Fish Assemblages of a Sub-Arctic Fjord Show Early Signals of Climate Change Response Contrary to the Benthic Assemblages

Abstract

Arctic benthic ecosystems are facing high-speed environmental changes, such as decreased sea ice coverage, increased temperature and precipitations, as well as the invasion by non-indigenous species. Few sub-arctic fjords have the particularity to have an inner-most part forming a basin in which water remains very cold. Those fjords may offer a refugee for cold-water arctic species as well as a small-scale “laboratory” of the changes that arctic assemblages located at higher latitudes might face soon. The Porsangerfjord in Northern Norway is a sub-arctic fjord with an inner arctic part and face red king crabs Paralithodes camtchasticus invasion since the end of the 1990s. It offers a case study of the dynamics of arctic ecosystems facing multiple stressors, i.e., climate change and invasive species. Based on a time series of megabenthic invertebrates and bentho-demersal fishes over 2007–2019, a complex multivariate analysis (STATICO) was used to identify the trends in the relationship between taxa and the environment. We showed the main environmental changes in the fjord were the freshening of the water, the increase of the seabed current, and the decrease of the maximum sea ice extent. A strong along-fjord gradient was visible for both benthic and fish assemblages. Species richness and Shannon diversity of fishes significantly increased into the fjord, due to the arrival of warm-water species over time that overlapped with cold-water species that have seen their biomass significantly reduced. No significant decrease in the biomass of the cold-water benthic species was visible, which could indicate an efficient refugee effect of the inner fjord. Yet, this refugee effect could be unbalanced by the red king crab invasion as it is a predator of several arctic species. In the Porsangerfjord, fish species thus respond to climate change while megabenthic assemblages are more threatened by invasive species.

Full text

fmars-09-822979 February 25, 2022 Time: 16:12 # 1
ORIGINAL RESEARCH
published: 03 March 2022
doi: 10.3389/fmars.2022.822979
Edited by:
Jorge L. Gutiérrez,
Consejo Nacional de Investigaciones
Científicas y Técnicas (CONICET),
Argentina
Reviewed by:
Jan Marcin Weslawski,
Institute of Oceanology (PAN), Poland
Ricardo Sahade,
CCT CONICET Córdoba, Argentina
*Correspondence:
Laurène Mérillet
Laurene.Merillet@hi.no
†
†
†ORCID:
Laurène Mérillet
orcid.org/0000-0003-2838-8740
Morten D. Skogen
orcid.org/0000-0002-6304-7282
Specialty section:
This article was submitted to
Global Change and the Future Ocean,
a section of the journal
Frontiers in Marine Science
Received: 26 November 2021
Accepted: 08 February 2022
Published: 03 March 2022
Citation:
Mérillet L, Skogen MD, Vikebø F
and Jørgensen LL (2022) Fish
Assemblages of a Sub-Arctic Fjord
Show Early Signals of Climate
Change Response Contrary to the
Benthic Assemblages.
Front. Mar. Sci. 9:822979.
doi: 10.3389/fmars.2022.822979
Fish Assemblages of a Sub-Arctic
Fjord Show Early Signals of Climate
Change Response Contrary to the
Benthic Assemblages
Laurène Mérillet1,2*†, Morten D. Skogen1,2†, Frode Vikebø1,2 and Lis Lindal Jørgensen3
1Institute of Marine Research, Bergen, Norway, 2Bjerknes Centre for Climate Research, Bergen, Norway, 3Institute
of Marine Research, Tromsø, Norway
Arctic benthic ecosystems are facing high-speed environmental changes, such as
decreased sea ice coverage, increased temperature and precipitations, as well as the
invasion by non-indigenous species. Few sub-arctic fjords have the particularity to have
an inner-most part forming a basin in which water remains very cold. Those fjords may
offer a refugee for cold-water arctic species as well as a small-scale “laboratory” of
the changes that arctic assemblages located at higher latitudes might face soon. The
Porsangerfjord in Northern Norway is a sub-arctic fjord with an inner arctic part and face
red king crabs Paralithodes camtchasticus invasion since the end of the 1990s. It offers
a case study of the dynamics of arctic ecosystems facing multiple stressors, i.e., climate
change and invasive species. Based on a time series of megabenthic invertebrates and
bentho-demersal fishes over 2007–2019, a complex multivariate analysis (STATICO)
was used to identify the trends in the relationship between taxa and the environment. We
showed the main environmental changes in the fjord were the freshening of the water,
the increase of the seabed current, and the decrease of the maximum sea ice extent.
A strong along-fjord gradient was visible for both benthic and fish assemblages. Species
richness and Shannon diversity of fishes significantly increased into the fjord, due to the
arrival of warm-water species over time that overlapped with cold-water species that
have seen their biomass significantly reduced. No significant decrease in the biomass
of the cold-water benthic species was visible, which could indicate an efficient refugee
effect of the inner fjord. Yet, this refugee effect could be unbalanced by the red king crab
invasion as it is a predator of several arctic species. In the Porsangerfjord, fish species
thus respond to climate change while megabenthic assemblages are more threatened
by invasive species.
Keywords: biodiversity, invasive species, spatio-temporal dynamics, red king crab, coastal freshening
INTRODUCTION
Climate change has marked effects in the Arctic region: over the last 50 years, it has warmed three
times faster than the rest of the world, the extent of sea ice decreased by 43%, and rainfall increased
by 24% which increased freshwater runoff and surface stratification (Arctic Monitoring Assessment
Program, 2021). Arctic coastal areas, in particular, offer important ecosystem services supporting
Frontiers in Marine Science | www.frontiersin.org 1March 2022 | Volume 9 | Article 822979

fmars-09-822979 February 25, 2022 Time: 16:12 # 2
Mérillet et al. Fjords ‘Biodiversity Facing Anthropogenic Impacts
aquaculture and fisheries, and mitigate global climate
change because of high productivity and carbon storage
(Johnsen et al., 2009;Smith et al., 2015;Ortega et al., 2019)
as well as providing suitable areas for spawning grounds and
nurseries for commercially important species (Olsen et al., 2010).
Fjords systems are located at the interface of terrestrial,
oceanic, cryospheric, and atmospheric fields and are highly
vulnerable to climate change as they have to cope with
the cumulative changes that each of these fields is facing
(Białogrodzka et al., 2018;Bianchi et al., 2020). A key variable
to study climate change impacts from the terrestrial influence
is coastal light attenuation. Terrestrially derived organic matters
discharge has increased in recent years along the Norwegian
coastline due to cross effects between increased precipitations,
reduced sulfur deposition, and land-use change (Frigstad et al.,
2020). The resulting light attenuation is susceptible to affect
photosynthetic organisms, favor tactile predators (e.g., jellyfish)
over visual predators (e.g., fish), and finally modify the whole
community (Aksnes et al., 2009). On the other hand, changes in
the open ocean conditions can also affect the dynamics of species
inside the fjords, notably through coastal wind changes resulting
in upwelling or downwelling and the vertical stratification at
the coast (Svendsen, 1995;Asplin et al., 1999). For instance, an
episodic change in wind direction along the Norwegian coast
led to the upwelling of cold waters that induced a slowdown in
the growth of Pecten maximus inside the fjords (Jolivet et al.,
2015). Changes in ocean temperature also cause key predators
of sea urchins to fluctuate in population size (Fagerli et al.,
2014) which had then consequences on the whole ecosystem
due to the grazing pressure sea urchin exert on kelp forest
(Strand et al., 2020). Fauna from the open ocean can also enter
the fjord and modify its taxonomic composition. For instance,
local fjord conditions may attract open ocean megafauna such
as humpback whales (Megaptera novaeangliae) and killer whales
(Orcinus orca) feeding on wintering Norwegian spring-spawning
herring (Clupea harengus) (Jourdain and Vongraven, 2017).
In the south-eastern Barents Sea, 2012 and 2016 are the
two warmest years since 1950 regarding surface temperatures
while sea ice cover in December 2018 was the lowest since
1951 (González-Pola et al., 2019). These physical changes have
already resulted in biodiversity changes in the Barents Sea and
induced a poleward shift of fish assemblages and an increase of
warm water boreal species (Wassmann et al., 2011;Fossheim
et al., 2015;Frainer et al., 2017). Similarly, for megabenthic
species, a recent poleward expansion of boreal assemblages
and a corresponding decrease in the importance of arctic
assemblages were reported (Jørgensen et al., 2019). These changes
in megabenthic assemblages are also visible in arctic fjords
such as Kongsfjord in Svalbard (Kedra et al., 2010b) where
it has eventually led to a regime shift of the rocky-bottom
assemblages with a sharp increase of macroalgae cover (Kortsch
et al., 2012). Megabenthic species are considered as a good
indicator of long-term changes in climatic conditions since they
are largely non-mobile and long-lived, and thus representative
of the sampling site (Reynoldson and Metcalfe-Smith, 1992;
Kedra et al., 2010b;Blicher et al., 2015), while fish species,
more mobile, are hypothesized to react more quickly to climatic
changes (Kortsch et al., 2015). It is thus interesting to compare
fish and benthic assemblage trends facing climate change as fish
may provide early warning of the changes that might later also
affect benthic assemblages.
Climate change is not the only threat to fjord ecosystems. In
link with increased anthropogenic activities, 11 non-indigenous
species were introduced in the Barents Sea region over 1960–
2015, among which 55% got established (Chan et al., 2019). In
fjords of Northern Norway, fish and megabenthos assemblages
have to face invasion by red king crab Paralithodes camtschaticus,
which is one of the rare large, higher-trophic-level organisms
that has established itself into new areas (Jamieson et al., 1998;
Falk-Petersen et al., 2011). Red king crabs were introduced from
the Pacific to the Russian Barents Sea at the end of the 1960s,
and reached the Norwegian coast in the early 1990s (Oug et al.,
2018). Those are active predators (Jørgensen and Primicerio,
2007) that led to substantial structural and functional changes in
megabenthic assemblages with the decrease of large suspension
and surface deposit feeders in adjacent fjords while small, mobile,
shallow burrowing and predator species with planktonic larval
development increased (Oug et al., 2018). Red king crabs also
prey on benthic fish eggs (e.g., lumpfish and sculpin) but are of
minor importance in the diet of large demersal fish and cod that
prey on small and medium red king crabs (Pedersen et al., 2018).
Fjords can harbor relict population of cold-water species, as
it has been shown with the presence of the cold-water species
Bathyarca glacialis in the Olsofjord during the warming that
occurred in the younger Dryas (12,000 years ago) (Spjeldnæs,
1978). Today, sub-arctic fjords of Northern Norway are located
at a latitude where warm-water boreal taxa dominate offshore
(Johannesen et al., 2012;Jørgensen et al., 2015). Very few fjords
have an inner basin isolated from the rest of the fjord and open
ocean by a sill, in which water remains very cold (in Norway, it is
essentially Porsangerfjord, Balsfjord, and Ullsfjord; T. Pedersen
(UiT) personal communication). Such fjords harbor a local and
probably isolated population of cold-water arctic species, as it
has been observed in Porsanger (Oug and Fuhrmann, 2013;
Fuhrmann et al., 2015), and could act as a refugee area for these
species (Włodarska-Kowalczuk et al., 1998;Drewnik et al., 2017;
Yletyinen, 2019). Those fjords can be considered as a “mini arctic”
(Oug and Fuhrmann, 2013), providing unique insights into the
changes undergone by arctic assemblages facing climate change
and invasive species, that might be of relevance for Svalbard and
Northern Barents Sea. Here we focus on the Porsangerfjord as
a case study of the dynamics of the biodiversity of sub-arctic
fjords facing multiple stressors, i.e., climate change and invasive
species. This fjord was closed to shrimp fishing in the early 1970s
after intensive fishing caused the overexploitation of cod and
small young fishes (Søvik et al., 2020). Hence, it is considered
to be free of bottom fishing impacts. The fjord also harbors
a large population of red king crab, that entered the fjord in
the mid-1990s, and progressed toward the inner-most part of
the fjord that they reached after 2010 (Fuhrmann et al., 2015;
Pedersen et al., 2018). It is now an important commercial red
king crab fishery with 921 tones caught in the Porsangerfjord
in 2018 (Søvik et al., 2020). Climate change, through increased
water temperature and light attenuation, has been recorded
Frontiers in Marine Science | www.frontiersin.org 2March 2022 | Volume 9 | Article 822979

