ECOTOXICOLOGY: A Comprehensive Treatment - Chapter 24 pdf

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 conta

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 communitiesrange from computationally simple indices such as species richness to complex, computer-dependentalgorithms such as multivariate analyses The simplest community indices use species pres-ence/absence or abundance data to show how individuals in the community are distributed amongspecies The advantages of these indices are their intuitive meaning and their ability to reduce complexdata to a single number Only slightly more involved but retaining more information, species abund-ance curves described inChapter 22characterize the distribution of individuals among the species

by fitting abundance data to specified distributions Estimated distributional parameters from speciesabundance models provide a parsimonious description of the community Slightly more involvedcomposite 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 ofthe community than measures of species richness or species abundance models but have the advant-age of being focused primarily on human effects on communities or species assemblages Moreconvenient, but perhaps applying less ecology than warranted, distributions of individual specieseffect metrics (e.g., distributions of 96-h LC50 values) are used to predict “safe concentrations” thatpresumably 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 num-ber of data dimensions to an interpretable low number, and to quantify similarities or differencesamong sampling units These last methods tend to generate interpretive parsimony at the expense

of methodological simplicity and straightforward terminology; therefore, considerable caution isneeded to avoid errors during their application However, the value of these methods in identifyingclear explanations from complex data sets makes worthwhile any effort spent wading through obtusecomputer manuals or dealing with the associated jargon

Jargon, not argument, is your best ally in keeping him from the Church

(Lewis 1942)

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24.1.1 COMPARISON OFMULTIMETRIC ANDMULTIVARIATE

APPROACHES

Multimetric and multivariate approaches are applied to community data with the intent of ing the associated complex array of information to a more parsimonious form Because ecologicalassessments of biological integrity generally require analysis of numerous biotic and abiotic vari-ables, sophisticated statistical approaches are often necessary to examine the complex relationshipsbetween species assemblages and multiple environmental factors Multivariate approaches reducecomplex, multidimensional data to two or three dimensions, thus allowing researchers to identifykey environmental variables responsible for patterns of species abundance In contrast, multimetricindices integrate a diverse suite of measures, often across several levels of biological organization,

render-to assess biological integrity It is appropriate render-to consider these two approaches render-together because thecommunity data necessary to calculate a multimetric index or to conduct multivariate analyses areoften the same (e.g., abundance, richness, and composition)

In their comparison of multivariate and multimetric approaches, Reynoldson et al (1997) cluded that multivariate approaches provided greater accuracy and precision for assessing referenceconditions in streams Terlizzi et al (2005) showed that univariate measures of molluscan communitystructure (species richness) showed little response to contamination whereas multivariate analysesidentified significant differences between reference and polluted sites Thomas and Hall (2006) com-pared the ability of individual metrics, multivariate approaches, and multimetric indices to identifyimpairment in periphyton, macroinvertebrate, and fish communities Although some individual met-rics were associated with large-scale habitat gradients, multivariate approaches were most usefulfor identifying spatial and temporal differences in each community In a comprehensive analysis ofcommunity indices and multivariate approaches, Kilgour et al (2004) compared the relative sensit-ivity of seven benthic community metrics and three multivariate indices to contamination associatedwith mines, pulp and paper mills, and urbanization Multivariate approaches identified signific-ant differences associated with each of the perturbations and greater effect sizes compared to thecommunity metrics Although the examples described above seem to highlight the greater discrim-inatory ability of multivariate approaches, the usefulness of univariate and multivariate techniquesfor distinguishing between reference and contaminated sites will likely vary with the spatial scale

con-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 complexityand because results are often difficult to interpret (Fore et al 1996, Gerritsen 1995) The com-plex graphical representations of multivariate results are often of limited value to non-ecologistsand managers Although strict reliance on complex statistical algorithms may obscure importantbiological results, we believe that multivariate approaches are an essential set of tools for biolo-gical assessments of water quality Because community–environment relationships are inherentlymultidimensional, approaches such as multivariate analyses that consider interactions among pre-dictor 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 multivariatecommunity responses to contaminants in ways that are more accessible to managers and policy-makers We agree with the recommendations of Reynoldson et al (1997) that multivariate andmultimetric approaches are complementary and should be used in conjunction For example, thevariables used in multivariate analyses such as principal components could include species rich-ness, 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 macroin-vertebrate assemblages and environmental gradients Multivariate statistical analysis (redundancyanalysis) using metrics derived from an index of biotic integrity provided complementary res-ults to canonical correspondence analysis based on macroinvertebrate abundance Alternatively,

a multimetric index similar to Karr’s IBI could be developed using results of multivariate lyses Loading coefficients from canonical discriminant analyses, principal component analyses(PCA), and other multivariate procedures identify variables that are most important for separation

