Introduction
As the planet warms, the Arctic is warming at 2–4 times the global rate (Rantanen et al., 2022). This warming is impacting Arctic ecosystems through a process known as ‘shrubification,’ or the northward expansion and increased height and density of deciduous woody vegetation (Tape et al., 2006; Myers-Smith et al., 2011; Elmendorf et al., 2012; Sweet et al., 2015). Arctic shrubification alters the structure of tundra ecosystems, its regional biodiversity, food web structure, and nutrient availability (Elmendorf et al., 2012; Fauchald et al., 2017; Collins et al., 2018). Arctic shrubification also has implications for the climate system by reducing surface albedo (Sturm et al., 2005) and increasing atmospheric water vapor (Pearson et al., 2013) – both positive feedbacks that amplify high-latitude warming (Thompson et al., 2022). However, large uncertainties remain regarding the rate of future shrubification, such that the magnitude of these positive feedbacks is poorly constrained in predictive earth system models. One way to reduce future climate uncertainties is through the analysis of well-constrained paleoenvironmental records dating to warmer-than-present periods in Earth’s recent history (e.g., Tierney et al., 2020).
Traditionally, macrofossils and pollen have formed the backbone of Quaternary paleovegetation reconstructions (Birks, 2019). However, macrofossils are not consistently preserved in sedimentary records, and pollen production is restricted to seed-bearing taxa, which can be obscured by long-distant transport from sources thousands of kilometers away (Hyvärinen, 1970; Birks, 2003; Crump et al., 2019). For example, in Iceland, exotic tree pollen from taxa never reported as macrofossils since the Miocene and Pliocene (i.e.,
In this study, we present one new Icelandic record (Stóra Viðarvatn) and update the age model of a second previously published record from Iceland (Alsos et al., 2021). For these two lakes, we also compare existing pollen and
Results
Stóra Viðarvatn, Iceland
Stóra Viðarvatn (#4, Table 1, Figure 1) is a large lake (surface area: 2.4 km2, depth: 48 m) in northeast Iceland, with no preexisting records of Holocene vegetation change. In winter 2020, we recovered sediment core 20SVID-02 from 17.4 m water depth near the center of the lake. The core contains 893 cm of sediment, collected in 1.5 m increments. The basal 20 cm are laminated, clay-rich sediment, transitioning to organic gyttja by 873 cm depth, which characterizes the remainder of the record, interbedded with tephra (volcanic ash) deposits. The chronology of the Early to Middle Holocene portion of the record is based on five tephra layers (see ‘Materials and methods’), of which the lowest, at 886.5 cm depth, is identified as the Askja S tephra (10,830 ± 57 BP, Bronk Ramsey et al., 2015), and the highest, at 334 cm depth, as Hekla 4 (4200 ± 42 BP, Dugmore et al., 1995). Because the sedimentation rate between the six tephra layers is essentially linear, we extrapolate that rate from the Askja S tephra layer 7 cm to the base of the sediment core at 893.5 cm depth, which provides a minimum deglaciation age of ~10,950 BP (Figure 2A).
Figure 1.
Overview map of the North Atlantic.
Lake sedimentary ancient DNA (
Figure 2.
Lake sediment age models used in this study.
(A) Stóra Viðarvatn’s age model (this study), (B) Torfdalsvatn’s pollen and macrofossil age model modified from Rundgren, 1995; Rundgren, 1998 (see Geirsdóttir et al., 2020), and (C) Torfdalsvatn’s sedimentary ancient DNA (
Table 1.
Lake site information.
