# Low Density of Top Predators (Seabirds and Marine Mammals) in the High Arctic Pack Ice

Authors: Claude R. Joiris; Karin Boos; Diederik D’Hert; Dominik A. Nachtsheim
Journal: Scientifica
 (2016)

---

## Abstract

The at-sea distribution of top predators, seabirds and marine mammals, was determined in the high Arctic pack ice on board the icebreaker RVPolarstern in July to September 2014. In total, 1,620 transect counts were realised, lasting 30 min each. The five most numerous seabird species represented 74% of the total of 15,150 individuals registered: kittiwakeRissa tridactyla, fulmarFulmarus glacialis, puffinFratercula arctica, Ross’s gullRhodostethia rosea, and little aukAlle alle. Eight cetacean species were tallied for a total of 330 individuals, mainly white-beaked dolphinLagenorhynchus albirostris and fin whaleBalaenoptera physalus. Five pinniped species were represented by a total of 55 individuals and the polar bearUrsus maritimus was represented by 12 individuals. Four main geographical zones were identified: from Tromsø to the outer marginal ice zone (OMIZ), the Arctic pack ice (close pack ice, CPI), the end of Lomonosov Ridge off Siberia, and the route off Siberia and northern Norway. Important differences were detected between zones, both in species composition and in individual abundance. Low numbers of species and high proportion of individuals for some of them can be considered to reflect very low biodiversity. Numbers encountered in zones 2 to 4 were very low in comparison with other European Arctic seas. The observed differences showed strong patterns.


---

## Body

## 1. Introduction

The Central Arctic Ocean is one of the least studied regions on earth, mainly due to its inaccessibility caused by permanent ice cover. However, it is already threatened by climatic changes, which calls for a proper assessment and monitoring of this vulnerable ecosystem for future comparisons. There is a strong focus on research in the Arctic marine ecosystem in recent years due to many open questions, for example, regarding the ecology and physiology of polar organisms and the effects of climate change and sea ice decline. In view of these apparent changes, it is essential to monitor the state of an ecosystem to enable comparisons to baseline data and projections into future conditions. However, the distribution of top predators, seabirds and marine mammals, is not well understood, especially in the understudied and permanently ice-covered Central Arctic Ocean. Generally, the at-sea occurrence and density of top predators can be considered to reflect prey availability and thus integrate the structure and functioning of the ecosystems they belong to. In the frame of our long-term study on the distribution of seabirds and marine mammals in polar marine ecosystems, this paper reports on data collected in poorly known areas of the high Arctic pack ice, including Lomonosov Ridge.


## 2. Material and Methods

### 2.1. Counting Method and Environmental Factors

The quantitative at-sea distribution of marine mammals and seabirds was established during the PS86 (AURORA) expedition of icebreaker RVPolarstern from Tromsø and back, from 8 July to 3 August 2014, and the PS87 expedition from Tromsø to Bremerhaven, from 5 August to 8 October 2014, respectively. Transect counts lasted half an hour without width limitation from the bridge at 18 m above sea level on a continuous basis, allowing light and visibility; see description and discussion in [1, 2]. The animals were detected with the naked eye, and observations were confirmed and complemented with binoculars when useful. Photographic documents were also used, especially for rare species or those difficult to identify in the field.Water temperature (sea surface temperature, SST), salinity, and fluorescence (chlorophyll) were continuously recorded on board at subsurface sampling (keel, −10 m). Ice cover was evaluated from the bridge and expressed as % coverage within an approximated range of 500 m around the ship.Basic data have been included in the biodiversity datasets:http://ipt.biodiversity.aq/archive.do?r=rbins_joiris_2014_ps86_birds_mammals and http://ipt.biodiversity.aq/archive.do?r=rbins_joiris_2014_ps87_birds_mammals, respectively.