fmars-09-822979 February 25, 2022 Time: 16:12 # 3
Mérillet et al. Fjords ‘Biodiversity Facing Anthropogenic Impacts
in the fjord. Over 1986–2015, air temperature increased by
0.05◦C/year near the fjord mouth and 0.04◦C/year in the inner
basin (Cieszy´
nska and Stramska, 2018). Over 2009–2014, light
attenuation is estimated to be quite high in the inner part of
the Porsanger (up to Kd(PAR) = 1.96, Trine Bekkby (NIVA)
unpublished data, Frigstad, 2020). Light attenuation as a function
of increased precipitation was approximated here by including
riverine discharge among the environmental variables used in
the study. More generally, depth, sediment type, temperature,
current, and sea ice have been related to variation in the structure
of megabenthic assemblages in the Arctic and their effects were
thus investigated here (Gray, 2002;Piepenburg, 2005;Sejr et al.,
2009;O’Sadnick et al., 2020). For fish assemblages, depth and
temperature gradients appear to be the main structuring variables
in the Barents Sea (Johannesen et al., 2012), while sediment type
and seabed currents are also reported to be important structuring
variables of bentho-demersal fish assemblages (Vaz et al., 2007;
Pickens et al., 2021). Since there is a strong salinity gradient in
the Porsanger, salinity was also included as a potential driver of
assemblages in this study for both fish and megabenthos.
Here we aim at investigating the consequences of climate
change and the increase in red king crab biomass on fish
and megabenthic assemblages. More specifically, we investigated
which of these pressures are the most structuring variables for
fish and megabenthic assemblages, whether their importance
changed over 2007–2019, and if the mobile fish assemblages
reacted earlier than the less mobile megabenthic assemblages.
Two datasets covering 2007–2019 for fish and megabenthic
species respectively were explored as functions of environmental
variables known to influence fish and megabenthic species
distribution in fjords. We first investigated (1) temporal trends
in the biomass of each fish and megabenthic species and (2)
temporal changes in biodiversity indices. Then, we characterized
(3) whether a shift is visible in the relationship between species
and environmental variables (i.e., a dramatic change in species
composition or environment); (4) the average structure of
the association of fish and megabenthos with environmental
variables (the distinction between cold and warm water species)
and (5) the interannual variations or the importance of each
environmental variable.
MATERIALS AND METHODS
Study Area
The Porsanger, located in Finnmark above the polar circle is
one of Norway’s largest fjord. It is 123 km long and covers
an area of about 1,877 km2. This large and open sub-arctic
fjord is characterized by the heterogeneity of its environmental
conditions with a strong along-fjord gradient (Fuhrmann et al.,
2015). Three areas can be distinguished in the fjord: (1) the outer
fjord faces the Barents Sea and is influenced by warm and saline
coastal waters with depths around 300 m; (2) the center of the
fjord is separated from the outer fjord by a sill that is about 200
m deep: (3) the inner fjord is isolated from the center of the fjord
by a narrowing of the fjord width and a sill that is about 60 m
deep. In the inner fjord, seabed temperatures are below zero for
half of the year (Fuhrmann et al., 2015) and freshwater inputs
from the three rivers in the inner fjord make the salinity lower
than in the rest of the fjord (Myksvoll et al., 2012). This leads to a
sea ice coverage, present every year but varying in extent, during
late winter until the beginning of the spring (Megan O’Sadnick,
SINTEF, pers. comm.). This creates special conditions that enable
the presence of unique species compared to the adjacent fjords
at the same latitude (Søvik et al., 2020). Freshwater discharge
and stratification in the Porsanger are low (Svendsen, 1995).
There is an anticlockwise surface water circulation in the fjord
with high saline and warm surface waters entering the fjord
on the west and low saline water exiting the fjord on the east
(Svendsen, 1991).
Environmental Variables
Depth data were downloaded from the GEBCO website1and
sediments were taken from the “Marine—Seabed sediments
(grain size) detailed” product from the Geological Survey
of Norway (NGU) website.2The 17 categories of sediments
originally documented in the sediment data were re-coded into 3
main categories to avoid loss of power in the analysis: mud, sand,
and rough substratum. Surface and bottom temperatures, salinity
as well as seabed current were extracted from the NordKyst800
model outputs at 800 ×800 m resolution (Albretsen et al.,
2011;Asplin et al., 2020). An evaluation of this model was
performed (Supplementary Figure 1). The mean errors between
observations and model do not have significant temporal trends,
but they are significantly different between areas. Despite this
spatial bias, the along-fjord gradient was well reconstructed by
the model. Outputs of the NorKyst800 models could thus be
used to identify potential changes in the relationship between
environment and taxa along the fjord. River discharges (m3/s)
were taken from the outputs of an HBV (Hydrologiska Byråns
Vattenbalansavdelning) model run by the Norwegian Water
Resources and Energy Directorate (NVE) (Bergström, 1995;
Lindström et al., 1997). We selected the cells corresponding to
the inner and central/outer fjord and compute the annual mean.
Sea ice maximum extent was obtained from automatic analysis
of Moderate Resolution Imaging Spectroradiometer (MODIS)
images (O’Sadnick et al., 2020). Sea ice is only present in the
inner part of the fjord, so NAN was attributed to sampling sites
in the central and outer fjord. Due to the annual or less than
the annual frequency of our samplings of a large number of taxa
with different life cycles covered in this study, we used annual
means for the environmental variables that are used to potentially
explain shifts in the assemblage composition.
To identify potential temporal trends in surface temperature,
bottom temperature, surface salinity, bottom salinity, and seabed
currents a linear regression of the values over time was applied in
each pixel (800 ×800 m) (Figure 1). The slope of the regression
was printed only in the pixels with a p-value <0.1. For non-
spatialized variables (sea ice maximum extent and river runoff)
1https://www.gebco.net/data_and_products/gridded_bathymetry_data/arctic_
ocean/
2http://geo.ngu.no/download/ShoppingServlet
Frontiers in Marine Science | www.frontiersin.org 3March 2022 | Volume 9 | Article 822979

fmars-09-822979 February 25, 2022 Time: 16:12 # 4
Mérillet et al. Fjords ‘Biodiversity Facing Anthropogenic Impacts
FIGURE 1 | Temporal trends of the environmental variables. For spatialized data (A–E), linear regression was computed in each 800 ×800 m cell, only slopes with
p-values <0.1 are printed. For non-spatialized data (F–H), linear regression was performed on the annual mean (stratified biomass), for the inner fjord for sea ice
extent (F) and separated between inner (G) and central/outer (H) areas for river runoff.
Frontiers in Marine Science | www.frontiersin.org 4March 2022 | Volume 9 | Article 822979

