The Arctic Plant Aboveground Biomass Synthesis Dataset.

Plant biomass is a fundamental ecosystem attribute that is sensitive to rapid climatic changes occurring in the Arctic. Nevertheless, measuring plant biomass in the Arctic is logistically challenging and resource intensive. Lack of accessible field data hinders efforts to understand the amount, composition, distribution, and changes in plant biomass in these northern ecosystems. Here, we present The Arctic plant aboveground biomass synthesis dataset, which includes field measurements of lichen, bryophyte, herb, shrub, and/or tree aboveground biomass (g m-2) on 2,327 sample plots from 636 field sites in seven countries. We created the synthesis dataset by assembling and harmonizing 32 individual datasets. Aboveground biomass was primarily quantified by harvesting sample plots during mid- to late-summer, though tree and often tall shrub biomass were quantified using surveys and allometric models. Each biomass measurement is associated with metadata including sample date, location, method, data source, and other information. This unique dataset can be leveraged to monitor, map, and model plant biomass across the rapidly warming Arctic.

Recent range shifts of moths, butterflies, and birds are driven by the breadth of their climatic niche.

Species are altering their ranges as a response to climate change, but the magnitude and direction of observed range shifts vary considerably among species. The ability to persist in current areas and colonize new areas plays a crucial role in determining which species will thrive and which decline as climate change progresses. Several studies have sought to identify characteristics, such as morphological and life-history traits, that could explain differences in the capability of species to shift their ranges together with a changing climate. These characteristics have explained variation in range shifts only sporadically, thus offering an uncertain tool for discerning responses among species. As long-term selection to past climates have shaped species' tolerances, metrics describing species' contemporary climatic niches may provide an alternative means for understanding responses to on-going climate change. Species that occur in a broader range of climatic conditions may hold greater tolerance to climatic variability and could therefore more readily maintain their historical ranges, while species with more narrow tolerances may only persist if they are able to shift in space to track their climatic niche. Here, we provide a first-filter test of the effect of climatic niche dimensions on shifts in the leading range edges in three relatively well-dispersing species groups. Based on the realized changes in the northern range edges of 383 moth, butterfly, and bird species across a boreal 1,100 km latitudinal gradient over c. 20 years, we show that while most morphological or life-history traits were not strongly connected with range shifts, moths and birds occupying a narrower thermal niche and butterflies occupying a broader moisture niche across their European distribution show stronger shifts towards the north. Our results indicate that the climatic niche may be important for predicting responses under climate change and as such warrants further investigation of potential mechanistic underpinnings.

Plant traits poorly predict winner and loser shrub species in a warming tundra biome.

Climate change is leading to species redistributions. In the tundra biome, shrubs are generally expanding, but not all tundra shrub species will benefit from warming. Winner and loser species, and the characteristics that may determine success or failure, have not yet been fully identified. Here, we investigate whether past abundance changes, current range sizes and projected range shifts derived from species distribution models are related to plant trait values and intraspecific trait variation. We combined 17,921 trait records with observed past and modelled future distributions from 62 tundra shrub species across three continents. We found that species with greater variation in seed mass and specific leaf area had larger projected range shifts, and projected winner species had greater seed mass values. However, trait values and variation were not consistently related to current and projected ranges, nor to past abundance change. Overall, our findings indicate that abundance change and range shifts will not lead to directional modifications in shrub trait composition, since winner and loser species share relatively similar trait spaces.

ForestClim-Bioclimatic variables for microclimate temperatures of European forests.

Microclimate research gained renewed interest over the last decade and its importance for many ecological processes is increasingly being recognized. Consequently, the call for high-resolution microclimatic temperature grids across broad spatial extents is becoming more pressing to improve ecological models. Here, we provide a new set of open-access bioclimatic variables for microclimate temperatures of European forests at 25 × 25 m2 resolution.

Bioclimatic atlas of the terrestrial Arctic.

