Impact of ocean warming on sustainable fisheries management informs the Ecosystem Approach to Fisheries

Acknowledgements Serpetti N., Heymans J.J., and Burrows M.T. were funded by the Natural Environment Research Council and Department for Environment, Food and Rural Affairs under the Marine Ecosystems Research Programme (MERP) (grant No. NE/L003279/1). Baudron A. and Fernandes, P.G. were founded by Horizon 2020 European research projects MareFrame (grant No. 613571) and ClimeFish (grant No. 677039). Payne, B.L. was founded by the Natural Environment Research Council and Department for Environment under the ‘Velocity of Climate Change’ (grant No. NE/J024082/1).Peer reviewedPublisher PD

Order in Spontaneous Behavior

Brains are usually described as input/output systems: they transform sensory input into motor output. However, the motor output of brains (behavior) is notoriously variable, even under identical sensory conditions. The question of whether this behavioral variability merely reflects residual deviations due to extrinsic random noise in such otherwise deterministic systems or an intrinsic, adaptive indeterminacy trait is central for the basic understanding of brain function. Instead of random noise, we find a fractal order (resembling Lévy flights) in the temporal structure of spontaneous flight maneuvers in tethered Drosophila fruit flies. Lévy-like probabilistic behavior patterns are evolutionarily conserved, suggesting a general neural mechanism underlying spontaneous behavior. Drosophila can produce these patterns endogenously, without any external cues. The fly's behavior is controlled by brain circuits which operate as a nonlinear system with unstable dynamics far from equilibrium. These findings suggest that both general models of brain function and autonomous agents ought to include biologically relevant nonlinear, endogenous behavior-initiating mechanisms if they strive to realistically simulate biological brains or out-compete other agents

Railway operations, time-tabling and control

This paper concentrates on organising, planning and managing the train movement in a network. The three classic management levels for rail planning, i.e., strategic, tactical and operational, are introduced followed by decision support systems for rail traffic control. In addition, included in this paper are discussions on train operating forms, railway traffic control and train dispatching problems, rail yard technical schemes and performance of terminals, as well as timetable design. A description of analytical methods, simulation techniques and specific computer packages for analysing and evaluating the behaviour of rail systems and networks is also provided

Oak forest carbon and water simulations:Model intercomparisons and evaluations against independent data

Models represent our primary method for integration of small-scale, process-level phenomena into a comprehensive description of forest-stand or ecosystem function. They also represent a key method for testing hypotheses about the response of forest ecosystems to multiple changing environmental conditions. This paper describes the evaluation of 13 stand-level models varying in their spatial, mechanistic, and temporal complexity for their ability to capture intra- and interannual components of the water and carbon cycle for an upland, oak-dominated forest of eastern Tennessee. Comparisons between model simulations and observations were conducted for hourly, daily, and annual time steps. Data for the comparisons were obtained from a wide range of methods including: eddy covariance, sapflow, chamber-based soil respiration, biometric estimates of stand-level net primary production and growth, and soil water content by time or frequency domain reflectometry. Response surfaces of carbon and water flux as a function of environmental drivers, and a variety of goodness-of-fit statistics (bias, absolute bias, and model efficiency) were used to judge model performance. A single model did not consistently perform the best at all time steps or for all variables considered. Intermodel comparisons showed good agreement for water cycle fluxes, but considerable disagreement among models for predicted carbon fluxes. The mean of all model outputs, however, was nearly always the best fit to the observations. Not surprisingly, models missing key forest components or processes, such as roots or modeled soil water content, were unable to provide accurate predictions of ecosystem responses to short-term drought phenomenon. Nevertheless, an inability to correctly capture short-term physiological processes under drought was not necessarily an indicator of poor annual water and carbon budget simulations. This is possible because droughts in the subject ecosystem were of short duration and therefore had a small cumulative impact. Models using hourly time steps and detailed mechanistic processes, and having a realistic spatial representation of the forest ecosystem provided the best predictions of observed data. Predictive ability of all models deteriorated under drought conditions, suggesting that further work is needed to evaluate and improve ecosystem model performance under unusual conditions, such as drought, that are a common focus of environmental change discussions

Investigations of a compartmental model for leucine kinetics using nonlinear mixed effects models with ordinary and stochastic differential equations

