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Small birds, big effects: the little auk ( Alle alle ) transforms high Arctic ecosystems

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In some arctic areas, marine-derived nutrients (MDN) resulting from fish migrations fuel freshwater and terrestrial ecosystems, increasing primary production and biodiversity. Less is known, however, about the role of seabird-MDN in shaping ecosystems. Here, we examine how the most abundant seabird in the North Atlantic, the little auk (Alle alle), alters freshwater and terrestrial ecosystems around the North Water Polynya (NOW) in Greenland. We compare stable isotope ratios (δ¹⁵N and δ¹³C) of freshwater and terrestrial biota, terrestrial vegetation indices and physical-chemical properties, productivity and community structure of fresh waters in catchments with and without little auk colonies. The presence of colonies profoundly alters freshwater and terrestrial ecosystems by providing nutrients and massively enhancing primary production. Based on elevated δ¹⁵N in MDN, we estimate that MDN fuels more than 85% of terrestrial and aquatic biomass in bird influenced systems. Furthermore, by using different proxies of bird impact (colony distance, algal δ¹⁵N) it is possible to identify a gradient in ecosystem response to increasing bird impact. Little auk impact acidifies the freshwater systems, reducing taxonomic richness of macroinvertebrates and truncating food webs. These results demonstrate that the little auk acts as an ecosystem engineer, transforming ecosystems across a vast region of Northwest Greenland. © 2017 The Author(s) Published by the Royal Society. All rights reserved.
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Small birds, big effects: The Little Auk (Alle alle) transforms
high arctic ecosystems
Journal:
Proceedings B
Manuscript ID
RSPB-2016-2572.R1
Article Type:
Research
Date Submitted by the Author:
16-Jan-2017
Complete List of Authors:
Gonzalez-Bergonzoni, Ivan; Facultad de Ciencias, Universidad de la
Republica, Departamento de Ecologia y Evolucion
Johansen, Kasper; Aarhus Universitet, Department of Bioscience
Mosbech, Anders; Aarhus Universitet
Landkildehus, Frank; Aarhus Universitet, Department of Bioscience
Jeppesen, Erik; Aarhus Universitet, Department of Bioscience
Davidson, Thomas; Aarhus Universitet, Department of Bioscience
Subject:
Ecology < BIOLOGY, Environmental Science < BIOLOGY
Keywords:
Marine-derived nutrients, seabird colonies., ecosystem engineer, arctic
food webs, stable isotopes.
Proceedings B category:
Ecology
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Title page
1
Small birds, big effects: The little auk (Alle alle) transforms high arctic ecosystems
2
Ivan González-Bergonzoni
a,b,c
, Kasper L. Johansen
d
, Anders Mosbech
d
, Frank
3
Landkildehus
b
, Erik Jeppesen
b,e,f
, Thomas A. Davidson
b
4
Author affiliation:
5
a- Departamento de Ecología y Evolución, Facultad de Ciencias, Universidad de la
6
República, Iguá 4225, Malvín Norte, 11400, Montevideo, Uruguay
7
b- Department of Bioscience and Arctic Research Centre, Aarhus University,
8
Vejlsøvej, 25, 8600 Silkeborg, Denmark
9
c- Laboratorio de Etología, Ecología y Evolución, Instituto de Investigaciones
10
Biológicas Clemente Estable, Montevideo, Uruguay
11
d- Department of Bioscience and Arctic Research Centre, Aarhus University,
12
Frederiksborgvej 399, 4000 Roskilde, Denmark
13
e- Sino-Danish Centre for Education and Research, University of Chinese Academy
14
of Sciences (UCAS), Room N501, UCAS Teaching Building, Zhongguancun
15
Campus, Zhongguancun South 1st Alley, Haidian District, Beijing 100190, China
16
f- Greenland Climate Research Centre (GCRC), Greenland Institute of Natural
17
Resources, Kivioq 2, 3900 Nuuk, Greenland
18
19
Corresponding authors:
20
21
Ivan González-Bergonzoni
22
Departamento de Ecología y Evolución, Facultad de Ciencias, Universidad de la
23
Republica. Iguá 4225, 11400, Montevideo Uruguay
24
Telephone number: +598 93 356 908
25
E-mail: ivg@fcien.edu.uy
26
27
Thomas A. Davidson
28
Department of Bioscience and Arctic Research Centre, Aarhus University,
29
Vejlsøvej, 25, Silkeborg, 8600, Denmark
30
Telephone number: +4587159005
31
E-mail: thd@bios.au.dk
32
33
34
35
36
37
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Abstract
38
In some arctic areas marine-derived nutrients (MDN) resulting from fish migrations
39
fuel freshwater and terrestrial ecosystems, increasing primary production and
40
biodiversity. Less is known, however, about the role of seabird-MDN in shaping
41
ecosystems. Here, we examine how the most abundant seabird in the North Atlantic,
42
the little auk (Alle alle), alters freshwater and terrestrial ecosystems around the North
43
Water Polynya (NOW) in Greenland. We compare stable isotope ratios (δ
15
N and
44
δ
13
C) of freshwater and terrestrial biota, terrestrial vegetation indices and physical-
45
chemical properties, productivity and community structure of fresh waters in
46
catchments with and without little auk colonies. The presence of colonies profoundly
47
alters freshwater and terrestrial ecosystems by providing nutrients and massively
48
enhancing primary production. Based on elevated δ
15
N in MDN, we estimate that
49
MDN fuels > 85% of terrestrial and aquatic biomass in bird influenced systems.
50
Furthermore, by using different proxies of bird impact (colony distance, algal δ
15
N)
51
it is possible to identify a gradient in ecosystem response to increasing bird impact.
52
Little auk impact acidifies the freshwater systems, reducing taxonomic richness of
53
macroinvertebrates and truncating food webs. These results demonstrate that the
54
little auk acts as an ecosystem engineer, transforming ecosystems across a vast
55
region of Northwest Greenland.
56
57
Keywords: Marine-derived nutrients; nutrient subsidies; stable isotopes; arctic food
58
webs; ecosystem engineer; seabird colonies.
59
60
61
62
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Introduction
63
Migratory animals translocate energy and nutrients between ecosystems and may
64
support productivity and biomass in otherwise unproductive systems [1]. Species
65
responsible for such translocation of nutrients are often termed ‘ecosystem
66
engineers’ as they may profoundly change the recipient ecosystem (e.g. [2-4]). For
67
example, Pacific salmon species are responsible for large-scale transport of marine-
68
derived nutrients (MDN) to freshwater and terrestrial ecosystems in temperate and
69
arctic regions of North America and Asia [5-8]. Productivity and biodiversity
70
increase in systems with Pacific salmon as MDN are assimilated into stream biofilms
71
and terrestrial vegetation [7, 9].
72
In many locations around the globe, seabirds feeding at sea and breeding in colonies
73
in terrestrial systems are known to bring nutrients to land [9-13]. However, evidence
74
of the extent to which the seabird MDN subsidy alters ecosystems is sparse and what
75
exists is limited to detecting the presence of MDN in terrestrial soil, vegetation, and a
76
few soil invertebrates [14-17]. These studies rely on nitrogen stable isotope ratios
77
(δ
15
N), exploiting the fact that marine δ
15
N is higher than terrestrial δ
15
N and thus
78
easily traceable in freshwater and terrestrial biota (e.g. [17]). For example, δ
15
N was
79
found to be higher in soil and vegetation at bird-affected sites compared to bird-free
80
sites for a range of colonial seabird species in Antarctica [16], Florida, USA [18],
81
New Zealand [15, 19], and Svalbard [20].
82
The few studies considering change in ecosystem structure induced by seabirds (e.g.
83
[10, 11, 13]) suggest that seabird colonies alter terrestrial vegetation structure,
84
increase primary productivity and fuel terrestrial food webs both above and below
85
ground [13- 14]. Seabird guano may also alter the chemical properties of freshwaters
86
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by changing nutrient concentrations and pH [21]. However, these findings apply to
87
relatively small catchments and are mostly valid only for ecosystems located
88
immediately adjacent to specific bird colonies on islands [13, 18, 19].
89
Here, we sought to elucidate the nature and extent of the marine nutrient subsidy
90
from the extensive seabird colonies along the shores of the North Water Polynya
91
(NOW) in Northwest Greenland (Figure 1). This area is the main breeding ground of
92
the little auk (Alle alle), a small (approx. 160 g), zooplanktivorous alcid, which is the
93
most abundant seabird in the North Atlantic [22, 23]. Within a range of approx. 400
94
km, ca. 80% of the global little auk population, or 33 million breeding pairs, have
95
their nesting sites in dense colonies on talus slopes up to 10 km inland from the coast
96
[24, 25, 26]. Colonies are attended from early-May to mid-August when parents,
97
performing round-trips to at-sea foraging areas up to 100 km from the breeding site,
98
raise a single chick on a diet of lipid-rich Arctic copepods [27]. In the NOW, little
99
auks are estimated to be capable of consuming up to 24% of the copepod standing
100
stock [28], bringing vast quantities of MDN to land.
101
The overall aim of the study was to determine the contribution of seabird-MDN to
102
the biomass of primary producers and consumers in both terrestrial and aquatic
103
habitats. Specifically we sought to identify the effect of bird colonies on terrestrial
104
and freshwater primary productivity, freshwater physical-chemical characteristics
105
and biological community composition and also to investigate the potential
106
mechanisms behind any differences between affected and unaffected systems. We
107
hypothesize that: i) a very large proportion of the biomass of aquatic and terrestrial
108
primary producers and consumers in bird colony areas is fuelled by MDN; ii) little
109
auk colonies increase terrestrial and aquatic primary production, and alter physical-
110
chemical properties and community structure in recipient freshwater ecosystems,
111
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resulting in increased nutrient concentrations, algal biomass and taxa richness.
112
Methods
113
Study area
114
The NOW polynya in Smith Sound, Northern Baffin Bay lies between Greenland
115
and Canada. It covers around 85,000 km
2
and is the largest polynya in the Arctic
116
[29](Figure 1). The combination of year-round nutrient rich, open waters and
117
constant light in the summer makes the NOW one of the most productive marine
118
areas in the Arctic [30].
119
Sampling campaigns
120
In late-July and early-August 2014 and 2015, terrestrial and freshwater ecosystems
121
were sampled along the Greenlandic coastline of NOW from Savissivik in the south
122
to Siorapaluk in the north (Fig. 1). In the field, sampling sites were classified as
123
either “colony” or “control” sites. Sites located in the drainage catchment of a little
124
auk colony or under a flight corridor of birds commuting between a colony and at-
125
sea foraging areas (areas receiving bird droppings) were classified as colony sites.
126
Sites located in catchments without colonies or overflying little auks were classified
127
as control sites. Additionally, we used data on stable isotopes and nutrient
128
concentrations in lakes sampled in an area without little auks near Pituffik in 2001
129
(Supplementary Methods S1, Fig. 1).
130
Stable isotope sampling
131
At each site, samples were collected for analysis of C and N stable isotopes (δ
15
N
132
and δ
13
C). Samples were obtained from soil, terrestrial mosses, pooled terrestrial
133
plant leaves, excrement (little auk, geese, arctic hare (Lepus arcticus) and musk ox
134
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(Ovibos moschatus)), hair from arctic hare, and a skull from an arctic fox (Vulpes
135
lagopus). In freshwater habitats, filamentous algae, aquatic mosses, debris
136
(conditioned leaf litter), benthic biofilm, macroinvertebrates, seston, zooplankton,
137
profundal lake sediments, and fish were sampled. Samples were analysed at UC
138
Davis Stable Isotope Facilities, California, USA
139
(http://stableisotopefacility.ucdavis.edu). Isotopic data from animals were lipid-
140
corrected based on their C:N ratio following Equation 3 of Post et. al. (2007)[31].
141
Details of the stable isotope sampling and corrections are given in the Supplementary
142
Methods S1.
143
Sampling freshwater consumer taxa and physical-chemical properties
144
The richness of consumer taxa was measured as the number of aquatic consumer
145
taxa obtained during the sampling for stable isotopes, taxa being defined at family
146
level. Family level richness was used as species-level identification of
147
macroinvertebrates was not possible in the field. Family-level richness generally
148
correlates well with species richness, thus constituting a valid measure of richness
149
[e.g. 32]. Sampling effort was similar at the different sites, allowing comparison
150
between sites (see Supplementary Methods S1 for details). In each aquatic system,
151
we also took water samples for measuring nutrient concentrations and algal biomass
152
samples (chlorophyll-a). Key physical-chemical variables were recorded using a
153
multi-parameter probe (for details see Supplementary Methods S1). Information
154
about the physical-chemical characteristics of sites is available in Supplementary
155
Table S2 and details of taxa collected are given in Supplementary Table S3.
156
Terrestrial productivity
157
As a measure of terrestrial productivity, we used an Enhanced Vegetation Index
158
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(EVI) image from MODIS Terra, 28/7 - 12/8 2015 [33, 34]
.
Due to the coarse
159
resolution of the image (250x250 m), and the heterogeneous nature of the landscape
160
in the vicinity of the sampling sites (e.g. patches of vegetation in places where soil
161
formation is possible, interspaced with areas of bare rock and water), the maximum
162
EVI value within a 500 m radius of each sampling site was used in the statistical
163
analyses (for details see Supplementary Methods S1).
164
Comparison of colony and control sites
165
To avoid making type II errors, univariate tests of differences between colony and
166
control sites were preceded by two PERMANOVAs [35], one including δ
15
N and
167
δ
13
C of terrestrial and aquatic primary producer and consumer groups, and another
168
including grouped freshwater physical-chemical parameters. In the univariate tests,
169
variance was often heterogeneous and Generalized Least Squares models (GLS,
170
α=0.05, [36]) were used with the appropriate error structures to account for this.
171
Residual plots were checked for remaining heterogeneity and for spatial
172
autocorrelation [36]. Where residual spatial autocorrelation was detected, a spatial
173
weights matrix was integrated into the model and residuals were re-checked. Full
174
details of the models can be found in Supplementary Methods S1.
175
In relation to aquatic habitats, comparisons of parameters between colony and
176
control sites were made for all system types (lakes, ponds, and streams) pooled.
177
However, in the case of algal biomass, separate comparisons were made for lotic
178
(streams) and lentic (lakes+ponds) systems due to different units being used: In lotic
179
systems benthic algal biomass was in µg cm
-2
whereas in lentic systems
180
phytoplankton biomass was expressed in µg l
-1
.
181
Estimation of the contribution of seabird-MDN to biomass
182
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Enhanced δ
15
N at colony sites relative to control sites provides an unequivocal
183
marker of the presence of seabird-MDN. Following the procedure of Harding et al.
