Over 20% of marine fishes shifting in the North and Barents Seas, but not in the Norwegian Sea

Introduction

Climate warming induces poleward expansions and equatorward range contractions of species’ geographic distributions on a global scale. These shifts have already been documented for thousands of species (Chaudhary et al., 2021; Pinsky, Selden & Kitchel, 2020). Marine species are expected to show stronger and faster niche shifts in response to global warming than terrestrial species because their geographic ranges more closely match their thermal limits, the marine environment is highly connected, and many marine species have huge dispersal potential as adults and/or planktonic life-stages (Pinsky et al., 2019; Sunday, Bates & Dulvy, 2012).

While the geographic distribution of over 50% of studied taxa shifts in response to climate warming, the direction and magnitude of these shifts depend on local abiotic variables, biological interactions and individual taxa characteristics (Pinsky et al., 2013; Tekwa, Watson & Pinsky, 2022). For each species, the probability of range expansion or contraction depends on several biological traits, including functional traits such as diet, swimming behaviour or habitat, but also biogeographic traits, such as distribution range size, or physiological traits, such as thermal preference or thermal range size (Kitchel et al., 2022; Perry et al., 2005; Sunday et al., 2015). Despite multiple studies, there is still little agreement on which specific traits facilitate or hinder climate-induced species distribution shifts. For example, fish species shifting distributions in the North Sea were detected to have faster life cycles and smaller body sizes than non-shifting species, while other studies showed that larger fish species with higher swimming ability are more prone to settle into temperate habitats from the tropics (Feary et al., 2014; Perry et al., 2005; Sunday et al., 2015). Contrasting trait-mediated response is also apparent at higher latitudes: while in the Bering Sea, no relationship was found between marine fish species dispersal-related traits and the degree of species thermal niche tracking, larger and generalist species expanded in the Barents Sea (Frainer et al., 2017; Fredston et al., 2021).

In the Arctic, a global warming hotspot, recent climate-warming-induced boreal species’ range expansions have affected fish species richness and food-web structure (Frainer et al., 2017, 2021; Gordó-Vilaseca et al., 2023; Kortsch et al., 2015). These included several species northward expansions in the Norwegian Sea, including the Atlantic mackerel (Scomber scombrus) and the European hake (Merluccius merluccius) (Skaret et al., 2015; Wienerroither et al., 2011), and at least 11 boreal species in the Barents Sea (Pecuchet et al., 2020). These community changes resulted in higher functional diversity and increased boreal-like trait profile in the Barents Sea (larger, longer lived and more piscivorous than Arctic species) (Frainer et al., 2017, 2021). However, changes in community traits towards an increased dominance of boreal-like traits in the Barents Sea do not necessarily indicate that these traits facilitate species distributional shifts in the area. The observed changes in the trait composition of the Barents Sea fish communities can be due to the overall community shifting, i.e., all species within the boreal community expanded poleward, or it can result from the range-shift of only few boreal species sharing shifting-related traits. Which of these processes dominate is unknown as the direct relationship between species traits and the magnitude of their climate-induced geographic shift has never been addressed in this region. Moreover, although previous studies confirmed the presence of species shifts (Fossheim et al., 2015; Pecuchet et al., 2020), a quantification of the magnitude and direction of these shifts is currently lacking. Understanding whether and which species respond faster to climate warming is crucial for informing effective conservation and management strategies (Pinsky, Selden & Kitchel, 2020).

Species biomass is theoretically maximised at temperatures corresponding to the species’ optimal temperature and tend to decline at warmer and colder temperatures than this optimum (Burrows et al., 2019). According to this principle, assuming a linear relationship between temperature and latitude, and considering that temperature is the main driver of most species at biogeographic scales (Rutterford et al., 2023), the geographic distribution of species’ biomass centroids would be at latitudes where mean temperatures correspond to species’ “preferred temperatures”. As sea temperatures increase, these distributions may follow the increase in temperature across latitude. Areas that were previously too cold for a certain species, might approach species’ preferred temperatures, resulting in population increases. Conversely, areas that were previously at species’ preferred temperature might become too warm, resulting in population declines with warming in this region (Fig. 1).

