Explore the vast range of titles available.

Small-scale fisheries (SSF) receive increasing international attention for landing around 40% of global marine fisheries catches and employing millions of people globally. Their contributions to food security and poverty alleviation, especially in developing countries, make it relevant to consider them when discussing sustainable development goals (SDGs). Achieving SDGs by supporting SSF means understanding fisheries in their broader context, from the health of marine ecosystems to social and economic features such as employment, public health, culture, and the effects of global change. Social–ecological relationships in SSF are complex and poorly understood, thus challenging the identification of policies that could improve and preserve the contributions of SSF to sustainable development. Here, we developed an expert-based rapid appraisal framework to identify and characterize the relationships between SSF and SDGs. The framework serves as a diagnostic tool for identifying strengths and gaps in SSF potential in enhancing SDG achievement in data-limited situations. Our structured approach extends beyond SDG 14 and target 14.b, offering insights into SSF's contributions to 11 other SDGs. As a proof of concept, we illustrate the approach and its potential contributions in two case studies in Madagascar. The method effectively captured the multiple dimensions of the SSF through the SDG lens, providing a contextually relevant understanding of how global UN goals can be achieved locally. Further research is needed to define mechanisms for aggregating and reporting the multiple, case-specific contributions of SSF to monitor progress toward the SDGs at national and global levels.

Small-scale fisheries (SSFs) contribute significantly to the economies of coastal developing nations, offering employment and food, and supporting sustainable development goals (SDGs). Despite increasing focus on SSFs, data, and knowledge gaps persist in SSFs research and their contribution to SDGs. Ten fisheries were assessed in Kenya for their contributions to 12 SDGs, highlighting different levels of contributions. Small pelagic and shrimp fisheries display higher overall contributions to SDGs, appearing to perform strongly in more SDGs, while handline and octopus fisheries display lower contributions to SDGs. Specific contributions can vary depending on the characteristics of individual fisheries, such as their structures and markets. This study provides valuable insights from an under-represented part of the world on the under-researched topic of SSFs and SDGs. It also contributes significantly to research on sustainable development in developing coastal nations and highlights gaps and areas for improvement in achieving the SDGs within the context of SSFs.

The challenges of Reproducibility and Replicability (R & R) in computer science experiments have become a focus of attention in the last decade, as efforts to adhere to good research practices have increased. However, experiments using Deep Learning (DL) remain difficult to reproduce due to the complexity of the techniques used. Challenges such as estimating poverty indicators (e.g., wealth index levels) from remote sensing imagery, requiring the use of huge volumes of data across different geographic locations, would be impossible without the use of DL technology. To test the reproducibility of DL experiments, we report a review of the reproducibility of three DL experiments which analyze visual indicators from satellite and street imagery. For each experiment, we identify the challenges found in the data sets, methods and workflows used. As a result of this assessment we propose a checklist incorporating relevant FAIR principles to screen an experiment for its reproducibility. Based on the lessons learned from this study, we recommend a set of actions aimed to improve the reproducibility of such experiments and reduce the likelihood of wasted effort. We believe that the target audience is broad, from researchers seeking to reproduce an experiment, authors reporting an experiment, or reviewers seeking to assess the work of others. Plain Language Summary This paper aims to help researchers understand the challenges of reproducing Deep Learning (DL) publications, mitigate reproducibility gaps, and make their own work more reproducible. We build on the work of others and add recommendations organized by (a) the quality of the data set (and associated metadata), (b) the DL methodology, (c) the implementation methodology, and the infrastructure used. To our knowledge, this is the first initiative of its kind to address the problem of reproducibility in remote sensing imagery and DL problems for real‐world tasks. We hope this paper lowers the barrier to entry for the DL community to improve research. Following the lifecycle mantra: reproduce!, then replicate! With the goal of improving reproducibility! Key Points We discuss the reproducibility challenges faced in research by Deep Learning approaches using Big Data We provide advice for pre‐screening papers (before experiments) to avoid poorly invested effort We present a recipe with a set of mitigation strategies to address common errors users (researchers, authors, reviewers) may encounter