184
(2004), the contribution of seabird-MDN to the biomass of different terrestrial and
185
aquatic primary producer and consumer groups at colony sites was estimated by
186
means of mass balance models for N. Details of methodology are provided in
187
Supplementary Methods S1.
188
Changes along a gradient of bird impact
189
To study how terrestrial and freshwater ecosystems were affected along a gradient of
190
bird impact, we investigated the relationships between distance to nearest little auk
191
colony and EVI, aquatic algal biomass (lotic and lentic Chl-a) and δ
15
N of freshwater
192
benthic algae. Further, in a detailed case study of Savissivik Island, where GPS
193
tracking of breeding little auks was conducted, we examined how EVI and
194
freshwater benthic algal δ
15
N varied in relation to overflight intensity of little auks
195
(proxy of bird dropping intensity) at five sample sites at varying distances from the
196
tracking colony. We also modelled the drainage pattern from the Savissivik colony to
197
evaluate its effect on the spread of nutrients in the landscape. All details are
198
provided in Supplementary Methods S1.
199
The combined results of these analyses strongly indicated that benthic algal δ
15
N is a
200
good proxy of the relative magnitude of bird nutrient input in fresh waters, reflecting
201
true impact much better than distance to nearest little auk colony (see Results and
202
Discussion). We therefore used benthic algal δ
15
N to detect changes in freshwater
203
physical-chemical and community characteristics (pH, total nitrogen, total
204
phosphorous, algal biomass, and consumer taxa richness) along a gradient of bird
205
impact. The use of δ
15
N as an indicator of MDN input has been supported in diverse
206
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studies (e.g. 7, 12, 21, 37). Details of the models used to test the relationships are
207
provided in Supplementary Methods S1.
208
Potential drivers of changes in freshwater ecosystems along a gradient of bird
209
impact
210
Finally, in order to identify potential mechanisms behind changes in freshwater
211
community structure along a gradient of bird impact, we tested for relationships
212
between environmental variables changing with bird impact, i.e. nutrient
213
concentrations and pH, versus algal biomass (phytoplankton Chl-a in lentic and
214
benthic algal Chl-a in lotic systems) and consumer taxa richness. Statistical
215
procedures are described in Supplementary Methods S1.
216
Results and Discussion
217
Comparison of colony and control sites
218
Unequivocal evidence of fertilization by seabird-derived nutrients was reflected in
219
the different isotopic fingerprints of C and N in terrestrial and aquatic primary
220
producers and consumers at little auk colony sites compared with control sites
221
(PERMANOVA F=9.9; df
res
=11, p<0.01), in particular by the circa ten-fold
222
difference in their δ
15
N (GLS: p<0.05, Table 1, Supplementary Figure S1). The
223
greater statistical significance of the difference in δ
15
N compared with δ
13
C between
224
colony and control sites reflects the fact that whilst marine derived nitrogen is
225
incorporated in both terrestrial and freshwater ecosystems, carbon of marine origin is
226
only incorporated in freshwater systems [e.g. 15]. Specifically aquatic mosses and
227
chironomids were enriched in δ
13
C at colony sites. The pattern of elevated δ
15
N at
228
colony sites was significant in terrestrial systems and across all fresh waters – lakes,
229
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ponds and streams (Table 1, Supplementary Figure S1). Lake sediment, seston,
230
zooplankton, and hair from arctic hare (Lepus arcticus) also had higher δ
15
N values
231
at colony sites, although this could not be tested statistically due to lack of replicates
232
(Table 1, Supplementary Figure S1). The observed ten-fold increase in δ
15
N is larger
233
than that recorded in systems where migratory fish transfer MDN to terrestrial and
234
aquatic ecosystems (i.e. a 3- to 4-fold δ
15
N increase in fish impacted vs. control sites
235
[7, 37]).
236
In agreement with our first hypothesis, the modelling of the MDN subsidy indicated
237
that an overwhelming majority of both terrestrial and freshwater primary producer
238
and consumer biomass was fuelled by MDN at little auk colony sites (Table 1). The
239
mass balance models always yielded values >85% at colony sites. While directly
240
comparable with other studies [e.g. 5, 11], there are uncertainties associated with
241
these estimates due to possible variation in the fractionation of marine nitrogen from
242
guano to its final uptake product, which is dependent on various microbial processes
243
[11]. For example, the process of conversion of uric acid to ammonia involves the
244
volatilization of ammonia, a powerful fractionation process leaving the remaining
245
substrate enriched by approximately 40
0
/
00
in δ
15
N [38]. In contrast, the fractionation
246
of nitrogen during nitrification depletes δ
15
N of the substrate with about -25
0
/
00
[39].
247
For seston and zooplankton, the uncertainty is higher due to low sample size from
248
colony sites (Table 1). Notwithstanding these uncertainties, the data provide strong
249
evidence of a very large MDN subsidy of terrestrial and aquatic ecosystem
250
production at colony sites. This is highlighted by the fact that the proportions of
251
MDN assimilated into freshwater organisms described here (always >85%) are much
252
higher than those reported for biota in New Zealand streams related to seabird
253
colonies using directly comparable methods (28 to 38% of biomass generated from
254
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MDN) [11]. Our proportions are also much higher than those estimated in MDN
255
subsidy studies of Pacific salmon (e.g. 23 and 25% of marine-derived biomass in
256
aquatic organisms and terrestrial vegetation, respectively; [7]). The higher values
257
compared with other studies probably reflect both the large quantity of the MDN
258
input in little auk colonies and the paucity of other nutrient sources at these high
259
latitudes (76-78 deg. N).
260
In agreement with our second hypothesis, we found the physical-chemical
261
characteristics of freshwater systems differed significantly between colony and
262
control sites (PERMANOVA F=8.8, df
res
=26; p<0.01, Table 2). Nutrient
263
concentrations were significantly higher at colony sites, the only exception being the
264
marginal significance of phosphate concentrations (GLS: p=0.07, Table 2). Algal
265
biomass was also significantly higher at colony sites – ca. 20 fold for phytoplankton
266
biomass in lentic systems and ca. 10 fold for benthic algal biomass in lotic systems
267
(GLS: p<0.05; Table 2). The nutrient levels and algal biomass observed in the
268
aquatic systems at colony sites are the highest reported for Greenland, where most
269
systems are characterized by nutrient limitation (e.g. [40, 41]). Correspondingly, in
270
terrestrial systems, there were significantly higher EVI values (c.a. 2 fold higher) at
271
colony sites (GLS: p<0.01; Table 2). However, contrary to expectations, freshwater
272
consumer taxa richness was lowest at the nutrient enriched, bird-impacted sites
273
(GLS: p<0.05; Table 2, Supplementary Table S3).
274
Among the chemical properties of the freshwaters, pH differed significantly with
275
colony sites being more acidic than control sites (GLS: p<0.05; Table 2). This effect
276
appears to be a particular characteristic of little auk colonies, contrasting with
277
findings from Devon Island, Canada, where pools under a northern fulmar (Fulmarus
278
glacialis) colony exhibited increased pH values relative to control sites without birds
279
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[21]. In Svalbard it has been observed that zooplanktivorous seabirds, such as the
280
little auk, promote soil acidification, whereas piscivorous seabirds do not [42].
281
Changes along a gradient of bird impact
282
At a broad scale a decrease in the distance to nearest little auk colony was associated
283
with an increase in EVI (GLS: t=-2.6 p<0.05, pseudo r
2=
0.06, n=29), benthic algae
284
biomass (lotic Chl-a) (GLS: t=-4.7 p<0.0001, pseudo r
2=
0.07, n=17) and δ
15
N of
285
freshwater benthic algae (GLS: t=-5.6 p<0.00001, pseudo r
2=
0.29, n=27), whereas
286
the relationship with phytoplankton biomass (lentic Chl-a) was not significant
287
(Supplementary Figure S2). When considering only sites closer than 2500 m from
288
colonies the relationships became more clear: EVI increased strongly with proximity
289
to colony (GLS: t= -5.1 p<0.001, pseudo r
2
=0.52, n=27) as did benthic algal biomass
290
(GLS: t=-4.5 p<0.0001, pseudo r
2=
0.12, n=15) and benthic algal δ
15
N (GLS: t=-4.6
291
p<0.0001, pseudo r
2=
0.17, n=21) (Supplementary Figure S2). However, from the
292
scatterplots it is evident that distance to colony is a relatively poor predictor as a site
293
can be proximal to a colony and remain relatively unaffected by MDN.
294
An explanation of this is provided by the case study of Savissivik Island, where, due
295
to the GPS tracking, we were in the unique position of being able to relate EVI and
296
δ
15
N of benthic algae with an estimate of overflight intensity of birds (proxy of bird
297
dropping intensity) (Figure 2). Acknowledging that we only have five sample sites
298
on Savissivik Island, these indicate strong, positive correlations between overflight
299
intensity of little auks and both EVI (LM: p<0.01; r
2
= 0.88; n=5) and δ
15
N of
300
freshwater benthic algae (LM: p<0.01; r
2
= 0.88; n=5). Further, benthic algal δ
15
N
301
and EVI were strongly, positively correlated at the sample sites (LM: p<0.001; r
2
=
302
0.94; n=5). The drainage pattern from the colony seemingly also corresponds with
303
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the EVI and benthic algal δ
15
N values at the sample sites (Fig. 2). Thus, whilst it is
304
clear that the little auk colony is the main source of the nutrients on Savissivik
305
Island, the relative importance of overflight vs. drainage in the spread of nutrients
306
could not be discerned.
307
The Savissivik case clearly demonstrates that little auks use distinct flight paths
308
when commuting between their breeding colony and offshore foraging areas, and
309
that local drainage systems transport the nutrients in particular directions. Thus, it is
310
possible to have a low impact site close to a large colony, if the site is located outside
311
the flight path and upstream of the drainage from the colony/flight path. This appears
312
to be the main reason for the inadequacy of distance to colony as predictor of aquatic
313
and terrestrial primary producer biomass. The EVI evidence suggests that relatively
314
fertile terrestrial areas do exist outside the influence of little auk colonies, primarily
315
in conjunction with meadows. However, EVI values >0.25 are almost exclusively
316
found in association with little auk colonies, and terrestrial productivity is clearly
317
elevated over large areas of the Greenlandic coastal forelands of the NOW due to the
318
extensive little auk colonies (Figure 3).
319
The fact that freshwater benthic algal δ
15
N values were circa 15-fold higher at
320
colony sites compared to control sites, decrease with distance to colony, and, in the
321
case of Savissivik island, are tightly coupled to overflight intensity and drainage
322
input from the colony, indicated that it is a robust indicator of the intensity of MDN
323
impact. This is in line with findings in Canadian seabird colonies [12, 21] and
324
relationships observed between δ
15
N of terrestrial vegetation and distance to a
325
salmon stream [7, 37] or relative salmon carcass density [37]. Thus, benthic algal
326
δ
15
N was employed as a proxy of little auk impact to investigate changes of
327
freshwater physical-chemical properties and biotic community structure along a
328
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14
gradient of impact. As δ
15
N of benthic algae rose there was a significant increase in
329
concentrations of total nitrogen (GAM: F=2.5, p<0.0001, r
2
=0.63, n=34) and
330
phosphorous (GAM: F=3.5, p<0.0001, r
2
=0.36, n=34) and system acidity (LM:
331
p<0.0001, r
2
=0.53, n=32) (Figure 4). With increasing benthic algal δ
15
N, algal
332
biomass rose in both lentic (GAM: F=2.1, p<0.05, r
2
= 0.39) and lotic systems
333
(GAM: F=2.8, p<0.001, r
2
= 0.55), whereas freshwater consumer taxa richness
334
decreased (GAM: F=10.8, p<0.01, r
2
=0.21) (Figure 4).
335
336
Potential drivers of changes in freshwater ecosystems along a gradient of bird
337
impact
338
We attribute the increase in nutrients to be the main driver of increased algal biomass
339
in lakes and streams (Supplementary Figure S3). In both lotic and lentic systems, the
340
increase in total nitrogen concentrations promoted increased algal biomass (LMs:
341
p<0.0001, r
2
= 0.77, n=16 and p<0.0001, r
2
= 0.89, n=11 for lotic and lentic systems,
342
respectively) as did the total phosphorous concentration, which was also strongly
343
related to algal biomass in both the lentic (LM: p<0.0001, r
2
=0.99, n=16) and lotic
344
systems (LM: p<0.0001, r
2
= 0.75, n=11) (Supplementary Fig. S3). In nutrient-poor
345
systems, increased nutrient concentrations enhance primary producer biomass,
346
resulting in bottom-up effects [13, 43] that increase the abundance and biomass of
347
primary and secondary consumers [4], often creating higher taxonomic richness [44].
348
This matches our field observations of terrestrial systems where sites located below
349
bird colonies were the most productive and greenest with most observations of foxes,
350
hares, geese and muskoxen, whereas control sites were largely barren (e.g. Fig. 3).
351
However, in the freshwater systems, the enhanced algal biomass with increasing bird
352
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15
impact was associated with decreasing taxonomic richness. The acidification
353
associated with little auk impact is a potential driver as pH was found to be
354
negatively correlated with consumer taxa richness in both lotic (GAM: p<0.001,
355
r
2
=0.44, n=17) and lentic systems (LM: p=0.06, r
2
=0.49, n=12) (Supplementary Fig.
356
S3). Some bird-impacted sites had extremely low pH, down to 3.4, and such low
357
levels cause biodiversity loss in lakes and streams [45, 46]. At two colony sites, the
358
harsh environment with low pH values meant that neither fish nor
359
macroinvertebrates were found during the sampling (Supplementary Table S3).
360
Conclusions
361
In the study region, the approximately 60-70 million breeding little auks [26] alter
362
both terrestrial and freshwater ecosystems by promoting primary and secondary
363
production. Whilst these findings are similar to studies of MDN subsidy produced by
364
migrating Pacific salmon species, the magnitude of the MDN subsidy reported here
365
is much higher and the consequences for freshwater consumers differ significantly.
366
In association with little auk impact on freshwater systems, we found a decrease in
367
species richness of higher consumers and truncated food webs without fish. This
368
highlights the key relevance of the identity of the vector of nutrient subsidies in order
369
to understand and predict ecosystem-wide consequences of engineer species that
370
translocate nutrients between ecosystems.
371
As the total horizontal extent of breeding colonies is approx. 400 km [24], a
372
significant proportion the coastal forelands around the NOW has been transformed
373
by this single species: the little auk. Similar significant changes in the terrestrial
374
environment related to the presence of seabird colonies have also been reported for
375
islands in Alaska where in total >10 million birds nest [10]. Here the introduction of
376
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16
arctic fox (Vulpes lagopus) on some islands in the late 19
th
century resulted in
377
decreased bird abundance, reducing the nutrient subsidies by seabirds to terrestrial
378
productivity, and consequently the landscape shifted from grasslands to tundra [10].
