attention has been given to a series of controversial papers purportedly showing that many of world's fish populations have been overharvested (e.g., Myers and Worm 2003; but see Hilborn 2007). Whether or not the dire state of fisheries has been exaggerated, there is no doubt that overharvesting and low population biomasses are a concern (Mullon et al. 2005; Worm et al. 2009). As a result, current fisheries management practices, which rely heavily on single-species approaches (Rose andCowan 2003), are being called into question, and multispecies and ecosystem approaches are being advocated (Latour et al. 2003; Plagányi 2007; Cury et al. 2008).

The emergence of end-to-end models is also timely because advances in the modeling of hydrodynamics and HTL organisms, combined with improvements in computing power, now make it possible to link component submodels explicitly. Recent advances in physical modeling include accurate and efficient solution schemes, sufficient spatial resolution to simulate mesoscale features, the nesting of grids to enable fine-scale simulation within a relatively broad spatial domain, and the ability to reproduce patterns in field data for nearshore and coastal areas (e.g., upwelling regions) that are utilized by many HTL organisms during their life cycle (Ito et al. 2010; Lett et al. 2009). We are now able to simulate mesoscale phenomena in the physical and LTL models, which provides more realistic spatial and temporal temperature, salinity, and food fields for the simulation of the reproduction, growth, mortality, and movement of individual fish as well as the short-term population dynamics. The physical and LTL models can also reconstruct decadal-scale phenomena (e.g., regime shifts) critical for simulating the low-frequency variation in HTL organisms. We do not want to overstate the current abilities of the physical and LTL models. The degree to which simulations with these models match reality can always be questioned. For example, artificial numerical “fixes” are still used in many LTL models to ensure that certain behaviors are realistic (Cropp and Norbury 2009). However, recent advances in physical and LTL modeling have partially shifted the emphasis from inadequate modeling methods to the lack of appropriate data for parameterization and validation (see Anderson 2005; Flynn 2005).

There have also been advances in the modeling of HTL organisms. There is now far greater appreciation of the ways to handle ontogeny at scales that are appropriate for end-to-end models. While the differential and difference equation representations are still very useful in many situations, especially for the low and mid trophic levels (e.g., Gentleman et al. 2008), an individual-based approach that simulates discrete Lagrangian entities offers several advantages over the more traditional age- and stage-based approaches at the higher trophic levels. The individual-based approach has become very popular in ecology (DeAngelis and Mooij 2005) and is well suited for representing HTL organisms in end-to-end models. The individual-based approach allows for conceptually straightforward linking of the HTL processes of growth, mortality, and reproduction to the detailed spatial and temporal scales of the physical and NPZ submodels. Competitive and predatory food web interactions can be represented on local scales, and physical-based and behavioral movement can be implemented (DeAngelis and Rose 1992; Breckling et al. 2006). The individual-based approach has been used in single- and multiple-species models (DeAngelis and Mooij 2005), but far less in community-level analyses because of computational costs (Rose and Sable 2009). Data collection has also progressed, the advances including remote sensing that permits synoptic measurements to be made over broad areas, the ability to obtain spatially resolved measurements in the field, and the ability to acquire fine-scale information (such as that obtained through acoustic and sonic tagging) on the movements of animals (Rose et al. 2001). A mix of stage-, age-, and individual-based modeling approaches will probably be needed to represent the complex food webs and community dynamics of HTL organisms in end-to-end models.

Computing power is ever increasing, such that end-to-end models are now computationally feasible. We have the computing power to simulate physical phenomena at fine scales using highly resolved models with the spatial coverage (domain) and ability to perform the decadal-length simulations that are needed to capture the dynamics of HTL organisms. Computing limitations will always be present, however. High-end computing is not widely available and requires special expertise, and there are issues related to the storage of and efficient access to the massive data and model output that can be generated. One could easily formulate an end-to-end model that would be limited by computing power, especially in terms of data fitting and uncertainty analysis. At present, however, uncertainty about biological processes is more limiting than computing power.

Another development that favors an end-to-end approach is the dynamic and spatially variable nature of the factors that control marine ecosystem dynamics. Knowing the relative roles of bottom-up, top-down, and wasp-waist controls (Cury et al. 2000; Frank et al. 2005; Bakun 2006; Field et al. 2006)—and how they operate simultaneously and vary in time and space—is vital for making accurate projections of the responses of ecosystems to changing conditions (Cury et al. 2008). Evidence of the complexity of inter–trophic level controls in coastal and marine systems (Hutchings et al. 1998; Hunt and Stabeno 2002; Frank et al. 2005; Ware and Thompson 2005) and the complexity of the relationships between environmental conditions and fish population dynamics (Rose 2000; Breitburg et al. 2009) is accumulating. While it has long been known that nutrients are related to fish production (Caddy 1993; Nixon and Buckley 2002), some studies have suggested that fish themselves can affect nutrient availability (Hjerne and Hansson 2002; Wilson et al. 2009). How to represent the complexity of the food web continues to be investigated for NPZ models (e.g., Friedrichs et al. 2007), and it is even more complicated when HTL organisms are added to the model. End-to-end models will need to have the structure and feedbacks to represent a complex mix of controlling processes (e.g., an alternation of bottom-up and top-down processes). In some situations, this will involve feedbacks among the physical, NPZ, and HTL components.

In this paper, we discuss nine issues related to the further development and application of end-to-end models. These are (1) the role of zooplankton as a link between the lower and upper trophic levels, (2) the inclusion of new organisms such as macroinvertebrates and humans, (3) the melding of multiple temporal and spatial scales across submodels, (4) acclimation and adaptation by organisms, (5) the behavioral movement of upper-trophic-level organisms, (6) the software and technology for coding and sharing algorithms in an open-access community, (7) one-way and two-way solution techniques, (8) the assessment of model performance and forecasting, and (9) the challenges posed by the interdisciplinary aspects of end-to-end modeling. We conclude with a general statement about the future of end-to-end modeling and its relevance to management decision making. This paper is based on the discussions at a workshop entitled “Bridging the Gap between Lower and Higher Trophic Levels” that was held February 2009 in Plymouth, England. This paper is not meant to be an exhaustive review of end-to-end modeling of marine ecosystems. Our goal is to make the participants' deliberations at the workshop on this rapidly advancing field available to a wider audience.

Zooplankton as the Link between Lower and Upper Trophic Levels

A major topic of discussion at the workshop was whether the current representation of zooplankton in the traditional NPZ models is an appropriate to way link LTL organisms to fish and other HTL organisms. Zooplankton will be a linchpin as we develop end-to-end models because of their importance as a consumer of primary production and as prey for HTL organisms. Historically, zooplankton was included in NPZ models mostly to achieve realistic biogeochemical cycling (Le Quere et al. 2005), while in models of fish population dynamics, food (LTL) dynamics were either not considered explicitly (e.g., age-structured models; Caswell 2001) or represented very simply (e.g., Ecopath with Ecosim; Walters et al. 1997).

In end-to-end models, the goal of representing zooplankton shifts from obtaining realistic nutrient and chlorophyll concentrations in a biogeochemical context to representing the quantity and quality of food available to multiple species of fish over their lifetimes. Two likely differences between traditional NPZ models and end-to-end models are the representation of biogeochemical processes and the definition of functional groups.

Shifting the focus of zooplankton means that many of the processes represented in traditional NPZ model formulations need to be revisited. For example, traditional NPZ simulations (e.g., Fasham et al. 1990) have either made reference to a single nutrient (typically nitrogen) or assumed fixed, Redfield-like stoichiometry. Setting aside the question whether Monod–Redfield models can sufficiently describe phytoplankton growth (Flynn 2010), variable elemental stoichiometry has been shown to be important in controlling NPZ dynamics (Andersen et al. 2004; Mitra et al. 2007). Whether stoichiometry is a first-order problem or not is unknown, but the importance of this issue (and others) needs to be assessed in the context of incorporating NPZ models into end-to-end models.

Another issue is the amount of detail needed in the algorithms that represent prey selection by zooplankton. Relating prey selection to food quality in a simplified manner in NPZ models is a nontrivial matter (Mitra and Flynn 2006), while including it in structured copepod models can have profound impacts on copepod dynamics (Flynn and Irigoien 2009). We do not know the extent to which the traditional representations of processes in NPZ models will require revision in the context of the demands imposed by end-to-end models, but we caution against simply adopting existing formulations.

