Presence-absence of marine macrozoobenthos does not generally predict abundance and biomass

ABSTRACT Many monitoring programmes of species abundance and biomass increasingly face financial pressures. Occupancy is often easier and cheaper to measure than abundance or biomass. We,

therefore, explored whether measuring occupancy is a viable alternative to measuring abundance and biomass. Abundance- or biomass-occupancy relationships were studied for sixteen

macrozoobenthos species collected across the entire Dutch Wadden Sea in eight consecutive summers. Because the form and strength of these relationships are scale-dependent, the analysis was

completed at different spatiotemporal scales. Large differences in intercept and slope of abundance- or biomass-occupancy relationships were found. Abundance, not biomass, was generally

positively correlated with occupancy. Only at the largest scale, seven species showed reasonably strong abundance-occupancy relationships with large coefficients of determination and small

differences in observed and predicted values (RMSE). Otherwise, and at all the other scales, intraspecific abundance and biomass relationships were poor. Our results showed that there is no

generic relationship between a species’ abundance or biomass and its occupancy. We discuss how ecological differences between species could cause such large variation in these relationships.

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FISH COMMUNITIES Article Open access 14 July 2021 INTRODUCTION Most conservation efforts depend on monitoring different species to obtain estimates of spatial distributions and population

sizes1. Specimen collection and identification is expensive and labour intensive, and in practice monitoring programmes are constrained by the number of sampling units that can be afforded.

As costs need to be reduced, many long-term monitoring programmes are now under pressure2,3. Cost-reductions could involve new technology that reduces the effort involved in species

identification1, or shifting interest from estimating the abundance of animals to estimating their occupancy, i.e. the proportion of sampling sites in which a species was present4. Because

measures of occupancy are often much easier and cheaper to measure than abundance or biomass5, the question arises whether occupancy is a reliable predictor of a species abundance or

biomass, and whether occupancy sampling could thus reduce the cost of long-term monitoring programmes. Occupancy-abundance relationships are among the most widespread empirical patterns

described in macroecological studies6,7,8,9. The most commonly studied pattern is the interspecific (between species) abundance-occupancy relationship that describes how abundance and

occupancy correlate between species in a particular area. Positive interspecific abundance-occupancy relationships are reported for many different taxa at different spatial scales and in a

wide variety of ecosystems8,9,10,11,12. Even though the underlying mechanisms remain elusive10, possible ecological processes underlying positive abundance-occupancy patterns involve habitat

use6,13,14 and population dynamics, e.g. colonization and extinction rates8,10,15,16,17. Abundance-occupancy relationships have also been studied within species18,19,20,21. Such

intraspecific relationships are divided into temporal and spatial relationships. The intraspecific temporal relationship describes the correlation between abundance and occupancy of a single

species across time. Compared to between-species abundance-occupancy relationships, there have been fewer studies on intraspecific relationships. There is support for positive intraspecific

relationships18,22, but the strength of these relationships depends on the characteristics of each species, e.g., life-history, dispersal, and longevity9,12,23,24. Negative relationships

have, however, also been found8,10 and the generality of a positive relationship remains unresolved25. The intraspecific spatial relationship describes the correlation between abundance and

occupancy of a species at a single point in time across space. Intraspecific spatial relationships are rarely studied and there is no agreement whether the shape of spatial intraspecific

relationships should be positive10. Both types of intraspecific abundance-occupancy relationships have been studied mainly in terrestrial systems26, but rarely in marine systems9,11,23,24.