379
During the breeding season, little auks depends on lipid-rich copepods species
380
associated with cold water, and consequently it has been suggested that little auk
381
populations will decline in response to the current warming of the Arctic [47, 48]. If
382
so, a landscape shift comparable to the one observed by [10] may be expected for the
383
NOW.
384
Ethics
385
The procedures used conform to the legal requirements of the country and
386
institutional guidelines.
387
Data accessibility
388
The datasets supporting this article have been uploaded as part of the supplementary
389
material.
390
Competing interests
391
We have no competing interests
392
Author’s contributions
393
IGB, AM, KJ, EJ and TD participated in the conception and design of the study. All
394
authors carried out the fieldwork. IGB, TD and KJ analysed the data. IGB carried out
395
lab work and drafted the manuscript. AM, KJ, FL, EJ, TD helped drafting the
396
manuscript. TD coordinated the study. All authors gave final approval for
397
publication
398
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Acknowledgements
399
We thank Anne Mette Poulsen for manuscript editing, and we wish to extend our
400
warmest thanks for help during fieldwork to the local communities Siorapaluk,
401
Savissivik and Qaanaaq, and, at Thule Air Base, to liaison officer Kim Mikkelsen,
402
and Tony Rønne Pedersen and Erland Søndergaard of Greenland Contractors. We
403
would also like to thank the crew of the ships Minna Martek, Blue Jay and Hot
404
Toddy.
405
406
Funding
407
This study is part of The North Water Project (NOW) funded by the funded by the
408
Velux Foundation, the Villum Foundation, and the Carlsberg Foundation of
409
Denmark. EJ was further supported by the MARS project (Managing Aquatic
410
ecosystems and water Resources under multiple Stress) funded under the 7th EU
411
Framework Programme, Theme 6 (Environment including Climate Change),
412
Contract No.: 603378 (http://www.mars-project.eu). IGB was supported by SNI
413
(Agencia Nacional de Investigación e Innovación, ANII, Uruguay).
414
415
References
416
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43.Huryn A.D. 1998 Ecosystem-level evidence for top-down and bottom-up control
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44.VanderMeulen M.A., A.J. H., M. S.S. 2001 Three evolutionary hypotheses for the
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hump-shaped productivity–diversity curve. Evol. Ecol. Res. 3, 379-392.
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45.Layer K., Hildrew A.G., Jenkins G.B., Riede J., Rossiter S.J., Townsend C.R.,
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Woodward G. 2011 Long-term dynamics of a well-characterised food web: Four
549
decades of acidification and recovery in the Broadstone Stream model system. Adv.
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46.Schindler D.W. 1990 Experimental perturbations of whole lakes as tests of
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hypotheses concerning ecosystem structure and function. Oikos 57(1), 25-41.
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(doi:10.2307/3565733).
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47.Grémillet D., Fort J., Amélineau F., Zakharova E., Le Bot T., Sala E., Gavrilo M.
555
2015 Arctic warming: nonlinear impacts of sea-ice and glacier melt on seabird
556
foraging. . Glob. Change Biol. 21(3), 1116-1123.
557
48.Jakubas D., Trudnowska E., Wojczulanis-Jakubas K., Iliszko L., Kidawa D.,
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Darecki M., Błachowiak-Samołyk K., Stempniewicz L. 2013 Foraging closer to the
559
colony leads to faster growth in little auks. MEPS 489, 263-278.
560
561
Figure and table captions
562
Figure 1. The investigation area in Northwest Greenland encompassing the entire range of 563
little auk breeding colonies associated with the North Water Polynya (NOW). Red areas: 564
little auk breeding colonies after (25). Black dots: sites sampled in 2014-15. White dots: sites 565
sampled in 2001 and used for comparison. Black squares: permanently inhabited 566
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24
settlements. On the overview map, the solid blue area represents the approximate late 567
winter/early spring extent of the NOW. 568
Figure 2: Relationships between flight intensity of little auks, Enhanced Vegetation Index 569
(EVI), and δ
15
N of freshwater benthic algae at Savissivik Island where GPS tracking of little 570
auks was conducted. A) Relative flight intensity of little auks (km of track line per square 571
km) on a color scale from blue (low) to red (high). Coastline depicted as black line, little auk 572
colony as hatched area, and deployment site used during GPS-tracking as a white dot. 573
Freshwater sampling sites from Savissivik Island (NOW 9-13) are labelled on the map and 574
the symbol size is scaled according to the benthic algal δ
15
N value of the site. B) Enhanced 575
Vegetation Index (EVI) from MODIS Terra, 28/7 - 12/8 2015 (33), on a color scale from 576
blue (low) to red (high). The black, hatched region extending from the little auk colony 577
indicates drainage from the colony, modelled on the basis of a digital elevation model of 578
Savissivik Island (see Supplementary Methods S1 for details). 579
Figure 3: Terrestrial productivity is elevated in little auk colonies. Little auk colonies (black 580
polygons; after (24)) and Enhanced Vegetation Index (EVI) values below 0.25 (light green) 581
and above 0.25 (dark green) in the northern part of the investigation area. The EVI image is 582
from MODIS Terra, 28/7 - 12/8 2015 (33). Despite of many EVI values missing along the 583
coastline due to mixed pixels, the map clearly shows that EVI values above 0.25 are almost 584
exclusively found in association with little auk colonies. In inserts A-D landscape photos 585
illustrate the contrasting terrestrial productivity at colony and control sites. A) Productive 586
landscape in catchment of little auk colony on Savissivik Island (site 9); C) Productive 587
landscape in catchment of little auk colony at Annikitsoq on the south coast of Cape York 588
(sites 25-32); B) Barren landscape at control site on Savissivik Island (site 10); D) Barren 589
landscape at control site close to Booth Sound (site 15-16). See Fig. 1 and Supplementary 590
Table S2 for exact positions. 591
592
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Figure 4: Changes in environmental and biotic characteristics induced by increasing little 593
auk impact (using δ
15
N of benthic algae as a proxy of impact). Sampling sites in catchments 594
with little auk colonies are marked with black dots and sites in control areas with open 595
circles. A) Decrease in pH with increasing bird influence. B) Increase in total nitrogen 596
concentrations with increasing bird influence. C) Increase in total phosphorous 597
concentrations with increasing bird influence. D) Increase in phytoplankton biomass in lentic 598
systems with increasing bird influence. E) Increase in stream benthic algal biomass with 599
increasing bird influence. F) Decrease in consumer taxa richness with increasing bird 600
influence. Model details are shown in each panel figure. 601
Table 1: Nitrogen and carbon isotopic signatures (δ
15
N and δ
13
C,
o
/
oo
, mean±SD) at 602
colony and control sites, and estimated contribution of marine-derived nitrogen (MDN) 603
to the biomass of primary producers and consumers at colony sites. Calculations of the 604
contribution of MDN to biomass were performed with mass balance models using as 605
marine nitrogen source δ
15
N of peat at colony sites, adding 2
o
/
oo
enrichment in case of 606
aquatic resources and consumers. This 2
o/oo
enrichment represents the hydrolysable 607
proportion of nitrogen reaching aquatic ecosystems. This estimate is tentative as the 608
variability in δ
15
N reaching freshwaters is high due to diverse microbial processes 609
affecting guano and soil δ
15
N, implying that values >100% may occur. 610
Table 2: Physical-chemical and biotic characteristics of freshwater systems in 611
catchments with and without little auk colonies sites (colony vs. control sites). Values 612
are given as mean±SD. PERMANOVA and pairwise GLS test parameters are given to 613
allow comparison of each parameter between colony and control sites. Significant 614
differences are marked in bold and marginal p values are given in italics. nt =not tested 615
due to lack of replicates. Generally, the GLS tests were conducted for all system types 616
pooled (lakes, ponds streams). However, differences in algal biomasses were tested 617
separately for lentic and lotic systems due to different measurement methods (see 618
Methods). 619
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Supplementary material:
620
Supplementary Methods S1. Detailed description of sampling and data analysis
621
methodology.
622
Supplementary Appendix S2. Supplementary tables and figures including: Table
623
S1:
Summary of site locations and physical-chemical characteristics;
Table S2: Summary
624
of collected freshwater organisms; Figure S1:
Carbon and nitrogen isotopic signatures of 625
main producers and consumers found in lakes, streams, ponds, and terrestrial ecosystems 626
with little auk colonies in their catchment and at control sites without colonies;
Figure S2:
627
Relationships between distance to nearest little auk colony and algal biomasses in
628
freshwater systems, Enhanced Vegetation Index (EVI), and freshwater benthic algal
629
δ
15
N; Figure S3: Relationships between nutrient concentrations and algal biomasses
630
in freshwater systems, and between water pH and freshwater consumer taxa richness.
631
Supplementary Material S3. R script and data tables used in the statistical analysis.
632
633
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Table 1: Nitrogen and carbon isotopic signatures (δ
15
N and δ
13
C,
o
/
oo
, mean±SD) at colony and control sites, and estimated contribution of marine-1
derived nitrogen (MDN) to the biomass of primary producers and consumers at colony sites. Calculations of the contribution of MDN to biomass were 2
performed with mass balance models using as marine nitrogen source δ
15
N of peat at colony sites, adding 2
o
/
oo
enrichment in case of aquatic 3
resources and consumers. This 2
o/oo
enrichment represents the hydrolysable proportion of nitrogen reaching aquatic ecosystems. This estimate is 4
tentative as the variability in δ
15
N reaching freshwaters is high due to diverse microbial processes affecting guano and soil δ
15
N, implying that values 5
>100% may occur. 6
Number of samples
analysed δ
13
C (Mean ± SD) δ
15
N (Mean ± SD) GLS test parameters (T; Df
res
; p-value)
Mass balance
model. Mean
contribution of
MDN to
biomass (%)
Sample Colony, control Colony Control Colony Control Test for δ13C Test for δ15N Colony
PERMANOVA (all isotopic signatures) 4; 9 δ13C and δ15N: F=9.9; df
res
=11; p<0.01
Freshwater environment
Terrestrial debris 3; 6 -27.2 ± 2.9 -29.8 ± 1.0 8.4 ± 6.5 -1.3 ± 2.7 2.0; 7; p=0.08 2.8; 7; p<0.05 nt
Profundal lake sediment 2; 5 -24.2 ± 0.3 -22.4 ± 5.2 20.7 ± 2.4 1.9 ± 0.4 nt nt nt
Aquatic moss 15; 14 -23.9 ± 1.7 -28.7 ± 4.9 17.3 ± 5.8 1.8 ± 2.5 3.4; 26; p<0.01 8.7; 26; p<0.0001 116.5
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2
7
Benthic algae 39; 25 -19.9 ± 4.4 -21.7 ± 6.6 17.9 ± 8.8 1.1 ± 2.4 0.87; 33; p>0.1 4.8; 33; p<0.0001 127
Chironomids 33; 42 -17.5 ± 3.3 -23.9 ± 4.7 16.2 ± 7.1 4.4 ± 3.2 4.5; 31 p<0.0001 3.9; 31; p<0.001 87.9
Other invertebrates 4; 31 -21.5 ± 1.8 -21.3 ± 3.8 15.9 ± 5.0 4.6 ± 1.9 -0.11; 12; p>0.1 4,3; 12; p<0.0001 86.1
Seston 1; 3 -19 ± 0 -24.6 ± 2.6 20.8 ± 0.0 3.5 ± 0.7 nt nt 128.7
Zooplankton 1; 9 -16.8 ± 0 -25.3 ± 3.4 34.2 ± 0.0 4.2 ± 2.3 nt nt 229.5
Terrestrial environment
Peat soil 5; 6 -26.1 ± 1.9 -25.3 ± 0.9 11.1 ± 3.5 0.4 ± 1.5 -0.9; 9; p>0.1 6.8; 9; p<0.001 nt
Terrestrial vegetation 9; 9 -28.7 ± 1.1 -28.5 ± 1.4 16.5 ± 6.7 5.3 ± 6.6 -1.0;16; p>0.1 5.5; 16 p<0.001 97.8
Arctic hare 1; 2 -25.2 ± 0 -25.7 ± 0.5 14.5 ± 0.0 5.2 ± 2.9 nt nt nt
Arctic fox 1 -16.7 13.5 nt nt nt
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1
Table 2: Physical-chemical and biotic characteristics of freshwater systems in catchments 1
with and without little auk colonies sites (colony vs. control sites). Values are given as 2
mean±SD. PERMANOVA and pairwise GLS test parameters are given to allow comparison 3
of each parameter between colony and control sites. Significant differences are marked in 4
bold and marginal p values are given in italics. nt =not tested due to lack of replicates. 5
Generally, the GLS tests were conducted for all system types pooled (lakes, ponds 6
streams). However, differences in algal biomasses were tested separately for lentic and 7
lotic systems due to different measurement methods (see Methods). 8
Lentic systems Lotic systems All systems GLS test parameters
Colony Control Colony Control Colony Control All systems (t; df
res
;
p-value)
Lentic
(F; df
res
;p-
value)
Lotic
(F; df
res
;p-
value)
Water physical-chemical
parameters
PERMANOVA (all physical-
chemical parameters) F=8 .87; df
res
=26;
p<0.01
pH* 5.7 ± 1.8 7.2 ±1.4 5.1 ± 1.4 6.9 ± 0.8 5.3 ± 1.5 7.1 ± 1.1 -2.59; 30; p<0.05
Conductivity (ms cm
-2
) 0.1 ± 0.2 0.07 ± 0.06 0.04 ± 0.03 0.08 ± 0.08 0.1 ± 0.1 0.1 ± 0.1 0.08; 29; p>0.1
Dissolved oxygen (mg l
-1
) 13.1 ± 1.6 12.0 ± 0.6 13.2 ± 1.1 12.1 ± 1.5 13.2 ± 1.1 12.1 ± 1.1 2.49; 29; p<0.05
Total nitrogen (mg l
-1
) 1.5 ± 1.3 0.4 ± 0.2 1.7 ± 1.6 0.3 ± 0.09 1.7 ±1.5 0.4 ± 0.2 2 .35; 32; p<0.05
NO
2
+ NO
3
( mg N l
-1
) 0.7 ± 0.8 0.05 ± 0.09 1 .3 ± 1.2 0.1 ± 0.09 1.1 ±1.1 0.1 ± 0.1 3.55; 32; p<0.01
Total phosphorous (mg. l
-
1
) 0.2 ± 0.2 0.01 ± 0.005 0.1 ±0.1 0.007 ± 0.004 0.12 ± 0.14 0.009 ± 0.005 3.3; 32; p<0.05
PO
4
(mg P. l
-1
) 0.08 ± 0.1 0.01 ± 0.01 0.08 ± 0.1 0.002 ± 0.001 0.088 ± 0.11 0. 004 ± 0.003 1.8; 32; p=0.07
Biotic structure
Enhanced Vegetation Index (EV I) 0 .27 ± 0.07 0. 16 ± 0.06 3.5; 33; p<0.01
Algal biomass (lentic Chla-µg.l
1
;
lotic Chla-µg.cm
-2
) 41.5 ± 34.9 2.2 ± 0.9 3.4 ± 2.8 0.4 ± 0.09
nt
2.3; 11;
p<0.05 3.3; 15; p<0.01
Richness of aquatic consumer tax a
(N
o
of taxa) 1.3 ± 0.5 2.7 ± 1.4 1 ± 0.6 1.8 ± 1.2 1.1 ±0.6 2.4 ± 1.3 -3.29; 31; p<0.01
9
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The investigation area in Northwest Greenland encompassing the entire range of little auk breeding colonies
associated with the North Water Polynya (NOW). Red areas: little auk breeding colonies after (25). Black
dots: sites sampled in 2014-
15. White dots: sites sampled in 2001 and used for comparison. Black squares:
permanently inhabited settlements. On the overview map, the solid blue area represents the approximate
late winter/early spring extent of the NOW.
