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(BQ) Part 2 book “Ecotoxicology – A comprehensive treatment” has contents: Disturbance ecology and the responses of communities to contaminants, community responses to global and atmospheric stressors, effects of contaminants on trophic structure and food webs, effects of contaminants on trophic structure and food webs,… and other contents.
of 24 Application Multimetric and Multivariate Approaches in Community Ecotoxicology The most distinct and beautiful statement of any truth must take at last the mathematical form (Henry David Thoreau, in Walls 1999) 24.1 INTRODUCTION Methods to assess the effects of contaminants and other anthropogenic stressors on communities range from computationally simple indices such as species richness to complex, computer-dependent algorithms such as multivariate analyses The simplest community indices use species presence/absence or abundance data to show how individuals in the community are distributed among species The advantages of these indices are their intuitive meaning and their ability to reduce complex data to a single number Only slightly more involved but retaining more information, species abundance curves described in Chapter 22 characterize the distribution of individuals among the species by fitting abundance data to specified distributions Estimated distributional parameters from species abundance models provide a parsimonious description of the community Slightly more involved composite measures require additional knowledge about community qualities (e.g., the trophic status of a species) to produce indices developed specifically to gauge diminished community integrity due to anthropogenic stressors Currently, the most popular of these composite indices is Karr’s (1981) index of biological integrity (IBI) These composite indices require more ecological knowledge of the community than measures of species richness or species abundance models but have the advantage of being focused primarily on human effects on communities or species assemblages More convenient, but perhaps applying less ecology than warranted, distributions of individual species effect metrics (e.g., distributions of 96-h LC50 values) are used to predict “safe concentrations” that presumably protect all but a specified, low percentage of the species making up the community Even more computationally intense methods, such as multivariate analyses, aim to reduce the number of data dimensions to an interpretable low number, and to quantify similarities or differences among sampling units These last methods tend to generate interpretive parsimony at the expense of methodological simplicity and straightforward terminology; therefore, considerable caution is needed to avoid errors during their application However, the value of these methods in identifying clear explanations from complex data sets makes worthwhile any effort spent wading through obtuse computer manuals or dealing with the associated jargon Jargon, not argument, is your best ally in keeping him from the Church (Lewis 1942) 473 Clements: “3357_c024” — 2007/11/9 — 18:34 — page 473 — #1 474 Ecotoxicology: A Comprehensive Treatment 24.1.1 COMPARISON OF MULTIMETRIC AND MULTIVARIATE APPROACHES Multimetric and multivariate approaches are applied to community data with the intent of rendering the associated complex array of information to a more parsimonious form Because ecological assessments of biological integrity generally require analysis of numerous biotic and abiotic variables, sophisticated statistical approaches are often necessary to examine the complex relationships between species assemblages and multiple environmental factors Multivariate approaches reduce complex, multidimensional data to two or three dimensions, thus allowing researchers to identify key environmental variables responsible for patterns of species abundance In contrast, multimetric indices integrate a diverse suite of measures, often across several levels of biological organization, to assess biological integrity It is appropriate to consider these two approaches together because the community data necessary to calculate a multimetric index or to conduct multivariate analyses are often the same (e.g., abundance, richness, and composition) In their comparison of multivariate and multimetric approaches, Reynoldson et al (1997) concluded that multivariate approaches provided greater accuracy and precision for assessing reference conditions in streams Terlizzi et al (2005) showed that univariate measures of molluscan community structure (species richness) showed little response to contamination whereas multivariate analyses identified significant differences between reference and polluted sites Thomas and Hall (2006) compared the ability of individual metrics, multivariate approaches, and multimetric indices to identify impairment in periphyton, macroinvertebrate, and fish communities Although some individual metrics were associated with large-scale habitat gradients, multivariate approaches were most useful for identifying spatial and temporal differences in each community In a comprehensive analysis of community indices and multivariate approaches, Kilgour et al (2004) compared the relative sensitivity of seven benthic community metrics and three multivariate indices to contamination associated with mines, pulp and paper mills, and urbanization Multivariate approaches identified significant differences associated with each of the perturbations and greater effect sizes compared to the community metrics Although the examples described above seem to highlight the greater discriminatory ability of multivariate approaches, the usefulness of univariate and multivariate techniques for distinguishing between reference and contaminated sites will likely vary with the spatial scale of an investigation (Quintino et al 2006) Despite their growing popularity in Canada and Europe, multivariate approaches have received considerably less attention in the United States (Resh et al 1995) Multivariate analyses have been criticized because of their inherent statistical complexity and because results are often difficult to interpret (Fore et al 1996, Gerritsen 1995) The complex graphical representations of multivariate results are often of limited value to non-ecologists and managers Although strict reliance on complex statistical algorithms may obscure important biological results, we believe that multivariate approaches are an essential set of tools for biological assessments of water quality Because community–environment relationships are inherently multidimensional, approaches such as multivariate analyses that consider interactions among predictor variables and their effects on multiple response variables are necessary New approaches, such as the application of principal response curves (Pardal et al 2004), quantify multivariate community responses to contaminants in ways that are more accessible to managers and policymakers We agree with the recommendations of Reynoldson et al (1997) that multivariate and multimetric approaches are complementary and should be used in conjunction For example, the variables used in multivariate analyses such as principal components could include species richness, abundance of sensitive groups, or other measures typically included in a multimetric index Griffith et al (2003) used this approach in their evaluation of the relationship between macroinvertebrate assemblages and environmental gradients Multivariate statistical analysis (redundancy analysis) using metrics derived from an index of biotic integrity provided complementary results to canonical correspondence analysis based on macroinvertebrate abundance Alternatively, Clements: “3357_c024” — 2007/11/9 — 18:34 — page 474 — #2 Application of Multimetric and Multivariate Approaches in Community Ecotoxicology 475 a multimetric index similar to Karr’s IBI could be developed using results of multivariate analyses Loading coefficients from canonical discriminant analyses, principal component analyses (PCA), and other multivariate procedures identify variables that are most important for separation of groups (generally locations, sampling stations) Variables shown to be responsible for separation of reference and impacted stations could be combined in a multimetric index Integration of multivariate and multimetric approaches may be necessary to detect perturbations when relatively weak relationships between stressors and community structure exist (Chenery and Mudge 2005) Finally, we note that our enthusiasm for multimetric and multivariate approaches in communitylevel bioassessment is not shared by all researchers Weiss and Reice (2005) remind us that neither of these approaches provides causal linkages between stressors and community-level responses These researchers advocate an alternative approach in which effects of stressors on individual taxa with known species-level tolerances are employed to develop an overall assessment of community-level impact 24.2 MULTIMETRIC INDICES A principal objective of the 1972 Federal Water Pollution Control Act and its 1977 and 1987 amendments is to restore and maintain the biological integrity of the nation’s waters (Miller et al 1988) One of the most significant advances in the field of biological assessments over the past 20 years was the development and application of multimetric approaches for measuring ecological integrity Because no single measure of impairment will respond to all classes of contaminants, and because some individual metrics may show unexpected changes (e.g., increased species richness at polluted sites), multimetric indices are an effective tool for measuring effects of stressors (Fausch et al 1990, Karr 1981, Kerans and Karr 1994, Plafkin et al 1989) The individual metrics in a multimetric index reflect different characteristics of life history, community