environmental risk assessment and management from a landscape perspective (kapustka/environmental...

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LINKING REGIONAL ANDLOCAL RISK ASSESSMENT

Rosana Moraes and Sverker Molander

Physical or biological processes that affect the fluxes of organisms or stressors canfunctionally define regions, such as wide-scale watersheds or physiographic provinces(Hunsaker et al. 1990). Stressors, sources, and their associated risks exist along a con-tinuum of spatial scales, and it is impossible to draw an objective line between what isa regional and what is a local environmental situation. For instance, regional changecan be caused either by a local phenomenon (stressor source or primary effect) thathas a regional consequence or by multiple local sources that, when combined, createchanges that qualify as regional or by diffuse sources—very many, very small. Despitethe lack of a clear demarcation between local and regional, we distinguish two cate-gories (i.e., regional and local) of ecological risk assessment (EcoRA), acknowledgingthat this is an arbitrary categorization that provides a practical way to distinguish localand regional risk assessment methods.

Local risk assessments cover, for example, evaluation of changes in the waterquality of a reach of a particular river related to discharges from one or many specificindustries. The scale in this case is small and the organisms are exposed in the area.In general, risk assessments at a small scale can comprise more refined analyses thanassessments made at a regional scale. On the other end of the scale, a regional riskassessment is more general and coarse-grained. Details are left out in order to aggregateinformation, sometimes in order to prioritize where more detailed assessment, on alocal scale, should be performed.

Environmental Risk and Management from a Landscape Perspective, edited by Kapustka and LandisCopyright © 2010 John Wiley & Sons, Inc.

122 LINKING REGIONAL AND LOCAL RISK ASSESSMENT

AIMS AND CHALLENGES OF REGIONAL RISK ASSESSMENTS

This discussion of regional risk assessment begins with a view of scale. Two additionalsubsections describe the consideration of multiple stressors and the challenges ofdealing with voids in regional-specific spatial data.

Scale

One of the purposes of EcoRA is to inform environmental management. Managersmust in many cases make decisions concerning environmental issues at a regionalscale (e.g., threats to natural reserves or large watersheds) rather than in single sitesor small geographic areas. Regional issues are not only larger in scale than local ones,but are also larger in scope, since they may involve multiple stressors, multiple recep-tors, and spatially heterogeneous natural systems. The evaluation of ecological risksposed by environmental problems over large geographical areas requires regional riskassessments. In these circumstances the applicability of traditional site-specific EcoRAprocedures, such as the ones designed for assessing contaminated sites published byagencies in different jurisdictions such as Canada (CCME 1996, EC 1994), the UnitedStates (US EPA 1998), Australia (A NEPC, 1999a, b), the Netherlands (Rutgers et al.2001), the United Kingdom (UK EA 2008), and Basque Country (IHOBE 2003) mayhave limited applicability.

Participants of the “Workshop on Characterizing Ecological Risk at the WatershedScale” (US EPA, 2000) indeed recognized the problem of dealing with large geograph-ical areas. Since the event of that workshop, several papers have been published onwatershed risk assessments methods and case studies (e.g., US EPA 2002a, 2000b;Wickham and Wade 2002, Serveiss 2002, Serveiss et al. 2004). In Europe, recentchanges in environmental directives and strategies (e.g., Water Framework Directive2000/60/EC, CEC 2000) will require that currently used approaches of risk assessmentshift from small scale to the watershed level and will need a better understanding ofdifferent media and habitats across many spatial and temporal scales (Apitz et al.2006). Regarding prospective investigations the European legislation requires studiesof Strategic Environmental Assessments on larger scales according to the directive2001/42/EC on the assessment of the effects of certain plans and programs on theenvironment (CEC 2001).

