The aim of this study is to investigate whether eliminating plastics entirely under existing waste infrastructure and management practices could have an adverse effect on climate change, using a case study on the hypothetical substitution of Polyethylene Terephthalate (PET) with glass as the material for bottling liquids in the domestic sector in Cornwall, England. A life cycle environmental impacts-based model was created using high resolution local data on household waste and current management practices in combination with Life Cycle Assessment (LCA) datasets. The model allows users to define key system parameters such as masses of materials, transport options and end-of-life processes and produces results for 11 environmental impact categories including the Global Warming Potential (GWP). The results from the application of this model on the case study of Cornwall have shown that the substitution of PET with glass as the material for bottling under the current waste infrastructure and management practices could lead to significant increases in GWP and hinder efforts to tackle climate change. A sensitivity analysis of the glass/PET mass ratio suggests that in order to achieve equal GWP the glass bottles need to become approximately 38% of the weight they are now. Increasing the recycled content and decreasing losses during the recycling processes could also help lower the GWP by 18.9% and 14.5%, respectively. This model can be expanded further to include more types of plastics and other regions to evaluate designs of new regional circular economy with less plastics waste and pollution. Our study suggests that it is necessary and crucial to consider the specific waste infrastructure and management practices in place and use science-based models that incorporate life cycle thinking to evaluate any solutions to plastics pollution in order to avoid problem shifting.

The use of linear mixed effects models (LMMs) is increasingly common in the analysis of biological data. Whilst LMMs offer a flexible approach to modelling a broad range of data types, ecological data are often complex and require complex model structures, and the fitting and interpretation of such models is not always straightforward. The ability to achieve robust biological inference requires that practitioners know how and when to apply these tools. Here, we provide a general overview of current methods for the application of LMMs to biological data, and highlight the typical pitfalls that can be encountered in the statistical modelling process. We tackle several issues regarding methods of model selection, with particular reference to the use of information theory and multi-model inference in ecology. We offer practical solutions and direct the reader to key references that provide further technical detail for those seeking a deeper understanding. This overview should serve as a widely accessible code of best practice for applying LMMs to complex biological problems and model structures, and in doing so improve the robustness of conclusions drawn from studies investigating ecological and evolutionary questions.

Naturally dark nighttime environments are being widely eroded by the introduction of artificial light at night (ALAN). The biological impacts vary with the intensity and spectrum of ALAN, but have been documented from molecules to ecosystems. How globally severe these impacts are likely to be depends in large part on the relationship between the spatio-temporal distribution of ALAN and that of the geographic ranges of species. Here, we determine this relationship for the Cactaceae family. Using maps of the geographic ranges of cacti and nighttime stable light composite images for the period 1992 to 2012, we found that a high percentage of cactus species were experiencing ALAN within their ranges in 1992, and that this percentage had increased by 2012. For almost all cactus species (89.7%) the percentage of their geographic range that was lit increased from 1992-1996 to 2008-2012, often markedly. There was a significant negative relationship between the species richness of an area, and that of threatened species, and the level of ALAN. Cacti could be particularly sensitive to this widespread and ongoing intrusion of ALAN into their geographic ranges, especially when considering the potential for additive and synergistic interactions with the impacts of other anthropogenic pressures.

Macroecology emerged at the end of the 1980's as a synthetic discipline aimed at describing and understanding biodiversity patterns at large spatial and temporal scales. Here, we review the state of the art of macroecology in Mexico by identifying: (i) the main theoretical and applied contributions made by Mexican researchers; (ii) knowledge gaps and theoretic-methodological challenges, and (iii) perspectives of this research field in Mexico. After conducting systematic searches in the Web of Science database and targeted specific topic searches we found 163 articles published by Mexican scientists, most of them in the last decade. Thirty seven percent of these papers were focused at a regional scale and 31% at a national scale, and most of them (90%) corresponded to terrestrial habitats. Mammals, plants and birds were the groups better represented (33%; 21%, and 17%, respectively). The main contributions were developed in the study of diversity patterns, ecological niche, analyses of diversity at different scales (alpha, beta and gamma diversity), and the distributional area. It is important to incorporate subjects such as abundance, phylogenetic and functional diversity and transform the discipline into a more predictive one able to contribute in solving environmental issues at national and global scales.

The rapid growth in electric light usage across the globe has led to increasing presence of artificial light in natural and semi-natural ecosystems at night. This occurs both due to direct illumination and skyglow - scattered light in the atmosphere. There is increasing concern about the effects of artificial light on biological processes, biodiversity and the functioning of ecosystems. We combine intercalibrated Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS) images of stable night-time lights for the period 1992 to 2012 with a remotely sensed landcover product (GLC2000) to assess recent changes in exposure to artificial light at night in 43 global ecosystem types. We find that Mediterranean-climate ecosystems have experienced the greatest increases in exposure, followed by temperate ecosystems. Boreal, Arctic and montane systems experienced the lowest increases. In tropical and subtropical regions, the greatest increases are in mangroves and subtropical needleleaf and mixed forests, and in arid regions increases are mainly in forest and agricultural areas. The global ecosystems experiencing the greatest increase in exposure to artificial light are already localized and fragmented, and often of particular conservation importance due to high levels of diversity, endemism and rarity. Night time remote sensing can play a key role in identifying the extent to which natural ecosystems are exposed to light pollution.