Understanding the effects of scale is essential to the understanding of natural ecosystems, particularly in marine environments where sampling is more limited and sporadic than in terrestrial environments. Despite its recognized importance, scale is rarely considered in benthic habitat mapping studies. Lack of explicit statement of scale in the literature is an impediment to better characterization of seafloor pattern and process. This review paper highlights the importance of incorporating ecological scaling and geographical theories in benthic habitat mapping. It reviews notions of ecological scale and benthic habitat mapping, in addition to the way spatial scale influences patterns and processes in benthic habitats. We address how scale is represented in geographic data, how it influences their analysis, and consequently how it influences our understanding of seafloor ecosystems. We conclude that quantification of ecological processes at multiple scales using spatial statistics is needed to gain a better characterization of species–habitat relationships. We offer recommendations on more effective practices in benthic habitat mapping, including sampling that covers multiple spatial scales and that includes as many environmental variables as possible, adopting continuum-based habitat characterization approaches, using statistical analyses that consider the spatial nature of data, and explicit statement of the scale at which the research was conducted. We recommend a set of improved standards for defining benthic habitat. With these standards benthic habitats can be defined as ‘areas of seabed that are (geo)statistically significantly different from their surroundings in terms of physical, chemical and biological characteristics, when observed at particular spatial and temporal scales’.

Information on the distribution of cold-water corals in Newfoundland and Labrador (NL) waters largely comes from scientific multi-species trawl surveys and commercial fisheries observer programs. As a result, knowledge of coral distribution has been influenced by the type of gear used and by fishing effort distribution, leaving large knowledge gaps beyond the fishing footprint along the edge of the continental shelf. In support of international efforts to preserve marine biodiversity, maximum entropy (Maxent) species distribution models (SDMs) were generated for cold-water coral functional groups and individual species to predict their distributions throughout the NL region. Although functional group models have also been produced using a random forest (RF) approach in the past, the species level models provided here are thought to be the first of their kind in the NL region. Models generated for this study were found to be statistically robust, even for species with limited observation data, with average area under the receiver operating curve and true skill statistic values of 0.914 and 0.684, respectively. Findings indicated that models for functional groups typically overgeneralized habitat suitability and did not always reflect the distribution of the individual species they were based on. Furthermore, suitable habitats delineated by Maxent fit more closely to in situ observations than existing models generated for this region, providing more realistic estimates of habitat suitability at bathyal and abyssal depths. In general, the highest suitability was found along the continental shelf break and within canyons on the upper continental shelf, with Maxent models capturing variations in habitat suitability not previously described in the region.

Seascape ecology, the marine-centric counterpart to landscape ecology, is rapidly emerging as an interdisciplinary and spatially explicit ecological science with relevance to marine management, biodiversity conservation, and restoration. While important progress in this field has been made in the past decade, there has been no coherent prioritisation of key research questions to help set the future research agenda for seascape ecology. We used a 2-stage modified Delphi method to solicit applied research questions from academic experts in seascape ecology and then asked respondents to identify priority questions across 9 interrelated research themes using 2 rounds of selection. We also invited senior management/conservation practitioners to prioritise the same research questions. Analyses highlighted congruence and discrepancies in perceived priorities for applied research. Themes related to both ecological concepts and management practice, and those identified as priorities include seascape change, seascape connectivity, spatial and temporal scale, ecosystem-based management, and emerging technologies and metrics. Highestpriority questions (upper tercile) received 50% agreement between respondent groups, and lowest priorities (lower tercile) received 58% agreement. Across all 3 priority tiers, 36 of the 55 questions were within a ±10% band of agreement. We present the most important applied research questions as determined by the proportion of votes received. For each theme, we provide a synthesis of the research challenges and the potential role of seascape ecology. These priority questions and themes serve as a roadmap for advancing applied seascape ecology during, and beyond, the UN Decade of Ocean Science for Sustainable Development (2021–2030).