Even studies concerning effects on small geographic areas should consider effectslinked to broader scales than the exposed area because of fluxes of organisms orstressors from the exposed area to the surrounding landscape and vice versa (Gardner1998, McLaughlin and Landis 2000). For instance, impacted sites whose communitieshave been extinguished by acute exposures may be recolonized by immigrants fromnoncontaminated areas (Johnson 2002). On the other hand, migratory species that havetraveled from exposed sites may exhibit effects when they live in a nonexposed area.None of the EcoRA procedures listed above (US EPA, CCME, UK EPA, AustralianNEPC, Basque Country IHOBE, etc.) explicitly incorporates this issue. Moreover,air and water pollutants can travel long distances, and the understanding of thosepollutants’ sources is relevant, even if they are outside of the boundary of the studied

AIMS AND CHALLENGES OF REGIONAL RISK ASSESSMENTS 123

site (Efroymson et al. 2005). A further aspect that has been put forward by Colnar andLandis (2007) is effects caused by invasive species where the sources of these speciesare outside the studied area. It is clear that in order to cover significant influences onthe assessment endpoint, also the connection to sources of various stressors occurringoutside the study area need to be included. The point is to keep flexibility of what toinclude so that the relevant linkages from stressor sources to endpoints are captured.

Multiple Kinds of Stressors

Another case in which traditional EcoRA procedures have limited application is asso-ciated with the assessment of multiple kinds of stressors. The importance of identifyingand evaluating the adverse effects of biological, physical, and chemical stressors inecosystems and their components is explicitly mentioned in the US EPA Guidelinesfor Ecological Risk Assessment , but that procedure does not offer specific guidance onthe quantitative assessment of multiple kinds of stressors in the context of an EcoRA.Even though a deterioration of natural ecosystems is typically the result of cumulativeimpacts of chemical, physical, and biological origin, most published EcoRA studiesanalyze the effects of only one kind of stressor. This is clearly unsatisfactory butunderstandable due to practical constraints.

In most cases the stressors examined are exclusively chemicals, such asPCBs, metals, PAHs, or pesticides. EcoRA studies less frequently concern physicalstressors—that is, stressors that lead to reduction in availability and accessibilityof receptor-specific, structurally defined habitat (Hope 2005). Examples of physicalstressors include habitat fragmentation, sound pollution, and visual pollution. Evenrarer are EcoRA studies concerning exclusively biological stressors, such as presenceof pathogens, or the removal of resources needed, such as dead wood, which is aprerequisite for woodpeckers serving both as substrate for food resources and nesting(Butler et al. 2004).

All three types of stressors may interact in such ways that modify a chemicalstressor’s contribution to the overall risk to a given receptor (Hope 2005). Further-more, the interaction of different types of stressors are unlikely to be additive. Thedevelopment of quantitative and qualitative assessments of multiple kinds of stressorsand their incorporation in EcoRA procedures still remain a difficult challenge.

Lack of Region Specific Spatial Data

Few models have been suggested for the purpose of dealing with risks at regionalscale; and most of these are constrained in terms of data availability, because the costof data collection at large scales is often high. For instance, Graham et al. (1991)published a prototype assessment to evaluate risks associated with elevated ozone ina forested region in the United States. The regional risk assessment used a stochasticspatial simulation model of land-cover change linked with a water quality regressionmodel. Positive aspects of the model include consideration of spatial heterogeneityand the linkages between terrestrial and aquatic systems. Limitations comprise theneed of spatially explicitly data such as water quality data for a larger region.


Another example is the multiple regression model relating anthropogenic activ-ities in watersheds in Ohio, USA, and several stream conditions variables related tobiological quality proposed by Gordon and Majumder (2000). Useful accomplish-ments of the model include a broad view of the degree and significance of the factorsimpacting ecological resources on a regional scale. Once again, limitations are relatedto needs of large input dataset. Just as an illustration, the authors ran the model usingdata from 276 locations across 25 watersheds in Ohio.

In contrast, the procedure proposed for risk assessments by the Commission ofEuropean Communities (CEC 1996) does include evaluations of risk at both theregional and local scale, but is designed for generic environments, not actual sites,and is restricted to assessments of chemical stressors, one chemical at a time. Themodel used can be adapted to represent aspects of a local environment, but in thatcase, locally gathered data will be needed.

THE RELATIVE RISK MODEL (RRM) FOR REGIONAL RISKASSESSMENTS

An alternative approach used to overcome some of the limitations described in theparagraphs above is the Relative Risk Model (RRM) developed by Landis and Weigers(1997). The merits of the RRM are not only related to low demand for input data,and consequently low cost, but also to the transparency of its assessment models,plus the fact that its outputs are easy to use in risk communication and for creatingpriorities for management actions or for the identification of locations for local riskassessments.

