# Viewpoints in bioerosion research-are we really disagreeing? A reply to the comment by Silbiger and DeCarlo (2017)

Christine H. L. Schönberg, Aline Tribollet, James K. H. Fang, Marina

Carreiro-Silva, Max Wisshak

► To cite this version:

Christine H. L. Schönberg, Aline Tribollet, James K. H. Fang, Marina Carreiro-Silva, Max Wisshak. Viewpoints in bioerosion research-are we really disagreeing? A reply to the comment by Silbiger and DeCarlo (2017). ICES Journal of Marine Science, 2017, 24 (9), pp.2494-2500. 10.1093/icesjms/fsx167. hal-01644334

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ICES Journal of Marine Science (2017), 74(9), 2494–2500. doi:10.1093/icesjms/fsx167

## Reply

# Viewpoints in bioerosion research—are we really disagreeing?
A reply to the comment by Silbiger and DeCarlo (2017)

Schönberg, C. H. L., Tribollet, A., Fang, J. K.-H., Carreiro-Silva, M., Wisshak, M., Viewpoints in bioerosion research—are we really disagreeing? A reply to the comment by Silbiger and DeCarlo (2017). – ICES Journal of Marine Science, 74: 2494–2500; advance access publication 3 October 2017.

A recent literature review by Schönberg *et al.* (2017) on bioerosion under ocean acidification and global change led to a detailed comment by Silbiger and DeCarlo (2017). We use the opportunity to reply to this comment, to correct misinterpreted data and to further stimulate the discussion in bioerosion science. We still believe that our paper and the included method discussion were timely. Most discussed studies captured only a partial bioeroder community, for which upscaling and generalization remains difficult. However, after re-visiting Silbiger *et al.* (2014) we would like to propose a new interpretation: Their net bioerosion/accretion did not change along a natural gradient, but may have responded to localized pH “anomalies”. In general, we encourage future authors to more often consider that biological responses can be non-linear and discuss this in the context of bathymetry-related variables. We agree with Silbiger and DeCarlo (2017) in that conclusions should be reached by methods and data relevant to that particular situation and by sufficient sample sizes, all being strongly influenced by the research design and the targeted bioeroder taxa. We further agree that field studies are not automatically flawed, but needed to confirm controlled aquarium experiments and to assess multiple factor interactions in natural settings.

### What led to the comment by Silbiger and DeCarlo (2017)

We recently reviewed the present knowledge on bioerosion in the context of ocean acidification (OA) and global change (Schönberg *et al.*, 2017). When compiling and assessing the available information, we noticed that sample sizes or experimental conditions were rarely representative for bioerosion in general. Moreover, comparing different studies was very difficult, because different standards, units, or subgroups of bioeroders were used. This situation largely prevents assessment across different scales, or generalization. Also, while partial assessments often favoured one functional guild over the other and reflected only specific local conditions, many authors still interpreted their data as generally applicable to bioerosion under changing environmental conditions. Overall, we found that

there were methodological issues inherent to bioerosion research that may create difficulties, and can often be unavoidable in standard work environments. We therefore included a subchapter on method-related matters in Schönberg *et al.* (2017), aiming to raise the awareness about this (“pattern recognition” and Supplement 10 in Schönberg *et al.*, 2017). This required support by citations, but it was also potentially offensive to the respective authors—who despite occasional limitations to the scope of data interpretation had all significantly contributed to bioerosion research. We particularly looked into what was available under natural settings, which was very little. In order to illustrate the difficulties to extrapolate from such data we examined whether they could possibly be interpreted in different ways and showed the examples that we thought allowed conceivable alternatives.

Two authors responded and were quite critical about our approaches of exploratory data mining and some of our views on bioerosion research, and stated their own viewpoints (Silbiger and DeCarlo, 2017). We welcome this reaction, because it increases the potential for discussions, scientific exchange and networking in bioerosion research. We feel that interaction in this field of science can be improved and that the few people working on bioerosion often do so in isolation or in the context of other research areas (see “pattern recognition” in Schönberg *et al.*, 2017). We are also glad about the opportunity to address some misunderstandings and to own up to mistakes. The chapter titles below refer to the main points of criticism that Silbiger and DeCarlo (2017) expressed. We all seemed to agree about the main principles of bioerosion, however, so hopefully our reply will get us back on the same page.