ana-of groups (generally locations, sampling stations) Variables shown to be responsible for tion of reference and impacted stations could be combined in a multimetric index Integration ofmultivariate and multimetric approaches may be necessary to detect perturbations when relativelyweak relationships between stressors and community structure exist (Chenery and Mudge 2005).Finally, we note that our enthusiasm for multimetric and multivariate approaches in community-level bioassessment is not shared by all researchers Weiss and Reice (2005) remind us that neither ofthese approaches provides causal linkages between stressors and community-level responses Theseresearchers advocate an alternative approach in which effects of stressors on individual taxa withknown species-level tolerances are employed to develop an overall assessment of community-levelimpact

separa-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 yearswas 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 becausesome individual metrics may show unexpected changes (e.g., increased species richness at pollutedsites), 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 indexreflect different characteristics of life history, community structure, and functional organization Ingeneral, as the number of metrics increases (up to some reasonable number), the ability to separatecontaminant effects from natural variation increases (Karr 1993) (Figure 24.1) In addition, because

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 theblack 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 1 in Karr (1993).)

individual metrics respond differently to different classes of contaminants, multimetric approachesare useful for assessing a diverse suite of stressors and measuring impacts in systems receivingmultiple stressors The individual metrics included in a multimetric index may vary among perturba-tions, 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 valuethat 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 aquaticcommunities The IBI was developed in response to the federally legislated mandate to “restore andmaintain 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 richnessand composition, trophic composition, and fish abundance and condition The individual metrics areassigned scores (1, 3, 5) based on their similarity to expected values in undisturbed or least impactedstreams Expected values for the individual metrics are obtained by sampling a large number ofknown reference sites in a region Alternatively, expected values can be derived from surveys ofreference and impacted sites and using the “best” values from these samples (Simon and Lyons1995) 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), withlarger values indicating healthy fish assemblages The IBI is sensitive to a diverse array of physicaland 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 environmentalrequirements and historic distributions of this group are well known This greatly facilitates estab-lishment of expected values for individual metrics The structural and functional metrics included inthe IBI are biologically relevant, and each individual metric responds to known gradients of degrad-ation (Fausch et al 1990) The general approach outlined in the IBI has been modified for otherecosystems (e.g., lakes and estuaries) and applied to other taxonomic groups (e.g., benthic macroin-vertebrates 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 indexare consistent among approaches A multimetric index for benthic macroinvertebrate communitieswas used to distinguish polluted from reference sites in rivers of the Tennessee Valley (Kerans andKarr 1994) The benthic IBI (B-IBI) was found to be highly effective because benthic macroin-vertebrates generally respond to chemical and physical degradation in a predictable fashion TheIBI 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 impactedsite to the expected values for the region (Figure 24.2a) As described above, because somemetrics (e.g., species richness) are greatly influenced by stream order and watershed area, theseexpected values must be adjusted to reflect natural variation (Figure 24.2b) Assuming thatcommunity responses to other landscape variables are predictable, a logical extension of thisapproach 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, catch-ment area, elevation) and year sampled accounted for greater than 50% of the variation amongreference sites The performance of several bioassessment metrics was significantly improvedwhen a predictive model that included this geographic variation was employed to identifyimpacted sites The conventional approach of comparing metric values at impacted sites withexpected values at reference sites has now advanced to the point where we can characterize hab-itat variation within subregions using more sophisticated multivariate statistics (Figure 24.2c).The application of multivariate techniques for assessing reference conditions is describedbelow

Test site 2

Test site 3

Test site 2

Range of expected metric

values at reference sites

95% Confidence ellipsoids

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 Valuesare within the expected range for metric 1, but below the range of expected values for metric 2 (b) Metricvalues are adjusted to reflect expected changes in habitat characteristics along a gradient Although the metricvalue at test site 2 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 Testsites 1 and 2 are within the expected values whereas test site 3 falls outside the 95% confidence ellipsoid

24.2.1 MULTIMETRICAPPROACHES FORTERRESTRIAL

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 sensitivityand rapid response to environmental stressors, terrestrial arthropods would be especially useful forassessing biological integrity (Kremen et al 1993) Nelson and Epstein (1998) investigated theresponses of lepidopterans to habitat modifications and concluded that butterfly communities integ-rate important structural and functional characteristics of terrestrial ecosystems Kremen (1992)evaluated the indicator properties of butterfly communities and reported that this group was quiteresponsive 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 Sur-veys, 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 communit-ies, developing a suite of ecologically relevant indicators for this group will be a challenge In

Species richness of birds

FIGURE 24.3 The relationship between species richness of birds and butterflies at 6 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 thatbutterflies, which are relatively easy to monitor, can be used as a surrogate to predict the response of birds tostressors (Modified from Figure 1 in Blair (1999).)

