Animal-Sediment Relationships Re-Visited: Characterising Species

Journal of Experimental Marine Biology and Ecology 366 (2008) 16–27

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Journal of Experimental Marine Biology and Ecology

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Animal-sediment relationships re-visited: Characterising species' distributions along an environmental gradient using canonical analysis and quantile regression splines

Marti J. Anderson ⁎

Department of Statistics, University of Auckland, Private Bag 92019, Auckland, New Zealand article info abstract

Keywords: Benthic soft-sediment organisms generally show strong relationships with the grain-size characteristics of Canonical analysis of principal coordinates the sediments they inhabit. These relationships, when characterised from ﬁeld data, tend to be asymmetrical, Predictive models non-linear and heteroscedastic, due to the existence of multiple other potentially important and interacting Quantile regression splines factors, some of which are inevitably unmeasured. For multivariate data, canonical analysis of principal Sediment texture coordinates (CAP) can be used to isolate particular gradients of interest, despite the presence of other Soft-sediment assemblages potentially important factors. For univariate abundance data, models focusing on upper quantiles of species' Species-environment relationships distributions can ameliorate the problem of heterogeneity induced by other variables. Here, a multivariate model of the relationship between benthic inter-tidal estuarine soft-sediment assemblages (sampled over a period of 3 years from 70 sites across the Auckland region) and the percentage of mud in the sediments was generated using CAP. To characterise changes in assemblage structure, quantile regression splines (of the 0.95 quantile) were used to model each of the twenty most abundant individual taxa along the gradient in percentage mud. This approach provided an effective instrumental quantitative predictive model of species' turnover, while allowing for the asymmetric, non-linear animal-sediment relationships and heterogeneous scatter observed in species' abundances along the mud gradient. © 2008 Elsevier B.V. All rights reserved.

1. Introduction complex, since a number of subsidiary parameters are influenced by sediment characters and the subsidiary factors may in fact be the The strong association between the structure of benthic marine soft- limiting ones.” sediment communities and the texture of the sediments they inhabit is a Models of species' abundances along environmental gradients well-known phenomenon, as outlined in the landmark paper by John S. have seen a fairly long history of development (e.g., see the review by Gray (1974). Professor Gray's work on animal-sediment relationships has Austin, 2007). Species have been thought to show unimodal response provided a touchstone for many soft-sediment ecologists (e.g., Constable, patterns along environmental gradients, and symmetric unimodal or 1999; Ellingsen, 2002; Ysebaert et al., 2002; Thrush et al., 2003). gaussian models have been used to estimate their optima and Although the existence of animal-sediment relationships in these tolerances (ter Braak, 1985, 1986). However, there is no compelling habitats is undisputed, many other factors can also play important reason why models should necessarily be symmetric; indeed, many roles in structuring the temporal and spatial heterogeneity of soft- species show asymmetric responses (or, albeit less commonly, multi- sediment assemblages. These may include (but are certainly not modal patterns) along gradients. This has led to the use of more limited to): predation (Peterson and Skilleter, 1994; Hines et al., 1997), flexible response functions, including splines and generalized bioturbation (Levinton, 1995), physical disturbance (Probert, 1984; additive models (GAMs, Hastie and Tibshirani, 1990; Leathwick Thrush and Dayton, 2002), sedimentation (Peterson, 1985; Norkko et al., 2005; Zhu et al., 2005; Yee, 2006). Not only do species- et al., 2002), pollution (Gray,1992; Gray et al.,1990) or factors affecting environment relationships tend to be inherently asymmetric and colonisation (Zajac et al., 1998; Hewitt et al., 2003; Lundquist et al., nonlinear, they also tend to show heterogeneous scatter. The variance 2006). In order specifically to model and characterise animal- calculated from the abundances of a particular species at each fixed sediment relationships, scientists may need to take other factors point along the environmental gradient will differ at different points. into account, either through modeling or by using a carefully targeted Specifically, variances are inevitably smaller where mean abundance stratified sampling strategy. In the words of Gray (1974): “… values are small, where environmental conditions are unsuitable. consideration of the relationship of organisms to sediments is Furthermore, the distribution of abundances at a fixed point along the gradient will be strongly right-skewed (long-tailed). All these characteristics are often a consequence of the fact that other ⁎ Tel.: +64 9 373 7599x85052; fax: +64 9 373 7000. unmeasured variables also limit abundances and can interact with E-mail address: [email protected]. the (measured) system in complex ways. Quantile regression

(Koenker and Bassett, 1978; Cade and Noon, 2003; Koenker, 2005)is (in the present case, the environmental gradient of interest is the an effective tool that can be used to ameliorate the heterogeneity percentage of mud in the sediments); (ii) the fact that species caused by such unmeasured factors (Cade et al., 2005). More generally display asymmetric non-linear relationships in their relative specifically, the idea of modeling not the mean but the upper (or abundances along environmental gradients and (iii) the idea that a lower) quantiles not only deals nicely with the intrinsic hetero- model of the outer “envelope” can be more meaningful and useful for geneity, but also aligns directly with the ecological concept of characterising individual species' patterns along a gradient than a limiting factors acting as constraints on organisms (Thomson et al., model based on the mean. 1996; Cade et al., 1999; Lancaster and Belyea 2006). More specifically, using a reasonably large set of monitoring data Recently, Thrush et al. (2003, 2005) modeled the relationship from estuarine intertidal soft-sediment habitats across the Auckland between soft-sediment benthic fauna and the percentage of mud region, the approach taken here consisted of essentially two steps. (b63 µm) in sediments, using data obtained from sampling mud-to- First, a multivariate predictive model of the relationship between sand transects in 19 estuaries and harbours across the North Island of benthic infaunal assemblages and the percentage mud of the New Zealand. It was recognised that the distributions of abundances sediments was generated, using canonical analysis of principal of species along the gradient in percentage mud would be hetero- coordinates (CAP, Anderson and Robinson, 2003; Anderson and Willis, geneous: species might well be functionally able to occur within a 2003). Second, the gradient in assemblage structure for a suite of the particular range of percentage mud, but other factors may come into most abundant species was characterised using quantile regression play so that variation in their abundances within that range would be splines (Koenker, 2005). This approach not only provides a working large. Outside of this range (i.e., for those parts of the mud gradient quantitative model of community change and individual species' where the species has low tolerance), variation would necessarily be responses along an important environmental gradient, it also allows relatively small, due to limited abundance. Thus, Thrush et al. (2003, predictions for future change scenarios, such as expected increases in 2005) modeled the maximum abundances of species along the the percentage mud content of sediments (Thrush et al., 2004), against gradient, rather than modeling their mean abundance. This resulted which future monitoring data can be examined. in a model for each species or taxon that appeared like an “envelope”, clearly allowing for intrinsic heterogeneity along the mud gradient. 2. Methods Here, the animal-sediment relationships of Gray (1974) are revisited, following also in the footsteps of Thrush et al. (2003, 2005) in 2.1. Sampling design and database an effort to model soft-sediment fauna specifically along a gradient in sediment texture. Several refinements are suggested which specifi- The data used here form part of a monitoring programme, funded cally cater for: (i) the fact that organisms will respond in the field by the Auckland Regional Council (ARC), examining the potential long- simultaneously to multiple gradients, whereas one may wish to focus term effects of urbanization and sediment inputs from surrounding in some cases (for modeling and/or prediction) on only one of these catchments on benthic intertidal estuarine infauna (Anderson et al.,

