How individual movement response to habitat edges affects population persistence and spatial spread

How individual-level movement decisions in response to habitat edges influence population-level patterns of persistence and spread of a species is a major challenge in spatial ecology and conservation biology. Here, we integrate novel insights into edge behavior, based on habitat preference and movement rates, into spatially explicit growth-dispersal models. We demonstrate how crucial ecological quantities (e.g., minimal patch size, spread rate) depend critically on these individual-level decisions. In particular, we find that including edge behavior properly in these models gives qualitatively different and intuitively more reasonable results than those of some previous studies that did not consider this level of detail. Our results highlight the importance of new empirical work on individual movement response to habitat edges. © 2013 by The University of Chicago.

Spatial Distribution of Euschistus heros (F.) (Hemiptera: Pentatomidae) in Soybean

Soybean bugs are major crop pests that cause significant reduction in harvest yield and influence grain quality. The aim of this study was to verify the spatial distribution of Euschistus heros (F.) (Hemiptera: Pentatomidae) in conventional and transgenic soybean cultivars. The experiment was conducted during the 2010-2011 crop season in UNESP/FCAV, Jaboticabal, SP, Brazil, in two fields of 10,000-m2 area that were subdivided into 100 plots (10 m × 10 m). The cultivars sown were M 7908 RR and its isoline M-SOY 8001. The number of the first to fifth instars and the number of adults were determined. To evaluate insect dispersion in the area, the following indices were used: variance/mean ratio, Morisita index, Green coefficient, and the k exponent of the negative binomial distribution. To study probabilistic models to describe the spatial distribution of the insects, the adjustments of the Poisson and negative binomial distributions were tested. The first to third instars showed aggregated spatial distribution, whereas the fourth and fifth instars, and adults, isolated or grouped, showed variation in the arrangement, ranging from moderately aggregated to randomly dispersed. During the adjustment of probability distributions, the negative binomial distribution model showed adjustment for the first to third instars, fourth and fifth instars, adults, and fourth and fifth instars plus adults. © 2013 Sociedade Entomológica do Brasil.

Cellular automata-based spatial dynamic modeling for analyzing urban land use change

This paper analyzes land use change in Rio Claro City and its surroundings, located in the southeastern state of Sao Paulo, in the period from 1988 to 1995, using air-borne digital imagery and a cellular automata model. The simulation experiment was carried out in the Dinamica EGO platform and the results revealed a constrained urban sprawl, resulting from both the densification of residential areas implemented in previous years and the economic recession that led to an internal financial crisis in Brazil during the early 1990s. The simulation outputs were validated using a multi-resolution procedure based on a fuzzy similarity index and showed a satisfactory fitness in relation to the historical reference data. © 2013 IEEE.

Thermal phase transition for some spin-boson models

In this work we study two different spin-boson models. Such models are generalizations of the Dicke model, it means they describe systems of N identical two-level atoms coupled to a single-mode quantized bosonic field, assuming the rotating wave approximation. In the first model, we consider the wavelength of the bosonic field to be of the order of the linear dimension of the material composed of the atoms, therefore we consider the spatial sinusoidal form of the bosonic field. The second model is the Thompson model, where we consider the presence of phonons in the material composed of the atoms. We study finite temperature properties of the models using the path integral approach and functional methods. In the thermodynamic limit, N→∞, the systems exhibit phase transitions from normal to superradiant phase at some critical values of temperature and coupling constant. We find the asymptotic behavior of the partition functions and the collective spectrums of the systems in the normal and the superradiant phases. We observe that the collective spectrums have zero energy values in the superradiant phases, corresponding to the Goldstone mode associated to the continuous symmetry breaking of the models. Our analysis and results are valid in the limit of zero temperature β→∞, where the models exhibit quantum phase transitions. © 2013 Elsevier B.V. All rights reserved.

Spatial Distribution of the Risk of Dengue and the Entomological Indicators in Sumare, State of Sao Paulo, Brazil

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Magnetic susceptibility and diffuse reflectance spectroscopy to characterize the spatial variability of soil properties in a Brazilian Haplustalf

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Spatial Variation in Morphometry in Vanzosaura rubricauda (Squamata, Gymnophthalmidae) from Open Habitats of South America and its Environmental Correlates

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A Spatial and Temporal Prediction Model of Corn Grain Yield as a Function of Soil Attributes

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Determining spatial resolution in spatial load forecasting using a grid-based model

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Information-entropic measure of energy-degenerate kinks in two-field models

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Search for quark contact interactions and extra spatial dimensions using dijet angular distributions in proton–proton collisions at √s = 8 TeV

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Community-wide spatial and temporal discordances of seed-seedling shadows in a tropical rainforest

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Field-scale spatial correlation between contents of iron oxides and CO2 emission in an Oxisol cultivated with sugarcane

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Estimating the extent of spatial association of Mycobacterium bovis infection in badgers in Ireland

Mycobacterium bovis infects the wildlife species badgers Meles meles who are linked with the spread of the associated disease tuberculosis (TB) in cattle. Control of livestock infections depends in part on the spatial and social structure of the wildlife host. Here we describe spatial association of M. bovis infection in a badger population using data from the first year of the Four Area Project in Ireland. Using second-order intensity functions, we show there is strong evidence of clustering of TB cases in each the four areas, i.e. a global tendency for infected cases to occur near other infected cases. Using estimated intensity functions, we identify locations where particular strains of TB cluster. Generalized linear geostatistical models are used to assess the practical range at which spatial correlation occurs and is found to exceed 6 in all areas. The study is of relevance concerning the scale of localized badger culling in the control of the disease in cattle.

Space–time zero-inflated count models of Harbor seals

Environmental data are spatial, temporal, and often come with many zeros. In this paper, we included space–time random effects in zero-inflated Poisson (ZIP) and ‘hurdle’ models to investigate haulout patterns of harbor seals on glacial ice. The data consisted of counts, for 18 dates on a lattice grid of samples, of harbor seals hauled out on glacial ice in Disenchantment Bay, near Yakutat, Alaska. A hurdle model is similar to a ZIP model except it does not mix zeros from the binary and count processes. Both models can be used for zero-inflated data, and we compared space–time ZIP and hurdle models in a Bayesian hierarchical model. Space–time ZIP and hurdle models were constructed by using spatial conditional autoregressive (CAR) models and temporal first-order autoregressive (AR(1)) models as random effects in ZIP and hurdle regression models. We created maps of smoothed predictions for harbor seal counts based on ice density, other covariates, and spatio-temporal random effects. For both models predictions around the edges appeared to be positively biased. The linex loss function is an asymmetric loss function that penalizes overprediction more than underprediction, and we used it to correct for prediction bias to get the best map for space–time ZIP and hurdle models.