Fog is an important environmental phenomenon affecting, among other things, geochemical cycles via atmospheric deposition pathways. It is generally accepted that fog contributes substantially to ...atmospheric deposition fluxes especially in mountain forests. Nevertheless, due to intrinsic constraints, fog pathway has thus far been neglected in the quantification of atmospheric deposition and fog pathway has not been accounted for in nation-wide spatial patterns of atmospheric deposition of air pollutants. In this review we explore the causes as to why it is so complex to create a spatial pattern of fog contribution to atmospheric ion deposition fluxes on a national scale. Physical and chemical principles of fog formation are presented and factors influencing the abrupt temporal and spatial changes in both fog occurrence and fog chemistry are elucidated. The focus is on both constituents essential for fog deposition flux quantification, i.e. (i) hydrological input on fog water and (ii) chemistry of fog water.
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•Fog as a cross-cutting issue•Fog pathway desirable to include to atmospheric deposition•Complexity of including of fog contribution to deposition spatial patterns highlighted•Factors influencing fog deposition•Abrupt changes in fog deposition due both to fog water and fog chemistry
Implementation of wildfire- and climate-adaptation strategies in seasonally dry forests of western North America is impeded by numerous constraints and uncertainties. After more than a century of ...resource and land use change, some question the need for proactive management, particularly given novel social, ecological, and climatic conditions. To address this question, we first provide a framework for assessing changes in landscape conditions and fire regimes. Using this framework, we then evaluate evidence of change in contemporary conditions relative to those maintained by active fire regimes, i.e., those uninterrupted by a century or more of human-induced fire exclusion. The cumulative results of more than a century of research document a persistent and substantial fire deficit and widespread alterations to ecological structures and functions. These changes are not necessarily apparent at all spatial scales or in all dimensions of fire regimes and forest and nonforest conditions. Nonetheless, loss of the once abundant influence of low- and moderate-severity fires suggests that even the least fire-prone ecosystems may be affected by alteration of the surrounding landscape and, consequently, ecosystem functions. Vegetation spatial patterns in fire-excluded forested landscapes no longer reflect the heterogeneity maintained by interacting fires of active fire regimes. Live and dead vegetation (surface and canopy fuels) is generally more abundant and continuous than before European colonization. As a result, current conditions are more vulnerable to the direct and indirect effects of seasonal and episodic increases in drought and fire, especially under a rapidly warming climate. Long-term fire exclusion and contemporaneous social-ecological influences continue to extensively modify seasonally dry forested landscapes. Management that realigns or adapts fire-excluded conditions to seasonal and episodic increases in drought and fire can moderate ecosystem transitions as forests and human communities adapt to changing climatic and disturbance regimes. As adaptation strategies are developed, evaluated, and implemented, objective scientific evaluation of ongoing research and monitoring can aid differentiation of warranted and unwarranted uncertainties.
There are plenty of mesoscale meteorological discontinuities in the atmosphere, acting as atmospheric internal boundaries (AIBs). In conjunction with the atmospheric boundary layer in the vertical ...direction, they form confined three-dimensional structures that significantly affect air pollution. However, the role of AIBs in regional pollution has not been systematically elucidated. Based on surface observations, this study investigates PM2.5 pollution distributions under the forcing of various AIBs in the North China Plain. A total of 98 regional pollution episodes are identified during the autumn and winter of 2014–2020, and are further classified according to the impact of AIBs. In the pollution formation-maintenance stage, there are three categories. The frontal category (with a frequency of 41%), including the frontal trough type and frontal inverted trough type, displays the most polluted air masses along the mountains. The frontal AIB defines the lateral border of the pollution zone and forms a frontal inversion above, creating a closed and stable structure wherein the highest concentration of PM2.5 accumulates. The wind shear category (29%) is decided by the dynamic convergence AIB, which causes lighter PM2.5 pollution with diverse spatial patterns corresponding to west-southwest shear, southeast-east shear, and south-north shear. The topographic obstruction category (14%) presents as a narrow arc-shaped pollution belt at the foot of the windward mountains, resulting from the cold air damming AIB with dynamical obstruction and thermal stratification. Pollution diffuses in three ways: northwest, west, and northeast, respectively. The first one is the strongest and most frequent (42%), with both strong horizontal wind and vertical mixing. The second category is relatively rare (17%), characterized by foehn-induced active vertical ventilation. The last one is frequent (41%), but relatively weak, mainly relying on horizontal diffusion. Some evolution details of the AIB affecting PM2.5 pollution are also illustrated by a typical case.
