First trait-based characterization of Arctic ice meiofauna taxa

Abstract

Trait-based approaches connect the traits of species to ecosystem functions to estimate the functional diversity of communities and how they may respond to environmental change. For the first time, we compiled a traits matrix across 11 traits for 28 species of Arctic ice meiofauna, including Copepoda (Subclass), Nematoda (Phylum), Acoela (Order), Rotifera (Phylum), and Cnidaria (Phylum). Over 50 years of pan-Arctic literature were manually reviewed, and trait categories were assigned to enable future trait–function connections within the threatened ice-associated ecosystem. Approximately two-thirds of the traits data were found at the genus or species level, ranging from 44% for Nematoda to 100% for Cnidaria. Ice meiofauna were shown to possess advantageous adaptations to the brine channel network within sea ice, including a majority with small body widths < 200 μm, high body flexibility, and high temperature and salinity tolerance. Diets were found to be diverse outside of the algal bloom season, with most organisms transitioning to ciliate-, omnivore-, or detritus-based diets. Eight species of the studied taxa have only been recorded within sea ice, while the rest are found in a mixture of sympagic–pelagic–benthic habitats. Twelve of the ice meiofauna species have been found with all life stages present in sea ice. Body width, temperature tolerance, and salinity tolerance were identified as traits with the largest research gaps and suffered from low-resolution taxonomic data. Overall, the compiled data show the degree to which ice meiofauna are adapted to spending all or portions of their lives within the ice.

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Polar Biology (2022) 45:1673–1688
https://doi.org/10.1007/s00300-022-03099-0
ORIGINAL PAPER
First trait‑based characterization ofArctic ice meiofauna taxa
EvanPatrohay1· RolfGradinger1· MiriamMarquardt1· BodilA.Bluhm1
Received: 19 August 2022 / Revised: 31 October 2022 / Accepted: 1 November 2022 / Published online: 18 November 2022
© The Author(s) 2022
Abstract
Trait-based approaches connect the traits of species to ecosystem functions to estimate the functional diversity of communi-
ties and how they may respond to environmental change. For the first time, we compiled a traits matrix across 11 traits for 28
species of Arctic ice meiofauna, including Copepoda (Subclass), Nematoda (Phylum), Acoela (Order), Rotifera (Phylum),
and Cnidaria (Phylum). Over 50years of pan-Arctic literature were manually reviewed, and trait categories were assigned
to enable future trait–function connections within the threatened ice-associated ecosystem. Approximately two-thirds of
the traits data were found at the genus or species level, ranging from 44% for Nematoda to 100% for Cnidaria. Ice mei-
ofauna were shown to possess advantageous adaptations to the brine channel network within sea ice, including a majority
with small body widths < 200μm, high body flexibility, and high temperature and salinity tolerance. Diets were found to be
diverse outside of the algal bloom season, with most organisms transitioning to ciliate-, omnivore-, or detritus-based diets.
Eight species of the studied taxa have only been recorded within sea ice, while the rest are found in a mixture of sympagic–
pelagic–benthic habitats. Twelve of the ice meiofauna species have been found with all life stages present in sea ice. Body
width, temperature tolerance, and salinity tolerance were identified as traits with the largest research gaps and suffered from
low-resolution taxonomic data. Overall, the compiled data show the degree to which ice meiofauna are adapted to spending
all or portions of their lives within the ice.
Keywords Functional traits· Arctic sea ice· Sea ice meiofauna· Trait database
Introduction
Arctic sea ice contains an extensive brine channel net-
work that serves as habitat for both protozoans and meta-
zoans (Horner etal. 1992). Those metazoans which range
from ~ 20 to ~ 500μm in size are grouped into a category
called sea ice, or sympagic, meiofauna. The most common
taxa found in sea ice are species from Copepoda (Sub-
class), Nematoda (Phylum), Acoela (Order), and Rotifera
(Phylum), but other taxa like Cnidaria (Phylum) were also
found (Marquardt etal. 2011; Bluhm etal. 2017). Some ice
meiofauna species also live in the benthic or pelagic zones
and are believed to settle within the ice via active migra-
tion, incorporation during ice formation, or through sedi-
ment incorporation on shelves (Carey and Montagna 1982;
Gradinger etal. 2009; Kiko etal. 2017). Other species may
be endemic and colonize new ice from summer multi-year
ice refuges (Bluhm etal. 2017). Ice meiofauna are consid-
ered one of the most poorly studied groups in the Arctic with
large knowledge gaps related to their diversity, abundance,
and ecological functions (Bluhm etal. 2018).
The sea ice brine channel network forms during the freez-
ing of sea water (Assur 1958). Ice crystals, being pure fresh-
water, expel all sea salts and concentrate them into the liquid
in between, called brine because of its often high salinity.
This interstitial environment serves as the in-ice habitat for
meiofauna (Bluhm etal. 2017) and its volume within the
ice varies with temperature and salinity, ranging from 5 to
25% of the total sea ice volume (Golden etal. 1998). The ice
crystal matrix itself is impenetrable for meiofauna.
All types of sea ice contain this narrow brine channel
network, which is characterized by large vertical gradi-
ents within the ice cover. In its upper layers, ice insitu
temperatures can fall below -10°C and brine salinities
may soar above 150 (Assur 1958; Gradinger and Schnack-
Schiel 1998). These values become less extreme toward
the ice–water interface, where brine temperatures and
* Evan Patrohay
evanrp99@gmail.com
1 Department ofArctic andMarine Biology, UiT – The Arctic
University ofNorway, Tromsø, Norway
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1674 Polar Biology (2022) 45:1673–1688
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salinities are typically close to the freezing point of sea
water (−1.8°C) and full marine salinity around 35,
respectively (Gradinger and Schnack-Schiel 1998). Ice
conditions are highly seasonal and in winter months ice
insitu temperatures are typically lower and brine salini-
ties higher, with more concentrated brine than in summer
(Assur 1958; Gradinger etal. 2009). During summer melt
the ice–water interface can become brackish or even fresh
(Spindler 1994; Hop etal. 2000), and ice meiofauna must
be well adapted to these extreme and variable conditions
to survive. The brine volume fraction differs with age of
ice, driven by the gravitational loss of dense brine and a
resulting smaller brine volume fraction with increasing
age of ice floes. For example, the surface decimeters of
multi-year sea ice are essentially freshwater and in winter
are devoid of a brine channel network in contrast to saltier
and younger first-year ice. In summer, warming then cre-
ates brine channels in multi-year ice, allowing meiofauna
to inhabit the interior and upper parts of the ice (Friedrich
1997). Overall, all ice types possess the largest brine vol-
ume fraction in their bottom 10–30cm where the brine
channel network is generally more open to the underlying
seawater (Petrich and Eicken 2017).