Here, we analysed 9,522 research trawls to compare the pace of latitudinal range shifts across 83 marine fish species’ biomass centroids, from the North Sea (data from 1998 to 2020) the Norwegian Sea (data from 2000 to 2020), and the Barents Sea (data from 2004 to 2020). We explored the relationship between species’ geographic shifts, their ability to track thermal envelope shifts, and the relationship between species traits and their observed geographic shifts. To do so, we calculate (1) each species’ geographic latitudinal shift in km/yr, (2) each species’ thermal envelope latitudinal shift in km/yr, (3) the difference between these two as a measure of the tracking capacity of the species, and (4) we study to what extent can these geographic shifts be explained by species traits. We hypothesize that marine fish species will generally track their thermal envelope, as observed in other areas (Pinsky, Selden & Kitchel, 2020), and that species with higher dispersal potential (higher fecundity and pelagic habitat) and smaller body lengths show faster geographic shifts following shifts in their geographic thermal envelope, than large and slower maturing fish species, in accordance with previous studies in the North Sea (Perry et al., 2005). Although our data covers several species distribution ranges only partially, species range shifts should still be detected when occurring, if the relationship between species’ biomass and latitude follows a bell-shape curve, which is a wide-spread assumption (Burrows et al., 2019) (Fig. 1).

Methods

Study area

Our study area was the continental shelf and slope from the North Sea into the Arctic Ocean, from 51°N to 80°N latitude, and from 4°W to 47°E longitude. The area comprises a temperature gradient, with average annual bottom water temperatures over 8 °C in the North Sea to −1 °C in the northern region of the Barents Sea. Based on different ecoregions (Costello et al., 2017; Yaragina & Dolgov, 2009), but also on temporal and spatial distribution of the data, we distinguished three study regions due to data collection differences between them (Fig. 2, Table 1 and Fig. S1). We restricted the analysis to offshore areas defined as being at least 20 km from the coast.

Table 1:

Data included in each study area.

Region	First year	Last year	Years surveyed	Months surveyed	Hauls
Barents Sea	2004	2020	14 (not 2014 and 2016)	July, August and September	2,306
Norwegian Sea	2000	2020	21	October, November and December	628
North Sea	1998	2020	22	July, August and September	6,588

DOI: 10.7717/peerj.15801/table-1

Results

A generalized northward shift of species’ biomass centroids was clearly detected over the last decades in the North Sea (from 1998 to 2022) (Wilcoxon signed rank test, p < 0.05), but not in the Norwegian Sea (from 2000 to 2020) (Student t-test, p = 0.6); nor in the Barents Sea (from 2004 to 2020) (Wilcoxon signed rank test, p = 0.09) (Fig. 3). The (pseudo) median shifting rates were 0.7 km/yr, 95% CI [0.1–1.4] in the North Sea, 0.4 km/yr, 95% CI [−1.3 to 2.2] in the Norwegian Sea, and 1.6 km/yr, 95% CI [−0.2 to 4.3] in the Barents Sea (Fig. 3). When each species was assessed individually, 15 species of 58 showed clear northward shifts of their biomass centroid in the North Sea (26%), while two species showed a clear southward shift (4%) (LR, p < 0.05) (Fig. S2). In the Norwegian Sea, two species were detected to shift northwards and two species southwards of a total of 26 species (8%) (LR, p < 0.05) (Fig. S3), and seven species of 35 showed clear northward shifts in the Barents Sea (20%), while two species showed a clear southward shift (6%) (LR, p < 0.05) (Fig. S4) (Table 2).

Table 2:

Linear effect of Year on mean latitude of marine fish species in the Barents Sea (Bar), Norwegian Sea (Now) and North Sea (Nor).

