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Abstract The Lena Delta in Siberia is the largest delta in the Arctic and as a snow-dominated ecosystem particularly vulnerable to climate change. Using the two decades of MODerate resolution Imaging Spectroradiometer satellite acquisitions, this study investigates interannual and spatial variability of snow-cover duration and summer vegetation vitality in the Lena Delta. We approximated snow by the application of the normalized difference snow index and vegetation greenness by the normalized difference vegetation index (NDVI). We consolidated the analyses by integrating reanalysis products on air temperature from 2001 to 2021, and air temperature, ground temperature, and the date of snow-melt from time-lapse camera (TLC) observations from the Samoylov observatory located in the central delta. We extracted spring snow-cover duration determined by a latitudinal gradient. The ‘regular year’ snow-melt is transgressing from mid-May to late May within a time window of 10 days across the delta. We calculated yearly deviations per grid cell for two defined regions, one for the delta, and one focusing on the central delta. We identified an ensemble of early snow-melt years from 2012 to 2014, with snow-melt already starting in early May, and two late snow-melt years in 2004 and 2017, with snow-melt starting in June. In the times of TLC recording, the years of early and late snow-melt were confirmed. In the three summers after early snow-melt, summer vegetation greenness showed neither positive nor negative deviations. Whereas, vegetation greenness was reduced in 2004 after late snow-melt together with the lowest June monthly air temperature of the time series record. Since 2005, vegetation greenness is rising, with maxima in 2018 and 2021. The NDVI rise since 2018 is preceded by up to 4 °C warmer than average June air temperature. The ongoing operation of satellite missions allows to monitor a wide range of land surface properties and process

Here we describe the LegacyPollen 1.0, a dataset of 2831 fossil pollen records with metadata, a harmonized taxonomy, and standardized chronologies. A total of 1032 records originate from North America, 1075 from Europe, 488 from Asia, 150 from Latin America, 54 from Africa, and 32 from the Indo-Pacific. The pollen data cover the late Quaternary (mostly the Holocene). The original 10 110 pollen taxa names (including variations in the notations) were harmonized to 1002 terrestrial taxa (including Cyperaceae), with woody taxa and major herbaceous taxa harmonized to genus level and other herbaceous taxa to family level. The dataset is valuable for synthesis studies of, for example, taxa areal changes, vegetation dynamics, human impacts (e.g., deforestation), and climate change at global or continental scales. The harmonized pollen and metadata as well as the harmonization table are available from PANGAEA (https://doi.org/10.1594/PANGAEA.929773; Herzschuh et al., 2021). R code for the harmonization is provided at Zenodo (https://doi.org/10.5281/zenodo.5910972; Herzschuh et al., 2022) so that datasets at a customized harmonization level can be easily established.

Relative pollen productivity (RPP) estimates are fractionate values, often in relation to Poaceae, that allow vegetation cover to be estimated from pollen counts with the help of models. RPP estimates are especially used in the scientific community in Europe and China, with a few studies in North America. Here we present a comprehensive compilation of available northern hemispheric RPP studies and their results arising from 51 publications with 60 sites and 131 taxa. This compilation allows scientists to identify data gaps in need of further RPP analyses but can also aid them in finding an RPP set for their study region. We also present a taxonomically harmonised, unified RPP dataset for the Northern Hemisphere and subsets for North America (including Greenland), Europe (including arctic Russia), and China, which we generated from the available studies. The unified dataset gives the mean RPP for 55 harmonised taxa as well as fall speeds, which are necessary to reconstruct vegetation cover from pollen counts and RPP values. Data are openly available at https://doi.org/10.1594/PANGAEA.922661 (Wieczorek and Herzschuh, 2020).