Arctic sea ice is rapidly disappearing and strongly reduc-
ing in area and volume (Barber etal. 2015; Meredith etal.
2019; Meier etal. 2021), which will have consequences for
sympagic meiofauna that depend on ice for grazing, ref-
uge, and reproduction (Bluhm etal. 2018). The survival and
colonization ability of endemic fauna may also be impacted
(Kiko etal. 2017). With conditions in the Arctic changing
so quickly, scientists are searching for efficient and accurate
methods of assessing ecosystem health and the impacts of
anthropogenic stressors. Traditional studies use taxonomic
richness, species composition, and the abundance of organ-
isms to gauge the status of ecosystems (Cifoni etal. 2021).
However, all species can directly influence ecosystem func-
tions such as nutrient and energy fluxes via their quantita-
tive (i.e., measurable) and qualitative (i.e., categorical) traits
(e.g., body size or feeding habits, respectively) (Chapin etal.
1996; Naeem 2002; Hooper etal. 2005). Some studies have
also shown that observed responses to ecosystem fluctua-
tions are due more to patterns of trait diversity than to the
richness or abundance of species (Gagic etal. 2015; Mar-
tini etal. 2020). Communities with low functional diver-
sity are also generally believed to be less resilient (Naeem
etal. 1994; Naeem and Li 1997). Therefore, trait-based
approaches are increasingly used to catalogue the func-
tional diversity of communities, based on the concept that
ecosystem resistance and resilience can be quantitatively
assessed through the functional redundancy of response
traits (Hooper etal. 2005; McGill etal. 2006; Martini etal.
2020). Spatially mapping functional diversity can also aid in
conservation and management strategies (Degen etal. 2018).
Only a few trait-based studies from the Arctic have been
published, possessing a clear focus on benthic invertebrates
and fish or targeting few traits (see Degen etal. (2018) for
a synopsis of existing papers). Trait information from the
polar regions in general is still very scarce. The Arctic Traits
Database (see Degen and Faulwetter 2019) is focused on
larger benthic species and, hence, several of its trait cat-
egories are not applicable to the unique environment of the
sea ice brine channel network. Our goal was, therefore, to
(1) compile a first traits matrix for common Arctic sea ice
meiofauna taxa to provide data for subsequent studies on
the functional response of sea ice biota to ice variability
and change and (2) characterize the adaptations of taxa that
may be well suited for inhabiting sea ice brine channels.
We hypothesized that sea ice meiofauna would be domi-
nated by morphological traits that allow them to fit into and
move through the narrow brine channel system and tolerate
the extreme environmental conditions of their habitat. To
our knowledge, the present study serves as the first compre-
hensive synthesis of ice meiofaunal traits and is hoped to
provide a solid base for future use in forming trait–function
connections within the ice-associated ecosystem. Trait–func-
tion connections are key elements in establishing a deeper
understanding of ecosystem responses to environmental
stressors and presently represent a large gap in Arctic com-
munity studies (Beauchard etal. 2017; Degen etal. 2018).
Methods
Compilation ofice meiofauna species analyzed
The traits of 28 species and/or morphotypes of sympagic
meiofauna were used for this study: nine copepods, six
nematodes, three acoels, nine rotifers, and the cnidarian
Sympagohydra tuuli (Table1). The species were chosen
based on a literature survey that established considerable
evidence of presence in ice (sources given in Table1), not
based on any abundance parameters. The taxa of this study
have overall been recorded throughout the Arctic, includ-
ing the Beaufort Sea (Gradinger etal. 2005), Greenland
(Gradinger etal. 1999), the Canadian Arctic Archipelago
and Hudson Bay (Grainger etal. 1985), Svalbard (Schüne-
mann and Werner 2005), the Siberian shelves (Tchesunov
and Riemann 1995), and the Transpolar Drift (Friedrich and
De Smet 2000). We combined information from across the
Arctic into one analysis, as most taxa have pan-Arctic wide
distributions that result from large-scale ice drift patterns
(Beaufort Gyre, Transpolar Drift) that connect various Arc-
tic regions on annual to decadal time scales. On a coarse tax-
onomic level, the pan-Arctic distribution of these taxa is well
recorded in Bluhm etal. (2018), recognizing, however, that
not all studies report species or genus level for all groups.
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1675Polar Biology (2022) 45:1673–1688
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Species rarely mentioned in the literature were not included
in the trait table but still recorded (see Online Resource #1).
Meroplankton larvae, common inhabitants of the brine chan-
nel network that peak in abundance in spring and summer
(Gradinger etal. 2009; Michelsen etal. 2016; Bluhm etal.
2018; Descôteaux etal. 2021), were also excluded from the
species list due to taxonomic identification challenges and
brief time spent within the ice. The Acoela were divided
into ‘morpho-species’ based on coloration consistently men-
tioned in several sources. Though distinct, the exact taxon-
omy of these sea ice inhabiting taxa is still unknown (Oliver
and Beatty 1996; Friedrich and Hendelberg 2001; Derraik
etal. 2010) and a topic of current research. The ‘white’ acoel
morphotype may possibly instead be a platyhelminth but
given the evolving research on the relationship of Acoela
and Platyhelminthes we have here grouped them together,
recognizing a separation may be likely (Achatz etal. 2013).
The literature survey was composed of both relatively
early observations of Arctic sea ice fauna (Carey and Mon-
tagna 1982; Chengalath 1985) and more recent compre-
hensive lists (Riemann and Sime-Ngando 1997; Friedrich
and De Smet 2000; Arndt and Swadling 2006; Pitusi 2021).