Site # | Lake name | Region | Latitude (°) | Longitude (°) | Reference |
---|---|---|---|---|---|
1 | Lake Qaupat | Baffin Island | 63.68 | –68.20 | Crump et al., 2019 |
2 | Bliss Lake | Greenland | 83.52 | –28.35 | Epp et al., 2015 |
3 | Torfdalsvatn | Iceland | 66.06 | –20.38 | Alsos et al., 2021 |
4 | Stóra Viðarvatn | Iceland | 66.24 | –15.84 | This study |
5 | Jodavannet | Svalbard | 77.34 | 16.02 | Voldstad et al., 2020 |
6 | Lake Skartjørna | Svalbard | 77.96 | 13.82 | Alsos et al., 2016b |
7 | Langfjordvannet | Norway | 70.15 | 20.54 | Alsos et al., 2022 |
8 | Eaštorjávri South | Norway | 70.43 | 27.33 | Alsos et al., 2022 |
9 | Nordvivatnet | Norway | 70.13 | 29.01 | Alsos et al., 2022 |
10 | Lake Ljøgottjern | Norway | 60.15 | 11.14 | ter Schure et al., 2021 |
Samples for
Figure 3.
DNA quality assessment for Stóra Viðarvatn’s entire Holocene record plotted against depth (cm).
Total raw DNA reads, CT values, proportion raw terrestrial reads, metabarcoding technical quality (MTQ), metabarcoding analytical quality (MAQ), and species richness.
Figure 4.
Comparison of paleoclimate and lake sedimentary ancient DNA (
(A) Presence/absence
Torfdalsvatn, Iceland
Torfdalsvatn (#3, Table 1, Figure 1) is a small lake (surface area: 0.41 km2, depth: 5.8 m) in north Iceland from which sediment cores have been analyzed since the early 1990s to create paleoecological records from pollen and macrofossils (Björck et al., 1992; Rundgren, 1995; Rundgren, 1998), and
Differences on the order of centuries between the pollen/macrofossil and
Figure 5.
Simplified paleovegetation records from Torfdalsvatn for two taxa:
Shown are pollen counts (bold red lines), where shaded regions indicate values above the mean (Rundgren, 1995), first occurrence of taxa macrofossils (black leaves, Rundgren, 1998), and DNA presence (red bubbles, Alsos et al., 2021).
Resolving Icelandic records of woody taxa colonization
Based on the original age model, Torfdalsvatn pollen records were interpreted to capture ecological changes associated with the abrupt climate oscillations between the Bølling-Allerød (14,700–12,900 BP) and Younger Dryas (12,900–11,700 BP) periods (Björck et al., 1992; Rundgren, 1995). However, recent calibration of the radiocarbon ages in the Torfdalsvatn cores from Björck et al., 1992 and Rundgren, 1995 suggests that the organic-rich portion of the record began less than 11,800 y ago (Figure 2B, Geirsdóttir et al., 2020). While there is sediment at deeper levels, it is described as silty clay (Björck et al., 1992) with sand and gravel (Rundgren, 1995). These characteristics suggest that the sediment likely originates from a rapidly deglaciating environment, perhaps with lingering ice sheet meltwater in the catchment and a rapid sediment accumulation rate that is difficult to constrain without secure age control. Therefore, we suggest that the maximum age for the final retreat of the Icelandic Ice Sheet from Torfdalsvatn’s catchment, when organic-rich sedimentation began, is ~11,800 BP, several thousand years younger than previously proposed (Björck et al., 1992; Rundgren, 1995). We use the updated age models to reconstruct the history of plant colonization in Iceland.
Differences in taxa presence/absence between the
Relative to Torfdalsvatn, the catchment of Stóra Viðarvatn deglaciated ~1000 y later at ~10,850 BP (Figure 4A). Based on our new
Figure 6.
Simplified paleovegetation records from northeast Iceland for two taxa:
Shown are Ytra-Áland pollen counts (bold green lines), where shaded regions indicate values above the mean (Karlsdóttir et al., 2014) and DNA presence from Stóra Viðarvatn (green bubbles, this study), where the bubble size is proportional to the number of PCR replicates.
