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Flux across the carbon cycle is generally characterized by contributions from plants, microbes, and abiotic systems. Animals, however, move vast amounts of carbon, both through ecosystem webs and across the landscape. Schmitz et al. review the different contributions that animal populations make to carbon cycling and discuss approaches that allow for better monitoring of these contributions. Science , this issue p. eaar3213 Predicting and managing the global carbon cycle requires scientific understanding of ecosystem processes that control carbon uptake and storage. It is generally assumed that carbon cycling is sufficiently characterized in terms of uptake and exchange between ecosystem plant and soil pools and the atmosphere. We show that animals also play an important role by mediating carbon exchange between ecosystems and the atmosphere, at times turning ecosystem carbon sources into sinks, or vice versa. Animals also move across landscapes, creating a dynamism that shapes landscape-scale variation in carbon exchange and storage. Predicting and measuring carbon cycling under such dynamism is an important scientific challenge. We explain how to link analyses of spatial ecosystem functioning, animal movement, and remote sensing of animal habitats with carbon dynamics across landscapes.

Arctic warming can influence tundra ecosystem function with consequences for climate feedbacks, wildlife and human communities. Yet ecological change across the Arctic tundra biome remains poorly quantified due to field measurement limitations and reliance on coarse-resolution satellite data. Here, we assess decadal changes in Arctic tundra greenness using time series from the 30 m resolution Landsat satellites. From 1985 to 2016 tundra greenness increased (greening) at ~37.3% of sampling sites and decreased (browning) at ~4.7% of sampling sites. Greening occurred most often at warm sampling sites with increased summer air temperature, soil temperature, and soil moisture, while browning occurred most often at cold sampling sites that cooled and dried. Tundra greenness was positively correlated with graminoid, shrub, and ecosystem productivity measured at field sites. Our results support the hypothesis that summer warming stimulated plant productivity across much, but not all, of the Arctic tundra biome during recent decades. Satellites provide clear evidence of greening trends in the Arctic, but high-resolution pan-Arctic quantification of these trends is lacking. Here the authors analyse high-resolution Landsat data to show widespread greening in the Arctic, and find that greening trends are linked to summer warming overall but not always locally.

It is clear that reducing greenhouse gas emissions alone is insufficient to avoid large global temperature increases. To avoid atmospheric concentrations of greenhouse gases that result in dangerous alterations of the climate, large reductions in carbon dioxide emissions from fossil fuel combustion and land use changes must be accompanied by an increase in atmospheric carbon dioxide sequestration. Natural Climate Solutions have become a major focus of climate policy. Land and ocean ecosystems remove and store atmospheric carbon, and forests play a major role. This focus collection includes papers that address three important aspects of the role for forests in meeting climate change mitigation goals: (i) Carbon Accounting of forest sinks and reservoirs, process emissions and carbon storage in forest products, (ii) the carbon dioxide dynamics of using Forest Bioenergy and (iii) the carbon cycle of Tropical Forests.

Vegetation composition shifts, and in particular, shrub expansion across the Arctic tundra are some of the most important and widely observed responses of high-latitude ecosystems to rapid climate warming. These changes in vegetation potentially alter ecosystem carbon balances by affecting a complex set of soil–plant–atmosphere interactions. In this review, we synthesize the literature on (a) observed shrub expansion, (b) key climatic and environmental controls and mechanisms that affect shrub expansion, (c) impacts of shrub expansion on ecosystem carbon balance, and (d) research gaps and future directions to improve process representations in land models. A broad range of evidence, including in-situ observations, warming experiments, and remotely sensed vegetation indices have shown increases in growth and abundance of woody plants, particularly tall deciduous shrubs, and advancing shrublines across the circumpolar Arctic. This recent shrub expansion is affected by several interacting factors including climate warming, accelerated nutrient cycling, changing disturbance regimes, and local variation in topography and hydrology. Under warmer conditions, tall deciduous shrubs can be more competitive than other plant functional types in tundra ecosystems because of their taller maximum canopy heights and often dense canopy structure. Competitive abilities of tall deciduous shrubs vs herbaceous plants are also controlled by variation in traits that affect carbon and nutrient investments and retention strategies in leaves, stems, and roots. Overall, shrub expansion may affect tundra carbon balances by enhancing ecosystem carbon uptake and altering ecosystem respiration, and through complex feedback mechanisms that affect snowpack dynamics, permafrost degradation, surface energy balance, and litter inputs. Observed and projected tall deciduous shrub expansion and the subsequent effects on surface energy and carbon balances may alter feedbacks to the climate system. Land models, including those integrated in Earth System Models, need to account for differences in plant traits that control competitive interactions to accurately predict decadal- to centennial-scale tundra vegetation and carbon dynamics.