While traditional EcoRAs estimate the level of exposure and effects to calculaterisk to a specific receptor at a local site (see Chapter 2), the assessment at regionallevel, deals with a level of complexity that is not the same. At a regional level, numer-ous existing sources in a certain region (e.g., industries, farms) can release differenttypes of stressors (e.g., chemicals, invasive species, soil erosion); multiple receptors(e.g., wildlife, fish) present in the various habitats (e.g., forest, wetland, rivers) in theregion can be affected by those stressors. The evaluation of exposure in this case willinvolve different types of measurements [concentrations of chemicals in water (mg/L),number of individuals of invasive species per m2 of wetland, surface of eroded soilin m2]. Although the three stressors can lead to the same effects (e.g., decline ofwetland indigenous species), the exposure levels are not easily comparable. In orderto address this issue, Landis and Weigers (1997) suggested the establishment of ranksand weighting factors to evaluate the risk. The model is based on the communityconditioning hypothesis (Matthews et al. 1996), which eliminates dependency of areference site and therefore opens up for relative risk assessment.

The basic steps of the RRM are:

• To identify management goals based on stakeholders’ values and regulatoryneeds and associate those goals into a spatial context

PETAR: A PROCEDURE TO INTEGRATE REGIONAL AND LOCAL RISK ASSESSMENTS 125

• To locate sources of stressors and habitats relevant to management goals on amap

• To delineate sub-areas based on combinations of identified management goalsand the location of stressors and habitats

• To build a conceptual model linking the multiple sources, stressors, habitats,and endpoints

• To assign a ranking scheme for each source, stressor, and habitat to allow thecalculation of relative risk to the assessment endpoint (details in the next pages)

• To calculate the relative risk based on the relationship between sources, habitat,and impact to assessment endpoints (details in the next pages)

• To evaluate the uncertainty and sensitivity analysis of the relative rankings• To generate testable hypotheses for future investigations to reduce uncertainties

and to confirm the risk rankings• To test those hypotheses• To communicate the risks.

There have been some recent applications of the RRM model demonstrating itsadaptability to a variety of scales and ecosystems (Landis and Wiegers 1997, Wiegerset al. 1998, Landis et al. 2000, Walker et al. 2001, Moraes et al. 2002, Obery andLandis 2002, Hamame 2002, Hart Hayes and Landis 2004, Landis et al. 2004, Colnarand Landis 2007). Most of those case studies were compiled in the book RegionalScale Ecological Risk Assessment Using the Relative Risk Model (Landis 2005). Areflection over the 10 years (1997–2007) of the Relative Risk Model and RegionalScale Ecological Risk Assessment was recently published by Landis and Wiegers(2007).

Here we describe a procedure that links regional and local risk assessments. Thelinkage is soft in that the regional assessment is used for identifying stressors, sources,and habitats with potentially affected endpoints in certain areas. This is apart from theconceptually different approach of Colnar and Landis (2007), who used hierarchicalmodeling (Wu and David 2002, Allen and Starr 1982) where local scale is linked toregional by an aggregative approach.

PETAR: A PROCEDURE TO INTEGRATE REGIONAL AND LOCAL RISKASSESSMENTS

In 2004, we proposed a procedure for EcoRA that incorporates qualitative and quan-titative methods for assessing multiple kinds of stressors related to environmentalproblems across a range of scales (Moraes and Molander 2004). It also includes Lan-dis and Weigers’ RRM and takes into account restrictions on data availability and openup for smaller-scale studies within the framework. The proposed procedure was giventhe name “PETAR” as an acronym for Procedure for Ecological Tiered Assessment ofRisks. The acronym also pays homage to the place in which the preliminary ideas forthe procedure were first conceived and put into practice, the Parque Estadual Turistico


do Alto Ribeira (PETAR), a Brazilian rain forest reserve. Results of several studiesleading up to the proposed procedure are also found in Moraes (2002), Moraes et al.(2003), and Gerhard et al. (2004).

The PETAR procedure consists of a three-tiered evaluation of risks (Fig. 7.1),with successive tiers requiring more detailed investigations. The procedure, whichstarts with large and ends with small geographical areas, recognizes restrictions indata availability and acquisition. It is based on a number of complementary meth-ods selected from the scientific literature regarding assessments of multiple kinds ofstressors and for establishing causality between exposure and effects. The three mainphases of the assessment (preliminary, regional, and local) will be detailed in the nextsections.