A second major issue that arises with end-to-end modeling is the representation of zooplankton functional groups. Much of the literature still focuses on ways to represent functional groups with respect to biogeochemical cycling (Hood et al. 2006); end-to-end modeling will dramatically shift the focus to ways to represent LTL organisms because the emphasis will be on their linkage to HTL organisms. Most real LTL dynamics operate through microzooplankton, as represented by protistan grazers and the juvenile states of mesozooplanton. Biogeochemically oriented NPZ models typically employ closure terms that operate on the mortality component of the zooplankton groups, which (often without being stated explicitly) describe the activities of HTL organisms (see Mitra 2009). End-to-end modeling provides explicit description of predatory activity in place of general closure terms.

End-to-end models inevitably require the inclusion of at least two zooplankton types: protistan zooplankton for realistic LTL dynamics and mesozooplankton (notably copepods) for the HTL link. The zooplankton groups need to be represented so that key shifts in the prey types eaten as fish develop can be realistically simulated. Larval fish often require small prey sizes and certain types (e.g., Checkley 1982; Munk 1997; Kühn et al. 2008), and subtle changes in mortality arising from growth differences or episodic events can dramatically affect annual recruitment (Houde 1989). Adult fish tend to eat larger prey and often consume a mix of invertebrate and fish prey (e.g., Scharf et al. 2000). Detailed stage-based models of specific copepod species have been successfully imbedded into three-dimensional spatial models (e.g., Moll and Stegert 2007), and others have invoked postprocessing of NPZ model output to obtain sufficiently detailed prey fields for modeling fish feeding (e.g., disaggregating biomass into size structure; Daewel et al. 2008). Several plankton–fish models have essentially abandoned the functional group approach and represented LTL organisms by means of size distributions (e.g., Zhou and Huntley 1997; Maury et al. 2007). Defining planktonic functional groups remains a longstanding challenge in NPZ modeling (Anderson 2005; Le Quere et al. 2005), and the additional requirement of accurately representing the energy flow pathways and phenology of multiple groups of zooplankton will further complicate it. The extent to which multistage LTL models will be required to describe the development of important LTL species groups (e.g., copepods) is a major unknown. Indeed, from a biological standpoint, the modeling of mesozooplankton may have more in common with modeling the more challenging HTL organisms than with modeling single-celled microbes (Carlotti and Poggiale 2010).

A critical element of making end-to-end models realistic is correctly describing the LTL organisms. In this regard the demands of end-to-end modeling differ from those for biogeochemical cycling. We anticipate that, at least in some situations, significant changes to traditional NPZ model formulations will be needed to fulfill the demands of end-to-end models. In other situations, more moderate changes may suffice. Data that describe the flux of materials and energy up through their pathways are needed to confirm the simulated dynamics of the fish-oriented zooplankton functional groupings. This will reinforce the drive for changes in the ways that phytoplankton and zooplankton are traditionally represented. We will need NPZ models that deal with multiple nutrients, that generate energy flows through functional or size-groups that provide a sound basis for ontogenetic changes in fish prey demands, and that are robust to changing environmental conditions. This issue will become especially important as we extend the use of models to conditions (e.g., global climate change) that are well outside the domain in which the models were calibrated and validated.

New Organisms

Two groups of organisms that have not been the focus of marine ecosystem modeling to date but that are likely to become important under changing climate scenarios are macroinvertebrates and very-high-trophic-level organisms. Macroinvertebrates such as salps, appendicularians, and jellyfish seem to fall in-between most NPZ and HTL modeling efforts. Salps can channel small-sized phytoplankton and aggregate them into large, fast-sinking particles, thereby making them unavailable to the small stages of crustaceans and interfering with the trophic link between zooplankton and fish (Le Fèvre et al. 1998). Given the worldwide concerns about jellyfish blooms (Mills 2001; Purcell 2005) and how they alter the energy flow through the food web (Brodeur et al. 2002) and affect zooplankton community dynamics (e.g., Kremer 1979) and fish growth and survival (Purcell and Arai 2001), the representation of jellyfish in end-to-end models requires careful consideration.

Demersal fish species are another group of organisms that will need to be considered in end-to-end models. While HTL models of demersal fish species have been developed (e.g., Bryant et al. 1995), they have tended to include simplified physical conditions and benthic prey dynamics. Most LTL–fish models that include detailed physical conditions have focused on the early life stages of pelagic fish species (Werner et al. 2001; Lett et al. 2009; North et al. 2009), while the initial attempts to formulate end-to-end models that include demersal organisms have been simplified in other aspects, such as the physical conditions or behavioral movements (see Plagányi 2007). In many situations, end-to-end models will need to consider demersal fish species as integral parts of the HTL community. This presents new challenges as to how to represent the interface not only with LTL organisms in the overlaying waters but also with the hydrodynamics and physical conditions near the bottom and the associated benthic food web. Some of the existing benthic modules within ecosystem models (e.g., Ebenhoh et al. 1995; Blackford 1997) and the spatially simplified models that include demersal species (e.g., Bryant et al. 1995; Okey et al. 2004) will provide a starting point, but much further work is required to determine the appropriate formulations for demersal-related processes and linkages to the physical conditions and pelagic food web.

Finally, many of the models that include LTL and HTL organisms have shifted the mortality closure term problem further up the food chain. We have moved from specifying zooplankton mortality in NPZ models to achieve realistic biogeochemical cycling to specifying HTL mortality in terms of predation by other HTL organisms and human activities such as fishing. In most models, fish mortality is treated in a simplified manner by assuming constant annual natural and fishing mortality rates. In end-to-end models, marine mammals, birds, and humans may need to be included as full members of the food web in order to obtain realistic representations of the mortality and dynamics of their prey. Integrating closure terms with model-generated predation rates for functional groups whose suite of possible predators is only partially represented by explicit simulation might require model-specific solutions (Fulton et al. 2003a). Representing very-high-trophic-level organisms can require very different modeling approaches than the traditional fish-oriented approaches (Perry et al. 2010) and result in model behaviors that would not be expected from the study of the natural and social systems separately (Liu et al. 2007).


There are scale differences between LTL and HTL dynamics that will need to be accommodated in end-to-end models. Dealing with multiple biological, spatial, and temporal scales is fundamental to all ecological modeling (Levin 1992) and becomes even more acute with end-to-end models. Deciding the best temporal and spatial scales and processes is the art of modeling. A model should be tailored to the specific questions to be addressed, but because there are no general guidelines for deciding the scales and processes to be included for a given question, model development includes judgments on the part of the model developers.

End-to-end models exacerbate the uncertainty in model building because they include a more diverse set of processes that operate on different scales and organisms that span a wide range of sizes and life histories. Examples include how to generate temperature and prey fields in an LTL model on scales that harmonize with the way that fish feeding is represented in the HTL model; how to include behavioral responses when modeling the movement of fish and other HTL organisms on a spatial grid dictated by hydrodynamics; and how to have sufficient temporal and spatial resolution in the physical conditions to realistically represent fast-reaction dynamics (e.g., phytoplankton and fish foraging) while simulating decadal dynamics over a broad enough spatial domain to allow for changes in large-scale migration and movement patterns. We are challenged to harmonize fine-scale physical models with biological processes that span a wide range of temporal and spatial scales for fish (Mullin 1993) and other HTL organisms. We are confident that working on a single temporal and spatial scale will not be sufficient (Fulton et al. 2003b). The issue of scaling is not new, but with end-to-end modeling it becomes even more challenging.

End-to-end models will probably create situations of complex systems behavior that will complicate the interpretation of model results. Complex systems behavior occurs when the composite has properties that are not obvious from the properties of the individual components (Auyang 1999). Models do not need to be complicated to exhibit complex systems behavior. For example, very simple models can generate chaotic and irregular patterns of population dynamics over time (e.g., May 2001). The wide range of scales in end-to-end models and the fact that the individual-based approach allows simple rules to be scaled up to the population and community levels via local interactions provide fertile ground for the emergence of complex systems behavior (Railsback 2001). While such model behavior is often beguiling, we must be prepared to detect and diagnose it—via analysis of model output and simulation experiments (Grimm et al. 2005)—to ensure that the output is indeed realistic and to fully understand the reasons for our results.

Acclimation and Adaptation

Acclimation refers to the biochemical, morphological, and behavioral ways an organism responds to reoccurring changes in its environment. Acclimation responses are usually short term and rapid and involve the expression of phenotypic plasticity (see Scheiner 1993). If the response results in genotype changes, then adaptation has occurred. The need to include acclimation and adaptation is not unique to end-to-end models; it arises from the general problem of how to model responses to changing environmental conditions.