Fire is a complex Earth system phenomenon that fundamentally affects vegetation distributions, biogeochemical cycling, climate, and human society across most of Earth's land surface. Fire regimes are currently changing due to multiple interacting global change drivers, most notably climate change, land use, and direct human influences via ignition and suppression. It is therefore critical to better understand the drivers, patterns, and impacts of these changing fire regimes now and continuing into the future. Our review contributes to this focus issue by synthesizing results from 27 studies covering a broad range of topics. Studies are categorized into (i) Understanding contemporary fire patterns, drivers, and effects; (ii) Human influences on fire regimes; (iii) Changes in historical fire regimes; (iv) Future projections; (v) Novel techniques; and (vi) Reviews. We conclude with a discussion on progress made, major remaining research challenges, and recommended directions.

This study shows that climate change could lead to a major redistribution of vegetation across the Arctic, with important implications for biosphere–atmosphere interactions, as well as for biodiversity conservation and ecosystem services. Woody vegetation is predicted to expand substantially over coming decades, causing more Arctic warming through positive climate feedbacks than previously thought. Climate warming has led to changes in the composition, density and distribution of Arctic vegetation in recent decades 1 , 2 , 3 , 4 . These changes cause multiple opposing feedbacks between the biosphere and atmosphere 5 , 6 , 7 , 8 , 9 , the relative magnitudes of which will have globally significant consequences but are unknown at a pan-Arctic scale 10 . The precise nature of Arctic vegetation change under future warming will strongly influence climate feedbacks, yet Earth system modelling studies have so far assumed arbitrary increases in shrubs (for example, +20%; refs  6 , 11 ), highlighting the need for predictions of future vegetation distribution shifts. Here we show, using climate scenarios for the 2050s and models that utilize statistical associations between vegetation and climate, the potential for extremely widespread redistribution of vegetation across the Arctic. We predict that at least half of vegetated areas will shift to a different physiognomic class, and woody cover will increase by as much as 52%. By incorporating observed relationships between vegetation and albedo, evapotranspiration and biomass, we show that vegetation distribution shifts will result in an overall positive feedback to climate that is likely to cause greater warming than has previously been predicted. Such extensive changes to Arctic vegetation will have implications for climate, wildlife and ecosystem services.

Avian diversity is under increasing pressures. It is thus critical to understand the ecological variables that contribute to large scale spatial distribution of avian species diversity. Traditionally, studies have relied primarily on two-dimensional habitat structure to model broad scale species richness. Vegetation vertical structure is increasingly used at local scales. However, the spatial arrangement of vegetation height has never been taken into consideration. Our goal was to examine the efficacies of three-dimensional forest structure, particularly the spatial heterogeneity of vegetation height in improving avian richness models across forested ecoregions in the U.S. We developed novel habitat metrics to characterize the spatial arrangement of vegetation height using the National Biomass and Carbon Dataset for the year 2000 (NBCD). The height-structured metrics were compared with other habitat metrics for statistical association with richness of three forest breeding bird guilds across Breeding Bird Survey (BBS) routes: a broadly grouped woodland guild, and two forest breeding guilds with preferences for forest edge and for interior forest. Parametric and non-parametric models were built to examine the improvement of predictability. Height-structured metrics had the strongest associations with species richness, yielding improved predictive ability for the woodland guild richness models (r(2) = ∼ 0.53 for the parametric models, 0.63 the non-parametric models) and the forest edge guild models (r(2) = ∼ 0.34 for the parametric models, 0.47 the non-parametric models). All but one of the linear models incorporating height-structured metrics showed significantly higher adjusted-r2 values than their counterparts without additional metrics. The interior forest guild richness showed a consistent low association with height-structured metrics. Our results suggest that height heterogeneity, beyond canopy height alone, supplements habitat characterization and richness models of forest bird species. The metrics and models derived in this study demonstrate practical examples of utilizing three-dimensional vegetation data for improved characterization of spatial patterns in species richness.