### Singling out two authors in particular

*“Their critiques primarily fell on our publications”.*

Our method chapter in Schönberg *et al.* (2017) was not intended to expose specific people to public censure, and we feel
dismayed that it was apparently received as such by Silbiger and DeCarlo (2017). We looked into every publication that was then available on bioerosion in the context of OA and global change, and the part of the text referring to possible caveats is crowded with citations. For publications investigating bioerosion along natural gradients, we displayed tentative alternative analyses where we could directly or indirectly access the original data. Including these field studies, our account discussed 27 different publications by 15 different first authors, our own work included. As in most areas of science, nearly every available paper contained issues that may potentially have reduced the application value of the published data, and Silbiger et al. (2014) and DeCarlo et al. (2015) were by no means the sole focus of this assessment (Supplement 10 in Schönberg et al., 2017).

With respect to bioerosion studies, we have gone through a learning process ourselves and have experienced avoidable failures. We thus intended to raise the awareness of possible difficulties. We further meant to encourage future investigators to critically plan their study designs and to seek expert advice, not just in order to avoid possible errors, but also to enhance the achievable scope and value of the data, and to enable comparison between studies by using common standards. We also meant to remind ourselves and other authors that the discussion of results should be limited to the specific setting that generated the data and should not always readily be generalized or directly be interpreted as effects of OA (see also Browman, 2016; McElhany, 2017).

## Mis-using and mis-interpreting published data of Silbiger et al. (2014) and DeCarlo et al. (2015)

> "Unfortunately, S17's assessments are plagued by critical errors [...] and ultimately their claims are not supported by our published data [...] S17 used high-order polynomials to re-fit our data but provided no mechanistic bases for why such relationships should exist. This is a recipe for finding artificial correlations".

The data analyses in “pattern recognition” in Schönberg et al. (2017) represented exploratory data mining for which we never claimed anything other than that alternative interpretations may exist for some of the published results and that some samples may not have been representative given the context. We fully agree that the use of high-order polynomial analyses and regression splines rarely describe mechanistic biological responses. We fitted polynomial trendlines to the order of two—the lowest level available. They allow a curvilinear fit, which is often a better representation of biological responses than a linear regression (see “general concerns” below). However, we allow that this was quite speculative, because the sample sizes of the data subsets that we investigated became very small. We therefore presently revisited two cases, and in the following account we will disentangle some of the specific issues.

### Silbiger and DeCarlo (2017) regarded our re-analysis of the Silbiger et al. (2014)

data as “unsubstantiated due to (1) misclassification of reef flat and slope data, (2) incorrect plotting of the data and re-moving outliers without justification, and (3) not accounting for high collinearity among environmental parameters”.

Silbiger et al. (2014) studied net bioerosion/accretion rates in 21 initially clean experimental settlement blocks deployed for 1 year on a reef in Kaneohe Bay (Hawaii). Their blocks contained an early stage bioeroder community that thus under-represented natural levels of macroboring (e.g. Kiene and Hutchings, 1994; Tribollet and Golubic, 2005; Tribollet et al., 2006), and accretion relied on coralline algae, barnacles, and oysters, not on corals (Silbiger et al., 2014, 2016). In Schönberg et al. (2017) we commented that this community did not appear to be representative for coral reef bioerosion in general, and that the sample size and the extent of the transect were small and not replicated, despite large parameter variations and often non-linear behaviour of several of the environmental variables. We felt that due to the circumstances a different scenario might have been feasible and looked for conceivable alternatives by using simplified approaches. Admittedly, two things led us astray: We did then not fully realize how Silbiger et al.’s (2014) use of residuals instead of raw data changed the behaviour and interpretation of their pH data. Moreover, we overlooked the reference for the raw net data for Silbiger et al. (2014) that was later provided in Silbiger et al. (2016), and the data for water depth and calcification/bioerosion were collected from Silbiger et al.’s (2014) Figure 1e and Supplementary Figure S7. Unfortunately this resulted in the misalignments Silbiger and DeCarlo (2017) pointed out.