### 2.2. Statistical Analysis

We tested the effects of the different zones on the abundance of selected species using a generalized linear mixed model (GLMM with Poisson-distributed error terms and log-link function) into which the fixed effects factors “zone” and “species” were included. The species were selected according to their total abundances and chosen, where numbers exceeded 200 individuals. This selection was important in order to warrant stable convergence of the model. Because the data originated from two different cruises, we included “cruise” as a random effects factor to account for grouped data per cruise. We tested and confirmed overdispersion by including an additive dispersion term as a random factor to the GLMM, which comprised as many factor levels as observations [4]. The variance of the (random) additive dispersion term was considerably higher than zero indicating overdispersion in the data. Also, the Bayesian Information Criterion (BIC) revealed the model including the additive dispersion term to be clearly more parsimonious than the original model (B
I
C
a
d
d
=
12956.71; B
I
C
o
r
i
g
=
46410.72). For this reason, we applied a negative binomial mixed model in order to account for overdispersion. Although there were many zeros in excess, we ruled out zero-inflation on the basis that the zero-inflation value was, generally by a factor of at least 6, smaller than the model’s estimated coefficients and, therefore, negligibly small (using the argument “zeroInflation = T”; zero-inflation
=
1.0
∗
10
-
6; and S
E
=
4.064
∗
10
-
9). We refrained from including the interaction term into the model, because data replicates were not available for all combinations of zones and species. Hence, the inclusion of an interaction term would lead to severe overparametrisation and possibly false results. We therefore focused solely on the two main effects.The final model was checked for stability through comparing the empirical data with 1000 randomly generated simulations of negative binomial distributions based on the present model predictions. The empirical data lay perfectly within the range of the simulated data. Therefore, the model was considered robust. Significance of the main effects was established by applying likelihood ratio tests (LRT), where the deviance of the respective full models was compared with that of the corresponding reduced models not comprising the respective factor of interest using the R function “anova.” All models were fitted in R, version 3.2.2 [5], using the function “glmer” from the R package “lme4” [6] and the function “glmmadmb” from the R package “glmmADMB” [7, 8].A separate analysis was conducted to test for the repeatability of the findings of the qualitative and quantitative species composition along the three consecutive subzones 1a, 1b, and 2a. Just as described above, we selected the species according to their overall abundance (i.e., >200 individuals). This led to the exclusion of Ross’s gull from the selected species. For the same reasons as above, we chose a negative binomial model to account for overdispersion and ruled out zero-inflation as described above (zero-inflation
=
1.0
∗
10
-
6; S
E
=
5.0
∗
10
-
9). Instead of including the random factor “cruise,” we included the “revisit” of each subzone as a random factor to account for grouping in the data. Model stability check was conducted as described above and showed a clear deviation from running within the expected ranges and, thereby, far more optimistic predictions than provided by the empirical data. Because the model did not converge properly with both factors included, we, therefore, refrained from keeping the factor “species” in the model and pooled the data for the three subzones. The comparisons between species are presented in a descriptive way.


## 2.1. Counting Method and Environmental Factors

The quantitative at-sea distribution of marine mammals and seabirds was established during the PS86 (AURORA) expedition of icebreaker RVPolarstern from Tromsø and back, from 8 July to 3 August 2014, and the PS87 expedition from Tromsø to Bremerhaven, from 5 August to 8 October 2014, respectively. Transect counts lasted half an hour without width limitation from the bridge at 18 m above sea level on a continuous basis, allowing light and visibility; see description and discussion in [1, 2]. The animals were detected with the naked eye, and observations were confirmed and complemented with binoculars when useful. Photographic documents were also used, especially for rare species or those difficult to identify in the field.Water temperature (sea surface temperature, SST), salinity, and fluorescence (chlorophyll) were continuously recorded on board at subsurface sampling (keel, −10 m). Ice cover was evaluated from the bridge and expressed as % coverage within an approximated range of 500 m around the ship.Basic data have been included in the biodiversity datasets:http://ipt.biodiversity.aq/archive.do?r=rbins_joiris_2014_ps86_birds_mammals and http://ipt.biodiversity.aq/archive.do?r=rbins_joiris_2014_ps87_birds_mammals, respectively.