Phytoplankton and zooplankton have relatively rapid turnover rates, so that the typical 50–100-year simulations needed for HTL organisms could include evolutionary adaptation on the part of plankton (Fussmann et al. 2003; Yoshida et al. 2003). Large-scale changes in plankton functional type dominance is possible. High-trophic-level organisms can also exhibit adaptive responses to selective pressures from harvest and global climate change within 50–100-year simulations (Jørgensen et al. 2007), although distinguishing between phenotypic (acclimation) and genetic responses (adaptation) is difficult (Naish and Hard 2008). The trade-offs among species traits as environmental conditions change can affect species' spatial distributions and productivity, predator–prey dynamics, and community structure (Litchman and Klausmeier 2008). Examples of the incorporation of acclimation and adaptation into models include photoacclimation by phytoplankton in response to changing light and nutrient environments (Geider et al. 1996; Flynn 2003; Pahlow 2005; Armstrong 2006); optimal acclimation of nutrient uptake by phytoplankton (Flynn 2003; Smith et al. 2009); the emergence of alternative phenotypes in phytoplankton (Follows et al. 2007); changes in the migration of fish (Mullon et al. 2002; Huse and Ellingsen 2008); and changes in the age at maturity of fish as a result of selective pressures and inheritability (Jager 2001). Methods are being developed, such as structural dynamic models (see Jorgensen 1997), which relate model structure and parameters to recent environmental and other conditions (e.g., Bendoricchio and Jørgensen 1997; Solidoro et al. 2010).

Long-term simulations under potentially highly variable environmental conditions and the diverse mix of organisms in end-to-end models will heighten the need for mechanistic representations of acclimation and adaptation. This is an active area of research, and the examples cited above (and others) should provide a basis for the eventual inclusion of acclimation and adaptation into end-to-end models. The challenge will be to avoid unsupportable and deleterious model complexity.

Behavioral Movement

End-to-end models that include the juvenile and adult stages of HTL organisms will need to deal with behavioral movement. Movement based on physical conditions has been investigated for a long time, often as particle tracking. This approach has been used extensively with the early life stages of fish (North et al. 2009), the swimming abilities of which can be simplified (i.e., vertical only) in favor of relying on advective and dispersive transport to move the particles around a spatial grid (Lett et al. 2009). Similar approaches can be taken (with caution) to simulating the transport of copepod stages (Speirs et al. 2006). As we include the juvenile and adult stages of HTL organisms, we must deal not only with movement based on physical conditions but also significant movement related to behavior. In contrast to the movement of plankton, that of adult fish and other HTL organisms (e.g., marine mammals) is poorly approximated by treating them as passive particles. Behavioral movement plays a strong role in many HTL organisms and is a function of both environmental factors and the state of the organism itself (Burrows 1994; Tyler and Rose 1994). Modeling behavioral movement has involved a diverse set of approaches (Giske et al. 1998) and is receiving increasing attention (Sugden and Pennisi 2006). Several of the commonly used approaches are fitness based (Railsback et al. 1999), kinesis (Humston et al. 2004), neural network with genetic algorithm (Huse and Giske 1998), rule based (Vabø and Nøttestad 1997; Roth et al. 2008), random walk (Huse 2001; Zhang et al. 2007), and event driven (Goodwin et al. 2006). There are also approaches that relate algorithms to the optimality of the overall spatial distribution (e.g., ideal free distribution; Kacelnik et al. 1992). At present, there are no general guidelines as to which approach is appropriate in certain situations or how the approaches relate to each other within a general framework. Yet the choice of a specific movement algorithm can affect the predictions of fish growth and spatial distribution (e.g., Wildhaber and Lamberson 2004). We recommend that a group effort be made to examine the methods of modeling behavioral movement. The group's charge would be to identify the approaches that are available, indicate how they relate to each other, and determine the conditions under which each shows promise.

Software and Technology

End-to-end models require a substantial amount of custom computer code, and an “off-the-shelf” end-to-end model is neither realistic nor desirable. For large-scale, complicated modeling, languages such as FORTRAN and C are still preferred, mostly for reasons of processing speed (Barnes and Hopkins 2003) and ease of coupling to existing model code. We cannot declare a single programming language the best for end-to-end models, but we do foresee continued reliance on sequential languages. End-to-end models will require extensive computer programming skills into the foreseeable future. Common data standards (including common units) and interfaces would make sharing, exchanging, and testing model codes easier.

We anticipate new algorithms for one-way and two-way coupling solutions, predator–prey interactions, behavioral movement, and evolutionary adaptation, to name a few. However, end-to-end models (and individual-based models in general) are still very much custom computer programs known and available only to the model developers and perhaps some of their collaborators. This creates challenges in comparing results and sharing the lessons learned about algorithms (Lorek and Sonnenschein 1999; Ropella et al. 2002). As the building of end-to-end models is still in its infancy, we have the opportunity to develop a community-based approach with open-source computer code and rapid exchange of information. Data standards, such as that of the Network Common Data Form (NETCDF), would enable easy sharing of input and output files. The community involvement in the Regional Ocean Modeling System (ROMS) modeling effort (Shchepetkin and McWilliams 2005) is a good example of an open-source, community approach to model development. If there is ever a time to establish a common framework for end-to-end modeling, it is now, when we are at the beginning of what we expect to be a substantial effort in this area.

Solution Techniques: One-Way Linking or Two-Way Coupling

The components or submodels of end-to-end models can be solved by means of either one-way linking or two-way coupling. Two-way coupling involves the solution of all submodels simultaneously, thereby allowing for dynamic feedbacks among them. Thus, the hydrodynamic, LTL, and HTL submodels can exchange information as the solution proceeds. Feedbacks are critical for the accurate simulation of HTL population dynamics because of the important role played by density-dependent processes in those dynamics (Rose et al. 2001). The simulated dynamics of HTL organisms, in turn, affect the realism of the dynamics of LTL organisms. Typically, fish consumption (and thus growth) is determined from the available zooplankton at that time step and then summed over the HTL individuals and fed back into the LTL submodel as zooplankton mortality (e.g., Travers et al., in press). In a tightly coupled system, fish consumption affects the population dynamics of the zooplankton, which then has a density-dependent effect on fish growth. Density-dependent feedbacks, which occur throughout the entire LTL–HTL system, impose bounds on the population dynamics and enable assessment of sustainable harvest levels of commercially and recreationally important HTL organisms. It is the compensatory density dependence in fish populations that allows increased mortality from harvesting to result in a stable population over time and makes possible the evaluation of sustainable harvest options (Rose and Cowan 2003).

One-way linking involves using the output of one submodel as input to another submodel without feedbacks. A common example is running the hydrodynamic component and then using the stored velocity, temperature, and other environmental fields to drive the LTL model. In another type of one-way coupling the output from one submodel is summarized in terms of aggregate indicators and input into the other submodels (e.g., Aydin et al. 2005). If the LTL submodel is solved with the physical conditions submodel but separately from the HTL submodel, the consumption of zooplankton by fish has no effect on the availability of zooplankton for other fish (i.e., the crowding of fish has no effect on food availability). The implicit assumption is that the ecological linkage is also unidirectional.

There are trade-offs between the one-way and two-way approaches. Computational issues arise with two-way coupling because of the disaggregation and long run times needed in end-to-end models. One viable way of dealing with this is to use coarser spatial and temporal scales in the HTL submodel than in the physical and LTL submodels (Fulton et al. 2003b; Travers et al. 2007). There may also be numerical solution algorithms that partially alleviate the computational limitations (e.g., Walters et al. 1999). In a two-way system, the mortality from fish consumption must be fed back into the LTL submodel at the spatial and temporal scales of that submodel. If one uses a coarser spatial grid and longer time step for the HTL submodel, there will be a disaggregation problem: how to relate predation mortality on one scale to prey dynamics on another. One-way linking reduces execution time and avoids disaggregation issues as long as the next submodel in the chain is at the same or coarser time and spatial scales (it is easier to aggregate going up the food chain than to disaggregate going down it), but at the cost of not allowing feedbacks. Possible feedbacks that can be too important to ignore relate not only to the density-dependent responses of HTL organisms (Rose et al. 2001) but also to the interaction between biological and physical processes. For example, biota can affect sea ice dynamics (e.g., Leck et al. 2004) and water temperatures (e.g., Marzeion et al. 2005). If we assume one-way linkages, we have to be very sure not only that the linkage is really one-way but that it will remain one-way under all plausible simulation scenarios, including climate change. This seems unlikely as a general rule, but it may be the case in specific situations. One can force the feedbacks (e.g., reduce growth rates according to the fish biomass in the same spatial area), but externally defined feedbacks are often difficult to parameterize and can be a major driver of model dynamics. It is usually easier to formulate and justify feedbacks that emerge from model processes and local considerations than ones that are simply imposed (e.g., Rose et al. 2001).

