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Coastal ecological risk assessment in regional scale: Application of the relative risk model to Xiamen Bay, China
Ocean & Coastal Management xxx (2014) 1e9
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Coastal ecological risk assessment in regional scale: Application of the relative risk model to Xiamen Bay, China Weiwei Yu a, b, Luoping Zhang a, *, Paolo F. Ricci c, Bin Chen b, Hao Huang b a
College of the Environment & Ecology, Xiamen University, Xiamen 361005, PR China The Third Institute of Oceanography, State Oceanic Administration, 178 Daxue Road, Xiamen 361005, PR China c Environmental Health Sciences, University of Massachusetts, Amherst, MA 10030, USA b
a r t i c l e i n f o
a b s t r a c t
Article history: Available online xxx
Ecological risk assessment has been regarded as an important tool for environmental management. Ecological risk assessment was conducted using Relative Risk Model in Xiamen Bay, China, the results of which were applied to inform coastal environmental management. The study area was divided into seven sub-regions, and the potentially ecological risks for both the whole bay and sub-regions were predicted and ranked by introducing a sourceestressorereceptoreendpoint filter. The results showed that: (i) Jiulong River Estuary was the sub-region with highest risk; the second highest being Tongan Bay; (ii) coastal engineering major works were the biggest sources of routine risk, followed by typhoons and storm surges; (iii) oil spills were the biggest accidents contributing to risk, followed by non-routine discharges; (iv) shallow water swamp ecosystem were most likely to be affected, followed by intertidal mudflat ecosystem; (v) species diversity was the endpoint most likely to be affected, with population abundance of protected species being second. Monte Carlo simulations were conducted to examine the effects of uncertainty on those risk prediction. Its results suggested that the probability distributions were consistent with other examples in the literature and as expected that the uncertainty that affects results does not alter the rankings from the relative risk analysis. Ó 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).
1. Introduction
magnitude and frequency, but their numbers have been increasing exponentially over the last several decades. Examples are algal blooms (Adam et al., 2011; Walters et al., 2013), oil spills (Mezi c et al., 2010; Peterson et al., 2012), species extinction (Edgar et al., 2010; Polidoro et al., 2010; Briggs, 2011), and alien species invasion (Occhipinti-Ambrogi and Galil, 2010; Seebens et al., 2013). Although natural factors might be influential elements for ecological risks, anthropogenic activities associated with increasing pressures on natural environment might be even more significant doing much to aggravate already existing natural pressure and stresses. Ecological risk assessment (ERA) is an important tool for environmental management, which has been widely applied to environmental decision making (Nash et al., 2005). ERA is a process for organizing data, information, assumptions, and uncertainties to evaluate the probability (e.g., likelihood) of adverse ecological effects that may occur as a result of exposure to one or more stressors from human activities (U.S.EPA, 1992, 1998, 2008). The theory and methods of ERA were largely developed from human health risk assessment in the USA, and new chemical risk assessment in Europe (REACH Directive); therefore, most of the early studies focused on ecological risks caused by chemical
Coastal ecosystems, located in the transitional zone between land and sea, are among the most valuable in the world, not only because they provide important habitats for abundant organisms, but also provide lots of ecological goods and services for humans, such as food production, storm buffering, pollution control, and so on (Barbier et al., 2008). However, these ecosystems, where nearly 41% of global population live within the coast (Martínez et al., 2007), are also experiencing unprecedented pressures due to increasing concentrations of human population and anthropogenic activities. Consequently, there has been increasing degradation, and they have become one of the most impacted and altered ecosystems (Adger, 2005). Undeniably, coastal deterioration contributes to increasing ecological risks and other hazards in several ways, and ecological risks in turn could make the already degraded coastal ecosystems much more vulnerable. Coastal ecological risks and hazards have been global issues. Ecological damages from accidental events are uncertainty as to * Corresponding author. Tel.: þ86 592 2185855; fax: þ86 592 2186913. E-mail address: [email protected] (L. Zhang).
http://dx.doi.org/10.1016/j.ocecoaman.2014.04.027 0964-5691/Ó 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).
Please cite this article in press as: Yu, W., et al., Coastal ecological risk assessment in regional scale: Application of the relative risk model to Xiamen Bay, China, Ocean & Coastal Management (2014), http://dx.doi.org/10.1016/j.ocecoaman.2014.04.027
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W. Yu et al. / Ocean & Coastal Management xxx (2014) 1e9
Fig. 1. Boundary of study area and its sub-regions.
pollutants. By contrast, regional ERA was developed in the early of 1990s (Hunsaker et al., 1990; Suter II, 1990). Since then, regional ERA has developed rapidly to deal with multiple sources of several stressors causing diverse endpoints. Among the approaches applied to regional ERA, the Relative Risk Model (RRM) is one of the most widely used methods, since it was first developed in 1997 (Landis and Wiegers, 1997, 2007). RRM has been applied successfully in numerous studies throughout the world, such as in marine environment in the Fjord of Port Valdez, Alaska, USA (Wiegers et al., 1998), Mountain River Catchment in Tasmanian, Australia (Walker et al., 2001), the Codorus Creek Watershed, Pennsylvania in USA (Obery and Landis, 2002), a near shore marine environment in Northwestern Washington, USA (Hayes and Landis, 2004), Luanhe River Basin in China (Liu et al., 2010), and Northern Tropical River in Australia (Bartolo et al., 2012). In this study, a modified RRM was developed and used to identify and prioritize ecological risks in terms of sub-regions, sources, accidental stressors, receptors and endpoints and then applied to Xiamen Bay. The aim of this study was to mitigate policydriven ecologically risks on coastal and marine ecosystems to enhance the sustainable use of coastal resources. Its results were used to inform coastal environmental management. Additionally, Monte Carlo analysis was used to examine the effects of uncertainty on risk predictions. 2. Materials and methods 2.1. Study area and sub-regions The study area presented herein was undertaken in Xiamen Bay, located on the south-east coast of China, which is a semi-enclosed bay with sea body of about 984.36 km2 (Fig. 1). It is an ecologically important area where Xiamen National Nature Reserve for Rare
Marine Species was authorized in 2000, as being characterized by high biodiversity and a large number of marine organisms, notably nationally protected and rare species such as Sousa chinensis (Osbeck) and Branchiostoma belcheri (Gray). The bay also plays a significant role in promoting economic and social development around it, where it lies within Xiamen Special Economic Zone authorized in 1980 and Western Taiwan Straits Economic Zones. However, the continuous increase in population, coupled with significant economic growth, has exerted several large pressures on the Bay, notably pollutant discharge (Xue et al., 2004; Zhang et al., 2007), coastal reclamation (Wang et al., 2010), port development and marine transportation (Xue et al., 2004). Intensive human disturbances have caused serious ecological degradation, such as seawater quality decline, mangrove wetland loss, abundance of B. belcheri (Gray) decline, and others. The study area was divided into seven sub-regions based on their morphological, ecological, environmental, and administrative characteristics: Jiulong River Estuary, Western Xiamen Waters, Eastern Xiamen Waters, Southern Xiamen Waters, Tongan Bay, Dadeng Waters, and Weitou Bay (Fig. 1). 2.2. Conceptual model Regional ERA is used to evaluate the interaction of three environmental components: sources that release stressors, habitats where receptors reside, and impacts on the endpoints or receptors considered (Landis and Wiegers, 1997) (Fig. 2). In this study, minor modifications were done to these components, as detailed in Fig. 2. Firstly, a sourceestressor filter was added to screen accidental stressors by source (i.e., sources might release multiple stressors, accidentally or not). In most of the RRM literature (e.g. Hayes and Landis, 2004; Serveiss et al., 2004), accidental and not-accidental
Please cite this article in press as: Yu, W., et al., Coastal ecological risk assessment in regional scale: Application of the relative risk model to Xiamen Bay, China, Ocean & Coastal Management (2014), http://dx.doi.org/10.1016/j.ocecoaman.2014.04.027
W. Yu et al. / Ocean & Coastal Management xxx (2014) 1e9
stressors have been taken into account (risks account for uncertain events). Here, we argue that risk assessment should focus on accidental pressures, because in conventional ecological impact assessment in China, it’s generally required that not-accidental stressors should be included regardless of their uncertainty. This is a critical distinction between ecological risk assessment and conventional ecological assessment. Secondly, stressors were defined over ecosystems instead of habitats. Because an ecosystem is regarded as a unit of interaction between a community of living organisms and its nonliving environment, in particular area, it’s assumed that stressors might affect both habitats and all living organisms in the ecosystem. Thus, ecosystems are more reasonable foci of concern as receptors than habitats. Here, an ecosystem as a receptor is confined to those of small scale, encompassing a limited and certain space characterized with specific abiotic and biotic factors, greatly different from adjacent spaces. The conceptual components of the modified RRM are depicted in Fig. 2: sources causing accidental stressors, in turn releasing them into ecosystems, and finally influencing the assessment endpoints. Risk evaluations were based on the following assumptions: (1) the greater of source density, the greater density of accidental stressors, and the greater the potential for exposure to ecosystems; (2) the greater the coverage of an ecosystem, the greater potential effect on endpoints in this ecosystem; (3) the greater exposure and effect, the greater the aggregate risk. The ranking scheme of source, ecosystem and each interaction between components was illustrated in Fig. 2, which was adapted from the RRM developed in the previous studies (e.g. Landis and Wiegers, 1997; Elmetri, 2007). Source was ranked on the likelihood of its density and occurrence; ecosystem was ranked on its coverage area. For ranking of interaction, the terms unlikely, somewhat likely and likely indicates none, minimal and intensive interaction between two components. The estimates of relative risks were attained by their combination.
