Long-term changes in ecological functioning of temperate shelf sea benthic communities

Journal Pre-proof Long-term changes in ecological functioning of temperate shelf sea benthic communities Mehdi Ghodrati Shojaei, Lars Gutow, Jennifer Dannheim, Alexander Schröder, Thomas Brey PII:

S0272-7714(20)30828-3

DOI:

https://doi.org/10.1016/j.ecss.2020.107097

Reference:

YECSS 107097

To appear in:

Estuarine, Coastal and Shelf Science

Received Date: 7 April 2020 Revised Date:

9 September 2020

Accepted Date: 10 November 2020

Please cite this article as: Shojaei, M.G., Gutow, L., Dannheim, J., Schröder, A., Brey, T., Long-term changes in ecological functioning of temperate shelf sea benthic communities, Estuarine, Coastal and Shelf Science (2020), doi: https://doi.org/10.1016/j.ecss.2020.107097. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Ltd.

M.G.H. conceptualized the study, carried out statistical analysis and wrote the initial draft of the manuscript.; L.G. conceptualized the study, helped draft the manuscript and critically revised the manuscript; J.D. participated in the design of the study, collected data and helped draft the manuscript; A.S. collected data and helped data curation; T.B. conceptualized and supervised the study, discussed methodology and revised the manuscript. All authors approved the final

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manuscript for publication and agreed to be accountable for the result presented therein.

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Long-term changes in ecological functioning of temperate shelf

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sea benthic communities

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Mehdi Ghodrati Shojaeia *, Lars Gutowb, Jennifer Dannheimb,d, Alexander Schröderc, Thomas

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Breyb,d,e

9 10

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13

of ro

b

c

re

12, 27570 Bremerhaven, Germany

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Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Am Handelshafen

Lower Saxony Water Management, Coastal Defense and Nature Conservation Agency,

lP

8

Modares University, 4641776489 Noor, Iran

Oldenburg, Germany

na

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Department of Marine Biology, Faculty of Natural Resources and Marine Sciences, Tarbiat

d

Helmholtz Institute for Functional Marine Biodiversity at the University Oldenburg (HIFMB),

ur

6

a

Ammerländer Heerstraße 231, 26129 Oldenburg, Germany e

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University of Bremen, Bibliothekstraße 1, 28359 Bremen, Germany

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* Corresponding author: Phone: +98 11 4499 9155; e-mail: [email protected]

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(MG.Shojaei)

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Abstract

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The functioning of ecosystems is decisively dependent on the composition and distribution of the

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functional traits of the constituent species. We used trait analysis to represent aspects of marine

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benthic functioning, using a 20-year time-series (1992-2011) on macrozoobenthos collected

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annually at four monitoring sites in the southern North Sea. Temporal patterns of trait

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composition and taxonomic composition were compared to test whether they exhibited similar or

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contrasting responses to environmental change. Temporal changes in trait composition were

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more similar among monitoring sites than the changes in the taxonomic composition suggesting

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that trait compositions converge towards a common structure sculpted by common

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environmental drivers. The relationship between species richness and functional diversity

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displayed a power-shaped curve with a shallow slope, implying considerable functional

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redundancy among species. The temporal trends in functional diversity were relatively stable as

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compared to taxonomic diversity, with only two irregularities coinciding with exceptionally cold

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winter events in the North Sea in 1995 and 2009. Following the temporary changes in ecological

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functioning, the functional diversity returned to previous levels within one year. The rapid

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functional recovery of the benthic infauna confirms the self-organizing ability of the ecosystem

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in response to stressors and may be attributed to the high functional redundancy in the temperate

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shelf sea system of the North Sea.

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Key words: Functional diversity, Biological traits, Trait-based approach, Long-term change,

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Macrozoobenthos

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Introduction

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Marine ecosystems experience an unprecedented range of natural and anthropogenic

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disturbances with an increasing frequency of occurrence over recent decades (Hewitt et al., 2008;

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Marques et al., 2009). Among other extrinsic drivers, rising sea water temperature and coastal

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water pollution have resulted in alteration of habitats and subsequent changes in overall

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community structure (Bremner et al., 2006; Krone et al., 2013). There is increasing evidence that

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these changes have significant implications for ecosystem functioning and stability of marine

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ecosystems (Brey, 2012). The impacts of such disturbances on marine biodiversity have

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primarily been documented in terms of changes in community composition based on species

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abundance, biomass or diversity, and single species responses (Bremner et al., 2006; Degen et

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al., 2018). Alternatively, the diversity of functional traits (e.g., feeding habit, body size, mobility)

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occurring among sets of species in a given ecosystem, often referred to as ‘functional diversity’,

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is increasingly being investigated to understand the responses of ecosystems to environmental

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fluctuations (Bremner et al., 2006; Degen et al., 2018; Törnroos et al., 2014; Weigel et al., 2016).

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Functional diversity is based on the assumption that the ability of an ecosystem to function and

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maintain itself may be more related to species-specific traits than on diversity itself (Hewitt et

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al., 2008; Hooper et al., 2005). Functional diversity within a community is crucial for ecosystem

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processes and the resilience to environmental changes.

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Measuring functional diversity enables us to generalize the functional contributions of species to

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ecosystem functions and envisage the ecological consequences of species loss (Degen et al.,

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2018; Teichert et al., 2017; Weigel et al., 2016). The relationship between taxonomic diversity

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and functional diversity is key in identifying the diversity effects on ecosystem functioning

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(Mason et al., 2005; Naeem et al., 2002). Theoretical and empirical evidence has shown that the

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strength and shape of the relationship between species diversity and functional diversity may

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differ, for example, when many functionally similar but taxonomically different species co-occur

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in a community. Previously proposed relationships between species diversity and functional

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diversity range from positive linear relationships with varying slopes (Van der Linden et al.,

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2016) to a variety of hump-shaped or even sigmoid logistic relationships (Sasaki et al., 2009;

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Weigel et al., 2016). Species with a great overlap in traits are expected to be functionally

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redundant, resulting in a low level of functional diversity within an assemblage (Naeem, 1998).

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Contrarily, species with little trait overlap are complementary and support a high level of

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functional diversity (Teichert et al., 2017). The degree of trait overlap is decisive for the

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sensitivity of ecological functioning towards the loss of species diversity. In a highly redundant

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community, a loss of species diversity causes only minor loss in ecosystem functioning, whereas,

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in a non-redundant community, the loss of any taxon may profoundly impact ecosystem

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functioning (Naeem, 1998; Walker, 1992). Characteristically, disturbed systems are

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characterized by a reduced functional diversity and, correspondingly, elevated redundancy (e.g.

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Micheli and Halpern, 2005). The unique characteristics of aquatic environments hamper our

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ability to directly transfer conclusions on species diversity effects on functional diversity from

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terrestrial to aquatic systems. Recently, the relationship between biodiversity and functional

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diversity has received scientific interest in the marine benthos (Clare et al., 2015; Danovaro,

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2012; Van der Linden et al., 2016; Weigel et al., 2016; Wong and Dowd, 2015). Besides its

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theoretical significance, much of the research has been motivated by the alarming loss of species

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seen in marine environments (Hewitt et al., 2008). Most studies found a strong positive

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relationship between the two indices (Danovaro, 2012; Van der Linden et al., 2016; Wong and

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Dowd, 2015), but the changes in functional diversity may not simply follow the changes in

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species diversity. Weigel et al. (2016) showed that functional diversity seemed to stabilize with

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increasing numbers of species, following a saturation curve pattern. Clare et al. (2015) carried

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out a long-term evaluation (40 years) of macrozoobenthos communities in the North Sea. The

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authors found that even when the community structure of the benthic fauna experienced temporal

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changes, functional diversity showed long-term stability, but with temporary disruptions. The

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marine benthos is a promising system to study the relationship between species diversity and

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functional diversity because marine benthic invertebrates are taxonomically and functionally

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diverse and support important ecosystem functions (Brey, 2012). The combined investigation of

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structure and functionality in marine benthic invertebrate communities can improve our

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understanding of the relationship between taxon diversity and ecosystem functions, which is

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crucial for the management and conservation of biodiversity and to maintain the goods and

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services provided by marine systems (Hooper et al., 2005; Nordström et al., 2015).

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The present study investigates long-term changes in macrozoobenthic communities of the

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southern North Sea in relation to various environmental factors. Characteristically, the temperate

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shelf sea system of the North Sea is affected by diverse anthropogenic activities such as

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shipping, oil and gas extractions, and offshore windfarming (Krone et al., 2013). Bottom

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trawling is a key source of physical disturbance in shallow North Sea areas, and is well known to

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affect the diversity, community structure, and functioning of marine organisms (Hiddink et al.,

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2006; Tillin et al., 2006). In addition to anthropogenic stressors, recent environmental changes in

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the North Sea region could be primarily linked to climate change-induced seawater temperature

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increase, sea-level rise and the variability of the North Atlantic Oscillation Index (NAOI) (Frid,

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2000; Meyer et al., 2018; Shojaei et al., 2016). These disturbances have resulted in a shift in

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benthic community structure, an increase in warm-temperate species and a decrease in cold-

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temperate species (Kröncke et al., 2013; Reiss et al., 2006; Shojaei et al., 2016; Wiltshire et al.,

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2010). Recent climate change also facilitates the development, survival, and successful spreading

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of non-native and resource-competing species, such as the Pacific oyster Magallana gigas

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(Anglès d’Auriac et al., 2017). The dominance of small opportunistic species was repeatedly

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reported for different regions of the North Sea over the last decades, ranging from intertidal to

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offshore areas (Kröncke et al., 2013, and references therin). Benthic communities respond to

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environmental stress in terms of species abundances (Kröncke et al., 2013; Shojaei et al., 2016)

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and traits, which are indicative of species sensitivity to environmental stressors (Bremner et al.,

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2006). However, functional change and potential functional recovery trajectories are not always

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matched by their structural counterparts (Bolam, 2012; Clare et al., 2015).

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In this study, we will investigate how changes in the species inventory may alter the functional

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properties of benthic communities making use of the combined application of a functional trait-

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based approach and a taxonomy-based approach. We used data from a continuous long-term

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monitoring program on macrozoobenthic communities at four sites in the southern North Sea, to

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determine the relationships between species diversity and functional diversity. Specifically, we

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ask whether temporal changes in species diversity in response to environmental changes are

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accompanied by changes in functional diversity.

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Material and Methods

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Study area and sites

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Benthic infauna was sampled annually in spring (i.e., prior to the major annual recruitment

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period) from 1992 to 2011 at four long-term monitoring sites in the southern North Sea (Fig. 1).

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The monitoring sites cover the most widespread benthic assemblages in this region, i.e. the

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Nucula nitidosa-association, the Tellina fabula-association, and the Amphiura filiformis-

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association (Salzwedel et al., 1985). The sediment at site SLT (Silt: 54° 03.00’ N – 8° 05.00’ E;

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depth 23 m) has a median grain size of 70 µm and consists of black and soft silt with the highest

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silt-clay content of 40%. Until 1980, sewage sludge from the Hamburg port area was disposed

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about 8.3 km east of this site resulting in a considerable organic load of the sediment. Sediments

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at site FSD (fine sand: 54° 22.50’ N – 7° 37.00’ E; depth 26 m) consisted of fine sand (median

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grain size 180 µm) with the lowest silt-clay fraction (1%). FSD is located at the center of a

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former dumping area about 27 km north-west of the island of Helgoland where acid-iron waste

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was discharged from 1969 to 1989 (Schröder, 2003). Sediment characteristics at sites SSD (silty

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sand: 54° 01.00’ N – 7° 49.00’ E; depth 36 m ) and WB (White Bank: 55° 00.00’ N – 6° 30.00’

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E; depth 42 m) were similar with a median grain size of 83 µm and a silt-clay content of 25%

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(Rachor, 1978; Schröder, 2003). At each sampling event, five replicate van Veen grab samples

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(0.1 m2 area, 10-20 cm penetration depth) were taken at each site, sieved over a 0.5 mm mesh,

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and preserved in 4% buffered formalin-seawater solution. The organisms were identified to

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species level as far as practicable, counted, and weighed (wet weight) to build a “taxon by station

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matrix”. For the analysis (see below), the biomass data were log-transformed to down-weight the

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influence of species with disproportionally high biomasses.

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Trait data

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A set of ten biological traits was selected to describe the life history, behavioural, and

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morphological characteristics of the benthic communities of the North Sea. There is currently no

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accepted consensus regarding the selection of most appropriate traits for a given study and

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usually the final selection is guided by the limited trait information available for benthic

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invertebrates (Bolam 2020; Bremner et al. 2006; Clare et al. 2015; Degen and Faulwetter, 2019).

