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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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
11 12
13
of ro
b
c
re
12, 27570 Bremerhaven, Germany
-p
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
Jo
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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/),
and
personal
expert
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-
219
correlations) among environmental variables. Environmental variables that showed evidence of
NCAR,
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Section,
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
255
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
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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
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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
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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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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.
Jo
ur
na
lP
re
-p
ro
of
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
na
lP
re
-p
ro
of
☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: