Plant Diversity of the Russian Arctic: Providing a Baseline for Arctic Change and Conservation Research

The Arctic tundra is one of the few biomes that have remained relatively untouched by the direct impact of economic activities. As the Arctic is warming almost four times faster than the global average (Chylek et al., 2022; Rantanen et al., 2022), pressure on the tundra is increasing, complicating efforts to conserve its ecosystems (Ernakovich et al., 2014; Niskanen et al., 2019; Reji Chacko et al., 2023). Plant diversity is a key component of the Arctic tundra as it forms the basis of ecosystem functioning. Plant diversity changes lead to cascading effects throughout the entire ecosystem, and also influence the global climate, primarily via the carbon and energy cycles (Heijmans et al., 2022; Loranty et al., 2014; Oehri et al., 2022). The importance of protecting plant diversity is recognized by Arctic countries through the Arctic Council, and conservation is facilitated by the Conservation of Arctic Flora and Fauna (CAFF) (Barry et al., 2020). About half of the Arctic tundra is located in Russia, a country where independent research is facing serious challenges. In the Russian Arctic climate change and economic expansion are putting pressure on the ecosystems and thus, weakening their ability to maintain plant diversity (Khapugin et al., 2020; Telyatnikov & Pristyazhuk, 2014; Yu et al., 2011). With half of the tundra being located in Russia, pan-arctic conservation strategies need to include the Russian territories in order to maintain the intactness of this biome, even though directly influencing the Russian government's conservation decisions may be difficult given the current political context. The successful development of these strategies requires a thorough scientific understanding of the ecosystems and their functioning informed by up-to-date data on the processes affecting the Russian Arctic tundra and its plant diversity, currently largely missing. Observations on plant diversity in the Russian Arctic have been scattered and mostly not accessible for a comprehensive pan-Arctic analysis. Therefore, in Chapter 1, we translated, standardized and digitized 4785 geobotanical plots collected in the Russian Arctic from 1927 to 2022 and presented them as the Russian Arctic Vegetation Archive (AVA-RU), now available to the international community. The plots document over 1770 plant and lichen species and subspecies, their habitats, and information on the vertical and horizontal structure of vegetation. Climate is changing fast and human activities are expanding across the Arctic, however, our understanding of how they shape tundra species richness is limited. Therefore, in Chapter 2, we utilized AVA-RU data to examine the relative impacts of environmental and anthropogenic factors on community-level plant species richness and its distribution in the Western Siberian Arctic – one of the Arctic regions most affected by anthropogenic pressure. The results reveal an increase in species richness from South-West to North-East, driven mainly by climatic factors, instead of the commonly expected decrease from South to North along the latitudinal gradient. We show that paleoclimatic factors exhibit higher predictive power (up to 21% of explained deviance) even when compared to modern climate, indicating a lasting impact of past climate on tundra vegetation. We suggest that while species richness distribution is mostly driven by environmental factors, a targeted study is needed to assess the human impact. We also show that existing protected areas cover only a fraction of the most species-rich areas. As the Arctic changes, areas with the most extreme climate are likely the most vulnerable to warming. Documenting their diversity and biomass becomes crucial for establishing a baseline to monitor future changes. Therefore, in Chapter 3, we assessed plant and lichen species richness, turnover and biomass, as well as their spatial distribution, in polar deserts — the northernmost biome on Earth. While we only identified 129 species within the 19 surveys, there is a major difference in species richness distribution and turnover. Particularly, 40% of the detected species were found exclusively in a single plot. We also showed that biomass varies widely across the sites, with its maximum on Vize Island, where the mean biomass is comparable to Arctic tundra levels. Overall, my thesis supports the stewardship of Arctic plant diversity in Russia with new baseline data and applications, with the overarching goal of informing and enhancing conservation strategies at both the national and pan-Arctic levels

Vegetation type is an important predictor of the arctic summer land surface energy budget