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A B
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A
C D
BPage 32 of 33
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0 5 10 15 20 25 30
3456789
δ15N
System pH
0 5 10 15 20 25 30
0 1 2 3 4 5
δ15N
TN (mg l1)
0 5 10 15 20 25 30
0.0 0.1 0.2 0.3
δ15N
TP (μg l1)
0 5 10 15 20 25
0 20 40 60
δ15N
Lentic Chla (μg l1)
0 5 10 15 20 25 30
0 2 4 6 8
δ15N
Lotic Chla (μg l1)
0 5 10 15 20 25 30
0 1 2 3 4 5
δ15N
Number of consumer taxa
δ15N in freshwater benthic algae (0/00)
System pH
System total nitrogen (mg.l-1)
Phytoplankton biomass (μg/l-1)
Periphyton biomass (μg/cm.-2)System total phosphorous (mg.l-1)
Richness of consumer taxa (No taxa)
A B
D
C
E F
Colony sites
Control sites
LM: p<0.0001, r2=0.53
GAM: F=2.5, p<0.0001, r2=0.63
GAM: F=3.5, p<0.0001, r2=0.36 GAM: F=2.1, p<0.05, r2= 0.39
GAM: F=2.8, p<0.001, r2= 0.55 GAM: F=10.8, p<0.01, r2=0.21
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Supplementary Methods!1!
Stable isotope sampling 2!
In terrestrial systems, we took integrated samples of soil (peat soil), terrestrial mosses, 3!
pooled terrestrial plant leaves, excrement (little auk, geese, arctic hare (Lepus 4!
arcticus), musk ox (Ovibos moschatus)), hairs from arctic hare, and a skull from an 5!
arctic fox (Vulpes lagopus). Whenever possible, material from at least three different 6!
locations within the same study area was integrated. In the freshwater systems, we 7!
sampled filamentous algae, aquatic mosses and debris (conditioned leaf litter) by hand 8!
picking, benthic biofilm by scraping at least five different, randomly selected, rocks 9!
and macroinvertebrates by dragging a 500 μm sweep net and disturbing the stream 10!
bed immediately upstream of the net for about 20 minutes. In all lakes, two sets of 11!
standard gill nets (Lundgren gillnets with 14 different mesh sizes ranging from 6.25 to 12!
75 mm) were set in littoral and profundal habitats for at least 12 hours to capture fish 13!
representing the size spectrum present. Fish were measured and weighed in the field, 14!
and a sample of flank muscle was collected. No fish were present in the ponds, which 15!
freeze solid during winter. In lakes, bulk zooplankton samples were obtained by 16!
towing a 140 μm mesh-size net in the water column, and a seston sample was 17!
obtained by pumping lake water (pre-filtered through a 50 μm net) through a 20 μm 18!
mesh-size net. Additionally, bottom sediment was obtained from the first 1 cm 19!
surface of a core sample taken at the deepest point in each lake. 20!
All stable isotope samples were kept frozen until analysis. In the lab, samples were 21!
taxonomically identified (in the case of macroinvertebrates), sorted, and cleaned with 22!
distilled water following standard stable isotope preparation protocols [1]. 23!
Macroinvertebrate samples usually consisted of bulk samples of 10-20 individuals for 24!
chironomids and 3-10 individuals for large crustaceans, covering all occurring size 25!
groups. All samples were freeze-dried for 48 h, ground into fine powder, weighed 26!
(0.5-1.5 mg for animal tissue, 2-4 mg for mosses, debris, sediments and periphyton), 27!
and submitted for stable isotope analysis at UC Davis Stable Isotope Facilities, 28!
California, USA (http://stableisotopefacility.ucdavis.edu), where they were analysed 29!
following standard procedures. 30!
In addition to the samples collected during the field campaign in 2014-15, we used 31!
stable isotope data from lakes sampled in an area without little auks near Pituffik in 32!
2001. The use of these older data is justified because no evidence suggests that 33!
nitrogen isotopic signatures could have changed over time because of the pristine and 34!
stable environmental nature of the region and because the differences between guano-35!
derived and terrestrial nitrogen are much larger than any other natural variation 36!
reported [2]. 37!
Isotopic C signatures were mathematically normalized for lipids by using Eq.3 from 38!
Post et al 2007 [3], which is developed for aquatic animals and based on 16 different 39!
species from contrasting ecosystems: 40!
δ
13Cnormalised =
δ
13Cuncorrected - 3.32 + (0.99* C:N) 41!
where
δ
13Cnormalised is the mathematically corrected value for
δ
13C,
δ
13Cuncorrected 42!
is the raw carbon isotopic signature, and C:N is the carbon-nitrogen biomass ratio of 43!
the sample in question. In several studies, this equation has been shown to accurately 44!
predict lipid corrected δ13C in animal muscle tissues, performing as well as the 45!
method based on chemical lipid extraction [3-6]. In a few studies, where chemical 46!
lipid extracted samples differ in carbon isotopic signature from equation-corrected 47!
samples, this difference is never > 2 0/00 [7,8]. As our focus is on differences much 48!
larger than this, we believe that mathematical lipid normalisation is sufficient in our 49!
case. 50!
With regard to the potential influence of carbonates on δ13C, we did not acid treat the 51!
zooplankton samples as is often routinely done to remove carbonates from a sample. 52!
The reason for this was that many of our samples were of very low mass, excluding 53!
the possibility to split samples, and acid treatment alters δ15N, which in our study is 54!
more important than δ13C. However, due to the low pH in most of our study systems 55!
(mean value of 5 ±1.5 at colony sites, and 7.1 ± 1.1 at control sites) it is unlikely that 56!
carbonates were present in concentrations that would alter the results significantly 57!
[e.g. 9 ]. The fact that δ13C in several marine subarctic and arctic zooplankton species 58!
seem unaffected by carbonates [10] may provide further evidence to allow us to 59!
disregard any confounding effect of carbonates in the δ13C values of our zooplankton 60!
samples. 61!
62!
Sampling physical-chemical properties of freshwater systems 63!
We sampled lakes (lentic systems with maximum depth >1 m and maximum diameter 64!
>20 m), ponds (<1 m deep lentic systems, maximum diameter <10 m) and streams 65!
(2nd and 3rd order streams). In each system, a YSI V6600 multi-parameter probe was 66!
used to measure water temperature (oC), conductivity (ms cm-2), dissolved oxygen 67!
(mg l-1) and pH. Integrated water samples were collected for analysis of nutrient 68!
concentrations. In lakes and ponds, the sample was integrated from the whole water 69!
column. In streams, the sample integrated three sites in a downstream – upstream 70!
direction within a reach of approx. 100 m. In lakes and ponds, samples for estimation 71!
of algal biomass standing stock as chlorophyll-a (Chl-a) were taken by filtering a 72!
known volume of a water sample, integrated from the whole water column, through a 73!
GFC filter. In streams, three benthic biofilm Chl-a samples were obtained by scraping 74!
the same surface area (12.5 cm2) of three randomly selected rocks followed by 75!
filtering through GFC filters. The water and Chl-a samples were kept frozen until 76!
analysis. In the lab, total phosphorus was determined as molybdate reactive 77!
phosphorus [11] following persulphate digestion [12] and total nitrogen as nitrite + 78!
nitrate after potassium persulphate digestion [13]. Chl-a was determined 79!
spectrophotometrically after ethanol extraction [14]. The physical-chemical 80!
characteristics of the freshwater sample sites are given in Supplementary Table S1. 81!
82!
Terrestrial productivity 83!
As basis for assessing terrestrial productivity, we used an Enhanced Vegetation Index 84!
(EVI) image from MODIS Terra with a spatial resolution of 250x250 m [15, 16]. For 85!
each sample site, the maximum EVI value within a radius of 500 m was extracted 86!
using ArcGIS 10.2 (ESRI, Redlands, CA, USA). We used the maximum value within 87!
a neighbourhood due to the nature of the vegetation impact associated with little auk 88!
colonies. Typically, a little auk colony consists of a bare scree, where the birds nest, 89!
surrounded by cliffs, and in the case of valleys, often with some freshwater system at 90!
the valley floor (many of our samples come from such freshwater systems and thus 91!
from cells with water). What sets the little auk colonies apart from the surrounding 92!
landscape are the patches of lush vegetation in-between these features in places where 93!
soil formation is possible. However, individual patches are often considerable smaller 94!
than the cells of the EVI image, where everything within 250x250 m is collapsed into 95!
one value. By using the extreme EVI value within a small neighborhood around each 96!
sample site we hope to detect the cell, which best intersects an area close to the 97!
sample site, where soil formation is possible, in the case of colonies capturing the 98!
green patches. We also experimented with using the mean EVI value, which may 99!
seem somewhat more intuitive, but found this to be unsuitable as it is to a large degree 100!
dictated by the amount of water and bare rock within the neighborhood. 101!
Using maximum EVI values instead of mean EVI values would make our analyses 102!
more prone to error if other strong nutrient point sources existed in our study area. 103!
However, this would equally affect colony and control sites, adding noise but not 104!
bias. Further, besides little auk colonies, the only significant nutrient point sources 105!
within our study area are other seabird colonies, human settlements and 106!
archaeological sites. None of the neighborhoods around sample sites included other 107!
seabird colonies, and only one site is close to a human settlement (NOW 33). The 108!
neighborhoods of four sites contained archaeological remains (NOW 4, 5, 8, 24), but 109!
these remains were observed in the field and the associated vegetation was so local in 110!
extent that it will have little or no influence on the EVI value of a 250x250 m cell. 111!
112!
Comparison of colony and control sites 113!
We used two different PERMANOVAs [17] to test for significant differences 114!
between colony and control sites. The first PERMANOVA involved δ15N and δ13C of 115!
grouped terrestrial and aquatic primary producers and consumers. The second 116!
PERMANOVA involved a combination of parameters that reflect the physical and 117!
chemical properties of the freshwater bodies, involving dissolved oxygen (DO), 118!
conductivity, pH, total nitrogen (TN), nitrate + nitrite (NO3 + NO2), total phosphorous 119!
(TP), phosphate (PO4) and Chl-a. Two separate PERMANOVAs were used as the 120!
responses being tested in each one differed. The first PERMANOVA sought to 121!
identify differences in MDN inputs and pathways between colony and control sites 122!
across freshwater and terrestrial ecosystems, whereas the second PERMANOVA can 123!
be seen as testing for differences in freshwater systems resulting from the different 124!
MDN inputs at colony and control sites. 125!
The multivariate PERMANOVA tests were followed by univariate tests of differences 126!
between colony and control sites of each individual parameter. Often, the assumptions 127!
of linear methods were not met, variance generally being larger at colony sites. We 128!
therefore used Generalized Least Squares models (GLS, α=0.05), which allow the use 129!
of different error structures to account for heterogeneity of variance. Thus, in many 130!
cases we used the VarIdent error structure, which allow different variance in different 131!
categories of predictors, in this case control and colony sites. Once the model was 132!
formulated residual plots were checked for patterns, and spatial autocorrelation was 133!
assessed by plotting the residuals against location and using semi-variograms [18]. 134!
Where residual spatial autocorrelation was detected, a spatial weights matrix was 135!
incorporated into the model to account for this, and the residuals were re-checked, 136!
following procedures of Zuur et al. (2009). Details of the models used in each 137!
individual case are given in the table S3 in Supplementary Tables and Figures. 138!
139!
Modelling seabird-MDN contribution to biomass 140!
To estimate the contribution of seabird-MDN to the biomass of each primary 141!
producer and consumer group, we created a mass balance model for N, using δ15N of 142!
consumers and their potential sources [19,20]. Following the procedure of Harding et 143!
al. (2004) and Chaloner et al. (2002), we used the equation: 144!
145!
146!
147!
where %Marine nutrient is the proportion of marine nitrogen incorporated into the 148!
organism at the marine nutrient-affected site (colony sites); δXmarine is the isotopic 149!
signature of the sample affected by marine input (colony sites); δXcontrol is the isotopic 150!
signature!of the sample unaffected by marine input (control sites); δXsource is the 151!
isotopic signature of the marine nitrogen source; CF is a correction factor per trophic 152!
level of the organism in question (i.e. 1 for primary producers); and δXTL is the 153!
assumed trophic level fractionation. As a potential marine source, we used the δ15N in 154!
soil at colony sites (as did Harding et al. (2004) and previously Chaloner et al. (2002)) 155!
since this is the most likely nitrogen pathway from guano to both terrestrial and 156!
aquatic biota [19]. For aquatic consumers, we added an enrichment of 20/00 to the soil 157!
isotopic signature (source signature), which represents the mean fractionation of soil 158!
δ15N due to hydrolization (it is the hydrolyzable fraction that ultimately reaches 159!
freshwater systems)[19-21]. As trophic fractionation, we used 0 when calculating the 160!
uptake from source to primary consumers (nitrogen is assimilated by primary 161!
producers with no fractionation [22]) and 2.20/00 when estimating the uptake from 162!
primary producers to consumers, following the average value in [23] (the procedure 163!
also used by Harding et al. 2004). 164!
165!
Changes along a gradient of bird impact 166!
Distance to nearest little auk colony 167!
As a coarse scale proxy of bird influence, we calculated the Euclidian (direct) 168!
distance to the nearest little auk colony (in meters) for each sample site, using ArcGIS 169!