Table 1 List of sea ice
meiofauna taxa used in the
present study
* Previously Halectinosoma finmarchicum
* Meroplankton was deliberately excluded from this list
Taxonomic group Species/genus/morphotype Source for observation in sea ice
Copepoda
Harpacticoida
Halectinosoma neglectum Carey and Montagna (1982)
*Halectinosoma elongatum Arndt and Swadling (2006)
Harpacticus superflexus Arndt and Swadling (2006)
Tisbe furcata Grainger (1991)
Pseudobradya sp.Carey and Montagna (1982)
Cyclopoida
Cyclopina schneideri Grainger (1991)
Cyclopina gracilis Carey and Montagna (1982)
Arctocyclopina pagonasta Mohammed and Neuhof (1985)
Calanoida
Pseudocalanus sp. Grainger and Mohammed (1990)
Nematoda
Theristus melnikovi Riemann and Sime-Ngando (1997)
Theristus sp. Pitusi (2021)
Cryonema tenue Riemann and Sime-Ngando (1997)
Cryonema crassum Riemann and Sime-Ngando (1997)
Hieminema obliquorum Tchesunov and Portnova (2005)
Halomonhystera sp. Pitusi (2021)
Acoela
Red morphotype Friedrich and Hendelberg (2001)
White morphotype Friedrich and Hendelberg (2001)
Orange morphotype Janssen and Gradinger (1999)
Rotifera
Encentrum graingeri Friedrich and De Smet (2000)
Proales reinhardti Friedrich and De Smet (2000)
Synchaeta bacillifera Friedrich and De Smet (2000)
Synchaeta cecilia Friedrich and De Smet (2000)
Synchaeta glacialis Friedrich and De Smet (2000)
Synchaeta hyperborea Friedrich and De Smet (2000)
Synchaeta tamara Friedrich and De Smet (2000)
Synchaeta sp. A Friedrich and De Smet (2000)
Cephalodella sp. A Chengalath (1985)
Cnidaria
Sympagohydra tuuli Bluhm etal. (2007)
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1676 Polar Biology (2022) 45:1673–1688
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Halectinosoma finmarchicum, appearing most recently in
Arndt and Swadling (2006), had been reclassified as H. elon-
gatum in Clément and Moore (2000). Therefore, species data
for both H. finmarchicum and H. elongatum were used in
this study and are grouped under the latter. There were two
instances where data on a species identified to genus level
were considered species-level information, these are the
Rotifera Synchaeta sp. A (Friedrich and De Smet 2000) and
Cephalodella sp. A (Chengalath 1985). In both cases these
organisms were observed in sea ice and identified as distinct
from all other Rotifera in their respective samples.
Analysis andorganization ofdata
A literature survey comprising over fifty years of research
on Arctic ice meiofauna was conducted to gather trait infor-
mation. Search terms for adequate literature included, for
example, ‘sea ice meiofauna,’ ‘sympagic fauna,’ and ‘sea
ice biota’ in addition to certain taxa names listed in over-
view articles and book chapters, such as Arndt and Swadling
(2006) and Bluhm etal. (2017). Reference lists to insightful
articles often pointed to additional sources. Occasionally,
early taxonomic papers would also be searched for to fill in
basic morphological data. When information pertaining to a
trait (e.g., body size) was identified, the source was directly
quoted or summarized and the taxonomic level of the mate-
rial was recorded in a trait table for that particular species
and trait. Based on this available evidence, a trait category
of best fit (e.g., small, large) and the degree to which the trait
is exhibited, known as a ‘fuzzy code’ (see ‘Fuzzy Coding
Procedure’ in Methods), was then assigned. Species-level
data were considered the most specific available information
and was always searched for first, but the decision to assign a
trait category often depended on material from multiple tax-
onomic levels and sources. If no information could be found
at the species level, genus-level data were then searched for,
continuing to the next highest taxonomic level until evidence
for every species-trait combination was found, an approach
used in earlier studies (Brun etal. 2017). A common caveat
of traits coding is that not all trait modalities are provided in
every record of a given taxon, hence limiting insights into
trait plasticity among regions. The trait-by-taxon table was
organized by taxonomic group (Copepoda, Nematoda, etc.)
and provided a column for species name, the assigned trait
category, a fuzzy code, sources and quotes, and the taxo-
nomic level of information. This trait table can be found in
Online Resource #2.
Selection oftraits
Before compiling a list of traits and trait categories, exist-
ing databases were consulted to gain a broader idea of
relevant traits commonly used in literature (Costello etal.
2015; Degen etal. 2018; Degen and Faulwetter 2019; Mar-
tini etal. 2020; Cifoni etal. 2021). Eleven traits consid-
ered especially applicable to fauna which spend at least
part of their life cycle in ice and encounter the extreme
conditions found within were chosen. These traits were
grouped into four broad categories as in Litchman etal.
(2013) and Degen etal. (2018): morphological, which
here included body length, body width, body shape, and
flexibility; physiological, which included temperature and
salinity tolerance; life history, here consisting of habitat
occurrence and if all life stages are found in sea ice; and
behavioral, which here included feeding mode while in
ice, diet while in ice, and mobility. These 11 traits were
further divided into trait categories and are summarized
in Table2.
“Body length” and “body width” were coded as the
range of adult body sizes of both males and females.
“Body shape” describes the form of an organism and can
be a combination of elongate, globulose, fusiform, or com-
pressed. All are relevant for determining which part of the
brine channel system a species can inhabit. “Flexibility”
describes how easily an organism can flex or manipulate
its body shape and is a proxy for rigidity, a trait relevant
for moving around tight brine channels. “Salinity toler-
ance” describes if an organism can survive fresh, brack-
ish, marine, or briny water and minimum tolerance is here
assumed to be 25–40 by default for marine organisms.
Note that we used the salinity definition of the practical
salinity scale (PSS) (Lewis, 1980), as most literature we
cite utilizes this scale. Because it is based on the ratio of
the conductivity of a water sample to a standard, it does
not have a unit. “Temperature tolerance” describes the
degree to which an organism can survive above- and/or
below-zero conditions and is here assumed to be at least
-2 to 0 by default for Arctic organisms. The combination
of temperature and salinity tolerance would determine
whether and at what time of year organisms can inhabit
the ice interior.
The “habitat occurrence” trait is intended to reflect
the degree a species relies on the sea ice (‘sympagic’
being most extreme) and its likely origin through its co-
occurrence in other realms (‘pelagic’ and/or ‘benthic’).
“All stages found in sea ice” likewise infers how reliant a
species is on the sea ice habitat throughout its life cycle,
although which life history stages are found in sea ice are
not always known or published. “Feeding mode while in
ice” describes the main method of ingestion of ice organ-
isms and “diet while in ice” characterizes their known food
sources during one or more seasons in sea ice. Both traits
are important for understanding sea ice nutrient and energy
fluxes. “Mobility” describes the various modes of trans-
port an organism uses and is relevant to ice colonization.
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1677Polar Biology (2022) 45:1673–1688
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Fuzzy coding procedure
Fuzzy coding is a procedure that assigns a trait category
to an organism based on the degree to which it exhibits
that trait category. The procedure reflects the existence of
biological plasticity, uncertainty, and the combination of
multiple categories within one organism (Chevenet etal.
1994). Fuzzy coding establishes a common code that
enables a comparison of data from multiple sources on a
per-category basis (Degen etal. 2018) and can also rep-
resent qualitative data in a quantitative way. This analysis
utilizes a code from 0 to 3 and follows the system of the
Arctic Traits Database (Table3) (Degen and Faulwetter
2019). While this procedure is not without shortcomings
and introduces some subjectivity into trait-based analyses,
Degen etal. (2018) found in an experiment that partici-
pants coded 83% of the trait categories of three common
Arctic benthic invertebrates identically. This suggests that
the introduced subjectivity remains low and will likely be
reduced as more experts are involved in the fuzzy coding
process (Degen etal. 2018).
For one coding example, all Copepoda possess rigid,
chitinous frames (Reid and Williamson 2001). Addition-
ally, Krembs etal. (2000) found that harpacticoid copepods
could only fit through ice capillaries that were 100% their
body width, confirming the rigidity of the exoskeleton. This
exclusive rigidity means all species of Copepoda are coded
as a “3” for the flexibility category “rigid.” As a more dif-
ficult example: white Acoela are described as “slender and
drop-shaped” in literature (Janssen and Gradinger 1999),
which does not fit directly into any of the available body
shape categories and must therefore be a combination of
them (Table2). With “drop-shaped” considered analogous
to a mixture of “globulose” and “fusiform”, and “slender”
synonymous with “elongate”, white acoels are here coded
as “2” in the fusiform, globulose, and elongate categories.
A specific coding procedure was followed for the temper-
ature and salinity tolerance traits, as ranges were involved. If
the tolerance of a species was fully within a single category
it was coded “3.” If it fully or mostly covered multiple cat-
egories, these were coded “2.” However, if the range only
covered a small portion of a category, this category was
coded “1.” For example, Halectinosoma spp. were noted as
found between salinities of 12.4 and 33.9 by Kramer and
Kiko (2011). This range covers most of the categories 0–25
and 25–40 and therefore each was coded as “2.” In the cases
where a species was recorded as surviving a specific salin-
ity or temperature but without movement, this was coded as
“1.” Ranges (despite their difficulty in fuzzy coding) were
Table 2 List of traits and trait categories chosen to characterize sea ice meiofauna
Trait Trait category
Morphological
Body length (adult forms) [μm] Small (< 200μm); Small-Medium (200–400μm); Medium (400–700μm); Medium-Large (700–1000μm);
Large (> 1000μm)
Body width (adult forms) [μm] Small (< 25μm); Small-Medium (25–50μm); Medium (50–100μm); Medium-Large (100–200μm); Large
(> 200μm)
Body shape Elongate (worm-like); Globulose (round); Fusiform (spindle-like); Compressed (laterally – copepods, dorso-
ventrally – flatworms)
Flexibility Rigid (hard skeleton); Semi-Flexible (no skeleton but protective structure); Flexible (no form of protective
structure)
Physiological
Salinity tolerance [unitless] Tolerant: 0–25; 25–40; 40–60; 60 +
Temperature tolerance [°C] Tolerant: < −4; −4 to −2; −2 to 0; 0 <
Life history
Habitat occurrence Sympagic only; Sympagic–pelagic; Sympagic–benthic; Sympagic–pelagic–benthic
All stages found in sea ice Yes; No
Behavioral
Feeding mode while in sea ice Filter/Suspension feeder; Scavenger/Predation; Grazer; Absorption; Non-Feeding
Diet while in sea ice Herbivore; Carnivore; Omnivore; Osmotrophic; Bacterio-/Ciliovore; Detritivore
Mobility Attached; Swimmer; Crawler; Immobile
Table 3 Fuzzy coding explanation after Degen and Faulwetter (2019)
Code Explanation
3 Taxon has total and exclusive affinity for a certain trait
category
2 Taxon has a high affinity for a certain trait category, but other
categories can occur with equal (2) or lower (1) affinity
1 Taxon has a low affinity for a certain trait category
0 Taxon has no affinity for a certain trait category
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1678 Polar Biology (2022) 45:1673–1688
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used in this study instead of maximum tolerances because
of the scarcity of temperature and salinity tolerance data.
Many species would have missing data or misleadingly low
tolerances if only one data point was used.
Results
Taxonomic resolution anddata availability
There were 308 trait-by-species instances in total from this
study (28 species × 11 traits). In all cases, the proportion of
information for each trait represents the highest taxonomic
resolution found, and information was often used from mul-
tiple taxonomic levels for a single trait (Fig.1). All data
generated and analyzed in this study are included in this
published article and its supplementary information files
(see Online Resource #2).
Around 50% of the data were found at the species level
and 20% at the genus level (Fig.1). Almost a quarter of all
data were found at the much coarser resolution levels of
phylum (15%) and order (7%) (Fig.1). However, in nearly
every case this low-resolution data was only used for basic
mobility and flexibility information; traits which do not vary
beyond coarse taxonomic classifications in the studied taxa.
The fraction of species-level specific information was high-
est for Cnidaria and Acoela at 100% and 76%, respectively.
Rotifera, Copepoda, and Nematoda possessed 53%, 44%, and
44% species-level coverage, respectively (Fig.1).
Trait modality composition insea ice meiofauna
Body length
Sympagic meiofauna length varied widely by taxa (Fig.2a).
