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A FRAMEWORK FOR THE DESIGN OF ECOLOGICAL MONITORINGPROGRAMS AS A TOOL FOR ENVIRONMENTAL AND NATURE

MANAGEMENT

P. VOS∗, E. MEELIS and W. J. TER KEURSEnvironmental Biology, Institute of Evolutionary and Ecological Sciences, Leiden University, P.O.

Box 9516, 2300 RA Leiden, The Netherlands(∗ author for correspondence, e-mail: [email protected])

(Received 28 October 1997; accepted 13 January 1999)

Abstract. Environmental and nature management can not operate effectively without reliable in-formation on changes in the environment and on the causes of those changes. Ecological monitoringcan represent an important source of information. However, many operational monitoring programsare not very effective, i.e., not very useful for decision-making. We present a conceptual frameworkfor the development and maintenance of effective ecological monitoring programs. In the decision-making process, two main functions for monitoring can be recognized: an early warning and an earlycontrol function. Both these functions require a high diagnostic power. This is used as a guidelinefor the design process. The design consists of choices concerning monitoring objectives, objects andvariables to be monitored, sampling strategy and design, data collection, data handling, maintenanceand organization. Arguments commonly put forward in literature and in practice to support thevarious choices are subjected to a critical analysis. The framework will be helpful in the designof effective monitoring systems as it avoids important components to be overlooked, clarifies therelation between the different components, maximizes the exploitation of existing possibilities andopportunities and identifies shortcomings in advance. This will result in monitoring programs thatshould be able to live up to their expectations.

1. Introduction

Without reliable information on changes in the quality of nature and the environ-ment, and on the causes of those changes, decision-making can not deal efficientlywith these issues. Therefore, many authors stress the need for long-term ecologicalmonitoring (e.g., Wolfeet al., 1987; Jeffers, 1989; Pimm, 1991). Many of theexisting programs are, however, not effective (Cullen, 1990; Spellerberg, 1991;Furnesset al., 1993b). What seems to be lacking is a general concept for thedesign of ecological monitoring systems (e.g., Cullen, 1990; Spellerberg, 1991).Even authors of general books on ecological monitoring, often with promisingtitles (e.g., Clarke, 1986; Goldsmith, 1991; Salankiet al., 1994), are apparentlyunaware of the need for a conceptual framework. They deal with a lot of detailsrather than a framework, while the principles proposed are hardly more than a listof diverse – though often useful – recommendations.

Environmental Monitoring and Assessment61: 317–344, 2000.© 2000Kluwer Academic Publishers. Printed in the Netherlands.

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In designing a monitoring system, one must address many different questions,such as what, how, where and when to measure, how to store and analyze the data,and how to present the results, to mention only the most obvious ones. All these– and other – questions have to be answered aiming at a program that contributesto an effective and efficient management of nature and the environment. Moreover,the answers to the various questions are not mutually independent. Needless tosay, the complete design of a monitoring system is a complex matter, and it isnot surprising that many operational monitoring programs are ‘poorly designed’(Furnesset al., 1993b) or ‘poorly planned’ (Spellerberg, 1991).

In order to improve the design process, this complexity has to be made manage-able. One way of doing this is to subdivide the entire design-process into separatecomponents, thereby making it easier to tackle one problem at a time, to addressthe components in an appropriate order and level of detail at every stage of thedesign process, and to apply the appropriate considerations to each component.Thus for the establishment of a well-designed monitoring program, a general andsystematic framework is needed.

In this article, we propose and discuss such a framework. This framework is aresult of many years of experience in designing new ecological monitoring pro-grams (e.g., Vos, 1992) and evaluating existing ones. We do not pretend to addcompletely new scientific insights to every individual component of the framework.What we do pretend is to present a comprehensive framework that clarifies therelation between components and, as a consequence, can give the right direction tothe choices within each component.

2. Scope of the Study

Monitoring can generally be defined as the repetitive measurement of a specifiedset of variables at one or more locations over an extended period of time accordingto prearranged schedules in space and time. However, to be effective, a monitoringprogram must be more than just data collection: it also involves all other activitiesneeded to present the results in an appropriate format to intended users, including,e.g., analysis and interpretation of data.

Although the general principles referred to in this article may apply to all typesof monitoring, we will focus our discussion on terrestrial biological monitoringprograms intended for environmental management and nature conservation, and,as a first approach, we will focus on monitoring of variables at the organizationallevel of plant and animal populations (e.g., densities of species, reproduction rates)or higher levels (e.g., number of species as one obvious aspect of biodiversity). Wewill refer to this as ‘ecological’ monitoring, thereby using this term to indicate thespecialization within the science of biology that has populations and ecosystemsas its object of study. This focus on ecological monitoring implies that we are con-cerned with measurements in the field, rather than with such techniques as remote

THE DESIGN OF ECOLOGICAL MONITORING PROGRAMS 319

sensing. These measurements will generally be performed at a set of well-selectedplots, in which case we speak of a monitoring network.

We will start our discussion with the functions of ecological monitoring indecision-making in politics and management, since this determines the desiredoutput of any monitoring program. Next, the framework will be introduced and therelevant aspects and components identified and described briefly. In the sectionsthereafter, we will discuss the individual components more thoroughly.

3. Functions of Ecological Monitoring

Surprisingly enough, many ecological monitoring systems are lacking clear pur-poses. A mere ‘knowing-what-is-going-on’ argument often seems to motivate theeffort. However, such a vague argument can not be used to derive clear objectivesand will easily lead to ‘datakleptomania’, i.e., the uncontrolled desire to collectmore data (Hellawell, 1991). A more fruitful starting point is to consider mon-itoring as part of a regulatory system with decision-makers and managers as thecontrollers (e.g., Ter Keurs and Meelis, 1986; Cullen, 1990; Maher and orris, 1990;Gray et al., 1991; Spellerberg, 1991; Kremenet al., 1994). This is schematic-ally represented in Figure 1. In this approach, the monitoring program providesinformation on the relevant ecosystem output and this information is confrontedwith aims or standards, which are set in relation to the desired functions of thesystem involved. Where these standards are not met, either new standards can beset, or remedial action can be undertaken. Subsequently, the information from themonitoring program can be used to control the effectiveness of the measures taken,i.e., whether aims are reached. Analogously, a monitoring program could be usedto assess the (anticipated) impacts of other large-scale human activities (e.g.,expostevaluation of Environmental Impact Assessments).

Obviously, the function within a regulatory system requires a monitoring designthat enables the identification of causes of detected change, i.e., there is a need fora strong analytical ordiagnostic power(e.g., Herrmann and Stottlemyer, 1991;Messeret al., 1991; Spellerberg, 1991; Slocombe, 1992; Furness and Greenwood,1993a; Greenwoodet al., 1993; Underwood, 1995). Davis (1993) draws a parallelwith the family physician, who not only assesses the patient’s current health, butalso identifies his illness and its causes, and suggests effective treatment.

