Long-term responses of Icelandic Arctic foxes to changes in marine and terrestrial ecosystems

Fanny Berthelot; Ester R Unnsteinsdóttir; Jennifer A Carbonell Ellgutter; Dorothee Ehrich

doi:10.1371/journal.pone.0282128

. 2023 Oct 4;18(10):e0282128. doi: 10.1371/journal.pone.0282128

Long-term responses of Icelandic Arctic foxes to changes in marine and terrestrial ecosystems

Fanny Berthelot ^1,^*, Ester R Unnsteinsdóttir ², Jennifer A Carbonell Ellgutter ³, Dorothee Ehrich ¹

Editor: Dorothée Drucker⁴

PMCID: PMC10550166 PMID: 37792685

Abstract

The long-term dynamics of predator populations may be driven by fluctuations in resource availability and reflect ecosystem changes such as those induced by climate change. The Icelandic Arctic fox (Vulpes lagopus) population has known major fluctuations in size since the 1950s. Using stable isotopes analysis of bone collagen over a long-time series (1979–2018), we aimed at identifying the main resources used by Icelandic Arctic foxes during periods of growth and decline to assess if the variations in their population size are linked to fluctuations in the availability of resources. We hypothesized that (1) the decline in Seabird abundance was responsible for the decrease in the fox population; and (2) that the growth in the fox population combined to fluctuations in main resources would lead to an increase in intra-specific competition, ultimately leading to variations in their isotopic niches at the population scale. The isotopic composition of Arctic fox bones differed clearly between inland and coast. Stable isotopes mixing models suggested that marine resources and rock ptarmigans were the most important food source and highlighted a rather stable diet in coastal habitats compared to inland habitats where more fluctuations in dietary composition were observed. Coastal foxes had a broader niche than inland foxes, and there was more variation in niche size in the inland habitat. Our results tend to confirm that a general decline in seabird populations drove the decline in Arctic foxes, especially in coastal habitats. For the inland foxes, our results suggest that the lack of marine resources might have led to an increased use of ptarmigans especially during the most recent period.

1. Introduction

Many Arctic ecosystems are characterized by a close link to adjacent marine environments. Around 80% of the terrestrial Arctic lays within 100 km from the coast [1], and marine subsidies often play an important role in terrestrial food webs [2, 3]. At present, high latitude ecosystems are subject to rapid changes under the influence of global warming [4, 5]. These changes may affect marine and terrestrial systems differently and thus modify the interplay between these neighboring ecosystems [6]. Warmer and longer summers may lead to increased primary production on land, and better conditions for some herbivores [7], whereas expanding boreal species can become serious competitors of arctic endemics [8]. Warming of the sea causes major changes in food webs and leads notably to declines of many seabird populations [9]. Several predator species, such as gulls (Larus spp.), skuas (Stercorarius spp.) or Arctic foxes (Vulpes lagopus) are able to exploit both marine and terrestrial resources, thereby constituting a link between both systems [3, 6]. As these opportunistic species are likely to reflect changes through complex ecological responses, their long-term monitoring is a challenging yet key process for a better understanding of the impacts of global warming on interconnected marine and terrestrial ecosystems [10].

The Arctic fox is a terrestrial mammalian predator endemic to the Arctic tundra. It has a circumpolar distribution and is abundant across most of its range, including most arctic islands. Different threats such as increasing competition with the red fox (Vulpes vulpes), habitat loss arising from climate change and declines in the abundance of key prey make Arctic foxes increasingly vulnerable in part of their range [11, 12]. Therefore, they have been chosen as a climate change flagship species by the International Union of Conservation of Nature [13]. They are also a target of international monitoring as their reliance on tundra ecosystems make them likely to highlight the impacts of climate change through species interactions [10, 14].

Arctic foxes have been attributed to two different resource use strategies that involve different reproductive patterns: lemming foxes and coastal foxes [15–17]. The first type behaves as an opportunistic lemming (Lemmus and Dicrostonyx spp.) specialist and adapts its breeding effort to the lemming cycle with large litters in peak years [3, 17, 18]. The second type is more generalist, and lives on Arctic islands deprived of lemmings such as Iceland, or Svalbard. These foxes feed on both marine and terrestrial resources [16, 19] and dispose of a more stable food supply, thus producing fewer cubs per year, but breeding more regularly [17]. While the population dynamics of lemming foxes primarily follow the cycles of their prey, coastal foxes are driven by trends in both terrestrial and marine resources [6, 17, 20].

Iceland makes up for a particularly interesting system when it comes to understanding Arctic fox population dynamics for several reasons. First, this specific coastal population is neither threatened by interspecific competition with the red fox, as Arctic foxes are the only canid species living in Iceland, nor by the collapse of rodent cycles because lemmings are absent from this island [19]. Second, the species being considered as a pest, hunting is known to be the main cause of mortality [21]. The hunting pressure is thought to be stable since 1950, and is regulated by Icelandic laws, which makes it an unlikely driver of the population dynamics, but the hunting statistics provide a long-term estimate of the population trends [20, 22]. The island is also free of infectious diseases that could potentially be fatal to foxes, such as rabies or distemper [23, 24].

Despite the stable hunting effort and the apparent absence of common ecological pressures, striking long-term fluctuations in Arctic fox numbers have been documented and attributed to variations in carrying capacity, likely driven by the distribution and fluctuations in abundance of prey [20, 22]. In addition, Hersteinsson & al. [25] found evidence for indirect climatic impacts through food availability. The hunting statistics indicated a decrease of the population from 1950 to 1970, which has been partly explained by a reduction in the rock ptarmigan (Lagopus muta) population [20, 26, 27]. This period of decline has been followed by a steady six-fold increase until 2008 which has been explained by a global rise in food abundance. Hersteinsson & al. [25] suggested that climatic variables such as the Sub-Polar Gyre, the North Atlantic Oscillation and summer temperature acted indirectly to increase the abundance of the main preys. Based on prey remains at dens, Pálsson & al. [20] documented an increased predation on waders and geese during this growth period, as well as an important use of fulmars. Using stable isotopes analysis, Carbonell Ellgutter & al. [28] highlighted the importance of marine resources and suggested that they might have supported the increase in the fox population.

Recent population estimates have shown important fluctuations in foxes’ numbers during the last decade, starting with a drastic drop reducing the population to half its size within 5 years (Fig 1). Since 2011, however, the population seems to have recovered. Stable isotopes of carbon and nitrogen reflect the main resources used by a consumer over a certain period and can thus provide a good insight into potential drivers of population changes [29]. Building on the study of Carbonell Ellgutter & al. [28], we used isotopic signatures of bone collagen over 40 years to identify the main resources used by Icelandic Arctic foxes during periods of population growth and decline. We investigate whether the recent fluctuations in the size of the population were associated with changes in main dietary components and assess whether they can be attributed to fluctuations in the availability of prey. As Carbonell Ellgutter & al. [28] highlighted that the steady growth in population was characterized by a rather constant diet and may thus have been driven by increasing seabird populations constituting a key resource, we hypothesized that the recent decline in several seabird species [9] might have negatively affected the foxes during the last decade, especially in coastal areas. In the inland, population fluctuations may be related to a switch in diet from rock ptarmigan to increased predation on geese [20]. The important growth of the fox population together with fluctuations in main resources may have also led to an increase in intra-specific competition. Unnsteinsdóttir & al. [22] showed that density dependence is one of the main drivers of Icelandic Arctic fox population, pointing out that foxes adapt their territory sizes in response to variations in carrying capacity. Therefore, we hypothesized an increase in inter-individual variability in the diet with increasing population size, potentially leading to variation in the niche breadth at the population level. A better understanding of what caused the important variations this population experienced in recent years will be important for further management and hunting recommendations, and shed new light on how the interplay between changes in the marine and terrestrial ecosystems affect a flexible arctic predator.

The population estimates (in black) are based on age cohort analysis and hunting statistics [30] and are plotted along with the hunting data per region from 1979 to 2018 (note the different scales on the right and left axis). From a sample of aged foxes, life tables were created, and used to backtrack the age cohorts and create a model that predicts the proportion of each age group alive each year. The age cohorts were then accumulated to get the estimated number of foxes alive each year. Foxes culled further than 50km from the coast were referred as inland and were mostly coming from the East (in green), while foxes culled within 5km from the coast were considered coastal and were mostly culled in the West (in blue). The dotted lines delimitate the different period of growth and decline we are using in the study: 1979–1996; 1997–2009; 2010–2012; 2013–2018.

2. Material and methods

2.1 Study area and species

Iceland is located in the Atlantic Ocean, close to the Arctic Circle. Its climate is influenced by the Gulf Stream and the temperature is considerably higher than expected at this latitude. Monthly mean temperatures vary from -3 to 3°C in January and reach 8 to 15°C in July. Precipitation is high, ranging from 400 to 4000 mm annually (climateknowledgeportal.worldbank.org). The temperatures usually prevent the shorelines from freezing throughout the year. On average, the mean annual temperature has been above 2°C during the last decades, whereas it was below 2°C during the last century. Iceland is also free from pack-ice and thereby remains isolated [31].

Icelandic Arctic foxes can adopt two different resource use strategies, referred to as coastal and inland. Interior habitats are more subject to seasonal variations in temperature influencing the availability of resources. The resident rock ptarmigan (Lagopus muta) and migrating birds like waders, geese and passerines make up for most of the inland foxes’ diet [16, 19, 25, 32]. The Western part of Iceland bears the most productive seashores, with a greater productivity than northern, southern and eastern Iceland combined [33]. It also supports most of the large seabird colonies that nest on the cliffs during summer [25]. Ice-free shores contribute to the stability of food supply [34], enabling coastal foxes to benefit from carrion from marine mammals, fish and marine invertebrates in addition to seabirds and some terrestrial preys [19]. All foxes can occasionally consume sheep (Ovis aries) and reindeer (Rangifer tarandus) carcasses, as well as cattle and horse carcasses that are used as baits by hunters [16]. Because of their more stable food resources, coastal foxes are thought to be more territorial whereas inland foxes are more mobile [19]. This subdivision is reflected in genetic differentiation between coastal foxes from the north-western part of Iceland and the foxes from the rest of the country [35]. Because of this clear distinction, we carried out all analyses addressing coastal and inland foxes separately.

2.2 Arctic fox samples

Fox mandibles were obtained from the collection of the Icelandic Institute of Natural History in Reykjavik, which consists of 12,200 mandibles from Arctic foxes culled from 1979 to present. Legally killed fox carcasses are donated voluntarily by hunters from all over Iceland. All foxes have been aged by counting annual cementum lines of canine tooth roots [36] at Matsons laboratory (United States). To include both coastal and inland foxes and assure a sufficient sample size, we chose jaws of foxes culled in Nordur Isafjardarsysla (henceforth NIS) and Nordur Múlasysla (NMU), two counties respectively representing the Western productive seashores and the Eastern inland areas (Fig 2). Individuals from NIS were culled within 5km from the coast and can thereby be qualified as coastal whereas foxes culled in NMU were located further than 50km from the coast and are thus defined as inland. We included 106 samples from Carbonell Ellgutter & al. [28] culled in the same counties, along with samples coming from 5 other counties whose ecology was either similar to NIS or NMU (Ester Unnsteinsdóttir, personal communication), and culled within 5km from the coast or further than 50km from respectively. Altogether, this added up to a total of 256 samples, 127 of them being from inland areas and 129 being from coastal areas, covering a period of 40 years, from 1979 to 2018. The last 14 years, characterized by large fluctuations in abundance, were sampled more intensively. All the foxes analyzed were between one and two years old. As previous research showed that resource use as inferred from δ¹³C and δ¹⁵N does in general not differ between sexes [32, 37] male and female individuals have been chosen at random (S1 and S2 Tables).

The isotopic composition of bone collagen has a slow turnover rate [38, 39]. It can reflect a lifetime average dietary intake although it is biased towards the period of greatest growth [3], which is until 8–9 months old for Arctic foxes [25]. Here, we will assume that the isotope signatures are representative of the diet of Arctic foxes during their first year of life, thereby reflecting their average resource use during this period. Collagen has been extracted from lower jaws following the same protocol as Carbonell Ellgutter & al. [28], based on a standardized method from Brown & al. [40] and modified according to Richard & Hedges [41]. Bone powder has been extracted with an electric drill and demineralized in a 0.25 M HCl solution for 6 days or until decalcification. After being filtered, rinsed and dried, the bone powder was grinded using a mixer mill (Tissuelyzer II, QIAGEN) before we proceeded to the lipid extraction. Lipid extraction was performed twice with 2:1 chloroform/methanol. The collagen samples were finally freeze dried before they were sent to analysis for stable isotopes of carbon and nitrogen at the Stable Isotopes in Nature Laboratory (SINLAB) at the Canadian Rivers Institute, University of New Brunswick. A Finnegan Delta V Plus isotope-ratio mass spectrometer was used coupled with a Finnegan Conflo IV and CE NC2500 Carlo Erba element analyzer. Stable isotope ratios were expressed using the standard δ notation in parts per thousand (‰) [42]. The international standards, i.e the Vienna PeeDee Belemnite for carbon and atmospheric air for nitrogen, were used as reference [42]. Data were calibrated using a three-point calibration curve and international reference material as standards (Nicotinamide, BLS, MLS, USGS61) were used for calibration while N2 and CH7 were used as check standards. Additional technical information can be found in S3 Table.

