Environmental Monitoring - Part 5 pdf

443 Discriminating between the Good and the Bad: Quality Assurance Is Central in Biomonitoring Studies G. Brunialti, P. Giordani, and M. Ferretti CONTENTS 20.1 Introduction 444 20.2 Quality Assurance 444 20.3 Errors 445 20.4 Monitoring in a Variable Environment Needs Proper Sampling Design 446 20.4.1 Environmental Factors as Source of Noise in Biomonitoring Data 447 20.4.2 Inherent Variability Requires Unambiguous Objectives 449 20.5 Environmental Factors and Sampling Design 449 20.6 Indicator Development 451 20.7 Observer and Measurement Errors 452 20.7.1 Too Complex or Too Lax Sampling Protocols May Induce Relevant Observer Errors 452 20.7.2 Type of Sampling Measurements 453 20.7.3 Taxonomic Skill of the Operators 454 20.7.4 Observer Error in Lichen Diversity Monitoring 455 20.7.5 Observer Error in Ozone Monitoring with Tobacco Plants 457 20.7.6 Observer Error in Tree Condition Surveys 458 20.7.7 Management of the Learning Factor 459 20.7.8 Time Required for Each Sampling Phase 459 20.8 Conclusions 460 References 460 20 L1641_C20.fm Page 443 Tuesday, March 23, 2004 9:00 PM © 2004 by CRC Press LLC 444 Environmental Monitoring 20.1 INTRODUCTION Variability is an inherent property of ecological systems and every attempt to measure and interpret the environment should consider it. Unfortunately, not only variability in the system is of concern for those involved in environmental monitoring, an entire range of actual and potential sources of variability connected to the survey design, methods, and operators should be taken into account. 1–3 Differences between methods, difference in the application of the same method, measurement error, sampling and nonsampling error, and errors related to model applications are all terms of the whole error budget that inevitably affects environmental surveys. 4 In this perspective, the extent to which the objective of the survey is matched depends very much on the ability to manage the various sources of variability. 1,5 While such a management is complex, it always depends on the documentation of the various steps of the investigations. Documentation allows tracking all the steps undertaken to carry out the investigation of concern and helps in identifying when and where problems occur. However, documentation can be properly achieved only by ade- quate quality assurance (QA). 1,6–8 Politicians, administrators, and decision makers may be not very interested to know with what degree of confidence a certain population parameter was estimated by the survey they are presenting to the public; for example, colored maps showing the spatial variation of lichen diversity as indicator of air pollution are usually much more attractive than statistical details. However, their attitude may change considerably when the survey results are used, for example, to stop a (supposed) harmful power plant and the owners of the plant challenge (in terms of money) such a decision in the court. In this case, every statistical detail about the accuracy and precision of the survey data will be very much welcome. This example suggests that, if biomonitoring should be taken as a serious basis for decision making, it needs to produce robust, defensible data of documented quality. In short, biomonitoring needs QA and it should make the differences between “good” (e.g., of documented and therefore known quality) and “bad” (e.g., undocumented and therefore of unknown quality) monitoring programs. The aim of this chapter is to recall the basic QA procedures, emphasize the need for a formal design in biomonitoring studies, and provide examples of data quality control in various fields of environmental biomonitoring, with special reference to air pollution monitoring by means of lichens, sensitive tobacco plants, and sponta- neous vegetation. 20.2 QUALITY ASSURANCE Some definitions of the main activities that have to be carried out in all phases of a biomonitoring program in order to assess the quality of the data are reported here. Reference will be made to these phases in the rest of the section using the abbrevi- ations given here. QA is an organized group of activities defining the way in which tasks are to be performed to ensure an expressed level of quality. 9 The main benefit of a QA plan is the improved consistency, reliability, and cost-effectiveness of a program L1641_C20.fm Page 444 Tuesday, March 23, 2004 9:00 PM © 2004 by CRC Press LLC Discriminating between the Good and the Bad 445 through time. 8 A QA plan is essential since it forces program managers to identify and evaluate most of the factors involved in the program. In addition, the assessment of data quality enables mathematical management of uncertainty due to the method used. 8 Cline and Burkman 6 consider four main activities in a QA program which take all the steps of the monitoring survey into account: 1. Quality Management (QM). This concerns the proper design of the project and its documentation. It ensures that the proper activities are performed in the proper way. QM activities include, for example, the choice of the proper sampling strategy to be adopted (i.e., where and how the sampling stations have to be located). 