3
2.2.1. Ecological assessment endpoints According to the key criteria from Suter II (1990) and U.S.EPA (1998), the ecological endpoints in Xiamen Bay were identified through synthesizing information from in-depth analysis of ecological characteristics and consultation developed from a questionnaire-based survey. Four ecological endpoints were identified and summarized in terms of different levels and components of ecosystem, specifically: (1) decline in population abundance of protected species, including Sousa chinensis (Osbeck), B. belcheri (Gray), Little Egret, and mangrove species; (2) ecologically important habitats impairment, including spawning ground, island, mangrove habitat, coastal wetland with abundant waterfowls, natural reserves; (3) decline in species diversity; and (4) fishery resources impairment. 2.2.2. Sources Because a large variety of natural and anthropogenic factors have exerted pressures on Xiamen Bay, the risk sources are a complicated mix of natural and anthropogenic, including landbased and marine-based sources. Based on the analysis of natural disturbances and anthropogenic activities in Xiamen Bay (Xue et al., 2004; Lin et al., 2006; Zhang et al., 2009), the major sources were identified and summarized into six categories, i.e. typhoon and storm surge, wastewater discharge, aquaculture, ports and shipping, tourism and recreation, and coastal engineering major works. 2.2.3. Accidental stressors Accidental stressors refer to those events that might occur with great uncertainty of location, time and influence. On the basis of review on historical accidental events occurred in Xiamen Bay, accidental stressors were identified mainly including oil spill (Wang et al., 2009), non-routine discharge, red tide (Zeng et al., 2011), biological diseases/poisoning, biological invasion (Liu et al., 2007), flow regimes changes and coastal erosion/deposition.
Fig. 2. Comparison of risk components of typical regional ERA and ERA in Xiamen Bay study case.
Please cite this article in press as: Yu, W., et al., Coastal ecological risk assessment in regional scale: Application of the relative risk model to Xiamen Bay, China, Ocean & Coastal Management (2014), http://dx.doi.org/10.1016/j.ocecoaman.2014.04.027
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2.2.4. Ecosystems Ecosystems types were classified according to typical marine ecosystems classification in China and referring to coastal habitat classification decision tree developed by NOAA’s Coastal Ocean Program (Thayer et al., 2003). Six kinds of ecosystems in Xiamen Bay were identified through extracting information from remote sensing data and marine chart, i.e., sandy inter-tidal ecosystem, inter-tidal mudflat ecosystem, mangrove ecosystem, salt marsh ecosystem, island ecosystem, and shallow water swamp ecosystem. 2.2.5. Conceptual model in Xiamen Bay Filters and interactions of sources-stressors-ecosystemecological adverse endpoints were defined on the basis of consultation with experts and managers, and analyses of historical information and environmental conditions of Xiamen Bay. Then, a conceptual model was developed to depict interactions among components as shown in Fig. 3. 2.3. Risk analysis 2.3.1. Sources ranking The ranking scheme for all risk components and filters was adapted from previous studies (e.g. Wiegers et al., 1998; Hayes and Landis, 2004; Elmetri, 2007) as shown in Fig. 2. The source ranking scheme was based on a two-point scale ranking with an interval of 0e6, where 0, 2, 4, and 6 represents no, low, moderate, and the greatest amount of source density, respectively. Sources ranking were defined from indicators representing the frequency and intensity of the sources, for example, aquaculture was scored based on the proportion of aquaculture area. Ranking criteria were specific to the study area: these were determined by temporal and spatial statistic analysis of source density and distribution among seven sub-regions of Xiamen Bay. The source data for each subregion integrated current situation and related implemented plans in recent years, indicating situations for the near future,
including marine functional zoning, port and shipping planning and so on. Ranks were assigned using criteria as shown in Table 1. 2.3.2. Ecosystems ranking Similar to source ranking scheme, ecosystem ranks also were assigned to four categories with a two-point scale as depicted in Fig. 2, categorizing the percentage cover of a particular ecosystem. Ranks criteria were defined specific to the study area as shown in Table 1. The data on percentage cover of ecosystems in study area was derived from remote sensing and data from marine charts. 2.3.3. Filters ranking Filters are numerical weighting factor used to determine the relationships between the risk components, indicating probability that an interaction between risk components occurs. As shown in Fig. 2, there are three filters in the model, namely, causing filter ajn, exposure filter bnk, and effect filter cml. Filter ranks were assigned in the interval of 0e1, with a 0.5-point scale as depicted in Fig. 2. If the interaction between two components is clear, or it was verified by historical events, then it is assigned a value of 1. If the interaction is unclear or relatively weak, then it is assigned a value of 0.5. If there is no or weak interaction between two components, then value of 0 is assigned. 2.3.4. Relative risk calculation As stated above and shown in Fig. 2, the magnitude of risks depends on several factors from the interactions of four components, including the density of sources, the strength of causal relationship between source and accidental stressor, the potential of stressor exposure to ecosystem, the density of ecosystem, and the strength of effect relationship of ecosystem change on endpoint. According to methodology of RRM (e.g. Wiegers et al., 1998; Hayes and Landis, 2004), ecological risk was calculated and ranked by multiplying ranks of risk components and filtering over each possible combination. Relative risks for a specific sub-region
Fig. 3. Conceptual model of the interactions among sources, stressors, ecosystems and ecological endpoints.
Please cite this article in press as: Yu, W., et al., Coastal ecological risk assessment in regional scale: Application of the relative risk model to Xiamen Bay, China, Ocean & Coastal Management (2014), http://dx.doi.org/10.1016/j.ocecoaman.2014.04.027
W. Yu et al. / Ocean & Coastal Management xxx (2014) 1e9
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Table 1 Ranking criteria for risk sources and ecosystems in Xiamen Bay. Indicator
Sources Typhoon and storm surge Wastewater discharge Aquaculture Ports and shipping Tourism and recreation Coastal engineering Ecosystems Sandy inter-tidal ecosystem Inter-tidal mudflat ecosystem Mangrove ecosystem Salt marsh ecosystem Island ecosystem Shallow water swamp ecosystem
Criteria ranks
The frequency of serious typhoon and storm surge per day The volume of pollutants into sea per unit area per year, including CODMn, TN, and TP The proportion of aquaculture area in study area The length of port shoreline The cover area of marine entertainment regions (including sea area and island) The cover area of planned reclamation engineering The The The The The The
cover cover cover cover cover cover
area area area area area area
of of of of of of
sandy inter-tidal ecosystem inter-tidal mudflat ecosystem mangrove ecosystem salt marsh ecosystem island ecosystem shallow water swamp ecosystem
were calculated by summing all sourceestressoreecosysteme adverse endpoint combination in a sub-region, as follows:
RSsubregioni ¼
X
Sij ajn bnk Eim cml
where: RS is the relative risk score for sub-region; i is the subregions series; j is the source series; n is the accidental stressors series; m is the ecosystem series; l is the endpoints series; Sij is the source rank in sub-regions; Eim is the ecosystem rank in subregions; ajn is the causing filter for each sourceeaccidental stressor combination in sub-regions; bnk is the exposure filter for each accidental stressoreecosystem combination in sub-regions; cml is the effect filter for ecosystemeendpoint combination in sub-regions. Similar to relative risk for a specific sub-region, relative risks were also calculated for a specific source, stressor, ecosystem, or endpoint by summing possible sourceeaccidental stressoreecosystemeendpoint combination.
