Assessing the effectiveness of marine nature-based solutions with climate risk assessments.

Prospective risks from climate change impacts in ocean and coastal systems are urging the implementation of nature-based solutions (NBS). These are climate-resilient strategies to maintain biodiversity and the delivery of ecosystem services, contributing to the adaptation of social-ecological systems and the mitigation of climate-related impacts. However, the effectiveness of measures like marine restoration or conservation is not exempt from the impacts of climate change, and the degree to which they can sustain biodiversity and ecosystem services remains unknown. Such uncertainty, together with the slow pace of implementation, causes decision-makers and societies to demand a better understanding of NBS effects. To address this gap, in this study, we use the risk mitigation capacity of marine NBS as a proxy for their effectiveness while providing a toolset for the implementation of the method. The method considers environmental data and relies on expert elicitation, allowing us to go beyond current practice to evaluate the effectiveness of NBS in reducing habitat or species risks under different future socio-political and climate-change scenarios. As a result, we present a ready-to-use tool, and supporting materials, for the implementation of the Climate Risk Assessment method and an illustrative example considering the application of the NBS "nature-inclusive harvesting" in two shellfisheries. The method works as a rapid assessment that guarantees comparability across sites and species due to its low data or resource demand, so it can be widely incorporated to adaptation policies across the marine realm.

Jellytoring: Real-Time Jellyfish Monitoring Based on Deep Learning Object Detection.

During the past decades, the composition and distribution of marine species have changed due to multiple anthropogenic pressures. Monitoring these changes in a cost-effective manner is of high relevance to assess the environmental status and evaluate the effectiveness of management measures. In particular, recent studies point to a rise of jellyfish populations on a global scale, negatively affecting diverse marine sectors like commercial fishing or the tourism industry. Past monitoring efforts using underwater video observations tended to be time-consuming and costly due to human-based data processing. In this paper, we present Jellytoring, a system to automatically detect and quantify different species of jellyfish based on a deep object detection neural network, allowing us to automatically record jellyfish presence during long periods of time. Jellytoring demonstrates outstanding performance on the jellyfish detection task, reaching an F1 score of 95.2%; and also on the jellyfish quantification task, as it correctly quantifies the number and class of jellyfish on a real-time processed video sequence up to a 93.8% of its duration. The results of this study are encouraging and provide the means towards a efficient way to monitor jellyfish, which can be used for the development of a jellyfish early-warning system, providing highly valuable information for marine biologists and contributing to the reduction of jellyfish impacts on humans.

A risk-based approach to the analysis of potential climate change effects on fish communities associated to Posidonia oceanica in the Mediterranean.

The Mediterranean is recognized as a climate change hotspot, with ongoing warming anticipated to impact its habitats and their associated fish fauna. Among these habitats, the seagrass Posidonia oceanica stands out as a foundational species, critical for the stability of coastal fish communities. However, our understanding of climate change consequences on P. oceanica associated fish fauna to date remains limited in part due to a lack of long-term data. This study aimed to highlight potential climate change risks to fish species associated with Posidonia, integrating data on species' thermal envelopes with their habitat and depth preferences into a climate change risk index. Specifically, 9 species, including three pipefish and several wrasse species of the genus Symphodus, emerged as being at higher potential risk from climatic change. A historical time series from Palma Bay (Balearic Islands, Spain), spanning 45 years and providing clear evidence of warming, was employed to evaluate trends in species abundance and occurrence in relation to their relative climate risk score. While certain high-risk species like Symphodus cinereus and Diplodus annularis showed an increase in abundance over time, others, such as the pipefish Syngnathus acus, Syngnathus typhle and Nerophis maculatus experienced declines. The absence of observed declines in some high-risk species could be attributed to several factors, such as acclimation, adaptation, or unmet response thresholds. However, this does not rule out the potential for future changes in these species. Factors such as increased nutrient influx due to growing human populations and changes in fishing regulations may also have contributed to the observed trends. These findings underscore the intricate interplay of environmental and anthropogenic factors and accentuate the pressing need for sustained, long-term data acquisition to fathom the implications of climate change on this highly important marine ecosystem.

The potential of Jellytoring 2.0 smart tool as a global jellyfish monitoring platform.

Despite the recent recognition of jellyfish as an important component of marine ecosystems and existing concerns on their potential population increase, they are rarely monitored at the appropriate spatial and temporal scales. Traditional jellyfish monitoring techniques are costly and generally restrict the spatial-temporal resolution limiting the quantity and quality of monitoring data. We introduce Jellytoring 2.0, an automatic recognition tool for jellyfish species based on convolutional neural networks (CNN). We trained Jellytoring 2.0 to identify 15 jellyfish species with a global distribution. Our aim is to offer Jellytoring 2.0 as an open-access tool to serve as the backbone for a system that promotes the creation of large-scale and long-term jellyfish monitoring data. Results reveal that Jellytoring 2.0 performed well in the identification of the 15 species with average precision values ranging between 90% and 99% for most of the species. Four of the species presented slightly lower values (75%-80%). Our system was trained on a relatively small dataset, implying that additional integration of image data will further improve the performance of the CNN. We show how the application of CNNs to image data can deliver a tool that will enable the cost-effective collection of jellyfish data on larger spatial and temporal scales. For Jellytoring 2.0 to become a truly global automatic identification system, we ask scientists and nonscientists to actively contribute with jellyfish image data to extend the number of species it can identify.

Data about marine area-based management tools to assess their contribution to the UN sustainable development goals.