10.2 (ESRI, Redlands, CA, USA). As basis for the calculation, we used information 170!
on the spatial distribution of little auk colonies from an aerial survey mapping of 171!
colonies conducted in 1994-95 [24].! This survey represents the only complete 172!
mapping of little auk colonies in Thule, and although conducted two decades ago, the 173!
information is still valid. Thus, parts of the Thule area was surveyed again in 2013 by 174!
ship, and the results of this survey correspond well with the mapping from the mid-175!
1990ies, suggesting that no major changes have taken place in the years between [25]. 176!
In a few colonies we have extracted cores from peat deposits and lake sediments, and 177!
14C-dates of these cores prove the colonies to be thousands of years old and very 178!
stable over time (Thomas A. Davidson in prep).!!179!
180!
Flight intensity of little auks at Savissivik Island 181!
To estimate flight intensity at sampling sites on Savissivik Island, we conducted GPS 182!
tracking of little auks from the breeding colony on the island in the period July 26 to 183!
August 2, 2014 (Figure 2A). Fifteen actively breeding little auks were caught by 184!
noose carpet close to their nesting site and fitted with miniature GPS loggers (Ecotone 185!
model Alle-60; Ecotone, Sopot, Poland; size!26 x 16 x 10 mm; weight 4.5 g; accuracy 186!
±50 m) with remote data download to a radio station placed in the colony. Loggers 187!
were attached to the central back feathers with strips of Tesa code 4965 tape (Tesa 188!
Tape Inc. Charlotte, NC, USA) after no more than 15 minutes of handling and 189!
programmed to record a position every 15 minutes starting 12 hours after release or 190!
upon first dive registered by the on-board dive sensor. The tracking resulted in 1050 191!
positions (excluding colony positions) on the basis of which 54 separate trips to at-sea 192!
foraging areas from 14 individuals could be reconstructed. Using the Spatial Analyst 193!
Extension of ArcGIS 10.2 (ESRI, Redlands, CA, USA), the foraging trips were 194!
represented as polylines, and within a grid of 100x100 m cells covering the Savissivik 195!
area relative flight intensity was estimated by calculating km polyline per km2 within 196!
a radius of 500 m around the center of each cell. At the five freshwater sampling sites 197!
on Savissivik Island (NOW 9-13), relative flight intensity was subsequently extracted 198!
from this grid.!199!
200!
Modelling drainage from the little auk colony at Savissivik Island 201!
To examine the effect of drainage from the little auk colony at Savissivik Island, we 202!
ran a flow accumulation model [26], using the Spatial Analyst Extension of ArcGIS 203!
10.2 (ESRI, Redlands, CA, USA). As basis of the model, we used a digital elevation 204!
model (DEM) of Savissivik Island with a spatial resolution of 30x30 m [27]. Initially, 205!
all sinks (cells lower than all their neighbouring cells) in the DEM were filled, 206!
ensuring that all water dropped on the colony would eventually run to the sea. We 207!
then calculated the flow direction in each grid cell. Finally, using the flow direction 208!
grid and a polygon representation of the little auk colony as inputs, we were able to 209!
calculate the flow accumulation from the colony – the number of cells from the 210!
colony that drains through each of the 30x30m cell on Savissivik island. As 211!
visualization of the area affected by drainage from the colony, we extracted all cells 212!
with a flow accumulation > 0. 213!
214!
Modelling changes along a gradient of bird impact 215!
In modelling changes along a gradient of bird impact we adhered to the principle of 216!
using the simplest model possible whilst accounting for the properties of the data and 217!
the assumptions of the methods. Where linear models (LM) using least squares 218!
regression were appropriate, they were used. Where variance was heterogeneous, we 219!
applied GLS with an error structure to account for this. In the case of non-linear 220!
response, a Generalised Additive Model (GAM) with an appropriate error was used. 221!
In all cases, model residuals were plotted against fitted values and explanatory 222!
variables to check for patterns and to assure that the assumptions of the methods were 223!
met, chiefly homogeneity of variance and independence [18]. Furthermore, residuals 224!
were plotted in space, and semi-variograms were used to check for spatial 225!
autocorrelation, the latter being absent in all cases. 226!
Including all sample sites, the most adequate model describing change in EVI in 227!
response to distance to nearest little auk colony was found using GLS with an 228!
exponential variance structure based on distance to colony. For sample sites within 229!
distances of less than 2500 m from nearest little auk colony, a LM was sufficient to 230!
model the response in EVI. For lotic Chl-a and
δ
15N of freshwater benthic algae, 231!
GLS with an exponential error structure was optimal for both the whole data set and 232!
for sample sites within 2500 m of nearest little auk colony. Although the average 233!
value of lentic Chl-a was certainly higher close to colonies, we were unable to fit any 234!
significant models relating Chl-a to distance to nearest colony. 235!
In the Savissivik case study, simple LMs were adequate for the regressions of EVI 236!
and δ15N of freshwater benthic algae against little auk flight intensity, and for the 237!
regression of EVI against δ15N of freshwater benthic algae. 238!
In the analyses of changes in freshwater physical-chemical properties and taxa 239!
richness along a gradient of bird impact, using
δ
15N of freshwater benthic algae as a 240!
proxy of the latter (the explanatory variable in the models), a simple LM was 241!
sufficient for pH. For TN, TP and consumer taxa richness we used GAMs with 242!
VarIdent error structures based on colony/control sites. Lentic and lotic Chl-a were 243!
also modelled with GAMs, but using a VarPower error structure based on the 244!
variance of
δ
15N. We found no residual spatial autocorrelation in any of these models. 245!
Models identifying potential drivers of changes in freshwater ecosystems along a 246!
gradient of bird impact 247!
The analyses of the bi-variate relationships between on the one hand nutrient 248!
concentrations (TN and TP) and Chl-a, on the other hand pH and consumer taxa 249!
richness, were conducted separately for lentic and lotic systems. In all but one of 250!
these six analyses, simple LMs were adequate. Only in the case of consumer taxa 251!
richness vs. pH in lotic systems did we encounter non-linearity and thus used a GAM. 252!
253!
References 254!
1. Levin L.A., Currin C. 2012 Stable isotope protocols: sampling and sample 255!
procesing. In Scripps Institution of Oceanography Technical Report (eScholarship, 256!
University of California. 257!
2. Peipoch M., Martí E., Gacia E. 2012 Variability in δ15N natural abundance of 258!
basal resources in fluvial ecosystems: a meta-analysis. Freshwater Science 31 1003-259!
1015. 260!
3.!! Post D.M., Layman C.A., Arrington D.A., Takimoto G., Quattrochi J., 261!
Montaña C.G. 2007 Getting to the fat of the matter: models, methods and assumptions 262!
for dealing with lipids in stable isotope analyses. Oecologia 152, 179-189. 263!
4. Schwamborn R., Giarrizzo T. 2015 Stable Isotope Discrimination by 264!
Consumers in a Tropical Mangrove Food Web: How Important Are Variations in C/N 265!
Ratio? Estuaries and Coasts 38(3), 813-825. 266!
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removal and mathematical normalization on carbon and nitrogen stable isotope 268!
compositions in beaked whale (Ziphiidae) bone. Rapid Commun. Mass Spectrom., 269!
30: 460–466. 270!
6. Elliott K.H., Davis M., Elliott J.E. (2014) Equations for lipid normalization of 271!
carbon stable isotope ratios in aquatic bird eggs. PLOS ONE 9(1): e83597. 272!
7. Mintenbeck, K. Brey T., Jacob U., Knust R., Struck U. 2008. How to account 273!
for the lipid effect on carbon stable-isotope ratio (δ13C): sample treatment effects and 274!
model bias.Journal of Fish Biology 72, 815–830$. 275!
8. De Lecea A.M., De Charmoy L. 2015. Chemical lipid extraction or 276!
mathematical isotope correction models: should mathematical models be widely 277!
applied to marine species? Rapid Commun. Mass Spectrom. 29, 2013–2025. 278!
9. Mook W. 2000. Chemistry of carbonic acid in water, in: Environmental 279!
isotopes in the hydrological cycle: principles and applications. Paris, 280!
INEA/UNESCO. 281!
10. Pomerleau C., Winkler G., Sastri A., Nelson R.J., Williams W.J. 2014 The 282!
effect of acidification and the combined effects of acidification/lipid extraction on 283!
carbon stable isotope ratios for sub-arctic and arctic marine zooplankton species. 284!
Polar Biology 37(10), 1541-1548. 285!
11 Murphy J., Riley J.R. 1972 A modified single solution method for the 286!
determination of phosphate in natural waters. Analytica Chimica Acta 27, 21–26. 287!
12. Koroleff F. 1970 Determination of total phosphorus in natural waters by 288!
means of the persulphate oxidation. An Interlab. . Journal du Conseil / Conseil 289!
Permanent International pour l'Exploration de la Mer report No 3,, 19–22 290!
13. Solórzano L., Sharp J.H. 1980 Determination of total dissolved nitrogen in 291!
natural waters. Limnology and Oceanography 25, 751-754. 292!
14. Jespersen A., Christoffersen K. 1987 Measurements of chlor- ophyll-a from 293!
phytoplankton using ethanol as extraction solvent. Archiv für Hydrobiologie 109, 294!
445-454. 295!
15. Didan K. 2015 MOD13Q1 MODIS/Terra Vegetation Indices 16-Day L3 296!
Global 250m SIN Grid V006. . (NASA EOSDIS Land Processes DAAC, 297!
https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod13q1. 298!
16. Huete A., Didan K., Miura T., Rodriguez E.P., Gao X., Ferreira L.G. 2002 299!
Overview of the radiometric and biophysical performance of the MODIS vegetation 300!
indices. Remote Sensing of Environment 83(1-2), 195-213. 301!
17. Anderson, M. J. (2001), A new method for non-parametric multivariate 302!
analysis of variance. Austral Ecology 26, 32–46. 303!
18. Zuur A.F., Ieno E.N., Walker N., Saveliev A.A., Smith G.M. 2009 Mixed 304!
effects models and extensions in ecology with R. New York, NY, Springer New 305!
York. 306!
19. Chaloner D.T., Martin K.M., Wipfli M.S., Ostrom P.H., Lamberti G.A. 2002 307!
Marine carbon and nitrogen in southeastern Alaska stream food webs: evidence from 308!
artificial and natural streams. Canadian Journal of Fisheries and Aquatic Sciences 59, 309!
1257–1265. 310!
20. Harding J.S., Hawke D.J., Holdaway R.N., Winterbourn M.J. 2004 311!
Incorporation of marine-derived nutrients from petrel breeding colonies into stream 312!
food webs. Freshwater Biology 49, 576–586.. 313!
21. Schidlowski M., Hayes J.M., Kaplan I.R. 1983 Isotopic inferences of ancient 314!
biochemistries: carbon, sulfur, hydrogen, and nitrogen. In Earth’s Earliest Biosphere: 315!
Its Origin and Evolution (ed. Schopf J.W.), pp. 149–186. Princeton, USA., Princeton 316!
University Press. 317!
22. Yoneyama T., Fujiwara H., Wilson J.W. 1998 Variations in fractionation of 318!
carbon and nitrogen isotopes in higher plants: N-metabolism and partitioning in 319!
phloem and xylem. In Stables Isotopes, Integration of Bio- logical, Ecological and 320!
Geochemical Processes (ed. Griffiths H.), pp. 99-109. Oxford, BIOS Scientific 321!
Publishers. 322!
23. McCutchan J.H., Lewis W.M., Kendall C., McGrath C.C. 2003 Variation in 323!
trophic shift for stable isotope ratios of carbon, nitrogen, and sulfur. Oikos 102(2), 324!
378-390. (doi:10.1034/j.1600-0706.2003.12098.x). 325!
24. Boertmann D., Mosbech A. 1998 Distribution of little auk (Alle alle) breeding 326!
colonies in Thule District, Northwest Greenland. . Polar Biol. 19, 206–210. 327!
25. Boertmann D. 2013. Seabird colonies in the Melville Bay, Northwest 328!
Greenland II. Final survey in August 2013. Aarhus University, DCE Danish Centre 329!
for Environment and Energy, 24 pp. Technical Report from DCE Danish Centre for 330!
Environment and Energy No. 32. http://dce2.au.dk/pub/TR32.pdf 331!
26. Jenson, S. K., and J. O. Domingue. 1988. "Extracting Topographic Structure 332!
from Digital Elevation Data for Geographic Information System Analysis." 333!
Photogrammetric Engineering and Remote Sensing 54 (11): 1593–1600. 334!
27. Howat, I., A. Negrete, and B. Smith. 2014. The Greenland Ice Mapping 335!
Project (GIMP) land classification and surface elevation data sets, The Cryosphere. 8. 336!
1509-1518. http://dx.doi.org/10.5194/tc-8-1509-2014 337!
338!
Supplementary Appendix S2: Figures and tables
Table&S1:!Summary of site locations and physical-chemical characteristics. Values of maximum depth correspond to sonde depth measurements
in ponds and lakes, whereas widths of streams were estimated visually. Temperature, pH, conductivity, and dissolved oxygen values are averages
of three recorded values in streams and ponds, and ca. 10 values recorded along the depth profile in lakes. Chl-a measurements correspond to the
mean values of three random rock scrapes of the same surface area in streams, (µg.cm-2), and an integrated water sample in ponds and lakes
(µg.cm-1). In streams, nutrient concentrations were estimated from an integrated water sample taken from different sections of the stream. In
ponds and lakes, the water samples for nutrient concentrations were integrated from the water column.
!
