Evolution of Helping and Harming in Viscous Populations When Group Size Varies

Funding: Balliol College and the Royal Society.Recent years have seen huge interest in understanding how demographic factors mediate the evolution of social behavior in viscous populations. Here we study the impact of variation in group size on the evolution of helping and harming behavior. Although variation in group size influences the degree of relatedness and the degree of competition between groupmates, we find that these effects often exactly cancel, so as to give no net impact of variation in group size on the evolution of helping and harming. Specifically, (1) obligate helping and harming are never mediated by variation in group size, (2) facultative helping and harming are not mediated by variation in group size when this variation is spatial only, (3) facultative helping and harming are mediated by variation in group size only when this variation is temporal or both spatial and temporal, and (4) when there is an effect of variation in group size, facultative helping is favored in big groups and facultative harming is favored in little groups. Moreover, we find that spatial and temporal heterogeneity in individual fecundity may interact with patch-size heterogeneity to change these predictions, promoting the evolution of harming in big patches and of helping in little patches.Publisher PDFPeer reviewe

Temporal and spatial variation of limnological variables and biomass of different macrophyte species in a Neotropical reservoir (São Paulo - Brazil)

Aim: This study reports an investigation of limnological characteristics and aquatic macrophyte occurrence in a neotropical reservoir in order to assess the spatio-temporal variation of water and sediment variables and their influence on plant distribution. Methods: Macrophytes, water and sediment samples were collected from a Brazilian reservoir in different seasons from four main arms of the reservoir. In total sixteen water-sediment variables were analyzed including N:P ratio and Trophic State Index. The plants were collected using a quadrat sampling procedure and the dry weight per sample was measured. MANOVA was performed to evaluate spatial and temporal variation of environmental variables as well as seasonal biomass differences. To assess the relationship among environmental variables and macrophytes an ordination analysis (using Canonical Correspondence Analysis: CCA) was carried out. Results: The spatial and temporal variation of limnological variables generated a heterogeneous system which supports the presence of different species of macrophyte. pH, dissolved oxygen and sediment composition were important predictors of Polygonum lapathifolium occurrence while nutrients were associated with Eichhornia crassipes and Pistia stratiotes. Inorganic substances were related to biomass variation of Eichhornia azurea and Myriophyllum aquaticum. Conclusions: The spatial variation of the environmental variables has caused heterogeneity in the reservoir and it may support the occurrence of different species of macrophyte. Limnological variables highlighted in CCA are important to predict the species occurrence and their control in the study area

Temporal and spatial variability in speakers with Parkinson's Disease and Friedreich's Ataxia

Speech variability in groups of speakers with Parkinson's disease (PD) and with Friedreich's ataxia was compared with healthy controls. Speakers repeated the same phrase 20 times at one of two rates (fast or habitual). A non-linear analysis of variability was performed which used some of the principles behind the spatio-temporal index (STI). The STI usually employs variation in lip displacement over repetitions of the same utterance and a linear analysis of such signals is conducted to represent the combined variation in spatial and temporal control. When working with patients, audio measures (here we used speech energy) are preferred over kinematics ones as they are minimally disruptive to speech. Non-linear methods allow spatial variability to be estimated separately from temporal variability. The results are tentatively interpreted as showing that PD speakers were distinguished from healthy control speakers in spatial variability and ataxic speakers were distinguished from controls in temporal variability. These findings are consistent with the speech symptoms reported for these disorders. We conclude that the non-linear analysis using the speech energy measure is worth investigating further as it is potentially revealing of the differences underlying these two pathologies

A Bayesian spatio-temporal model of panel design data: airborne particle number concentration in Brisbane, Australia

This paper outlines a methodology for semi-parametric spatio-temporal modelling of data which is dense in time but sparse in space, obtained from a split panel design, the most feasible approach to covering space and time with limited equipment. The data are hourly averaged particle number concentration (PNC) and were collected, as part of the Ultrafine Particles from Transport Emissions and Child Health (UPTECH) project. Two weeks of continuous measurements were taken at each of a number of government primary schools in the Brisbane Metropolitan Area. The monitoring equipment was taken to each school sequentially. The school data are augmented by data from long term monitoring stations at three locations in Brisbane, Australia. Fitting the model helps describe the spatial and temporal variability at a subset of the UPTECH schools and the long-term monitoring sites. The temporal variation is modelled hierarchically with penalised random walk terms, one common to all sites and a term accounting for the remaining temporal trend at each site. Parameter estimates and their uncertainty are computed in a computationally efficient approximate Bayesian inference environment, R-INLA. The temporal part of the model explains daily and weekly cycles in PNC at the schools, which can be used to estimate the exposure of school children to ultrafine particles (UFPs) emitted by vehicles. At each school and long-term monitoring site, peaks in PNC can be attributed to the morning and afternoon rush hour traffic and new particle formation events. The spatial component of the model describes the school to school variation in mean PNC at each school and within each school ground. It is shown how the spatial model can be expanded to identify spatial patterns at the city scale with the inclusion of more spatial locations.Comment: Draft of this paper presented at ISBA 2012 as poster, part of UPTECH projec