Species	Reg.	n	Lat shift (km/yr)	r2	P	Thermal envelope shift (km/yr)	r2	P
Amblyraja radiata	Bar	15	3.49	0.68	0.25	0.05	0.64	0.00
Anarhichas denticulatus	Bar	15	−2.93	0.57	0.41	0.05	0.53	0.00
Anarhichas lupus	Bar	15	−0.51	0.13	0.91	0.03	0.29	0.00
Anarhichas minor	Bar	15	1.54	0.64	0.46	0.05	0.70	0.00
Arctozenus risso	Bar	14	−9.36	0.62	0.02	0.01	0.60	0.00
Argentina silus	Bar	15	3.81	0.52	0.08	0.02	0.42	0.00
Artediellus atlanticus	Bar	15	−2.55	0.84	0.19	0.02	0.42	0.00
Boreogadus saida	Bar	15	1.97	0.50	0.49	0.03	0.40	0.00
Brosme brosme	Bar	14	3.71	0.59	0.38	0.03	0.48	0.00
Clupea harengus	Bar	11	13.60	0.44	0.15	0.01	0.15	0.05
Cottunculus microps	Bar	15	5.52	0.50	0.15	−0.02	0.21	0.02
Gadiculus argenteus	Bar	13	1.79	0.67	0.27	0.02	0.54	0.00
Gadus morhua	Bar	15	−3.33	0.83	0.07	0.03	0.46	0.00
Hippoglossoides platessoides	Bar	15	−0.30	0.89	0.77	0.05	0.71	0.00
Leptagonus decagonus	Bar	15	5.72	0.91	0.00	0.04	0.65	0.00
Leptoclinus maculatus	Bar	15	0.17	0.58	0.92	0.03	0.55	0.00
Liparis fabricii	Bar	14	8.37	0.66	0.03	0.03	0.36	0.00
Lumpenus lampretaeformis	Bar	15	3.54	0.68	0.18	0.04	0.58	0.00
Lycodes esmarkii	Bar	10	1.17	0.86	0.82	0.05	0.59	0.00
Lycodes gracilis	Bar	15	−4.50	0.61	0.15	0.04	0.63	0.00
Lycodes pallidus	Bar	15	14.32	0.65	0.01	0.07	0.69	0.00
Lycodes reticulatus	Bar	15	11.90	0.67	0.03	0.03	0.49	0.00
Lycodes rossi	Bar	15	11.07	0.74	0.00	0.04	0.68	0.00
Mallotus villosus	Bar	15	0.12	0.65	0.96	0.05	0.64	0.00
Melanogrammus aeglefinus	Bar	15	−2.07	0.57	0.44	0.09	0.49	0.00
Micromesistius poutassou	Bar	15	−3.51	0.77	0.04	0.02	0.59	0.00
Pollachius virens	Bar	12	−1.58	0.71	0.39	0.02	0.55	0.00
Rajella fyllae	Bar	13	−4.68	0.74	0.11	0.01	0.15	0.05
Reinhardtius hippoglossoides	Bar	15	−0.47	0.72	0.87	0.00	0.03	0.42
Sebastes mentella	Bar	15	0.12	0.71	0.96	0.02	0.52	0.00
Sebastes norvegicus	Bar	15	−3.32	0.42	0.71	0.04	0.37	0.00
Sebastes viviparus	Bar	15	0.47	0.22	0.87	0.03	0.62	0.00
Triglops murrayi	Bar	15	12.23	0.61	0.03	0.03	0.74	0.00
Triglops nybelini	Bar	15	11.38	0.85	0.00	0.02	0.32	0.00
Trisopterus esmarkii	Bar	12	−0.32	0.25	0.93	0.02	0.59	0.00
Anarhichas lupus	Now	18	7.48	0.57	0.02	0.00	0.00	0.91
Argentina silus	Now	21	−0.73	0.76	0.52	−0.01	0.08	0.17
Artediellus atlanticus	Now	18	7.37	0.58	0.01	0.00	0.05	0.28
Brosme brosme	Now	20	4.29	0.67	0.07	0.00	0.10	0.12
Chimaera monstrosa	Now	21	−0.10	0.67	0.95	0.00	0.00	0.74
Clupea harengus	Now	12	−8.66	0.36	0.18	0.01	0.24	0.01
Etmopterus spinax	Now	17	−1.36	0.70	0.51	0.01	0.27	0.01
Gadiculus argenteus	Now	19	3.49	0.40	0.25	0.00	0.03	0.39
Gadus morhua	Now	21	−0.97	0.46	0.69	0.00	0.04	0.31
Glyptocephalus cynoglossus	Now	18	−3.26	0.36	0.38	0.00	0.01	0.67
Hippoglossoides platessoides	Now	20	0.07	0.45	0.98	−0.01	0.43	0.00
Lepidorhombus whiffiagonis	Now	17	8.07	0.53	0.09	0.04	0.74	0.00
Lophius piscatorius	Now	16	2.69	0.29	0.60	0.01	0.52	0.00
Melanogrammus aeglefinus	Now	21	−2.83	0.77	0.02	0.01	0.08	0.17
Merlangius merlangus	Now	20	1.56	0.53	0.58	0.02	0.44	0.00
Merluccius merluccius	Now	12	−5.63	0.59	0.41	0.03	0.75	0.00
Micromesistius poutassou	Now	20	−5.86	0.68	0.02	0.01	0.39	0.00
Microstomus kitt	Now	20	−2.63	0.73	0.20	0.00	0.08	0.17
Molva molva	Now	17	5.52	0.46	0.23	0.01	0.37	0.00
Phycis blennoides	Now	15	0.86	0.59	0.80	0.01	0.13	0.07
Pollachius virens	Now	21	−0.58	0.88	0.44	0.02	0.37	0.00
Sebastes norvegicus	Now	21	1.04	0.49	0.54	0.01	0.23	0.01
Sebastes viviparus	Now	21	1.47	0.75	0.19	0.01	0.23	0.01
Triglops murrayi	Now	17	4.13	0.41	0.37	0.02	0.21	0.02