!"#$%%&'%
($)"'&%
*+#"#,-$%./0%
*'&)"#,-$%
.10%
2"3-%
"45+6#%
!78#$4%
9+:;%
<$5#=%.40%
1"-#=%
3+&)$%.40%
>$45%
.'?0%
5@%
?'&-,6#"A"#7%
.4);64BC0%
<"88'DA$-%
E:7)$&%
.4);DBC0%
<"88'DA$-%
E:7)$&%
.F0%
?=DB+%
.G);64BH%'3%
G);DBC0%
>'#+D%
/"#')$&%
.4);DBC0%
/EHI/EJ%%
.4)%/;DBC0%
>'#+D%
K='85='3',8%
.4)%;DBC0%
KEL%
.4);DBC0%
NOW$10'
Savissivik'Island'
76.04263'
$65.00198'
Control'
Lake'
'
'
5.4'
5.2'
0.009'
'
97'
2.8'
0.26'
0.017'
0.01'
0.005'
NOW$11'
Savissivik'Island'
76.03445'
$65.002341'
Control'
Pond'
<1'
'
6.9'
5.4'
0.026'
12.8'
104'
'
0.40'
0.28'
0.004'
0.003'
NOW$12'
Savissivik'Island'
76.024737'
$65.012637'
Colony'
Pond'
<1'
'
4.8'
4.9'
0.019'
'
100'
'
0.40'
0.29'
0.004'
0.003'
NOW$13'
Savissivik'Island'
76.019616'
$65.038849'
Colony'
Pond'
<1'
'
5.0'
6.0'
0.021'
'
98'
'
0.60'
0.44'
0.006'
0.003'
NOW$14'
Nuuliit'
76.800518'
$70.60262'
Control'
Lake'
1'
'
11.9'
8.3'
0.112'
11.4'
106'
3.4'
0.75'
0.01'
0.01'
0.006'
NOW$15'
Nuuliit'
76.880562'
$70.908773'
Control'
Lake'
3'
'
12.0'
7.9'
0.062'
11.6'
107'
0.8'
0.25'
0.004'
0.009'
0.002'
NOW$16'
Nuuliit'
76.878775'
$70.910512'
Control'
Stream'
'
0$1'
'
'
'
'
'
'
'
'
'
'
NOW$17'
Nuuliit'
76.802204'
$70.5925'
Control'
Stream'
'
1$2'
9.8'
7.6'
0.188'
10.38'
91'
0.4'
0.36'
0.04'
0.005'
0.002'
NOW$18'
Nuuliit'
76.802915'
$70.589354'
Control'
Stream'
'
2$3'
13.8'
7.8'
0.137'
10.63'
102'
0.5'
0.44'
0.02'
0.01'
0.004'
NOW$19'
Nuuliit'
76.804953'
$70.60259'
Control'
Lake'
1'
'
12.6'
8.1'
0.094'
11.6'
109'
1.8'
0.58'
0.02'
0.02'
0.011'
NOW$20'
Nuuliit'
76.812704'
$70.612803'
Control'
Pond'
<1'
'
13.4'
8.6'
0.186'
12.0'
115'
2.4'
0.67'
0.001'
0.01'
0.002'
NOW$24'
Paakitsoq'
76.26695'
$68.96526'
Colony'
Stream'
'
2$3'
3.6'
4.4'
0.047'
14.6'
110'
2.2'
2.43'
2.13'
0.09'
0.083'
NOW$25'
Annikitsoq'
76.033386'
$67.615256'
Colony'
Lake'
34'
'
4.0'
5'
0.027'
13.7'
104'
22.7'
1.70'
1.17'
0.14'
0.092'
NOW$26'
Annikitsoq'
76.033455'
$67.598419'
Colony'
Stream'
'
0$1'
2.1'
4.8'
0.075'
12.3'
111'
1.9'
2.95'
2.27'
0.20'
0.15'
NOW$27'
Annikitsoq'
76.042635'
$67.593026'
Colony'
Stream'
'
1$2'
4.7'
6.0'
0.008'
13.6'
106'
0.4'
0.34'
0.13'
0.006'
0.006'
NOW$28'
Annikitsoq'
76.031341'
$67.612562'
Colony'
Stream'
'
2$3'
6.5'
4.9'
0.026'
13.6'
110'
2.9'
1.30'
1.09'
0.13'
0.093'
NOW$29'
Annikitsoq'
76.030699'
$67.599994'
Colony'
Pond'
<1'
'
10.0'
5.8'
0.032'
11.2'
99'
67.5'
2.25'
0.005'
0.38'
0.102'
NOW$30'
Annikitsoq'
76.043883'
$67.701596'
Colony'
Pond'
<1'
'
10.1'
8.2'
0.621'
14.4'
128'
1.8'
0.45'
0.04'
0.01'
0.002'
NOW$31'
Annikitsoq'
76.049544'
$67.7273'
Colony'
Stream'
'
0$1'
12.8'
6.2'
0.03'
11.9'
112'
4.4'
1.90'
1.68'
0.04'
0.027'
NOW$32'
Annikitsoq'
76.04423'
$67.719559'
Colony'
Stream'
'
2$3'
2.9'
7.1'
0.113'
14.9'
110'
0.5'
0.11'
0.04'
0.04'
0.002'
NOW$33'
Siorapaluk'
77.787034'
$70.640888'
Colony'
Stream'
'
1$2'
3.4'
6.4'
0.018'
13.87'
104'
1.1'
0.31'
0.18'
0.02'
0.009'
NOW$34'
Meehan'Glacier'
77.875228'
$70.400516'
Control'
Lake'
23'
'
'
'
'
'
'
1.7'
0.31'
0.04'
0.01'
0.049'
NOW$35'
Meehan'Glacier'
77.880268'
$70.423403'
Control'
Stream'
'
1$2'
4.2'
6.4'
0.016'
13.3'
104'
0.4'
0.23'
0.15'
0.006'
0.002'
NOW$36'
Meehan'Glacier'
77.885912'
$70.405929'
Control'
Pond'
<1'
'
4.1'
6.7'
0.013'
12.9'
98'
0.5'
0.17'
0.05'
0.002'
0.002'
NOW$37'
Meehan'Glacier'
77.882327'
$70.415994'
Control'
Stream'
'
0$1'
4.5'
6.0'
0.012'
13.2'
102'
0.1'
0.39'
0.24'
0.003'
0.002'
NOW$38'
Meehan'Glacier'
77.865298'
$70.337633'
Control'
Stream'
'
2$3'
5.4'
7.0'
0.031'
13.2'
104'
0.2'
0.26'
0.16'
0.007'
0.001'
NOW$39'
Meehan'Glacier'
77.746706'
$70.427401'
Colony'
Stream'
'
2$3'
'
6.8'
'
'
'
0.3'
0.88'
0.75'
0.003'
0.001'
NOW$4'
Parker'Snow'Bay'
76.09061'
$68.3189'
Colony'
Stream'
'
1$2'
'
'
'
'
'
'
0.60'
0.41'
0.03'
0.034'
NOW$5'
Salve'Island'
76.04386'
$65.99149'
Colony'
Lake'
5.40'
'
2.4'
3.4'
0.035'
'
103'
74.0'
3.75'
2.21'
0.37'
0.294'
NOW$6'
Salve'Island'
76.049801'
$65.980854'
Colony'
Stream'
'
1$2'
2.8'
3.1'
0.057'
13.1'
97'
9.3'
5.55'
3.94'
0.36'
0.36'
NOW$7'
Salve'Island'
76.039792'
$65.976149'
Colony'
Stream'
'
1$2'
4.3'
4.3'
0.012'
12.8'
98'
2.8'
0.50'
0.36'
0.011'
0.005'
NOW$8'
Salve'Island'
76.042057'
$65.98902'
Colony'
Stream'
'
2$3'
3.0'
3.2'
0.041'
12.5'
93'
7.3'
3.45'
2.35'
0.36'
0.303'
NOW$9'
Savissivik'Island'
76.016159'
$65.061866'
Colony'
Stream'
'
2$3'
6.2'
3.6'
0.05'
11.56'
93'
2.6'
2.40'
2.08'
0.04'
0.019'
P1'
Pitufffik'
76.563168'
$68.452104'
Control'
Lake'
5.2'
'
8.4'
8.3'
0.185'
11.7'
'
'
'
'
0.875'
0.007'
P10'
Pitufffik'
76.547853'
$68.523766'
Control'
Lake'
2.1'
'
4.1'
7.7'
0.103'
'
100'
'
'
'
0.005'
0.003'
P9'
Pitufffik'
76.549192'
$68.49341'
Control'
Lake'
2.4'
'
4.0'
7.2'
0.101'
'
100'
'
'
'
0.005'
0.003'
!
Table S2: Presence-absence matrix of collected freshwater organisms, where X indicates that the consumer taxa was collected at the sampling
site.
'
System'
type'
Fish'
Anelidae'
Arachnidae'
Diptera'
Trichioptera'
Crustacea'
Site'no'
!
Salvelinus!
alpinus!
Anelidae'
Hydrachnidia'
Tipulida
e'
Muscidae'
Chironomidae'
pupae'
Chironominae'
Apatania!zonella!
Cladocerans'
Ostracoda'
Brachinecta!
paludosa!
Lepidurus!
arcticus!
NOW$4'
Stream'
'
'
'
X'
'
'
X'
'
'
'
'
'
NOW$5'
Lake'
'
'
'
'
'
'
X'
'
'
'
'
'
NOW$6'
Stream'
'
'
'
'
'
'
'
'
'
'
'
'
NOW$7'
Stream'
'
'
'
'
'
X'
X'
'
'
'
'
'
NOW$8'
Stream'
'
'
'
'
'
'
'
'
'
'
'
'
NOW$9'
Stream'
'
'
'
'
'
'
X'
'
'
'
'
'
NOW$10'
Lake'
X'
'
'
'
'
X'
X'
'
'
'
'
'
NOW$11'
Pond'
'
'
'
'
'
'
X'
'
'
'
'
'
NOW$12'
Pond'
'
'
'
'
'
X'
X'
'
'
'
'
'
NOW$13'
Pond'
'
'
'
'
'
'
X'
'
'
'
'
'
NOW$14'
Lake'
'
'
'
'
'
'
X'
'
X'
'
X'
X'
NOW$15'
Lake'
'
X'
'
'
'
X'
X'
'
X'
'
X'
'
NOW$16'
Stream'
'
X'
'
'
'
'
X'
'
'
'
'
'
NOW$17'
Stream'
'
'
X'
'
'
X'
X'
'
'
'
'
'
NOW$18'
Stream'
'
X'
X'
'
'
'
X'
'
'
'
'
X'
NOW$19'
Lake'
'
X'
X'
'
'
'
X'
'
X'
'
X'
X'
NOW$20'
Pond'
'
X'
'
'
'
'
X'
'
'
'
'
X'
NOW$24'
Stream'
'
'
'
'
'
'
X'
'
'
'
'
'
NOW$25'
Lake'
'
'
'
'
X'
'
X'
'
X'
'
'
'
NOW$26'
Stream'
'
'
'
'
'
'
X'
'
'
'
'
'
NOW$27'
Stream'
'
X'
'
'
'
'
X'
'
'
'
'
'
NOW$28'
Stream'
'
'
'
'
'
'
X'
'
'
'
'
'
NOW$29'
Pond'
'
X'
'
'
'
'
X'
'
'
'
'
'
NOW$30'
Pond'
'
'
'
'
'
'
X'
'
'
'
'
'
NOW$31'
Stream'
'
'
'
'
'
'
X'
'
'
'
'
'
NOW$32'
Stream'
'
'
'
'
'
'
X'
'
'
'
'
'
NOW$33'
Stream'
'
'
'
'
'
X'
X'
'
'
'
'
'
NOW$34'
Lake'
X'
X'
'
'
'
X'
X'
X'
'
'
'
'
NOW$35'
Stream'
'
'
'
'
'
X'
'
'
'
'
'
'
NOW$36'
Pond'
'
'
'
'
'
'
X'
'
'
'
'
'
NOW$37'
Stream'
'
'
'
'
'
'
X'
'
'
'
'
'
NOW$38'
Stream'
'
'
'
'
'
'
X'
'
'
'
'
'
NOW$39'
Stream'
'
'
'
'
'
'
X'
'
'
'
'
'
P$1'
Lake'
X'
'
'
'
'
'
X'
'
X'
'
'
'
P$10'
Lake'
'
'
X'
'
'
'
X'
'
X'
X'
'
'
P$9'
Lake'
X'
'
X'
'
X'
'
X'
'
X'
'
'
'
Table S3: Summary of models used in the comparison between colony and control sites
of isotopic signatures, and physical-chemical and community properties of freshwater
systems. A few parameters could not be tested due to the lack of replicates (indicated as
“NT”). The variables included in the two PERMANOVAs are marked with 1 and 2. For
freshwater systems, all parameters were compared for lakes, ponds and streams pooled,
except for algal biomass, which was tested separately for lotic and lentic systems (using
the same kind of model).
Parameter'
PERMANOVA'
Models'used'
Physico8
chemical'
paramters'
pH'
1"
GLS,'Spatial'Weights'Matrix'
Conductivity'(ms/cm)'
1"
GLS,'VarIdent'
Dissolved'oxygen'(mg.l81)'
1"
GLS,'Spatial'Weights'Matrix'
''
Total'nitrogen'(mg.l81)'
1"
GLS,'VarIdent'
''
NO2+'NO2'('mg'N.l81)'
1"
GLS,'VarIdent'
''
Total'phosphorous'(mg.l81)'
1"
GLS,'VarIdent,'Spatial'Weights'Matrix'
''
PO4'(mg'P.l81)'
1"
GLS,'VarIdent,'Spatial'Weights'Matrix'
Biotic'structure'
Algal'biomass'(lentic:'µg'Chla.l81,'lotic:'µg'Chla.cm82)'
1"
GLS,'VarIdent'
''
Richness'of'aquatic'consumer'taxa'(No.'of'taxa)'
''
GLS,'VarIdent'
''
Enhanced'Vegetation'Index'(EVI)'
''
GLS,'Spatial'Weights'Matrix'
δ15N'
Terrestrial'debris'(in'freshwaters)'
2"
GLS,'VarIdent'
''
Profundal'lake'sediment'
""
NT'
''
Aquatic'moss'
2"
GLS,'VarIdent'
''
Benthic'algae'
2"
GLS,'VarIdent,'Spatial'Weights'Matrix'
''
Chironomids'
2"
GLS,'VarIdent'
''
Other'invertebrates'
2"
GLS,'VarIdent'
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Seston'
""
NT'
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Zooplankton'
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NT'
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Peat'soil'
2"
GLS'
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Terrestrial'vegetation'
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Arctic'hare'
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δ13C'
Terrestrial'debris'(in'freswaters)'
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GLS,'VarIdent'
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Profundal'lake'sediment'
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NT'
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Aquatic'moss'
2"
GLS,'VarIdent'
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Benthic'algae'
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GLS'
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Chironomids'
2"
GLS'
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Other'invertebrates'
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GLS'
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Seston'
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Zooplankton'
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Peat'soil'
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Terrestrial'vegetation'
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Arctic'hare'
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!
!
Figure S1: Carbon and nitrogen isotopic signatures (mean ± SD) of main producers and
consumers in freshwater (lakes, streams, ponds) and terrestrial ecosystems with little
auk colonies in their catchment (left) and at control sites without colonies (right). The
number of sites included is shown at the top of each plot. Abbreviations: Zoo:
35 30 25 20 15 10
0 10 20 30
δ13C
δ15N
Basal resources
Primary consumers
Secondary consumers
Zoo.
Chi.
Inv.
Aqm.
Deb.
Ses.
Alg.
Sed.