Preliminary Assessment

A preliminary assessment aims at a qualitative evaluation of the stressors sources,ecosystems potentially at risk, characteristics of the stressors, and expected ecologicaleffects (Fig. 7.2). Based on the agreed goals and scope of the planning process, theassessor should gather information on all existing stressor sources in the study areaand ecosystems potentially at risk. The study area should include a broader spatialscale than the stressor sources of concern (e.g., a municipal district) or the specificexposed area of interest (e.g., a bay, a natural reserve, a fiord, a river). The boundariesof the study area may preferentially be of physical (e.g., airsheds or watersheds) orbiological nature (e.g., physiographic provinces) because these features are known toinfluence the flux of stressors and organisms from a source area to an exposed areaand to its neighborhood, and vice versa (Hunsaker et al. 1990, Efroymson et al. 2005).

Potential stressors should be listed on the basis of existing sources in the region.Physical, chemical, or biological characteristics of the stressors and ecosystem thatmay influence their fate and distribution in the environment as well as their potentialhazard should be compiled from the scientific literature (e.g., existing toxicologicaldatabases and studies done at other sites). These data are important for evaluation ofexpected ecological effects.

The outcome of this preliminary assessment regarding the initial survey of stres-sors sources, ecosystems potentially at risk, characteristics of stressors, and expectedecological effects will guide the problem formulation of the regional risk assessmenttier.

Regional Risk Assessment

The aim is to identify stressors having the greatest potential for ecological impact, thehabitats most at risk, and the sub-areas inside the study area most likely to be affectedby impacts. A semiquantitative evaluation of ecological risks over large geographicalareas is the result of the regional risk assessment.

In the PETAR approach, the RRM of Landis and Weigers is the suggested tool toevaluate risks at the regional level. The next paragraphs provide a general understand-ing of the RRM and its application within the PETAR procedure. The reader looking


Regulatory needs Public concern

Consideration of social,legal, political andeconomical factors

Scientific knowledge

Goals and scope of the assessment

As necessary: data acquisition, process iteration, results m

onitoring

PLANNING(risk manager, risk assessor, interested parties)

PETAR

PRELIMINARY ASSESSMENT

REGIONAL RISK ASSESSMENT

LOCAL RISK ASSESSMENT

Communication ofresults to the risk manager

Risk management

Figure 7.1. Overall process of the Procedure for Ecological Tiered Assessment of Risks (PETAR)

and related activities (planning process, risk communication, and risk management).

for a more detailed description of the RRM method and examples of its applicationshould refer to Landis (2005).

Like traditional local risk assessments, the overall process of the regional riskassessment includes three main steps: problem formulation, analysis phase, and riskcharacterization (Figure 7.3). During the problem formulation stage, assessment end-points are selected, conceptual models are created, and an analysis plan for theevaluation of exposure and effects at regional scale is designed.


PETAR

Goals and scope of the assessment

Gathering of available information

Existingstressorsources

Stressorcharacteristics



Expectedecological

effects

Ecosystemspotentially

at risk


Figure 7.2. Preliminary assessment within the overall process of the Procedure for Ecological

Tiered Assessment of Risks (PETAR). White rectangles represent steps in the process and

rounded rectangles represent products of the preliminary assessment.

The major criteria to be used when assessment endpoints are selected are theirecological relevance, their susceptibility to the known or potential stressors, and theirrepresentation of management goals (US EPA 1992, Barton and Sergeant 1998).According to Suter (1990), assessment endpoints for regional risk assessments maydiffer from those traditionally selected for local ecological risk assessments. In regionalrisk assessments, environmental characteristics valued by the public and by decision-makers should be given great weight in the choice of the assessment endpoint. Thesocietal values are usually linked to the region’s ability to provide food, clean waterand air, aesthetic experiences, recreation, and so on. It is also possible to relate thechoice of the assessment endpoint to the ecosystem services following the terminologyof the Millennium Ecosystem Assessment (2005).

