Zooplankton diversity in the deep “midnight zone” (>1000 m), where sunlight does not reach, remains largely unknown. Uncovering such diversity has been challenging because of the major difficulties in sampling deep pelagic fauna and identifying many (unknown) species that belong to these complex swimmer assemblages. In this study, we evaluated zooplankton diversity using two taxonomic marker genes: mitochondrial cytochrome oxidase subunit 1 (COI) and nuclear 18S ribosomal RNA (18S). We collected samples from plankton net tows, ranging from the surface to a depth of 5000 m above the Atacama Trench in the Southeast Pacific. Our study aimed to assess the zooplankton diversity among layers from the upper 1000 m to the ultra-deep abyssopelagic zone to test the hypothesis of decreasing diversity with depth resulting from limited carbon sources. The results showed unique, highly vertically structured communities within the five depth strata sampled, with maximal species richness observed in the upper bathypelagic layer (1000–2000 m). The high species richness of zooplankton (>750 OTUS) at these depths was higher than that found in the upper 1000 m. The vertical diversity trend exhibited a pattern similar to the well-known vertical pattern described for the benthic system. However, a large part of this diversity was either unknown (>50%) or could not be assigned to any known species in current genetic diversity databases. DNA analysis showed that the Calanoid copepods, mostly represented by Subeucalanus monachus, the Euphausiacea, Euphausia mucronata, and the halocypridade, Paraconchoecia dasyophthalma, dominated the community. Water column temperature, dissolved oxygen, particulate carbon, and nitrogen appeared to be related to the observed vertical diversity pattern. Our findings revealed rich and little-known zooplankton diversity in the deep sea, emphasizing the importance of further exploration of this ecosystem to conserve and protect its unique biota.

Extreme low pH events in estuaries and upwelling areas can modulate the phenotypic and genetic diversity of natural populations. To test this hypothesis, we explored the linkage between local scale extreme low pH events, genetic diversity, and variation in fecundity-related traits (body size, egg size, and egg production rate) in the broad-dispersal copepod Acartia tonsa. We assessed genetic and phenotypic characteristics of populations by contrasting extreme low pH environments (upwelling and temperate estuary) in the coastal Southeast Pacific, under natural and experimental conditions. These populations showed significant genetic differentiation with higher diversity in mitochondrial and nuclear loci (encoding mtCOI and 18S rRNA) in the estuarine population. Copepods from this population are exposed to more frequent extreme low pH events (< 7.7), and the adult females exhibit consistent phenotypic variation in body size, egg size, and egg production rate across different cohorts. Experimental acclimation to extreme low pH conditions revealed no significant differences in fecundity-related traits between A. tonsa populations. Although these results partially support our hypothesis, the experimental findings suggest other drivers might also influence phenotypic differences in the local environments.

The Chilean Patagonian fjords are globally renowned as one of the few remaining pristine environments on Earth; however, their ecosystems are under significant threat from climatic and anthropogenic pressures. Of particular concern is the lack of research into the impact of plastic pollution on the waters and biodiversity of these fjords. In this study, the marine environment of a secluded and sparsely populated fjord system in southern Patagonia was sampled to assess microplastics in seawater, beaches, bottom sediment, and zooplankton. Microplastics were found to be widespread across the water surface of the fjord, but with low abundances of 0.01 ± 0.01 particles m−3 (mean ± SD). The presence of microplastics in sedimentary environments (e.g., beaches and bottom sediments, 15.6 ± 15.3 and 9.8 ± 24 particles kg of dry sediment−1, respectively) provided additional evidence of plastic debris accumulation within the fjord system. Furthermore, microplastics were already bioavailable to key zooplankton species of the Patagonian food web (0.01 ± 0.02 particles individual−1), suggesting bioaccumulation. A comprehensive examination of potential microplastic inputs originating from coastal runoff, coupled with distribution of water masses, suggested minimal local contribution of microplastics to the fjord, strongly indicating that plastic litter is likely entering the area through oceanic currents. The composition and type of microplastics, primarily consisting of polyester fibers (approx. 60 %), provided further support for the proposed distant origin and transportation into the fjord by oceanographic drivers. These results raise significant concern as reveal that despite a lack of nearby population, industrial or agricultural activity, remote Patagonian fjords are still impacted by plastic pollution originating from distant sources. Prioritizing monitoring efforts is crucial for effectively assessing the future trends and ecological impact of plastic pollution in these once so-called pristine ecosystems.

