Combining modeling with novel field observations yields new insights into wintertime food limitation of larval fish

Recruitment success of marine fishes is generally considered to be highly dependent on larval growth and survival. In temperate ecosystems, fish larvae are sensitive to food limitation during the low productivity seasons, particularly if water temperatures and concomitant larval metabolic costs increase due to climate change. We combined 7 years of in situ sampling of larval fish, novel observations on zooplankton via automated image analyses, and larval physiological modeling to explore feeding conditions of Atlantic herring larvae (Clupea harengus) in the North Sea. The observed plankton size-structure was close to the theoretical optimum for larval foraging, but not the biomass. Our results for autumn larvae supported Hjort's critical period hypothesis: small first-feeding larvae were predicted to have a high probability of starvation, whereas larvae > 13 mm were able to reach their maximal growth capacity. In winter, the majority of herring larvae of all tested sizes (5–27 cm) experienced food-limitation with over 35% probability of starvation. Sensitivity analysis suggested that young herring larvae improve their growth performance and probability of survival if feed not only on copepods and their life-stages but include other microplankters in their diet. Given projected warming of the North Sea, our model predicts that herring larvae would require 28% (35%) more prey biomass in autumn (winter) to sustain their growth and survival in the future. This finding together with the ongoing low recruitment of North Sea herring underscore the importance of future micro- and mesoplankton monitoring within a scope of wintertime larval fish surveys.publishedVersio

Caught in the middle: bottom‑up and top‑down processes impacting recruitment in a small pelagic fsh

Understanding the drivers behind fluctuations in fish populations remains a key objective in fishery science. Our predictive capacity to explain these fluctuations is still relatively low, due to the amalgam of interacting bottom-up and top-down factors, which vary across time and space among and within populations. Gaining a mechanistic understanding of these recruitment drivers requires a holistic approach, combining field, experimental and modelling efforts. Here, we use the Western Baltic Spring-Spawning (WBSS) herring (Clupea harengus) to exemplify the power of this holistic approach and the high complexity of the recruitment drivers (and their interactions). Since the early 2000s, low recruitment levels have promoted intense research on this stock. Our literature synthesis suggests that the major drivers are habitat compression of the spawning beds (due to eutrophication and coastal modification mainly) and warming, which indirectly leads to changes in spawning phenology, prey abundance and predation pressure. Other factors include increased intensity of extreme climate events and new predators in the system. Four main knowledge gaps were identified related to life-cycle migration and habitat use, population structure and demographics, life-stage specific impact of multi-stressors, and predator–prey interactions. Specific research topics within these areas are proposed, as well as the priority to support a sustainable management of the stock. Given that the Baltic Sea is severely impacted by warming, eutrophication and altered precipitation, WBSS herring could be a harbinger of potential effects of changing environmental drivers to the recruitment of small pelagic fishes in other coastal areas in the world.publishedVersio

Uncertainty in humanities network visualization

Network visualization is one of the most widely used tools in digital humanities research. The idea of uncertain or “fuzzy” data is also a core notion in digital humanities research. Yet network visualizations in digital humanities do not always prominently represent uncertainty. In this article, we present a mathematical and logical model of uncertainty as a range of values which can be used in network visualizations. We review some of the principles for visualizing uncertainty of different kinds, visual variables that can be used for representing uncertainty, and how these variables have been used to represent different data types in visualizations drawn from a range of non-humanities fields like climate science and bioinformatics. We then provide examples of two diagrams: one in which the variables displaying degrees of uncertainty are integrated/pinto the graph and one in which glyphs are added to represent data certainty and uncertainty. Finally, we discuss how probabilistic data and what-if scenarios could be used to expand the representation of uncertainty in humanities network visualizations

Addressing the climate challenge

In 2021, colleagues from across the University of Birmingham community were invited to write articles about topics relevant to the COP26 climate change summit. In this series of articles, experts from across many different disciplines provide new insight and evidence on how we might all understand and tackle climate change

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Automated Plankton Classification With a Dynamic Optimization and Adaptation Cycle