structure, and functional organization In general, as the number of metrics increases (up to some reasonable number), the ability to separate contaminant effects from natural variation increases (Karr 1993) (Figure 24.1) In addition, because Ecological attribute One metric Two metrics Threshold value Level of stressor Level of stressor FIGURE 24.1 Hypothetical relationships between stressor levels and ecological attributes characterized using one or two metrics The threshold value of the ecological attribute is defined as the response that is considered to be biologically significant For example, a researcher may conclude that a 20% reduction in abundance of a sensitive species is a biologically significant response The responses of the individual metrics are represented as clouds of points and the level of the stressor known to affect the ecological attribute is represented by the black bar Note that addition of a second metric provides a more refined measure of the stressor level that causes a biologically significant response (Modified from Figure in Karr (1993).) Clements: “3357_c024” — 2007/11/9 — 18:34 — page 475 — #3 476 Ecotoxicology: A Comprehensive Treatment individual metrics respond differently to different classes of contaminants, multimetric approaches are useful for assessing a diverse suite of stressors and measuring impacts in systems receiving multiple stressors The individual metrics included in a multimetric index may vary among perturbations, but should reflect important structural and functional characteristics of the system In general, deviation of individual metrics from expected values at reference sites is estimated and a final value that includes the sum of all individual metrics is calculated Karr’s (1981) IBI is the most widely used multimetric index for assessing the health of aquatic communities The IBI was developed in response to the federally legislated mandate to “restore and maintain the chemical, physical, and biological integrity” of U.S waters (Clean Water Act 1977, PL 95-217, also 1987 PL 100-4) Originally employed in Midwestern streams in the United States, the IBI is based on 12 attributes of fish assemblages in three general categories: species richness and composition, trophic composition, and fish abundance and condition The individual metrics are assigned scores (1, 3, 5) based on their similarity to expected values in undisturbed or least impacted streams Expected values for the individual metrics are obtained by sampling a large number of known reference sites in a region Alternatively, expected values can be derived from surveys of reference and impacted sites and using the “best” values from these samples (Simon and Lyons 1995) Because expected values for species richness and total abundance vary with stream size, these metrics must be adjusted to reflect watershed area and other regional conditions The scores of the 12 metrics are summed to yield a total IBI score for a site (which ranges from 12 to 60), with larger values indicating healthy fish assemblages The IBI is sensitive to a diverse array of physical and chemical stressors, including industrial and municipal effluents, agricultural inputs, habitat loss, and introduction of exotic species The IBI works especially well for characterizing fish communities because environmental requirements and historic distributions of this group are well known This greatly facilitates establishment of expected values for individual metrics The structural and functional metrics included in the IBI are biologically relevant, and each individual metric responds to known gradients of degradation (Fausch et al 1990) The general approach outlined in the IBI has been modified for other ecosystems (e.g., lakes and estuaries) and applied to other taxonomic groups (e.g., benthic macroinvertebrates and diatoms) Although the specific metrics vary among these applications, comparison of measured values to expected values and integration of a suite of metrics into a single index are consistent among approaches A multimetric index for benthic macroinvertebrate communities was used to distinguish polluted from reference sites in rivers of the Tennessee Valley (Kerans and Karr 1994) The benthic IBI (B-IBI) was found to be highly effective because benthic macroinvertebrates generally respond to chemical and physical degradation in a predictable fashion The IBI now enjoys such popularity that the term, IBI, has come to be applied to any new composite or multimetric index Calculating multimetric indices involves comparing individual metrics measured at an impacted site to the expected values for the region (Figure 24.2a) As described above, because some metrics (e.g., species richness) are greatly influenced by stream order and watershed area, these expected values must be adjusted to reflect natural variation (Figure 24.2b) Assuming that community responses to other landscape variables are predictable, a logical extension of this approach is to create models to account for natural variation across broad geographical areas Bailey et al (1998) found that simple geographic characteristics (distance from source, catchment area, elevation) and year sampled accounted for greater than 50% of the variation among reference sites The performance of several bioassessment metrics was significantly improved when a predictive model that included this geographic variation was employed to identify impacted sites The conventional approach of comparing metric values at impacted sites with expected values at reference sites has now advanced to the point where we can characterize habitat variation within subregions using more sophisticated multivariate statistics (Figure 24.2c) The application of multivariate techniques for assessing reference conditions is described below Clements: “3357_c024” — 2007/11/9 — 18:34 — page 476 — #4 Application of Multimetric and Multivariate Approaches in Community Ecotoxicology (a) 477 (b) Range of expected metric values at reference sites Expected metric value Metric value Test site Metric al erv % 95 c en fid n Co nt ei Test site Test site Metric Habitat gradient Multivariate axis (c) 95% Confidence ellipsoids Test site Test site Test site Multivariate axis FIGURE 24.2 Multimetric and multivariate approaches for comparing test sites to expected values at reference sites (a) Two metric values at a test site (indicated by solid circles) are compared to expected values Values are within the expected range for metric 1, but below the range of expected values for metric (b) Metric values are adjusted to reflect expected changes in habitat characteristics along a gradient Although the metric value at test site is greater than at test site 1, it is less than the expected value and would indicate impact (c) Multivariate analysis of expected metric values based on regional differences in habitat characteristics Test sites and are within the expected values whereas test site falls outside the 95% confidence ellipsoid 24.2.1 MULTIMETRIC APPROACHES FOR TERRESTRIAL COMMUNITIES Although multimetric indices such as the IBI have been limited primarily to aquatic ecosystems, the general approach could be modified for terrestrial communities Because of their sensitivity and rapid response to environmental stressors, terrestrial arthropods would be especially useful for assessing biological integrity (Kremen et al 1993) Nelson and Epstein (1998) investigated the responses of lepidopterans to habitat modifications and concluded that butterfly communities integrate important structural and functional characteristics of terrestrial ecosystems Kremen (1992) evaluated the indicator properties of butterfly communities and reported that this group was quite responsive to anthropogenic disturbance Bird communities also offer opportunities for development of integrated measures of ecological integrity The abundance, distribution, and habitat requirements of birds are generally well known, especially in North America National monitoring programs, such as the Christmas Bird Counts conducted by the Audubon Society and Breeding Bird Surveys, have provided spatially extensive, long-term data on bird assemblages Finally, responses of bird populations to some environmental stressors, especially pesticides and habitat alterations, have been well documented However, given the logistical difficulties of sampling bird communities, developing a suite of ecologically relevant indicators for this group will be a challenge In Clements: “3357_c024” — 2007/11/9 — 18:34 — page 477 — #5 Ecotoxicology: A Comprehensive Treatment 478 Species richness of butterflies 16 14 12 10 10 15 20 25 30 Species richness of birds FIGURE 24.3 The relationship between species richness of birds and butterflies at sites along a gradient of urban development Obtaining quantitative data for certain taxonomic groups, such as birds and small mammals is often expensive and logistically challenging The close relationship between these measures suggests that butterflies, which are relatively easy to monitor, can be used as a surrogate to predict the response of birds to stressors (Modified from Figure in Blair (1999).) particular, surveys must be corrected to account for differences in detectability among species and among locations (Chambers et al 1999) One promising alternative is to predict effects of anthropogenic stressors on bird communities based on characteristics of surrogate taxonomic groups Blair (1999) reported a strong relationship between species richness of birds and butterflies along a gradient of urban development (Figure 24.3) Because butterfly surveys are relatively easy to conduct, Blair suggested that species richness of butterflies could be used as a surrogate for monitoring bird communities 24.2.2 LIMITATIONS OF MULTIMETRIC APPROACHES One major advantage of multimetric approaches is that they integrate several ecologically relevant responses