Effective management of coral reefs requires strategies tailored to cope with cumulative disturbances from human activities. In Brazil, where coral reefs are a priority for conservation, intensifying threats from local and global stressors are of paramount concern to management agencies. Using a cumulative impact assessment approach, our goal was to inform management actions for coral reefs in Brazil by assessing their exposure to multiple stressors (fishing, land-based activities, coastal development, mining, aquaculture, shipping, and global warming). We calculated an index of the risk to cumulative impacts: (i) assuming uniform sensitivity of coral reefs to stressors; and (ii) using impact weights to reflect varying tolerance levels of coral reefs to each stressor. We also predicted the index in both the presence and absence of global warming. We found that 16% and 37% of coral reefs had high to very high risk of cumulative impacts, without and with information on sensitivity respectively, and 42% of reefs had low risk to cumulative impacts from both local and global stressors. Our outputs are the first comprehensive spatial dataset of cumulative impact on coral reefs in Brazil, and show that areas requiring attention mostly corresponded to those closer to population centres. We demonstrate how the relationships between risks from local and global stressors can be used to derive strategic management actions.
Subtropical reefs are biogeographic transition zones, providing critical habitat for a range of tropical, subtropical and temperate biota, including many endemic species. To date, limited research has been conducted on assessing the level of SCUBA diving risks to subtropical benthic habitats. This study surveyed 407 SCUBA divers to determine the types and rates of contact presenting the greatest risk to benthic taxa. Data were aggregated to give the total number of severe contacts for each diver. Site-level analysis based on 95% confidence level showed that severe impacts were more probable as reef complexity increased vertically. A general linear regression model was used to assess the level of risk to habitat based on the contact type and benthic percentage cover. SCUBA tank, camera, diver's knee and untethered equipment created the greatest proportion of severe impacts to benthic taxa. As benthic percentage cover increased for Scleractinia, Echinodermata, Ascidiacea, Porifera, susceptibility and vulnerability to severe impacts also increased. Abrasions, breaks, compression and mucus release were common forms of impact. Risk assessment findings suggest that subtropical benthic taxa are highly susceptible to SCUBA diver impacts. Targeted risk reduction is required in future management strategies.
Marine mammals are impacted by many anthropogenic activities and mitigating these impacts requires knowledge about the geographic occurrence of threats. Here, we systematically reviewed, categorized and geo-referenced information from >1780 publications about threats affecting 121 marine mammal species worldwide between 1991 and 2016. We created risk maps by assigning threat to countries where they had been reported, further refining spatial allocation to specific ocean basins and Longhurst biogeographical provinces and subsequent intersection with mapped species' distributions. We superimposed risk maps for different taxa and threats to visualize geographic patterns of risks and quantify risk severity with respect to number of species affected. Almost all marine mammal species have been reported to face at least one threat. Incidental catch affected the most species (112 species), followed by pollution (99 species), direct harvesting (89 species) and traffic-related impacts (86 species). Direct human activities, mainly fisheries, urban development, whaling/hunting and tourism were the major source of threats affecting most species (>60 species). Risk areas were identified for 51% of marine mammal core habitat. Besides, the majority of local marine mammal communities are at high-risk in 47% of world coastal-waters. Hotspots were located mainly in temperate and polar coastal waters and in enclosed seas such as the Mediterranean or Baltic Sea. However, risk areas differed by threat types and taxa. Our maps show that human activities in coastal waters worldwide impose previously unrecognized levels of cumulative risk for most of marine mammal species, and provide a spatially explicit frame of reference for the assessment of mammals' species conservation status.