The Arctic is the region on Earth that is warming at the fastest rate. In addition to rising means of temperature-related variables, Arctic ecosystems are affected by increasingly frequent extreme weather events causing disturbance to Arctic ecosystems. Here, we introduce a new dataset of bioclimatic indices relevant for investigating the changes of Arctic terrestrial ecosystems. The dataset, called ARCLIM, consists of several climate and event-type indices for the northern high-latitude land areas > 45°N. The indices are calculated from the hourly ERA5-Land reanalysis data for 1950-2021 in a spatial grid of 0.1 degree (~9 km) resolution. The indices are provided in three subsets: (1) the annual values during 1950-2021; (2) the average conditions for the 1991-2020 climatology; and (3) temporal trends over 1951-2021. The 72-year time series of various climate and event-type indices draws a comprehensive picture of the occurrence and recurrence of extreme weather events and climate variability of the changing Arctic bioclimate.

The activity and functions of soil microbial communities in the Finnish sub-Arctic vary across vegetation types.

Due to climate change, increased microbial activity in high-latitude soils may lead to higher greenhouse gas (GHG) emissions. However, microbial GHG production and consumption mechanisms in tundra soils are not thoroughly understood. To investigate how the diversity and functional potential of bacterial and archaeal communities vary across vegetation types and soil layers, we analyzed 116 soil metatranscriptomes from 73 sites in the Finnish sub-Arctic. Meadow soils were characterized by higher pH and lower soil organic matter (SOM) and carbon/nitrogen ratio. By contrast, dwarf shrub-dominated ecosystems had higher SOM and lower pH. Although Actinobacteria, Acidobacteria, Alphaproteobacteria and Planctomycetes were dominant in all communities, there were significant differences at the genus level between vegetation types; plant polymer-degrading groups were more active in shrub-dominated soils than in meadows. Given that climate-change scenarios predict the expansion of shrubs at high latitudes, our results indicate that tundra soil microbial communities harbor potential decomposers of increased plant litter, which may affect the rate of carbon turnover in tundra soils. Additionally, transcripts of methanotrophs were detected in the mineral layer of all soils, which may moderate methane fluxes. This study provides new insights into possible shifts in tundra microbial diversity and activity due to climate change.

From white to green: Snow cover loss and increased vegetation productivity in the European Alps.

Mountains are hotspots of biodiversity and ecosystem services, but they are warming about twice as fast as the global average. Climate change may reduce alpine snow cover and increase vegetation productivity, as in the Arctic. Here, we demonstrate that 77% of the European Alps above the tree line experienced greening (productivity gain) and <1% browning (productivity loss) over the past four decades. Snow cover declined significantly during this time, but in <10% of the area. These trends were only weakly correlated: Greening predominated in warmer areas, driven by climatic changes during summer, while snow cover recession peaked at colder temperatures, driven by precipitation changes. Greening could increase carbon sequestration, but this is unlikely to outweigh negative implications, including reduced albedo and water availability, thawing permafrost, and habitat loss.

In-depth characterization of denitrifier communities across different soil ecosystems in the tundra.

BACKGROUND: In contrast to earlier assumptions, there is now mounting evidence for the role of tundra soils as important sources of the greenhouse gas nitrous oxide (N2O). However, the microorganisms involved in the cycling of N2O in this system remain largely uncharacterized. Since tundra soils are variable sources and sinks of N2O, we aimed at investigating differences in community structure across different soil ecosystems in the tundra. RESULTS: We analysed 1.4 Tb of metagenomic data from soils in northern Finland covering a range of ecosystems from dry upland soils to water-logged fens and obtained 796 manually binned and curated metagenome-assembled genomes (MAGs). We then searched for MAGs harbouring genes involved in denitrification, an important process driving N2O emissions. Communities of potential denitrifiers were dominated by microorganisms with truncated denitrification pathways (i.e., lacking one or more denitrification genes) and differed across soil ecosystems. Upland soils showed a strong N2O sink potential and were dominated by members of the Alphaproteobacteria such as Bradyrhizobium and Reyranella. Fens, which had in general net-zero N2O fluxes, had a high abundance of poorly characterized taxa affiliated with the Chloroflexota lineage Ellin6529 and the Acidobacteriota subdivision Gp23. CONCLUSIONS: By coupling an in-depth characterization of microbial communities with in situ measurements of N2O fluxes, our results suggest that the observed spatial patterns of N2O fluxes in the tundra are related to differences in the composition of denitrifier communities.