fmars-09-822979 February 25, 2022 Time: 16:12 # 5
Mérillet et al. Fjords ‘Biodiversity Facing Anthropogenic Impacts
FIGURE 2 | Sampling sites for megabenthic taxa (A) and fish taxa (B) in the inner (green), central (yellow), and outer (red) parts of the fjord.
similar linear regressions were computed independent of pixels.
For visualization, a GAM model was also fitted.
Biological Sampling
Benthic assemblages were sampled in 2007, 2008, 2009, 2010,
2014, 2016, and 2019. Each of these years, between 5 and 22
sites in total were visited at the outer, center, and inner parts
of the fjord (Figure 2). Hauls were conducted for 5 min at a
mean speed of 1.5 knots with a 2 m beam trawl (codend 4 mm)
and washed through a 5 mm sieve. The catch was then sorted,
and each taxon was counted and weighted (i.e., biomass is wet
mass). Megabenthic taxa were identified to the lowest possible
taxonomic level. However, as this was not always possible, some
taxa were nested (a species and its genus were present in the
database) (see Supplementary Table 1). Fish assemblages in the
Porsanger were sampled as part of the annual coastal ecosystem
survey in October. During 2007–2019, this survey sampled every
year between 2 and 8 sites in the fjord (Figure 2). Due to this
low number of samplings and since fish species are mobile in
the fjord, outer and central area were pooled for fish data. The
year 2008, which has only 2 sampling sites into the Porsanger was
removed from the dataset. Fish and megabenthic species (>1 cm,
Stratmann et al., 2020) were collected with a Campelen 1,800
bottom trawl, rigged with rockhopper ground gear, and towed on
double warps. Sampling effort was standardized to a fixed effort
of 15 min [equivalent to a towing distance of 0.75 nautical miles
(∼1.4 km)]. The horizontal opening of this trawl is 17 m, and
the vertical opening is 4 m (Engås and Ona, 1990). The mesh
size is 80 mm (stretched) in the front and 24 mm at the codend,
allowing the capture and retention of small fish and the largest
megabenthos (benthic megafauna larger than 4 mm) from the
seabed. This represents a total of 78 valid hauls.
Since the number of sampling sites varied between years
(Supplementary Figures 2A,C), a non-parametric Kruskal-
Wallis test was performed on each of the megabenthos and fish
datasets to determine if there was a significant difference in
the number of sites sampled between years. As the number of
sampling sites is low in some years, the power of the test was also
calculated with the kwpower function from the MultNonParam R
package (Kolassa and Jankowski, 2021).
Some species are not always correctly identified and were
grouped to a higher taxonomic level (i.e., Benthosema glacialis,
Myctophidae, and Myctophiformes under Myctophiformes;
Argentina silus and Argentina shyraena under Argentina;Sebastes
mentella,Sebastes norvegicus,Sebastes viviparous, and Sebastes
under Sebastes). For both fish and megabenthic taxa, to avoid
bias caused by sporadic catches of taxa that can be due to a
restricted spatial and temporal coverage of our data and/or the
non-homogenous accuracy in taxonomic identification across
different crews, only taxa occurring in more than 5% of all
sampling hauls and over more than one-third of the time series
were kept for analysis. A total of 24 fish and 71 megabenthic taxa
Frontiers in Marine Science | www.frontiersin.org 5March 2022 | Volume 9 | Article 822979

fmars-09-822979 February 25, 2022 Time: 16:12 # 6
Mérillet et al. Fjords ‘Biodiversity Facing Anthropogenic Impacts
were selected (the full list is given in Supplementary Table 1)
and their biomass was standardized by the area sampled during
each haul (computed from the distance trawled and the width
of the trawl). Biomass was log(x + 1)-transformed to reduce
variability caused by the catch of numerous individuals of the
same taxon that may occur for some of the fish schools or sessile
megabenthic taxa.
Temporal Trends in Biomass of the Taxa
and Biodiversity Indices
The trend of the biomass of each fish and megabenthic taxa
was computed as a function of time with a regression, in
each area of the fjord (inner, central, outer for megabenthic
taxa and whole fjords for fish taxa). Since the time series
are not very long and we are interested in trends, a simple
linear regression was computed. The stratified biomass in each
area was computed to account for the different number of
sampling sites in each area and each year (i.e., mean biomasses
for each taxa in each area and each year were multiplied by
the surface of the area and divided by the total surface of
the fjord, these weighed mean biomass were then summed to
get the total biomass of the whole fjord, Bellail et al., 2001).
For megabenthos, since each area was considered separately,
the stratified biomass was the mean biomass in each area
each year. Biomass was log(x + 1)-transformed to reduce
variability. Finally, the regression was performed on strictly
positive biomass only, as it is impossible to determine whether
a taxon was really absent in an area a specific year or if we
failed at sampling it.
Diversity indices were computed for each area for
megabenthos and for the whole fjord for fish data to characterize
the dynamics of taxa composition and the changes undergone in
the fjord in recent years. In each area for the megabenthos and
the whole fjord for megabenthos and fish, log(x + 1)-transformed
stratified biomass, log(x + 1)-transformed stratified abundance,
taxonomic richness, Shannon-Weiner diversity index that
informs on community diversity and Pielou’s evenness that
informs on the equality of biomass distribution between taxa
(Pielou, 1966) were computed. Pielou’s evenness is independent
of the number of taxa and is only influenced by the distribution
of individuals among taxa. Specifically, to follow the evolution
of the particular ecosystem in the inner fjord, the proportion of
the stratified biomass accounted by each taxon each year was
represented and the 5 taxa with the largest stratified biomass
were identified each year (see Supplementary Figure 3).
Statistical Analysis: The STATICO
Method
The STATICO analysis belongs to the ordination analysis
family that enables the study of the relationship between taxa
and their environment (Thioulouse et al., 2004). This analysis
has the particularity to handle complex relationships between
numerous taxa and environmental variables with spatially and
temporally resolved information. STATICO is based on a series
of k (number of years) times two tables: a site ×taxa table,
which provides biomass for each taxon at each site sampled,
and a sites ×environment table, which provides values for
each environmental variable at each site. To avoid placing
undue emphasis on the taxa with low frequency, Hellinger’s
transformation was applied on biomass according to Rao (1995).
Some sediment types were sampled many times while others
were not. To remove this unbalanced sampling among sediment
types (Supplementary Figures 2B,D), sediments were grouped
in three categories and coded as a numeric variable with larger
values representing larger particle sizes (mud = 1, sand = 2,
rough substrate = 3).
The first step of STATICO consists of co-inertia analysis
that combines, for each year, the sites ×taxa table with the
sites ×environments table. This yields a cross-covariance table
between taxa and environment for each year. A preliminary step
to the co-inertia analysis is to perform two normed principal
component analyses (within-PCA), to remove the effect of time:
one for the sites ×taxa table and one for the sites ×environments
table. During this step, in each of the two tables, variables
are standardized separately within each year (Thioulouse et al.,
2018). Since this step assumes a linear relationship between taxa’s
biomass and environmental variables, the relation between each
pair of taxon and environmental variables was visually verified
to be roughly linear. After the co-inertia analysis, a Monte-
Carlo permutation test of rows and columns of the taxa and
environment tables was performed, with the statico.krandtest
function from the ade4 R package (Thioulouse et al., 2018), for
each year, to test the significance of the co-variations between taxa
and environment. The second step of STATICO consists of partial
triadic analysis (PTA: Tucker, 1966;Thioulouse et al., 2018) to
analyze the series of cross-covariance tables across years.
1. The interstructure identifies similarities in the relationships
between taxa biomass and the environment between years
and enables to detect potential shift in the time series.
For each year with every other year, vectorial correlation
coefficients Rv are calculated between cross-covariance
(environments ×taxa) tables and gathered in a year ×year
Rv matrix to identify similarities between years. On
the correlation circle, the length of one arrow on the
first axis represents the weight of the cross-covariance
(environments ×taxa) table of this year in the computation
of the compromise (the averaged cross-covariance table).
The larger this length, the greater the contribution of a year
to the compromise.
2. The compromise assesses temporally stable relations
between taxa biomass and environmental variables. The
first eigenvector of the eigen analysis of the Rv matrix is
used to weight the cross-covariance tables and calculate
their weighted mean. A high weight for a given year
indicates that this year is representative of the relation
between taxa and the environment during the time series
while a low weight indicates an unusual relation this
year. Similar weights for all years indicate stable relations
between taxa and the environment. The compromise is then
calculated using a centered PCA.
3. The intrastructure assesses temporal changes in relations
between taxa biomass and environmental variables. To
Frontiers in Marine Science | www.frontiersin.org 6March 2022 | Volume 9 | Article 822979

