Central place foragers (CPFs) are well-suited for developing ecological models

of adaptive processes because their objective functions and operational constraints can be reasonably inferred. Central place foraging and provisioning theory provide the theoretical framework for this analysis. Analysis of CPF time allocation and energy budgets can provide insights into their strategies for responding to environmental variation. However, until recently, suitable high-resolution data on the behaviour of seabirds and other CPFs at sea have not been available. Previous studies of breeding seabirds have investigated variation in foraging trip duration and colony attendance, but few studies have Ro-3306 order analyzed variation in time allocation within foraging trips. Here, we develop a conceptual energy-based model for analysing variation in the time allocation of CPFs during foraging trips, and apply it to the movement patterns of Peruvian boobies (Sula variegata). Foraging trips of Peruvian boobies, recorded using high-resolution global positioning systems (GPS), were first partitioned into movement modes consistent with travel and foraging behaviours using a hidden Markov model (HMM) adapted to account for gaps in the GPS tracks associated with diving

behaviour. Analysis of the HMM results based on the conceptual model indicated that differences in foraging effort between two treatments were best explained by a combination of differences in travel time and in time spent searching for prey. The conceptual model provides the basis for an selleckchem integrated approach to analysis of variation in foraging strategies in which identification of various behaviours is coupled with assessments of time and energy budgets. This integrated approach can contribute DAPT concentration to greater understanding of the processes determining foraging strategies and the limits to these strategies in the context of competition for resources and global

climate change. (C) 2014 Elsevier B.V. All rights reserved.”
“PurposeIn real-time MRI serial images are generally reconstructed from highly undersampled datasets as the iterative solutions of an inverse problem. While practical realizations based on regularized nonlinear inversion (NLINV) have hitherto been surprisingly successful, strong assumptions about the continuity of image features may affect the temporal fidelity of the estimated reconstructions. Theory and MethodsThe proposed method for real-time image reconstruction integrates the deformations between nearby frames into the data consistency term of the inverse problem. The aggregated motion estimation (AME) is not required to be affine or rigid and does not need additional measurements. Moreover, it handles multi-channel MRI data by simultaneously determining the image and its coil sensitivity profiles in a nonlinear formulation which also adapts to non-Cartesian (e.g.