2.3.5. Uncertainty analysis The uncertainty in the ERA arises from a number of factors including paucity of data in the study area, poor data quality, and insufficient information on the relationships between components. The risk predictions in the RRM are point estimates derived from data and experts’ subjective judgments: hence they are uncertain. Monte Carlo simulation was applied to the RRM results to generate distributions of probable risk predictions for each risk components (Elmetri, 2007). The key idea of Monte Carlo methods as used in this paper consists of probabilistic simulation replacing integration to provide numerical approximations of statistical quantities such as the mean, variance, and approximate probability distributions about these parameters (Ricci, 2006). Monte Carlo simulation was conducted adopting from pervious studies (Hayes and Landis, 2004; Elmetri, 2007), using Crystal Ball 2000 Ó as a macro in Microsoft Excel 2003 as the following steps: (i) Uncertainty was assigned to each point estimate of ranks (sources and ecosystems) and filters, i.e. low, medium or high uncertainty. (ii) The discrete probability distributions and probabilities with range from 0 to 1 were assigned to ranks and filters with medium and high uncertainty, while those with low uncertainty were left as the original point estimate. For ranks with medium uncertainty, estimate was assigned a 0.8 probability,
0
2
4
6
<0.001/d <5 t/a.km2
0.001e0.02/d 5e100 t/a.km2
0.02e0.04/d 100e400 t/a.km2
>0.04/d >400 t/a.km2
<0.5% <5 km <10 km2
0.5e3% 5e15 km 10e20 km2
3%e5% 15e25 km 20e45 km2
>5% >25 km >45 km2
<1.0 km2
10.e3.0 km2
2.0e5.0 km2
>5.0 km2
<0.5 km2 <5 km2 <0.5 km2 <0.5 km2 <1.0 km2 <30 km2
0.5e3.0 km2 5e20 km2 0.5e1.0 km2 0.5e1.0 km2 1.0e3.0 km2 30e50 km2
3.0e10.0 km2 20e50 km2 1.0e2.0 km2 1.0e2.0 km2 3.0e8.0 km2 50e100 km2
>10.0 km2 >50 km2 >2.0 km2 >2.0 km2 >8.0 km2 >100 km2
with probability of 0.1 for two adjacent ranks each. For ranks with high uncertainty, estimate was assigned a 0.6 probability, and probability of 0.1, 0.3 respectively was assigned for two adjacent ranks respectively if point estimate was extreme value, and otherwise a 0.2 probability was assigned for two adjacent ranks respectively. For filters with medium uncertainty, point estimate was assigned a 0.8 probability, one adjacent rank was assigned probability of 0.2 if point estimate was extreme value, and otherwise two adjacent ranks were assigned a 0.1 probability respectively. The probability assignment scheme for filters with high uncertainty was the same as ranks with high uncertainty. (iii) The number of simulations was defined and obtained for 1 000 and 10 000 simulations. Increasing the trials improves the accuracy of the estimation of the empirical distributions used to represent the uncertainty in the overall calculations (Ricci, 2006).
3. Results 3.1. Relative risk ranks of sources The relative risk scores and rankings of sources were calculated based on RRM for Xiamen Bay and its sub-regions, as shown in the Fig. 4. For the whole region, the total risk of six sources indicated that coastal engineering major works had the maximum relative risk scores as high as 1439, followed by typhoon and storm surge (1 248), wastewater discharge (1 108), and ports and shipping (1 074) (Fig. 4a). For each sub-region, the RRM results showed that ports and shipping and wastewater discharge were the main risk sources for the Jiulong River estuary and Western Xiamen Waters; coastal engineering major works were the greatest source of risk for Dadeng Waters and Tongan Bay; typhoons and storm surges were the greatest sources for Eastern Xiamen Waters and Weitou Bay (Fig. 4b). The results also indicated that the risk score distribution of each source among sub-regions were different: the maximum scores of ports and shipping (517.5), wastewater discharge (400.5), typhoon and storm surge (236), and aquaculture (97.5) were all found in Jiulong River Estuary; the highest score for coastal engineering major works (483) was found in Dadeng Waters; and the highest score of tourism and recreation (174) was found in Tongan Bay (Fig. 4b).
Please cite this article in press as: Yu, W., et al., Coastal ecological risk assessment in regional scale: Application of the relative risk model to Xiamen Bay, China, Ocean & Coastal Management (2014), http://dx.doi.org/10.1016/j.ocecoaman.2014.04.027
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maximum score for flow regimes change (188) was found in Dadeng Bay; the maximum score for red tide (102.5) was found in Tongan Bay (Fig. 5b). 3.3. Relative risk ranks of ecosystems Relative risk of ecosystems as receptors was calculated and ranked as shown in Fig. 6. At the regional scale of Xiamen Bay, the ecosystem with highest risk score was shallow water swamp ecosystem (2 275), followed by inter-tidal mudflat ecosystem (1827), and the third was mangrove ecosystem (855) (Fig. 6a). For each sub-region, inter-tidal mudflat ecosystems were the receptors at the highest risk in: Tongan Bay, Dadeng Waters and Western Xiamen Water. Shallow water swamp was the ecosystem most likely to be affected both in Southern Xiamen Waters and Weitou Bay, and mangrove ecosystem was most likely to be affected in Jiulong River Estuary (Fig. 6b). For the highest score distribution of each ecosystem among sub-regions, the maximum scores for mangrove (522), salt marsh (102) and island ecosystems (172), all were found in Jiulong River Estuary. The greatest risk of inter-tidal mudflat ecosystem (535.5) was found for Tongan Bay, and the highest score of shallow water swamp ecosystem (451.5) was found for Weitou Bay (Fig. 6b). Fig. 4. Relative risk estimates for source in Xiamen Bay (a) and its sub-regions (b).
3.4. Relative risk ranks of endpoints 3.2. Relative risk ranks of accidental stressors The risk scores of accidental stressors were estimated as shown in the Fig. 5. For the whole Xiamen Bay, oil spills had the maximum risk scores (1 447.5) among seven accidental stressors, the second was non-routine discharge (1 305), and the third was coastal erosion/deposition (958.5) (Fig. 5a). For each sub-region, oil spills and non-routine discharges were the main stressors for most of sub-regions, except Dadeng Waters where its main stressors were coastal erosion/deposition and flow regimes changes (Fig. 5b). The maximum scores for oil spill (348), non-routine discharge (348), coastal erosion/deposition (241.5) and biological diseases/ poisoning (86), were all affecting the Jiulong River Estuary. The
On the whole Xiamen Bay, RRM results revealed that species diversity was most likely to be affected, with a score as high as 1723; the second was decline in population abundance of protected species (1 583.5); and the third was ecological important habitats impairment (1 436) (Fig. 7a). For each sub-region, decline in species diversity was the endpoint with highest score in all sub-regions, and also in population abundance of protected species was most likely to be affected in Dadeng Waters and Weitou Bay (Fig. 7b). For the highest score distribution of each endpoint among sub-regions, the maximum scores of decline in species diversity (449), ecological important habitats impairment (452.5), and decline in population abundance of protected species (398) were all found in Jiulong River Estuary, and the greatest risk of fishery resource was (172.5) was found in Weitou Bay (Fig. 7b). 3.5. Relative risk ranks of sub-regions RRM results indicated the relative risk ranks in terms of subregion: Jiulong River Estuary was the sub-region with the highest risk score, the second was Tongan Bay, and the third was Western Xiamen Waters as shown (Fig. 4b). 3.6. Uncertainty
Fig. 5. Relative risk estimates for accidental stressors in Xiamen Bay (a) and its subregions (b).
Monte Carlo simulations produced probability distribution of risk predictions for each sub-region, source, accidental stressor, ecosystem and endpoint, which appear to be symmetrical and approximate normal distributions. Wide and skewed probability distributions indicated higher uncertainty than distributions with narrow range. Simulations of 1 000 trials and 10 000 trials were similar results in that that there was not significant difference in mean, median, range minimum, range maximum, and standard deviation, although the output distributions of 10 000 trials were more smooth than that of 1 000 trails. The output distributions based on 1 000 trails are shown in Fig. 8. For relative risk of source, distribution of aquaculture was narrower than other sources, indicating higher confidence in the risk prediction for aquaculture than for others (Fig. 8a). For relative risk of accidental stressors, oil spill and abnormal discharge showed
Please cite this article in press as: Yu, W., et al., Coastal ecological risk assessment in regional scale: Application of the relative risk model to Xiamen Bay, China, Ocean & Coastal Management (2014), http://dx.doi.org/10.1016/j.ocecoaman.2014.04.027
W. Yu et al. / Ocean & Coastal Management xxx (2014) 1e9
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Fig. 6. Relative risk estimates for ecosystems in Xiamen Bay (a) and its sub-regions (b).