Silbiger et al.’s (2014) publication title was “Reefs shift from net accretion to net erosion along a natural environmental gradient”, and on page 39 of their paper they wrote “The pH model best explained patterns in the accretion-erosion balance along the transect in both the residual and the raw data models”. These statements inherently suggested that their statistic correlation referred to a spatial continuum, specifically from calcification to erosion when gradually moving towards a less alkaline environment. The pH changed slightly along their transect at right angle to the coast, but we were unable to match net bioerosion/accretion to such a continuum. Moreover, pH was not the only factor that changed along the transect in a relatively continuous way (pH increased, N/P decreased, $pCO_2$ subtly decreased, total alkalinity and DIC decreased across the flat and increased down the slope, and data for flow light or grazing pressure were not provided). One environmental parameter that changed antagonistically and maybe even stronger than pH was the DIN:DIP ratio (Figure 1 in Silbiger et al., 2014). Inorganic nutrients are known to stimulate microbioerosion (e.g. Carreiro-Silva et al., 2005, 2009, 2012). However, the potential nutrient effect may have been diluted by the grouping of several nutrient parameters as “resource availability”, because the used AIC model gives parameter groups with more elements a lower rank (Symonds and Moussalli, 2011). This point concerned us, because microborers likely constituted the dominant bioeroder in the studied blocks (e.g. Kiene and Hutchings, 1994; Tribollet and Golubic, 2005; Tribollet et al., 2006), and this group could not be fully assessed with the resolution used in the µCT technique. We therefore thought that some of natural environmental factors other than pH could still be good candidates affecting net bioerosion/accretion.

Confusingly, we could not see how either net bioerosion/accretion or bioerosion and accretion separately correlated to a spatial continuum, and we tried to find a better fit. By comparing net bioerosion/accretion with water depth, we aimed to detect a pattern through depth-related variables, which can behave nonlinear. However, we used the raw data (as the authors said they did as well, Silbiger et al., 2014: 39), which represented a very
<figure><img src="image_2.png" /><figcaption>**Figure 1.** Patterns related to the distribution of residuals in data extracted from Silbiger *et al.* (2014, 2016), who interpreted pH as the main environmental factor driving net bioerosion/accretion, and in particular bioerosion. As the distribution of the residuals or data deviations from the fitted line revealed a correlation between (a) pH and (b) bioerosion (Silbiger *et al.*, 2014, 2016), this may suggest that bioerosion responded to local pH “anomalies”, but a number of caveats require confirmation of this assumption (see main text). No such match appeared to exist between (c) accretion and pH, nor is there a conceivable spatial gradient of either bioerosion or accretion across the reef (low r² values). The variability in the biological responses was considerable and likely reflected the effect of more than one environmental parameter. All variables are fitted against the extent of the transect used in Silbiger *et al.* (2014, 2016), and black markers represent higher, grey markers lower than predicted values.</figcaption></figure>

different situation compared with the residuals the authors had also used, and our speculative interpretations kept disagreeing with Silbiger *et al.'s* (2014). In contrast to Silbiger *et al.* (2014), we were unable to correlate any of the raw carbonate system parameters convincingly with net bioerosion/accretion, either with separate simple regressions or by using a “gamm4” model that considered collinearity (Burnham and Anderson, 2002). This generated some of the doubts that we had about their study and led us to look into subsets of the data, even though the sample size admittedly became unacceptably small.

Revisiting the publication and the data we now realize that the confusion stemmed from the somewhat unusual approach of ultimately using residuals rather than raw data for pH. Employing this approach, Silbiger *et al.* (2014) had not assessed the behaviour of net bioerosion/accretion along a continuous spatial pH gradient, but a response to minute, small-scale pH “anomalies” or deviations from predicted values. When plotting mean pH against the extent of Silbiger *et al.'s* (2014) transect, pH continuously increased (Figure 1a). However, the measured mean pH slightly deviated from the calculated value at any increment of the transect. Silbiger *et al.'s* (2014, 2016) data analysis therefore suggested that at locations with negative pH “anomaly” net calcification/bioerosion became more negative as well. They stated that it was chiefly bioerosion that was affected (Figure 1b), while calcification did not appear to respond to the pH deviations (Figure 1c). This was erroneously represented as a “natural gradient” related to the lay of the transect. Moreover, given the extreme pH variations on the reef flat and the small size of the pH “anomalies”, this putative correlation requires confirmation. Further, the studied communities were not necessarily representative for normal conditions on coral reefs, as the bioeroders were likely not fully established and lacked macroborers such as sponges, and the calcifiers included coralline algae, oysters, and barnacles, but no corals. The authors adequately explained this particular situation, but occasionally still lapsed into generalization and used their observations to explain likely biological responses to OA, which should more accurately have been described as a response to a 1-year exposure to small pH “anomalies” (Browman, 2016; McElhaney, 2017). However, Silbiger *et al.'s* (2014) observations are generally in accordance with earlier experimental and observational findings and provide another piece of the puzzle (reviewed in Schönberg *et al.*, 2017). The aspect of biological responses to naturally occurring pH “anomalies” has not yet been considered in the context of bioerosion and is certainly highly relevant.