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One common approach to the use of traits is as indicators of ecosystem functions or of changes

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in the environment (Degen and Faulwetter, 2019). An overview of how each of these traits relate

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to key components of ecosystem functioning or respond to environmental changes or pressures is

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provided in Supplementary Material (Table S1). The traits also represent those for which

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biological information is relatively well-established for marine benthic invertebrates and thus

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commonly been used in similar studies (e.g, Bolam 2012; Bremner et al. 2006; Clare et al. 2015;

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Nordström et al. 2015; Törnroos et al. 2014; Weigel et al. 2016). Each trait was subdivided into

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distinct modalities, which describe how the trait is expressed by a species. For example, the trait

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‘larval development’ was subdivided into the following modalities: 1) direct (i.e., no larva), 2)

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planktotrophic, 3) lecithotrophic (Table 1). In total, 39 trait modalities were defined. Species

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may shift between modalities depending on, for instance, environmental conditions and resource

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availability (Usseglio-polatera et al., 2000). Accordingly, it is often not possible to assign a taxon

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(i.e., species or genus) to a single trait modality. Therefore, each taxon was scored according to

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its affinity to each trait modality using a score of 0 to 3 (0 = no affinity, 1 and 2 = partial affinity,

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and 3 = highest exclusive affinity). For example, the actinia Sagartia troglodytes mostly feeds as

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a predator/scavenger but may occasionally feed as a suspension feeder. Accordingly, the species

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was coded 3 for predator and 1 for suspension feeder for the trait feeding habit. Scores were

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given using the ‘fuzzy scoring’ approach, which allowed taxa to show more than a single

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modality for any given trait if needed (Chevene et al., 1994).

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Information on biological traits was compiled from peer-reviewed literature, identification

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guides,

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consultations. If trait information was not available at the species level, the information was

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adopted from other members of the genera. To give the same weight to each taxon and trait, the

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online

databases

(e.g.,

http://www.marlin.ac.uk/biotic/),

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personal

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scores were standardized by scaling the sum of all records for each trait of a taxon equal to one.

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This procedure allowed us to build the “taxon by trait” matrix. For each station, the standardized

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modality scores for each taxon were multiplied by the log-transformed biomass of that taxon and

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summed up over all taxa. The results provide a “trait by station matrix” providing the frequencies

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of occurrence of modalities for each site/time combination (4 sites and 20 years) (for details see

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Bremner et al., 2006). We then computed the community weighted mean of traits for each of

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four stations, which represented the average of each trait value weighted by the relative biomass

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of each species. The full data on the species traits with an attributed reference list is available

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through

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(http://doi.pangaea.de/10.1594/PANGAEA.813419).

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Similarity in temporal patterns

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Similarities in temporal patterns among the sampling sites in terms of taxonomic composition

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(i.e., taxon by station matrix) and trait composition (i.e., taxon by trait matrix) were analyzed

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using the Rv vectorial correlation coefficient (see Supplementary Material for details of the Rv

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coefficient). The method allows testing whether changes in trait composition are similar among

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the sites regardless of taxonomic composition differences. The Rv-coefficient is a

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multidimensional equivalent of the ordinary correlation coefficient between two or more subsets

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of variables (Klingenberg 2009; Robert and Escoufier 1976). It is based on multiple coinertia

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analysis, which enables the simultaneous ordination of a set of several tables (K-tables) (Dray et

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al., 2003). The Rv-coefficient ranges between 0 to 1 with values close to 1 indicating high

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similarity among sampling sites (Heo and Gabriel, 1998). The statistical significance of a given

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coefficient was tested using a Monte-Carlo permutation test with 999 permutations.

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Environmental

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Linking benthic functioning to environmental variables

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Eight environmental variables were selected based on available data. Mean sea surface

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temperature (SST) in winter (Dec.-March) and summer (July-Sept.) of the preceding year, mean

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(mean of all days within a year) salinity and dissolved inorganic nutrient concentrations

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(phosphate, dissolved inorganic nitrogen (DIN), and silicate) were taken from daily

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measurements of the Helgoland Roads time series (Wiltshire et al., 2010). The North Atlantic

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Oscillation annual (NAOI) and winter indices (NAOWI; Dec.-March) were obtained from the

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Climate

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(http://www.cgd.ucar.edu/staff/jhurrell/naointro.html).

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The environmental variables were lagged (1-year lag) to explore possible indirect or delayed

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effects of the environmental variables on trait composition and to examine the proportion of the

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variation that is explained by lagged and unlagged values. A non-parametric distance-based

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linear model (DISTLM) was used to assess the relationship between variations in environmental

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variables and the trait composition (Anderson, 2006). The DISTLM models the relationship

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between the environmental predictors and the multivariate biological trait composition based on

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multiple regression models (Nicastro and Bishop, 2013). Model selection was based on the

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‘Akaike information criterion’ (AIC) and the ‘BEST’ selection procedure. AIC was chosen as it

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identifies the most parsimonious model by adding a ‘penalty’ for increases in the number of

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predictor variables (Anderson, 2006). P-values were obtained from 999 permutations of the data.

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Distance-based redundancy analysis (dbRDA) based on the Bray-Curtis distance was used to

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visualize the DISTLM model in a 2-dimensional plane (Anderson, 2006). Prior to the DISTLM,

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we applied a variance inflation factor (VIF) analysis to avoid multi-collinearity (strong inter-

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correlations) among environmental variables. Environmental variables that showed evidence of

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Boulder,

USA

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skewness were transformed using a square root (for mild skewness) or log(x+1) transformation

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to improve the linear fit of the data.

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Species and functional diversity

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Simpson's diversity index and species richness were calculated for each site/time combination (4

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sites, 20 years). As a functional diversity metric, we used Rao’s quadratic entropy, which reflects

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the sum of the trait dissimilarities among all possible pairings of taxa weighted by the relative

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static measures of the taxa (Rao, 1982). Following Villéger et al. (2008), biomass was preferred

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over abundance as a weighting factor of functional traits as it better reflects the amount of energy

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and resources assimilated within a species (Brey, 2012). The relationship between species and

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functional diversity was tested by power regression analysis (significance level: P = 0.05) as it

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provided the best model fit. Response ratios (RR) were calculated to test for relationships

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between year-to-year fluctuations in species richness, species diversity, and FD. The response

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ratio was calculated from the ln of the ratio of species richness and FD values in one year divided

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by the corresponding value from the previous year (Micheli and Halpern, 2005). The calculated

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values thus quantify the percentage decrease or increase of FD with species richness over time.

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Temporal variation in species and functional diversity were plotted to explore the differential

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sensitivities of function and structure of macrozoobenthos towards environmental drivers.

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Interannual differences in FD at each site were analyzed using repeated measure analysis

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followed by Bonferroni post hoc comparison (significance level of P = 0.05). Analyses were

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performed in R using the packages ‘ade-4’, ‘tcltk’ and ‘vegan’ (R Development Core Team,

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2011).

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Results

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Similarity in temporal patterns

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Temporal variations were more similar among the sampling sites for the trait composition (mean

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Rv-coefficient = 0.353) than for the taxonomic composition (mean Rv-coefficient = 0.192)

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(Table 2) indicating that the long-term patterns in species composition differed between the sites

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whereas the long-term patterns in trait composition were more similar among the sites. The

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variations in trait composition were most similar at the sites SLT (silt) and SSD (silty sand) (Rv

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= 0.589), SSD and WB (White Bank) (Rv = 0.572) and SLT and WB (Rv = 0.396).

