Environmental microplastics are gaining interest due to their ubiquity and the threat they pose to environmental and human health. Critical studies have revealed the abundance of microplastics in nature, while others have tested the impacts of these small plastics on organismal health in the laboratory. Yet, there is often a mismatch between these two areas of research, resulting in major discrepancies and an inability to interpret certain findings. Here, we focus on several main lines of inquiry. First, even though the majority of environmental microplastics are plastic microfibers from textiles, laboratory studies still largely use spherical microbeads. There are also inconsistencies between the measurements of microplastics in the environment as compared to the concentrations that tend to be used in experimental studies. Likewise, the period of exposure occurring in experimental studies and in the environment are vastly different. Lastly, although experimental studies often focus on a particular subset of toxic chemicals present on microplastics, textile microfibers carry other dyes and chemicals that are understudied. They also cause types of physical damage not associated with microspheres. This review will analyze the literature pertaining to these mismatches, focusing on aquatic organisms and model systems, and seek to inform a path forward for this burgeoning area of research.

One of the goals of ecological biogeography is to accurately predict where major vegetation types, such as forests or grasslands, can establish themselves. Alas, our current understanding of the mechanisms by which environmental variables jointly influence vegetation types does not allow for such predictions. Here, we compile a database of local measurements of soil, weather, topography, and bedrock lithology for 6404 sites across the continental United States, which we complement with information about the vegetation around the measurement sites. By applying machine learning methods to a subset of the sites, we are able to train a machine that predicts - using only information about abiotic variables - the presence of forests or grasslands with nearly 90% accuracy on the rest of the sites. We find that while weather variables are statistically important for these predictions, a few soil variables prove to be the most powerful predictors. By separating the combined contributions of weather and soil, we show that soil texture is a better predictor under dry conditions than under wet conditions, while pH is important predictor regardless of the weather. Furthermore, from 136 sites undergoing grassland-to-forest succession, our analysis indicates that soil pH changes with a certain lag relative to vegetation change. Sites that have recently undergone a transition exhibit a higher pH, closer to that of grasslands, despite the presence of trees. The pH then decreases by about one unit over a 30-year period of forest development. Ultimately, our results indicate that fine-scale environmental measurements and historical ecological data can serve as a basis for quantitative bio-geographical predictions, which could eventually guide targeted conservation interventions.