35 30 25 20 15 10
0 10 20 30
δ13C
δ15N
S.alp
Chi. Inv.
Zoo
Aqm.
Deb.
Ses. Alg.
Sed.
35 30 25 20 15 10
0 10 20 30
δ13C
δ15N
Chi.
Inv.
Aqm.
Deb.
Alg.
R.Snw. Inv.
35 30 25 20 15 10
0 10 20 30
δ13C
δ15N
Chi.
Inv.
Aqm.
Deb.
Alg.
Alg.
35 30 25 20 15 10
0 10 20 30
δ13C
δ15N
Chi.
Inv.
Aqm. Alg.
35 30 25 20 15 10
0 10 20 30
δ13C
δ15N
Chi.
Inv.
Aqm.
Deb.
Alg.
Sed..
35 30 25 20 15 10
0 10 20 30
δ15N
Fox
L.auk
Hare.
G.exc.
H.exc.
L.exc.
Peat
T.veg.
M.exc.
35 30 25 20 15 10
0 10 20 30
δ15N
Hare
G.exc.
H.exc. Peat
T.veg.
Control sitesColony sites
Lakes (n=2) Lakes (n=8)
Streams(n=6)Streams (n=15)
Ponds (n=5) Ponds (n=3)
δ15N (0/00)
δ13C (0/00)
Terrestrial (n=7) Terrestrial (n=5)
zooplankton (copepods and cladocerans); Chi: chironomidae; Inv.: other invertebrates
found in low frequency (for details see Supplementary Table S2); S.alp.: arctic char (S.
alpinus); Ses: seston; Alg: benthic algae (rock scrapes and green algae); Sed: bottom
sediment; Aq-m: aquatic moss; Deb: debris (leaf litter); Peat: peat soil; T.veg.:
terrestrial vegetation; G.exc: goose excrement; M.exc: muskox (O. moschatus)
excrement; L.exc.: little auk (A. alle) excrement; H. exc.: arctic hare (L. arcticus)
excrement; Hare: hair from arctic hare; L.auk: little auk flesh; Fox: arctic fox (V.
lagopus) bone.
!
!
!
!
0 10000 20000 30000 40000 50000
0 20 40 60
Lentic Chla (μg l1)
500 1000 1500 2000 2500
0 20 40 60
Lentic Chla (μg l1)
0 10000 20000 30000 40000 50000
02468
Lotic Chla (μg l1)
0 500 1000 1500 2000
02468
Lotic Chla (μg l1)
0 10000 20000 30000 40000 50000
0.10 0.20 0.30 0.40
EVI.500.max
0 500 1000 1500 2000 2500
0.10 0.20 0.30 0.40
EVI.500.max
0 10000 20000 30000 40000 50000
0 5 10 15 20 25 30
Distance from colony (m)
δ15N
0 500 1000 1500
5 10 15 20 25 30
δ15N
Phytoplankton biomass (μg/l-1)
Periphyton biomass (μg/cm-2)
EVI
δ15N in freshwater benthic algae (0/00)
Distance to colony (m)
Colony sites
Control sites
A B
DC
E
H
F
G
Figure!S2: Changes in freshwater algal biomass, Enhanced Vegetation Index (EVI) and
freshwater benthic algae δ15N with increasing distance to nearest little auk colony.
Sample sites in catchments with little auk colonies are marked with black dots and sites
in control areas with open circles. Plots and models in left!hand!column!include!all!
sample!sites,!whereas!right!hand!column!only!includes!sample!sites!closer!than!
2500!m!from!nearest!little!auk!colony.!Regression!lines!are!the!results!of!GLS!or!
LM!models,!including!90!%!confidence!intervals!(see!Methods): A) Relationship
between lentic Chl-a (phytoplankton biomass) and distance to colony for the whole
dataset (not modelled); B) Relationship between lentic Chl-a (phytoplankton biomass)
and distance to colony for sites closer than 2500 m from nearest little auk colony (not
modelled); C) Relationship between lotic Chl-a (periphyton biomass) and distance to
colony for the whole dataset (GLS: t=-4.7 p<0.0001, pseudo r2= 0.07, n=17); D)
Relationship between lotic Chl-a (periphyton biomass) and distance to colony for sites
closer than 2500 m from nearest little auk colony (GLS: t=-4.5 p<0.0001, pseudo r2=
0.12, n=15); E) Relationship between EVI and distance to colony for the whole dataset
(GLS: t=-2.6 p<0.05, pseudo r2= 0.06, n=29); F) Relationship between EVI and
distance to colony for sites closer than 2500 m from nearest little auk colony (LM:
p<0.001, r2=0.52, n=27); G) Relationship between benthic algae δ15N and distance to
colony for the whole dataset (GLS: t=-5.6 p<0.00001, pseudo r2= 0.29, n=27); H)
Relationship between benthic algae δ15N and distance to colony for sites closer than
2500 m from nearest little auk colony (GLS: t=-4.6 p<0.0001, pseudo r2= 0.17, n=21).
!
!
!
Figure S3: Potential drivers of changes in algal biomass and consumer taxa richness
with increasing bird impact in the form of relationships between nutrients and algal
biomasses, and between pH and consumer taxa richness. Sample sites in catchments
with little auk colonies are marked with black dots and sites in control areas with open
0 1 2 3 4 5
02468
System total nitrogen
Stream Chl
linear regression p<0.0001, r2=0.77
0.0 0.1 0.2 0.3
02468
System total phosphorous
linear regression p<0.0001, r2=0.75
0.5 1.0 1.5 2.0 2.5 3.0 3.5
0 20 40 60
System total nitrogen
phytoplankton Chl
linear regression p<0.0001, r2=0.89
0.0 0.1 0.2 0.3
0 20 40 60
System total phosphorous
linear regression p<0.0001, r2=0.99
3 4 5 6 7 8
01234
Stream pH
Taxa richness
GAM p<0.0001, r2=0.44
45678
0 1 2 3 4 5
System pH
linear regression p<0.001, r2=0.49
Phytoplankton biomass (μg/l-1) Periphyton biomass (μg/cm-2)Richness of consumer taxa (No taxa)
Stream pH Lentic systems pH
Stream total nitrogen (mg.l-1)
Lentic systems total nitrogen (mg.l-1)
Stream total phosphorous (mg.l-1)
Lentic systems total phosphorous (mg.l-1)
A B
D
F
E
C
Colony sites
Control sites
LM, p<0.0001, r2=0.77 LM, p<0.0001, r2=0.75
LM, p<0.0001, r2=0.99
LM, p<0.0001, r2=0.89
LM, p<0.001, r2=0.49
GAM p<0.001, r2=0.44
circles. Regression!lines!are!the!results!of!LM!and!GAM!models,!including!90!%!
confidence!intervals!(statistics are shown in each panel).
!
... Nutrients were the only subsidies recorded in the early literature ; Figure 3) and accounted for 84.5% (n = 153) of all studies considered in this review. In contrast, the first publication to consider seabirds as vectors of pollutants was in 1991 (inorganic pollutants; Godzik, 1991). ...
... The deposition of excessive amounts of guano can cause guanotrophication, or seabird-induced eutrophication, as has been observed with a range of seabirds, including cormorants and gulls (Kolb et al., 2012;Otero et al., 2015). An excess in nutrients can indirectly lead to decreases in abundance and diversity of faunal groups (Signa et al., 2015), or directly lead to the destruction of veg- (Godzik, 1991;Otero et al., 2018). The literature regarding seabirds as vectors for such pollutants commonly focused on metals with very few studies exploring POPs (Figure 3). ...
Article
Full-text available
Seabird species worldwide are integral to both marine and terrestrial environments, connecting the two systems by transporting vast quantities of marine‐derived nutrients and pollutants to terrestrial breeding, roosting, and nesting grounds via the deposition of guano and other allochthonous inputs (e.g., eggs, feathers). We conducted a systematic review and meta‐analysis and provide insight into what types of nutrients and pollutants seabirds are transporting, the influence these subsidies are having on recipient environments, with a particular focus on soil, and what may happen if seabird populations decline. The addition of guano to colony soils increased nutrient levels compared to control soils for all seabirds studied, with cascading positive effects observed across a range of habitats. Deposited guano sometimes led to negative impacts, such a guanotrophication, or guano‐induced eutrophication, which was often observed where there was an excess of guano or in areas with high seabird densities. While the literature describing nutrients transported by seabirds is extensive, literature regarding pollutant transfer is comparatively limited, with a focus on toxic and bioaccumulative metals. Research on persistent organic pollutants and plastics transported by seabirds is likely to increase in coming years. Studies were limited geographically, with hotspots of research activity in a few locations, but data were lacking from large regions around the world. Studies were also limited to seabird species listed as Least Concern on the IUCN Red List. As seabird populations are impacted by multiple threats and steep declines have been observed for many species worldwide, gaps in the literature are particularly concerning. The loss of seabirds will impact nutrient cycling at localised levels and potentially on a global scale as well, yet it is unknown what may truly happen to areas that rely on seabirds if these populations disappear.
... although Abrolhos lacked islands without breeding seabirds for comparison. Within the space of each island, however, such differences between areas would be expected to occur between colony sites and nearby colony-free areas (e.g., see Caut et al., 2012;Gonz alez-Bergonzoni et al., 2017;Pascoe et al., 2021), but we observed the opposite spatial trend in Abrolhos. Higher δ 15 N values were consistently detected in control sites for all trophic levels, suggesting that the direct influence of seabirds inside the colony may have, in fact, caused a local depletion in 15 N, as we discuss below. ...
... Higher δ 15 N values were consistently detected in control sites for all trophic levels, suggesting that the direct influence of seabirds inside the colony may have, in fact, caused a local depletion in 15 N, as we discuss below. This finding suggests a more complex spatial pattern of the seabird isotopic influence on small islands than generally assumed, and could induce the misleading interpretation that seabirds had a larger ecological influence in areas outside their colonies, since high δ 15 N is a signal of guano incorporated into the ecosystem (Barrett et al., 2005;Ellis et al., 2006;Gonz alez-Bergonzoni et al., 2017;Szpak et al., 2012). ...
Article
Full-text available
Allochthonous resource fluxes mediated by organisms crossing ecosystem boundaries may be essential for supporting the structure and function of resource‐limited environments, such as tropical islands and surrounding coral reefs. However, invasive species, such as black rats, thrive on tropical islands and disrupt the natural pathways of nutrient subsidies by reducing seabird colonies. Here, we used stable isotopes of nitrogen and carbon to examine the role of seabirds in subsidizing the terrestrial food webs and adjacent coral reefs in the Abrolhos archipelago, southwest Atlantic Ocean. By sampling invasive rats and multiple ecosystem compartments (soil, plants, grasshoppers, tarantulas and lizards) within and outside seabird colonies, we showed that seabirds’ subsidies led to an overall enrichment in 15N across the food web in islands. However, contrary to other studies, δ15N values were consistently lower within the seabird colonies, suggesting that a higher seabird presence may potentially produce a localized depletion in 15N in small islands influenced by seabirds. In contrast, the %N in plants and soils was higher inside the colonies, corresponding to a higher effect of seabirds at the base of the trophic web. Among consumers, lizards and invasive rats seemed to obtain allochthonous resources from subsidized terrestrial organisms outside the colony. Inside the colony, however, they showed a more direct consumption of marine matter, suggesting that subsidies benefit these native and invasive animals both directly and indirectly. Nonetheless, in coral reefs, the scleractinian corals assimilated seabird‐derived nitrogen only around the two smaller and lower‐elevation islands, as demonstrated by the substantially higher δ15N values in relation to the reference areas. This provides evidence that island morphology may influence the incorporation of seabird nutrients in coral reefs around rat‐invaded islands, likely because guano lixiviation toward seawater is facilitated in small and low‐elevation terrains. Overall, these results showed that seabirds affect small islands across all trophic levels within and outside colonies, and that these effects spread outward to coral reefs, evidencing resiliency of seabird subsidies even within a rat‐invaded archipelago. As consumers of seabird chicks and eggs, however, rat eradication could potentially benefit the terrestrial and nearshore ecosystems through increased subsides carried by seabirds. This article is protected by copyright. All rights reserved.
... This satellite carried a multispectral radiometer, recording the first large-scale measurement of primary productivity at the ocean surface. Satellites with many other sensors followed, measuring a great variety of biotic and abiotic, static, and dynamic ocean variables (Goddijn-Murphy et al., 2021). Around the same time, microchip technologies allowed the design of satellite transmitters light enough to be carried by large fish, marine mammals, turtles, and seabirds (Timko and Kolz, 1982). ...
... For instance, in a provocative manner, one may consider that the little auk (Alle alle), one of the smallest seabirds in the North Atlantic, is marine megafauna. Indeed, the little auk is one of the most numerous seabirds in the world (40-80 million individuals), capable of extracting up to 24% of zooplankton stocks in certain areas, and of transforming entire coastal landscapes by carrying tonnes of nutrients from sea to shore with its guano (González-Bergonzoni et al., 2017). Therefore, for this review, we took a functional look at marine megafauna, by also including smaller (but trophically important) species. ...
Article
Satellite remote-sensing and wildlife tracking allow researchers to record rapidly increasing volumes of information on the spatial ecology of marine megafauna in the context of global change. This field of investigation is thereby entering the realm of big data science: Information technology allows the design of completely new frameworks for acquiring, storing, sharing, analysing, visualizing, and publicizing data. This review aims at framing the importance of big data for the conservation of marine megafauna, through intimate knowledge of the spatial ecology of these threatened, charismatic animals. We first define marine megafauna and big data science, before detailing the technological breakthroughs leading to pioneering “big data” studies. We then describe the workflow from acquiring megafauna tracking data to the identification and the prediction of their critical habitats under global changes, leading to marine spatial planning and political negotiations. Finally, we outline future objectives for big data studies, which should not take the form of a blind technological race forward, but of a coordinated, worldwide approach to megafauna spatial ecology, based on regular gap analyses, with care for ethical and environmental implications. Employing big data science for the efficient conservation of marine megafauna will also require inventing new pathways from research to action.
... While the Arctic is the summer breeding region to millions of seabirds, and hundreds of thousands of mammals that move in and out of the region annually, currently biota as a vector to the Arctic is thought to be minimal for most contaminants compared to atmospheric and oceanic pathways (Wania 1998;CACAR 2017). While biovectors are well studied in both chemical contaminants and nutrients (e.g., Brimble et al. 2009;González-Bergonzoni et al. 2017;Mosbech et al. 2018), only a handful of studies have examined how plastic pollution may be moved from the marine environment to terrestrial sites via birds (Hammer et al. 2016;Bourdages et al. 2021;Grant et al. 2021;Hamilton et al. 2021). ...