## 2.2. Statistical Analysis

We tested the effects of the different zones on the abundance of selected species using a generalized linear mixed model (GLMM with Poisson-distributed error terms and log-link function) into which the fixed effects factors “zone” and “species” were included. The species were selected according to their total abundances and chosen, where numbers exceeded 200 individuals. This selection was important in order to warrant stable convergence of the model. Because the data originated from two different cruises, we included “cruise” as a random effects factor to account for grouped data per cruise. We tested and confirmed overdispersion by including an additive dispersion term as a random factor to the GLMM, which comprised as many factor levels as observations [4]. The variance of the (random) additive dispersion term was considerably higher than zero indicating overdispersion in the data. Also, the Bayesian Information Criterion (BIC) revealed the model including the additive dispersion term to be clearly more parsimonious than the original model (B
I
C
a
d
d
=
12956.71; B
I
C
o
r
i
g
=
46410.72). For this reason, we applied a negative binomial mixed model in order to account for overdispersion. Although there were many zeros in excess, we ruled out zero-inflation on the basis that the zero-inflation value was, generally by a factor of at least 6, smaller than the model’s estimated coefficients and, therefore, negligibly small (using the argument “zeroInflation = T”; zero-inflation
=
1.0
∗
10
-
6; and S
E
=
4.064
∗
10
-
9). We refrained from including the interaction term into the model, because data replicates were not available for all combinations of zones and species. Hence, the inclusion of an interaction term would lead to severe overparametrisation and possibly false results. We therefore focused solely on the two main effects.The final model was checked for stability through comparing the empirical data with 1000 randomly generated simulations of negative binomial distributions based on the present model predictions. The empirical data lay perfectly within the range of the simulated data. Therefore, the model was considered robust. Significance of the main effects was established by applying likelihood ratio tests (LRT), where the deviance of the respective full models was compared with that of the corresponding reduced models not comprising the respective factor of interest using the R function “anova.” All models were fitted in R, version 3.2.2 [5], using the function “glmer” from the R package “lme4” [6] and the function “glmmadmb” from the R package “glmmADMB” [7, 8].A separate analysis was conducted to test for the repeatability of the findings of the qualitative and quantitative species composition along the three consecutive subzones 1a, 1b, and 2a. Just as described above, we selected the species according to their overall abundance (i.e., >200 individuals). This led to the exclusion of Ross’s gull from the selected species. For the same reasons as above, we chose a negative binomial model to account for overdispersion and ruled out zero-inflation as described above (zero-inflation
=
1.0
∗
10
-
6; S
E
=
5.0
∗
10
-
9). Instead of including the random factor “cruise,” we included the “revisit” of each subzone as a random factor to account for grouping in the data. Model stability check was conducted as described above and showed a clear deviation from running within the expected ranges and, thereby, far more optimistic predictions than provided by the empirical data. Because the model did not converge properly with both factors included, we, therefore, refrained from keeping the factor “species” in the model and pooled the data for the three subzones. The comparisons between species are presented in a descriptive way.


## 3. Results

During the 1,620 transect counts devoted to top predator census on board the RVPolarstern, expeditions PS86 and PS87 (partim North of 66°30′N) (Figure 1), a total of 15,150 birds were detected, belonging to 23 species, that is, an average of 9.3 identified individuals per count (Table 1). Five species represented the vast majority: kittiwakeRissa tridactyla, fulmarFulmarus glacialis, puffinFratercula arctica, Ross’s gullRhodostethia rosea, and little aukAlle alle with 2.2, 1.5, 1.3, 1.1, and 0.7 birds per count, respectively, representing 74% of the total. They were followed by ivory gullPagophila eburnea and Brünnich’s guillemotUria lomvia (0.3 and 0.2 per count). Eight cetacean species were counted for a total of 330 individuals, that is, 0.2 per count. The most numerous species were white-beaked dolphinLagenorhynchus albirostris (0.15 per count) and fin whaleBalaenoptera physalus (0.02 per count). Five pinniped species were represented by 55 individuals (0.03 per count), of which 0.01 were harp sealsPagophilus groenlandicus and 0.01 hooded sealsCystophora cristata. Polar bearUrsus maritimus was recorded in low numbers (12 individuals including a mother with two large cubs and another one with three small cubs plus one out of effort).Table 1
Seabirds and marine mammals encountered during the PS86 and PS87 expeditions of RVPolarstern in 2014; total numbers recorded; mean per counta; and n = number of 30 min counts.
Species
Species
Expedition>
PS86
PS87b
All
Period>
July
Aug-Sept
July–Sept
n >
387
1235
1622
Sum
Mean
Sum
Mean
Sum
Mean
Fulmar light
Fulmarus glacialis
920
2.31
362
0.29
1282
0.79
Fulmar dark
Fulmarus glacialis
835
2.16
323
0.26
1158
0.71
Fulmar all
Fulmarus glacialis
1755
4.53
721
0.43
2476
1.53
Manx shearwater
Puffinus puffinus
1
1
Fork-tailed storm-petrel
Oceanodroma furcata
2
2
Common guillemot
Uria aalge
14
1
15
Brünnich’s guillemot
Uria lomvia
93
0.23
262
0.18
355
0.22
Guillemot unidentified
Uria sp. (aalge/lomvia)
4
18
22
Little auk
Alle alle
498
1.25
568
0.46
1066
0.66
Black guillemot
Cepphus grylle
9
76
0.05
85
Puffin
Fratercula arctica
273
0.69
1829
1.48
2102
1.30
Razorbill
Alca torda
51
0.04
51
Gannet
Sula bassana
2
4
6
Great skua
Stercorarius skua
3
4
7
Pomarine skua
Stercorarius pomarinus
2
7
9
Arctic skua
Stercorarius parasiticus
8
25
33
Long-tailed skua
Stercorarius longicaudus
31
31
Ivory gull
Pagophila eburnea
200
0.50
332
0.27
532
0.33
Glaucous gull
Larus hyperboreus
11
18
29
Herring gull
Larus argentatus
5
27
32
Great black-backed gull
Larus marinus
14
17
31
Lesser black-backed gull
Larus fuscus
2
5
7
Kittiwake
Rissa tridactyla
278
0.68
3338
2.70
3616
2.23
Sabine’s gull
Xema sabini
1c
Ross’s gull
Rhodostethia rosea
1770
1.22
1770
1.09
Arctic tern
Sterna paradisaea
11
2
13
Tern unidentified
Sterna sp. (hirundo/paradisaea)
15
15
Phalarope unidentified
Phalaropus sp.
1
1
∑
b
i
r
d
s
(identified)
3214
9.19
11932
7.17
15146
9.34
Blue whale
Balaenoptera musculus
6
6
Fin whale
Balaenoptera physalus
34
0.085
4
38
0.02
Minke whale
Balaenoptera acutorostrata
1
1
Humpback whale
Megaptera novaeangliae
7
1
8
Sperm whale
Physeter macrocephalus
9
2
11
0.01
Killer whale
Orca orca
9
9
Large whale unidentified
Cetacea
35
6
41
0.03
Narwhal
Monodon monoceros
7
7
White-beaked dolphin
Lagenorhynchus albirostris
215
0.540
34
0.03
249
0.15
Dolphin unidentified
Delphinidae
3
14
17
0.01
∑
c
e
t
a
c
e
a
n
s (identified)
280
0.723
49
0.03
329
0.20
Bearded seal
Erignathus barbatus
5
5
Harp seal
Pagophilus groenlandicus
5
18
0.01
23
0.01
Ringed seal
Pusa hispida
7
2
9
Hooded seal
Cystophora cristata
16
0.038
2
18
0.01
Walrus
Odobenus rosmarus
1c
Seal unidentified
Pinnipedia
36
18
54
0.03
∑
p
i
n
n
i
p
e
d
s (identified)
33
0.085
22
0.01
55
0.03
Polar bear
Ursus maritimus
4
+
1
c
8
+
1
c
9
Polar bear tracks
Ursus maritimus
1
64
0.07
65
0.04
Arctic fox/wolf tracks
Canidae
3
3
aFor mean values above 0.2 per count for birds and above 0.01 for mammals; bpartim North of 66°30′N; cout of effort.Figure 1
Route of the PS86 (red) and PS87 (black) expeditions of RVPolarstern in July–September 2014; for definition of the zones, see text.Four major geographical zones were defined, mainly on the basis of ice coverage: zone 1 from Tromsø to the outer marginal ice zone (OMIZ), passing through the North-East Water (NEW) polynya. Zone 2 covered the ice-covered close pack ice (CPI), including the Lomonosov Ridge separating the Amerasian and Eurasian Basins. Zone 3 was basically the ice-free part of the Lomonosov Ridge off the New Siberian Islands and zone 4 the coastal waters off Siberia to northern Norway (Figure1). Ice conditions in the four zones are illustrated in Figure 2. Characterisation of the four zones and observations of the most numerous (main) species are shown in Table 2. Geographical differences were obvious. The analysis included the eight most numerous species (each >200 individuals). It revealed both factors “zone” and “species” to be highly significant and, therefore, having an effect on the observed abundances. Independent of the individual species, the results showed that the highest number of selected top predators was found in zone 1 (mean numbers per count, all eight species pooled: 7.92), followed by zone 4 (5.89), and the least amounts were found in zones 3 and 2 (3.38 and 1.17, resp.) (L