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Linking benthic functioning to environmental variables

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A combination of lagged values of environmental variables explained a higher proportion of

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temporal variation in the trait composition than the unlagged values (Table 3). According to the

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best DISTLM model, the lagged values of phosphate (PO4), dissolved inorganic nitrogen (DIN),

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and NAOWI explained the temporal variation (33% of the total variation) in trait composition

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best (P < 0.05; Table 3). In the distance-based redundancy analysis (dbRDA) ordination plot the

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first two RDA axes accounted for 94 % of the fitted variation from the model (Fig. 2).

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Species and functional diversity

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We plotted the biomass-weighted means of trait modalities to visualize the trait composition of

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all monitoring sites (Fig. 3). Macrozoobenthos communities at all stations were dominated by

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burrower, omnivore, planktotrophic larval development, high dispersal potential and small body

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size (1-10 mm) trait-categories. Deposit feeder was the most common feeding habit observed at

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WB and FSD, whereas interface feeder was the most common feeding habit at SSD and SLT.

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The 3-10 years of adult longevity was dominant at WB, SSD and SLT, whereas at FSD the 1-2

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years of adult longevity trait category was dominant. There was a significant positive

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relationship between species richness and FD (R2 = 0.45, P < 0.001, df = 399, Fig. 4a). Similarly,

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the positive relationship between Simpson’s diversity and FD was significant (R2 = 0.30, P <

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0.001, df = 399, Fig. 4b). In both cases, the power model explained the variability best indicating

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a high functional redundancy within the benthic assemblage. The temporal changes in FD were

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significantly correlated only with changes in species richness during the same time period (F1,77 =

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8.75, R2 = 0.25, P < 0.001, Fig. 5a). The inter-annual changes in Simpson diversity and FD were

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not related to each other (F1,77 = 1.28, R2 = 0.08, P > 0.05, Fig. 5b). The temporal trends in FD

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were stable despite a temporary decline at the sites WB and SSD in 1996 and 2009 (F19,76 = 6.55,

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P < 0.001, Fig. 6a). In contrast, patterns of temporal variation in the Simpson diversity index

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were conspicuous and widespread (Fig. 6b).

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Discussion

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For understanding the dynamics of ecosystems, it is essential to study not only the structure but

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also the functioning of the inherent biological communities (Hooper et al., 2005; Naeem, 1998;

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Weigel et al., 2016). We contrasted the taxonomic and the functional trait composition of

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macrozoobenthic assemblages in the North Sea and the temporal variations therein. The results

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revealed a clear relationship between taxonomic and functional diversity. However, the temporal

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variations in functional and taxonomic diversity were different, suggesting differential

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sensitivities of structure and functioning towards environmental drivers. The stability of trait

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composition indicates that substitutions of functionally similar benthic species may occur

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commonly and across multiple temporal scales, ensuring the sustainability of ecological

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functioning (Clare et al., 2015; Frid and Caswell, 2015; Naeem, 1998).

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Relationship between taxonomic and functional diversity

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A positive power function best explained the relationship between taxonomic and functional

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diversity in macrozoobenthos assemblages in the North Sea. This indicates that in species-rich

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assemblages, the functional redundancy among the species would allow for only minor changes

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in functionality even at considerable variations in taxonomic diversity. This is in agreement with

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the findings from previous studies in marine and estuarine systems (Frid and Caswell, 2015;

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Törnroos et al., 2014; Van der Linden et al., 2016; Weigel et al., 2016). For example, the trait

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composition and, hence, the ecological functioning of the benthic system of the North Sea was

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maintained despite taxonomic changes indicating a considerable degree of redundancy within the

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benthic communities (Frid and Caswell, 2015). Similarly, a reduction in the species inventory of

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the coastal macrozoobenthos of the Baltic Sea from 151 to 105 taxa (30% loss) caused only a 2%

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reduction in trait modalities expressed (Törnroos et al., 2014).

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In some previous studies, the relationship between taxonomic and functional diversity followed a

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linear model indicating much lower functional redundancy (e.g. Gasbarro et al., 2018; Wong and

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Dowd, 2015) For example, low functional redundancy is reported for microbenthic invertebrates

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across seagrass habitats in Atlantic Canada (Wong and Dowd, 2015) indicating that the

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functioning is sensitive to changes in biodiversity. The relatively high functional redundancy of

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the macrozoobenthos in this study may be the result of a high taxonomic diversity as compared

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with, for example, macrozoobenthos assemblages in northeast Pacific fjord (Gasbarro et al.,

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2018). Functionally redundant communities are expected to be particularly resistant to

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environmental disturbance (Walker, 1992) because species loss is buffered by mutual

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compensation of functionally similar species (Naeem, 1998).

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Temporal variations in functionality

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The high functional redundancy of the benthic assemblages may suggest a temporally stable

311

functionality of the system even in a fluctuating environment. However, the functional diversity

312

at the sites WB and SSD declined in 1996 and 2009 in response to exceptionally cold winters

313

and a negative NAO index. Both SSD and WB exhibit similar sediment characteristics and

314

similar species and trait compositions (Salzwedel et al., 1985; Schröder, 2003; Shojaei et al.,

315

2016). The temporary decline in functional diversity at these sites indicates a disappearance of

316

redundant species from the assemblages or species with specific trait modalities for which there

317

is little functional substitution (Naeem, 1998).

318

Cold winters substantially affect the structure and functioning of the subtidal benthos in the

319

North Sea (Kröncke et al., 2013; Neumann and Kröncke, 2011). For example, the cold winter

320

1995/96 led to a remarkable decrease in species richness, abundance, and biomass of benthic

321

macrozoobenthos across the North Sea (Reiss et al., 2006). Similarly, large-scale atmospheric

322

oscillations, such as the NAO, induce dynamics in marine ecosystems, as indicated by

323

remarkable variations at the individual, population, and assemblage level (Ottersen et al., 2001).

324

The effects of cold winters and NAO on the functioning of the benthic system of the North Sea

325

were evident, although the thermal sensitivity of the organisms was not explicitly considered in

326

the trait matrix. The functional response of the benthic assemblage to cold winters and NAO

327

fluctuations reveals that the extreme meteorological events affect organisms beyond the direct

328

metabolic effects of temperature. Temperature indirectly affects ecosystem functionality by

329

changing species interactions (Kordas et al., 2011). The temperature could also elicit

330

idiosyncratic changes in functioning due to the variability in the behavioral responses of the

331

species to changes, though the strength and direction of the effects might be species-specific

332

(Eklöf et al., 2013). Accordingly, the effects of cold winters may be transmitted through all

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trophic levels and cause drastic ecological and functional consequences (Brey, 2012; Kröncke et

334

al., 2013; Neumann and Kröncke, 2011).