Based on the
Discussion
Postglacial
Eight additional records of vascular plant
Baffin Island
Lake Qaupat (#1, Figure 1) is a small lake (surface area: 0.08 km2, depth: 9.2 m) situated at 35 m asl on southern Baffin Island. Cosmogenic 10Be exposure dating of Lake Qaupat’s impounding moraine constrains the timing of deglaciation to 9100 ± 700 BP (Crump et al., 2019). Lake Qaupat and most of its catchment were below sea level until 7700 ± 300 BP when postglacial isostatic recovery raised the basin above the ocean and its catchment was available for vascular plant colonization. Salicaceae is present in the record shortly after isolation from the ocean at 7400 BP, but no vascular plants are documented by
Greenland
Bliss Lake (#2, Figure 1) is a small lake (depth: 9.8 m) situated at 17 m asl on the northern coastline of Greenland (Peary Land). Bliss Lake initially deglaciated 11,000 BP (Olsen et al., 2012) and the first appearance of Salicaceae
Svalbard
Jodavannet (#5, Figure 1) is a small lake (depth: 6.4 m) situated at 140 m asl on the east coast of Wijdefjorden on northern Spitsbergen. Regional cosmogenic exposure dating suggests the area began to deglaciate between 14,600 and 13,800 BP (Hormes et al., 2013), although the base of Jodavannet’s sediment record suggests a minimum timing of lake deglaciation by 11,900 BP (Voldstad et al., 2020). Salicaceae
Norway
Langfjordvannet (#7, Figure 1) is a small lake (surface area: 0.55 km2, depth: 34.8 m) situated at 66 m asl on the coast of northern Norway (Rijal et al., 2021). Langfjordvannet deglaciated by 16,150 BP, Salicaceae is first identified in
Betulaceae colonization is delayed relative to Salicaceae in the circum North Atlantic
The timing of postglacial Salicaceae and Betulaceae colonization varies across the North Atlantic regions, with first appearance dates ranging from 15,500 BP in Norway to 5900 BP on Baffin Island (Figure 7). Salicaceae appears immediately after inferred deglaciation at 4 out of 10 sites, while colonization dates for the other 6 locations range from 200 to 2100 y after deglaciation and exhibit no clear spatio-temporal pattern (Figure 8). For Betulaceae, colonization times range from 800 to 6150 y after deglaciation, with sites closest to source refugia south of the ice sheets (Figure 1) having generally shorter colonization delays (Figure 8), consistent with seedling viability having a higher probability of success with shorter distance from source (Nathan, 2006). The one exception is Langfjordvannet, in northern Norway (site #7, Figure 1, Alsos et al., 2022), which deglaciated at 16,150 BP (Figure 7). Considering that Betulaceae only arrives during the Holocene Epoch (last 11,700 y) across all North Atlantic sites, the anomalously long delay of Betulaceae to Langfjordvannet suggests that this taxon may have required the Holocene’s relatively stable climate, as reflected by Greenland oxygen isotope records (Figure 7, Seierstad et al., 2014), to successfully establish itself. Alternatively, the colonization delay of Betulaceae to Langfjordvannet may reflect the slow development of stable soils, which are required for Betulaceae establishment, due to the lake’s steep catchment (Otterå, 2012). In contrast to Betulaceae, Salicaceae appears to have been an efficient colonizer to Langfjordvannet during the colder and less stable climate of the Late Glacial period and earliest Holocene (Figure 7, Seierstad et al., 2014). Finally, except for Lake Ljøgottjern in southern Norway (site #10, Figure 1) where Betulaceae and Salicaceae apparently colonize at about the same time (ter Schure et al., 2021), all other sites show that Betulaceae colonization is delayed by 800–5500 y relative to Salicaceae colonization dates (Figure 7).
Figure 7.
Timing of postglacial Salicaceae and Betulaceae colonization in the circum North Atlantic at 10 locations (see Figure 1 and Table 1).
(A) GISP2 δ18O values reflective of regional North Atlantic temperature variability (Seierstad et al., 2014) and (B) lake
Figure 8.
Colonization time after deglaciation (years) versus lake distance from the closest possible source south of the Last Glacial Maximum (LGM) ice sheet margin (km).
For all circum North Atlantic sites, Salicaceae is shown in red and Betulaceae in blue. Data point for Langfjordvannet is open for Betulaceae.