Interestingly, 18 species that represented Cnidaria, every
entity of Copepoda and Nematoda and a majority of Acoela
were listed in literature as capable of growing beyond
the ~ 500-μm threshold commonly used to define mei-
ofauna (Bluhm etal. 2017). On average, Rotifera possessed
the smallest body size. All taxa except Nematoda typically
grow to a maximum of less than 1mm long, although some
Fig. 1 The taxonomic level
of information found for 28
sea ice meiofauna covering 11
traits *Information pertaining
directly to Acoela morphotypes
is included as species-level data.
atol. is an abbreviation for toler-
ance. bocc. is an abbreviation
for occurrence
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1679Polar Biology (2022) 45:1673–1688
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Fig. 2 Relative composition of trait categories within 11 traits coded for 28 sea ice meiofauna taxa. Traits are labeled from A-K accordingly
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1680 Polar Biology (2022) 45:1673–1688
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Copepoda taxa such as Pseudocalanus sp. can exceed this
(Krøyer 1845).
Body width
The majority (57%) of species had body widths < 200μm,
consisting predominantly of Nematoda, Rotifera, and
Cnidaria. However, all Copepoda species and a majority
of Acoela species can grow wider than 200μm (Fig.2b)
(Tchesunov and Riemann 1995; Friedrich 1997; Janssen and
Gradinger 1999; Krembs etal. 2000; Bluhm etal. 2007;
Kramer etal. 2011).
Body shape
Elongate and compressed body shapes represent the two
most common categories found in ice meiofauna and are
applicable to nearly all the taxa (Fig.2c). In addition, many
species in Copepoda and Acoela are characterized as “fusi-
form” or tapered in shape (Sars 1911; Smirnov 1932; Jans-
sen and Gradinger 1999).
Flexibility
All three flexibility trait categories are considerably repre-
sented in the fauna (Fig.2d). Copepoda taxa were coded as
rigid because they all possess a firm exoskeleton (Reid and
Williamson 2001). Species of Nematoda are flexible in lat-
eral movement but less yielding by cross-section due to the
presence of a cuticle (Page etal. 2014) and were therefore
coded as semi-flexible. Acoela, Rotifera, and Cnidaria are
well documented as exhibiting high plasticity of their soft
bodies (Krembs etal. 2000; Schünemann and Werner 2005;
Piraino etal. 2008) and were hence coded as flexible.
Salinity tolerance
Most groups can tolerate salinities at either extreme; at least
20% of three taxa groups could tolerate brine salinities of
60 + and at least 25% of four taxa groups could tolerate 0–25
(Fig.2e). Brine tolerance was best documented in Cnidaria
(yet n = 1 only) and Rotifera and brackish tolerance was best
documented in Nematoda and Copepoda. Otherwise, salinity
tolerance was relatively uniform across all taxa. Almost all
studied species could tolerate 0–25 to some degree (Heip
etal. 1985; Grainger and Mohammed 1990; Friedrich and
De Smet 2000; Moens and Vincx 2000). As earlier stated, it
is assumed that Arctic marine organisms tolerate a salinity
of 25–40 as a baseline.
Temperature tolerance
Less than 10% of sea ice Nematoda, Copepoda, and Rotifera
are documented as capable of tolerating temperatures below
−4°C (Fig.2f). At least 17% of all taxa except Nematoda
are documented as tolerating −2 to −4°C. Survival at low
temperatures is best documented in sea ice Cnidaria and
Acoela. All species except S. tuuli are believed to tolerate
above-zero temperatures (Johnson and Olson 1948; Ber-
zins and Peljer 1989; Moens and Vincx 2000; Siebert etal.
2009). As earlier stated, it is assumed that Arctic organisms
tolerate the thermal range 0 to −2°C as a baseline.
Habitat occurrence
Harpacticoida are mainly found in both sympagic and
benthic habitats, while most Cyclopoida are found in sym-
pagic–pelagic habitats (Fig.2g) (Grainger etal. 1985;
Horner and Murphy 1985; Mohammed and Neuhof 1985;
Grainger 1991; Carey 1992; Schünemann and Werner 2005).
Most Nematoda are either only sympagic or sympagic–ben-
thic (Fig.2g) (Tchesunov and Riemann 1995; Tchesunov
and Portnova 2005; Portnova etal. 2019; Pitusi 2021), All
Acoela are only known from the ice (Fig.2g). Rotifera of
the genus Synchaeta are known as sympagic–pelagic, while
Encentrum, Proales, and Cephalodella are sympagic–ben-
thic (Fig.2g) (Friedrich and De Smet 2000). The Cnidaria S.
tuuli is only known from sea ice (Siebert etal. 2009).
All life stages found insea ice
The bulk of Copepoda and Nematoda are found in all life
stages in the ice, and no species of Acoela or Rotifera has
been found in all life stages (Fig.2h) (Grainger etal. 1985;
Tchesunov and Riemann 1995; Friedrich 1997; Janssen and
Gradinger 1999; Friedrich and De Smet 2000; Schünemann
and Werner 2005). Sympagohydra tuuli exists in the sea ice
at all life stages (Fig.2h) (Siebert etal. 2009).
Feeding mode whileinsea ice
For feeding mode information (Fig.2i), 64% of the taxa were
coded as “grazer,” consisting mostly of Copepoda, Nema-
toda, and Acoela (Grainger and Hsiao 1990; Tchesunov and
Riemann 1995; Janssen and Gradinger 1999; Kramer 2011;
Moens etal. 2013). “Filter/suspension feeding” describes
32% of taxa, mostly pelagic species of Rotifera and Copep-
oda (Pourriot 1979; Grainger and Hsiao 1990; Fontaneto
and De Smet 2015; Wilke etal. 2020). “Scavenger/preda-
tion” included 14% of species and consists of Acoela, the
Nematoda species Cryonema crassum, and the predatory
Cnidaria S. tuuli (Friedrich 1997; Bluhm etal. 2007; Siebert
etal. 2009; Kramer 2011). Many species are described by
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1681Polar Biology (2022) 45:1673–1688
1 3
multiple feeding types. Though inconclusive, the sympagic
Nematoda Theristus melnikovi and C. crassum are suggested
to absorb dissolved organic matter (DOM) for sustenance by
Tchesunov and Riemann (1995) and Tchesunov and Port-
nova (2005) after many individuals were found with empty
stomach contents. DOM has been experimentally shown to
be a food source for some marine Nematoda (Pape etal.
2013).