In the process described above, two separate functions of monitoring can bedistinguished, each of which offers specific benefits:• An early-warning function. In an early stage, information from the monitoring

program can detect changes in the environment which might need remedialaction, and identify (possible or likely) causes of those changes to indicate thekind of remedial action needed. The benefits aimed at are the prevention ofpossible future damage. Costs required for the restoration can be saved.

• An early-control function. In an early stage, information from the monitoring

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Figure 1.The role of an ecological monitoring program as part of a regulatory system. Adapted fromTer Keurs and Meelis (1986).

program can be used to check whether remedial action is successful or not,and to evaluate the predicted or expected consequences of specific measuresor activities. Here, the benefits are doing away with inefficient measures oractivities with undesirable ecological side effects and replacing them by moreefficient measures. In addition, the political and financial support for measuresshown to be efficient can be reinforced.

Note that the two functions may require a different methodological approach to thecollected data and, as a consequence, may result in a different kind of managementdecision. In the case of the early-control function, the monitoring program maybe seen as a research program aimed at the testing of hypotheses concerning theecological consequences (‘effects’) of human activities (‘treatments’): the manage-ment decisions are considered ‘uncontrolled experiments’ (Underwood, 1995). Inthe case of the early-warning function, the monitoring program serves as a researchprogram that – through inductive reasoning or pattern recognition – can only aimat the formulation of hypotheses: is there an important ecological change, and if so,can any set of likely and manageable causes be identified? An implication of theabove is that the early-control function may indeed lead to direct action (to con-tinue, alter, or stop a measure as far as compatible with other important interests),while in the case of the early-warming function, caution is appropriate: only strongevidence for a particular hypothesis, or maybe application of the precautionaryprinciple (Grayet al., 1991; Peterman and M’Gonigle, 1992), may lead to directaction. In all other cases additional research aimed at testing the hypotheses whichhave been formulated may be needed.


Figure 2.A conceptual model of the system to be controlled.

4. The Framework

4.1. PURPOSES

Evidently, the design of any monitoring program should start with the identificationof the purposes. This can only be done if both the ‘decision-making system’ forwhich the information is needed and the (eco)system to be monitored are clearlyidentified, i.e., delimited and described.

The identification of the decision-making system is more than identifying thedecision-makers (i.e., the intended users) and must at least include the domainsand extents of their power. The identification of the system to be monitored ismore than delimiting the system in space and time (Musterset al., 1998). Basedon a conceptual model of the system, the relevant inputs, systems characteristics,and outputs have to be identified and subsystems may have to be distinguishedand described. An important distinction of (natural or anthropogenic) inputs isthat between ‘controlled’ (i.e., manageable by the identified decision-makers, bydefinition only anthropogenic) and ‘uncontrolled’ inputs (both natural and anthro-pogenic, the latter may be manageable by other decision-makers). For ecologicalmonitoring, the relevant outputs (‘valued endpoints’) are populations of speciesand communities. These valued endpoints can be chosen for their socio-economicvalues, either usefulness (fisheries, outdoor recreation) or harmfulness (pests), orfor their ‘intrinsic’ value as perceived by the public. All in between input and outputmust be considered as features of the system itself. They must be important forthe valued endpoints or for the relation between input and valued endpoints. Theycan be either permanent (e.g., soil type) or changeable, i.e., affected by the input(e.g., toxins in biota, reproduction rate). Terminology is summarized in Figure 2.During the process of the technical design, the conceptual model and (sub)systemboundaries may be adapted and refined repeatedly.

For a policy-orientated ecological monitoring program, the purposes ultimatelyconcern a check on and an increase in the efficiency of decision-making in envir-onmental and nature conservation policy and management. Therefore, the actual

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use of information resulting from a monitoring program is aconditio sine qua nonfor reaching the benefits of a monitoring program. As a consequence – and unlikemuch of the current practice (e.g., Spellerberg, 1991; Furness and Greenwood,1993a; Davis, 1993) – a program design should start with the measures taken byenvironmental managers as well as their future options, and with identifying thekind of information they need to make the right decisions.

4.2. TECHNICAL COMPONENTS

To identify the relevant technical components of a monitoring program, Green(1984) and Maher and Norris (1990) took the accepted scientific sequence as astarting point. This leads to four distinct components: monitoring objectives (ques-tions and hypotheses), sampling strategy (model of the system and sampling design),data collection (actual sampling), and data handling (analysis, interpretation, andpresentation). However, environmental monitoring is more than ‘just’ a one timeand unique experimental research concerning a limited number of questions andhypotheses concerning a specific object and set of variables. On the contrary, amonitoring program is often intended as a continuous activity designed to answeran array of current as well as future questions concerning an array of objects andvariables. One complication is that the choice of objects and variables is worth dis-tinguishing from the objectives. Furthermore, the continuous flow of data requiresspecial attention to data storage (as part of the component ‘data handling’). Also,the possible involvement of many persons and organizations in the monitoring pro-gram requires the dintinction of both ‘maintenance’ and ‘organization’ as separatecomponents. This leads to seven main components of the monitoring program:1. Monitoring objectives. Basically, two types of general objectives can be din-

stinguished: assessment of state and detection of change. Further specifica-tions concern desired precision and confidence, spatial resolution and timescale. Moreover, taking the information needs of managers as a starting point,identification of (possible or likely) causes of detected change or violation ofstandards should be part of the objectives.

2. Objects and variables. Objects and variables describing ecological states andtrends can be categorized into three types, depending on the argumentationused to select them: variables describing valued endpoints (‘final variables’),variables earlier in the causal chain (‘intermediate variables’, possibly earlywarners) and variables that may be used as surrogates for either final or in-termediate variables (‘indicators’). In order to identify causes of change, in-formation is also needed on the input, both ‘natural’ and anthropogenic, thelatter with a high priority for those variables that are directly or indirectlymanageable by the intended users.

3. Sampling strategy. Important choices concern methods of site selection (spe-cific, representative, regular, or random) and the possible subdivision of thetotal monitoring area (stratification). Depending on the set of variables to be


monitored, sampling (and as a consequence, stratification) can be one- or multi-stage. The sampling strategy is strongly related to the purposes and objectivesof the monitoring program (and more in particular, to the need to identifycauses of change) and to the intended statistical analysis: the strategy shouldenable the statistical analysis and interpretations needed to relate cause andeffect.

4. Data collection. During the design, choices concerning the (field)methods tobe applied, the total effort and its allocation over time and space (number andlocation of sites or plots, measurement frequency), must lead to a completesampling scheme. In the operational stage of ecological monitoring, the col-lection of data will put a substantial claim on the available means. Therefore,assessment and optimization of costs and effectiveness are important. Such op-timization is based on the intended statistical analysis, using statistical poweras an important measure of effectiveness.

