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.
The immense energy potential of the oceans is being increasingly recognized the world over, at the same time the integrity of marine ecosystems is challenged by pressure from multiple human activities. For good reasons environmental licensing procedures are precautionary and new industries must declare their detrimental impacts and provide mitigation measures. New ocean energy industries target renewable energy sources thus, on a grand scale, partly mitigating climate change. However, on-site environmental impacts are yet to be established. In this review we compare ocean energy industries with a wide range of conventional, better understood, human activities and outline environmental risks and research priorities. Results show that ocean energy systems are thought to incur many pressures, some familiar and others with yet unknown effects. Particular uncertainties regard ocean thermal energy conversion (OTEC) and large fast-moving turbines. Ocean energy industries should not be considered in isolation because the significance of environmental impacts depend on the full spectra of human activities in each area. Marine spatial planning provides a platform for holistic assessments and may facilitate the establishment of ocean energy industries, as long as risk-related uncertainties are reduced.
China is amongst the countries most severely affected by coastal and marine disasters. In this study, the annual variation and geographic distribution of the direct economic losses and fatalities caused by rapid-onset coastal and marine disasters in China have been analysed. This was based on a collection of historical documents and official records. The five main hazards include storm surges, rough seas, sea ice, red tides and green tides. The results show that: (1) Storm surges caused the most economic losses (92% of the total); (2) At national scale, direct economic losses induced by coastal and marine disasters fluctuated with no clear trend; the number of fatalities per year declined, and in relative terms both economic losses and fatalities decreased dramatically throughout time; (3) Substantial heterogeneity exists across the 11 provincial-level administrative regions in terms of the spatial pattern and temporal trends of coastal and marine hazards, exposure, vulnerability and observed impacts. Guangzhou, Fujian, Zhejiang and Hainan provinces experienced the highest direct economic losses and fatalities due to repeated typhoon-induced storm surges. The decline in adverse impacts caused by hazards is due to substantial progress in coastal and marine disaster prevention and migration in China, largely thanks to institutional measures, plus adaptation and mitigation actions at both national and regional levels. Coastal China still faces growing risks due to socio-economic development, climate change, as well as subsidence and new emerging marine disasters (e.g. green tides). Further management needs to promote integrated solutions across socio-economic development, disaster risk reduction and environmental conservation in coastal regions. This should happen at national and international levels as disasters can affect neighboring countries, and their marine environments and socio-ecological systems. Lessons may be learnt from countries experiencing similar problems over the long-term.
Conservation efforts strive to protect significant swaths of terrestrial, freshwater and marine ecosystems from a range of threats. As climate change becomes an increasing concern, these efforts must take into account how resilient protected spaces will be in the face of future drivers of change such as warming temperatures. Climate landscape metrics, which signal the spatial magnitude and direction of climate change, support a convenient initial assessment of potential threats to and opportunities within ecosystems to inform conservation and policy efforts where biological data are not available. However, inference of risk from purely physical climatic changes is difficult unless set in a meaningful ecological context. Here, we aim to establish this context using historical climatic variability, as a proxy for local adaptation by resident biota, to identify areas where current local climate conditions will remain extant and future regional climate analogues will emerge. This information is then related to the processes governing species’ climate-driven range edge dynamics, differentiating changes in local climate conditions as promoters of species range contractions from those in neighbouring locations facilitating range expansions. We applied this approach to assess the future climatic stability and connectivity of Japanese waters and its network of marine protected areas (MPAs). We find 88% of Japanese waters transitioning to climates outside their historical variability bounds by 2035, resulting in large reductions in the amount of available climatic space potentially promoting widespread range contractions and expansions. Areas of high connectivity, where shifting climates converge, are present along sections of the coast facilitated by the strong latitudinal gradient of the Japanese archipelago and its ocean current system. While these areas overlap significantly with areas currently under significant anthropogenic pressures, they also include much of the MPA network that may provide stepping-stone protection for species that must shift their distribution due to climate change.
With increasing pressure on the oceans from environmental change, there has been a global call for improved protection of marine ecosystems through the implementation of marine protected areas (MPAs). Here, we used species distribution modelling (SDM) of tracking data from 14 seabird species to identify key marine areas in the southwest Atlantic Ocean, valuing areas based on seabird species occurrence, seasonality and extinction risk. We also compared overlaps between the outputs generated by the SDM and layers representing important human threats (fishing intensity, ship density, plastic and oil pollution, ocean acidification), and calculated loss in conservation value using fishing and ship density as cost layers. The key marine areas were located on the southern Patagonian Shelf, overlapping extensively with areas of high fishing activity, and did not change seasonally, while seasonal areas were located off south and southeast Brazil and overlapped with areas of high plastic pollution and ocean acidification. Non-seasonal key areas were located off northeast Brazil on an area of high biodiversity, and with relatively low human impacts. We found support for the use of seasonal areas depending on the seabird assemblage used, because there was a loss in conservation value for the seasonal compared to the non-seasonal approach when using ‘cost’ layers. Our approach, accounting for seasonal changes in seabird assemblages and their risk of extinction, identified additional candidate areas for incorporation in the network of pelagic MPAs.
Understanding plastic pollution from a systems perspective requires a way of conceptualizing sources, distribution and dynamics in the environment; identifying or quantifying impacts on wildlife, humans and other assets; and identifying and evaluating potential management responses. The uncertainties in our knowledge and the difficulty in resolving them satisfactorily can be challenging, given that we are confined to working with largely observational data because experiments at scale are difficult or impossible. To advance this area of research, we suggest applying a conceptual framework that allows us to break the components into smaller parts that can integrate uncertainty and connect variables of interest to outcomes of interest. We identify four specific questions inherent to a risk framework: the first three focus on risk analysis, and the fourth on risk management or mitigation. We present examples that are both data rich and data poor and discuss the value of integrating a systems perspective, connecting sources and drivers to dynamics and distribution to impacts and management responses. We also propose applying risk analysis to the plastics pollution issue as we acknowledge and embrace uncertainty, noting the precautionary principle and its application to risk management.