R
T
z
o
n
e: d
e
v
i
a
n
c
e
=
608.66, d
f
=
13, and p
<
0.0001).Table 2
Data obtained in the four main geographical zones defined during the PS86 and PS87 expeditions of RVPolarstern, July–September 2014; n: number of 30 min counts; total number; mean per count∗; and mean N per km (see text and Figure 1).
Zone
n
Salinity
Temperature °C
Ice cover %
Fluorescence (chlorophyll)
Speed knots
Fulmar
Brünnich’s guillemot
Little auk
Puffin
Ivory gull
Fulmarus glacialis
Uria lomvia
Alle alle
Fratercula arctica
Pagophila eburnea
Mean (min–max)
Mean (min–max)
Mean (min–max)
Mean (min–max)
Mean (min–max)
Total
Mean
Total
Mean
Total
Mean
Total
Mean
Total
Mean
1
419
33.4 (29.3–35.1)
3.3 (−1.7–12.1)
10 (0–80)
2.1 (0.06–12.7)
9.2 (2.8–12.7)
2121
5.06
252
0.6
1054
2.52
509
1.21
78
0.57
2
449
31.1 (29.2–33.3)
−1.4 (−1.7–1.3)
75 (0–100)
1.2 (0.1–5.5)
4.4 (1.1–11.2)
116
0.26
1
4
0
156
0.35
3
160
26.2 (24.3–29.9)
0.2 (−1.3–1.11)
80 (0–100)
1.1 (0.7–2.3)
4.9 (1.2–10.5)
0
1
0
0
105
0.97
4
580
28.6 (24.3–35.0)
0.9 (−1.7–10.8)
2 (0–10)
1.3 (0.6–3.9)
5.7 (2.3–12.2)
126
0.22
100
0.17
8
2
162
0.28
Zone
Ross’s gull
Kittiwake
White-beaked dolphin
Fin whale
Harp seal
Hooded seal
Polar bear
Rhodostethia rosea
Rissa tridactyla
Lagenorhynchus albirostris
Balaenoptera physalus
Pagophilus groenlandicus
Cystophora cristata
Ursus maritimus
Total
Mean
Total
Mean
Total
Mean
Total
Mean
Total
Mean
Total
Mean
Total
Mean
1
0
239
0.57
239
0.57
37
0.09
4
0.01
2
0.01
3
0.01
2
0
112
0.25
0
0
2
14
5
0.01
3
190
1.19
155
0.97
0
0
0
0
0
4
38
0.07
2931
5.05
10
0.02
1
17
0.03
0
0
∗For mean values above 0.1 per count for birds and above 0.01 for mammals.Figure 2
Ice conditions during the PS86 and PS87 expeditions of RVPolarstern in July–September 2014; ship’s position indicated by a star: 9 August (a); 19 August (b); 27 September (c); and 30 September (d). Institute of Environmental Physics, University of Bremen, Germany: https://seaice.uni-bremen.de/amsr2/index.html.
(a)
(b)
(c)
(d)These important differences seem to reflect differences in biological productivity, considering chlorophyll to be a good index: maximal values were 12.7, 4.1, 2.3, and 3.9, respectively, in the four zones (Table2, Figure 3).Figure 3
Environmental conditions registered during the PS86 and PS87 expeditions of RVPolarstern in July–September 2014: water temperature (SST, °C) (a); salinity (b).
(a)
(b)Pooled over all zones, kittiwake presented the highest numbers (2.12 per count), including three individuals belonging to the Siberian subspeciesR. t. pollicaris (zone 3, 81.1°N). It was followed by fulmar (1.46) and little auk (0.66). Considerably lower but similar numbers were found for ivory gull and puffin (0.33 and 0.31, resp.). Lowest numbers were detected for Brünnich’s guillemot (0.33), Ross’s gull (0.14), and white-beaked dolphin (0.15) (Table 2). Overall, puffins and white-beaked dolphins were almost exclusively found in zone 1a (n
=
506 and 239, resp.). The pattern described above for the overall abundances in the four zones was true for Brünnich’s Guillemots, fulmars, little auks, puffins, and white-beaked dolphins. This pattern was reversed in kittiwakes and ivory gulls, which were sighted in highest numbers in zone 4. Ross’s gulls were the only species found in highest numbers in zone 3. The main species in zone 1 were fulmar (4 individuals per count, mean value), little auk (2 per count), kittiwake (0.5) and Brünnich’s guillemot (0.45), white-beaked dolphin (0.4), and fin whale (0.07). Using chlorophyll (fluorescence) as an indicator, biological productivity is also the highest in this area. The ice-covered area (zone 2) showed by far the lowest densities, with significant numbers of kittiwakes (0.2) and ivory gulls (0.04), as well as polar bears (0.02). Biological productivity (chlorophyll) was low. Zone 3 showed low numbers as well: Ross’s gull (1.2) and ivory gull and kittiwake (1 each) being the most abundant; biological productivity (chlorophyll) was low as well. Zone 4 showed intermediate abundances: highest numbers of kittiwakes (5 per count, including higher concentrations at the local ice edge around 130°E; see Figure 