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The gig economy and gig work have grown quickly in recent years and have drawn much attention from researchers in different fields. Because the platform mediated gig economy is a relatively new phenomenon, studies have produced a range of interesting findings; of interest here are the socio‐technical issues that this work has surfaced. This systematic literature review (SLR) provides a snapshot of a range of socio‐technical issues raised in the last 12 years of literature focused on the platform mediated gig economy. Based on a sample of 515 papers gathered from nine databases in multiple disciplines, 132 were coded that specifically studied the gig economy, gig work, and gig workers. Three main socio‐technical themes were identified: (1) the digital workplace, which includes information infrastructure and digital labor that are related to the nature of gig work and the user agency; (2) algorithmic management, which includes platform governance, performance management, information asymmetry, power asymmetry, and system manipulation, relying on a diverse set of technological tools including algorithms and big data analytics; (3) ethical design, as a relevant value set that gig workers expect from the platform, which includes trust, fairness, equality, privacy, and transparency. A social informatics perspective is used to rethink the relationship between gig workers and platforms, extract the socio‐technical issues noted in prior research, and discuss the underexplored aspects of the platform mediated gig economy. The results draw attention to understudied yet critically important socio‐technical issues in the gig economy that suggest short‐ and long‐term opportunities for future research directions. [ABSTRACT FROM AUTHOR]

Cetaceans can serve as sentinel species in marine environments as long-lived, apex predators that can concentrate environmental contaminants with potential health consequences. Thus, monitoring the well-being of these species may provide an additional indicator of ecosystem health. In cetaceans, one method for assessing individual and population health is by examining skin conditions. For decades, skin lesions have been increasingly documented in coastal populations of bottlenose dolphins (Tursiops spp.) worldwide. Lesion presence can indicate diminished health and may reflect environmental stressors. Herein, we document the prevalence of epidermal lesions/marks in bottlenose dolphins from the waters off New York and New Jersey during their seasonal presence from spring to fall. Photographs of distinct individuals sighted from May to October 2017 to 2021 were compiled into a catalog, and skin lesions were categorized and counted. Annually, the lowest skin lesion prevalence was in 2021 (p = 0.31), and the highest was in 2017 (p = 0.81). By month, prevalence generally decreased from spring to fall. Overall lesion prevalence in this population was higher than reported estimates for other populations in the coastal waters of the United States. The five most common categories were cloudy white spots, dark spots, white amorphous lesions, tattoo lesions, and dark fringe lesions; and the three most common groups were potentially pathogenic lesions, hypopigmentation, and rake mark-associated potentially pathogenic lesions. Some of the observed lesions have been associated with viral infections that may be exacerbated by environmental stressors. This research establishes an important baseline for further studies into bottlenose dolphin population health in and around the New York--New Jersey Harbor Estuary, particularly given the continued expansion of anthropogenic activities, including those related to forthcoming offshore wind development. [ABSTRACT FROM AUTHOR]

The 20th century commercial whaling industry severely reduced populations of great whales throughout the Southern Hemisphere. The effect of this exploitation on genetic diversity and population structure remains largely undescribed. Here, we compare pre- and post-whaling diversity of mitochondrial DNA (mtDNA) control region sequences for 3 great whales in the South Atlantic, such as the blue, humpback, and fin whale. Pre-whaling diversity is described from mtDNA extracted from bones collected near abandoned whaling stations, primarily from the South Atlantic island of South Georgia. These bones are known to represent the first stage of 20th century whaling and thus pre-whaling diversity of these populations. Post-whaling diversity is described from previously published studies reporting large-scale sampling of living whales in the Southern Hemisphere. Despite relatively high levels of surviving genetic diversity in the post-whaling populations, we found evidence of a probable loss of mtDNA lineages in all 3 species. This is evidenced by the detection of a large number of haplotypes found in the pre-whaling samples that are not present in the post-whaling samples. A rarefaction analysis further supports a loss of haplotypes in the South Atlantic humpback and Antarctic blue whale populations. The bones from former whaling stations in the South Atlantic represent a remarkable molecular archive for further investigation of the decline and ongoing recovery in the great whales of the Southern Hemisphere. [ABSTRACT FROM AUTHOR]