Fig. 7. Relative risk estimates for ecological endpoints in Xiamen Bay (a) and its subregions (b).
wide distributions, indicating less confidence of the risk model predictions, while narrow distribution of biological diseases/ poisoning suggest high confidence of those predictions (Fig. 8b). Distributions for ecosystems also revealed less confidence of risk predictions for shallow water swamp ecosystem and inter-tidal mudflat ecosystem, but high confidence for sandy inter-tidal and salt marsh ecosystems (Fig. 8c). Distributions of four ecological endpoints were relatively wide, suggesting less confidence of risk predications (Fig. 8d). For relative risk for sub-regions, output probability distributions of Jiulong River Estuary, Dadeng Water and Tongan Bay were relatively wide and skewed indicating less confidence in the risk model predictions, while probability distributions of Southern Xiamen Waters and Eastern Xiamen Waters suggest high confidence in the predictions (Fig. 8e).
Fig. 8. Monte Carlo uncertainty distributions for source (a), accidental stressors (b), ecosystems (c), endpoints (d) and sub-regions (e).
Although there was less confidence of some risk predictions, the output probabilities charts mostly agreed with the expected results from RRM, maintaining the relative risk orders among sub-regions, sources, accidental stressors, ecosystems and endpoints, which suggests that the distributions were close to the predicted risks and the uncertainty does not materially alter the rankings of relative risks.
Please cite this article in press as: Yu, W., et al., Coastal ecological risk assessment in regional scale: Application of the relative risk model to Xiamen Bay, China, Ocean & Coastal Management (2014), http://dx.doi.org/10.1016/j.ocecoaman.2014.04.027
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4. Discussions ERA was widely regarded as an important tool to support and improve environmental decision-making. In this study, initial assessment of potential sources, accidental stressors and effects on the Xiamen Bay were conducted using the RRM. The preliminary RRM results provide useful information for managers to plan future efforts on controlling dominant sources, preventing risks from key stressors, and protecting and restoring susceptible ecosystems and endpoints in Xiamen Bay as follows: (i) the highest risk was found in Jiulong River Estuary among the seven sub-regions, indicating that anthropogenic activities (especially, port and shipping, and wastewater discharge) couldn’t be intensified, and effective measures should be taken to protect ecosystem from potentially risk in Jiulong River Estuary as the key region of risk prevention; (ii) more efforts should be conducted to control the risk sources of the coastal engineering major works (especially in Dadeng Waters), typhoon and storm surge, wastewater discharge and ports and shipping (especially in Jiulong River Estuary and Western Xiamen Waters); (iii) more attentions should be paid to prevent the potential risk from oil spill and non-routine discharge (especially in Jiulong River Estuary); (iv) measures should be taken to protect shallow water swamp (especially in Southern Xiamen Waters), inter-tidal mudflat (especially in Tongan Bay) and mangrove ecosystem (especially in Jiulong River Estuary) from potentially risks; (v) efforts should be focused on protecting and restoring protected species and those habitats with rich species diversity (especially in Jiulong River Estuary). Although the RRM applied in the study was a rough analysis that ranks the risk among multiple sub-regions, sources, accidental stressors, ecosystems and ecological endpoints, it informs important information for coastal management. The study here confirmed that RRM is a rapid and effective tool for improving coastal strategic decision making in terms of ecological risk. Monte Carlo simulations are effective to examine the uncertainty of RRM model predictions. The results indicated that the output probabilities distributions were generally consistent with the predicted results from RRM, and the uncertainty considered in this paper does not alter the rankings of relative risk model results. Moreover, running 1 000 and 10 000 Monte Carlo simulations yields similar results, and thus 1 000 simulations was sufficient to capture the uncertainty. Because of great difficulty in quantifying ecological factors, ecological assessments often are confronted with challenges to quantify the ecological impacts with absolutely. Because complicated interrelations among multiple sources, accidental stressors, ecosystems and endpoints occur at the regional scale, it’s very difficult to evaluate ecological risk by quantifying likelihood or probability of adverse ecological effects. The primary goal of ecological assessment or ERA is to provide information for environmental management, and thus the approach is semiquantitative, with the relative value of ecological impact satisfying management requirements to improve its decisions. In this study, RRM is a semi-quantitative approach to determine an overall ecological risk ranking, which might deal with this problem and also meet the management need to help decision-making under uncertainty. RRM also can be widely applied to other studies related with ecological assessment, such as biodiversity assessment, habitat quality evaluation and so on. However, there are some limitations in applications of RRM. Relative risk estimates are determined by combining source and
habitat ranks for the specific region, and thus these risks are relative and cannot be used to compare against other regions. Besides, assigning scores for ranks and filters in the RRM model is a crucial factor for quantifying the right risk ranks to the greatest extent, and therefore uncertainty analysis is necessary because of great uncertainty derived from lack of data and poor quality of data. Acknowledgments This research was supported by the Public Science and Technology Research Funds Projects of Ocean of China (No. 200905005 and No. 201105012). The authors would like to thank anonymous reviewers for their helpful comments and suggestions. References Adam, A., Mohammad-Noor, N., Anton, A., Saleh, E., Saad, S., Muhd Shaleh, S.R., 2011. Temporal and spatial distribution of harmful algal bloom (HAB) species in coastal waters of Kota Kinabalu, Sabah, Malaysia. Harmful Algae 10 (5), 495e 502. Adger, W.N., 2005. Social-ecological resilience to coastal disasters. Science 309, 1036e1039. Barbier, E.B., Koch, E.W., Silliman, B.R., Hacker, S.D., Wolanski, E., Primavera, J., Granek, E.F., Polasky, S., Aswani, S., Cramer, L.A., 2008. Coastal ecosystem-based management with nonlinear ecological functions and values. Science 319, 321e 323. Bartolo, R.E., van Dam, R.A., Bayliss, P., 2012. Regional ecological risk assessment for Australia’s Tropical Rivers: application of the relative risk model. Hum. Ecol. Risk Assess. Int. J. 18 (1), 16e46. Briggs, J.C., 2011. Marine extinctions and conservation. Mar. biol. 158 (3), 485e488. Edgar, G.J., Banks, S.A., Brandt, M., Bustamante, R.H., Chiriboga, A., Earle, S.A., Garske, L.E., Glynn, P.W., Grove, J.S., Henderson, S., 2010. El Niño, grazers and fisheries interact to greatly elevate extinction risk for Galapagos marine species. Glob. Change Biol. 16 (10), 2876e2890. Elmetri, I., 2007. Application of the Relative Risk Model (RRM) to Investigate Multiple Risks to the Miranda Ramsar Site. Environment Waikato, Hamilton East. Hayes, E.H., Landis, W.G., 2004. Regional ecological risk assessment of a near shore marine environment: Cherry Point, WA. Hum. Ecol. Risk Assess. Int. J. 10 (2), 299e325. Hunsaker, C., Graham, R., Suter, G., O’Neill, R., Barnthouse, L., Gardner, R., 1990. Assessing ecological risk on a regional scale. Environ. Manag. 14 (3), 325e332. Landis, W.G., Wiegers, J.A., 1997. Design considerations and a suggested approach for regional and comparative ecological risk assessment. Hum. Ecol. Risk Assess. Int. J. 3 (3), 287e297. Landis, W.G., Wiegers, J.K., 2007. Ten years of the relative risk model and regional scale ecological risk assessment. Hum. Ecol. Risk Assess. Int. J. 13 (1), 25e38. Lin, T., Xue, X., Lu, C., 2006. Safety stress analysis on coastal ecosystem: a study case in Xiamen. Mar. Environ. Sci. 35, 71e74. Liu, J., Chen, Q., Li, Y., 2010. Ecological risk assessment of water environment for Luanhe River Basin based on relative risk model. Ecotoxicology 19 (8), 1400e 1415. Liu, J., Zhu, X., Yang, S., 2007. Present status of marine biological invasions in Xiamen. J. Xiamen Univ. (Natural Science) 46, 181e185. Martínez, M.L., Intralawan, A., Vázquez, G., Pérez-Maqueo, O., Sutton, P., Landgrave, R., 2007. The coasts of our world: ecological, economic and social importance. Ecol. Econ. 63 (2e3), 254e272. Mezi c, I., Loire, S., Fonoberov, V.A., Hogan, P., 2010. A new mixing diagnostic and Gulf oil spill movement. Science 330, 486e489. Nash, C.E., Burbridge, P.R., Volkman, J.K., 2005. Guidelines for Ecological Risk Assessment of Marine Fish Aquaculture. U.S. Department of Commerce, National Oceanic and Atmospheric Administration (NOAA), National Marine Fisheries Service. Obery, A., Landis, W., 2002. A regional multiple stressor risk assessment of the Codorus Creek Watershed applying the relative risk model. Hum. Ecol. Risk Assess. Int. J. 8 (2), 405e428. Occhipinti-Ambrogi, A., Galil, B., 2010. Marine alien species as an aspect of global change. Adv. Oceanogr. Limnol. 1 (1), 199e218. Peterson, C.H., Anderson, S.S., Cherr, G.N., Ambrose, R.F., Anghera, S., Bay, S., Blum, M., Condon, R., Dean, T.A., Graham, M., 2012. A tale of two spills: novel science and policy implications of an emerging new oil spill model. Bioscience 62 (5), 461e469. Polidoro, B.A., Carpenter, K.E., Collins, L., Duke, N.C., Ellison, A.M., Ellison, J.C., Farnsworth, E.J., Fernando, E.S., Kathiresan, K., Koedam, N.E., 2010. The loss of species: mangrove extinction risk and geographic areas of global concern. PLoS One 5, e10095. Ricci, P.F., 2006. Environmental and Health Risk Assessment and Management: Principles and Practices. Springer. Seebens, H., Gastner, M., Blasius, B., 2013. The risk of marine bioinvasion caused by global shipping. Ecol. Lett. 16 (6), 782e790.
