The Mediterranean Rhodes Gyre is a cyclonic gyre with high primary production due to local upwelling of nutrients and occasional deep overturning up to 1 km depth. This nutrient-rich state is in sharp contrast to other parts of the Eastern Mediterranean which are oligotrophic. Here we investigated the upwelling system central to the Rhodes Gyre and the impact of different stressors like meteorological changes, acidification and fishing pressure up to the year 2100. A water column model spanning the physical, chemical and biological system up to top predators (GOTM-ERSEM-BFM-EwE) was used to simulate the pelagic environment under single and combined stressors. Results show that due to increasing winter temperatures, deep overturning events will become more rare in the future until they stop occurring around 2060 under the Paris-agreement climate scenario (RCP4.5) or around 2040 under the business-as-usual climate scenario (RCP8.5). Stratification will become stronger as temperature effects outweigh salinity effects in the surface mixed layer. Together with the lack of deep overturning, this stronger stratification limits the nutrient supply to the euphotic zone, significantly reducing primary production. Phytoplankton species shift towards smaller species as nutrients become more scarce, mimicking the situation found currently on the edge of the gyre. Climatic changes and fishing pressure will affect higher trophic levels in an additive way for some species (sardines, dolphins), while in a synergistic way for others (anchovy, mackerel). Acidification impacts are negligible. Fish stocks will reduce significantly under all scenarios involving climate change effects: ~30% under scenarios imposing RCP4.5 and ~40% under scenarios imposing RCP8.5. The beneficial impact of maximum sustained yield-level fishing is very limited, indicating a need for mitigating measures beyond fleet control.

Climate change is affecting large-scale oceanic processes. How and when these changes will impact those reliant on marine resources is not yet clear. Here we use end-to-end modeling to track the impacts of expected changes through the marine ecosystem on a specific, small community: Cochamó, in the Gulf of Ancud wider area, Chile. This area is important for Chilean fisheries and aquaculture, with Cochamó reliant on both lower and upper trophic level marine resources. We applied the GOTM-ERSEM-BFM coupled hydro-biogeochemical watercolumn model to gauge lower-trophic level marine ecological community response to bottom-up stressors (climate change, ocean acidification), coupled to an existing Ecopath with Ecosim model for the area, which included top-down stressors (fishing). Social scientists also used participatory modeling (Systems Thinking and Bayesian Belief Networking) to identify key resources for Co chamó residents and to assess the community’s vulnerability to possible changes in key resources. Modeling results suggest that flagellate phytoplankton abundance will increase at the cost of other species (particularly diatoms), resulting in a greater risk of harmful algae blooms. Both climate change and acidification slightly increased primary production in the model. Higher trophic level results indicate that some targeted pelagic resources will decline (while benthic ones may benefit), but that these effects might be mitigated by strong fisheries management efforts. Participatory modeling suggests that Cochamó inhabitants anticipate marine ecosystem changes but are divided about possible adaptation strategies. For climate change impact quantification, detailed experimental studies are recommended based on the dominant threats identified here, with specific local species.

Abstract. End-to-end modelling is a rapidly developing strategy for modelling in marine systems science and management. However, problems remain in the area of data matching and sub-model compatibility. A mechanism and novel interfacing system (Couplerlib) is presented whereby a physical–biogeochemical model (General Ocean Turbulence Model–European Regional Seas Ecosystem Model, GOTM–ERSEM) that predicts dynamics of the lower trophic level (LTL) organisms in marine ecosystems is coupled to a dynamic ecosystem model (Ecosim), which predicts food-web interactions among higher trophic level (HTL) organisms. Coupling is achieved by means of a bespoke interface, which handles the system incompatibilities between the models and a more generic Couplerlib library, which uses metadata descriptions in extensible mark-up language (XML) to marshal data between groups, paying attention to functional group mappings and compatibility of units between models. In addition, within Couplerlib, models can be coupled across networks by means of socket mechanisms. As a demonstration of this approach, a food-web model (Ecopath with Ecosim, EwE) and a physical–biogeochemical model (GOTM–ERSEM) representing the North Sea ecosystem were joined with Couplerlib. The output from GOTM–ERSEM varies between years, depending on oceanographic and meteorological conditions. Although inter-annual variability was clearly present, there was always the tendency for an annual cycle consisting of a peak of diatoms in spring, followed by (less nutritious) flagellates and dinoflagellates through the summer, resulting in an early summer peak in the mesozooplankton biomass. Pelagic productivity, predicted by the LTL model, was highly seasonal with little winter food for the higher trophic levels. The Ecosim model was originally based on the assumption of constant annual inputs of energy and, consequently, when coupled, pelagic species suffered population losses over the winter months. By contrast, benthic populations were more stable (although the benthic linkage modelled was purely at the detritus level, so this stability reflects the stability of the Ecosim model). The coupled model was used to examine long-term effects of environmental change, and showed the system to be nutrient limited and relatively unaffected by forecast climate change, especially in the benthos. The stability of an Ecosim formulation for large higher tropic level food webs is discussed and it is concluded that this kind of coupled model formulation is better for examining the effects of long-term environmental change than short-term perturbations.

