Predictable invasion dynamics in North American populations of the Eurasian collared dove Streptopelia decaocto.

Species invasions represent a significant dimension of global change yet the dynamics of invasions remain poorly understood and are considered rather unpredictable. We explored interannual dynamics of the invasion process in the Eurasian collared dove (Streptopelia decaocto) and tested whether the advance of the invasion front of the species in North America relates to centrality (versus peripherality) within its estimated fundamental ecological niche. We used ecological niche modelling approaches to estimate the dimensions of the fundamental ecological niche on the Old World distribution of the species, and then transferred that model to the New World as measures of centrality versus peripherality within the niche for the species. Although our hypothesis was that the invasion front would advance faster over more favourable (i.e. more central) conditions, the reverse was the case: the invasion expanded faster in areas presenting less favourable (i.e. more peripheral) conditions for the species as it advanced across North America. This result offers a first view of a predictive approach to the dynamics of species' invasions, and thereby has relevant implications for the management of invasive species, as such a predictive understanding would allow better anticipation of coming steps and advances in the progress of invasions, important to designing and guiding effective remediation and mitigation efforts.

Mahalanobis distances for ecological niche modelling and outlier detection: implications of sample size, error, and bias for selecting and parameterising a multivariate location and scatter method.

The Mahalanobis distance is a statistical technique that has been used in statistics and data science for data classification and outlier detection, and in ecology to quantify species-environment relationships in habitat and ecological niche models. Mahalanobis distances are based on the location and scatter of a multivariate normal distribution, and can measure how distant any point in space is from the centre of this kind of distribution. Three different methods for calculating the multivariate location and scatter are commonly used: the sample mean and variance-covariance, the minimum covariance determinant, and the minimum volume ellipsoid. The minimum covariance determinant and minimum volume ellipsoid were developed to be robust to outliers by minimising the multivariate location and scatter for a subset of the full sample, with the proportion of the full sample forming the subset being controlled by a user-defined parameter. This outlier robustness means the minimum covariance determinant and the minimum volume ellipsoid are highly relevant for ecological niche analyses, which are usually based on natural history observations that are likely to contain errors. However, natural history observations will also contain extreme bias, to which the minimum covariance determinant and the minimum volume ellipsoid will also be sensitive. To provide guidance for selecting and parameterising a multivariate location and scatter method, a series of virtual ecological niche modelling experiments were conducted to demonstrate the performance of each multivariate location and scatter method under different levels of sample size, errors, and bias. The results show that there is no optimal modelling approach, and that choices need to be made based on the individual data and question. The sample mean and variance-covariance method will perform best on very small sample sizes if the data are free of error and bias. At larger sample sizes the minimum covariance determinant and minimum volume ellipsoid methods perform as well or better, but only if they are appropriately parameterised. Modellers who are more concerned about the prevalence of errors should retain a smaller proportion of the full data set, while modellers more concerned about the prevalence of bias should retain a larger proportion of the full data set. I conclude that Mahalanobis distances are a useful niche modelling technique, but only for questions relating to the fundamental niche of a species where the assumption of multivariate normality is reasonable. Users of the minimum covariance determinant and minimum volume ellipsoid methods must also clearly report their parameterisations so that the results can be interpreted correctly.

Novel Methods in Disease Biogeography: A Case Study with Heterosporosis.

Disease biogeography is currently a promising field to complement epidemiology, and ecological niche modeling theory and methods are a key component. Therefore, applying the concepts and tools from ecological niche modeling to disease biogeography and epidemiology will provide biologically sound and analytically robust descriptive and predictive analyses of disease distributions. As a case study, we explored the ecologically important fish disease Heterosporosis, a relatively poorly understood disease caused by the intracellular microsporidian parasite Heterosporis sutherlandae. We explored two novel ecological niche modeling methods, the minimum-volume ellipsoid (MVE) and the Marble algorithm, which were used to reconstruct the fundamental and the realized ecological niche of H. sutherlandae, respectively. Additionally, we assessed how the management of occurrence reports can impact the output of the models. Ecological niche models were able to reconstruct a proxy of the fundamental and realized niche for this aquatic parasite, identifying specific areas suitable for Heterosporosis. We found that the conceptual and methodological advances in ecological niche modeling provide accessible tools to update the current practices of spatial epidemiology. However, careful data curation and a detailed understanding of the algorithm employed are critical for a clear definition of the assumptions implicit in the modeling process and to ensure biologically sound forecasts. In this paper, we show how sensitive MVE is to the input data, while Marble algorithm may provide detailed forecasts with a minimum of parameters. We showed that exploring algorithms of different natures such as environmental clusters, climatic envelopes, and logistic regressions (e.g., Marble, MVE, and Maxent) provide different scenarios of potential distribution. Thus, no single algorithm should be used for disease mapping. Instead, different algorithms should be employed for a more informed and complete understanding of the pathogen or parasite in question.

IS THERE CONVERGENCE ACROSS COUNTRIES? A SPATIAL APPROACH.

We analyze convergence across countries over the last half century as a result of globalizing forces. Drawing on theories of modernization, dependency, the world-system, political trade blocs, and the world-society, we consider economic, demographic, knowledge, financial, and political dimensions of convergence. Using a new methodology, we calculate the minimum volume ellipsoid encompassing different groupings of countries, finding that during the 1960-2009 period, countries have not evolved significantly closer or similar to one another, although groups of countries based on their core-periphery status or membership in trade blocs exhibit increasing internal convergence and divergence between one another.

Distribution of variables by method of outlier detection.

The presence of outliers can very problematic in data analysis, leading statisticians to develop a wide variety of methods for identifying them in both the univariate and multivariate contexts. In case of the latter, perhaps the most popular approach has been Mahalanobis distance, where large values suggest an observation that is unusual as compared to the center of the data. However, researchers have identified problems with the application of this metric such that its utility may be limited in some situations. As a consequence, other methods for detecting outlying observations have been developed and studied. However, a number of these approaches, while apparently robust and useful have not made their way into general practice in the social sciences. Thus, the goal of this study was to describe some of these methods and demonstrate them using a well known dataset from a popular multivariate textbook widely used in the social sciences. Results demonstrated that the methods do indeed result in datasets with very different distributional characteristics. These results are discussed in light of how they might be used by researchers and practitioners.