The quality of the inferred isotopic composition of the collagen samples was checked following Guiry and Szpak [43] using the atomic C:N ratio as quality criteria. Based on collagen composition reported by the authors and on relationships between δ¹³C and C:N in our data we excluded data with an atomic C:N ratio exceeding 3.33, the highest ratio reported for mammalian collagen (see S4 Table for a justification of this threshold). We thus included 25 samples with an atomic C:N ratio between 3.28 and 3.33, which need to be considered with caution as they may be contaminated to a certain degree with non-collagen material. We excluded 23 samples with an atomic C:N ratios above 3.33.

Coastal areas are shown in blue, and inland areas in green. Culling locations are shown as a circle when hunting does not occur in the whole region. The number of foxes included in our dataset is specified under the abbreviation for each region. Reprinted from the Icelandic Institute for National History under a CC BY license, with permission from Hans H. Hansson, original copyright 2023.

2.3 Prey samples

Greylag goose (Anser anser) muscle and egg samples along with northern fulmar (Fulmarus glacialis) muscle samples have been provided by the Icelandic Institute of National History. Both muscle and egg samples have been prepared for stable isotope analysis following the method from Ehrich & al. [44]. Egg samples were dried and subdivided into a sample to analyze for δ¹⁵N, and a sample subjected to lipid extraction with 2:1 chloroform-methanol, before measuring δ¹³C. Muscle samples were cleaned with ethanol, dried, grinded in a mixer mill and subjected to chemical lipid extraction. Samples were analyzed for stable isotopes in carbon and nitrogen at SINLAB, along with the collagen samples. Additional prey signatures of ptarmigan, common eider (Somateria mollissima), wood mouse (Apodemus sylvaticus), golden plover (Pluvialis apricaria), whimbrel (Numenius phaeopus), sheep, horse, kittiwake (Rissa tridactyla), starfish (Asteria rubens), redshank (Tringa totanus), common snipe (Gallinago gallinago), and black guillemot (Cepphus grille) were obtained from Carbonell Ellgutter & al. [28].

2.4 Data analysis

The statistical analysis was performed using R version 4.0.4 [45]. We corrected both foxes and preys’ raw δ¹³C values for the Suess effect, which consists in a depletion in δ¹³C in the biosphere driven by the input of CO₂ from fossil fuel since the Industrial Revolution [46]. To be consistent with Carbonell Ellgutter & al. [28], we followed the same method, i.e using a mean δ¹³C rate of change of -0.026% per year [47] and correcting all the δ¹³C isotopic ratios to levels, which correspond to the first year of the study (1979). The resulting correction was close to measurements obtained from terrestrial ecosystems [48].

The distribution of Arctic foxes’ isotopic signatures with respect to their potential preys was assessed graphically by plotting individual values from both habitats. Prey values were corrected for isotopic discrimination, which corresponds to the amount of change in isotope ratios occurring as a resource is incorporated into the consumer’s tissue [42, 49]. Since the discrimination factor for Arctic fox bone collagen has not been determined experimentally, we used a combination of the experimentally determined trophic discrimination values from Arctic fox blood cells (+0.49‰ and +2.56‰ for δ¹³C and δ¹⁵N respectively) [37], and a correction factor between red blood cells and bone collagen estimated for wolves by Adams & al. [50] (+2.6‰ and +0.8‰ for δ¹³C and δ¹⁵N respectively). The resulting estimate for the discrimination factor from the diet to bone collagen of Arctic foxes was used to adjust our prey signatures with: 3.09 ± 0.25‰ and 3.36 ± 0.37‰ for δ¹³C and δ¹⁵N respectively. This discrimination factor is an approximation and cannot fully account for the complexity of all the molecular processes involved in isotope discrimination during resource assimilation, thus some uncertainty about true discrimination remained.

Temporal changes in Arctic fox isotopic values were analyzed using generalized additive models (GAM) with the mgcv package to allow for non-linearity [51]. Changes in δ¹³C and δ¹⁵N have been modelled as a smooth function of the birth year of each individual. An interaction allowed to fit different changes over time for inland and coastal foxes, and their different means with respect to the two isotopes ratios were modeled as a fixed effect. We chose the restricted maximum likelihood method and used the default parameters of the package for both smoothing parameter and the number of basic functions. Both gam.check and concurvity functions were used to test for the fit of the model as recommended by Ross & al. [52] (S5 Table).

The proportions of different prey in Arctic foxes’ diet over time were estimated using Bayesian stable isotope mixing models as implemented in the MixSIAR package [53] and were illustrated with boxplots. As mixing models perform best with few potential sources, the different prey items were grouped considering the similarity of their isotopic signatures and their ecological relevance. We created four distinct groups: cliff nesting seabirds (black guillemot, northern fulmar and kittiwake) called seabirds, eider ducks that had distinct isotopic signatures from the other seabirds were grouped with whimbrels to marine resources, the rock ptarmigan was kept as a focal species, and all other terrestrial prey (common snipe, greylag goose, goose egg, redshank, golden plover, wood mouse, horse, sheep) were grouped to terrestrial resources. Because of their more marine signature, whimbrels were placed with eider ducks, but the other wader species were placed in the terrestrial resources. To address potential changes in dietary composition, we defined four periods characterized either by growth or decline of the Arctic fox population (Fig 1). The mixing models were run separately for coastal and inland foxes. To account for the uncertainty considering the discrimination factor and because the results of mixing models can depend on how correct the discrimination value is [49], the analysis was repeated with the discrimination factor used by Carbonell Ellgutter & al. [28] (S6 Table, S1 Fig), in addition to the factor described above. We ran the analysis following the MixSIAR manual recommendations, and did 1 000 000 or 300 000 000 MCMC replicates, (preceded by 500 000 or 1 500 000 burn-in), depending on when convergence was reached, and used a residual*process error [54]. The performance of mixing models being sensitive to the selection of priors [55], and as all priors are informative in a mixing context, we used priors based on the known dietary preferences of the foxes from both habitats, i.e. predominantly marine diet for coastal foxes and terrestrial diet for inland foxes [16, 32]. Thus, the prior attributed to coastal foxes was 2/3 of marine diet components (1/3 seabirds and 1/3 marine resources) and 1/3 terrestrial diet components shared between the two groups (1/6 rock ptarmigan and 1/6 terrestrial resources). For inland foxes the prior was opposite with 2/3 terrestrial diet components shared between the two groups and 1/3 marine diet components. The analyses were also run with uniform priors for comparison. The convergence of the MCMC estimations was assessed based on Gelman-Rubin and Geweke diagnostics, and we also inspected the correlation between different sources. In addition, we checked that the posterior distributions were unimodal. The isospace plots for the converging models are shown in S2 Fig.

The use of stable isotopes to infer a population’s trophic niche width is increasing as isotopic niches are considered a good proxy for ecological niches [56–58]. For each period and habitat, we determined the isotopic niche breadth using standard ellipses containing 80% of the data as calculated by the SIBER package. The areas of those ellipses were computed using the Bayesian approach (SEAb, parametrized as detailed in Jackson et al. [59], S3 Fig). The ellipses being unbiased for sample size, it was possible to compare them even though the periods did not contain an equal number of individuals.

3. Results

The prey species showed the typical distinction between terrestrial species with lower δ¹⁵N and δ¹³C values and marine species with higher isotopic compositions for both carbon and nitrogen (Fig 3). The fox values were within the polygon delimited by the prey and covered the whole gradient from terrestrial to marine resources. Many coastal foxes had isotopic signatures that placed them close to the marine prey, whereas inland foxes were in general placed at the other end of the coast-inland gradient. Accordingly, both carbon and nitrogen isotopic compositions showed a significant difference between foxes of the two habitats, with an average difference of respectively 5‰ and 3‰ in δ¹³C and δ¹⁵N values between coastal and inland areas (Table 1).

Table 1. Parameter estimates from generalized additives models assessing the effect of birth year and habitat for (a) carbon isotopes (δ¹³C) and (b) nitrogen isotopes (δ¹⁵N) from bone collagen, along with their corresponding smooth terms values.

	Estimate	Std. Error	t value	P
(a) δ¹³C ⎪ Formula: δ¹³C ~ s(Year, by = Habitat) + Habitat
Intercept	-16.8597	0.1759	-95.87	<2e-16
Habitat (inland)	-3.3818	0.2498	-13.54	<2e-16
(b) δ¹⁵N ⎪ Formula: δ¹⁵N ~ s(Year, by = Habitat) + Habitat
Intercept	11.7296	0.2297	51.07	<2e-16
Habitat (inland)	-5.1021	0.3293	-15.49	<2e-16
	edf	Ref.df	F	p-value
(a) δ¹³C ⎪ Formula: δ¹³C ~ s(Year, by = Habitat) + Habitat
Coastal	3.943	4.871	1.988	0.122
Inland	1	1.001	1.259	0.263
(b) δ¹⁵N ⎪ Formula: δ¹⁵N ~ s(Year, by = Habitat) + Habitat
Coastal	1.644	2.036	0.496	0.598
Inland	4.537	5.561	1.895	0.063

Open in a new tab

Prey species were corrected with a discrimination factor resulting from the combination of the Arctic fox blood to diet discrimination from Lecomte & al. [37], and the blood to collagen variation of wolves from Adams & al. [50]. The prey isotopic composition corrected for trophic discrimination are plotted with their respective standard deviation.

For each model, the fixed effect of the difference between habitats is given (Habitat (inland)) as well as the estimates for non-linearity (edf—effective degree of freedom) for foxes from each habitat.

Significant effects are shown in bold

The GAM did not reveal significant trends in δ¹³C or δ¹⁵N over the study period (Fig 4). For coastal foxes, non-linear variations were detected (edf = 3.943; p = 0.122) with fluctuations in the start of the study period and more stable and slightly higher values in recent years. For inland foxes, δ¹³C were stable over the study period. Regarding nitrogen, for coastal foxes, the values of δ¹⁵N were close to remain stable throughout the whole study period (edf = 1.644; p = 0.598; Fig 4b) while more fluctuations were observed for inland foxes (edf = 4.537), with a noticeable increase at the end of the study period that was close to significant (p = 0.063). The gam.check function showed full convergence for both models, as well as randomly distributed residuals (p > 0.05 in both cases), thus confirming that the default parameters of the program were adequate. The concurvity function showed no evidence for concurvity between variables (S5 Table).

According to the results of the mixing models, the dietary composition of coastal foxes remained stable throughout the study period (Fig 5a) and the models was very close to converge with respect to the discrimination factor chosen (S6 Table, S1 Fig). As expected, marine diet components dominated for coastal foxes, but contrary to our expectations seabirds were less important that other marine resources. However, the posterior diet proportions of seabirds and alternative marine resources were highly correlated (-0.94), indicating that the distinction between these two prey groups was difficult to estimate reliably. This was reflected in considerable overlap in the credibility intervals of the dietary proportions estimated (Fig 5a). Among terrestrial preys, the rock ptarmigan was clearly the most important resource. Inland foxes had a somewhat more variable diet with respect to the different periods (Fig 5b) and the models also converged with both discrimination factors (S4 Table, S1 Fig). Ptarmigans were always the most used resource, but their proportion varied over time, representing nearly 70% of the diet during the decline phase. Marine preys were the second most important prey type, but their use decreased slightly during the decline phase. Other terrestrial resources were less important. The credibility intervals of seabirds did not exclude 0 in all periods, and this also applied to terrestrial resource during the two first periods. The results of models using a uniform prior were nearly identical. The MixSIAR analysis using the discrimination factor from Carbonell Ellgutter & al. [28] also provided similar results, with slightly clearer temporal fluctuations for inland foxes (S6 Table, S1 Fig).

As for the mixing models, the variations in the isotopic niche breadths estimated as standard ellipse areas were more important for inland foxes (Fig 6a). The niches kept a similar width until the decline phase when they shrank considerably. Coastal foxes’ niches remained stable and overlapped during the different periods (Fig 6b). Moreover, the estimated niche areas of coastal foxes were overall greater than the ones of inland foxes (S3 Fig).

Fig 6 — Isotopic niches were calculated as standard ellipses areas containing 80% of the data with the “SIBER” R-package.

4. Discussion

4.1 Variations in resource use

Our results confirmed the previously described differences in diet between coastal and inland foxes, with coastal foxes having overall more marine isotopic signatures than inland foxes [28, 34]. The stable carbon isotope composition among inland foxes over the study period was combined with a slight increase in nitrogen istotopic composition in recent years. Coastal foxes, on the opposite, showed no statistical evidence for changes in δ¹³C and δ¹⁵N values, and the changes in δ¹³C they underwent during the beginning of the study had a low statistical support, and were likely due to the small number of individuals sampled during the first years of the study. These overall rather stable diets both at the coast and in inland habitats were in agreement with the estimations of dietary composition from isotopic mixing models. Both analyses also suggested some slight fluctuations, although not significant, in the inland. This was in agreement with the temporal changes in isotopic niche breadths, which showed important changes in the resource use of inland foxes while coastal foxes seemed to have a rather stable diet and constant niche width throughout the years.