In this study, we explore whether occupancy can provide accurate estimates of a species’ abundance and thus provide a cost-effective alternative to traditional sampling methods. We analysed

intraspecific abundance-occupancy relationships8, also called distribution-abundance relationships10, for sixteen marine macrozoobenthos species that were collected across the Dutch Wadden

Sea over a period of eight years27,28: the bivalves _Cerastoderma edule_, _Limecola balthica_, _Mya arenaria_, _Abra tenuis, Ensis leei, Mytilus edulis, Scrobicularia plana_, and

_Macomangulus tenuis_, the polychaetes _Scoloplos armiger_, _Heteromastus filiformis_, _Hediste diversicolor_, _Nephtys hombergii_, _Lanice conchilega_, _Marenzelleria viridis_, and

_Arenicola marina_, and the gastropod _Peringia ulvae_. Since the form and strength of abundance-occupancy relationships are scale-dependent9, they were analysed across different

spatiotemporal scales. At the regional scale, yearly variation in abundance and occupancy was analysed across the entire Dutch Wadden Sea. At the local scale, yearly variation in abundance

and occupancy was analysed within the ten tidal basins of the Dutch Wadden Sea (Fig. 1a). We also examined whether abundance-occupancy relationships occurred across geographic space at a

single point in time. For this intraspecific spatial abundance-occupancy relationship, variation in abundance and occupancy between tidal basins within years was analysed. In

abundance-occupancy modelling, abundance is generally measured as the density of the number of individuals8. However, many monitoring programmes are aimed at estimating a species’ biomass

(e.g., as a possible food source for predators29), therefore biomass-occupancy relationships were also considered. RESULTS REGIONAL TEMPORAL RELATIONSHIPS On the regional scale of the entire

Dutch Wadden Sea, the temporal relationships between abundance and occupancy were variable but mainly positive (Table 1, Figs 1b,c, 2a–d, 3a–d, 4, and Supplementary Figs. S1a–d and S2a–d).

The steepest slopes were found for the bivalves _M. arenaria, M. edulis, C. edule_. and _E. leei_, and the polychaete _S. armiger_ (Table 1). Strong relationships, characterised by a large

coefficient of determination (>50%) and small back-transformed RMSE (<50%), were observed in the bivalves _M. edulis_ and _S. plana_, the polychaetes _H. filiformis_ and _S. armiger_,

and the gastropod _P. ulvae_; Weak relationships, characterised by a small coefficient of determination and large RMSEbt, were observed in the bivalves _E. leei_, _M. arenaria_ and _A.

tenuis_, and the polychaete _M. viridis_ (Table 1 and Fig. 5a). Interestingly, the coefficients of determination for the bivalves _M. arenaria_, _L. balthica_ and _C. edule_ were reasonably

large (R2 > 0.5), but the RMSEbt was also large (RMSEbt > 49%, Table 1 and Fig. 5a). On average across the remaining species, small coefficients of variation and large RMSEbt were

found (Table 1 and Fig. 5a). At the regional scale, relationships between biomass and occupancy were highly variable and ranged from positive to negative (Table 2, Figs 2e–h, 3e–h, 4, and

Supplementary Figs S1e–h and S2e–h). The steepest positive slopes were found for the polychaetes _L. conchilega_ and _S. armiger_, and the steepest negative slopes were found for the

bivalves _M. edulis_ and _E. leei_ (Table 2). Strong relationships were observed in the bivalves _L. balthica, M. edulis, E. leei_, and the polychaetes _H. filiformis_ and _S. armiger_; Weak

relationships were found in the bivalves _A. tenuis_, _M. arenaria_, and the polychaete _L. conchilega_ (Table 2 and Fig. 5b). There were several species with small RMSEbt, but they also

had small coefficients of determination (e.g., the polychaetes _A. marina_, _N. hombergii_, and the gastropod _P. ulvae_, Table 2 and Fig. 5b). For these species the overall mean was a

better predictor of biomass than occupancy. Comparing abundance-occupancy and biomass-occupancy relationships showed striking differences for some species (Fig. 4). Although for the bivalves

_M. edulis_, _E. leei_ and _C. edule_ positive relationships were observed between abundance and occupancy, the relationships between biomass and occupancy were negative (Tables 1 and 2,