fmars-09-822979 February 25, 2022 Time: 16:12 # 7
Mérillet et al. Fjords ‘Biodiversity Facing Anthropogenic Impacts
assess potential spatial patterns in taxa–environment
relations, we projected the rows (sampling sites) of the
initial sites ×taxa and sites ×environments tables onto the
compromise space as supplementary elements (Thioulouse
et al., 2018). These projections provide an overview
of annual variations in taxa–environment relations, also
called trajectories.
One STATICO analysis was run for megabenthic taxa and
one for fish taxa. At the compromise step, a K-means clustering
algorithm was applied to the taxa in the compromise space to
determine groups of taxa with a common relationship with the
environment. The number of groups was given by the Calinsky-
Harabasz criterion (Calinski and Harabasz, 1974). Taxa with the
largest biomass in each of the fjord areas were computed based on
the stratified biomass of each taxon each year. Finally, stratified
biomasses of taxa belonging, respectively, to each K-means
cluster (warm-water and cold-water taxa) were summed and
log(x + 1) transformed. A linear regression of these log(x + 1)
transformed stratified biomass of each group was fitted to
evaluate the temporal trend of their biomass. Finally, at the
intrastructure step, to disentangle the cause of the variations in
the stable variables (i.e., depth and sediment), a Kruskal-Wallis
and a Pearson’s Chi-square test were computed. This enables to
disentangle if there was a significant difference in sampling across
depth or sediments or if taxa were moving.
RESULTS
Environmental Variables
Over 2007–2019, a significant decrease in surface and bottom
salinity is visible in outer, central, and inner parts of the
fjord with a loss of 0.01–0.05 psu/year (Figures 1C,D). Seabed
current shows some significant increase of 0–0.001 m/s near the
coast of the central fjord (Figure 1E). There are no significant
trends for surface and bottom temperature (Figures 1A,B). Sea
ice maximum extent shows some decreasing trends between
2010 and 2015, but no significant decrease over 2007–2019
(Figure 1F). River runoff into the inner and medium/outer parts
of the fjord has increasing, but not significantly (Figures 1G,H).
Temporal Trend of the Biomass of Each
Taxon
For megabenthic taxa, the biomass of Norwegian shrimp
Pontophilus norvegicus shows a significant increase in the outer
fjord while the biomass of brittlestars Ophiuridae significantly
decrease in the inner fjord similarly to Island scallop Chlamys
and sea anemones Actiniaria in the central fjord (Table 1).
In addition, some non-significant trends also appear important
to be reported: red king crab Paralithodes camtschaticus have
an increase of its biomass in the inner fjord. Regarding fish,
the biomass of haddock Melanogramus aeglefinus and Atlantic
hookhead sculpin Artediellus atlanticus significantly decrease
while the biomass of redfishes Sebastes significantly increase.
A non-significant trend is also worth noting, with the decrease
in the biomass of Atlantic wolffish Anarhichas lupus in the fjord
over 2007–2019 (Table 2).
Temporal Trend of the Diversity Indices
at the Scale of the Assemblage
Taxonomic richness and Shannon diversity index significantly
increased over 2007–2019 for the fish dataset (Table 2). For
megabenthic taxa, there is a significant decrease of Pielou’s
evenness in the whole fjord, which indicates that the biomass
is becoming less evenly distributed between taxa, with the
emergence of a dominant taxon (Table 2). More specifically,
toward the end of the time series (2014 and 2019), the biomass
of the red king crab become among the 5 largest biomasses in
the inner fjord (Supplementary Figure 3). In the inner fjord,
a decrease in the abundance of megabenthos is also visible,
but non-significant. Finally, a non-significant increase in the
biomass of megabenthic taxa in the central fjord can also be
noted (Table 2).
STATICO Analysis
For both megabenthic and fish taxa, the Kruskal-Wallis tests
indicated no significant differences in the number of sampling
sites between years (for megabenthos: Kruskal-Wallis Chi-
squared = 7, p= 0.429, power = 0.999; For fish: Kruskal-Wallis
Chi-squared = 12, p= 0.446, power = 0.999).
The co-variation between taxa and environment was
significant all years, except in 2010 and 2019 for megabenthic
taxa, and in 2009 for fish. This highlights that those years the
assemblages were likely driven by other variables than those
considered in this study.
The interstructure step of the STATICO analysis quantifies
how much the relationship between taxa and environment is
similar between years. The first axis of the interstructure accounts
for the largest share of the similarity in the taxa-environment
relationship between years. For megabenthic taxa, the first
axis shows moderate common structure between years (30.0%
of explained variability) (Figure 3B), while for fish the first
axis explained more of the correlation between years (46.62%)
(Figure 3E). In addition, for megabenthic taxa, values of the
vectorial correlation coefficient Rv are moderate to low which
indicate that relation between taxa and the environment tend
to vary between years (Figure 3C). Conversely, for fish, the
relation between taxa and the environment was more stable
as shown by the moderate to high values of Rv (Figure 3F).
Similarly, the length of the arrows on the first axis of the fish
correlation circle is long and quite even (Figure 3D), indicating
relative stability of the taxa-environment relation through the
years. For megabenthic taxa, the length of the arrows on the
first axis is less even, pointing again toward the variability of the
taxa-environment relation for those taxa (Figure 3A).
The compromise analysis informs on the average relation
between taxa and environment through the time series. For
megabenthic taxa, the first axis accounts for 79.1% of the
variability in the compromise space, and the second axis 13.5%.
For fish, the first axis explains 84.6% of the variability in the
compromise space while the second axis accounts for 14.2%.
Frontiers in Marine Science | www.frontiersin.org 7March 2022 | Volume 9 | Article 822979

fmars-09-822979 February 25, 2022 Time: 16:12 # 8
Mérillet et al. Fjords ‘Biodiversity Facing Anthropogenic Impacts
TABLE 1 | Coefficient and p-value of the linear regression of the log-transformed stratified biomass of megabenthic and fish taxa.
Taxa Area Coefficient linear regression p-value
Megabenthic taxa
Pontophilus norvegicus Outer 1.10 ×10−18.30 ×10−3
Actiniaria Central −3.10 ×10−11.45 ×10−2
Chlamys Central −6.10 ×10−22.22 ×10−2
Ophiuridae Inner −1.30 ×10−13.68 ×10−2
Paralithodes camtschaticus Inner 8.80 ×10−26.07 ×10−2
Astarte Outer −5.20 ×10−26.78 ×10−2
Fish taxa
Melanogrammus aeglefinus Whole fjord −1.40 ×10−18.67 ×10−3
Artediellus atlanticus Whole fjord −1.50 ×10−23.43 ×10−2
Sebastes Whole fjord 1.40 ×10−14.82 ×10−2
Anarhichas lupus Whole fjord −3.50 ×10−25.16 ×10−2
For each taxon, the stratified biomass in each area (inner, central, and outer fjord for megabenthos and central and outer for fish) is computed, and values of 0-biomass
are removed before the regression, as it is unsure whether the taxon was absent or not detected. Only regressions with p <0.05 are shown. Regression with slightly
higher p-values (p <0.07) are also shown in gray as they can be informative as well.
TABLE 2 | Coefficient and p-value of the linear regression of the biodiversity indices (taxonomic richness, log(x + 1) transformed stratified biomass, log(x + 1) transformed
stratified abundance, Shannon diversity, and Pielou’s evenness) for megabenthic and fish taxa over time.
Biodiversity indices Area Coefficient linear regression p-value
Taxonomic richness—fish Whole fjord 6.60 ×10−15.52 ×10−5
Shannon diversity index—fish Whole fjord 5.1 ×10−29.77 ×10−3
Pielou’s evenness—fish Whole fjord 9.2 ×10−35.26 ×10−2
Biomass—megabenthos Central 1.4 ×10−15.86 ×10−2
Abundance—megabenthos Inner −1.3 ×10−16.17 ×10−2
Pielou’s evenness—megabenthos Whole fjord −2.3 ×10−22.39 ×10−2
Each index is computed for the whole fjord for fish and megabenthos as well as for the sub-area (inner, central, and outer) for megabenthic taxa. Regressions with
p≤0.05 are in black while regressions with 0.05 <p<0.07 are in gray. The corresponding regression plots are presented in Supplementary Figure 4.
In both cases, the first axis represents the strong structuring
importance of the along-fjord gradient, with the outer fjord deep
sampling sites with high values of temperature, salinity, and
currents on the left and the inner fjord shallow sampling sites
with more river runoff, and sea ice on the right (Figures 4B,D).
The second axis, in both cases, is associated with sediments, with
a gradient of grain size from the bottom to the top of the second
axis (mud, then sand, then coarse sediments). For megabenthos,
bottom salinity, followed by surface temperature, river runoff,
sea ice extent, and bottom temperature and depth are the main
drivers, while for fish assemblages it is bottom salinity, then
depth, surface salinity and surface temperature.
The clustering of the taxa along the scores on the first axis
of the compromise lead to two groups for both megabenthos,
and fish taxa (Figures 4A,C). Megabenthic taxa can be
distinguished between cold water taxa (Figure 4A, cluster 1)
associated with high values of sea ice extent and river runoff
and warm waters taxa (Figure 4A, cluster 2) associated with
high values of temperature, salinity, and current. The largest
biomass of cold-water megabenthic taxa are formed by ascidians
Ascidiacea, cookies cutter sea star Ctenodiscus crispatus, red
king crab Paralithodes camtschaticus, whelks Buccinidae, and
soft coral Nephtheidae while the largest biomass of warm
water megabenthic taxa originate from shrimps Pandalidae, sea
anemones Actiniaria, brittlestars Ophiuridae, Nephtyidae and
Sevenline shrimp Sabinea septemcarinata. Similarly, fish taxa
can also be partitioned between warm water taxa (Figure 4C,
cluster 1) associated with warm and saline deep water with
strong current and cold-water taxa (Figure 4D, cluster 2)
found in shallower waters with lower values of temperature,
salinity, and current.
The biomass of the warm-water and the cold-water
megabentic groups did not show any significant temporal
trends (Supplementary Figure 5A). It can be noted that when
removing red king crab from the cold-water taxa, no significant
trends were observed either (Supplementary Figure 5B). The
biomass of the cold-water fish taxa significantly decreased in
the whole fjord while the biomass of warm-water fish taxa did
not vary significantly over time (Figure 5). In addition, the
importance of bottom temperature as a structuring variable
of the assemblages sharply increased after 2016 (Figure 6B).
This variable was the one showing the largest variation in
its importance in structuring the assemblages (large size of
the ellipse) (Figure 6A). Conversely, for megabenthos the
structuring effect of environmental variables appears relatively
stable through the period considered (2007–2019) as indicated by
the medium size of the ellipses (Supplementary Figures 6A,B).
Fish and megabenthos taxa maintained stable relations with the
Frontiers in Marine Science | www.frontiersin.org 8March 2022 | Volume 9 | Article 822979