Trisopterus esmarkii	Now	20	0.52	0.86	0.63	0.02	0.52	0.00
Trisopterus minutus	Now	15	−4.28	0.46	0.18	0.02	0.54	0.00
Agonus cataphractus	Nor	23	1.03	0.65	0.59	−0.01	0.17	0.04
Amblyraja radiata	Nor	23	−0.28	0.85	0.60	0.00	0.01	0.72
Ammodytes marinus	Nor	23	−4.22	0.82	0.01	0.00	0.09	0.15
Anarhichas lupus	Nor	22	2.41	0.63	0.04	0.00	0.04	0.31
Arnoglossus laterna	Nor	23	2.12	0.69	0.00	0.01	0.21	0.02
Brosme brosme	Nor	12	3.81	0.92	0.14	0.00	0.01	0.71
Buglossidium luteum	Nor	23	1.62	0.79	0.00	0.02	0.33	0.00
Callionymus lyra	Nor	23	0.67	0.82	0.26	0.00	0.19	0.02
Chelidonichthys cuculus	Nor	17	0.18	0.86	0.94	0.00	0.01	0.57
Chelidonichthys lucerna	Nor	23	−1.60	0.40	0.32	0.01	0.25	0.01
Clupea harengus	Nor	23	−0.16	0.83	0.66	0.01	0.24	0.01
Echiichthys vipera	Nor	23	0.82	0.46	0.21	0.01	0.05	0.27
Enchelyopus cimbrius	Nor	23	0.65	0.89	0.43	0.00	0.00	0.80
Engraulis encrasicolus	Nor	14	−0.80	0.74	0.71	0.01	0.05	0.27
Eutrigla gurnardus	Nor	23	0.43	0.90	0.20	0.01	0.13	0.07
Gadiculus argenteus	Nor	23	0.79	0.13	0.41	0.00	0.09	0.15
Gadus morhua	Nor	23	1.25	0.96	0.05	−0.01	0.11	0.09
Glyptocephalus cynoglossus	Nor	23	−0.02	0.73	0.98	−0.02	0.11	0.10
Helicolenus dactylopterus	Nor	14	9.06	0.65	0.06	−0.01	0.08	0.16
Hippoglossoides platessoides	Nor	23	0.54	0.93	0.06	0.00	0.00	0.94
Hippoglossus hippoglossus	Nor	13	7.97	0.78	0.04	0.00	0.01	0.55
Hyperoplus lanceolatus	Nor	23	0.09	0.38	0.90	−0.01	0.08	0.17
Lepidorhombus whiffiagonis	Nor	23	0.84	0.65	0.28	0.00	0.02	0.47
Leucoraja naevus	Nor	23	−1.62	0.60	0.16	0.01	0.06	0.22
Limanda limanda	Nor	23	−0.09	0.87	0.72	0.02	0.23	0.01
Lophius piscatorius	Nor	23	−0.84	0.93	0.24	−0.01	0.13	0.07
Lumpenus lampretaeformis	Nor	22	1.66	0.62	0.12	−0.04	0.22	0.01
Maurolicus muelleri	Nor	13	7.35	0.79	0.01	−0.03	0.15	0.05
Melanogrammus aeglefinus	Nor	23	0.97	0.93	0.01	0.00	0.01	0.59
Merlangius merlangus	Nor	23	−0.32	0.96	0.07	0.00	0.01	0.57
Merluccius merluccius	Nor	23	1.67	0.95	0.00	0.00	0.11	0.09
Microchirus variegatus	Nor	11	5.09	0.87	0.05	0.01	0.08	0.15
Micromesistius poutassou	Nor	23	2.02	0.58	0.13	−0.01	0.19	0.03
Microstomus kitt	Nor	23	0.16	0.87	0.55	0.00	0.06	0.22
Molva molva	Nor	23	2.06	0.78	0.02	−0.01	0.17	0.04
Mullus surmuletus	Nor	23	−0.29	0.42	0.88	0.01	0.04	0.31
Myoxocephalus scorpius	Nor	23	−2.37	0.62	0.18	0.00	0.00	0.73
Myxine glutinosa	Nor	23	3.40	0.56	0.01	0.00	0.03	0.39
Platichthys flesus	Nor	19	−3.70	0.58	0.09	−0.01	0.11	0.10
Pleuronectes platessa	Nor	23	−0.12	0.94	0.64	0.01	0.13	0.07
Pollachius pollachius	Nor	12	−4.50	0.66	0.30	−0.01	0.09	0.13
Pollachius virens	Nor	23	1.12	0.91	0.00	−0.01	0.14	0.06
Raja clavata	Nor	15	6.12	0.81	0.06	0.00	0.00	0.88
Raja montagui	Nor	16	5.07	0.77	0.10	0.00	0.00	0.94
Scomber scombrus	Nor	23	0.63	0.94	0.03	0.00	0.02	0.51
Scophthalmus maximus	Nor	23	−0.62	0.46	0.78	0.01	0.29	0.00
Scophthalmus rhombus	Nor	18	2.48	0.45	0.38	0.01	0.26	0.01
Scyliorhinus canicula	Nor	23	−1.64	0.80	0.11	0.01	0.06	0.21
Sebastes viviparus	Nor	20	0.71	0.71	0.74	0.00	0.00	0.74
Solea solea	Nor	23	5.84	0.49	0.05	0.01	0.29	0.00
Sprattus sprattus	Nor	23	−0.70	0.81	0.19	−0.01	0.09	0.14
Squalus acanthias	Nor	23	3.75	0.68	0.07	−0.01	0.03	0.44
Trachinus draco	Nor	13	−7.78	0.55	0.11	0.02	0.23	0.01
Trachurus trachurus	Nor	23	0.93	0.98	0.07	−0.04	0.13	0.07
Trisopterus esmarkii	Nor	23	0.20	0.72	0.69	−0.01	0.16	0.05
Trisopterus luscus	Nor	18	−3.17	0.57	0.03	0.01	0.04	0.33
Trisopterus minutus	Nor	23	4.22	0.97	0.00	0.00	0.01	0.70
Zeus faber	Nor	15	−10.27	0.82	0.10	0.02	0.13	0.07