particular, surveys must be corrected to account for differences in detectability among species andamong locations (Chambers et al 1999) One promising alternative is to predict effects of anthropo-genic 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 gradi-ent 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 birdcommunities

24.2.2 LIMITATIONS OFMULTIMETRICAPPROACHES

One major advantage of multimetric approaches is that they integrate several ecologically relevantresponses 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 limitationshave 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 taxonomicgroup, development and application of multimetric approaches require a thorough understanding ofthe 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, mostmultimetric approaches cannot be employed to identify specific causes of environmental impacts.This criticism reflects two mutually exclusive goals of many biological monitoring programs Whilechemical-specific, diagnostic indicators may allow researchers to identify a single source of perturb-ation, more general measures such as the IBI are required to characterize the integrity of systemsreceiving multiple stressors It is possible that the responses of individual metrics in a multimetricindex could offer some insight into the specific source of contamination For example, a mul-timetric index for benthic macroinvertebrates might include metrics for abundance and speciesrichness of mayflies, stoneflies, and caddis-flies All three groups are generally sensitive to organicenrichment; however, many caddis-flies and some stoneflies are tolerant of heavy metals (Clem-ents et al 1988, Clements and Kiffney 1995) Analysis of the responses of component metrics may

allow researchers to quantify the relative importance of individual stressors in systems affected bymultiple perturbations Third, multimetric indices may not respond to some types of perturbationbecause changes in one metric may be offset by changes in another metric Again, the obvioussolution to this problem is to report not only the integrated scores but also the responses of com-ponent metrics Finally, multimetric indices based on attributes of community composition will beless 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 streamsrequires that many of the community-level metrics be replaced by life history and population-levelresponses

24.3 MULTIVARIATE APPROACHES

Multivariate data sets are broadly defined here as those in which more than two dependent or pendent variables are collected for each sampling unit These variables typically include communitycharacteristics (e.g., species abundances) that change or might be influenced together in complexways A wide range of multivariate statistical methods has been used to analyze these types of data

inde-In contrast to the methods described to this point, multivariate analyses are not based on gical concepts but are statistical constructs that reduce complex data sets to potentially meaningfulpatterns involving a few variables Some, such as ordination methods, combine species abundanceinformation for many sites or sampling units into functions that capture a portion of the total vari-ance in the data A small number of uncorrelated, linear combinations of the species abundancesmight 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 todescribe trends among sampling units Other methods, such as cluster analysis, separate samplesinto groups in hopes of identifying some ecological or toxicological pattern that may emerge toexplain the groupings Another type of analysis might be applied to species abundance data toidentify which qualities weigh most heavily in discriminating among known groups Regardless ofthe applied method, the overarching idea is that multivariate analysis of the measured variables canreveal hidden or unmeasured qualities

ecolo-As with most parametric analyses, transformation of species abundance data is often advisablebefore applying a multivariate method Transformation might be done to reduce the influence of onevariable relative to others in the linear combinations of variables One variable might have a muchwider range of values and, in the absence of transformation, would have a disproportionately heavyinfluence on variance In such a case, each variable (e.g., species’ abundances at all sampling sites)may be standardized to a mean of 0 and standard deviation of 1 If a skewed distribution was to occurwith the species abundance distributions, some transformation such as the square root or anotherpower of abundance might be employed prior to standardization and multivariate analysis This isoften necessary when a few species are very abundant at some sites

24.3.1 SIMILARITYINDICES

Although generally not included in treatment of multivariate analyses, similarity indices also reducecomplex, multispecies data for the purpose of comparing communities among locations or overtime Similarity indices quantify the correspondence between two communities based on eitherpresence–absence or abundance data These indices are especially useful for comparing communitiesfrom regional reference sites to impacted sites Alternatively, similarity indices are appropriate instudies of well-defined pollution gradients, where similarity to reference conditions is expected toincrease with distance from a pollution source The simplest and most frequently used similarityindex based on presence–absence data is the Jaccard Index:

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 similarityindices, such as the Morisita–Horn measure, compare the relative abundance of taxa between twocommunities The Morisita–Horn Index is given as

MH= 2
(an i × bni )/(da + db)aN × bN, (24.2)

where an i = 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

ation was effective, the relative dissimilarity between reference and impacted sites would be expected

to decrease over time

0 0.2 0.4 0.6 0.8 1

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 overtime as a result of remediation