To expand knowledge of Pseudo-nitzschia species in the Southeast Pacific, we isolated specimens from coastal waters of central Chile (36°S–30°S), the Gulf of Corcovado, and the oceanic Robinson Crusoe Island (700 km offshore) and grew them into monoclonal strains. A total of 123 Pseudo-nitzschia strains were identified to 11 species based on sequencing of the ITS region of the nuclear rDNA and on ultrastructural and morphometric analyses of the frustule in selected representatives of each clade: P. australis, P. bucculenta, P. cf. chiniana, P. cf. decipiens, P. fraudulenta, P. hasleana, P. multistriata, P. plurisecta, P. cf. sabit, the new species P. dampieri sp. nov., and one undescribed species. Partial 18S and 28S rDNA sequences, including the hypervariable V4 and D1–D3 regions used for barcoding, were gathered from representative strains of each species to facilitate future metabarcoding studies. Results showed different levels of genetic, and at times ultrastructural, diversity among the above-mentioned entities, suggesting morphological variants (P. bucculenta), rapidly radiating complexes with ill-defined species boundaries (P. cf. decipiens and P. cf. sabit), and the presence of new species (P. dampieri sp. nov., Pseudo-nitzschia sp. 1, and probably P. cf. chiniana). Domoic acid (DA) was detected in 18 out of 82 strains tested, including those of P. australis, P. plurisecta, and P. multistriata. Toxicity varied among species mostly corresponding to expectations from previous reports, with the prominent exception of P. fraudulenta; DA was not detected in any of its 10 strains tested. In conclusion, a high diversity of Pseudo-nitzschia exists in Chilean waters, particularly offshore.

Large subduction earthquakes induce complex postseismic deformation, primarily driven by afterslip and viscoelastic relaxation, in addition to interplate relocking processes. However, these signals are intricately intertwined, posing challenges in determining the timing and nature of relocking. Here, we use six years of continuous GNSS measurements (2015–2021) to study the spatiotemporal evolution of afterslip, seismicity and locking after the 2015 Illapel earthquake (M_w 8.3). Afterslip is inverted from postseismic displacements corrected for nonlinear viscoelastic relaxation modeled using a power-law rheology, and the distribution of locking is obtained from the linear trend of GNSS stations. Our results show that afterslip is mainly concentrated in two zones surrounding the region of largest coseismic slip. The accumulated afterslip (corresponding to M_w 7.8) exceeds 1.5 m, with aftershocks mainly occurring at the boundaries of the afterslip patches. Our results reveal that the region experiencing the largest coseismic slip undergoes rapid relocking, exhibiting the behavior of a persistent velocity weakening asperity, with no observed aftershocks or afterslip within this region during the observed period. The rapid relocking of this asperity may explain the almost regular recurrence time of earthquakes in this region, as similar events occurred in 1880 and 1943.

Fishing has provided mankind with a protein-rich source of food and labor, allowing for the development of an important industry, which has led to the overexploitation of most targeted fish species. The sustainable management of these natural resources requires effective control of fish landings and, therefore, an accurate calculation of fishing quotas. This work proposes a deep learning-based spatial-spectral method to classify five pelagic species of interest for the Chilean fishing industry, including the targeted Engraulis ringens, Merluccius gayi, and Strangomera bentincki and non-targeted Normanichthtys crockeri and Stromateus stellatus fish species. This proof-of-concept method is composed of two channels of a convolutional neural network (CNN) architecture that processes the Red-Green-Blue (RGB) images and the visible and near-infrared (VIS-NIR) reflectance spectra of each species. The classification results of the CNN model achieved over 94% in all performance metrics, outperforming other state-of-the-art techniques. These results support the potential use of the proposed method to automatically monitor fish landings and, therefore, ensure compliance with the established fishing quotas.