Figs 2 and 4, and Supplementary Fig. S2). Compared to biomass-occupancy relationships, the RMSEbt were similar but coefficients of determination were larger for abundance-occupancy

relationships (median R2 of abundance and biomass relationships were respectively 0.49 and 0.18). The exceptions were the two bivalves _L. balthica_ and _E. leii_ that showed stronger

occupancy-relationships for biomass than abundance (Tables 1 and 2, and Fig. 5a and b). LOCAL TEMPORAL RELATIONSHIPS On the local scale within tidal basins, the between-year variation in

abundance generally correlated positively with occupancy (Supplementary Fig. S3). However, between tidal basins and between species, we observed large variation in median intercepts (range =

 1.85–4.29) and slopes (range = 0.04–0.40) (Supplementary Fig. S3). Additionally, the coefficients of determination were small (median R2 = 0.37) and Root Mean Squared Error large (median

RMSEbt = 51%, Fig. 5c). The weakest relationships were found for the bivalves _M. edulis_ and _A. tenuis_, and the polychaete _L. conchilega_. Compared to the regional scale, relationships

between abundance and occupancy at local scales were more variable (the Inter Quartile Range of slopes were 0.30 and 0.12 for respectively local and regional relationships) as well as weaker

(the median RMSEbt of local and regional relationships were respectively 51 and 34%) with the exception of the bivalves _M. edulis, S. plana_, and the gastropod _P. ulvae_ (Supplementary

Fig. S5a). For biomass, positive, negative or no relationships with occupancy were found within species across tidal basins (Supplementary Fig. S3). Between species, there was also a large

variation in the relationships between biomass and occupancy (median slopes ranged from −0.18 to 0.40, Supplementary Fig. S3), and none of them were strong (Fig. 5d). Compared to the

regional scale relationships, relationships of between-year variation in biomass and occupancy within tidal basins were weaker with similar R2 but larger RMSEbt (median RMSEbt of the local

and regional scale were respectively 63 and 31%, Supplementary Fig. S5b). SPATIAL RELATIONSHIPS In general, positive spatial abundance-occupancy relationships were found, but these

relationships were weak (median slopes ranged from −0.03 to 0.33, Supplementary Fig. S4) and highly variable between years. Only the polychaete _S. armiger_ showed strong spatial

abundance-occupancy relationships (R2 = 0.66 and RMSEbt = 22%, Fig. 5e), but this was still weaker than its temporal relationship at the regional scale (R2 = 0.83 and RMSEbt = 15%, Fig. 5a).

Weakest relationships were found for the bivalves _M. edulis_, _M. arenaria_, the polychaete _L. conchilega_, and the gastropod _P. ulvae_. Comparing spatial relationships with regional

temporal relationships showed that spatial relationships were generally weaker, especially for the bivalves _M. edulis_ (slope of respectively 0.29 and 0.53) and _S. plana_ (slope of

respectively 0.03 and 0.09), and the gastropod _P. ulvae_ (slope of respectively 0.21 and 0.28) (Supplementary Fig. S5c). Only in the cases of the bivalve _E. leei_ and the polychaete _M.

viridis_ did the R2 increase and RMSEbt decrease, but these relationships were still weak (RMSEbt > 68%, Fig. 5e). The spatial relationships were similarly weak as the local temporal

relationships (Supplementary Fig. S4c–f). Only for two polychaete species the spatial relationships were stronger than the local temporal relationships: _S. armiger_ and _A. marina_,

(Supplementary Fig. S5e). However, the regional temporal relationships for these two species were still stronger than both the spatial and local temporal (Fig. 5). For biomass, the spatial

biomass-occupancy relationships revealed large variation in median intercept (range = −0.28–2.57) and slope (range = −0.05–0.56, Supplementary Fig. S4). Moreover, none of the spatial

biomass-occupancy relationships were strong (median R2 = 0.18 and RMSEbt = 82%, Fig. 5f). One of the weakest relationships was observed for the bivalve _M. edulis_ (R2 = 0.19 and RMSEbt = 