While similarity indices provide a simple way to compare community composition, there arepotential 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 werestrongly influenced by initial community composition and the nature of the perturbation Althoughsimilarity indices are useful when comparing communities from two locations, more sophisticatedtechniques are necessary to compare multiple sites Cluster analysis, a logical extension of sim-ilarity indices, is applicable for comparing communities from several locations or for comparingthe similarity of a single site with a group of sites Cluster analysis employs a variety of similar-ity measures based on either presence–absence or abundance data These data are often expressedusing a dendrogram, with the most similar sites combined into a single cluster Additional sites areincluded based on their similarity to the existing clusters Several different clustering algorithmshave 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 sitesand clusters should be joined have been published (Gauch 1982) These methods will be describedbelow

24.3.2 ORDINATION

Ordination is a process in which a large set of variables is reduced to a few variables with the intent ofenhancing conceptual parsimony and tractability With ordination analysis of community abundancedata, the measured variables (e.g., abundance of each species for each sampling unit) are used toidentify hidden patterns or unmeasured factors explaining the data structure Mathematical constructsare 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 andselected for analysis As noted above, the data might require transformation prior to use (2) Thecorrelation 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 1= b1X1+ b2X2+ b3X3+ b4X4+ · · · , (24.3)

where X i = 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 isrepeated 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 theremaining 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 becalculated for placement along each function Plots for all sampling units using the formulatedfunctions as axes should reveal an interpretable pattern In this process, a matrix of many speciesabundances is reduced to a few sampling unit positions on a two- or three-dimensional plot Forexample, 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

Grasslands with few metal-tolerant species Grasslands with numerous metal-tolerant species

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 referencesites Soil qualities and the history of agricultural use of the sites are also noted as potential confoundingfactors After data transformation, ordination analysis results in three orthogonal, linear functions that areassigned interpretations of the influence of soil quality, soil metal concentrations, and agricultural history Thefive mine sites clearly cluster away from the reference sites There is a gradient of communities relative to soilquality and agricultural history Ordination axes can be rotated to enhance interpretation using orthogonal andoblique 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 thevariables and the function (Comrey 1973) For example, very high loadings in Function 2 forspecies known to be tolerant to toxic metals and low or negative loadings for metal-sensitive specieswould suggest the influence of metal exposure on community composition For Function 3, highloadings for species known to flourish in active agricultural areas might suggest the impact of activeagriculture on community structure The final result at this stage for ordination analyses would be toconstruct 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 24.1and24.2) PCA was the first, and remainsthe most popular method (Sparks 2000, Sparks et al 1999) Using PCA, linear combinations ofthe original variables are extracted that sequentially account for the residual variance in a series oforthogonal (uncorrelated) components The first component contains the most variance; the second

Box 24.1 Pollution’s Signature on the Diversity of Estuarine Benthic Communities

To assess the influence of pollution on estuarine benthos, Diaz (1989) plotted species diversity

on principal component axes generated from physical and chemical data for several James River(Virginia, USA) locations Admittedly, one might object to this example because ordination isnot being used directly to summarize community data However, the study is a good illustra-tion of applying two multivariate methods to interpret pollution effects on communities Thedirect application of ordination to species abundance data will be described inBox 24.2afterillustrating key aspects of ordination analysis with this example

The challenge faced by Diaz (1989) was to assess the influence of pollution on benthic munities relative to several other confounding variables Stations were sampled at 5 nauticalmile intervals from the fall line to within 10 miles of the river’s mouth Factors potentiallyinfluencing the benthic communities were measured, including sediment qualities, site-specificpoint discharges, and general water quality characteristics Prior to ordination analysis, sites

com-at salinity extremes were omitted to elimincom-ate this obvious factor with a strong influence oncommunity diversity

Ordination analysis of physical and chemical data from James River sites was done afternormalizing data with the formula

Z ij= X ij− Mj

SDj

where Z ij = the standardized score of a datum for the jth variable of the ith site, Xij = the datum

for the jth variable for the ith site, and M jand SDj = the mean and standard deviation of the data

for the jth variable, respectively The normalized data were analyzed by principal components

methods with no mention of any rotation of axes Whether or not a rotation procedure wouldhave produced more parsimonious principal components remains ambiguous

Table 24.1 summarizes the PCA results The percentage of total variance accounted for

by each of the first five principal components is provided at the top of the table Loadings(eigenvectors) for each chemical or physical factor are given for each principal component with

Discharge biochemical oxygen demand 0.33 0.37 0.00 0.20 0.05

Discharge chemical oxygen demand 0.24 0.46 −0.07 0.20 0.16

Discharge total suspended solids 0.23 −0.19 −0.02 0.10 0.73

Ammonia concentration in water 0.13 0.02 −0.13 −0.70 0.03

Nitrite/nitrate concentration in water −0.14 0.49 −0.26 −0.20 −0.03

Biochemical oxygen demand in water 0.39 0.14 −0.02 −0.32 0.00