In agreement with Carbonell Ellgutter & al. [28], who highlighted the importance of marine resources in the diet of Icelandic Arctic foxes, the mixing model analysis showed that marine preys were the main resource consumed by coastal foxes, and that they also were important to a certain degree for inland individuals. However, contrary to Carbonell Ellgutter & al. [28], we separated the marine resources in two groups and our results suggested that preys from lower trophic levels such as common eiders and whimbrels could be more important than cliff-nesting seabirds. Although the separation between the two groups of marine resources is not strongly supported by the mixing model (high correlation between the posterior distributions and overlap of credibility intervals), it appears likely that marine resources like common eiders, which are the most common waterfowl available throughout the year and are widely distributed in coastal areas, could represent an important part of the diet of Icelandic Arctic foxes. However, predation on eiders in Iceland is thought to be lower than for other populations in other areas since their protection is one of the reasons for the Icelandic fox culling program [26, 60]. A high importance of common eider in Arctic fox diet is also contrary to findings from prey remains at dens [20], where fulmars were the most common species. This unexpected result could possibly be due to the fact that many of the coastal foxes were culled at eider colonies (Unnsteinsdóttir, personal communication).

Alternatively, whimbrels, which were included in our marine preys, could have been an important resource for foxes from both habitats as they are abundant and accessible during the breeding season [61]. Unfortunately, the grouping used in the mixing models analysis makes it hard to determine whether or not this particular wader species was more important than the others, and no information was found in literature to either support or contest this assumption.

The rock ptarmigan, also a year-round resident in Iceland, appeared to be the preferred terrestrial resource. They were especially important for inland foxes but were also consumed in significant amounts by coastal foxes (Fig 5b).

During the breeding season, waders and geese are increasingly available to foxes, and goose populations are especially increasing [20]. In contrast with Pálsson & al. [20] who suggested that these resources were the main preys available to inland foxes, our results suggested that these prey items were of minor importance. This confirmed the results from Carbonell Ellgutter & al. [28] who did not find support for an increased use of geese, despite isolating the greylag goose as a focal source in their mixing model analysis. The lack of species such as the pink-footed goose in our prey signatures could explain the apparent minor importance of this group since this species has been shown to be especially important for Iceland Arctic foxes [16, 20]. The inclusion of terrestrial species like reindeer, which can occasionally be consumed by the foxes, would have given a more detailed picture of the diet of inland individuals, while the lack of marine species such as puffins (Fratercula arctica), crustaceans and other invertebrates might result in a underestimation of the proportion of marine resources, especially in the diet of coastal foxes. The use of freshwater prey by Arctic foxes in Iceland has not been reported in the literature [16, 19]. Although the isotopic composition of aquatic resources can be influenced by complex and diverse factors [39], the turnover rate of collagen reflects the lifetime dietary intake of the foxes. Thus, in the event of an occasional consumption of freshwater resources, it is unlikely that it would have affected our results. It is also important to note that the discrimination factor we used is an estimation as the fractionation has not been calculated for collagen in Arctic foxes yet. However, we assessed the robustness of our analysis using also the values from Carbonell Ellgutter & al. [28].

4.2 Driver of population change

As suggested by Unnsteinsdóttir & al. [22], the fluctuations in the population size of Arctic foxes likely result from changes in carrying capacity due to changes in abundance of main resources. Accordingly, the results of Carbonell Ellgutter & al. [28] showing a constant and important use of marine resources indicated that increasing populations of seabirds could have been a major driver of the long population increase of the foxes from the 1980’s. For coastal foxes, our results showed a stable diet composition over the whole study period, despite considerable fluctuations in population size (Fig 1). This is also consistent with a bottom-up regulation of the fox population. Indeed, after a period of increase for many seabirds in Iceland up to the turn of the millennium, populations stabilized and some dramatically declined during the last decades [62, 63] when the numbers of foxes hunted in coastal areas started to fluctuate considerably. Breeding failure of several species was observed in 2005, while in 2010–2011 Puffin reproduction failed totally [64]. This was attributed to a lack of sandeel (Ammodytes marinus), a major resource for many seabird species [64, 65] notably for Fulmars [66], a key prey of Arctic foxes [20].

The strong decrease in fox numbers in 2011–2012 that was more pronounced for the coastal population might be a direct consequence of this event. Moreover, our results suggested that Common Eider is likely to be an important prey for Arctic foxes. Their populations stabilized and partly declined in Iceland in the 2000s after an overall increase in the end of the last century [67] and notably declined in Westfjords in western Iceland after 2000 [68]. This resource may thus also have contributed to flattening out and periodic decline of the coastal fox population.

For inland foxes, Ptarmigan were the main resources during the whole study period, but their population trends cannot really explain the increase in the Arctic fox population as many Icelandic populations showed declining trends in the last decades [69]. As suggested by Carbonell Ellgutter & al. [28] it is likely that the increase in marine resources also resulted to an improved situation for inland foxes during the growth period. This is supported by the fact that the crash of seabird populations together with declining Eider populations in 2010–2011 led to an abrupt decline in foxes hunted in the inland as well, although the decline was not as strong as for the coastal population. Notably fulmars were indeed breeding on cliffs far inland and preyed upon by Arctic foxes, but at present, these inland colonies are less active (ER Unnsteinsdóttir, personal observations). During these years, the marine input in the diet of the foxes declined and the proportion of ptarmigan increased. However, according to Fuglei & al. [69] ptarmigan populations were in the low phase in these years and could thus not compensate the lack of marine resource for the foxes. After this decline phase, the diet of inland foxes returned to its previous state. The non-significant trend for an increase in nitrogen isotopic composition in the end of the study period might indicate increased use of geese and waders that experience positive population trends in inland areas [20], at least by some foxes–although such a diet shift was not detected by the MixSIAR analysis. In the future, inland foxes might adapt their diet to the changing resource situation shifting from declining Ptarmigan and seabirds to other terrestrial preys.

4.3 Population isotopic niche breadth

The high hunting pressure in Iceland leads to a high turnover in territorial foxes, and Unnsteinsdóttir & al. [22] suggested that Icelandic Arctic foxes engage in contest competition as they adapt their territory size in response to variations in carrying capacity.

Consequently, we predicted that Arctic foxes’ niche breadth would vary over time, but found no support for this hypothesis in the coastal habitat. Although the diet of coastal foxes seemed not to vary over the study period, one could have expected that the decline in the availability of seabirds would have led to a narrower niche breadth or to a shift in the diet. The apparent consistency in their isotopic niche at the population level could hide some variations at a finer scale—the individual scale.

The results from isotopic niche analysis suggested that coastal foxes globally have broader niches than inland foxes. In previous research, Dalerum & al. [34] pointed out the same phenomenon and suggested that these wider niches were due to a diversification of individual strategies likely dictated by the local abundance of resources. The heterogeneity of coastal areas could lead to increased individual specialization of some foxes, especially since coastal foxes are more territorial [19]. This assumption would support that the fluctuations in carbon isotope composition observed among coastal foxes are likely to be influenced by individual variations in the diet rather than a global shift in the resource used at the population scale. This specialization would be a way of reducing the potential dietary overlap among foxes, in response to an increasing intra-specific competition pressure [70].

Inland foxes on the contrary showed more variations in isotopic niche space over time, as well as a marked reduction in their niche breadth during the period of decline. This fits with the increased use of rock ptarmigan suggested by the mixing model results during the decline period. In previous research, Hersteinsson [19] showed that the number of occupied fox dens was positively correlated to ptarmigan abundance, highlighting the importance of this prey item.

The reduction in the niche space matches the years of low productivity of seabirds, and illustrates the low availability of these resources, thus narrowing the niche. As the fox population recovered, the niche size widened, which could indicate that some marine species were available again.

5. Conclusion

Both marine and terrestrial ecosystems in Iceland are at present changing under the direct and indirect effects of climate change. Our results showed how the Arctic fox, a generalist top predator that uses different resources in western (coastal) and eastern (inland) Iceland, reacted to changes in resources availability. Coastal foxes that benefit from the productive seashores of western Iceland exhibited a constant marine dominated diet over the 40 years of our study. When seabird populations experienced reproductive failure, the fox population declined, probably because there were no alternative resources accessible. Arctic foxes are indeed genuine generalists able to exploit a wide variety of resources. As the seabird species, the foxes are experiencing the profound changes in the marine food web related to lower reproduction of the sandeels that is related both to ocean warming and to structural changes of the marine food web [62, 64]. Inland foxes may have changed their diet more and therefore possibly experienced a somewhat less dramatic population decline. From being a stronghold of the Icelandic Arctic fox, with climate change the coastal areas may become a habitat with less reliable resources, whereas the prey basis in the inland areas may become more productive.

Supporting information

S1 Fig. Boxplot representing the proportion of different prey items in the diet of (a) inland and (b) coastal Arctic foxes using the discrimination factor from Carbonell Ellgutter & al. [28].

(JPEG)

Click here for additional data file.^{(115.5KB, jpeg)}

S2 Fig. Isospace plots generated by MixSIAR on the convergent runs for (a) coastal and (b) inland habitats.

The discrimination factor used for this model was based on a combination of the fractionation values of Arctic fox blood from Lecomte & al. [37], and the blood to collagen variation of wolves from Adams & al. [50].

(JPEG)

Click here for additional data file.^{(138.2KB, jpeg)}

S3 Fig. Bayesian standard ellipse area determined with the SIBER package for (a) coastal and (b) inland foxes over the four periods. Parametrized as detailed in Jackson & al. [59].

(PNG)

Click here for additional data file.^{(44.3KB, png)}

S1 Table. Year of birth, location and sex of the foxes used in this study.

(TIF)

Click here for additional data file.^{(1.5MB, tif)}

S2 Table. Extractions.

Extraction of Carbonell Ellgutter & al. [28] along with the one carried out in the present study.

(TIF)

Click here for additional data file.^{(553KB, tif)}

S3 Table. Technical details: Stable isotopes standards values.

(XLSX)

Click here for additional data file.^{(13.2KB, xlsx)}

S4 Table. Raw isotopic data and collagen quality criteria.

Guiry and Szpak [43] recommend using a threshold of 3.28 in atomic C:N ratio to assess the quality of modern collagen samples of mammals. According to their study, samples with higher values result in a significant correlation between atomic C:N and δ¹³C, and δ¹³C values for these samples may be contaminated by non-collagen material. Among our samples, 48 had C:N values > 3.28. Not to exclude excessively many samples we decided to use a threshold of 3.33, that corresponds to the maximum ratio determined based on amino-acid composition by Guiry and Szpack [43]. Including samples up to this threshold resulted in a significant correlation between C:N and δ¹³C and we may thus have included samples with slight contamination. However, given the coefficient of the regression of δ¹³C against C:N was -8.13, a difference in C:N of 0.05 (3.28 to 3.33) would only lead to a change in δ¹³C of 0.4, corresponding to less than 0.2 standard deviations of our δ¹³C values. The bias this might have introduced would thus be negligible.

(XLSX)

Click here for additional data file.^{(26.2KB, xlsx)}

S5 Table. Outputs from (a) gam.check and (b) concurvity functions to test for the fit of the generalized additive models.

Estimated with the default parameters of the gam function in the mgcv package.

(XLSX)

Click here for additional data file.^{(9.9KB, xlsx)}

S6 Table. Overview of the runs performed in MixSIAR.

Foxes were analyzed separately depending on their habitat and time was included as a categorical (four periods) covariate. Coastal and inland foxes were analyzed separately. We used two different discrimination factors. The first was the one used in Carbonell Ellgutter & al. [28], and the second one was the addition of the values from Arctic fox blood from Lecomte & al. [37] and the variation from blood to collagen of wolves from Adams & al. [50]. We also ran the models with uniform priors (P1) and with informative priors (Pinf). Convergence was assessed based on the Gelman-Rubin and Geweke diagnostics. The detailed results of the Geweke diagnostics are shown in percentage of number of variables outside +/- 1.96 per chain. The maximal correlation between two sources is given, and sources are abbreviated as Ptarmigan–P, Alt. terrestrial–T, Alt. Marine–M, Seabirds–S. Models which had satisfactory convergence diagnostics are shown in bold.

(XLSX)

Click here for additional data file.^{(9.8KB, xlsx)}

Acknowledgments

The authors would like to thank Sissel Kaino for her help in the lab.

Data Availability

All relevant data are within the paper and its Supporting information files.

Funding Statement

The analyses and publication costs were covered by UiT – The Arctic University of Tromsø to Fanny Berthelot (master thesis costs and publication fund). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

1.Ims RA & Ehrich D. Terrestrial ecosystems. In Meltofte H. (Ed.), Arctic biodiversity assessment: Status and trends in Arctic biodiversity. 2013. pp. 384–440). Akureyri: CAFF. [Google Scholar]
2.Gauthier G, Berteaux D, Bety J, Tarroux A, Therrien JF, Mc- Kinnon L, et al. The tundra food web of Bylot Island in a changing climate and the role of exchanges between ecosystems. Ecoscience. 2011. 18:223–35. [Google Scholar]
3.Roth JD. Variability in marine resources affects arctic fox population dynamics. J Anim Ecol. 2003. 72:668–676. doi: 10.1046/j.1365-2656.2003.00739.x [DOI] [PubMed] [Google Scholar]
4.Chylek P, Folland C, Klett JD, Wang M, Hengartner N, Lesins G, et al. Annual mean Arctic Amplification 1970–2020: Observed and simulated by CMIP6 climate models. Geophysical Research Letters. 2022. 49. [Google Scholar]
5.Constable AJ, Harper S, Dawson J, Holsman K, Mustonen T, Piepenburg D, et al. Cross-Chapter Paper 6: Polar Regions. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Pörtner HO, Roberts DC, Tignor M, Poloczanska ES, Mintenbeck K, Alegría A, Craig M, Langsdorf S, Löschke S, Möller V, Okem A, Rama B (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA. 2022. pp. 2319–2368
6.Nater CR, Eide NE, Pederson ÅØ, Yoccoz NG, Fuglei E. Contributions from terrestrial and marine resources stabilize predator populations in a rapidly changing climate. Ecosphere. 2021. 12(6):e03546 [Google Scholar]
7.Albon SD, Irvine RJ, Halvorsen O, Langvatn R, Loe LE, Ropstad E, et al. Contrasting effects of summer and winter warming on body mass explain population dynamics in a food‐limited Arctic herbivore. Global Change Biology. 2017. 23, 1374–1389. doi: 10.1111/gcb.13435 [DOI] [PubMed] [Google Scholar]
8.Elmhagen B, Berteaux D, Burgess R, Ehrich D, Gallant D, Henttonen H, et al. Homage to Hersteinsson and Macdonald: climate warming and resource subsidies cause red fox range expansion and Arctic fox decline. Polar Res. 2017. [Google Scholar]
9.CAFF. State of the Arctic Marine Biodiversity Report Update: Seabirds. Conservation of Arctic Flora and Fauna International Secretariat: Akureyri, Iceland. 2021. https://caff.is/seabirds-cbirds
10.Ehrich D, Ims RA, Yoccoz NG, Lecomte N, Killengreen ST, Fuglei E, et al. What can stable isotope analysis of top predator tissues contribute to monitoring of tundra ecosystems? Ecosystems. 2015. 18:404–416. [Google Scholar]
11.Killengreen ST, Ims RA, Yoccoz NG, Bråthen KA, Henden JA, Schott T. Structural characteristics of a Low Arctic tundra ecosystem and the retreat of the Arctic fox. Biological Conservation. 2007. 135, 459–472 [Google Scholar]
12.Ims RA, Killengreen ST, Ehrich D, Flagstad Ø, Hamel S, Henden JA, et al. Ecosystem drivers of an Arctic fox population at the western fringe of the Eurasian Arctic. Polar Res. 2017. [Google Scholar]
13.Sillero-Zubiri C, Angerbjørn A. Arctic foxes and climate change. Out-foxed by Arctic warming. The IUCN Red List of Threatened Species. 2009.
14.Berteaux D, Thierry AM, Alisauskas R, Angerbjörn A, Buchel E, Doronina L, et al. Harmonizing circumpolar monitoring of arctic fox: benefits, opportunities, challenges and recommendations. Polar Res. 2017. 36:2. [Google Scholar]
15.Braestrup FW. A study on the arctic fox in Greenland. Meddelelser om Grønland. 1941. 13(4): 1–101. [Google Scholar]
16.Hersteinsson P, Macdonald DW. Diet of arctic foxes (Alopex lagopus) in Iceland. J Zool. 1996. 240:457–474. [Google Scholar]
17.Elmhagen B, Tannerfeldt M, Verucci P & Angerbjörn A. The arctic fox (Alopex lagopus): an opportunistic specialist. Journal of Zoology, London. 2000. 251, 139–149. [Google Scholar]
18.Bêty J, Gauthier G, Korpimäki E & Giroux JF. Shared predators and indirect trophic interactions: lemming cycles and arctic-nesting geese. Journal of Animal’ Ecology. 2002. 71, 88–98. [Google Scholar]
19.Hersteinsson P. The behavioural ecology of the arctic fox (Alopex lagopus) in Iceland. PhD thesis, Oxford: University of Oxford, UK. 1984.
20.Pálsson S, Hersteinsson P, Unnsteinsdóttir ER, Nielsen ÓK. Population limitation in a non-cyclic arctic fox population in a changing climate. Oecologia. 2016. 180:1147–1157. doi: 10.1007/s00442-015-3536-7 [DOI] [PubMed] [Google Scholar]
21.Hersteinsson P. Demography of the arctic fox (Alopex lagopus) population in Iceland. In: McCullough DR, Barrett RH (eds) Wildlife 2001: populations. Elsevier, London. 1992. pp 954–964 [Google Scholar]
22.Unnsteinsdóttir ER, Hersteinsson P, Pálsson S, Angerbjörn A. The fall and rise of the Icelandic arctic fox (Vulpes lagopus): a 50-year demographic study on a non-cyclic arctic fox population. Oecologia. 2016. 181. doi: 10.1007/s00442-016-3635-0 [DOI] [PubMed] [Google Scholar]
23.Gunnarsson E, Hersteinsson P, Adalsteinsson S. Rannsóknir á sjúkdómum í íslenska melrakkanum (Studies on diseases in the Icelandic arctic fox). In: Hersteinsson P, Sigbjarnarsson G (eds) Villt íslensk spendýr (e. Reykjavík, Wild Icelandic Mammals). Hið íslenska náttúrufræðifélag, Reykjavík. 1993. pp 49–58. [Google Scholar]
24.Fooks AR, Roberts DH, Lynch M, Hersteinsson P, Runolfsson H. Rabies in the United Kingdom, Ireland and Iceland. In: King AA, Fooks AR, Aubert M, Wandeler AI (eds) Historical perspective of rabies in Europe and the Mediterranean Basin: a testament to rabies by Dr Arthur A. King. World Organization for Animal Health, Paris. 2004. pp 25–32. [Google Scholar]
25.Hersteinsson P, Yom-Tov Y, Geffen E. Effect of sub-polar gyre, north Atlantic oscillation and ambient temperature on size and abundance in the Icelandic arctic fox. Glob Chang Biol. 2009. 15(6):1423–1433. [Google Scholar]
26.Hersteinsson P, Angerbjörn A, Frafjord K, Kaikusalo A. The arctic fox in Fennoscandia and Iceland: management problems. Biol Conserv. 1989. 49:67–81 [Google Scholar]
27.Angerbjörn A, Hersteinsson P, Tannerfeldt M. Arctic fox (Alopex lagopus). In: Sillero-Zubiri C, Hoffmann M, Macdonald DW (eds) Canids: foxes, wolves, jackals and dogs. Status survey and conservation action plan. IUCN/SSC Canid specialist group, Switzerland. 2004b. [Google Scholar]
28.Carbonell Ellgutter JA, Ehrich D, Killengreen ST, Ims RA, Unnsteinsdóttir ER. Dietary variation in Icelandic arctic fox (Vulpes lagopus) over a period of 30 years assessed through stable isotopes. Oecologia. 2020. 192, 403–414. [DOI] [PubMed] [Google Scholar]
29.Kelly JF. Stable isotopes of carbon and nitrogen in the study of avian and mammalian trophic ecology. Can J Zool. 2000. 78(1):1–27. [Google Scholar]
30.Unnsteinsdóttir ER. Refastofninn réttir úr sér, Fréttatilkynning frá Náttúrufræðistofnun Íslands. 2021. Icelandic. http://www.ni.is/sites/ni.is/files/atoms/files/frettatilkynning_stofnmat-a-refum_mai-2021.pdf
31.Geffen E, Waidyaratne S, Dalén L, Angerbjörn A, Vila C, Hersteinsson P, et al. Sea ice occurrence predicts genetic isolation in the Arctic fox. Mol Ecol. 2007. 16:4241–4255. doi: 10.1111/j.1365-294X.2007.03507.x [DOI] [PubMed] [Google Scholar]
32.Angerbjörn A, Hersteinsson P, Lidén K, Nelson E. Dietary variation in arctic foxes (Alopex lagopus) an analysis of stable carbon isotopes. Oecologia. 1994. 99:226–232. doi: 10.1007/BF00627734 [DOI] [PubMed] [Google Scholar]
33.Ingólfsson A. Life on seashores. Rit Landverndar. 1975. 4, 61–99. Icelandic. [Google Scholar]
34.Dalerum F, Perbro A, Magnusdottir R, Hersteinsson P, Angerbjörn A. The influence of coastal access on isotope variation in Icelandic arctic foxes. PLoS One. 2012. 7(3):e32071. doi: 10.1371/journal.pone.0032071 [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Norén K., Angerbjörn A., and Hersteinsson P. Population structure in an isolated Arctic fox, Vulpes lagopus, population: the impact of geographical barriers. Biol. J. Linn. Soc. 2009. 97(1): 18–26 [Google Scholar]
36.Allen DH, Melfi RC. Improvements in techniques for ageing mammals by dental cementum annuli. Proc Iowa Acad Sci. 1985. 92(3):100–102 [Google Scholar]
37.Lecomte N, Ahlstrøm Ø, Ehrich D, Fuglei E, Ims RA, et al. Intrapopulation variability shaping isotope discrimination and turnover: experimental evidence in arctic foxes. PLoS ONE. 2011. 6: e21357. doi: 10.1371/journal.pone.0021357 [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Tieszen LL, Boutton TW, Tesdahl KG, Slade NA. Fractionation and turnover of stable carbon isotopes in animal tissues: implications for δ13C analysis of diet. Oecologia. 1983. 57, 32–37. [DOI] [PubMed] [Google Scholar]
39.Guiry E. Complexities of stable carbon and nitrogen isotope biogeochemistry in ancient freshwater ecosystems: implications for the study of past subsistence and environmental change. Frontiers in Ecology and Evolution 7. 2019. [Google Scholar]
40.Brown TA, Nelson DE, Vogel JS, Southon JR. Improved collagen extraction by modified Longin method. Radiocarbon. 1998. 30(2):171–177. [Google Scholar]
41.Richard MP, Hedges REM. Stable isotopes evidence for similarities in the types of marine foods used by Late Mesolithic humans at sites along the Atlantic coast of Europe. J Archaeol. 1999. Sci 26:717–722. [Google Scholar]
42.Ben-David M, Flaherty EA. Stable isotopes in mammalian research: a beginner’s guide. J Mammal. 2012. 93:312–328. [Google Scholar]
43.Guiry EJ, Szpak P. Quality control for modern bone collagen stable carbon and nitrogen isotope measurements. Methods Ecol Evol. 2020. 11: 1049–1060. [Google Scholar]
44.Ehrich D, Tarroux A, Stien J, Lecomte N, Killengreen S, Berteaux D, et al. Stable isotopes analysis: modelling lipid normalization for muscle and eggs from arctic mammals and birds. Methods Ecol Evol. 2011. 2:66–76. [Google Scholar]
45.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2021.
46.Keeling RF, Graven HD, Welp LR, Resplandy L, Bi J, Piper S, et al. Atmospheric evidence for a global secular increase in carbon isotopic discrimination of land photosynthesis. Proceedings of the National Academy of Sciences of the United States of America. 2017. 114(39), 10,361–10,366. doi: 10.1073/pnas.1619240114 [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Olsen A, Omar AM, Bellerby RGJ, Johannessen T, Ninnemann U, Brown KR, et al. Magnitude and origin of the anthropogenic CO2 increase and 13C Suess effect in the Nordic seas since 1981. 2006. Glob Biogeo- chem Cycles.
48.Belmecheri S, Lavergne A. Compiled records of atmospheric CO2 concentrations and stable carbon isotopes to reconstruct climate and derive plant ecophysiological indices from tree rings. 20202. Dendrochronologia, Volume 63, 125748, ISSN 1125-7865.
49.Bond AL, & Diamond AW. Recent Bayesian stable isotope mixing models are highly sensitive to variation in discrimination factors. Ecological Applications. 2011. 21, 1017–1023. doi: 10.1890/09-2409.1 [DOI] [PubMed] [Google Scholar]
50.Adams LG, Farley SD, Stricker CA, Demma DJ, Roffler GH, Miller DC, et al. Are inland wolf-ungulate systems influenced by marine subsidies of Pacific salmon? Ecological Applications. 2010. 20, 251–262. doi: 10.1890/08-1437.1 [DOI] [PubMed] [Google Scholar]
51.Wood SN. Generalized Additive Models: an introduction with R (2nd edition), CRC. 2017.
52.Ross N, Miller D, Simpson GL, Pedersen EJ. Generalized Additive Models in R: A Free Interactive Course. 2019. noamross.github.io/gams-in-r-course
53.Stock BC, Jackson AL, Ward EJ, Parnell AC, Phillips DL, Semmens BX. Analyzing mixing systems using a new generation of Bayesian tracer mixing models. PeerJ. 2018. 6:e5096. doi: 10.7717/peerj.5096 [DOI] [PMC free article] [PubMed] [Google Scholar]
54.Stock BC, Semmens BX. Unifying error structures in commonly used biotracer mixing models. Ecology. 2016b. 97(10):2562–2569. doi: 10.1002/ecy.1517 [DOI] [PubMed] [Google Scholar]
55.Phillips DL, Inger R, Bearhop S, Jackson AL, Moore JW, Parnell AC, et al. Best practices for use of stable isotope mixing models in food-web studies. Canadian Journal of Zoology. 2014. 92(10): 823–835. [Google Scholar]
56.Layman CA, Arrington DA, Montaña CG, Post DM. Can stable isotope ratios provide quantitative measures of trophic diversity within food webs? Ecology. 2007a. 88, 42–48. [DOI] [PubMed] [Google Scholar]
57.Newsome SD, Tinker MT, Monson DH, Oftedal OT, Ralls K, Staedler MM, et al. Using stable isotopes to investigate individual diet specialization in California sea otters (Enhydra lutris nereis). Ecology. 2009. 90, 961–974. doi: 10.1890/07-1812.1 [DOI] [PubMed] [Google Scholar]
58.Tarroux A, Bety J, Gauthier G, Berteaux D. The marine side of a terrestrial carnivore: intra-population variation in use of allochthonous resources by Arctic foxes. PLoS ONE. 2012. 7:e42427. doi: 10.1371/journal.pone.0042427 [DOI] [PMC free article] [PubMed] [Google Scholar]
59.Jackson AL, Inger R, Parnell AC, Bearhop S. Comparing isotopic niche widths among and within communities: SIBER—Stable Isotope Bayesian Ellipses in R. J. Anim. Ecol. 2011. 80, 595–602. doi: 10.1111/j.1365-2656.2011.01806.x [DOI] [PubMed] [Google Scholar]
60.Wood KA, Thorstensen S, Lúðvíksson SJ, Brides K and Petersen A. Long-term trends in the survival rates of adult female Common Eider Somateria mollissima at three colonies in Iceland. Ibis. 2021. 163: 671–683. [Google Scholar]
61.Skarphéðinsson. Spói (Numenius phaeopus). 2018. https://www.ni.is/numenius-phaeopus
62.Hansen ES, Sandvik H, Erikstad KE, Yoccoz NG, Anker-Nilssen T, Bader J, et al. Centennial relationships between ocean temperature and Atlantic puffin production reveal shifting decennial trends. Glob Change Biol. 2021. 27:3753–3764. doi: 10.1111/gcb.15665 [DOI] [PubMed] [Google Scholar]
63.Linnebjerg JF, Frederiksen M, Kolbeinsson Y et al. Non-breeding areas of three sympatric auk species breeding in three Icelandic colonies. Polar Biol. 2018. 41, 1951–1961. [Google Scholar]
64.Lilliendahl K, Hansen ES, Bogason V, Sigursteinsson M, Magnúsdóttir ML, Jónsson PM, et al. Viðkomubrestur lunda og sandsílis við Vestmannaeyjar [Recruitment failure of puffin and sand eel in Vestmannaeyjar]. Náttúrufræðingurinn. 2013. 83, 65–79. Icelandic. [Google Scholar]
65.Hansen Erpur. Stofnvöktun lunda-Lokakýrsla 2017–2019. 2017. Icelandic.
66.Phillips R, Petersen M, Lilliendahl K et al. Diet of the northern fulmar Fulmarus glacialis: reliance on commercial fisheries? Marine Biology. 1999. 135, 159–170. [Google Scholar]
67.D’Alba L, Monahan P and Nager RG. Advances in laying date and increasing population size suggest positive responses to climate change in Common Eiders Somateria mollissima in Iceland. Ibis. 2010. 152: 19–28. [Google Scholar]
68.Jónsson JE, Gardarsson A, Gill JA, Pétursdóttir UK, Petersen A, et al. Relationships between Long-Term Demography and Weather in a Sub-Arctic Population of Common Eider. PLoS ONE. 2013. 8(6): e67093. doi: 10.1371/journal.pone.0067093 [DOI] [PMC free article] [PubMed] [Google Scholar]
69.Fuglei E, Henden JA, Callahan CT, Gilg O, Hansen J, Ims RA et al. Circumpolar status of Arctic ptarmigan: population dynamics and trends. Ambio. 2020. doi: 10.1007/s13280-019-01191-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
70.Svanbäck Richard and Bolnick Daniel I. Intraspecific competition drives increased resource use diversity within a natural population. Proc. R. Soc. 2007. B.274839–844 doi: 10.1098/rspb.2006.0198 [DOI] [PMC free article] [PubMed] [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0282128.r001