575%). Compared to the regional temporal biomass-occupancy relationships, the spatial relationships were worse (RMSEbt of respectively 31 and 82%), especially for the bivalves _M. edulis, E.

leei_ and _L. balthica_ (Supplementary Fig. S5d). Also compared to the local temporal abundance-occupancy relationships (median RMSEbt = 63%), the spatial relationships were weaker, e.g.,

for the bivalves _M. edulis_ and _E. leei_ (Supplementary Fig. S5f). When comparing the spatial abundance-occupancy with spatial biomass-occupancy relationships, the biomass-occupancy

relationships were more variable than the abundance-occupancy relationship. That is, the Inter Quartile Ranges of intercepts were 0.65 and 1.45 for the abundance and biomass relationships,

0.18 and 0.32 for the slopes, and 53% and 133% for the RMSEbt respectively (Supplementary Fig. S4). DISCUSSION At the scale of the entire Dutch Wadden Sea, the intraspecific

abundance-occupancy relationships were generally positive. Also, occupancy was usually positively related with biomass, but relationships were more variable than the abundance-occupancy

relationships, and even negative for some species. The local temporal relationships and the spatial relationships were more often negative and weaker, in the cases of both abundance and

biomass, than the regional temporal relationships. These findings suggest that occupancy data at large spatial scales could be informative about the abundance or biomass of selected species

(e.g., the bivalves _L. balthica, S. plana_, _E. leei_ and _M. edulis_, the polychaetes _S. armiger_ and _H. filiformis_, and the gastropod _P. ulvae_.). However, for most species the

predictive power of abundance and/or biomass from occupancy was low, i.e. the coefficients of determination were small and the difference in observed and predicted values large. Moreover,

there were large differences in intercepts and slopes of the relationships between species. Within species, these relationships also varied between years and across geographic space, which

further showed that there was a lack of generality for predicting a species’ abundance or biomass from its occupancy; especially at smaller scales. Several modelling and empirical studies

show that the slope of abundance-occupancy relationships is consistently shallower and weaker for rare species12,20,30. A related factor that could affect abundance-occupancy relationships

is the distribution range (variation) of a species’ measured abundance and occupancy8. In our study, _A. tenuis_ and _M. tenuis_ were relatively rare with little variation in occupancy

(respectively 2–5% and 0–3%), and indeed they showed shallow (slope close to zero) and weak relationships. However, even though _S. plana_ was also rare and had a small occupancy range

(2–5%), its abundance-occupancy relationship was among the strongest, but also with a shallow slope. Likewise, commonness and a large range of occupancies were no guarantee for strong

abundance-occupancy and biomass-occupancy relationships. _M. viridis_ had an occupancy range of 15–54% but weak abundance-occupancy and biomass-occupancy relationships. The variation in

abundance-occupancy patterns observed in this study could be understood by differences in life-histories between species. Theory predicts that abundance-occupancy relationships can be

explained by: niche differentiation in resource and/or environmental use, which result in differences in vital rates and thus abundances along gradients of resources and/or the

environment6,8,10,13,15, or population dynamics mediated by the movement of organisms between sites, which can be driven by competition for resources14,17. Based on the latter mechanism,

weak abundance-occupancy relationships are predicted for species with low dispersal rates16. Thus a species that experiences little dispersion and aggregates locally is expected to have

reduced occupancy compared to a more dispersive species26. A comparison between marine invertebrates showed that dispersal propensity affected abundance-occupancy relationships23. In our

study, _A. tenuis_ has limited dispersal capabilities (it deposits egg masses locally into the sediment31) and indeed showed a shallow slope and weak abundance-occupancy relationship.