An area worthy of exploration is coupling that is intermediate between strictly one-way and two-way coupling. Data assimilation techniques (Robinson and Lermusiaux 2002) may be useful in linking LTL and HTL submodels. In some situations they may permit existing models to be used without major reparameterization (e.g., one can still use the closure mortality term in NPZ models). The assimilation method would enable some correcting of the two coupled submodels during their simultaneous solution (Libralato and Solidoro 2010). Data assimilation may also allow communication between submodels at key points in time while allowing them to be run in one-way mode at others. The issue of solution techniques depends on the specific situation at hand, but in general it is a major unresolved problem in end-to-end modeling.

Other issues related to solving end-to-end models are the need for long-term simulations and the complications arising from the representation of local interactions, such as predator–prey encounters. Fish and other HTL organisms have complex life cycles that require multiple generations for changes in environmental and biological conditions to be fully expressed at the population and community levels. This translates into long spin-up times and decadal or longer simulations, especially if long-lived species (e.g., predatory fish and marine mammals) are included. Both HTL and LTL submodels require physical representations on relatively fine scales in order to capture the important spatial and temporal dynamics. For numerical reasons, the super-individual approach (Scheffer et al. 1995) is often used in models of HTL organisms that couple to LTL models. Each such super-individual is worth some number of identical individuals, which decreases through time via mortality processes. The activities (e.g., feeding) and abundance of the super-individuals are scaled up to the population level by adjusting them by their population worths (i.e., how many other identical individuals they represent). Using super-individuals addresses the problems of representing the variability within large populations (billions of fish in real populations) without representing every individual and maintaining fixed-size arrays in the computer program. However, super-individuals should be used carefully to avoid introducing unwanted numerical artifacts into the solutions (Parry and Evans 2008). Predator–prey interactions between HTL organisms and LTL groups usually involve the meshing of Lagrangian and Eulerian representations (Bian 2003; Goodwin et al. 2006). Interactions between HTL organisms (e.g., competition for food and predator–prey) introduce issues as to their spatial scale and how to maintain mass balance while super-individuals eat a diverse set of prey, including other super-individuals (e.g., McDermot and Rose 1999; Shin and Cury 2001).

Model Confidence and Forecasting

End-to-end models involve a very diverse set of calculations that need calibration and validation to enable one to judge a model's “skill” and how much confidence to place in it. The calibration and validation of hydrodynamics and LTL dynamics (especially in NPZ versions) differ from those usually done with HTL models. As with any modeling effort, calibration, validation, and measures of model skill depend on the questions to be addressed and the quantity and quality of the available data (Stow et al. 2009). In general, partial skill assessment can be achieved with the hydrodynamics component by using relatively short-term, spatially detailed measurements of the physical conditions. Evaluation of some phenomena (e.g., the physical responses to the El Niño–Southern Oscillation) requires long-term and broader-scale measurements. The LTL component (especially as regards mesozooplankton) is more challenging, as the temporal and spatial patchiness in species' distributions and their multiple life stages make synoptic and comprehensive monitoring more difficult. Finally, the complex life cycles and longevity of HTL organisms have always presented an enormous challenge to skill assessment for fish population models (Schnute and Richards 2001), especially when those models provide the foundation for fisheries stock assessment and management decisions (Hilborn and Walters 2001). Long-standing issues with calibration and validation carry forward and will be amplified when fish and other HTL organisms are imbedded in end-to-end models. Even if sufficient data are available (which they never are), simply getting the model to match the spatial and temporal patterns in physical conditions, nutrients, and HTL organisms will not be sufficient for some applications. Different sets of assumptions and parameter values will probably generate reasonable patterns within the resolution of the available data, and the particular assumptions and parameter values can greatly affect the predicted dynamics under new conditions. The diversity of information needed for the calibration and validation of end-to-end models also has implications for how data should be collected (e.g., common currency; sampling locations and frequency; synoptic measurements) to facilitate model–data comparisons.

While end-to-end modeling is developing at encouraging speed, we urge restraint in using end-to-end models in a true forecasting mode (Clark et al. 2001) until we gain more experience regarding their performance. In the near term, end-to-end modeling has many potential applications in strategic scenario analysis, pushing the envelope of modeling, and identifying data needs for future analyses. End-to-end models may appear to be an ideal way to meet the need for ecosystem-based approaches, but we should resist the temptation of using them to make forecasts for management until their performance and robustness are adequately tested. We need to develop more end-to-end models to work through the algorithms, identify the critical knowledge gaps, and evaluate model performance with different test cases. We believe that end-to-end models will eventually reach the point at which they can be used not only for scenario analysis but also for direct management applications. At present, however, there is a great deal of work to be done before they can be used in a truly predictive mode. Fisheries management has become extremely controversial (National Research Council 2002), and allowing end-to-end models to be prematurely pushed (or pulled) into that arena could hinder further model development. Certainly, end-to-end modeling can and should be used on a case-specific basis to inform management, particularly about the possible outcomes of long-term strategies. This is all the more so because there are often large uncertainties associated with the models currently being used, especially those involving long-term responses to alternative scenarios. But looking to the future, the need to forecast should be a major criterion guiding end-to-end model development.

True Interdisciplinary Efforts

End-to-end modeling will further challenge our ability to forge true interdisciplinary research teams. There are many multidisciplinary efforts under way in marine ecosystem modeling. We now need to move even further—from multidisciplinary to truly interdisciplinary efforts. We view the difference between multidisciplinary and interdisciplinary in terms of the degree of communication and feedback that occurs between the various research groups. A group of people working on the same problem but in a quasi-independent manner with periodic meetings to report on progress may be considered multidisciplinary but not truly interdisciplinary. Interdisciplinary research is characterized by groups that work together closely, with the constant exchange of information and adjustments to plans in response to the needs of the other groups.

The interdisciplinary challenge is not trivial. We must recognize that end-to-end modeling is as much a people challenge as a technical one. It requires respecting and understanding others' perspectives and approaches and involves communicating information in a form that is understandable by colleagues who speak the languages of other disciplines. At times, it will require trusting others' abilities without fully understanding what they are saying. The issues of communication and trust can be as challenging as ensuring that all of the needed disciplines are represented on the team.

End-to-end modeling will require the involvement of a wide variety of disciplines (e.g., hydrodynamics, LTL organisms, climate, fisheries, community ecology, economics, sociology, anthropology, etc.), which will challenge the classical research models. Involvement of a wide variety of disciplines will present a challenge to funding agencies as to how to evaluate proposals as well as to institutions taking the “silo” approach of grouping like disciplines together and adhering to the idea that credit is diluted when people from different organizations are involved. End-to-end modeling will be an excellent case study of interdisciplinary research and collaboration among groups comprised of disciplines with different research traditions.

Concluding Remarks

The above comments are presented in the hope that they will facilitate progress in the conceptualization and development of end-to-end models of marine ecosystems. Not all of the issues noted will be important in every situation, and our list of issues is not comprehensive. While the tasks ahead are formidable, they are feasible and many are project critical. At present, several research groups are developing large-scale end-to-end models. We envision more such groups getting involved and development activities proceeding in parallel, with close collaboration among the groups. The immediate next step is exploratory.

Most likely, the various research groups will continue their development of end-to-end models largely adhering to their own philosophies, structures, and algorithms. We are in the proof-of-concept phase to show that such models can be developed and implemented and in a learning phase about the solution techniques and algorithm development. In due course, a joining of forces that brings the experiences of the groups together will lead to the formulation of the next generation of end-to-end models. While the modeling community will grow and become more complex, we advocate a unified comparative analysis involving model intercomparisons (e.g., models applied to common systems). Early agreement on common-language issues would facilitate communication among the groups. We are at the beginning of what we think will be a growing and long-term effort toward the development and application of end-to-end modeling. We are being presented with a unique opportunity to set up a community-based effort. We urge anyone interested to get involved in what appears to be an exciting scientific, collaborative, and computational journey.


This paper resulted from discussions at a workshop entitled “Bridging the Gap between Lower and Higher Trophic Levels” held February 10–12, 2009, in Plymouth, England, and led by J. I. Allen. The workshop was funded by the UK Natural Environment Research Council International Opportunities Fund through the Advances in Marine Ecosystem Modeling Research program. We wish to acknowledge the following participants in the workshop, who contributed to the free-flowing discussions that made it a success: M. N. Aita, J. Aldridge, J. Beecham, M. Bunke, M. Butenschön, A. Christensen, K. Edwards, I. Grigorov, J. Heard, T. Hirata, W. Kühn, V. Lauria, P. Licandro, K. Lygre, N. Mountford, L. Polimene, A. Rajan, E. Ramírez, A. Samuelsen, K. Tsiaras, S. van Leeuwen, and M. Zavatarelli. The participation of B. Megrey in preparing this paper is noted as research contribution EcoFOCI-0729 to NOAA's Ecosystem and Fisheries–Oceanography Coordinated Investigations. Preparation of this paper by K.A.R. was partially supported by the National Oceanic and Atmospheric Administration, Center for Sponsored Coastal Ocean Research (CSCOR) NGOMEX06 grant NA06NOS4780131 awarded to the University of Texas and by NOAA grant NA09NMF4720183 awarded to Rutgers University as part of the Comparative Analysis of Marine System Organization program. This is publication 127 of NOAA's CSCOR NGOMEX06 program.