Adequate response to risks affecting coasts requires an integrated and coordinated multi-risk governance system, with ongoing evaluation of statutory planning documents and responsible stakeholders. Traditionally, such analyses have been carried out using mainly qualitative approaches. This paper adopts a more systemic and quantitative perspective on assessing planning systems and stakeholder relationships in connection with coastal risk. We apply network analysis to the Catalan coast (Northwestern Mediterranean Basin), paying special attention to the level of climate change integration in the planning system, as an aggravating factor of current risk dynamics. Our results demonstrate and quantify the complexity of Catalan coastal risk planning, which requires dealings with multi-level legal and administrative frameworks. Also highlighted is dissimilar management traditions according to risk type: the perspective on flooding risk is more unified and multi-risk focused, whereas coastal erosion (a significant issue for the Catalan coast) is managed more sectorially from a centralized administrative level. Climate change, moreover, is weakly accounted for in current statutory planning. We also acknowledge the relevance of using qualitative information as an important complement in interpreting results and making policy recommendations.
Rising seas will impact millions of coastal residents in coming decades. The vulnerability of coastal populations exposed to inundation will be greater for some sub-populations due to differences in their socio-demographic characteristics. Many climate risk and vulnerability assessments, however, model current populations against future environments. We advance sea-level rise risk assessments by dynamically modeling environmental change and socio-demographic change. We project three scenarios of inundation exposure due to future sea-level rise in coastal Georgia from 2010 to 2050. We align the sea-level rise projections with five population projection scenarios of socially vulnerable sub-populations via the Hamilton-Perry method and the theory of demographic metabolism. Our combined fast sea-level rise and middle population scenarios project a near doubling of the population exposed, and a more than five-fold increase for those at risk (i.e., residing in a census tract with high social vulnerability) and most at risk (i.e., high social vulnerability and high exposure) compared to the same estimate based on 2010 population data. Of vulnerable sub-populations, women had the largest absolute increase in exposure for all scenario combinations. The Hispanic/Latinx population's exposure increased the largest proportionally under the fast and medium sea-level rise projections and elderly people's (65+) under the slow sea-level rise scenario. Our findings suggest that for coastal areas experiencing rapid growth (or declines) in more socially vulnerable sub-populations, estimates based on current population data are likely to underestimate (or overestimate) the proportion of such groups' risk to inundation from future sea-level rise.
Assessing the stock status of mixed and/or multi-species fishery resources is challenging. This is especially true in highly diverse systems, where landed catches are small, but comprise many species. In these circumstances, whole-of-ecosystem management requires consideration of the impact of harvesting on a plethora of species. However, this is logistically infeasible and cost prohibitive. To overcome this issue, selected ‘indicator’ species are used to assess the risk to sustainability of all ‘like’ species susceptible to capture within a fishery resource. Indicator species are determined via information on their (1) inherent vulnerability, i.e. biological attributes; (2) risk to sustainability, i.e. stock status; and (3) management importance, i.e. commercial prominence, social and/or cultural amenity value of the resource. These attributes are used to determine an overall score for each species which is used to identify ‘indicator’ species. The risk status (i.e. current risk) of the indicator species then determines the risk-level for the biological sustainability of the entire fishery resource and thus the level of priority for management, monitoring, assessment and compliance. A range of fishery management regimes are amenable to the indicator species approach, including both effort limited fisheries (e.g. individually transferable effort systems) and output controlled fisheries (e.g. species-specific catch quotas). The indicator species approach has been used and refined for fisheries resources in Western Australia over two decades. This process is now widely understood and accepted by stakeholders, as it focuses fishery dependent- and/or independent-monitoring, biological sampling, stock assessment and compliance priorities, thereby optimising the use of available jurisdictional resources.