Likewise, the species that spawn in the water column and/or have a planktonic juvenile phase, in which the currents can disperse individuals over large distances32,33,34, had the strongest

abundance-occupancy relationships (e.g., _L. balthica_, _M. edulis_, _P. ulvae_). As an example, _M. edulis_ can have a planktonic larval phase of up to two months35 and can potentially

disperse very far. Our findings are, however, not conclusive as some species (e.g., _M. tenuis, L. conchilega, M. viridis_) that have a planktonic phase and should be capable to disperse

over large distances, showed weak abundance- or biomass-occupancy relationships. For many macrozoobenthos species, recruitment dominates population dynamics36,37. Recruits are often

superabundant and disproportionally affect abundance, biomass, and occupancy. That is, small but numerous recruits occupy large areas but with little contribution to biomass, and _vice

versa_ few adults contribute considerably to biomass but survive in restricted areas. Recruitment events could explain the opposing signs of abundance-occupancy versus biomass-occupancy

relationships that were found for _M. edulis_, _E. leei_ and _C. edule_. For instance, in 2011 _C. edule_ had a uniquely strong recruitment with maximum densities of almost 19,000 juveniles

per square meter38. Across the eight years of this study, 2011 had the largest abundance and occupancy, and indeed the smallest biomass as well. Similarly, old and large individuals can also

dominate abundance, biomass and occupancy. Moreover, theory predicts that longevity would cause shallow slopes12,16. _M. arenaria_ can live for 28 years, reach 15 cm in length39, and the

abundance and occupancy of old individuals is small, but biomass is large. Indeed, the weakest relationships for _M. arenaria_ was found. Moreover, longevity introduces strong temporal

autocorrelation, due to cohort effects persisting through time, which might influence abundance-occupancy relationships further. Local conditions can synchronize biomass-variation between

macrozoobenthos species40, e.g., _C. edule, M. edulis, M. arenaria_. Therefore, local temporal relationships (within tidal basins) are predicted to be stronger than regional relationships

(across the entire Dutch Wadden Sea), i.e. spatial variance is reduced. However, the abundance-occupancy relationships on the local scale were generally weaker, particularly for the

above-mentioned species. Perhaps this is caused by smaller sample sizes as we scaled down to tidal basins. Alternatively, it could hint at large-scale processes that synchronise population

dynamics of these species. For instance, the population dynamics of many marine macrozoobenthic species are affected by large-scale weather patterns. Cold winters can cause adult mortality,

and mild winters can cause failed recruitment40,41. Large-scale weather patterns have indeed been found to synchronise population dynamics of different species over large spatial

scales40,42, e.g., _M. edulis_, _M. arenaria_, _L. conchilega_, _C. edule_. Whether these large-scale processes underlie abundance-occupancy relationships, or perhaps influence their shape,

needs to be investigated in further detail. In this study, occupancy was measured in the traditional way by morphological taxonomy. Over the past years there has been an explosion in the use

of environmental DNA (eDNA) metabarcoding as a tool for aiding monitoring programmes1,43,44,45,46,47,48. Studies have shown that eDNA sampling is accurate in collecting presence-absence

data49,50, which could provide a cost-effective alternative to measuring occupancy. One should, however, be careful extrapolating our traditionally measured abundance-occupancy relationships

to occupancies measured with eDNA techniques. There is some evidence that eDNA methods have higher detection rates than traditional field methods, particularly when species occur at low

densities51. Sampling benthic invertebrates with small sediment cores can underestimate a species’ occupancy, i.e. imperfect detection leads to zero-inflated abundances5,52. This might

especially be the case for _M. arenaria_ that live partly below the reach of the sampling core (>30 cm) when they are very large (up to 15 cm)39,53. Indeed, this species showed the

weakest abundance-occupancy relationships. The strength of abundance-occupancy relationships presented in this study could thus be underestimated compared to eDNA sampling techniques.