Fig. 1. Map showing the positions of the 7 estuaries in the Auckland region included as part of the monitoring programme. 18 M.J. Anderson / Journal of Experimental Marine Biology and Ecology 366 (2008) 16–27

then deflocculated for at least 4 hours (using Calgon 5 g per litre) and wet-sieved. Each fraction (N500, 250-499, 125-249, 63-124 and b63 µm) was dried, weighed and calculated as a percentage of the total weight. The fraction less than 63 µm (percentage of mud) was the environmental variable of interest for analysis here. For more details on all sampling methods, see Anderson et al. (2004, 2007). Data from the monitoring programme that were included in analyses were drawn from the period of August 2004 – April 2007, inclusive, so that all methods used in the treatment of sediments and in taxonomic resolution of biota were consistent. This included 12 times of sampling (4 in each of 3 years). Previous extensive analyses demonstrated that temporal variation was quite small compared to spatial variation among sites: assemblages at individual sites were clearly identifiable, stable through time over this period, and differentiable from assemblages at other sites (Anderson et al., 2007). Grain sizes of ambient sediments at a given site were also quite stable over time. The coefficient of variation (CV, the standard deviation over the mean) in percent mud through time for each site ranged from 0.23 to 1.17 and 97% of the sites had a CV of less than 1.0. Fig. 2. Canonical analysis of principal coordinates (CAP) to model the percentage of mud All analyses were therefore based on the averages calculated for each (b63 µm) in ambient sediment using fauna (153 taxa) from each site. Each point is an site across all 12 times of sampling (6 cores×12 times=72 cores). average of 6 cores×12 time points=72 cores. The analysis was based on Bray-Curtis Models related the averages for faunal abundances to the average dissimilarities calculated from square-root transformed abundances, using m=20 percentage mud at that site over the same period. principal coordinates.

2.2. Model relating assemblage structure to percentage mud 2007). Sampling has been done 4 times per year (twice in each of two seasons) from each of 70 sites across the Auckland region, with 10 Canonical analysis of principal coordinates (CAP, Anderson and intertidal sites (10's to 100's of metres apart) situated from the mouth Robinson, 2003; Anderson and Willis, 2003) was used to model to the inner reaches (labeled from 1 to 10, respectively) of each of 7 changes in the community along a gradient in the percentage mud of estuaries (Puhoi, Waiwera, Orewa, Okura, Mangemangeroa, Turanga ambient sediments across the region. The analysis was based on Bray- and Waikopua, Fig. 1). Tidal height relative to mean sea level ranged Curtis dissimilarities calculated from square-root transformed abun- from -0.6 to 1.6 m. At each site (measuring 50 m×25 m) and at each dances, using the PERMANOVA+ add-on package for PRIMER v6 time of sampling, biota were sampled using 6 randomly placed cores (Clarke and Gorley, 2006; Anderson et al., 2008). The model built was (measuring 13 cm in diameter by 15 cm deep). Each core was sieved in purposefully spatial, using averages in faunal abundances and the field using 0.5 mm mesh. Material retained on the sieve was percentage mud from 12 times of sampling at each of the 70 sites, brought back to the laboratory, preserved in 70% isopropyl alcohol thus integrating temporal variation. The advantage to using CAP here with 0.01% rose bengal, sorted and all organisms identified to the is that it is specifically designed to find an axis through the lowest practical level. There were 153 taxa identified and included in multivariate cloud of data which has the strongest relationship with this study (see the Appendix A, which includes the names, abbrevia- the environmental variable(s) of interest. The method seeks this tions and also the taxonomic authorities for all species). particular gradient out, even in the presence of potentially high Ambient sediments were also sampled adjacent to each faunal variation in other directions of the data cloud that might be due to core, using an open 20 ml syringe (2 cm diameter) to a depth of 2 cm. other factors. CAP is done using principal coordinates (PCO, Gower, The six sediment samples from each site were combined into a single 1966) from the resemblance matrix and a check on overparameter- sample (weighing~60 g) for analysis. To characterise the grain size isation (i.e., to avoid including too many axes and finding spurious fractions, samples were first dried and treated with 9% hydrogen relationships) is needed. This was achieved by choosing the number of peroxide until fizzing ceased to dissolve organic matter. Samples were PCO axes (m) that minimised a leave-one-out residual sum of squares then dried again and weighed to obtain a total dry weight. They were (Anderson and Robinson, 2003).

Fig. 3. Relationship between Austrovenus stutchburyi (cockles) and percentage mud in ambient sediment. Each point is an average of 6 cores×12 time points=72 cores at each site. The regression spline model for the 95th percentile is shown, with the maximum from the model (interpretable as an estimated optimum for the species) indicated by a vertical line. M.J. Anderson / Journal of Experimental Marine Biology and Ecology 366 (2008) 16–27 19

