Marine fishery stakeholders are beginning to consider and implement adaptation strategies in the face of growing consumer demand and potential deleterious climate change impacts such as ocean warming, ocean acidification, and deoxygenation. This study investigates the potential for development of a novel climate change-tolerant sea urchin fishery in southern California based on Strongylocentrotus fragilis (pink sea urchin), a deep-sea species whose peak density was found to coincide with a current trap-based spot prawn fishery (Pandalus platyceros) in the 200–300-m depth range. Here we outline potential criteria for a climate change-tolerant fishery by examining the distribution, life-history attributes, and marketable qualities of S. fragilis in southern California. We provide evidence of seasonality of gonad production and demonstrate that peak gonad production occurs in the winter season. S. fragilis likely spawns in the spring season as evidenced by consistent minimum gonad indices in the spring/summer seasons across 4 years of sampling (2012–2016). The resiliency of S. fragilis to predicted future increases in acidity and decreases in oxygen was supported by high species abundance, albeit reduced relative growth rate estimates at water depths (485–510 m) subject to low oxygen (11.7–16.9 µmol kg−1) and pHTotal (<7.44), which may provide assurances to stakeholders and managers regarding the suitability of this species for commercial exploitation. Some food quality properties of the S. fragilis roe (e.g. colour, texture) were comparable with those of the commercially exploited shallow-water red sea urchin (Mesocentrotus franciscanus), while other qualities (e.g. 80% reduced gonad size by weight) limit the potential future marketability of S. fragilis. This case study highlights the potential future challenges and drawbacks of climate-tolerant fishery development in an attempt to inform future urchin fishery stakeholders.

Dispersal has far‐reaching implications for individuals, populations, and communities, especially in sessile organisms. Escaping competition with conspecifics and with kin are theorized to be key factors leading to dispersal as an adaptation. However, manipulative approaches in systems in which adults are sessile but offspring have behaviors is required for a more complete understanding of how competition affects dispersal. Here, we integrate a series of experiments to study how dispersal affects the density and relatedness of neighbors, and how the density and relatedness of neighbors in turn affects fitness. In a marine bryozoan, we empirically estimated dispersal kernels and found that most larvae settled within ~1 m of the maternal colony, although some could potentially travel at least 10s of meters. Larvae neither actively preferred or avoided conspecifics or kin at settlement. We experimentally determined the effects of spreading sibling larvae by manipulating the density and relatedness of settlers and measuring components of fitness in the field. We found that settler density reduced maternal fitness when settler neighbors were siblings compared with when neighbors were unrelated or absent. Genetic markers also identified very few half sibs (and no full sibs) in adults from the natural population, and rarely close enough to directly interact. In this system, dispersal occurs over short distances (meters) yet, in contrast with expectations, there appears to be limited kinship between adult neighbors. Our results suggest that the limited dispersal increases early offspring mortality when siblings settle next to each other, rather than next to unrelated conspecifics, potentially reducing kinship in adult populations. High offspring production and multiple paternity could further dilute kinship at settlement and reduce selection for dispersal beyond the scale of 10s of meters.

Technology advances and lower equipment costs are enabling non-invasive, convenient recording of brain data outside of clinical settings in more real-world environments, and by non-experts. Despite the growing interest in and availability of brain signal datasets, most analytical tools are made for experts in the specific device technology, and have rigid constraints on the type of analysis available. We developed BrainEx to support interactive exploration and discovery within brain signals datasets. BrainEx takes advantage of algorithms that enable fast exploration of complex, large collections of time series data, while being easy to use and learn. This system enables researchers to perform similarity search, explore feature data and natural clustering, and select sequences of interest for future searches and exploration, while also maintaining the usability of a visual tool. In addition to describing the distributed architecture and visual design for BrainEx, this paper reports on a benchmark experiment showing that it outperforms other existing systems for similarity search. Additionally, we report on a preliminary user study in which domain experts used the visual exploration interface and affirmed that it meets the requirements. Finally, it presents a case study using BrainEx to explore real-world, domain-relevant data.