Diet whileinsea ice
Diet while in sea ice information is very diverse (Fig.2j).
While 71% of species were coded in some way as herbi-
vores, most species show additional variety in their diets.
For instance, Kramer (2011) stated that only Harpacticoida
taxa Tisbe spp. and Halectinosoma spp. could be classified
as almost exclusively herbivorous, while she classified the
rest of the studied sea ice meiofauna as carnivorous–omniv-
orous–bacterivorous in the spring pre-bloom. Cyclopoida
were considered carnivorous–omnivorous–detritivorous,
relying much more on metazoan-derived food sources and
less on diatoms than previously known. Nematoda, while
also feeding on a high proportion of diatoms, were said to
possess a “rather ciliate-based diet,” be herbivorous–bacte-
rivorous in summer and cilivorous–omnivorous–bacterivo-
rous–detritivorous in spring. Red Acoela were considered
herbivorous–cilivorous and white Acoela (that could be
platyhelminths) herbivorous–detritivorous. Rotifera also
feed more on ciliates than diatoms and were classified as cil-
ivorous–omnivorous–bacterivorous–detritivorous (Kramer
2011). The Cnidaria S. tuuli is exclusively a top predator in
the brine channel network and is assumed to play a key role
in the sympagic ecosystem (Piraino etal. 2008).
Mobility
Swimming and crawling are the two most common mobil-
ity trait categories of sea ice meiofauna (Fig.2k) and were
observed in some capacity in 100% of the studied taxa. In
addition, Harpacticoida are often particle attached (Kiør-
boe 2000; Koski etal. 2005) and can adhere to suspended
particles. Rotifera also possess a cement gland and can tem-
porarily attach to surfaces (Allen 1968; Yang and Hochberg
2018).
Discussion
High taxonomic resolution butlimited data
availability exists inice meiofauna studies
Example imagery of each taxa group studiedispresented
belowfor reference (Fig.3).The high proportion of spe-
cies- and genus-level data found in the literature review
Fig. 3 a Harpacticoida, b Nematoda, c Cnidaria, d Acoela, e Rotif-
era, and f Calanoida. Photographs: a Julia Ehrlich, University of
Hamburg, b, c, e Miriam Marquardt, UiT The Arctic University of
Norway/UNIS, d Kyle Dilliplaine, University of Alaska Fairbanks, f
Russell Hopcroft, University of Alaska Fairbanks
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

1682 Polar Biology (2022) 45:1673–1688
1 3
(Fig.1) strengthens the confidence that most trait catego-
ries assigned in this study apply either directly or closely
to the 28 species. Furthermore, although data on Cnidaria
and Acoela (albeit only morpho-species for Acoela) pos-
sess a taxonomic resolution far higher than that of Rotifera,
Copepoda, and Nematoda (Fig.1), the former possess few
species known from the ice. Acoela has just three morpho-
species and Cnidaria just one, leading to a high proportion
of species-level studies in the literature. The lower resolution
of the other taxa was in part related to some genera being
in taxonomic limbo like Pseudocalanus sp. in Copepoda
(Holmborn etal. 2011) or remaining unidentified to species
level like Synchaeta sp. A in Rotifera.
General trends in the availability and resolution of trait
information can be inferred by examining the percent cov-
erage heat map of Fig.1. This indicates research gaps and
identifies directions where traits require more research. First,
body length and body shape were the traits with the highest
coverage. In contrast, flexibility and mobility possessed the
lowest resolution, largely because basic order- or phylum-
level information was sufficient to cover these traits. Salin-
ity and temperature tolerance were the two traits most dif-
ficult to find information for, likely as specific experimental
approaches are necessary with sufficient animals to estab-
lish tolerance ranges. Species-level salinity tolerance infor-
mation for ice meiofauna was only found for Copepoda in
Grainger and Mohammed (1990), Cnidaria in Siebert etal.
(2009), and a variety of sympagic meiofauna in Friedrich
(1997). In general, more salinity tolerance than tempera-
ture tolerance studies existed, and cold tolerance was often
neglected in favor of heat tolerance. Surprisingly, besides
Friedrich (1997) and Siebert etal. (2009), no below-zero
experimental temperature tolerance studies were found for
ice meiofauna. Body width also possessed a low resolution,
for species lengths were much more commonly listed than
widths and very old taxonomic papers were often required
to find this information (Krøyer 1845; Bastian 1895; Willey
1920). Feeding mode and diet possessed good resolution for
all taxa except Rotifera.
Ice meiofauna morphological characteristics are
well adapted tobrine channel structure
Size is an important variable limiting access of biota to
the sea ice brine channel network, where brine channels
typically fill 5 to 25% of the sea ice volume (Golden etal.
1998). Individual channels vary in diameter from less than
1µm to over 1mm, while length can range from pockets
just handfuls of µm long to several dm long drainage chan-
nels, depending on location in ice floe and season (Cole and
Shapiro 1998). Therefore size matters and provides a strong
constraint on what species may inhabit the ice. Because
most of the ice meiofauna species studied grow to less than
1mm (Fig.2a), they can fit their entire bodies within many
of these channels, presumably providing refuge from larger
predators (Bluhm etal. 2017). Taxa with large body sizes
are usually assumed to possess disadvantages in the sea ice
brine channel network and may be unable to access certain
brine capillaries. Yet, factors like small body widths or high
flexibility can confound this trait and still allow for channel
penetration (Krembs etal. 2000). For example, sympagic
Nematoda couple extensive length with high flexibility and
narrow body widths (Tchesunov and Riemann 1995). How-
ever, limited information is available of the confining role of
body length and width for ice meiofauna, where in contrast
to sediment habitats, individual ice crystals cannot be moved
and the habitat is dimensionally fixed.
A diameter of < 200μm is a threshold which represents
the approximate width of roughly half the capillaries that
comprise the brine channel network (Krembs etal. 2000).
This study reveals that the majority of sympagic meiofauna
species likewise possess widths < 200μm (Fig.2b) and
hence are adapted to utilize these small channels for protec-
tion and feeding on microorganisms (Krembs etal. 2000).
However, this also demonstrates that nearly 50% of the brine
channels might not be inhabitable for meiofauna taxa, reduc-
ing grazing pressure on channel-inhabiting algae, protozoa,
and bacteria.