2. Quality Assurance (QA). This concerns the first steps of evaluation of the quality of the data. It includes the use and documentation of standard operating procedures. All the activities defined in the sampling protocol are examples of QA procedures. In the case of lichen moni- toring, these activities include the selection of sampling subplots, the selection of standard trees, and the positioning of the sampling grid on the tree. 3. Quality Control (QC). This concerns mostly the training, calibration, and control phases. It ensures that data are collected appropriately and that QA is carried out. 4. Quality Evaluation (QE). This concerns mainly the statistical evaluation of the data quality. These activities enable the precision and accuracy of the data collected by the operators to be evaluated, providing the basis for comparability of the data. 20.3 ERRORS Environmental data for large areas are generally assessed by sample-based methods. The objective of a sample-based survey is to select a subset — the sample — from the population of interest and to estimate population parameters based on probability theory. 10 Obviously, these parameter estimates differ from the true population as they are subject to different sources of errors. 5 Errors can be classified into four major categories: 2,5,11 1. Sampling errors: These errors are generated by the nature of the sampling itself and by the degree of data variability. In general, sampling errors can be reduced by increasing the sample size and by introducing a more cost- efficient sampling design. 2,5,11 2. Assessment errors: These errors incorporate measurement and classifica- tion errors. They can occur when the methodology is poorly standardized, when insufficient care is devoted to its application, or when there are problems with instrument calibration. 2 3. Prediction errors: Many attributes in environmental resources assessment are not directly assessed but derived by models. In this case it is assumed that based on the input values the true population value is derived. Models L1641_C20.fm Page 445 Tuesday, March 23, 2004 9:00 PM © 2004 by CRC Press LLC 446 Environmental Monitoring and functions, however, are subject to errors, which are defined prediction errors (see Kohl et al. 5 for a complete review). 4. Nonstatistical errors: These kinds of error include human errors and may affect the data quality in all the phases of the sampling. They are frequent, ubiquitous, and can be very serious. They usually originate from errors in measurement, sampling, and/or data processing. Examples are mistakes in data entry, programming errors, and errors in defining the sample frame. 2 In order to improve the interpretation of survey results and to review the benefit of the retrieved information, the total error of estimates has to be quantified. Some authors introduced the terms “error budget” 5,12,13 or “total sampling design” 14 to define this parameter. The error budget provides a calculation of the total error affecting the survey estimates, which can be achieved by a mathematical model that accounts for the various error sources. A general parameter of the studied population adopted to calculate the total survey error is the mean square error of an estimate. Köhl et al. 5 report the following formula by Kish 12 : (20.1) where is the mean square error, ∑ r S r 2 is the sum of all variance terms ( S r ) from multiple error sources, and ( ∑ r B r ) the squared sum of the biases ( B r ) 20.4 MONITORING IN A VARIABLE ENVIRONMENT NEEDS PROPER SAMPLING DESIGN According to Yoccoz et al., 15 we can define monitoring as the process of gathering information about some system-state variables at different points in time for the purpose of assessing the state of the system and making inferences about changes in state over time. If our focus is on the monitoring of biological diversity, the systems of interest to us are typically ecosystems or components of such systems (e.g., com- munities and populations), and the variables of interest include quantities such as species richness, species diversity, biomass, and population size. 15 In the assessment of environmental quality by means of biomonitors, it is important to control the variability of biological data, which often affects the forecasting precision of these techniques. 16 According to Kovacs, 3 the quality of the data orig- inating from biological measurements depends heavily on at least three factors: (1) variability of the biomonitoring organisms (interactions between the organisms and environmental factors), (2) operators involved in data collection (especially for methods requiring taxonomic knowledge), and (3) type of sampling (sampling design, density of sampling points). The selection of a proper (suitable) sampling design represents the first step to reduce data variability due to sampling error. When selecting the proper design, the objectives of the survey and environmental variability should be taken carefully into account. MSE(y) 2 =+       ∑∑ SB r r r r 2 MSE(y) L1641_C20.fm Page 446 Tuesday, March 23, 2004 9:00 PM © 2004 by CRC Press LLC Discriminating between the Good and the Bad 447 20.4.1 E NVIRONMENTAL F ACTORS AS S OURCE OF N OISE IN B IOMONITORING D ATA Environmental factors such as geomorphology, climatic variables, and substrate could have a great impact on the ecosystem property being studied in order to assess environmental quality, such as the rate of indicator species, the biodiversity of a community, or the presence of injuries on organisms. For this reason, it is important to understand the environmental processes and the interactions underlying the changes they undergo. Large-scale monitoring programs cover large geographical regions and raise the question of how to deal with the great differences among ecosystems found in the various areas. 17 Different species show various patterns of geographical distribution irrespective of anthropogenic effects on the environment; neither is it reasonable to expect their ranges of distribution to be constant over time even in the absence of human activities. The factors that determine the ranges of distribution and geographical patterns in species diversity are not well understood, and, in some cases, it is therefore difficult to separate the natural pattern of variations from the effects of human activities. 17 Due to this variability, study of environmental properties with known cause– effect relationship is to be preferred. Indeed, the higher the potential to isolate the cause, the lesser is the error in interpreting the data. For some methodologies the cause–effect relationship is more obvious, for example, in the case of ecotoxicolog- ical experiments or in the case of ozone-sensitive tobacco, in which noise is easily identified since the cultivar Bel W3 is preferentially sensitive to ozone-related inju- ries. For other types of biomonitoring, it is more difficult to discriminate between the influence of a single variable and that of the others. With reference, above all, to biodiversity studies, we should remember that environmental processes are dynamic: populations of organisms are in a constant state of flux. Intensive small-scale studies to calibrate the response of subsets of all species will enhance understanding of the generality and predictability of the trends indicated by the response of subsets. 18 This type of approach enables a valid model for interpreting data obtained on a large scale to be developed. 18 Several examples 19 carried out a study aimed at standardizing Lichen Biodiversity monitoring in a Mediterranean region. In particular, they analyzed the influence of the great geomorphological variability and of substrate characteristics on epiphytic lichen vegetation. The results obtained in a small area suggested the use of less restrictive parameters in the sampling protocol (namely, olive trees with an inclination of the trunk >30˚ were also suitable for biomonitoring relevés). Further information on the applicability of this method was obtained by means of an in- depth study on three tree species monitored in the same microclimatic conditions, showing that the results obtained in the sample area could be extended to vaster areas. 20 In general, these investigations are useful in the preliminary stages of preparation of the sampling protocol and for developing models enabling the infor- mation obtained to be extended on a large scale. A wide array of models has been developed to cover aspects as diverse as biogeography, conservation biology, climate L1641_C20.fm Page 447 Tuesday, March 23, 2004 9:00 PM © 2004 by CRC Press LLC of such an approach are present in the literature (see, for example, References 19, 20, and 21). Giordani et al. 448 Environmental Monitoring change research, and habitat or species management. Conceptual considerations should relate to selecting appropriate procedure for model selection. Testing the model in a wider range of situations (in space and time) will enable the range of applications for which the model predictions are suitable to be defined. 22 An important issue is selectivity which seems particularly important in ecological measurement. A protocol is selective if the response provided as a measurement depends only on the intended ecosystem property. 