At this stage we provide specific answers to the three points of criticism above:

(i) As our primary concern was focused on factors changing with depth, we divided Silbiger *et al.'s* (2014) data into “reef flat” (same depth around 0 m) and “slope” (depth gradient), irrespective of groupings by the authors.

(ii) Exploratory data mining for Silbiger *et al.* (2014) was conducted with data for water depth and calcification/bioerosion read off their figures (Figure 1e and Supplementary Figure S7). This approximation was accepted as good enough to recognize patterns should there be any, but resulted in the misalignments Silbiger and DeCarlo (2017) pointed out. We furthermore omitted one outlier (Aggarwal, 2015), not outliers, that varied about twice as strong from neighbouring values as the other values did along the bathymetric gradient. However, given the extremely small sample size of the data subset this was ultimately counterproductive for providing a convincing alternative to interpret the published data. Failing to find reasonable patterns when re-analyzing the full and corrected data set in R using the function “gamm4” from the gamm4 library (v. 3.3.1, R core development team; Burnham and Anderson, 2002; Bates *et al.*, 2015) presently prompted us to have a closer look at the effect of using residuals.
(iii) We did not ignore collinearity but were motivated by it. In the case of Silbiger et al. (2014) we thought it was inconven- ient that a multitude of environmental factors change with depth, and that some of these factors were not assessed, but likely behaved in a non-linear way and interacted (e.g. light, flow, grazing). Using residuals instead of the raw data can alleviate such problems of co-dependancy, but it may intro- duce bias and creates a different study focus (Freckleton, 2002): away from “natural gradients” and towards effects caused by localized “anomalies”.

**Silbiger and DeCarlo (2017) raised objections that we "chose to use the in situ data for all the reef sites in DeCarlo et al. (2015), except for Panama where [we] chose to use the [World Ocean Atlas] WOA data".**

DeCarlo et al. (2015) studied levels of macrobioerosion in coral cores sampled across the Pacific and assessed this in correlation to water chemistry. Although the authors did not find a correla- tion with different reef environments and depths, some samples were very small and the variation of macrobioerosion per site very large. By sampling live coral in which bioeroders do not rep- resent the equilibrium communities that can be found in “dead substrates”, and by sampling the central axis, the more eroded basal and marginal parts were avoided. This would in most cases exclude >50% of the dominant macrobioerosion and likely in- creased the patchiness of observations (Schönberg et al., in press).

We did not as such deliberately choose different data sources to look into DeCarlo et al.'s (2015) data, but used what they pro- vided in their Table 1. For their *in situ* water chemistry data some gaps occurred for aragonite saturation and nitrate at Wake Atoll, and wide data ranges were given for nitrate at Panama. Their data also listed WOA values, but with no indication which exact values the authors themselves had entered in their analyses. Replotting their % bioerosion against the local aragonite saturation with the respective WOA value for Wake Atoll produced a figure like their Figure 3 and erroneously suggested that the authors had inserted WOA data where necessary.

In Schönberg et al. (2017) we thereby arrived at the puzzling observation that we could not confirm the author’s statements about a nutrient effect by itself. And here we are misrepresented ourselves, because Silbiger and DeCarlo (2017) wrote:

> "This led to S17's claims that (1) there is a parabolic effect of nitrate on bioerosion".

This is incorrect. We wrote in Schönberg et al. (2017):

> "When plotting [DeCarlo et al.'s (2015)] nutrient effect on macrobioerosion separating the oligo- and the eutrophic sites, their material did not show a clear nutrient effect, when this should have been the case at least for the eutrophic location".

The misunderstanding apparently stems from our attempt to show how bad the fit in our replotted data was by displaying a trendline, which very obviously does not adequately represent the very few—four—data points it connects. We should have left the line out of the figure.

Figure 2a and c in Silbiger and DeCarlo (2017) do not quite capture what we meant to display in Figure 8d in Schönberg et al. (2017). We separated the oligotrophic from the eutrophic sites

and ran two separate trial analyses. We now replotted this situa- tion, using a value of ~7 µM for nitrate at Panama (as estimated from Figure 2 in Silbiger and DeCarlo, 2017) and 0.2 µM WOA for Wake Atoll. This generated a figure similar to Figure 2a in Silbiger and DeCarlo (2017). When then plotting the nutrient effect on macrobioerosion separately for the high and low nutrient sites, we now achieved a correlation—but only under high nutrients and without a very good fit (r² = 0.59). Likewise, the *p* provided in Figure 2c of Silbiger and DeCarlo (2017) seems to be close to 0.05, even after removing the effect of aragonite saturation.