into a single measure, a characteristic that appeals to many water resource managers However, some researchers are skeptical of multimetric indices and argue that a better approach is to assess an array of ecosystem responses, which provide a direct linkage between cause and effect (Suter 1993) Detailed critiques of multimetric indices as well as a discussion of their limitations have been published previously (Simon and Lyons 1995, Fausch et al 1990, Suter 1993) Only a summary of the major limitations will be presented here First, multimetric indices are data intensive Regardless of the specific system or taxonomic group, development and application of multimetric approaches require a thorough understanding of the ecology and habitat requirements of species as well as their tolerances for environmental stressors For some taxonomic groups and in some systems, these data will not be available Second, most multimetric approaches cannot be employed to identify specific causes of environmental impacts This criticism reflects two mutually exclusive goals of many biological monitoring programs While chemical-specific, diagnostic indicators may allow researchers to identify a single source of perturbation, more general measures such as the IBI are required to characterize the integrity of systems receiving multiple stressors It is possible that the responses of individual metrics in a multimetric index could offer some insight into the specific source of contamination For example, a multimetric index for benthic macroinvertebrates might include metrics for abundance and species richness of mayflies, stoneflies, and caddis-flies All three groups are generally sensitive to organic enrichment; however, many caddis-flies and some stoneflies are tolerant of heavy metals (Clements et al 1988, Clements and Kiffney 1995) Analysis of the responses of component metrics may Clements: “3357_c024” — 2007/11/9 — 18:34 — page 478 — #6 Application of Multimetric and Multivariate Approaches in Community Ecotoxicology 479 allow researchers to quantify the relative importance of individual stressors in systems affected by multiple perturbations Third, multimetric indices may not respond to some types of perturbation because changes in one metric may be offset by changes in another metric Again, the obvious solution to this problem is to report not only the integrated scores but also the responses of component metrics Finally, multimetric indices based on attributes of community composition will be less effective in areas with low species richness or naturally impoverished assemblages Fausch et al (1990) note that the low species richness of fish assemblages in western coldwater streams requires that many of the community-level metrics be replaced by life history and population-level responses 24.3 MULTIVARIATE APPROACHES Multivariate data sets are broadly defined here as those in which more than two dependent or independent variables are collected for each sampling unit These variables typically include community characteristics (e.g., species abundances) that change or might be influenced together in complex ways A wide range of multivariate statistical methods has been used to analyze these types of data In contrast to the methods described to this point, multivariate analyses are not based on ecological concepts but are statistical constructs that reduce complex data sets to potentially meaningful patterns involving a few variables Some, such as ordination methods, combine species abundance information for many sites or sampling units into functions that capture a portion of the total variance in the data A small number of uncorrelated, linear combinations of the species abundances might be identified Ecotoxicological meaning can be assigned to the positions of sampling units (e.g., sites) along these linear functions Alternatively, the researcher may simply use the results to describe trends among sampling units Other methods, such as cluster analysis, separate samples into groups in hopes of identifying some ecological or toxicological pattern that may emerge to explain the groupings Another type of analysis might be applied to species abundance data to identify which qualities weigh most heavily in discriminating among known groups Regardless of the applied method, the overarching idea is that multivariate analysis of the measured variables can reveal hidden or unmeasured qualities As with most parametric analyses, transformation of species abundance data is often advisable before applying a multivariate method Transformation might be done to reduce the influence of one variable relative to others in the linear combinations of variables One variable might have a much wider range of values and, in the absence of transformation, would have a disproportionately heavy influence on variance In such a case, each variable (e.g., species’ abundances at all sampling sites) may be standardized to a mean of and standard deviation of If a skewed distribution was to occur with the species abundance distributions, some transformation such as the square root or another power of abundance might be employed prior to standardization and multivariate analysis This is often necessary when a few species are very abundant at some sites 24.3.1 SIMILARITY INDICES Although generally not included in treatment of multivariate analyses, similarity indices also reduce complex, multispecies data for the purpose of comparing communities among locations or over time Similarity indices quantify the correspondence between two communities based on either presence–absence or abundance data These indices are especially useful for comparing communities from regional reference sites to impacted sites Alternatively, similarity indices are appropriate in studies of well-defined pollution gradients, where similarity to reference conditions is expected to increase with distance from a pollution source The simplest and most frequently used similarity index based on presence–absence data is the Jaccard Index: J = j/(a + b − j), Clements: “3357_c024” — 2007/11/9 — 18:34 — page 479 — #7 (24.1) Ecotoxicology: A Comprehensive Treatment 480 where a = the number of species in community a, b = the number of species in community b, and j = the number of species common to both sites Because the Jaccard Index does not account for differences in abundance between locations, rare species and abundant species are weighted equally Thus, it is likely that the Jaccard Index will be relatively insensitive to low or moderate levels of contamination More sophisticated similarity indices, such as the Morisita–Horn measure, compare the relative abundance of taxa between two communities The Morisita–Horn Index is given as MH = (ani × bni )/(da + db)aN × bN, (24.2) where ani = the number of individuals of the ith species at site a, bni = the number of individuals of the ith species at site b, aN = the total number of individuals at site a, and bN = the total number of individuals at site b The terms da and db in the Equation 24.2 are calculated as da = ani2 /aN , db = bni2 /bN Dissimilarity between/dissimilarity among The Morisita–Horn measure of similarity is favored by some researchers because it is relatively insensitive to sample size and species richness (Magurran 1988, Wolda 1981) Dissimilarity among locations or between time points can also be used to evaluate responses to environmental stressors Philippi et al (1998) quantified spatial and temporal responses to perturbations by comparing the pairwise dissimilarity between sites with the average dissimilarity among replicate samples These researchers noted that measures of dissimilarity (or similarity) can be employed to evaluate changes in community composition during recovery (Figure 24.4) If remediation was effective, the relative dissimilarity between reference and impacted sites would be expected to decrease over time 0.8 0.6 0.4 0.2 0 Time since remediation FIGURE 24.4 Hypothetical changes in community similarity between reference and impacted sites as a function of time since remediation was initiated The relationship shows that the index of dissimilarity (expressed as the ratio of dissimilarity between sites to the average dissimilarity among sites) is reduced over time as a result of remediation Clements: “3357_c024” — 2007/11/9 — 18:34 — page 480 — #8 Application of Multimetric and Multivariate Approaches in Community Ecotoxicology 481 While similarity indices provide a simple way to compare community composition, there are potential problems with these measures Boyle et al (1990) evaluated the ability of similarity indices to discriminate effects of simulated perturbations based on initial community structure, sensitivity to community change, stability in response to reduced richness and abundance, and consistency These researchers concluded that some similarity indices were misleading because results were strongly influenced by initial community composition and the nature of the perturbation Although similarity indices are useful when comparing communities from two locations, more sophisticated techniques are necessary to compare multiple sites Cluster analysis, a logical extension of similarity indices, is applicable for comparing communities from several locations or for comparing the similarity of a single site with a group of sites Cluster analysis employs a variety of similarity measures based on either presence–absence or abundance data These data are often expressed using a dendrogram, with the most similar sites combined into a single cluster Additional sites are included based on their similarity to the existing clusters Several different clustering algorithms have been developed, and relatively simple software packages are available for most analyses Details of the different clustering techniques and the justification for deciding how different sites and clusters should be joined have