Despite the importance of high-latitude surface energy budgets (SEBs) for land-climate interactions in the rapidly changing Arctic, uncertainties in their prediction persist. Here, we harmonize SEB observations across a network of vegetated and glaciated sites at circumpolar scale (1994–2021). Our variance-partitioning analysis identifies vegetation type as an important predictor for SEB-components during Arctic summer (June-August), compared to other SEB-drivers including climate, latitude and permafrost characteristics. Differences among vegetation types can be of similar magnitude as between vegetation and glacier surfaces and are especially high for summer sensible and latent heat fluxes. The timing of SEB-flux summer-regimes (when daily mean values exceed 0 Wm−2) relative to snow-free and -onset dates varies substantially depending on vegetation type, implying vegetation controls on snow-cover and SEB-flux seasonality. Our results indicate complex shifts in surface energy fluxes with land-cover transitions and a lengthening summer season, and highlight the potential for improving future Earth system models via a refined representation of Arctic vegetation types

The High–Low Arctic boundary: How is it determined and where is it located?

Geobotanical subdivision of landcover is a baseline for many studies. The High–Low Arctic boundary is considered to be of fundamental natural importance. The wide application of different delimitation schemes in various ecological studies and climatic scenarios raises the following questions: (i) What are the common criteria to define the High and Low Arctic? (ii) Could human impact significantly change the distribution of the delimitation criteria? (iii) Is the widely accepted temperature criterion still relevant given ongoing climate change? and (iv) Could we locate the High–Low Arctic boundary by mapping these criteria derived from modern open remote sensing and climatic data? Researchers rely on common criteria for geobotanical delimitation of the Arctic. Unified circumpolar criteria are based on the structure of vegetation cover and climate, while regional specifics are reflected in the floral composition. However, the published delimitation schemes vary greatly. The disagreement in the location of geobotanical boundaries across the studies manifests in poorly comparable results. While maintaining the common principles of geobotanical subdivision, we derived the boundary between the High and Low Arctic using the most up‐to‐date field data and modern techniques: species distribution modeling, radar, thermal and optical satellite imagery processing, and climatic data analysis. The position of the High–Low Arctic boundary in Western Siberia was clarified and mapped. The new boundary is located 50–100 km further north compared to all the previously presented ones. Long‐term anthropogenic press contributes to a change in the vegetation structure but does not noticeably affect key species ranges. A previously specified climatic criterion for the High–Low Arctic boundary accepted in scientific literature has not coincided with the boundary in Western Siberia for over 70 years. The High–Low Arctic boundary is distinctly reflected in biodiversity distribution. The presented approach is appropriate for accurate mapping of the High–Low Arctic boundary in the circumpolar extent

Russian Arctic Vegetation Archive—A new database of plant community composition and environmental conditions

Motivation: The goal of the Russian Arctic Vegetation Archive (AVA-RU) is to unite and harmonize data of plot-based plant species and their abundance, vegetation structure and environmental variables from the Russian Arctic. This database can be used to assess the status of the Russian Arctic vegetation and as a baseline to document biodiversity changes in the future. The archive can be used for scientific studies as well as to inform nature protection and restoration efforts. Main types of variables contained: The archive contains 2873 open-access geobotanical plots. The data include the full species. Most plots include information on the horizontal (cover per species and morphological group) and vertical (average height per morphological group) structure of vegetation, site and soil descriptions and data quality estimations. In addition to the open-access data, the AVA-RU website contains 1912 restricted-access plots. Spatial location and grain: The plots of 1–100 m2 size were sampled in Arctic Russia and Scandinavia. Plots in Russia covered areas from the West to the East, including the European Russian Arctic (Kola Peninsula, Nenets Autonomous district), Western Siberia (Northern Urals, Yamal, Taza and Gydan peninsulas), Central Siberia (Taymyr peninsula, Bolshevik island), Eastern Siberia (Indigirka basin) and the Far East (Wrangel island). About 72% of the samples are georeferenced. Time period and grain: The data were collected once at each location between 1927 and 2022. Major taxa and level of measurement: Plots include observations of >1770 vascular plant and cryptogam species and subspecies. Software format: CSV files (1 file with species list and abundance, 1 file with environmental variables and vegetation structure) are stored at the AVA-RU website (https://avarus.space/), and are continuously updated with new datasets. The open-access data are available on Dryad and all the datasets have a backup on the server of the University of Zurich. The data processing R script is available on Dryad