T. Andersen, J. J. Elser, and D. O. Hessen . 2004. Stoichiometry and population dynamics. Ecology Letters 7:884–900. Google Scholar


T. R. Anderson 2005. Plankton functional type modelling: running before we can walk? Journal of Plankton Research 27:1073–1081. Google Scholar


R. A. Armstrong 2006. Optimality-based modeling of nitrogen allocation and photoacclimation in photosynthesis. Deep-Sea Research Part II 53:513–531. Google Scholar


S. Y. Auyang 1999. Foundations of complex-system theories in economics, evolutionary biology, and statistical physics. Cambridge University Press. Cambridge, UK. Google Scholar


K. Y. Aydin, G. A. McFarlane, J. R. King, B. A. Megrey, and K. W. Myers . 2005. Linking oceanic food webs to coastal production and growth rates of Pacific salmon (Oncorhynchus spp.) using models on three scales. Deep-Sea Research 5:757–780. Google Scholar


A. Bakun 2006. Wasp-waist populations and marine ecosystem dynamics: navigating the “predator pit” topographies. Progress in Oceanography 68:271–288. Google Scholar


D. J. Barnes and T. R. Hopkins . 2003. The impact of programming paradigms on the efficiency of an individual-based simulation model. Simulation Modelling: Practice and Theory 11:557–569. Google Scholar


R. J. Beamish, A. J. Benson, R. M. Sweeting, and C. M. Neville . 2004. Regimes and the history of the major fisheries off Canada's West Coast. Progress in Oceanography 60:55–385. Google Scholar


G. Bendoricchio and S. E. Jørgensen . 1997. Exergy as goal function of ecosystems dynamic. Ecological Modelling 102:5–15. Google Scholar


L. Bian 2003. The representation of the environment in the context of individual-based modeling. Ecological Modelling 159:279–296. Google Scholar


J. C. Blackford 1997. An analysis of benthic biological dynamics in a North Sea ecosystem model. Journal of Sea Research 38:213–230. Google Scholar


B. Breckling, U. Middelhoff, and H. Reuter . 2006. Individual-based models as tools for ecological theory and application: understanding the emergence of organizational properties in ecological systems. Ecological Modelling 194:102–113. Google Scholar


D. L. Breitburg, J. K. Craig, R. S. Fulford, K. A. Rose, W. R. Boynton, D. Brady, B. J. Ciotti, R. J. Diaz, K. D. Friedland, J. D. Hagy III, D. R. Hart, A. H. Hines, E. D. Houde, S. E. Kolesar, S. W. Nixon, J. A. Rice, D. H. Secor, and T. E. Targett . 2009. Nutrient enrichment and fisheries exploitation: interactive effects on estuarine living resources and their management. Hydrobiologia 629:31–47. Google Scholar


R. D. Brodeur, H. Sugisaki, and G. L. Hunt . 2002. Increases in jellyfish biomass in the Bering Sea: implications for the ecosystem. Marine Ecology Progress Series 233:89–103. Google Scholar


A. D. Bryant, M. R. Heath, N. Broekhuizen, J. G. Ollason, W. S. C. Gurney, and S. P. R. Greenstreet . 1995. Modeling the predation, growth, and population dynamics of fish within a spatially resolved shelf sea ecosystem model. Netherlands Journal of Sea Research 33:407–421. Google Scholar


M. T. Burrows 1994. An optimal foraging and migration model for juvenile plaice. Evolutionary Ecology 8:125–149. Google Scholar


J. F. Caddy 1993. Toward a comparative evaluation of human impacts on fishery ecosystems of enclosed and semienclosed seas. Reviews in Fisheries Science 1:57–95. Google Scholar


F. Carlotti and J. C. Poggiale . 2010. Towards methodological approaches to implement the zooplankton component in “end to end” food web models. Progress in Oceanography 84:20–38. Google Scholar


H. Caswell 2001. Matrix population models: construction, analysis, and interpretation. Sinauer Associates. Sunderland, Massachusetts. Google Scholar


F. P. Chavez, J. Ryan, S. E. Lluch-Cota, and M. Ňiquen . 2003. From anchovies to sardines and back: multidecadal change in the Pacific Ocean. Science 299:217–221. Google Scholar


D. M. Checkley 1982. Selective feeding by Atlantic herring (Clupea harengus) larvae on zooplankton in natural assemblages. Marine Ecology Progress Series 9:245–253. Google Scholar


J. S. Clark, S. Carpenter, M. Barber, S. Collins, A. Dobson, J. Foley, D. Lodge, M. Pascual, R. Pielke, W. Pizer, C. Pringle, W. Reid, K. Rose, O. Sala, W. Schlesinger, D. Wall, and D. Wear . 2001. Ecological forecasts: an emerging imperative. Science 293:657–660. Google Scholar


R. Cropp and J. Norbury . 2009. Parameterizing plankton functional type models: insights from a dynamical systems perspective. Journal of Plankton Research 31:939–963. Google Scholar


P. Cury, A. Bakun, R. J. M. Crawford, A. Jarre-Teichmann, R. A. Quinones, L. J. Shannon, and H. M. Verheye . 2000. Small pelagics in upwelling systems: patterns of interaction and structural changes in “wasp-waist” ecosystems. ICES Journal of Marine Science 57:603–618. Google Scholar


P. M. Cury, Y-J. Shin, B. Planque, J. M. Durant, J-M. Fromentin, S. Kramer-Schadt, N. C. Stenseth, M. Travers, and V. Grimm . 2008. Ecosystem oceanography for global change in fisheries. Trends in Ecology and Evolution 23:338–346. Google Scholar


U. Daewel, M. A. Peck, C. Schrum, and M. A. St. John . 2008. How best to include the effects of climate-driven forcing on prey fields in larval fish individual-based models. Journal of Plankton Research 30:1–5. Google Scholar


D. L. DeAngelis and W. M. Mooij . 2005. Individual-based modeling of ecological and evolutionary processes. Annual Review of Ecology, Evolution, and Systematics 36:147–168. Google Scholar


D. L. DeAngelis and K. A. Rose . 1992. Which individual-based approach is most appropriate for a given problem? Pages 67–87. in D. L. DeAngelis and L. J. Gross . editors. Individual-based approaches in ecology: populations, communities, and ecosystems. Routledge. Chapman, and Hall, New York. Google Scholar


W. Ebenhoh, C. Kohlmeier, and P. J. Radford . 1995. The benthic biological submodel in the European regional seas ecosystem model. Netherlands Journal of Sea Research 33:423–452. Google Scholar


M. J. R. Fasham, H. W. Ducklow, and S. M. McKelvie . 1990. A nitrogen-based model of plankton dynamics in the oceanic mixed layer. Journal of Marine Research 48:591–639. Google Scholar


W. Fennel and T. Neumann . 2004. Introduction to the modelling of marine systems. Elsevier. Amsterdam. Google Scholar


J. C. Field, R. C. Francis, and K. Aydin . 2006. Top-down modeling and bottom-up dynamics: linking a fisheries-based ecosystem model with climate hypotheses in the northern California Current. Progress in Oceanography 68:238–270. Google Scholar


K. J. Flynn 2003. Modelling multinutrient interactions in phytoplankton; balancing simplicity and realism. Progress in Oceanography 56:249–279. Google Scholar


K. J. Flynn 2005. Castles built on sand: dysfunctionality in plankton models and the inadequacy of dialogue between biologists and modelers. Journal of Plankton Research 27:1205–1210. Google Scholar


K. J. Flynn 2010. Ecological modelling in a sea of variable stoichiometry: dysfunctionality and the legacy of Redfield and Monod. Progress in Oceanography 84:52–65. Google Scholar


K. J. Flynn and X. Irigoien . 2009. Why aldehyde-induced insidious effects cannot be considered as a diatom defence mechanism against copepods. Marine Ecology Progress Series 377:79–89. Google Scholar


M. J. Follows, S. Dutkiewicz, S. Grant, and S. W. Chisholm . 2007. Emergent biogeography of microbial communities in a model ocean. Science 315:1843–1846. Google Scholar


