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.
Identifying the relative risk human activities pose to a habitat, and the ecosystem services they provide, can guide management prioritisation and resource allocation. Using a combination of expert elicitation to assess the probable effect of a threat and existing data to assess the level of threat exposure, we conducted a risk assessment for 38 human-mediated threats to eight marine habitats (totalling 304 threat-habitat combinations) in Spencer Gulf, Australia. We developed a score-based survey to collate expert opinion and assess the relative effect of each threat to each habitat, as well as a novel and independent measure of knowledge-based uncertainty. Fifty-five experts representing multiple sectors and institutions participated in the study, with 6 to 15 survey responses per habitat (n = 81 surveys). We identified key threats specific to each habitat; overall, climate change threats received the highest risk rankings, with nutrient discharge identified as a key local-scale stressor. Invasive species and most fishing-related threats, which are commonly identified as major threats to the marine environment, were ranked as low-tier threats to Spencer Gulf, emphasising the importance of regionally-relevant assessments. Further, we identified critical knowledge gaps and quantified uncertainty scores for each risk. Our approach will facilitate prioritisation of resource allocation in a region of increasing social, economic and environmental importance, and can be applied to marine regions where empirical data are lacking.
- Impacts of bottom fishing, particularly trawling and dredging, on seabed (benthic) habitats are commonly perceived to pose serious environmental risks. Quantitative ecological risk assessment can be used to evaluate actual risks and to help guide the choice of management measures needed to meet sustainability objectives.
- We develop and apply a quantitative method for assessing the risks to benthic habitats by towed bottom-fishing gears. The method is based on a simple equation for relative benthic status (RBS), derived by solving the logistic population growth equation for the equilibrium state. Estimating RBS requires only maps of fishing intensity and habitat type – and parameters for impact and recovery rates, which may be taken from meta-analyses of multiple experimental studies of towed-gear impacts. The aggregate status of habitats in an assessed region is indicated by the distribution of RBS values for the region. The application of RBS is illustrated for a tropical shrimp-trawl fishery.
- The status of trawled habitats and their RBS value depend on impact rate (depletion per trawl), recovery rate and exposure to trawling. In the shrimp-trawl fishery region, gravel habitat was most sensitive, and though less exposed than sand or muddy-sand, was most affected overall (regional RBS = 91% relative to un-trawled RBS = 100%). Muddy-sand was less sensitive, and though relatively most exposed, was less affected overall (RBS = 95%). Sand was most heavily trawled but least sensitive and least affected overall (RBS = 98%). Region-wide, >94% of habitat area had >80% RBS because most trawling and impacts were confined to small areas. RBS was also applied to the region's benthic invertebrate communities with similar results.
- Conclusions. Unlike qualitative or categorical trait-based risk assessments, the RBS method provides a quantitative estimate of status relative to an unimpacted baseline, with minimal requirements for input data. It could be applied to bottom-contact fisheries world-wide, including situations where detailed data on characteristics of seabed habitats, or the abundance of seabed fauna are not available. The approach supports assessment against sustainability criteria and evaluation of alternative management strategies (e.g. closed areas, effort management, gear modifications).
Guanabara Bay is characterized by predominant eutrophication and anoxic sediments with a mixture of pollutants. The risk prognosis associated with the dumping of its dredged sediments into the open ocean was addressed by our algorithm. Our algorithm could prioritize areas, characterize major processes related to dredging, measure the potential risk of sediments, and predict the effects of sediment mixing. The estimated risk of dredged sediment was > 10-fold than that of ocean sediments. Among metals, mercury represented 50–90% of the total risk. The transfer of dredged material into the ocean or internal dumping in the bay requires a 1:10 dilution to mitigate the risk and bring the risk levels close to that in the EPA criteria, below which there is less likelihood of adverse effects to the biota, and a 1:100 dilution to maintain the original characteristics of the ocean disposal control area. Our algorithm indicator can be used in the design of both aquatic and continental disposal of dredged materials and their management.
Low-frequency noise that is part of the acoustic environment for baleen whales has increased in many areas of the Northeast Pacific Ocean that contain whale habitat. We conducted a spatially explicit risk assessment of noise from commercial shipping to blue, fin, and humpback whale habitats in Southern California waters and explored how noise is affected by several place-based management techniques: a National Marine Sanctuary, an Area to be Avoided (ATBA), and a Traffic Separation Scheme (TSS). We used shipping data to model noise at 2 frequencies that are part of the acoustic environment for these species and capture the variable contributions from shipping to noise. Predicted noise levels in Southern California waters suggest high, region-wide exposure to shipping noise. Our risk assessment identified several areas where the acoustic environment may be degraded for blue, fin, and humpback whales because their habitat overlaps with areas of elevated noise from shipping traffic and 2 places where blue and humpback whale feeding areas overlap with lower predicted noise levels. One of the places with lower predicted noise occurs in the Channel Islands National Marine Sanctuary (CINMS). Noise has not been directly managed within the CINMS; instead, reduced noise in this portion of the CINMS is likely an ancillary benefit of the ATBA surrounding most of the Sanctuary. Areas of elevated noise in the CINMS also occur, primarily where a TSS intersects the Sanctuary’s boundaries. Our risk assessment framework can be used to evaluate how shipping traffic affects acoustic environments and explore management strategies.