Most ice meiofauna possess elongate, compressed, or
fusiform body shapes (Fig.2c). Species with especially
elongated body shapes are better able to squeeze and
snake their way through the narrow brine channel network
(Krembs etal. 2000; Bluhm etal. 2017), comparable to ben-
thic meiobenthos which share a similarly interstitial habi-
tat (Urban-Malinga 2013). A tapered, fusiform shape may
exemplify another adaptation to the narrow brine channel
network, as this configuration may reduce the likelihood of
becoming stuck.
As noted in Fig.2d, all taxa except Copepoda are coded
as either flexible or semi-flexible. Flexible and semi-flexible
organisms possess an advantage in the narrow and twist-
ing brine channel network. These traits are also common in
organisms living in interstitial sediment habitats (Curini-
Galletti etal. 2020) and may be a reason why benthic organ-
isms like Nematoda and Acoela thrive in the ice (Krembs
etal. 2000).
Salinity andtemperature tolerance are key adaptations
possessed byall ice meiofauna taxa
The relative uniformity of salinity tolerance depicted in
Fig.2e confirm our hypothesis that most ice meiofauna spe-
cies are well adapted to the extreme salinity dynamics of
the brine channel network (Grainger and Mohammed 1990;
Gradinger etal. 1991; Friedrich 1997; Riemann and Sime-
Ngando 1997; Arndt and Swadling 2006).
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1683Polar Biology (2022) 45:1673–1688
1 3
However, few studies describing full tolerance ranges
existed, and most species-level information was instead
inferred from the temperature and salinity at which an organ-
ism was sampled (e.g., from Friedrich and De Smet 2000;
Marquardt etal. 2011). Most data were recorded at genus or
family level and this lower taxonomic resolution, coupled
with the fact that many sympagic species share genera and
family (Friedrich and De Smet 2000; Bluhm etal. 2017;
Pitusi 2021), naturally caused the data to tend toward uni-
formity among taxa. Additionally, brackish water tolerance
was better documented than brine tolerance, and the fact
that almost all studied species could tolerate 0–25 to some
degree introduces a bias toward lower salinities in the data.
Tolerance in the 40–60 and 60 + categories may exist at a
higher frequency than documented, especially given the har-
diness that taxa like Harpacticoida and Nematoda exhibit in
interstitial sediment habitats, hypersaline ponds, and other
extreme environments (Ranade 1957; Heip etal. 1985; Dam-
gaard and Davenport 1994; Zeppilli etal. 2018; Hotos 2021).
Personal observations from experiments on unidentified fast
ice Nematoda and the orange Acoela morphotype indeed
confirm salinity tolerance upward of 60 (Kaufman, Bluhm
and Gradinger, personal communication).
Tolerance of above-zero temperatures was far better docu-
mented than below-zero (Fig.2f), which provides evidence
that every species of all groups except Cnidaria can tolerate
above-zero temperatures (Johnson and Olson 1948; Ber-
zins and Peljer 1989; Moens and Vincx 2000; Siebert etal.
2009). Data in Fig.2f exhibit the same uniformity as data
in Fig.2e and likewise suggest adaptations to the thermal
fluctuations of the brine channel network (Friedrich 1997).
Just as for salinity, limited data coupled with high rates of
genus- and family-level information created taxa-wide uni-
formities that confound the results.
Habitat occurrence andportion oflife cycle spent
withinsea ice varies bytaxa andspecies
Sea ice meiofauna do not only occur within the ice, but
a variety of habitats. The habitats Copepoda are found
in breakdown predictably based on their order (Fig.2g).
Harpacticoida, common inhabitants of the benthos, are
found in both sympagic and benthic habitats. Cyclopoida
and Calanoida, regular inhabitants of the pelagic zone, are
mostly found in sympagic–pelagic habitats. Most Nema-
toda, not typical inhabitants of the pelagic realm, are pre-
dictably either sympagic or sympagic–benthic (Moens etal.
2013). The fact that all Acoela are only known from the
ice may be influenced by their unknown taxonomic status
(Janssen and Gradinger 1999; Friedrich and Hendelberg
2001) and the fact that Acoela in general – along with other
soft-bodied meiofauna – often do not preserve well with
common preservatives and frequently remain unidentified
(Curini-Galletti etal. 2020). Generally, however, Acoela
and Platyhelminthes (again we note one of the ice Acoela
may be a platyhelminth) are most prominent at the seafloor
(Achatz etal. 2013). Additionally, there is debate on whether
the Cnidaria S. tuuli is endemic to the sea ice or not. Bluhm
etal. (2007) and Marquardt etal. (2011) assume it has a
sympagic–benthic lifestyle, supported by the fact it was
found in seasonal sea ice within an isolated ord and in very
shallow coastal waters. Siebert etal. (2009), who found it
above 3000-m deep water in the central Arctic Ocean and
cite its extreme salinity and temperature tolerance, believe
it is fully sympagic.
Some ice meiofauna species live in the sea ice for all
stages of life, while others do not. Most Copepoda and Nem-
atoda have been found at all life stages (Fig.2h), thanks to
numerous studies that identified these life stages to the spe-
cies level (Cross 1982;Grainger etal. 1985; Friedrich 1997;
Portnova etal. 2019). The reason no species of Acoela or
Rotifera has been found in all life stages may potentially be
a result of the limited research effort on sea ice meiofauna.
Difficulty at distinguishing juvenile Acoela from adults was
also noted in Friedrich (1997). Siebert etal. (2009) found
reproducing individuals of S. tuuli within the ice.
These conclusions reinforce the fact that most stages of
sympagic meiofauna utilize the sea ice as a nursery and
feeding environment (Grainger 1991; Bluhm etal. 2017,
2018), irrespective of the additional habitats they are found
in. Sympagic meiofauna species are often found in higher
concentrations within the ice than in the pelagic or benthic
zones (Carey and Montagna 1982; Grainger 1991; Bluhm
etal. 2017), meaning that losing ice cover could have a dis-
proportionate impact even if the same taxa are found in other
habitats. Losses and reductions of sea ice meiofauna have
already been reported due to changes in ice dynamics (Mel-
nikov etal. 2001; Kiko etal. 2017; Leasi etal. 2021). A rich
sea ice community contributes to strong sympagic–pelagic
and sympagic–benthic coupling in the Arctic Ocean through
processes, such as the vertical pump (Søreide 2013; Wied-
mann etal. 2020), where the sinking of small invertebrates
and algae out of the ice bring energy and nutrients from the
surface toward the sea floor. Therefore, losses in diversity
and abundance of ice meiofauna and the larger sea ice com-
munity due to habitat loss will not only impact sympagic
food webs, but also pelagic and benthic ones if fewer mei-
ofauna melt out of sea ice in the spring (Leasi etal. 2021).