Suitable types of endpoints can then be connected to valued populations such asendangered, commercial, or recreational species in the region, provided that the status



Assessmentendpoints

Conceptualmodels

Analysisplan

Densityof sources

ExposureCharacterization

EffectsCharacterization

Risk Estimation(per sub-area, per source, per stressor)

Risk Description


Communication of resultsto the risk manager

Uncertaintiesreduction

Hypothesesgeneration

Priorities setting forrisk mitigation

Densityof habitats

Amount of stressorper source

Endpointvulnerability

REGIONAL RISK ASSESSMENTPF

AP

RC

Figure 7.3. Regional risk assessment in the overall process of the Procedure for Ecological

Tiered Assessment of Risks (PETAR): PF, problem formulation; AP, analysis phase; and RC, risk

characterization. White rectangles represent steps in the process, and rounded rectangles

represent products of the assessment.


of the selected populations integrates physical, chemical, and biological disturbances.At the community level, changes in community type can be suitable for assessmentsat a regional scale.

The problem formulation also produces a conceptual model, which is the linkbetween stressor sources, stressors, habitats, and effects linkages (e.g., pool of indus-tries, air emissions, primary forest, and decline of sensitive plant species). A strategyfor creating clear and complete conceptual models for complex EcoRAs with multi-ple stressors and multiple assessment endpoints was presented by Suter (1999). Theconceptual model can also be constructed as a hierarchical model following Wu andDavid (2002) and Colnar and Landis (2007).

The final product of the problem formulation should be the analysis plan, whichincludes criteria for the delimitation of sub-areas of the region under study. Thecriteria may be analytical factors (e.g., watershed limits) as well as human-derivedorganizational factors (e.g., political, administrative or socioeconomical boundaries).With the use of Geographical Information Systems (GIS), the assessor should createmaps showing the sub-areas and, within them, the distribution of sources, habitats,and assessment endpoints. Afterwards, the density of sources and habitats per sub-areacan be estimated.

The analysis phase consists of a semiquantitative evaluation of exposure andeffects at the regional scale. It includes the design of:

1. A ranking based on the density of sources per subarea (rank sources, RS)

2. A ranking based on the density of habitats per subarea (rank habitats RH)

3. Weighting factors for stressors (WS) to account for differences in amounts ofa stressor that can be released from diverse sources

4. Weighting factors for effects (WE) to evaluate the likelihood and extent towhich exposure to different stressors may harm the habitat as a function ofthe system’s sensitivity and its ability to adapt to new conditions

For an illustration of how designing of ranking systems and weighting factors can beconducted, see Moraes et al. (2002).

The use of RRM may require simplifying assumptions because empirical data onexposure and effects are usually limited. The designing of weighting factors also reliesto a great degree on inferred preferences and data availability. To evaluate the likeli-hood and extent of harm to the habitat, the assessor may use, for instance, informationon the number of potential stressors of each type (e.g., number of organic compoundsand their toxicity relative to the number of metals and their toxicity included in each“type” of stressor) and the temporal and spatial variability of the stressor. Relevantinformation for design of effect weighting factors includes the possibility of previousexposure by natural sources in the region (e.g., metal containing ores), the duration ofthe exposure (chronic or acute), and consequent opportunity for organisms to developtolerance and adaptation mechanisms.

Risk characterization is based on the three following assumptions of the RRM:First, the greater the density of sources in a sub-area, the amount of stressor released


by the source, and the density of habitats where the endpoint resides in a sub-area, thegreater the potential for exposure; second, the greater the vulnerability of an endpointto a stressor, the greater the potential for effects; and third, the greater the potentialcombination of exposure and effects, the greater the risk.

The risk of adverse effects in sub-area i for the habitat h is determined by

Risk_Sub-areaih =∑

Risk_Sub-areaijh (1)

Risk_Sub-areaijh = Exposureijh × Effectjh (2)

where i represents the sub-area, j represents the stressor, and h represents the habitat.The likelihood of the endpoint to be exposed to a stressor depends on the abun-

dance of sources (RS) that release such a stressor, the amount of stressors releasedin the environment by each source (WS), and the abundance of habitats where theendpoint can be found in a certain area (RH). The exposure factor in area i to stressorj for the habitat h was determined by

Exposureijh =∑

k=source1...n

(RSki × WSjk) × RHhi (3)

where k represents the sources of a particular type (e.g., industries or landfills); RSki

is the rank chosen for the sub-area i according to the density of the source k (e.g.,ranks determined by the number of industries in the sub-area); WSjk is the weightingfactor for the stressor j according to the amounts produced by source k; and RHhi isthe rank chosen for the area i according to the density of the habitat h.

