Anderson MJ 2001.Permanova

INTRODUCTION

The analysis of multivariate data in ecology is becom-ing increasingly important. Ecologists often need to testhypotheses concerning the effects of experimentalfactors on whole assemblages of species at once. Thisis important for core ecological research and in studiesof biodiversity or environmental impacts in manyhabitats, including marine subtidal environments(Warwick et al. 1988; Gray et al. 1990; Chapman et al.1995; Glasby 1997), mangroves (Skilleter 1996;Kelaher et al. 1998), freshwater systems (Faith et al.1995; Quinn et al. 1996) and terrestrial systems (Oliver& Beattie 1996; Anderson & Clements, in press).

Univariate analysis of variance (ANOVA) provides anextremely powerful and useful tool for statistical testsof factors and their interactions in experiments(Underwood 1981, 1997). Partitioning variation, as inmultifactorial ANOVA, is particularly important for test-ing hypotheses in complex ecological systems with nat-ural temporal and spatial variability. This partitioningis also needed to test multivariate hypotheses in ecol-ogy for experimental designs involving several factors.

This paper describes a new non-parametric test ofthe general multivariate hypothesis of differences in thecomposition and/or relative abundances of organismsof different species (variables) in samples from differ-ent groups or treatments. This test is a significantadvance on previous methods because it can be basedon any measure of dissimilarity and can partitionvariation directly among individual terms in a multi-factorial ANOVA model. The test is applicable to anysituation where the simultaneous responses of manypotentially non-independent variables (usually abun-dances of species in an assemblage) have been meas-ured in samples from a one-factor or multifactorialANOVA design.

Powerful multivariate statistical methods, such as thetraditional multivariate analysis of variance (MANOVA),have existed for decades (Hotelling 1931; Wilks 1932;Fisher 1936; Bartlett 1939; Lawley 1939; Pillai 1955),but tests using these statistics rely on assumptions thatare not generally met by ecological data. The assump-tion that the data conform to a multivariate normal dis-tribution is particularly unrealistic for most ecologicaldata sets. This is because the distributions of abun-dances of individual species are usually highly aggre-gated or skewed (e.g. Gaston & McArdle 1994). Also,abundances take discrete values rather than beingcontinuous, species with small means often haveasymmetric distributions because they are necessarily

Austral Ecology (2001) 26, 3246

A new method for non-parametric multivariate analysis of variance

MARTI J. ANDERSONCentre for Research on Ecological Impacts of Coastal Cities, Marine Ecology Laboratories A11,University of Sydney, New South Wales 2006, Australia

Abstract Hypothesis-testing methods for multivariate data are needed to make rigorous probability statementsabout the effects of factors and their interactions in experiments. Analysis of variance is particularly powerful forthe analysis of univariate data. The traditional multivariate analogues, however, are too stringent in their assumptionsfor most ecological multivariate data sets. Non-parametric methods, based on permutation tests, are preferable.This paper describes a new non-parametric method for multivariate analysis of variance, after McArdle andAnderson (in press). It is given here, with several applications in ecology, to provide an alternative and perhapsmore intuitive formulation for ANOVA (based on sums of squared distances) to complement the description pro-vided by McArdle and Anderson (in press) for the analysis of any linear model. It is an improvement on previousnon-parametric methods because it allows a direct additive partitioning of variation for complex models. It doesthis while maintaining the flexibility and lack of formal assumptions of other non-parametric methods. The test-statistic is a multivariate analogue to Fishers F-ratio and is calculated directly from any symmetric distance ordissimilarity matrix. P-values are then obtained using permutations. Some examples of the method are given fortests involving several factors, including factorial and hierarchical (nested) designs and tests of interactions.

Key words: ANOVA, distance measure, experimental design, linear model, multifactorial, multivariate dissimilarity,partitioning, permutation tests, statistics.

*Present address: Department of Statistics, University ofAuckland, Private Bag 92019, Auckland, New Zealand (Email:[email protected]).

Accepted for publication March 2000.

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SuzanaCaixa de textoPode usar qualquer medida de dissimilaridade entre os objetos (por exemplo, Gower, Jaccard, Bray-curtis, Dudi.Hillsmith) e particionar a varincia na matriz de distncia.

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truncated at zero, and rare species contribute lots ofzeros to the data set. MANOVA test statistics are notparticularly robust to departures from multivariate nor-mality (Mardia 1971; Olson 1974; Johnson & Field1993). Finally, many of these test statistics are simplyimpossible to calculate when there are more variablesthan sampling units, which often occurs in ecologicalapplications.

Many non-parametric methods for tests of differ-ences among a priori groups of observations (as inMANOVA) have been developed (Mantel 1967; Mantel& Valand 1970; Hubert & Schultz 1976; Mielke et al.1976; Clarke 1988, 1993; Smith et al. 1990; Excoffieret al. 1992; Edgington 1995; Pillar & Orlci 1996;Legendre & Anderson 1999). These methods gener-ally have two things in common. First, they are basedon measures of distance or dissimilarity between pairsof individual multivariate observations (which I willrefer to generally as distances) or their ranks. A statis-tic is constructed to compare these distances amongobservations in the same group versus those in differ-ent groups, following the conceptual framework ofANOVA. Second, they use permutations of the obser-vations to obtain a probability associated with the nullhypothesis of no differences among groups.

These non-parametric methods generally fall intotwo categories. First, there are those that can be basedon any chosen distance measure. There are many suchmeasures and these have different properties, whichmake them appropriate for different kinds of data(Legendre & Legendre 1998). For example, to expressdifferences in community structure, the semimetricBrayCurtis measure of ecological distance (Bray &Curtis 1957) or Kulczynskis (1928) semimetric meas-ure are generally preferred over metric measures, likeEuclidean distance (Odum 1950; Hajdu 1981; Faithet al. 1987; Clarke 1993). The methods that are flexi-ble enough to be used with any such distance measure(e.g. Mantel 1967; Hubert & Schultz 1976; Smith et al. 1990; Clarke 1993) have much to recommendthem for this reason.

The drawback to using these methods is that they arenot able to cope with multifactorial ANOVA. That is, theyare not able to partition variation across the manyfactors that form part of the experimental design.Consequently, for most complex designs, one mustanalyse data as one-way analyses in multiple subsetswithin particular levels of factors. These multiple one-way analyses and qualitative interpretations of ordin-ation plots are then used to infer something aboutinteractions or variability at different spatial scales (e.g.Anderson & Underwood 1994; Kelaher et al. 1998).

Some of the proposed non-parametric methods doallow partitioning for a complex design (e.g. Excoffieret al. 1992; Edgington 1995; Pillar & Orlci 1996), butthese are restricted for use with metric distance meas-ures, which are not ideal for ecological applications.

Furthermore, even if these statistics were to be used,there has been disagreement concerning appropriatepermutational strategies for complex ANOVA, particu-larly for tests of interactions (e.g. Edgington 1995;Manly 1997). There have been some recent examplesof direct statistical analyses of BrayCurtis distances(Faith et al. 1995; Underwood & Chapman 1998).These are restricted, however, to very specific experi-mental designs or hypotheses and cannot be used forany multifactorial ANOVA design.

Ecologists need a non-parametric multivariatemethod that can partition variation based on any dis-tance measure in any ANOVA design. The method needsto be robust, interpretable by reference to the experi-mental design, and should lack formal assumptionsconcerning distributions of variables. The purpose ofthis paper is to outline just such a method and to givesome ecological examples of its use. The more generalmathematical theory underlying this method, alongwith simulations and a comparison with the relatedapproach of Legendre and Anderson (1999), isdescribed elsewhere (McArdle & Anderson, in press).

STRATEGY FOR NON-PARAMETRICMULTIVARIATE ANALYSIS

An outline for a general approach to the analysis ofmultivariate data in ecology was given by Clarke andGreen (1988) and Clarke (1993). For experimentaldesigns used to test hypotheses defined a priori, thereare essentially four steps: (i) a choice is made con-cerning an appropriate transformation and/or standard-ization (if any) to apply to the data, given the hypothesisand the scales and nature of the species variables; (ii)a choice is made concerning the distance measure tobe used as the basis of the analysis (e.g. BrayCurtis,Euclidean, x2 or other measure); (iii) ordination(and/or clustering) is performed in order to visualizepatterns of resemblance among the observations basedon their community composition; and (iv) a non-parametric multivariate test for differences amonggroups is done to obtain a rigorous probabilistic statement concerning multivariate effects of a priorigroups. Note that (iii) is not essential in terms of the statistical test; ordination simply gives a visual representation by reducing the dimensionality of thedata. In this paper, I focus on step (iv) of this pro-cedure, which currently poses a problem for multi-factorial designs.