5. Data handling, which includes data storage, (statistical) analysis of the data,and interpretation and presentation of the results. Especially, the possibly largeand surely continuous flow of data requires an operational data-base to beelaborated in advance and in place before the flow gets under way. Also, themethods for statistical analyses must be determined in advance to check forcompatibility with earlier choices concerning objects and variables, samplingstrategy, and data collection. Finally, the possibilities for presentation of theresults in an aggregated form need to be considered, balancing between datareduction and interpretability.

6. Maintenance, which includes not only a regular quality control of data collec-tion (e.g., by mutual field visits) and handling (e.g., by peer reviews), but alsoa regular evaluation of the entire program in the light of changing informationneeds and changes in the environment (e.g., a large-scale shift in land-use).Methods and moments of evaluation and quality control must be anticipatedduring the design process.

7. Organization, including all management aspects of the components of an op-erational program: data collection, data handling and maintenance.

4.3. COSTS AND BENEFITS

The benefits aimed for – an increase in efficiency of environmental management –do not come for free: the operational monitoring program itself will have structuralcosts of the actual data collection, data handling, maintenance and organization.In the design process, the allocation of the available means over these four com-ponents needs explicit attention: in practice there is a tendency to use the availablemeans to a great extent for data collection only, leaving valuable data idle dueto lack of manpower for data handling. Ideally, in the design process – and thesubsequent evaluations of an operational monitoring program – costs and benefitshave to be weighted against each other in an analysis of costs and benefits. This

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may lead to a change in the means made available, a reallocation of the means overthe various components, or an adaptation of the purposes with consequences for allthe components of the monitoring program. Such an analysis should also underliethe choice whether or not to actually implement a proposed program, or to continueor alter an operational one.

4.4. EXTERNAL CONSTRAINTS

Finally, options for each of the components can be limited by external constraints.These can be ecological (e.g., maximum resolution in relation to home ranges),technical (e.g., availability of field methods), methodological (e.g., lack of appro-priate control sites), statistical (e.g., impossibility to meet the assumptions for aparticular test), or physical (e.g., accessibility of sites). Note that, in principle,we do not consider the available means an external financial constraint: ratherwe would speak of the means made available as a management choice based onthe outcome of a cost/benefit analysis, thus making the choice part of the designprocess. In practice, however, a quantification of the benefits of the monitoringprogram will often be beyond reach and environmental managers will put a fixedpercentage of total management costs available for monitoring. In those cases, theavailable means can be considered an external constraint.

The described framework is schematically represented in Figure 3. The hier-archy and numbering of components suggest a chronological sequence of stepsin the design process. In this view, choices on any component will determine theoptions for following components (e.g., sampling strategy will strongly guide siteselection). However, options for any component can also be limited by externalconstraints, thus limiting possibilities for the preceding components (e.g., access-ibility of sites will determine the feasible sampling strategy). Thus feedback loopscan be thought to exist between any pair of components. In theory, all previouscomponents need to be reconsidered with any next step. As a consequence, thedesign will be an iterative process, running through all components several times,increasing the level of specification each time, testing specifications against ex-ternal constraints and adjusting choices. So the framework presented is more thanjust a step down plan (as proposed by Davis, 1993): aspects are too mutuallydependent for such an approach.

To illustrate the framework, in the following sections we will discuss eachcomponent in more detail.

5. Monitoring Objectives

The formulation of clear objectives may seem an obvious first step, but the lackof clearly defined objectives in current monitoring programs is regularly reported(e.g., Cullen 1990; Goldsmith, 1991; Furness and Greenwood, 1993a). Yet, many


Figure 3. Schematic representation of the framework for the design and evaluation of monitoringprograms.

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authors have explicitly stressed the need for clear-cut objectives, some of whomalso deal with the aspects to be specified: the entities to be assessed and the quant-itative characteristics of those entities (‘objects and variables’, dealt with in section7), the amounts and types of change to be detected (e.g., a decline of 5% per year),the area within which change must be detected (e.g., 20% of the total area), the timeallowed for detection (e.g., within 10 yr), and the desired levels of significance andpower or probability of detection. Such quantified objectives allow the evaluationof a monitoring program in terms of effectiveness: the degree to which a programmeets the objectives.

However, these specifications focus on the statistical power rather than the dia-gnostic power, which may be considered equally important. Only in combination,they make monitoring a powerful tool for decision-making. For the early-controlfunction, specifying the desired diagnostic power is straightforward: it means aspecification of the management measures or actions to be controlled for theireffectiveness or impact. Such an approach, driven by clear hypotheses about theimpact to be tested, though regularly advocated (e.g., National Research Coun-cil, 1990; MacDonald and Smart, 1993; Stout, 1993; Montgomeryet al., 1995;Morrison and Marcot, 1995), is not common practice. Yet, only this allows anevaluation of the diagnostic power: the degree to which differences in detectedchange between the different types of area can be causally connected to the mana-gerial actions under consideration. For the early-warning function, specification ofthe desired diagnostic power is less straightforward. Since there are no hypothesesto be tested, a specification would imply a complete enumeration of all possiblecauses of all possible ecological changes. This is, of course, beyond reach andwe have to content ourselves with specifying main categories of possible causes(e.g., the different types of ‘natural’ and anthropogenic factors). Subsequently, onecan evaluate whether all the necessary ingredients for data analysis and interpret-ation are present. These necessary ingredients will mainly concern measurementof those inputs and changeable features that would enable exclusion of possiblecausal mechanisms if they are not active.

6. Objects and Variables

6.1. TERMINOLOGY

There can be much confusion about the terminology for the different kind of vari-ables. One source of confusion is the use of the terms ‘variables’ and ‘indicators’,often used as synonyms. We prefer a clear distinction between the two, using theterm ‘variable’ for any quantitative aspect of an object of concern (e.g., local dens-ities as estimates for total population size), and using the term ‘indicator’ only forvariables used as ‘surrogates’ for other ones: their ‘target’ variables (e.g., speciesrichness in one taxon as surrogate for total species richness). Also, in distinguishing


Figure 4.The terminology used for the variables describing the different aspects of the system.

different types of variables, different approaches to terminology can be mixed up.For its general applicability, we will use the terminology related to the systemapproach. The terminology used is summarized in Figure 4.

There is a strong relationship with Figure 2, which can be considered as arepresentation of the objects of interest. Following De Groot (1992), output orvalued endpoints (e.g., population sizes of species) is described with the term‘final variables’ (e.g., numbers and densities per species). At the input side wedistinguish ‘controlled variables’, to describe anthropogenic inputs manageable byintended users (e.g., depositions/ha/yr; management regimes), and ‘uncontrolledvariables’ to describe (both natural and anthropogenic) inputs that are unmanage-able by intended users (e.g., weather conditions). Permanent features of the systemare describes as ‘condition variables’ (e.g., soil types) and changeable features as‘intermediate variables’ (e.g., concentrations of toxic matter in biota, mortality,reproduction rates). In the statistical context, both controlled and uncontrolledvariables are the independent (explaining or predictor) variables, final variablesare the dependent (effect or response) variables. Intermediate variables can playboth roles, depending on the question under consideration. Condition variables canbe seen as the covariables.

Finally, for all of the above types of variables, indicator variables can be used assurrogates: ‘values-indicators’ for final variables, ‘process-indicators’ for interme-diate variables, and ‘factor-indicators’ as surrogates for controlled and uncontrolledvariables. In the remainder of this section we will briefly discuss the argumentationused en to be used to select the different kind of variables.

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6.2. OUTPUTS: VALUED ENDPOINTS AND FINAL VARIABLES

Hinds (1984) identified the selection of ‘biotic conditions and activities to be mon-itored’ (i.e., final variables) as the major ecological difficulty in ecological mon-itoring. Spellerberg (1991) expressed the idea that ‘ideally the choice of variablesand processes (to be monitored) should have a wholly ecological basis’. Beingaware of the countless possibilities, many ecologists have spent much time andeffort to set priorities based on ‘ecological importance’, using descriptions such as‘sensitive’, ‘vulnerable’, ‘umbrella’, ‘representative’, ‘rare’ and ‘keystone’ species(e.g., Noss, 1990; Messeret al., 1991; Davis, 1993; Silsbee and Peterson, 1993;Kremenet al., 1994). All these attempts and statements share the idea that thequestion what final variables to monitor is a scientific one. For monitoring as amanagement tool, this idea is false. To be actually used in decision-making, themonitoring results must be meaningful not (only) for biologists, but above all theymust represent a societal and public interest. So the choice of final variables mustbe based on what managers think of as valuable and important enough to serve as atarget for management action: the choice concerns a normative, and thus political,rather than a scientific question.

There is, of course, a role for the science of ecology: not to decide what is tobe considered important, but rather to develop cost-effective field methods (thusdiminishing the need for a strong selection), and to inform decision-makers aboutthe logistic consequences (costs: finance, time, effort) of their options. These costsdo not have a one-to-one relationship with the number of selected species: theargument that biologists will be ‘outnumbered by species to be monitored’ (Keddy,1991) does not do justice to the practice where – once the field method has beenchosen – often all species that can be sampled with the method chosen are sampledanyway (including species that are rare, vulnerable, etc.). So the choice must focuson groups of species and the field methods to be used, rather than on a selectionof individual species. Biologists will not easily be ‘outnumbered’ by standard fieldmethods. The decision whether or not to use a certain field method – and thuswhether or not to sample a group of species considered of value – can then betaken on the basis of the effort needed (‘costs’) and the expected quality and utilityof the information gathered (‘effectiveness’).

However, the final choice of final variables remains a political one. In contrast,once the final variables are selected, a choice to use indicators instead, or to useintermediate variables should be based on scientific arguments.

6.3. VALUE-INDICATORS

In the literature, many requirements are mentioned which indicators should meet.They can be reduced to two main points: there should be a known and unambigu-ous relation between the indicator variable and the target variable, and it shouldbe more efficient to measure the indicator variable than the target variable itself,


either because the measurement is easier, cheaper or more accurate, or because theindicator has an earlier reponse.

In fact, the search for ‘umbrella’, ‘representative’, or ‘keystone’ species men-tioned earlier can be considered a search for value-indicators. Also, a selection ofsensitive or vulnerable species is often proposed as ‘early warners’, i.e., as indicatorfor future damage to (other) valuable components of the ecosystem. These attemptshave failed, not only because of questionable concepts (Millset al., 1993; Landreset al., 1988; Humphreyet al., 1990), but also because of the first requirement: aknown and unambiguous relation with their target variables: the final variables tobe indicated (which are, by the way, rarely specified). Obviously, the use of anindicator can only be recommended if its efficiency has been demonstrated in anadequate study (Landreset al., 1988). Murtaugh (1996) outlines the principles ofsuch research.

6.4. CHANGEABLE FATURES OF THE SYSTEM: INTERMEDIATE VARIABLES

There are several arguments to measure not only final variables, but also interme-diate variables somewhere in the causal chain between input and valued endpoint(e.g., concentrations of toxic matter in biota, physical condition of organisms, re-production rates). There is, however, a big danger of datakleptomania, especiallywhen an unspecified ‘better understanding of the system’ is the only purpose. Werecognize two valid arguments for measuring intermediate variables.

In the first place, such measurements may be advocated because they couldserve as ‘early warmers’ and could provide an earlier response to environmentalstress than the final variables. I.e., intermediate variables are used as value-indicatorsand could replace measurement of final variables. A strong relation may be ex-pected to exist when using variables ‘near the final variables’ (e.g., reproductionrate as an indicator for future trends in population size). Though this is a strongercase than using sensitive or vulnerable species, caution is still due (e.g., as long asrecruitment is sufficient, a decrease in reproduction rate will not necessarily lead toa change in population size): here also, a known and unambiguous relation betweenthe early warner and the target variable is required.

Secondly, measurements of intermediate variables may be advocated if they areshown to improve the diagnostic power of the monitoring program: they couldbe used to explain detected changes in final variables and help to identify causesof those changes. On the basis of this argument, measurement of intermediatevariables is additional to, rather than instead of, measurement of final variables.Obviously, in this case intermediate variables should only be monitored to theextent that improvements of the diagnostic power are needed.

6.5. INPUTS: CONTROLLED AND UNCONTROLLED VARIABLES

Many ecological monitoring programs focus on measurement of final variables,i.e., dependent variables, only. Most of these were initiated as volunteer programs,

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primarily motivated by an interest in the taxon under consideration. Many nationalbird monitoring programs are obvious examples. How useful these schemes maybe to detect trends and to draw attention to declining populations, they often failto serve as a management tool: the lack of information on independent variablesstrongly limits any diagnostic power. Measurement of independent, explainingvariables on the same temporal and spatial scales is aconditio sine qua nontodemonstrate relations beween input and output.

However, as at the output side, we can not measure everything of interest at theinput side either and priorities have to be set. A first priority is, of course, meas-urement of variables describing the management actions to be controlled for theirefficiency. They can be derived directly from the early control monitoring object-ives. In addition, measurement of a selection of other – controlled and uncontrolled– independent variables may be needed to be able to separate the impact of naturaland uncontrolled anthropogenic factors from the impact of the controlled variablesof interest. In the selection of ‘important’ independent variables, ecological sciencemust play a major role in pointing out the factors that are likely or are expected tohave an important ecological impact.