K. T. Frank, B. Petrie, J. A. Choi, and W. C. Leggett . 2005. Trophic cascades in a formerly cod-dominated ecosystem. Science 308:1621–1623. Google Scholar


M. A. M. Friedrichs, J. Dusenberry, L. Anderson, R. Armstrong, F. Chai, J. Christian, S. C. Doney, J. Dunne, M. Fujii, R. Hood, D. McGillicuddy, K. Moore, M. Schartau, Y. H. Sptiz, and J. Wiggert . 2007. Assessment of skill and portability in regional marine biogeochemical models: role of multiple phytoplankton groups. Journal of Geophysical Research 112 C08001.  DOI: 1029/2006JC003852.  Google Scholar


E. A. Fulton, A. D. M. Smith, and C. R. Johnson . 2003a. Mortality and predation in ecosystem models: is it important how these are expressed? Ecological Modelling 169:157–178. Google Scholar


E. A. Fulton, A. D. M. Smith, and C. R. Johnson . 2003b. Effect of complexity on marine ecosystem models. Marine Ecology Progress Series 253:1–16. Google Scholar


E. A. Fulton, A. D. M. Smith, and C. R. Johnson . 2004a. Biogeochemical marine ecosystem models, I. IGBEM: a model of marine bay ecosystems. Ecological Modelling 174:267–307. Google Scholar


E. A. Fulton, A. D. M. Smith, and A. E. Punt . 2004b. Ecological indicators of the ecosystem effects of fishing. Australian Fisheries Management Authority. Report R99/1546,. Canberra. Google Scholar


G. F. Fussmann, S. P. Ellner, and N. G. Hairston . 2003. Evolution as a critical component of plankton dynamics. Proceedings of the Royal Society of London B 270:1015–1022. Google Scholar


R. J. Geider, H. L. MacIntyre, and T. M. Kana . 1996. A dynamic model of photoadaptation in phytoplankton. Limnology and Oceanography 41:1–15. Google Scholar


W. C. Gentleman, A. B. Neuheimer, and R. G. Campbell . 2008. Modelling copepod development: current limitations and a new realistic approach. ICES Journal of Marine Science 65:399–413. Google Scholar


J. Giske, G. Huse, and Ø Fiksen . 1998. Modelling spatial dynamics of fish. Reviews in Fish Biology and Fisheries 8:57–91. Google Scholar


R. A. Goodwin, J. M. Nestler, J. J. Anderson, L. J. Weber, and D. P. Loucks . 2006. Forecasting 3-D fish movement behavior using a Eulerian–Lagrangian–agent method (ELAM). Ecological Modelling 192:197–223. Google Scholar


V. Grimm, E. Revilla, U. Berger, F. Jeltsch, W. M. Mooij, S. F. Railsback, H-H. Thulke, J. Weiner, T. Wiegand, and D. L. DeAngelis . 2005. Pattern-oriented modeling of agent-based complex systems: lessons from ecology. Science 310:987–991. Google Scholar


R. Hilborn 2007. Reinterpreting the state of fisheries and their management. Ecosystems 10:1362–1369. Google Scholar


R. Hilborn and C. J. Walters . 2001. Quantitative fisheries stock assessment: choice, dynamics, and uncertainty. Kluwer Academic. Boston. Google Scholar


O. Hjerne and S. Hansson . 2002. The role of fish and fisheries in Baltic Sea nutrient dynamics. Limnology and Oceanography 47:1023–1032. Google Scholar


R. R. Hood, E. A. Laws, R. A. Armstrong, N. R. Bates, C. W. Brown, C. A. Carlson, F. Chai, S. C. Doney, P. G. Falkowski, R. A. Feely, M. A. M. Friedrichs, M. R. Landry, J. K. Moore, D. M. Nelson, T. L. Richardson, B. Salihoglu, M. Schartau, D. A. Toole, and J. D. Wiggert . 2006. Pelagic functional group modeling: progress, challenges, and prospects. Deep-Sea Research 53:459–512. Google Scholar


E. D. Houde 1989. Subtleties and episodes in the early life of fishes. Journal of Fish Biology 35:29–38. Google Scholar


R. Humston, D. B. Olson, and J. S. Ault . 2004. Behavioral assumptions in models of fish movement and their influence on population dynamics. Transactions of the American Fisheries Society 133:1304–1328. Google Scholar


G. L. Hunt and P. J. Stabeno . 2002. Climate change and the control of energy flow in the southeastern Bering Sea. Progress in Oceanography 55:5–22. Google Scholar


G. Huse 2001. Modelling habitat choice in fish using adapted random walk. Sarsia 86:477–483. Google Scholar


G. Huse and I. Ellingsen . 2008. Capelin migrations and climate change: a modelling analysis. Climatic Change 87:177–197. Google Scholar


G. Huse and J. Giske . 1998. Ecology in the Mare Pentium: an individual-based spatio-temporal model for fish with adapted behaviour. Fisheries Research 37:163–178. Google Scholar


L. Hutchings, M. Barange, S. F. Bloomer, A. J. Boyd, R. J. M. Crawford, J. A. Huggett, M. Kerstan, J. L. Korrubel, J. A. A. De Oliveira, S. J. Painting, A. J. Richardson, L. J. Shannon, F. H. Schulein, C. D. Van der Lingen, and H. M. Verheye . 1998. Multiple factors affecting South African anchovy recruitment in the spawning, transport, and nursery areas. South African Journal of Marine Science 19:211–255. Google Scholar


S. Ito, K. A. Rose, A. J. Miller, K. Drinkwater, K. Brander, J. E. Overland, S. Sundby, E. Curchitser, J. W. Hurrell, and Y. Yamanaka . 2010. Ocean ecosystem responses to future global change scenarios: a way forward. Pages 287–322. in M. Barange, J. G. Field, R. H. Harris, E. Hofmann, R. I. Perry, and F. Werner . editors. Global change and marine ecosystems. Oxford University Press. New York. Google Scholar


H. I. Jager 2001. Individual variation in life history characteristics can influence extinction risk. Ecological Modelling 144:61–76. Google Scholar


S. Jennings, M. J. Kaiser, and J. D. Reynolds . 2001. Marine fisheries ecology. Blackwell Scientific Publications. Malden, Massachusetts. Google Scholar


C. Jørgensen, K. Enberg, E. S. Dunlop, R. Arlinghaus, D. S. Boukal, K. Brander, B. Ernande, A. Gårdmark, F. Johnston, S. Matsumura, H. Pardoe, K. Raab, A. Silva, A. Vainikka, U. Dieckmann, M. Heino, and A. D. Rijnsdorp . 2007. Ecology: managing evolving fish stocks. Science 318:1247–1248. Google Scholar


S. E. Jorgensen 1997. Introduction to the development of models with dynamic structure for marine ecosystems. Ecological Modelling 102:1–3. Google Scholar


A. Kacelnik, J. R. Krebs, and C. Bernstein . 1992. The ideal free distribution and predator–prey populations. Trends in Ecology and Evolution 7:50–55. Google Scholar


P. Kremer 1979. Predation by the ctenophore Mnemiopsis leidyi in Narragansett Bay, Rhode Island. Estuaries 2:97–105. Google Scholar


W. Kühn, M. A. Peck, H-H. Hinrichsen, U. Daewel, A. Moll, T. Pohlmann, C. Stegert, and S. Tamm . 2008. Defining habitats suitable for larval fish in the German Bight (southern North Sea): an IBM approach using spatially and temporally resolved, size-structured prey fields. Journal of Marine Systems 74:329–342. Google Scholar


R. Latour, M. J. Brush, and C. F. Bonzek . 2003. Toward ecosystem-based fisheries management: strategies for multispecies modeling and associated data requirements. Fisheries 28 (9):10–22. Google Scholar


C. Leck, M. Tjernström, P. Matrai, E. Swietlicki, and K. Bigg . 2004. Can marine microorganisms influence melting of the Arctic pack ice? Eos 85:25–36. Google Scholar


J. Le Fèvre, L. Legendre, and R. B. Rivkin . 1998. Fluxes of biogenic carbon in the Southern Ocean; roles of large microphagous zooplankton. Journal of Marine Systems 17:325–345. Google Scholar


P. Lehodey, J. Alheit, M. Barange, T. Baumgartner, G. Beaugrand, K. Drinkwater, J. M. Fromentin, S. R. Hare, G. Ottersen, R. I. Perry, C. Roy, C. D. Van der Lingen, and F. Werner . 2006. Climate variability, fish, and fisheries. Journal of Climate 19:5009–5030. Google Scholar


