Ecosystem Risk Research Articles

Storm resilience of subtidal soft‐bottom mussel beds: Mechanistic insights, threshold quantification and management implications

AbstractWith the projected escalation of extreme storm events, coastal ecosystems risk undergoing catastrophic shifts and losing essential ecosystem services. Subtidal soft‐bottom mussel beds, vital components of these ecosystems, are particularly vulnerable to hydrodynamically‐induced dislodgement (i.e., detachment of mussel clumps from the bed), especially during storms. However, the mechanisms underlying the resilience—comprising both resistance and recovery—of these beds to storms remain unclear, despite being essential for informed management.This study addresses this knowledge gap regarding subtidal soft‐bottom mussel beds by: (i) quantifying their dislodgement threshold (i.e., the hydrodynamics causing widespread dislodgement of mussel clumps) using novel in situ monitoring methodologies in a representative region, namely the Dutch Wadden Sea; (ii) unveiling the influence of prior life history (here, wave exposure extent) and storm durations on their dislodgement thresholds through a flume study; and (iii) assessing the impacts of repeated storms and prior life histories (here, wave exposure extent and substrate types) on their recovery (i.e. mussel re‐aggregation) through mesocosm experiments.Integrated experimental evidence indicates that: (i) hydrodynamic‐induced dislodgement is a sudden process characterized by distinct near‐bed orbital velocity thresholds, which were identified at our study site to be between 0.45 and 0.50 m s−1; (ii) peak storm intensity, rather than storm duration, primarily drives the dislodgement of subtidal soft‐bottom mussel beds, and prior wave exposure extent regulates the dislodgement threshold; (iii) repeated storms do not seem to affect the recovery of these beds following storm‐related disturbances when the conditions between storms are conducive to mussel re‐aggregation, whereas substrate type significantly impacts recovery.Synthesis and applications. Overall, concerns regarding subtidal soft‐bottom mussel beds degradation primarily stem from increasing storm intensity and their limited resistance to such events. The methodology we developed enables low‐cost quantification of mussel resistance thresholds across broad spatiotemporal scales, facilitating the pinpointing of vulnerable areas. Our findings inform strategic management by highlighting the influential role of prior life histories in shaping mussel bed resistance and the potential to accelerate mussel bed recovery through substrate modification (e.g., shell additions). Both our methodology and findings hold promise for application in comparable ecosystems, such as oyster and coral reefs.

Microplastics abundance associated with farmland use types and the impact on soil microbial communities: A case study in Southern China

To investigate the impact of various farmland use types on the spatial distribution of microplastics (MPs) and their correlation with microbial communities, 78 soil samples from distinct farmland use types (orchard, paddy field, and vegetable field) in Southern China were examined. Results revealed that the abundance of MPs ranged from 528 to 39,864 items/kg with a mean abundance of 10,562 items/kg, which were primarily 0–30 μm in size. A total of 32 polymer types were identified, with the main polymer being polyethylene terephthalate (PET, 28.8%), followed by polyvinyl chloride (PVC, 13.5%) and fluororubber (FKM, 9.5%). The abundance of MPs was highest in orchard field (8,896 ± 5,745 items/kg), followed by paddy field (4,176 ± 2,976 items/kg) and lastly vegetable field (2,967 ± 3,698 items/kg). Results of 16 S rRNA gene amplicon sequencing showed that the presence of MPs affected the bacterial distribution patterns. Compared with soil bacterial communities, the spatial dispersion and dissimilarity of plasticsphere communities were less variable. Notably, the predicted sequences related to xenobiotics biodegradation and metabolism became more abundant, thereby affecting the ecological function of soil. Overall, this study provides important data for further research on MP ecosystem risks associated with farmland use types.