been published (Gauch 1982) These methods will be described below 24.3.2 ORDINATION Ordination is a process in which a large set of variables is reduced to a few variables with the intent of enhancing conceptual parsimony and tractability With ordination analysis of community abundance data, the measured variables (e.g., abundance of each species for each sampling unit) are used to identify hidden patterns or unmeasured factors explaining the data structure Mathematical constructs are sought to help interpret correlations among variables There are five steps to ordination analysis, regardless of the specific method applied (Comrey 1973) (1) The relevant data are generated and selected for analysis As noted above, the data might require transformation prior to use (2) The correlation matrix for the variables is calculated (3) Factors (mathematical functions) are extracted (4) The factors might be rotated to enhance interpretation (5) The factors are then interpreted Ideally, plots of the sampling unit positions along the first few mathematical constructs reveal explanatory, or at least consistent, themes As an example, linear functions can be defined such as Function = b1 X1 + b2 X2 + b3 X3 + b4 X4 + · · · , (24.3) where Xi = the normalized ln(abundance + 1) for each species sampled at the site A first function is constructed that incorporates as much of the variance in the data as possible, and the process is repeated for additional functions with the remaining variance Residual correlations after extraction of the first factor are used to produce a second, uncorrelated function that explains as much of the remaining variance as possible The process is repeated to produce a series of functions Ideally, most of the variance will be explained in the first few functions A score for each sampling unit can be calculated for placement along each function Plots for all sampling units using the formulated functions as axes should reveal an interpretable pattern In this process, a matrix of many species abundances is reduced to a few sampling unit positions on a two- or three-dimensional plot For example, the entire species abundance data set for a site might be reduced to one point in a two- or three-dimensional plot The X, Y , and perhaps, Z dimensions are constructs that can be given physical meaning such as the influences of soil type (Function 1), heavy metal contamination (Function 2), and agricultural activity (Function 3) (Figure 24.5) Insight from additional information on soils, agricultural history, and soil metal concentrations might be used to interpret the distribution of the sampled plant communities along these three functions The magnitude and signs of the b values (loading coefficients) in the linear functions are used to identify an underlying theme for each axis Clements: “3357_c024” — 2007/11/9 — 18:34 — page 481 — #9 Ecotoxicology: A Comprehensive Treatment 482 Agriculture Grasslands with few metal-tolerant species Grasslands with numerous metal-tolerant species So il q ua lity tals Function Me on ti nc Fu Function FIGURE 24.5 A hypothetical ordination analysis of plant communities relative to heavy metal contamination (top panel) Abundances of species are quantified at five sites near abandoned mines and another eight reference sites Soil qualities and the history of agricultural use of the sites are also noted as potential confounding factors After data transformation, ordination analysis results in three orthogonal, linear functions that are assigned interpretations of the influence of soil quality, soil metal concentrations, and agricultural history The five mine sites clearly cluster away from the reference sites There is a gradient of communities relative to soil quality and agricultural history Ordination axes can be rotated to enhance interpretation using orthogonal and oblique methods (bottom panel) These loadings represent the extent to which the variables are related to the hypothetical factor For most factor extraction methods, these loadings may be thought of as correlations between the variables and the function (Comrey 1973) For example, very high loadings in Function for species known to be tolerant to toxic metals and low or negative loadings for metal-sensitive species would suggest the influence of metal exposure on community composition For Function 3, high loadings for species known to flourish in active agricultural areas might suggest the impact of active agriculture on community structure The final result at this stage for ordination analyses would be to construct a table with rows of variables and associated loadings for each relevant factor (i.e., a table of unrotated factor loadings) Several types of ordination methods exist (Boxes 24.1 and 24.2) PCA was the first, and remains the most popular method (Sparks 2000, Sparks et al 1999) Using PCA, linear combinations of the original variables are extracted that sequentially account for the residual variance in a series of orthogonal (uncorrelated) components The first component contains the most variance; the second Clements: “3357_c024” — 2007/11/9 — 18:34 — page 482 — #10 Index 839 Habitat associations, 582, 747 fragmentation, 195, 541, 552 keystone, see Keystone habitat loss, 195, 388, 476, 546, 725 mosaic, 195, 253, 255, 256, 354 patch, 19, 254 stability, 293, 507, 523 Half-life (biological), 119 Hard Soft Acid Base (HSAB) theory, 103, 104, 108, 156, 158, 190 Hardy–Weinberg equilbrium, 317, 326 expectations, 204, 319, 323 model, 320 polynomial, 318 principle, 313, 318, 340, 355 Hartley–Bartlett test, 176 Harvest method, 637 Harvested, renewable resource, 247, 258 Hatchling sex determination, 289, 355 Hazard, 152, 169, 217, 226, 227 baseline, 226, 227 concentration (HCp ), 171, 207 cumulative, 152, 225 function, 224, 225 proportional, 153, 226 quotient, 217 rate, 152, 153 relative, 153 Head kidney, 70, 71 Health, 139, 158, 171, 197, 235, 236, 353, 476 Heat shock, 30 protein, 30, 72; see also Stress protein Hemapoietic tissues, 67 Hematocrit, 67, 129 Heme synthesis, 24, 32–34, 37, 67 Hemeoxygenase, 30 Hemocyanin, 84 Hemocyte, 70 Hemoglobin, 32, 67, 69 Hemolymph, 104 Henderson–Hasselbalch relationship, 36, 83, 102, 115 Hepatic artery, 106 Hepatocyte, 44, 52, 68, 73 epinephrine-stimulated, 86 Hepatopancreas, 68, 99 cells, 55 Hepatotoxicity, 68 Heritability, 343–345, 347, 348 broad sense, 344, 347 narrow sense, 344, 345, 347, 348 Heritable differences, 331 trait, 331, 332 Herring gull, 756–757 Heterogeneous variances, 175, 176 Heterophilic communication/exchange, 815, 816, 823, 826 Heterophily, 815 Heteroplasmy, 23 Heterosis, 325 multiple-locus/multiple, 324, 325 Heterostasis, 138, 145 Clements: Heterotrophic, 538, 556, 644–645, 669, 740, 754 Heterozygosity, 317, 320–322, 324, 325 average, 317 individual level (HI ), 320 multiple locus, 324, 327 subpopulation (HS ), 320 total (HT ), 320 Heterozygote, 317, 319, 338 deficiency, 318, 320, 323, 327, 356 Heterozygous effect, 341 Hexokinase, 322 Hierarchy theory, 619, 622–623, 627, 635 High altitude conditions, 202 Hill, Sir Austin, 215, 228 Hill’s nine aspects of disease association, 228, 230, 353, 825 Hilsenhoff’s Biotic Index, 370, 421–422 Hiroshima atomic bomb survivors, 309 Histopathology, 43 “Hockey stick” exposure–effect curve, 231 Holism, 5, 7, 8, 363–365, 582, 607, 815 Holon, 4, 5, Homeostasis, 90, 138, 190, 284, 285, 294, 295 Homocysteine, 98 Homogeneous variances, 174, 175 test of, 174, 175 Homophilic communication, 815 groups, 819 Homophily, 815 Homozygote, 319, 324, 339 Honeybee, 72 Hormesis, 89, 90, 144, 145, 147, 148, 158, 167, 170, 179, 266, 285, 300 Hormoligosis, 285 Hormone, 138 catatoxic, 139, 158, 190 mimics, 86 sex, 139 steroid, 87 syntoxic, 139, 141, 158, 190 Host, 237, 354 Hubbard Brook Experimental Forest, 442, 458, 561, 566, 618, 651, 706 Hueppe’s rule, 145, 285 Humoral immunological deficiencies, 70 Hunter-gatherer social group, 818 Hybrid vigor, 325 Hydration sphere, 96 Hydrogen peroxide, 31, 66 Hydroxyl radical, 31, 290, 308 Hyperexcitation, 72 Hyperplasia, 49–51, 53, 57, 66, 189 compensatory, 50 mucus cell, 66 neoplastic, 53, 57 physiologic, 53, 54 Hypersensitivity, 215 Hypertension, 46, 139 Hypertrophy, 49–51, 57, 66, 189 heart, 67 Hypothalamic-pituitary-adrenal system response, 138 Hypothalamic-pituitary-gonadal axis, 86, 87 Hypothalamo-pituitary-interrenal system response, 138 “3357_c037” — 2007/11/9 — 12:41 — page 839 — #11 Index 840 Hypothalamus, 87 Hypothesis testing, 163, 169–173, 178–180, 190, 265, 339, 463, 466, 701, 822 Hypoxia, 155, 488, 523, 742, 823, 824 Hysteresis, 119 I Idiosyncratic model, 502 Idol of certainty, 815 Idola quantitatis, 296 Image analysis systems, 296 Imitation, 817 strategy of, 818 Immune suppression, 236, 237 Immune system, 67, 138, 139, 190 ontology of, 70 Immunocompetence, 69, 70, 73, 235, 237 Impact assessment, 242, 525 Imposex, 166, 228, 229 Inactivation hypothesis, 336 Inbreeding, 323, 356 coefficient, 321 depression, 324 Incidence, 228, 237 cancer, 221 rate, 218, 219, 221 rate difference (IRD), 219, 220 rate ratio (RR), 219, 221, 222 Independent joint action, 145 Index of biological integrity (IBI), 475–478, 488 Indicator species, 20, 410–411 Indirect effects, xxvi, 199, 327, 367, 393–402, 555, 569, 594, 690, 783 Individual -based paradigm, 241 effective dose, 141, 158, 201, 203 lethal dose, 141 tolerance theory/hypothesis, 143, 145, 343 Industrial melanism, 196, 197, 199, 334, 346 Inert radionuclide tracer, 131 Infection, 70, 235 Infectious agent, 140 disease, 354 disease triad, 235–237, 354 Inference, Strength of, 8; see also Strong inference Inferential statistics, 426, 439, 450, 603 Infestation, 70 Infinitesimal rate of increase, see Intrinsic rate of increase Inflammation, 47, 48, 57, 64, 67, 69, 138, 139, 158, 189, 813 four cardinal signs of, 49 pulmonary, 65 Information correlative, 217 mechanistic (cause–effect), 217 –theoretic approach, 463 Informational mimicry, 818 Ingestion, 