7 resultados para Spatial dynamic modeling

em Plymouth Marine Science Electronic Archive (PlyMSEA)


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The Scotia Sea has been a focus of biological- and physical oceanographic study since the Discovery expeditions in the early 1900s. It is a physically energetic region with some of the highest levels of productivity in the Southern Ocean. It is also a region within which there have been greater than average levels of change in upper water column temperature. We describe the results of three cruises transecting the central Scotia Sea from south to north in consecutive years and covering spring, summer and autumn periods. We also report on some community level syntheses using both current-day and historical data from this region. A wide range of parameters were measured during the field campaigns, covering the physical oceanography of the region, air–sea CO2 fluxes, macro- and micronutrient concentrations, the composition and biomass of the nano-, micro- and mesoplankton communities, and the distribution and biomass of Antarctic krill and mesopelagic fish. Process studies examined the effect of iron-stress on the physiology of primary producers, reproduction and egestion in Antarctic krill and the transfer of stable isotopes between trophic layers, from primary consumers up to birds and seals. Community level syntheses included an examination of the biomass-spectra, food-web modelling, spatial analysis of multiple trophic layers and historical species distributions. The spatial analyses in particular identified two distinct community types: a northern warmer water community and a southern cold community, their boundary being broadly consistent with the position of the Southern Antarctic Circumpolar Current Front (SACCF). Temperature and ice cover appeared to be the dominant, over-riding factors in driving this pattern. Extensive phytoplankton blooms were a major feature of the surveys, and were persistent in areas such as South Georgia. In situ and bioassay measurements emphasised the important role of iron inputs as facilitators of these blooms. Based on seasonal DIC deficits, the South Georgia bloom was found to contain the strongest seasonal carbon uptake in the ice-free zone of the Southern Ocean. The surveys also encountered low-production, iron-limited regions, a situation more typical of the wider Southern Ocean. The response of primary and secondary consumers to spatial and temporal heterogeneity in production was complex. Many of the life-cycles of small pelagic organisms showed a close coupling to the seasonal cycle of food availability. For instance, Antarctic krill showed a dependence on early, non-ice-associated blooms to facilitate early reproduction. Strategies to buffer against environmental variability were also examined, such as the prevalence of multiyear life-cycles and variability in energy storage levels. Such traits were seen to influence the way in which Scotia Sea communities were structured, with biomass levels in the larger size classes being higher than in other ocean regions. Seasonal development also altered trophic function, with the trophic level of higher predators increasing through the course of the year as additional predator-prey interactions emerged in the lower trophic levels. Finally, our studies re-emphasised the role that the simple phytoplankton-krill-higher predator food chain plays in this Southern Ocean region, particularly south of the SACCF. To the north, alternative food chains, such as those involving copepods, macrozooplankton and mesopelagic fish, were increasingly important. Continued ocean warming in this region is likely to increase the prevalence of such alternative such food chains with Antarctic krill predicted to move southwards.

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Modeling of global climate change is moving from global circulation model (GCM)-type projections with coupled biogeochemical models to projections of ecological responses, including food web and upper trophic levels. Marine and coastal ecosystems are highly susceptible to the impacts of global climate change and also produce significant ecosystem services. The effects of global climate change on coastal and marine ecosystems involve a much wider array of effects than the usual temperature, sea level rise, and precipitation. This paper is an overview for a collection of 12 papers that examined various aspects of global climate change on marine ecosystems and comprise this special issue. We summarized the major features of the models and analyses in the papers to determine general patterns. A wide range of ecosystems were simulated using a diverse set of modeling approaches. Models were either 3-dimensional or used a few spatial boxes, and responses to global climate change were mostly expressed as changes from a baseline condition. Three issues were identified from the across-model comparison: (a) lack of standardization of climate change scenarios, (b) the prevalence of site-specific and even unique models for upper trophic levels, and (c) emphasis on hypothesis evaluation versus forecasting. We discuss why these issues are important as global climate change assessment continues to progress up the food chain, and, when possible, offer some initial steps for going forward.