Accumulation characteristics and source analysis of microplastics derived traffic of soil in the Tibetan Plateau

The development of transportation infrastructure can stimulate economic growth. However, the environmental and human health risks associated with the release of traffic-derived microplastics (MPs) into the ecosystem must also be considered. This study examined four types of soil samples collected from the Qinghai-Tibetan Plateau along the Gemang Highway. The abundance, size, morphology, and types of MPs were analyzed using laser direct infrared (LDIR) spectroscopy to gain a better understanding of their distribution and characteristics. The average abundance of MPs in the study area was 2,247.92 ± 2,053.82n/kg. It showed the characteristics of wasteland (4,726.67 ± 2956.76n/kg) > wolfberry plantation (3,294.44 ± 2,881.93n/kg) > sandy land (1,519.44 ± 282.65n/kg) > soil along the highway (1,144.44 ± 611.01n/kg). 67.25% of MPs had a particle size of less than 100 μm, posing greater environmental risks. The 6 factors identified by the PMF model suggested a variety of potential sources of MPs and highlighted their connections to the transportation sector. The ecological risk assessment of MPs demonstrated a significant degree of risk, with particular concern regarding more toxic plastics use. This study provides fundamental data for the source analysis and ecosystem risk assessment of MPs derived traffic.

Interactions between nanobiochar and arsenic: Effects of biochar aging methods on arsenic binding capacity and mechanisms

Nano-biochar (nanoBC), produced from biochar aging, exhibits significant molecular heterogeneity that may affect the fate and toxicity of co-occurring pollutants. However, the interaction between nanoBC and arsenic (As) remains unclear. Herein, we simulated biochar aging through water erosion, photoaging, and thermal chemical decomposition to generate three types of nanoBC (nUBC, nPBC, and nHBC). We then investigated their distinct binding affinities and interaction mechanisms with arsenite (AsIII) and arsenate (AsV). Complementary analysis using optical spectrophotometer and high-resolution mass spectrometry revealed significant differences in properties and chemical compositions among the three nanoBCs at a size of 100 nm. Specifically, nHBC had higher yield, nPBC had higher aromaticity, and nUBC had more intricate molecular compositions and larger molecular weights. Binding experiments showed that nHBC and nUBC exhibited the highest conditional distribution coefficient (KD) for AsIII and AsV, respectively. In nHBC, a higher proportion of humic-like fluorescent component C3 enhanced its affinity for AsIII, attributed to lignin-like molecules with CHONS formulas where thiol acted as active binding sites. In contrast, the robust AsV binding capacity of nUBC stemmed from its richness in humic-like fluorescent component C1 and tryptophan-like fluorescent component C2. This is facilitated by lipid-like molecules and CHO formulas in C1 and aliphatic/peptide-like molecules and CHON formulas in C2, which provided oxygenic and nitrogen-containing groups for binding. All nanoBC had a significantly higher binding affinity for As than bulk BC. These findings provide a deeper understanding of As-nanoBC binding mechanisms at the molecular level, facilitating more accurate prediction of As fate in biochar-amended soil and associated ecosystem risks.

Petroleum hydrocarbons in arctic urban lake sediments (Murmansk, Russia)

The level of pollution of lake sediments located in the city of Murmansk (Russia) with petroleum hydrocarbons was studied. The most urban lakes can be classified as slightly polluted, representing the maximum acceptable risk for human health and ecosystems; Lake Yuzhnoe – as moderately polluted with a negative impact on the aquatic environment, and Lake Ledovoe located near the highway, gas station, and car service, – as dangerously polluted, posing a threat to human health and requiring remediation. The background content of organic compounds identified as petroleum hydrocarbons in the sediments of Murmansk lakes ranges from 76 to 307 mg TPH/kg. High background concentrations of hydrocarbons can be associated with a large amount of organic matter in the sediments of lakes. The standards for the content of hydrocarbons in sediments, developed for other regions, give an ambiguous assessment of their content in the sediments of lakes in Murmansk. It is not entirely correct to use the background content of hydrocarbons in the sediments of lakes in other regions, which differ in particle size distribution, content of organic matter, and chemical composition, to assess the degree of pollution. Additional research is needed to determine the content of hydrocarbons in the sediments of background lakes in the Murmansk region and to develop standards for the content of hydrocarbons in the bottom sediments of Arctic cities.