Occupancy estimates could be improved by modelling detection probabilities5. To fully assess the validity of predicting abundance and biomass from eDNA occupancy data, traditional and eDNA

sampling should be carried out simultaneously at the same locations. In the future, however, new genomic technologies could allow estimating abundance and biomass directly from eDNA sampling

data54,55. In summary, we find support for positive, as well as negative, intraspecific abundance- or biomass-occupancy relationships that could partly and non-conclusively be explained by

ecological differences in life-histories between species. Abundance and biomass of some species could be accurately predicted from occupancy data, but only at the large scale of the entire

Dutch Wadden Sea. At present, there is no generic relationship for predicting a species’ abundance or biomass from its occupancy. For the foreseeable, we therefore need to rely on

traditional sampling technology for estimating a species’ abundance and/or biomass. MATERIAL AND METHODS STUDY SYSTEM The Dutch Wadden Sea (53°16′N, 5°24′E) covers roughly 2500 km2 of which

50% is tidal mud flats56. Due to natural tidal divides, the system is divided into ten physical units of tidal basins separated by watersheds (Fig. 1a). FIELD SAMPLING From 2008 to 2015, the

abundance and biomass of macrozoobenthic invertebrates were sampled across all 1151 km2 of intertidal mudflats in the Dutch Wadden Sea (Fig. 1) from June to September (SIBES Synoptic

Intertidal BEnthic Survey)27,28. Sampling stations were arranged according to a grid sampling design with 0.5 km inter-sample distance and 15 to 20% additional sampling stations randomly

placed onto gridlines27. In total between 3,159 and 4,818 stations were sampled and analysed for the sampling campaigns from 2008 to 2015, with the exception of 2015 where 1,289 samples of

the random sample points have currently been analysed. In 2008, the Ems-Dollard estuary was not sampled. Within tidal basins, the surface area of the intertidal mudflats varies between 25

and 311 km2. Thus on average 75 to 1071 stations were sampled per tidal basin. Sampling stations were located by handheld GPS (Garmin 60 and Dakota 10). At each station, two sediment cores

(1/112 m² each) were taken to a depth of 25–30 cm, washed over a 1-mm square mesh sieve, and then transported to the laboratory. Because of the time between collection and processing, large

bivalves were stored in the freezer and the polychaetes, crustaceans and small bivalves were kept on formalin. In the laboratory, species were identified, individuals counted, and biomass

(g) was determined as ash-free dry mass of the flesh (AFDMflesh)28. The shell and flesh of the gastropod _Peringia ulvae_ were not separated, thus flesh was assumed to contribute 17% of the

AFDM57. For bivalves, shell length (mm) was also measured. ANALYSES Prior to analyses, outliers in AFDMflesh were identified with a non-linear local regression of the log10 of AFDMflesh and

the log10 of shell length58 (R-script available in Supplementary Material Appendix A1). If residuals exceeded twice the Inter Quartile Range (IQR) they were defined as outliers. Because the

lengths of polychaetes could not be accurately determined, they were divided into size-classes (juvenile or adult) and AFDMflesh of outliers was estimated from their size class. _H.

filiformis_ could not be divided into size classes, therefore, outliers were estimated with mean log10 AFDMflesh. If AFDMflesh was not measured, it was estimated from their length (bivalves)

or size class (polychaetes). If both AFDMflesh and length or size class were absent, the measurement was removed from the analyses (0.9% of measurements). Abundance of a single species at a

single sampling location was calculated as the number of individuals divided by the sampled surface area. To obtain the biomass of a single species at a single sampling location, the

AFDMflesh (g) of all individuals was summed. Mean abundance (n m−2) and biomass (g AFDM m−2) were then calculated by averaging abundances and biomasses of a species within occupied patches

only (local mean abundance)59. To calculate presence-absence data, a species was classified as absent (0) or present (1) for each sampling station. Occupancy was calculated as the sum of the

total number of presences divided by the number of stations visited. For the analyses of intraspecific temporal relationships at the regional scale, mean abundance, biomass and occupancy of

a species was calculated across the Dutch Wadden Sea in each year. To evaluate the local temporal relationship, abundances, biomasses and occupancy were averaged for each tidal basins in

each year. The analyses of temporal relationships within tidal basins were restricted to those tidal basins where the species was observed at least six out of eight years. To examine spatial

relationships, we examined the average abundance, biomass and occupancy between tidal basins within a single year, and then for each year separately to assess the yearly variation and

robustness of these relationships across time. Intraspecific relationships were modelled by fitting linear regressions between the log10 of abundance or biomass and the logit of occupancy.

