Joint effects of population density and toxicant exposure on population dynamics of Capitella sp I

Very few studies have analyzed the dependence of population growth rate on population density, and even fewer have considered interaction effects of density and other stresses, such as exposure to toxic chemicals. Yet without such studies we cannot know whether chemicals harmful at low density have effects on carrying capacity or, conversely, whether chemicals reducing carrying capacity are also harmful at low density, impeding a population's capacity to recover from disturbance. This study examines the combined effects of population density and a toxicant (fluoranthene) on population growth rate (pgr) and carrying capacity using the deposit-feeding polychaete Capitella sp. I as a test organism. Populations were initiated with a stable age distribution, and population density and age/size distribution were followed during a period of 28 wk. Fluoranthene (FLU), population density, and their interaction influenced population growth rate. Population growth rate declined linearly with the logarithm of population biomass, but the slope of the relationship was steeper for the control populations than for populations exposed to 50 mug FLU/(g sediment dry mass). Populations exposed to 150 mug FLU/(g sediment dry mass) went extinct after 8 wk of exposure. Despite concerns that toxicant effects would be exacerbated at high density, we found the reverse to be the case, and effects of fluoranthene on population growth rate were much reduced in the region of carrying capacity. Fluoranthene did. reduce carrying capacity by 46%, and this could haven important implications for interacting species and/or sediment biogeochemical processes.

Function of the hydration layer around an antifreeze protein revealed by atomistic molecular dynamics simulations

Atomistic molecular dynamics simulations are used to investigate the mechanism by which the antifreeze protein from the spruce budworm, Choristoneura fumiferana, binds to ice. Comparison of structural and dynamic properties of the water around the three faces of the triangular prism-shaped protein in aqueous solution reveals that at low temperature the water structure is ordered and the dynamics slowed down around the ice-binding face of the protein, with a disordering effect observed around the other two faces. These results suggest a dual role for the solvation water around the protein. The preconfigured solvation shell around the ice-binding face is involved in the initial recognition and binding of the antifreeze protein to ice by lowering the barrier for binding and consolidation of the protein:ice interaction surface. Thus, the antifreeze protein can bind to the molecularly rough ice surface by becoming actively involved in the formation of its own binding site. Also, the disruption of water structure around the rest of the protein helps prevent the adsorbed protein becoming covered by further ice growth.

Modelling fixed plant and algal dynamics in rivers: an application to the River Frome

The development of eutrophication in river systems is poorly understood given the complex relationship between fixed plants, algae, hydrodynamics, water chemistry and solar radiation. However there is a pressing need to understand the relationship between the ecological status of rivers and the controlling environmental factors to help the reasoned implementation of the Water Framework Directive and Catchment Sensitive Farming in the UK. This research aims to create a dynamic, process-based, mathematical in-stream model to simulate the growth and competition of different vegetation types (macrophytes, phytoplankton and benthic algae) in rivers. The model, applied to the River Frome (Dorset, UK), captured well the seasonality of simulated vegetation types (suspended algae, macrophytes, epiphytes, sediment biofilm). Macrophyte results showed that local knowledge is important for explaining unusual changes in biomass. Fixed algae simulations indicated the need for the more detailed representation of various herbivorous grazer groups, however this would increase the model complexity, the number of model parameters and the required observation data to better define the model. The model results also highlighted that simulating only phytoplankton is insufficient in river systems, because the majority of the suspended algae have benthic origin in short retention time rivers. Therefore, there is a need for modelling tools that link the benthic and free-floating habitats.

Growth and public infrastructure

The paper analyzes a multicountry extension of the Barro model of productive public expenditure. In the presence of positive infrastructural externalities between countries, the provision of infrastructure will be inefficiently low if countries do not coordinate. This provides a role for a supranational body, such as the European Union, to coordinate the policies of the individual governments. It is shown how intervention by a supranational body can raise welfare by internalizing the infrastructural externality. Infrastructural externalities increase the importance of tax policy in the growth process and distribute the benefits of taxation across countries.

Sensitivity analysis of a cyanobacterial growth and movement model under two different flow regimes

Bloom-forming and toxin-producing cyanobacteria remain a persistent nuisance across the world. Modelling cyanobacterial behaviour in freshwaters is an important tool for understanding their population dynamics and predicting the location and timing of the bloom events in lakes, reservoirs and rivers. A new deterministic–mathematical model was developed, which simulates the growth and movement of cyanobacterial blooms in river systems. The model focuses on the mathematical description of the bloom formation, vertical migration and lateral transport of colonies within river environments by taking into account the major factors that affect the cyanobacterial bloom formation in rivers including light, nutrients and temperature. A parameter sensitivity analysis using a one-at-a-time approach was carried out. There were two objectives of the sensitivity analysis presented in this paper: to identify the key parameters controlling the growth and movement patterns of cyanobacteria and to provide a means for model validation. The result of the analysis suggested that maximum growth rate and day length period were the most significant parameters in determining the population growth and colony depth, respectively.