Overview of Methylation and Demethylation Mechanisms and Influencing Factors of Mercury in Water.

Mercury, particularly in its methylated form, poses a significant environmental and health risk in aquatic ecosystems. While the toxicity and bioaccumulation of mercury are well documented, there remains a critical gap in our understanding of the mechanisms governing mercury methylation and demethylation in aquatic environments. This review systematically examines the complex interplay of chemical, biological, and physical factors that influence mercury speciation and transformation in natural water systems. We provide a comprehensive analysis of methylation and demethylation processes, specifically focusing on the dominant role of methanogenic bacteria. Our study highlights the crucial function of hgcAB genes in facilitating mercury methylation by anaerobic microorganisms, an area that represents a frontier in current research. By synthesizing the existing knowledge and identifying key research priorities, this review offers novel insights into the intricate dynamics of mercury cycling in aquatic ecosystems. Our findings provide a theoretical framework to inform future studies and guide pollution management strategies for mercury and its compounds in aquatic environments.

Differential exposure to second-generation anticoagulant rodenticides in raptors from continental and insular regions of the Iberian Peninsula

The global impact of anticoagulant rodenticides (ARs) on non-target species is well-recognized. Birds of prey, as apex predators, are highly vulnerable to AR exposure and are widely used as biomonitors for priority pollutants in Europe. This study investigates differential SGAR exposure in raptors from insular versus continental regions, hypothesizing greater exposure in insular areas due to ecological factors like reduced prey diversity, intensive rodenticide use, and resistant rodent populations. We analyzed the livers of 190 common kestrels (Falco tinnunculus) and 104 common buzzards (Buteo buteo) across the Iberian Peninsula and its archipelagos using LC-MS/MS to assess their role as AR sentinels and the differences between insular and continental areas. Results revealed a high prevalence (>80%) of second-generation anticoagulant rodenticides (SGARs), with brodifacoum and bromadiolone, being the most frequent. Multiple SGAR detections were also common (≈50%). A binomial logistic regression showed that species and region significantly influence the likelihood of SGAR exposure. Kestrels had a greater probability of exceeding 100 ng/g wet weight (ww) compared to buzzards. Raptors from insular territories were ten times more likely to have higher SGAR concentrations than those from continental areas. However, the legal restriction on SGAR bait concentrations that came into effect in 2018 did not significantly impact exposure levels. This study highlights the need for targeted conservation efforts to mitigate AR exposure risk in vulnerable island ecosystems.

Observation-constrained projections reveal longer-than-expected dry spells

Climate models indicate that dry extremes will be exacerbated in many regions of the world1,2. However, confidence in the magnitude and timing of these projected changes remains low3,4, leaving societies largely unprepared5,6. Here we show that constraining model projections with observations using a newly proposed emergent constraint (EC) reduces the uncertainty in predictions of a core drought indicator, the longest annual dry spell (LAD), by 10–26% globally. Our EC-corrected projections reveal that the increase in LAD will be 42–44% greater, on average, than ‘mid-range’ or ‘high-end’ future forcing scenarios currently indicate. These results imply that by the end of this century, the global mean land-only LAD could be 10 days longer than currently expected. Using two generations of climate models, we further uncover global regions for which historical LAD biases affect the magnitude of projected LAD increases, and we explore the role of land–atmosphere feedbacks therein. Our findings reveal regions with potentially higher- and earlier-than-expected drought risks for societies and ecosystems, and they point to possible mechanisms underlying the biases in the current generation of climate models.

