Locally developed models improve the accuracy of remotely assessed metrics as a rapid tool to classify sandy beach morphodynamics.

Classification of beaches into morphodynamic states is a common approach in sandy beach studies, due to the influence of natural variables in ecological patterns and processes. The use of remote sensing for identifying beach type and monitoring changes has been commonly applied through multiple methods, which often involve expensive equipment and software processing of images. A previous study on the South African Coast developed a method to classify beaches using conditional tree inferences, based on beach morphological features estimated from public available satellite images, without the need for remote sensing processing, which allowed for a large-scale characterization. However, since the validation of this method has not been tested in other regions, its potential uses as a trans-scalar tool or dependence from local calibrations has not been evaluated. Here, we tested the validity of this method using a 200-km stretch of the Brazilian coast, encompassing a wide gradient of morphodynamic conditions. We also compared this locally derived model with the results that would be generated using the cut-off values established in the previous study. To this end, 87 beach sites were remotely assessed using an accessible software (i.e., Google Earth) and sampled for an in-situ environmental characterization and beach type classification. These sites were used to derive the predictive model of beach morphodynamics from the remotely assessed metrics, using conditional inference trees. An additional 77 beach sites, with a previously known morphodynamic type, were also remotely evaluated to test the model accuracy. Intertidal width and exposure degree were the only variables selected in the model to classify beach type, with an accuracy higher than 90% through different metrics of model validation. The only limitation was the inability in separating beach types in the reflective end of the morphodynamic continuum. Our results corroborated the usefulness of this method, highlighting the importance of a locally developed model, which substantially increased the accuracy. Although the use of more sophisticated remote sensing approaches should be preferred to assess coastal dynamics or detailed morphodynamic features (e.g., nearshore bars), the method used here provides an accessible and accurate approach to classify beach into major states at large spatial scales. As beach type can be used as a surrogate for biodiversity, environmental sensitivity and touristic preferences, the method may aid management in the identification of priority areas for conservation.

The relative contribution of non-selection and selection processes in marine benthic assemblages.

We tested the hypothesis that the ubiquity of marine meiofaunal nematodes and their indiscriminate passive dispersal create assemblages that are less limited by its environment; whereas the relatively smaller population sizes of macrofauna, associated with their ability to track environmental conditions before settlement, renders their distribution more environmentally-restricted. We compared the empirical distribution of macrofauna and nematode species with that of communities simulated under different assumptions of selection (e.g. environmental filtering) and non-selection (e.g. dispersal limitation) processes. Selection processes were the prime driver of both meio- and macrofauna assemblages, with rare species strongly contributing to this component. The total number of species explained by non-selection processes was 27% higher in nematodes than in macrofauna. Our results underline the importance of a species-level approach to determine the contribution of selection and non-selection assembly processes. Moreover, they highlight the important yet overlooked role of dispersal and stochastic processes in determining species dynamics.

Laundering and textile parameters influence fibers release in household washings.

Synthetic fibers represent one of the main forms of microplastics in marine environment and recently were related to household washings as a source. Although other types of fiber, like natural, do not rely under this classification, there is a potential for them to act as a vector of toxic substances to biota in the same way as microplastics do. Consequently all types of fiber have the potential to cause variable ecologic and socioeconomic impacts. In this scenario, the present study aimed to investigate the effects of washing parameters in the emission of fibers on textiles with different characteristics and fiber content: cotton, acrylic, polyester and polyamide. For this purpose individual garments were sequentially washed with and without detergent. Results showed that the use of a detergent reduced significantly the mass of particles emitted from synthetic garments but not from cotton, which, in relative terms, was responsible for the highest emissions. Textile characteristics such as mass availability and fiber cohesion influenced results, where shorter irregular fibers and lower tenacities dealt to higher releases. For all types of garments tested, 10 sequential cycles decreased particles' release, with peaks in three firsts washes (from 37% to 76%). Taking into account a regular washing machine filter, a considerable mass of fibers (from 40% to 75%) was not retained by this device, indicating a potential for improvement. Together, simple solutions as the use of detergents, three pre-washes and superimposed filter meshes, could diminish >53% of this type of pollution. Besides this potential reduction, globally, in one year, domestic washing machines would still contribute with around 15 thousand tonnes of cotton and synthetic fibers. A structured and sustained solution for this problem should advance in an interdisciplinary approach, fomenting responsibility from plural actors, taken in all stages of products' life cycle.