fmars-09-822979 February 25, 2022 Time: 16:12 # 9
Mérillet et al. Fjords ‘Biodiversity Facing Anthropogenic Impacts
FIGURE 3 | Interstructure analysis with correlation circle with scores for years for megabenthos (A) and fish (D).(B,E) Eigenvalues of the Rv matrix that gathers
vectorial correlation in taxa-environment covariances between 2 years). (C,F) Histogram of the Rv values between 2 years.
FIGURE 4 | Compromise analysis for megabenthos and fish. (A,C) The space of the taxa and (B,D) the space of the environmental variables.
Frontiers in Marine Science | www.frontiersin.org 9March 2022 | Volume 9 | Article 822979

fmars-09-822979 February 25, 2022 Time: 16:12 # 10
Mérillet et al. Fjords ‘Biodiversity Facing Anthropogenic Impacts
FIGURE 5 | Linear regression of the log(x + 1) transformed stratified biomass of fish warm-water and cold-water taxa (from the K-means clustering on the score of
the two first axis of the compromise) over time. Equation of the regression, its r2, and its p-value are printed for each group for the whole fjord.
FIGURE 6 | Intrastructure analysis for fish: (A) The yearly average position of environmental variables in the space of the compromise is the center of the ellipses and
each point is the yearly position (two last numbers of the year are printed), (B) score of the environmental variables on axis 1 of the compromise.
environment through the years (Supplementary Figures 6C–
F). For variables that do not change in time, depth and
sediments, their variations could be interpreted either because
the significantly different depth and sediments were sampled
through time or because taxa moved. For depth, there is no
significant difference in the sampled values between years (For
megabenthos: Kruskal-Wallis Chi-squared = 10.64, p-value = 0.1;
for fish: Kruskal-Wallis Chi-squared = 2.39, p-value = 0.967) as
well as for sediments (for megabenthos: Chi-squared = 12.653,
p= 0.554; for fish: Chi-squared = 2.81, p-value = 0.993) meaning
that the observed variability can be caused by taxa moving
among different sediment types and depth.
For each part of the fjord, the 5 megabenthic taxa with
the largest stratified biomass were reported: (1) for inner
fjord: cookie-cutter seastar Ctenodiscus crispatus, sea urchins
Strongylocentrotus, shrimps Pandalidae, sea urchins Echinus,
Frontiers in Marine Science | www.frontiersin.org 10 March 2022 | Volume 9 | Article 822979

fmars-09-822979 February 25, 2022 Time: 16:12 # 11
Mérillet et al. Fjords ‘Biodiversity Facing Anthropogenic Impacts
and basket star Gorgonocephalus; (2) for central fjord: red
king crab Paralithodes camtschaticus, soft coral Nephtheidae,
shrimps Pandalidae, ascidians Ascidiacea and polychaetes worms
Maldanidae; (3) for outer fjord: sea anemone Actiniaria, shrimps
Pandalidae, hairy cockle Ciliatocardium ciliatum, brittle star
Ophiuridae and polychaetes worms Ampharetidae. Similarly, fish
taxa with the largest biomass in the whole fjord were haddock
Melanogrammus aeglefinus, cod Gadus morhua, American plaice
Hippoglossoides platessoides, whitch flounder Glyptocephalus
cynoglossus, starry ray Amblyraja radiata.
DISCUSSION
We used a complex multivariate analysis to identify the trends
of the co-variation with the environment of megabenthic and
fish taxa in a sub-arctic fjord facing both climate change effects
and invasion by red king crabs. Our results show that the
fish assemblages are increasingly driven by bottom temperature,
especially after 2016. Those assemblages are markedly changing
at the detriment of the cold-water boreal fish taxa whose
biomass significantly decreased over 2007–2019, while the
biomass of warm-water fish taxa did not show a significant
trend. In particular, we observed a significant decreasing trend
for the cold-water haddock Melanogrammus aeglefinus and
Atlantic hookhead Artediellus atlanticus. Increased taxonomic
richness and Shannon diversity index indicate that warm-
water fish species that were occasionally present in the fjord
at the beginning of the time series tend to become present
every year, toward the end of the time series. This included
argentine Argentina, blue whiting Micromesistius poutassou,
lanternfishes Myctophiformes, and round ray Rajella fyllae
(Supplementary Figure 7).
Fish and megabenthic taxa co-variated along the fjord,
discriminating between warm-water taxa present in the outer
part of the fjord, and cold-water taxa present in the central
and inner part. Such co-variation of fish and megabenthos with
environmental gradients has been mapped for the Barents Sea
(Johannesen et al., 2017) but without investigating temporal
responses to climate change between fish and megabenthic
assemblages, as done in our work. Over the studied period, the
main change in environmental variables that the taxa had to
cope with was the freshening of the water in the fjord and the
decrease of the maximum sea ice extent. This density reduction
could strengthen water stratification in the fjord, which has led to
dissolved oxygen decline in fjords in southwest Norway (Aksnes
et al., 2019). However, the rate of renewal of the deep water below
the sill in the inner basin (Askeland, personal communication)
would likely prevent the Porsanger from future anoxia. This
seems confirmed by the O2measurements realized in the whole
fjord in May 2019, with the lowest values >6.5 mmol.m−3
(IMR coastal survey, unpublished data). A decrease in sea ice
maximum extent is particularly visible in 2010–2015. Yet, over
2007–2019, no significant decrease in maximum sea ice extent,
increase in surface or bottom temperature could be detected
in the fjord. However, the warming of waters along the North
Norwegian coast due to a larger penetration of warm Atlantic
waters (Asbjørnsen et al., 2020;Skagseth et al., 2020) has most
likely driven the arrival of warm-water fish species similar to what
has been reported in the Barents Sea (Fossheim et al., 2015).
With climate change and increased sea temperature,
megabenthic species composition is also hypothesized to change
toward more warm-water species (Kedra et al., 2010b). These
changes are thought to be particularly strong for arctic benthic
species that are documented to have a narrow environmental
tolerance (i.e., stenothermal species) and lower upper thermal
limit compared to arcto-boreal and boreal species (Morley et al.,
2019;Renaud et al., 2019). In the Porsanger, including in the
Arctic inner part of the fjord, no significant decrease of the
biomass of cold-water species was recorded. However, given that
we did not observe a significant increase in bottom temperature
inside the fjord, this finding might not be surprising. This lack of
a significant trend in megabenthic diversity is in line with results
from previous studies. For instance, W˛esławski et al. (2017)
reported that marked changes in the physical environment
had little effect on the benthic fauna of two fjords of the West
Spitsbergen, because of the fauna’s adaptation to the scale of
the ongoing changes. Indeed, megabenthic assemblages might
show some degree of stability facing climate change due to
their inherent adaptive capacities to survive long periods of
seasonally low food availability (Sun et al., 2009) as well as
opportunistic feeding strategy (Iken et al., 2010;Weslawski
et al., 2011). Renaud et al. (2019), which modeled megabenthic
species habitat under an end-of-century scenario of warming and
acidification, found that mean habitat loss was small (0–11%)
and that there was no significant difference in habitat loss
between arctic and arcto-boreal species. In addition, the response
of megabenthic cold-water taxa to climate change sometimes
cannot be fully evaluated considering the environment the taxa
live in, and notably their thermic preferences. Other parameters
not considered in this study, such as metabolic characteristics,
that enable species to feed on zooplankton (Sun et al., 2009)
or macroalgae detritus (Renaud et al., 2015) instead of on
sea-ice dependent micro-algae, might also play a major role in
climate change adaptation. Lowering in size could also favor
the survival of species as it reduces their metabolic energy
demand (Garilli et al., 2015). Further, macroalgae coverage
experienced a very sharp decrease due to the burst in sea urchin
abundance that happened into the fjord from the beginning
of the 1970s following the overexploitation of Norwegian
spring-spawning herring, large cod, and saith (Larsen, 2010).
Macroalgae offer important habitats for numerous benthic
species and juvenile fish (Larsen, 2010). The recovering in the
extent of macroalgae might have been a favorable parameter
for megabenthic species that might compensate to some extent
the changes in environmental variables (decreased salinity,
increase bottom current, and decreased sea ice extent). Finally,
this lack of significant trends could also arise from bias in
our data. The patchiness of the sediments, especially in the
south-central fjord, makes it difficult to get good coverage of
the different sediment types where to follow a trend analysis
in time. Homogenous areas for long-term monitoring should
be identified and sampled every year. Further, only species
occurring in more than 5% of the sampling sites and one third
Frontiers in Marine Science | www.frontiersin.org 11 March 2022 | Volume 9 | Article 822979