Mathematical model for managing marine ecosystem risks

The article presents the results of the research aimed at developing and verifying a mathematical model for managing the ecosystem risks of marine areas. The main purpose of the research is to create an effective tool for assessing and managing the risks arising from anthropogenic influence, climate change and other natural factors. The proposed model is based on the existing theory of sea ecosystem risks management and takes into account the relationship between natural and anthropogenic factors, condition of marine ecosystems, their vulnerability and ecosystem services. The model uses mathematical methods, in particular, differential equations, statistical analyses, simulation modeling and network analysis, allowing creation of detailed models that reflect marine ecosystems' dynamics and their response to external influence. Numerous experiments were conducted in order to verify the model. They demonstrate its ability to predict changes in the condition of marine ecosystems under influence of various factors. The results of the research show that the proposed model can be an effective tool for optimizing management strategies, minimizing ecosystem risks and improving sustainability of marine ecosystems. Practical recommendations developed on the basis of the results of the research include strategies for preventing, minimizing and adapting to potential threats, taking into consideration each region's and ecosystem's specifics. The proposed model helps to optimize management decisions, reducing risks and improving the condition of marine ecosystems affected by anthropogenic impact and climate change. It is a reliable tool for ensuring sustainability and preservation of marine ecosystems. This approach allows for evidence-based risk management that increases sustainability of marine ecosystems and their preservation for future generations. The model also considers key factors influencing marine ecosystems, including biological diversity, water quality, industrial fisheries, tourism and recreation, and allows evaluation of effectiveness of various management decisions in the context of such factors. The application of this model in ecosystem management practices allows making more informed decisions regarding protection and preservation of marine resources. It contributes to their sustainable development and provision of ecosystem services having critical importance for society.

Assessing the destabilization risk of ecosystems dominated by carbon sequestration based on interpretable machine learning method

Increasing carbon sequestration (CS) in soils and biomass is an important land-based solution in mitigating global warming. Ecosystems provide a wide range of ecosystem services (ESs). The necessity to augment CS may engender alterations in the interrelationships among ESs, thereby heightening the probability of ecosystem destabilization. This study developed a framework that integrates machine learning and interpretable predictions to evaluate the destabilization risk resulting from alterations in ecosystem service relationships dominated by CS. We selected Northeastern China as study area to estimate six ESs and identified areas of destabilization risk among the three services most relevant to CS, including food production (FP), soil retention (SR), and habitat quality (HQ). Subsequently, we compared three machine learning models (random forest, extreme gradient boosting, and support vector machine) and introduced the Shapley additive interpretation (SHAP) method for driving mechanism analysis. The results showed that: (1) CS-FP had 30.28% of its area at destabilization risk and is the most significant ecosystem service pair; (2) Heilongjiang Province was the region with the highest destabilization risk of CS, with CS-FP and CS-SR accounting for 44.76% and 52.89% of all regions, respectively; (3) a non-linear relationship and the presence of threshold features between socio-ecological factors and the prediction of destabilization risk. The study has potential practical value for destabilization risks prevention, while also providing a scientific basis for formulating comprehensive carbon management policies and maintaining ecosystem stability.

Tailor-made ammonia nitrogen risk management with machine learning models for aquatic environments in the Mainland of China

Efficient management of pollutant risks in water bodies is crucial for public health and aquatic ecosystem sustainability. However, the toxicities of pollutants, such as ammonia nitrogen (NH3-N), are often affected by multiple water quality factors, including the pH and water temperature. Extensive spatial and temporal variability in these factors hinders tailor-made management of risk. This study used high-frequency monitoring data collected over 1 year to evaluate the long-term NH3-N risk in China’s aquatic ecosystems. High accuracy and interpretability were achieved by decomposing NH3-N risk into the contributions of key influencing factors using random forest models and Shapley Additive Explanations. Two distinct types of NH3-N risk hotspots were identified across 18 cities: 15 cities with high NH3-N concentrations and 3 cities with low environmental carrying capacity due to high pH levels or elevated water temperatures. For the former, rapid NH3-N abatement measures are necessary to bring NH3-N concentrations back below the environmental capacity. For the latter, it is recommended that NH3-N related industries are relocated to regions with high environmental capacities because fragile environments are not suitable for such industries. Importantly, this study investigated methods for attributing pollutant risks in the context of non-linear influencing factors, and the risk of NH3-N was predicted to increase by 6.1 % by the end of 2100 in the context of increasing temperatures under the SSP 2–4.5 scenario. The methodology is also adaptable and suitable for integration into global ecosystem risk management efforts to balance development and aquatic ecological sustainability.

