Tearing Out the Income Tax by the (Grass)Roots

Landscapes are increasingly fragmented, and conservation programs have started to look at network approaches for maintaining populations at a larger scale. We present an agent-based model of predator–prey dynamics where the agents (i.e. the individuals of either the predator or prey population) are able to move between different patches in a landscaped network. We then analyze population level and coexistence probability given node-centrality measures that characterize specific patches. We show that both predator and prey species benefit from living in globally well-connected patches (i.e. with high closeness centrality). However, the maximum number of prey species is reached, on average, at lower closeness centrality levels than for predator species. Hence, prey species benefit from constraints imposed on species movement in fragmented landscapes since they can reproduce with a lesser risk of predation, and their need for using anti-predatory strategies decreases.authorCount :

An Updated Algorithm for the Generation of Neutral Landscapes by Spectral Synthesis

Background: Patterns that arise from an ecological process can be driven as much from the landscape over which the process is run as it is by some intrinsic properties of the process itself. The disentanglement of these effects is aided if it possible to run models of the process over artificial landscapes with controllable spatial properties. A number of different methods for the generation of so-called ‘neutral landscapes’ have been developed to provide just such a tool. Of these methods, a particular class that simulate fractional Brownian motion have shown particular promise. The existing methods of simulating fractional Brownian motion suffer from a number of problems however: they are often not easily generalisable to an arbitrary number of dimensions and produce outputs that can exhibit some undesirable artefacts. Methodology: We describe here an updated algorithm for the generation of neutral landscapes by fractional Brownian motion that do not display such undesirable properties. Using Monte Carlo simulation we assess the anisotropic properties of landscapes generated using the new algorithm described in this paper and compare it against a popular benchmark algorithm. Conclusion/Significance: The results show that the existing algorithm creates landscapes with values strongly correlated in the diagonal direction and that the new algorithm presented here corrects this artefact. A number of extensions of the algorithm described here are also highlighted: we describe how the algorithm can be employed to generate landscapes that display different properties in different dimensions and how they can be combined with an environmental gradient to produce landscapes that combine environmental variation at the local and macro scales

The Dispersal Ecology of Rhodesian Sleeping Sickness Following Its Introduction to a New Area

Tsetse-transmitted human and animal trypanosomiasis are constraints to both human and animal health in sub-Saharan Africa, and although these diseases have been known for over a century, there is little recent evidence demonstrating how the parasites circulate in natural hosts and ecosystems. The spread of Rhodesian sleeping sickness (caused by Trypanosoma brucei rhodesiense) within Uganda over the past 15 years has been linked to the movement of infected, untreated livestock (the predominant reservoir) from endemic areas. However, despite an understanding of the environmental dependencies of sleeping sickness, little research has focused on the environmental factors controlling transmission establishment or the spatially heterogeneous dispersal of disease following a new introduction. In the current study, an annually stratified case-control study of Rhodesian sleeping sickness cases from Serere District, Uganda was used to allow the temporal assessment of correlations between the spatial distribution of sleeping sickness and landscape factors. Significant relationships were detected between Rhodesian sleeping sickness and selected factors, including elevation and the proportion of land which was “seasonally flooding grassland” or “woodlands and dense savannah.” Temporal trends in these relationships were detected, illustrating the dispersal of Rhodesian sleeping sickness into more ‘suitable’ areas over time, with diminishing dependence on the point of introduction in concurrence with an increasing dependence on environmental and landscape factors. These results provide a novel insight into the ecology of Rhodesian sleeping sickness dispersal and may contribute towards the implementation of evidence-based control measures to prevent its further spread

Forest landscape ecology and global change: an introduction

Forest landscape ecology examines broad-scale patterns and processes and their interactions in forested systems and informs the management of these ecosystems. Beyond being among the richest and the most complex terrestrial systems, forest landscapes serve society by providing an array of products and services and, if managed properly, can do so sustainably. In this chapter, we provide an overview of the field of forest landscape ecology, including major historical and present topics of research, approaches, scales, and applications, particularly those concerning edges, fragmentation, connectivity, disturbance, and biodiversity. In addition, we discuss causes of change in forest landscapes, particularly land-use and management changes, and the expected structural and functional consequences that may result from these drivers. This chapter is intended to set the context and provide an overview for the remainder of the book and poses a broad set of questions related to forest landscape ecology and global change that need answers