127, 128, 395, 642, 750 route, 67, 73, 99, 128 Clements: In-groups, 819; see also Homophilic group Inhibin, 87 Inhibition concentration (ICp ), 179 Innovation, 815, 817, 826 diffusion of, 816 Innovator, 816 Insect emergence, 394, 548, 696 Instantaneous mortality rate/failure, 152, 225, 247 Insurance hypothesis, 721 Integration, see Conceptual systems theory Integument, 72, 190 Interaction, 179 coefficient, 156 toxicant, 145 Intermediate disturbance hypothesis (IDH), 507–508 Internal redistribution, 116 International Biological Program (IBP), 618 Interspecies/Interspecific interactions, 168, 363, 382 selection, 510–511 Interstitial water, 103, 104, 108 Intestinal mucins, 67 Intracellular signaling, 86 Intrinsic rate of increase (r), 244–246, 251, 252, 263, 267–269, 291, 346 Ion, 103 channel, 35, 96, 107 gated, 96 pump or pumping, 83, 96 flux, 84 regulation, 66, 67, 82, 90, 137, 190, 293 transport, 50, 65 Ionic conditions, 137 homology, 98 hypothesis, 103, 104, 108, 156, 190 mimicry, 98 Ionization, 102, 155 tracks, 306 Ionocyte, see Chloride cell Irradiation, see Radiation Ischemia, 46 Island biogeography, 258, 386–387, 402, 448, 545 Isle Royale National Park, 368, 545, 589 Isocitrate dehydrogenase (Icd), 319, 322, 335 Isomotic conditions, 137 Isopods, 64, 347, 398–399, 774 Isozyme, 312 Iteroparity, 282 J Jaccard index, 479–480 Janus context, 189 Joint action model, 149, 157 independent action, 157 probability, 234, 235 Jonkheere–Terpstra test, 174, 175 Juvenile mortality, 292 “3357_c037” — 2007/11/9 — 12:41 — page 840 — #12 Index 841 K K-(equilibrial) selection/strategy, 281, 282, 299, 387, 413 K-selected species, 281, 282, 299 κ (Kappa)-rule, 284, 285 triage, 291 Kangaroo rats, 390 Kaplan–Meier method, 151, 152 Karyolysis, 44, 45 Karyorrhexis, 44 Karyotype instability, 54, 57 Karyotyping, 52 Kelp, 585–586, 589, 816 Kent chart, 825 Kesterson Wildlife Refuge, 167 Keystone habitat, 195, 236, 237, 254, 256, 259, 354 population, 237 species, 20, 205, 207, 388–390, 401, 442, 490, 502–503, 585, 723 Kidney, 69, 83 Killdeer, 394–395 Killer whales, 586, 816 Kinetic food web models, 743 Kolmogorov–Smirnov test, 176 Kow , 35, 36, 101, 102, 108, 124, 155, 739, 743–746 Kraft pulp mill, 35, 36, 101, 102, 108, 124, 155 Kreb’s cycle, 24 enzyme, 335 metabolites, 336 -related loci, 325 Kuhn, Thomas S., Kullback–Leibler, 463 Kupffer cell, 68, 98 Kurtosis, 298 L Laboratory toxicity tests, 141, 299, 365, 431, 464, 607, 688 Lactate dehydrogenase, 47 Lag time, 130, 247 Lake Mendota, 588, 667 morphology, 756 trout, 71, 368, 596, 738, 752, 759–760 Landscape, 253, 258, 354 characteristics, 755–757 ecology, 256 heterogeneous, 236 patches, 254 Langmuir isotherm model, 141 Late maturation, 243 Latency period, 57, 229 Latent tumor cell, 54 Lateral line nerves, 72 Law of independence (general probability), 149 Law of succession in time (Kant’s), 216, 217 Lead gasoline, 65 poisoning, 217 shot, 67, 217 Clements: Leaf fall, 107 Lehman set of functions, 227 Lepidopterans, 477 Lesion, 230 liver, 230 necrotic, 230 neoplastic, 230, 231 preneoplastic, 230, 231 proliferative, 230 Leucocrit, 236 Leucylglycylglycine peptidase (lgg), 322 Leukemia, 220, 221 Leukocytes, 46, 49, 66, 70, 236 Level III fugacity model, 124 Levene test, 176 Lewis acid–base, 156 Leydig cells, 87 Liebig’s law of the minimum, 14, 16, 20 Life cycle, 17, 164, 200, 236, 276, 277, 282, 331, 337, 338, 346, 348 events, 164 stages, 291 test, 169, 275 theory, 165 Life expectancy, 86, 89, 218, 266 average, 218, 264, 265 Life history, 20, 88, 90, 200, 207, 288, 289, 355, 365, 373, 375, 475, 515, 523, 548, 747 adaptation, 281 analysis, 87, 242 events, 165 evolution, 291 phenotypes, 281 shifts, 293 strategy, 7, 81, 141, 145, 164 theory, 165, 196, 208, 291, 353, 355 trait, 14, 20, 243, 281, 286, 287, 293, 294, 299, 300 plasticity, 291, 294 Life span, 282 Life stage, 144, 164, 263, 264, 274, 347 sensitivity, 346 Life table/schedule, 200, 225, 242, 264, 265, 267, 272, 277, 353 analysis, 151, 152 cohort, 264 composite, 264 horizontal, 264 Light–dark bottles, 637 Likelihood ratio, 823, 824 Limited life span theory, 289, 290, 300 Limiting resource, 387–388, 401, 508 Lindeman, Raymond, 363, 366, 582, 616 Lineages, 344, 345 Lineweaver–Burk plot, 117 Linkage strength, 752 Lipid(s), 285 barrier, 102, 108 peroxidation, 23, 66, 68, 290 route, 96, 107 solubility, 105, 115, 190 Lipofusin, 56, 57 Lipophilicity, 35, 101, 102, 108, 125, 154, 594, 738–741 Litterbags, 677, 679, 692 Liver, 68, 106, 125, 126, 139, 158, 190, 220 “3357_c037” — 2007/11/9 — 12:41 — page 841 — #13 Index 842 Loading coefficients, 475, 481, 486 Loblolly pines, 543–544, 794 Local adaptation syndrome (LAS), 138 Loci heterozygous, 326 Log logistic model, 143, 158, 206, 244 Log normal distribution, 141, 142, 343, 414–415, 603 model, 143, 146, 151, 158, 206, 207, 412–413, 490 Log odds, 147, 227 Log-rank test, 226 Logistic model, 147–149, 206, 245, 247 θ- 245, 252 Logistic regression model, 227, 231 binary, 227, 237, 353 Logit, 227 transformation, 147 transformed, 147 Longevity, 144, 236, 289, 290, 291, 299, 300, 346 Long-term studies, 390, 450, 515, 542, 545–546, 595, 605, 625, 787 Loon, 99, 756 Lotic intersite nitrogen experiment (LINX), 654, 800 Love Canal, 221 Low dimensionality, 216, 217 Lowest Observed Adverse Effect Concentration (LOAEC), 171 Lowest Observed Effect Concentration or Level (LOEC or LOEL), 169, 176, 178, 452–453, 490 Luciferase, 35 Lungs, 65, 66, 73, 106 Lupus erthematosis, 46 Luteinizing hormone (LH), 87 lx schedule, see Life schedule lx mx table/schedule, 266 Lymph, 106 Lymph nodes, 138, 139 Lymphatic structure, 139 Lymphocyte, 53 Lymphoma, 220, 221 M MacArthur–Wilson model, 258, 386, 448 Macroevolution, 331 Macroexplanation, 6–8, 10, 203, 204 Macroinvertebrate, 415, 422, 426, 488, 511, 515–517, 559, 564, 567, 643, 657, 695, 747, 757–768, 774, 784, 799 Macrolesion, 52 Macrophage, 68 aggregate, 48, 56 infiltration, 69 Malabsorption, 99, 107 Malate dehydrogenase (Mdh), 332, 335 Male fertility, 44 Malic enzyme (Me), 332 Malpighian tubules, 69 Malthus, Thomas, 331, 347 Manipulative experiments, 366, 392, 434, 442, 444, 566, 591, 603 Mannose-6-phosphate isomerase (Mpi), 332 Clements: Mantel–Haenszel test, 174 Marginal habitat, 232 Margination, 49 Marine benthic communities, 505, 570 Markov Chain Monte Carlo method, 321 Mass balance approach, 129, 614, 624, 667 Mating nonrandom, 314, 317 success, 338 Matrix, 277 algebra, 270 Lefkovitch, 270, 274 Leslie, 270, 272, 274, 275 multiplication, 270 transpose, 271, 275 Maturation basal rate of, 284 late, 292 Maulstick incongruity, 171 Mauna Loa observatory, 535–536 Maximum Acceptable Toxicant Concentration (MATC), 207, 263, 297, 490, 694 life span, 289 population size, 249 possible yield, 249 sustainable harvest (population), 248 loss (population), 248, 258 yield (population), 248 Mayflies, 525, 562, 595, 668–670, 741, 749 Mean absorption time (MAT), 127 expected length of life, 264 generation time (Tc ), 266, 327 residence time (MRT), 126 oral (MRToral ), 127 variance of (VRT), 126 Mechanistic model, 116 Medfly, 51 Median effective concentration (EC50), 205, 207 inhibition concentration (IC50), 205 lethal concentration (LC50), 142, 148, 150, 153, 203, 205, 207, 277, 297, 367, 423, 489, 667, 816 lethal dose (LD50), 142, 270 Meiofauna, 595–596, 689, 691, 785 Meiotic drive, see Selection, component, meiotic drive Melanism, 199 Melanistic morph, 198 Membrane irritation, 154 macrodomains, 96 patches, 96 vesicles, 118 Memory, 138 Mendelian inheritance/genetics, 312, 331, 332, 348 Menge and Sutherland model, 384, 391, 400–401 Menkes syndrome, 70 Mesocosm, 287, 339, 445–457, 484, 687–701, 784, 797 Mesopelagic fish, 19, 756 Meta-analysis, 393, 590, 674, 727, 786 Metabolic costs, 165, 293 “3357_c037” — 2007/11/9 — 12:41 — page 842 — #14 Index 843 currency, 88, 284 differences, 336, 337 disorders, 196 efficiency, 336 maintenance, 285 pathway, 141 rate, 84, 89, 90, 285, 290 Metabolism, 84, 336 anabolism, 24 basal, 285 carbohydrate, 84–86 catabolism, 24 stimulating hormones, 86 Metabolomics, 23, 36, 189 Metal bioavailable, 103, 680, 694, 697, 757, 798 Class A, 56, 156 Class B 56, 156, 306 deficiency, 29 divalent, 309 essential, 28, 29, 68, 738 -fume fever, 49, 65 intermediate, 56 ions, 156 -ligand binding tendencies, 104, 105, 158 nonessential, 28, 29 sequestration, 55 Metalloenzyme, 29 Metallothionein, 23, 29, 30, 37, 55, 68, 69, 98, 371, 510, 524, 748 Metamorphosis, 165 Metanephridia, 69 Metapopulation, 19, 195, 196, 208, 254–256, 258, 259, 303, 347, 353, 354 Metastasis, 54 Metchnikoff, Elie, 48 Methyl tertiary butyl ether (MTBE), 27 Methylation, 27, 798 Meyer–Overton rule, 154 Michaelis–Menten equation, 117 model, 117, 118 parameters, 118 Microarthropods, 558, 568, 699, 705, 786 Microbial activity, 675, 677–678, 691, 696, 699, 703, 795, 798 Microconstant, 122 Microcosm, 446, see also Mesocosm conceptual, 124 Microevolution, 81, 197, 281, 293, 297, 299, 331, 347 Microexplanation, 6, 8, 10, 205 Microhabitat, 380, 425, 560 Microlayer of gill surface, 102 Microlesion, 52, 57 Micronuclei, 53, 57 assay, 52 Midparent trait, 344 Migration, 254, 256, 257, 263, 264, 274, 282, 314, 317, 327, 339, 346, 347, 354 barrier, 317 direction, 314 rates, 204, 256, 257, 305, 313–315, 326, 355, 541 Milk, 107, 125 Minamata Bay, 197 Clements: Mindguard, 819 Mineralization, 505, 558, 570, 596, 650, 656, 677, 700, 777, 778, 795 Minimum concentration/dose, 144 population size, see Population, minimum size time-to-death, 144 Mink, 68, 569, 594, 753 Mitochondrial oxygen uptake, 86 Mitotic apparatus, 54 Mixed function oxidase (MFO) system/activity, see Cytochrome P450 monooxygenase Mixed order reaction, 116, 131 Mixing zone, 233 Mixtures, 125, 149, 154, 157, 190, 423, 668, 692, 694 Mode of action, 23, 145, 155, 221, 336 Model systems, 446, see also Microcosm; Mesocosm Modified Janus context, 4, 6–8, 10 Molecular connectivity, 143 genetic, 321 markers, 311 techniques, 311 homology, 98 