Deep-water emergence (DWE) is the phenomenon where marine species normally found at great depths (i.e., below 200 m), can be found locally occurring in significantly shallower depths (i.e., euphotic zone, usually shallower than 50 m). Although this phenomenon has been previously mentioned and deep-water emergent species have been described from the fjord regions of North America, Scandinavia, and New Zealand, local or global hypotheses to explain this phenomenon have rarely been tested. This publication includes the first literature review on DWE. Our knowledge of distribution patterns of Chilean marine invertebrates is still very scarce, especially from habitats below SCUBA diving depth. In our databases, we have been gathering occurrence data of more than 1000 invertebrate species along the Chilean coast, both from our research and from the literature. We also distributed a list of 50 common and easily in situ-identifiable species among biologically experienced sport divers along the Chilean coast and recorded their sighting reports. Among other findings, the analysis of the data revealed patterns from 28 species and six genera with similar longitudinal and bathymetric distribution along the entire Chilean coast: along the Chilean coast these species are typically restricted to deep water (>200 m) but only in some parts of Chilean Patagonia (>39°S–56°S), the same species are also common to locally abundant at diving depths (<30 m). We found 28 of these ‘deep’ species present in shallow-water of North Patagonia, 32 in Central Patagonia and 12 in South Patagonia. The species belong to the phyla Cnidaria (six species), Mollusca (four species), Arthropoda (two species) and Echinodermata (16 species). We ran several analyses comparing depth distribution between biogeographic regions (two-way ANOVA) and comparing abiotic parameters of shallow and deep sites to search for correlations of distribution with environmental variables (Generalized Linear Models). For the analyses, we used a total of 3328 presence points and 10635 absence points. The results of the statistical analysis of the parameters used, however, did not reveal conclusive results. We summarize cases from other fjord regions and discuss hypotheses of DWE from the literature for Chilean Patagonia.

Coastal waters are highly productive and diverse ecosystems, often dominated by marine submerged aquatic vegetation (SAV) and strongly affected by a range of human pressures. Due to their important ecosystem functions, for decades, both researchers and managers have investigated changes in SAV abundance and growth dynamics to understand linkages to human perturbations. In European coastal waters, monitoring of marine SAV communities traditionally combines diver observations and/or video recordings to determine, for example, spatial coverage and species composition. While these techniques provide very useful data, they are rather time consuming, labor‐intensive, and limited in their spatial coverage. In this study, we compare traditional and emerging remote sensing technologies used to monitor marine SAV, which include satellite and occupied aircraft operations, aerial drones, and acoustics. We introduce these techniques and identify their main strengths and limitations. Finally, we provide recommendations for researchers and managers to choose the appropriate techniques for future surveys and monitoring programs. Integr Environ Assess Manag 2022;18:892–908. © 2021 SETAC

Traditional monitoring (e.g., in-water based surveys) of eelgrass meadows and perennial macroalgae in coastal areas is time and labor intensive, requires extensive equipment, and the collected data has a low temporal resolution. Further, divers and Remotely Operated Vehicles (ROVs) have a low spatial extent that cover small fractions of full systems. The inherent heterogeneity of eelgrass meadows and macroalgae assemblages in these coastal systems makes interpolation and extrapolation of observations complicated and, as such, methods to collect data on larger spatial scales whilst retaining high spatial resolution is required to guide management. Recently, the utilization of Unoccupied Aerial Vehicles (UAVs) has gained popularity in ecological sciences due to their ability to rapidly collect large amounts of area-based and georeferenced data, making it possible to monitor the spatial extent and status of SAV communities with limited equipment requirements compared to ROVs or diver surveys. This paper is focused on the increased value provided by UAV-based, data collection (visual/Red Green Blue imagery) and Object Based Image Analysis for gaining an improved understanding of eelgrass recovery. It is demonstrated that delineation and classification of two species of SAV ( Fucus vesiculosus and Zostera marina) is possible; with an error matrix indicating 86–92% accuracy. Classified maps also highlighted the increasing biomass and areal coverage of F. vesiculosus as a potential stressor to eelgrass meadows. Further, authors derive a statistically significant conversion of percentage cover to biomass ( R2 = 0.96 for Fucus vesiculosus, R2 = 0.89 for Zostera marina total biomass, and R2 = 0.94 for AGB alone, p < 0.001). Results here provide an example of mapping cover and biomass of SAV and provide a tool to undertake spatio-temporal analyses to enhance the understanding of eelgrass ecosystem dynamics.

Knowledge about the spatial distribution of seagrasses is essential for coastal conservation efforts. Imagery obtained from unoccupied aerial systems (UAS) has the potential to provide such knowledge. Classifier choice and hyperparameter settings are, however, often based on time-consuming trial-and-error procedures. The presented study has therefore investigated the performance of five machine learning algorithms, i.e., Bayes, Decision Trees (DT), Random Trees (RT), k-Nearest Neighbor (kNN), and Support Vector Machine (SVM) when used for the object-based classification of submerged seagrasses from UAS-derived imagery. The influence of hyperparameter tuning and training sample size on the classification accuracy was tested on images obtained from different altitudes during different environmental conditions. The Bayes classifier performed well (94% OA) on images obtained during favorable environmental conditions. The DT and RT classifier performed better on low-altitude images (93% and 94% OA, respectively). The kNN classifier was outperformed on all occasions, while still producing OA between 89% and 95% in five out of eight scenarios. The SVM classifier was most sensitive to hyperparameter tuning with OAs ranging between 18% and 97%; however, it achieved the highest OAs most often. The findings of this study will help to choose the appropriate classifier and optimize related hyperparameter settings.