To assess the environmental impacts of human interventions, species indicators can be useful to evaluate the effects of different designs. The so-called Species Sensitivity Distribution (SSD) uses causal relationships between exposure level and effect level of individual species. By using exposure-effect data of multiple types of animals and plants, the sensitivity distribution of these species can be used to assess the risk of human interference more quantitatively. Although the SSD was originally developed to assess the ecological risk of toxicants, it also appeared to be applicable for non-toxic stressors. For instance, SSDs have been developed specifically for assessing the risk of suspended clay-particles and sedimentation for the environmental impact analysis of offshore oil and gas drilling activities. One advantage of having SSDs for non-toxic stressors is that they can be easily combined with those of toxic stressors, resulting in a single impact indicator - although such combinations remain to be fully validated.
Eco-toxicological risk assessment often makes use of Predicted Environmental Concentrations (PECs), i.e. the concentration that is expected after human interference, and Predicted No Effect Concentrations (PNECs). PECs, are usually calculated with an appropriate model. The selection of such a model depends on the nature and source of the toxicant and the environmental system at hand. PNECs are usually based on lab studies and are often determined by applying an assessment factor to the most sensitive species (see box below). Traditionally, simply the ratio between PEC and PNEC is determined: a ratio larger than one indicates a potential ecological risk, whereas a value less than one indicates no risk . A disadvantage of this approach is that it doesn't quantify the risk: when the PEC:PNEC ratio is larger than one, we know that there is a potential risk but we don't know its magnitude. In other words, we expect that sensitive species are affected, but we don’t know which fraction of the community as a whole is at risk. Hence this approach is highly conservative, which can be a disadvantage.
There are four possible options for eco-toxicological risk assessment:
The SSD uses information on the sensitivity of all tested species (instead of only the most sensitive), by determining its statistical distribution. Of course, this is only possible if sufficient representative species have been tested. By using available information on all test species, SSD allows for the quantification of the ecological risk at a specific exposure level. This can be done by expressing the risk as the potentially affected fraction (PAF) of species. If the combined risk of multiple stressors needs to be assessed, the overall risk can be determined by superposition (total number of species affected by one or more stressors), resulting in a multi-stressor PAF (msPAF).
In an SSD, each marker represents the end-point of a toxicity test with a specific species, in other words: species sensitivity. The y-axis shows the cumulative probability distribution of species sensitivity, which can be interpreted as the PAF (37% in the presented example) at a certain exposure level. SSDs can be used in two ways: to derive 'safe' thresholds (PNECs), and to quantify the risk level at a specific PEC.
There are some important assumptions, on which both the SSD and the assessment factor approach rely:
Although there are some well-known critiques (e.g., Forbes and Forbes, 1993; Forbes et al., 2001), the SSD approach (for toxicants) has been validated by several studies (e.g., Hose and Van den Brink, 2004; Selck et al., 2002; Van Wijngaarden et al., 2005). As indicated above, SSDs have originally been developed for risk assessment of toxicants. Only in recent years has the method been implemented to assess risk from non-toxic stressors. For toxic stressors laboratory test protocols are well defined and standardised, which reduces uncertainty in the risk calculations with SSDs. For non-toxic stressors, such standardisation is often not in place. This must lead to a higher uncertainty in the SSD risk calculations, but the extent to which this is the case remains to be investigated.
The SSD has shown to be a practical and useful tool in environmental risk assessment. It is a relatively straightforward approach which can report environmental impact by a single indicator and potentially allows integration of risks from multiple stressors. These properties are advantageous in environmental risk management.
Building with Nature interest
In BwN-projects, SSD can be used in various ways. In adaptive management, for instance, SSDs can be used to continuously evaluate the risk of human interference based on in situ data. SSDs can also be used to estimate the effects of different alternatives in advance (e.g. BwN concepts versus a traditional approach), via exposure effect relations for relevant stressors. For the latter, they have to be used in combination with tools that estimate environmental exposure levels (PECs). SSDs are therefore relevant for BwN phases ranging from design to (adaptive) construction management.