Multi-messenger observations of a binary neutron star merger

On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ~1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of 40+8-8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 Mo. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ~40 Mpc) less than 11 hours after the merger by the One- Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ~10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ~9 and ~16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta

Computer-aided prediction of short-term tumor growth in sporadic vestibular schwannomas using both structural and dynamic-contrast enhanced MR imaging

Introduction:Recent studies have demonstrated that microvascular parameters derived from dynamic-contrast enhanced (DCE) MR imaging significantly correlate with tumor growth in vestibularschwannomas (VS). Other studies provide evidence that the use of artificial intelligence (AI)on structural MR data provides similar predictive value for tumor growth.Methods: This prospective study investigates the combination of structural and DCE imaging data for AI to predict short-term tumor growth in VS. A total of 110 newly diagnosed unilateral sporadic VS patients underwent both T2-weighted and DCE MR imaging. Established pipelines were used to estimate the values of DCE-derived parameters Ktrans, ve, and vp. Subsequently, tumors were delineated and only voxel values within the delineation were considered for the AI model development. Radiomic features were extracted from both the structural images and DCE-derived parameter maps. A classifier was trained on the radiomic features to predict tumor growth.Results: Growth was observed in 69 (63%) of the 110 patients during follow-up. A support vector machine (SVM) model was trained on Ktrans and ve radiomic features using five-fold-crossvalidation. This model resulted in an accuracy of 82.5%, sensitivity of 81.2%, specificity of 82.9%, and area-under-the-curve of 0.85. The predictive value of structural MR imaging features is currently under investigation, as well as the use of more complex AI models. It is hypothesized that the addition of structural features and increase in model complexity will improve the model's predictive power. Conclusion: Preliminary results have shown that DCE-derived parameter values exhibit a high predictive value for tumor growth prediction in sporadic VS. Other radiomic features and model types will be analyzed in order to investigate whether they improve the current AI model. These results will be presented during the conference.<br/

Computer-aided prediction of short-term tumor growth in sporadic vestibular schwannomas using both structural and dynamic-contrast enhanced MR imaging

Introduction:Recent studies have demonstrated that microvascular parameters derived from dynamic-contrast enhanced (DCE) MR imaging significantly correlate with tumor growth in vestibularschwannomas (VS). Other studies provide evidence that the use of artificial intelligence (AI)on structural MR data provides similar predictive value for tumor growth.Methods: This prospective study investigates the combination of structural and DCE imaging data for AI to predict short-term tumor growth in VS. A total of 110 newly diagnosed unilateral sporadic VS patients underwent both T2-weighted and DCE MR imaging. Established pipelines were used to estimate the values of DCE-derived parameters Ktrans, ve, and vp. Subsequently, tumors were delineated and only voxel values within the delineation were considered for the AI model development. Radiomic features were extracted from both the structural images and DCE-derived parameter maps. A classifier was trained on the radiomic features to predict tumor growth.Results: Growth was observed in 69 (63%) of the 110 patients during follow-up. A support vector machine (SVM) model was trained on Ktrans and ve radiomic features using five-fold-crossvalidation. This model resulted in an accuracy of 82.5%, sensitivity of 81.2%, specificity of 82.9%, and area-under-the-curve of 0.85. The predictive value of structural MR imaging features is currently under investigation, as well as the use of more complex AI models. It is hypothesized that the addition of structural features and increase in model complexity will improve the model's predictive power. Conclusion: Preliminary results have shown that DCE-derived parameter values exhibit a high predictive value for tumor growth prediction in sporadic VS. Other radiomic features and model types will be analyzed in order to investigate whether they improve the current AI model. These results will be presented during the conference.<br/

Immigration Rates in Fragmented Landscapes – Empirical Evidence for the Importance of Habitat Amount for Species Persistence