On the scale of tidal basins, abundance, biomass and occupancy data contained zeros. Before taking their logarithm or logit, we therefore added the smallest measured value of abundance or

biomass within tidal basins, and for occupancy half times one over the sample size. To evaluate the form of the abundance- or biomass-occupancy relationships, the intercept and slope were

extracted from linear regression models. Because we were particularly interested in whether occupancy could predict abundance and/or biomass, we also extracted the coefficient of

determination (R2) and the Root Mean Squared Error (RMSE). The coefficient of determination describes the proportion of variance in abundance or biomass explained by occupancy. For

presentation purposes, the Root Mean Squared Error was back-transformed (RMSEbt) by taking the anti-log of RMSE, subtracted by 1, and multiplied by 100. The resulting RMSEbt provided the

percentage difference between observed and predicted values. A strong relationship should be characterised by a large R2 and small RMSEbt, whereas a weak relationship should be characterised

by a small R2 and large RMSEbt. All data was analysed in R v3.2.360. DATA AVAILABILITY All data analysed in this study is available at https://doi.org/10.5281/zenodo.1120347. REFERENCES *

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ACKNOWLEDGEMENTS We are in very grateful to the large numbers of staff, students and particularly volunteers that collected and analysed the huge amount of data that was used in this study.

We also thank the crew of RV Navicula for the logistical support during field work. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * NIOZ Royal Netherlands Institute for Sea Research,

Department of Coastal Systems, and Utrecht University, P.O. Box 59, 1790 AB, Den Burg, The Netherlands Allert I. Bijleveld, Tanya J. Compton, Lise Klunder, Sander Holthuijsen, Job ten Horn, 

Anita Koolhaas, Anne Dekinga, Jaap van der Meer & Henk W. van der Veer Authors * Allert I. Bijleveld View author publications You can also search for this author inPubMed Google Scholar

* Tanya J. Compton View author publications You can also search for this author inPubMed Google Scholar * Lise Klunder View author publications You can also search for this author inPubMed 

Google Scholar * Sander Holthuijsen View author publications You can also search for this author inPubMed Google Scholar * Job ten Horn View author publications You can also search for this

author inPubMed Google Scholar * Anita Koolhaas View author publications You can also search for this author inPubMed Google Scholar * Anne Dekinga View author publications You can also

search for this author inPubMed Google Scholar * Jaap van der Meer View author publications You can also search for this author inPubMed Google Scholar * Henk W. van der Veer View author

publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS A.I.B., T.C., L.K., J.v.d.M. and H.v.d.V. conceived the study and designed methodology; S.H., J.t.H.,

A.K., and A.D. collected the data; A.I.B. analysed the data and led the writing of the manuscript. All authors contributed critically to the drafts and gave final approval for publication.

CORRESPONDING AUTHOR Correspondence to Allert I. Bijleveld. ETHICS DECLARATIONS COMPETING INTERESTS Part of the sampling has been funded by the Nederlandse Aardolie Maatschappij (NAM) and by

The Netherlands Organization for Scientific Research (NWO) via Project 839.08.251 of the National Ocean and Coastal Research Programme (ZKO). ADDITIONAL INFORMATION PUBLISHER'S NOTE:

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Compton, T.J., Klunder, L. _et al._ Presence-absence of marine macrozoobenthos does not generally predict abundance and biomass. _Sci Rep_ 8, 3039 (2018).

https://doi.org/10.1038/s41598-018-21285-1 Download citation * Received: 29 June 2017 * Accepted: 02 February 2018 * Published: 14 February 2018 * DOI:

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