The likelihood of undesired effects will depend on the vulnerability of the endpointto the stressor (WE). The effect factor to stressor j for the habitat h was determined by

Effectjh = WEjh (4)

where WEjh is the effect-weighting factor based on the vulnerability of endpoints inhabitat h to stressor j .

The risk of adverse effects due to the source k for the habitat h is determined byadding the risks of adverse effects of each stressor released by the source k:

Risk_Sourcekh =∑

j=stressor1...n

Risk_Stressorjh (5)

The risk of adverse effects due to the stressor j in the habitat h was determinedby

Risk_Stressorjh = Exposurejh × Effectjh (6)

The probability of exposure to stressor j to the endpoints living in the habitat h isa product of the abundance of the different sources (RS) that release that stressor,


the amount of that stressor released by each source (WS), and the abundance of thehabitat h (RH) where that endpoint lives:

Exposure_Stressorjh =∑

i=sub-area1...n

⎡⎣ ∑

k=source1...n

(RSki × WSjk) × RHhi

⎤⎦ (7)

The likelihood of undesired effects will depend on the vulnerability of the endpointto the stressor (WE). The effect factor to stressor j for the habitat h was determinedby Eq. (4).

The application of the RMM in retrospective studies results in (a) a ranking ofstressors having the greatest potential for ecological impact, (b) habitats most at risk,and (c) sub-areas inside the study area that are more likely to be impacted. The resultscan be used for risk mitigation in specific sub-areas and, furthermore, to generatetestable hypotheses and direct investigations for the local risk assessment.

Many uncertainties are involved in the estimations of effects, which typicallyoccur in any EcoRA. The RRM is no exception. The uncertainties, overestimating orunderestimating the risk, can be divided into two groups. In the first group are uncer-tainties related to the model structure, which are the result of a possible disregard ofkey elements in the conceptual models due to oversimplifications or lack of knowl-edge. The second group consists of uncertainties related to assigning the input valuesto the model. This type of uncertainty is a consequence of extrapolations, assump-tions, and lack of knowledge. Follow-up studies can add knowledge and, to a certaindegree, reduce uncertainties by filling data gaps with site-specific data. Methods forthe collection, analysis, and interpretation of site-specific data are presented in thenext section.

Local Risk Assessment

The final tier of the PETAR procedure is a more detailed quantitative and site-specificrisk assessment, designed for relatively small geographical areas. The starting pointof the local risk assessment is the hypotheses generated based on the regional riskassessment regarding stressors with greatest potential for impact, habitats most atrisk, and sub-areas more likely to be impacted. One of the aims of the site-specificinvestigations is to verify the occurrence of the ecological effects in specific habitatsand in specific sub-areas and to relate observed effects to specific stressors.

Probably the most difficult task of retrospective risk assessments at the local scaleis to provide data sufficient to conclude that a particular human-induced stressor causesan observed ecological effect in natural systems. Without knowing the cause, decision-makers are not able to properly tackle an environmental problem. The approachsuggested here involves the construction of several lines of evidence (comparisonsbetween measured concentrations and toxicity endpoints from toxicity tests, biomark-ers, biological surveys, etc.). The different lines of evidence should then be evaluatedin a formal and systematic way with a weight-of-evidence approach (Suter and Barn-thouse 1993, Menzie et al. 1996, Hull and Swanson 2006) that uses a collection ofepidemiological criteria to evaluate causality (Hill 1965, Fox 1991, Suter et al. 2002).


The problem formulation of the local risk assessment has three main outputs:assessment endpoints, a conceptual model, and an analysis plan (Fig. 7.4). The maindifference between the regional scale and the local scale assessments is the level ofdetail in which the site-specific data are incorporated.

In the case of the local risk assessment, it is particularly desirable for the assess-ment endpoint to have a biological significance. In particular, that an effect on thelevel of biological organization to which the assessment endpoint belongs has implica-tions for the next higher level of biological organization (Suter 1990). For the EcoRAprocedure proposed here, the assessment endpoint for the local risk assessment may(but not necessarily) be the same as the one selected for the regional risk assessment.Generally speaking, population-level assessment endpoints may be the most usefulin local risk assessments because (a) populations of many species have economical,recreational, aesthetic, and biological significance that is easily appreciated by the pub-lic and is likely to influence management decisions and (b) population responses arewell-defined and easier to predict with available data and methods (see, for instance,Munkittrick et al. 2000) in comparison to community and ecosystem responses (Suter1990). Regarding the choice of ecological entity, individual or sub-individual levels ofbiological organization often have low societal value and are of controversial biolog-ical significance. However, observations on these levels of organization can be usedas evidence underpinning particular cause–effect relationships (Moraes et al. 2003).