Table 1 chosen. If another of the models had an AICc value within 2 units of the Estimated optimum percentage mud (and 95% confidence interval based on 10,000 chosen model (so could be deemed essentially equivalent from the bias-adjusted bootstrap samples) for each of the top 20 most abundant taxa (i.e., having point of view of parsimony, Burnham and Anderson, 2002) and also the largest total average abundance at the site level, summed across all sites) obtained fi from quantile regression spline models (of indicated degree) had a better visual tted shape to the scatterplot of the data, then it was chosen in preference. Abbrev. Name Group Rank Degree Est. opt. 95% CI For each species' model, the value at which the predicted density abund. %mud † from the model achieved a maximum along the gradient was Papaus Paphies australis Bivalvia 6 3 3.4 ( 3.3, 4.5) fi Colspp Colurostylis spp. Cumacea 10 3 3.4 (†3.4, †3.4) identi ed and taken as an estimated optimum percentage mud for Antaur Anthopleura Anthozoa 16 4 4.0 (†0.0, 10.0) that species. Ninety-five percent bootstrap confidence intervals (e.g., aureoradiata Manly, 2006) were obtained for the estimated optimum using bias- Waibre Waitangi brevirostris Amphipoda 12 5 7.5 (4.2, 13.0) corrected percentiles from re-application of the model to each of Aonoxy Aonides oxycephala Spionidae 15 5 7.9 (†3.3, 16.1) 10,000 bootstrapped sample pairs, using the polynomial degree that Ausstu Austrovenus Bivalvia 2 5 11.3 (7.7, 14.8) stutchburyi was chosen for the original data. Species were also ranked in order of Nuchar Nucula hartvigiana Bivalvia 3 4 11.7 (10.0, 14.1) their estimated optima along the gradient. This allowed specific Aquauc Aquilaspio Spionidae 4 4 12.2 (8.2, 39.0) predictions regarding the expected change in the structure of the aucklandica assemblage with changes in mud content in terms of the numerically Barnac Barnacles Cirripedia 1 4 12.8 (8.6, 32.9) Exonae Exogoninae Syllidae 20 5 14.6 (11.9, 17.4) dominant species across the region. To demonstrate this turnover and Maclil Macomona liliana Bivalvia 11 3 16.6 (10.2, 26.4) to relate the quantile models to the CAP model, the proportional Artbif Arthritica bifurcata Bivalvia 17 3 17.5 (9.4, 39.2) abundances of taxa occurring at a series of representative sites along Hetfil Heteromastus Capitellidae 5 4 23.4 (19.6, 32.3) the mud gradient were calculated and compared with the ordered list filiformis † of estimated optima for the abundant taxa. Orbins Orbinids Orbiniidae 14 2 23.5 ( 0.3, 38.1) Capoli Capitella spp. and Polychaeta 7 2 28.0 (†3.4, 41.2‡) Note that the purpose of the univariate models of individual taxa Oligochaetes was not to “test for the significance” of their relationship with ‡ Polyco Polydorid complex Spionidae 9 4 30.4 (11.8, 43.0 ) percentage mud, but rather simply to characterise the mud gradient ‡ Cordae Corophidae Amphipoda 8 3 41.2 (30.7, 44.6 ) identified by the CAP analysis in terms of variation in the most Helmac Helice, Decapoda 18 3 41.2 (28.5, 41.8‡) Macrophthalmus prominent and abundant fauna. Admittedly, some other criterion Nerdae Nereidae Nereidae 13 5 NA (rather than greatest total average abundance) might have been used Parspp Paracalliope spp. Amphipoda 19 4 NA to choose the appropriate taxa to model. For example, one might Two taxa did not show any clear relationship with percentage mud (“NA”). All models choose to model those taxa that achieved more than X% of the total examined the τ=0.95 quantile, except for Exogoninae, for which τ=0.90 was used, to abundance at Y (or more) sites, rather than over the dataset as a avoid undue influence of a single outlier. Confidence intervals that are equal to or more whole, especially to avoid modelling only the most ubiquitous ‡ † extreme than either the maximum ( ) or minimum ( ) values for percentage mud in the species and instead to target those that might discriminate along the modeled dataset should be viewed with caution. gradient. However, the values of X and Y that might be chosen here would be arbitrary at best, and, at worst, might over-emphasise 2.3. Characterising changes in terms of component fauna species with spatially erratic responses. It is also recognised that rarer taxa will almost certainly also show patterns of change along To characterise the changes in assemblage structure occurring this gradient (Ellingsen et al., 2007) and, indeed, the CAP analysis along this gradient, the 20 most abundant taxa in the dataset (i.e., was used to ensure that the gradient model was built using the having the largest total average abundance at the site level, summed entire multivariate assemblage. Robust sampling of rarer taxa is, across all sites) were each modeled individually along the mud however, difficult to ensure and has a higher dependency on sample gradient. Admittedly, sediment mud content may not be the only size, so the focus here was restricted to the more abundant taxa for factor influencing the abundance of an individual species, but it may univariate models. well limit the maximum abundance attainable by a given species within a given environment (Thrush et al., 2003). The maximum can 3. Results be fairly volatile, however, as a statistic, particularly for species abundance data which are expected to show long-tailed distributions There was a very strong relationship between changes in (e.g., Aitchison and Ho, 1989). Thus, quantile regression spline models assemblage structure among these 70 sites across the region and the (Koenker et al., 1994; Koenker, 2005) were built for each taxon for the physical gradient of ambient percent mud, with a canonical correla- 95th percentile (i.e., the value below which 95% of the abundances are tion (using m=20 principal coordinate axes) of δ=0.938 (Fig. 2). The expected to fall, called the τ=0.95 quantile), which is less sensitive to muddiest sites were from the inner reaches (sites 8, 9 and 10) of the outliers, along the regional gradient in percentage mud. One exception southern estuaries (Mangemangeroa, Turanga and Waikopua). How- was the taxon Exogoninae, for which the 90th percentiles were ever, each of the estuaries had sites that were spread along the modeled instead in order to avoid undue influence of a single outlier. majority of this gradient. All models were fitted using the function rq() (part of the “quantreg” A scatterplot of one of the most prominent species, the cockle, package of Koenker, 2007) combined with function bs() (part of the Austrovenus stutchburyi, revealed a non-linear asymmetric unimodal “splines” package, see Hastie, 1993) in the R computer programming relationship, with significant heterogeneity for different values of language (R Development Core Team, 2007). The function bs() is a percent mud (Fig. 3, left). This type of relationship and scatter flexible way to construct B-spline basis expansions and will fita motivated the models used here (quantile regression splines on the piecewise polynomial of specified degree. The appropriate degree for 95th percentile), which form a type of “envelope” around the the polynomial (resulting in a given number of parameters for the abundance values. For Austrovenus, the model indicated a peak in spline model) was determined for each species using the small- abundance at 11.3% mud, which is taken here as an estimated sample-correction version of Akaike's information criterion (AICc, see optimum for this species (Fig. 3, right). The breadth of the 95% Hurvich and Tsai, 1989 and Burnham and Anderson, 2002). This confidence interval (7.7% – 14.8% mud, Table 1) measures the precision criterion was also used by Cade et al. (2005) with quantile regression of the estimated optimum for this species. models. For each taxon, the model having the smallest AICc value out Models and results for the 20 most numerically abundant taxa, of the set of models having polynomial of degree=2, 3, 4 or 5 was ordered from lowest to highest in terms of their estimated optimum 20 M.J. Anderson / Journal of Experimental Marine Biology and Ecology 366 (2008) 16–27 percentage mud, are shown in Fig. 4 and Table 1. Two out of the 20 4. Discussion individual taxa, namely Nereidae and Paracalliope spp., had scatterplot patterns that were potentially multi-modal (Fig. 4, bottom two Characterising animal-sediment relationships is a core theme in panels). Although the spline approach can certainly be used for benthic soft-sediment ecology (Gray, 1974; Constable, 1999). Follow- multi-modal distributions, a clear and biologically meaningful inter- ing and extending the ideas presented in Thrush et al. (2003),a pretation, in the present case, would be doubtful, so no model was multivariate model was developed of changes in assemblage fitted. For the rest, relationships were one of: (i) decreasing with structure with increasing sediment mud content for estuarine increasing mud content (e.g., Paphies australis, Colurostylis spp., intertidal fauna across the Auckland region. This model was based Anthopleura aureoradiata), (ii) unimodal with fairly high precision in on abundances recorded for each of 153 taxa from a total of 5040 the estimated optimum (e.g., Waitangi brevirostris, Aonides oxycephala, cores obtained over a period of 3 years from 7 different estuaries. The Nucula hartvigiana, Exogoninae), (iii) unimodal with fairly low model presented here can be considered as quite useful and robust precision in the estimated optimum (e.g., Austrovenus stutchburyi, for several reasons. First, rather than identifying only a few species Macomona liliana, Heteromastus filiformis, Orbinids), or (iv) increasing for the model, all of the available taxa were used to develop a with increasing mud content (Corophidae and crabs) (Fig. 4). canonical gradient. Multivariate community responses are more The list of taxa, ordered from smallest to largest in their estimated sensitive to environmental changes than single indicator species or optimum percentage mud from these models, suggested a gradual diversity indices (Underwood and Peterson, 1988; Clarke, 1993), and turnover of dominant taxa at sites should occur along the canonical rare species may play an important role in distinguishing habitats gradient - from greater abundances of the bivalve Paphies australis, the (Ellingsen et al., 2007). What is more, as a monitoring tool, new cumacean Colurostylis spp. and anemones Anthopleura aureoradiata at samples can be placed into this canonical space in future, based only the sandy end, through to greater abundances of crabs, corophid on the abundances of taxa (Anderson and Robinson, 2003), enabling amphipods and polydorid spionid worms at the muddy end of the ongoing assessment of the positions of sites along this gradient spectrum (Table 1). To further explore this idea, the top ten taxa in through time and with anticipated region-wide increases in the mud terms of proportional abundance were examined along a series of content of sediments (Thrush et al., 2004). representative sites from sandy through to muddy habitats (Fig. 5), Second, the model was built at the site level from points obtained and compared with the list in Table 1 (which is also given as averages over a fairly long period of time (3 years) for both the schematically for reference at the bottom of Fig. 5). At the sandy end community data and ambient percent mud. Means of individual of the spectrum (e.g., Waiwera site 3), there was indeed a variables are well-known to have good statistical properties (approx- predominance of Paphies australis (Papaus), the amphipods Waitangi imate normality, for a given site, that is) and lower variance (inversely brevirostris (Waibre) and cumaceans Colurostylis spp. (Colspp) (Fig. 5). proportional to sample size), according to the central limit theorem Moving along the gradient (e.g., Puhoi site 4), these species were still (Lindeberg, 1922; Feller, 1968). By integrating over the temporal present and abundant, but were no longer the primary dominants – variation, one is able to more successfully model the (quite non- they were replaced by barnacles (Barnac) and cockles (Ausstu) and the normal) spatial variation in relative abundances among sites for a appearance of other taxa within the top ten, such as Nucula given average percentage of mud. hartvigiana (Nuchar). Further along (e.g., Orewa site 5), cockles were Third, these data spanned a reasonably large geographical area and the dominant taxon, and other species also began to appear in range of habitats. Although the extent of the sampling from this abundance, such as Aquilaspio aucklandica (Aquauc), orbinids (Orbins) monitoring programme is not nearly as broad as the area spanned by and the bivalve Macomona liliana (Maclil). the Thrush et al. (2003) study, the area covered by the present study is At sites having even greater mud content (e.g., Okura site 9), nevertheless highly pertinent for making predictions within the numerical dominance shifted towards other species: Heteromastus Auckland region. In addition, although the range in percent mud filiformis (Hetfil) and Aquilaspio aucklandica (Aquauc), although found in Thrush et al. (2003) was larger (from near zero to over 80% cockles, Nucula and Macomona were also still present and fairly mud) than the range for the models presented here (which was 3.4% - abundant. There was also greater evenness in these communities in 41.2%), this is probably due to the fact that averages from many core themiddleofthegradient– the dominance curve was much less samples were used here. The range in mud content measured from steep (the most abundant species here occupies only about 20% of individual cores used in the present study was actually quite similar to the total abundance, rather than 40 - 50% or more, which occurred that of Thrush et al. (2003) and ranged from 0.19% to 86.7%. in sites at the extreme ends of the spectrum). Moving towards still Finally, changes along this gradient were characterised using non- muddier sites (e.g., Waikopua site 10), the dominant taxa were linear quantile regression splines on the 95th percentile for the 20 capitellids and oligochaetes (Capoli) and crabs (Helmac), although most numerically abundant taxa. Eighteen of these 20 taxa showed some bivalves (Ausstu, Maclil) still appeared within the top ten reasonably clear relationships along the mud gradient. Models of the taxa. Finally, at the muddiest site (on average) in this study (i.e., 95th percentile will be less influenced by outliers than models of Turanga site 9), corophid amphipods (Cordae) were dominant, maxima. Non-linear quantile regression splines also provide more along with crabs (Helmac) and capitellids and oligochaetes (Capoli). flexibility with respect to the shape of the inherently non-linear No bivalves occurred within the top ten most abundant taxa at this responses of species to the gradient. These models are by no means site. presented as “the best possible models”, but are, instead, considered Although occurrences of rarer taxa were not considered individu- to provide a flexible approach for characterising community changes ally here, they also would naturally contribute to the turnover of for abundant taxa along the mud gradient. species along this gradient, and were included in the full canonical The choice to model the 95th percentile (as opposed to the model (Fig. 2). The individual univariate models of abundant taxa maximum, or the 90th percentile, for example) was taken with some (Fig. 4) do, nevertheless, provide a very useful characterisation of the care after exploring potential alternatives. First, modelling the 95th nature of the transitions in community structure predicted with percentile places special emphasis on the sites having large abun- changes in the mud content of ambient sediments (Fig. 5). dances. This is appropriate if one wishes to obtain a model as a kind of