mimicry, 98 toxicology, 23, 36 Monogenetic differences, 142, 346 Monotonicity, 179 Monte Carlo simulation, 277 Moose, 368, 389, 545, 589 Morisita–Horn index, 480 Mortality, 243, 266, 268, 289, 300 compensatory, 283 curve, Deevey Type I, II, or III, 282 density-dependent, 283 density-independent, 283, 354 early life stage, 283 rate, 364, 272, 281 size-dependent, 288 Most sensitive life stage, 20, 200, 283, 355 Motor-related organs, 72, 73 Mt St Helens, 513, 515 Mucins, 67 Mucociliary escalator process, 107 Multicompartment system, 118 Multidisciplinary, 611, 613, 625 Multidrug resistance (MDR) transporter protein, 19, 28, 30, 813 Multigenetic differences, 142 Multilevel selection theory, 364 Multimetric index, 475–479, 604, see also Index of biological integrity Multiple stressors, 232, 422, 451, 478, 512, 523 working hypotheses, 8, 816, 819, 821, 625, 825 Multitrophic communities, 508, 724 Multivariate, 474, 479–490, 518, 604, 740, 749 Multixenobiotic resistance (MXR), 19, 28, 36 Muscle, 126, 138 Mutagen, 52, 326 Mutation, 25, 198, 229, 290, 306, 307, 309, 317, 355 accumulation, 309 accumulation theory of aging, 25, 37, 291 adverse, 310 “3357_c037” — 2007/11/9 — 12:41 — page 843 — #15 Index 844 Mutation (continued) neutral, 305, 316 rate, 229, 309–311, 313, 315, 317, 326, 327, 355 neutral, 305 Mutualism, 391–392 Myocardial injury, 67 Myoglobin, 32 Mysids, 596 N N-acetylcysteine, 97, 98 NADPH-cytochrome P450 reductase, 26 Narcosis, 35–37, 136, 154, 155, 189 Narcotic nonpolar, 155 polar, 36, 155 Natality, 264, 266, 269, 270, 272, 327 National water-quality assessment program (NAWQA), 424 Natural baseline response levels, 170 disturbance, 362, 372, 458, 497–499, 504–505, 522–523, 582, 605, 620, 728 experiments, 443–444, 461, 465, 516, 562, 589 frequencies, 822, 824 mortality, 147 selection, 88, 196, 197, 285, 305, 313, 326, 331, 333, 334, 340, 347, 348, 355, 356 syllogism of, 332 Necessary (relative to appearance of disease), 232, 233 Necrobiosis, 44 Necrosis, 43, 47, 54, 57 caseous (caseation or cheesy), 44, 46 coagulative, 44, 46, 57, 189 fat, 44, 46 enzymatic, 46 tramatic, 46 fibrinoid, 46 gangrenous, 46 liquefactive (cytolytic or liquefaction), 46 liver, 45 vessel wall, 46 Zenker’s (hyaline or waxy), 44, 46 Negative interactions, 235 Nematodes, 282, 289, 427, 511, 596, 657, 697, 699, 705 Nephrocytes, 69 Nephrotoxicity, 69 Nerve cell, 136 Nervous system, 72, 73, 136, 190 Net primary production (NPP), 537–539, 543, 636–644, 655, 689, 696, 771, 776, 778, 790–794 global patterns, 639 limiting factors, 638 methods of measurement, 637 Net production efficiency, 641, 761 Net reproductive rate (R0 ), 266 Neurons, 56, 72 cerebellar granule, 72 cerebral cortical, 72 GnRH, 87 Neurotransmitter, 72, 89 Neutral theory, 326 Clements: Neutrophils, 44, 68 Neyman–Pearson theory, 171 Niche (Ecological), 16, 622 complementarity, 718 fundamental, 16, 17 realized, 16, 413 Nickel refinery/smelter workers, see Welsh smelter/refinery workers Nitrate (NO3 ), 567, 674, 680, 692, 771–797 Nitrification, 649–650, 674, 679–680, 692–693, 697, 700, 776, 778 Nitrogen assimilation, 650, 779 cascade, 771–772 cycle, 649, 680 deposition, 771–779 fixation, 649–651, 700 flux, 674 saturation, 772, 775 experiments (NITREX), 775–778 Nitrogenous waste, 69 No Observed Adverse Effect Level (NOAEL), 144, 171 No Observed Effect Concentration or Level (NOEC or NOEL), 169, 177, 179, 200, 205–207, 263, 270, 277, 297, 451, 453, 490, 695, 816 community, 206 Noncompartment-based method, 130, 132 Nonoverlapping generations, 243, 246, 315 Nonrandom breeding, 204 species loss, 724 Nontarget species, 251, 334 Nonthreshold model, 55 Non-equilibrium communities, 499 Norepinephrine, 138 Normal distribution, 151, 218 equivalent deviation (NED), 146, 147 Normality test, 174–176, 298 North Carolina biotic index, 422 Northern pike, 588, 590 Novel stressors, 505, 523–526 NOx , 533, 569, 571, 665, 727, 772–775 Nucleophilicity, 154 Null hypothesis, 178, 298, 305, 314, 320, 462–463, 625–626, 821 Nutrient budget, 651–652, 667 cycling, 674, 677, 679, 699, 702, 723, 725, 786, 789 injection studies, 651–652 marine-derived, 653 spiraling, 650–651 Nutrition, 232 O Oak-pine forests, 457, 704 Ockham’s razor, 320, 323 Octanol:water partition coefficient, 101, 155, see also Kow Octave, 412–414 Odds, 222 posterior, 822 “3357_c037” — 2007/11/9 — 12:41 — page 844 — #16 Index 845 prior, 822 ratio (OR), 218, 222 Odum, Eugene P., 582, 616 Offspring, 338, 347 nonviable, 306 number of, 291 size of, 291 Olfactory discrimination, 72 Oligotrophic, 509, 556–558, 637, 653, 754, 777, 784 Omnivory matrix, 746 Oncogene, 53 One-sided test, 175 Ontogenetic, 581, 619, 737, 758 Opinion leader, 816, 826 Opportunistic species, 506, 512, 522 Optimal energy allocation, 293 foraging, 394 metabolic efficiency, 324, 325 Optimality, 165 Optimization theory, 164, 165 Oral exposure, 99 route, 135 Ordination, 382, 479, 481–485, 490 Organ toxicity, 63 Organic anions, 97 Organic anion transporters (OATs), 97, 98, 104, 107 Oscillations damped, 250, 251, 258 stable, 250, 251, 258 Osmoconformer, 82 Osmoregulation, 66, 82, 83, 90, 97, 190, 293 Osmoregulator, 82 Osmotic conditions, 82 Osteoderms, see Dermal, plates Ova, 337 Ovaries, 87 Overcompensation, 90, 145 Overlapping generations, 243, 315 Oxidative burden, 66 damage, 55, 56, 290 phosphorylation, 36, 44, 336 uncoupling of, 35, 37, 136, 137, 189 stress, 31, 37, 44, 71, 97, 189, 310 hypothesis of aging, 290, 300 Oxygen binding affinity, 84 consumption, 84 dissolved, 487, 547, 637, 654, 690, 772, 823–824 uptake, 86 Oxyhemocyanin, 84 Oxyradical, 31, 290 -producing molecules, 31 Ozone (O3 ), 552–555, 569, 572, 781, 786 Ozone depletion zone, 552, 780 P P450, see Cytochrome P450 monooxygenase P-glycoprotein (P-gp), 27, 28, 108 overexpression, 19 Clements: Paleoecological, 540–541, 549, 625, 798 Paleolimnological, 563, 773 Paracellular route, 96, 104, 107, 190 Paradigm, 13, 189, 241, 813, 817 core, 241 failing, 815 shift, 816 Paralysis, 72 Parasite, 55, 236, 237, 354, 533 Parasitism, 235, 392, 459 Parent-offspring combinations/pairs, 338, 344 Partial kills, 158 Particulate organic matter, see Coarse particulate organic material Partition coefficient/constant, 124, 155 l-α-dimyristoylphosphatidylcholine:water, 155 octanol:water, 155 Passive reabsorption, 106 Patch extinction, 254 Patches, 257, 314 Pathogen, 235 Pathological resistance, 818 science, 14 P:B ratio, 640 Peppered moth, 197, 198, 229, 346 Per capita growth rate, see Intrinsic rate of increase Peripheral compartment, 118, 122 Periphyton, 398, 430, 454, 511–512, 519, 557, 559, 595, 629, 651, 654, 669, 690, 693–695, 741, 754 Permeability, 102 skin, 64 Peroxidase, 32 Peroxisomes, 56 Peroxyradicals, 290 Pesticide efficiacy, 335 registration pH; see also Acidification blood, 35, 83, 84, 90, 137 gut, 116 low water, 95, 325 -Partition theory, 96, 102, 106, 108, 190 regulation, 84, 190 -relevant ions, 83 urine, 116 Phagocyte, 70 infiltration, 49 Phagocytosis, 96, 98, 102 Phase I reaction, 25–27, 37, 64, 69, 97, 104, 306 hydrolysis, 35 Phase II reaction, 25–27, 37, 64, see also Conjugate conjugation, 37 Phase III reaction, 27 Phenogenetics, 281 Phenol poisoning, 67 Phenomenological model, 243, 249, 354 Phenotype, 145, 198, 281, 286, 294, 299, 300, 356 Phenotypic limitations, 285 plasticity, 20, 142, 286, 288, 291, 293, 355 range of, 286, 299 variation, 286, 298, 343, 344 Phosphoglucomutase (Pgm), 322 “3357_c037” — 2007/11/9 — 12:41 — page 845 — #17 Index 846 Phospholipid, 66, 96, 97 Phosphorus cycle, 649 Photoactivation, 374 Photodegradation, 570, 785, 799 Photoinhibition, 782 Photolyase, 307 Photomodification, 56 Photoprotection, 553, 560 Photorepair, 558, 560, 780 Photosensitization, 56 Photosynthesis, 89, 646, 648–649, 668, 690, 693, 780–787, 790, 792, 795, 797 Photosynthetically active radiation (PAR), 552, 638, 654, 781–782 Photosystem II 553, 780 Phototoxic, 64, 561 Photo-induced toxicity, 56 Physicochemical property-activity relationship, 154 Physiological functioning, 291 strategy, 82 stress, 399 tolerance, 14–16 Physiologically-based pharmacokinetic (PBPK) model, 81, 90, 125, 126, 132, 190 Physiology, 163, 166, 180, 190 Phytochelatin, 30, 37 Phytoplankton, 368–369, 387, 428, 449–450, 454, 511, 547, 554–557, 560, 564, 572, 588, 593, 596, 615, 637–638, 640, 654, 657, 702, 745, 753–754, 758–759, 780–783, 788, 798 Pica, 99 Picciano pilot study, 220 Pied flycatcher, 369–370 Pine marten, 753 Pinocytosis, 96 Piscivores, 369, 615 Pituitary cells, 71 gland, 87 pKa , 35, 36, 155 Placenta, 107 Plant, 90, 167, 197, 292, 296 C3 and C4, 541, 543 metal-tolerant/intolerant, 90, 286 Plasma, 122, 140 Plausibility, 231, 235, 821, 822, 826 judgments of, 820 Pleiotropic genes, 291 Pocket mouse, 390 Point estimation, 169, 170, 172, 173, 179, 180, 190 Poisson distribution, 218, 257 trend test, 174 Polar bears, 749 ozone depletion, 7, see also Ozone depletion zone Pollution-induced community tolerance (PICT), 510–512, 605 Pollution-tolerant species, 370, 410, 423, 721, 748 Polychaetes, 69, 204, 427, 488, 505, 743 Polygenic control, 332, 346 Polymerase chain reaction (PCR), 312 Polymorphic loci, 319 Clements: Polymorphism, 312 Polyphenism, 20, 288, 299, 300, 355 Polyploidy, 306 Popper, Sir Karl R., 4, 441, 625 Popperian falsification, 820 Population cryptic, 320, 322, 327 demographics, 19 density, 244 density-dependent growth, 244 doubling time (td ), 244 ecotoxicology, 196, 353 genetics, 19, 196, 208, 305, 353 growth rate (r), 248, 256, 274, 275, 281 harvested, 247, 248 isolation, 305 minimum size (M), 249, 252, 257, 277 projection, 275, 353 reappearance probability, 254 reference, 219 size, 305 harmonic mean of, 315 stability, 250, 268 stationary, 267 structure, 314, 317, 321, 324, 327, 356, 497 ephemeral, 336 substructuring, 204 vector, 275 viability, 196, 248, 258, 306 Population-based paradigm, 241 Porphyria, 33, 69 acute intermittent, 33 Porphyrins, 33, 34, 56, 69 synthesis, 32 Portfolio effect, 720 