Perceptions of health and ecosystem risks from free-roaming domestic animals in Mediterranean wetlands: One health perspective

Perceptions of health and ecosystem risks from free-roaming domestic animals in Mediterranean wetlands: One health perspective

TERRITORY'S INNOVATION ECOSYSTEM: TOOLS FOR MANAGING DEVELOPMENT IN THE DIGITAL ENVIRONMENT

Purpose of the study. The innovation ecosystem of the territory was set to be the format of adaptation of the regional innovation system to the digital environment. This management model is rapidly developing, based on the generalization of empirical experience and multidirectional theoretical research. However, the diversity of management concepts requires building a logical relationship in terms of their application to ensure the successful operation of the ecosystem. Therefore, the purpose of this article is to substantiate management approaches and tools that contribute to improving the performance of the territory's innovation ecosystem in the digital environment. Conclusions. It is proposed to use the model for assessing its maturity as an information basis for managing the development of the territory's innovation ecosystem. A comparative analysis of approaches to the assessment of ecosystem maturity has been carried out. The key aspects of assessment are defined, and the expediency of building a structured dynamic complex of assessment models is substantiated. Promising models of ecosystem management (hybrid orchestration, multi-orchestration, multi-tier orchestration) are identified, which allow for non-hierarchical coordination of relatively independent partners in the process of creating a common value. The landscape of trajectories of possible development of the innovation ecosystem of the territory is constructed, which defines a controlled transition between possible states depending on the ratio of maturity levels of each of the participants and the ecosystem as a whole. The importance of taking risks into account for the possibility of realizing the optimal development trajectory is emphasized. A methodological approach to ecosystem risk assessment is proposed, combining: 1) a map of risks of the territory's innovation ecosystem, structured by life cycle stages and management level (an individual participant and the ecosystem as a whole), 2) risk ranking based on the assessment of their impact on the performance indicators of the territory's innovation ecosystem.

A novel-approach for identifying sources of fluvial DOM using fluorescence spectroscopy and machine learning model

Rivers are well known as one of the most threatened aquatic environments, whose structure and water quality can be deeply impacted by intensive anthropogenic activities. Despite the fact that anthropogenic influences on river ecosystems could indeed be deduced from the composition and chemistry of fluvial dissolved organic matter (DOM), sources of anthropogenic loading to DOM are still poorly explored. Here, by uniting fluorescence excitation-emission matrices (EEM) and principal component absolute coefficient, four sources of DOM from seventeen rivers in major drainage basins of China could be identified, i.e., originating from municipal sewage, domestic wastewater, livestock wastewater, and natural origins, and thus being defined as MS-DOM, DW-DOM, LW-DOM, NO-DOM, respectively. Based on the random forest model, special nodes in EEM could be traced from four sources, respectively. According to parallel factor analysis, DOM mainly contained protein-like, microbial humic-like, and fulvic-like fluorescence substances, among which protein-like was dominant in MS-DOM and DW-DOM, microbial humic-like in LW-DOM, and fulvic-like in NO-DOM. Based on key peaks and essential nodes in EEM, the identifying source indices were first proposed, which could be introduced to simply distinguish the different anthropogenic-derived sources of fluvial DOM. It was associated with intensity ratios of the key peaks and the essential nodes of EEM spectra from four sources, i.e., municipal sewage (MS-SI: Ex/Em = 280/(335, 410) nm), domestic wastewater (DW-SI: Ex/Em = 280/(340, 410) nm), livestock wastewater (LW-SI: Ex/Em = 235/(345, 380) nm), and natural origins (NO-SI: Ex/Em = 260/(380, 430) nm). By statistical analysis, the high identifying source indices of municipal sewage (>0.5) and natural origins (>0.4) values could be related to MS-DOM and NO-DOM, respectively. The identifying source indices of domestic wastewater with 0.1–0.3 might be linked to DW-DOM and the identifying source indices of livestock wastewater with 0.3–0.4 to LW-DOM. Compared with conventional optical indices, the novel identifying source indices showed remarkable discrimination for the sources of fluvial DOM with different forms of anthropogenic disturbances. Hence the innovative approach could be relatively convenient and accurate to evaluate water quality or pollution risk in river ecosystems.

