Evaluation of the trophic structure of the West Florida Shelf in the 2000s using the ecosystem model OSMOSE

Grüss, Arnaud; Schirripa, Michael J.; Chagaris, David; Drexler, Michael; Simons, J.D.; Verley, Philippe; Shin, Yunne-Jaï; Karnauskas, Mandy; Oliveros‐Ramos, Ricardo; Ainsworth, Cameron H.

doi:10.1016/j.jmarsys.2014.11.004

Cited by 41 publications

(58 citation statements)

References 45 publications

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“…Beyond vessel behaviour, POSEIDON spatially represents a coastline, ocean area, ports and fish biology. Fish biology can be represented as simply as with non‐age‐structured logistic growth models with diffusion among grid cells, or as complexly as full ecosystem models, such as OSMOSE (Grüss et al, ; Shin & Cury, , ). Although it is possible to tailor both fleet and biological components of POSEIDON to a specific fishery (Carrella et al, ), here we use a simple conceptual version simulating a hypothetical fishery with one port (Figures ), and one or more non‐interacting fish species having logistic growth (Schaefer, ).…”

Section: Poseidon Modelmentioning

confidence: 99%

Opportunities for agent‐based modelling in human dimensions of fisheries

et al. 2020

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Models of human dimensions of fisheries are important to understanding and predicting how fishing industries respond to changes in marine ecosystems and management institutions. Advances in computation have made it possible to construct agent-based models (ABMs)-which explicitly describe the behaviour of individual people, firms or vessels in order to understand and predict their aggregate behaviours. ABMs are widely used for both academic and applied purposes in many settings including finance, urban planning and the military, but are not yet mainstream in fisheries science and management, despite a growing literature. ABMs are well suited to understanding emergent consequences of fisher interactions, heterogeneity and bounded rationality, especially in complex ecological, social and institutional contexts. For these reasons, we argue that ABMs of human behaviour can contribute significantly to human dimensions of fisheries in three areas: (a) understanding interactions between multiple management institutions; (b) incorporating cognitive and | 571 BURGESS Et al. 7 ABM CHALLENGES 579 7.1 Scope 579 7.2 Calibration and validation 579 7.3 Balancing realism and complexity 578 7.4 Resource and time investment 578 7.5 Documentation and reproducibility 578 8 CONCLUSION: FROM STRATEGIC TO TACTICAL ABMS 581 ACKNOWLEDGEMENTS 581 DATA AVAILABILITY STATEMENT 581 REFERENCES 581 APPENDIX 1 586

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Section: Poseidon Modelmentioning

confidence: 99%

Opportunities for agent‐based modelling in human dimensions of fisheries

et al. 2020

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“…In the simplest case we have small numbers of non-interacting fish species, with population density that grows locally (per spatial cell) according to a simple logistic model, and diffuses spatially according to the local gradient (following Soulié and Thébaud 2006;Cabral et al 2010). A significantly more sophisticated option for the biology is the OSMOSE model Cury 2001, 2004;Grüss et al 2015) a computational model of fish dynamics simulated at school level. All options are described fully in the ESM, and in examples shown in the main paper the logistic model is used throughout.…”

Section: The Modelmentioning

confidence: 99%

A computational approach to managing coupled human–environmental systems: the POSEIDON model of ocean fisheries

et al. 2018

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Sustainable management of complex human-environment systems, and the essential services they provide, remains a major challenge, felt from local to global scales. These systems are typically highly dynamic and hard to predict, particularly in the context of rapid environmental change, where novel sets of conditions drive coupled socio-economic-environmental responses. Faced with these challenges, our tools for policy development, while informed by the past experience, must not be unduly constrained; they must allow equally for both the fine-tuning of successful existing approaches and the generation of novel ones in unbiased ways. We study ocean fisheries as an example class of complex human-environmental systems, and present a new model (POSEIDON) and computational approach to policy design. The model includes an adaptive agentbased representation of a fishing fleet, coupled to a simplified ocean ecology model. The agents (fishing boats) do not have programmed responses based on empirical data, but respond adaptively, as a group, to their environment (including policy constraints). This conceptual model captures qualitatively a wide range of empirically observed fleet behaviour, in response to a broad set of policies. Within this framework, we define policy objectives (of arbitrary complexity) and use Bayesian optimization over multiple model runs to find policy parameters that best meet the goals. The trade-offs inherent in this approach are explored explicitly. Taking this further, optimization is used to generate novel hybrid policies. We illustrate this approach using simulated examples, in which policy prescriptions generated by our computational methods are counterintuitive and thus unlikely to be identified by conventional frameworks.

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“…OSMOSE has often been coupled with lower trophic level systems based on satellite chlorophyll a (Grüss et al 2015) and biogeochemical models (Halouani et al 2016;Travers-Trolet et al 2014). Travers-Trolet et al (2014) investigated the effects of the combined pressures of fishing and climate forcing in the southern Benguela ecosystem using a hydrodynamic lower trophic level OSMOSE-coupled model.…”

Section: Biogeochemical Model Coupled With Individual-based Modelmentioning

confidence: 99%

Modeling the coastal ecosystem complex: present situation and challenges

et al. 2018

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To enhance numerical modeling of the coastal ecosystem complex (CEC), we reviewed the CEC and related concepts along with the current coastal ecosystem model framework in this study. We identified two model implementation paths from the initial objectives to numerical models: specific model building, and the use of existing model frameworks. As the CEC is still at the conceptual stage, both paths are possible. Four important ecological features of CEC modeling (population connectivity, habitat heterogeneity, ontogeny of organisms, and trophic interactions) were also identified. Models for population connectivity, species distributions, life histories, and food webs were categorized using these features. We found that some previously established concepts (between-habitat interactions, coastal ecosystem mosaic, and seascape nursery) overlap with the CEC concept. Several existing integrated model frameworks were reviewed, focusing on their potential to simulate CEC processes. Building specific models for the CEC at the current conceptual stage will be challenging, and modification of existing models will be needed if they are to be used for CEC modeling. Habitat function, ontogenetic development in early life stages, and recruitment variability are important factors when modifying existing models for the development of CEC models. Although model complexity should become high to reproduce observed ecoclogical processes, an intermediate level of model ccomplexity is feasible to decrease parameter uncertainty in models for fisheries management.

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Evaluation of the trophic structure of the West Florida Shelf in the 2000s using the ecosystem model OSMOSE

Cited by 41 publications

References 45 publications

Opportunities for agent‐based modelling in human dimensions of fisheries

Opportunities for agent‐based modelling in human dimensions of fisheries

A computational approach to managing coupled human–environmental systems: the POSEIDON model of ocean fisheries

Modeling the coastal ecosystem complex: present situation and challenges

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