Explore the vast range of titles available.

Aim: Biotic disturbances (BD, including insects, pathogens and wildlife herbivory) can alter forest structure and the capability of forests to deliver ecosystem services. Impact assessments, however, are limited by the lack of reliable and timely disturbance data at large spatial scales. This review synthesizes empirical data on the magnitude and distribution of spatiotemporal impacts of BD. Location: Northern Hemisphere. Methods: Based on large-scale, multi-year BD data sets, covering c. 46% of the global forest, we calculated annual disturbance fractions Df (percentage of forest area affected) and their inter-annual variability at a grid cell resolution of 1°. The impact of BD on forest carbon pools was determined by overlaying Df with data on forest cover and carbon density. Results: Overall, 43.9 million hectares (Mha) (Df=2.6%) of forests were affected annually by BD, particularly by insects (36.5 Mha, Df=2.2%). Our synthesis demonstrates that fractions affected by BD (1) vary greatly over space and time, mainly in response to ephemeral bark beetle and defoliator outbreaks, (2) show temporal trends that are inconsistent across regions, yet are largely increasing over recent decades, and (3) are substantially higher than Df caused by fire and other abiotic disturbances. Tree mortality was estimated over an area of 3.3 Mha year⁻¹ (medium estimate which assumed mortality at 7.5% of the affected area), with associated committed carbon fluxes from living biomass to litter and the atmosphere at 129.9 Mt C year⁻¹. Main conclusions: BD are key drivers of forest dynamics, making a contribution to tree mortality of a similar magnitude to fire. Despite inherent uncertainties, the data reported can be used to improve the representation of BD in global ecosystem models. Our findings call for future forest monitoring approaches to provide accessible, precise and consistent data on the occurrence and severity of BD which are harmonized across jurisdictions.

Future climate change and increasing atmospheric CO2 are expected to cause major changes in vegetation structure and function over large fractions of the global land surface. Seven global vegetation models are used to analyze possible responses to future climate simulated by a range of general circulation models run under all four representative concentration pathway scenarios of changing concentrations of greenhouse gases. All 110 simulations predict an increase in global vegetation carbon to 2100, but with substantial variation between vegetation models. For example, at 4 °C of global land surface warming (510–758 ppm of CO2), vegetation carbon increases by 52–477 Pg C (224 Pg C mean), mainly due to CO2 fertilization of photosynthesis. Simulations agree on large regional increases across much of the boreal forest, western Amazonia, central Africa, western China, and southeast Asia, with reductions across southwestern North America, central South America, southern Mediterranean areas, southwestern Africa, and southwestern Australia. Four vegetation models display discontinuities across 4 °C of warming, indicating global thresholds in the balance of positive and negative influences on productivity and biomass. In contrast to previous global vegetation model studies, we emphasize the importance of uncertainties in projected changes in carbon residence times. We find, when all seven models are considered for one representative concentration pathway × general circulation model combination, such uncertainties explain 30% more variation in modeled vegetation carbon change than responses of net primary productivity alone, increasing to 151% for non-HYBRID4 models. A change in research priorities away from production and toward structural dynamics and demographic processes is recommended.

Fire is an important climate-driven disturbance in terrestrial ecosystems, also modulated by human ignitions or fire suppression. Changes in fire emissions can feed back on the global carbon cycle, but whether the trajectories of changing fire activity will exacerbate or attenuate climate change is poorly understood. Here, we quantify fire dynamics under historical and future climate and human demography using a coupled global climate–fire–carbon cycle model that emulates 34 individual Earth system models (ESMs). Results are compared with counterfactual worlds, one with a constant preindustrial fire regime and another without fire. Although uncertainty in projected fire effects is large and depends on ESM, socioeconomic trajectory, and emissions scenario, we find that changes in human demography tend to suppress global fire activity, keeping more carbon within terrestrial ecosystems and attenuating warming. Globally, changes in fire have acted to warm climate throughout most of the 20th century. However, recent and predicted future reductions in fire activity may reverse this, enhancing land carbon uptake and corresponding to offsetting ∼5 to 10 y of global CO₂ emissions at today’s levels. This potentially reduces warming by up to 0.11 °C by 2100. We show that climate–carbon cycle feedbacks, as caused by changing fire regimes, are most effective at slowing global warming under lower emission scenarios. Our study highlights that ignitions and active and passive fire suppression can be as important in driving future fire regimes as changes in climate, although with some risk of more extreme fires regionally and with implications for other ecosystem functions in fire-dependent ecosystems.

The maximum photosynthetic carboxylation rate (V cmax) is an influential plant trait that has multiple scaling hypotheses, which is a source of uncertainty in predictive understanding of global gross primary production (GPP). Four trait-scaling hypotheses (plant functional type, nutrient limitation, environmental filtering, and plant plasticity) with nine specific implementations were used to predict global V cmax distributions and their impact on global GPP in the Sheffield Dynamic Global Vegetation Model (SDGVM). Global GPP varied from 108.1 to 128.2 PgC yr−1, 65% of the range of a recent model inter-comparison of global GPP. The variation in GPP propagated through to a 27% coefficient of variation in net biome productivity (NBP). All hypotheses produced global GPP that was highly correlated (r = 0.85–0.91) with three proxies of global GPP. Plant functional type-based nutrient limitation, underpinned by a core SDGVM hypothesis that plant nitrogen (N) status is inversely related to increasing costs of N acquisition with increasing soil carbon, adequately reproduced global GPP distributions. Further improvement could be achieved with accurate representation of water sensitivity and agriculture in SDGVM. Mismatch between environmental filtering (the most data-driven hypothesis) and GPP suggested that greater effort is needed understand V cmax variation in the field, particularly in northern latitudes.

Dynamic global vegetation models (DGVMs) are powerful tools to project past, current and future vegetation patterns and associated biogeochemical cycles. However, most models are limited by how they define vegetation and by their simplistic representation of competition. We discuss how concepts from community assembly theory and coexistence theory can help to improve vegetation models. We further present a trait- and individual-based vegetation model (aDGVM2) that allows individual plants to adopt a unique combination of trait values. These traits define how individual plants grow and compete. A genetic optimization algorithm is used to simulate trait inheritance and reproductive isolation between individuals. These model properties allow the assembly of plant communities that are adapted to a site's biotic and abiotic conditions. The aDGVM2 simulates how environmental conditions influence the trait spectra of plant communities; that fire selects for traits that enhance fire protection and reduces trait diversity; and the emergence of life-history strategies that are suggestive of colonization–competition trade-offs. The aDGVM2 deals with functional diversity and competition fundamentally differently from current DGVMs. This approach may yield novel insights as to how vegetation may respond to climate change and we believe it could foster collaborations between functional plant biologists and vegetation modellers.

AIM: To evaluate the climate sensitivity of model‐based forest productivity estimates using a continental‐scale tree‐ring network. LOCATION: Europe and North Africa (30–70° N, 10° W–40° E). METHODS: We compiled close to 1000 annually resolved records of radial tree growth for all major European tree species and quantified changes in growth as a function of historical climatic variation. Sites were grouped using a neural network clustering technique to isolate spatiotemporal and species‐specific climate response patterns. The resulting empirical climate sensitivities were compared with the sensitivities of net primary production (NPP) estimates derived from the ORCHIDEE‐FM and LPJ‐wsl dynamic global vegetation models (DGVMs). RESULTS: We found coherent biogeographic patterns in climate response that depend upon (1) phylogenetic controls and (2) ambient environmental conditions delineated by latitudinal/elevational location. Temperature controls dominate forest productivity in high‐elevation and high‐latitude areas whereas moisture sensitive sites are widespread at low elevation in central and southern Europe. DGVM simulations broadly reproduce the empirical patterns, but show less temperature sensitivity in the boreal zone and stronger precipitation sensitivity towards the mid‐latitudes. MAIN CONCLUSIONS: Large‐scale forest productivity is driven by monthly to seasonal climate controls, but our results emphasize species‐specific growth patterns under comparable environmental conditions. Furthermore, we demonstrate that carry‐over effects from the previous growing season can significantly influence tree growth, particularly in areas with harsh climatic conditions – an element not considered in most current‐state DGVMs. Model–data discrepancies suggest that the simulated climate sensitivity of NPP will need refinement before carbon‐cycle climate feedbacks can be accurately quantified.

Aim: This paper presents a tool for long-term global change studies; it is an update of the History Database of the Global Environment (HYDE) with estimates of some of the underlying demographic and agricultural driving factors. Methods: Historical population, cropland and pasture statistics are combined with satellite information and specific allocation algorithms (which change over time) to create spatially explicit maps, which are fully consistent on a 5' longitude/latitude grid resolution, and cover the period 10,000 BC to AD 2000. Results: Cropland occupied roughly less than 1% of the global ice-free land area for a long time until AD 1000, similar to the area used for pasture. In the centuries that followed, the share of global cropland increased to 2% in AD 1700 (c. 3 million km 2 ) and 11% in AD 2000 (15 million km²), while the share of pasture area grew from 2% in AD 1700 to 24% in AD 2000 (34 million km²) These profound land-use changes have had, and will continue to have, quite considerable consequences for global biogeochemical cycles, and subsequently global climate change. Main conclusions: Some researchers suggest that humans have shifted from living in the Holocene (emergence of agriculture) into the Anthropocene (humans capable of changing the Earth's atmosphere) since the start of the Industrial Revolution.But in the light of the sheer size and magnitude of some historical land-use changes (e. g. as result of the depopulation of Europe due to the Black Death in the 14th century and the aftermath of the colonization of the Americas in the 16th century) we believe that this point might have occurred earlier in time. While there are still many uncertainties and gaps in our knowledge about the importance of land use (change) in the global biogeochemical cycle, we hope that this database can help global (climate) change modellers to close parts of this gap.

Great uncertainty exists in the global exchange of carbon between the atmosphere and the terrestrial biosphere. An important source of this uncertainty lies in the dependency of photosynthesis on the maximum rate of carboxylation (Vcmax) and the maximum rate of electron transport (Jmax). Understanding and making accurate prediction of C fluxes thus requires accurate characterization of these rates and their relationship with plant nutrient status over large geographic scales. Plant nutrient status is indicated by the traits: leaf nitrogen (N), leaf phosphorus (P), and specific leaf area (SLA). Correlations between Vcmax and Jmax and leaf nitrogen (N) are typically derived from local to global scales, while correlations with leaf phosphorus (P) and specific leaf area (SLA) have typically been derived at a local scale. Thus, there is no global‐scale relationship between Vcmax and Jmax and P or SLA limiting the ability of global‐scale carbon flux models do not account for P or SLA. We gathered published data from 24 studies to reveal global relationships of Vcmax and Jmax with leaf N, P, and SLA. Vcmax was strongly related to leaf N, and increasing leaf P substantially increased the sensitivity of Vcmax to leaf N. Jmax was strongly related to Vcmax, and neither leaf N, P, or SLA had a substantial impact on the relationship. Although more data are needed to expand the applicability of the relationship, we show leaf P is a globally important determinant of photosynthetic rates. In a model of photosynthesis, we showed that at high leaf N (3 gm−2), increasing leaf P from 0.05 to 0.22 gm−2 nearly doubled assimilation rates. Finally, we show that plants may employ a conservative strategy of Jmax to Vcmax coordination that restricts photoinhibition when carboxylation is limiting at the expense of maximizing photosynthetic rates when light is limiting. Great uncertainty exists in the global exchange of carbon between the atmosphere and the terrestrial biosphere. To reduce this uncertainty we analysed data collected in the literature from across the globe on the maximum rate of carboxylation (Vcmax) and the maximum rate of electron transport (Jmax) in relation to plant nutrient status indicated by the traits: leaf nitrogen (N), leaf phosphorus (P), and specific leaf area (SLA). Vcmax was strongly related to leaf N and increasing leaf P substantially increased the sensitivity of Vcmax to leaf N and in a model of photosynthesis we showed that at high leaf N (3 gm−2) increasing leaf P from 0.05 to 0.22 gm−2 nearly doubled assimilation rates. Finally we show that plants may employ a conservative strategy of Jmax to Vcmax co‐ordination that restricts photoinhibition when carboxylation is limiting at the expense of maximising photosynthetic rates when light is limiting.

