Sub-arctic mosses and lichens show idiosyncratic responses to combinations of winter heatwaves, freezing and nitrogen deposition.

Arctic ecosystems are increasingly exposed to extreme climatic events throughout the year, which can affect species performance. Cryptogams (bryophytes and lichens) provide important ecosystem services in polar ecosystems but may be physiologically affected or killed by extreme events. Through field and laboratory manipulations, we compared physiological responses of seven dominant sub-Arctic cryptogams (three bryophytes, four lichens) to single events and factorial combinations of mid-winter heatwave (6°C for 7 days), re-freezing, snow removal and summer nitrogen addition. We aimed to identify which mosses and lichens are vulnerable to these abiotic extremes and if combinations would exacerbate physiological responses. Combinations of extremes resulted in stronger species responses but included idiosyncratic species-specific responses. Species that remained dormant during winter (March), irrespective of extremes, showed little physiological response during summer (August). However, winter physiological activity, and response to winter extremes, was not consistently associated with summer physiological impacts. Winter extremes affect cryptogam physiology, but summer responses appear mild, and lichens affect the photobiont more than the mycobiont. Accounting for Arctic cryptogam response to multiple climatic extremes in ecosystem functioning and modelling will require a better understanding of their winter eco-physiology and repair capabilities.

Identifying climate thresholds for dominant natural vegetation types at the global scale using machine learning: Average climate versus extremes.

The global distribution of vegetation is largely determined by climatic conditions and feeds back into the climate system. To predict future vegetation changes in response to climate change, it is crucial to identify and understand key patterns and processes that couple vegetation and climate. Dynamic global vegetation models (DGVMs) have been widely applied to describe the distribution of vegetation types and their future dynamics in response to climate change. As a process-based approach, it partly relies on hard-coded climate thresholds to constrain the distribution of vegetation. What thresholds to implement in DGVMs and how to replace them with more process-based descriptions remain among the major challenges. In this study, we employ machine learning using decision trees to extract large-scale relationships between the global distribution of vegetation and climatic characteristics from remotely sensed vegetation and climate data. We analyse how the dominant vegetation types are linked to climate extremes as compared to seasonally or annually averaged climatic conditions. The results show that climate extremes allow us to describe the distribution and eco-climatological space of the vegetation types more accurately than the averaged climate variables, especially those types which occupy small territories in a relatively homogeneous ecological space. Future predicted vegetation changes using both climate extremes and averaged climate variables are less prominent than that predicted by averaged climate variables and are in better agreement with those of DGVMs, further indicating the importance of climate extremes in determining geographic distributions of different vegetation types. We found that the temperature thresholds for vegetation types (e.g. grass and open shrubland) in cold environments vary with moisture conditions. The coldest daily maximum temperature (extreme cold day) is particularly important for separating many different vegetation types. These findings highlight the need for a more explicit representation of the impacts of climate extremes on vegetation in DGVMs.

Global maps of soil temperature.

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km2 resolution for 0-5 and 5-15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km2 pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (-0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications.

Monitoring Winter Stress Vulnerability of High-Latitude Understory Vegetation Using Intraspecific Trait Variability and Remote Sensing Approaches.

In this study, we focused on three species that have proven to be vulnerable to winter stress: Empetrum nigrum, Vaccinium vitis-idaea and Hylocomium splendens. Our objective was to determine plant traits suitable for monitoring plant stress as well as trait shifts during spring. To this end, we used a combination of active and passive handheld normalized difference vegetation index (NDVI) sensors, RGB indices derived from ordinary cameras, an optical chlorophyll and flavonol sensor (Dualex), and common plant traits that are sensitive to winter stress, i.e. height, specific leaf area (SLA). Our results indicate that NDVI is a good predictor for plant stress, as it correlates well with height (r = 0.70, p < 0.001) and chlorophyll content (r = 0.63, p < 0.001). NDVI is also related to soil depth (r = 0.45, p < 0.001) as well as to plant stress levels based on observations in the field (r = -0.60, p < 0.001). Flavonol content and SLA remained relatively stable during spring. Our results confirm a multi-method approach using NDVI data from the Sentinel-2 satellite and active near-remote sensing devices to determine the contribution of understory vegetation to the total ecosystem greenness. We identified low soil depth to be the major stressor for understory vegetation in the studied plots. The RGB indices were good proxies to detect plant stress (e.g. Channel G%: r = -0.77, p < 0.001) and showed high correlation with NDVI (r = 0.75, p < 0.001). Ordinary cameras and modified cameras with the infrared filter removed were found to perform equally well.