DOI: 10.7717/peerj.15801/table-2

Note:

Each model included a linear effect of year, overall annual mean and median latitude, and a smooth effect of depth, to correct for variability across surveys.

Thermal envelope models

Species thermal envelope models including SBT, SST, SIC and depth, presented a heterogeneous but reasonable fit to the data. Area Under the Curve (AUC) evaluation of taxa presence/absence model accuracy (part one of the GAM) ranged between 0.78 and 0.99 with a mean of 0.92. Models of non-zero observations (part two of the GAM) explained 1.9–54 % of the deviance in the data, with a mean of 21 % (Fig. S5). Most of the individual species’ thermal envelopes moved poleward in the Barents and Norwegian Seas (94% and 84% respectively), but less than half in the North Sea (43%) (Table 2).

No significant correlation was detected between species shifting rates and their thermal envelope in the Norwegian and Barents Sea, and a negative significant correlation was detected in the North Sea (Pearson correlation = −0.28, p-val = 0.03) of the study areas (Fig. 4). In the Barents Sea, 60% of the species shifted in the same direction as their thermal envelope, and 95% of them did it northward (Table 3). In the Norwegian Sea, 54% of species shifted in accordance with their thermal envelope, 86% of them northward (Table 3). In the North Sea, 31% of the species shifted in the same direction as their thermal envelope, and of these, 61% shifted northward.

Table 3:

Percentage of correspondence between latitudinal direction of observed change vs thermal envelope change in each study area.

Direction	Barents Sea	Norwegian Sea	North Sea
Observed shift ↓ but thermal envelope shift ↑	37%	39%	24%
Observed shift ↑, thermal envelope shift ↓	3%	8%	45%
Observed + thermal envelope shifts ↓	3%	8%	12%
Observed + thermal envelope shifts ↑	57%	46%	19%

DOI: 10.7717/peerj.15801/table-3

We detected a significant effect of species’ records in the absolute difference between the geographic and the thermal shifts, implying that both estimations were more different in poorly sampled species than for common and well-sampled species (LRs, p-val < 0.05). To include this in the analysis, we conducted the correlation tests between species geographical shifts and species shifts in thermal envelope, adding one species at a time ordered by species frequency of occurrence (correlation test with the three most abundant species, then the four most abundant species, the five most abundant, the six, etc.), a significant correlation was only detected in the North Sea, when including all species lead to a negative correlation between species’ thermal envelope shift, and species’ realized latitudinal shift (Fig. S6).