Fig. 4. Relationship between individual taxa (as indicated) and percentage mud in ambient sediment. Each point is an average of 6 cores×12 time points=72 cores at each site. The regression spline model for the 95th percentile is shown, with the maximum from the model (interpretable as an estimated optimum for the species) indicated by a vertical line. Two taxa (bottom two panels) showed no clear relationships. For Exogoninae, the 90th percentile was modeled (see text for details). M.J. Anderson / Journal of Experimental Marine Biology and Ecology 366 (2008) 16–27 21 22 M.J. Anderson / Journal of Experimental Marine Biology and Ecology 366 (2008) 16–27

Fig. 4 (continued). M.J. Anderson / Journal of Experimental Marine Biology and Ecology 366 (2008) 16–27 23

Fig. 5. Species turnover along the canonical gradient in percentage mud of ambient sediment for a series of representative sites, as indicated. Individual barplots show proportional abundances for the top ten numerically dominant taxa at each of the sites. The list of species expected to turnover along the gradient from sandy to muddy habitats, as given in Table 1, is shown schematically at bottom, for reference. Abbreviations for taxa are given in Appendix A.

“envelope” to characterise responses. If the agenda were merely to Exogoninae), a model of the 90th percentile was more reasonable, avoid the issue of long-tailed distributions, then the median (50th however, to avoid the undue influence of a single site having large percentile) could simply be modelled rather than the mean, but this abundances (Fig. 4). For τ=0.95, the estimated optimum percent mud approach would place virtually no emphasis on sites having large for this taxon was reduced rather dramatically from 14.6 down to abundance values and would not lead to an envelope-type model 12.0% (with confidence interval 9.6 to 15.2%). This, however, is simply (conceptually linked to the idea of environmental constraints on the value of percent mud at the one site where the average abundance organisms). A certain balance must be struck here, therefore, given the of Exogoninae was greatest. Other taxa having large abundances at a size of the available dataset. Modelling the maximum, would generally single site were not unduly affected in this way when the 95th tend to do little more than “connect-the-dots” along an upper bound, percentile was used for modelling (e.g., Arthritica bifurcata, Antho- paying little or no heed to the underlying shape of the distribution of pleura aureoradiata, see Fig. 4). One possible explanation is that points at less-than-maximum values. On the other hand, models of the Exogoninae were not very abundant in the dataset as a whole (ranked 75th (or even 90th) percentiles can ignore sites having very large 20th out of the 20 taxa modelled). Some exploratory analyses of each abundances, if there are relatively few of these for a given species. For taxon to be modelled is therefore recommended in order to gain an the present dataset, the 95th percentile was found to strike a understanding of how models are affected by outlying values when reasonable balance for the majority of taxa, in terms of visual fitas different quantiles are chosen and to achieve an appropriate balance. an “envelope” and yet with a view to estimating optima in a way that Despite differences in approach, the results obtained here were did not overly emphasise outlying single values. For one taxon (i.e., quite consistent, qualitatively, with those presented by Thrush et al. 24 M.J. Anderson / Journal of Experimental Marine Biology and Ecology 366 (2008) 16–27