335

Following the temporary changes in 1996 and 2009, the functional diversity returned to previous

336

levels within one year. Similarly, Clare et al. (2015) reported that the trait composition of the

337

macrozoobenthos in the western North Sea remains stable or recovers quickly after temporary

338

variations despite strong taxonomic variations over a 40-year period. Similar changes and

339

recovery of benthos functional diversity have been observed in response to episodic hypoxia in

340

the Baltic Sea (Gogina et al., 2014). Moreover, Bêche and Resh (2007) report that the trait

341

composition of benthic macroinvertebrates in Californian streams varies only little over

342

timescales of 6-19 years despite high taxonomic turnover. Recovery of functional diversity is a

343

critical consideration in macrozoobenthos community restoration. Functional overlap between

344

species increases a system's ability to resist disturbance and thus ensuring the long-term

345

functioning of the marine benthos (Peterson et al., 1998).

346

Effects of anthropogenic activities on benthic functioning

347

Irrespective of the site, some characteristics, such as small body size and deposit-feeding, were

348

relatively common in the benthic assemblages, whereas other characteristics, such as a sessile

349

lifestyle and suspension-feeding, were relatively rare. The dominance of species with

350

opportunistic life-history strategies has repeatedly been reported for the North Sea benthos

351

(Bremner et al., 2006; Kröncke et al., 2013; Shojaei et al., 2016; Tillin et al., 2006). In addition

352

to natural environmental fluctuations, anthropogenic activities, such as bottom trawling, are

353

maintaining the dominance of specific traits in the benthic system. Additionally, previous studies

354

indicate that trawling induced disturbance and natural disturbance have similar effects on benthic

355

communities in this region (e.g., Hiddink et al., 2006). Both sources of disturbance cause

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declines in long-living animals resulting in a community of species with an opportunistic

357

lifestyle. Deposit-feeders can be favored by bottom trawling, which greatly enhances the

358

availability of organic matter on the sediment surface, whereas suspension feeders mostly suffer

359

from suspended sediments (Frid, 2000; Tillin et al., 2006). Accordingly, human activities are

360

inducing pressure on the benthic fauna that selects for specific functions in the ecosystem (Clare

361

et al., 2015). The resultant changes can have strong effects on the functional composition and,

362

thus, on crucial ecosystem services to humans (Mouillot et al., 2006; Törnroos et al., 2014).

363

The results of the current study suggest that in a highly disturbed benthic system, with high

364

species diversity, where assemblages are experiencing temporal changes in species composition

365

over time, a high functional overlap among species would imply a considerable capacity of the

366

ecosystem to maintain or recover key processes and functions in response to disturbance. The

367

results also indicate that environmental stressors can cause an acute temporary decline in

368

functioning, even in ecosystems characterized by long-term functional stability. Differential

369

sensitivities of community and functional structure to stressors highlight the need for combined

370

analysis of both structures for a comprehensive understanding of long-term dynamics of benthic

371

ecosystems.

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Acknowledgments

374

We are deeply indebted to all persons who contributed to sampling, sample sorting, and

375

taxonomic identification. We thank the crew of all research vessels involved, especially “RV

376

Heincke” and “RV Uthörn” for their help with sampling throughout the years. We gratefully

377

acknowledge the partial support from Earth System Sciences Research School (ESSReS) and

378

Tarbiat Modares University. The authors thank Ruth Alheit for a language check that greatly

17

379

improved the manuscript. This work was carried out within the framework of the PACES II

380

program of the Helmholtz Association.

381 382

References

383

Anderson, M.J., 2006. Distance based tests for homogeneity of multivariate dispersions. Biometrics 62, 245–253. https://doi.org/10.1111/j.1541-0420.2005.00440.x

384

Anglès d’Auriac, M.B., Rinde, E., Norling, P., Lapegue, S., Staalstrøm, A., Hjermann, D.Ø.,

386

Thaulow, J., 2017. Rapid expansion of the invasive oyster Crassostrea gigas at its northern

387

distribution limit in Europe: Naturally dispersed or introduced? PLoS One 12, e0177481.

388

https://doi.org/10.1371/journal.pone.0177481

-p

ro

of

385

Bêche, L. a., Resh, V.H., 2007. Biological traits of benthic macroinvertebrates in California

390

mediterranean-climate streams: long-term annual variability and trait diversity patterns.

391

Fundamental

392

https://doi.org/10.1127/1863-9135/2007/0169-0001

Applied

Limnology/Archiv

für

Hydrobiologie

169,

1–23.

lP

and

re

389

Bolam, S.G., 2012. Impacts of dredged material disposal on macrobenthic invertebrate

394

communities: A comparison of structural and functional (secondary production) changes at

395

disposal sites around England and Wales. Marine Pollution Bulletin 64, 2199–2210.

396

https://doi.org/10.1016/j.marpolbul.2012.07.050

Jo

ur

na

393

397

Bolam, S.G., Eggleton, J., Barry, J., 2020. Spatial variability of macrofaunal traits across the

398

Greater

399

https://doi.org/10.1016/j.seares.2020.101923

400

Bremner, J., Rogers, S.I., Frid, C.L.J., 2006. Matching biological traits to environmental

401

conditions in marine benthic ecosystems. Journal of Marine Systems 60, 302–316.

402

https://doi.org/10.1016/j.jmarsys.2006.02.004

North

Sea.

Journal

of

Sea

Research

163,

1-10.

101923.

403

Brey, T., 2012. A multi-parameter artificial neural network model to estimate macrobenthic

404

invertebrate productivity and production. Limnology and Oceanography: Methods 10, 581–

405

589. https://doi.org/10.4319/lom.2012.10.581

406

Chevene, Fran., Doléadec, S., Chessel, D., 1994. A fuzzy coding approach for the analysis of

407

long term

408

https://doi.org/10.1111/j.1365-2427.1994.tb01742.x 18

ecological

data.

Freshwater

Biology

31,

295–309.