6.6. FACTOR- AND PROCESS-INDICATORS

Instead of direct measurement of the controlled variables or intermediate variables,indicators could be used. In fact, many authors seem to believe that monitoringbiotic variables is of value only if they indicate abiotic environmental conditionsor change (e.g., Gray, 1980; Morrison, 1986; Temple and Wiens, 1989; Maherand Norris, 1990; Koskimies, 1990; Furnesset al., 1993b; Salankiet al., 1994).Not only does this approach ignore the value of the organisms themselves, gener-ally there also are serious doubts concerning the efficiency of biotic variables as‘surrogates’ for abiotic factors: they depend on many different factors at the sametime, and they can rarely be measured more easily, cheaply or accurately than directmeasurements of environmental factors.

Of course, once a set of final variables is selected, these variables can be used –not to indicate values of controlled variables – but as help to get an idea about theactive mechanisms behind detected ecological change. Such ‘ecological indication’is possible only if the necessary ecological knowledge (e.g., concerning habitatrequirements) is available. This may apply to macrofauna in the water environment(Hellawell, 1991), to vegetation (e.g., using the figures of Ellenberg, 1974), and –to a much lesser extent – to birds (Furness and Greenwood, 1993a). Note that thereis an essential difference between such ‘ecological indication’ and the use of factor-indicators: there is no attempt to indicate values of specific independent variables,and there is no need for an unambiguous relation with any abiotic environmentalvariable to defend their inclusion.

One of the examples of ‘real’ factor-indicators is the use of ‘bioaccumulators’,which is fairly well-established in the water environment (Hellawell, 1991). For


most independent variables, however, no indicators are available, nor necessary: in-formation on most controlled variables can be collected from management admin-istration or other measurement systems, including physical-chemical monitoringprograms.

7. Sampling Strategy

Once the objectives have been set, and the objects and variables have been selected,a general sampling strategy has to be elaborated. The strategy is the methodolo-gical core of the program: it determines the confidence with which statisticallysignificant (spatial differences in) ecological changes can be related to specificcauses. Basically, what we want is to be able to make (future) statistical compar-isons of ecological change between areas that differ in ‘treatment’ concerning onecontrolled variable, but which are otherwise equal. Such ‘ceteris paribus’ compar-isons are our strongest diagnostic tool. To maximize future possibilities forceterisparibus comparisons, we need both a stratification on permanent features of thesystem (on ‘condition variables’) and aa priori stratification on the expected ‘treat-ments’: i.e., the expected changes in the anthropogenic inputs or ‘controlled vari-ables’. Note that these stratifications imply a specification of the general conceptualmodel of the system, mainly by identifying relevant subsystems.

Within the resulting strata, there are four general possibilities for site selection(Silsbee and Peterson, 1993; Greenwood, 1996): specific, ‘representative’, regularand random site selection. The latter is clearly the best: it allows all statisticalanalyses based on randomness, while not excluding model-based analysis, and itallows conclusions to be generalized to the larger areas (strata) from which thesites were drawn. With randomized site selection, there is still the choice betweenrenewed random selection at every new round of measurement (‘reallocation’) andthe use of randomly selected, but permanent plots (‘resampling’). As demonstratedby Green (1989), resampling generally results in improved statistical power, as spa-tial variability can be excluded from the noise. It is only at very low spatial scalesthat reallocation might be preferable (e.g., in order to avoid observer-induced bias,such as trampling effects). Therefore, we advocate a stratified random, resamplingstrategy for any policy-orientated monitoring program.

A stratification on condition variables must be based on relatively permanent,relevant features (Careyet al., 1995). What has to be considered as ‘relativelypermanent’ depends on the time-horizon specified in the monitoring objectives.E.g., on a time scale of years, vegetation structure may be a permanent feature, ona time scale of decennia, it may be a changeable feature of the system, or even avalued endpoint. ‘Relevant’ permanent features are those which can be expected todetermine the relations between controlled variables and final variables (e.g., theecological impact of acidification on vegetation will depend on soil type). Thus,within the resulting strata, a set of specific relations between controlled inputs and

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valued endpoints are thought to exist. In the absence of mapped information onpermanent features, in practice there is a tendency to use information on final vari-ables instead (e.g., the distinction of ‘good’, ‘average’ and ‘bad’ areas). Obviously,this should be avoided: the resulting strata will not reflect potentials (dependingon condition variables), but rather the realization of these potentials (i.e., alsodepending on input variables and the stochasticity in population dynamics).

The resulting strata might be referred to by terms like ecozones, ecoregions,ecodistricts, landscape types, or landscape elements, depending on the spatial scalerelevant for the considered objects. When aiming for a (national or international)ecological monitoring network which integrates measurements on objects rangingfrom large mammals to vegetation, a hierarchical stratification is needed. Wienset al. (1986) and Wiens (1989) recognize intuitively and arbitrarily four scales forgeneral utility. Multiple examples of hierarchical land classifications can be foundin Wiersmaet al. (1996).

Note that, for long term ecological monitoring systems (operational for muchlonger than the time-horizon specified in the monitoring objectives), a regularcheck is required on the quantities and spatial distribution of the distinguishedlandscape units. In those cases, they must be added to the list of variables to bemonitored, albeit on a different time scale.

A stratification on controlled variables must distinguish areas that differ in thevalue of controlled variables, or in changes in those values, expected or predictedon the basis of the actual and intended policy or management. Here, thea pri-ori hypothesis-testing advocated by several authors (e.g., Green, 1984; Wolfeetal., 1987; Maher and Norris, 1990) is introduced in the design process, using themanagement actions as treatments in ‘experiments’, albeit uncontrolled by science(Underwood, 1995). In the simplest case, the areas to be distinguished coincidewith the areas where a specific measure will or will not be taken (e.g., hydrologicalunits where the water table will or will not be changed). In many cases, however,the place where the actual measure is taken and the places where its impact may beexpected will differ. The most obvious example is a measure to control the emissionof a toxic substance from a particular source. The ecological impact will dependon the distance from the source and we may distinguish several strata along thisgradient. For both types, we will refer to the resulting strata as ‘treatment areas’.

There is one frequently heard objection against such a pre-stratification on con-trolled inputs: since the number of strata is an exponential function of the numberof controlled variables used for stratification, it would lead to an unmanageablylarge number of strata. However, this problem is not avoided by abandoning apre-stratification, but only postponed until the analysis of the data. Moreover, theproblem may not be as large as it seems: when analyzing the impact of a specificclass of treatments, data might be lumped over all other treatments, as long as theceteris paribuscondition is still valid. Nevertheless, there is, of course, a limit tothe number of controlled variables to be used, depending on the feasible number


of sampling plots. Priorities have to be set, based on the extent and spatial scale ofthe expected impact.

