• Energy Research
• 15. Life on land close
• 3. Good health close
• CN close
• GB close
• DE close
• PLoS ONE close

Although the impacts of climate change on biodiversity are increasing worldwide, few studies have attempted to forecast these impacts on Amazon Tropical Forest. In this study, we estimated the impact of climate change on Amazonian avian assemblages considering range shifts, species loss, vulnerability of ecosystem functioning, future effectiveness of current protected areas and potential climatically stable areas for conservation actions. Species distribution modelling based on two algorithms and three different scenarios of climate change was used to forecast 501 avian species, organized on main ecosystem functions (frugivores, insectivores and nectarivores) for years 2050 and 2070. Considering the entire study area, we estimated that between 4 and 19% of the species will find no suitable habitat. Inside the currently established protected areas, species loss could be over 70%. Our results suggest that frugivores are the most sensitive guild, which could bring consequences on seed dispersal functions and on natural regeneration. Moreover, we identified the western and northern parts of the study area as climatically stable. Climate change will potentially affect avian assemblages in southeastern Amazonia with detrimental consequences to their ecosystem functions. Information provided here is essential to conservation practitioners and decision makers to help on planning their actions.

Climate change affects the timing of phenological events, such as the start, end, and length of the growing season of vegetation. A better understanding of how the phenology responded to climatic determinants is important in order to better anticipate future climate-ecosystem interactions. We examined the changes of three phenological events for the Mongolian Plateau and their climatic determinants. To do so, we derived three phenological metrics from remotely sensed vegetation indices and associated these with climate data for the period of 1982 to 2011. The results suggested that the start of the growing season advanced by 0.10 days yr-1, the end was delayed by 0.11 days yr-1, and the length of the growing season expanded by 6.3 days during the period from 1982 to 2011. The delayed end and extended length of the growing season were observed consistently in grassland, forest, and shrubland, while the earlier start was only observed in grassland. Partial correlation analysis between the phenological events and the climate variables revealed that higher temperature was associated with an earlier start of the growing season, and both temperature and precipitation contributed to the later ending. Overall, our findings suggest that climate change will substantially alter the vegetation phenology in the grasslands of the Mongolian Plateau, and likely also in biomes with similar environmental conditions, such as other semi-arid steppe regions.

Individual trees have been shown to exhibit strong relationships between DBH, height and volume. Often such studies are cited as justification for forest volume or standing biomass estimation through remote sensing. With resolution of common satellite remote sensing systems generally too low to resolve individuals, and a need for larger coverage, these systems rely on descriptive heights, which account for tree collections in forests. For remote sensing and allometric applications, this height is not entirely understood in terms of its location. Here, a forest growth model (SERA) analyzes forest canopy height relationships with forest wood volume. Maximum height, mean, H₁₀₀, and Lorey's height are examined for variability under plant number density, resource and species. Our findings, shown to be allometrically consistent with empirical measurements for forested communities world-wide, are analyzed for implications to forest remote sensing techniques such as LiDAR and RADAR. Traditional forestry measures of maximum height, and to a lesser extent H₁₀₀ and Lorey's, exhibit little consistent correlation with forest volume across modeled conditions. The implication is that using forest height to infer volume or biomass from remote sensing requires species and community behavioral information to infer accurate estimates using height alone. SERA predicts mean height to provide the most consistent relationship with volume of the height classifications studied and overall across forest variations. This prediction agrees with empirical data collected from conifer and angiosperm forests with plant densities ranging between 10²-10⁶ plants/hectare and heights 6-49 m. Height classifications investigated are potentially linked to radar scattering centers with implications for allometry. These findings may be used to advance forest biomass estimation accuracy through remote sensing. Furthermore, Lorey's height with its specific relationship to remote sensing physics is recommended as a more universal indicator of volume when using remote sensing than achieved using either maximum height or H₁₀₀.

Climatic warming is often predicted to reduce wheat yield and grain quality in China. However, direct evidence is still lacking. We conducted a three-year experiment with a Free Air Temperature Increase (FATI) facility to examine the responses of winter wheat growth and plant N accumulation to a moderate temperature increase of 1.5°C predicted to prevail by 2050 in East China. Three warming treatments (AW: all-day warming; DW: daytime warming; NW: nighttime warming) were applied for an entire growth period. Consistent warming effects on wheat plant were recorded across the experimental years. An increase of ca. 1.5°C in daily, daytime and nighttime mean temperatures shortened the length of pre-anthesis period averagely by 12.7, 8.3 and 10.7 d (P<0.05), respectively, but had no significant impact on the length of the post-anthesis period. Warming did not significantly alter the aboveground biomass production, but the grain yield was 16.3, 18.1 and 19.6% (P<0.05) higher in the AW, DW and NW plots than the non-warmed plot, respectively. Warming also significantly increased plant N uptake and total biomass N accumulation. However, warming significantly reduced grain N concentrations while increased N concentrations in the leaves and stems. Together, our results demonstrate differential impacts of warming on the depositions of grain starch and protein, highlighting the needs to further understand the mechanisms that underlie warming impacts on plant C and N metabolism in wheat.

Large-scale climate processes influence many aspects of ecology including breeding phenology, reproductive success and survival across a wide range of taxa. Some effects are direct, for example, in temperate-zone birds, ambient temperature is an important cue enabling breeding effort to coincide with maximum food availability, and earlier breeding in response to warmer springs has been documented in many species. In other cases, time-lags of up to several years in ecological responses have been reported, with effects mediated through biotic mechanisms such as growth rates or abundance of food supplies. Here we use 23 years of data for a temperate woodland bird species, the great tit (Parus major), breeding in deciduous woodland in eastern England to demonstrate a time-lagged linear relationship between the on-set of egg laying and the winter index of the North Atlantic Oscillation such that timing can be predicted from the winter index for the previous year. Thus the timing of bird breeding (and, by inference, the timing of spring events in general) can be predicted one year in advance. We also show that the relationship with the winter index appears to arise through an abiotic time-lag with local spring warmth in our study area. Examining this link between local conditions and larger-scale processes in the longer-term showed that, in the past, significant relationships with the immediately preceding winter index were more common than those with the time-lagged index, and especially so from the late 1930s to the early 1970s. However, from the mid 1970s onwards, the time-lagged relationship has become the most significant, suggesting a recent change in climate patterns. The strength of the current time-lagged relationship suggests that it might have relevance for other temperature-dependent ecological relationships.

More than 60% of the total area of tree plantations in China is in subtropical, and over 70% of subtropical plantations consist of pure stands of coniferous species. Because of the poor ecosystem services provided by pure coniferous plantations and the ecological instability of these stands, a movement is under way to promote indigenous broadleaf plantation cultivation as a promising alternative. However, little is known about the carbon (C) stocks in indigenous broadleaf plantations and their dependence on stand age. Thus, we studied above- and below-ground biomass and C stocks in a chronosequence of Mytilaria laosensis plantations in subtropical China; stands were 7, 10, 18, 23, 29 and 33 years old. Our assessments included tree, shrub, herb and litter layers. We used plot-level inventories and destructive tree sampling to determine vegetation C stocks. We also measured soil C stocks by analyses of soil profiles to 100 cm depth. C stocks in the tree layer dominated the above-ground ecosystem C pool across the chronosequence. C stocks increased with age from 7 to 29 years and plateaued thereafter due to a reduction in tree growth rates. Minor C stocks were found in the shrub and herb layers of all six plantations and their temporal fluctuations were relatively small. C stocks in the litter and soil layers increased with stand age. Total above-ground ecosystem C also increased with stand age. Most increases in C stocks in below-ground and total ecosystems were attributable to increases in soil C content and tree biomass. Therefore, considerations of C sequestration potential in indigenous broadleaf plantations must take stand age into account.

Although the importance of addressing ecosystem service benefits in regional land use planning and decision-making is evident, substantial practical challenges remain. In particular, methods to identify priority areas for the provision of key ecosystem services and other environmental services (benefits from the environment not directly linked to the function of ecosystems) need to be developed. Priority areas are locations which provide disproportionally high benefits from one or more service. Here we map a set of ecosystem and environmental services and delineate priority areas according to different scenarios. Each scenario is produced by a set of weightings allocated to different services and corresponds to different landscape management strategies which decision makers could undertake. Using the county of Cornwall, U.K., as a case study, we processed gridded maps of key ecosystem services and environmental services, including renewable energy production and urban development. We explored their spatial distribution patterns and their spatial covariance and spatial stationarity within the region. Finally we applied a complementarity-based priority ranking algorithm (zonation) using different weighting schemes. Our conclusions are that (i) there are two main patterns of service distribution in this region, clustered services (including agriculture, carbon stocks, urban development and plant production) and dispersed services (including cultural services, energy production and floods mitigation); (ii) more than half of the services are spatially correlated and there is high non-stationarity in the spatial covariance between services; and (iii) it is important to consider both ecosystem services and other environmental services in identifying priority areas. Different weighting schemes provoke drastic changes in the delineation of priority areas and therefore decision making processes need to carefully consider the relative values attributed to different services.