DESCRIPTION OF THE TEST: ONE-WAYDESIGN

The two essential considerations for the test are: (i) theconstruction of the test-statistic, and (ii) the calculation

NON-PARAMETRIC MANOVA FOR ECOLOGY 33







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of a P-value using some method of permutation. I willdescribe the method, which I shall simply call non-parametric MANOVA, first for the one-way design andthen for more complex designs, followed by some eco-logical examples. I deal here only with the case ofbalanced ANOVA designs, but analogous statistics for anylinear model, including multiple regression and/orunbalanced data, can be constructed, as described byMcArdle and Anderson (in press).

The test statistic: an F-ratio

The essence of analysis of variance is to compare vari-ability within groups versus variability among differentgroups, using the ratio of the F-statistic. The larger thevalue of F, the more likely it is that the null hypothesis(H0) of no differences among the group means (i.e.locations) is false. For univariate ANOVA, partitioningof the total sum of squares, SST, is achieved by calcu-lating sums of squared differences (i) between indiv-idual replicates and their group mean (SSW, thewithin-group sum of squares; Table 1a), and (ii)between group means and the overall sample mean(SSA, the among-group sum of squares). Next, considerthe multivariate case where p variables are measuredsimultaneously for each of n replicates in each of agroups, yielding a matrix of data where rows are obser-vations and columns are variables. A natural multi-variate analogue may be obtained by simply adding upthe sums of squares across all variables (Table 1b). AnF-ratio can then be constructed, as in the univariatecase.

This multivariate analogue can also be thought ofgeometrically (e.g. Calinski & Harabasz 1974; Mielkeet al. 1976; Edgington 1995; Pillar & Orlci 1996), asshown in Fig. 1 for the case of two groups and two vari-ables (dimensions). Here, SSW is the sum of thesquared Euclidean distances between each individualreplicate and its group centroid (the point corres-ponding to the averages for each variable, Fig. 1 and

34 M. J. ANDERSON

Table 1. Calculations of within-group sums of squares for partitioning in (a) univariate ANOVA, (b) a multivariate analogueobtained by summing across variables, (c) a multivariate analogue equivalent to (b) obtained using sums of squared Euclideandistances, (d) the traditional MANOVA approach, which yields an entire matrix (W) of within-group sums of squares and crossproducts, and (e) the partitioning using inter-point distances advocated here, equivalent to (b) and (c) if Euclidean distancesare used

Univariate(a) One variable SSW 5 S

ai 5 1 S

nj 5 1 ( yij yi.)2

Multivariate(b) Summed across variables SSW 5 S

ai 5 1 S

nj 5 1 S

pk 5 1 ( yijk yi.k)2

(c) Geometric approach SSW 5 Sai 5 1 S

nj 5 1 ( yij yi.)T( yij yi.)

(inner product, a scalar, based on Euclidean distances, correlations between variables ignored)

(d) Traditional MANOVA W 5 Sai 5 1 S

nj 5 1 ( yij yyi.)( yij yyi.)T

(outer product, a matrix, based on Euclidean distances, correlations between variables matter)

(e) Inter-point geometric approach(a scalar, based on any distance measure, correlations between variables ignored) SS1 5

1S

N 1i 5 1 S

Nj 5 i 1 1 d

2ij eijn

yij, univariate observation of the jth replicate (j 5 1,, n) in the ith group (i 5 1,, a); yijk, observation of yij for the kthvariable (k 5 1,, p); yij, vector of length p, indicating a point in multivariate space according to p variables (dimensions) forobservation j in group i. A superscript )T9 indicates the transpose of the vector, bars over letters indicate averages and a dotsubscript indicates averaging was done over that subscripted variable.

Fig. 1. A geometric representation of MANOVA for twogroups in two dimensions where the groups differ in location.The within-group sum of squares is the sum of squared dis-tances from individual replicates to their group centroid. Theamong-group sum of squares is the sum of squared distancesfrom group centroids to the overall centroid. () Distancesfrom points to group centroids; () distances from groupcentroids to overall centroid; (q), overall centroid; (h), groupcentroid; (d), individual observation.


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Table 1c). Note that this additive partitioning using ageometric approach yields one value for each of SSW,SSA and SST as sums of squared Euclidean distances.This geometric approach gives sums of squares equiv-alent to the sum of the univariate sums of squares(added across all variables) described in the previousparagraph. This differs from the traditional MANOVAapproach, where partitioning is done for an entirematrix of sums of squares and cross-products (e.g.Mardia et al. 1979; Table 1d).

The key to the non-parametric method describedhere is that the sum of squared distances between pointsand their centroid is equal to (and can be calculateddirectly from) the sum of squared interpoint distancesdivided by the number of points. This importantrelationship is illustrated in Fig. 2 for points in twodimensions. The relationship between distances tocentroids and interpoint distances for the Euclideanmeasure has been known for a long time (e.g. Kendall & Stuart 1963; Gower 1966; Calinski &Harabasz 1974; Seber 1984; Pillar & Orlci 1996;Legendre & Legendre 1998; see also equation B.1 inAppendix B of Legendre & Anderson 1999). What isimportant is the implication this has for analyses based on non-Euclidean distances. Namely, an additive partitioning of sums of squares can be obtainedfor any distance measure directly from the distancematrix, without calculating the central locations ofgroups.

Why is this important? In the case of an analysisbased on Euclidean distances, the average for each vari-able across the observations within a group constitutesthe measure of central location for the group inEuclidean space, called a centroid. For many distancemeasures, however, the calculation of a central locationmay be problematic. For example, in the case of thesemimetric BrayCurtis measure, a simple averageacross replicates does not correspond to the centrallocation in multivariate BrayCurtis space. Anappropriate measure of central location on the basis of BrayCurtis distances cannot be calculated easily directly from the data. This is why additive


Fig. 2. The sum of squared distances from individualpoints to their centroid is equal to the sum of squared inter-point distances divided by the number of points.

Fig. 3. Schematic diagramfor the calculation of (a) a dis-tance matrix from a raw datamatrix and (b) a non-para-metric MANOVA statistic for aone-way design (two groups)directly from the distancematrix. SST, sum of squareddistances in the half matrix(j) divided by N (total number of observations); SSW,sum of squared distanceswithin groups ( ) divided byn (number of observations per group). SSA 5 SST SSWand F = [SSA/(a 1)]/[SSW/(N a)], where a 5 the num-ber of groups.




partitioning (in terms of average differences amonggroups) has not been previously achieved usingBrayCurtis (or other semimetric) distances. However,the relationship shown in Fig. 2 can be applied toachieve the partitioning directly from interpointdistances.

Thus, consider a matrix of distances between everypair of observations (Fig. 3a). If we let N 5 an, the totalnumber of observations (points), and let dij be the dis-tance between observation i 5 1,, N and observationj 5 1,, N, the total sum of squares is

1SST 5 S

N1

i 5 lSN

j 5 i 11d2ij (1)N

That is, add up the squares of all of the distances inthe subdiagonal (or upper-diagonal) half of the distancematrix (not including the diagonal) and divide by N(Fig. 3b). In a similar fashion, the within-group orresidual sum of squares is

1SSW 5 S

N1

i 5 lSN

j 5 i 11d2ij eij (2)n

where eij takes the value 1 if observation i and obser-vation j are in the same group, otherwise it takes thevalue of zero. That is, add up the squares of all of thedistances between observations that occur in the samegroup and divide by n, the number of observations pergroup (Fig. 3b). Then SSA 5 SST SSW and a pseudoF-ratio to test the multivariate hypothesis is

SSA /(a 1)F 5 (3)

SSW /(N a)

If the points from different groups have different cen-tral locations (centroids in the case of Euclidean dis-tances) in multivariate space, then the among-groupdistances will be relatively large compared to the within-group distances, and the resulting pseudo F-ratio willbe relatively large.

