Erosion of natural darkness in the geographic ranges of cacti.

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.

A brief introduction to mixed effects modelling and multi-model inference in ecology.

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.