P. Lehodey, F. Chai, and J. Hampton . 2003. Modelling climate-related variability of tuna populations from a coupled ocean–biogeochemical–population dynamics model. Fisheries Oceanography 12:483–494. Google Scholar


C. Le Quere, S. P. Harrison, I. C. Prentice, E. T. Buitenhuis, O. Aumonts, L. Bopp, H. Claustre, L. C. da Cunha, R. Geider, X. Giraud, C. Klaas, K. E. Kohfeld, L. Legendre, M. Manizza, T. Platt, R. B. Rivkan, S. Sathyendranath, J. Uitz, A. J. Watson, and D. Wolf-Galdrow . 2005. Ecosystem dynamics based on plankton functional types for ocean biogeochemistry models. Global Change Biology 11:2016–2040. Google Scholar


C. Lett, K. A. Rose, and B. A. Megrey . 2009. Biophysical models. Pages 88–111. in D. Checkley, C. Roy, J. Alheit, and Y. Oozeki . editors. Climate change and small pelagic fish. Cambridge University Press. Cambridge, UK. Google Scholar


S. A. Levin 1992. The problem of pattern and scale in ecology. Ecology 73:1943–1967. Google Scholar


S. Libralato and C. Solidoro . 2009. Bridging biogeochemical and food web models for an end-to-end representation of marine ecosystem dynamics: the Venice Lagoon case study. Ecological Modelling 220:2960–2971. Google Scholar


E. Litchman and C. A. Klausmeier . 2008. Trait-based community ecology of phytoplankton. Annual Review of Ecology, Evolution, and Systematics 39:615–639. Google Scholar


J. Liu, T. Dietz, S. R. Carpenter, M. Alberti, C. Folke, E. Moran, A. N. Pell, P. Deadman, T. Kratz, J. Lubchenco, E. Ostrom, Z. Quyang, W. Provencher, C. L. Redman, S. H. Schneider, and W. W. Taylor . 2007. Complexity of coupled human and natural systems. Science 317:1513–1516. Google Scholar


H. Lorek and M. Sonnenschein . 1999. Modelling and simulation software to support individual-based ecological modelling. Ecological Modelling 115:199–216. Google Scholar


B. Marzeion, A. Timmermann, R. Murtugudde, and F-F. Jin . 2005. Biophysical feedbacks in the tropical Pacific. Journal of Climate 18:58–70. Google Scholar


O. Maury, Y-J. Shin, B. Faugeras, T. Ben Ari, and F. Marsac . 2007. Modeling environmental effects on the size-structured energy flow through marine ecosystems, part 2. Simulations. Progress in Oceanography 74:500–514. Google Scholar


R. M. May 2001. Stability and complexity in model ecosystems. Princeton University Press. Princeton, New Jersey. Google Scholar


D. McDermot and K. A. Rose . 1999. An individual-based model of lake fish communities: application to piscivore stocking in Lake Mendota. Ecological Modelling 125:67–102. Google Scholar


C. E. Mills 2001. Jellyfish blooms: are populations increasing globally in response to changing ocean conditions? Hydrobiologia 451:55–68. Google Scholar


A. Mitra 2009. Are closure terms appropriate or necessary descriptors of zooplankton loss in nutrient–phytoplankton–zooplankton–type models? Ecological Modelling 220:611–620. Google Scholar


A. Mitra and K. J. Flynn . 2006. Accounting for variation in prey selectivity by zooplankton. Ecological Modelling 199:82–92. Google Scholar


A. Mitra, K. J. Flynn, and M. J. R. Fasham . 2007. Accounting correctly for grazing dynamics in nutrient–phytoplankton–zooplankton models. Limnology and Oceanography 52:649–661. Google Scholar


A. Moll and C. Stegert . 2007. Modelling Pseudocalanus elongatus population dynamics embedded in a water column ecosystem model for the northern North Sea. Journal of Marine Systems 64:35–46. Google Scholar


M. M. Mullin 1993. Webs and scales: physical and ecological processes in marine fish recruitment. University of Washington Press. Seattle. Google Scholar


C. Mullon, P. Cury, and P. Penven . 2002. Evolutionary individual-based model for the recruitment of anchovy (Engraulis capensis) in the southern Benguela. Canadian Journal of Fisheries and Aquatic Sciences 59:910–922. Google Scholar


C. Mullon, P. Freon, and P. Cury . 2005. The dynamics of collapse in world fisheries. Fish and Fisheries 6:111–120. Google Scholar


R. A. Myers and B. Worm . 2003. Rapid worldwide depletion of predatory fish communities. Nature (London) 423:280–283. Google Scholar


P. Munk 1997. Prey size spectra and prey availability of larval and small juvenile cod. Journal of Fish Biology 51:340–351. Google Scholar


K. A. Naish and J. J. Hard . 2008. Bridging the gap between the genotype and the phenotype: linking genetic variation, selection, and adaptation in fishes. Fish and Fisheries 9:396–422. Google Scholar


National Research Council. 2002. Science and its role in the National Marine Fisheries Service. National Academy Press. Washington, D.C. Google Scholar


S. W. Nixon and B. A. Buckley . 2002. “A strikingly rich zone”: nutrient enrichment and secondary production in coastal marine ecosystems. Estuaries 25:782–796. Google Scholar


E. W. North, A. Gallego, and P. Petitgas . 2009. Manual of recommended practices for modelling physical–biological interactions during fish early life. ICES Cooperative Research Report 295.  Google Scholar


T. A. Okey, G. A. Vargo, S. Mackinson, M. Vasconcellos, B. Mahmoudi, and C. A. Meyer . 2004. Simulating community effects of sea floor shading by plankton blooms over the West Florida Shelf. Ecological Modelling 172:339–359. Google Scholar


M. Pahlow 2005. Linking chlorophyll–nutrient dynamics to the Redfield N:C ratio with a model of optimal phytoplankton growth. Marine Ecology Progress Series 287:33–43. Google Scholar


H. R. Parry and A. J. Evans . 2008. A comparative analysis of parallel processing and super-individual methods for improving the computational performance of a large individual-based model. Ecological Modelling 214:141–152. Google Scholar


D. Pauly, V. Christensen, and C. Walters . 2000. Ecopath, Ecosim, and Ecospace as tools for evaluating ecosystem impact of fisheries. ICES Journal of Marine Science 57:697–706. Google Scholar


R. I. Perry, R. E. Ommer, M-C. Badjeck, M. Barange, K. Cochrane, L. Hamilton, A. Jarre, R. Quiñones, and U. R. Sumaila . 2010. Interactions between changes in marine ecosystems and human communities. Pages 221–251. in M. Barange, J. G. Field, R. H. Harris, E. Hofmann, R. I. Perry, and F. Werner . editors. Global change and marine ecosystems. Oxford University Press. New York. Google Scholar


E. K. Pikitch, C. Santora, E. A. Babcock, A. Bakun, R. Bonfil, D. O. Conover, P. Dayton, P. Doukakis, D. Fluharty, B. Heneman, E. D. Houde, J. Link, P. A. Livingston, M. Mangel, M. K. McAllister, J. Pope, and K. J. Sainsbury . 2004. Ecosystem-based fishery management science. Science 305:346–347. Google Scholar


É. E. Plagányi 2007. Models for an ecosystem approach to fisheries. FAO (Food and Agriculture Organization of the United Nations) Fisheries Technical Paper 477.  Google Scholar


J. E. Purcell 2005. Climate effects on formation of jellyfish and ctenophore blooms: a review. Journal of the Marine Biological Association of the UK 85:461–476. Google Scholar


J. E. Purcell and M. N. Arai . 2001. Interactions of pelagic cnidarians and ctenophores with fish: a review. Hydrobiologia 451:27–44. Google Scholar


T. J. Quinn and R. B. Deriso . 1999. Quantitative fish dynamics. Oxford University Press. Oxford, UK. Google Scholar


S. F. Railsback 2001. Concepts from complex adaptive systems as a framework for individual-based modelling. Ecological Modelling 139:47–62. Google Scholar


S. F. Railsback, R. H. Lamberson, B. C. Harvey, and W. E. Duffy . 1999. Movement rules for individual-based models of stream fish. Ecological Modelling 123:73–89. Google Scholar


A. R. Robinson and P. F. J. Lermusiaux . 2002. Data assimilation for modeling and predicting coupled physical–biological interactions in the sea. Pages 475–536. in A. R. Robinson, J. J. McCarthy, and B. J. Rothschild . editors. The sea: biological–physical interactions in the sea, volume 12. Wiley. New York. Google Scholar


G. E. Ropella, S. F. Railsback, and S. K. Jackson . 2002. Software engineering considerations for individual-based models. Natural Resource Modeling 15:5–22. Google Scholar