Vegetation type is an important predictor of the arctic summer land surface energy budget

Despite the importance of high-latitude surface energy budgets (SEBs) for land-climate interactions in the rapidly changing Arctic, uncertainties in their prediction persist. Here, we harmonize SEB observations across a network of vegetated and glaciated sites at circumpolar scale (1994-2021). Our variance-partitioning analysis identifies vegetation type as an important predictor for SEB-components during Arctic summer (June-August), compared to other SEB-drivers including climate, latitude and permafrost characteristics. Differences among vegetation types can be of similar magnitude as between vegetation and glacier surfaces and are especially high for summer sensible and latent heat fluxes. The timing of SEB-flux summer-regimes (when daily mean values exceed 0 Wm(-2)) relative to snow-free and -onset dates varies substantially depending on vegetation type, implying vegetation controls on snow-cover and SEB-flux seasonality. Our results indicate complex shifts in surface energy fluxes with land-cover transitions and a lengthening summer season, and highlight the potential for improving future Earth system models via a refined representation of Arctic vegetation types.An international team of researchers finds high potential for improving climate projections by a more comprehensive treatment of largely ignored Arctic vegetation types, underscoring the importance of Arctic energy exchange measuring stations.Peer reviewe

Vegetation type is an important predictor of the arctic summer land surface energy budget

Despite the importance of high-latitude surface energy budgets (SEBs) for land-climate interactions in the rapidly changing Arctic, uncertainties in their prediction persist. Here, we harmonize SEB observations across a network of vegetated and glaciated sites at circumpolar scale (1994–2021). Our variance-partitioning analysis identifies vegetation type as an important predictor for SEB-components during Arctic summer (June-August), compared to other SEB-drivers including climate, latitude and permafrost characteristics. Differences among vegetation types can be of similar magnitude as between vegetation and glacier surfaces and are especially high for summer sensible and latent heat fluxes. The timing of SEB-flux summer-regimes (when daily mean values exceed 0 Wm−2) relative to snow-free and -onset dates varies substantially depending on vegetation type, implying vegetation controls on snow-cover and SEB-flux seasonality. Our results indicate complex shifts in surface energy fluxes with land-cover transitions and a lengthening summer season, and highlight the potential for improving future Earth system models via a refined representation of Arctic vegetation types

Pan Arctic Species List (PASL) matching script

<p>This script matches a provided species list with the Panarctic Species List (PASL) (Raynolds et al, 2013) to obtain the commonly accepted scientific names of the plants, including short names and full names.</p> <p>Inputs:</p> <ul> <li>Species list: CSV file containing a list of species names, as provided by the geobotanist;</li> <li>PASL: a text file with the Panarctic Species List names, including the short and full scientific names of the plants (with synonyms).</li> </ul> <p>Outputs:</p> <ul> <li>A new file with the matched species list, including the short and full scientific names of the plants as found in the PASL.</li> </ul> <p><span>References:</span></p> <ul> <li> <span>Raynolds, M.K., Breen, A.L., Walker, D.A., Elven, R., Belland, R., Konstantinova, N., Kristinsson, H. & Hennekens, S. (2013). The Pan-Arctic Species List (PASL). </span>Arctic Vegetation Archive (AVA) Workshop.</li> </ul><p><span>R software (R version 4.2.2)</span></p> <p><span>References:</span></p> <ul> <li> <span>R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL</span><a href="https://www.r-project.org/"> https://www.R-project.org/</a> </li> </ul><p>Funding provided by: Swiss Government Excellence Scholarship*<br>Crossref Funder Registry ID: <br>Award Number: 2019.0075</p><p>The script processes text to translate the names of a target dataset to the PASL format. It does this by removing special characters, replacing "sp" with "species," and adjusting for any 2-symbol differences between matching tables.</p&gt