As the world’s population grows to a projected 11.2 billion by 2100, the number of people living in low-lying areas exposed to coastal hazards is projected to increase. Critical infrastructure and valuable assets continue to be placed in vulnerable areas, and in recent years, millions of people have been displaced by natural hazards. Impacts from coastal hazards depend on the number of people, value of assets, and presence of critical resources in harm’s way. Risks related to natural hazards are determined by a complex interaction between physical hazards, the vulnerability of a society or social-ecological system and its exposure to such hazards. Moreover, these risks are amplified by challenging socioeconomic dynamics, including poorly planned urban development, income inequality, and poverty. This study employs a combination of machine learning clustering techniques (Self Organizing Maps and K-Means) and a spatial index, to assess coastal risks in Latin America and the Caribbean (LAC) on a comparative scale. The proposed method meets multiple objectives, including the identification of hotspots and key drivers of coastal risk, and the ability to process large-volume multidimensional and multivariate datasets, effectively reducing sixteen variables related to coastal hazards, geographic exposure, and socioeconomic vulnerability, into a single index. Our results demonstrate that in LAC, more than 500,000 people live in areas where coastal hazards, exposure (of people, assets and ecosystems) and poverty converge, creating the ideal conditions for a perfect storm. Hotspot locations of coastal risk, identified by the proposed Comparative Coastal Risk Index (CCRI), contain more than 300,00 people and include: El Oro, Ecuador; Sinaloa, Mexico; Usulutan, El Salvador; and Chiapas, Mexico. Our results provide important insights into potential adaptation alternatives that could reduce the impacts of future hazards. Effective adaptation options must not only focus on developing coastal defenses, but also on improving practices and policies related to urban development, agricultural land use, and conservation, as well as ameliorating socioeconomic conditions.
Marine ecosystems are increasingly threatened by the cumulative effects of multiple human pressures. Cumulative effect assessments (CEAs) are needed to inform environmental policy and guide ecosystem-based management. Yet, CEAs are inherently complex and seldom linked to real-world management processes. Therefore we propose entrenching CEAs in a risk management process, comprising the steps of risk identification, risk analysis and risk evaluation. We provide guidance to operationalize a risk-based approach to CEAs by describing for each step guiding principles and desired outcomes, scientific challenges and practical solutions. We reviewed the treatment of uncertainty in CEAs and the contribution of different tools and data sources to the implementation of a risk based approach to CEAs. We show that a risk-based approach to CEAs decreases complexity, allows for the transparent treatment of uncertainty and streamlines the uptake of scientific outcomes into the science-policy interface. Hence, its adoption can help bridging the gap between science and decision-making in ecosystem-based management.
Spatially explicit risk assessment is an essential component of Marine Spatial Planning (MSP), which provides a comprehensive framework for managing multiple uses of the marine environment, minimizing environmental impacts and conflicts among users. In this study, we assessed the risk of the exposure to high intensity vessel traffic areas for the three most abundant cetacean species (Stenella coeruleoalba, Tursiops truncatus and Balaenoptera physalus) in the southern area of the Pelagos Sanctuary, which is the only pelagic Marine Protected Area (MPA) for marine mammals in the Mediterranean Sea. In particular, we modeled the occurrence of the three cetacean species as a function of habitat variables in June by using hierarchical Bayesian spatial-temporal models. Similarly, we modelled the marine traffic intensity in order to find high risk areas and estimated the potential conflict due to the overlap with the cetacean home ranges. Results identified two main hot-spots of high intensity marine traffic in the area, which partially overlap with the area of presence of the studied species. Our findings emphasize the need for nationally relevant and transboundary planning and management measures for these marine species.
Environmental impact assessment (EIA) is used globally to manage the impacts of development projects on the environment, so there is an imperative to demonstrate that it can effectively identify risky projects. However, despite the widespread use of quantitative predictive risk models in areas such as toxicology, ecosystem modelling and water quality, the use of predictive risk tools to assess the overall expected environmental impacts of major construction and development proposals is comparatively rare. A risk-based approach has many potential advantages, including improved prediction and attribution of cause and effect; sensitivity analysis; continual learning; and optimal resource allocation. In this paper we investigate the feasibility of using a Bayesian belief network (BBN) to quantify the likelihood and consequence of non-compliance of new projects based on the occurrence probabilities of a set of expert-defined features. The BBN incorporates expert knowledge and continually improves its predictions based on new data as it is collected. We use simulation to explore the trade-off between the number of data points and the prediction accuracy of the BBN, and find that the BBN could predict risk with 90% accuracy using approximately 1000 data points. Although a further pilot test with real project data is required, our results suggest that a BBN is a promising method to monitor overall risks posed by development within an existing EIA process given a modest investment in data collection.