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•The effects of atmospheric internal boundaries on regional air pollution are elucidated.•A method to identify various atmospheric internal boundaries based on surface observations is proposed.•Three categories, six types of atmospheric internal boundaries are classified under polluted conditions in North China Plain.
Eigenvector-mapping methods such as Moran’s eigenvector maps (MEM) are derived from a spatial weighting matrix (SWM) that describes the relations among a set of sampled sites. The specification of ...the SWM is a crucial step, but the SWM is generally chosen arbitrarily, regardless of the sampling design characteristics. Here, we compare the statistical performances of different types of SWMs (distance-based or graph-based) in contrasted realistic simulation scenarios. Then, we present an optimization method and evaluate its performances compared to the arbitrary choice of the most-widely used distance-based SWM. Results showed that the distance-based SWMs generally had lower power and accuracy than other specifications, and strongly underestimated spatial signals. The optimization method, using a correction procedure for multiple tests, had a correct type I error rate, and had higher power and accuracy than an arbitrary choice of the SWM. Nevertheless, the power decreased when too many SWMs were compared, resulting in a trade-off between the gain of accuracy and the loss of power. We advocate that future studies should optimize the choice of the SWM using a small set of appropriate candidates. R functions to implement the optimization are available in the adespatial package and are detailed in a tutorial.
In near-natural basins, zooplankton are key hubs for maintaining aquatic food webs and organic matter cycles. However, the spatial patterns and drivers of zooplankton in streams are poorly ...understood. This study registered 165 species of zooplankton from 147 sampling sites (Protozoa, Rotifers, Cladocera and Copepods), integrating multiple dimensions (i.e., taxonomic, functional, and phylogenetic) and components (i.e., total, turnover, and nestedness) of α and β diversity. This study aims to reveal spatial patterns, mechanisms, correlations, and relative contribution of abiotic factors (i.e., local environment, geo-climatic, land use, and spatial factors) through spatial interpolation (ordinary kriging), mantel test, and variance partitioning analysis (VPA). The study found that α diversity is concentrated in the north, while β diversity is more in the west, which may be affected by typical habitat, hydrological dynamics and underlying mechanisms. Taxonomic and phylogenetic β diversity is dominated by turnover, and metacommunity heterogeneity is the result of substitution of species and phylogeny along environmental spatial gradients. Taxonomic and phylogenetic β diversity were strongly correlated (r from 0.91 to 0.95), mainly explained by historical/spatial isolation processes, community composition, generation time, and reproductive characteristics, and this correlation provides surrogate information for freshwater conservation priorities. In addition, spatial factors affect functional and phylogenetic α diversity (26%, 28%), and environmental filtering and spatial processes combine to drive taxonomic α diversity (10%) and phylogenetic β diversity (11%). Studies suggest that spatial factors are key to controlling the community structure of zooplankton assemblages in near-natural streams, and that the relative role of local environments may depend on the dispersal capacity of species. In terms of diversity conservation, sites with high variation in uniqueness should be protected (i) with a focus on the western part of the thousand islands lake catchment and (ii) increasing effective dispersal between communities to facilitate genetic and food chain transmission.
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•Unravelling the relative roles of abiotic factors on zooplankton α and β diversity.•Phylogenetic and taxonomic β diversity are highly correlated.•Spatial factors strongly affect phylogenetic and functional α diversity.•Environmental filtering and spatial processes co-drive phylogenetic β diversity.•Zooplankton diversity protection should focus on the western TIL catchment.
•We quantify large scale afforestation projects impact on forest spatial patterns.•Remote sensing time series is used to analyze the effects of restoration projects.•Clear footprints were observed on ...the forest landscape of the Loess Plateau.•Core and fragmented forests greatly increased following afforestation projects.•Analysis of spatial pattern dynamics offers a new view on forest ecosystems.
The spatial pattern of forests impacts on biodiversity, stability and sustainability of forest ecosystems. Afforestation and reforestation projects have massively increased forested areas on the Chinese Loess Plateau, however, the spatial pattern of the new and old forests, as well as their interaction, remains unknown. Here we study the spatiotemporal dynamics of old and new forests for the period 2001–2016, and found that 84.21% of the old forests existed throughout the study period. Moreover, core forests (defined as a forest area which is surrounded by other forest areas) significantly increased (2585 km2 yr−1, in total 39, 597 km2). Two ecological restoration projects have left clear footprints on the forest landscape of the Loess Plateau: (1) The Natural Forest Conservation Project, aiming at expanding old forest, has resulted in the establishment of considerable areas of new forest surrounding old forest. Consequently, this has promoted new core forest areas to emerge. (2) The Grain for Green Project has mainly caused a fragmented landscape of forest islets which gradually connect to core forest areas. The general increase in core forest areas can be considered an ecological improvement, and the assessment method presented here may guide stakeholders in measuring the success of forest restoration activities that goes beyond a classical quantification of forest cover.
Evidence is mounting that the immense diversity of microorganisms and animals that live belowground contributes significantly to shaping aboveground biodiversity and the functioning of terrestrial ...ecosystems. Our understanding of how this belowground biodiversity is distributed, and how it regulates the structure and functioning of terrestrial ecosystems, is rapidly growing. Evidence also points to soil biodiversity as having a key role in determining the ecological and evolutionary responses of terrestrial ecosystems to current and future environmental change. Here we review recent progress and propose avenues for further research in this field.
Celotno besedilo
Dostopno za:
DOBA, IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
High aerosol loadings are observed not only in megacities on continents but also in oceanic regions like the Bohai Sea. This work provides a comprehensive analysis of the spatial and temporal ...variations in Aerosol Optical Depth (AOD) across different ocean regions worldwide over the past four decades, using remote sensing reanalysis data. The mean AOD value across all oceanic grids is approximately 0.112, with higher levels recorded in the Central Atlantic (~0.206), followed by the North Indian Ocean (~0.201), and the Western North Pacific (~0.197). A latitudinal analysis reveals that high AOD values are predominantly found in the Northern Hemisphere's oceanic regions, especially between latitudes 0° and 70° N. Except for the Gulf of California and Hudson Bay, AOD values in the other fourteen surveyed inland seas surpass the mean levels found at similar latitudes in oceanic regions. Among which, the Bohai Sea stands out as the most polluted oceanic region with AOD value of 0.35. Over the last four decades, AOD trends have revealed a significant decrease across about 89.5 % of global oceanic grids, while an increase in AOD is observed in low-latitude oceanic areas (30° S–30° N). Investigation into inland seas shows that nearly two-thirds have experienced a declining AOD trend, while sharply upward trends in AOD are primarily found in Asia. The Bohai Sea shows the largest increase in AOD, with an annual growth rate of 1.4 %. The turning-points of the AOD in each inland sea confirm the success of regional emission control policies initiated on the adjacent continents. To improve air quality in inland seas like the Bohai Sea, adjusting industrial layouts, such as relocating heavy industries from the surrounding coastal cities' proximities to areas near open seas, could significantly benefit public health.
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•High AOD predominantly occurs in oceanic regions of the Northern Hemisphere.•The Bohai Sea is a critical hotspot for oceanic aerosol pollution.•Oceanic AOD trends decline globally, except for increases in Asia seas.•Inland seas record higher AOD levels compared to the open oceans.
Climate factors (CFs) are key variables shaping the interannual variability (IAV) of terrestrial ecosystem carbon sinks. However, the dominant CFs influencing the IAV of terrestrial carbon sinks ...remains debated, as CFs are coupled via land-atmosphere interactions. Here, the dominant factors influencing the IAV of global terrestrial net ecosystem production (NEP) were quantified using the convergent cross-mapping (CCM) technique. This analysis was conducted with distinct global terrestrial NEP datasets deriving from process-based ecosystem models, machine learning techniques, and eddy covariance flux towers. Results revealed that the spatial patterns of IAV of global terrestrial NEP were dominated by water availability (WA) and temperature (Ts). Ts mainly controlled the IAV of terrestrial NEP in mid to high-latitude regions of the Northern Hemisphere (NH), while WA exerted dominance over low and mid-latitude regions in both the NH and the Southern Hemisphere. Moreover, the energy limitation and water limitation explained the spatial pattern of Ts and WA dominant on NEP. Further analysis found that WA and Ts also played a dominant role in gross primary productivity (GPP) and terrestrial ecosystem respiration (TER), proving that WA and Ts were the dominant factors affecting NEP. In addition, we found a weakening trend in causal linkages of CFs to NEP in the temporal domain. This study used causal analysis to reveal the spatial patterns of water and heat dominating the NEP, providing support for improved assessment and prediction of terrestrial carbon sinks under climate change.
•Causal effects of CFs to CVs were quantified using CCM.•Spatial patterns of IAV of terrestrial NEP were dominated by WA and Ts.•ELI explained the spatial patterns of Ts and WA dominant on NEP.•Strength of causal effect dependence of IAV of NEP on ELI and AI gradient
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