Decision Letter 0

Dorothée Drucker

6 Mar 2023

PONE-D-23-03420Long-term responses of Icelandic Arctic foxes to changes in marine and terrestrial ecosystemsPLOS ONE

Dear Dr. Berthelot,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Both reviewers highlighted the interest of this work. They present a set of recommendations, including critical points that need to be revised to allow the publication of the manuscript. Reviewer 2 insists on listing all raw data and applying quality criteria. I concur with reviewer 2 that not only modern or sub-modern samples may be contaminated, but we also need to push up our isotopic studies on modern and past ecosystem to the same standards. This includes giving full access to the raw data, as well as complete analytical procedures and full description of calibration and standards. It is also mandatory to provide such information to comply with the FAIR principles for scientific data management included in the PLOS ONE policies. Reviewer 2 indicates several points and literature sources that will be helpful to the authors. There is a need to specify the TEF values and the collagen-to-diet isotopic offset. It is not unusual to see some confusion between collagen-to-collagen and collagen/other tissue-to-diet isotopic offset in modern and past ecosystem trophic studies. As mentioned by reviewer 2, the Suess effect is not linear, and several papers provide detailed data or formula to take into account the exponential tendency of the change in atmospheric ¹³C amounts (e.g. Dombrosky 2020, The Holocene). Reviewer 1 states relevant questions and comments about the application of the mixing model. Altogether, addressing the comments of both reviewers through a major revision process will help the authors to enhance the impact of their study.

Please submit your revised manuscript by Apr 20 2023 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Dorothée Drucker

Academic Editor

PLOS ONE

Journal requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. Please include a complete copy of PLOS’ questionnaire on inclusivity in global research in your revised manuscript. Our policy for research in this area aims to improve transparency in the reporting of research performed outside of researchers’ own country or community. The policy applies to researchers who have travelled to a different country to conduct research, research with Indigenous populations or their lands, and research on cultural artefacts. The questionnaire can also be requested at the journal’s discretion for any other submissions, even if these conditions are not met. Please find more information on the policy and a link to download a blank copy of the questionnaire here: https://journals.plos.org/plosone/s/best-practices-in-research-reporting. Please upload a completed version of your questionnaire as Supporting Information when you resubmit your manuscript.”

3. We note that the grant information you provided in the ‘Funding Information’ and ‘Financial Disclosure’ sections do not match.

When you resubmit, please ensure that you provide the correct grant numbers for the awards you received for your study in the ‘Funding Information’ section.

4. We note that you have stated that you will provide repository information for your data at acceptance. Should your manuscript be accepted for publication, we will hold it until you provide the relevant accession numbers or DOIs necessary to access your data. If you wish to make changes to your Data Availability statement, please describe these changes in your cover letter and we will update your Data Availability statement to reflect the information you provide.

5. We note that Figure 2 in your submission contain [map/satellite] images which may be copyrighted. All PLOS content is published under the Creative Commons Attribution License (CC BY 4.0), which means that the manuscript, images, and Supporting Information files will be freely available online, and any third party is permitted to access, download, copy, distribute, and use these materials in any way, even commercially, with proper attribution. For these reasons, we cannot publish previously copyrighted maps or satellite images created using proprietary data, such as Google software (Google Maps, Street View, and Earth). For more information, see our copyright guidelines: http://journals.plos.org/plosone/s/licenses-and-copyright.

We require you to either (1) present written permission from the copyright holder to publish these figures specifically under the CC BY 4.0 license, or (2) remove the figures from your submission:

a. You may seek permission from the original copyright holder of Figure 2 to publish the content specifically under the CC BY 4.0 license.

We recommend that you contact the original copyright holder with the Content Permission Form (http://journals.plos.org/plosone/s/file?id=7c09/content-permission-form.pdf) and the following text:

“I request permission for the open-access journal PLOS ONE to publish XXX under the Creative Commons Attribution License (CCAL) CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). Please be aware that this license allows unrestricted use and distribution, even commercially, by third parties. Please reply and provide explicit written permission to publish XXX under a CC BY license and complete the attached form.”

Please upload the completed Content Permission Form or other proof of granted permissions as an "Other" file with your submission.

In the figure caption of the copyrighted figure, please include the following text: “Reprinted from [ref] under a CC BY license, with permission from [name of publisher], original copyright [original copyright year].”

b. If you are unable to obtain permission from the original copyright holder to publish these figures under the CC BY 4.0 license or if the copyright holder’s requirements are incompatible with the CC BY 4.0 license, please either i) remove the figure or ii) supply a replacement figure that complies with the CC BY 4.0 license. Please check copyright information on all replacement figures and update the figure caption with source information. If applicable, please specify in the figure caption text when a figure is similar but not identical to the original image and is therefore for illustrative purposes only.

The following resources for replacing copyrighted map figures may be helpful:

USGS National Map Viewer (public domain): http://viewer.nationalmap.gov/viewer/

The Gateway to Astronaut Photography of Earth (public domain): http://eol.jsc.nasa.gov/sseop/clickmap/

Maps at the CIA (public domain): https://www.cia.gov/library/publications/the-world-factbook/index.html and https://www.cia.gov/library/publications/cia-maps-publications/index.html

NASA Earth Observatory (public domain): http://earthobservatory.nasa.gov/

Landsat: http://landsat.visibleearth.nasa.gov/

USGS EROS (Earth Resources Observatory and Science (EROS) Center) (public domain): http://eros.usgs.gov/#

Natural Earth (public domain): http://www.naturalearthdata.com/

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The manuscript discusses changes in the diet and trophic niches of Arctic foxes over a period of nearly 40 years. The authors examined the stable δ13C and δ15N isotope values from the bone collagen of the foxes. The study is very informative and shows very well the changes in diet over the last 40 years and the relationship between foxes and prey availability. However, I have some major and minor comments which should still be considered:

Major comments:

1. Line 127 f: The authors write that the populations were determined using "age cohort analysis and hunting statistics". Could this be a bit more in-depth? What is the relationship between population size and number of kills? Figure 1 gives the impression that the population is always twice the number of kills. I think this is too simplistic. I would ask the authors to elaborate on this.

2. Line 183 ff: The authors write that they extracted collagen according to a specific protocol. It would be desirable to say more about this. Please add a sentence or two describing the steps involved. This will help to classify the values obtained.

3. Line 185: Please enter the laboratory specifications here. This helps to classify how accurate the measurements are. Information about the standards used is also missing.

4. Line 193 ff: This section describes the prey samples. As far as I know, only muscle and egg samples are taken. It would also be important to briefly describe the method of preparation. Can muscle or egg isotopes be compared to collagen isotopes without calibration? If not, the formula for calibration must be mentioned here.

5. Line 214 f: I am not sure with these numbers if this is the correlation of collagen to muscle, or the TEF (Trophic Enrichment Factor) value, or the combination. Please make this clearer. The reference to Fig. 3 is even more confusing as it seems to include TEF values as well. Please clarify what your TEF values are and what the correlation values are.

6. Line 256 ff: Why did the authors use only 300,000 MCMC replicates when the cited manual recommends 1,000,000 MCMC for publications? I also know from many other publications using MixSIAR that 1,000,000 MCMC replicates is the standard size to get a clean running model. In addition, the results of the error diagnostics (Geweke & Gelman-Rubin) to evaluate the usability of the model are missing. These need to be included.

7. Line 269 ff: This section describes how SIBER was used. However, what is missing is the information on how the SEA was calculated. Did the authors use the 40% SEA, which is the core niche, or a 95% SEA, or some other SEA? This information needs to be added. The other niche metrics (e.g., TA and other layman metrics) that can be calculated with SIBER would also be desirable. SIBER also calculates SEAb (Bayesian SEA), which can be used to compare populations and time periods. The different sample sizes do not play a decisive role for this analysis. I strongly recommend including these calculations.

Minor comments:

- Citation in the text: When referring directly to a study, do not say “As [28] showed …” but the name followed by the number. If the authors use Citavi or Endnote, this can be fixed very quickly: Highlight the citation, click Edit, and set it to “display as author(year).

- Line 178: What did the authors mean with “one collagen”?

- Notation of δ13C and δ15N: Please use the international notation for δ13C and δ15N values. The number is superscripted, and the delta is italicized.

- Line 260: Did the authors run the model without priors and is it very different from the model with priors? Maybe the authors can put something like that in the SI.^

- Line 354: The authors should add a small paragraph here about how to deal with missing resources. The selected prey resources cannot reflect 100% of the Arctic fox diet. There may be a resource that was not considered that completely changes the picture. This should be briefly discussed.

- Line 444: This is where the SEAb (or SEAc) comparison mentioned in point 7 would fit in very well to support the statement about the size of the niche.

Reviewer #2: This manuscript was a pleasure to read and I would like to commend the authors on what appears to be an exciting study. That said, I recommend the following changes, which are aimed at enhancing the paper’s ecological and isotopic background and clarifying language around some interpretations. Note that I consider compliance with Comment's 1 and 2 to be critical to the paper's publishability.