(2003). Ten of the 13 species modeled by Thrush et al. (2003) were multivariate data cloud that can discriminate or predict positions of also identified here as abundant enough for individual models. In sites along the environmental gradient?” The answer will almost addition, the relative ordering of taxa along the mud gradient, in terms certainly be “yes” if there are large numbers of species, which is why a of estimated maxima, was very similar in these two studies (compare check on over-parameterisation for a CAP model is important Fig. 4 in Thrush et al., 2003 with Figs. 4 and 5 herein). This study (Anderson and Robinson, 2003). A non-parametric multivariate therefore supports and extends the work of Thrush et al. (2003), analogue to this “swapping of roles” is the ENV-BIO approach of Clarke which indicates that the response of benthic macrofauna to and Warwick (2001), also demonstrated in Clarke and Gorley (2006), percentage mud is very strong and highly relevant for assessing which uses a global BEST test to identify a suite of biological (species) longer term “press” responses of communities to changing environ- variables that will yield resemblances among samples having max- mental conditions of anticipated increased muddiness in estuaries. imum rank correlation with the inter-sample distances among those None of the 20 species chosen for individual modelling showed a samples along an environmental gradient. Whereas the CAP approach pattern of having a high value for optimum percent mud but with a uses all taxa in the community and explicitly models the environ- narrow confidence interval (i.e., corresponding to an inability to mental gradient directly, ENV-BIO will search for an optimum subset tolerate low mud content). This could have been an artefact of the of variables purely on the basis of the match in rank-ordered distances. choice to model species that had large total average abundance across The inherent role-change in the consideration of species-environ- the entire dataset, so excluding species that might have moderate ment relationships is the main difference between CAP and the related abundances but only at the muddy sites. Alternatively, it could reflect technique of distance-based (or dissimilarity-based) redundancy ana- the fact that few prominent species are genuine mud-specialists, as lysis (dbRDA, Legendre and Anderson, 1999; McArdle and Anderson, might be suggested by the low relative abundance and diversity of 2001). It also is what distinguishes it as appropriate for purposes of macrofauna generally found at muddy sites in these systems. There is plucking out and characterising particular gradients of interest in the clearly scope for more ecological research to investigate this. face of potentially much larger heterogeneity in other directions of the The lack of an apparent trend or single mode along the mud gradient multivariate data cloud, due to other measured or unmeasured factors. for two of the reasonably abundant taxa, Nereidae and Paracalliope spp., With characteristic insight, Gray (1974) observed: “Although grain- could have been caused by a number of factors. It is possible that these size preferences will restrict organisms to a narrower area of sediment species genuinely are not affected or limited by the percentage mud in than could be explained by responses to other physical parameters, sediments, at least over the range examined. It is more likely, however, nevertheless many populations are not distributed uniformly over a given that patterns in their relationship with percentage mud were masked by grain-size range.” Gray (1974) goes on to identify many factors which can other unmeasured factors (despite the attempt to alleviate this using the interact with grain sizes in determining distributions of soft-sediment quantile modelling approach), such as estuary-specific effects (Warwick benthic populations, such as responses to light, pressure, salinity or et al., 1991) or interactions with spatial scale (Thrush et al., 2005). Low presence of adults during larval settlement, movements geared to tidal taxonomic resolution can also mask patterns: if species show different rhythms, the presence of organic material or living micro-organisms, individual responses to sediment texture, these can clearly be lost when interactions between suspension-feeders and deposit-feeders, and they are lumped together into a broader category. Lumping of taxa can alterations to sediment properties by the organisms themselves. also cause the confidence intervals of individual taxa to be enlarged Clearly, the gap between existing quantitative models and causal (corresponding to a decrease in the precision of the estimated mechanistic models that incorporate the realistic complexities of optimum). For example, the group “Capitella spp. and Oligochaetes” is multiple factors in soft-sediment systems is still wide. At least the a very broad taxonomic grouping that is likely to contain several species instrumentalist approach adopted here provides an initial predictive with disparate responses. Not surprisingly, the confidence interval is framework, acknowledging that substantial intrinsic heterogeneity broad for this group. from unmeasured factors inevitably remains. In addition to the above limitations of the models, percent mud, as a variable, can act as a proxy for many other environmental factors in Acknowledgements these estuarine systems, such as relative exposure, wave action, permeability, porosity or oxygen content (Gray, 1974). These models Data for this study were obtained from contract research to are therefore merely about describing observed patterns in relation- UniServices Auckland, funded by the Auckland Regional Council ships, rather than attributing cause (e.g., Ysebaert et al., 2002). (ARC). Support for this project provided by G. Putt, N. White, K. Furthermore, by modelling only the upper quantiles of distributions, it Robinson and S. Em (from UniServices), M. Stewart, S. Kelly and G. is assumed that the effects of unmeasured variables will cause Barnes (from the ARC) and A. Cozens and J. Montgomery (from the abundances to decrease (i.e. to be further limiting), rather than being Leigh Marine Laboratory) is gratefully acknowledged. Technical facilitative (Cade et al., 2005). Clearly, more research is required to expertise in the field and in the laboratory was provided by R. Ford, understand mechanisms underlying observed patterns (Snelgrove and M. Pawley, C. Williams, C. Bedford, N. Harwood and B. Sandall. Butman, 1994) and to incorporate this knowledge into future models. Assistance with taxonomy was provided by W. Blom from the Auckland Multivariate methods generally provide models with greater War Memorial Museum. [SS] sensitivity to small changes in assemblage structure than those based on univariate summary statistics, such as diversity indices or Appendix A richness (Gray et al., 1990; Warwick and Clarke, 1991). The multi- species CAP method (Anderson and Robinson, 2003; Anderson and List of the 153 taxa identified and included in analyses. Willis, 2003) provides a very useful technique for focussing on a single gradient in the face of multiple factors affecting field-based data. No. Abbrev. Name Group Phylum Taxonomic The approach is based on a suitable resemblance measure among Authority samples (such as Bray-Curtis, Clarke et al., 2006), thus generally 1 Aglmac Aglaophamus Nephtyidae Annelida Schmarda avoiding explicit assumptions regarding the distributions of original macroura (1861) variables. Rather than attempting to find environmental variables, or 2 Alphsp Alpheus sp. Decapod Arthropoda some combination of them, that “best explain” patterns in community 3 Amalsp Amalda sp. Gastropoda Mollusca data (e.g., as in RDA or CCA, ter Braak, 1986; or as in BIO-ENV, Clarke 4 Ampcre Amphibola Gastropoda Mollusca Gmelin crenata (1791) and Ainsworth, 1993; Freeman and Rogers, 2003), the CAP approach 5 Ampdae Ampharetidae Terebellidae Annelida rather turns this on its head to ask: “Is there an axis through the M.J. Anderson / Journal of Experimental Marine Biology and Ecology 366 (2008) 16–27 25