Comparison of Five Spatio-Temporal Satellite Image Fusion Models over Landscapes with Various Spatial Heterogeneity and Temporal Variation

In recent years, many spatial and temporal satellite image fusion (STIF) methods have been developed to solve the problems of trade-off between spatial and temporal resolution of satellite sensors. This study, for the first time, conducted both scene-level and local-level comparison of five state-of-art STIF methods from four categories over landscapes with various spatial heterogeneity and temporal variation. The five STIF methods include the spatial and temporal adaptive reflectance fusion model (STARFM) and Fit-FC model from the weight function-based category, an unmixing-based data fusion (UBDF) method from the unmixing-based category, the one-pair learning method from the learning-based category, and the Flexible Spatiotemporal DAta Fusion (FSDAF) method from hybrid category. The relationship between the performances of the STIF methods and scene-level and local-level landscape heterogeneity index (LHI) and temporal variation index (TVI) were analyzed. Our results showed that (1) the FSDAF model was most robust regardless of variations in LHI and TVI at both scene level and local level, while it was less computationally efficient than the other models except for one-pair learning; (2) Fit-FC had the highest computing efficiency. It was accurate in predicting reflectance but less accurate than FSDAF and one-pair learning in capturing image structures; (3) One-pair learning had advantages in prediction of large-area land cover change with the capability of preserving image structures. However, it was the least computational efficient model; (4) STARFM was good at predicting phenological change, while it was not suitable for applications of land cover type change; (5) UBDF is not recommended for cases with strong temporal changes or abrupt changes. These findings could provide guidelines for users to select appropriate STIF method for their own applications

Phytoplankton production in the Delaware Estuary: temporal and spatial variation.

ABSTRACT: Phytoplankton production in the Delaware Estuary (USA) was measured over several seasonal cycles (1980-1985). Seasonal variability in daily area1 production (JP; g C m-2 d-l) was dlrectly related to chlorophyll concentrations in the upper estuary, ranging from a maximum of 1.1 g C m-\u27 d-\u27 In summer to a minlmum of d-l) dunng summer in the presence of low phytoplankton biomass (2 to 10 kg Chl I-\u27), and in mid-estuary [2.6 g C d-l) during the spring diatom bloom (50 to 60 yg Chll-l). Desplte the occurrence of maximum nutnent concentrations in the freshwater region, highest JP and 90 % of the annual production occurred in the lower estuary, down-stream from the turbidity maximum. The presence of the turbidity maximum immediately downstream from major anthropogenic nutrient sources restricts phytoplankton growth, and limits biomass accumulation below nuisance levels. Annual production for the 1981-1985 period averaged 307 g Cm-2 and displayed marked inter-annual variability. Llght availability is the predominant regulator of production in the estuary. Although growth was light-limited, neither chlorophyll specific produchon nor the light intensity at which photosynthesis saturates was related to the mean light intensity in the mixed surface-layer. These results suggest that photoadaptive response times are slower than the vertical mlxing rate and that photoadaptation is of mlnor significance to overall production in the system

The selective advantage of reaction norms for environmental tolerance

A tolerance curve defines the dependence of a genotype's fitness on the state of an environmental gradient. It can be characterized by a mode (the genotype's optimal environment) and a width (the breadth of adaptation). It seems possible that one or both of these characters can be modified in an adaptive manner, at least partially, during development. Thus, we extend the theory of environmental tolerance to include reaction norms for the mode and the width of the tolerance curve. We demonstrate that the selective value of such reaction norms increases with increasing spatial heterogeneity and between-generation temporal variation in the environment and with decreasing within-generation temporal variation. Assuming that the maintenance of a high breadth of adaptation is costly, reaction, norms are shown to induce correlated selection for a reduction in this character. Nevertheless, regardless of the magnitude of the reaction norm, there is a nearly one to one relationship between the optimal breadth of adaptation and the within-generation temporal variation perceived by the organism. This suggests that empirical estimates of the breadth of adaptation may provide a useful index of this type of environmental variation from the organism's point of view