Modelling phosphorus dynamics in multi-branch river systems: A study of the Black River, Lake Simcoe, Ontario, Canada

High rates of nutrient loading from agricultural and urban development have resulted in surface water eutrophication and groundwater contamination in regions of Ontario. In Lake Simcoe (Ontario, Canada), anthropogenic nutrient contributions have contributed to increased algal growth, low hypolimnetic oxygen concentrations, and impaired fish reproduction. An ambitious programme has been initiated to reduce phosphorus loads to the lake, aiming to achieve at least a 40% reduction in phosphorus loads by 2045. Achievement of this target necessitates effective remediation strategies, which will rely upon an improved understanding of controls on nutrient export from tributaries of Lake Simcoe as well as improved understanding of the importance of phosphorus cycling within the lake. In this paper, we describe a new model structure for the integrated dynamic and process-based model INCA-P, which allows fully-distributed applications, suited to branched river networks. We demonstrate application of this model to the Black River, a tributary of Lake Simcoe, and use INCA-P to simulate the fluxes of P entering the lake system, apportion phosphorus among different sources in the catchment, and explore future scenarios of land-use change and nutrient management to identify high priority sites for implementation of watershed best management practises.

Root growth models: towards a new generation of continuous approaches

Models of root system growth emerged in the early 1970s, and were based on mathematical representations of root length distribution in soil. The last decade has seen the development of more complex architectural models and the use of computer-intensive approaches to study developmental and environmental processes in greater detail. There is a pressing need for predictive technologies that can integrate root system knowledge, scaling from molecular to ensembles of plants. This paper makes the case for more widespread use of simpler models of root systems based on continuous descriptions of their structure. A new theoretical framework is presented that describes the dynamics of root density distributions as a function of individual root developmental parameters such as rates of lateral root initiation, elongation, mortality, and gravitropsm. The simulations resulting from such equations can be performed most efficiently in discretized domains that deform as a result of growth, and that can be used to model the growth of many interacting root systems. The modelling principles described help to bridge the gap between continuum and architectural approaches, and enhance our understanding of the spatial development of root systems. Our simulations suggest that root systems develop in travelling wave patterns of meristems, revealing order in otherwise spatially complex and heterogeneous systems. Such knowledge should assist physiologists and geneticists to appreciate how meristem dynamics contribute to the pattern of growth and functioning of root systems in the field.

Linking pesticide exposure and spatial dynamics: An individual-based model of wood mouse (Apodemus sylvaticus) populations in agricultural landscapes

The wood mouse is a common and abundant species in agricultural landscape and is a focal species in pesticide risk assessment. Empirical studies on the ecology of the wood mouse have provided sufficient information for the species to be modelled mechanistically. An individual-based model was constructed to explicitly represent the locations and movement patterns of individual mice. This together with the schedule of pesticide application allows prediction of the risk to the population from pesticide exposure. The model included life-history traits of wood mice as well as typical landscape dynamics in agricultural farmland in the UK. The model obtains a good fit to the available population data and is fit for risk assessment purposes. It can help identify spatio-temporal situations with the largest potential risk of exposure and enables extrapolation from individual-level endpoints to population-level effects. Largest risk of exposure to pesticides was found when good crop growth in the “sink” fields coincided with high “source” population densities in the hedgerows. Keywords: Population dynamics, Pesticides, Ecological risk assessment, Habitat choice, Agent-based model, NetLogo

Reconciling observed and modelled phytoplankton dynamics in a major lowland UK river system, the Thames

This study aims to elucidate the key mechanisms controlling phytoplankton growth and decay within the Thames basin through the application of a modified version of an established river-algal model and comparison with observed stream water chlorophyll-a concentrations. The River Thames showed a distinct simulated phytoplankton seasonality and behaviour having high spring, moderate summer and low autumn chlorophyll-a concentrations. Three main sections were identified along the River Thames with different phytoplankton abundance and seasonality: (i) low chlorophyll-a concentrations from source to Newbridge; (ii) steep concentration increase between Newbridge and Sutton; and (iii) high concentrations with a moderate increase in concentration from Sutton to the end of the study area (Maidenhead). However, local hydrologic (e.g. locks) and other conditions (e.g. radiation, water depth, grazer dynamics, etc.) affected the simulated growth and losses. The model achieved good simulation results during both calibration and testing through a range of hydrological and nutrient conditions. Simulated phytoplankton growth was controlled predominantly by residence time, but during medium–low flow periods available light, water temperature and herbivorous grazing defined algal community development. These results challenge the perceived importance of in-stream nutrient concentrations as the perceived primary control on phytoplankton growth and death.