The problem formulation also results in a detailed conceptual model constructedon the basis of hypothesized cause–effect chains and experience from the regionalrisk assessment. The model should describe (1) the stressors pathways from sourcesto receptors, considered to have the greatest potential for ecological impact, and (2)expected effects on the assessment endpoint. It should include all known stressors exis-tent in the study area that can cause similar effects, including stressors of both anthro-pogenic and natural origin. Included potential effects is suggested to cover effects atthe same and lower levels of biological organization to which the assessment endpointbelongs, since lower-level effects can be the controller of, or the constraint on, theeffects on the assessment endpoint. In addition, the conceptual model should also con-sider potential implications for the next higher level of biological organization—forexample, from the species level to the community level, since ecological and societalrelevance may be perceived as larger on higher levels of organization.

The process of evaluation of the hypothesized cause–effect chains to be madeduring the analysis phase characterizes receptor, ecosystem, exposure, and effects. Thecharacterization of the receptor organism, population, or community is the evaluationof the life history characteristics (e.g., habitat use, behavior, trophic relations) of theendpoint that can influence the exposure pathways and the response. The characteri-zation of receptors at several locations is also vital to distinguish between patterns ofdisturbed and undisturbed receptors in order to recognize differences between observedvariations caused by natural variability (e.g., differences in habitat complexity betweensampling sites) and those caused by anthropogenic stressors.

Ecosystem characterization consists of the qualitative or quantitative evaluationof ecosystem features, related to both site-specific and larger-scale phenomena, thatmay influence stressors’ fate and distribution or the receptor sensitivity to the stressors



Assessmentendpoints

DetailedConceptual models

Analysisplan

Effects on differentlevel of biological

organization

Spatio-temporaldistributionof stressors

Disturbed andnot disturbedcommunities

Baselineand naturalvariability

EffectsCharacterization

ExposureCharacterization

ReceptorCharacterization

EcosystemCharacterization

Risk Estimation(weight-of-evidence)

Risk Description

Communication of resultsto the risk manager

Uncertaintiesreduction

Hypothesesgeneration

Likelihood of effects and theirecological significance estimation

Priorities setting for risk mitigation


LOCAL RISK ASSESSMENTPF

AP

RC

Figure 7.4. Local risk assessment in the overall process of the Procedure for Ecological

Tiered Assessment of Risks (PETAR): PF, problem formulation; AP, analysis phase; and RC, risk

characterization. White rectangles represent steps in the process, and rounded rectangles

represent products of the assessment.


(e.g., water chemical and physical parameters). Some features can even be used asindicators of responses to human-induced alterations (e.g., pH, nutrient concentration).The ecosystem characterization may be based on available maps and reports togetherwith site-specific observations in field studies. Detailed site-specific investigationsfor analysis of exposure and effects should only start after a careful selection ofreference sites, which is based on evaluation of baseline conditions of ecosystems anda preliminary distinction between disturbed and undisturbed receptors.

Exposure characterization may include evaluation of number, size, and distributionof natural and anthropogenic sources by digital processing of maps on land use, aerialphotographs, or satellite images. In addition, exposure characterization should includeassessment of spatial and temporal distribution of stressors by an evaluation of litera-ture data and databases or (preferentially) by field measurements on different media.Evaluation of pathways from the source to the receptor and quantification of stressors’concentration and distribution can also, for chemical stressors, be based on transportand fate models, or on other assessment tools for biological and physical stressors.

Effects characterization comprises complementary methodologies to be used forinference or measures of biological effects in natural systems. The selection of methodsto use, and the levels of organization to be investigated, will depend on resourceavailability. However, it is important to note that adding different methodologies andstudies on effects at different levels of biological organization will greatly improvethe quality of the assessment. A single community sample (e.g., fish) can be usedfor several studies—for instance, body burden, enzyme activity, condition factor,reproductive stage, diversity, and so on [see, e.g., Moraes et al. (2003)].