Nonlinear mixed effects models represent a powerful tool to simultaneously analyze data from several individuals. In this study a compartmental model of leucine kinetics is examined and extended with a stochastic differential equation to model non-steady state concentrations of free leucine in the plasma. Data obtained from tracer/tracee experiments for a group of healthy control individuals and a group of individuals suffering from diabetes mellitus type 2 are analyzed. We find that the interindividual variation of the model parameters is much smaller for the nonlinear mixed effects models, compared to traditional estimates obtained from each individual separately. Using the mixed effects approach, the population parameters are estimated well also when only half of the data are used for each individual. For a typical individual the amount of free leucine is predicted to vary with a standard deviation of 8.9% around a mean value during the experiment. Moreover, leucine degradation and protein uptake of leucine is smaller, proteolysis larger, and the amount of free leucine in the body is much larger for the diabetic individuals than the control individuals. In conclusion nonlinear mixed effects models offers improved estimates for model parameters in complex models based on tracer/tracee data and may be a suitable tool to reduce data sampling in clinical studies

Models for an Ecosystem Approach to Fisheries

This document is one outcome from a workshop held in Gizo in October 2010 attended by 82 representatives from government, NGO's private sector, and communities. The target audience for the document is primarily organizations planning to work with coastal communities of Solomon Islands to implement Community-Based Resource Management (CBRM). It is however also envisaged that the document will serve as a reference for communities to better understand what to expect from their partners and also for donors, to be informed about agreed approaches amongst Solomon Islands stakeholders. This document does not attempt to summarize all the outcomes of the workshop; rather it focuses on the Solomon Islands Coral Triangle Initiative (CTI) National Plan of Action (NPoA): Theme 1: Support and implementation of CBRM and specifically, the scaling up of CBRM in Solomon Islands. Most of the principles given in this document are derived from experiences in coastal communities and ecosystems as, until relatively recently, these have received most attention in Solomon Islands resource management. It is recognized however that the majority of these principles will be applicable to both coastal and terrestrial initiatives. This document synthesizes information provided by stakeholders at the October 2010 workshop and covers some basic principles of engagement and implementation that have been learned over more than twenty years of activities by the stakeholder partners in Solomon Islands. The document updates and expands on a summary of guiding principles for CBRM which was originally prepared by the Solomon Islands Locally Managed Marine Area Network (SILMMA) in 2007

Progress and challenges in coupled hydrodynamic-ecological estuarine modeling

© The Author(s), 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Estuaries and Coasts 39 (2016): 311-332, doi:10.1007/s12237-015-0011-y.Numerical modeling has emerged over the last several decades as a widely accepted tool for investigations in environmental sciences. In estuarine research, hydrodynamic and ecological models have moved along parallel tracks with regard to complexity, refinement, computational power, and incorporation of uncertainty. Coupled hydrodynamic-ecological models have been used to assess ecosystem processes and interactions, simulate future scenarios, and evaluate remedial actions in response to eutrophication, habitat loss, and freshwater diversion. The need to couple hydrodynamic and ecological models to address research and management questions is clear because dynamic feedbacks between biotic and physical processes are critical interactions within ecosystems. In this review, we present historical and modern perspectives on estuarine hydrodynamic and ecological modeling, consider model limitations, and address aspects of model linkage, skill assessment, and complexity. We discuss the balance between spatial and temporal resolution and present examples using different spatiotemporal scales. Finally, we recommend future lines of inquiry, approaches to balance complexity and uncertainty, and model transparency and utility. It is idealistic to think we can pursue a “theory of everything” for estuarine models, but recent advances suggest that models for both scientific investigations and management applications will continue to improve in terms of realism, precision, and accuracy.NKG, ALA, and RPS acknowledge support from the USGS Coastal and Marine Geology Program. DKR gratefully acknowledges support from NSF (OCE-1314642) and NIEHS (1P50-ES021923-01). MJB and JMPV gratefully acknowledge support from NOAA NOS NCCOS (NA05NOS4781201 and NA11NOS4780043). MJB and SJL gratefully acknowledge support from the Strategic Environmental Research and Development Program—Defense Coastal/Estuarine Research Program (RC-1413 and RC-2245)