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W. Yu et al. / Ocean & Coastal Management xxx (2014) 1e9 Serveiss, V.B., Bowen, J.I., Dow, D., Valiela, I., 2004. Using ecological risk assessment to identify the major anthropogenic stressor in the Waquoit Bay Watershed, Cape Cod, Massachusetts. Environ. Manag. 33 (5), 730e740. Suter II, G.W., 1990. Endpoints for regional ecological risk assessments. Environ. Manag. 14 (1), 9e23. Thayer, G.W., McTigue, T.A., Bellmer, R.J., Burrows, F.M., Merkey, D.H., Nickens, A.D., Lozano, S.J., Gayaldo, P.F., Polmateer, P.J., Pinit, P.T., 2003. Science-based Restoration Monitoring of Coastal Habitats. In: Volume One: a Framework for Monitoring Plans under the Estuaries and Clean Waters Act of 2000 (Public Law 160-457). NOAA Coastal Ocean Program Decision Analysis Series No. 23, vol. 1. NOAA National Centers for Coastal Ocean Science, Sliver Spring, MD. U.S.EPA, 1992. Framework for Ecological Risk Assessment. U.S. Environmental Protection Agency, Washington, DC. U.S.EPA, 1998. Guidelines for Ecological Risk Assessment. Risk Assessment Forum, U.S.EPA, Washington, DC. U.S.EPA, 2008. Application of Watershed Ecological Risk Assessment Methods to Watershed Management. National Center for Environmental Assessment, Washington, DC. National Technical Information Services, Springfield, VA. Walker, R., Landis, W., Brown, P., 2001. Developing a regional ecological risk assessment: a case study of a Tasmanian agricultural catchment. Hum. Ecol. Risk Assess. 7 (2), 417e439.
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Walters, S., Lowerre-Barbieri, S., Bickford, J., Tustison, J., Landsberg, J.H., 2013. Effects of Karenia brevis red tide on the spatial distribution of spawning aggregations of sand seatrout Cynoscion arenarius in Tampa Bay, Florida. Mar. Ecol. Prog. Ser. 479, 191e202. Wang, J., Pan, W., Zhang, G., Ma, T., 2009. Analysis and risk assessment of the oil spill in Xiamen Bay. J. Oceanogr. Taiwan Strait 28 (4), 534e539. Wang, X., Chen, W., Zhang, L., Jin, D., Lu, C., 2010. Estimating the ecosystem service losses from proposed land reclamation projects: a case study in Xiamen. Ecol. Econ. 69 (12), 2549e2556. Wiegers, J.K., Feder, H.M., Mortensen, L.S., Shaw, D.G., Wilson, V.J., Landis, W.G., 1998. A regional multiple-stressor rank-based ecological risk assessment for the Fjord of Port Valdez, Alaska. Hum. Ecol. Risk Assess. Int. J. 4 (5), 1125e1173. Xue, X., Hong, H., Charles, A.T., 2004. Cumulative environmental impacts and integrated coastal management: the case of Xiamen, China. J. Environ. Manag. 71 (3), 271e283. Zeng, Y., Chen, J., Li, X., 2011. Study on the relationship between red tides and trophical cyclones at the Xiamen Bay. Mar. Forecasts 28 (4), 23e29. Zhang, L., Jiang, Y., Chen, W., 2009. Study on Mathematical Modeling and Environment in Xiamen Bay, Fujian. Ocean Press, Beijing. Zhang, L., Ye, X., Feng, H., Jing, Y., Ouyang, T., Yu, X., Liang, R., Gao, C., Chen, W., 2007. Heavy metal contamination in western Xiamen Bay sediments and its vicinity, China. Mar. Pollut. Bull. 54 (7), 974e982.
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Contents lists available at ScienceDirect
Ocean & Coastal Management journal homepage: www.elsevier.com/locate/ocecoaman
Coastal ecological risk assessment in regional scale: Application of the relative risk model to Xiamen Bay, China Weiwei Yu a, b, Luoping Zhang a, *, Paolo F. Ricci c, Bin Chen b, Hao Huang b a
College of the Environment & Ecology, Xiamen University, Xiamen 361005, PR China The Third Institute of Oceanography, State Oceanic Administration, 178 Daxue Road, Xiamen 361005, PR China c Environmental Health Sciences, University of Massachusetts, Amherst, MA 10030, USA b
a r t i c l e i n f o
a b s t r a c t
Article history: Available online xxx
Ecological risk assessment has been regarded as an important tool for environmental management. Ecological risk assessment was conducted using Relative Risk Model in Xiamen Bay, China, the results of which were applied to inform coastal environmental management. The study area was divided into seven sub-regions, and the potentially ecological risks for both the whole bay and sub-regions were predicted and ranked by introducing a sourceestressorereceptoreendpoint filter. The results showed that: (i) Jiulong River Estuary was the sub-region with highest risk; the second highest being Tongan Bay; (ii) coastal engineering major works were the biggest sources of routine risk, followed by typhoons and storm surges; (iii) oil spills were the biggest accidents contributing to risk, followed by non-routine discharges; (iv) shallow water swamp ecosystem were most likely to be affected, followed by intertidal mudflat ecosystem; (v) species diversity was the endpoint most likely to be affected, with population abundance of protected species being second. Monte Carlo simulations were conducted to examine the effects of uncertainty on those risk prediction. Its results suggested that the probability distributions were consistent with other examples in the literature and as expected that the uncertainty that affects results does not alter the rankings from the relative risk analysis. Ó 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).
1. Introduction
magnitude and frequency, but their numbers have been increasing exponentially over the last several decades. Examples are algal blooms (Adam et al., 2011; Walters et al., 2013), oil spills (Mezi c et al., 2010; Peterson et al., 2012), species extinction (Edgar et al., 2010; Polidoro et al., 2010; Briggs, 2011), and alien species invasion (Occhipinti-Ambrogi and Galil, 2010; Seebens et al., 2013). Although natural factors might be influential elements for ecological risks, anthropogenic activities associated with increasing pressures on natural environment might be even more significant doing much to aggravate already existing natural pressure and stresses. Ecological risk assessment (ERA) is an important tool for environmental management, which has been widely applied to environmental decision making (Nash et al., 2005). ERA is a process for organizing data, information, assumptions, and uncertainties to evaluate the probability (e.g., likelihood) of adverse ecological effects that may occur as a result of exposure to one or more stressors from human activities (U.S.EPA, 1992, 1998, 2008). The theory and methods of ERA were largely developed from human health risk assessment in the USA, and new chemical risk assessment in Europe (REACH Directive); therefore, most of the early studies focused on ecological risks caused by chemical
Coastal ecosystems, located in the transitional zone between land and sea, are among the most valuable in the world, not only because they provide important habitats for abundant organisms, but also provide lots of ecological goods and services for humans, such as food production, storm buffering, pollution control, and so on (Barbier et al., 2008). However, these ecosystems, where nearly 41% of global population live within the coast (Martínez et al., 2007), are also experiencing unprecedented pressures due to increasing concentrations of human population and anthropogenic activities. Consequently, there has been increasing degradation, and they have become one of the most impacted and altered ecosystems (Adger, 2005). Undeniably, coastal deterioration contributes to increasing ecological risks and other hazards in several ways, and ecological risks in turn could make the already degraded coastal ecosystems much more vulnerable. Coastal ecological risks and hazards have been global issues. Ecological damages from accidental events are uncertainty as to * Corresponding author. Tel.: þ86 592 2185855; fax: þ86 592 2186913. E-mail address: [email protected] (L. Zhang).
http://dx.doi.org/10.1016/j.ocecoaman.2014.04.027 0964-5691/Ó 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).