Forage fishes are a critical food web link in marine ecosystems, aggregating in a hierarchical patch structure over multiple spatial and temporal scales. Surface‐level forage fish aggregations (FFAs) represent a concentrated source of prey available to surface‐ and shallow‐foraging marine predators. Existing survey and analysis methods are often imperfect for studying forage fishes at scales appropriate to foraging predators, making it difficult to quantify predator–prey interactions. In many cases, general distributions of forage fish species are known; however, these may not represent surface‐level prey availability to predators. Likewise, we lack an understanding of the oceanographic drivers of spatial patterns of prey aggregation and availability or forage fish community patterns. Specifically, we applied Bayesian joint species distribution models to bottom trawl survey data to assess species‐ and community‐level forage fish distribution patterns across the US Northeast Continental Shelf (NES) ecosystem. Aerial digital surveys gathered data on surface FFAs at two project sites within the NES, which we used in a spatially explicit hierarchical Bayesian model to estimate the abundance and size of surface FFAs. We used these models to examine the oceanographic drivers of forage fish distributions and aggregations. Our results suggest that, in the NES, regions of high community species richness are spatially consistent with regions of high surface FFA abundance. Bathymetric depth drove both patterns, while subsurface features, such as mixed layer depth, primarily influenced aggregation behavior and surface features, such as sea surface temperature, sub‐mesoscale eddies, and fronts influenced forage fish diversity. In combination, these models help quantify the availability of forage fishes to marine predators and represent a novel application of spatial models to aerial digital survey data. [ABSTRACT FROM AUTHOR]

North Atlantic right whale (NARW) distribution shifts have led to increased presence in the US mid‐Atlantic, which includes the New York Bight (NYB), an area with substantial vessel traffic. NARW vessel strikes are mitigated by a seasonal management area (SMA) that is active November–April. Within the SMA, vessels ≥ 19.8 m must travel at ≤10 knots. However, until recently, surveys of the NYB were not conducted after the NARW distribution shifted. Thus, it was unclear whether the SMA captured NARW presence in the NYB. Outside the SMA, voluntary speed restrictions are implemented over a limited area when NARW calls are acoustically detected; therefore, it was important to quantify NARW vocal activity. Acoustic recordings (2016–2020) were analyzed to determine NARW presence, vocal states, and diel pattern of vocal activity. NARWs were detected outside SMA boundaries, and medium/high vocal states (21–69 calls over 3–6 h) occurred while the SMA was inactive. Therefore, the SMA spatiotemporal boundaries should be revised. Vocal activity was highest at night (22:00–1:00), and NARWs were acoustically detected when they were not detected by aerial surveys, illustrating acoustic detections are a viable method for triggering mitigation measures when NARWs are vocalizing but are not visually observed. [ABSTRACT FROM AUTHOR]

Rapid changes in the Arctic from shifting climate and human use patterns are affecting previously reported distributions and movements of marine mammals. The underwater soundscape, a key component of marine mammal habitats, is also changing. This study integrates acoustic data, collected at a site in the northern Bering Sea, with information on sound sources to quantify their occurrence throughout the year and identify deviations in conditions and dominant soundscape components. Predictive models are applied to explain variation in sound levels and to compare the relative contributions of various soundscape components. Levels across all octave bands were influenced most strongly by the variation in abiotic environment across seasons. The presence of commercial ships did not have a discernible effect on sound levels at this location and period of time. The occurrence of sources was compared to a second site, where we documented how higher levels of shipping changed that soundscape. This study demonstrated the value of acoustic monitoring to characterize the dominant acoustic features in a soundscape and the importance of preserving soundscapes based on dominant features rather than level of sound. Using a soundscape approach has relevance for protecting marine mammals and for the food security of Alaska Native communities that depend upon them. [ABSTRACT FROM AUTHOR]

Machine learning algorithms are often used to model and predict animal habitat selection—the relationships between animal occurrences and habitat characteristics. For broadly distributed species, habitat selection often varies among populations and regions; thus, it would seem preferable to fit region- or population-specific models of habitat selection for more accurate inference and prediction, rather than fitting large-scale models using pooled data. However, where the aim is to make range-wide predictions, including areas for which there are no existing data or models of habitat selection, how can regional models best be combined? We propose that ensemble approaches commonly used to combine different algorithms for a single region can be reframed, treating regional habitat selection models as the candidate models. By doing so, we can incorporate regional variation when fitting predictive models of animal habitat selection across large ranges. We test this approach using satellite telemetry data from 168 humpback whales across five geographic regions in the Southern Ocean. Using random forests, we fitted a large-scale model relating humpback whale locations, versus background locations, to 10 environmental covariates, and made a circumpolar prediction of humpback whale habitat selection. We also fitted five regional models, the predictions of which we used as input features for four ensemble approaches: an unweighted ensemble, an ensemble weighted by environmental similarity in each cell, stacked generalization, and a hybrid approach wherein the environmental covariates and regional predictions were used as input features in a new model. We tested the predictive performance of these approaches on an independent validation dataset of humpback whale sightings and whaling catches. These multiregional ensemble approaches resulted in models with higher predictive performance than the circumpolar naive model. These approaches can be used to incorporate regional variation in animal habitat selection when fitting range-wide predictive models using machine learning algorithms. This can yield more accurate predictions across regions or populations of animals that may show variation in habitat selection. [ABSTRACT FROM AUTHOR]