One can calculate the sums of squares in equations(1) and (2) and the statistic in equation (3) from a distance matrix obtained using any distance measure.The statistic in equation (3) corresponds exactly to the statistic in equation (4) of McArdle and Anderson(in press), who have shown more generally how partitioning for any linear model can be done directlyfrom the distance matrix, regardless of the distancemeasure used. Another important aspect of the stat-istic described above is that, in the case of a Euclideandistance matrix calculated from only one variable,equation (3) gives the same value as the traditionalparametric univariate F-statistic.

This is proposed as a new non-parametric MANOVAstatistic that is intuitively appealing, due to its analogywith univariate ANOVA, and that is extremely relevantfor ecological applications. The results (in terms ofsums of squares, mean squares and pseudo F-ratios)obtained for individual terms in a multivariate analysis

can be interpreted in the same way as they usually arefor univariate ANOVA. The difference is that the hypoth-esis being tested for any particular term is a multivariatehypothesis.

OBTAINING A P-VALUE USINGPERMUTATIONS

The multivariate version of the F-statistic describedhere is not distributed like Fishers F-ratio under thenull hypothesis. This is so because (i) we do not expectthe individual variables to be normally distributed, and(ii) we do not expect that the Euclidean distance willnecessarily be used for the analysis. Even if each of thevariables were normally distributed and the Euclideandistance used, the mean squares calculated for the mul-tivariate data would not each consist of sums of inde-pendent x2 variables, because, although individualobservations are expected to be independent, individ-ual species variables are not independent of oneanother. Thus, traditional tabled P-values cannot beused. A distribution of the statistic under the nullhypothesis can be created, however, using permutationsof the observations (e.g. Edgington 1995; Manly1997). The only situation in which one could use thetraditional tabled probabilities would be if one had asingle variable that could reasonably be assumed to benormally distributed and one used Euclidean distancesfor the analysis.

Suppose the null hypothesis is true and the groupsare not really different (in terms of their compositionand/or their relative abundances of species, as measuredby the BrayCurtis distances). If this were the case,then the multivariate observations (rows) would beexchangeable among the different groups. Thus, thelabels on the rows that identify them as belonging to aparticular group could be randomly shuffled (per-muted) and a new value of F obtained (called, say, Fp).This random shuffling and re-calculation of Fp is thenrepeated for all possible re-orderings of the rows rela-tive to the labels. This gives the entire distribution ofthe pseudo F-statistic under a true null hypothesis forour particular data. Comparing the value of F obtainedwith the original ordering of the rows to the distributioncreated for a true null by permuting the labels, a P-valueis calculated as

(No. of F p F)P 5 (4)

(Total no. of F p)

Note that we consider the original observed value of Fto be a member of the distribution of Fp under per-mutation (i.e. it is one of the possible orderings of thelabels on the rows). The usual scientific convention ofan a priori significance level of a 5 0.05 is generallyused for interpreting the significance of the result, asin other statistical tests. It is also possible to view the

36 M. J. ANDERSON




P-value as a measure of confidence concerning the nullhypothesis (Fisher 1955; Freedman & Lane 1983).

With a groups and n replicates per group, the num-ber of distinct possible outcomes for the F-statistic ina one-way test is (an)!/(a!(n!)a) (Clarke 1993). As it isusually not practical to calculate all possible permuta-tions, because of the time involved, P can be calculatedusing a large random subset of all possible permutations(Hope 1968). However, the precision of the P-value willincrease with increasing numbers of permutations.Generally, at least 1000 permutations should be donefor tests with an a-level of 0.05 and at least 5000 per-mutations should be done for tests with an a-level of0.01 (Manly 1997).

ASSUMPTIONS

The only assumption of the test is that the observations(rows of the original data matrix) are exchangeableunder a true null hypothesis. To assume exchangeabilityunder the null hypothesis is generally to assume thatthe observations are independent and that they havesimilar distributions (e.g. Boik 1987; Hayes 1996). Bysimilar distributions, I mean similar multivariate dis-persions of points, not that the points are necessarilymultivariate normal. The test described here is a testfor differences in location (means or centroids) amonggroups of multivariate observations based on thechosen distance measure. Like its univariate counter-part, which is sensitive to heterogeneity of variances,this test and its predecessors that use permutations, likeANOSIM (Clarke 1993), will also be sensitive to differ-ences in the dispersions of points, even if the locationsdo not differ.

The sensitivity of ANOSIM to differences in dispersionhas been suggested as an advantage by Clarke (1993).This is because it was introduced in the context ofdetecting environmental impacts, for which detectionof differences of any kind between control and impactedlocations is very important for environmental reasons.Here, I simply suggest that caution be exercised ininterpreting the results of tests of significance.Determining if significant differences among groupsmay be due to differences in dispersion versus differ-ences in location (or some combination of the two) isan important statistical and ecological issue. The useof permutation tests to obtain P-values does not avoidthis issue.

A useful comparative index of multivariate dispersionhas been given by Warwick and Clarke (1993). Also, aseparate permutation test for significant differences inmultivariate dispersions (after removing effects of dif-ferences in location), as an accompaniment to the non-parametric MANOVA approach given here, will bedescribed elsewhere (Anderson, Dutilleul, Lapointe &Legendre, unpubl. data).

DISTINCTION FROM TRADITIONAL TESTSTATISTICS

Although the statistic described here is sensitive to dif-ferences in the relative dispersion of points amonggroups, it takes no account of the correlations amongvariables. In traditional MANOVA, the test-statistics (suchas Wilks Lambda) use information contained in thebetween-group and/or within-group sample variancecovariance matrices (e.g. Table 1d, see Olson 1974;Johnson & Field 1993). The traditional MANOVA testsassume not only that the variance for each variableremains constant across different groups (i.e. thepoints in different groups have similar scatter), they alsoassume that the relationships among the variables (theircovariances or correlations) do not differ across groups.

These differences in the sensitivities of differentmultivariate test statistics are shown diagrammaticallyin Fig. 4 for two variables (two dimensions). Figure 4(a)shows two groups that differ in their correlation struc-ture, but not in their variances or location. Figure 4(b)shows two groups that differ only in their dispersions,but not in their correlation or location. Although allMANOVA statistics are designed to test for differences inlocation, the traditional statistics will also be sensitiveto differences in correlations (Fig. 4a) as well as dif-ferences in dispersion (Fig. 4b). The method of non-parametric MANOVA described here will only be sensitiveto differences in dispersion (Fig. 4b). The correlationsamong variables play no role in the analysis. If differ-ences in the relationships amongst variables form ahypothesis of interest, then some other non-parametrictechniques may be relevant (e.g. Biondini et al. 1991;Krzanowski 1993).

ONE-WAY EXAMPLE: EFFECTS OFGRAZERS

Consider the following example, taken from an eco-logical study by Anderson and Underwood (1997). Thestudy was designed to test the hypothesis that grazingby gastropods affects intertidal estuarine assemblages.The experiment was done at an intertidal oyster farmfrom January to July 1994 in Quibray Bay, south ofSydney, New South Wales, Australia. Experimentalsurfaces (10 cm 3 10 cm) were enclosed in cages toexclude gastropod grazers, while other surfaces wereleft open to grazing. A third treatment consisted ofcaged areas where natural densities of grazers wereincluded. This was a control for the effect of the cageitself on assemblages. There were n 5 20 surfaces ineach of the three treatments. The numbers of indi-viduals of each of 21 taxa (invertebrates and algae) colonizing each surface were recorded.

The rationale for increasing the severity of the trans-formation to increase the relative contribution of rare







versus abundant species in the analysis, given by Clarkeand Green (1988), is followed here. Note that the trans-formation is not done in an effort to make data con-form to any assumptions of the analysis. In thisexample, the data contained some species that occurredon a very large relative scale of abundance (e.g.Spirorbid worms occurred in the thousands), so thedata were transformed by taking double-square rootsbefore the analysis. To visualize the multivariate patterns among observations, non-metric multi-dimensional scaling (MDS) was performed on theBrayCurtis distances (Kruskal & Wish 1978), usingthe PRIMER computer program. Non-parametricMANOVA was then done on BrayCurtis distances, asdescribed in the previous section, using the computerprogram NPMANOVA, written by the author in FORTRAN.

The number of possible permutations for the one-way test in the case of the grazing experiment is9.6 3 1025. A random subset of 4999 permutations wasused (Fig. 5). In this case, the null hypothesis of nodifferences among groups was rejected, as the observedvalue was much larger than any of the values obtainedunder permutation (Fig. 5, Table 2).