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.

Maintaining forest cover to enhance temperature buffering under future climate change.

Forest canopies buffer macroclimatic temperature fluctuations. However, we do not know if and how the capacity of canopies to buffer understorey temperature will change with accelerating climate change. Here we map the difference (offset) between temperatures inside and outside forests in the recent past and project these into the future in boreal, temperate and tropical forests. Using linear mixed-effect models, we combined a global database of 714 paired time series of temperatures (mean, minimum and maximum) measured inside forests vs. in nearby open habitats with maps of macroclimate, topography and forest cover to hindcast past (1970-2000) and to project future (2060-2080) temperature differences between free-air temperatures and sub-canopy microclimates. For all tested future climate scenarios, we project that the difference between maximum temperatures inside and outside forests across the globe will increase (i.e. result in stronger cooling in forests), on average during 2060-2080, by 0.27 ± 0.16 °C (RCP2.6) and 0.60 ± 0.14 °C (RCP8.5) due to macroclimate changes. This suggests that extremely hot temperatures under forest canopies will, on average, warm less than outside forests as macroclimate warms. This knowledge is of utmost importance as it suggests that forest microclimates will warm at a slower rate than non-forested areas, assuming that forest cover is maintained. Species adapted to colder growing conditions may thus find shelter and survive longer than anticipated at a given forest site. This highlights the potential role of forests as a whole as microrefugia for biodiversity under future climate change.

ForestTemp - Sub-canopy microclimate temperatures of European forests.

Ecological research heavily relies on coarse-gridded climate data based on standardized temperature measurements recorded at 2 m height in open landscapes. However, many organisms experience environmental conditions that differ substantially from those captured by these macroclimatic (i.e. free air) temperature grids. In forests, the tree canopy functions as a thermal insulator and buffers sub-canopy microclimatic conditions, thereby affecting biological and ecological processes. To improve the assessment of climatic conditions and climate-change-related impacts on forest-floor biodiversity and functioning, high-resolution temperature grids reflecting forest microclimates are thus urgently needed. Combining more than 1200 time series of in situ near-surface forest temperature with topographical, biological and macroclimatic variables in a machine learning model, we predicted the mean monthly offset between sub-canopy temperature at 15 cm above the surface and free-air temperature over the period 2000-2020 at a spatial resolution of 25 m across Europe. This offset was used to evaluate the difference between microclimate and macroclimate across space and seasons and finally enabled us to calculate mean annual and monthly temperatures for European forest understories. We found that sub-canopy air temperatures differ substantially from free-air temperatures, being on average 2.1°C (standard deviation ± 1.6°C) lower in summer and 2.0°C higher (±0.7°C) in winter across Europe. Additionally, our high-resolution maps expose considerable microclimatic variation within landscapes, not captured by the gridded macroclimatic products. The provided forest sub-canopy temperature maps will enable future research to model below-canopy biological processes and patterns, as well as species distributions more accurately.

Statistical upscaling of ecosystem CO2 fluxes across the terrestrial tundra and boreal domain: Regional patterns and uncertainties.