General Comments

1) Methods Section. There is an essential, non-negotiable need to include reference to established collagen quality criteria (QC) for bone collagen isotopic compositions. This means reporting and referencing atomic C-to-N ratios (Atomic C:N, hereafter just “C:N”) and the carbon and nitrogen concertation data (%C and %N) that are used calculate them. I cannot stress the importance of ensuring that these data are included for all samples enough. This is the primary means by which the data quality all bone collagen samples (modern or archaeological) is evaluated, a convention that has been widely accepted by the those working with bone collagen stable isotope research for decades. In that context, not only is it key that you include these QC data to follow rudimentary standards of data hygiene practices, but including these QC data will also likely be key for ensuring more interdisciplinary engagement with your study. For instance, many broader meta-analyses (of the kind that are increasingly making big discoveries in retrospective animal isotopic compositions studies today) would exclude data that has no associated QC information. Below, I have offered a brief overview of C:N ratios with reference to the some of the nuances of which approach is best suited for work with modern samples.

1a) C:N ratios have long been a widely accepted standard across disciplines (as per the 3500+ citations of these key QC criteria papers: DeNiro, 1985, Ambrose, 1990) for ensuring that the isotopic compositions (especially for δ13C) of collagen are not altered by contamination. While these QC criteria were developed initially for ancient samples, they have long been used in contemporary ecology and have recently been revised (Guiry and Szpak, 2020) to further improve their ability to ensure data from modern samples are more accurate. In a nutshell, unlike, more commonly analyzed materials from modern specimens, such as muscle, collagen has a highly conserved, and specific Atomic C:N across taxa, which can provide a robust marker for contamination detection. For collagen from mammalian tissues, Atomic C:N that falls outside the 3.00-3.28 range (see Guiry and Szpak 2020) are contaminated with non-collagenous materials (typically lipids or other proteins) that have been incompletely removed from the bone sample during cleaning and collagen extraction. As these materials have isotopic compositions that can differ drastically (and unpredictably) from that of collagen, samples that fall outside of the acceptable range of Atomic C:N should be discarded, or used with caution and a clear caveat. I should note that, even within the acceptable Atomic C:N range, there can be meaningful impacts of on δ13C. If you’re worried about this, you can test for it by plotting your δ13C against Atomic C:N and looking for relationships. Important notes:

i) Atomic C:N (as opposed to molecular C:N) is used for collagen C:N work by widely accepted convention and is calculated as follows: (%C/%N)x(14.007/12.011).

ii) For dataset of the size you are presenting here, these QC data (i.e., C:N, %C, and %N) are typically provided in an ESM in the same table as your isotopic compositions for individual samples (see Comment 2 below).

2) Results Section. I do not see your raw data included in the manuscript or its supplements. The summary averages presented in your tables cannot stand in place of providing the raw data. Unlike ecology, where in the past original data have sometimes not published, it has become a fundamental requirement for studies using bone collagen data that all isotopic compositions and quality control metrics data are provided so that readers can assess the data for themselves and conduct inter-study comparisons. Globally, in the past 35 years, hundreds of thousands of isotopic compositions from extracted, purified bone collagen have been published in full in the archaeological, historical ecological, and paleontological literatures and data from previous studies are routinely used for comparative purposes to support interpretations in new studies. It is critical that we make our data as freely available and useful to future researchers as possible. A key reason for this is that isotopic analyses are a destructive process – the samples we cut from bones and then analyse can never be analyzed again. It therefore behoves us all to ensure that our data can contribute to wider discussions in the future. If you do not provide δ13C and δ15N values from extracted bone collagen, future studies will not be able to link datasets with yours – this may serve to discourage inclusion of your work in future debates. In other words, because isotopic analyses destroy unique, un-replaceable samples we have an ethical responsibility to make all raw data freely available. It is there for an essential requirement that all isotopic data used in this paper is made available to your readership in a supplement.

Specific Comments

3) Fig 1 caption. Please outline what the blue and green dots show in your caption.

4) Lines 184-185. More information is need here, particularly with respect to analytical reporting. Your collagen extraction methods derive from the archaeological and paleontological literatures, and this make sense as these are discipline areas with the most experience working out the nuances of preparing and analysing bone collagen isotopic compositions. However, you have stopped short of what current best practices in this (and other isotope- focused) disciplines require. In that context, I encourage you to consider following current best practices for reporting on bone collagen work as outlined by Szpak et al. (2017). Note that most of this can be done in an electronic supplementary materials section.

a) Explicitly note how data were calibrated (e.g., a one-, two-, or more-point calibration curve).

b) With respect to the standards you have used you should note:

i) what these are,

ii) which are check standards and which are calibration standards,

iii) the accepted or long-term average observed isotopic composition for each,

c) Report tables with the “n=”, means (for check standards), and standard deviations (for calibration standards and check standards) for analyses run in each analytical session.

d) Report tables with the “n=”, individual values, means, and standard deviations for all sample replicates.

e) Consider calculating uncertainty calculation following recommendations from Szpak et al. (2017).

5) Lines 204 to 209. You have glossed over a great deal of complexity here and it would be useful for readers, especially those who may not have an isotope background, to see explicit references to some of the sources of uncertainty built into all these data treatment variables as follows:

a) How have extraction methods for non-bone collagen tissues been dealt with in terms of potential for lipid contamination? What steps have you taken to ensure the best possible comparability between these data types?

b) As you mention, you don’t have a direct isotopic offset for fox bone collagen-to-diet and you have come up with a best guess based on stacked inter-tissue offsets. I think perhaps even adding an explicit, brief note that there are uncertainties that can’t be accounted for here is worthwhile, particularly in light of the vary different processes that underly each of the offsets you have come up with. For instance, while there are large isotopic offsets between both the δ13C and δ15N of an animal’s collagen and its diet, the underlying processes behind these and the extent to which they have been studied differs in some big ways. Diet-consumer bone collagen shifts in δ15N result from trophic enrichment of 15N and this has undergone a lot of study. The offset value you have selected here is broadly in line with results from relevant meta-analyses. By contrast, diet-consumer bone collagen shifts in δ13C are not the result of large trophic enrichment shifts, but instead derive largely from intra-individual, inter-tissue differences that are inherently difficult to study (due to the slow turnover of bone collagen and length of time needed for controlled feeding studies). In that context, it is more challenging to see if the offset you have come up with for δ13C is in the right ballpark.

c) The Susse correction is not linear. Instead, the rate of change in our atmosphere’s δ13C is accelerating more and more with each passing year. Because this process continues to build momentum, constant-rate Suess corrections are not well suited for samples from recent decades. I encourage you to consider using a more nuanced approach (Clark et al., 2021) that considers rate of change in your Susse correction. Alternatively, if you can calculate that this rate of change is inconsequential in the context of your interpretations (which, given the wide spread of δ13C you have observed, is entirely plausible), you could simply add a caveat to that effect.

d) When and where are the samples used for baseline data from? How have you accounted for potential systematic changes (aside from the Susse effect) in baseline prey isotope compositions? Is it not possible that baseline prey isotopic compositions could vary through time due to changes in prey behavior or environmental conditions (climatic or anthropogenic) influencing relevant C and N cycles? Have you made an attempt to source prey baseline data over the same period of time for which you exampling foxes?

6) Line 221. For Figure 3, I suggest the following changes to improve legibility:

a) Reduce the integer interval for your vertical axis. An interval of, say, 2‰ might make this a bit less cluttered and easier to read.

b) Add the standard reference materials to relevant axis titles (i.e., AIR, VPDB).

c) Consider changing the color of one of the fox groups. For instance, if you were to make inland fox “x” symbols black, this would help them ‘pop’ a bit better. As it stands, in some areas of the plot, the data forms a cloud that is difficult to make sense of.

7) Lines 243-251. Somewhere (elsewhere would be fine if you prefer) you should deal with the potential for freshwater food use by: 1) acknowledging that freshwater foods represent a tremendous range of potential variation in source isotope values (for review see Guiry, 2019) and, 2) outline the extent to which such resources might have been available to foxes. I suspect, based on the fact that you haven’t touched on this as a possibility, that there is a low likelihood that foxes have had substantial access to freshwater resources. However, reference to the fact your aware of this as a potential interpretive issue would be useful for your readers who are not familiar with there are (and could therefore misunderstand this as an omission of potentially relevant baseline source data).

8) Line 310. “that” should be “than”. Note that, though I have seen several potential word choice issues, I have not been made a systematic attempt to comment on these. I suggest you go over the manuscript in detail prior to resubmission.

9) Line 346. Here and throughout the manuscript (e.g., line 379, 458) please be more specific about what kind of values you are referring to. It is not good practice to use vague terminologies like “nitrogen ratios”, “carbon ratios”, or “carbon signature”. In the case of “nitrogen ratios”, for instance, this is because this could technically refer to a ratio involving the concentration of nitrogen (i.e., %N, which should also be reported as a key QC metrice (see Comment 1 above) or nitrogen isotope compositions (i.e., δ15N). I understand what you mean here but it is important to use correct terminology to help avoid confusion among readers with less experience in this area (for a review on terminology see Coplen, 2011).

References

AMBROSE, S.H. 1990. Preparation and characterization of bone and tooth collagen for isotopic analysis. Journal of Archaeological Science 17: 431-451.

CLARK, C.T., CAPE, M.R., SHAPLEY, M.D., MUETER, F.J., FINNEY, B.P. & MISARTI, N. 2021. SuessR: Regional corrections for the effects of anthropogenic CO2 on δ13C data from marine organisms. Methods in Ecology and Evolution 12: 1508-1520.

COPLEN, T.B. 2011. Guidelines and recommended terms for expression of stable‐isotope‐ratio and gas‐ratio measurement results. Rapid communications in mass spectrometry 25: 2538-2560.

DENIRO, M.J. 1985. Postmortem preservation and alteration of in vivo bone collagen isotope ratios in relation to palaeodietary reconstruction. Nature 317: 806-809.

GUIRY, E. 2019. Complexities of stable carbon and nitrogen isotope biogeochemistry in ancient freshwater ecosystems: implications for the study of past subsistence and environmental change. Frontiers in Ecology and Evolution 7.

GUIRY, E. & SZPAK, P. 2020. Quality Control for Modern Bone Collagen Stable Carbon and Nitrogen Isotope Measurements. Methods in Ecology and Evolution.

SZPAK, P., METCALFE, J.Z. & MACDONALD, R.A. 2017. Best practices for calibrating and reporting stable isotope measurements in archaeology. Journal of Archaeological Science: Reports 13: 609-616.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

Attachment

Submitted filename: Review to Berthelot et al.pdf

Click here for additional data file.^{(100.1KB, pdf)}

Attachment

Submitted filename: Reviewer Comments - Guiry March 2 2023.docx

Click here for additional data file.^{(32.9KB, docx)}

PLoS One. 2023 Oct 4;18(10):e0282128. doi: 10.1371/journal.pone.0282128.r002

Author response to Decision Letter 0

18 Jul 2023

Dear Editors,

1. We did our best to follow PLOS one's requirement.

2. We filled in the questionnaire on inclusivity with the information we had available and attached it in "other".

3. The funding information has been modified.

4. This statement was a mistake and we would appreciate help to make our data available.

5. We followed the guidelines for copyright issues and added the permission in the "other" attached files.

Attachment

Submitted filename: Answer to reviewers.docx

Click here for additional data file.^{(27.7KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0282128.r003

Decision Letter 1

Dorothée Drucker

10 Aug 2023

PONE-D-23-03420R1Long-term responses of Icelandic Arctic foxes to changes in marine and terrestrial ecosystemsPLOS ONE

Dear Dr. Berthelot,

Both reviewers and I acknowledge the thorough revision of the manuscript by the authors. Some minor revisions should be done before complete acceptance of the paper.

I encourage the authors to consider the following points (line references according to the text without tracked changes):

please answer comment 2) of reviewer 1 and add the results of the statistical tests in table S6
Line 201-202: Are all the standards quoted equivalent to international reference material such as USGS or is there any internal (=inhouse) reference material (which is totally fine)?
Line 208-209 “We thus included 25 samples with a C:N ratio between 2.28 and 3.33” while the authors convincingly argue about considering isotopic results on collagen with C:N atomic ratio up to 3.33, I do not see any argument to keep samples with C:N below 3.0 as recommended by Guiry &Szpak (not Szpack as in table S4) 2020 and could not spot any atomic C:N under 3.09 in your table S4 anyway. I guess you confused with the molecular C/N ratio of column J. Please specify that J column is a molecular C/N ratio and K column is an atomic C/N ratio (named C:N by convention), avoiding further confusion, and amend your main text accordingly. You may not necessarily refer to a specific literature source for the calculation of the atomic C/N ratio, since it corresponds to chemical convention (14.007 and 12.011 being the atomic weight of N and C; please note that initially C and N contents where measured by the Kjeldahl method giving automatically C/N atomic ratios that are still our landmarks today despite being provided with molecular ratio through the mass spectrometer facility).
Line 385-386: “no statistical evidence for changes in nitrogen isotopic composition, and the changes in δ¹³C” please replace by “ no statistical evidence for changes in δ¹³C and δ¹⁵N values” for sake of consistency
Line 429-431: I guess it is where you consider missing resources as recommended by reviewer 1. Could you add a sentence about the consequences of missing terrestrial resources in your reconstruction, as you did for freshwater resources, to better address the point of the reviewer? In other words, beside t the pink-footed goose, which other terrestrial species contribution may have been over- or underestimated?

After addressing these minor points, I will be glad to recommend the manuscript for publication and congratulate the authors for their work.

Please submit your revised manuscript by Sep 24 2023 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Dorothée Drucker

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: I see that the authors have implemented most of my recommendations. Overall, the manuscript now looks much completer and more understandable . I have only three comments that should be considered

1) This is just a small comment and is more about retrieving the authors changes. The lines the authors specified do not match either of the two uploaded versions of your manuscript. This makes retrieval very difficult.

2) The authors wrote that they have more information about Geweke a nd Gelman Rubin diagnostics in table S6. But this information is very incomplete. It is not enough to just write whether their model is convergent or not. It would be better to give the specifications. How many variables have been tested? How many of them w ere in the Gelman Rubin diagnostic above 1.05 (at best none). How many variables were unequal in the Geweke disgnostic and in which chains (at best less than 5%)? This information is important to measure the quality of Bayesian models using MCMC and must b e published.

3) My final point is again about missing resources. The authors wrote that they added a few sentences about this in line 641. This brings us back to the problem in 1): There is the b ibliography in this line in both manuscripts. Unfortunately, I could no t find this place at all. The easiest way would be to make a small chapter on this topic in the SI, because this is an important

point to classify the food reconstructions made.

Reviewer #2: Thank you for revising you manuscript. You have satisfactorily addressed my recommendations. I have now recommended the paper be accepted for publication.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: Yes: Eric Guiry

**********

PLoS One. 2023 Oct 4;18(10):e0282128. doi: 10.1371/journal.pone.0282128.r004

Author response to Decision Letter 1

21 Aug 2023

We hopefully fulfilled all the requirements and provided all the information that was required from us by the editor and the reviewers.

The reference list has also been reviewed.

Attachment

Submitted filename: Answer to reviewers2.docx

Click here for additional data file.^{(17.2KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0282128.r005

Decision Letter 2

Dorothée Drucker

4 Sep 2023

Long-term responses of Icelandic Arctic foxes to changes in marine and terrestrial ecosystems

PONE-D-23-03420R2

Dear Dr. Berthelot,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Dorothée Drucker

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0282128.r006

Acceptance letter

Dorothée Drucker

11 Sep 2023

PONE-D-23-03420R2

Long-term responses of Icelandic Arctic foxes to changes in marine and terrestrial ecosystems

Dear Dr. Berthelot:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Dorothée Drucker

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Fig. Boxplot representing the proportion of different prey items in the diet of (a) inland and (b) coastal Arctic foxes using the discrimination factor from Carbonell Ellgutter & al. [28].

(JPEG)

Click here for additional data file.^{(115.5KB, jpeg)}

S2 Fig. Isospace plots generated by MixSIAR on the convergent runs for (a) coastal and (b) inland habitats.

(JPEG)

Click here for additional data file.^{(138.2KB, jpeg)}

S3 Fig. Bayesian standard ellipse area determined with the SIBER package for (a) coastal and (b) inland foxes over the four periods. Parametrized as detailed in Jackson & al. [59].

(PNG)

Click here for additional data file.^{(44.3KB, png)}

S1 Table. Year of birth, location and sex of the foxes used in this study.

(TIF)

Click here for additional data file.^{(1.5MB, tif)}

S2 Table. Extractions.

Extraction of Carbonell Ellgutter & al. [28] along with the one carried out in the present study.

(TIF)

Click here for additional data file.^{(553KB, tif)}

S3 Table. Technical details: Stable isotopes standards values.

(XLSX)

Click here for additional data file.^{(13.2KB, xlsx)}

S4 Table. Raw isotopic data and collagen quality criteria.

(XLSX)

Click here for additional data file.^{(26.2KB, xlsx)}

S5 Table. Outputs from (a) gam.check and (b) concurvity functions to test for the fit of the generalized additive models.

Estimated with the default parameters of the gam function in the mgcv package.

(XLSX)

Click here for additional data file.^{(9.9KB, xlsx)}

S6 Table. Overview of the runs performed in MixSIAR.

(XLSX)

Click here for additional data file.^{(9.8KB, xlsx)}

Attachment

Submitted filename: Review to Berthelot et al.pdf

Click here for additional data file.^{(100.1KB, pdf)}

Attachment

Submitted filename: Reviewer Comments - Guiry March 2 2023.docx

Click here for additional data file.^{(32.9KB, docx)}

Attachment

Submitted filename: Answer to reviewers.docx

Click here for additional data file.^{(27.7KB, docx)}

Attachment

Submitted filename: Answer to reviewers2.docx

Click here for additional data file.^{(17.2KB, docx)}

Data Availability Statement

All relevant data are within the paper and its Supporting information files.

[pone.0282128.ref001] 1.Ims RA & Ehrich D. Terrestrial ecosystems. In Meltofte H. (Ed.), Arctic biodiversity assessment: Status and trends in Arctic biodiversity. 2013. pp. 384–440). Akureyri: CAFF. [Google Scholar]

[pone.0282128.ref002] 2.Gauthier G, Berteaux D, Bety J, Tarroux A, Therrien JF, Mc- Kinnon L, et al. The tundra food web of Bylot Island in a changing climate and the role of exchanges between ecosystems. Ecoscience. 2011. 18:223–35. [Google Scholar]

[pone.0282128.ref003] 3.Roth JD. Variability in marine resources affects arctic fox population dynamics. J Anim Ecol. 2003. 72:668–676. doi: 10.1046/j.1365-2656.2003.00739.x [DOI] [PubMed] [Google Scholar]

[pone.0282128.ref004] 4.Chylek P, Folland C, Klett JD, Wang M, Hengartner N, Lesins G, et al. Annual mean Arctic Amplification 1970–2020: Observed and simulated by CMIP6 climate models. Geophysical Research Letters. 2022. 49. [Google Scholar]

[pone.0282128.ref005] 5.Constable AJ, Harper S, Dawson J, Holsman K, Mustonen T, Piepenburg D, et al. Cross-Chapter Paper 6: Polar Regions. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Pörtner HO, Roberts DC, Tignor M, Poloczanska ES, Mintenbeck K, Alegría A, Craig M, Langsdorf S, Löschke S, Möller V, Okem A, Rama B (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA. 2022. pp. 2319–2368

[pone.0282128.ref006] 6.Nater CR, Eide NE, Pederson ÅØ, Yoccoz NG, Fuglei E. Contributions from terrestrial and marine resources stabilize predator populations in a rapidly changing climate. Ecosphere. 2021. 12(6):e03546 [Google Scholar]

[pone.0282128.ref007] 7.Albon SD, Irvine RJ, Halvorsen O, Langvatn R, Loe LE, Ropstad E, et al. Contrasting effects of summer and winter warming on body mass explain population dynamics in a food‐limited Arctic herbivore. Global Change Biology. 2017. 23, 1374–1389. doi: 10.1111/gcb.13435 [DOI] [PubMed] [Google Scholar]

[pone.0282128.ref008] 8.Elmhagen B, Berteaux D, Burgess R, Ehrich D, Gallant D, Henttonen H, et al. Homage to Hersteinsson and Macdonald: climate warming and resource subsidies cause red fox range expansion and Arctic fox decline. Polar Res. 2017. [Google Scholar]

[pone.0282128.ref009] 9.CAFF. State of the Arctic Marine Biodiversity Report Update: Seabirds. Conservation of Arctic Flora and Fauna International Secretariat: Akureyri, Iceland. 2021. https://caff.is/seabirds-cbirds

[pone.0282128.ref010] 10.Ehrich D, Ims RA, Yoccoz NG, Lecomte N, Killengreen ST, Fuglei E, et al. What can stable isotope analysis of top predator tissues contribute to monitoring of tundra ecosystems? Ecosystems. 2015. 18:404–416. [Google Scholar]

[pone.0282128.ref011] 11.Killengreen ST, Ims RA, Yoccoz NG, Bråthen KA, Henden JA, Schott T. Structural characteristics of a Low Arctic tundra ecosystem and the retreat of the Arctic fox. Biological Conservation. 2007. 135, 459–472 [Google Scholar]

[pone.0282128.ref012] 12.Ims RA, Killengreen ST, Ehrich D, Flagstad Ø, Hamel S, Henden JA, et al. Ecosystem drivers of an Arctic fox population at the western fringe of the Eurasian Arctic. Polar Res. 2017. [Google Scholar]

[pone.0282128.ref013] 13.Sillero-Zubiri C, Angerbjørn A. Arctic foxes and climate change. Out-foxed by Arctic warming. The IUCN Red List of Threatened Species. 2009.

[pone.0282128.ref014] 14.Berteaux D, Thierry AM, Alisauskas R, Angerbjörn A, Buchel E, Doronina L, et al. Harmonizing circumpolar monitoring of arctic fox: benefits, opportunities, challenges and recommendations. Polar Res. 2017. 36:2. [Google Scholar]

[pone.0282128.ref015] 15.Braestrup FW. A study on the arctic fox in Greenland. Meddelelser om Grønland. 1941. 13(4): 1–101. [Google Scholar]

[pone.0282128.ref016] 16.Hersteinsson P, Macdonald DW. Diet of arctic foxes (Alopex lagopus) in Iceland. J Zool. 1996. 240:457–474. [Google Scholar]

[pone.0282128.ref017] 17.Elmhagen B, Tannerfeldt M, Verucci P & Angerbjörn A. The arctic fox (Alopex lagopus): an opportunistic specialist. Journal of Zoology, London. 2000. 251, 139–149. [Google Scholar]

[pone.0282128.ref018] 18.Bêty J, Gauthier G, Korpimäki E & Giroux JF. Shared predators and indirect trophic interactions: lemming cycles and arctic-nesting geese. Journal of Animal’ Ecology. 2002. 71, 88–98. [Google Scholar]

[pone.0282128.ref019] 19.Hersteinsson P. The behavioural ecology of the arctic fox (Alopex lagopus) in Iceland. PhD thesis, Oxford: University of Oxford, UK. 1984.

[pone.0282128.ref020] 20.Pálsson S, Hersteinsson P, Unnsteinsdóttir ER, Nielsen ÓK. Population limitation in a non-cyclic arctic fox population in a changing climate. Oecologia. 2016. 180:1147–1157. doi: 10.1007/s00442-015-3536-7 [DOI] [PubMed] [Google Scholar]

[pone.0282128.ref021] 21.Hersteinsson P. Demography of the arctic fox (Alopex lagopus) population in Iceland. In: McCullough DR, Barrett RH (eds) Wildlife 2001: populations. Elsevier, London. 1992. pp 954–964 [Google Scholar]

[pone.0282128.ref022] 22.Unnsteinsdóttir ER, Hersteinsson P, Pálsson S, Angerbjörn A. The fall and rise of the Icelandic arctic fox (Vulpes lagopus): a 50-year demographic study on a non-cyclic arctic fox population. Oecologia. 2016. 181. doi: 10.1007/s00442-016-3635-0 [DOI] [PubMed] [Google Scholar]

[pone.0282128.ref023] 23.Gunnarsson E, Hersteinsson P, Adalsteinsson S. Rannsóknir á sjúkdómum í íslenska melrakkanum (Studies on diseases in the Icelandic arctic fox). In: Hersteinsson P, Sigbjarnarsson G (eds) Villt íslensk spendýr (e. Reykjavík, Wild Icelandic Mammals). Hið íslenska náttúrufræðifélag, Reykjavík. 1993. pp 49–58. [Google Scholar]

[pone.0282128.ref024] 24.Fooks AR, Roberts DH, Lynch M, Hersteinsson P, Runolfsson H. Rabies in the United Kingdom, Ireland and Iceland. In: King AA, Fooks AR, Aubert M, Wandeler AI (eds) Historical perspective of rabies in Europe and the Mediterranean Basin: a testament to rabies by Dr Arthur A. King. World Organization for Animal Health, Paris. 2004. pp 25–32. [Google Scholar]

[pone.0282128.ref025] 25.Hersteinsson P, Yom-Tov Y, Geffen E. Effect of sub-polar gyre, north Atlantic oscillation and ambient temperature on size and abundance in the Icelandic arctic fox. Glob Chang Biol. 2009. 15(6):1423–1433. [Google Scholar]

[pone.0282128.ref026] 26.Hersteinsson P, Angerbjörn A, Frafjord K, Kaikusalo A. The arctic fox in Fennoscandia and Iceland: management problems. Biol Conserv. 1989. 49:67–81 [Google Scholar]

[pone.0282128.ref027] 27.Angerbjörn A, Hersteinsson P, Tannerfeldt M. Arctic fox (Alopex lagopus). In: Sillero-Zubiri C, Hoffmann M, Macdonald DW (eds) Canids: foxes, wolves, jackals and dogs. Status survey and conservation action plan. IUCN/SSC Canid specialist group, Switzerland. 2004b. [Google Scholar]

[pone.0282128.ref028] 28.Carbonell Ellgutter JA, Ehrich D, Killengreen ST, Ims RA, Unnsteinsdóttir ER. Dietary variation in Icelandic arctic fox (Vulpes lagopus) over a period of 30 years assessed through stable isotopes. Oecologia. 2020. 192, 403–414. [DOI] [PubMed] [Google Scholar]

[pone.0282128.ref029] 29.Kelly JF. Stable isotopes of carbon and nitrogen in the study of avian and mammalian trophic ecology. Can J Zool. 2000. 78(1):1–27. [Google Scholar]