Arctic browning: Impacts of extreme climatic events on heathland ecosystem CO2 fluxes.

Extreme climatic events are among the drivers of recent declines in plant biomass and productivity observed across Arctic ecosystems, known as "Arctic browning." These events can cause landscape-scale vegetation damage and so are likely to have major impacts on ecosystem CO2 balance. However, there is little understanding of the impacts on CO2 fluxes, especially across the growing season. Furthermore, while widespread shoot mortality is commonly observed with browning events, recent observations show that shoot stress responses are also common, and manifest as high levels of persistent anthocyanin pigmentation. Whether or how this response impacts ecosystem CO2 fluxes is not known. To address these research needs, a growing season assessment of browning impacts following frost drought and extreme winter warming (both extreme climatic events) on the key ecosystem CO2 fluxes Net Ecosystem Exchange (NEE), Gross Primary Productivity (GPP), ecosystem respiration (Reco ) and soil respiration (Rsoil ) was carried out in widespread sub-Arctic dwarf shrub heathland, incorporating both mortality and stress responses. Browning (mortality and stress responses combined) caused considerable site-level reductions in GPP and NEE (of up to 44%), with greatest impacts occurring at early and late season. Furthermore, impacts on CO2 fluxes associated with stress often equalled or exceeded those resulting from vegetation mortality. This demonstrates that extreme events can have major impacts on ecosystem CO2 balance, considerably reducing the carbon sink capacity of the ecosystem, even where vegetation is not killed. Structural Equation Modelling and additional measurements, including decomposition rates and leaf respiration, provided further insight into mechanisms underlying impacts of mortality and stress on CO2 fluxes. The scale of reductions in ecosystem CO2 uptake highlights the need for a process-based understanding of Arctic browning in order to predict how vegetation and CO2 balance will respond to continuing climate change.

Contrasting survival and physiological responses of sub-Arctic plant types to extreme winter warming and nitrogen.

MAIN CONCLUSION: Evergreen plants are more vulnerable than grasses and birch to snow and temperature variability in the sub-Arctic. Most Arctic climate impact studies focus on single factors, such as summer warming, while ecosystems are exposed to changes in all seasons. Through a combination of field and laboratory manipulations, we compared physiological and growth responses of dominant sub-Arctic plant types to midwinter warming events (6 °C for 7 days) in combination with freezing, simulated snow thaw and nitrogen additions. We aimed to identify if different plant types showed consistent physiological, cellular, growth and mortality responses to these abiotic stressors. Evergreen dwarf shrubs and tree seedlings showed higher mortality (40-100%) following extreme winter warming events than Betula pubescens tree seedlings and grasses (0-27%). All species had growth reductions following exposure to - 20 °C, but not all species suffered from - 10 °C irrespective of other treatments. Winter warming followed by - 20 °C resulted in the greatest mortality and was strongest among evergreen plants. Snow removal reduced the biomass for most species and this was exacerbated by subsequent freezing. Nitrogen increased the growth of B. pubescens and grasses, but not the evergreens, and interaction effects with the warming, freezing and snow treatments were minor and few. Physiological activity during the winter warming and freezing treatments was inconsistent with growth and mortality rates across the plants types. However, changes in the membrane fatty acids were associated with reduced mortality of grasses. Sub-Arctic plant communities may become dominated by grasses and deciduous plants if winter snowpack diminishes and plants are exposed to greater temperature variability in the near future.

Evolution of complex symbiotic relationships in a morphologically derived family of lichen-forming fungi.

We studied the evolutionary history of the Parmeliaceae (Lecanoromycetes, Ascomycota), one of the largest families of lichen-forming fungi with complex and variable morphologies, also including several lichenicolous fungi. We assembled a six-locus data set including nuclear, mitochondrial and low-copy protein-coding genes from 293 operational taxonomic units (OTUs). The lichenicolous lifestyle originated independently three times in lichenized ancestors within Parmeliaceae, and a new generic name is introduced for one of these fungi. In all cases, the independent origins occurred c. 24 million yr ago. Further, we show that the Paleocene, Eocene and Oligocene were key periods when diversification of major lineages within Parmeliaceae occurred, with subsequent radiations occurring primarily during the Oligocene and Miocene. Our phylogenetic hypothesis supports the independent origin of lichenicolous fungi associated with climatic shifts at the Oligocene-Miocene boundary. Moreover, diversification bursts at different times may be crucial factors driving the diversification of Parmeliaceae. Additionally, our study provides novel insight into evolutionary relationships in this large and diverse family of lichen-forming ascomycetes.

Climatic and biotic extreme events moderate long-term responses of above- and belowground sub-Arctic heathland communities to climate change.

Climate change impacts are not uniform across the Arctic region because interacting factors causes large variations in local ecosystem change. Extreme climatic events and population cycles of herbivores occur simultaneously against a background of gradual climate warming trends and can redirect ecosystem change along routes that are difficult to predict. Here, we present the results from sub-Arctic heath vegetation and its belowground micro-arthropod community in response to the two main drivers of vegetation damage in this region: extreme winter warming events and subsequent outbreaks of the defoliating autumnal moth caterpillar (Epirrita autumnata). Evergreen dwarf shrub biomass decreased (30%) following extreme winter warming events and again by moth caterpillar grazing. Deciduous shrubs that were previously exposed to an extreme winter warming event were not affected by the moth caterpillar grazing, while those that were not exposed to warming events (control plots) showed reduced (23%) biomass from grazing. Cryptogam cover increased irrespective of grazing or winter warming events. Micro-arthropods declined (46%) following winter warming but did not respond to changes in plant community. Extreme winter warming and caterpillar grazing suppressed the CO2 fluxes of the ecosystem. Evergreen dwarf shrubs are disadvantaged in a future sub-Arctic with more stochastic climatic and biotic events. Given that summer warming may further benefit deciduous over evergreen shrubs, event and trend climate change may both act against evergreen shrubs and the ecosystem functions they provide. This is of particular concern given that Arctic heath vegetation is typically dominated by evergreen shrubs. Other components of the vegetation showed variable responses to abiotic and biotic events, and their interaction indicates that sub-Arctic vegetation response to multiple pressures is not easy to predict from single-factor responses. Therefore, while biotic and climatic events may have clear impacts, more work is needed to understand their net effect on Arctic ecosystems.

Synergies and trade-offs between provisioning and climate-regulating ecosystem services in reindeer herding ecosystems.

Reindeer (Rangifer tarandus) pastoralism utilizes vast boreo-arctic taiga and tundra as grazing land. Highly fluctuating population sizes pose major challenges to the economy and livelihood of indigenous herder communities. In this study we investigated the effect of population fluctuations on core provisioning and regulating ecosystem services in two Sámi reindeer herding districts with contrasting fluctuation trends. We compared 50-year long time series on herd size, meat production, forage productivity, carbon footprint, and CO2-equivalence metrics for surface albedo change based on the radiative forcing concept. Our results show, for both districts, that the economic benefits from the provisioning services were higher than the costs from the regulating services. Still, there were major contrasts; the district with moderate and stable reindeer density gained nearly the double on provisioning services per unit area. The costs from increasing heat absorption due to reduction in surface albedo caused by replacement of high-reflective lichens with low-reflective woody plants, was 10.5 times higher per unit area in the district with large fluctuations. Overall, the net economic benefits per unit area were 237 % higher in the district with stable reindeer density. These results demonstrate that it is possible to minimize trade-offs between economic benefits from reindeer herding locally and global economic costs in terms of climate-regulating services by minimizing fluctuations in herds that are managed at sustainable densities.

The northernmost hyperspectral FLoX sensor dataset for monitoring of high-Arctic tundra vegetation phenology and Sun-Induced Fluorescence (SIF).

A hyperspectral field sensor (FloX) was installed in Adventdalen (Svalbard, Norway) in 2019 as part of the Svalbard Integrated Arctic Earth Observing System (SIOS) for monitoring vegetation phenology and Sun-Induced Chlorophyll Fluorescence (SIF) of high-Arctic tundra. This northernmost hyperspectral sensor is located within the footprint of a tower for long-term eddy covariance flux measurements and is an integral part of an automatic environmental monitoring system on Svalbard (AsMovEn), which is also a part of SIOS. One of the measurements that this hyperspectral instrument can capture is SIF, which serves as a proxy of gross primary production (GPP) and carbon flux rates. This paper presents an overview of the data collection and processing, and the 4-year (2019-2021) datasets in processed format are available at: https://thredds.met.no/thredds/catalog/arcticdata/infranor/NINA-FLOX/raw/catalog.html associated with https://doi.org/10.21343/ZDM7-JD72 under a CC-BY-4.0 license. Results obtained from the first three years in operation showed interannual variation in SIF and other spectral vegetation indices including MERIS Terrestrial Chlorophyll Index (MTCI), EVI and NDVI. Synergistic uses of the measurements from this northernmost hyperspectral FLoX sensor, in conjunction with other monitoring systems, will advance our understanding of how tundra vegetation responds to changing climate and the resulting implications on carbon and energy balance.