The regional variability in tundra and boreal carbon dioxide (CO2 ) fluxes can be high, complicating efforts to quantify sink-source patterns across the entire region. Statistical models are increasingly used to predict (i.e., upscale) CO2 fluxes across large spatial domains, but the reliability of different modeling techniques, each with different specifications and assumptions, has not been assessed in detail. Here, we compile eddy covariance and chamber measurements of annual and growing season CO2 fluxes of gross primary productivity (GPP), ecosystem respiration (ER), and net ecosystem exchange (NEE) during 1990-2015 from 148 terrestrial high-latitude (i.e., tundra and boreal) sites to analyze the spatial patterns and drivers of CO2 fluxes and test the accuracy and uncertainty of different statistical models. CO2 fluxes were upscaled at relatively high spatial resolution (1 km2 ) across the high-latitude region using five commonly used statistical models and their ensemble, that is, the median of all five models, using climatic, vegetation, and soil predictors. We found the performance of machine learning and ensemble predictions to outperform traditional regression methods. We also found the predictive performance of NEE-focused models to be low, relative to models predicting GPP and ER. Our data compilation and ensemble predictions showed that CO2 sink strength was larger in the boreal biome (observed and predicted average annual NEE -46 and -29 g C m-2  yr-1 , respectively) compared to tundra (average annual NEE +10 and -2 g C m-2  yr-1 ). This pattern was associated with large spatial variability, reflecting local heterogeneity in soil organic carbon stocks, climate, and vegetation productivity. The terrestrial ecosystem CO2 budget, estimated using the annual NEE ensemble prediction, suggests the high-latitude region was on average an annual CO2 sink during 1990-2015, although uncertainty remains high.

Forest microclimates and climate change: Importance, drivers and future research agenda.

Forest microclimates contrast strongly with the climate outside forests. To fully understand and better predict how forests' biodiversity and functions relate to climate and climate change, microclimates need to be integrated into ecological research. Despite the potentially broad impact of microclimates on the response of forest ecosystems to global change, our understanding of how microclimates within and below tree canopies modulate biotic responses to global change at the species, community and ecosystem level is still limited. Here, we review how spatial and temporal variation in forest microclimates result from an interplay of forest features, local water balance, topography and landscape composition. We first stress and exemplify the importance of considering forest microclimates to understand variation in biodiversity and ecosystem functions across forest landscapes. Next, we explain how macroclimate warming (of the free atmosphere) can affect microclimates, and vice versa, via interactions with land-use changes across different biomes. Finally, we perform a priority ranking of future research avenues at the interface of microclimate ecology and global change biology, with a specific focus on three key themes: (1) disentangling the abiotic and biotic drivers and feedbacks of forest microclimates; (2) global and regional mapping and predictions of forest microclimates; and (3) the impacts of microclimate on forest biodiversity and ecosystem functioning in the face of climate change. The availability of microclimatic data will significantly increase in the coming decades, characterizing climate variability at unprecedented spatial and temporal scales relevant to biological processes in forests. This will revolutionize our understanding of the dynamics, drivers and implications of forest microclimates on biodiversity and ecological functions, and the impacts of global changes. In order to support the sustainable use of forests and to secure their biodiversity and ecosystem services for future generations, microclimates cannot be ignored.

Consistent trait-environment relationships within and across tundra plant communities.

A fundamental assumption in trait-based ecology is that relationships between traits and environmental conditions are globally consistent. We use field-quantified microclimate and soil data to explore if trait-environment relationships are generalizable across plant communities and spatial scales. We collected data from 6,720 plots and 217 species across four distinct tundra regions from both hemispheres. We combined these data with over 76,000 database trait records to relate local plant community trait composition to broad gradients of key environmental drivers: soil moisture, soil temperature, soil pH and potential solar radiation. Results revealed strong, consistent trait-environment relationships across Arctic and Antarctic regions. This indicates that the detected relationships are transferable between tundra plant communities also when fine-scale environmental heterogeneity is accounted for, and that variation in local conditions heavily influences both structural and leaf economic traits. Our results strengthen the biological and mechanistic basis for climate change impact predictions of vulnerable high-latitude ecosystems.

Decreasing snow cover alters functional composition and diversity of Arctic tundra.