[pone.0282128.ref030] 30.Unnsteinsdóttir ER. Refastofninn réttir úr sér, Fréttatilkynning frá Náttúrufræðistofnun Íslands. 2021. Icelandic. http://www.ni.is/sites/ni.is/files/atoms/files/frettatilkynning_stofnmat-a-refum_mai-2021.pdf

[pone.0282128.ref031] 31.Geffen E, Waidyaratne S, Dalén L, Angerbjörn A, Vila C, Hersteinsson P, et al. Sea ice occurrence predicts genetic isolation in the Arctic fox. Mol Ecol. 2007. 16:4241–4255. doi: 10.1111/j.1365-294X.2007.03507.x [DOI] [PubMed] [Google Scholar]

[pone.0282128.ref032] 32.Angerbjörn A, Hersteinsson P, Lidén K, Nelson E. Dietary variation in arctic foxes (Alopex lagopus) an analysis of stable carbon isotopes. Oecologia. 1994. 99:226–232. doi: 10.1007/BF00627734 [DOI] [PubMed] [Google Scholar]

[pone.0282128.ref033] 33.Ingólfsson A. Life on seashores. Rit Landverndar. 1975. 4, 61–99. Icelandic. [Google Scholar]

[pone.0282128.ref034] 34.Dalerum F, Perbro A, Magnusdottir R, Hersteinsson P, Angerbjörn A. The influence of coastal access on isotope variation in Icelandic arctic foxes. PLoS One. 2012. 7(3):e32071. doi: 10.1371/journal.pone.0032071 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0282128.ref035] 35.Norén K., Angerbjörn A., and Hersteinsson P. Population structure in an isolated Arctic fox, Vulpes lagopus, population: the impact of geographical barriers. Biol. J. Linn. Soc. 2009. 97(1): 18–26 [Google Scholar]

[pone.0282128.ref036] 36.Allen DH, Melfi RC. Improvements in techniques for ageing mammals by dental cementum annuli. Proc Iowa Acad Sci. 1985. 92(3):100–102 [Google Scholar]

[pone.0282128.ref037] 37.Lecomte N, Ahlstrøm Ø, Ehrich D, Fuglei E, Ims RA, et al. Intrapopulation variability shaping isotope discrimination and turnover: experimental evidence in arctic foxes. PLoS ONE. 2011. 6: e21357. doi: 10.1371/journal.pone.0021357 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0282128.ref038] 38.Tieszen LL, Boutton TW, Tesdahl KG, Slade NA. Fractionation and turnover of stable carbon isotopes in animal tissues: implications for δ13C analysis of diet. Oecologia. 1983. 57, 32–37. [DOI] [PubMed] [Google Scholar]

[pone.0282128.ref039] 39.Guiry E. Complexities of stable carbon and nitrogen isotope biogeochemistry in ancient freshwater ecosystems: implications for the study of past subsistence and environmental change. Frontiers in Ecology and Evolution 7. 2019. [Google Scholar]

[pone.0282128.ref040] 40.Brown TA, Nelson DE, Vogel JS, Southon JR. Improved collagen extraction by modified Longin method. Radiocarbon. 1998. 30(2):171–177. [Google Scholar]

[pone.0282128.ref041] 41.Richard MP, Hedges REM. Stable isotopes evidence for similarities in the types of marine foods used by Late Mesolithic humans at sites along the Atlantic coast of Europe. J Archaeol. 1999. Sci 26:717–722. [Google Scholar]

[pone.0282128.ref042] 42.Ben-David M, Flaherty EA. Stable isotopes in mammalian research: a beginner’s guide. J Mammal. 2012. 93:312–328. [Google Scholar]

[pone.0282128.ref043] 43.Guiry EJ, Szpak P. Quality control for modern bone collagen stable carbon and nitrogen isotope measurements. Methods Ecol Evol. 2020. 11: 1049–1060. [Google Scholar]

[pone.0282128.ref044] 44.Ehrich D, Tarroux A, Stien J, Lecomte N, Killengreen S, Berteaux D, et al. Stable isotopes analysis: modelling lipid normalization for muscle and eggs from arctic mammals and birds. Methods Ecol Evol. 2011. 2:66–76. [Google Scholar]

[pone.0282128.ref045] 45.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2021.

[pone.0282128.ref046] 46.Keeling RF, Graven HD, Welp LR, Resplandy L, Bi J, Piper S, et al. Atmospheric evidence for a global secular increase in carbon isotopic discrimination of land photosynthesis. Proceedings of the National Academy of Sciences of the United States of America. 2017. 114(39), 10,361–10,366. doi: 10.1073/pnas.1619240114 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0282128.ref047] 47.Olsen A, Omar AM, Bellerby RGJ, Johannessen T, Ninnemann U, Brown KR, et al. Magnitude and origin of the anthropogenic CO2 increase and 13C Suess effect in the Nordic seas since 1981. 2006. Glob Biogeo- chem Cycles.

[pone.0282128.ref048] 48.Belmecheri S, Lavergne A. Compiled records of atmospheric CO2 concentrations and stable carbon isotopes to reconstruct climate and derive plant ecophysiological indices from tree rings. 20202. Dendrochronologia, Volume 63, 125748, ISSN 1125-7865.

[pone.0282128.ref049] 49.Bond AL, & Diamond AW. Recent Bayesian stable isotope mixing models are highly sensitive to variation in discrimination factors. Ecological Applications. 2011. 21, 1017–1023. doi: 10.1890/09-2409.1 [DOI] [PubMed] [Google Scholar]

[pone.0282128.ref050] 50.Adams LG, Farley SD, Stricker CA, Demma DJ, Roffler GH, Miller DC, et al. Are inland wolf-ungulate systems influenced by marine subsidies of Pacific salmon? Ecological Applications. 2010. 20, 251–262. doi: 10.1890/08-1437.1 [DOI] [PubMed] [Google Scholar]

[pone.0282128.ref051] 51.Wood SN. Generalized Additive Models: an introduction with R (2nd edition), CRC. 2017.

[pone.0282128.ref052] 52.Ross N, Miller D, Simpson GL, Pedersen EJ. Generalized Additive Models in R: A Free Interactive Course. 2019. noamross.github.io/gams-in-r-course

[pone.0282128.ref053] 53.Stock BC, Jackson AL, Ward EJ, Parnell AC, Phillips DL, Semmens BX. Analyzing mixing systems using a new generation of Bayesian tracer mixing models. PeerJ. 2018. 6:e5096. doi: 10.7717/peerj.5096 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0282128.ref054] 54.Stock BC, Semmens BX. Unifying error structures in commonly used biotracer mixing models. Ecology. 2016b. 97(10):2562–2569. doi: 10.1002/ecy.1517 [DOI] [PubMed] [Google Scholar]

[pone.0282128.ref055] 55.Phillips DL, Inger R, Bearhop S, Jackson AL, Moore JW, Parnell AC, et al. Best practices for use of stable isotope mixing models in food-web studies. Canadian Journal of Zoology. 2014. 92(10): 823–835. [Google Scholar]

[pone.0282128.ref056] 56.Layman CA, Arrington DA, Montaña CG, Post DM. Can stable isotope ratios provide quantitative measures of trophic diversity within food webs? Ecology. 2007a. 88, 42–48. [DOI] [PubMed] [Google Scholar]

[pone.0282128.ref057] 57.Newsome SD, Tinker MT, Monson DH, Oftedal OT, Ralls K, Staedler MM, et al. Using stable isotopes to investigate individual diet specialization in California sea otters (Enhydra lutris nereis). Ecology. 2009. 90, 961–974. doi: 10.1890/07-1812.1 [DOI] [PubMed] [Google Scholar]

[pone.0282128.ref058] 58.Tarroux A, Bety J, Gauthier G, Berteaux D. The marine side of a terrestrial carnivore: intra-population variation in use of allochthonous resources by Arctic foxes. PLoS ONE. 2012. 7:e42427. doi: 10.1371/journal.pone.0042427 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0282128.ref059] 59.Jackson AL, Inger R, Parnell AC, Bearhop S. Comparing isotopic niche widths among and within communities: SIBER—Stable Isotope Bayesian Ellipses in R. J. Anim. Ecol. 2011. 80, 595–602. doi: 10.1111/j.1365-2656.2011.01806.x [DOI] [PubMed] [Google Scholar]

[pone.0282128.ref060] 60.Wood KA, Thorstensen S, Lúðvíksson SJ, Brides K and Petersen A. Long-term trends in the survival rates of adult female Common Eider Somateria mollissima at three colonies in Iceland. Ibis. 2021. 163: 671–683. [Google Scholar]

[pone.0282128.ref061] 61.Skarphéðinsson. Spói (Numenius phaeopus). 2018. https://www.ni.is/numenius-phaeopus

[pone.0282128.ref062] 62.Hansen ES, Sandvik H, Erikstad KE, Yoccoz NG, Anker-Nilssen T, Bader J, et al. Centennial relationships between ocean temperature and Atlantic puffin production reveal shifting decennial trends. Glob Change Biol. 2021. 27:3753–3764. doi: 10.1111/gcb.15665 [DOI] [PubMed] [Google Scholar]

[pone.0282128.ref063] 63.Linnebjerg JF, Frederiksen M, Kolbeinsson Y et al. Non-breeding areas of three sympatric auk species breeding in three Icelandic colonies. Polar Biol. 2018. 41, 1951–1961. [Google Scholar]

[pone.0282128.ref064] 64.Lilliendahl K, Hansen ES, Bogason V, Sigursteinsson M, Magnúsdóttir ML, Jónsson PM, et al. Viðkomubrestur lunda og sandsílis við Vestmannaeyjar [Recruitment failure of puffin and sand eel in Vestmannaeyjar]. Náttúrufræðingurinn. 2013. 83, 65–79. Icelandic. [Google Scholar]

[pone.0282128.ref065] 65.Hansen Erpur. Stofnvöktun lunda-Lokakýrsla 2017–2019. 2017. Icelandic.

[pone.0282128.ref066] 66.Phillips R, Petersen M, Lilliendahl K et al. Diet of the northern fulmar Fulmarus glacialis: reliance on commercial fisheries? Marine Biology. 1999. 135, 159–170. [Google Scholar]

[pone.0282128.ref067] 67.D’Alba L, Monahan P and Nager RG. Advances in laying date and increasing population size suggest positive responses to climate change in Common Eiders Somateria mollissima in Iceland. Ibis. 2010. 152: 19–28. [Google Scholar]

[pone.0282128.ref068] 68.Jónsson JE, Gardarsson A, Gill JA, Pétursdóttir UK, Petersen A, et al. Relationships between Long-Term Demography and Weather in a Sub-Arctic Population of Common Eider. PLoS ONE. 2013. 8(6): e67093. doi: 10.1371/journal.pone.0067093 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0282128.ref069] 69.Fuglei E, Henden JA, Callahan CT, Gilg O, Hansen J, Ims RA et al. Circumpolar status of Arctic ptarmigan: population dynamics and trends. Ambio. 2020. doi: 10.1007/s13280-019-01191-0 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0282128.ref070] 70.Svanbäck Richard and Bolnick Daniel I. Intraspecific competition drives increased resource use diversity within a natural population. Proc. R. Soc. 2007. B.274839–844 doi: 10.1098/rspb.2006.0198 [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Long-term responses of Icelandic Arctic foxes to changes in marine and terrestrial ecosystems

Fanny Berthelot

Ester R Unnsteinsdóttir

Jennifer A Carbonell Ellgutter

Dorothee Ehrich

Roles

Abstract

1. Introduction

Fig 1. Icelandic Arctic fox population estimates and hunting data per region from 1979 to 2018.

2. Material and methods

2.1 Study area and species

2.2 Arctic fox samples

Fig 2. Map of Iceland displaying the different culling locations.

2.3 Prey samples

2.4 Data analysis

3. Results

Fig 3. Isotopic signatures (‰) of foxes from coastal and inland habitats plotted along with their potential prey species corrected for trophic discrimination.

Table 1. Parameter estimates from generalized additives models assessing the effect of birth year and habitat for (a) carbon isotopes (δ13C) and (b) nitrogen isotopes (δ15N) from bone collagen, along with their corresponding smooth terms values.

Fig 4. Isotopic signatures in (a) δ13C and (b) δ15N of coastal and inland foxes plotted according to their year of birth.

Fig 5. Boxplots representing the proportion of different prey items in the diet of (a) inland and (b) coastal Arctic foxes during 4 different periods.

Fig 6. Standard ellipses representing the isotopic niche breadths of (a) inland and (b) coastal foxes during four different periods.

4. Discussion

4.1 Variations in resource use

4.2 Driver of population change

4.3 Population isotopic niche breadth

5. Conclusion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Dorothée Drucker

Roles

Author response to Decision Letter 0

Decision Letter 1

Dorothée Drucker

Roles

Author response to Decision Letter 1

Decision Letter 2

Dorothée Drucker

Roles

Acceptance letter

Dorothée Drucker

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases

Table 1. Parameter estimates from generalized additives models assessing the effect of birth year and habitat for (a) carbon isotopes (δ¹³C) and (b) nitrogen isotopes (δ¹⁵N) from bone collagen, along with their corresponding smooth terms values.

Fig 4. Isotopic signatures in (a) δ¹³C and (b) δ¹⁵N of coastal and inland foxes plotted according to their year of birth.