Please cite this article in press as: Yu, W., et al., Coastal ecological risk assessment in regional scale: Application of the relative risk model to Xiamen Bay, China, Ocean & Coastal Management (2014), http://dx.doi.org/10.1016/j.ocecoaman.2014.04.027
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Fig. 1. Boundary of study area and its sub-regions.
pollutants. By contrast, regional ERA was developed in the early of 1990s (Hunsaker et al., 1990; Suter II, 1990). Since then, regional ERA has developed rapidly to deal with multiple sources of several stressors causing diverse endpoints. Among the approaches applied to regional ERA, the Relative Risk Model (RRM) is one of the most widely used methods, since it was first developed in 1997 (Landis and Wiegers, 1997, 2007). RRM has been applied successfully in numerous studies throughout the world, such as in marine environment in the Fjord of Port Valdez, Alaska, USA (Wiegers et al., 1998), Mountain River Catchment in Tasmanian, Australia (Walker et al., 2001), the Codorus Creek Watershed, Pennsylvania in USA (Obery and Landis, 2002), a near shore marine environment in Northwestern Washington, USA (Hayes and Landis, 2004), Luanhe River Basin in China (Liu et al., 2010), and Northern Tropical River in Australia (Bartolo et al., 2012). In this study, a modified RRM was developed and used to identify and prioritize ecological risks in terms of sub-regions, sources, accidental stressors, receptors and endpoints and then applied to Xiamen Bay. The aim of this study was to mitigate policydriven ecologically risks on coastal and marine ecosystems to enhance the sustainable use of coastal resources. Its results were used to inform coastal environmental management. Additionally, Monte Carlo analysis was used to examine the effects of uncertainty on risk predictions. 2. Materials and methods 2.1. Study area and sub-regions The study area presented herein was undertaken in Xiamen Bay, located on the south-east coast of China, which is a semi-enclosed bay with sea body of about 984.36 km2 (Fig. 1). It is an ecologically important area where Xiamen National Nature Reserve for Rare
Marine Species was authorized in 2000, as being characterized by high biodiversity and a large number of marine organisms, notably nationally protected and rare species such as Sousa chinensis (Osbeck) and Branchiostoma belcheri (Gray). The bay also plays a significant role in promoting economic and social development around it, where it lies within Xiamen Special Economic Zone authorized in 1980 and Western Taiwan Straits Economic Zones. However, the continuous increase in population, coupled with significant economic growth, has exerted several large pressures on the Bay, notably pollutant discharge (Xue et al., 2004; Zhang et al., 2007), coastal reclamation (Wang et al., 2010), port development and marine transportation (Xue et al., 2004). Intensive human disturbances have caused serious ecological degradation, such as seawater quality decline, mangrove wetland loss, abundance of B. belcheri (Gray) decline, and others. The study area was divided into seven sub-regions based on their morphological, ecological, environmental, and administrative characteristics: Jiulong River Estuary, Western Xiamen Waters, Eastern Xiamen Waters, Southern Xiamen Waters, Tongan Bay, Dadeng Waters, and Weitou Bay (Fig. 1). 2.2. Conceptual model Regional ERA is used to evaluate the interaction of three environmental components: sources that release stressors, habitats where receptors reside, and impacts on the endpoints or receptors considered (Landis and Wiegers, 1997) (Fig. 2). In this study, minor modifications were done to these components, as detailed in Fig. 2. Firstly, a sourceestressor filter was added to screen accidental stressors by source (i.e., sources might release multiple stressors, accidentally or not). In most of the RRM literature (e.g. Hayes and Landis, 2004; Serveiss et al., 2004), accidental and not-accidental
Please cite this article in press as: Yu, W., et al., Coastal ecological risk assessment in regional scale: Application of the relative risk model to Xiamen Bay, China, Ocean & Coastal Management (2014), http://dx.doi.org/10.1016/j.ocecoaman.2014.04.027
W. Yu et al. / Ocean & Coastal Management xxx (2014) 1e9
stressors have been taken into account (risks account for uncertain events). Here, we argue that risk assessment should focus on accidental pressures, because in conventional ecological impact assessment in China, it’s generally required that not-accidental stressors should be included regardless of their uncertainty. This is a critical distinction between ecological risk assessment and conventional ecological assessment. Secondly, stressors were defined over ecosystems instead of habitats. Because an ecosystem is regarded as a unit of interaction between a community of living organisms and its nonliving environment, in particular area, it’s assumed that stressors might affect both habitats and all living organisms in the ecosystem. Thus, ecosystems are more reasonable foci of concern as receptors than habitats. Here, an ecosystem as a receptor is confined to those of small scale, encompassing a limited and certain space characterized with specific abiotic and biotic factors, greatly different from adjacent spaces. The conceptual components of the modified RRM are depicted in Fig. 2: sources causing accidental stressors, in turn releasing them into ecosystems, and finally influencing the assessment endpoints. Risk evaluations were based on the following assumptions: (1) the greater of source density, the greater density of accidental stressors, and the greater the potential for exposure to ecosystems; (2) the greater the coverage of an ecosystem, the greater potential effect on endpoints in this ecosystem; (3) the greater exposure and effect, the greater the aggregate risk. The ranking scheme of source, ecosystem and each interaction between components was illustrated in Fig. 2, which was adapted from the RRM developed in the previous studies (e.g. Landis and Wiegers, 1997; Elmetri, 2007). Source was ranked on the likelihood of its density and occurrence; ecosystem was ranked on its coverage area. For ranking of interaction, the terms unlikely, somewhat likely and likely indicates none, minimal and intensive interaction between two components. The estimates of relative risks were attained by their combination.
3
2.2.1. Ecological assessment endpoints According to the key criteria from Suter II (1990) and U.S.EPA (1998), the ecological endpoints in Xiamen Bay were identified through synthesizing information from in-depth analysis of ecological characteristics and consultation developed from a questionnaire-based survey. Four ecological endpoints were identified and summarized in terms of different levels and components of ecosystem, specifically: (1) decline in population abundance of protected species, including Sousa chinensis (Osbeck), B. belcheri (Gray), Little Egret, and mangrove species; (2) ecologically important habitats impairment, including spawning ground, island, mangrove habitat, coastal wetland with abundant waterfowls, natural reserves; (3) decline in species diversity; and (4) fishery resources impairment. 2.2.2. Sources Because a large variety of natural and anthropogenic factors have exerted pressures on Xiamen Bay, the risk sources are a complicated mix of natural and anthropogenic, including landbased and marine-based sources. Based on the analysis of natural disturbances and anthropogenic activities in Xiamen Bay (Xue et al., 2004; Lin et al., 2006; Zhang et al., 2009), the major sources were identified and summarized into six categories, i.e. typhoon and storm surge, wastewater discharge, aquaculture, ports and shipping, tourism and recreation, and coastal engineering major works. 2.2.3. Accidental stressors Accidental stressors refer to those events that might occur with great uncertainty of location, time and influence. On the basis of review on historical accidental events occurred in Xiamen Bay, accidental stressors were identified mainly including oil spill (Wang et al., 2009), non-routine discharge, red tide (Zeng et al., 2011), biological diseases/poisoning, biological invasion (Liu et al., 2007), flow regimes changes and coastal erosion/deposition.
Fig. 2. Comparison of risk components of typical regional ERA and ERA in Xiamen Bay study case.