SSDs can be used by eco(toxico)logists with sufficient knowledge of the ecosystem and its species. The application of SSDs is one form of probabilistic effect analysis (PEA). Please refer to the tool Probabilistic analysis of ecological effects - Cause-effect chain modeling, for a description of another form of PEA.
1. Appliccability of SSDs for environmental impact assessments
The first steps are to determine whether SSDs may be of use to evaluate the effects of human interventions:
The next steps apply when SSDs are expected to be useful. There is no readily available approach for applying SSDs to a project, but some general steps can be followed. For offshore drilling activities an approach - the Environmental Impact factor (EIF) - has been developed (see practical applications). This may serve serve as an example of how SSDs can be used:
2. Use of the Environmental Impact Factor
As already mentioned, the Environmental Impact factor (EIF) approach has been developed for offshore drilling activities. The drilling discharge model (EIFDD) runs in the Marine Environmental Modelling Workbench (MEMW), developed by SINTEF, Norway. Practically anyone with the proper (few days) training can operate this model. SINTEF’s office in Trondheim can be contacted for inquiries on the model. Keep in mind, however, that this implementation focuses on drilling discharges and produces a generic impact assessment. As indicated before, the model might be adapted to marine construction activities, like dredging.
As a case study one may want developing an SSD for tropical corals. In that case (preferably consistent) effect data on tropical corals needs to be collected or may already be available from other tasks within Building with Nature. Once SSDs have been developed, they should be combined with predicted or measured turbidity levels.
3. Spatial and temporal issues
For adaptive construction management this may not be sufficient, as operations are time-dependent and operational decisions need to be made at short notice. Another issue to be considered is therefore the factor 'time'. First of all, in toxicology, SSDs are based on chronic end-points, which is actually a worst-case approach. The idea behind this is that if a species is not affected after chronic exposure, it will certainly not be affected after a shorter exposure. Unfortunately, examples of time-dependent SSDs are scarce in literature. In fact, there is only one publication (Smit et al., 2008c) in which a time-dependent SSD (for hydrogen peroxide exposure) is actually developed. In this approach several constant concentrations were tested, and effects on several species were recorded as a function of time. This gives a simple and straightforward relationshio between exposure time and effect, known as Habers' Law (Karman, 2000). To achieve the same effect with higher exposure concentrations, a shorter exposure duration is required and vice versa. Note that this relationship applies to constant stress levels and is not suitable for fluctuating exposure levels.
For toxicants models are available that can describe effects of fluctuating exposure concentrations. For example some models consider the kinetics (uptake and excretion speeds) of toxicants. With such kinetic information internal concentration in a species can be calculated from a (fluctuating) external concentration. This internal concentration can then be compared with internal effect threshold levels, in order to determine effects as a function of time (see for example Figure, Graphical representation of inverse hazard model, applied to three hypothetical cases, ref Karman, 2000). A similar, more mechanistic approach (e.g., considering uptake, storage and excretion processes and target sites in the organism) would also be possible for non-toxic stressors (such as suspended matter), although this requires knowledge on the mechanism through which these stressors affect the organism, as well as data to quantify them.
Although the latter approach (fig 4c) accounts for the factor 'time' in a more realistic and sophisticated way, it doesn't take the recovery potential of a species into account. Furthermore, it is difficult to implement this approach in a simple SSD approach, as effects will not only depend on the exposure time and the current exposure level, but also on the exposure history.
4. Conclusions and recommendations
SSDs can be applied:
The SSD approach has been implemented by the offshore oil and gas industry to evaluate the environmental impact of drilling discharge scenarios. Such discharge plumes are to some extent comparable with dredging plumes. Therefore, the approach may also be applicable to evaluate the effects of dredging. The implementation of SSDs in the EIF by the oil and gas industry is illustrated in this section.