Silver nanoparticles: A review of the production techniques to reduce toxicological risk in ecosystems and in human health

Silver nanoparticles: A review of the production techniques to reduce toxicological risk in ecosystems and in human health

Disentangling drivers of annual grass invasion: Abiotic susceptibility vs. fire-induced conversion to cheatgrass dominance in the sagebrush biome

Invasive annual grasses are often facilitated by fire, yet they can become ecologically dominant in susceptible locations even in the absence of fire. We used an extensive vegetation plot database to model susceptibility to the invasive annual grass cheatgrass (Bromus tectorum L.) in the sagebrush biome as a function of climate and soil water availability variables. We built random forest models predicting cheatgrass presence or dominance (>15 % relative cover) under unburned (37,219 plots) and burned conditions (6340 plots). We mapped predicted probability of cheatgrass presence and dominance, conditional on burning. We combined predicted susceptibility with burn probability to quantify the 10-year total risk of cheatgrass dominance. Finally, we identified portions of the landscape (1) at risk of fire-induced conversion to cheatgrass dominance, (2) consistently susceptible to cheatgrass dominance, or (3) consistently resistant to cheatgrass dominance. At the scale of the sagebrush biome, we found that abiotic susceptibility to cheatgrass dominance drives total risk, regardless of fire. At local scales (i.e., individual 30 m pixels), burning increased the probability of cheatgrass dominance by a median of 14 %. Threshold-based analyses indicate that 10–31 % of the sagebrush biome was at risk of fire-induced dominance, with 55 % exhibiting abiotic resistance and 5 % exhibiting abiotic susceptibility to dominance regardless of fire. Burn probability was higher in areas predicted to be susceptible to dominance, illustrating how cheatgrass invasion can cause ecosystem conversions that are then sustained by grass-fire cycles. Disentangling the influence of abiotic conditions and fire contributes to our understanding of the mechanisms driving invasion dynamics, and modeling the probability of dominance can help anticipate where ecological transformations are at risk of occurring. Our approach can facilitate the prioritization of management actions in the sagebrush biome and be used as a framework for modeling invasion risk in other disturbance-prone ecosystems.

A novel colorimetric sensor through peroxymonosulfate activated by CoFe@CN nanozymes for on-site resorcinol detection

The presence of resorcinol (RS) in water contaminated with pollutants may engender environmental and ecosystem risks. In this study, we developed rapid and efficient colorimetric methods for the on-site quantitative determination of RS. Specifically, a synthetic nanozyme composed of nitrogen-doped carbon-coated cobalt-iron (CoFe@CN) was utilized to initiate the oxidation reaction of 3,3′,5,5′-tetramethylbenzidine (TMB) with the activator of peroxymonosulfate (PMS). Compared to the activator of hydrogen peroxide (H2O2), PMS demonstrated a sixfold enhancement in catalytic efficacy, markedly elevating the detection performance. Density Functional Theory calculations indicate that RS can serve as a reducing agent for oxTMB, confirming the theoretical feasibility of this process. A linear relationship between inhibition rate (IR %) of RS concentration and absorbance was established and the colorimetric analysis revealed a linear relationship between RS concentration and gray scale values over the range of 0–45 μM, with a detection limit of 3.39 μM. Furthermore, smartphone-based analysis of color images allows for on-site quantitative analysis of RS concentration, leveraging portability and real-time detection capabilities. This study provides pioneering analytical support for on-site water quality monitoring within the ecological environment.