2(c)), ivory gulls (0.3), and fulmar (0.2); harp seals were present at a few stations only (0.03, 120°E).In addition to the above, a “first” zone was covered three times during both expeditions and divided into three subzones (subzones 1a, 1b, and 2a; see Figure1), deserving special attention. Clear geographical differences were detected (Table 3). Pooled together, top predators were highest in zone 1a (12.6 per count), followed by zones 1b (7.6) and 2a (2.1). In zone 1a (Greenland Sea), fulmar was the most numerous (7.55 per count) followed by puffin (2), Brünnich’s guillemot (0.9), kittiwake (0.7), white-beaked dolphin (0.9), and fin whale (0.1), with high productivity (chlorophyll) of 2.8. In zone 1b (NEW polynya), species were present with little auk (6.4), fulmar (1.2), and ivory gull (0.7), with productivity of 0.9. In zone 2a (CPI), the present species were ivory gull (1), fulmar (0.8), kittiwake (0.2), hooded seal (0.1), and polar bear (0.03) with intermediate productivity (1.2). The tested reproducibility of data between successive transects was high for some species, reflecting good reproducibility of the counting method, fulmar, Brünnich’s guillemot, ivory gull, and kittiwake, but not for other species, reflecting the heterogeneity of their distribution, little auk and puffin. The analysis of the selected and pooled species revealed the factor “subzone” to be significantly effective (L
R
T
s
u
b
z
o
n
e: deviance = 8.52, d
f
=
5, and p
=
0.014). In fact, the results showed that subzones 1a and 1b were significantly similar with respect to the abundances (p
=
0.13), even though the abundances in subzone 1a were higher than those in subzone 1b. This is most likely due to high variations and many excess zeros in the data. Subzone 2a hosted clearly less individuals than the subzones 1a and 1b (p
=
0.0001 and 0.013, resp.).Table 3
Observations of top predators during the same transect visited three times, between Tromsø and the high Arctic pack ice (83°N): main species; total number; mean per count∗; and mean N per km.
Zone∗
∗
Polarsternexpedition
n
Latitude °N
Longitude °E
Salinity
Temperature °C
Ice cover %
Fluorescence (chlorophyll)
Speed knots
Fulmar
Brünnich’s guillemot
Little auk
Fulmarus glacialis
Uria lomvia
Alle alle
Mean (min–max)
Mean (min–max)
Mean (min–max)
Mean (min–max)
Mean (min–max)
Total
Mean
N/km
Total
Mean
N/km
Total
Mean
N/km
1a
PS86
101
70.1/78.4
20.2/−2.4
34.7 (33.2–35.1)
7.1 (2.5–10.6)
0
11.2 (6.0–11.5)
407
4.0
0.36
21
0.21
0.02
4
1a
PS86
89
70.7/77.8
6.0/19.0
34.3 (34.2–35.1)
8.6 (2.7–12.0)
0
10.9 (3.9–12.7)
1140
12.8
1.17
62
0.75
0.07
460
4.30
0.39
1a
PS87
68
70.2/77.98
20.2/2.44
34.6 (32.5–35.1)
8.0 (3.2–12.4)
0
9.0 (4.1–11.0)
424
5.7
6.19
150
2.21
0.24
32
0.47
0.05
∑
1
a
258
70.1/78.4
20.2/−2.4
34.5 (32.5–35.1)
7.9 (2.5–12.4)
0
2.79 (0.08–12.7)
10.4 (3.9–11.5)
1971
7.64
0.76
233
0.90
0.09
496
1.92
0.18
1b
PS86
28
78.5/79.2
−2.80/−7.3
30.25 (29.9–31.7)
−0.63 (−1.3–0.31)
6.3 (3.8–9.0)
31
1.1
0.17
0
10
0.08
0.1
1b
PS86
53
79.2/81.3
6.0/−5.6
31.15 (29.6–35.1)
0.39 (−0.97–3.73)
8.9 (4.9–11.5)
73
1.38
0.16
2
28
0.39
0.04
1b
PS87
83
78.1/81.5
2.17/−6.65
30.22 (29.3–33.8)
0.34 (−1.1–4.3)
5.99 (2.80–7.50)
89
1.07
0.18
11
0.13
0.02
528
6.36
1.06
∑
1
b
164
78.1/81.5
2.17/−6.65
30.54 (29.3–35.1)
0.03 (−1.3–4.3)
17 (0–80)
0.88 (0.47–2.3)
7.06 (2.80–11.5)
193
1.18
0.16
13
0.08
0.01
566
3.45
0.49
2a
PS86
115
79.2/82.9
−5.7/−9.8
30.54 (29.9–31.5)
−0.86 (−1.30–1.60)
6.1 (3.8–9.0)
94
0.82
0.13
1
4
2a
PS87
32
81.5/82.9
−4.52/−6.61
31.06 (29.3–31.1)
−1.47 (−0.97– −1.70)
4.4 (2.50–6.10)
19
0.59
0.13
0
0
∑
2
a
147
79.2/82.9
−9.8/−4.52
30.80 (29.30–31.5)
−1.16 (−0.97– −1.70)
70 (20–90)
1.19 (0.13–5.5)
8.4 (2.50–9.0)
113
0.77
0.09
1
4
Zone
∗
∗
Puffin
Ivory gull
Kittiwake
Wh.-beaked dolphin
Fin whale
Harp seal
Hooded seal
Fratercula arctica
Pagophila eburnea
Rissa tridactyla
Lagenorhynchus  albirostris
Balaenoptera physalus
Pagophilus groenlandicus
Cystophora cristata
Total
Mean
N/km
Total
Mean
N/km
Total
Mean
N/km
Total
Mean
N/km
Total
Mean
N/km
Total
Mean
N/km
Total
1a
157
1.6
0.14
1
49
0.50
0.04
8
4
0
0
1a
116
1.3
0.12
0
117
1.3
0.12
207
2.3
30
0.3
0.03
0
0
1a
1824
26.8
2.98
0
16
0.24
0.03
24
3
0.05
0.005
0
0
∑
1
a
2097
8.13
0.78
1
182
0.71
0.07
239
0.94
37
0.14
0.01
0
0
1b
0
8
0.28
0.04
5
0.18
0.03
0
3
0.11
0.01
0
1b
0
14
0.26
0.02
64
1.21
0.14
0
0
1
1b
3
55
0.66
0.11
134
1.61
0.27
0
1
2
∑
1
b
3
77
0.47
0.07
203
1.18
0.16
0
4
3
2a
0
143
1.24
0.20
33
0.29
0.05
0
2
0.02
0.003
14
2a
0
4
23
0.72
0.16
0
0
0
∑
2
a
0
147
1.00
0.12
56
0.38
0.045
0
2
14
∗For mean values above 0.1 per count for birds and above 0.01 for mammals.
∗
∗From south to north: 1a, open water; 1b, NEW polynya; and 2a, pack ice (OMIZ: outer marginal ice zone).