Petri net synthesis deals with the problem whether, given a labelled transition system TS, one can find a Petri net N with an initial marking M0 such that the reachability graph of (N, M0) is isomorphic to TS. This may be preceded by a pre-synthesis phase that will quickly reject ill-formed transition systems (and give structural reasons for the failure) and otherwise build data structures needed by the proper synthesis. The last phase proceeds by solving systems of linear inequalities, and may still fail but for less transparent reasons. In this paper, we consider an extended problem. A finite set of transition systems {TS 1,..., TSm} shall be called simultaneously Petri net solvable if there is a single Petri net N with several initial markings {M01,..., M0m}, such that for every i = 1,... ,m, the reachability graph of (N, M0i) is isomorphic to TSi. The focus will be on choice-free nets, that is, nets without structural choices, and we explore how previously published efficient algorithms for the pre-synthesis and proper synthesis of bounded and choice-free Petri nets can be generalised for the simultaneous pre-synthesis and synthesis of such multi-marked nets. At the same time, the choice-free pre-synthesis of a single transition system shall be strengthened by introducing new structural checks.

We examined spatial and temporal relationships between snow crabChionocetes opilio, shrimpPandalusspp., Atlantic codGadus morhuaand the environment (depth, temperature and salinity) on the Newfoundland–Labrador Shelf from 1995 to 2009 using autumn multi-species trawl survey data. First, the core habitat of snow crab and shrimp was determined based on cumulative distribution functions of species abundance over depth and bottom temperature. On average, this method predicted the presence of crab and shrimp at 95 and 99% of trawl locations, respectively, and indicated 90% of crab and shrimp inhabited temperature ranges of −1 to 4 and 0 to 4°C and depths of 100 to 500 and 150 to 450 m, respectively. Then geographically weighted regressions, based on trawl stations where species presence was predicted, indicated spatial non-stationarity between invertebrates and explanatory variables at scales <200 km. Snow crab abundance was best predicted by environmental variables, suggesting bottom-up influences are important, whereas shrimp abundance was influenced by both the environment and cod (predator) abundance. We discuss how averaged ecological relationships within large marine ecosystems central to fisheries management mask processes operating at smaller scales, with reference to the northern cod ecosystem under present conditions of warming waters and increasing cod.

In this paper, we study the problem of scheduling hard real-time periodic tasks with static priority pre-emptive algorithms. We consider tasks which are characterized by a period, a hard deadline, a computation time and an offset (the time of the first request), where the offsets may be chosen by the scheduling algorithm, hence the denomination offset free systems.We study the rate monotonic and the deadline monotonic priority assignments for this kind of system and we compare the offset free systems and the asynchronous systems in terms of priority assignment. Hence, we show that the rate and the deadline monotonic priority assignments are not optimal for offset free systems.

Changes in species abundances caused by climatic variability have long been linked to alterations in community composition, species interactions and maintenance of biodiversity in marine ecosystems. Here we use multivariate regression tree (MRT) analyses to quantify how changes in species abundances and environmental variability contributed to observed patterns of community composition in the Gulf of St. Lawrence during 2 contrasting periods (the cooler and less saline period 1991 to 1995 and the warmer and more saline period 1997 to 2003). Broad-scale patterns of community composition in both periods were consistently explained by the depth and salinity of the benthic environment, but biological factors differed. In the cold period, the previous year’s catches of snow crabChionoecetes opilioand northern shrimp (mainlyPandalus borealis) were most important, while in the warm period the previous year’s catch of Atlantic codGadus morhuadominated. MRT models further identified spatially discrete areas where communities are characterized by relatively high abundances of these species. These results indicate that environmental variability leads to dynamic and spatially explicit responses not only of single species, but of marine communities. Applications of ecosystem management in the face of climate change must take this into account.