Complementor or Depreciator: Relationship Risks in Digital Ecosystems

Complementor or Depreciator: Relationship Risks in Digital Ecosystems

Ecological and geological security risk assessment of underground space development in cold and arid canyon cities-Taking Ping'an District, Haidong City, Qinghai Province, as an example.

The ecological and geological problems caused by the rise of groundwater level due to the development of underground space in cold and arid canyon cities are particularly typical. Reasonably assessing the ecological and geological security risks of utilizing underground space is conducive to reducing the occurrence of ecological and geological problems during the construction and operation of underground engineering projects. Taking Ping'an District of Haidong City as an example, the topography and geomorphology of the research area were investigated in the field, and the distribution of topography and geomorphology in the research area was understood; through geological drilling and geotechnical engineering testing, the distribution of different strata in the research area was obtained; through pumping and seepage experiments, the recharge, runoff, and discharge relationship between surface water and groundwater in the research area and the water abundance of different strata are obtained, and the causes and mechanisms of geological safety risks in forest and grassland ecosystems, farmland ecosystems, and human settlements ecosystems were analyzed based on literature. Corresponding ecological geological safety risk assessment index systems and methods were established, and the ecological geological safety risks before and after the development of underground rail transit projects along both banks of the Huangshui River in the study area were evaluated. PRACTITIONER POINTS: The development of underground space in canyon type cities can easily lead to ecological and geological problems. Taking Haidong City, Qinghai Province, as an example, this study investigates the causes of ecological and geological problems caused by the development of underground spaces in canyon type cities. An ecological geological security risk assessment index system and method for canyon-type cities were established. An evaluation was conducted on the ecological and geological safety risks before and after the development of the underground rail transit projects on both sides of the research area.

Ecosystem risk from human use of ocean space and resources: A case study from the Norwegian coast

Coastal and adjacent shelf waters are generally highly productive ecosystems harboring important ecological processes and exposed to a range of anthropogenic pressures from land-based and marine sectors. Ensuring that the cumulative pressures from human activities do not cause unacceptable, permanent harm to the ecosystem is challenging but crucial for sustainable management of these regions. Linkage frameworks and ecological risk assessments have proven to be useful tools for holistic evaluations of cumulative human pressures as a guide to managers and policy makers for prioritization of risk factors. Here, we present the first holistic assessment of ecosystem risk from human activities along the Norwegian coast. Pressures from coastal sectors are identified and weighted by the exposure to and potential impact on ecosystem components following the ODEMM (Options for Delivering Ecosystem-based Marine Management) framework. We focus on four coastal regions with contrasting scales of human activities. Two southern regions with multiple anthropogenic activities are associated with higher cumulative risk of negative impacts compared to northern areas where less extensive activities have a lower potential of harming the coastal ecosystems. Despite latitudinal differences in human use of the coastline, the pressures and ecosystem components associated with the greatest risk of cumulative impacts are relatively similar between the regions. Contaminants and underwater noise stand out as high-risk pressures, associated with multiple sectors with a high spatiotemporal footprint and with the potential to negatively impact a range of ecosystem components. Nevertheless, a confidence assessment also highlights the need for more in-depth analysis on the input, spread and effect of these pressures on coastal ecosystems. We discuss strengths and weaknesses of the risk assessment framework and suggest new directions which may enhance the utility and uptake of such assessments for sustainable management of coastal ecosystems.