17 Regarding this aspect, Yoccoz et al. 15 suggest that quantitative state variables characterizing the system well should be privileged. For example, when defining management objectives in terms of changes of densities of indicator species, the program should incorporate tests to ensure that selected species are indeed indicators of the process and variables of interest. 15 From the point of view of application, some examples of processing for limiting errors due to environmental variability in interpreting ILB values (Index of Lichen Biodiversity) are reported by Ferretti and Erhardt 2 and by Loppi et al. 23 According to this method, the ILB values are calculated as percentage deviations from natu- ral/normal conditions (Table 20.1), i.e., from a maximum ILB potentially measurable in a natural area. In Italy, ILB values are interpreted according to different scales 21,23 depending on the bioclimatic regions, 24 determined on the basis of the distribution of indicator lichen species and of the main meteorological and climatic parameters (rainfall, altitude, and temperature). This approach was also used in other types of biomonitoring, as, for example, in the case of bioaccumulation of trace elements in lichens. 25–27 The advantage of this interpretation is that it enables data measured on TABLE 20.1 Interpretative Scales for Index of Lichen Biodiversity Values Scored in the Humid Sub-Mediterranean Bioclimatic Region (Thyrrenian Italy) and in the Dry Sub-Mediterranean Bioclimatic Region (Adriatic) in Italy % Deviation from Natural Condition IBL Score (Thyrrenian Italy) IBL Score (Adriatic Italy) Naturality/Alteration Classes 100 0 0 Lichen desert 76–100 1–25 1–20 Alteration 51–75 26–50 21–40 Semi-alteration 26–50 51–75 41–70 Semi-naturality 25 >75 >70 Naturality Note: The scales are based on percentage deviation from maximum score potentially assessed in background natural conditions. Source: Modified from Loppi, S. et al., A new scale for the interpretation of lichen biodiversity values in the Thyrrenian side of Italy, in Progress and Problems in Lichenology at the Turn of the Millennium, 4th IAL Symposium (IAL 4) , Llimona, X., Lumbsh, H.T., and Ott, S., Eds., Bibliotheca Lichenologica , 82, J. Cramer in der Gebruder Borntraeger Verlagsbuchhandlung, Berlin, Stuttgart, 2002, 235 and unpublished data. 21 L1641_C20.fm Page 448 Tuesday, March 23, 2004 9:00 PM © 2004 by CRC Press LLC Discriminating between the Good and the Bad 449 a regional scale to be compared with data on a national scale. Inevitably, however, these developments are only approximations that do not always lead to a reduction in error, since they tend to simplify the effects of the interactions between organisms and the environment. For this reason, it is important that the quality level of the data that can actually be achieved should be consistent with the level of predictability suggested by the interpretation of the results. Some authors, 28,29 for example, have observed different bioaccumulation rates in different lichen species collected under the same environmental conditions. As a consequence, the combined use of different species in the same bioaccumulation survey has to be verified previously to check the correlation among elemental concentration in the accumulator species. Examples of calibration of interpretation scales in different bioclimatic regions are also reported in other field of environmental monitoring such as in bioaccumulation in mosses 26 or in macroinvertebrates for the assessment of fresh water quality. 30 20.4.2 I NHERENT V ARIABILITY R EQUIRES U NAMBIGUOUS O BJECTIVES An explicit and well-defined objective is the major driver of the whole design process. 2 Once the nature of the study is identified, the unambiguous definition of the objectives involves the explicit identification of: • Assessment question: Careful attention should be paid in the phase of definition of the objectives of the program. Different objectives require different monitoring designs. 31 As a consequence, the scope of inference of the study and the data collected depend on the aim of the study. If the monitoring objectives are clearly stated, it will be easier to describe the statistical methods to be used to analyze the data. 17 • Target population: Unfortunately, monitoring programs do not always define their target population in an explicit statement. In many cases, the statement is insufficiently clear to determine whether a potential sample unit is included or excluded from the target population. 17 • Geographical coverage: The area to be considered by the investigation, as also the characteristic of the area, are important when considering the proper sampling design; e.g., large vs. small survey areas or flat vs. geomorphological complex areas. 20.5 ENVIRONMENTAL FACTORS AND SAMPLING DESIGN As previously reported, depending on the objectives of the investigation, environ- mental data for large geographical units are generally collected by sample surveys. The objective is therefore to select a subset from the population of interest (the sample) that allows inferences about the whole population. A good sampling design is essential to collect data amenable to statistical analyses and to control errors in relation to the costs. Many environmental programs address the assessment of abundance and richness as state variables of interest. 15 However, when these results have to be extended to L1641_C20.fm Page 449 Tuesday, March 23, 2004 9:00 PM © 2004 by CRC Press LLC 450 Environmental Monitoring a vast geographical range, it is important to estimate the detection probabilities associated with the selected count statistics and survey methods. McCune and Lesica 32 found tradeoffs between species capture and accuracy of cover estimates for three different within-site sample designs for inventory of mac- rolichen communities in forest plots. On average, whole-plot surveys captured a higher proportion of species than did multiple microplots, while giving less accurate cover estimates for species. The reverse was true for microplots, with lower species captures and much better cover estimates for common species. Belt transects fell between the over two sample designs. Similar results were obtained by Humphrey et al. 33 in a study to assess the differences in the species richness of lichen and bryophyte communities between planted and seminatural stands. A high percentage of species was recorded only once and very few species were common to more than half the plots. This “local rarity” phenomenon has been noted in other studies 34–36 and is partially related to sampling area. The authors of that study observe that it is possible that a 1-ha sampling plot used is too small to capture a representative sample of lower plant diversity in forest stands. For example, Rose 37 recommends a minimum sampling area of 1 km 2 , but again, this depends on the objective of the survey. Recently, the influence of different sampling tactics in the evaluation of lichen biodiversity was performed in a test study in Italy. In the first sampling method (Table 20.2), the five trees nearest to the center of the sampling unit were selected, within a Primary Sampling Unit of 1 km 2 . In the second sample, the operator moved from the center of the square in all of the cardinal directions, took a circular plot with radius of 56 m (Secondary Sampling Units — SSUs) and selected (if there were) TABLE 20.2 Sampling Procedures in Four Methods for Assessing Lichen Biodiversity Sampling Tactic Plot Dimension and Shape No. of Trees and Selection Procedures Method 1 1 km 2 primary unit The 5 trees nearest to the center of the primary unit Method 2 4 circular subplots (secondary sampling units — SSU) with 56-m radius, within 1 km 2 primary unit The 3 trees nearest to the center of each plot Method 3 4 circular subplots (secondary sampling units — SSU) with 125-m radius, within 1 km 2 primary unit The 3 trees nearest to the center of each plot Method 4 10 circular random plots with 30-m radius, within 1 km 2 primary unit All the trees within the plots Note: Details in the text. L1641_C20.fm Page 450 Tuesday, March 23, 2004 9:00 PM © 2004 by CRC Press LLC Discriminating between the Good and the Bad 451 the three trees nearest to the center of each plot. In the third sampling tactic, the SSUs had a radius of 125 m. Finally, in the fourth method, used to obtain the true average ILB value, a random selection of 30-m-radius SSU was adopted. In each secondary unit a census of the trees within the plot was carried out. Significant differences in average ILB values were found between the checked tactics. As a result, the third tactic gives the better estimation of the average ILB of the sampling units and it can find a sufficient number of sampling trees, whereas a 56-m-radius SSU is too small to find a proper number of trees. The first tactic (five trees nearest the center) often takes to a “clustering error,” i.e., the trees selected are grouped in a small portion of the sampling units and are not representative of the whole area. Another important aspect to consider is the sampling density, which needs to be defined in relation to the objectives of the study and to its spatial scale. Ferretti et al. 38 used two datasets of lichen diversity (LD) surveys at the subnational level in Italy for establishing the sampling density that can be cost-effectively adopted in medium- to large-scale biomonitoring studies. As expected, in both cases the relative error on the mean LD values and the error associated with the interpolation of LD values for (unmeasured) grid cells increase with decreasing sampling density. How- ever, it was possible to identify sampling density able to provide acceptable errors with quite a strong reduction of sampling efforts. This is important, as reduction of sampling effort can result in a considerable saving of resources that can be used for more detailed investigation in potentially problematic areas. 20.6 INDICATOR DEVELOPMENT We can define “indicator” as a character or an entity that can be measured to estimate status and trends of the target environmental resource. 39 Further, an “index” is a characteristic, usually expressed as a score, that describes the status of an indicator. 8 Response indicators should demonstrate the following features: 2,39–41 • Correlate with changes in processes or other unmeasured components such as the stressor of concern • Have a broad application, both at local and at large scale • Integrate effects over time • Provide early warning on future changes in ecosystem condition • Provide distinctive information, e.g., cause–effect • Be related to the overall structure and function of ecosystems • Have a low and standard measurement error • Have a sufficient reference on the effective applicability on the field • Be cost effective The development of indicator and indices is important in environmental moni- toring 39 above all to obtain concise information from a complex environment. The process for indicator development should be taken into account including all the phases of the sampling, from the assessment question to the selection of core indicators and the evaluation of the performance of the indicators adopted. L1641_C20.fm Page 451 Tuesday, March 23, 2004 9:00 PM © 2004 by CRC Press LLC 452 Environmental Monitoring For this reason it is important to establish a priori the variables of interest in a sampling protocol. An example of a rigorous selection of the variables to be measured is given by EMAN, the Ecological Monitoring and Assessment Network implemented in Can- ada. 42 In this program a core of suitable variables capable of identifying departures from normal ranges of fluctuations in key ecosystem parameters were selected to detect early warning of ecosystem change. The criteria for this selection were based on data quality, applicability, data collection, repeatability, data analysis and inter- pretation, and cost-effectiveness. 20.7 OBSERVER AND MEASUREMENT ERRORS In implementing the guidelines for biomonitoring methods, the documentation of standard operating procedures and a proper sampling design are only the first steps towards meeting Quality Assurance Objectives. 6 To assess the reliability and consistency of the data, two activities above all are fundamental: training of the personnel involved in data collection and field checks on reproducibility of data. 43 Observer and measurement errors are important issues, especially in large-scale and long-term studies involving many surveyors and sub- jective estimates of a given attribute. 43–45 Metric measurements, often considered in forest health assessment (dbh, distance between trees, etc.), depend closely on the precision of the instrument used and they are generally easily repeatable and repro- ducible. When considering methods based on visual estimation, the instrument is the human eye. Visual assessments are quickly made and do not require expensive equipment, chemical tests, or highly trained personnel, but their subjective nature is a matter for concern. 46 Measurements based on visual estimation are consequently less precise and repeatable and less accurately reproducible. Many different factors may contribute to the variability of application of a visually based assessment method: the operational manual used and the accuracy and precision of the operator, which in turn define his/her position on the calibration curve. The main causes of error due to the operators involved in biomonitoring surveys imental protocol used: the wrong type of measurement and imprecision of the instruments, the need for more experience in taxonomy, and faulty timing of the various phases. 20.7.1 T OO C OMPLEX OR T OO L AX S AMPLING P ROTOCOLS MAY I NDUCE RELEVANT OBSERVER ERRORS A sampling protocol that calls for excessively complex procedures might not be easily applicable on a large scale and by inexperienced operators. The applica- bility of a protocol can be assessed by measuring precision and reproducibility in the framework of resampling surveys. The development of preparatory studies, conducted perhaps on a small scale and in controlled conditions, is a good method for assessing both the applicability and the repeatability of an experimental protocol. 47 L1641_C20.fm Page 452 Tuesday, March 23, 2004 9:00 PM © 2004 by CRC Press LLC are reported in Table 20.3. It is possible to distinguish errors relating to the exper- [...]... study, Conserv Biol., 10, 99, 1996 53 Beattie, A.J and Oliver, I., Taxonomic minimalism, Trends Ecol Evol., 9, 488, 1994 54 Oliver, I and Beattie, A.J., A possible method for the rapid assessment of biodiversity, Conserv Biol., 7, 56 2, 1993 55 Will-Wolf, S., Esseen, P.-A., and Neitlich, P., Monitoring biodiversity and ecosystem function: forests, in Monitoring with Lichens Monitoring Lichens, Nimis, P.L.,... Ecol., 157 , 1 65, 2001 25 Nimis, P.L et al., Biomonitoring of trace elements with lichens in Veneto (NE Italy), Sci Total Environ., 255 , 97, 2000 26 Bargagli, R., Trace Elements in Terrestrial Plants: An Ecophysiological Approach to Biomonitoring and Biorecovery, Springer-Verlag & R.G Landes, Berlin, 1998, chap 8 and 9 27 Bargagli, R and Mikhailova, I., Accumulation of inorganic contaminants, in Monitoring. .. S.L and Kutz, F.W., Environmental data in decision making in EPA regional offices, Environ Monit Assess., 51 , 15, 1998 17 Olsen, A.R et al., Statistical issues for monitoring ecological and natural resources in the United States, Environ Monit Assess., 54 , 1, 1999 18 Will-Wolf, S and Scheidegger, C., Monitoring lichen diversity and ecosystem function, in Monitoring with Lichens — Monitoring Lichens,... data quality in lichen biomonitoring studies: the Italian experience, Environ Monit Assess., 75, 271, 2002. 45 TABLE 20 .5 Percentage Accuracy Scored by the Operators in Lichen Biomonitoring Surveys Before and After a 7-Month Training Period Before Quantitative ILB Number of species Taxononomic identification After 72.4 ± 14.4 64.6 ± 14.3 34 .5 ± 13.01 84.6 ± 10.1 81.1 ± 8.6 56 .2 ± 17.9 Source: Adapted... agreement (no difference) was reached in 49% of cases, while deviations ±1 class were obtained in 35% of cases © 2004 by CRC Press LLC L1641_C20.fm Page 458 Tuesday, March 23, 2004 9:00 PM 458 Environmental Monitoring n = 4 350 60 ±1.96*Dev Std ±1.00*Dev Std Media Frequency (%) 50 40 30 20 10 0 0 1 2 3 4 5 6 7 8 Difference between Observers FIGURE 20.1 Difference between observers: 0 means no difference... biomonitoring (and other) studies REFERENCES 1 Wagner, G., Basic approaches and methods for quality assurance and quality control in sample collection and storage for environmental monitoring, Sci Total Environ., 264, 3, 19 95 2 Ferretti, M and Erhardt, W., Key issues in designing biomonitoring programmes Monitoring scenarios, sampling strategies and quality assurance, in Monitoring with Lichens — Monitoring. .. C.T., New concepts in environmental monitoring: the question of indicators, Sci Total Environ., Suppl., 77, 1993 40 Breckenridge, R.P., Kepner, W.G., and Mouat, D.A., A process to select indicators for monitoring of rangeland health, Environ Monit Assess., 36, 45, 19 95 41 Muir, P.S and Mc Cune, B., Index construction for foliar symptoms of air pollution injury, Plant Dis., 71, 55 8, 1987 42 Tegler, B.,... Environ., 2 75, 43, 2001 30 Houston, L et al., A multi-agency comparison of aquatic macroinvertebrate-based stream bioassessment methodologies, Ecol Indicat., 1, 279, 2002 31 Knopman, D.S and Voss, C.I., Multi-objective sampling design for parameter estimation and model discrimination in groundwater solute transport, Water Resour Res., 25, 22 45, 1989 32 McCune, B and Lesica, P., The trade-off between... Netherlands, 2002, chap 14 56 Will-Wolf, S., Scheidegger, C., and McCune, B., Methods for monitoring biodiversity and ecosystem function, in Monitoring with Lichens — Monitoring Lichens, Nimis, P.L., Scheidegger, C., and Wolseley, P., Eds., Kluwer Academic, Dordrecht, Netherlands, 2002, p 11 57 McCune, B et al., Repeatability of community data: species richness versus gradient scores in large-scale lichen studies,... results and conclusions The use of guilds or morphological groups as indicators for monitoring changes in ecosystem function has been considered by several authors 55, 56 as a good compromise between the need for specialized knowledge and rapid field procedures employing nonspecialist technicians In particular, McCune et al .57 observed that random subsamples of species (community level) tended to produce . Italy) Naturality/Alteration Classes 100 0 0 Lichen desert 76–100 1– 25 1–20 Alteration 51 – 75 26 50 21–40 Semi-alteration 26 50 51 – 75 41–70 Semi-naturality 25 > 75 >70 Naturality Note: The scales are based. Operators 454 20.7.4 Observer Error in Lichen Diversity Monitoring 455 20.7 .5 Observer Error in Ozone Monitoring with Tobacco Plants 457 20.7.6 Observer Error in Tree Condition Surveys 458 20.7.7. the L1641_C20.fm Page 455 Tuesday, March 23, 2004 9:00 PM © 2004 by CRC Press LLC and quality control procedures. Table 20.4 shows an intercalibration ring test con- 456 Environmental Monitoring groups