Several possibilities may contribute to the delay in Betulaceae colonization compared to Salicaceae colonization. First, the northernmost Salicaceae species reach bioclimate subzone A (1–3°C July temperature), whereas the northernmost Betulaceae species rarely go beyond bioclimate subzone D (8–9°C July temperature) (Walker et al., 2005). The different environmental tolerances may explain why Salicaceae generally arrives earlier when regional temperatures are still climbing compared to the timing of Betulaceae colonization when warmth is more broadly established (Figure 7). In support, Alsos et al., 2022 showed that at least for Fennoscandian
Finally, modern observations broadly replicate the records of delayed woody taxa colonization in the North Atlantic. Studies tracking the migration of Salicaceae and Betulaceae in the forefields of retreating glaciers and to new islands also show a rapid and faster colonization of Salicaceae compared to Betulaceae (Whittaker, 1993; Magnússon et al., 2009; Burga et al., 2010; Synan et al., 2021). As an example, following the formation of the volcanic island Surtsey in 1964 CE, Salicaceae (
Environmental dispersal mechanisms of Salicaceae and Betulaceae
Coupling the timing of postglacial woody shrub colonization in the North Atlantic with genetic approaches provides a valuable opportunity to identify probable environmental dispersal mechanisms (e.g., wind, sea ice, driftwood, and birds) for long-distance transport. Previously, Alsos et al., 2015 compared amplified fragment length polymorphism (AFLP) data from living plants from Iceland, Greenland, Svalbard, the Faroe Islands, and Jan Mayen to AFLP data from the same species from potential source regions. Genetic distances suggest that
Sea ice has been suggested to be a dispersal mechanism for postglacial vascular plants in the Arctic (Alsos et al., 2016a). While seasonal sea ice persisted throughout the Holocene around Svalbard (Pieńkowski et al., 2021) and northern Greenland (Syring et al., 2020), general ocean circulation patterns require source populations of vascular plants in Arctic Siberia, the dominant source region for sea ice export to the North Atlantic, for sea ice to be a viable vector. Considering that
Future outlook
As the planet continues to warm, glacier and ice sheet mass loss is accelerating (Hugonnet et al., 2021), providing new territory for woody shrubs to colonize – the Greenland Ice Sheet currently occupies 1.7 million km2 (Morlighem et al., 2017) with >400,000 km2 more covered by glaciers elsewhere in the Arctic (RGI Consortium, 2017). Global warming will also allow woody plants to migrate poleward across unglaciated Arctic tundra (e.g., Myers-Smith et al., 2011), which occupies a substantially larger area compared to glaciated regions (5 billion km2, Walker et al., 2005). In this study, we demonstrate that Salicaceae can migrate into deglaciating North Atlantic habitats rapidly, whereas Betulaceae may be delayed by millennia. However, the ability of these taxa to successfully follow the rapid pace of ongoing range shifts in non-glaciated tundra (e.g., Alaska and Siberia) remains an open question (e.g., Davis and Shaw, 2001; Thuiller et al., 2008). This is partially due to different baseline conditions of the tundra ecosystem compared to glacial regions upon deglacial warming, which already contain established ecosystems and nutrient-rich soils bound in the permafrost (e.g., Heijmans et al., 2022).
While the causes and rates of present-day climate change differ from the preceding postglacial period (e.g., Tierney et al., 2020), the factors that we identify as potentially driving the colonization patterns (i.e., environmental tolerance, modes of reproduction, and soil development) are relevant in both scenarios. As a result, circum North Atlantic shrubification in deglaciating regions may be prolonged – ultimately dampening the rate of change, at least temporarily, in regional biodiversity and food web structure, as well as high-latitude temperature amplification. An important next step for constraining future shrubification rates is developing detailed paleoecological reconstructions of plant dynamics in multiple tundra regions (e.g., Clarke et al., 2020; Huang et al., 2021), continuing these efforts in glaciated regions, and pairing plant histories with independent local records of climate, such as temperature and precipitation. The latter paleoclimate reconstructions will enable a more detailed assessment of plant colonization lags relative to climate (e.g., Crump et al., 2019). By pairing paleoecological and paleoenvironmental records from formerly glaciated and tundra regions and integrating these with predictive models (e.g., Braconnot et al., 2012), the community will be able to reduce uncertainties and better quantify future shrubification rates, which are essential to inform ecological predictions for policymakers.