Models of complex systems with n components typically have order n2 parameters because each component can potentially interact with every other. When it is impractical to measure these parameters, one may choose random parameter values and study the emergent statistical properties at the system level. Many influential results in theoretical ecology have been derived from two key assumptions: that species interact with random partners at random intensities and that intraspecific competition is comparable between species. Under these assumptions, community dynamics can be described by a community matrix that is often amenable to mathematical analysis. We combine empirical data with mathematical theory to show that both of these assumptions lead to results that must be interpreted with caution. We examine 21 empirically derived community matrices constructed using three established, independent methods. The empirically derived systems are more stable by orders of magnitude than results from random matrices. This consistent disparity is not explained by existing results on predator-prey interactions. We investigate the key properties of empirical community matrices that distinguish them from random matrices. We show that network topology is less important than the relationship between a species’ trophic position within the food web and its interaction strengths. We identify key features of empirical networks that must be preserved if random matrix models are to capture the features of real ecosystems.

From economics to natural sciences, many disciplines use complex models and simulations to better understand the world, but the unknown parameters of these models can be difficult to find. Looking to optimise the search for such parameters, many turn to the high parallelism afforded by general purpose Graphical Processing Unit (GP-GPU) programming. This paper discusses the challenges faced and lessons learned when porting such a marine ecology simulation from a pure-CPU implementation to make use of GPU technology. While this is a specific implementation, many of the problems we encountered apply generally to GPU-based simulations. They therefore hint at the potential for reusable solutions to GPU-based environmental simulations, and pave the way for a generic GPU-middleware for natural sciences.

Regular grids or lattices are frequently used to study ecosystems, for observations, experiments and simulations. The regular rectangular or square grid is used more often than the hexagonal grid, but their relative merits have been little discussed. Here we compare rectangular and hexagonal grids for ecological applications. We focus on the reasons some researchers have preferred hexagonal grids and methods to facilitate the use of hexagonal grids. We consider modelling and other applications, including the role of nearest neighbourhood in experimental design, the representation of connectivity in maps, and a new method for performing field surveys using hexagonal grids, which was demonstrated on montane heath vegetation.The rectangular grid is generally preferred because of its symmetrical, orthogonal coordinate system and the frequent use of rasters from Geographic information Systems. Cells in a rectangular grid can also easily be combined to produce new grids with lower resolutions. However, efficient co-ordinate systems and multi-resolution partitions using the hexagonal grid are available. The nearest neighbourhood in a hexagonal grid is simpler and less ambiguous than in a rectangular grid. When nearest neighbourhood, movement paths or connectivity are important, the rectangular grid may not be suitable. We also investigate important differences between visualizations using hexagonal and rectangular grids. A survey of recent uses of grids in Ecological Modelling suggested that hexagonal grids are rarely used, even in applications for which they are more suitable than rectangular grids, e.g. connectivity and movement paths. Researchers should consider their choice of grid at an early stage in project development, and authors should explain the reasons for their choices. (c) 2007 Elsevier B.V. All rights reserved.

An ecological economic model of trawling is presented to demonstrate the effect of trawling location choice strategy on net input (rate of economic gain of fish caught per time spent less costs). Fishing location choice is considered to be a dynamic process whereby trawlers chose from among a repertoire of plastic strategies that they modify if their gains fall below a fixed proportion of the mean gains of the fleet as a whole. The distribution of fishing across different areas of a fishery follows an approximate ideal free distribution (IFD) with varying noise due to uncertainty. The least-productive areas are not utilised because initial net input never reaches the mean yield of better areas subject to competitive exploitation. In cases, where there is a weak temporal autocorrelation between fish stocks in a specific location, a plastic strategy of local translocation between trawls mixed with longer-range translocation increases realised input. The trawler can change its translocation strategy in the light of information about recent trawling success compared to its long-term average but, in contrast to predictions of the Marginal Value Theorem (MVT) model, does not know for certain what it will find by moving, so may need to sample new patches. The combination of the two types of translocation mirrored beam-trawling strategies used by the Dutch fleet and the resultant distribution of trawling effort is confirmed by analysis of historical effort distribution of British otter trawling fleets in the North Sea. Fisheries exploitation represents an area where dynamic agent-based adaptive models may be a better representation of the economic dynamics of a fleet than classically inspired optimisation models.