fmars-09-822979 February 25, 2022 Time: 16:12 # 12
Mérillet et al. Fjords ‘Biodiversity Facing Anthropogenic Impacts
of the years of the time series were kept in this analysis. Rare
species could provide early signals of environmental shifts and
their absence from this dataset could also explain the lack of
significant trends observed.
Climate change was also hypothesized to lead to more
homogenous benthic assemblages (Kedra et al., 2010b;Weslawski
et al., 2011). But such homogenization was not observed in
the Porsanger since there were no significant decreasing trends
of Shannon diversity for megabenthic taxa. In the whole fjord,
the significant decrease of Pielou’s evenness for megabenthic
taxa highlights that the biomass of some few taxa become
dominant through 2007–2019. The increase of red king crab
biomass in the inner fjord (non-significant), as well as its biomass
becoming amongst the 5 largest in the inner fjord from 2014,
points toward red king crab becoming a dominant taxon in the
inner fjord. Red king crabs were reported to negatively impact
the productivity and the biomass of megabenthic assemblages
(Pedersen et al., 2018). Here we observed a decrease in the
abundance of megabenthic organisms in the inner fjord, which
is a sign of red king crab impact on benthic assemblages that
was also reported by Falk-Petersen et al. (2011) in invaded
areas in the Barents Sea. The significant decrease of brittle stars
Ophuroidae in the inner fjord, as well as the one of Island
scallops Chlamys in the medium fjord, could thus originate from
the predation of the red king crabs on these taxa (Jørgensen
and Primicerio, 2007). Since its introduction, benthic taxa
composition has changed in three fjord areas in Northern
Norway including Porsanger, with a decrease of echinoderms
and most bivalves while small polychaetes prospered (Oug
et al., 2018). Some further decrease of in the abundance of
benthic taxa, especially in the cold-water assemblages in the
inner fjord, are thus expected as the red king crab continues its
progression. Red king crabs are estimated to consume between
1 and 18% of the production of benthic invertebrates in the
Porsanger (Fuhrmann et al., 2015). Søvik et al. (2020) indeed
noted the attractiveness for red king crabs of the inner fjord
since those areas were later invaded and therefore probably
contain more food. Among megabenthic taxa with the largest
biomass in the inner part of the fjord, cookie-cutter sea star
Ctenodiscus crispatus and sea urchins Strongylocentrotus are
widely distributed through the Barents Sea (Jørgensen et al.,
2015). The inner fjord megabenthic assemblage thus appears
as a mix between sub-arctic and a few arctic taxa, such as
basket stars Gorgonocephalus, Iceland scallop Chlamys islandica,
hairy cockle Clinocardium ciliatum, Northern yoldia Yoldia
hyperborean, or crangonid shrimp Sclerocrangon ferox. This is
in line with the dominance of sub-arctic (arctic-boreal) taxa
observed in the arctic fjord Hornsund (Svalbard) (Kedra et al.,
2010a). In addition, C. crispatus is a detritivore sea star feeding
on small particles that settle on the seabed. This might indicate
a weak current regime of the ecosystem with a large quantity
of detrital matter that represents C. crispatus food supply and
allow it to sustain its large population (Jørgensen et al., 2015).
C. crispatus also appears to be a prey of red king crabs (Jørgensen
and Primicerio, 2007) with the potential to eradicate it from
the area, as it happened in an adjacent fjord (Varangerfjorden)
over 1994–2007 (Oug et al., 2011). A major regime shift is
thus a potential threat for the inner cold parts of the fjord
if red king crabs continue their progression. In addition, a
decrease in the condition of red king crabs has been reported
by fishermen, at the beginning of 2021, with a large share of the
catch composed of small crabs with poor meat content where
catches were previously dominated by large crabs with good
meat content (H.K. Strand, IMR, personal communication). This
could indicate that red king crabs have reached their carrying
capacity into the fjord.
Porsangerfjorden, as other fjords in Norway and Svalbard,
has experienced significant changes in the last 40 years, with
the collapse of the spring spawning herring, cod, and saith
fishery, the burst in sea urchin that down grazed macroalgae
in the 1970s, the arrival of red king crabs in the 2000s, the
recent recovery of macroalgae (Strand, 2019) and the effects of
climate change becoming tangible. In this study, we showed that
fish assemblages changed with climate change, with an increase
of the warm water fishes (significant increase in biomass of
Sebastes and increase in the presence frequency of argentine
Argentina, blue whiting Micromesistius poutassou, lanternfishes
Myctophiformes and round ray Rajella fyllae) at the detriment
of cold-water fish (significant decrease in biomass of haddock
Melanogrammus aeglefinus and Atlantic hookhead Artediellus
atlanticus). The changes undergone by megabenthic assemblages,
on the other hand, were caused by the ongoing invasion of
red king crabs that lead to a decrease in the abundance of
megabenthic taxa and in particular brittle stars Ophiuridae
and Iceland scallops Chlamys. The cold-water megabenthic
assemblage of the inner part of the fjord should be monitored
with caution as it can be closed to the tipping point where
red king crab had overgrazed the whole assemblages and
durably disrupt their functioning. Overall, this study suggests
that invasive species, before climate change effects, would be
a major threat to megabenthic assemblages of the subarctic
and arctic fjords.
DATA AVAILABILITY STATEMENT
The raw data supporting the conclusions of this article will be
made available by the authors, without undue reservation.
AUTHOR CONTRIBUTIONS
LM, LJ, MS, and FV designed the study and edited the
manuscript. LJ collected the benthic invertebrate’s data. MS
provided information and codes on how to extract NorKyst800
model outputs. LM performed the statistical analysis and wrote
the first draft. All authors contributed to the article and approved
the submitted version.
FUNDING
This study was conducted in the frame of the project FACE-
IT (The Future of Arctic Coastal Ecosystems—Identifying
Frontiers in Marine Science | www.frontiersin.org 12 March 2022 | Volume 9 | Article 822979

fmars-09-822979 February 25, 2022 Time: 16:12 # 13
Mérillet et al. Fjords ‘Biodiversity Facing Anthropogenic Impacts
Transitions in Fjord Systems and Adjacent Coastal Areas).
FACE-IT has received funding from the European Union’s
Horizon 2020 Research and Innovation Program under (grant
agreement no. 869154).
ACKNOWLEDGMENTS
We would like to thank all who made possible the samplings
during the coastal surveys and the benthic surveys in the
Porsangerfjord. We are grateful to Berengere Husson for her
comments on this work. We also thank Jon Albretsen for
sharing the river runoff data, Megan O’Sadnick for her insights
into sea ice formation into the Porsanger, Ingrid Johnsen
Askeland for sharing her data on the water exchanges periodicity
between the fjord and open ocean, Stig Dalsøren for his relevant
comments onto the NorKyst800 model outputs evaluation,
Hans Kristian Strand and Torstein Pedersen for sharing their
knowledge of the fjord.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fmars.
2022.822979/full#supplementary-material
REFERENCES
Aksnes, D. L., Aure, J., Johansen, P. O., Johnsen, G. H., and Vea Salvanes, A. G.
(2019). Multi-decadal warming of Atlantic water and associated decline of
dissolvegd oxygen in a deep fjord. Estuar. Coast. Shelf Sci. 228:106392. doi:
10.1016/j.ecss.2019.106392
Aksnes, D. L., Dupont, N., Staby, A., Fiksen, Ø., Kaartvedt, S., and Aure, J. (2009).
Coastal water darkening and implications for mesopelagic regime shifts in
Norwegian fjords. Mar. Ecol. Prog. Ser. 387, 39–49. doi: 10.3354/meps08120
Albretsen, J., Sperrevik, A. K., Staalstrøm, A., Sandvik, A. D., Vikebø, F., and Asplin,
L. (2011). NorKyst-800 Report no. 1: User Manual and Technical Descriptions.
Fisken Og Havet. Bergen: Institute of Marine Research.
Arctic Monitoring Assessment Program (2021). Arctic Climate Change Update
2021: Key Trends and Impacts. Tromsø: Arctic Monitoring and Assessment
Programme.
Asbjørnsen, H., Årthun, M., Skagseth, Ø., and Eldevik, T. (2020). Mechanisms
underlying recent arctic atlantification. Geophys. Res. Lett. 47:e2020GL088036.
doi: 10.1029/2020GL088036
Asplin, L., Albretsen, J., Johnsen, I. A., and Sandvik, A. D. (2020). The
hydrodynamic foundation for salmon lice dispersion modeling along the
Norwegian coast. Ocean Dyn. 70, 1151–1167. doi: 10.1007/s10236-020- 01378-0
Asplin, L., Salvanes, A. G. V., and Kristoffersen, J. B. (1999). Nonlocal wind-driven
fjord-coast advection and its potential effect on plankton and fish recruitment.
Fish. Oceanogr. 8, 255–263. doi: 10.1046/j.1365-2419.1999.00109.x
Bellail, R., Mahé, J. C., Newton, A., Officer, R., Reid, D., Stokes, D., et al.
(2001). IPROSTS Project International Program of Standardised Trawl Surveys.
Commission of European Comunities. Paris: IPROSTS.
Bergström, S. (1995). “The HBV model,” in Computer Models of Watershed
Hydrology, ed. V. P. Singh (Highlands Ranch, CO: Water Resources
Publications), 443–476.
Białogrodzka, J., Stramska, M., Ficek, D., and Wereszka, M. (2018). Total
suspended particulate matter in the Porsanger fjord (Norway) in the summers
of 2014 and 2015. Oceanologia 60, 1–15. doi: 10.1016/j.oceano.2017.06.002
Bianchi, T. S., Arndt, S., Austin, W. E. N., Benn, D. I., Bertrand, S., Cui, X., et al.
(2020). Fjords as aquatic critical zones (ACZs). Earth Sci. Rev. 203:103145.
doi: 10.1016/j.earscirev.2020.103145
Blicher, M. E., Hammeken Arboe, N., Jørgensen, L. L., Burmeister, A.,
Gudmundsson, G., Olafsdottir, S. H., et al. (2015). Development of
Minimum Standards for Long-Term Monitoring of Marine Bottom-Living
Fauna Communities in the Arctic-Atlantic: Pilot Study in Greenland (Issue
94). Available Online at: https://natur.gl/wp-content/uploads/2013/01/94-
Development-of- minimum-standards- for-long- term-monitoring- of-marine-
bottom-living-fauna-communities- in-the- Arctic-Atlantic.pdf (accessed
March 2021).
Calinski, T., and Harabasz, J. (1974). A dendrite method for cluster analysis.
Commun. Stat. 3, 1–27.
Chan, F. T., Stanislawczyk, K., Sneekes, A. C., Dvoretsky, A., Gollasch, S., Minchin,
D., et al. (2019). Climate change opens new frontiers for marine species in the
Arctic: current trends and future invasion risks. Glob. Change Biol. 25, 25–38.
doi: 10.1111/gcb.14469
Cieszy´
nska, A., and Stramska, M. (2018). Climate-related trends and
meteorological conditions in the Porsanger fjord, Norway. Oceanologia
60, 344–366. doi: 10.1016/j.oceano.2018.01.003
Drewnik, A., W˛esławski, J. M., and Włodarska-Kowalczuk, M. (2017). Benthic
Crustacea and Mollusca distribution in Arctic fjord – case study of patterns in
Hornsund, Svalbard. Oceanologia 59, 565–575. doi: 10.1016/j.oceano.2017.01.
005
Engås, A., and Ona, E. (1990). Day and night fish distribution pattern in the net
mouth area of the Norwegian bottom sampling trawl. Rapp. P. V. Réun. 189,
123–127.
Fagerli, C. W., Norderhaug, K. M., Christie, H., Pedersen, M. F., and Fredriksen, S.
(2014). Predators of the destructive sea urchin Strongylocentrotus droebachiensis
on the Norwegian coast. Mar. Ecol. Prog. Ser. 502, 207–218. doi: 10.3354/
meps10701
Falk-Petersen, J., Renaud, P., and Anisimova, N. (2011). Establishment and
ecosystem effects of the alien invasive red king crab (Paralithodes camtschaticus)
in the Barents Sea – a review. ICES J. Mar. Sci. 68, 479–488.
Fossheim, M., Primicerio, R., Johannesen, E., Ingvaldsen, R. B., Aschan, M. M.,
and Dolgov, A. V. (2015). Recent warming leads to a rapid borealization of
fish communities in the Arctic. Nat. Clim. Change 5, 673–677. doi: 10.1038/
nclimate2647
Frainer, A., Primicerio, R., Kortsch, S., Aune, M., Dolgov, A. V., Fossheim, M., et al.
(2017). Climate-driven changes in functional biogeography of Arctic marine
fish communities. Proc. Natl. Acad. Sci. U.S.A. 114:201706080. doi: 10.1073/
pnas.1706080114
Frigstad, H. (2020). Increased Light Attenuation in Norwegian Coastal Waters. Oslo:
Norsk institutt for vannforskning.
Frigstad, H., Kaste, Ø., Deininger, A., Kvalsund, K., Christensen, G.,
Bellerby, R. G. J., et al. (2020). Influence of riverine input on norwegian
coastal systems. Front. Mar. Sci. 7:332. doi: 10.3389/fmars.2020.0
0332
Fuhrmann, M. M., Pedersen, T., Ramasco, V., and Nilssen, E. M. (2015).
Macrobenthic biomass and production in a heterogenic subarctic fjord after
invasion by the red king crab. J. Sea Res. 106, 1–13. doi: 10.1016/j.seares.2015.
09.003
Garilli, V., Rodolfo-Metalpa, R., Scuderi, D., Brusca, L., Parrinello, D., Rastrick,
S. P. S., et al. (2015). Physiological advantages of dwarfing in surviving
extinctions in high-CO 2 oceans. Nat. Clim. Change 5, 678–682. doi: 10.1038/
nclimate2616
González-Pola, C., Larsen, K. M. H., Fratantoni, P., and Beszczynska-Möller, A.
(2019). ICES Report on Ocean Climate 2018. Copenhagen: International Council
for the Exploration of the Sea. doi: 10.17895/ices.pub.5461
Gray, J. S. (2002). Species richness of marine soft sediments. Mar. Ecol. Prog. Ser.
244, 285–297. doi: 10.3354/meps244285
Iken, K., Bluhm, B., and Dunton, K. (2010). Benthic food-web structure
under differing water mass properties in the southern Chukchi Sea. Deep
Sea Res. II Top. Stud. Oceanogr. 57, 71–85. doi: 10.1016/j.dsr2.2009.08
.007
Jamieson, G. S., Grosholz, E. D., Armstrong, D. A., and Elner, R. W.
(1998). Potential ecological implications from the introduction of the
Frontiers in Marine Science | www.frontiersin.org 13 March 2022 | Volume 9 | Article 822979