BACKGROUND: The total amount of native vegetation is an important property of fragmented landscapes and is known to exert a strong influence on population and metapopulation dynamics. As the relationship between habitat loss and local patch and gap characteristics is strongly non-linear, theoretical models predict that immigration rates should decrease dramatically at low levels of remaining native vegetation cover, leading to patch-area effects and the existence of species extinction thresholds across fragmented landscapes with different proportions of remaining native vegetation. Although empirical patterns of species distribution and richness give support to these models, direct measurements of immigration rates across fragmented landscapes are still lacking. METHODOLOGY/PRINCIPAL FINDINGS: Using the Brazilian Atlantic forest marsupial Gray Slender Mouse Opossum (Marmosops incanus) as a model species and estimating demographic parameters of populations in patches situated in three landscapes differing in the total amount of remaining forest, we tested the hypotheses that patch-area effects on population density are apparent only at intermediate levels of forest cover, and that immigration rates into forest patches are defined primarily by landscape context surrounding patches. As expected, we observed a positive patch-area effect on M. incanus density only within the landscape with intermediate forest cover. Density was independent of patch size in the most forested landscape and the species was absent from the most deforested landscape. Specifically, the mean estimated numbers of immigrants into small patches were lower in the landscape with intermediate forest cover compared to the most forested landscape. CONCLUSIONS/SIGNIFICANCE: Our results reveal the crucial importance of the total amount of remaining native vegetation for species persistence in fragmented landscapes, and specifically as to the role of variable immigration rates in providing the underlying mechanism that drives both patch-area effects and species extinction thresholds

Experimental Beetle Metapopulations Respond Positively to Dynamic Landscapes and Reduced Connectivity

Interactive effects of multiple environmental factors on metapopulation dynamics have received scant attention. We designed a laboratory study to test hypotheses regarding interactive effects of factors affecting the metapopulation dynamics of red flour beetle, Tribolium castaneum. Within a four-patch landscape we modified resource level (constant and diminishing), patch connectivity (high and low) and patch configuration (static and dynamic) to conduct a 23 factorial experiment, consisting of 8 metapopulations, each with 3 replicates. For comparison, two control populations consisting of isolated and static subpopulations were provided with resources at constant or diminishing levels. Longitudinal data from 22 tri-weekly counts of beetle abundance were analyzed using Bayesian Poisson generalized linear mixed models to estimate additive and interactive effects of factors affecting abundance. Constant resource levels, low connectivity and dynamic patches yielded greater levels of adult beetle abundance. For a given resource level, frequency of colonization exceeded extinction in landscapes with dynamic patches when connectivity was low, thereby promoting greater patch occupancy. Negative density dependence of pupae on adults occurred and was stronger in landscapes with low connectivity and constant resources; these metapopulations also demonstrated greatest stability. Metapopulations in control landscapes went extinct quickly, denoting lower persistence than comparable landscapes with low connectivity. When landscape carrying capacity was constant, habitat destruction coupled with low connectivity created asynchronous local dynamics and refugia within which cannibalism of pupae was reduced. Increasing connectivity may be counter-productive and habitat destruction/recreation may be beneficial to species in some contexts

Correlation of exhaled breath temperature with bronchial blood flow in asthma

In asthma elevated rates of exhaled breath temperature changes (Δe°T) and bronchial blood flow (Q(aw)) may be due to increased vascularity of the airway mucosa as a result of inflammation. We investigated the relationship of Δe°T with Q(aw )and airway inflammation as assessed by exhaled nitric oxide (NO). We also studied the anti-inflammatory and vasoactive effects of inhaled corticosteroid and β(2)-agonist. Δe°T was confirmed to be elevated (7.27 ± 0.6 Δ°C/s) in 19 asthmatic subjects (mean age ± SEM, 40 ± 6 yr; 6 male, FEV(1 )74 ± 6 % predicted) compared to 16 normal volunteers (4.23 ± 0.41 Δ°C/s, p < 0.01) (30 ± 2 yr) and was significantly increased after salbutamol inhalation in normal subjects (7.8 ± 0.6 Δ°C/ s, p < 0.05) but not in asthmatic patients. Q(aw), measured using an acetylene dilution method was also elevated in patients with asthma compared to normal subjects (49.47 ± 2.06 and 31.56 ± 1.6 μl/ml/min p < 0.01) and correlated with exhaled NO (r = 0.57, p < 0.05) and Δe°T (r = 0.525, p < 0.05). In asthma patients, Q(aw )was reduced 30 minutes after the inhalation of budesonide 400 μg (21.0 ± 2.3 μl/ml/min, p < 0.05) but was not affected by salbutamol. Δe°T correlates with Q(aw )and exhaled NO in asthmatic patients and therefore may reflect airway inflammation, as confirmed by the rapid response to steroids