Refining area of occupancy to address the modifiable areal unit problem in ecology and conservation

The ‘modifiable areal unit problem’ is prevalent across many aspects of spatial analysis within ecology and conservation. The problem is particularly manifest when calculating metrics for extinction risk estimation, for example, area of occupancy (AOO).Although embedded into the International Union for the Conservation of Nature (IUCN) Red List criteria, AOO is often not used or is poorly applied. Here we evaluate new and existing methods for calculating AOO from occurrence records and present a method for determining the minimum AOO using a uniform grid. We evaluate the grid cell shape, grid origin and grid rotation with both real-world and simulated data, reviewing the effects on AOO values, and possible impacts for species already assessed on the IUCN Red List. We show that AOO can vary by up to 80% and a ratio of cells to points of 1:1.21 gives the maximum variation in the number of occupied cells. These findings potentially impact 3% of existing species on the IUCN Red List, as well as species not yet assessed. We show that a new method that combines both grid rotation and moving grid origin gives fast, robust and reproducible results and, in the majority of cases, achieves the minimum AOO. As well as reporting minimum AOO, we outline a confidence interval which should be incorporated in to existing tools that support species risk assessment. We also make further recommendations for reporting AOO and other areal measurements within ecology, leading to more robust methods for future species risk assessment

Erasmus+ higher education impact study

Between 2014 and 2018, two million students and staff in higher education undertook a learning, training or teaching period abroad with the new Erasmus+ programme. The objective of the study was to assess the impact of Erasmus+ mobilities on staff, students and higher education institutions. It was based on almost 77,000 survey responses from these groups

A balanced view of scale in spatial statistical analysis

Concepts of spatial scale, such as extent, grain, resolution, range, footprint, support and cartographic ratio are not interchangeable. Because of the potential confusion among the definitions of these terms, we suggest that authors avoid the term "scale" and instead refer to specific concepts. In particular, we are careful to discriminate between observation scales, scales of ecological phenomena and scales used in spatial statistical analysis. When scales of observation or analysis change, that is, when the unit size, shape, spacing or extent are altered, statistical results are expected to change. The kinds of results that may change include estimates of the population mean and variance, the strength and character of spatial autocorrelation and spatial anisotropy, patch and gap sizes and multivariate relationships, The First three of these results (precision of the mean, variance and spatial autocorrelation) can sometimes be estimated using geostatistical support-effect models. We present four case studies of organism abundance and cover illustrating some of these changes and how conclusions about ecological phenomena (process and structure) may be affected. We identify the influence of observational scale on statistical results as a subset of what geographers call the Modifiable Area Unit Problem (MAUP). The way to avoid the MAUP is by careful construction of sampling design and analysis. We recommend a set of considerations for sampling design to allow useful tests for specific scales of a phenomenon under study. We further recommend that ecological studies completely report all components of observation and analysis scales to increase the possibility of cross-study comparisons

Comprehensive analysis of correlation coefficients estimated from pooling heterogeneous microarray data

Background The synthesis of information across microarray studies has been performed by combining statistical results of individual studies (as in a mosaic), or by combining data from multiple studies into a large pool to be analyzed as a single data set (as in a melting pot of data). Specific issues relating to data heterogeneity across microarray studies, such as differences within and between labs or differences among experimental conditions, could lead to equivocal results in a melting pot approach. Results We applied statistical theory to determine the specific effect of different means and heteroskedasticity across 19 groups of microarray data on the sign and magnitude of gene-to-gene Pearson correlation coefficients obtained from the pool of 19 groups. We quantified the biases of the pooled coefficients and compared them to the biases of correlations estimated by an effect-size model. Mean differences across the 19 groups were the main factor determining the magnitude and sign of the pooled coefficients, which showed largest values of bias as they approached ±1. Only heteroskedasticity across the pool of 19 groups resulted in less efficient estimations of correlations than did a classical meta-analysis approach of combining correlation coefficients. These results were corroborated by simulation studies involving either mean differences or heteroskedasticity across a pool of N \u3e 2 groups. Conclusions The combination of statistical results is best suited for synthesizing the correlation between expression profiles of a gene pair across several microarray studies

A spatial analysis of employment multipliers in the US

The actual effectiveness of employment promotion policies depends on the ability of the intervention at creating new jobs in the targeted sector, but also, to a large extent, on the impact they have on other parts of the local economy. Estimating the latter effect is therefore quite important for regional economic development policies. Along the lines of Moretti (Am Econ Rev Pap Proc 100: 373-377, 2010), we present an empirical analysis of local employment multipliers using data on 123 US Metropolitan Statistical Areas over the period 1980-2010. From the methodological point of view, in this work not only endogeneity (via instrumental variables estimates), but also spatial spillovers are taken into account. According to the results, the magnitude of the multiplier could be rather limited. On the other hand, there is clear indication that the impact of these interventions is not fully contained within the local economy and they have a positive effect on closely surrounding ones

Empirical Legal Studies Before 1940: A Bibliographic Essay

The modern empirical legal studies movement has well-known antecedents in the law and society and law and economics traditions of the latter half of the 20th century. Less well known is the body of empirical research on legal phenomena from the period prior to World War II. This paper is an extensive bibliographic essay that surveys the English language empirical legal research from approximately 1940 and earlier. The essay is arranged around the themes in the research: criminal justice, civil justice (general studies of civil litigation, auto accident litigation and compensation, divorce, small claims, jurisdiction and procedure, civil juries), debt and bankruptcy, banking, appellate courts, legal needs, legal profession (including legal education), and judicial staffing and selection. Accompanying the essay is an extensive bibliography of research articles, books, and reports