A POSTERIORI TESTS

As in univariate ANOVA where there is a significant resultin a comparison of 3 or more treatments, we may wishto ask for the multivariate case: wherein does the sig-nificant difference lie? This can be done by using thesame test, given above for the one-way comparison ofgroups, but where individual pair-wise comparisonsbetween particular groups are done. To continue withthe logic of the analogous univariate situation, we can use a t-statistic (which is simply the square root ofthe value of the F-statistic described above) for these

38 M. J. ANDERSON

Fig. 4. Two variables in each of two groups of observationswhere (a) the groups differ in correlation between variables,but not in location or dispersion and (b) the groups differ in dispersion, but not in location or correlation betweenvariables.

Fig. 5. Distribution of the non-parametric MANOVAF-statistic for 4999 permutations of the data on assemblagesin different grazing treatments. The real value of F for thesedata is very extreme by reference to this distribution(F 5 36.62): thus there are strong differences among theassemblages in different grazing treatments.

Table 2. Non-parametric MANOVA on BrayCurtis dis-tances for assemblages of organisms colonizing intertidal sur-faces in estuaries in three grazing treatments (grazersexcluded, grazers inside cages, and surfaces open to grazers)

Source d.f. SS MS F P

Grazers 2 18 657.65 9328.83 36.61 0.0002Residual 57 14 520.89 254.75Total 59 33 178.54

Comparison* t P

Open versus caged 8.071 0.0002Open versus cage control 3.268 0.0002Caged versus cage control 6.110 0.0002

*Pair-wise a posteriori tests among grazing treatments.

pairwise comparisons. These have the same inter-pretation as univariate t-tests, but they test the generalmultivariate hypothesis of no difference between thegroups on the basis of the BrayCurtis (or otherchosen) distances. This is Students univariate t-statistic if Euclidean distances are chosen for theanalysis of only one variable. P-values for each test areobtained using separate sets of permutations that areonly done across the pair of groups being compared.In this example, a random subset of 4999 permutationswas used (out of a possible 6.8 3 109) for each pair-wise comparison.

For the analysis of the experimental removal of graz-ers, there was a significant difference among all pairsof treatments: assemblages colonizing surfaces in cagesdiffered from those in the open or in cage controls(t 5 8.07, P 5 0.0002 and t 5 6.11, P 5 0.0002, respec-tively). Grazers had a significant effect on the assem-blages, which is consistent with the pattern ofseparation of points corresponding to different treat-ments in the non-metric MDS plot (Fig. 6). The factthat assemblages on open surfaces also differed signifi-cantly from those in cage controls (t 5 3.27,P 5 0.0002) suggested that there was some additionalartifact due to the presence of a cage in the experiment.

An important point here is that the a posteriori com-parisons just described did not make any correction forexperiment-wise error rate (Day & Quinn 1989).Similarly, the multivariate pair-wise tests available inthe computer program NPMANOVA are not corrected forexperiment-wise error rate. This means that with an apriori significance level of a 5 0.05, one should expectto obtain a significant result in one out of every 20

independent tests by chance alone. Nevertheless, theP-value obtained under permutation for any individualpair-wise test is exact. Many of the methods used forcorrecting error rates for multiple comparisons, suchas the Bonferroni method, are very conservative butmay be applied.

MORE COMPLEX DESIGNS

For more complex designs, we can start by consider-ing the situation with two factors in a factorial (ororthogonal) design. The principles used to partition thevariation directly from the distance matrix and to obtainthe statistics and permutation methods for individualterms in the model can be readily generalized to othermultifactorial cases. The logic applied to multifactorialANOVA of univariate data (e.g. see Underwood 1981,1997) also applies to the analysis of multivariate datausing this non-parametric procedure. For example,tests of main effects should be examined after tests forinteractions.

Calculating the statistic

Let A designate factor 1 with a levels (treatments orgroups) and B designate factor 2 with b levels, with nreplicates in each of the ab combinations of the two fac-tors. The total number of observations is N 5 abn. Thetotal sum of squares in the analysis is calculated as forthe one-way case according to equation (1). To partitionthe variation, the within-group sum of squares for factorA, ignoring any influence of B, is calculated as

1SSW(A) 5 S

N1

i 5 1SN

j 5 i 11d2ij eij

(A) (5)bn

where eij(A) takes the value 1 if observation i and obser-

vation j are in the same group of factor A, otherwise ittakes the value of zero. Similarly, the within-group sumof squares for factor B, ignoring any influence of A, is

1SSW(B) 5 S

N1

i 5 lSN

j 5 i 11d2ij eij

(B) (6)an

Then, the corresponding sums of squares for each ofthe main effects in the analysis are SSA 5 SST SSW(A)and SSB 5 SST SSW(B).

The residual sum of squares is calculated by con-sidering the interpoint distances within each of the abcombinations of factor A and B, thus:

1SSR 5 S

N1

i 5 lSN

j 5 i 11d2ij eij

(AB) (7)n

where eij(AB) takes the value 1 if observation i and obser-

vation j are in the same combination of factors A andB, otherwise it takes the value of zero. We then can


Fig. 6. Non-metric MDS plot of assemblages colonizingintertidal surfaces in Quibray Bay in each of three differentgrazing treatments: (m), cage control; (s), open; ( ) caged.

easily obtain the sum of squares corresponding to theinteraction term: SSAB 5 SST SSA SSB SSR. It maybe easier to consider the squared distances beingsummed in equations (5) through (7) by reference totheir physical location in the distance matrix itself, asillustrated in Fig. 7.

In the case of a two-factor design where one factoris nested in the other, the same general approach isused. In this case, however, there is no interaction termin the analysis and we have instead SSB(A) 5 SST SSA SSR, where B(A) denotes that factor B is nested infactor A.

Having obtained appropriate sums of squares, theconstruction of the pseudo F-statistic for each term inthe analysis for non-parametric MANOVA then followsthe same rules and formulae as it would for the usualunivariate ANOVA. The construction of the F-ratiodepends on the experimental design, that is, whetherfactors are nested or factorial and whether they are fixedor random, exactly as for univariate ANOVA (e.g.Underwood 1981, 1997; Winer et al. 1991).

Doing the permutations

The method of permutation required to obtain an exacttest is not so simple if there is more than one factor inthe design. The choice of an appropriate permutationmethod is not trivial and should be considered care-fully for each term in the model. Indeed, the lack ofexact tests or knowledge of how the available approxi-mate permutation tests might behave for complexmodels has been a sticking point in the developmentof multivariate non-parametric methods (e.g. Crowley1992; Clarke 1993). To construct exact tests, twoimportant issues must be considered (Anderson & terBraak, unpublished data). First, which units should bepermuted (i.e. what are exchangeable under the nullhypothesis) and second, should any restrictions beimposed on the permutations to account for otherfactors in the design?

In many important situations, such as tests of inter-actions, no exact permutation test can be done. Also,there are times when the exact test imposes so many

40 M. J. ANDERSON

Fig. 7. Schematic diagram of the interpoint distances usedto partition the variability in the multivariate data set and tocalculate the sum of squares for each term in a two-factororthogonal design (each factor has two groups or levels). (a)SSW(A) 5 sum of squared distances within groups of A (j),divided by (bn). (b) SSW(B) 5 sum of squared distances within groups of B ( ), divided by (an). (c) SSR 5 sum ofsquared distances within combinations of AB ( ), dividedby (n) (residual sum of squares). SST 5 sum of squared distances in the total half matrix, divided by (abn), SSA 5 SST SSW(A), SSB 5 SST SSW(B), SSAB 5 SST SSA SSB SSR.

restrictions as to render the test meaningless, due tothere being too few possible permutations left. In thesecases, approximate permutation tests should be used,of which there are several alternatives, includingpermutation of residuals and permutation of raw dataacross all terms in the analysis (e.g. Freedman & Lane1983; ter Braak 1992; Manly 1997). Some empiricalcomparisons of these methods are provided byGonzalez and Manly (1998) and Anderson andLegendre (1999).