The environmental impact factor for produced water (EIF PW) was developed in the year 2000 by the oil and gas exploration and production companies active on the Norwegian shelf, as a risk assessment tool for environmental management of produced water discharges. The EIF PW is an indicator of environmental risk, with the purpose to aid the industry in developing a ‘zero harm’ strategy and selecting cost-benefit based solutions. The Norwegian authorities presently require using this tool in reporting and planning of management actions to reduce potentially harmful environmental effects associated with produced water discharges (Singaas et al., 2008). In order to further develop the toolbox for environmental management, the Environmental Risk Management System (ERMS) Joint Industry Project was established to develop an EIF for drilling discharges (EIF DD). T he framework for the EIF DD indicates the different steps in the risk assessment process (Singaas et al., 2008) (see figure). Six stressors related to the discharge of drilling waste to the marine environment were identified (see step I in figure and table below), of which two occur in the water column (toxicity of chemical substances and physical effects of suspended clay particles) and four in the sediment (toxicity of chemical substances, burial of organisms, oxygen depletion, and change in sediment structure).
Table 1: Stressors of drilling and dredging operations
To assess the exposure (step II) the fate of each of the stressors is modelled. The effect assessment (step III) is based on laboratory tests and field measurements reported in literature. For each stressor identified a species sensitivity distribution (SSD) was constructed in such a way that the 5th percentile of the distribution corresponds to the Predicted No Effect Concentration (PNEC) of the constructed species. For risk assessment (step IV) for single stressors, the ratio of predicted environmental concentration (PEC) and PNEC is prescribed by the EU Technical Guidance Document on risk assessment (Anonymous, 2003). Sensitivity distributions for the various stressors were used to calculate the potentially affected fraction of species as a result of the simultaneous exposure to multiple stressors (msPAF). Monitoring information was available for validation purposes. Generally, a good correlation was found between model results and measured data (Singaas et al., 2008).
For suspended clays the data collection resulted in a database with effect concentrations for marine species, distinguishing different effect types, such as survival, feeding behaviour, growth, mobility, reproduction, oxygen consumption, and effects on the gastrointestinal tract (Smit et al., 2008a). Data were obtained from exposure studies with clay-sized particles (i.e. attapulgite, bentonite, clays, and barite) and also with various types of water-based drilling fluids. Most data were available for the species groups phytoplankton, zooplankton, crustaceans (excluding zooplankton), molluscs, and fish.
The SSDs, based on collected 50% effect concentration (EC50) values for mortality resulting from suspended barite and bentonite exposure, present median values and 5-95% confidence intervals (see SSDs with corresponding hazardous concentration) (Smit et al., 2008a). Filter feeding zooplankton and molluscs are, together with algae, among the sensitive taxonomic groups, while benthic crustacean and siphon feeding molluscs are relatively insensitive (Smit et al., 2008a). This supports the hypothesis that organisms living in the benthic boundary layer of fine sediments are accommodated to deal with elevated turbidity and sedimentation.
The value of the EIF is related to the recipient water volume and sediment surface area where msPAF exceeds 5% (Smit et al., 2008b) (se spatial risk map). The selected unit for the EIF for the 2 compartments is a water volume of in units of 100 m x 100 m x 10 m for the water column and 100 m x 100 m surface area for the sediment. An advantage of the EIF method is that it calculates the overall risk and the contribution to it from each stressor (see output of DREAM).
The EIF in its present form should be regarded as a management tool to identify and rank the most harmful discharges (and components of those discharges). This enables operators of production fields to evaluate mitigating measures on a cost/benefit basis, when applying the Best Available Technology (BAT) for treatment of produced water of all production fields (Smit et al., 2003). The EIF provides the possibility to assess different management options, enabling the field operator to prioritise the implementation of BAT for the specific field and to document Best Environmental Practice (BEP) (see EIF application for management option).