Appendix A (continued) Appendix A (continued) No. Abbrev. Name Group Phylum Taxonomic No. Abbrev. Name Group Phylum Taxonomic Authority Authority 6 Ampoth Amphipod other Amphipoda Arthropoda 57 Hesdae Hessionid Hesionidae Annelida 7 Aneoth Anemone other Anthozoa Cnidaria 58 Hetfil Heteromastus Capitellidae Annelida Claparede 8 Antaur Anthopleura Anthozoa Cnidaria Stuckey filiformis (1864) aureoradiata (1909) 59 Holoth Holothuroidia Echinoderm Echinodermata 9 Antdae Anthuridae Isopoda Arthropoda other 10 Antisp Antiguraleus sp. Gastropoda Mollusca 60 Insect Insect Insect Arthropoda 11 Aonoxy Aonides Spionidae Annelida Sars (1862) 61 Isooth Isopod other Isopoda Arthropoda oxycephala 62 Lepdae Lepidontidae Polynoidae Annelida 12 Aquauc Aquilaspio Spionidae Annelida Augener 63 Leptsp Leptograpsus sp. Decapod Arthropoda aucklandica (1923) 64 Lignov Ligia Isopoda Arthropoda Dana (1853) 13 Aricsp Aricidea sp. Paraonidae Annelida novaezelandiae 14 Armmac Armandia Opheliidae Annelida Webster 65 Lumdae Lumbrinereidae Lumbrineridae Annelida maculata (1884) 66 Macdae Mactridae Bivalvia Mollusca 15 Artbif Arthritica bifurca Bivalvia Mollusca Webster 67 Maclil Macomona liliana Bivalvia Mollusca Iredale (1908) (1915) 16 Asyamp Asychis Maldanidae Annelida Ehlers (1897) 68 Macste Macroclymenella Maldanidae Annelida Augener amphiglypta stewartensis (1926) 17 Ausstu Austrovenus Bivalvia Mollusca Wood (1828) 69 Magspp Magelona spp. Magelonidae Annelida stutchburyi 70 Manshr Mantis shrimp Decapod Arthropoda 18 Barnac Barnacles Cirripedia Arthropoda 71 Melasp Melagraphia sp. Gastropoda Mollusca 19 Bulquo Bulla quoyi Opistobranchia Mollusca Gray (1843) 72 Melcyl Melanochlamys Opistobranchia Mollusca Cheeseman 20 Capoli Capitella spp. and Capitellids+ Annelida cylindrica (1881) Oligochaetes Oligochaetes 73 Micrsp Micrelenchus sp. Gastropoda Mollusca 21 Chaeto Chaetognath Chaetognath Chaetognatha 74 Minspp Minuspio spp. Spionidae Annelida 22 Chiton Chiton Polyplacophora Mollusca 75 Mite Mite Chelicaerata Arthropoda 23 Cirdae Cirratulidae Cirratulidae Annelida 76 Modimp Modiolarca Bivalvia Mollusca Hermann 24 Cirssp Cirsonella sp. Gastropoda Mollusca impacta (1782) 25 Cirzel Cirsotrema Gastropoda Mollusca Dunker 77 Mundae Munnidae Isopoda Arthropoda zelebori (1866) 78 Murdae Muricidae Gastropoda Mollusca 26 Colspp Colurostylis spp. Cumacean Arthropoda 79 Mussen Musculista Bivalvia Mollusca Benson in 27 Comads Cominella Gastropoda Mollusca Brugiere senhousia Cantor adspersa (1789) (1842) 28 Comgla Cominella Gastropoda Mollusca Reeve (1847) 80 Myastr Myadora striata Bivalvia Mollusca Quoy and glandiformis Gaimard 29 Commac Cominella Gastropoda Mollusca Martyn (1835) maculosa (1784) 81 Mysida Mysidacea Mysidacea Arthropoda 30 Comquo Cominella Gastropoda Mollusca A. Adams 82 Mytadg Mytilus edulis Bivalvia Mollusca Lamarck quoyana (1854) galloprovinciallis (1819) 31 Cordae Corophidae Amphipoda Arthropoda 83 Nebala Nebalace Nebalace Arthropoda 32 Corzea Corbula Bivalvia Mollusca Quoy and 84 Nemert Nemertean Nemertean Nemertina zealandica Gaimard 85 Neogsp Neoguraleus sp. Gastropoda Mollusca (1835) 86 Nerdae Nereidae Nereidae Annelida 33 Coscon Cossura consimilis Cossuridae Annelida Read (2000) 87 Nodant Nodilittorina Gastropoda Mollusca Philippi 34 Cragig Crassostrea gigas Bivalvia Mollusca Thunberg antipodum (1847) (1793) 88 Notosp Notomastus sp. Capitellidae Annelida 35 Dilsub Diloma Gastropoda Mollusca Gray (1835) 89 Notspp Notoacmea spp. Gastropoda Mollusca subrostrata 90 Nuchar Nucula Bivalvia Mollusca Dohrn (1864) 36 Diopsp Diopatra sp. Onuphidae Annelida hartvigiana 37 Dorvil Dorvilleidae Dorvilleidae Annelida 91 Nucoth Other Nuculidae Bivalvia Mollusca group1 92 Odospp Odostomia spp. Gastropoda Mollusca 38 Dosspp Dosinia spp. Bivalvia Mollusca 93 Onudae Onuphidae Onuphidae Mollusca 39 Edwasp Edwardsia sp. Anthozoa Cnidaria 94 Ophoth Opheliidae other Opheliidae Annelida 40 Epiten Epitonium Gastropoda Mollusca Hutton, 95 Opisto Opistobranch Opistobranchia Mollusca tenellum (1885) (Philine type) 41 Euchsp Euchone sp. Sabellidae Annelida 96 Orbins Orbinids Orbiniidae Annelida 42 Eundae Eunicidae Eunicidae Annelida 97 Owefus Owenia fusiformis Oweniidae Annelida delle Chiaje 43 Eurcoo Eurylana cookii Isopoda Arthropoda Filhol (1885) (1844) 44 Evechl Evechinus Echinoderm Echinodermata Valenciennes 98 Pagsp Pagurus sp. Decapod Arthropoda chloroticus (1846) 99 Palaff Palaemon affinis Decapod Arthropoda H. Milne 45 Exodae Exogoninae Syllidae Annelida Edwards 46 Exospp Exosphaeroma spp. Isopoda Arthropoda (1837) 47 Felaze Felaniella Bivalvia Mollusca Gray (1835) 100 Papaus Paphies australis Bivalvia Mollusca Gmelin zelandica (1791) 48 Felzel Fellaster zelandiae Echinoderm Echinodermata Gray (1855) 101 Papsub Paphies Bivalvia Mollusca Wood (1828) 49 Glyspp Glycera spp. Glyceriidae Annelida subtriangulata 50 Gnathi Gnathiidea Isopoda Arthropoda 102 Paramp Paralepidonotus Polynoidae Annelida Grube (1878) 51 Gnatho Gnathostomulida Gnathostomulida Gnathostomulida ampulliferus 52 Gondae Goniadidae Glyceriidae Annelida 103 Paroth Paraonid other Paraonidae Annelida 53 Halspp Halicarcinus spp. Decapod Arthropoda 104 Parspp Paracalliope spp. Amphipoda Arthropoda 54 Hamzel Haminoea Opistobranchia Mollusca Gray (1843) 105 Pecaus Pectinaria Pectinarid Annelida Ehlers (1904) zelandiae australis 55 Harmsp Harmothoe sp. Polynoidae Annelida 106 Percan Perna canaliculus Bivalvia Mollusca Gmelin 56 Helmac Helice, Decapod Arthropoda (1791) Hemigrapsus, 107 Phitar Philinopsis Opistobranchia Mollusca Allan (1933) Macrophthalmus taronga

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Appendix A (continued) Number, vol. 348. URL: http://www.arc.govt.nz/plans/technical-publications/ technical-publications-301-350.cfmx. No. Abbrev. Name Group Phylum Taxonomic Anderson, M.J., Gorley, R.N., Clarke, K.R., 2008. PERMANOVA+ for PRIMER: Guide to Authority Software and Statistical Methods. PRIMER-E, Plymouth, UK. 108 Phoron Phoronid Phoronid Phoronida Austin, M., 2007. Species distribution models and ecological theory: A critical 109 Phoxos Phoxocephalids Amphipoda Arthropoda assessment and some possible new approaches. Ecol. Model. 200, 1–19. 110 Physpp Phyllodocid Phyllodocidae Annelida Burnham, K.P., Anderson, D.R., 2002. Model selection and multi-model inference: a 111 Pinnsp Pinnotheres sp. Decapod Arthropoda practical information-theoretic approach, 2nd edition. Springer, New York. 112 Platyh Platyhelminth Platyhelminth Platyhelminth Cade, B.S., Noon, B.R., 2003. A gentle introduction to quantile regression for ecologists. Front. Ecol. Environ. 1, 412–420. 113 Polyco Polydorid Spionidae Annelida Cade, B.S., Noon, B.R., Flather, C.H., 2005. Quantile regression reveals hidden bias and complex uncertainty in habitat models. Ecology 86, 786–800. 114 Polyno Polynoid Polynoidae Annelida Cade, B.S., Terrell, J.W., Schroeder, R.L., 1999. Estimating effects of limiting factors with 115 Ponaus Pontophilus Decapod Arthropoda Thomson regression quantiles. Ecology 80, 311–323. australis (1879) Clarke, K.R., 1993. Non-parametric multivariate analyses of changes in community 116 Powesp Powellisetia sp. Gastropoda Mollusca structure. Aust. J. Ecol. 18, 117–143. 117 Priapu Priapulid priapulid Priapulida Clarke, K.R., Ainsworth, M., 1993. A method of linking multivariate community structure 118 Pycnog Pycnogonid Chelicaerata Arthropoda to environmental variables. Mar. Ecol. Prog. Ser. 92, 205–219. 119 Risnae Rissoninae Gastropoda Mollusca Clarke, K.R., Gorley, R.N., 2006. PRIMER v6: User manual/Tutorial. PRIMER-E Ltd, 120 Sabdae Sabellidae Sabellidae Annelida Plymouth, UK. 121 Scadae Scalibregmatidae Scalibregmatidae Annelida Clarke, K.R., Warwick, R.M., 2001. Change in marine communities, 2nd edition. PRIMER- 122 Scoben Scolecolepides Orbiniidae Annelida Ehlers (1907) E Ltd, Plymouth, UK. fi benhami Clarke, K.R., Somer eld, P.J., Chapman, M.G., 2006. On resemblance measures for ecological studies, including taxonomic dissimilarities and a zero-adjusted Bray- 123 Scospp Scolelepis spp. Spionidae Annelida Curtis coefficient for denuded assemblages. J. Exp. Mar. Biol. Ecol. 330, 55–80. 124 Serdae Serpulidae Serpulidae Annelida Constable, A.J., 1999. Ecology of benthic macro-invertebrates in soft-sediment 125 Sipunc Sipunculid Sipunculid Sipunculida environments: A review of progress towards quantitative models and predictions. 126 Solpar Solemya Bivalvia Mollusca E.A. Smith Aust. J. Ecol. 24, 452–476. parkinson (1874) Ellingsen, K.E., 2002. Soft-sediment benthic biodiversity on the continental shelf in 127 Solsil Soletellina siliqua Bivalvia Mollusca Reeve (1857) relation to environmental variability. Mar. Ecol. Prog. Ser. 232, 15–27. 128 Sphdae Sphaerodoridae Sphaerodoridae Annelida Ellingsen, K.E., Hewitt, J.E., Thrush, S.F., 2007. Rare species, habitat diversity and 129 Sphguo Sphaeroma Isopoda Arthropoda H. Milne functional redundancy in marine benthos. J. Sea Res. 58, 291–301. quoyanum Edwards Feller, W.F.,1968. An Introduction to Probability Theory and its Applications, 3rd edition. (1840) John Wiley and Sons, New York. 130 Spioth Spionid other Spionidae Annelida Freeman, S.M., Rogers, S.I., 2003. A new analytical approach to the characterization of 131 Spirsp Spirorbis sp. Serpulidae Annelida macro-epibenthic habitats: linking species to the environment. Estuar. Coast. Shelf – 132 Syldae Syllinae Syllidae Annelida Sci. 56, 749 764. Gray, J.S., 1974. Animal–sediment relationships. Oceanogr. Mar. Biol. Annu. Rev. 12, 223–261. 133 Tanaid Tanaidacea Tanaidacea Arthropoda Gray, J.S.,1992. Biological and ecological effects of marine pollutants and their detection. 134 Terdae Terebellidae Terebellidae Annelida Mar. Pollut. Bull. 25, 48–50. 135 Thashr Thalassinoidia Decapod Arthropoda Gray, J.S., Clarke, K.R., Warwick, R.M., Hobbs, G., 1990. Detection of initial effects of 136 Thelub Theora lubrica Bivalvia Mollusca Gould (1861) pollution on marine benthos: an example from the Ekofisk and Eldfisk oilfields, 137 Traole Travisia olens Opheliidae Annelida Ehlers (1897) North Sea. Mar. Ecol. Prog. Ser. 66, 285–299. 138 Troden Trochodota Echinoderm Echinodermata Mortensen Gower, J.C., 1966. Some distance properties of latent root and vector methods used in dendyi (1925) multivariate analysis. Biometrika 53, 325–338. 139 Turbsp Turbonilla sp. Gastropoda Mollusca Hastie, T.J., 1993. Generalized additive models. In: Chambers, J.M., Hastie, T.J. (Eds.), 140 Tursma Turbo smaragdus Gastropoda Mollusca Gmelin Statistical Models. S. Chapman and Hall, New York, pp. 249–307. (1791) Hastie, T., Tibshirani, R., 1990. Generalized Additive Models. Chapman and Hall, London. 141 Unbiva Unidentified Bivalvia Mollusca Hewitt, J.E., Cummings, V.J., Ellis, J.I., Funnell, G., Norkko, A., Talley, T.S., Thrush, S.F., Bivalve 2003. The role of waves in the colonization of terrestrial sediments deposited in the – 142 Uncrab Unidentified Crab Decapod Arthropoda marine environment. J. Exp. Mar. Biol. Ecol. 290, 19 47. 143 Uncrus Unidentified Crustacean Arthropoda Hines, A.H., Whitlach, R.B., Thrush, S.F., Hewitt, J.E., Cummings, V.J., Dayton, P.K., Legendre, P., 1997. Nonlinear foraging response of a large marine predator to Crustacean benthic prey: eagle ray pits and bivalves in a New Zealand sandflat. J. Exp. Mar. Biol. 144 Ungast Unidentified Gastropoda Mollusca Ecol. 216, 191–210. Gastropod Hurvich, C.M., Tsai, C.-L., 1989. Regression and time-series model selection in small fi 145 Unpoly Unidenti ed Polychaete Annelida samplesBiometrika, 76, pp. 297–307. Polychaete Koenker, R., 2005. Quantile Regression. Cambridge University Press, New York, USA. 146 Veniae Venericardiae Bivalvia Mollusca Koenker, R. 2007. quantreg: Quantile Regression. R package version 4.10. http:// 147 Waibre Waitangi Amphipoda Arthropoda Cooper and www.r-project.org. brevirostris Fincham Koenker, R., Bassett, G., 1978. Regression quantiles. Econometrica 46, 33–50. (1977) Koenker, R., Ng, P., Portnoy, S., 1994. Quantile smoothing splines. Biometrika 81, 673–680. 148 Xenspp Xenostrobus spp. Bivalvia Mollusca Lancaster, J., Belyea, L.R., 2006. Defining limits to local density: alternative views of 149 Xymsp Xymene sp. Gastropoda Mollusca abundance-environment relationships. Freshw. Biol. 51, 783–796. 150 Zealut Zeacumantus Gastropoda Mollusca Kiener Leathwick, J.R., Rowe, D., Richardson, J., Elith, J., Hastie, T., 2005. Using multivariate lutulentus (1841) adaptive regression splines to predict the distributions of New Zealand's freshwater diadromous fish. Freshw. Biol. 50, 2034–2052. 151 Zeaspp Zeacolpus spp. Gastropoda Mollusca Legendre, P., Anderson, M.J., 1999. Distance-based redundancy analysis: testing multi- 152 Zegten Zegalerus tenuis Gastropoda Mollusca Gray (1867) species responses in multifactorial ecological experiments. Ecol. Monogr. 69, 1–24. 153 Zenasp Zenatia sp. Bivalvia Mollusca Levinton, J., 1995. Bioturbators as ecosystem engineers: control of the sediment fabric, inter-individual interactions, and material fluxes. In: Jones, C.G., Lawton, J.H. (Eds.), Linking Species and Ecoystems. Chapman and Hall, New York, pp. 29–36. References Lindeberg, J.W.,1922. Eine neue Herleitung des Exponentialgesetzes in der Wahrschein- lichkeitsrechnung. Math. Z. 15, 211–225. Aitchison, J., Ho, C.H., 1989. The multivariate Poisson-log normal distribution. Lundquist, C.J., Thrush, S.F., Hewitt, J.E., Halliday, J., MacDonald, I., Cummings, V.J., Biometrika 76, 643–653. 2006. Spatial variability in recolonisation potential: influence of organism Anderson, M.J., Robinson, J., 2003. Generalized discriminant analysis based on behaviour and hydrodynamics on the distribution of macrofaunal colonists. distances. Aust. N.Z. J. Stat. 45, 301–318. Mar.Ecol.Prog.Ser.324,67–81. Anderson, M.J., Willis, T.J., 2003. Canonical analysis of principal coordinates: a useful Manly, B.F.J., 2006. Randomization, Bootstrap and Monte Carlo Methods in Biology, 3rd method of constrained ordination for ecology. Ecology 84, 511–525. edition. Chapman and Hall, London. Anderson, M.J., Ford, R.B., Feary, D.A., Honeywill, C., 2004. Quantitative measures of McArdle, B.H., Anderson, M.J., 2001. Fitting multivariate models to community data: a sedimentation in an estuarine system and its relationship with intertidal soft- comment on distance-based redundancy analysis. Ecology 82, 290–297. sediment infauna. Mar. Ecol. Prog. Ser. 272, 33–48. Norkko, A., Thrush, S.F., Hewitt, J.E., Cummings, V.J., Norkko, J., Ellis, J.I., Funnell, G.A., Anderson, M.J., Pawley, M.D.M., Ford, R.B., Williams, C.L., 2007. Temporal variation in Schultz, D., MacDonald, I., 2002. Smothering of estuarine sandflats by terrigenous benthic estuarine assemblages of the Auckland region. Prepared by UniServices clay: the role of wind-wave disturbance and bioturbation in site-dependent for Auckland Regional Council. Auckland Regional Council Technical Publication macrofaunal recovery. Mar. Ecol. Prog. Ser. 234, 23–41. M.J. Anderson / Journal of Experimental Marine Biology and Ecology 366 (2008) 16–27 27

Peterson, C.H., 1985. Patterns of lagoonal bivalve mortality after heavy sedimentation Thrush, S.F., Hewitt, J.E., Cummings, V.J., Ellis, J.I., Hatton, C., Lohrer, A.M., Norkko, A., and their paleoecological significance. Paleobiology 11, 139–153. 2004. Muddy waters: elevating sediment input to coastal and estuarine habitats. Peterson, C.H., Skilleter, G.A., 1994. Control of foraging behaviour of individuals within Front. Ecol. Environ. 2, 299–306. an ecosystem context: the clam Macoma balthica, flow environment, and siphon- Thrush, S.F., Hewitt, J.E., Herman, P.M.J., Ysebaert, T., 2005. Multi-scale analysis of cropping fishes. Oecologia 100, 256–267. species-environment relationships. Mar. Ecol. Prog. Ser. 302, 13–26. Probert, P.K., 1984. Disturbance, sediment stability and trophic structure of soft-bottom Underwood, A.J., Peterson, C.H., 1988. Towards an ecological framework for investigat- communities. J. Mar. Res. 42, 893–921. ing pollution. Mar. Ecol. Prog. Ser. 46, 227–234. R Development Core Team, 2007. R: A language and environment for statistical Warwick, R.M., Clarke, K.R., 1991. A comparison of some methods for analyzing changes computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3- in benthic community structure. J. Mar. Biol. Assoc. U.K. 71, 225–244. 900051-07-0, URL http://www.R-project.org. Warwick, R.M., Goss-Custard, J.D., Kirby, R., George, C.L., Pope, N.D., Rowden, A.A., 1991. Snelgrove, P.V.R., Butman, C.A., 1994. Animal-sediment relationships revisited: cause Static and dynamic environmental factors determining the community structure of versus effect. Oceanogr. Mar. Biol. Annu. Rev. 32, 111–177. estuarine macrobenthos in SW Britain: Why is the Severn Estuary different? J. Appl. ter Braak, C.J.F., 1985. Correspondence analysis of incidence and abundance data: Ecol. 28, 329–345. properties in terms of a unimodal response model. Biometrics 41, 859–873. Yee, T.W., 2006. Constrained additive ordination. Ecology 87, 203–213. ter Braak, C.J.F.,1986. The analysis of vegetation-environment relationships by canonical Ysebaert, T., Meire, P., Herman, P.M.J., Verbeek, H., 2002. Macrobenthic species response correspondence analysis. Vegetatio 69, 69–77. surfaces along estuarine gradients: prediction by logistic regression. Mar. Ecol. Prog. Thomson, J.D., Weiblen, G., Thomson, B.A., Alfaro, S., Legendre, P., 1996. Untangling Ser. 225, 79–95. multiple factors in spatial distributions: lilies, gophers, and rocks. Ecology 77, Zajac, R.N., Whitlatch, R.B., Thrush, S.F., 1998. Recolonization and succession in soft- 1698–1715. sediment infaunal communities: the spatial scale of controlling factors. Hydro- Thrush, S.F., Dayton, P.K., 2002. Disturbance to marine benthic habitats by trawling and biologia 376, 227–240. dredging: implications for marine biodiversity. Annu. Rev. Ecol. Syst. 33, 449–473. Zhu, M., Hastie, T.J., Walter, G., 2005. Constrained ordination analysis with flexible Thrush, S.F., Hewitt, J.E., Norkko, A., Nicholls, P.E., Funnell, G.A., Ellis, J.I., 2003. Habitat response functions. Ecol. Model. 187, 524–536. change in estuaries: predicting broad-scale responses of intertidal macrofauna to sediment mud content. Mar. Ecol. Prog. Ser. 263, 101–112.