Arctic tundra is becoming greener and shrubbier due to recent warming. This is impacting climate feedbacks and wildlife, yet the spatial distribution of plant biomass in tundra ecosystems is uncertain. In this study, we mapped plant and shrub above-ground biomass (AGB; kg m−2) and shrub dominance (%; shrub AGB/plant AGB) across the North Slope of Alaska by linking biomass harvests at 28 field sites with 30 m resolution Landsat satellite imagery. We first developed regression models (p < 0.01) to predict plant AGB (r2 = 0.79) and shrub AGB (r2 = 0.82) based on the normalized difference vegetation index (NDVI) derived from imagery acquired by Landsat 5 and 7. We then predicted regional plant and shrub AGB by combining these regression models with a regional Landsat NDVI mosaic built from 1721 summer scenes acquired between 2007 and 2016. Our approach employed a Monte Carlo uncertainty analysis that propagated sampling and sensor calibration errors. We estimated that plant AGB averaged 0.74 (0.60, 0.88) kg m−2 (95% CI) and totaled 112 (91, 135) Tg across the region, with shrub AGB accounting for ~43% of regional plant AGB. The new maps capture landscape variation in plant AGB visible in high resolution satellite and aerial imagery, notably shrubby riparian corridors. Modeled shrub AGB was strongly correlated with field measurements of shrub canopy height at 25 sites (rs = 0.88) and with a regional map of shrub cover (rs = 0.76). Modeled plant AGB and shrub dominance were higher in shrub tundra than graminoid tundra and increased between areas with the coldest and warmest summer air temperatures, underscoring the fact that future warming has the potential to greatly increase plant AGB and shrub dominance in this region. These new biomass maps provide a unique source of ecological information for a region undergoing rapid environmental change.

The Global Ecosystem Dynamics Investigation (GEDI) lidar began data acquisition from the International Space Station in March 2019 and is expected to make over 10 billion measurements of canopy structure and topography over two years. Previously, airborne lidar data with limited spatial coverage have been used to examine relationships between forest canopy structure and faunal diversity, most commonly bird species. GEDI's latitudinal coverage will permit these types of analyses at larger spatial extents, over the majority of the Earth's forests, and most importantly in areas where canopy structure is complex and/or poorly understood. In this regional study, we examined the impact that GEDI-derived Canopy Structure variables have on the performance of bird species distribution models (SDMs) in Sonoma County, California. We simulated GEDI waveforms for a two-year period and then interpolated derived Canopy Structure variables to three grid sizes of analysis. In addition to these variables, we also included Phenology, Climate, and other Auxiliary variables to predict the probability of occurrence of 25 common bird species. We used a weighted average ensemble of seven individual machine learning models to make predictions for each species and calculated variable importance. We found that Canopy Structure variables were, on average at our finest resolution of 250 m, the second most important group (32.5%) of predictor variables after Climate variables (35.3%). Canopy Structure variables were most important for predicting probability of occurrence of birds associated with Conifer forest habitat. Regarding spatial analysis scale, we found that finer-scale models more frequently performed better than coarser-scale models, and the importance of Canopy Structure variables was greater at finer spatial resolutions. Overall, GEDI Canopy Structure variables improved SDM performance for at least one spatial resolution for 19 of 25 species and thus show promise for improving models of bird species occurrence and mapping potential habitat.

Large-extent maps of three-dimensional vegetation structure are important for understanding the hydrologic cycle, climate, carbon fluxes, and habitat. We aggregated over 7 billion lidar shots from the Global Ecosystem Dynamics Investigation (GEDI) to produce analysis-ready, gridded rasters of 36 vegetation structure metrics at three spatial resolutions (1, 6, and 12 km). We used 8 statistics to grid shots in every pixel, specifically the mean, bootstrapped standard error of the mean, median, standard deviation, interquartile range, Shannon’s Diversity Index, and shot count. We quantified uncertainty of the mean by randomly selecting 100 subsets of shots (i.e. bootstrapping) within each pixel. We also assessed the accuracy of several gridded metrics using fine spatial resolution airborne laser scanning data. The gridded metrics are generally more accurate at mid latitudes due to higher shot density and lower density of vegetation. Statistics associated with the central or maximum tendency of a metric are more accurate than statistics related to variability of metric values within the pixel.