## 4. Discussion

Not only were mammal numbers very low but they were moreover very limited spatially: dolphins and fin whales in zone 1a, especially along the shelf slope, where autumn aggregations were already encountered [10]. Hooded seal and polar bear were present on the OMIZ, zone 2a, and harp seal in a few counts off Siberia (zone 4, OMIZ, 120°E).Two of the three high Arctic gulls deserve special comments, Sabine’s gullXema sabini being absent in our counts, one observation only, out of effort.The breeding range of the ivory gull includes the Canadian Arctic, Greenland, Svalbard, and Russian Arctic islands [11]. Northeast (NE) Greenland in particular seems to be a hotspot for breeding sites [12], apparently due to the vicinity to an attractive feeding ground: the NEW polynya. It usually breeds in colonies, either inland on steep cliffs and nunataks or coastal on barren islands and lowlands. In rare cases, it uses gravel-covered sea ice close to the coast as breeding platform: a breeding site on an ice floe covered with gravel was discovered in Independence Fjord, NE Greenland [13]. We recently reported an even more extreme breeding habitat: a gravel-covered iceberg 70 km off NE Greenland, close to the NEW polynya (81°N, 9°W) [14]. During this study, 60 more adults plus an unknown number of chicks, not included in our calculations, were detected (the position is indicated by a star on Figure 2(a)). Dramatic population declines have already been observed in Canada and Greenland during the last decades [12, 15–17]. Our own data, collected from the same observation platform with the same methodology, show a decrease in ivory gull abundance in the Greenland Sea from a mean of 1.7 individuals per 30 min count in the 1990–1993 period [9] to 0.4 and 0.3 in 2008 and 2011, respectively [10, 18, 19]. Its relative abundance deeply changed as well; it was one of the three most abundant species in the 1990s, together with fulmar and kittiwake, but now does not often even belong to the top ten any more. The ivory gull is listed as “Endangered” under the Species at Risk Act in Canada and as “Near Threatened” by the International Union for Conservation of Nature (IUCN) Red List of Threatened Species [20, 21]. The global population is estimated to be between 19,000 and 27,000 individuals with still high uncertainties due to its occurrence in highly remote areas [21]. Its breeding range includes the Canadian Arctic, Greenland, Svalbard, and Russian Arctic islands. Northeast (NE) Greenland in particular seems to be a hotspot for breeding sites [11, 12, 22–24], possibly due to the vicinity to an attractive feeding ground: the NEW polynya. In this frame the numbers encountered in zones 3 (0.7 per count) and 4 (0.3 per count) represent a significant part of the world population, probably belonging to the Russian/Siberian breeding sites. Half of them were juveniles and 10% immature: this seems to reflect a good breeding success for this population.Ross’s gulls are breeding mainly in the deltas of the Kolyma and Khroma rivers, Siberia (142° to 160°E). The world population is estimated at 45,000 to 55,000 individuals, probably >27,000 and possibly 100,000 [22–24]. Adults used to be regularly observed in the Greenland Sea at the end of the breeding season during the 1990s (end of June; Table 4; [9]) but not after 1994 (this team: numerous papers and unpublished data). It is not clear how far this had to be interpreted as movements following breeding failure or as large nonbreeding population. In this study, they represent the most abundant species in zone 3, off its main Siberian breeding grounds, with a total of 190 individuals (1.2 per count). The majority were breeding adults (60%), 25% juveniles, and some nonbreeding adults close to the end of the breeding season in late July-August [22–25]. This again seems to reflect a good breeding success for this species. More information can be found in Figure 4.Table 4
Observations of Ross’s gullsRhodostethia rosea in the NEW polynya area; n = number of 30 min counts [9].
Date
Year
n
Ross’s gull
From
To
Total
Mean/count
25 May
2 June
1993
239
0
9 June
19 June
1991
451
0
28 June
31 July
1993
529
92
0.18
21 July
5 August
1993
113
107
0.95
22 July
10 August
1992
25
20
0.80Figure 4
Distribution maps of the main top predator species encountered during the PS86 and PS87 expeditions of RVPolarstern in July–September 2014: fulmarFulmarus glacialis (a), Brünnich’s guillemotUria lomvia (b), little aukAlle alle (c), puffinFratercula arctica (d), ivory gullPagophila eburnea (e), Ross’s gullRhodostethia rosea (f), kittiwakeRissa tridactyla (g), white-beaked dolphinLagenorhynchus albirostris (h), fin whaleBalaenoptera physalus (i), harp sealPagophilus groenlandicus and hooded sealCystophora cristata (j), and polar bearUrsus maritimus (k).
(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)
(i)
(j)
(k)A comparison between our seabird data and the model predictions of Arctic seabird distribution [26] shows a full compatibility in trends: modelled bird diversity varied from 17–20 species in our zone 1 (more than 20 close to the coast) to 0–4 in the high Arctic pack ice (our zones 2 and 4), with a slight increase corresponding to our zone 3 (Figure 5, [3]). Our values were however much lower, mean numbers varying from 1.85 species per count in zone 1 (6 maximum) to 1.95 in subzone 1a, followed by 0.68 in zone 3 (3 maximum), 0.23 in zone 4 (5 maximum), and 0.14 in zone 2 (3 maximum); in all zones, many counts were free of contact. This quantitative discrepancy can partially be explained by the limited period and area covered by our study, while the model was based on broader datasets concerning more species.Figure 5
Seabird species diversity of 27 modelled seabird species in the Arctic with shipping lanes overlain: Figure  2(a) in [3], with authorization.