Materials and methods
Lake sediment cores and age control
Stóra Viðarvatn (66.24°N, 15.84°W) is a large (2.4 km2), deep lake (48 m) located at 151 m asl in northeast Iceland (Figure 1, Axford et al., 2007). In winter 2020, we recovered a composite 8.93-m-long sediment core (20SVID-02) from 17.4 m water depth using lake ice as a coring platform. The sediment was collected in seven drives of ~150 cm each. The core sections were subsequently stored at 4°C before opening for sediment subsampling. The chronology of 20SVID-02 is based on five marker tephra layers (volcanic ash) of known age: Askja S, G10ka Series, Kverkfjöll/Hekla, Kverkfjöll, and Hekla 4 tephra layers, with ages of 10,830 ± 57 BP (Bronk Ramsey et al., 2015), 10,400–9900 BP (Óladóttir et al., 2020), 6200 BP (Óladóttir et al., 2011), 5200 BP (Óladóttir et al., 2011), and 4200 ± 42 BP (Dugmore et al., 1995), respectively. Each tephra layer was sampled along the vertical axis, sieved to isolate glass fragments between 125 and 500 μm, and embedded in epoxy plugs. Individual glass shards were analyzed at the University of Iceland on a JEOL JXA-8230 election microprobe using an acceleration voltage of 15 kV, beam current of 10 nA, and beam diameter of 10 μm. The international A99 standard was used to monitor for instrumental drift and maintain consistency between measurements. Tephra origin was then assessed following the systematic procedures outlined in Jennings et al., 2014 and Harning et al., 2018. Briefly, based on SiO2 wt% vs. total alkali (Na2O + K2O) wt%, we determine whether the tephra volcanic source is mafic (tholeiitic or alkalic), intermediate, and/or rhyolitic. From here, we objectively discriminate the source volcanic system through a detailed series of bielemental plots produced from available compositional data on Icelandic tephra (Harning et al., 2018). The major oxide composition of each tephra layer is provided in Figure 2—source data 1. A Bayesian age model was generated using the R package rbacon and default settings (Blaauw and Christen, 2011; Figure 2A).
Torfdalsvatn (66.06°N, 20.38°W) is a small (0.4 km2), shallow (5.8 m) lake located at 52 m asl in north Iceland (Figure 1, Rundgren, 1998). Paleovegetation records from this lake are based on pollen, macrofossil assemblages (Björck et al., 1992; Rundgren, 1995; Rundgren, 1998; Rundgren and Ingólfsson, 1999), and
DNA metabarcoding
We subsampled the Stóra Viðarvatn sediment core halves immediately upon splitting in a dedicated clean lab facility with no PCR products in the Trace Metal Lab, University of Colorado Boulder. All surfaces and tools were treated with 10% bleach and 70% ethanol before use. Personnel wearing complete PPE took a total of 75 samples for
We performed DNA extraction and metabarcoding library generation in a dedicated ancient DNA laboratory at the Paleogenomics Lab, University of California Santa Cruz. While working in this laboratory, we followed commonly used practices for minimizing contamination of ancient and degraded samples described previously (Fulton and Shapiro, 2019). We initially cleaned all instruments and surfaces using 10% bleach and 70% ethanol before UV irradiation for at least 1 hr. From each homogenized sample of the core, we aliquoted two 50 mg subsamples and isolated DNA following the column purification method described in Rohland et al., 2018 using Binding Buffer D and eluting 50 µl volumes. We pooled extraction eluates derived from the same original core sample prior to subsequent experiments and included one extraction control (containing no sample) for every 12 samples processed to assess the presence of external contaminants or cross-contamination.
We performed quantitative PCR (qPCR) to determine the ideal dilution of extract to add to the metabarcoding PCR, as well as the ideal number of PCR cycles. We added 2 µl of neat or diluted extract to wells containing 1× QIAGEN Multiplex master mix, as well as a final concentration of 0.16 µm of the forward and reverse trnL primer and 0.12× SYBR Green I Dye. Cycling conditions were as follows: a 15 min 95°C activation, followed by 40 cycles of 94°C for 30 s, 57°C for 30 s, and 72°C for 60 s. We tested 1:10 and 1:100 dilutions of extract to water alongside neat extract. We used cycle threshold (CT) values to compare PCR efficiency between dilutions and determined the ideal cycle number for the metabarcoding PCR as the number of cycles at which the PCR curve reached the plateau phase.