Survival, reproduction and population growth of the bee pollinator, Osmia rufa (Hymenoptera: Megachilidae), along gradients of heavy metal pollution

1. Bees are one of the most important groups of pollinators in the temperate zone. Although heavy metal pollution is recognised to be a problem affecting large parts of the European Union, we currently lack insights into the effects of heavy metals on wild bee survival and reproduction. 2. We investigated the impact of heavy metal pollution on the wild bee Osmia rufa (Hymenoptera: Megachilidae) by comparing their survival, reproduction and population dynamics along two independent gradients of heavy metal pollution, one in Poland and the other in the United Kingdom. We used trap nests to evaluate the response of fitness and survival parameters of O. rufa. To quantify the levels of pollution, we directly measured the heavy metal concentration in provisions collected by O. rufa. 3. We found that with increasing heavy metal concentration, there was a steady decrease in number of brood cells constructed by females and an increase in the proportion of dead offspring. In the most polluted site, there were typically 3–4 cells per female with 50–60% dead offspring, whereas in unpolluted sites there were 8 to 10 cells per female and only 10–30% dead offspring. Moreover, the bee population growth rate (R0) decreased along the heavy metal pollution gradients. In unpolluted sites, R0 was above 1, whereas in contaminated sites, the values tended to be below 1. 4. Our findings reveal a negative relationship between heavy metal pollution and several fitness parameters of the wild bee O. rufa, and highlight a mechanism whereby the detrimental effects of heavy metal pollution may severely impact wild bee communities.

Forecasting US output growth with non-linear models in the presence of data uncertainty

We consider the impact of data revisions on the forecast performance of a SETAR regime-switching model of U.S. output growth. The impact of data uncertainty in real-time forecasting will affect a model's forecast performance via the effect on the model parameter estimates as well as via the forecast being conditioned on data measured with error. We find that benchmark revisions do affect the performance of the non-linear model of the growth rate, and that the performance relative to a linear comparator deteriorates in real-time compared to a pseudo out-of-sample forecasting exercise.

Energy supply, consumption and access dynamics in Botswana

The growing dependence on electricity for economic growth in all countries prompts the need to manage current resources for future sustainability. In today’s world, greater emphasis is placed on energy conservation for energy security and for the development of every economy. However, for some countries understanding the basic drivers to such achievements is farfetched. The research presented in this paper investigates the electricity generation and access potential for Botswana. In addition detailed documentation and 13 years energy consumption and generation trends are investigated. Using questionnaires and empirical studies the energy demand for the entire nation was estimated. From the research it was established that current energy generation trends account for 38- 39% of the country’s population with access to electricity. Considering the percentage rate of sector energy demand, the proposed total installed capacity of 1332 MW, would not meet the country's energy demand at 100% access. The likely consequence of the lack of adequate supply would cumulate to significant increase of imports and/or load shedding to meet demand.

Modeling growth strategy in a biotechnology startup firm

The top managers of a biotechnology startup firm agreed to participate in a system dynamics modeling project to help them think about the firm's growth strategy. The article describes how the model was created and used to stimulate debate and discussion about growth management. The paper highlights several novel features about the process used for capturing management team knowledge. A heavy emphasis was placed on mapping the operating structure of the factory and distribution channels. Qualitative modeling methods (structural diagrams, descriptive variable names, and friendly algebra) were used to capture the management team's descriptions of the business. Simulation scenarios were crafted to stimulate debate about strategic issues such as capacity allocation, capacity expansion, customer recruitment, customer retention, and market growth, and to engage the management team in using the computer to design strategic scenarios. The article concludes with comments on the impact of the project.

Incoherent scatter radar observations related to magnetospheric dynamics

Over the past decade incoherent scatter radars have provided fundamental observations of velocities and plasma parameters in the high-latitude ionosphere which relate to the dynamical processes responsible for the excitation of flow in the coupled solar wind-magnetosphere-ionosphere system. These observations have played a central role in inspiring a change of paradigm from a picture of quasi-steady flows parameterised by the direction of the interplanetary magnetic field to a picture of inherently time-dependent flows driven by coupling processes at the magnetopause and in the tail. Flows and particle precipitation in the dayside ionosphere are reasonably well understood in principle in terms of the effects of time-dependent reconnection at the magnetopause, though coordinated high- and low-altitude observations are lacking. Related phenomena also appear to occur in the tail, forming the “equatorward-drifting arcs” which are present during quiet times, as well as during the growth and early expansion phases of substorms. At expansion onset, the substorm bulge forms well equatorward of the arc formation region, and may take ∼ 10 min or more to reach it in its poleward expansion. Nightside ionospheric flows are then considerably perturbed by the effects of strong precipitation-induced conductivity gradients.

Microbial community dynamics in the forefield of glaciers

Retreating ice fronts (as a result of a warming climate) expose large expanses of deglaciated forefield, which become colonized by microbes and plants. There has been increasing interest in characterizing the biogeochemical development of these ecosystems using a chronosequence approach. Prior to the establishment of plants, microbes use autochthonously produced and allochthonously delivered nutrients for growth. The microbial community composition is largely made up of heterotrophic microbes (both bacteria and fungi), autotrophic microbes and nitrogen-fixing diazotrophs. Microbial activity is thought to be responsible for the initial build-up of labile nutrient pools, facilitating the growth of higher order plant life in developed soils. However, it is unclear to what extent these ecosystems rely on external sources of nutrients such as ancient carbon pools and periodic nitrogen deposition. Furthermore, the seasonal variation of chronosequence dynamics and the effect of winter are largely unexplored. Modelling this ecosystem will provide a quantitative evaluation of the key processes and could guide the focus of future research. Year-round datasets combined with novel metagenomic techniques will help answer some of the pressing questions in this relatively new but rapidly expanding field, which is of growing interest in the context of future large-scale ice retreat.