The dataset presented in this article contains information about marine Area-Based Management Tools (ABMTs) used to assess their contribution to the United Nations 2030 Sustainable Development Goals. Following the scope of the analysis, ABMTs were identified by scrutinizing international and regional legal sources related to ocean management in the fields of marine conservation, fisheries, deep sea bed mining, underwater natural and cultural heritage, environmental conservation, and marine spatial planning. Legal sources were screened to depict the following characteristics of individual ABMTs: i) management objectives; ii) authorities responsible for delivering such objectives; iii) the system of management and planning entailed in the ABMT including the zoning type; and iv) the specific spatial scope and domain each ABMT refer to in vertical depth and horizontal domain. Data were generated through an internal expert elicitation. Experts, initially trained in the data analysis and related protocol, contributed to the data production because of their specific knowledge and experience in ocean management. This dataset represents a unique source of information for advancing research about monitoring and assessment of the achievement of sustainable development goals that encompasses different types of ABMTs.

A Graph Theory approach to assess nature's contribution to people at a global scale.

The use of Graph Theory on social media data is a promising approach to identify emergent properties of the complex physical and cognitive interactions that occur between humans and nature. To test the effectivity of this approach at global scales, Instagram posts from fourteen natural areas were selected to analyse the emergent discourse around these areas. The fourteen areas, known to provide key recreational, educational and heritage values, were investigated with different centrality metrics to test the ability of Graph Theory to identify variability in ecosystem social perceptions and use. Instagram data (i.e., hashtags associated to photos) was analysed with network centrality measures to characterise properties of the connections between words posted by social media users. With this approach, the emergent properties of networks of hashtags were explored to characterise visitors' preferences (e.g., cultural heritage or nature appreciation), activities (e.g., diving or hiking), preferred habitats and species (e.g., forest, beach, penguins), and feelings (e.g., happiness or place identity). Network analysis on Instagram hashtags allowed delineating the users' discourse around a natural area, which provides crucial information for effective management of popular natural spaces for people.

In the blind-spot of governance - Stakeholder perceptions on seagrasses to guide the management of an important ecosystem services provider.

Seagrass ecosystems have been identified as important marine ecosystem service (ES) providers, they contribute to coastal protection, fisheries provision and mitigate climate change among others. Yet, they are declining globally at alarming rates. While the ecological dimensions of this social-ecological system have been well studied, its associated social aspects remain largely unexplored. Here, we show how the analysis of stakeholders' perceptions on seagrass ES, their drivers of change, links to wellbeing and governance structures can provide a path towards a more sustainable management. Stakeholders identified seagrass regulatory ES as crucial for the maintenance of social and economic wellbeing and the potential causes and consequences associated to seagrass decline. Power imbalances, an over-compartmentalized legislation and a generalized lack of awareness were highlighted as key aspects to redress in order to achieve a more just governance system. Stakeholders' empirical evidence on the importance of particular ES and on negative drivers of change can also provide an understanding of areas where financial investment would gather wider public support and therefore be more successfully implemented. We showed how the different dimensions highlighted through stakeholders' perspectives can contribute to the consecution of a more inclusive sustainable management, a crucial aspect in the maintenance of seagrass ecosystems.

Preference classes in society for coastal marine protected areas.

Marine protected areas (MPAs) are increasingly being used as conservation tools in the marine environment. Success of MPAs depends upon sound scientific design and societal support. Studies that have assessed societal preferences for temperate MPAs have generally done it without considering the existence of discrete groups of opinion within society and have largely considered offshore and deep-sea areas. This study quantifies societal preferences and economic support for coastal MPAs in Wales (UK) and assesses the presence of distinct groups of preference for MPA management, through a latent class choice experiment approach. Results show a general support for the protection of the marine environment in the form of MPAs and that society is willing to bear the costs derived from conservation. Despite a general opposition toward MPAs where human activities are completely excluded, there is some indication that three classes of preferences within society can be established regarding the management of potentially sea-floor damaging activities. This type of approach allows for the distinction between those respondents with positive preferences for particular types of management from those who experience disutility. We conclude that insights from these types of analyses can be used by policy-makers to identify those MPA designs and management combinations most likely to be supported by particular sectors of society.

Citizen science in data and resource-limited areas: A tool to detect long-term ecosystem changes.

Coral reefs are threatened by numerous global and local stressors. In the face of predicted large-scale coral degradation over the coming decades, the importance of long-term monitoring of stress-induced ecosystem changes has been widely recognised. In areas where sustained funding is unavailable, citizen science monitoring has the potential to be a powerful alternative to conventional monitoring programmes. In this study we used data collected by volunteers in Southeast Sulawesi (Indonesia), to demonstrate the potential of marine citizen science programmes to provide scientifically sound information necessary for detecting ecosystem changes in areas where no alternative data are available. Data were collected annually between 2002 and 2012 and consisted of percent benthic biotic and abiotic cover and fish counts. Analyses revealed long-term coral reef ecosystem change. We observed a continuous decline of hard coral, which in turn had a significant effect on the associated fishes, at community, family and species levels. We provide evidence of the importance of marine citizen science programmes in detecting long-term ecosystem change as an effective way of delivering conservation data to local government and national agencies. This is particularly true for areas where funding for monitoring is unavailable, resulting in an absence of ecological data. For citizen science data to contribute to ecological monitoring and local decision-making, the data collection protocols need to adhere to sound scientific standards, and protocols for data evaluation need to be available to local stakeholders. Here, we describe the monitoring design, data treatment and statistical analyses to be used as potential guidelines in future marine citizen science projects.