ECOLOGICAL EXAMPLES

Two-way factorial design

The first example is from an experiment in MiddleHarbour (part of Sydney Harbour) to test for the effectof shade and proximity to the seafloor on assemblagesof invertebrates and algae colonizing subtidal hard sur-faces near marinas (Glasby 1999). The experiment wasa two-way crossed (orthogonal) design with n 5 4 repli-cate settlement plates (15 cm 3 15 cm sandstone)either far from or near to the seafloor (the factor ofposition; all plates were at a similar depth of approxi-mately 2 m below low water) and in one of three shading treatments: (i) shade (an opaque plexiglassroof), (ii) a procedural control (a clear plexiglass roof),and (iii) no shade. Organisms colonizing the plates after33 weeks were counted and a total of 46 taxa wereincluded in the analyses. Organisms that occurred only once across the entire data set were not included.Non-parametric MANOVA was done on BrayCurtis distances calculated from double-root transformed datausing the FORTRAN program NPMANOVA. The samplesize was reasonably small for this study (n < 5), so the

test was done using unrestricted permutation of rawdata (e.g. Manly 1997; Gonzalez & Manly 1998) with4999 random permutations. Similar results wereobtained using permutation of residuals under areduced model (not shown).

There was no significant interaction of shade andposition, but both main effects were significant(Table 3, Fig. 8). It was then of interest to compare thegroups corresponding to different shading treatmentsusing a posteriori tests (Table 3). It was not necessaryto do this for the effect of position, because this factoronly had two groups. Assemblages of organisms onsettlement plates near the bottom were extremely differ-ent from those far away from the bottom (Fig. 8). Also,assemblages on shaded plates were significantly differ-ent from those on either the procedural control or onunshaded plates, which themselves did not differ(Table 3, Fig. 8). This analysis also shows how the effectof position relative to the bottom was much greater thanthe effect of shading on assemblages in this experiment(compare the values of their mean squares in Table 3).The non-parametric approach advocated here allowstests of significance, but it also allows relative sizes ofeffects to be compared directly through the partitioningof the variation and examination of mean squares.

Three-way design, including nesting

The second example comes from an experiment to testthe hypothesis that the size of a patch available for


Table 3. Non-parametric MANOVA on BrayCurtis dis-tances for assemblages of organisms colonizing subtidal sand-stone settlement panels after 33 weeks in an estuary atdifferent distances from the seafloor (positions either near orfar) and in three different shading treatments


Position 1 5595.40 5595.40 13.536 0.0002Shade 2 3566.44 1783.22 4.314 0.0006Position 3 2 1238.94 619.47 1.499 0.1394

shadeResidual 18 7440.66 413.37Total 23 17 841.43

Comparison* t P

Shade versus control 1.783 0.0154Shade versus no shade 1.987 0.0018Control versus no shade 0.866 0.5560

*Pair-wise a posteriori tests among shading treatments.

Fig. 8. Two-factor non-metric MDS plot of subtidal assemblages colonizing sandstone settlement plates after 33 weeks in Middle Harbour that were either near to or farfrom the seafloor and in one of three shading treatments. (r),Shaded; ( ), control; (s), no shade; 1, far from the sea floor;2, close to the sea floor.

colonization would affect the succession of assemblagesin equal areas on those patches (Anderson 1998). Thishypothesis was tested using wooden panels of three different sizes (10 cm 3 10 cm, 20 cm 3 20 cm and40 cm 3 40 cm). Two panels of a given size wereattached to sticks that were then strapped to the structure of an intertidal oyster farm in the PortStephens estuary in New South Wales, Australia inJanuary of 1995 (see Anderson 1998 for details). Sixsticks (two for each of the three patch sizes) were thencollected independently after periods of 3, 6, 9, 12 and 18 months of exposure to colonization. The experi-mental design thus consisted of three factors: time (5 periods of submersion), patch (3 sizes) and sticks(2 sticks per time 3 patch combination, a randomnested factor), with n 5 2 panels per stick. Organismscolonizing panels were quantified in a 10 cm 3 10 cmarea from each panel (chosen randomly from the larger-sized panels). A total of 33 taxa were included in multivariate analyses.

The analysis was done using NPMANOVA on BrayCurtis distances calculated on double-root trans-formed data, as for the previous examples. In this case,however, an exact permutation test for the nested factor(sticks) was carried out by permuting the observationsrandomly across sticks, but only within the 5 3 3 com-binations of levels of time 3 patch. Then, for the testof the upper-level terms (the main effects of time, patchsize and their interaction), individual replicates on astick were permuted together as a unit (i.e. whole stickswere permuted). This is done so that the upper-leveleffects can be tested against the variability across sticks,not across individual replicates, as is necessary underthe null hypothesis for a nested hierarchy (e.g. Clarke1993). For all tests, a subset of 4999 permutations wasused.

The nested factor of sticks was not significant in themultivariate analysis, but the time 3 patch size inter-action was significant (Table 4). Individual pair-wisecomparisons of effects of patch size for each time werenearly identical to the one-way tests given in Anderson(1998) using analysis of similarities (ANOSIM, Clarke1993). Assemblages were significantly different on thesmallest patches compared to the other sized patchesafter 3, 6, 9 or 12 months. Assemblages on the twolarger sized patches did not differ significantly from oneanother except after 12 months. After 18 months,assemblages were similar on all patch sizes.

Non-metric MDS plots helped to interpret theseresults. Two separate ordinations were done on thesedata, as the stress value for the non-metric MDS plot that included all observations was too high for areasonable interpretation. The effect of different patchsizes appeared to be fairly consistent (in terms of its magnitude and direction) after 3, 6 or 9 months(Fig. 9a). After 12 or 18 months, the observationsbecome more scattered within and across the groups

and the effects of patch size become less clear (Fig. 9b).Increased dispersion (variability in assemblages) afterthese longer periods of time, compared to earlierperiods, is seen clearly in the two-factor plot of stickcentroids (Fig. 9c, which includes all data). As notedearlier, the tests are sensitive to such differences in dis-persion. Overall, although the two factors did interact,the effect of time (i.e. succession) was relatively moreimportant in distinguishing assemblages than the sizeof the patch (compare their mean squares in Table 4),and effects of patch size decreased through time forthese assemblages (see Anderson 1998 for furtherdetails).

DISCUSSION

Natural temporal and spatial variability is intrinsic toecological systems. Indeed, variability might be con-sidered the currency of ecological scientific work. It isfor this reason that statistical analysis plays such animportant role in the development of ecology as ascience. In Design of Experiments, R. A. Fisher (1935,p. 4) wrote:

We may at once admit that any inference from theparticular to the general must be attended withsome degree of uncertainty, but this is not thesame as to admit that such inference cannot beabsolutely rigorous, for the nature and degree ofuncertainty may itself be capable of rigorousexpression.

Quantitative statistical inference is indeed what isneeded for the rigorous interpretation of mensurativeor manipulative ecological experiments. Although ourconclusions may be uncertain, they are still rigorous inthe sense that the degree of uncertainty can beexpressed in terms of mathematical probability. In uni-variate analysis, W. S. Gosset (Student 1908) madethis possible for comparisons of two treatments, whileR. A. Fisher made this possible for many treatmentsand experimental factors. In a complex and intrinsi-cally variable world, ANOVA allows us to identify simul-taneous effects and interactions of more than onefactor, and to identify the uncertainty of our inferenceswith rigour (e.g. Underwood 1981, 1997).

An important advance in the analysis of multivariatedata in ecology was the development of non-parametricmethods for testing hypotheses concerning wholecommunities (e.g. Clarke 1988, 1993; Smith et al.1990; Biondini et al. 1991). Some parallel advanceswere made in the context of tests for significant clus-ters in cluster analysis (e.g. Good 1982; Gordon 1994).Before these applications, particularly ANOSIM (Clarke1993), became widely available, most multivariateanalyses in ecology focused on the reduction of dimen-sionality to produce and interpret patterns (ordination

42 M. J. ANDERSON

methods) and the use of numerical strategies for placingobservations into natural groups (clustering). Thesemethods, although extremely useful towards theirpurpose, do not rigorously express the nature anddegree of uncertainty concerning a priori hypotheses.Methods like Mantels test (Mantel 1967), ANOSIM(Clarke 1993) and multiresponse permutation pro-cedures (Mielke et al. 1976) allow such rigorousprobabilistic statements to be made for multivariateecological data.