Please cite this article in press as: Yu, W., et al., Coastal ecological risk assessment in regional scale: Application of the relative risk model to Xiamen Bay, China, Ocean & Coastal Management (2014), http://dx.doi.org/10.1016/j.ocecoaman.2014.04.027
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2.2.4. Ecosystems Ecosystems types were classified according to typical marine ecosystems classification in China and referring to coastal habitat classification decision tree developed by NOAA’s Coastal Ocean Program (Thayer et al., 2003). Six kinds of ecosystems in Xiamen Bay were identified through extracting information from remote sensing data and marine chart, i.e., sandy inter-tidal ecosystem, inter-tidal mudflat ecosystem, mangrove ecosystem, salt marsh ecosystem, island ecosystem, and shallow water swamp ecosystem. 2.2.5. Conceptual model in Xiamen Bay Filters and interactions of sources-stressors-ecosystemecological adverse endpoints were defined on the basis of consultation with experts and managers, and analyses of historical information and environmental conditions of Xiamen Bay. Then, a conceptual model was developed to depict interactions among components as shown in Fig. 3. 2.3. Risk analysis 2.3.1. Sources ranking The ranking scheme for all risk components and filters was adapted from previous studies (e.g. Wiegers et al., 1998; Hayes and Landis, 2004; Elmetri, 2007) as shown in Fig. 2. The source ranking scheme was based on a two-point scale ranking with an interval of 0e6, where 0, 2, 4, and 6 represents no, low, moderate, and the greatest amount of source density, respectively. Sources ranking were defined from indicators representing the frequency and intensity of the sources, for example, aquaculture was scored based on the proportion of aquaculture area. Ranking criteria were specific to the study area: these were determined by temporal and spatial statistic analysis of source density and distribution among seven sub-regions of Xiamen Bay. The source data for each subregion integrated current situation and related implemented plans in recent years, indicating situations for the near future,
including marine functional zoning, port and shipping planning and so on. Ranks were assigned using criteria as shown in Table 1. 2.3.2. Ecosystems ranking Similar to source ranking scheme, ecosystem ranks also were assigned to four categories with a two-point scale as depicted in Fig. 2, categorizing the percentage cover of a particular ecosystem. Ranks criteria were defined specific to the study area as shown in Table 1. The data on percentage cover of ecosystems in study area was derived from remote sensing and data from marine charts. 2.3.3. Filters ranking Filters are numerical weighting factor used to determine the relationships between the risk components, indicating probability that an interaction between risk components occurs. As shown in Fig. 2, there are three filters in the model, namely, causing filter ajn, exposure filter bnk, and effect filter cml. Filter ranks were assigned in the interval of 0e1, with a 0.5-point scale as depicted in Fig. 2. If the interaction between two components is clear, or it was verified by historical events, then it is assigned a value of 1. If the interaction is unclear or relatively weak, then it is assigned a value of 0.5. If there is no or weak interaction between two components, then value of 0 is assigned. 2.3.4. Relative risk calculation As stated above and shown in Fig. 2, the magnitude of risks depends on several factors from the interactions of four components, including the density of sources, the strength of causal relationship between source and accidental stressor, the potential of stressor exposure to ecosystem, the density of ecosystem, and the strength of effect relationship of ecosystem change on endpoint. According to methodology of RRM (e.g. Wiegers et al., 1998; Hayes and Landis, 2004), ecological risk was calculated and ranked by multiplying ranks of risk components and filtering over each possible combination. Relative risks for a specific sub-region
Fig. 3. Conceptual model of the interactions among sources, stressors, ecosystems and ecological endpoints.
Please cite this article in press as: Yu, W., et al., Coastal ecological risk assessment in regional scale: Application of the relative risk model to Xiamen Bay, China, Ocean & Coastal Management (2014), http://dx.doi.org/10.1016/j.ocecoaman.2014.04.027
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Table 1 Ranking criteria for risk sources and ecosystems in Xiamen Bay. Indicator
Sources Typhoon and storm surge Wastewater discharge Aquaculture Ports and shipping Tourism and recreation Coastal engineering Ecosystems Sandy inter-tidal ecosystem Inter-tidal mudflat ecosystem Mangrove ecosystem Salt marsh ecosystem Island ecosystem Shallow water swamp ecosystem
Criteria ranks
The frequency of serious typhoon and storm surge per day The volume of pollutants into sea per unit area per year, including CODMn, TN, and TP The proportion of aquaculture area in study area The length of port shoreline The cover area of marine entertainment regions (including sea area and island) The cover area of planned reclamation engineering The The The The The The
cover cover cover cover cover cover
area area area area area area
of of of of of of
sandy inter-tidal ecosystem inter-tidal mudflat ecosystem mangrove ecosystem salt marsh ecosystem island ecosystem shallow water swamp ecosystem
were calculated by summing all sourceestressoreecosysteme adverse endpoint combination in a sub-region, as follows:
RSsubregioni ¼
X
Sij ajn bnk Eim cml
where: RS is the relative risk score for sub-region; i is the subregions series; j is the source series; n is the accidental stressors series; m is the ecosystem series; l is the endpoints series; Sij is the source rank in sub-regions; Eim is the ecosystem rank in subregions; ajn is the causing filter for each sourceeaccidental stressor combination in sub-regions; bnk is the exposure filter for each accidental stressoreecosystem combination in sub-regions; cml is the effect filter for ecosystemeendpoint combination in sub-regions. Similar to relative risk for a specific sub-region, relative risks were also calculated for a specific source, stressor, ecosystem, or endpoint by summing possible sourceeaccidental stressoreecosystemeendpoint combination.
2.3.5. Uncertainty analysis The uncertainty in the ERA arises from a number of factors including paucity of data in the study area, poor data quality, and insufficient information on the relationships between components. The risk predictions in the RRM are point estimates derived from data and experts’ subjective judgments: hence they are uncertain. Monte Carlo simulation was applied to the RRM results to generate distributions of probable risk predictions for each risk components (Elmetri, 2007). The key idea of Monte Carlo methods as used in this paper consists of probabilistic simulation replacing integration to provide numerical approximations of statistical quantities such as the mean, variance, and approximate probability distributions about these parameters (Ricci, 2006). Monte Carlo simulation was conducted adopting from pervious studies (Hayes and Landis, 2004; Elmetri, 2007), using Crystal Ball 2000 Ó as a macro in Microsoft Excel 2003 as the following steps: (i) Uncertainty was assigned to each point estimate of ranks (sources and ecosystems) and filters, i.e. low, medium or high uncertainty. (ii) The discrete probability distributions and probabilities with range from 0 to 1 were assigned to ranks and filters with medium and high uncertainty, while those with low uncertainty were left as the original point estimate. For ranks with medium uncertainty, estimate was assigned a 0.8 probability,
0
2
4
6
<0.001/d <5 t/a.km2
0.001e0.02/d 5e100 t/a.km2
0.02e0.04/d 100e400 t/a.km2
>0.04/d >400 t/a.km2
<0.5% <5 km <10 km2
0.5e3% 5e15 km 10e20 km2
3%e5% 15e25 km 20e45 km2
>5% >25 km >45 km2
<1.0 km2
10.e3.0 km2
2.0e5.0 km2
>5.0 km2
<0.5 km2 <5 km2 <0.5 km2 <0.5 km2 <1.0 km2 <30 km2
0.5e3.0 km2 5e20 km2 0.5e1.0 km2 0.5e1.0 km2 1.0e3.0 km2 30e50 km2
3.0e10.0 km2 20e50 km2 1.0e2.0 km2 1.0e2.0 km2 3.0e8.0 km2 50e100 km2
>10.0 km2 >50 km2 >2.0 km2 >2.0 km2 >8.0 km2 >100 km2
with probability of 0.1 for two adjacent ranks each. For ranks with high uncertainty, estimate was assigned a 0.6 probability, and probability of 0.1, 0.3 respectively was assigned for two adjacent ranks respectively if point estimate was extreme value, and otherwise a 0.2 probability was assigned for two adjacent ranks respectively. For filters with medium uncertainty, point estimate was assigned a 0.8 probability, one adjacent rank was assigned probability of 0.2 if point estimate was extreme value, and otherwise two adjacent ranks were assigned a 0.1 probability respectively. The probability assignment scheme for filters with high uncertainty was the same as ranks with high uncertainty. (iii) The number of simulations was defined and obtained for 1 000 and 10 000 simulations. Increasing the trials improves the accuracy of the estimation of the empirical distributions used to represent the uncertainty in the overall calculations (Ricci, 2006).
3. Results 3.1. Relative risk ranks of sources The relative risk scores and rankings of sources were calculated based on RRM for Xiamen Bay and its sub-regions, as shown in the Fig. 4. For the whole region, the total risk of six sources indicated that coastal engineering major works had the maximum relative risk scores as high as 1439, followed by typhoon and storm surge (1 248), wastewater discharge (1 108), and ports and shipping (1 074) (Fig. 4a). For each sub-region, the RRM results showed that ports and shipping and wastewater discharge were the main risk sources for the Jiulong River estuary and Western Xiamen Waters; coastal engineering major works were the greatest source of risk for Dadeng Waters and Tongan Bay; typhoons and storm surges were the greatest sources for Eastern Xiamen Waters and Weitou Bay (Fig. 4b). The results also indicated that the risk score distribution of each source among sub-regions were different: the maximum scores of ports and shipping (517.5), wastewater discharge (400.5), typhoon and storm surge (236), and aquaculture (97.5) were all found in Jiulong River Estuary; the highest score for coastal engineering major works (483) was found in Dadeng Waters; and the highest score of tourism and recreation (174) was found in Tongan Bay (Fig. 4b).