Large scale analyses of climatic or ecological data are important to understand complex relationships. Often, such data are available in open repositories or national measurement programmes, others are only made available via the responsible researcher. However, merging data from various sources is often not straightforward, due to issues with the data itself or the metadata. Nevertheless, the application of such compilations offers various possibilities. In our working group, two large-scale compilations are currently constructed and applied. The Northern Hemispheric Pollen Compilation consists of data from NEOTOMA, European Pollen Database (EPD), PANGAEA and various authors. With the help of this compilation, we reconstruct climate and vegetation of large spatial and temporal scales. The circumpolar soil temperature dataset consist of data from the Global Terrestrial Network for Permafrost (GTN-P), Roshydromet, PANGAEA, Nordicana D and the National Science Foundation (NSF) Arctic Data Center. In its first version, the compilation has already been successfully applied to validate the ESA CCI Permafrost soil temperature map. The various sources of errors and problems will be shown by the two compilations of (i) sedimentary pollen data and (ii) soil temperature data. The most general problem and error source are wrong or inaccurate coordinates. These errors arise out of coordinates provided with two decimals only, wrong conversion of DMS to decimal format, wrong coordinates etc. For most analyses, the most exact geographic position is a prerequisite, as e.g. lake size is an important parameter when reconstructing vegetation out of sedimentary pollen data. Sedimentary pollen records not located in a lake according to their given location thus need manual reposition according to the main researcher of a dataset or satellite maps. Further challenges concerning the pollen dataset pose various naming conventions or variable resolution in time. Furthermore, taxonomic resol

Boreal forests in Siberia store huge amounts of aboveground carbon. Global warming potentially threatens this carbon storage due to more frequent droughts or other disturbances such as fires. These disturbances can change recruitment patterns, and thus may have long-lasting impacts on population dynamics. Assessing high-resolution forest stand structures and forecasting their response for the upcoming decades with detailed models is needed to understand the involved key processes and consequences of global change. We present forest stand inventories derived from UAV imagery and a developed processing chain including Individual Tree Detection (ITD) and species determination for 56 sites on a bioclimatic gradient at the Tundra-Taiga-Ecotone in Northeastern Siberia. We will use these and further 58 traditional count and measurement data as starting points for the detailed individual-based spatially explicit forest model LAVESI to predict future forest dynamics covering multiple sites across the Siberian treeline. In our analyses, we will focus on assessing future structural changes of the forests and their aboveground biomass dynamics. For our discussion, we will evaluate the reliability of UAV-derived forest inventories by measuring the impact strength of error sources introduced in the methodology on the forecasts.

Pollen records from Siberia are mostly absent in global or Northern Hemisphere synthesis works. Here we present a taxonomically harmonized and temporally standardized pollen dataset that was synthesized using 173 palynological records from Siberia and adjacent areas (northeastern Asia, 42–75∘ N, 50–180∘ E). Pollen data were taxonomically harmonized, i.e. the original 437 taxa were assigned to 106 combined pollen taxa. Age–depth models for all records were revised by applying a constant Bayesian age–depth modelling routine. The pollen dataset is available as count data and percentage data in a table format (taxa vs. samples), with age information for each sample. The dataset has relatively few sites covering the last glacial period between 40 and 11.5 ka (calibrated thousands of years before 1950 CE) particularly from the central and western part of the study area. In the Holocene period, the dataset has many sites from most of the area, with the exception of the central part of Siberia. Of the 173 pollen records, 81 % of pollen counts were downloaded from open databases (GPD, EPD, PANGAEA) and 10 % were contributions by the original data gatherers, while a few were digitized from publications. Most of the pollen records originate from peatlands (48 %) and lake sediments (33 %). Most of the records (83 %) have ≥3 dates, allowing the establishment of reliable chronologies. The dataset can be used for various purposes, including pollen data mapping (example maps for Larix at selected time slices are shown) as well as quantitative climate and vegetation reconstructions. The datasets for pollen counts and pollen percentages are available at https://doi.org/10.1594/PANGAEA.898616 (Cao et al., 2019a), also including the site information, data source, original publication, dating data, and the plant functional type for each pollen taxa.

Larix populations at the tundra-taiga ecotone in northern Siberia are highly under-represented in population genetic studies, possibly due to the remoteness of these regions that can only be accessed at extraordinary expense. The genetic signatures of populations in these boundary regions are therefore largely unknown. We aim to generate organelle reference genomes for the detection of single nucleotide polymorphisms (SNPs) that can be used for paleogenetic studies. We present 19 complete chloroplast genomes and mitochondrial genomic sequences of larches from the southern lowlands of the Taymyr Peninsula (northernmost range of Larix gmelinii (Rupr.) Kuzen.), the lower Omoloy River, and the lower Kolyma River (both in the range of Larix cajanderi Mayr). The genomic data reveal 84 chloroplast SNPs and 213 putatively mitochondrial SNPs. Parsimony-based chloroplast haplotype networks show no spatial structure of individuals from different geographic origins, while the mitochondrial haplotype network shows at least a slight spatial structure with haplotypes from the Omoloy and Kolyma populations being more closely related to each other than to most of the haplotypes from the Taymyr populations. Whole genome alignments with publicly available complete chloroplast genomes of different Larix species show that among official plant barcodes only the rcbL gene contains sufficient polymorphisms, but has to be sequenced completely to distinguish the different provenances. We provide 8 novel mitochondrial SNPs that are putatively diagnostic for the separation of L. gmelinii and L. cajanderi, while 4 chloroplast SNPs have the potential to distinguish the L. gmelinii/L. cajanderi group from other Larix species. Our organelle references can be used for a targeted primer and probe design allowing the generation of short amplicons. This is particularly important with regard to future investigations of, for example, the biogeographic history of Larix by screening ancient sedimentary DN

