Inferring long memory processes in the climate network via ordinal pattern analysis

We use ordinal patterns and symbolic analysis to construct global climate networks and uncover long and short term memory processes. The data analyzed is the monthly averaged surface air temperature (SAT field) and the results suggest that the time variability of the SAT field is determined by patterns of oscillatory behavior that repeat from time to time, with a periodicity related to intraseasonal oscillations and to El Ni\~{n}o on seasonal-to-interannual time scales.Comment: 10 pages, 13 figures Enlarged version, new sections and figures. Accepted in Chao

Perceived Diversity of Complex Environmental Systems: Multidimensional Measurement and Synthetic Indicators

The general attitude towards the sustainable management of environmental resources is evolving towards the implementation of ‘participatory’ (as opposed to the classical ‘command and control’) and, especially at local scale, ‘bottom up’ (as opposed to the classical ‘top down’) approaches. This progress pushes a major interest in the development and application of methodologies able to ‘discover’ and ‘measure’ how environmental systems tend to be perceived by the different Stakeholders. Due to the ‘nature’ of the investigated systems, often too ‘complex’ to be treated through a classical deterministic approach, as typical for ‘hard’ physical/mathematical sciences, any ‘measurement’ has necessarily to be multidimensional. In the present report an approach, more typical of ‘soft’ social sciences, is presented and applied to the analysis of the sustainable management of water resources in seven Southern and Eastern Mediterranean Watersheds. The methodology is based on the development and analysis (explorative factor analysis, multidimensional scaling) of a questionnaire and is aimed at the ‘discovery’ and ‘measurement’ of a latent multidimensional ‘underlying structure’ (‘conceptual map’). It is the opinion of the authors, that the identification of a set of ‘consistent’, ‘independent’, ‘bottom up’ and ‘shared’ synthetic indicators (aggregated indices) could be strongly facilitated by the interpretation of the dimensions of the emerging ‘underlying structure’.Participative Approach, Cognitive Map, Factor Analysis, Indicators of Sustainability, Sustainable Water, Management

Structured additive regression for multicategorical space-time data: A mixed model approach

In many practical situations, simple regression models suffer from the fact that the dependence of responses on covariates can not be sufficiently described by a purely parametric predictor. For example effects of continuous covariates may be nonlinear or complex interactions between covariates may be present. A specific problem of space-time data is that observations are in general spatially and/or temporally correlated. Moreover, unobserved heterogeneity between individuals or units may be present. While, in recent years, there has been a lot of work in this area dealing with univariate response models, only limited attention has been given to models for multicategorical space-time data. We propose a general class of structured additive regression models (STAR) for multicategorical responses, allowing for a flexible semiparametric predictor. This class includes models for multinomial responses with unordered categories as well as models for ordinal responses. Non-linear effects of continuous covariates, time trends and interactions between continuous covariates are modelled through Bayesian versions of penalized splines and flexible seasonal components. Spatial effects can be estimated based on Markov random fields, stationary Gaussian random fields or two-dimensional penalized splines. We present our approach from a Bayesian perspective, allowing to treat all functions and effects within a unified general framework by assigning appropriate priors with different forms and degrees of smoothness. Inference is performed on the basis of a multicategorical linear mixed model representation. This can be viewed as posterior mode estimation and is closely related to penalized likelihood estimation in a frequentist setting. Variance components, corresponding to inverse smoothing parameters, are then estimated by using restricted maximum likelihood. Numerically efficient algorithms allow computations even for fairly large data sets. As a typical example we present results on an analysis of data from a forest health survey

Valuing Biodiversity in Life Cycle Impact Assessment

Erratum published on 13 March 2020, see Sustainability 2020, 12(6), 2270. https://doi.org/10.3390/su11205628In this article, the authors propose an impact assessment method for life cycle assessment (LCA) that adheres to established LCA principles for land use-related impact assessment, bridges current research gaps and addresses the requirements of different stakeholders for a methodological framework. The conservation of biodiversity is a priority for humanity, as expressed in the framework of the Sustainable Development Goals (SDGs). Addressing biodiversity across value chains is a key challenge for enabling sustainable production pathways. Life cycle assessment is a standardised approach to assess and compare environmental impacts of products along their value chains. The impact assessment method presented in this article allows the quantification of the impact of land-using production processes on biodiversity for several broad land use classes. It provides a calculation framework with degrees of customisation (e.g., to take into account regional conservation priorities), but also offers a default valuation of biodiversity based on naturalness. The applicability of the method is demonstrated through an example of a consumer product. The main strength of the approach is that it yields highly aggregated information on the biodiversity impacts of products, enabling biodiversity-conscious decisions about raw materials, production routes and end user products

Knowing and doing vocational education and training reform: evidence, learning and the policy process

Much of VET policy internationally draws on a toolkit that has been seriously questioned for its logic, international relevance and effectiveness by considerable amounts of academic research. Reflecting primarily on our experiences of leading a complex, multi-country policy study, we develop an account that seeks to explore ways in which the apparent incommensurability between academic and policy knowledge can be addressed. This leads on to a broader discussion of key issues of contestation in the debates about knowledge for policy as they relate to international education and development more generally. We consider three key turns in the discourse of international education policy and research: to "governing by numbers", "what works" and policy learning, and ask what happens when these discursive trends travel to Southern and VET contexts. We suggest that this analysis implies that policymakers need both to be more modest and reflexive in their expectations of what knowledge can be mobilised for policy purposes and more serious in their commitment to supporting the generation of the types of knowledge that they claim to value. For international and comparative educators, we stress the importance of being clearer in seeking to shape research agendas; more rigorous in our approaches to research; and better in our external communication of our findings. Given the particular focus of this special issue on VET, we end by reiterating the particular challenge of reawakening research on VET-for-development from twenty years of slumbers

Assessing the Health of Richibucto Estuary with the Latent Health Factor Index

The ability to quantitatively assess the health of an ecosystem is often of great interest to those tasked with monitoring and conserving ecosystems. For decades, research in this area has relied upon multimetric indices of various forms. Although indices may be numbers, many are constructed based on procedures that are highly qualitative in nature, thus limiting the quantitative rigour of the practical interpretations made from these indices. The statistical modelling approach to construct the latent health factor index (LHFI) was recently developed to express ecological data, collected to construct conventional multimetric health indices, in a rigorous quantitative model that integrates qualitative features of ecosystem health and preconceived ecological relationships among such features. This hierarchical modelling approach allows (a) statistical inference of health for observed sites and (b) prediction of health for unobserved sites, all accompanied by formal uncertainty statements. Thus far, the LHFI approach has been demonstrated and validated on freshwater ecosystems. The goal of this paper is to adapt this approach to modelling estuarine ecosystem health, particularly that of the previously unassessed system in Richibucto in New Brunswick, Canada. Field data correspond to biotic health metrics that constitute the AZTI marine biotic index (AMBI) and abiotic predictors preconceived to influence biota. We also briefly discuss related LHFI research involving additional metrics that form the infaunal trophic index (ITI). Our paper is the first to construct a scientifically sensible model to rigorously identify the collective explanatory capacity of salinity, distance downstream, channel depth, and silt-clay content --- all regarded a priori as qualitatively important abiotic drivers --- towards site health in the Richibucto ecosystem.Comment: On 2013-05-01, a revised version of this article was accepted for publication in PLoS One. See Journal reference and DOI belo