The present study is the first to consider human and nonhuman consumers together to reveal several general patterns of plant utilization. We provide evidence that at a global scale, plant apparency and phylogenetic isolation can be important predictors of plant utilization and consumer diversity. Using the number of species or genera or the distribution area of each plant family as the island “area” and the minimum phylogenetic distance to common plant families as the island “distance”, we fitted presence–area relationships and presence–distance relationships with a binomial GLM (generalized linear model) with a logit link. The presence–absence of consumers among each plant family strongly depended on plant apparency (family size and distribution area); the diversity of consumers increased with plant apparency but decreased with phylogenetic isolation. When consumers extended their host breadth, unapparent plants became more likely to be used. Common uses occurred more often on common plants and their relatives, showing higher host phylogenetic clustering than uncommon uses. On the contrary, highly specialized uses might be related to the rarity of plant chemicals and were therefore very species‐specific. In summary, our results provide a global illustration of plant–consumer combinations and reveal several general patterns of plant utilization across humans, insects and microbes. First, plant apparency and plant phylogenetic isolation generally govern plant utilization value, with uncommon and isolated plants suffering fewer parasites. Second, extension of the breadth of utilized hosts helps explain the presence of consumers on unapparent plants. Finally, the phylogenetic clustering structure of host plants is different between common uses and uncommon uses. The strength of such consistent plant utilization patterns across a diverse set of usage types suggests that the persistence and accumulation of consumer diversity and use value for plant species are determined by similar ecological and evolutionary processes.

The cement industry confronts significant challenges in raw materials, energy demands, and CO₂ emissions reduction, which are global and local environmental concerns. Life cycle assessment (LCA) has been used in many studies to assess the environmental impact of cement production and investigate ways to improve environmental performance. This study aims to analyse the environmental impact of Portland cement (CEM I) on the South African cement industry using the life cycle impact assessment (LCIA), based on the Recipe 2016 v 1.04 midpoint method. The study was conducted using data modeled after the South African cement plant, considered a cradle-to-gate system boundary, starting from the extraction of the raw material to the cement production process that produces cement as the main product. The data were obtained from the Ecoinvent database v3.7.1, integrated with SimaPro 9.1.1. software, used to assess the impact categories. For simplicity, the study merged the entire production process into five processes, i.e., raw materials usage, fuel consumption, clinker production, transportation and electricity. The impact categories of the five production stages were assessed using the LCA methodology. The impact categories investigated were classified into three categories: atmospheric, resource depletion and toxicity categories. According to the results, clinker production and electricity usage stages contribute the most to atmospheric impact (global warming, which causes climatic change due to high CO₂ emissions), followed by raw materials and fuel consumption, contributing to the toxicity and resource depletion impact category. These stages contribute more than 76% of CO₂ eq. and 93% of CFC-11 eq. In the midpoint method, CO₂ is the most significant pollutant released. Therefore, replacing fossil fuels with alternative fuels can reduce fossil fuel use and the atmospheric impact of cement kilns.

The swimming behavior of Pseudomonas putida was analyzed with a tracking microscope to quantify its run time and turn angle distributions. Monte Carlo computer simulations illustrated that the bimodal turn angle distribution of P. putida reduced collisions with obstacles in porous media in comparison to the unimodal distribution of Escherichia coli.