Positive Predictive Value (PPV), 177, 178 Postexposure mortality, 137, 200, 202 Postreproductive individuals, 289 Potency, 202 drug, 143 relative, 145, 146, 149, 154 Potential nitrification, 680 Potentiation, 145 Power, 158, 170, 172, 177, 178, 314, 326, 335, 689, 701 Prairie voles, 399 Precipitate explanation, 143 Precipitating factor, 232, 233 Predation, 285, 292, 365, 368, 371, 382, 388, 391–397, 400, 442, 455, 525, 545, 556–557, 584–586, 588, 594, 754–755, 761 adult, 338 pressure, 254 Predator, 55, 198, 259, 293; see also Predation avoidance, 168, 394–395 barrier, 64 –prey interactions/relationships, 89, 205, 393–396, 545, 550, 555, 589 visual, 338, 560 Predisposing factor, 232 Preen, 64, 99 Preponderance of evidence, 235, 825 Prereproductive individual, 289 Presence-absence data, see Data, presence-absence Press disturbance, 500–501, 514 “3357_c037” — 2007/11/9 — 12:41 — page 846 — #18 Index 847 Presystemic metabolism, 128 Prevalence (of disease), 237 hepatic cancer, 231 Prey species, 216, 365, 391, 394–395, 402, 455, 569, 586–587, 591, 628, 742, 747–748, 754 Primary productivity, see Gross primary production; Net primary production Principal components analysis (PCA), 475, 482–486, 739, 750 Principle of allocation, 88, 284, 285, 299, 355 of instant pathogen, 235, 236 of parsimony, 323 Probability density function, 142, 224 of dying, 264 prior, 177, 821 Probit, 283 model, 149, 158, 179, 201–203, 207 transform, 140, 146, 147 Procarcinogen, 25 Product limit method (Kaplan–Meier method), 225 Proneness to die/fail, 152, 225 Pronephros, 67, 70 Propagule rain, 255, 259, 354 Proportion expected to die, 146 Proportional hazards, 151, 353 model, 151, 152, 224, 226, 227, 237, 265, 342 Proportionality constant, 116 Protein -binding theory, 35, 36 –DNA crosslinking, see DNA, cross-link with protein electrophoresis, see electrophoresis, protein synthesis, 293 turn over, 285, 293 Proteinuria, 56 Proteomics, 23, 24, 30, 36, 189 Proteotoxicity, 30, 189 Protonephridia, 69 Protooncogene, 25, 27, 53 Protozoan communities, 454, 511 Proximal tubules, 69, 97, 106 Psycho-physics, 141 Pseudoreasoning, 216 Pseudoreplication, 439, 519, 562 Pseudoscience, 216 Pulse, 138 disturbance, 500–501 Push-mechanism context of Descartes, 215 Pyknosis, 44, 45 Pyrimidine dimmers, 307 Q Qualitative and quantitative sampling, 425–426 Quantitative ion character-activity relationship (QICAR), 103, 105, 108, 156–158, 190 Quantitative structure-activity relation (QSAR), 105, 125, 154, 155, 157, 158 R r-(opportunistic) selection/strategy, 282, 505, 590 Clements: r-selected species, 282, 387, 413, 512, 522 r-selection, 281, 282, 289, 387, 413 Radiation, 265, 285, 306, 309–311, 457, 704 ultraviolet, see UV-A; UV-B Radioactive tracers, 592, 618 Radioecology, 618 Raft hypothesis, 96 Rainbow trout, 741 Random amplification of polymorphic DNA (RAPD), 312 drift, 314, 331 sampling experiment, 314 Rangeland, 543 Rank sum test, 152 Rapid bioassessment protocols, 423–430 Rapoport’s rule, 20, 384 Raptors, 196, 228 Rare species, 389–390, 413, 417, 419–421, 480, 504 Rarefaction, 416, 425 Rate constant, 116, 131 based model, 116, 131, 190 first order, 126, 127 Rate of living theory of aging, 290, 300 Reaction norm, 20, 286, 287, 300, 338, 355 Reaction time assay, 151 Reactive oxygen metabolites, 290 species, 66, 70 Realized dose, 65, 127 Reasonable doubt, 233, 825 Recessive gene, 341 Recognizable taxonomic units (RTUs), 427 Recovery, 54 community, 512–521; see also Resilience ecosystem, 505, 693, 779 time, 461, 497, 505, 513, 519–520, 605 Recycling ratio, 654–655 Red blood cell, 67, 69, 129 Red spruce, 567 Redox cycle, 31, 306 Reductionism, 5, 7, 10, 363–366, 444, 446, 582, 613, 616, 622, 627 –holism debate, 363–366, 815 Redundancy analysis, 474, 484 functional, 502, 511, 635, 691, 694–695, 719–721 Redundant species hypothesis, 207 Refugia, 346, 347, 513, 723 Regional reference condition, 430–431, 479 Regression analysis, 168–170, 423, 450–451, 453, 490, 658, 689, 760, 782 Reinforcing factor, 232 Relative likelihood, 823 Remediation, 480, 514–516, 605, 671, 751 Renal elimination, 106, 108 tubule, 106 Renewable resource managers, 249, 367, 409, 478, 823 Reproduction, 163–166, 169, 180, 190, 195, 243, 284, 285, 287, 288, 355, 371, 397, 456, 488, 522, 545, 560 Reproductive damage, 229 differences, 342 “3357_c037” — 2007/11/9 — 12:41 — page 847 — #19 Index 848 Reproductive (continued) disadvantage, 339 effort, 291, 293 failure, 229 individual, 289 investment, 291 onset, 281 production, 88 pulse, 336 rate of, 281 stage, 274 success, 331 traits, 337 value (VA ), 263, 267, 268, 273–275, 291, 331, 355 age-/stage-specific, 267, 273, 274 Rescue effect, 255, 259, 354 Reservoir host, 234 Residual bodies, 56 Resilience, 505–506, 513, 516, 519, 521–522, 585, 605, 615, 645, 688, 693, 728–730 stability, 499–502, 585, 729 Resistance, 140, 197, 310, 311, 331, 335, 346, 398, 497, 521–522, 568, 585, 605, 615, 645, 688, 728–730 enhanced, 331 genetic, 86 to infection, 235 stability, 499–502, 585, 729 to toxic action, 310 Resource allocation, 87, 287, 288 injury compensation, 215 Respiration, 24, 84 ecosystem, 371, 386, 448, 505, 536, 614, 621, 636–637, 641, 648, 668, 674–676, 694, 697, 761, 777, 789 microbial, 539, 672–673, 675, 679, 692, 697, 775 Respiratory organs, 72, 190 pigments, 84, 90 route, 135 strategy, 100, 116 uncoupling, 154 uptake, 100 Restoration ecology, 372, 442, 514 Restriction fragment length polymorphisms (RFLP), 312 Retinol, see Vitamin, A1 Rheumatoid arthritis, 49, 139 Ricker model, 46 θ-, 245, 246 Right censored, see Censoring, right Risk, 125, 139, 171, 205, 217, 237, 277, 346, 356 assessment, 99, 205, 217, 220, 233, 242, 249, 257, 258, 263, 264, 292, 297, 354, 611, 676–677, 688, 739, 744, 780, 819, 820 cancer, 309 ecological, 193 exaggeration, 823 extinction, 353, 512, 546 factor, 215, 218, 222–224, 227, 231, 232 genetic, 51 mutation, 309 perceived vs actual, 221 population, 51 Clements: predation, 368, 396 ratio, 222 relative, 218, 222, 224, 342 somatic, 24, 25, 51, 57 River continuum concept, 382, 430, 644–645 Rivet (Popper) hypothesis, 207, 729 RNA polymerase, 23 Root, 100 elongation, 90 exudate, 107 Routes of exposure/entry, 99, 100 S Safe concentration, 169 Sage thrasher, 461 Sagittal otoliths, 288 Salinity, 15, 16, 83, 483, 487–488, 570 Saprobien system, 370, 410–411, 604 Satisficing, 816, 819 Saturation kinetics, 116, 118 Saturnine gout, 56 Scalar, 270 Scaling, 81, 140; see also Allometry Scatchard plot, 117 Science hierarchical, 814 innovative, 813 normal, 813, 825, 826 pre-paradigm stage of, 817 Scientific community, 815 paradigm, 813 Scope of growth, 293 Screwfly, 51 Sea otter, 100, 586 Second law of thermodynamics, 614 Secondary production, 640–644, 648, 653, 657, 668–670, 704, 749, 761 techniques, 642–643 Sediment, 135 anoxic, 103, 104 contaminants, 430, 743, 749–750 quality triad, 431 reworking activity, 168 test, 169 Seed bank, 255 Seedling success, 90 Selander’s D, 319, 320, 323, 356 Selection, 81, 333, 337, 338, 341, 818 -based theory, 289 coefficient, 315, 340, 342, 348 components, 337, 346, 347, 356 analysis, 337, 338, 339 balancing, 339 fecundity selection, 337, 339, 342, 348, 356 gametic selection, 337, 338, 348, 356 meiotic drive, 337, 348, 356 sexual selection, 337–340, 342, 348, 356 viability (zygotic) selection, 337–339, 348 differentials, 339–340 directional, 332, 348 disruptive, 332, 348 “3357_c037” — 2007/11/9 — 12:41 — page 848 — #20 Index 849 interdemic, 333, 334 nonvisual, 199 normalizing, 332, 348 somatic 362 stabilizing, 332 viability, 334, 338 Selective feeding, 131, 394 predation, 388, 402, 594 Selectivity index (SI), 131 Self -censoring, 819 -contradictory systems, 13, 815, 816 Selye, Hans, 138, 139, 284 Semelparity, 282 Senescence, 290 Sensitivity, 275 analysis, 275, 276, 446, 744 of λ, 275 Sensory organs, 72, 73 Sequestration, 24, 29, 37, 104; see also Carbon sequestration; Metal sequestration Serotonin, 89 Sewage sludge, 72, 680 Sex, 140, 141, 158, 277, 342, 355 determination, 74, 289, 299, 300 ratio, 327, 371, 548 Sexual adult, 334 maturity, 246, 345, 355 selection, see Selection, component, sexual Shannon–Wiener diversity index, 417–420 Shapiro–Wilk’s test, 176, 298 Shelford’s law of tolerances, 16, 17, 20 Shell gland, 216, 229 Shenandoah National Park, 562 Shifting balance theory, see Wright’s shifting balance theory Shorttail shrew, 747 Shredders, 459–460, 595, 629, 645–646, 657, 666, 669, 671–673, 691, 703, 774 Sibship, 345 Sickle cell anemia, 197 Sigmoidal growth model, 246 Signal-to-noise ratio, 518 Silent Spring, 17, 163, 334, 737 Silver -mercury amalgam, 34 transport, 29, 97, 118 Similar joint action, 145, 149, 157 Similarity indices, 479–481 Simpson’s index, 417 Simulation models, 790–792 Simultaneously extracted metals (SEM), 104, 108 Single species test approach, 18, 205 Single-step test, 173, 179 Sister chromatid, 52 exchange, 52, 57 Site of action, 95, 202 Size, 141, 158 at maturity, 291 Skeptic (role of), 820 Skewness, 298 Skin, 64 Clements: Smoking, 223, 228 Smoothing, 179 Snails, 44, 63, 71, 141, 228, 236, 272, 395, 398, 454, 596, 694, 779 SO2 , 90, 369, 533, 562, 566, 569,650,774; see also Acidic deposition and acidification Social dominance, 86 interactions, 86 psychology, 814 structure, 168 Sodium channels, 72 Softness index, 156, 157 Soil communities, 568, 596, 696, 699–700, 726, 785 microbial processes, 674–676, 688, 697 microcosms, 456, 596, 697, 699–700 test, 169 Solar ambush hypothesis, 555 bottleneck hypothesis 555–557 cascade hypothesis, 555–556 Solvent drag, 95 Somatic circulation, 190 death, 136–138 maintenance, 89, 164, 284 mutations, 291 production, 88, 292 Songbirds, 432, 462, 545 Source-sink dynamics, 254, 259, 354 habitats, 195 Spatial cline, 195 heterogeneity, 263 scale, 361, 374, 380, 428–429, 448, 457, 464, 534, 539, 606, 629 Spatially extensive, 373, 539, 563–565, 572, 673, 756, 775 Spatiotemporal scale, 371, 440, 443, 448–450, 461, 464, 542, 611, 722, 