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.

31 32The use of linear mixed effects models (LMMs) is increasingly common in the analysis 33 of biological data. Whilst LMMs offer a flexible approach to modelling a broad range of 34 data types, ecological data are often complex and require complex model structures, 35 and the fitting and interpretation of such models is not always straightforward. The 36 ability to achieve robust biological inference requires that practitioners know how and 37 when to apply these tools. Here, we provide a general overview of current methods for 38 the application of LMMs to biological data, and highlight the typical pitfalls that can be 39 encountered in the statistical modelling process. We tackle several issues relating to the 40 use of information theory and multi-model inference in ecology, and demonstrate the 41 tendency for data dredging to lead to greatly inflated Type I error rate (false positives) 42 and impaired inference. We offer practical solutions and direct the reader to key

Abstract: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.

Societal Impact Statement Crop wild relatives (CWR) are plant taxa closely related to crops and are a source of high genetic diversity that can help adapt crops to the impacts of global change, particularly to meet increasing consumer demand in the face of the climate crisis. CWR provide vital ecosystem services and are increasingly important for food and nutrition security and sustainable and resilient agriculture. They therefore are of major biological, social, cultural and economic importance. Assessing the extinction risk of CWR is essential to prioritise in situ and ex situ conservation strategies in Mesoamerica to guarantee the long‐term survival and availability of these resources for present and future generations worldwide. Summary Ensuring food security is one of the world's most critical issues as agricultural systems are already being impacted by global change. Crop wild relatives (CWR)—wild plants related to crops—possess genetic variability that can help adapt agriculture to a changing environment and sustainably increase crop yields to meet the food security challenge. Here we report the results of an extinction risk assessment of 224 wild relatives of some of the world's most important crops (i.e. chilli pepper, maize, common bean, avocado, cotton, potato, squash, vanilla and husk tomato) in Mesoamerica—an area of global significance as a centre of crop origin, domestication and of high CWR diversity. We show that 35% of the selected CWR taxa are threatened with extinction according to The International Union for Conservation of Nature (IUCN) Red List demonstrates that these valuable genetic resources are under high anthropogenic threat. The dominant threat processes are land use change for agriculture and farming, invasive and other problematic species (e.g. pests, genetically modified organisms) and use of biological resources, including overcollection and logging. The most significant drivers of extinction relate to smallholder agriculture—given its high incidence and ongoing shifts from traditional agriculture to modern practices (e.g. use of herbicides)—smallholder ranching and housing and urban development and introduced genetic material. There is an urgent need to increase knowledge and research around different aspects of CWR. Policies that support in situ and ex situ conservation of CWR and promote sustainable agriculture are pivotal to secure these resources for the benefit of current and future generations.

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 relating to the use of information theory and multi-model inference in ecology, and demonstrate the tendency for data dredging to lead to greatly inflated Type I error rate (false positives) and impaired inference. 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.