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We present a unique view of mackerel (Scomber scombrus) in the North Sea based on a new time series of larvae caught by the Continuous Plankton Recorder (CPR) survey from 1948-2005, covering the period both before and after the collapse of the North Sea stock. Hydrographic backtrack modelling suggested that the effect of advection is very limited between spawning and larvae capture in the CPR survey. Using a statistical technique not previously applied to CPR data, we then generated a larval index that accounts for both catchability as well as spatial and temporal autocorrelation. The resulting time series documents the significant decrease of spawning from before 1970 to recent depleted levels. Spatial distributions of the larvae, and thus the spawning area, showed a shift from early to recent decades, suggesting that the central North Sea is no longer as important as the areas further west and south. These results provide a consistent and unique perspective on the dynamics of mackerel in this region and can potentially resolve many of the unresolved questions about this stock

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Traditionally, marine ecosystem structure was thought to be bottom-up controlled. In recent years, a number of studies have highlighted the importance of top-down regulation. Evidence is accumulating that the type of trophic forcing varies temporally and spatially, and an integrated view – considering the interplay of both types of control – is emerging. Correlations between time series spanning several decades of the abundances of adjacent trophic levels are conventionally used to assess the type of control: bottom-up if positive or top-down if this is negative. This approach implies averaging periods which might show time-varying dynamics and therefore can hide part of this temporal variability. Using spatially referenced plankton information extracted from the Continuous Plankton Recorder, this study addresses the potential dynamic character of the trophic structure at the planktonic level in the North Sea by assessing its variation over both temporal and spatial scales. Our results show that until the early-1970s a bottom-up control characterized the base of the food web across the whole North Sea, with diatoms having a positive and homogeneous effect on zooplankton filter-feeders. Afterwards, different regional trophic dynamics were observed, in particular a negative relationship between total phytoplankton and zooplankton was detected off the west coast of Norway and the Skagerrak as opposed to a positive one in the southern reaches. Our results suggest that after the early 1970s diatoms remained the main food source for zooplankton filter-feeders east of Orkney–Shetland and off Scotland, while in the east, from the Norwegian Trench to the German Bight, filter-feeders were mainly sustained by dinoflagellates.

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We review and compare four broad categories of spatially-explicit modelling approaches currently used to understand and project changes in the distribution and productivity of living marine resources including: 1) statistical species distribution models, 2) physiology-based, biophysical models of single life stages or the whole life cycle of species, 3) food web models, and 4) end-to-end models. Single pressures are rare and, in the future, models must be able to examine multiple factors affecting living marine resources such as interactions between: i) climate-driven changes in temperature regimes and acidification, ii) reductions in water quality due to eutrophication, iii) the introduction of alien invasive species, and/or iv) (over-)exploitation by fisheries. Statistical (correlative) approaches can be used to detect historical patterns which may not be relevant in the future. Advancing predictive capacity of changes in distribution and productivity of living marine resources requires explicit modelling of biological and physical mechanisms. New formulations are needed which (depending on the question) will need to strive for more realism in ecophysiology and behaviour of individuals, life history strategies of species, as well as trophodynamic interactions occurring at different spatial scales. Coupling existing models (e.g. physical, biological, economic) is one avenue that has proven successful. However, fundamental advancements are needed to address key issues such as the adaptive capacity of species/groups and ecosystems. The continued development of end-to-end models (e.g., physics to fish to human sectors) will be critical if we hope to assess how multiple pressures may interact to cause changes in living marine resources including the ecological and economic costs and trade-offs of different spatial management strategies. Given the strengths and weaknesses of the various types of models reviewed here, confidence in projections of changes in the distribution and productivity of living marine resources will be increased by assessing model structural uncertainty through biological ensemble modelling.

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We review and compare four broad categories of spatially-explicit modelling approaches currently used to understand and project changes in the distribution and productivity of living marine resources including: 1) statistical species distribution models, 2) physiology-based, biophysical models of single life stages or the whole life cycle of species, 3) food web models, and 4) end-to-end models. Single pressures are rare and, in the future, models must be able to examine multiple factors affecting living marine resources such as interactions between: i) climate-driven changes in temperature regimes and acidification, ii) reductions in water quality due to eutrophication, iii) the introduction of alien invasive species, and/or iv) (over-)exploitation by fisheries. Statistical (correlative) approaches can be used to detect historical patterns which may not be relevant in the future. Advancing predictive capacity of changes in distribution and productivity of living marine resources requires explicit modelling of biological and physical mechanisms. New formulations are needed which (depending on the question) will need to strive for more realism in ecophysiology and behaviour of individuals, life history strategies of species, as well as trophodynamic interactions occurring at different spatial scales. Coupling existing models (e.g. physical, biological, economic) is one avenue that has proven successful. However, fundamental advancements are needed to address key issues such as the adaptive capacity of species/groups and ecosystems. The continued development of end-to-end models (e.g., physics to fish to human sectors) will be critical if we hope to assess how multiple pressures may interact to cause changes in living marine resources including the ecological and economic costs and trade-offs of different spatial management strategies. Given the strengths and weaknesses of the various types of models reviewed here, confidence in projections of changes in the distribution and productivity of living marine resources will be increased by assessing model structural uncertainty through biological ensemble modelling.