To conclude, we agree with DeCarlo et al. (2015) and Silbiger and DeCarlo (2017) that the above data collected across the Pacific suggest that natural levels of pH had an effect on—pre- dominantly mollusc?—macrobioerosion. We also agree that nu- trient levels can have a strong impact on bioerosion (Schönberg et al., 2017), but this does not become as clear from DeCarlo et al. (2015) as the pH correlation. In a large-scale field setting, each site will be quite different from the next, introducing noise.

Effects may possibly have become clearer had dead substrate been studied and microborer and grazer erosion included (e.g. Tribollet and Golubic, 2005).

**Addressing general concerns in bioerosion research as discussed by Silbiger and DeCarlo (2017)**

**According to Silbiger and DeCarlo (2017) we claimed that bioerosion studies along natural pH gradients are *per se* compromised by co-variables, and that we had no grounds to call published works flawed:**

> "S17 go on to claim that previous investigations of bioerosion sensitivities across natural pH gradients are 'compromised' by co-variations within the natural environment [...] field studies are naturally characterized by co-variability, but this does not make them "flawed"".

We did not claim that field studies are automatically flawed. Science requires studies at different scales and in different set- tings, and each approach has a justification and purpose. While our original concerns were partly misguided, they referred to spe- cific situations, not to a general science approach. Therefore, we consciously expressed our thoughts as possibilities, not as demon- strated facts (Schönberg et al., 2017). For example with regards to the data replotted by us we said:

> "trendlines were tentatively used to explore possible correlations between bioerosion and environmental factors" (Figure 8 in Schönberg et al., 2017).

In the main text we said re Silbiger et al. (2014):

> "This suggested the possibility that observed patterns may have been also, or were exclusively influenced by other environmental factors".

Re DeCarlo et al. (2015):

> "When replotting [...] their material did not show a clear nutrient effect".
We feel that based on the experimental circumstances and our earlier and present exploratory analyses these statements are justified.

Silbiger and DeCarlo (2017) expressed themselves that in nature a multitude of factors comes into play and that collinearity and interaction effects can be important modifiers of the outcomes. When exploring other people's data, we did not primarily use this to irrefutably disprove their conclusions, but to encourage a more cautionary approach in wording for respective conclusions as other possibilities might apply. In fact, we "believe" in the same cause-effect relationships as the commenting authors: chiefly, that most types of bioerosion can be enhanced by eutrophication, increased availability of suitable substrate and an "erosion-friendly" water chemistry (Schönberg et al., 2017). Our point was that we are all too quick with generalizations that are not always adequately supported by respective data. It is not the natural, variable environment that is wrong, but it can be imprudent to identify a causative relationship without discussing possible alternatives in more detail.

Silbiger and DeCarlo (2017) agreed that

"a key requirement of reporting an observed pattern is to support it with mechanistic foundation".

Therefore none of the presently available studies on the behaviour of bioerosion in experimentally acidified aquarium water can be directly extrapolated for prognoses for the future (Browman, 2016; McElhaney, 2017). Exposure times were mostly insufficient, different bioeroder taxa often incompletely quantified, and environmental factors remain difficult to assess in field settings (Schönberg et al., 2017). We agree with Silbiger and DeCarlo (2017) that this challenge should not stop us, but reassure us in our efforts.

In the last paragraph of our paper we discussed that natural co-variation can make field studies more difficult to interpret and called for more field studies. Only by repeating pioneer work such as that of Silbiger et al. (2014) and DeCarlo et al. (2015) in different contexts, such findings can be validated. Field studies are needed to corroborate results from aquarium settings, and depending on the context it is often desirable to study how different organisms and parameters interact and affect the outcome.

**How does bioerosion change across a bathymetric gradient in general? Silbiger and DeCarlo (2017) asked:**

"do any of those correlations imply causation? To our knowledge, there is no published explanation of why depth (a change in pressure) would drive bioerosion directly [...] we can think of no mechanism to explain why the relationship would be parabolic".

No, our data mining did not necessarily confirm causation. We explored whether alternative interpretations may have been possible in some publications. For Silbiger et al. (2014) we chiefly considered the possible influence of depth-related parameters, because non-linear drivers might have generated more promising explanations for the variable pattern of net bioerosion/accretion. In this context we did not predominantly refer to pressure, but to the same range of depth-related underlying parameters as listed by Silbiger and DeCarlo (2017), of which pressure is just one in many.