The combination of the two above mentioned stratifications will result in unitsthat may be regarded as our future basic strata. They represent relatively homogen-eous areas characterized by a unique combination of values for condition variablesas well as controlled variables. We shall refer to these strata as our ‘target areas’.Obviously, these target areas do not have to be spatially continuous: often they willconsist of a set of fragments, especially on smaller spatial scales, i.e., smaller units.

Once the target areas have been identified and mapped, a more specific formula-tion of testable questions and monitoring objectives is appropriate. E.g., from ‘doesthe policy of emission reduction of matter X have an impact on the abundance ofspecies A?’ to ‘is there a difference in trend in abundances of species A betweentarget areas that differ only with respect to the change in immissions of matter X?’This enables a separate evaluation of the statistical power (probabilities of detectinga specified difference in trend) and the diagnostic power (ability to link detectedtrends to the treatment under consideration).

8. Data Collection

Obviously, the target areas also constitute the basis for our future choice of fieldsampling sites. However, first we must address the question of the amount ofsampling effort needed per target area, i.e., field methods including sample areaor plot size, sampling frequency (maybe on various time scales), the number ofsampling sites (maybe on various spatial scales), and the number of replications oneach sampling occasion and site.

An overview of field methods can be found in Clarke (1986) and Salankiet al.(1994). In order to maximize observer reliability, there is a strong preference forsimple field methods with as few subjective elements as possible: no interpretationin the field, counts rather than estimates, and the like. Given a field method, choicesof frequency and number of sites and replications will determine the effectivenessof the monitoring program in terms of statistical power. At the same time, they de-termine the costs of actual data collection. The costs associated with the collectionof field data will represent a substantial part of the total costs of an ecologicalmonitoring program. Therefore, it is important that these choices are based onthe calculated cost-effectiveness of the different alternatives (Hinds, 1984; Maherand Norris, 1990). In many operational monitoring programs, this optimizationproblem has been neglected (Green, 1989) and, surprisingly enough, attention tothis problem and the related topic of statistical power is limited and of fairly recentdate in the ecological literature.

The principles of such an optimization are quite simple: for each alternativefield method used to measure a variable, we need to formulate both costs andeffectiveness as a function of sampling frequency, number of sampling sites, and

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number of replicates. Formulas to calculate costs can generally be simple withoutsubstantially challenging reality, but the calculation is less simple for the effect-iveness. First of all, we need a measure to express the extent to which monitoringobjectives are being met in practice. For this we need to quantify our monitoringobjectives as described earlier (section 5). A convenient measure of effectivenessis the probability of detection, given all other parameters and a specified statisticaltechnique. For a general discussion on statistical power, see for instance Peterman(1990).

The power does not depend solely on the sampling effort and allocation ofmeans over time and space, but also on the variability related to different sourcesand on different temporal and spatial scales (e.g., from overall yr-to-yr fluctu-ations to measurement error). These can be derived from the literature or estimatedin a pilot program (MacDonald and Smart, 1993; Silsbee and Peterson, 1993).After variances and costs have been estimated for each alternative field method,an analysis of the cost-effectiveness should indicate the best field method andoptimal allocation of means over space and time for that method. The resultswill, of course, be species-dependent. For field methods used for more species, theallocation of means can be based on the amount of power lost when choosing sub-optimal allocation (in combination with the value attributed to individual species),or on ‘average’ variabilities within the group of species. Vos (1992) used the latterapproach for a monitoring network for meadow burds.

In the described approach, there is one major problem: the data underlyingestimates in the literature are rarely collected on the proper space and time scales,and also pilot programs are limited in space and time. Appropriate estimates ofvariability and costs can only be extracted from the monitoring program itself. Theimplication is that a ‘final’ optimal allocation of the available means can only becalculated and implemented after the monitoring program has been operational forseveral yr.

Once the total available means and the effort per target area needed for a desiredpower are known, choices can be made concerning the question whether all finalvariables can or must be sampled in all target areas. This is, of course, an iterativeprocess: given the total available means, lowering the standards for effectivenessper target area will increase the number of target areas that can be sampled suffi-ciently intensive. Here again, the desired diagnostic power, directly related to thepossibilities forceteris-paribuscomparisons, should be an important additionalcriterion. The information required to evaluate these possibilities can be deriveddirectly from the results of the stratifications.

The last step in the preparation for the actual data collection consists of theselection of the exact locations for sites and plots (random within each target area),the exact dates for the measurements (random or regular within a season or ‘indexsample period’) and the assignment of observers to sites and plots. The latter needssome special attention. For practical reason, observers are often assigned in a sys-tematic way according to geographical position, land ownership and management


control. As a consequence, observer turnover will strongly correlate with thesefactors, and thereby with treatment patterns: any difference in observer qualities –the literature on the inter- and intra-observer-reliability alone would fill a bookshelf– will lead to biased estimates of trends and treatment impact. Instead, a yearlyrandomization of observers over sites, though at first glance not very practicable,must be recommended: it offers possibilities to use ‘observer’ as an additionalexplaining variable in the statistical analysis and thereby to correct for differencesbetween observers. If no observer correction is used, random assignment will leadto more noise rather than to bias, as will be the case with systematic assignment.Obviously, in this case noise is to be preferred over bias, since the latter makesit impossible to exclude the change in observers as a potential cause for detectedtrends in the data.

The above will lead to a complete sampling scheme for the field work withinthe monitoring system. This field monitoring will be mainly restricted to the meas-urement of final and intermediate variables. In addition, information on controlledand uncontrolled variables is needed: most of this information can be collectedfrom management administration and other (e.g., physical and chemical) measure-ment systems. Finally, for ecological monitoring systems meant to be operationalfor a long period, or even permanently, a regular measurement of the quantitiesand spatial distribution of the dinstinguished landscape units is necessary. Theuse of remote sensing techniques is in this respect self-evident. For the long run,this would lead to a monitoring system which uses remote sensing techniques tomonitor quantities of landscape units, and field work to monitor their ecologicalqualities.

9. Data Handling

Once a sampling program has been established and implemented, data will flowin regularly over a long period. This continuous flow requires well-consideredmethods for data handling: not only storage and analysis of the data, but alsopresentation and publication of the results. Or, as Stafford (1993) puts it: ‘do untodata before data do unto you’.

Of crucial importance is the timely design and implementation of a computer-ized data management system, in order to prevent an accumulation of informationon paper. Though a database management system can be developed by standardmethods, it will take substantial time and money before it is operational. In ourexperience, underestimation of the effort needed has led too often to postponement,thus creating a situation where the lack of a proper database system is a (verynarrow) bottleneck in the functioning of the operational monitoring program.