K. A. Rose 2000. Why are quantitative relationships between environmental quality and fish populations so elusive? Ecological Applications 10:367–385. Google Scholar


K. A. Rose and J. H. Cowan . 2003. Data, models, and decisions in US marine fisheries management: lessons for ecologists. Annual Review of Ecology, Evolution, and Systematics 34:127–151. Google Scholar


K. A. Rose, J. H. Cowan, K. O. Winemiller, R. A. Myers, and R. Hilborn . 2001. Compensatory density dependence in fish populations: importance, controversy, understanding, and prognosis. Fish and Fisheries 2:293–327. Google Scholar


K. A. Rose and S. E. Sable . 2009. Multispecies modeling of fish populations. Pages 373–397. in B. A. Megrey and E. Moksness . editors. Computers in fisheries research, 2nd edition Springer. Google Scholar


B. M. Roth, K. A. Rose, L. S. Rozas, and T. J. Minello . 2008. The relative influence of landscape configuration and inundation on brown shrimp (Farfantepenaeus aztecus) production in northern Gulf of Mexico salt marshes. Marine Ecology Progress Series 359:185–202. Google Scholar


F. S. Scharf, F. Juanes, and R. A. Rountree . 2000. Predator size–prey size relationships of marine fish predators: interspecific variation and effects of ontogeny and body size on trophic-niche breadth. Marine Ecology Progress Series 208:229–248. Google Scholar


M. Scheffer, J. M. Baveco, D. L. DeAngelis, K. A. Rose, and E. H. van Nes . 1995. Super-individuals: a simple solution for modelling large populations on an individual basis. Ecological Modelling 80:161–170. Google Scholar


S. M. Scheiner 1993. Genetics and evolution of phenotypic plasticity. Annual Review of Ecology and Systematics 24:35–68. Google Scholar


J. T. Schnute and L. J. Richards . 2001. Use and abuse of fishery models. Canadian Journal of Fisheries and Aquatic Sciences 58:10–17. Google Scholar


A. F. Shchepetkin and J. C. McWilliams . 2005. The regional oceanic modeling system: a split-explicit, free-surface, topography-following-coordinate ocean model. Ocean Modelling 9:347–404. Google Scholar


Y. Shin and P. Cury . 2001. Exploring fish community dynamics through size-dependent trophic interactions using a spatialized individual-based model. Aquatic Living Resources 14:65–80. Google Scholar


S. L. Smith, Y. Yamanaka, M. Pahlow, and A. Oschlies . 2009. Optimal uptake kinetics: physiological acclimation explains the pattern of nitrate uptake by phytoplankton in the ocean. Marine Ecology Progress Series 384:1–12. Google Scholar


C. Solidoro, G. Cossarini, S. Libralato, and S. Salon . 2010. Remarks on the redefinition of system boundaries and model parameterization for downscaling experiments. Progress in Oceanography 84:134–137. Google Scholar


D. C. Speirs, W. S. C. Gurney, M. R. Heath, W. Horbelt, S. N. Wood, and B. A. de Cuevas . 2006. Ocean-scale modelling of the distribution, abundance, and seasonal dynamics of the copepod Calanus finmarchicus. Marine Ecology Progress Series 313:173–192. Google Scholar


C. A. Stow, J. Jolliff, D. J. McGillicuddy, S. C. Doney, J. I. Allen, M. A. M. Friedrichs, K. A. Rose, and P. Wallhead . 2009. Skill assessment for coupled biological–physical models of marine systems. Journal of Marine Systems 76:4–15. Google Scholar


A. Sugden and E. Pennisi . 2006. When to go, where to stop. Science 313:775. Google Scholar


M. Travers, Y-J. Shin, S. Jennings, and P. Cury . 2007. Towards end-to-end models for investigating the effects of climate and fishing in marine ecosystems. Progress in Oceanography 75:751–770. Google Scholar


M. Travers, Y-J. Shin, S. Jennings, E. Machu, J. A. Huggett, J. G. Field, and P. M. Cury . In press. Two-way coupling versus one-way forcing of plankton and fish models to predict ecosystem changes in the Benguela. Ecological Modelling.  Google Scholar


S. Tuljapurkar and H. Caswell . 1997. Structured-population models in marine, terrestrial, and freshwater systems. Chapman and Hall. New York. Google Scholar


J. A. Tyler and K. A. Rose . 1994. Individual variability and spatial heterogeneity in fish population models. Reviews in Fish Biology and Fisheries 4:91–123. Google Scholar


R. Vabø and L. Nøttestad . 1997. An individual-based model of fish school reactions: predicting antipredator behaviour as observed in nature. Fisheries Oceanography 6:55–171. Google Scholar


C. Walters, V. Christensen, and D. Pauly . 1997. Structuring dynamic models of exploited ecosystems from trophic mass balance assessments. Reviews in Fish Biology and Fisheries 7:139–172. Google Scholar


C. Walters, V. Christensen, W. Walters, and K. A. Rose . In press. Representation of multistanza life histories in Ecospace models for spatial organization of ecosystem trophic interaction patterns. Bulletin of Marine Science.  Google Scholar


C. Walters, D. Pauly, and V. Christensen . 1999. Ecospace: prediction of mesoscale spatial patterns in trophic relationships of exploited ecosystems, with emphasis on the impacts of marine protected areas. Ecosystems 2:539–554. Google Scholar


D. M. Ware and R. E. Thompson . 2005. Bottom-up ecosystem trophic dynamics determine fish production in the Northeast Pacific. Science 308:1280–1284. Google Scholar


F. E. Werner, J. A. Quinlan, R. G. Lough, and D. R. Lynch . 2001. Spatially explicit individual-based modeling of marine populations: a review of the advances in the 1990s. Sarsia 86:411–421. Google Scholar


M. L. Wildhaber and P. J. Lamberson . 2004. Importance of the habitat choice behavior assumed when modeling the effects of food and temperature on fish populations. Ecological Modelling 175:395–409. Google Scholar


R. W. Wilson, F. J. Millero, J. R. Taylor, P. J. Walsh, V. Christensen, S. Jennings, and M. Grosell . 2009. Contribution of fish to the marine inorganic carbon cycle. Science 323:359–362. Google Scholar


B. Worm, R. Hilborn, J. K. Baum, T. A. Branch, J. S. Collie, C. Costello, M. J. Fogarty, E. A. Fulton, J. A. Hutchings, S. Jennings, O. P. Jensen, H. K. Lotze, P. M. Mace, T. R. McClanahan, C. Minto, S. R. Palumbi, A. M. Parma, D. Ricard, A. A. Rosenberg, R. Watson, and D. Zeller . 2009. Rebuilding global fisheries. Science 325:578–585. Google Scholar


T. Yoshida, L. E. Jones, S. P. Ellner, G. F. Fussmann, and N. G. Hairston . 2003. Rapid evolution drives ecological dynamics in a predator–prey system. Nature (London) 424:303–306. Google Scholar


X. Zhang, S. N. Johnson, J. W. Crawford, P. J. Gregory, and I. M. Young . 2007. A general random walk model for the leptokurtic distribution of organism movement: theory and application. Ecological Modelling 200:79–88. Google Scholar


M. Zhou and M. E. Huntley . 1997. Population dynamics theory of plankton based on biomass spectra. Marine Ecology Progress Series 159:61–73. Google Scholar


[1] 1 Throughout this paper, we use the term “NPZ” to refer to a common class of mass-based models of LTL organisms that simulate the dynamics of nutrients, phytoplankton, and zooplankton. We also include models that represent multiple nutrients, multiple functional groups of phytoplankton and zooplankton, and detritus under this term. The term “LTL models” is a more general one that includes NPZ models as well as other types of models, such as stage-based population dynamics models of copepods.

Kenneth A. Rose, J. Icarus Allen, Yuri Artioli, Manuel Barange, Jerry Blackford, François Carlotti, Roger Cropp, Ute Daewel, Karen Edwards, Kevin Flynn, Simeon L. Hill, Reinier HilleRisLambers, Geir Huse, Steven Mackinson, Bernard Megrey, Andreas Moll, Richard Rivkin, Baris Salihoglu, Corinna Schrum, Lynne Shannon, Yunne-Jai Shin, S. Lan Smith, Chris Smith, Cosimo Solidoro, Michael St. John, and Meng Zhou "End-To-End Models for the Analysis of Marine Ecosystems: Challenges, Issues, and Next Steps," Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science 2010(2010), 115-130, (19 April 2010).
Received: 29 November 2009; Accepted: 23 December 2009; Published: 19 April 2010
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