With recent advances in Machine Learning techniques based on Deep Neural Networks (DNNs), automated plankton image classification is becoming increasingly popular within the marine ecological sciences. Yet, while the most advanced methods can achieve human-level performance on the classification of everyday images, plankton image data possess properties that frequently require a final manual validation step. On the one hand, this is due to morphological properties manifesting in high intra-class and low inter-class variability, and, on the other hand is due to spatial-temporal changes in the composition and structure of the plankton community. Composition changes enforce a frequent updating of the classifier model via training with new user-generated training datasets. Here, we present a Dynamic Optimization Cycle (DOC), a processing pipeline that systematizes and streamlines the model adaptation process via an automatic updating of the training dataset based on manual-validation results. We find that frequent adaptation using the DOC pipeline yields strong maintenance of performance with respect to precision, recall and prediction of community composition, compared to more limited adaptation schemes. The DOC is therefore particularly useful when analyzing plankton at novel locations or time periods, where community differences are likely to occur. In order to enable an easy implementation of the DOC pipeline, we provide an end-to-end application with graphical user interface, as well as an initial dataset of training images. The DOC pipeline thus allows for high-throughput plankton classification and quick and systematized model adaptation, thus providing the means for highly-accelerated plankton analysis

Viability of coastal fish larvae under ocean alkalinity enhancement: from organisms to communities

Ocean alkalinity enhancement (OAE) stands as a promising carbon dioxide removal technology. Yet, this solution to climate change entails shifts in water chemistry with unknown consequences for marine fish that are critical to ecosystem health and food security. With a laboratory and mesocosm experiment, we show that early life stages of fish can be resistant to OAE. We examined metabolic rate, swimming behavior, growth and survival in Atlantic herring (Clupea harengus) and other temperate coastal fish species. Neither direct physiological nor indirect food web-mediated impacts of OAE were apparent. This was despite non-CO2-equilibrated OAE (ΔTA = +600 µmol kg-1) that induces strong perturbations (ΔpH = +0.7, pCO2 = 75 µatm) compared to alternative deployment scenarios. Whilst our results give cause for optimism regarding the large-scale application of OAE, other life history stages (embryos) and habitats (open ocean) may prove more vulnerable. Still, our study across ecological scales (organism to community) and exposure times (short- to long-term) suggests that some fish populations, including key fisheries species, may be resilient to the carbonate chemistry changes under OAE

Identifying and addressing the anthropogenic drivers of global change in the North Sea: a systematic map protocol

BACKGROUND: Anthropogenic pressures on marine ecosystems have increased over the last 75 years and are expected to intensify in the future with potentially dramatic cascading consequences for human societies. It is therefore crucial to rebuild marine life-support systems and aim for future healthy ecosystems. Nowadays, there is a reasonable understanding of the impacts of human pressure on marine ecosystems; but no studies have drawn an integrative retrospective analysis of the marine research on the topic. A systematic consolidation of the literature is therefore needed to clearly describe the scientific knowledge clusters and gaps as well as to promote a new era of integrative marine science and management. We focus on the five direct anthropogenic drivers of biodiversity loss defined by the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES): (1) climate change; (2) direct exploitation; (3) pollution; (4) biological invasions; and (5) sea-use change. Our systematic map’s regional focus lies on the North Sea, which is among the most impacted marine ecosystems around the globe. The goal of the present study is to produce the first comprehensive overview of how marine research on anthropogenic drivers in the North Sea has grown and changed over the past 75 years. Ultimately, this systematic map will highlight the most urgent challenges facing the North Sea research domain. METHODS: The search will be restricted to peer-reviewed articles, reviews, meta-analyses, book chapters, book reviews, proceeding papers and grey literature using the most relevant search engines for literature published between 1945 and 2020. All authors will participate in the adjustment of the search in order to consider all relevant studies analyzing the effect of the direct anthropogenic drivers on the North Sea marine ecosystem. The references will be screened for relevance according to a predefined set of eligibility/ineligibility criteria by a pool of six trained reviewers. At stage one, each abstract and title will be independently screened by two reviewers. At stage two, potentially relevant references will be screened in full text by two independent reviewers. Subsequently, we will extract a suite of descriptive meta-data and basic information of the relevant references using the SysRev platform. The systematic map database composed will provide the foundation for an interactive geographical evidence map. Moreover, we will summarize our findings with cross-validation plots, heat maps, descriptive statistics, and a publicly available narrative synthesis. The aim of our visualization tools is to ensure that our findings are easily understandable by a broad audience