409

Clare, D.S., Robinson, L.A., Frid, C.L.J., 2015. Community variability and ecological

410

functioning: 40 years of change in the North Sea benthos. Marine Environmental Research

411

107, 24–34. https://doi.org/10.1016/j.marenvres.2015.03.012

412

Danovaro, R., 2012. Extending the approaches of biodiversity and ecosystem functioning to the

413

deep ocean. Marine Biodiversity and Ecosystem Functioning: Frameworks, methodologies,

414

and integration, edited by: Solan, M., Aspden, RJ, and Paterson, DM, Oxford University

415

Press, Oxford 115–126. Degen, R., Aune, M., Bluhm, B.A., Cassidy, C., Kędra, M., Kraan, C., Vandepitte, L.,

417

Włodarska-Kowalczuk, M., Zhulay, I., Albano, P.G., 2018. Trait-based approaches in

418

rapidly changing ecosystems: A roadmap to the future polar oceans. Ecological Indicators

419

91, 722–736. https://doi.org/10.1016/j.ecolind.2018.04.050

ro

of

416

Degen, R., Faulwetter, S., 2019. The Arctic Traits Database–a repository of Arctic benthic

421

invertebrate traits. Earth System Science Data 11, 301–322. https://doi.org/10.5194/essd-11-

422

301-2019

re

lP

423

-p

420

Dray, S., Chessel, D., Thioulouse, J., 2003. Co inertia analysis and the linking of ecological data tables. Ecology 84, 3078–3089. https://doi.org/10.1890/03-0178

na

424

Eklöf, A., Jacob, U., Kopp, J., Bosch, J., Castro-Urgal, R., Chacoff, N.P., Dalsgaard, B., de

426

Sassi, C., Galetti, M., Guimarães, P.R., Lomáscolo, S.B., Martín González, A.M., Pizo,

427

M.A., Rader, R., Rodrigo, A., Tylianakis, J.M., Vázquez, D.P., Allesina, S., 2013. The

428

dimensionality

429

https://doi.org/10.1111/ele.12081

430

ur

425

Jo

ecological

networks.

Ecology

Letters

16,

577–583.

Frid, C., 2000. Long-term changes in the benthic communities on North Sea fishing grounds. ICES Journal of Marine Science 57, 1303–1309. https://doi.org/10.1006/jmsc.2000.0900

431 432

of

Frid, C.L.J., Caswell, B.A., 2015. Is long-term ecological functioning stable: The case of the

433

marine

benthos?

Journal

of

434

https://doi.org/10.1016/j.seares.2014.08.003

Sea

Research

98,

15–23.

435

Gasbarro, R., Wan, D., Tunnicliffe, V., 2018. Composition and functional diversity of

436

macrofaunal assemblages on vertical walls of a deep northeast Pacific fjord. Marine

437

Ecology Progress Series 597, 47–64. https://doi.org/10.3354/meps12599

438

Gogina, M., Darr, A., Zettler, M.L., 2014. Approach to assess consequences of hypoxia

439

disturbance events for benthic ecosystem functioning. Journal of Marine Systems 129, 203– 19

213. https://doi.org/10.1016/j.jmarsys.2013.06.001

440 441

Heo, M., Gabriel, K.R., 1998. A permutation test of association between configurations by

442

means of the RV coefficient. Communications in Statistics-Simulation and Computation 27,

443

843–856. https://doi.org/10.1080/03610919808813512

444

Hewitt, J.E., Thrush, S.F., Dayton, P.D., 2008. Habitat variation, species diversity and ecological

445

functioning in a marine system. Journal of Experimental Marine Biology and Ecology 366,

446

116–122. https://doi.org/10.1016/j.jembe.2008.07.016 Hiddink, J.G., Jennings, S., Kaiser, M.J., 2006. Indicators of the Ecological Impact of BottomTrawl

Disturbance

on

Seabed

Communities.

449

https://doi.org/10.1007/s10021-005-0164-9

Ecosystems

9,

1190–1199.

ro

448

of

447

Hooper, D.U., Chapin, F.S., Ewel, J.J., Hector, A., Inchausti, P., Lavorel, S., Lawton, J.H.,

451

Lodge, D.M., Loreau, M., Naeem, S., 2005. Effects of biodiversity on ecosystem

452

functioning: a consensus of current knowledge. Ecological Monographs 75, 3–35.

453

https://doi.org/10.1890/04-0922

lP

re

-p

450

Klingenberg, C.P., 2009. Morphometric integration and modularity in configurations of

455

landmarks: tools for evaluating a priori hypotheses. Evolution and Development 11, 405–

456

421. https://doi.org/10.1111/j.1525-142X.2009.00347.x

na

454

Kordas, R.L., Harley, C.D.G., O’Connor, M.I., 2011. Community ecology in a warming world:

458

the influence of temperature on interspecific interactions in marine systems. Journal of

459

Experimental

460

https://doi.org/10.1016/j.jembe.2011.02.029

ur

457

Jo

Marine

Biology

and

Ecology

400,

218–226.

461

Kröncke, I., Reiss, H., Dippner, J.W., 2013. Effects of cold winters and regime shifts on

462

macrofauna communities in shallow coastal regions. Estuarine, Coastal and Shelf Science

463

119, 79–90. https://doi.org/10.1016/j.ecss.2012.12.024

464

Krone, R., Gutow, L., Brey, T., Dannheim, J., Schröder, A., 2013. Mobile demersal megafauna

465

at artificial structures in the German Bight–likely effects of offshore wind farm

466

development.

467

https://doi.org/10.1016/j.ecss.2013.03.012

Estuarine,

Coastal

and

Shelf

Science

125,

1–9.

468

Marques, J.C., Basset, A., Brey, T., Elliott, M., 2009. The ecological sustainability trigon–a

469

proposed conceptual framework for creating and testing management scenarios. Marine

470

Pollution Bulletin 58, 1773–1779. https://doi.org/10.1016/j.marpolbul.2009.08.020 20

471

Mason, N.W.H., Mouillot, D., Lee, W.G., Wilson, J.B., 2005. Functional richness, functional

472

evenness and functional divergence: the primary components of functional diversity. Oikos

473

111, 112–118. https://doi.org/10.1111/j.0030-1299.2005.13886.x

474

Meyer, J., Nehmer, P., Moll, A., Kröncke, I., 2018. Shifting south-eastern North Sea macrofauna

475

community structure since 1986: A response to de-eutrophication and regionally decreasing

476

food

477

https://doi.org/10.1016/j.ecss.2018.08.010

478

supply?

Estuarine,

Coastal

and

Shelf

Science

213,

115–127.

Micheli, F., Halpern, B.S., 2005. Low functional redundancy in coastal marine assemblages. Ecology Letters 8, 391–400. https://doi.org/10.1111/j.1461-0248.2005.00731.x

of

479

Mouillot, D., Spatharis, S., Reizopoulou, S., Laugier, T., Sabetta, L., Basset, A., Do Chi, T.,

481

2006. Alternatives to taxonomic based approaches to assess changes in transitional water

482

communities. Aquatic Conservation: Marine and Freshwater Ecosystems 16, 469–482.

483

https://doi.org/10.1002/aqc.769

-p

re

Naeem, S., 1998. Species redundancy and ecosystem reliability. Conservation Biology 12, 39–

lP

484

ro

480

45. https://doi.org/10.1046/j.1523-1739.1998.96379.x

485

Naeem, S., Loreau, M., Inchausti, P., 2002. Biodiversity and ecosystem functioning: the

487

emergence of a synthetic ecological framework. In: Loreau, M., Naeem, S., Inchausti, P.

