Remembering our roots: A possible connection between loss of ecological memory,alien invasions and ecological restoration

When a community or ecosystem is lost, some of its properties may remain, leaving behind an ecological memory. The soil properties, spores, seeds, stem fragments, mycorrhizae, species, populations and other remnants of the previous inhabitants contribute to shaping the replacement community and building a new ecosystem. The loss of ecological memory for the natural stability domain of a site reduces ecosystem resilience and enables alien invasive species to become established more easily. These invasives may eventually create a new ecosystem with its own ecological memory and resilience. These new ecosystems are described here as novel ecosystems and are placed in the context of adaptive cycles. Ecological restoration of urban ecosystems requires removing the ecological legacy of invasive alien species. To be successful, invasive species control must address both internal within patch memory of invasives and external between patch memory. The restoration of Garry oak ecosystems (Quercus garryana), by students of the Restoration of Natural Systems Program at the University of Victoria, British Columbia, and a number of other examples are presented here that highlight why ecological memory is especially important in urban ecosystems.     

On Testing Equality of Distributions of Technical Efficiency Scores

The challenge of the econometric problem in production efficiency analysis is that the efficiency scores to be analyzed are unobserved. Statistical properties have recently been discovered for a type of estimator popular in the literature, known as data envelopment analysis (DEA). This opens up a wide range of possibilities for well-grounded statistical inference about the true efficiency scores from their DEA estimates. In this paper we investigate the possibility of using existing tests for the equality of two distributions in such a context. Considering the statistical complications pertinent to our context, we consider several approaches to adapting the Li test to the context and explore their performance in terms of the size and power of the test in various Monte Carlo experiments. One of these approaches shows good performance for both the size and the power of the test, thus encouraging its use in empirical studies. We also present an empirical illustration analyzing the efficiency distributions of countries in the world, following up a recent study by Kumar and Russell (2002), and report very interesting results.     

Robust selection of variables in linear discriminant analysis

A commonly used procedure for reduction of the number of variables in linear discriminant analysis is the stepwise method for variable selection. Although often criticized, when used carefully, this method can be a useful prelude to a further analysis. The contribution of a variable to the discriminatory power of the model is usually measured by the maximum likelihood ratio criterion, referred to as Wilks’ lambda. It is well known that the Wilks’ lambda statistic is extremely sensitive to the influence of outliers. In this work a robust version of the Wilks’ lambda statistic will be constructed based on the Minimum Covariance Discriminant (MCD) estimator and its reweighed version which has a higher efficiency. Taking advantage of the availability of a fast algorithm for computing the MCD a simulation study will be done to evaluate the performance of this statistic. The presentation of material in this article does not imply the expression of any opinion whatsoever on the part of Austro Control GmbH and is the sole responsibility of the authors.     

Discussion of “The power of monitoring: how to make the most of a contaminated multivariate sample” by Andrea Cerioli,Marco Riani,Anthony C. Atkinson and Aldo Corbellini

This paper discusses the contribution of Cerioli et al. (Stat Methods Appl, 2018), where robust monitoring based on high breakdown point estimators is proposed for multivariate data. The results follow years of development in robust diagnostic techniques. We discuss the issues of extending data monitoring to other models with complex structure, e.g. factor analysis, mixed linear models for which S and MM-estimators exist or deviating data cells. We emphasise the importance of robust testing that is often overlooked despite robust tests being readily available once S and MM-estimators have been defined. We mention open questions like out-of-sample inference or big data issues that would benefit from monitoring.     

Connectivity of public urban grasslands: implications for grassland conservation and restoration in cities

An important limitation of studies on the ecological value of urban habitats is the question of habitat connectivity. While connectivity allows spread and genetic exchange of species, isolation can lead to the extinction of populations through inbreeding and loss of genetic diversity. However, while practitioners already start improving urban habitat patches by means of restoration, issues of connectivity have not been taken in account although being crucial for a self-sustaining habitat network. In our study, we evaluate the connectivity of public urban grasslands such as lawns, grasslands in residential areas, playgrounds or parks in the city of Münster, Germany. Employing a graph theory approach, which uses a map (graph) of habitat patches to calculate functional connectivity measures using patch area size and patch connectivity, we studied four groups of different plant dispersal distances (2 m, 20 m, 44 m and 100 m). This approach can be recommended for planning issues due to the output of different indicator values and comparably low data demands. Our results show for the first time, that connectivity of urban grasslands is extremely low for species of short-distance dispersal but slightly increases for long-distance dispersal. Except two larger conglomerations of well-connected patches, urban grasslands were found to be mostly sparsely connected. Thus, future research should focus on options to improve connectivity and to prioritize patches for ecological improvement. The graph theory approach turned out to be a useful tool to analyse urban habitat connectivity but also to illustrate results obtained.

     

Optimum Competitive Area: Romania's Economic Integration in Southeast Europe

This paper presents a factual exposition of an optimum competitive area, tentatively referred to as a certain pattern of spatially defined areas conducive to competitive development for industries or firms in such a way that benefits from competition are maximized. This argument articulates around Romania's regional position and concludes that the most valuable source for enriched competitive advantages resides in its integrative process in Southeast Europe. There are economic facts that explain the range of industries and scope of businesses that emerge as main drivers of changing competitive advantages. This economically self-supporting space of competitive advantages basically rests on a set of five observations relative to the following facts: business strategies are more easily conceived; the value chain is more valuably exploited; the role Romania plays as a test market; the existence of spillover effects of political arrangements; and the existence of speculative returns and attractive growth rates of an emergent market. The conclusions take stock of the implications to conceive in specific terms that deliberate policies which would appear as necessary from this economic and business perspective.     

Compositional Tables Analysis in Coordinates

Compositional tables – a continuous counterpart to the contingency tables – carry relative information about relationships between row and column factors; thus, for their analysis, only ratios between cells of a table are informative. Consequently, the standard Euclidean geometry should be replaced by the Aitchison geometry on the simplex that enables decomposition of the table into its independent and interactive parts. The aim of the paper is to find interpretable coordinate representation for independent and interaction tables (in sense of balances and odds ratios of cells, respectively), where further statistical processing of compositional tables can be performed. Theoretical results are applied to real‐world problems from a health survey and in macroeconomics.     

Monotone fitting for developmental variables

In order to study developmental variables, for example, neuromotor development of children and adolescents, monotone fitting is typically needed. Most methods, to estimate a monotone regression function non-parametrically, however, are not straightforward to implement, a difficult issue being the choice of smoothing parameters. In this paper, a convenient implementation of the monotone B-spline estimates of Ramsay [Monotone regression splines in action (with discussion), Stat. Sci. 3 (1988), pp. 425–461] and Kelly and Rice [Montone smoothing with application to dose-response curves and the assessment of synergism, Biometrics 46 (1990), pp. 1071–1085] is proposed and applied to neuromotor data. Knots are selected adaptively using ideas found in Friedman and Silverman [Flexible parsimonous smoothing and additive modelling (with discussion), Technometrics 31 (1989), pp. 3–39] yielding a flexible algorithm to automatically and accurately estimate a monotone regression function. Using splines also simultaneously allows to include other aspects in the estimation problem, such as modeling a constant difference between two groups or a known jump in the regression function. Finally, an estimate which is not only monotone but also has a ‘levelling-off’ (i.e. becomes constant after some point) is derived. This is useful when the developmental variable is known to attain a maximum/minimum within the interval of observation.