fmars-09-822979 February 25, 2022 Time: 16:12 # 14
Mérillet et al. Fjords ‘Biodiversity Facing Anthropogenic Impacts
european green crab, carcinus maenas (Linneaus), to British Columbia,
Canada, and Washington, USA. J. Nat. Hist. 32, 1587–1598. doi: 10.1080/
00222939800771121
Johannesen, E., Høines, Å. S., Dolgov, A. V., and Fossheim, M. (2012). Demersal
fish assemblages and spatial diversity patterns in the arctic-atlantic transition
zone in the barents sea. PLoS One 7:e34924. doi: 10.1371/journal.pone.0034924
Johannesen, E., Jørgensen, L. L., Fossheim, M., Primicerio, R., Greenacre, M.,
Ljubin, P. A., et al. (2017). Large-scale patterns in community structure of
benthos and fish in the Barents Sea. Polar Biol. 40, 237–246. doi: 10.1007/
s00300-016-1946-6
Johnsen, G., Volent, Z., Sakshaug, E., Sigemes, F., and Pettersson, L. H. (2009).
“Remote sensing in the Barents Sea,” in Ecosystem Barents Sea, Vol. 6, eds
E. Sakshaug, G. Johnsen, and K. Kovacs (Norway: Tapir Academic Press),
139–166.
Jolivet, A., Asplin, L., Strand, O., Thebault, J., and Chauvaud, L. (2015). Coastal
upwelling in norway recorded in great scallop shells. Limnol. Oceanogr. 60,
1265–1275. doi: 10.1002/lno.10093
Jørgensen, L. L., Ljubin, P., Skjoldal, H. R., Ingvaldsen, R. B., Anisimova, N.,
and Manushin, I. (2015). Distribution of benthic megafauna in the Barents
Sea: baseline for an ecosystem approach to management. ICES J. Mar. Sci. 72,
595–613.
Jørgensen, L. L., and Primicerio, R. (2007). Impact scenario for the invasive red
king crab Paralithodes camtschaticus (Tilesius, 1815) (Reptantia, Lithodidae)
on Norwegian, native, epibenthic prey. Hydrobiologia 590, 47–54. doi: 10.1007/
s10750-007-0756-9
Jørgensen, L. L., Primicerio, R., Ingvaldsen, R. B., Fossheim, M., Strelkova, N.,
Thangstad, T. H., et al. (2019). Impact of multiple stressors on sea bed fauna
in a warming Arctic. Mar. Ecol. Prog. Ser. 608, 1–12. doi: 10.3354/meps12
803
Jourdain, E., and Vongraven, D. (2017). Humpback whale (Megaptera
novaeangliae) and killer whale (Orcinus orca) feeding aggregations for
foraging on herring (Clupea harengus) in Northern Norway. Mamm. Biol. 86,
27–32. doi: 10.1016/j.mambio.2017.03.006
Kedra, M., Gromisz, S., Jaskuła, R., Legezy´
nska, J., Maciejewska, B., Malec, E.,
et al. (2010a). Soft bottom macrofauna of an all taxa biodiversity site: hornsund
(77◦N, Svalbard). Pol. Polar Res. 31, 309–326. doi: 10.2478/v10183-010-0008-y
Kedra, M., Włodarska-Kowalczuk, M., and Wesławski, J. M. (2010b). Decadal
change in macrobenthic soft-bottom community structure in a high Arctic fjord
(Kongsfjorden, Svalbard). Polar Biol. 33:1. doi: 10.1007/s00300-009-0679-1
Kolassa, J. E., and Jankowski, S. (2021). ‘MultNonParam’ Mutlivariate
Nonparametric Methods. R package. Available Online at: https://cran.r-
project.org/package=MultNonParam [accessed June 3, 2021].
Kortsch, S., Primicerio, R., Beuchel, F., Renaud, P. E., Rodrigues, J., Lønne, O. J.,
et al. (2012). Climate-driven regime shifts in Arctic marine benthos. Proc. Natl.
Acad. Sci. U.S.A. 109, 14052–14057. doi: 10.1073/pnas.1207509109
Kortsch, S., Primicerio, R., Fossheim, M., Dolgov, A. V., and Aschan, M. (2015).
Climate change alters the structure of arctic marine food webs due to poleward
shifts of boreal generalists. Proc. R. Soc. B Biol. Sci. 282:20151546. doi: 10.5061/
dryad.73r6j
Larsen, T. H. (2010). Population Dynamics of Cod, Gadus morhua, in
Porsangerfjord, Northern Norway. Tromsø: University of Tromsø.
Lindström, G., Johansson, B., Persson, M., Gardelin, M., and Bergström, S. (1997).
Development and test of the distributed HBV-96 hydrological model. J. Hydrol.
201, 272–288.
Morley, S. A., Barnes, D. K. A., and Dunn, M. J. (2019). Predicting which species
succeed in climate-forced polar seas. Front. Mar. Sci. 5:507. doi: 10.3389/fmars.
2018.00507
Myksvoll, M. S., Sandvik, A. D., Skarðhamar, J., and Sundby, S. (2012). Importance
of high resolution wind forcing on eddy activity and particle dispersion in a
Norwegian fjord. Estuar. Coast. Shelf Sci. 113, 293–304. doi: 10.1016/j.ecss.2012.
08.019
Olsen, E., Aanes, S., Mehl, S., Holst, J. C., Aglen, A., and Gjøsæter, H. (2010).
Cod, haddock, saithe, herring, and capelin in the Barents Sea and adjacent
waters: a review of the biological value of the area. ICES J. Mar. Sci. 67, 87–101.
doi: 10.1093/icesjms/fsp229
Ortega, A., Geraldi, N. R., Alam, I., Kamau, A. A., Acinas, S. G., Logares, R., et al.
(2019). Important contribution of macroalgae to oceanic carbon sequestration.
Nat. Geosci. 12, 748–754. doi: 10.1016/j.biortech.2016.03.060
O’Sadnick, M., Petrich, C., Brekke, C., and Skarðhamar, J. (2020). Ice extent in
sub-arctic fjords and coastal areas from 2001 to 2019 analyzed from MODIS
imagery. Ann. Glaciol. 61, 210–226. doi: 10.1017/aog.2020.34
Oug, E., Cochrane, S. K. J., Sundet, J. H., Norling, K., and Nilsson, H. C. (2011).
Effects of the invasive red king crab (Paralithodes camtschaticus) on soft-bottom
fauna in Varangerfjorden, northern Norway. Mar. Biodivers. 41, 467–479. doi:
10.1007/s12526-010-0068-6
Oug, E., and Fuhrmann, M. M. (2013). Bunndyrsamfunn i Foreslått Marint
Verneområde i Indre Porsangerfjorden Artssammensetning og Biomasse før
Invasjon av Kongekrabben 2011. Oslo: Norsk institutt for vannforskning.
Oug, E., Sundet, J. H., and Cochrane, S. K. J. (2018). Structural and functional
changes of soft-bottom ecosystems in northern fjords invaded by the red king
crab (Paralithodes camtschaticus). J. Mar. Syst. 180, 255–264. doi: 10.1016/j.
jmarsys.2017.07.005
Pedersen, T., Fuhrmann, M. M., Lindstrøm, U., Nilssen, E. M., Ivarjord, T.,
Ramasco, V., et al. (2018). Effects of the invasive red king crab on food web
structure and ecosystem properties in an Atlantic fjord. Mar. Ecol. Prog. Ser.
596, 13–31.
Pickens, B. A., Carroll, R., Schirripa, M. J., Forrestal, F., Friedland, K. D., and Taylor,
J. C. (2021). A systematic review of spatial habitat associations and modeling of
marine fish distribution: a guide to predictors, methods, and knowledge gaps.
PLoS One 16:e0251818. doi: 10.1371/journal.pone.0251818
Pielou, E. C. (1966). Species-diversity and pattern-diversity in the study of
ecological succession. J. Theor. Biol. 10, 370–383. doi: 10.1016/0022-5193(66)
90133-0
Piepenburg, D. (2005). Recent research on Arctic benthos: common notions need
to be revised. Polar Biol. 28, 733–755. doi: 10.1007/s00300-005-0013-5
Rao, C. R. (1995). A review of canonical coordinates and an alternative to
correspondence analysis using Hellinger distance. Questiio 19, 23–26.
Renaud, P. E., Sejr, M. K., Bluhm, B. A., Sirenko, B., and Ellingsen, I. H. (2015). The
future of Arctic benthos: expansion, invasion, and biodiversity. Prog. Oceanogr.
139, 244–257. doi: 10.1016/j.pocean.2015.07.007
Renaud, P. E., Wallhead, P., Kotta, J., Włodarska-Kowalczuk, M., Bellerby, R. G. J.,
Rätsep, M., et al. (2019). Arctic sensitivity? Suitable habitat for benthic taxa is
surprisingly robust to climate change. Front. Mar. Sci. 6:538. doi: 10.3389/fmars.
2019.00538
Reynoldson, T. B., and Metcalfe-Smith, J. L. (1992). An overview of the assessment
of aquatic ecosystem health using benthic invertebrates. J. Aquat. Ecosyst.
Health 1, 295–308. doi: 10.1007/BF00044171
Sejr, M. K., Blicher, M. E., and Rysgaard, S. (2009). Sea ice cover affects inter-
annual and geographic variation in growth of the Arctic cockle Clinocardium
ciliatum (Bivalvia) in Greenland. Mar. Ecol. Prog. Ser. 389, 149–158. doi: 10.
3354/meps08200
Skagseth, Ø., Eldevik, T., Årthun, M., Asbjørnsen, H., Lien, V. S., and Smedsrud,
L. H. (2020). Reduced efficiency of the Barents Sea cooling machine. Nat. Clim.
Change 10, 661–666. doi: 10.1038/s41558-020-0772-6
Smith, R. W., Bianchi, T. S., Allison, M., Savage, C., and Galy, V. (2015). High rates
of organic carbon burial in fjord sediments globally. Nat. Geosci. 8, 450–453.
doi: 10.1038/NGEO2421
Søvik, G., Nedreaas, K., Zimmermann, F., Husson, B., Strand, H. K.,
Jørgensen, L. L., et al. (2020). Kartlegging av Fjordøkosystemene i Tana-
og Porsangerfjorden. Available Online at: https://www.hi.no/hi/nettrapporter/
rapport-fra- havforskningen-2020-39 (accessed March 2021).
Spjeldnæs, N. (1978). Ecology of selected late- and post-glacial marine faunas in
the Oslo Fjord area. Geol. Foreiiirigeris Sfockholrn Forh. 100, 189–202. doi:
10.1080/11035897809454459
Strand, H. K. (2019). Porsanger 2.0. Available Onlie at: https://www.hi.no/hi/
nettrapporter/porsangerfjorden-2.0- final.docx (accessed April 2021).
Strand, H. K., Christie, H., Fagerli, C. W., Mengede, M., and Moy, F. (2020).
Optimizing the use of quicklime (CaO) for sea urchin management — a lab
and field study. Ecol. Eng. 6:100018. doi: 10.1016/j.ecoena.2020.100018
Stratmann, T., van Oevelen, D., Martínez Arbizu, P., Wei, C.-L., Liao, J.-X., Cusson,
M., et al. (2020). The BenBioDen database, a global database for meio-, macro-
and megabenthic biomass and densities - R code. Sci. Data 7, 1–12. doi: 10.5281/
zenodo.3843149
Sun, M. Y., Clough, L. M., Carroll, M. L., Dai, J., Ambrose, W. G., and Lopez,
G. R. (2009). Different responses of two common Arctic macrobenthic species
(Macoma balthica and Monoporeia affinis) to phytoplankton and ice algae: will
Frontiers in Marine Science | www.frontiersin.org 14 March 2022 | Volume 9 | Article 822979