Feeding mode anddiet ofice meiofauna are
reflective ofseasonal changes insea ice community
Most sea ice meiofauna species were at least coded as graz-
ers (Fig.2i). Of these, sympagic Copepoda possess mouth
appendages specialized for grasping and holding (Grainger
and Hsiao 1990). Certain Nematoda also feature buccal
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1684 Polar Biology (2022) 45:1673–1688
1 3
teeth for scraping (Moens etal. 2013) and Acoela have
been observed eating diatoms (Friedrich 1997; Janssen and
Gradinger 1999). Suspension feeding Rotifera use cilia to
filter small particles from the water column (Pourriot 1979).
Sympagohydra tuuli has been observed predating on Nauplii
and Rotifera, meaning the Cnidaria is a rare predator within
the ice brine channel network (Siebert etal. 2009).
Although most sympagic meiofauna are thought to feed
on microalgae (Grainger and Hsiao 1990; Nozais etal.
2001), this comprehensive literature study showed that
diatoms make up a relatively small proportion of diet data
(Kramer 2011) (Fig.2j). This is probably largely thanks to
seasonal flexibility, where species switch from a diatom-
based diet in the productive period to an omnivore-based diet
during other seasons (Kramer 2011; Gradinger and Bluhm
2020). Kramer (2011) used stable isotopes, fatty acids, and
feeding experiments and discovered sympagic meiofauna
possess a highly diverse diet year-round that does not match
the earlier, mostly algae-based prey assessment that was
only conducted in the productive period (Grainger and Hsiao
1990). Flexible feeding strategies can be an adaptation to the
dynamic sea ice habitat which is both seasonal and extreme
(Kramer 2011) and may prove to be an advantage given
uncertainties of future diatom bloom timing and length
(Bluhm etal. 2018). In summary, this compilation supports
the emerging notion that the food web inside the sea ice is
more complex than previously appreciated (Gradinger and
Bluhm 2020).
Mobility modes are uniform acrosstaxa,
butvariable indegree
Swimming and crawling were the two most common mobil-
ity categories of the species studied, but some taxa are bet-
ter swimmers and crawlers than others (Fig.2k). Crawling
can be of benefit in interstitial habitats, like the ice brine
channel network (Krembs etal. 2000). Of less certainty is
how poorly swimming organisms, including Copepoda and
Nematoda, can traverse the pelagic zone and make it into
the ice (Kramer 2011; Kiko etal. 2017). Suggestions have
ranged from being swept up from shallow sediments during
storms at ice formation, captured with frazil ice crystals,
or even via crustacean parasitism (Janssen and Gradinger
1999; Gradinger etal. 2009; Kiko etal. 2017). Taxa with
better swimming ability like Rotifera, Acoela, and Cnidaria
can enable quick access to food sources and may explain
their more cilivorous and predatory natures (Kramer 2011).
Ice-endemic organisms with low dispersal ability may have
difficulties recolonizing new ice as seasonal differences in
ice extent increase (Kiko etal. 2017).
Conclusion & Recommendations
In the present study, a range of morphological, physi-
ological, life history, and behavioral trait categories were
assigned to 28 species of Arctic sea ice meiofauna. This
work is intended to be of use in establishing trait–function
relationships in Arctic ecosystems and the trait informa-
tion listed here can be paired with species abundance data
to form spatial maps of characteristic traits, functional
diversity, and vulnerability from a pan-Arctic perspective.
The 11 traits provided in this study are by no means
exhaustive in characterizing the life history strategies and
adaptations of sympagic meiofauna. Time/frequency of
breeding, reproductive strategy, larval development, and
life cycle duration could all be useful traits to code for,
especially given the rapidly reducing duration of ice cover
(Meredith etal. 2019) and the importance of sea ice to
the protection and grazing of juvenile meiofauna (Bluhm
etal. 2017, 2018). However, such trait data are difficult to
generate as it requires lengthy experiments and establish-
ing cultures.
To fill major gaps in literature scientists should record
as much high-resolution species-level data as possible for
sympagic fauna. Works which comprehensively study mul-
tiple traits at once can also be extremely useful for trait-
based researchers and enable them to rely much less on
low-resolution taxonomic data. Easy access to trait infor-
mation will make trait-based methods, so valuable for the
study of rapidly changing ecosystems, more accessible to
the scientific community.
Supplementary Information The online version contains supplemen-
tary material available at https:// doi. org/ 10. 1007/ s00300- 022- 03099-0.
Acknowledgements The authors would like to thank the US Fulbright
Scholar program and the Norwegian Fulbright Commission for ena-
bling a year of Arctic study abroad for EP, without which this study
would not be possible. Support by the Nansen Legacy is also acknowl-
edged (RCN #276730), a project dedicated to the advancement of sci-
entific knowledge in the Arctic Barents Sea and the mentoring of a new
generation of young researchers. Additionalsupport was providedby
ArcticSIZE, a research group on the productive Marginal Ice Zone at
UiT (grant no. 01vm/h15). Lastly, we are grateful for the assistance of
Johanna Hovinen (UiT) in the lab and the two anonymous reviewers
whose comments this manuscript has benefited from.
Author contributions The concept and idea for this project were ini-
tially created by BB and further developed through discussions with
EP. EP designed and filled the trait table, reviewed the literature used
in this study, and wrote the manuscript. All authors discussed the traits,
categories, and species to be included. BB, RG, and MM advised EP
on the research process. All authors read and approved the manuscript.
Funding Open access funding provided by UiT The Arctic University
of Norway (incl University Hospital of North Norway).
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1685Polar Biology (2022) 45:1673–1688
1 3
Declarations
Competing interests The authors declare no competing interests.
Conflict of interest The authors have no competing interests to declare
that are relevant to the content of this article.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
were made. The images or other third party material in this article are
included in the article's Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not included in
the article's Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a
copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.
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