726–727, 793 Spearman–Karber method, 148, 158 Species abundance models, 411–415 -area relationships, 365–387 diversity, 381, 382–385, 417, 502–504, 509, 715–718, 720–721, 726–727 diversity and ecosystem function, see Community structure–function relationship indicator, 410–411, 604 interactions, 361, 364–365, 382–383, 391–401, 455, 502, 583, 596, 603, 628 nontarget, 197, 251, 334, 394 richness, 371, 381, 384, 415–416, 420–421, 425, 427, 453, 478, 502–503, 507, 517, 519–520, 691, 718, 720–721, 725, 728 saturation, 719–720 sensitivity distribution approach, 20, 207, 208 -specific sensitivity, 20 Specificity of association, 228, 231 Sperm, 87, 337 Sperm whale, 64 Spindle dysfunction, 53 “3357_c037” — 2007/11/9 — 12:41 — page 849 — #21 Index 850 Spiraling length, 650 Spiraling of pollutants, 754 Spleen, 70, 139 Spontaneous mortality, 144, 147, 148, 158; see also Natural mortality Spotted frogs, 395, 398 Spotted owl, 411 Springtails, 524 Stable isotopes, 592–593, 758–761 Stable population/age structure, 267, 269, 273, 275, 278 Stability, see also Resilience stability; Resistance stability criteria, 251 regions, 251 Stage-structure matrix model, see Matrix, Lefkovitch Standardized aquatic microcosm, 447 Starfish, 48, 64 Static aquatic toxicity test, 135 Statistical model, 224, 413, 463, 542, 729, 760 moments formulation/approach, 125 power, see Power Steady-state models, 121, 745 Stenothermal species, 546–547 Step-down test, 173, 174, 179 Sterile Insect technique (SIT), 51 Sternopleural bristles, 298, 299 Steroid, 71, 87, 97 anabolic, 71 Steady state conditions, 121 Steel’s many-one rank test, 174, 176 Steric hinderance, 154 qualities, 154 Stochastic changes, 305 model, 263, 276, 721 processes, 305, 311, 326, 498, 517 Stochasticity hypothesis, 143, 202, 203 theory, 143 Stock assessment, 589 Stomach ulcers, see Gastric ulcers Stomatal entry, 100 function, 90 Stoneflies, 385–396, 455, 525, 591 Strength of association, 230 of evidence, 232, 233, 237 of inference, 237 of weak ties theory, 816, 826 Stress, 88, 284, 293, 310, 336 adaptation, 297 -based theories of aging, 290, 300 fetal, 296 growth, 293 mortality, 293, 294 oxidative, see Oxidative stress psychological, 223 resistance, 290 selyean, 284 Stress protein, 23, 28, 30, 37, 69 Clements: chaperon or cpn60, 30 low molecular weight (LMW), 30 stress70, 30 stress90, 30 Stressor identification, 665 interactions, 453–454, 692–693, 796 Strong association, 228 electrophile, 27 inference, 9, 825, 826 Strongest possible inference, 8, 10, 189, 820, 821, 825, 826 Structural hierarchy, 629 measures, 362, 459, 605, 637, 666, 695, 697 Subalpine communities, 351, 522, 533, 795 Subinhibitory dose/concentration, 145 Subsampling, 425 Subsidy–stress gradients, 508–509, 667, 670 Succession, 20, 216 ecological, 364, 413, 456, 498, 541, 543, 621 Sufficient (relative to appearance of disease), 232, 233 Sufficient challenge, 285 Sulfate, 26, 27, 779 Sulfide, 95, 108 iron and manganese, 103 Sulfotransferases, 67 Sulfur cycle, 650 Sulfur dioxide, 199; see also SO2 Superorganism, 364–365, 613, 621 Superoxide anion, 31 dismutase, 56 radical, 290 Suppressor gene, 53 Surplus young, 248, 258 Survival, 164, 165, 169, 195, 224, 242, 285, 331 analysis, 151, 335 curves, 226 probability, 274 rates, 265 schedule, 204, 264 Survival time modeling/methods, 140, 158, 190, 326, 342, 354 fully parametric, 151, 153 nonparametric, 151 semiparametric, 151 Survivorship, 281 Susceptibility, 141, 311, 371, 454, 461, 525, 572, 594, 606, 693 to predation, 365, 395, 455, 761 Sustainable harvest, 247, 268 Swallows, 87, 167, 749 Switches, 289, 293 Switching developmental, 289 prey, 597 Symbiont exposure route, 100 Sympatric populations, 393, 441, 443 Synecology, 14, 16, 18, 191 Synecotoxicology, see Ecotoxicology Synergism, 145 “3357_c037” — 2007/11/9 — 12:41 — page 850 — #22 Index 851 Synergistic interactions, 145, 164, 395, 561, 570, 596, 606, 796 T T3 (3,5,3 -triiodothyonine), 86, 87 T4 (3,5,3 ,5 -tetraiodothyronine), 86, 87, 89 Tamhane–Dunnette test, 175 Target organ, 63 Taxonomic resolution, 427–429 Teeth, 296 Telomere function, 54 Temperature, 310, 793; see also Global warming ambient, 293 Temporal sequence/succession (of cause and then effect), 228, 229, 231 Teratogenic effects, 25, 166 Terrestrial ecosystem model (TEM), 639 Territoriality, 278 Testes, 87 Testosterone, 87 Thalidomide, 229 Theory of stress, 139 Thermal effluent, 235 pollution, 292 stress, 292 Thiosulfate, 95, 104 Threatened species, 215 Threshold, 148, 158, 179, 234, 237 for acceptable “excess mortality” 825 biological, 169, 170, 475 dose/concentration, 55, 57, 144, 146, 147 ecological, see Ecological threshold evidence, 234 exposure-effect curve, 231 lethal, 147, 158, 200 model, 55 population size, see Population, minimum size proof-of-hazard, 169 proof-of-safety, 169 response, 144 time, 138 Thrombocytes, 67 Thymus, 70, 138, 139 Thyroid, 71, 86, 87, 89 Thyroid-stimulating hormone (TSH), 87 Thyroxine, see T4 Time delays, 246, 251, 252 Time between litters, 291 Time of parturition, 291 Time-tocancer, 224 death, 153, 224, 226, 335, 336, 344, 345 disease onset, 224 event methods, 150, 151, 224, 226 fatal cancer, 226 flower, 151 partition, 151 recover, 299 stupification, 151 symptom presentation, 224 Clements: Tolerance, 90, 141, 142, 157, 311, 325, 331, 345, 384, 509–512, 560–561, 747; see also Resistance acquisition, 345, 346, 348, 524–525 accelerated, 347 enhancement, 284, 356 individual tolerance hypothesis, 142, 201, 203 values, 422, 453 Top-down, trophic cascades, 368–369, 555, 587–590, 594, 643 Topoplogy, 154 Total molecular surface area, 154 oxyradical scavenging capacity, 31 Toxicity test, 136, 201, 242, 256, 277, 344 early life-stage, 168, 169 life cycle, 168, 169 partial life-cycle, 168, 169, 275 Toxicological endpoint, 197 Trade-off, 81, 86, 90, 164, 165, 285, 287, 288, 291, 293, 299, 300, 338, 374, 398, 424, 428, 568, 754–755 curve, 293, 294 Trait mendelian, 333 quantitative, 333 variation, 332 Transcription, 23, 24, 307 Transcriptomics, 23, 36, 189 Transferrin protein, 97 Translation, 23, 24 Transport constant (D), 123 Tricarboxylic acid cycle, 33 Triglyceride cycles, 89 Trimmed Spearman–Karber method, 148 Trimming rule, 148 Trophic cascades, see Top-down trophic cascades complexity, 366, 401, 454, 585, 753 dynamic concept, 582, 596, 616, 640 interactions, 591, 594, 605–606, 670 level efficiency, 641 magnification factor, 758, 760 pyramids, 366, 582, 583, 737 status, 454, 506, 753–754 transfer, 21, 55, 125, 740, 746, 760 web, see Food webs Tryptophan pyrrolase, 32 Tsetse fly, 51 t-test, 174, 298 Tuberculosis, 46 Tubular reabsorption, 106 Tumor, 54, 232 cell, 70 suppressor gene, 25 Turnover time, 647–648 Two-sided test, 175 Type I error, 172, 175, 177, 320, 625–626, 820 experimentwise, 172, 305 Type II error, 625–626, 820 U UDP-glucuronosyltransferase, 27 “3357_c037” — 2007/11/9 — 12:41 — page 851 — #23 Index 852 Ultraviolet (UV), 37, 64, 307; see also UV-A; UV-B; UV-C light, 307 radiation (UVR), 552–561, 780–787 Unassimilated contaminant, 130 Unfixed cause-effect-significance concatenation, 4, Unit world, 124, 125 Uptake, 116 length, 650–651, 654 Urban development, 478 Urchins, 205, 549, 585–586, 730, 816 Urea, 69, 97 Uric acid, 31, 69 Urinary glutamine transaminase K, 56 Urine, 116 Uroporphyrinogen I synthetase deficiency, 33, 34 U.S numerical water quality criteria, 205, 741 UV enhancement and exclusion, 781 UV-A, 552, 554, 781–783 UV-B, 552–561, 780–787, 797–799 UV-C, 552 V Vasoconstriction, 67 Vector, 227, 270, 275 column, 271 Vegetation ecosystem modeling and analysis project (VEMAP), 791 Ventilation rate, 72, 84, 85, 90 Viability (survival), 331 selection, see Selection, components, viability success, 337 Visual predator, 198, 560 Vital rate, 19, 204, 263, 264, 272, 277, 281, 285, 355 age-, stage-, sex-dependent, 263 Vitamin, 31 A, 31 A1 , 68 C, 31 E, 31 Vitellogenin, 71, 87 Volumes of distribution (Vd ), 121–123 apparent, 121 effective, 121 Vomiting, 67 Vostok ice core, 535–536 W Wahland effect/principle, 204, 317, 318, 320, 322–324, 356 Walleye, 71, 588 Water reabsorption, 106 Weak inference, 237 Weakest link incongruity, 20, 263, 355 Weather, 254, 257 Weber–Fechner law, 141 Weibull function/model, 265 Weight, 227, 342 -of-evidence methods, 217, 431–432, 439 -specific ration, 121 Welsh refinery/smelter workers, 65, 221, 222 Whitefish, 740, 760 White-footed mouse, 747 Whole ecosystem manipulations, see Ecosystem manipulations effluent toxicity (WET) test, 179 Wiebull, 147, 148, 151, 158 Wilcoxon rank sum test (= Mann–Whitney U test), 174, 176, 226 Wildlife management, 243, 278 Williams’ test, 173–176 Wolves, 545, 589 Woolf plot, 117 Workplace mobbing, 818 Wright’s F statistics, 320–323, 327, 356 FIS , 321–323 FIT , 321–323 FST , 321–323 shifting balance theory, 333, 334, 347 X Xenobiotic estrogen, 289 X-ray, 310 Y Yellowstone National Park, 500, 513, 795 Yushckenko, Viktor, 64 Z Zebra mussels, 747, 750, 782 Zero order reaction, 116, 117 Zinc transporter system, 98, 103 Zone of deficiency, 89 stress, 89 tolerance, 88, 89 Zooxanthellae, symbiotic, 100, 551 Zooplanktivorous fish, 588 Zooplankton, 368–369, 381, 389, 401, 449, 550, 555–557, 560–561, 564, 590, 670, 695, 745, 758–759 Zygote, 334, 337 Zygotic selection, see Selection, components, viability Clements: “3357_c037” — 2007/11/9 — 12:41 — page 852 — #24 ... right along the first axis, reflecting an increase in abundance of Chironomidae, Planorbidae, Hirudinea, and Lymnaeidea, and a decrease in Gammaridae and Asellidae Clements: “3357_c 024 ” — 20 07/11/9... data are called factors A factor is an unobservable variable that has attributes of a subset of the observed variables In contrast to PCA in which components are calculated directly as linear... Boca Raton, FL, 20 01, pp 11 9–1 32 Pardal, M .A. , Cardoso, P.G., Sousa, J.P., Marques, J.C., and Raffaelli, D., Assessing environmental quality: A novel approach, Mar Ecol Prog Ser., 26 7, 1–8 , 20 04