The analysis phase of a local risk assessment is based on an assessment of thebaseline condition of the ecosystem and its natural range of variation that precedesany detailed site-specific study for establishing cause-effect relations. Considerablevariability in the distribution and intensity of factors such as river flow, temperature,and nutrient concentration occurs naturally. Human interventions in their patterns ofvariability may cause important ecological effects. As environmental factors alreadyshow a range of natural variability, it is difficult (but critical) to predict when human-induced changes result in significant alterations outside the boundaries of the baselinerange for a particular ecosystem (Dorward-King et al. 2001). The evaluation of thebaseline conditions of ecosystems, together with the distinction between disturbed andundisturbed communities, is an important element in a careful selection of samplingsites. A failure to have baseline data on the natural variability of ecosystems may thuscast doubt on the causal links between stressor, exposure, and effects, and the probabil-ity to establish a cause–effect relation will be higher at sites where ecosystem baselineconditions are comparable to both previously and currently unexposed similar sites.

Detection of effects on different levels of biological organization can be achievedby using complementary methods. The greater the amount of evidence indicating theoccurrence (or not) of biological effects at different levels of high specificity andlevels of high ecological relevance, the better the quality of the effect assessment.

The final stage of the local risk assessment is the risk characterization duringwhich evidence that a particular human-induced stressor causes observed biologi-cal effects at specific sites is analyzed. Evaluation of causal relationships between


exposure and effects suggested by the PETAR procedure consists of two steps: elimi-nation of potential causes (Suter et al. 2002) followed by a risk estimation step usinga weight-of-evidence approach for the remaining causes.

The weight-of-evidence approach suggested by the PETAR procedure is largelysimilar to the approach suggested by Suter et al. (2000). Lines of evidence can beresults of laboratory studies, manipulations of field situations, and results of biologi-cal surveys. The main difference between the PETAR procedure and other publishedEcoRA approaches is that measures of effects at different levels of biological organi-zation are divided into individual lines of evidence. So when analyzed, for example,biochemical, physiological, organism, reproductive biomarkers, population, or commu-nity characteristics are evaluated separately. Epidemiological criteria for an evaluationof each line of evidence may include: strength of the association, consistency andspecificity of the association; biological gradient and plausibility; and temporality(Adams 2003). For an illustration of how to establish causality between effects andstressors using the suggested weight-of-evidence, [see e.g., Moraes et al. (2003)].

CONCLUSIONS

The EcoRA procedure presented here integrates methods for assessing multiple typesof stressors related to environmental problems at both a regional and local scale. Italso includes methods for establishing causal relationships between exposures andeffects. Furthermore, three additional characteristics of the PETAR procedure can beconsidered to be improvements over existing EcoRA procedures: the way cause–effectchains are incorporated in the detailed conceptual model, the integration of a methodfor the selection of sites for exposure and effect analyses, and an adaptation of theweight-of-evidence approach for establishing causality.

While getting as close as possible to a corroboration, significant simplifications areperformed since interaction between entities in ecosystems is very complex. Simplifi-cations imply uncertainties in assessments and leave room for much criticism (Powerand Adams 1997, Holdway 1997). The extent to which extrapolations, assumptions,and lack of knowledge can influence the scientific validity of the assessment will varyfrom case to case, and in particular this is evident for the regional risk assessmentin PETAR. Therefore it is essential to include uncertainty and sensitivity analysesin the regional risk assessment (Landis and Weigers 1997) and, when needed, iter-ate the procedure and invest in further data gathering or model refinements. Despitepossible criticism regarding simplifications that the PETAR and other EcoRA proce-dures may encounter, decisions regarding environmental problems will continue tobe made regardless of our current (and endlessly) incomplete scientific knowledgeof ecosystems. Nevertheless, it is also important to recognize that the majority ofdecision-makers do not require the degree of certainty that scientists seek to achieveto make decisions (US EPA 2000).

The further development of the method will probably be related to the furtherdevelopment of remote sensing techniques. For instance, the characterizations ofland cover (Ju et al. 2005) may, in conjunction with methods for identification of

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human activities from satellite images and the establishment of links between theseand environmental effects, make it possible to perform regional risk assessments thatcover wide areas at low costs. Such methods may also, assisted by various models,contribute to prospective regional risk assessments that can serve as input to strategicenvironmental assessments. However, what might be even more important is thedevelopment of methods to estimate the uncertainty, which in these studies is morerelated to model than to measurements.

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