Pan Arctic Species List (PASL) matching script

<p>This script matches a provided species list with the Panarctic Species List (PASL) (Raynolds et al, 2013) to obtain the commonly accepted scientific names of the plants, including short names and full names.</p> <p>Inputs:</p> <ul> <li>Species list: CSV file containing a list of species names, as provided by the geobotanist;</li> <li>PASL: a text file with the Panarctic Species List names, including the short and full scientific names of the plants (with synonyms).</li> </ul> <p>Outputs:</p> <ul> <li>A new file with the matched species list, including the short and full scientific names of the plants as found in the PASL.</li> </ul> <p><span>References:</span></p> <ul> <li> <span>Raynolds, M.K., Breen, A.L., Walker, D.A., Elven, R., Belland, R., Konstantinova, N., Kristinsson, H. & Hennekens, S. (2013). The Pan-Arctic Species List (PASL). </span>Arctic Vegetation Archive (AVA) Workshop.</li> </ul><p><span>R software (R version 4.2.2)</span></p> <p><span>References:</span></p> <ul> <li> <span>R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL</span><a href="https://www.r-project.org/"> https://www.R-project.org/</a> </li> </ul><p>Funding provided by: Swiss Government Excellence Scholarship*<br>Crossref Funder Registry ID: <br>Award Number: 2019.0075</p><p>The script processes text to translate the names of a target dataset to the PASL format. It does this by removing special characters, replacing "sp" with "species," and adjusting for any 2-symbol differences between matching tables.</p&gt

Vegetation type is an important predictor of the arctic summer land surface energy budget

Despite the importance of high-latitude surface energy budgets (SEBs) for land-climate interactions in the rapidly changing Arctic, uncertainties in their prediction persist. Here, we harmonize SEB observations across a network of vegetated and glaciated sites at circumpolar scale (1994–2021). Our variance-partitioning analysis identifies vegetation type as an important predictor for SEB-components during Arctic summer (June-August), compared to other SEB-drivers including climate, latitude and permafrost characteristics. Differences among vegetation types can be of similar magnitude as between vegetation and glacier surfaces and are especially high for summer sensible and latent heat fluxes. The timing of SEB-flux summer-regimes (when daily mean values exceed 0 Wm−2) relative to snow-free and -onset dates varies substantially depending on vegetation type, implying vegetation controls on snow-cover and SEB-flux seasonality. Our results indicate complex shifts in surface energy fluxes with land-cover transitions and a lengthening summer season, and highlight the potential for improving future Earth system models via a refined representation of Arctic vegetation types.ISSN:2041-172

Vegetation type is an important predictor of the arctic summer land surface energy budget

Despite the importance of high-latitude surface energy budgets (SEBs) for land-climate interactions in the rapidly changing Arctic, uncertainties in their prediction persist. Here, we harmonize SEB observations across a network of vegetated and glaciated sites at circumpolar scale (1994–2021). Our variance-partitioning analysis identifies vegetation type as an important predictor for SEB-components during Arctic summer (June-August), compared to other SEB-drivers including climate, latitude and permafrost characteristics. Differences among vegetation types can be of similar magnitude as between vegetation and glacier surfaces and are especially high for summer sensible and latent heat fluxes. The timing of SEB-flux summer-regimes (when daily mean values exceed 0 Wm−2) relative to snow-free and -onset dates varies substantially depending on vegetation type, implying vegetation controls on snow-cover and SEB-flux seasonality. Our results indicate complex shifts in surface energy fluxes with land-cover transitions and a lengthening summer season, and highlight the potential for improving future Earth system models via a refined representation of Arctic vegetation types. Supplemental files are attached below