Functional traits contribution to explaining species’ shifts

Each species’ number of records had a significant negative effect on species’ latitudinal shifts in the Barents Sea, for which we included this variable in all trait regressions in that study area (weighted generalised least squares model, p < 0.05, Table S2). In the other study areas, no significant effect of species records was detected on species latitudinal shifts (weighted generalised least squares model, p > 0.05, Table S2).

Species’ biogeography, and preferred mean temperature were also significant explanatory variables for species latitudinal shifts in the Barents Sea, even when regressed in multiple regression including species records (weighted GLS, p-val < 0.05) (Table 4). Among these, species biogeography was a better explanatory variable in terms of explanatory power, and the only one significant when both were included in one multiple regression model, which suggest that Arctic species, and colder adapted species, shifted northward faster than warmer-water boreal species. In the North Sea, species’ minimum age at maturity and maximum depth were positively related to species latitudinal shifts, respectively (Table 4 and Fig. 5). Finally, no trait was detected to significantly explain species shifts in the Norwegian Sea (Table 4 and Fig. 5).

Table 4:

Collection of traits that showed a signifcant (p < 0.05) and non-significant (showing only p < 0.05–0.1) relationship with species latitudinal shift in each region, using weighted generalised multiple least squared regression with each trait.

Study area	Trait	Slope		r 2	p
Barents Sea	Preferred temperature		−0.7 km yr−1 °C−1	0.25	<0.01
Barents Sea	Temperature range		−0.5 km yr−1 °C−1	0.20	0.1
Barents Sea	Habitat: pelagic (vs demersal)		−2.5 km yr−1	0.20	0.06
Barents Sea	Zoogeography: Arctic-Boreal (vs Arctic)		−5.0 km yr−1	0.58	0.02
Barents Sea	Zoogeography: Boreal (vs Arctic)		−7.9 km yr−1	0.58	<0.01
Barents Sea	Zoogeography: deep-water (vs Arctic)		−18.8 km yr−1	0.58	<0.01
Barents Sea	Trophic level		−3.1 km yr−1	0.28	0.07
Norwegian Sea	Preferred temperature		−0.8 km yr−1 °C−1	0.09	0.08
Norwegian Sea	Habitat: pelagic (vs demersal)		−2.8 km yr−1	0.09	0.1
North Sea	Minimum age at maturity		0.3 km yr−1 °C-1	0.12	0.01
North Sea	Maximum depth		0.002 km yr−1 m−1	0.10	<0.01

DOI: 10.7717/peerj.15801/table-4

Discussion and Conclusion

Our results show overall significant northward shifts in the North Seas but not in the Norwegian Sea nor in the Barents Sea, where community-wide species’ geographical shifts did not clearly differ from zero. However, the varying spatial footprint in these areas was higher than in the North Sea, and the sample size smaller (number of years of data, and number of species), which has an effect in the estimation of species geographical latitudinal shifts, and in the p-value calculation during hypothesis testing (Murtaugh, 2014). At the individual species level, for all areas we detected clear northward migrations for only a fraction of the species studied, when assessed at the species level. Even though there has been a borealization of the Barents Sea community in the last years (Fossheim et al., 2015; Gordó-Vilaseca et al., 2023; Pecuchet et al., 2020), obvious northward migration patterns for most species were not detected here, nor were they a decade ago (Aschan et al., 2013), which suggests that climate warming-induced species geographic shifts can be hard to detect at the individual species level but become apparent when considering enough species across a longer period. On the contrary, most species’ thermal envelopes shifted northwards in the Norwegian and Barents Sea, suggesting that either (1) species’ response to shifts in thermal envelope lag behind those shifts, or (2) the potential data gathering, and analysis artifacts challenge the detection of species’ realized geographical shifts more, than the detection of species’ thermal envelope shifts, or (3) a combination of those two processes is occurring.

Although several species shifted in the direction of their thermal envelope (most of which northward), no positive correlation between shifts and thermal envelope shifts was detected in any of the study areas, because several species whose thermal envelope shifted northwards, shifted in the opposite direction. For example, two pelagic species, blue whiting (Micromesistius poutassou) and spotted barracudina (Arctozenus risso), shifted southwards in the Barents Sea, even though their thermal envelope moved northwards. However, these species are pelagic, and this discrepancy could be due to the species changing their vertical distribution, which cannot be detected here using bottom-dwelling sampling gear. Overall, this lack of positive correlation, and even a negative correlation in the North Sea, between species’ shift in thermal envelope and species realized shift in distribution is both surprising, and partially line with previous global studies including the Barents Sea (Lenoir et al., 2020), and suggest a strong influence of other factors, such as biological interactions, in determining several species’ recent geographic shifts in the area. Moreover, we detected high variability in species shifting rates, showing that not all species in the community are responding coherently and homogeneously. Instead, some species are closely following their thermal envelope (i.e., polar cod (Boreogadus saida) in the Barents Sea, or whiting (Merlangius merlangus) in the Norwegian Sea), while others are shifting faster (i.e., Arctic eelpout (Lycodes reticulatus) in the Barents Sea) or slower (i.e., Pollock (Pollachius pollachius) in the North Sea) than would be expected by the geographic shift in their thermal envelope.

Some other studies have assessed the proportion and magnitude of species shifting within fish communities, and our results mostly align within them: In the Northeast United States continental shelf, 17 species of 36 studied (47%) shifted their biomass centroid poleward, six species of 29 studied (21%) also shifted northwards in the Californian Current System, 13 of 19 studied fish species in the Japan Sea (68%), and at least 20% of the whole fish community shifted poleward in south-eastern Australia, a climate-warming hotspot (Hsieh et al., 2008; Last et al., 2011; Nye et al., 2009; Tu, Tian & Hsieh, 2015). Lenoir et al. (2020) did not provide regional estimations of species shifts, but only a global estimation of approximately 5 km/yr of northward shifts in the global marine fauna, while Pinsky et al. (2013) estimated most of species northward or southward regional latitudinal shifts along the north American coast, to be of less than 2 km/yr (Lenoir et al., 2020; Pinsky et al., 2013). In Iceland’s sub-arctic coastal waters, 82 fish species showed distribution shifts at a mean northward rate of 1.6 km/yr during 22 years with warming conditions (Campana et al., 2020).

Within our study area, a previous study in the North Sea, described poleward species geographical shifts in 13 species (of 35 studied), mostly of <3 km/yr, between 1977 and 2001 (Perry et al., 2005). We detected significant poleward shifts in 15 species in the North Sea (of 58 studied), of similar magnitude (mostly <3 km/yr), from 1998 to 2020. Of the 13 species detected to shift northward in Perry et al. (2005), three of them continued to shift northwards in our study (Arnoglossus laterna, Gadus morhua & Trisopterus minutus), one showed a significant southward shift (Trisopterus luscus), and nine others did not show any trend (Hippoglossoides platessoides, Merlangius merlangus, Eutrigla gurnardus, Lumpenus lampretaeformis, Lophius piscatorius, Enchelyopus cimbrius, Echiichthys vipera, Callionymus lyra & Limanda limanda). Moreover, one of the two species detected to shift south in Perry et al. (2005), shifted northwards in our study (Solea solea), while the other was not detected to shift (Trisopterus esmarkii). Finally, from the 19 species showing no significant shifts in Perry et al. (2005), we detected seven significant northward shifts (Anarhichus lupus, Buglossidium luteum, Melanogrammus aeglefinus, Merluccius merluccius, Molva molva, Myxine glutinosa, Pollachius virens), while the remaining 12 species did not show distributional changes in our study either. Overall, even though we report similar absolute numbers of species shifting poleward, the relation to overall species included in the analyses differ substantially (25% of studied species shifts in our study, vs 36% in Perry et al. (2005)). Considering that both shared the same minimum data requirements (excluding species caught at less than five stations each year, during at least ten years), our results suggest that when a higher number of less-abundant species is included in the analysis, thanks to increased effort in sampling those species, and increased taxonomic effort, the absolute number of species shifting northwards is maintained, and the proportion of the community that shifts is therefore reduced. This estimation could further improve if enough data on the remaining species in the North Sea, which overall hosts over 220 species of marine fish, was available to be included in the analysis (Ducrotoy, Elliott & De Jonge, 2000).

In the Barents Sea, the only estimation of marine fish shifting rates previous to this study was published earlier this year, and its mean northward shift was estimated to be around 4.4 km/yr using Ecospace modelling and including 108 functional groups between 2004 and 2013 (Nascimento et al., 2023). We did not detect clear community-wide shifts in a smaller number of species during a longer time period (2004–2020), although the low p-value achieved when the whole community was considered deserves future reanalysis with more extensive data and species. The estimation of species shifts and shifting rates are often challenged by the low data availability of most species and study areas, which can result in wrong estimations of shifting rates because of the difficulty in discerning true range shifts from sampling variability artefacts (Bates et al., 2015; Brown et al., 2016). In the Barents Sea, the data availability remains very low for several species, despite the remarkably increasing sampling effort in the last decades. From the total pool of species, estimated to be of around 160 fish species (Mecklenburg et al., 2018), only about a fifth (35 species) met the data requirements to be included in this study, and 20% of them showed significant northward shifts. The differences between Nascimento et al. (2023) and our study may be due to different criteria in including/excluding rare species, as well as different study periods, and different methodological approach. Additionally, surveys in the Barents and Norwegian Seas are not spatially standardised, and different locations are sampled in several years (Johannesen et al., 2021). This introduces a source of bias that, although partly accounted for in our method (by explicitly accounting for different annual latitudes, and by filtering the raw data to keep the years with best spatial coverage), the effect of varying spatial footprint of the data cannot be completely eliminated.

Rare species are more prone to show false positive shifting rates due to fewer data points and higher variability in the biomass centroid estimation. This process occurs in both directions (poleward and equatorward) (Bates et al., 2015) and should not affect the central tendency of the estimated range shifts of a community. Even though we eliminated the rarest species, following common practice data curation criteria (Perry et al., 2005), this is likely the reason for which a significant negative effect of species number of records in species absolute shifting rate was detected in all study areas. However, in the Barents Sea, species number of records also showed a significant negative effect on non-absolute species shifting rates, which could indicate that rare species are shifting at faster rates than widespread species, maybe as a result of smaller niche breadth associated with higher vulnerability to climate-warming-induced habitat loss (Thuiller, Lavorel & Araújo, 2005). Increased vulnerability in rare Arctic species may induce a faster contraction of their trawling edge distribution, and therefore show a faster shift in the biomass centroid towards the north. However, higher uncertainty remains for species with less data where few records can have bigger influence on the centroid estimation, and further data collection focused on those species would help to confirm this hypothesis.

Regarding other species’ biological traits, several studies pointed to the lack of explanatory power of several live-history and biogeographic traits, for marine fish distribution shifts, including no effect of asymptotic length, age at maturation or geographical affinity in the Japan Sea, no effect of maximum length, length or age at maturation, fecundity or trophic level in the California Current System, and a very small contribution of size, trophic group or geographic range around Australian reef-fishes (Hsieh et al., 2008; Stuart-Smith et al., 2021; Tu, Tian & Hsieh, 2015). In line with these studies, we show that species’ life-history traits were mostly unable to capture species’ latitudinal shifts variability. However, species biogeography was key in the Barents Sea, with Arctic species shifting at a higher rate than boreal species, and with evidence of warmer-water species shifting faster than cold-water species. In the North Sea, we found that slower maturing species shifted faster than faster maturing species, which is the opposite of what previous studies showed did not detect any significant differences between maximum length or age at maturity between shifting and non-shifting species, despite previous results suggesting these differences (Perry et al., 2005). However, if only species significantly shifting northwards are considered, as done in Perry et al. (2005), no significant differences appeared in age at maturity between shifting and not shifting species. The inclusion of more species in the analysis, together with potential changes in the shifting community between study periods could explain these differences between studies.

The Barents Sea contains several different community assemblages separated across its thermal and bathymetric gradients which may respond differently to present and future warming and may show different vulnerability to species gains and losses (Husson et al., 2022; Johannesen et al., 2012; Wiedmann et al., 2014). Understanding how geographic shifts affect marine communities at a finer geographic scale within the study areas considered here remains a present and future challenge. Even though very recent studies report no clear sub-regional temporal community trends across the Barents Sea, these could be hard to detect due to interannual variability in community composition at smaller scales (Pecuchet et al., 2022). Moreover, our results contain surveys from only one season in each study region, which could be partially influenced by seasonal changes in species abundance and biomass, as observed in other marine ecosystems (Lloret-Lloret et al., 2021; Vilas et al., 2020). Future data collection at finer temporal and spatial scales would help to discern these community changes linked to geographic shifts from the natural (seasonal and spatial) variability of species communities.

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