The Arctic is one of the least human-impacted parts of the world, but, in turn, tundra biome is facing the most rapid climate change on Earth. These perturbations may cause major reshuffling of Arctic species compositions and functional trait profiles and diversity, thereby affecting ecosystem processes of the whole tundra region. Earlier research has detected important drivers of the change in plant functional traits under warming climate, but studies on one key factor, snow cover, are almost totally lacking. Here we integrate plot-scale vegetation data with detailed climate and snow information using machine learning methods to model the responsiveness of tundra communities to different scenarios of warming and snow cover duration. Our results show that decreasing snow cover, together with warming temperatures, can substantially modify biotic communities and their trait compositions, with future plant communities projected to be occupied by taller plants with larger leaves and faster resource acquisition strategies. As another finding, we show that, while the local functional diversity may increase, simultaneous biotic homogenization across tundra communities is likely to occur. The manifestation of climate warming on tundra vegetation is highly dependent on the evolution of snow conditions. Given this, realistic assessments of future ecosystem functioning require acknowledging the role of snow in tundra vegetation models.

SoilTemp: A global database of near-surface temperature.

Current analyses and predictions of spatially explicit patterns and processes in ecology most often rely on climate data interpolated from standardized weather stations. This interpolated climate data represents long-term average thermal conditions at coarse spatial resolutions only. Hence, many climate-forcing factors that operate at fine spatiotemporal resolutions are overlooked. This is particularly important in relation to effects of observation height (e.g. vegetation, snow and soil characteristics) and in habitats varying in their exposure to radiation, moisture and wind (e.g. topography, radiative forcing or cold-air pooling). Since organisms living close to the ground relate more strongly to these microclimatic conditions than to free-air temperatures, microclimatic ground and near-surface data are needed to provide realistic forecasts of the fate of such organisms under anthropogenic climate change, as well as of the functioning of the ecosystems they live in. To fill this critical gap, we highlight a call for temperature time series submissions to SoilTemp, a geospatial database initiative compiling soil and near-surface temperature data from all over the world. Currently, this database contains time series from 7,538 temperature sensors from 51 countries across all key biomes. The database will pave the way toward an improved global understanding of microclimate and bridge the gap between the available climate data and the climate at fine spatiotemporal resolutions relevant to most organisms and ecosystem processes.

The effect of summer drought on the predictability of local extinctions in a butterfly metapopulation.

The ecological impacts of extreme climatic events on population dynamics and community composition are profound and predominantly negative. Using extensive data of an ecological model system, we tested whether predictions from ecological models remain robust when environmental conditions are outside the bounds of observation. We observed a 10-fold demographic decline of the Glanville fritillary butterfly (Melitaea cinxia) metapopulation on the Åland islands, Finland in the summer of 2018 and used climatic and satellite data to demonstrate that this year was an anomaly with low climatic water balance values and low vegetation productivity indices across Åland. Population growth rates were strongly associated with spatiotemporal variation in climatic water balance. Covariates shown previously to affect the extinction probability of local populations in this metapopulation were less informative when populations were exposed to severe drought during the summer months. Our results highlight the unpredictable responses of natural populations to extreme climatic events.

El Efecto de la Sequía Estival sobre la Previsibilidad de las Extinciones Locales en una Metapoblación de Mariposas Resumen Los impactos ecológicos de los eventos climáticos extremos sobre las dinámicas metapoblacionales y la composición de la comunidad son profundos y predominantemente negativos. Con los extensos datos de un sistema de modelos ecológicos probamos si las predicciones de los modelos ecológicos todavía son sólidos cuando las condiciones ambientales se encuentran fuera de los límites de observación. Observamos una declinación demográfica ocurrir diez veces en la metapoblación de la mariposa Melitaea cinxia en las Islas Aland de Finlandia durante el verano de 2018. Usamos datos climáticos y satelitales para demostrar que ese año fue una anomalía al contar con valores bajos de balance hídrico e índices bajos de productividad de la vegetación en todas las islas. Las tasas de crecimiento poblacional estuvieron fuertemente asociadas con la variación espaciotemporal del balance hídrico climático. Las covarianzas que previamente han afectado a la probabilidad de extinción de las poblaciones locales de esta metapoblación fueron menos informativas cuando las poblaciones estuvieron expuestas a sequías severas durante los meses de verano. Nuestros resultados resaltan las respuestas impredecibles de las poblaciones naturales ante los eventos climáticos extremos.