Please cite this article in press as: Yu, W., et al., Coastal ecological risk assessment in regional scale: Application of the relative risk model to Xiamen Bay, China, Ocean & Coastal Management (2014), http://dx.doi.org/10.1016/j.ocecoaman.2014.04.027
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maximum score for flow regimes change (188) was found in Dadeng Bay; the maximum score for red tide (102.5) was found in Tongan Bay (Fig. 5b). 3.3. Relative risk ranks of ecosystems Relative risk of ecosystems as receptors was calculated and ranked as shown in Fig. 6. At the regional scale of Xiamen Bay, the ecosystem with highest risk score was shallow water swamp ecosystem (2 275), followed by inter-tidal mudflat ecosystem (1827), and the third was mangrove ecosystem (855) (Fig. 6a). For each sub-region, inter-tidal mudflat ecosystems were the receptors at the highest risk in: Tongan Bay, Dadeng Waters and Western Xiamen Water. Shallow water swamp was the ecosystem most likely to be affected both in Southern Xiamen Waters and Weitou Bay, and mangrove ecosystem was most likely to be affected in Jiulong River Estuary (Fig. 6b). For the highest score distribution of each ecosystem among sub-regions, the maximum scores for mangrove (522), salt marsh (102) and island ecosystems (172), all were found in Jiulong River Estuary. The greatest risk of inter-tidal mudflat ecosystem (535.5) was found for Tongan Bay, and the highest score of shallow water swamp ecosystem (451.5) was found for Weitou Bay (Fig. 6b). Fig. 4. Relative risk estimates for source in Xiamen Bay (a) and its sub-regions (b).
3.4. Relative risk ranks of endpoints 3.2. Relative risk ranks of accidental stressors The risk scores of accidental stressors were estimated as shown in the Fig. 5. For the whole Xiamen Bay, oil spills had the maximum risk scores (1 447.5) among seven accidental stressors, the second was non-routine discharge (1 305), and the third was coastal erosion/deposition (958.5) (Fig. 5a). For each sub-region, oil spills and non-routine discharges were the main stressors for most of sub-regions, except Dadeng Waters where its main stressors were coastal erosion/deposition and flow regimes changes (Fig. 5b). The maximum scores for oil spill (348), non-routine discharge (348), coastal erosion/deposition (241.5) and biological diseases/ poisoning (86), were all affecting the Jiulong River Estuary. The
On the whole Xiamen Bay, RRM results revealed that species diversity was most likely to be affected, with a score as high as 1723; the second was decline in population abundance of protected species (1 583.5); and the third was ecological important habitats impairment (1 436) (Fig. 7a). For each sub-region, decline in species diversity was the endpoint with highest score in all sub-regions, and also in population abundance of protected species was most likely to be affected in Dadeng Waters and Weitou Bay (Fig. 7b). For the highest score distribution of each endpoint among sub-regions, the maximum scores of decline in species diversity (449), ecological important habitats impairment (452.5), and decline in population abundance of protected species (398) were all found in Jiulong River Estuary, and the greatest risk of fishery resource was (172.5) was found in Weitou Bay (Fig. 7b). 3.5. Relative risk ranks of sub-regions RRM results indicated the relative risk ranks in terms of subregion: Jiulong River Estuary was the sub-region with the highest risk score, the second was Tongan Bay, and the third was Western Xiamen Waters as shown (Fig. 4b). 3.6. Uncertainty
Fig. 5. Relative risk estimates for accidental stressors in Xiamen Bay (a) and its subregions (b).
Monte Carlo simulations produced probability distribution of risk predictions for each sub-region, source, accidental stressor, ecosystem and endpoint, which appear to be symmetrical and approximate normal distributions. Wide and skewed probability distributions indicated higher uncertainty than distributions with narrow range. Simulations of 1 000 trials and 10 000 trials were similar results in that that there was not significant difference in mean, median, range minimum, range maximum, and standard deviation, although the output distributions of 10 000 trials were more smooth than that of 1 000 trails. The output distributions based on 1 000 trails are shown in Fig. 8. For relative risk of source, distribution of aquaculture was narrower than other sources, indicating higher confidence in the risk prediction for aquaculture than for others (Fig. 8a). For relative risk of accidental stressors, oil spill and abnormal discharge showed
Please cite this article in press as: Yu, W., et al., Coastal ecological risk assessment in regional scale: Application of the relative risk model to Xiamen Bay, China, Ocean & Coastal Management (2014), http://dx.doi.org/10.1016/j.ocecoaman.2014.04.027
W. Yu et al. / Ocean & Coastal Management xxx (2014) 1e9
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Fig. 6. Relative risk estimates for ecosystems in Xiamen Bay (a) and its sub-regions (b).
Fig. 7. Relative risk estimates for ecological endpoints in Xiamen Bay (a) and its subregions (b).
wide distributions, indicating less confidence of the risk model predictions, while narrow distribution of biological diseases/ poisoning suggest high confidence of those predictions (Fig. 8b). Distributions for ecosystems also revealed less confidence of risk predictions for shallow water swamp ecosystem and inter-tidal mudflat ecosystem, but high confidence for sandy inter-tidal and salt marsh ecosystems (Fig. 8c). Distributions of four ecological endpoints were relatively wide, suggesting less confidence of risk predications (Fig. 8d). For relative risk for sub-regions, output probability distributions of Jiulong River Estuary, Dadeng Water and Tongan Bay were relatively wide and skewed indicating less confidence in the risk model predictions, while probability distributions of Southern Xiamen Waters and Eastern Xiamen Waters suggest high confidence in the predictions (Fig. 8e).
Fig. 8. Monte Carlo uncertainty distributions for source (a), accidental stressors (b), ecosystems (c), endpoints (d) and sub-regions (e).
Although there was less confidence of some risk predictions, the output probabilities charts mostly agreed with the expected results from RRM, maintaining the relative risk orders among sub-regions, sources, accidental stressors, ecosystems and endpoints, which suggests that the distributions were close to the predicted risks and the uncertainty does not materially alter the rankings of relative risks.
Please cite this article in press as: Yu, W., et al., Coastal ecological risk assessment in regional scale: Application of the relative risk model to Xiamen Bay, China, Ocean & Coastal Management (2014), http://dx.doi.org/10.1016/j.ocecoaman.2014.04.027
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4. Discussions ERA was widely regarded as an important tool to support and improve environmental decision-making. In this study, initial assessment of potential sources, accidental stressors and effects on the Xiamen Bay were conducted using the RRM. The preliminary RRM results provide useful information for managers to plan future efforts on controlling dominant sources, preventing risks from key stressors, and protecting and restoring susceptible ecosystems and endpoints in Xiamen Bay as follows: (i) the highest risk was found in Jiulong River Estuary among the seven sub-regions, indicating that anthropogenic activities (especially, port and shipping, and wastewater discharge) couldn’t be intensified, and effective measures should be taken to protect ecosystem from potentially risk in Jiulong River Estuary as the key region of risk prevention; (ii) more efforts should be conducted to control the risk sources of the coastal engineering major works (especially in Dadeng Waters), typhoon and storm surge, wastewater discharge and ports and shipping (especially in Jiulong River Estuary and Western Xiamen Waters); (iii) more attentions should be paid to prevent the potential risk from oil spill and non-routine discharge (especially in Jiulong River Estuary); (iv) measures should be taken to protect shallow water swamp (especially in Southern Xiamen Waters), inter-tidal mudflat (especially in Tongan Bay) and mangrove ecosystem (especially in Jiulong River Estuary) from potentially risks; (v) efforts should be focused on protecting and restoring protected species and those habitats with rich species diversity (especially in Jiulong River Estuary). Although the RRM applied in the study was a rough analysis that ranks the risk among multiple sub-regions, sources, accidental stressors, ecosystems and ecological endpoints, it informs important information for coastal management. The study here confirmed that RRM is a rapid and effective tool for improving coastal strategic decision making in terms of ecological risk. Monte Carlo simulations are effective to examine the uncertainty of RRM model predictions. The results indicated that the output probabilities distributions were generally consistent with the predicted results from RRM, and the uncertainty considered in this paper does not alter the rankings of relative risk model results. Moreover, running 1 000 and 10 000 Monte Carlo simulations yields similar results, and thus 1 000 simulations was sufficient to capture the uncertainty. Because of great difficulty in quantifying ecological factors, ecological assessments often are confronted with challenges to quantify the ecological impacts with absolutely. Because complicated interrelations among multiple sources, accidental stressors, ecosystems and endpoints occur at the regional scale, it’s very difficult to evaluate ecological risk by quantifying likelihood or probability of adverse ecological effects. The primary goal of ecological assessment or ERA is to provide information for environmental management, and thus the approach is semiquantitative, with the relative value of ecological impact satisfying management requirements to improve its decisions. In this study, RRM is a semi-quantitative approach to determine an overall ecological risk ranking, which might deal with this problem and also meet the management need to help decision-making under uncertainty. RRM also can be widely applied to other studies related with ecological assessment, such as biodiversity assessment, habitat quality evaluation and so on. However, there are some limitations in applications of RRM. Relative risk estimates are determined by combining source and
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Please cite this article in press as: Yu, W., et al., Coastal ecological risk assessment in regional scale: Application of the relative risk model to Xiamen Bay, China, Ocean & Coastal Management (2014), http://dx.doi.org/10.1016/j.ocecoaman.2014.04.027