Once data are being collected and stored, statistical analysis of data should takeplace at regular intervals. Though no general recommendations for the statisticalmethods can be made, the basic principles for data originating from a stratified ran-

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dom design are fairly clear. For the early-control function, the different treatmentareas represent the different treatments. Statistical analysis aims at the testing ofhypotheses concerning the impact of these treatments. Thus analysis starts withthe grouping of sites according to the relevant controlled variables, followed bya statistical comparison of the ecological changes between the treatment categor-ies. For the early-warning function of the monitoring program, analysis is moreexploratory and aims at detecting ecological change and formulation of hypo-theses concerning the possible causes of that change. Here, analysis may startwith estimating site-specific change in dependent variables (e.g., by regressionor change-point analysis) and a subsequent grouping of sites according to theestimated change (e.g., by cluster analysis or principal component analysis). Ananalysis of the spatial and temporal pattern of change and the spatial and temporalpatterns of (change in) the measured controlled and uncontrolled variables can leadto hypotheses about causes of change.

Note that the described principles of analysis require information on, or meas-urement of, independent variables. This holds also for the early control function,where actual measurement of independent variables is necessary to check whetherthe pre-stratification on controlled variables is justified. In fact, a pre-stratificationis nothing more than an attempt to assure a balanced design as the result of apost-stratification.

Whatever techniques will be used, the analysis of the collected data – and sub-sequent publication of the results – will be time consuming and a sufficient largepart of the available means has to be reserved for this purpose.

Evidently, the target audience being managers and politicians rather than sci-entists, there are special requirements for the presentation and publication of theresults. Basically, a presentation must have a transparent content in an aggregatedand synthesized form. Weighty reports crammed with text, tables and figures showa tendency to end up in bottom drawers, but reducing ecological quality to one fig-ure makes results impossible to interpret, and thereby susceptible to manipulation.Currently, integrated measures or indices of environmental and ecological qualityare in development (e.g., Indices of Biological Integrity, examples can be found inWiersmaet al., 1998). Although such integrated measures have been criticized inthe context of data analysis (e.g., Suter, 1993), and can be criticized for their lack oftransparency, they can be useful for concise presentation if accompanied in someway by the underlying information. Ten Brink (1991) proposes a graphic present-ation which combines conciseness and transparency, but which is hard to use inrepresenting time series. However, these attempts are important steps towards abalance between the two.

A second requirement concern the possible use of the information; not only eco-logical change needs to be described, but also the possible causes, and perhaps eventhe different management alternatives for remedial action. Another requirementconcerns explicit presentation of the reliability of the information. Risk assessmentof different alternative management choices (e.g., whether or not to apply the pre-


cautionary principle) is impossible if estimates are not accompanied by confidenceintervals, and statistical tests not by information on statistical power (Petermande M’Gonigle, 1992; Van Strienet al., 1994). Finally, for a proper functioningof democracy, all parties directly or indirectly involved in environmental policymaking should have access to the same information. Therefore, regular reportingin public media is important. All aspects mentioned are necessary to ensure aproper use of the information, which is aconditio sine qua nonfor an efficientpolicy-orientated monitoring program.

10. Maintenance

Sampling strategy and design, data collection and data handling represent the bodyof the monitoring program. Clearly, a regular evaluation or quality control of eachof these components is necessary to maintain the quality of the program. Duringthe design and planning of a monitoring program, it should be established howquality control will take place and again, the required means should be set asidefor this purpose.

Regular evaluation of the sampling strategy (i.e., the stratifications) is neededsince both the stratifications based on management ‘treatments’ and those based onrelatively permanent features may ‘run out of data’. Also, new ecological know-ledge or changes in information needs may call for adaptations. Finally, the actualdesign (numbers of sites, frequency) demands regular re-optimization based onestimates of costs and variabilities, using data of the monitoring program itself.However, any adaptation will result in a change in sampling locations, therebyinterrupting time series. The pros and cons for a change of sampling locationsmust therefore always be carefully weighed.

Quality control of the data collection should not primarily aim at the highestfeasible, but rather at the highest sustainable quality. Any change in quality willlead to biased estimates of change: quality needs to be constant rather than as highas possible. However, in ecological monitoring, many field methods are difficultto standardize. At the same time, there will be a turnover in observers. As a con-sequence, the requirement of maintaining constant quality will always constitute aproblem. This requirement can partly be met by choosing simple field methods withno or few subjective elelements. Other possibilities include joint field visits to in-troduce the field method standards to new observers, and to readjust the differencesbetween observers. Attempts to readjust the data by ‘labeling’ different observerson the basis of separate experiments is not to be recommended: the accuracy withwhich these labels should be assessed seems well out of reach.

The most important aspects of the quality control of data handling concernsthe statistical analysis and the subsequent interpretation of the results. To assurequality, the National Research Council (1990) proposes regular peer reviews. Wewould in addition advocate a free access (e.g., via World Wide Web) to the database

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for all parties interested: managers, researchers, non-governmental organizations(NGO’s) as well as environmentalists. This would guarantee permanent mutualquality control. Unfortunately, since information represents money and power, inmany cases the reality is still far away from this ideal (but see, for instance, Haleet al., 1998).

11. Organization

Last, but not least, the organizational aspects of the monitoring program – respons-ibility for the data collection, data handling and maintenance – must be consideredduring the design process. This is, of cource, of extreme importance in large-scalemonitoring programs involving a great many people. Obviously, affiliation withorganizations possessing the relevant expertise and capacity should be sought. Ingeneral, these will be either public bodies and institutes, or NGO’s made up ofvolunteers.

Ideally, the organizations responsible for the monitoring program should beindependent (Ter Keurs and Meelis, 1986), in order to avoid a situation where theyhave an interest in a certain output. Governmental bodies and institutes can not, ofcourse, be considered independent of the decision-makers and managers. There isalways a danger that governmental bodies will present results in a way that placestheir own policy in a favorable light. In practice, complete independence is notrealistic, if only for financial reasons. This enhances the argument in favour of freeand easy access to the database for all interested parties. Also, to avoid criticism inretrospect on the information gathered, it is important for all interested parties toagree on the information needs and general design beforehand. For this reason, allthese parties should be involved in the planning of a monitoring program at an earlystage. A ‘scoping’ procedure, like in Environmental Impact Assessments, could beappropriate.