488

(eds.) Biodiversity and ecosystem functioning: synthesis and perspectives, Oxford

489

University Pres, Oxford, UK, 3–11.

ur

na

486

Neumann, H., Kröncke, I., 2011. The effect of temperature variability on ecological functioning

491

of epifauna in the German Bight. Marine Ecology 32, 49–57. https://doi.org/10.1111/j.1439-

492

0485.2010.00420.x

Jo

490

493

Nicastro, A., Bishop, M.J., 2013. Weak and habitat-dependent effects of nutrient pollution on

494

macrofaunal communities of southeast Australian estuaries. PloS One 8, e65706–e65706.

495

https://doi.org/10.1371/journal.pone.0065706

496

Nordström, M.C., Aarnio, K., Törnroos, A., Bonsdorff, E., 2015. Nestedness of trophic links and

497

biological traits in a marine food web. Ecosphere 6, 1–14. https://doi.org/10.1890/ES14-

498

00515.1

499

Ottersen, G., Planque, B., Belgrano, A., Post, E., Reid, P.C., Stenseth, N.C., 2001. Ecological

500

effects

501

https://doi.org/10.1007/s004420100655 21

of

the

North

Atlantic

Oscillation.

Oecologia

128,

1–14.

502

Peterson, G., Allen, C.R., Holling, C.S., 1998. Ecological resilience, biodiversity, and scale. Ecosystems 1, 6–18. https://doi.org/10.1007/s100219900002

503 504

R Development Core Team, R., 2011. R: A language and environment for statistical computing.

505

Rachor, E., 1978. Changes of macrobenthos in a sublittoral sand area of the German Bight, 1967 to 1975. Rapports et Procès-Verbaux des Réunions 172, 418-431.

506 507

Rao, C.R., 1982. Diversity and dissimilarity coefficients: a unified approach. Theoretical Population Biology 21, 24–43. https://doi.org/10.1016/0040-5809(82)90004-1

508

Reiss, H., Meybohm, K., Kröncke, I., 2006. Cold winter effects on benthic macrofauna

510

communities in near- and offshore regions of the North Sea. Helgoland Marine Research

511

60, 224–238. https://doi.org/10.1007/s10152-006-0038-3

ro

of

509

Robert, P., Escoufier, Y., 1976. A unifying tool for linear multivariate statistical methods: the

513

RV coefficient. Journal of the Royal Statistical Society: Series C (Applied Statistics) 25,

514

257–265. https://doi.org/10.2307/2347233

re

-p

512

Salzwedel, H., Rachor, E., Gerdes, D., 1985. Benthic macrofauna communities in the German

516

Bight. Veröffentlichungen des Instituts für Meeresforschung Bremerhaven 20, 199–267.

517

Sasaki, T., Okubo, S., Okayasu, T., Jamsran, U., Ohkuro, T., Takeuchi, K., 2009. Two-phase

518

functional redundancy in plant communities along a grazing gradient in Mongolian

519

rangelands. Ecology 90, 2598–2608. https://doi.org/10.1890/08-1850.1

ur

na

lP

515

Schröder, A., 2003. Community dynamics and development of soft bottom macrozoobenthos in

521

the German Bight ( North Sea ) 2, 1969–2000. Berichte zur Polarforschung 494, 1–179

522

Shojaei, M.G., Gutow, L., Dannheim, J., Rachor, E., Schröder, A., Brey, T., 2016. Common

523

trends in German Bight benthic macrofaunal communities: Assessing temporal variability

524

and the relative importance of environmental variables. Journal of Sea Research 107, 25–

525

33. https://doi.org/10.1016/j.seares.2015.11.002

Jo

520

526

Teichert, N., Lepage, M., Sagouis, A., Borja, A., Chust, G., Ferreira, M.T., Pasquaud, S.,

527

Schinegger, R., Segurado, P., Argillier, C., 2017. Functional redundancy and sensitivity of

528

fish assemblages in European rivers, lakes and estuarine ecosystems. Scientific Reports 7,

529

1–11. https://doi.org/10.1038/s41598-017-17975-x

530

Tillin, H., Hiddink, J., Jennings, S., Kaiser, M., 2006. Chronic bottom trawling alters the

531

functional composition of benthic invertebrate communities on a sea-basin scale. Marine

532

Ecology Progress Series 318, 31–45. https://doi.org/10.3354/meps318031 22

533

Törnroos, A., Bonsdorff, E., Bremner, J., Blomqvist, M., Josefson, A.B., Garcia, C., Warzocha,

534

J., 2014. Marine benthic ecological functioning over decreasing taxonomic richness. Journal

535

of Sea Research 98, 49–56. https://doi.org/10.1016/j.seares.2014.04.010

536

Usseglio-polatera, P., Bournaud, M., Richouxy, P., 2000. Biological and ecological traits of

537

benthic freshwater macroinvertebrates: relationships and definition of groups with similar

538

traits. Freshwater Biology, 43, 175–205. https://doi.org/10.1046/j.1365-2427.2000.00535.x Van der Linden, P., Marchini, A., Dolbeth, M., Patrício, J., Veríssimo, H., Marques, J.C., 2016.

540

The performance of trait-based indices in an estuarine environment. Ecological Indicators

541

61, 378–389. https://doi.org/10.1016/j.ecolind.2015.09.039

of

539

Villéger, S., Mason, N. W., Mouillot, D., 2008. New multidimensional functional diversity

543

indices for a multifaceted framework in functional ecology. Ecology, 89, 2290-2301.

544

https://doi.org/10.1890/07-1206.1

-p

Walker, B.H., 1992. Biodiversity and ecological redundancy. Conservation Biology 6, 18–23.

re

545

ro

542

https://doi.org/10.1046/j.1523-1739.1992.610018.x

lP

546

Weigel, B., Blenckner, T., Bonsdorff, E., 2016. Maintained functional diversity in benthic

548

communities in spite of diverging functional identities. Oikos 125, 1421–1433.

549

https://doi.org/10.1111/oik.02894

na

547

Wiltshire, K.H., Kraberg, A., Bartsch, I., Boersma, M., Franke, H.-D., Freund, J., Gebühr, C.,

551

Gerdts, G., Stockmann, K., Wichels, A., 2010. Helgoland roads, North Sea: 45 years of

552

change. Estuaries and Coasts 33, 295–310. https://doi.org/10.1007/s12237-009-9228-y

553

Wong, M.C., Dowd, M., 2015. Patterns in taxonomic and functional diversity of macrobenthic

554

invertebrates across seagrass habitats: a case study in Atlantic Canada. Estuaries and Coasts

555

38, 2323–2336. https://doi.org/10.1007/s12237-015-9967-x

Jo

ur

550

23

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Figure captions

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Figure 1. Positions of the four long-term monitoring sites (i.e. FSD = fine sand, SLT = silt, SSD

557

= silty sand, WB = White Bank) for benthic macrozoobenthos in the North Sea.

Jo

556

25

2010 2007

5

2002 2004

2008 2003

2011 1994 1992 1993

0 2005

NAOWI-L1

2000 1998

2006

ro

19951996

-p

2009 1999

-5

of

2001

1997

PO4-L1

re

DIN-L1

lP

dbRDA2 (34.5% of fitted, 11.3% of total variation)

10

-5 0 5 dbRDA1 (60% of fitted, 19.6% of total variation)

10

ur

-10

na

-10

Jo

558 559

Figure 2. Distance-based redundancy analysis (dbRDA) plot of the DISTLM analysis based on

560

the environmental predictors fitted to the variation in benthic trait composition. Symbols and

561

vectors represent trait composition in each sampling year (1992-2011) and environmental

562

variables, respectively. The length of the vectors indicates the effect strength of the

563

environmental predictors on the trait composition. PO4-L1= lagged values of phosphate (1-year

564

lag), DIN-L1= dissolved inorganic nitrogen (1-year lag), NOWI-L1 = North Atlantic Oscillation

565

winter index (1-year lag).

26

of ro -p re lP na ur Jo 566 567 568

Figure 3. Biomass-weighted means of trait modalities, for all monitoring sites (i.e. FSD, SLT,

569

SSD, WB). Color codes represent the trait affiliation; individual bars represent the trait modality

570

expression. For trait modalities labels see Table 1.

27

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Figure 4. Relationship between (a) functional diversity and species richness (y = 1.249 x0.285),

573

and (b) functional and Simpson’s diversity (y = 1.854 x0.336; b). Each data point represents the

574

diversity/richness values from 20 years of monitoring (1992-2011) at each sampling site.

28

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577 578 579

Figure 5. Relationship between year-to-year variations in functional diversity and (a) species

580

richness (y = 0.069x + 0.002) and (b) Simpson’s diversity (y = 0.672 – 0.0007) across four

581

sampling sites in the southern North Sea. Year-to-year variations in richness and diversity are

582

measured as the ln of the ratio of values from year t divided by the corresponding value from

583

year t-1 (ln R+1).

584

29

2.2

SLT

SSD

1.9 1.6 1.3

2010

ro

-p

SLT

SSD

WB

lP

FSD

na

0.95 0.9

ur

0.85

Jo

0.8 0.75

re

b

1

2006

2004

2002

2000

1998

1996

1994

1992

Year

of

1 0.7

Simpson’s diversity

WB

2008

Functional diversity

FSD

a

2010

2008

2006

2004

2002

2000

1998

1996

1994

1992

0.7

Year

585

Figure 6. Interannual variability of (a) functional diversity and (b) Simpson’s diversity at four

586

sampling sites in the SE North Sea (FSD = fine sand, SLT = silt, SSD = silty sand, WB = white

587

bank).

30

588

Table 1. Traits and trait modalities used for functional characterization of benthic species 589 Modalities Surface deposit feeder Sub-surface deposit feeder Suspension feeder Interface feeder Predator Grazer Parasite

code F.SDF F.SSDF F.SF 590 F.IF F.PR F.GR F.PA 591

Adult movement

Swimmer Crawler Burrower Sessile

AM.SW AM.CR AM.B 592 AM.SE

Diet type

Omnivore Carnivore Herbivore

DT.O DT.C DT.H

ro

Direct Lecithotrophic Planktotrophic <1 1-2 3-10 10+

Habit

Burrow dweller Free living Tubiculous Attached

LD.D LD.L LD.P

AL.1 AL.2 595 AL.10 AL.10p

lP

na

Ha.BD 596 Ha.FL Ha.TB Ha.A 597

<1 1-2 3-4 4+

MA.1 MA.2 MA.4 598 MA.4p

Brooded or laid egg Short term planktonic Long term planktonic

LM.B LM.S LM.L

Maximum size of organism (cm)

<1 1-10 11-20 20+

SO.1 SO.10 601 SO.20 SO.20p

Dispersal potential

Low Medium High

DP.L DP.M DP.H

Jo

Larval phase mobility

ur

Age at maturity (years)

594

re

Adult longevity (years)

593

-p

Larval development

of

Traits Feeding habit

604

31

599 600

602 603

605

Table 2. Rv-coefficient analyses on two distinct matrices i.e. taxonomic and functional

606

composition of benthic assemblages in the North Sea, providing similarity measure among

607

sampling sites (where a coefficient of 1 would indicate identical tables). Asterisks indicate that

608

the temporal variations of trait composition between monitoring sites are significantly similar at

609

significance level of P < 0.05.

RV Taxonomic composition

SSD

Trait composition

0.111

0.337

FSD

0.174

0.212

0.192

Jo

ur

na

lP

re

-p

* Significance level of P < 0.05.

FSD

0.125

0.196

WB

610

SLT

WB

32

SLT

WB

of

FSD

ro

Sampling sites

*

0.572

0.159

0.589* 0.210 0.396*

Table 3. Distance-based linear model (DistLM) marginal and sequential tests describing the

612

association between environmental variables and temporal pattern in functional composition of

613

macrozoobenthos assemblages in the North Sea. The marginal test indicates the proportion of

614

variance explained by each variable separately. The sequential test shows the cumulative

615

variation described by a set of environmental variables based on ‘BEST’ selection procedure.

616

The P-values were obtained using 9999 permutations of the data. Prop. = the proportion of

617

variability explained by each predictor variable. SSTw= Sea Surface temperature in winter

618

(Dec.-March), SiO2= silicate, PO4 = phosphate, DIN = dissolved inorganic nitrogen, NOWI =

619

North Atlantic Oscillation winter index. L1 indicates the lagged value of variables.

Marginal test

b.

620 621

33

0.626 0.525 0.840 0.100 0.469 0.354 0.191 0.046 0.017 0.016

0.035 0.041 0.021 0.098 0.045 0.053 0.075 0.078 0.144 0.154

lP

re

Prop.

Square-root-transformed for the DISTLM analyses.

Jo

a.

P

na

SSTw SiO2 PO4 DINa NAOWI SSTw-L1 SiO2-L1 PO4-L1 DIN-L1a NAOWI-L1

b

ur

Variables

PseudoF 0.652 0.762 0.394 1.966 0.846 1.004 1.458 1.524 3.017 3.280

-p

ro

of

611

Significant values (P < 0.05) are showed in bold values.

NAOWI-1 PO4-1 DIN-1

PseudoF 3.280 2.436 1.600

Sequential test Pb

Prop.

0.018 0.038 0.170

0.154 0.106 0.067

Small body size and deposit-feeding are common among benthic invertebrates of the North Sea. Taxonomic and functional diversity showed contrasting responses to environmental drivers. Trait composition recovered rapidly after extreme events.

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A high functional redundancy may stabilize ecological functioning of temperate shelf sea benthic communities

Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Jo

ur

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lP

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☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: