1. Rapid biodiversity loss has triggered decades of research on the relationships between biodiversity and community stability. Recent studies highlighted the importance of species traits for understanding biodiversity-stability relationships. The species with high growth rates ('fast' species) are expected to be less resistant to environmental stress but recover faster if disturbed; in contrast, the species with slow growth rates ('slow' species) can be more resistant but recover more slowly if disturbed. Such a 'fast-slow' trait continuum provides a new perspective for understanding community stability, but its validity has mainly been examined in plant communities. Here, we investigate how 'fast-slow' trait composition, together with species richness and environmental factors, regulate avian community stability at a continental scale. 2. We used bird population records from the North American Breeding Bird Survey during 1988-2017 and defined avian community stability as the temporal invariability of total community biomass. We calculated species richness and the community-weighted mean (CWM) and functional diversity (FD) of four key life-history traits, including body size, nestling period (i.e. period of egg incubation and young bird fledging), life span and clutch size (i.e. annual total number of eggs). Environmental factors included temperature, precipitation and leaf area index (LAI). 3. Our analyses showed that avian community stability was mainly driven by the CWM of the 'fast-slow' trait. Communities dominated by 'fast' species (i.e. species with small body size, short nestling period and life span and large clutch size) were more stable than those dominated by 'slow' species (i.e. species with large body size, long nestling period and life span and small clutch size). Species richness and the FD of the 'fast-slow' trait explained much smaller proportions of variation in avian community stability. Temperature had direct positive effects on avian community stability, while precipitation and leaf area index affected community stability indirectly by influencing species richness and trait composition. 4. Our study demonstrates that composition of 'fast-slow' traits is the major biotic driver of avian community stability over North America. Temperature is the most important abiotic factor, but its effect is weaker than that of the 'fast-slow' trait. An integrated framework combining 'fast-slow' trait composition and temperature is needed to understand the response of avian communities in a changing environment.

Global warming is posing a threat to the health and condition of forests as the amount and length of biotic and abiotic disturbances increase. Most methods for detecting disturbances and measuring forest health are based on multi- and hyperspectral imaging. We conducted a test with spruce and pine trees using a hyperspectral Lidar instrument in a laboratory to determine the capability of combined range and reflectance measurements to investigate forest health. A simple drought treatment was conducted by leaving the harvested trees outdoors without a water supply for 12 days. The results showed statistically significant variation in reflectance after the drought treatment for both species. However, the changes differed between the species, indicating that drought-induced alterations in spectral characteristics may be species-dependent. Based on our results, hyperspectral Lidar has the potential to detect drought in spruce and pine trees.

Most trees emit volatile organic compounds (VOCs) continuously throughout their life, but the rate of emission and spectrum of emitted VOCs become substantially altered when the trees experience stress. Despite this, models to predict the emissions of VOCs do not account for perturbations caused by biotic plant stress. Considering that such stresses have generally been forecast to increase in both frequency and severity in the future climate, the neglect of stress-induced plant emissions in models might be one of the key obstacles for realistic climate change predictions, since changes in VOC concentrations are known to greatly influence atmospheric aerosol processes. Thus, we constructed a model to study the impact of biotic plant stresses on new particle formation and growth throughout a full growing season. We simulated the influence on aerosol processes caused by herbivory by the European gypsy moth (Lymantria dispar) and autumnal moth (Epirrita autumnata) feeding on pedunculate oak (Quercus robur) and mountain birch (Betula pubescens var. pumila), respectively, and also fungal infections of pedunculate oak and balsam poplar (Populus balsamifera var. suaveolens) by oak powdery mildew (Erysiphe alphitoides) and poplar rust (Melampsora larici-populina), respectively. Our modelling results indicate that all the investigated plant stresses are capable of substantially perturbing both the number and size of aerosol particles in atmospherically relevant conditions, with increases in the amount of newly formed particles by up to about an order of magnitude and additional daily growth of up to almost 50 nm. We also showed that it can be more important to account for biotic plant stresses in models for local and regional predictions of new particle formation and growth during the time of infestation or infection than significant variations in, e.g. leaf area index and temperature and light conditions, which are currently the main parameters controlling predictions of VOC emissions. Our study thus demonstrates that biotic plant stress can be highly atmospherically relevant. To validate our findings, field measurements are urgently needed to quantify the role of stress emissions in atmospheric aerosol processes and for making integration of biotic plant stress emission responses into numerical models for prediction of atmospheric chemistry and physics, including climate change projection models, possible.

While held to be a means for climate change adaptation and mitigation, nature-based solutions (NbS) themselves are vulnerable to climate change. To find ways of compensating for this vulnerability we combine a focused literature review on how information technology has been used to strengthen positive social-ecological-technological feedback, with the development of a prototype decision-support tool. Guided by the literature review, the tool integrates recent advances in using globally available remote sensing data to elicit information on functional diversity and ecosystem service provisioning with information on human service demand and population vulnerability. When combined, these variables can inform climate change adaptation strategies grounded in local social-ecological realities. This type of integrated monitoring and packaging information to be actionable have potential to support NbS management and local knowledge building for context-tailored solutions to societal challenges in urban environments.

Resolving the debate surrounding the nature and controls of seasonal variation in the structure and metabolism of Amazonian rainforests is critical to understanding their response to climate change. In situ studies have observed higher photosynthetic and evapotranspiration rates, increased litterfall and leaf flushing during the Sunlight-rich dry season. Satellite data also indicated higher greenness level, a proven surrogate of photosynthetic carbon fixation, and leaf area during the dry season relative to the wet season. Some recent reports suggest that rainforests display no seasonal variations and the previous results were satellite measurement artefacts. Therefore, here we re-examine several years of data from three sensors on two satellites under a range of sun positions and satellite measurement geometries and document robust evidence for a seasonal cycle in structure and greenness of wet equatorial Amazonian rainforests. This seasonal cycle is concordant with independent observations of solar radiation. Weattribute alternative conclusions to an incomplete study of the seasonal cycle, i. e. the dry season only, and to prognostications based on a biased radiative transfer model. Consequently, evidence of dry season greening in geometry corrected satellite data was ignored and the absence of evidence for seasonal variation in lidar data due to noisy and saturated signals was misinterpreted as evidence of the absence of changes during the dry season. Our results, grounded in the physics of radiative transfer, buttress previous reports of dry season increases in leaf flushing, litterfall, photosynthesis and evapotranspiration in well-hydrated Amazonian rainforests.

The Arctic tundra plays an important role in the carbon cycle as it stores 50% of global soil organic carbon reservoirs. The processes (fluxes) regulating these stocks are predicted to change due to direct and indirect effects of climate change. Understanding the current and future carbon balance calls for a summary of the level of knowledge regarding chamber-derived carbon dioxide (CO2) flux studies. Here, we describe progress from recently (2000-2016) published studies of growing-season CO2 flux chamber measurements, namely GPP (gross primary production), ER (ecosystem respiration), and NEE (net ecosystem exchange), in the tundra region. We review the study areas and designs along with the explanatory environmental drivers used. Most of the studies were conducted in Alaska and Fennoscandia, and we stress the need for measuring fluxes in other tundra regions, particularly in more extreme climatic, productivity, and soil conditions. Soil respiration and other greenhouse gas measurements were seldom included in the studies. Although most of the environmental drivers of CO2 fluxes have been relatively well investigated (such as the effect of vegetation type and soil microclimate on fluxes), soil nutrients, other greenhouse gases and disturbance regimes require more research as they might define the future carbon balance. Particular attention should be paid to the effects of shrubification, geomorphology, and other disturbance effects such as fire events, and disease and herbivore outbreaks. An improved conceptual framework and understanding of underlying processes of biosphere-atmosphere CO2 exchange will provide more information on carbon cycling in the tundra.

Background: Canopy structure, defined by leaf area index (LAI), fractional vegetation cover (FCover) and fraction of absorbed photosynthetically active radiation (fAPAR), regulates a wide range of forest functions and ecosystem services. Spatially consistent field-measurements of canopy structure are however lacking, particularly for the tropics. Methods: Here, we introduce the Global LAI database: a global dataset of field-based canopy structure measurements spanning tropical forests in four continents (Africa, Asia, Australia and the Americas). We use these measurements to test for climate dependencies within and across continents, and to test for the potential of anthropogenic disturbance and forest protection to modulate those dependences. Results: Using data collected from 887 tropical forest plots, we show that maximum water deficit, defined across the most arid months of the year, is an important predictor of canopy structure, with all three canopy attributes declining significantly with increasing water deficit. Canopy attributes also increase with minimum temperature, and with the protection of forests according to both active (within protected areas) and passive measures (through topography). Once protection and continent effects are accounted for, other anthropogenic measures (e.g. human population) do not improve the model. Conclusions: We conclude that canopy structure in the tropics is primarily a consequence of forest adaptation to the maximum water deficits historically experienced within a given region. Climate change, and in particular changes in drought regimes may thus affect forest structure and function, but forest protection may offer some resilience against this effect.

Remote sensing of the fraction of absorbed Photosynthetically Active Radiation (fPAR) has become a timely option to monitor forest productivity. However, only a few studies have had ground reference fPAR datasets containing both forest canopy and understory fPAR from boreal forests for the validation of satellite products. The aim of this paper was to assess the performance of two currently available satellite-based fPAR products: MODIS fPAR (MOD15A2, C5) and GEOV1 fPAR (g2_BIOPAR_FAPAR), as well as an NDVI-fPAR relationship applied to the MODIS surface reflectance product and a Landsat 8 image, in a boreal forest site in Finland. Our study area covered 16 km(2) and field data were collected from 307 forest plots. For all plots, we obtained both forest canopy fPAR and understory fPAR. The ground reference total fPAR agreed better with GEOV1 fPAR than with MODIS fPAR, which showed much more temporal variation during the peak-season than GEOV1 fPAR. At the chosen intercomparison date in peak growing season, MODIS NDVI based fPAR estimates were similar to GEOV1 fPAR, and produced on average 0.01 fPAR units smaller fPAR estimates than ground reference total fPAR. MODIS fPAR and Landsat 8 NDVI based fPAR estimates were similar to forest canopy fPAR.