Different Sources Of Heterogeneity Research Articles

Large-scale questions and small-scale data: empirical and theoretical methods for scaling up in ecology

Most experimental work is done at small spatial scales due to scientific, logistical, and financial constraints. Unfortunately, ecologists (and society) require answers to problems about dynamics that arise at larger scales than often can be studied experimentally. Thus, it is imperative to develop empirical and theoretical methods for scaling up the results of small-scale studies to predictions at larger scales. These methods should also identify the limits to scaling up: i.e., clarify when results from small-scale experiments cannot be extrapolated to larger scales. In this special topic, we identify three important hurdles associated with scaling up in population biology and methods of addressing these problems: (1) increased spatial heterogeneity with increasing spatial scale, (2) changes to species pools and species identities with changes in spatial scale, and (3) behaviors and trait-mediated indirect effects that emerge at larger scales, but are absent in small, relatively homogeneous experimental settings. Larger spatial scales will usually incorporate greater abiotic and biotic heterogeneity than small scales, including greater genetic or phenotypic variation among individuals. Simple extrapolations that assume linear scaling relationships will not provide good predictions when there are both spatial heterogeneities and nonlinear interactions among species or between species and abiotic resources. Thus it may be troubling that spatial heterogeneity is ubiquitous and many ecological interactions are known to be non-linear. Fortunately, if both the degree of nonlinearity and the amount of spatial heterogeneity at different scales were quantified, available methods may allow integrating large-scale observational studies with small-scale experiments to account for the influence of heterogeneities on the outcome of nonlinear interactions. Alternatively, models or experiments can be used to directly examine the effects of changing levels of variance and to determine the importance of different sources of heterogeneity in different systems. Three of the contributions illustrate ways to account for effects of increasing heterogeneity and non-linearities. Melbourne and Chesson use scale-transition theory to develop an expression for regional population dynamics that accounts for non-linear local density dependence and spatial heterogeneity in resources. Incorporating spatial variances and covariances, they predict the regional responses of caddisfly populations to riffle disturbance regimes. Inouye describes data collected at two nested spatial scales on the variances and covariances of distributions of competitors in a patchy and ephemeral habitat. These data are used to parameterize a model for the regional population dynamics of these competitors that includes spatial heterogeneity at both spatial scales. Helms and Hunter show that using observed spatial variation in local population growth parameters can produce regional population dynamics that are different from those obtained using average parameter values. A second hurdle in scaling up is that at larger scales, one is likely to encounter additional species or habitats within the domain of the study. In moving from studying a single species or pair of species interacting within a microcosm to studying the same species and interaction embedded in a landscape, the indirect effects of additional species may swamp whatever direct effects were measured in the microcosm. To address these problems, we need to include information on how species pools change as the spatial scales of studies increases. Srivastava addresses the relative roles of local biotic interactions and regional abiotic influences in aquatic treehole communities across a range of spatial scales and levels of diversity. Although local treehole communities are similarly structured around the world, communities Communicated by Craig W. Osenberg

Testing Heterogeneity within the Euro Area Using a Structural Multi-Country Model

In this paper, we investigate the sources of heterogeneity within the euro area. For this purpose, we build an optimization-based multi-country model (MCM) that allows different sources of heterogeneity across countries, both in terms of behavior of economic agents and in terms of asymmetry of shocks. Using Bayesian techniques, we estimate the MCM and several constrained versions of this model. We then test different sources of heterogeneity within the euro area. We show that the main source is the asymmetry of shocks affecting the different economies. In contrast, the heterogeneity of behaviors does not seem to be of empirical relevance for the euro area.

Conflicting criteria in embedded system design

The complexity of the design problem becomes apparent when considering the huge design space of possible implementations on the one hand and the many conflicting design goals on the other. Automated design tools are necessary in order to handle the complexity of today's systems and to support the designer in finding the trade-off that best fits the market requirements. This article presents a methodology to cope with the simultaneous optimization of multiple competing objectives and the different sources of heterogeneity in embedded system design.

A furrow irrigation model to improve irrigation practices in the Gharb valley of Morocco

In developing countries, modernization of surface irrigation is the most common solution to water management problems in irrigated areas because it is well adapted to the socio-economical context. This solution was adopted in the Gharb area near Kenitra in Morocco where an experimental site was set up to obtain irrigation and drainage references. Meaningful improvements in irrigation efficiency and better crop yields have yet to result from the modernization effort. Different sources of heterogeneity affecting the infiltration process can hinder the improvement of irrigation efficiency even in a modernized furrow irrigation context. The respective impact of deterministic and stochastic heterogeneity sources on the advance-infiltration process is analyzed. Then, a model based on the spatial and temporal variability of infiltration is developed to simulate the impact of irrigation practices on water saving during an irrigation season. This work will later contribute to the elaboration of a modelling approach simulating fertilization and irrigation practices.

The association between vasectomy and prostate cancer: a systematic review of the literature

Objective: To evaluate the possible association between vasectomy and prostate cancer. Design: Systematic review of the literature. Patient(s): Fourteen original studies published between January 1985 and December 1996 that addressed the association between vasectomy and prostate cancer. Main Outcome Measure(s): The strength of the association was estimated with the use of a meta-analysis (DerSimonian and Laird method). A sensitivity analysis was conducted to assess the impact of different sources of heterogeneity. Result(s): Fourteen original papers were reviewed (5 cohort and 9 case-control studies). Relative risks ranged between 0.44 (95% confidence interval [CI] = 0.1–4.0) and 6.70 (95% CI = 2.1–21.6). The overall relative risk (DerSimonian and Laird estimate) was 1.23 (95% CI = 1.01–1.49). The sensitivity analysis showed that this measure was very sensitive to the study base, the type of design used, and the possibility of bias. Further, the funnel plot demonstrated the probable existence of publication bias. Conclusion(s): No causal association was found between vasectomy and prostate cancer. Individuals who have undergone vasectomy are not at high risk for the development of prostate cancer.