We present a synthesis of the land-atmosphere carbon flux from land use and land cover change (LULCC) in Asia using multiple data sources and paying particular attention to deforestation and forest regrowth fluxes. The data sources are quasi-independent and include the U.N. Food and Agriculture Organization-Forest Resource Assessment (FAO-FRA 2015; country-level inventory estimates), the Emission Database for Global Atmospheric Research (EDGARv4.3), the 'Houghton' bookkeeping model that incorporates FAO-FRA data, an ensemble of 8 state-of-the-art Dynamic Global Vegetation Models (DGVM), and 2 recently published independent studies using primarily remote sensing techniques. The estimates are aggregated spatially to Southeast, East, and South Asia and temporally for three decades, 1980-1989, 1990-1999 and 2000-2009. Since 1980, net carbon emissions from LULCC in Asia were responsible for 20%-40% of global LULCC emissions, with emissions from Southeast Asia alone accounting for 15%-25% of global LULCC emissions during the same period. In the 2000s and for all Asia, three estimates (FAO-FRA, DGVM, Houghton) were in agreement of a net source of carbon to the atmosphere, with mean estimates ranging between 0.24 to 0.41 Pg C yr−1, whereas EDGARv4.3 suggested a net carbon sink of −0.17 Pg C yr−1. Three of 4 estimates suggest that LULCC carbon emissions declined by at least 34% in the preceding decade (1990-2000). Spread in the estimates is due to the inclusion of different flux components and their treatments, showing the importance to include emissions from carbon rich peatlands and land management, such as shifting cultivation and wood harvesting, which appear to be consistently underreported.

The larch (Larix spp.) forest in eastern Siberia is the world's largest coniferous forest. Its persistence is considered to depend on near‐surface permafrost, and thus, forecast warming over the 21st century and consequent degradation of near‐surface permafrost is expected to affect the larch forest in Siberia. However, predictions of these effects vary greatly, and many uncertainties remain about land – atmosphere interactions within the ecosystem. We developed an integrated land surface model to analyze how the Siberian larch forest will react to current warming trends. This model analyzed interactions between vegetation dynamics and thermo‐hydrology, although it does not consider many processes those are considered to affect productivity response to a changing climate (e.g., nitrogen limitation, waterlogged soil, heat stress, and change in species composition). The model showed that, under climatic conditions predicted under gradual and rapid warming, the annual net primary production of larch increased about 2 and 3 times, respectively, by the end of the 21st century compared with that in the previous century. Soil water content during the larch‐growing season showed no obvious trend, even when surface permafrost was allowed to decay and result in subsurface runoff. A sensitivity test showed that the forecast temperature and precipitation trends extended larch leafing days and reduced water shortages during the growing season, thereby increasing productivity. The integrated model also satisfactorily reconstructed latitudinal gradients in permafrost presence, soil moisture, tree leaf area index, and biomass over the entire larch‐dominated area in eastern Siberia. Projected changes to ecosystem hydrology and larch productivity at this geographical scale were consistent with those from site‐level simulation. This study reduces the uncertainty surrounding the impact of current climate trends on this globally important carbon reservoir, and it demonstrates the need to consider complex ecological processes to make accurate predictions. Larch forest ecosystem in eastern Siberia is considered to be dependent on near‐surface permafrost through its function of inhibiting subsurface water drainage. We examined whether this ecosystem has endurance for a forecasted climatic change by the end of the 21st century. Although surface permafrost is forecasted to be disappeared in eastern Siberia, resulting in subsurface water drainage, as both air temperature and annual precipitation are forecasted to increase, soil water content during growing season would not decrease, and hence, larch forest ecosystem is shown to be sustained in eastern Siberia.