Extracting tectonic transient displacements on the Earth’s surface from Global Navigation Satellite System (GNSS) time series remains a challenge, because GNSS station displacements depend on multiple processes occurring simultaneously, along with noise that obscures low-magnitude transient signals. We present a novel method for automatic detection of slow slip events (SSEs) in time series of a GNSS network by training a supervised machine learning (ML) model for classification. The proposed methodology detects both temporally and spatially the signatures of SSEs or regional transients within a GNSS network. The time series of a GNSS network were transformed into grayscale images, from which descriptors, including Bag of Visual Words (BoW) and Extended Local Binary Patterns (ELBP), were extracted. These descriptors served as input features for two distinct ML models: Support Vector Machines (SVM) and Artificial Neural Networks (NN). To train and test the ML classification model, two 3-year synthetic datasets were generated, one with GNSS networks featuring slow slip events (SSEs) of varying location, duration, onset time, and magnitude, and the other without SSEs, resulting in positive and negative sets, respectively. For each GNSS network, an image was created by combining the east and north components of the time series, which have been previously detrended and common mode error filtered. Each image is further divided into sub-images corresponding to 60 days time windows, in order to temporarily detect the existence of a transient. For training and testing, the datasets were separated into 75% for training and 25% for testing, each with 50% positive and 50% negative cases. In the final step, we analyze the positively classified images, representing the time windows in which the classifier detected transients. Within each of these windows, we identify the network’s time series with the highest velocity, indicating the stations and geographic area where the detected transients occurred. The test results demonstrate that both ML models achieved high performance using both ELBP and BoW descriptors as features. Finally, our ML models were validated on a real dataset with a transient signal recorded before the 2014 Iquique earthquake in Chile, and they effectively detected this anomalous signal. The proposed method can effectively detect transient signals related to SSEs with high accuracy, sensitivity, and specificity in both the test and instrumentally recorded datasets.

Fishing landings in Chile are inspected to control fisheries that are subject to catch quotas. The control process is not easy since the volumes extracted are large and the numbers of landings and artisan shipowners are high. Moreover, the number of inspectors is limited, and a non-automated method is utilized that normally requires months of training. In this work, we propose, design, and implement an automated fish landing control system. The system consists of a custom gate with a camera array and controlled illumination that performs automatic video acquisition once the fish landing starts. The imagery is sent to the cloud in real time and processed by a custom-designed detection algorithm based on deep convolutional networks. The detection algorithm identifies and classifies different pelagic species in real time, and it has been tuned to identify the specific species found in landings of two fishing industries in the Biobío region in Chile. A web-based industrial software was also developed to display a list of fish detections, record relevant statistical summaries, and create landing reports in a user interface. All the records are stored in the cloud for future analyses and possible Chilean government audits. The system can automatically, remotely, and continuously identify and classify the following species: anchovy, jack mackerel, jumbo squid, mackerel, sardine, and snoek, considerably outperforming the current manual procedure.

The release of vast quantities of sulfide from the sediment into the water column, known as a sulfidic event, has detrimental consequences on fish catches, including downstream effects on other linked element cycles. Despite being frequent occurrences in marine upwelling regions, our understanding of the factors that moderate sulfidic event formation and termination are still rudimentary. Here, we examined the biogeochemical and hydrodynamic conditions that underpinned the formation/termination of one of the largest sulfur plumes to be reported in the Peruvian upwelling zone. Consistent with previous research, we find that the sulfur-rich plume arose during the austral summer when anoxic conditions (i.e., oxygen and nitrate depletion) prevailed in waters overlying the upper shelf. Furthermore, the shelf sediments were organically charged and characterized by low iron-bound sulfur concentrations, further enabling the diffusion of benthic-generated sulfide into the water column. While these biogeochemical conditions provided a predicate to sulfidic event formation, we highlight that attenuations in local wind intensity served as an event trigger. Namely, interruptions in local wind speed constrained upwelling intensity, causing increased stratification over the upper shelf. Moreover, disturbances in local wind patterns likely placed additional constraints on wind-driven mesoscale eddy propagation, with feedback effects on coastal elemental sulfur plume (ESP) formation. We suggest ESP development occurs as a result of a complex interaction of biogeochemistry with regional hydrodynamics.