We performed metabarcoding PCR on each extract using the best dilution as determined by qPCR and versions of the chloroplast
Table 2.
Primer sequences used in metabarcoding and qPCR experiments.
Primer | Sequence 5′–3′ |
---|---|
truseq_trnL_g |
|
truseq_trnL_h |
We then used indexing PCR to attach the full truseq adapter and unique identifier. We added 5 µl of cleaned PCR product from the metabarcoding PCR to 1× Kapa Hifi master mix and dual-unique truseq style indices at a concentration of 0.4 µM. Thermocycling began with an activation step at 98°C for 3 min, followed by 8 cycles of 98°C for 20 s, 65°C for 30 s, 72°C for 40 s, and a final extension at 72°C for 2 min. We cleaned reactions using SPRI select beads and quantified DNA concentration using the NanoDrop 8000 Spectrophotometer. We pooled PCR replicates from all samples in equimass ratios and sequenced the pools on an Illumina Nextseq 550 platform. Sequencing was performed using Illuminas v3 chemistry and a 150-cycle mid-output sequencing kit with paired end reads of 76 base pair length.
We processed raw fastq files and performed taxonomic assignment using the Anacapa Toolkit (Curd et al., 2019) and the ArcBorBryo library, which contains 2280 sequences of Arctic and boreal vascular plants and bryophytes (Sønstebø et al., 2010). Briefly, we removed adapter sequences from the raw fastq data using cutadapt and trimmed low-quality bases using FastX-toolkit before reads were separated into paired and unpaired files (Hannon, 2010; Martin, 2011). We then used dada2 for further filtering of chimeric sequences and for merging of paired reads (Callahan et al., 2016.). We dereplicated identical sequences present in the data using dada2 and generated ASV (amplicon sequence variant) tables and fastq files for each of the read types (paired merged, paired unmerged, unpaired forward, and unpaired reverse). ASVs were assigned taxonomy by globally aligning to a CRUX database generated from the ArcBorBryo library using Bowtie2 before assignment with Anacapa’s custom BLCA algorithm (Langmead and Salzberg, 2012). Outputted taxonomy site frequency tables containing taxonomic assignments and read counts were subsequently analyzed using R. ASV sequences for Salicaceae and Betulaceae identified in our samples are included in Table 3.
Table 3.
ASV (amplicon sequence variant) sequences identified in our samples for Salicaceae and Betulaceae.
Taxon | ASV sequence |
---|---|
Salicaceae |
|
Betulaceae |
|
To minimize the chance of false positives, we retained only sequences that (1) were not detected in negative controls, (2) had 100% match to sequences in the library, (3) had a minimum of 10 reads per PCR replicate and occurred in a minimum of two of five PCR replicates, and (4) had a minimum of 100 reads across all PCR replicates (Table 4). We verified non-native taxa identified by this pipeline via comparison with the NCBI nucleotide database using BLAST (Sayers et al., 2022) (http://www.ncbi.nlm.nih.gov/blast/) and removed these due to the low likelihood of correct taxonomic assignment. Finally, DNA quality scores were calculated following Rijal et al., 2021.
Table 4.
Total read data remaining after each data filtering step.
Step | Total read data remaining |
---|---|
Raw | 18,653,132 |
Minimum 10 reads per PCR replicate, occurred in 2 out of 5 PCR replicates, and minimum 100 reads across PCR replicates | 17,303,887 |
Non-native taxa and blank contaminants | 15,909,615 |
Published
To place the Icelandic plant
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Abstract
As the Arctic continues to warm, woody shrubs are expected to expand northward. This process, known as ‘shrubification,’ has important implications for regional biodiversity, food web structure, and high-latitude temperature amplification. While the future rate of shrubification remains poorly constrained, past records of plant immigration to newly deglaciated landscapes in the Arctic may serve as useful analogs. We provide one new postglacial Holocene sedimentary ancient DNA (
You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer
Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer