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Intact native forests under negligible large-scale human pressures (i.e. high-integrity forests) are critical for biodiversity conservation. However, high-integrity forests are declining worldwide due to deforestation and forest degradation. Recognizing the importance of high-integrity ecosystems (including forests), the Kunming-Montreal Global Biodiversity Framework (GBF) has directly included the maintenance and restoration of ecosystem integrity, in addition to ecosystem extent, in its goals and targets. Yet, the headline indicators identified to help nations monitor forest ecosystems and their integrity can currently track changes only in (1) forest cover or extent, and (2) the risk of ecosystem collapse using the IUCN Red List of Ecosystems (RLE). These headline indicators are unlikely to facilitate the monitoring of forest integrity for two reasons. First, focusing on forest cover not only misses the impacts of anthropogenic degradation on forests but can also fail to detect the effect of positive management actions in enhancing forest integrity. Second, the risk of ecosystem collapse as measured by the ordinal RLE index (from Least Concern to Critically Endangered) makes it unlikely that changes to the continuum of forest integrity over space and time would be reported by nations. Importantly, forest ecosystems in many biodiverse African and Asian nations remain unassessed with the RLE. As such, many nations will likely resort to monitoring forest cover alone and therefore inadequately report progress against forest integrity goals and targets. We concur that monitoring changes in forest cover and the risk of ecosystem collapse are indeed vital aspects of conservation monitoring. Yet, they are insufficient for the specific purpose of tracking progress against crucial ecosystem integrity components of the GBF’s goals. We discuss the pitfalls of merely monitoring forest cover, a likely outcome with the current headline indicators. Augmenting forest cover monitoring with indicators that capture change in absolute area along the continuum of forest integrity would help monitor progress toward achieving area-based targets related to both integrity and extent of global forests.

Increased environmental threats require proper monitoring of animal communities to understand where and when changes occur. Ecoacoustic tools that quantify natural acoustic environments use a combination of biophony (animal sound) and geophony (wind, rain, and other natural phenomena) to represent the natural soundscape and, in comparison to anthropophony (technological human sound) can highlight valuable landscapes to both human and animal communities. However, recording these sounds requires intensive deployment of recording devices and storage and interpretation of large amounts of data, resulting in large data gaps across the landscape and periods in which recordings are absent. Interpolating ecoacoustic metrics like biophony, geophony, anthropophony, and acoustic indices can bridge these gaps in observations and provide insight across larger spatial extents and during periods of interest. Here, we use seven ecoacoustic metrics and acoustically-derived bird species richness across a heterogeneous landscape composed of densely urbanized, suburban, rural, protected, and recently burned lands in Sonoma County, California, U.S.A., to explore spatiotemporal patterns in ecoacoustic measurements. Predictive models of ecoacoustic metrics driven by land-use/land-cover, remotely-sensed vegetation structure, anthropogenic impact, climate, geomorphology, and phenology variables capture landscape and daily differences in ecoacoustic patterns with varying performance (avg. R ^2 = 0.38 ± 0.11) depending on metric and period-of-day and provide interpretable patterns in sound related to human activity, weather phenomena, and animal activity. We also offer a case study on the use of the data-driven prediction of biophony to capture changes in soniferous species activity before (1–2 years prior) and after (1–2 years post) wildfires in our study area and find that biophony may depict the reorganization of acoustic communities following wildfires. This is demonstrated by an upward trend in activity 1–2 years post-wildfire, particularly in more severely burned areas. Overall, we provide evidence of the importance of climate, spaceborne-lidar-derived forest structure, and phenological time series characteristics when modeling ecoacoustic metrics to upscale site observations and map ecoacoustic biodiversity in areas without prior acoustic data collection. Resulting maps can identify areas of attention where changes in animal communities occur at the edge of human and natural disturbances.

Changes in vegetation distribution are underway in Arctic and boreal regions due to climate warming and associated fire disturbance. These changes have wide ranging downstream impacts—affecting wildlife habitat, nutrient cycling, climate feedbacks and fire regimes. It is thus critical to understand where these changes are occurring and what types of vegetation are affected, and to quantify the magnitude of the changes. In this study, we mapped live aboveground biomass for five common plant functional types (PFTs; deciduous shrubs, evergreen shrubs, forbs, graminoids and lichens) within Alaska and northwest Canada, every five years from 1985 to 2020. We employed a multi-scale approach, scaling from field harvest data and unmanned aerial vehicle-based biomass predictions to produce wall-to-wall maps based on climatological, topographic, phenological and Landsat spectral predictors. We found deciduous shrub and graminoid biomass were predicted best among PFTs. Our time-series analyses show increases in deciduous (37%) and evergreen shrub (7%) biomass, and decreases in graminoid (14%) and lichen (13%) biomass over a study area of approximately 500 000 km ^2 . Fire was an important driver of recent changes in the study area, with the largest changes in biomass associated with historic fire perimeters. Decreases in lichen and graminoid biomass often corresponded with increasing shrub biomass. These findings illustrate the driving trends in vegetation change within the Arctic/boreal region. Understanding these changes and the impacts they in turn will have on Arctic and boreal ecosystems will be critical to understanding the trajectory of climate change in the region.

Forest characteristics, structure, and dynamics within the North American boreal region are heavily influenced by wildfire intensity, severity, and frequency. Increasing temperatures are likely to result in drier conditions and longer fire seasons, potentially leading to more intense and frequent fires. However, an increase in deciduous forest cover is also predicted across the region, potentially decreasing flammability. In this study, we use an individual tree-based forest model to test bottom-up (i.e. fuels) vs top-down (i.e. climate) controls on fire activity and project future forest and wildfire dynamics. The University of Virginia Forest Model Enhanced is an individual tree-based forest model that has been successfully updated and validated within the North American boreal zone. We updated the model to better characterize fire ignition and behavior in relation to litter and fire weather conditions, allowing for further interactions between vegetation, soils, fire, and climate. Model output following updates showed good agreement with combustion observations at individual sites within boreal Alaska and western Canada. We then applied the updated model at sites within interior Alaska and the Northwest Territories to simulate wildfire and forest response to climate change under moderate (RCP 4.5) and extreme (RCP 8.5) scenarios. Results suggest that changing climate will act to decrease biomass and increase deciduous fraction in many regions of boreal North America. These changes are accompanied by decreases in fire probability and average fire intensity, despite fuel drying, indicating a negative feedback of fuel loading on wildfire. These simulations demonstrate the importance of dynamic fuels and dynamic vegetation in predicting future forest and wildfire conditions. The vegetation and wildfire changes predicted here have implications for large-scale changes in vegetation composition, biomass, and wildfire severity across boreal North America, potentially resulting in further feedbacks to regional and even global climate and carbon cycling.

Widespread changes in the distribution and abundance of plant functional types (PFTs) are occurring in Arctic and boreal ecosystems due to the intensification of disturbances, such as fire, and climate-driven vegetation dynamics, such as tundra shrub expansion. To understand how these changes affect boreal and tundra ecosystems, we need to first quantify change for multiple PFTs across recent years. While landscape patches are generally composed of a mixture of PFTs, most previous moderate resolution (30 m) remote sensing analyses have mapped vegetation distribution and change within land cover categories that are based on the dominant PFT; or else the continuous distribution of one or a few PFTs, but for a single point in time. Here we map a 35 year time-series (1985–2020) of top cover (TC) for seven PFTs across a 1.77 × 10 ^6 km ^2 study area in northern and central Alaska and northwestern Canada. We improve on previous methods of detecting vegetation change by modeling TC, a continuous measure of plant abundance. The PFTs collectively include all vascular plants within the study area as well as light macrolichens, a nonvascular class of high importance to caribou management. We identified net increases in deciduous shrubs (66 × 10 ^3 km ^2 ), evergreen shrubs (20 × 10 ^3 km ^2 ), broadleaf trees (17 × 10 ^3 km ^2 ), and conifer trees (16 × 10 ^3 km ^2 ), and net decreases in graminoids (−40 × 10 ^3 km ^2 ) and light macrolichens (−13 × 10 ^3 km ^2 ) over the full map area, with similar patterns across Arctic, oroarctic, and boreal bioclimatic zones. Model performance was assessed using spatially blocked, nested five-fold cross-validation with overall root mean square errors ranging from 8.3% to 19.0%. Most net change occurred as succession or plant expansion within areas undisturbed by recent fire, though PFT TC change also clearly resulted from fire disturbance. These maps have important applications for assessment of surface energy budgets, permafrost changes, nutrient cycling, and wildlife management and movement analysis.

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.

Understanding spatial patterns of diversity in tropical forests is indispensable for their sustainable use and conservation. Recent studies have reported relationships between forest structure and α -diversity. While tree α -diversity is difficult to map via remote sensing, large-scale forest structure models are becoming more common, which would facilitate mapping the relationship between tree α -diversity and forest structure, contributing to our understanding of biogeographic patterns in the tropics. We developed a methodology to map tree α -diversity in tropical forest regions at 50 m spatial resolution using α -diversity estimates from forest inventories as response variables and forest structural metrics and environmental variables as predictors. To include forest structural metrics in our modelling, we first developed a method to map seven of these metrics integrating discrete light detection and ranging (LiDAR), multispectral, and synthetic aperture radar imagery (SAR). We evaluated this methodology in the Chocó region of Colombia, a tropical forest with high tree diversity and complex forest structure. The relative errors (REs) of the random forest models used to map the seven forest structural variables ranged from low (6%) to moderate (35%). The α -diversity maps had moderate RE; the maps of Simpson and Shannon diversity indices had the lowest RE (9% and 13%), followed by richness (17%), while Shannon and Simpson effective number of species indices had the highest RE, 27% and 47%, respectively. The highest concentrations of tree α -diversity are located along the Pacific Coast from the centre to the northwest of the Chocó Region and in non-flooded forest along the boundary between the Chocó region and the Andes. Our results reveal strong relationships between canopy structure and tree α -diversity, providing support for ecological theories that link structure to diversity via niche partitioning and environmental conditions. With modification, our methods could be applied to assess tree α -diversity of any tropical forest where tree α -diversity field observations coincident with LiDAR data.

Abstract Drivers of forest wildfire severity include fuels, topography and weather. However, because only fuels can be actively managed, quantifying their effects on severity has become an urgent research priority. Here we employed GEDI spaceborne lidar to consistently assess how pre-fire forest fuel structure affected wildfire severity across 42 California wildfires between 2019–2021. Using a spatial-hierarchical modeling framework, we found a positive concave-down relationship between GEDI-derived fuel structure and wildfire severity, marked by increasing severity with greater fuel loads until a decline in severity in the tallest and most voluminous forest canopies. Critically, indicators of canopy fuel volumes (like biomass and height) became decoupled from severity patterns in extreme topographic and weather conditions (slopes >20°; winds > 9.3 m/s). On the other hand, vertical continuity metrics like layering and ladder fuels more consistently predicted severity in extreme conditions – especially ladder fuels, where sparse understories were uniformly associated with lower severity levels. These results confirm that GEDI-derived fuel estimates can overcome limitations of optical imagery and airborne lidar for quantifying the interactive drivers of wildfire severity. Furthermore, these findings have direct implications for designing treatment interventions that target ladder fuels versus entire canopies and for delineating wildfire risk across topographic and weather conditions.

Previous research indicates that the effects of climate warming, including shrub expansion and increased fire frequency may lead to declining lichen abundance in arctic tundra and northern alpine areas. Lichens are important forage for caribou ( Rangifer tarandus ), whose populations are declining throughout most of North America. To clarify how lichen cover might affect caribou resource selection, ecologists require better data on the spatial distribution and abundance of lichen. Here, we use a combination of field data and satellite imagery to model lichen cover for a 583 200 km ^2 area that fully encompasses nine caribou ranges in interior Alaska and Yukon. We aggregated data from in situ vegetation plots, aerial survey polygons and unmanned aerial vehicle (UAV) imagery to align with 30 m resolution Landsat pixels. We used these data to train a random forest model with a suite of environmental and spectral predictors to estimate lichen cover. We validated our lichen cover model using reserved training data and existing external datasets, and found that reserved data from aerial survey polygons ( R ^2 = 0.77) and UAV imagery ( R ^2 = 0.71) provided the best fit. We used our lichen cover map to evaluate the influence of estimated lichen cover on caribou resource selection in the Fortymile Herd from 2012 to 2018 during summer and winter. In both seasons, caribou avoided lichen-poor areas (0%–5% lichen cover) and showed stronger selection as lichen cover increased to ∼30%, above which selection leveled off. Our results suggest that terrestrial lichen cover is an important factor influencing caribou resource selection in northern boreal forests across seasons. Our lichen cover map goes beyond existing maps of lichen abundance and distribution because it incorporates extensive field data for model training and validation and estimates lichen cover over a much larger spatial extent. We expect our landscape-scale map will be useful for understanding trends in lichen abundance and distribution, as well as for caribou research, management and conservation.

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.

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 .

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.

Warming in recent decades has triggered shrub expansion in Arctic and alpine tundra, which is transforming these temperature-limited ecosystems and altering carbon and nutrient cycles, fire regimes, permafrost stability, land-surface climate-feedbacks, and wildlife habitat. Where and when Arctic shrub expansion happens in the future will depend in part on how different shrub communities respond to warming air temperatures. Here, we analyze a shrub ring-width network of 18 sites consisting of Salix spp. and Alnus viridis growing across the North Slope of Alaska (68–71 ° N; 164–149 ° W) to assess shrub temperature sensitivity and compare radial growth patterns with satellite NDVI (normalized difference vegetation index) data since 1982. Regardless of site conditions and taxa, all shrubs shared a common year-to-year growth variability and had a positive response to daily maximum air temperatures (Tmax) from ca. May 31 (i.e. Tmax ∼6 ° C) to early July (i.e. Tmax ∼12 ° C), two-thirds of which were significant correlations. Thus, the month of June had the highest shrub growth-temperature sensitivity. This period coincides with the seasonal increase in temperature and phenological green up on the North Slope indicated by both field observations and the seasonal cycle of NDVI (a proxy of photosynthetic activity). Nearly all of the sampled shrubs (98%) initiated their growth after 1960, with 74% initiated since 1980. This post-1980 shrub-recruitment pulse coincided with ∼2 °C warmer June temperatures compared to prior periods, as well as with positive trends in shrub basal area increments and peak summer NDVI. Significant correlations between shrub growth and peak summer NDVI indicate these radial growth patterns in shrubs reflect tundra productivity at a broader scale and that tundra vegetation on the North Slope of Alaska underwent a greening trend between 1980 and 2012.

Climate change is impacting forested ecosystems worldwide, particularly in the Northern Hemisphere where warming has increased at a faster rate than the rest of the globe. As climate warms, trembling aspen ( Populus tremuloides ) is expected to become more successful in northern boreal forests because of its current presence in drier areas of North America. However, large-scale productivity decline of aspen has recently been documented throughout the United States and Canada as a result of drought and insect outbreaks. We used tree ring measurements (basal area increment (BAI) and stable carbon isotopes ( δ ^13 C)) and remote sensing indices of vegetation productivity (NDVI) to study the impact of climate and damage by the aspen epidermal leaf miner ( Phyllocnistis populiella ) on aspen productivity and physiology in interior Alaska. We found that productivity decreased with greater leaf mining and was not sensitive to growing season (GS) moisture availability. Although productivity decreased during high leaf mining years, it recovered to pre-outbreak levels during years of low insect damage, suggesting a degree of resilience to P. populiella mining. Climate and leaf mining interacted to influence tree ring δ ^13 C, with greater leaf mining resulting in decreased δ ^13 C when GS moisture availability was low. We also found that NDVI was negatively associated with leaf mining, and positively correlated with BAI and the δ ^13 C decrease corresponding to mining. This suggests that NDVI is capturing not only variations in productivity, but also changes in physiology associated with P. populiella . Overall, these findings indicate that the indirect effects of P. populiella mining have a larger impact on aspen productivity and physiology than climate under current conditions, and is essential to consider when assessing growth, physiology and NDVI trends in interior Alaska.

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

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.

As the permafrost region warms, its large organic carbon pool will be increasingly vulnerable to decomposition, combustion, and hydrologic export. Models predict that some portion of this release will be offset by increased production of Arctic and boreal biomass; however, the lack of robust estimates of net carbon balance increases the risk of further overshooting international emissions targets. Precise empirical or model-based assessments of the critical factors driving carbon balance are unlikely in the near future, so to address this gap, we present estimates from 98 permafrost-region experts of the response of biomass, wildfire, and hydrologic carbon flux to climate change. Results suggest that contrary to model projections, total permafrost-region biomass could decrease due to water stress and disturbance, factors that are not adequately incorporated in current models. Assessments indicate that end-of-the-century organic carbon release from Arctic rivers and collapsing coastlines could increase by 75% while carbon loss via burning could increase four-fold. Experts identified water balance, shifts in vegetation community, and permafrost degradation as the key sources of uncertainty in predicting future system response. In combination with previous findings, results suggest the permafrost region will become a carbon source to the atmosphere by 2100 regardless of warming scenario but that 65%–85% of permafrost carbon release can still be avoided if human emissions are actively reduced.

In arctic tundra and boreal forest ecosystems vegetation structural and functional influences on the surface energy balance can strongly influence permafrost soil temperatures. As such, vegetation changes will likely play an important role in permafrost soil carbon dynamics and associated climate feedbacks. Processes that lead to changes in vegetation, such as wildfire or ecosystem responses to rising temperatures, are of critical importance to understanding the impacts of arctic and boreal ecosystems on future climate. Yet these processes vary within and between ecosystems and this variability has not been systematically characterized across the arctic-boreal region. Here we quantify the distribution of vegetation productivity trends, wildfire, and near-surface soil carbon, by vegetation type, across the zones of continuous and discontinuous permafrost. Siberian larch forests contain more than one quarter of permafrost soil carbon in areas of continuous permafrost. We observe pervasive positive trends in vegetation productivity in areas of continuous permafrost, whereas areas underlain by discontinuous permafrost have proportionally less positive productivity trends and an increase in areas exhibiting negative productivity trends. Fire affects a much smaller proportion of the total area and thus a smaller amount of permafrost soil carbon, with the vast majority occurring in deciduous needleleaf forests. Our results indicate that vegetation productivity trends may be linked to permafrost distribution, fire affects a relatively small proportion of permafrost soil carbon, and Siberian larch forests will play a crucial role in the strength of the permafrost carbon climate feedback.

Countries participating in climate change mitigation via the United Nations Framework Convention on Climate Change reducing emissions from deforestation and forest degradation mechanism are required to establish national forest monitoring systems. The design of national forest monitoring system includes provision of transparent, consistent and accurate estimates of emissions and removals from forests, while also taking into account national circumstances and capabilities. One key component of these systems lies in satellite remote sensing approaches and techniques to determine baseline data on forest loss against which future rates of change can be evaluated. Advances in approaches meeting these criteria for measuring, reporting and verification purposes are therefore of tremendous interest. A robust example advancing such approaches, focused on Peru, is provided in the recent paper of Potapov et al (2014 Environ. Res. Lett. http://dx.doi.org/10.1088/1748-9326/9/12/124012 9 http://dx.doi.org/10.1088/1748-9326/9/12/124012 ).

This paper presents an overview of the state of measurement and monitoring capabilities for forests in the context of REDD+ needs, with a focus on what is currently possible, where improvements are needed, and what capabilities will be advanced in the near-term with new technologies already under development. We summarize the role of remote sensing (both satellite and aircraft) for observational monitoring of forests, including measuring changes in their current and past extent for setting baselines, their carbon stock density for estimating emissions in areas that are deforested or degraded, and their regrowth dynamics following disturbance. We emphasize the synergistic role of integrating field inventory measurements with remote sensing for best practices in monitoring, reporting and verification. We also address the potential of remote sensing for enforcing safeguards on conservation of natural forests and biodiversity. We argue that capabilities exist now to meet operational needs for REDD+ measurement, reporting, and verification and reference levels. For some other areas of importance for REDD+, such as safeguards for natural forests and biodiversity, monitoring capabilities are approaching operational in the near term. For all REDD+ needs, measurement capabilities will rapidly advance in the next few years as a result of new technology as well as advances in capacity building both within and outside of the tropical forest nations on which REDD+ is primarily focused.

Recent advances in remote sensing and ecological modeling warrant a timely and robust investigation of the ecological variables that underlie large-scale patterns of breeding bird species richness, particularly in the context of intensifying land use and climate change. Our objective was to address this need using an array of bioclimatic and remotely sensed data sets representing vegetation properties and structure, and other aspects of the physical environment. We first build models of bird species richness across breeding bird survey (BBS) routes, and then spatially predict richness across the coterminous US at moderately high spatial resolution (1 km). Predictor variables were derived from various sources and maps of species richness were generated for four groups (guilds) of birds with different breeding habitat affiliation (forest, grassland, open woodland, scrub/shrub), as well as all guilds combined. Predictions of forest bird distributions were strong ( R ^2 = 0.85), followed by grassland (0.76), scrub/shrub (0.63) and open woodland (0.60) species. Vegetation properties were generally the strongest determinants of species richness, whereas bioclimatic and lidar-derived vertical structure metrics were of variable importance and dependent upon the guild type. Environmental variables (climate and the physical environment) were also frequently selected predictors, but canopy structure variables were not as important as expected based on more local to regional scale studies. Relatively sparse sampling of canopy structure metrics from the satellite lidar sensor may have reduced their importance relative to other predictor variables across the study domain. We discuss these results in the context of the ecological drivers of species richness patterns, the spatial scale of bird diversity analyses, and the potential of next generation space-borne lidar systems relevant to vegetation and ecosystem studies. This study strengthens current understanding of bird species–climate–vegetation relationships, which could be further advanced with improved canopy structure information across spatial scales.

Recent large and frequent fires above the Alaskan arctic circle have forced a reassessment of the ecological and climatological importance of fire in arctic tundra ecosystems. Here we provide a general overview of the occurrence, distribution, and ecological and climate implications of Alaskan tundra fires over the past half-century using spatially explicit climate, fire, vegetation and remote sensing datasets for Alaska. Our analyses highlight the importance of vegetation biomass and environmental conditions in regulating tundra burning, and demonstrate that most tundra ecosystems are susceptible to burn, providing the environmental conditions are right. Over the past two decades, fire perimeters above the arctic circle have increased in size and importance, especially on the North Slope, indicating that future wildfire projections should account for fire regime changes in these regions. Remote sensing data and a literature review of thaw depths indicate that tundra fires have both positive and negative implications for climatic feedbacks including a decadal increase in albedo radiative forcing immediately after a fire, a stimulation of surface greenness and a persistent long-term (>10 year) increase in thaw depth. In order to address the future impact of tundra fires on climate, a better understanding of the control of tundra fire occurrence as well as the long-term impacts on ecosystem carbon cycling will be required.

Arctic vegetation communities are rapidly changing with climate warming, which impacts wildlife, carbon cycling, and climate feedbacks. Accurately monitoring vegetation change is thus crucial, but scale mismatches between field and satellite-based monitoring cause challenges. Remote sensing from unmanned aerial vehicles (UAVs) has emerged as a bridge between field data and satellite-based mapping. We assessed the viability of using high-resolution UAV imagery and UAV-derived Structure from Motion to predict cover, height, and aboveground biomass (henceforth biomass) of Arctic plant functional types (PFTs) across a range of vegetation community types. We classified imagery by PFT, estimated cover and height, and modeled biomass from UAV-derived volume estimates. Predicted values were compared to field estimates to assess results. Cover was estimated with a root-mean-square error (RMSE) of 6.29%–14.2%, and height was estimated with an RMSE of 3.29–10.5 cm depending on the PFT. Total aboveground biomass was predicted with an RMSE of 220.5 g m−2, and per-PFT RMSE ranged from 17.14 to 164.3 g m−2. Deciduous and evergreen shrub biomass was predicted most accurately, followed by lichen, graminoid, and forb biomass. Our results demonstrate the effectiveness of using UAVs to map PFT biomass, which provides a link towards improved mapping of PFTs across large areas using earth observation satellite imagery.

Interest in ecoacoustics has resulted in an influx of acoustic data and novel methodologies to classify and relate landscape sound activity to biodiversity and ecosystem health. However, indicators used to summarize sound and quantify the effects of disturbances on biodiversity can be inconsistent when applied across ecological gradients. This study used an acoustic dataset of 487,148 min from 746 sites collected over 4 years across Sonoma County, California, USA, by citizen scientists. We built a custom labeled dataset of soundscape components and applied a deep learning framework to test our ability to predict these soundscape components: human noise (Anthropophony), wildlife vocalizations (Biophony), weather phenomena (Geophony), Quiet periods, and microphone Interference. These soundscape components allowed us to balance predicting variation in environmental recordings and relative time to build a custom labeled dataset. We used these data to quantify soundscape patterns across space and time that could be useful for environmental planning, ecosystem conservation and restoration, and biodiversity monitoring. We describe a pre-trained convolutional neural network, fine-tuned with our sound reference data, with classification achieving an overall F0.75-score of 0.88, precision of 0.94, and recall of 0.80 across the five target soundscape components. We deployed the model to predict soundscape components for all acoustic data and assess their hourly patterns. We noted an increase in Biophony in the early morning and evening, coinciding with peak animal community vocalization (e.g., dawn chorus). Anthropophony increased during morning/daylight hours and was lowest in the evenings, coinciding with diurnal patterns in human activity. Further, we examined soundscape patterns related to geographic properties at recording sites. Anthropophony decreased with increasing distance to major roads, while Quiet increased. Biophony and Quiet were comparable to Anthropophony at more urban/developed and agriculture/barren sites, while Biophony and Quiet were significantly higher than Anthropophony at less-developed shrubland, oak woodland, and conifer forest sites. These results demonstrate that acoustic classification of broad soundscape components is possible with small datasets, and classifications can be applied to a large acoustic dataset to gain ecological knowledge.

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.

Abstract Signatory countries to the Convention on Biological Diversity (CBD) are formulating goals and indicators through 2050 under the post‐2020 Global Biodiversity Framework (GBF). Among the goals is increasing the integrity of ecosystems. The CBD is now seeking input toward a quantifiable definition of integrity and methods to track it globally. Here, we offer a schema for using Earth observations (EO) to monitor and evaluate global forest ecosystem integrity (EI). Our approach builds on three topics: the concept of EI, the use of satellite‐based EO, and the use of “essential biodiversity variables” to monitor and report on it. Within this schema, EI is a measure of the structure, function, and composition of an ecosystem relative to the range of variation determined by climatic–geophysical environment. We use evaluation criteria to recommend eight potential indicators of EI that can be monitored around the globe using Earth Observations to support the efforts of nations to monitor and report progress to implement the post‐2020 GBF. If operationalized, this schema should help Parties to the CBD take action and report progress on achieving ecosystem commitments during this decade.

Abstract Climate change‐driven droughts and insect outbreaks are becoming more frequent and widespread, increasing forest vulnerability to mortality. By addressing the impacts of climate and insects on tree growth preceding death, we can better understand tree mortality risk under a changing climate. Here, we used tree stature and interannual growth (basal area increment; BAI) to assess processes leading to trembling aspen (Populus tremuloides) survival or mortality during an unprecedented leaf miner (Phyllocnistis populiella) outbreak in boreal North America. We identified eight sites (22 plots) in the longest running forest monitoring network in Alaska, spanning ~350 km of latitude, that experienced ≥0.25 Mg·ha−1·yr−1 aspen mortality during the outbreak. We compared the size and canopy position, growth patterns, and sensitivity to climate and leaf mining of aspen that survived (living; n = 84) vs. died (dying; n = 76) and linked the normalized difference vegetation index (NDVI) to plot‐level aspen growth and stand biomass recruitment, growth, and mortality. Dying aspen were in the subcanopy, smaller in diameter, and after a drought in 1957 had lower growth than living aspen until death. Before the outbreak, growth of all trees was positively influenced by moisture and negatively by temperature, but only living trees maintained this climate response during the outbreak. Leaf mining reduced growth of both groups, exerting at least a twofold greater impact than climate. The NDVI captured plot‐level tree growth and stand biomass growth and mortality, yet it was nearly two times more strongly associated with living than dying tree growth and 12 times more strongly associated with biomass growth than mortality. These differences suggest that NDVI may inadequately detect insect‐driven dieback and dispersed mortality of aspen across the boreal biome. Our findings reveal that a historic drought triggered a multi‐decadal growth decline that predisposed aspen to mortality during the leaf miner outbreak and that while aspen growth is influenced by moisture and temperature, it is more strongly affected by P. populiella. We conclude that as the climate warms and insect outbreaks increase in frequency and magnitude at high latitudes, we should expect to see persistent and greater declines in aspen growth and increases in mortality.