Arctic treelines are facing a strong temperature increase as a result of recent global warming, causing possible changes in forest extent, which will alter vegetation-climate feedbacks. However, the mode and strength of the response is rather unclear, as potential changes are happening in areas that are very remote and difficult to access, and empirical data are still largely lacking. Here, we assessed the current population structure and genetic differentiation of Larix Mill. tree stands within the northernmost latitudinal treeline reaching ~ 72° N in the southern lowlands of the Taymyr Peninsula (~ 100° E). We sampled 743 individuals belonging to different height classes (seedlings, saplings, trees) at 11 locations along a gradient from ‘single tree’ tundra over ‘forest line’ to ‘dense forest’ stands and conducted investigations applying eight highly polymorphic nuclear microsatellites. Results suggest a high diversity within sub-populations (HE = 0.826–0.893), coupled, however, with heterozygote deficits in all sub-populations, but pronounced in ‘forest line’ stands. Overall, genetic differentiation of sub-populations is low (FST= 0.005), indicating a region-wide high gene flow, although ‘forest line’ stands harbour few rare and private alleles, likely indicating greater local reproduction. ‘Single tree’ stands, located beyond the northern forest line, are currently not involved in treeline expansion, but show signs of a long-term refuge, namely asexual reproduction and change of growth-form from erect to creeping growth, possibly having persisted for thousands of years. The lack of differentiation between the sub-populations points to a sufficiently high dispersal potential, and thus a rapid northward migration of the Siberian arctic treeline under recent global warming seems potentially unconstrained, but observations show it to be unexpectedly slow

Siberian boreal forests are expected to expand northwards in the course of global warming. However, processes of the treeline ecotone transition, as well astiming and related climate feedbacks are still not understood. Here, we present ‘Larix Vegetation Simulator’ LAVESI, an individual-based spatially-explicit model that can simulate Larix gmelinii (Rupr.) Rupr. stand dynamics in an attempt to improve our understanding about past and future treeline movements under changing climates. The relevant processes (growth, seed production and dispersal, establishment and mortality) are incorporated and adjusted to observation data mainly gained from the literature. Results of a local sensitivity analysis support the robustness of the model’s parameterization by giving relatively small sensitivity values. We tested the model by simulating tree stands under modern climate across the whole Taymyr Peninsula, north-central Siberia (c. 64–80° N; 92–119° E). We find tree densities similar to observed forests in the northern to mid-treeline areas, but densities are overestimated in the southern parts of the simulated region. Finally, from a temperature-forcing experiment, we detect that the responses of tree stands lag the hypothetical warming by several decades, until the end of 21st century. With our simulation experiments we demonstrate that the newly-developed model captures the dynamics of the Siberian latitudinal treeline.

Clarity of definitions is fundamental to the successful completion of any interdisciplinary project. In this chapter, we focus on defining a number of key terms that recur throughout the volume, and thus it acts as both a foundation and glossary for understanding the material covered. Ideas of resilience, self-organization and complexity are widely used across in constituent disciplines discussed in Chap. 1, but their use varies and we attempt to define this variation and thus the context of use in the book. There is also a strong emphasis on pattern, so we provide here an initial definition, to be followed up in more detail in Chaps. 7 and 8. We then move onto the specific nature of drylands and the need to understand land degradation within them and through them. How the mode of study affects our understanding is the next theme, in particular how case studies based in different places can be generalized, given the variations in landscape and vegetation type within them. Following an initial summary of the preceding material to evaluate why self-organization and complexity are useful frameworks for understanding patterns and processes in drylands, the chapter concludes with an overview of the deterministic and stochastic frameworks for understanding pattern.