The drawback to such non-parametric tests is thatthey cannot easily be extended to the multifactorialdesigns so common now in ecological studies. Twosticking points prevented this: (i) the lack of a gener-alized statistic for partitioning variation, and (ii) the lackof appropriate permutation methods (e.g. Clarke 1993;Legendre & Anderson 1999). Although traditional test-statistics used for MANOVA allow partitioning, their restrictive assumptions have prevented theireffective use in ecology. The method of distance-based redundancy analysis (Legendre & Anderson


Table 4. Non-parametric MANOVA on BrayCurtis dis-tances for assemblages of organisms colonizing woodensettlement panels of three different sizes after 3, 6, 9, 12 or18 months on an intertidal oyster farm


Time 4 30 305.71 7576.43 20.50 0.0002Patch Size 2 6414.99 3207.49 8.68 0.0002Time 3 patch 8 6224.03 778.004 2.10 0.0062Sticks (time 3 15 5544.66 369.64 1.28 0.3384

patch)Residual 30 8697.09 289.90Total 59 57 186.48

3 6 9 12 18Comparison months months months months months

Small versus 2.24* 1.86* 1.48* 2.00* 1.38medium

Small versus 2.30* 2.87* 2.38* 2.54* 3.02*large

Medium versus 1.49 1.47 1.69 1.75* 1.43large

*P < 0.05; pair-wise a posteriori tests among patch sizeswithin each time using the t-statistic. Sticks were ignored inthe pair-wise tests. There were 35 possible permutations foreach.

Fig. 9. Two-factor non-metric MDS plots of assemblagescolonizing intertidal wooden settlement panels of three dif-ferent sizes for (a) periods of 3, 6 or 9 months and (b) peri-ods of 12 or 18 months for raw data and (c) where centroidswere plotted corresponding to each stick across all combi-nations of time 3 patch size. The points corresponding toassemblages after particular times of submersion (numbersindicate the period in months) have been outlined for clar-ity. (d), Small; (h), medium; (m), large.

1999) largely solved these issues, but it has many rather complicated steps and involves the use of acorrection constant to distances. Although thiscorrection does not adversely affect the test (generallymaking it more conservative, if anything), accurateP-values are not given by this method in the case ofanything other than a one-factor design (McArdle &Anderson, in press).

The method presented here has, in some sense,combined the best of both worlds. Like the traditionaltest-statistics, it can partition variation according to any ANOVA design. Like the most flexible non-parametric methods, it can be based on any sym-metric dissimilarity or distance measure (or theirranks) and provides a P-value using appropriate per-mutation methods. That is, one can still choose a rel-evant transformation and an appropriate distancemeasure (or use ranks of distances), consistent with themethod of ordination used to visualize patterns. Byusing permutations, the test requires no specificassumption concerning the number of variables or thenature of their individual distributions or correlations.The statistic used is analogous to Fishers F-ratio andis constructed from sums of squared distances (or dissimilarities) within and between groups. Anotherfeature of this statistic is that it is equal to to Fishersoriginal F-ratio in the case of one variable and whenEuclidean distances are used.

It is perhaps important to point out that theBrayCurtis measure of dissimilarity may or may notbe the most appropriate measure to use in any givensituation. A point still commonly ignored is thatBrayCurtis and related measures, such as Kulczynskiscoefficient, will tend to under-estimate true ecologicaldistances when distances become large (i.e. whenobservations have very few species in common), as determined by simulations (Faith et al. 1987; Belbin 1991). The BrayCurtis measure may thereforeonly be useful insofar as it will produce reasonableecological ordinations, through the ranks used forMDS. In light of this issue, Faith (1990) proposeddoing MANOVA on ordination scores obtained fromMDS.

An analysis based only on ordination scores will omitsome portion of the ecological information containedin the original distance matrix and will depend on thenumber of dimensions chosen for the MDS (Clarke1993). The severity of this potential problem willobviously increase with increases in the stress valueassociated with an MDS plot. The actual amount and the kind of information lost in reducing dimensionsusing MDS are generally unknown and will depend on the particular data set. Also, any subsequentstatistical inferences on ordination scores (usingtraditional MANOVA, as described in Faith 1990; orbased on permutation tests) would be limited to points in the ordination plots, rather than being applic-able to the original observations.

It is not possible to identify a single best distancemeasure for ecological data. The method describedhere may be used with any distance measure chosen(or on ranks of distances). It is useful to have the flex-ibility to choose a distance measure appropriate for thedata and hypothesis being tested. Although theBrayCurtis measure has now become commonplacein ecological studies (perhaps due to its availability inthe PRIMER computer program, or due to its intuitiveinterpretation as percentage difference, or due to theresults presented in Faith et al. 1987), there are stillmany rivals. Over 60 measures of similarity or dissim-ilarity have been described, with very few actual com-parisons of their performance with different kinds of ecological data (see Lamont & Grant 1979;Legendre & Legendre 1998). This is undoubtedly anarea needing further research.

The approach advocated here is that multifactorialanalysis of variance, as successfully applied to univariatedata in ecology (e.g. Underwood 1981, 1997), can andshould also be applied to multivariate data for testinghypotheses in a logical and rigorous way. It stands toreason that the developments in experimental designfor ecology that require multifactorial ANOVA, in orderto, for example, avoid pseudo-replication (Hurlbert1984), test for generality (Beck 1997) or test for envir-onmental impact (e.g. Green 1979, 1993; Underwood1993; Glasby 1997), should be incorporated into multivariate analysis. The method described hereallows that to happen, but within a framework that isgeneral enough to suit our need for few assumptionsand flexibility in the multivariate analysis of ecologicaldata.

ACKNOWLEDGEMENTS

I am indebted to those who have worked on and devel-oped multivariate methods and permutation tests thathave led to the ideas in this manuscript, including K. R. Clarke, E. S. Edgington, R. A. Fisher, P. Legendre, B. F. J. Manly, N. Mantel, B. H. McArdle,L. Orlci, V. D. P. Pillar, E. P. Smith, and C. J. F. terBraak. I also owe a great deal to A. J. Underwood forhis work in the statistical analysis of ecological experi-ments using ANOVA, which inspired my pursuit of thistopic for multivariate analysis. T. Glasby kindlyprovided data for the two-way factorial example. ThePRIMER computer program was provided courtesy of M. R. Carr and K. R. Clarke, Plymouth MarineLaboratories, UK. My colleagues at the SpecialResearch Centre for Ecological Impacts of CoastalCities provided logistic support, tested out the com-puter program and commented on earlier versions ofthe manuscript. The computer program NPMANOVA isavailable from the author. This research was supportedby a U2000 Post-doctoral Fellowship at the Universityof Sydney.

44 M. J. ANDERSON

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FORUM

An Entomologist Guide to Demystify Pseudoreplication: Data Analysisof Field Studies With Design Constraints

LUIS FERNANDO CHAVES1,2

J. Med. Entomol. 47(3): 291298 (2010); DOI: 10.1603/ME09250

ABSTRACT Lack of independence, or pseudoreplication, in samples from ecological studies ofinsects reects the complexity of working with living organisms: the nite and limited input ofindividuals, their relatedness (ecological and/or genetic), and the need to group organisms intofunctional experimental units to estimate population parameters (e.g., cohort replicates). Severaldecades ago, when the issue of pseudoreplication was rst recognized, it was highlighted thatmainstream statistical tools were unable to account for the lack of independence. For example, thevariability as a result of differences across individuals would be confounded with that of the exper-imental units where they were observed (e.g., pans for mosquito larvae), whereas both sources ofvariability now can be separated using modern statistical techniques, such as the linear mixed effectsmodel, thatexplicitlyconsider thedifferent scalesofvariability inadataset (e.g.,mosquitoesandpans).However, the perception of pseudoreplication as a problem without solution remains. This studypresents concepts to critically appraise pseudoreplication and the linear mixed effects model as astatistical solution for analyzing data with pseudoreplication, by separating the different sources ofvariability and thereby generating correct inferences from data gathered in studies with constraintsin randomization.

KEYWORDS linear mixed effects model, Culex quinquefasciatus, Anopheles nuneztovari, bootstrap,data analysis

Pseudoreplication is probably one of the most widelycited and misunderstood concepts in the statisticalanalysis of ecological studies on insects and otherorganisms. Pseudoreplication is dened as the use ofinferential statistics to test for treatment effects withdata from experiments where either treatments arenot replicated . . . or replicates are not statistically dif-ferent . . . (Hurlbert 1984). This concept has beenvery inuential and pervasive, to the extent that pseu-doreplication is widely cited as a major aw of mosteld studies (Heffner et al. 1996). Hurlberts majorclaim was correct, and he basically showed that main-stream statistical tools at that time (e.g., analysis ofvariance) were not suitable for the analysis of mostexperimental designs. However, the uncritical ap-praisal of his study has been a major barrier for thepublicationof results and, therefore, the advancementof ecology (Oksanen 2001). Hurlberts study did notprevent the unsuitable analysis of valuable datasets orthe proliferation of unsound experimental practices,such as the movement of sampling units to control forspatial/temporal variability (Alto and Juliano 2001a,2001b; Reiskind and Wilson 2004). As thoughtfully

presented by Oksanen (2001), the goal of ecologicalstudies is not the application of statistical analysis toecological data per se, but rather its application to theunderstandingof ongoing ecological phenomena fromvariation of individual phenotypic traits to the assem-blages of organisms in populations, communities, andecosystems. Unlike physics or chemistry, in which thesupply of individual objects of study is practicallyunlimited, the objects of study for an entomologist (ormoregenerally anaturalist) arenite andconstrained.Thus, limitations in randomizationwill likely arise, andthe scienceof statistics has developednewsolutions tocorrectly analyze the lack of independence in elddata since Hurlberts study (Millar and Anderson2004). The current forum article presents the follow-ing: 1) key concepts of experimental design to criti-cally appraise and demystify the concept of pseu-doreplication; 2) linear mixed effects models(LMEMs) as powerful tools to analyze data originatedfrom constrained designs or to produce more generalinferences from classical randomized designs (e.g.,blocks); and 3) how these tools can be used to furthergain insights from the data that can strengthen ourunderstanding of insect ecology. In developing point2, equations and a guide to interpret them as modelsused to analyze common entomological data are pre-sented, as well as the implementation of this type ofanalysis in the open source software R.

1 Corresponding author: Department of Environmental Studies,Emory University, 400 Dowman Drive, Suite E510, Atlanta GA 30322(e-mail: [email protected]).

2 Laboratorio de Biologa Teorica, Instituto de Zoologa y EcologaTropical, Facultad de Ciencias, Universidad Central de Venezuela,Caracas, Venezuela.

0022-2585/10/02910298$04.00/0 2010 Entomological Society of America

Field Studies, Experiments, and Statistical DataAnalysis

Experiments are one of the major tools for hypoth-esis testing (Fisher 1935). In general, the idea is tosubject individual units of observation to varying de-grees of independent and/or controllable factor(s),and to determine how the levels of variation explain agiven pattern (Box 1980). Field studies focus on theimpacts of natural (or controlled) variation in envi-ronmental factors on individual units of observation.Hypothesis testing has been central to the develop-ment of modern science, to the point that hypothesis-driven experiments or eld studies are one of themostprominent requirements for project support by fund-ing agencies. One of the major reasons for the wide-spread appeal of hypothesis-driven experiments andeld studies has been their close associationwith toolsfor data analysis to determine the impact of differentindependent variables. The best example of a statis-tical tool guiding experimental design is the use of thelinear model (LM). This model assumes that variabil-ity across a set of individual units of observation (yi)is explained by a series of n independent variables (x1,x2, . . . , xn) and by a unique source of unexplainedvariability, normally referred to as error (). Thesemodels are linear, because the parameters enter lin-early into the equation that relates the independentvariables to the outcome (Faraway 2006, Chaves andPascual 2007). A major constraint of these models isthat they assume total independence among the sub-jects of study, i.e., individual observation units areunrelated at least within strata (i.e., after accountingfor the explanatory variables), which is the formaldenition of replication. When there is a lack ofindependenceacross objects of study(i.e., pseudorep-lication), the use of LMs with a unique source ofvariability is inappropriate, because the variability ismodeled incorrectly and can lead to spurious infer-ences. For example, if mosquitoes are reared in pans(orkissingbugs in jars) tomeasurebodysizeofemerg-ing adults from different experimental conditions, thelack of independence that arises from the aggregationinto pans (or jars) will inate the error value (a.k.a.residual variance) of the LM, in some cases leading toincorrect inferences when the LM is compared with amodel that explicitlymodels the lack of independencebecause of the aggregation into a functional experi-mental unit (i.e., the pan or jar). More than 20 yr ago,because of the limited statistical toolbox in ecology,this issue was a major problem for the correct analysisof datasets from studieswith design constraints (Hurl-bert 1984).However, strategies to handle the problemof pseudoreplication were around at the time. Forexample, in evolutionary ecology, individuals havedifferent degrees of common descent, and this vari-ability by itself is often a subject of study. In the 1980s,it was common to use nested half-sibling designs toestimate the variance of families and individuals be-longing to those families (Conner and Hartl 2004).Also, the use of dened designs such as Greco-Romansquares, Roman squares, and fractional factorials was

well established in theeldof engineering andprocesscontrol (Montgomery 2005). Some of these balanceddesigns were even used in studies of medically im-portant insects (Carpenter 1982, Chesson 1984). Infact, sophistication in randomization, when possible,can be very useful to evaluate the impact of strategiesto control human-vector contact (Kirby et al. 2008).For example, randomized control trials have beenused to demonstrate the importance of mosquitoscreening in reducing the risk of malaria transmission(Kirby et al. 2009). However, one of the major limi-tations of designs that handle pseudoreplication is theneed for balanced designs, i.e., an equal number ofreplicates per treatment. The inability to analyze un-balanced designs with unequal number of replicatesper treatment has been overcome with the develop-ment of maximum likelihood methods, especially therestricted maximum likelihood method and its appli-cation to estimate LMEM (Pinheiro and Bates 2000).LMEM has the same fundamental assumptions of theLM; it tries to explain the sources of variability acrossa set of individual units of observation (yi) as functionof a series of n independent variables (x1, x2, . . . ., xn),referred to as xed factors, but it can incorporateadditional sources of variability (the random factors),besides the error (). The random factors can accom-modate the lack of independence among the individ-ual units of observation as a result of spatial, temporal,genetic, or any exogenous environmental factor that isnot fully randomized. For example, the variance offunctional experimental units such as pans for mos-quitoes or jars for kissing bugs can be explicitly mod-eled, thus allowing the proper estimation of theerror variance, and thus limiting the chances ofcommitting a type II error, i.e., rejecting the nullhypothesis when true. Therefore, LMEMs allow thestatistical analysis of pseudoreplicated data. Thenext section will provide a series of examples illus-trating the use of LMEMs and how they comparewith similar LMs. The data used in the examples andcode to perform the analyses using the open sourcestatistical software R are included as supplementaryonline material (http://www.envs.emory.edu/research/Chaves_SOM_Pseudoreplication.html).

LMs Versus LMEMs

Factorial Designs. To illustrate the most basic dif-ferences betweenLMsandLMEMs, I reference aeldexperiment designed to study oviposition by Culexquinquefasciatus Say in Atlanta, GA (Chaves et al.2009). Cx. quinquefasciatus larvae are normally absentfrom lotic systems, suchas rivers andcreeks.However,several cities have relic sewage treatment systemswhere runoff water and sewage are combined in thesame system, and after large rainfall events the com-bined sewage efuent can overow into urban waterbodies (Chaves et al. 2009). In this experiment, theeffects of combined sewage overow water and nu-trient addition on oviposition site selection by thismosquito species were studied using 10 experimentalpools (water containers) at four sites in a forest patch.

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For this example, data will be used from the experi-mentwhen egg raftswere removed daily.Data use hasbeen restricted to a randomly extracted subsamplefrom the original data (only four pools per site) tohave a dataset similar to that of a balanced design. Inthis experiment, oviposition (y) was measured bycounting the total number of egg rafts oviposited over5 d. The experiment has three independent variables(i.e., n 3): 1) x1 water quality (with two levels:combined sewage overow water and tap water ascontrol); 2) x2 nutrient addition (added or absentas control); and 3) x3 sites (four in total). Only x1and x2 are factors (each with two levels), because x3is an independent variable considered to test theblock effects of forest site on oviposition. Becauseall treatments were present at each site, this is arandomized block 2 2 factorial design, which israndomized because both factors were present in allfour sites (blocks in the model), and 2 2 factorialbecause each factor has two levels. The goal of afactorial experiment is to test whether the factorsinteract, which can be expressed by the followingLM:

yil 1x1 2x2 3x1x2 4x3l il [1]

where is the average value of the observations; 1,2, and 4 quantify the impact of each independentvariable; 3 the interaction of water quality and nu-trient addition; and is the error, which is assumed tobe normally distributed. The subscript l denotes block(site within the forest patch), and i is for individualpools within a block (containers in a forest site).Therefore, yil is the total number of rafts from agiven pool and block (i.e., container in a site). Table1 shows the results of the analysis of variance for thedata using the model presented in (1). The naturallogarithm transformation of ln (y 1) is done tonormalize the data and fulll model assumptions.Table 1 shows that neither block nor the interactionbetween water quality and nutrient addition wassignicant (P 0.05).

The inuence of water quality and nutrients on Cx.quinquefasciatus oviposition can be reanalyzed using aLMEM. By contrast with the LM analysis, in whichinferences are done over blocks, the LMEMassumesthat blocks are random samples from a larger pop-

ulation and models block variability as a randomfactor (Fig. 1). The equivalent to equation 1 for aLMEM is:

yil 1x1 2x2 3x1x2 l il [2]

where , the s, and y and have the same interpre-tation as in equation 1, and quanties the variabilityacross the blocks, which is assumed to be normallydistributed. The signicance of factors can be testedusing F tests, which work well when designs are bal-anced (equal number of samples per treatment andblock) and parameters can be estimated using maxi-mum likelihood. More generally, signicance may betested using parametric bootstraps, for balanced orunbalanced designs where parameters are estimatedby restrictedmaximum likelihood (Pinheiro andBates2000, Faraway 2006). Table 2 shows the results of ananalysis of deviance, with parameters of equation 2estimated using restricted maximum likelihood andinference based on 1000 replications of parametricbootstrap (an analysis in which datasets are simulatedand the results of likelihood ratio tests for studiedfactors are compared with those of the true data tocompute the signicance of factors). In this example,inference about the impact of water quality and nu-trient addition is qualitatively similar using LM orLMEM. However, LMEM provides additional insight;it indicates oviposition is nely grained. The varianceat the individual container level is larger than at theblock level (Table 2: 0.059 and 0.024), apattern also observed in the original study for the fulldataset (Chaves et al. 2009). The data can be observedin Fig. 2.Constrained Designs. One of the major limitations

of theLMis that it is not suited toanalyzedatasetswithconstraints in randomization. For example, all repli-cates from a treatment should be present in all blocks.This is a frequent limitation in eld studies in whichfeatures of a given landscape cannot be altered, anunderlying motivation behind split-plot designs, inwhich some treatments do not vary across blocks.Split-plots are widely used in agriculture (Faraway2006) and economic entomology (Blumberg et al.1997,Haile et al. 2000,Oyediran et al. 2007).However,constraints in randomization can also arise as a prod-uct of other trade-offs in experimentation or by thenature of the questions asked. For example, the orig-inal design of Chaves et al. (2009) was unbalanced inthe sense that each block had an unequal number ofreplicates foreachoneof the treatments.However, foreach block the amount of total nutrients and waterquality was constant; one of the questions was todetermine the grain of mosquito perception for ovi-position choices. The largest variance was among theindividual oviposition containers, indicating a nelygrained perception, in contrast to a scenario ofcoarsely grained mosquito perception, in which thelargest variance would be expected for the blocks orsites. To illustrate the analysis of constrained designs,the original data of Chaves et al. (2009) are sampledin such a way that all experimental pools with nutri-ents added and combined sewage overow water

Table 1. Analysis of variance for the effects of water qualityand nutrient addition on the ln of total number of egg rafts 1oviposited over 5 d by Culex quinquefasciatus in Atlanta, GA(Chaves et al. 2009)

Factor dfSum

squareMeansquare

F Value Pr(F)

Water (1) 1 0.653 0.653 10.1401 0.01111*Nutrient (2) 1 50.634 50.634 786.0312 4.54E-10*Water nutrient (3) 1 0.013 0.013 0.2055 0.66105Block (4) 3 0.467 0.156 2.4186 0.13339Error () 9 0.58 0.064

This design was balanced. Original data and R code for analysis arein the supplementary online material (http://www.envs.emory.edu/research/Chaves_SOM_Pseudoreplication.html).

*Statistically signicant (P 0.05).

May 2010 CHAVES: DEMYSTIFYING PSEUDOREPLICATION 293

came from the same block (Fig. 2), and therefore thedesign becomes unbalanced (Fig. 3). Thus, the LMfrom equation 1 cannot be employed to analyze thedata, but the LMEM from equation 2 is suitable forsuch an analysis. Results are presented in Table 3 andare similar to those presented in Table 2, showing adecreased variability in the blocks and a larger error.In summary, LMEMs can uncover the same variancepattern in data under pseudoreplication, a major ad-vantage over LMs.

Spatial Variability

Organisms can be clustered in space, for example,the larvae of mosquitoes can be associated with onlycertain habitats where eggs are oviposited, thus mak-ing their abundance autocorrelated in space (Pitcairnet al. 1994). Several statistical tools can accommodatethe lack of spatial independence in data from eldstudies (Fortin and Dale 2005), and they have beenwidely used with insects of medical importance(Koenraadt et al. 2007, 2008; Vazquez-Prokopec et al.2008). However, their description is outside the scopeof this article. LMEM can also be used to considerspatial variability. LMEM are especially suitable forcases when spatial scales are nested. For example,mosquito larval samples coming from containers inseveral houses that belong to the same neighborhoodare hierarchically nested. Several studies have usedthis approach in recent studies onmedically importantinsects (Harrington et al. 2008, Chaves et al. 2009,Gurtler et al. 2009). The LMEM tting procedure issimilar to the one used next to consider the lack oftemporal independence in longitudinal studies.

Longitudinal Studies

The fact that observations are repeated throughtime in the same place (or from the same organisms)can lead to data that are not independent and areautocorrelated in time. One approach to this problem

Fig. 1. Blocks: xed or random? The left panel shows the case for blocks as a xed factor in LM, in which the assumptionis that inferences are exclusive for the observedblocks (within squares). The right panel shows the case for blocks as a randomfactor inLMEMs, inwhich the observedblocks (within squares) come froma larger population (i.e., blocks inside andoutsidesquares). (Online gure in color.)

Table 2. Analysis of deviance for the effects of water qualityand nutrient addition on the ln of total number of egg rafts 1oviposited over 5 d by Culex quinquefasciatus in Atlanta, GA(Chaves et al. 2009)

Fixed df Log likelihood LRT P

Water (1) 1 7.241 5.874 0.009*Nutrient (2) 1 31.089 53.569 0.000*Water nutrient (3) 1 4.304 8.612 0.664

Random Mean square (variance)

Blocks () 0.024

Error () 0.059

This design was balanced. Original data and R code for analysis arein the supplementary online material (http://www.envs.emory.edu/research/Chaves_SOM_Pseudoreplication.html). LRT, likelihood ra-tio test.

Obtained with a parametric bootstrap.*Statistically signicant (P 0.05).

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is to use repeated measurements analysis (Faraway2006) and time series analysis techniques (Shumwayand Stoffer 2000, Chaves and Pascual 2007). Timeseries techniques have been used for longitudinalstudies of some vectors (Hayes and Downs 1980,Strickman 1988, Feliciangeli and Rabinovich 1998,Scott et al. 2000, Salomon et al. 2004). Alternatively,LMEM can model the lack of temporal independenceas a random factor, which is one of the many methods

for repeated measurements analysis (Faraway 2006).Modeling the lack of temporal independence will beillustrated by examining data from a study on thebiting and resting behavior of anophelines using ex-perimental huts in three villages ofwesternVenezuela(Rubio-Palis and Curtis 1992). Mosquitoes were col-lected during two nights per month and by catchingthe landing mosquitoes on the legs of two catchersbetween 1900 and 0700 hours, inside and outside ex-perimental huts. Although several specieswere found,only data for Anopheles nuneztovari Gabaldon fromGuaquitas collected between August 1988 and Octo-ber 1989 will be analyzed in this study (Fig. 4). In thiscase, the response or dependent vari

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