## 5. Conclusion

Two factors can be considered the reflection of very low biodiversity: the very low numbers of species and the high relative numbers of individuals for some of them. This was especially the case for zones 2 (Arctic pack ice CPI), 3 (Siberian end of the Lomonosov Ridge), and 4. Numbers of species and individuals were high in zone 1: Greenland Sea and Fram Strait for fulmar, little auk, puffin, and Brünnich’s guillemot, as well as white-beaked dolphin and fin whale, comparable to the values usually obtained in the area (e.g., [9, 10, 18]). Ivory gull was present in all zones, with maxima being in zones 2 and 4. Kittiwake was present in all zones as well, with low abundance in zone 2 (CPI) and very high abundance in zone 4. The main conclusion is that numbers were very low for all species in zones 2 to 4, with the exception of Ross’s gull in zone 3 (Tables 2 and 3). The observed geographical differences seem to reflect differences in biological productivity of the ecosystems, as illustrated by differences in chlorophyll (fluorescence) data. These baseline data could be integrated in other datasets [27] and in broader discussions, for example, about changing ice conditions and human activities.These findings provide confirmation of the profound difference between both polar regions. The Arctic pack ice is generally characterised by low top predator density, while Antarctic pack ice is supporting high numbers of penguins, mainly AdéliePygoscelis adeliaeand chinstrapP. antarctica, and of seals, the majority being crabeater sealsLobodon carcinophagascattered on CPI [28]. Such a major difference might be due to differences in sea ice conditions and as a consequence in ecological structure, the Arctic being mainly covered by multiyear ice and the Antarctic by one-year ice. This situation is in full evolution as a consequence of climate changes. In the Arctic, summer pack ice is strongly reduced, while winter pack ice decreases in a very limited extent only. As a result, the Arctic might be changing into a one-year pack ice ecosystem. The possible ecological consequences of this situation are difficult to evaluate for the time being.



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*Source: 1982534-2016-09-29.xml*