fmars-09-822979 February 25, 2022 Time: 16:12 # 15
Mérillet et al. Fjords ‘Biodiversity Facing Anthropogenic Impacts
climate change impacts be species specific? J. Exp. Mar. Biol. Ecol. 376, 110–121.
doi: 10.1016/j.jembe.2009.06.018
Svendsen, H. (1991). Preliminary Results from a Hydrophysical Investigation of
Porsangerfjord, Altafjord and Adjacent Coastal Waters, June-August 1990.
Bergen: University of Bergen.
Svendsen, H. (1995). “Physical oceanography of coupled fjord coast systems in
northern Norway with special focus on frontal dynamics and tides,” in Ecology
of Fjords and Coastal Waters, eds H. R. Skjoldal, C. Hopkins, K. E. Erikstad, and
H. P. Leinaas (Amsterdam: Elsevier Science B.V).
Thioulouse, J., Dufour, A. B., Jombart, T., Dray, S., Siberchicot, A., and Pavoine, S.
(2018). Multivariate Analysis of Ecological Data with Ade4, Vol. 4. New York,
NY: Springer, 1–294. doi: 10.1007/978- 1-4939-8850-1
Thioulouse, J., Simier, M., and Chessel, D. (2004). Simultaneous analysis of a
sequence of paired ecological tables with the {STATICO} method. Ecology 85,
272–283. doi: 10.1890/02-0605
Tucker, L. R. (1966). Some mathematical notes on three-mode factor analysis.
Psychometrika 31, 279–311. doi: 10.1007/BF02289464
Vaz, S., Carpentier, A., and Coppin, F. (2007). Eastern English channel fish
assemblages: measuring the structuring effect of habitats on distinct sub-
communities. ICES J. Mar. Sci. 64, 271–287. doi: 10.1093/icesjms/fsl031
Wassmann, P., Duarte, C. M., Agusti, S., and Serj, M. (2011). Footprints of climate
change in the Arctic marine ecosystem. Glob. Change Biol. 17, 1235–1249.
doi: 10.1111/j.1365-2486.2010.02311.x
W˛esławski, J. M., Buchholz, F., Głuchowska, M., and Weydmann, A. (2017).
Ecosystem maturation follows the warming of the Arctic fjords. Oceanologia
59, 592–602. doi: 10.1016/j.oceano.2017.02.002
Weslawski, J. M., Kendall, M. A., Włodarska-Kowalczuk, M., Iken, K., Kedra,
M., Legezynska, J., et al. (2011). Climate change effects on Arctic fjord and
coastal macrobenthic diversity-observations and predictions. Mar. Biodivers.
41, 71–85. doi: 10.1007/s12526-010- 0073-9
Włodarska-Kowalczuk, M., We¸sawski, J. M., and Kotwicki, L. (1998). Spitsbergen
glacial bays macrobenthos - a comparative study. Polar Biol. 20, 66–73. doi:
10.1007/s003000050277
Yletyinen, J. (2019). Arctic climate resilience. Nat. Clim. Change 9, 805–806. doi:
10.1038/s41558-019-0616-4
Conflict of Interest: The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be construed as a
potential conflict of interest.
Publisher’s Note: All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their affiliated organizations, or those of
the publisher, the editors and the reviewers. Any product that may be evaluated in
this article, or claim that may be made by its manufacturer, is not guaranteed or
endorsed by the publisher.
Copyright © 2022 Mérillet, Skogen, Vikebø and Jørgensen. This is an open-access
article distributed under the terms of the Creative Commons Attribution License
(CC BY). The use, distribution or reproduction in other forums is permitted, provided
the original author(s) and the copyright owner(s) are credited and that the original
publication in this journal is cited, in accordance with accepted academicpractice. No
use, distribution or reproduction is permitted which does not comply with theseterms.
Frontiers in Marine Science | www.frontiersin.org 15 March 2022 | Volume 9 | Article 822979