Silbiger et al. (2016) wrote:

> "...bioerosion is linearly related to ocean acidity and temperature, but calcification exhibits both linear and non-linear responses".

Silbiger and DeCarlo (2017) also seem to imply that a parabolic response of marine bioeroders to environmental conditions would be unusual. In this, we disagree. A curvilinear fit can often be distinctly more appropriate to describe responses of living organisms than linear models (e.g. Calabrese, 2002; Doney et al., 2009; Wang et al., 2009), and bioeroders are not excluded. Early or low-dose responses can often be well described with linear regressions. However, physiological optima and thresholds necessarily apply to bioeroders as much as to other living organisms (Achlatis et al., 2017; Fang et al., unpublished data; Tribollet et al., unpublished data). This would result in a parabolic relationship with a central maximum. The opposite trend (initial high, intermediate low, final high) also occurs in nature, e.g. when measuring severity of bleaching in reef-building corals as a function of temperature (e.g. Hoegh-Guldberg et al., 2005; Chen et al., 2013; Heron et al., 2016). In fact, Sibiger et al.'s (2014, 2016) environmental data for total alkalinity and DIC described such relationships along their transect. It stands to reason that bioeroding or calcifying biota might react to these two factors.

Ample literature corroborates that regardless of climate and environment bioerosion changes with water depth and involves a multitude of environmental parameters. This concerns bioerosion rates (e.g. Kobluk and Kozelj, 1985; Vogel et al., 2000; Glaub et al., 2001; Wisshak et al., 2010; Weinstein et al., 2014), as well as bioeroder abundances (e.g. Bergman, 1983; Lissner, 1983; Perry, 1998; Perry and Macdonald, 2002; Schönberg and Ortiz, 2009; Cordeiro et al., 2014) and the diversity and species composition of bioeroders (e.g. Perry, 1998; Vogel et al., 2000; Cerrano et al., 2001; Schönberg, 2001; Perry and Macdonald, 2002; Radtke and Golubic, 2005; Wisshak et al., 2005, 2011; Gektidis et al., 2007; Tribollet et al., 2011; Wisshak, 2012; Schönberg, 2015; Schönberg et al., unpublished data). Respective spatial responses may easily move beyond linear or even parabolic relationships as different factors may affect different subsets of the data, which may make it very difficult to identify the underlying forces.

**Conclusions**

Although we entirely agree with Silbiger and DeCarlo (2017) by accepting that nutrient conditions and pH are strong factors driving the behaviour of bioerosion in general (Schönberg et al., 2017, in press), we would again like to caution against generalization when the sample does not fully reflect a common or "normal" situation. Bioeroders remove substrate by chemical and mechanical means, with only microborers eroding exclusively chemically, so that it is usually important to know the proportion of their contribution, especially in the context of OA (Schönberg et al., 2017). Macroboring is mostly performed by molluscs, sponges and worms, and sponges globally contribute the lion share (Schönberg et al., in press). When only a partial bioeroder community is sampled, it needs to be made clear which bioeroders are present, what their particular mode of bioerosion is, and how that affects the outcome of the study. The discussion
needs to be prudent in referring to the specific situation. Although a larger context should to be discussed, generalization should be avoided.

Christine H. L. Schönberg*,
School of Earth and Environment and Oceans Institute,
The University of Western Australia, Crawley, WA 6009, Australia

Aline Tribollet,
Laboratoire IPSL-LOCEAN, IRD-Sorbonne Universités (Univ. Paris 6)
UPMC-CNRS-MNHN, 32 Avenue Henri Varagnat, 93143 Bondy,
France

James K.-H. Fang,
Department of Applied Biology and Chemical Technology,
The Hong Kong Polytechnic University, Hung Hom, Kowloon,
Hong Kong

Marina Carreiro-Silva,
MARE – Marine and Environmental Sciences Centre, IMAR – Centre
of the Institute of Marine Research, Rua Frederico Machado 4, 9901-
862 Horta, Portugal

Max Wisshak,
Marine Research Department, Senckenberg am Meer, 26382
Wilhelmshaven, Germany

*Corresponding author: e-mail: christine.schonberg@uwa.edu.au

## Acknowledgements

We thank B. Radford and R. Fisher from the Australian Institute of Marine Science, Perth for discussing various statistical aspects with us, which significantly assisted in structuring this reply.

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Handling editor: Joanna Norkko