A substantial contribution of volunteers seems indispensable in large-scale eco-logical field monitoring. In fact, much of the data in current ecological monitoringprograms is collected by volunteers and coordinated by NGO’s. How wonderfulthis ‘information for free’ may be, it does raise several problems. One of theseconcerns the public access to data: many NGO’s prefer to have full control overthe use of their data, not only for financial reasons, but also to prevent ‘misuse’.Another problem is that volunteer organizations tend to survey species-rich areasrather than randomly chosen plots, leading to substantial bias (Fulleret al., 1985;Verstraelet al., 1990; Palmer, 1993). In the Netherlands, the methodological gapsin the design for a nation-wide ecological monitoring program, taking the presentvolunteer-based programs as a starting point, have yet to be determined, but theresulting additional plots are thought to be sampled by professionals. Whether theavailable professional capacity will be enough to fill the gaps remains to be seen,while also this approach risks to introduce bias, assigning professionals system-


atically to the species-poorer areas. Meanwhile, supervising the volunteers maynot be as difficult as it seems: in England, the Breeding Bird Survey has changedto randomly chosen plots without any substantial problem (Marchant, 1994). Ifvolunteers are convinced of the importantce of a proper spatial distribution of plotswith the consequent increase in usefulness of their joint efforts – and are regularlyinformed about results – they will be willing to sample randomly chosen plots.The involvement of private organizations in the design process is important in thisrespect as well.

12. Discussion

Above, we have discussed what we believe to be the most important aspects ofmonitoring programs that need attention during the planning and use of a monit-oring program. However, two issues may deserve some further discussion. Firstly,to serve as management tools in decision-making, monitoring systems must havea high diagnostic power. In the methodological sense, this means a high abilityto infer causal relationship between management actions and valued endpoints.At the same time, monitoring is restricted to observational data, and one mightquestion the possibility to infer causal relationships from observational data atall. Secondly, the monitoring systems that result from a strict application of thepresented framework may end up being quite complex, particularly when aimingfor large scale, integrated ecological monitoring. One might question whether suchcomplicated monitoring systems can be realized at all.

The use of observational data to establish causal relationships is controversial.Some authors (e.g., Tilman, 1989; Drew, 1994) state that only experiments canidentify causality, and this thought is spoon-fed to students in many courses. Thatwould be a very sad message for environmental scientists, since the spatial andtemporal scale inherent to most of the important environmental problems (e.g., cli-mate change) exclude experimentation as an option. However, in our view, realityis not as black and white: even the most elegant experiments leave space for doubts,and at the other hand well-designed observational studies can minimize doubt. Inneither case, one hundred percent certainty can be guaranteed.

The conditions necessary to demonstrate causality are comprehensively dis-cussed in the literature (e.g., Smith and Sugden, 1988; Olsen and Schreuder, 1997)and can be summarized in three main criteria related to experimental design:1. consistency: at any place and at any time, there should be a strong association

between level of putative stressor and level of symptoms;2. responsiveness and temporality: at any place and at any time, exposure to the

putative stessor should reproduce the symptoms;3. exclusion of alternative explanations: there should be no consistency and/or

responsiveness with alternative hypothetical stessors.

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The essential difference between experiment and observational studies concerns, ofcourse, the assignment of treatments. In experiments, a random assignment is usedto exclude causal factors other than the one under investigation. As a consequence,no associations are to be expected of the treatment with other independent factors,nor with the response variables, and comparisons of effects between the differenttreatments will beceteris paribus. As a consequence, the exclusion of alternat-ive explanations is relatively easy. In observational studies such associations canbe expected to exist. This holds particularly for those studies where the ‘treat-ments’ consist of decision-making in politics and management (and which, aswe may hope, will not be randomly assigned): e.g., nature conservation measureswill be concentrated on locations where nature is threatened (i.e., associated withstressors), but still valuable (i.e., associated with response variables). Also, in ex-periments, treatments can be applied repeatedly and to multiple plots, whereas inobservational environmental studies, there may be only a ‘one time’ treatment inone location. Though the use of multiple controls can resolve the problem of pseu-doreplication (Hurlbert, 1984; Underwood, 1992), the one time one place nature ofthe treatment prevents the criteria of consistency and responsiveness to be met. Sofor policy-orientated ecological monitoring, we do seem to have a problem.

The stratification proposed in this paper, not only the one based on conditionvariables, but especially the one based on controlled input variables, is part of thesolution. Though stratification can not eliminate associations between stressors inthe real world, it can, however, minimize associations within the collected data.Also, and equally important, it can demonstrate the remaining associations. Thusit enables ana priori evaluation of the possibilities forceteris paribuscomparis-ons. Also, the measurement of well-chosen additional (intermediate) variables, andecological indication derived from final variables, can help to exclude alternativeexplanations. Finally, the integration of measurements on many different speciesgroups into one monitoring system can help: not only may one group of speciesserve as intermediate variables for another group, also ecological indication willbe more convincing if supported by different groups of species. All strategies to-gether will add to the ability to exclude alternative explanations and to indicatethe active mechanisms and processes, thus minimizing uncertainty. The remaininguncertainty has to be dealt with by the decision-makers. For important issues witha high degree of uncertainty, they have two options: start up additional research (ifpossible) or, with the precautionary principle in mind, implement remedial action.

If one is convinced of the usefulness of policy-orientated ecological monitoring,the question remains whether it can be done. For small scale monitoring systems,limited to a specific field of policy and limited to a specific group of species, thereis no problem. Following the framework presented, the design and implementationcan be relatively straightforward. For a national ecological monitoring system to beeffective, keywords are ‘integrated measurements’ and ‘multiple scale hierarchicalstratification’. The elaboration of the design for such a system will be complex, and,at the operational stage, many people will be involved. This makes the design not


really straightforward and implementation difficult. Moreover, the design processwill not be as centrally organized as implicitly suggested by the framework. Thismeans that it is not realistic to aim for an implementation of the complete design allat once. The other extreme – continuing a set of separate, incoherent monitoringsystems – is, of course, undesirable. In our view, the framework can be used todescribe an ‘ideal’ integrated ecological monitoring system as a long term target. Aclear image of, and consensus about, such a desirable future monitoring system canguide adaptations of, and additions to, existing monitoring networks. This wouldcontribute to the realization of the ideal monitoring system in the long run.

In the absence of comparably comprehensive frameworks in the internationalliterature, we developed and used the presented framework for the design and eval-uation of several policy-orientated ecological monitoring systems (for an example,see Vos, 1992). It proved to be very helpful, especially in ensuring that all relevantchoices are in sight, and that these choices are made at the right moments andbased on the right arguments. The framework also brings order in the complexityresulting from many interrelated choices, so that they can be handled more eas-ily and possibilities and opportunities can be exploited to the full. However, thepossibly most important feature of this systematic approach is that shortcomingscan be identified in advance. This means that the decision whether or not to imple-ment a designed monitoring system can be based on objective information aboutthe performance to be expected. As a consequence, in the operational stage, theprogramme will be able to live up to the expectations.

Acknowledgments

This article is mainly based on work that was conducted under funding by theDutch Ministry of Housing, Spatial Planning and the Environment and the DutchMinistry of Agriculture, Nature Conservation and Fisheries.

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