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Full-text available
Plastic pollution (including microplastics) has been reported in a variety of biotic and abiotic compartments across the circumpolar Arctic. Due to their environmental ubiquity, there is a need to understand not only the fate and transport of physical plastic particles, but also the fate and transport of additive chemicals associated with plastic pollution. Further, there is a fundamental research gap in understanding long-range transport of chemical additives to the Arctic via plastics as well as their behavior under environmentally relevant, Arctic conditions. Here, we comment on the state of the science of plastic as carriers of chemical additives to the Arctic, and highlight research priorities going forward. We suggest further research on the transport pathways of chemical additives via plastics from both distant and local sources and laboratory experiments to investigate chemical behavior of plastic additives under Arctic conditions, including leaching, uptake, and bioaccumulation. Ultimately, chemical additives need to be included in strategic monitoring efforts to fully understand the contaminant burden of plastic pollution in Arctic ecosystems.
... The little auk Alle alle is the most abundant seabird in the North Atlantic (Barrett et al., 2006) breeding almost exclusively at high latitudes Wojczulanis-Jakubas et al., 2021). It has a strong influence on both marine and terrestrial ecosystem functioning and acts as an ecosystem engineer by consuming large amounts of zooplankton and by transporting vast amounts of nutrients from sea to land (Barrett et al., 2006;Zwolicki et al., 2016;Gonzaĺez-Bergonzoni et al., 2017). In Svalbard during the breeding season, the little auk feeds primarily on the Arctic copepod, C. glacialis, but the Atlantic C. finmarchicus may also represent an important part of its diet (e.g., Karnovsky et al., 2003;Jakubas et al., 2011;Hovinen et al., 2014;Jakubas et al., 2016;Jakubas et al., 2020). ...
Article
Full-text available
Global warming, combined with an increasing influence of Atlantic Waters in the European Arctic, are causing a so-called Atlantification of the Arctic. This phenomenon is affecting the plankton biomass and communities with potential consequences for the upper trophic levels. Using long-term data (2005-2020) from a high Arctic zooplanktivorous seabird, the little auk (Alle alle), we tested the hypothesis that the Atlantification affects its diet, body condition and demography. We based our study on data collected in three fjords in West Spitsbergen, Svalbard, characterized by distinct oceanographic conditions. In all three fjords, we found a positive relationship between the inflow of Atlantic Waters and the proportion of Atlantic prey, notably of the copepod Calanus finmarchicus, in the little auk chick diet. A high proportion of Atlantic prey was negatively associated with adult body mass (though the effect size was small) and with chick survival (only in one fjord where chick survival until 21 days was available). We also found a negative and marginally significant effect of the average proportion of Atlantic prey in the chick diet on chick growth rate (data were available for one fjord only). Our results suggest that there are fitness costs for the little auk associated with the Atlantification of West Spitsbergen fjords. These costs seem especially pronounced during the late phase of the chick rearing period, when the energetic needs of the chicks are the highest. Consequently, even if little auks can partly adapt their foraging behaviour to changing environmental conditions, they are negatively affected by the ongoing changes in the Arctic marine ecosystems. These results stress the importance of long-term monitoring data in the Arctic to improve our understanding of the ongoing Atlantification and highlight the relevance of using seabirds as indicators of environmental change.
... Seabirds that gather at colonies to breed, deposit significant amounts of materials that can include guano, dropped food, and carcasses through mortality (González-Bergonzoni et al., 2017;De La Peña-Lastra, 2021). This can have evident impacts on surrounding environments that range from enhancing primary production (Ellis, 2005) to contamination by deposition of persistent organic contaminants and toxic elements (Evenset et al., 2007). ...
Article
Seabirds are important biovectors of contaminants, like mercury, moving them from marine to terrestrial environments around breeding colonies. This transfer of materials can have marked impacts on receiving environments and biota. Less is known about biotransport of contaminants by generalist seabirds that exploit anthropogenic wastes compared to other seabird species. In this study, we measured total mercury (THg) in O-horizon soils at four herring gull (Larus smithsoniansus) breeding colonies in southern Nova Scotia, Canada. At colonies with dry substrate, THg was significantly higher in soils collected from gull colonies compared to nearby reference soils with no nesting gulls. Further, THg was distinct in soils among study colonies and was likely influenced by biotransport from other nesting seabird species, most notably Leach's storm-petrels (Hydrobates leucorhous). Our research suggests gulls that scavenge on anthropogenic wastes at local industrial sites are biovectors moving THg acquired at these sites to their colonies and may increase the spatial footprint of contaminants from these industries.
... vultures Accipitridae, African hornbills Bucerotidae) (Buechley et al. 2018;Capoccia et al. 2018;Trail 2007) or can act as ecosystem engineers in selected ecosystems (e.g., Little Auk Alle alle, Black-backed Woodpecker, Picoides arctius) (González-Bergonzoni et al. 2017;Tremblay et al. 2015). Birds support a variety of ecosystem services, including global cycling of nutrients (Otero et al. 2018) and soil formation (Simas et al. 2007;Souza et al. 2014), and serve as indicators of environmental health (Furness and Greenwood 1993;Gregory and Strien 2010). ...
Article
Full-text available
A literature review of bioaccumulation and biotransformation of organic chemicals in birds was undertaken, aiming to support scoping and prioritization of future research. The objectives were to characterize available bioaccumulation/biotransformation data, identify knowledge gaps, determine how extant data can be used, and explore the strategy and steps forward. An intermediate approach balanced between expediency and rigor was taken given the vastness of the literature. Following a critical review of > 500 peer-reviewed studies, > 25,000 data entries and 2 million information bytes were compiled on > 700 organic compounds for ~ 320 wild species and 60 domestic breeds of birds. These data were organized into themed databases on bioaccumulation and biotransformation , field survey , microsomal enzyme activity , metabolic pathway , and bird taxonomy and diet . Significant data gaps were identified in all databases at multiple levels. Biotransformation characterization was largely fragmented over metabolite/pathway identification and characterization of enzyme activity or biotransformation kinetics. Limited biotransformation kinetic data constrained development of an avian biotransformation model. A substantial shortage of in vivo biotransformation kinetics has been observed as most reported rate constants were derived in vitro. No metric comprehensively captured all key contaminant classes or chemical groups to support broad-scope modeling of bioaccumulation or biotransformation. However, metrics such as biota-feed accumulation factor, maximum transfer factor, and total elimination rate constant were more readily usable for modeling or benchmarking than other reviewed parameters. Analysis demonstrated the lack of bioaccumulation/biotransformation characterization of shorebirds, seabirds, and raptors. In the study of bioaccumulation and biotransformation of organic chemicals in birds, this review revealed the need for greater chemical and avian species diversity, chemical measurements in environmental media, basic biometrics and exposure conditions, multiple tissues/matrices sampling, and further exploration on biotransformation. Limitations of classical bioaccumulation metrics and current research strategies used in bird studies were also discussed. Forward-looking research strategies were proposed: adopting a chemical roadmap for future investigations, integrating existing biomonitoring data, gap-filling with non-testing approaches, improving data reporting practices, expanding field sampling scopes, bridging existing models and theories, exploring biotransformation via avian genomics, and establishing an online data repository.
... Long-distance seasonal migrations to breed in the Arctic are particularly common in birds, both in seabirds and terrestrial birds. Some of these species are also extremely abundant and therefore constitute considerable biomasses (Fox et al. 2019;Gonzalez-Bergonzoni et al. 2017). The migration routes and modes of bird migrations to the Arctic are also very diverse (Fig. 1, Table 1). ...
Chapter
Full-text available
Many animals are highly mobile and have evolved long-distance migrations that make them actors in multiple ecosystems through the year and throughout their life. Most long-distance migrations are adaptations to seasonality and more generally to spatio-temporal patterns in food availability, weather, risk of parasite and pathogen infections, and predation risk. Due to the considerable seasonality in high-latitude environments, with Arctic as extremes, long-distance seasonal migrations are a distinct characteristic of the fauna in these Northern regions. The Arctic is also a seasonal melting pot during the productive summer, when large numbers of organism move in from all over the world. Travelling animals connect ecosystems and serve as spatial vectors, of energy and nutrients and of other organisms that follow (sometimes as active hitchhikers) for parts or the entire route. We provide an overview of central concepts and main spatial and temporal (phenology) patterns of animal migrations, with a focus on migrations to and from as well as within northern regions (i.e. arctic and sub-arctic regions). In particular, we characterize the role of migratory animals as vectors and hosts for infectious agents, and we discuss the concepts of migratory escape, migratory culling, and migratory separation. Understanding drivers and patterns of migrations is essential for understanding the dynamics of diseases and must therefore be considered in veterinary and human medicine and the One Health perspective. We show how climate change and human stressors impact migrations and how these changes may interact with the animals’ capacity to transport parasites and other infectious agents. Throughout, we stress the evolutionary ecology of migrations, a plastic trait under natural selection with complex ecological consequences.
Preprint
Animal-borne telemetry devices provide essential insights into the life-history strategies of far-ranging species and allow us to understand how they interact with their environment. Many species in the seabird family Alcidae undergo a synchronous moult of all primary flight feathers during the non-breeding season, making them flightless and more susceptible to environmental stressors, including severe storms and prey shortages. However, the timing and location of moult remains largely unknown, with most information coming from studies on birds killed by storms or shot at sea. Using light-level geolocators with saltwater immersion loggers, we develop a method for determining flightless periods in the context of the annual cycle. Four Atlantic puffins (Fratercula arctica) were equipped with geolocator/immersion loggers on each leg to attempt to overcome issues of leg-tucking in plumage while sitting on the water, which confounds the interpretation of logger data. Light level and saltwater immersion time-series data were combined to correct for this issue. This approach was adapted and applied to 40 puffins equipped with the standard practice deployments of geolocators on one leg only. Flightless periods consistent with moult were identified in the dual-equipped birds, whereas moult identification in single-equipped birds was less definitive and should be treated with caution. Within the dual-equipped sample, we present evidence for two flightless moult periods per non-breeding season in two puffins that undertook more extensive migrations (> 2000km), and were flightless for up to 76 days in a single non-breeding season. A biannual flight feather moult is highly unusual among non-passerine birds, and may be unique to birds that undergo catastrophic moult, i.e. become flightless when moulting. Though our conclusions are based on a small sample, we have established a freely available methodological framework for future investigation of the moult patterns of this and other seabird species.
Article
Accelerometry has been widely used to estimate energy expenditure in a broad array of terrestrial and aquatic species. However, a recent reappraisal of the method showed that relationships between dynamic body acceleration (DBA) and energy expenditure weaken as the proportion of non-mechanical costs increase. Aquatic air breathing species often exemplify this pattern, as buoyancy, thermoregulation and other physiological mechanisms disproportionately affect oxygen consumption during dives. Combining biologging with the doubly-labelled water method, we simultaneously recorded daily energy expenditure (DEE) and triaxial acceleration in one of the world's smallest wing-propelled breath-hold divers, the dovekie (Alle alle). These data were used to estimate the activity-specific costs of flying and diving and to test whether overall dynamic body acceleration (ODBA) is a reliable predictor of DEE in this abundant seabird. Average DEE for chick-rearing dovekies was 604±119 kJ/d across both sampling years. Despite recording lower stroke frequencies for diving than for flying (in line with allometric predictions for auks), dive costs were estimated to surpass flight costs in our sample of birds (flying: 7.24, diving: 9.37 X BMR). As expected, ODBA was not an effective predictor of DEE in this species. However, accelerometer-derived time budgets did accurately estimate DEE in dovekies. This work represents an empirical example of how the apparent energetic costs of buoyancy and thermoregulation limit the effectiveness of ODBA as the sole predictor of overall energy expenditure in small shallow-diving endotherms.
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RationaleChemical lipid extractions, as means of standardizing sample preparations, have been identified as important for comparability of studies. Unfortunately, these methods are expensive, because of the costly chemicals and the need to analyse two sets of samples, one for δ13C values (treated) and another for δ15N values (untreated). To avoid this, studies have suggested mathematical solutions to the problem. Our study intends to (i) determine the applicability of the five most common mathematical correction models and (ii) which of the widely applied chemical extraction methods is the most suitable for a variety of marine organisms.Methods Muscle, heart and liver samples were collected from eight different species. The tissues were treated with Bligh and Dyer, Folch and Soxhlet extraction methods and analysed in a Europa 20-20 mass spectrometer. Predicted lipid-extracted δ13C values were calculated from untreated tissue values using the five most common mathematical models.ResultsThe results indicated that the mathematical methods could not be accurately applied to any of the eight species used in this study, highlighting current issues with accepted isotope methodologies. The Folch chemical extraction removed the highest amount of lipid, suggesting it is the most suitable delipidation method.Conclusions Analysing two samples, one treated one not, remains the best method to obtain accurate δ13C isotope values of muscle tissue. By using this approach every study will obtain two datasets, eventually providing a suitable collective dataset for determining how isotopic signatures are affected by delipidation and potentially producing better mathematical correction models in future. Copyright © 2015 John Wiley & Sons, Ltd.
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Arctic climate change has profound impacts on the cryosphere, notably via shrinking sea-ice cover and retreating glaciers, and it is essential to evaluate and forecast the ecological consequences of such changes. We studied zooplankton-feeding little auks (Alle alle), a key sentinel species of the Arctic, at their northernmost breeding site in Franz-Josef Land (80°N), Russian Arctic. We tested the hypothesis that little auks still benefit from pristine arctic environmental conditions in this remote area. To this end, we analysed remote sensing data on sea-ice and coastal glacier dynamics collected in our study area across 1979–2013. Further, we recorded little auk foraging behaviour using miniature electronic tags attached to the birds in the summer of 2013, and compared it with similar data collected at three localities across the Atlantic Arctic. We also compared current and historical data on Franz-Josef Land little auk diet, morphometrics and chick growth curves. Our analyses reveal that summer sea-ice retreated markedly during the last decade, leaving the Franz-Josef Land archipelago virtually sea-ice free each summer since 2005. This had a profound impact on little auk foraging, which lost their sea-ice-associated prey. Concomitantly, large coastal glaciers retreated rapidly, releasing large volumes of melt water. Zooplankton is stunned by cold and osmotic shock at the boundary between glacier melt and coastal waters, creating new foraging hotspots for little auks. Birds therefore switched from foraging at distant ice-edge localities, to highly profitable feeding at glacier melt-water fronts within <5 km of their breeding site. Through this behavioural plasticity, little auks maintained their chick growth rates, but showed a 4% decrease in adult body mass. Our study demonstrates that arctic cryosphere changes may have antagonistic ecological consequences on coastal trophic flow. Such nonlinear responses complicate modelling exercises of current and future polar ecosystem dynamics.