Fine-grained climate velocities reveal vulnerability of protected areas to climate change.

Climate change velocity is an increasingly used metric to assess the broad-scale climatic exposure and climate change induced risks to terrestrial and marine ecosystems. However, the utility of this metric in conservation planning can be enhanced by determining the velocities of multiple climatic drivers in real protected area (PA) networks on ecologically relevant scales. Here we investigate the velocities of three key bioclimatic variables across a nation-wide reserve network, and the consequences of including fine-grained topoclimatic data in velocity assessments. Using 50-m resolution data describing present-day and future topoclimates, we assessed the velocities of growing degree days, the mean January temperature and climatic water balance in the Natura 2000 PA network in Finland. The high-velocity areas for the three climate variables differed drastically, indicating contrasting exposure risks in different PAs. The 50-m resolution climate data revealed more realistic estimates of climate velocities and more overlap between the present-day and future climate spaces in the PAs than the 1-km resolution data. Even so, the current temperature conditions were projected to disappear from almost all the studied PAs by the end of this century. Thus, in PA networks with only moderate topographic variation, far-reaching climate change induced ecological changes may be inevitable.

Machine-learning based reconstructions of primary and secondary climate variables from North American and European fossil pollen data.

We test several quantitative algorithms as palaeoclimate reconstruction tools for North American and European fossil pollen data, using both classical methods and newer machine-learning approaches based on regression tree ensembles and artificial neural networks. We focus on the reconstruction of secondary climate variables (here, January temperature and annual water balance), as their comparatively small ecological influence compared to the primary variable (July temperature) presents special challenges to palaeo-reconstructions. We test the pollen-climate models using a novel and comprehensive cross-validation approach, running a series of h-block cross-validations using h values of 100-1500 km. Our study illustrates major benefits of this variable h-block cross-validation scheme, as the effect of spatial autocorrelation is minimized, while the cross-validations with increasing h values can reveal instabilities in the calibration model and approximate challenges faced in palaeo-reconstructions with poor modern analogues. We achieve well-performing calibration models for both primary and secondary climate variables, with boosted regression trees providing the overall most robust performance, while the palaeoclimate reconstructions from fossil datasets show major independent features for the primary and secondary variables. Our results suggest that with careful variable selection and consideration of ecological processes, robust reconstruction of both primary and secondary climate variables is possible.

Snow is an important control of plant community functional composition in oroarctic tundra.

The functional composition of plant communities is a critical modulator of climate change impacts on ecosystems, but it is not a simple function of regional climate. In the Arctic tundra, where climate change is proceeding the most rapidly, communities have not shifted their trait composition as predicted by spatial temperature-trait relationships. Important causal pathways are thus missing from models of trait composition change. Here, we study causes of plant community functional variation in an oroarctic tundra landscape in Kilpisjärvi, Finland. We consider the community-weighted means of plant vegetative height, as well as two traits related to the leaf economic spectrum. Specifically, we model their responses to locally measured summer air temperature, snow conditions, and soil resource levels. For each of the traits, we also quantify the importance of intraspecific trait variation (ITV) for between-community functional differences and trait-environment matching. Our study shows that in a tundra landscape (1) snow is the most influential abiotic variable affecting functional composition, (2) vegetation height is under weak local environmental control, whereas leaf economics is under strong local environmental control, (3) the relative magnitude of ITV differs between traits, and (4) ITV is not very consequential for community-level trait-environment relationships. Our analyses highlight the importance of winter conditions for community functional composition in seasonal areas. We show that winter climate change can either amplify or counter the effects summer warming, depending on the trait.