The compatibility of agricultural intensification in a global hotspot of smallholder agrobiodiversity (Bolivia)

Integrating the conservation of biodiversity by smallholder farmers with agricultural intensification is increasingly recognized as a leading priority of sustainability and food security amid global environmental and socioeconomic change. An international research project investigated the smallholder agrobiodiversity of maize (corn) in a global hotspot (Bolivia) undergoing significant intensification. Peach-based intensification was pronounced (300–400%) and prolonged (2000–2010) in study areas. Intensification and maize agrobiodiversity were found to co-occur within smallholder landscapes. Interactions of these field systems did not trigger land-change tipping points leading to landrace extirpation. By 2010 maize landraces in the study areas still demonstrated high levels of taxonomic and ecological biodiversity and contributed significantly to this crop’s agrobiodiversity at national (31%) and hemispheric (3%) scales. Social and ecological resilience and in situ conservation of the maize agrobiodiversity by Bolivian smallholders was enabled through robust linkages to off-farm migration; resource access and asset capabilities among both traditional and nontraditional growers; landrace agroecology and food uses; and innovative knowledge and skills. The smallholders’ resilience resulting from these linkages was integral to the conditional success of the in situ conservation of maize agrobiodiversity. Environment–development interactions both enabled smallholders’ agrobiodiversity resilience and influenced the limits and vulnerability of agrobiodiversity. Scientific policy recommendations regarding land-use planning and sustainability analysis are targeted to specific Río+20 priorities for agrobiodiversity.     

脑震荡患者恢复期智力测试成绩的特征分析

目的分析脑震荡患者恢复期智力测试（智测）成绩，总结认知功能损害规律，寻求进行干预的可能性。方法77例脑震荡患者在发病后30d左右（恢复期）接受了中国成人韦氏智力测试量表（WAIS-RC）评估，并与48例正常人（对照组）测试结果比较。结果脑震荡组WAIS-RC智测成绩中，知识、算术、相似性、数字广度、数字符号、填图、木块图、图形排列、图形拼凑等分量表评分均明显低于对照组（t=2．063～3．277，均P〈0．01或0．05）。在按性别分组后，男性脑震荡患者（41例）的算术、数字广度、填图和图形排列等分量表评分均明显低于女性患者（36例）（t=2．153～3．132，均P〈0．01或0．05）。在按脑外伤部位分组后，前部损伤组的知识、领悟、算术、数字广度、数字符号、填图、木块图、图形排列和图形拼凑等分量表评分均明显低于后部损伤组（t=2．027～3．148，均P〈0．01或0．05）。结论脑震荡患者恢复期常伴有明确的认知功能损害特征，后者又常与患者性别和损伤部位密切相关。     

中小面积烧伤患者不同时期的抑郁症状表现

目的观察中小面积烧伤患者不同时期的抑郁症状表现。方法65例中小面积烧伤患者（烧伤面积〈39%）在急性期和恢复期接受汉密尔顿抑郁量表（HAMD）调查,并与41例正常人（对照组）的测试结果比较。结果烧伤患者急性期和恢复期的大部分HAMD因子评分和总分均明显高于对照组（P〈0.05-0.01）,恢复期的抑郁情绪、入睡困难、早醒、精神性焦虑、躯体性焦虑、全身症状、性症状、疑病、昼夜变化、人格解体、能力感减退、绝望感和自卑感等症状评分和总分明显高于急性期（P〈0.05~0.01）,而睡眠不深、激越、胃肠道症状、体重减轻、偏执症状和强迫症状等症状评分均明显低于急性期（P〈0.05-0.01）。结论中小面积烧伤患者发病后不同时期抑郁症状表现不完全相同,心理干预应有针对性。     

A machine-learning approach to map landscape connectivity in Aedes aegypti with genetic and environmental data

Mapping landscape connectivity is important for controlling invasive species and disease vectors. Current landscape genetics methods are often constrained by the subjectivity of creating resistance surfaces and the difficulty of working with interacting and correlated environmental variables. To overcome these constraints, we combine the advantages of a machine-learning framework and an iterative optimization process to develop a method for integrating genetic and environmental (e.g., climate, land cover, human infrastructure) data. We validate and demonstrate this method for the Aedes aegypti mosquito, an invasive species and the primary vector of dengue, yellow fever, chikungunya, and Zika. We test two contrasting metrics to approximate genetic distance and find Cavalli-Sforza–Edwards distance (CSE) performs better than linearized F_ST. The correlation (R) between the model’s predicted genetic distance and actual distance is 0.83. We produce a map of genetic connectivity for Ae. aegypti’s range in North America and discuss which environmental and anthropogenic variables are most important for predicting gene flow, especially in the context of vector control.

Landscape genetics—explicitly quantifying the effects of a heterogenous landscape on gene flow—is an important tool for both conservation biology and the control of invasive species and disease vectors including the “yellow fever mosquito” (Aedes aegypti) (1, 2). We demonstrate that current limitations in landscape genetics can be addressed with a machine-learning approach integrated into an iterative optimization process. Isolation by distance (IBD) is a classical model in population genetics that assumes dispersal is limited in proportion to geographic distance, resulting in increasing genetic differentiation with increasing geographic distance between populations (3–5). Although this pattern is commonly seen in nature, factors such as history and dispersal limitations caused by the environment (i.e., “isolation by resistance”) (6) can produce deviations from IBD. Landscape resistance (alias friction) and its inverse, connectivity, determine how organisms move through a landscape (7). Modeling landscape connectivity can be used to identify the environmental variables that affect the organisms’ gene flow and genetic structure; predict how climate and land use change will affect their gene flow and distribution in the future; and inform conservation, vector control, and other management decisions (1, 8–13). Our goals are to use environmental data (the predictors) to build a model of genetic connectivity (the observed data) that improves on IBD and to identify environmental drivers of gene flow patterns.We implement a machine-learning approach that offers a number of advantages over classical methods in landscape genetics: The machine-learning approach is more objective, it allows the inclusion of correlated variables, and it is able to account for different shapes and magnitudes of correlations between predictor and response variables at different locations in the landscape (14–17). In comparison, a common approach in landscape genetics called resistance surface mapping involves the subjective process of creating resistance surfaces for environmental variables, in which each pixel represents a hypothesized resistance to the organism’s movement often based on expert opinion (6, 18). Effective landscape distances through the resistance surfaces can be found with least cost path or circuit theory analysis (19) and then analyzed for associations with genetic distance (20).One option to circumvent the subjectivity of creating resistance surfaces is to model genetic connectivity directly from environmental data. Bouyer et al. (7) took this approach and used a maximum-likelihood method to integrate genetic data and environmental data to map landscape resistance in tsetse flies. Additionally, they introduced an iterative optimization approach in which each subsequent iteration used least cost path lines through the previously predicted resistance surface—an improvement over modeling organism movement as straight lines (16, 17). While this presented a major advance, the maximum-likelihood methodology requires exclusion of correlated data, establishing the relationship between environmental variables and genetic distance before building the model, and transforming or discretizing nonlinear relationships. Additionally, this approach assumes one relationship between each environmental variable and the genetic data across the whole landscape. To build on previous advances while overcoming some of their limitations, we combine iterative optimization with a machine-learning method called random forest (RF).RF is a nonlinear classification and regression tree analysis that can handle many inputs, including redundant or irrelevant variables, as well as continuous and categorical data types (14, 15). RF creates many internal training/testing subdatasets and aggregates the predictors, resulting in stable and consistent results that generally do not overfit the data and can be evaluated through validation processes (14). It is easier to tune and less likely to overfit noisy data than another machine-learning method we considered, gradient boosting (21). Additionally, RF has been successfully incorporated into ecological studies (22) and a small number of landscape genetics studies (16, 17, 23). These studies considered only the environmental predictor values at the genetic collection sites (23) or along straight lines between each pair of sites (16, 17), in contrast to the least cost path analysis we implement here (7).We demonstrate the efficacy of our method to map landscape connectivity for an important disease vector. Ae. aegypti is highly invasive and the primary vector of yellow fever, Zika, dengue, and chikungunya. Except for yellow fever, there are no reliable, widely used vaccines for these diseases, so vector control is essential. Ae. aegypti originated in Africa and is now found throughout the tropics and increasingly in temperate regions (24–26). The species is temperature constrained, preferring warm, humid areas close to humans (the females’ preferred source for bloodmeals outside their native African range) (27). In the United States, it has a patchy distribution throughout southern states, especially Texas, Florida, and California (28). Although Ae. aegypti can disperse >1 km, its usual lifetime dispersal is only around 200 m (29–32). Passive “hitchhiking” via human transportation networks is responsible for long-distance invasions and worldwide spread of Ae. aegypti and its close relative (33–35). Climate change is also expanding the range of Aedes species, which could expose nearly 1 billion additional people to diseases carried by these mosquitoes for the first time (26).Although IBD is common in nature and a helpful null model in landscape genetics (20), geographic distance is often an inadequate sole predictor of genetic distance (as in the case of our dataset; SI Appendix, Fig. S1). Therefore, a more complex model is needed to explain and predict genetic distance and corresponding landscape connectivity. In this paper we introduce an iterative machine-learning approach to integrate environmental predictors and genetic observation data and apply it to map landscape connectivity for the Ae. aegypti mosquito in North America. We also find and examine the most important variables for building the connectivity model and provide validation of our proposed method.     

Spatial and evolutionary predictability of phytochemical diversity

To cope with environmental challenges, plants produce a wide diversity of phytochemicals, which are also the source of numerous medicines. Despite decades of research in chemical ecology, we still lack an understanding of the organization of plant chemical diversity across species and ecosystems. To address this challenge, we hypothesized that molecular diversity is not only related to species diversity, but also constrained by trophic, climatic, and topographical factors. We screened the metabolome of 416 vascular plant species encompassing the entire alpine elevation range and four alpine bioclimatic regions in order to characterize their phytochemical diversity. We show that by coupling phylogenetic information, topographic, edaphic, and climatic variables, we predict phytochemical diversity, and its inherent composition, of plant communities throughout landscape. Spatial mapping of phytochemical diversity further revealed that plant assemblages found in low to midelevation habitats, with more alkaline soils, possessed greater phytochemical diversity, whereas alpine habitats possessed higher phytochemical endemism. Altogether, we present a general tool that can be used for predicting hotspots of phytochemical diversity in the landscape, independently of plant species taxonomic identity. Such an approach offers promising perspectives in both drug discovery programs and conservation efforts worldwide.

Phytochemical diversity describes the richness and abundance of the specialized metabolites produced by vegetation. It is a key aspect of plant functional diversity and, thus, affects plant fitness (1), ecosystem functioning (2), and services to humankind (3). Despite its relevance, chemical ecologists still struggle to understand both the evolutionary origin of phytochemical diversity and its variation across ecosystems (4). Only a small fraction of the >300,000 currently described phytochemicals (5) has been ascribed to a known ecosystem function or process (6). This is because most identification work has been undertaken on model organisms, such as crop plants (7), and because drug discovery programs have so far been based on prior ethno-medicinal knowledge or random sampling, rather than systematic sampling from the tree of life (8) or guided by ecologically relevant information (9). The ability to better predict the presence and diversity of phytochemicals of interest from phylogenetic information, or from specific environments or habitat types, could uncover the full spectrum and function of phytochemicals in the landscape while also orienting drug discovery research (10). Moreover, documenting landscape variability in phytochemical diversity is particularly important in the context of land use change, which is causing losses of plants that possess a yet-unknown value to medicine and science (11).The plant metabolome includes both primary functions, expected to be conserved across species, and specialized functions, associated to specific lineages or environments (1). Thus, phytochemical variation in the landscape is expected to arise from a combination of evolutionary (12, 13) and ecological (14, 15) constraints. From a macroevolutionary standpoint, some classes of phytochemical compounds are specific to plant clades (e.g., glucosinolates in Brassicaceae, or tropane alkaloids in Solanales; ref. 16). Such lineage-dependent variation is thought to be driven by chemical defense innovations followed by coevolutionary dynamics with herbivores (17, 18). In particular, the escape-and-radiate model (13) predicts that plant lineages diversify by creating novel, more potent, or complex chemical mixtures in response to biotic pressure (19). Therefore, plant lineages that have experienced more evolutionary split events are predicted to have evolved higher levels of phytochemical diversity (13). From an ecological perspective, phytochemical diversity is expected to be the result of plant adaptation to abiotic and biotic conditions, both of which vary along ecological gradients in landscapes (2, 20). For example, habitats that impose constraints on plant growth, such as cold and resource-poor environments, may be expected to drive selection toward potent chemical defense mechanisms that reduce tissue loss (21). At the same time, it is well established that herbivores and pathogens can promote divergent selection between plant congeners, leading to increasing chemical dissimilarity (22). As such, species relatedness alone is a poor predictor of site-level phytochemical diversity.Here, we questioned whether phytochemical diversity can be predicted from the phylogenetic and ecological heterogeneity observed in the landscape. We hypothesized that phytochemical diversity is not only related to local plant species diversity but is also constrained by other ecological factors, especially trophic, climatic, edaphic, and topographic variation. We developed a methodological framework involving: 1) comprehensive sampling of plant species along ecological transects that cover the entire range of regional vegetation ecological boundaries; 2) assessing species-level phytochemical composition and combining it with species distribution models (SDMs) for extrapolating phytochemical diversity across the landscape based on species occurrences; 3) extracting climatic and topographical variables associated with each unique molecule observed across all species to build molecular distribution models (MDMs); and 4) projecting phytochemical diversity and composition across the landscape based on these MDMs (SI Appendix, Fig. S1). Here, we consider phytochemical diversity both as the richness of clustered metabolic features and the presence/absence of the families of compounds they represent. We expected that phytochemical diversity values compiled from the projected MDMs would better explain plot-level phytochemical diversity and composition than phytochemical diversity calculated from plant species composition alone.     

Geomorphic controls on elevational gradients of species richness

Elevational gradients of biodiversity have been widely investigated, and yet a clear interpretation of the biotic and abiotic factors that determine how species richness varies with elevation is still elusive. In mountainous landscapes, habitats at different elevations are characterized by different areal extent and connectivity properties, key drivers of biodiversity, as predicted by metacommunity theory. However, most previous studies directly correlated species richness to elevational gradients of potential drivers, thus neglecting the interplay between such gradients and the environmental matrix. Here, we investigate the role of geomorphology in shaping patterns of species richness. We develop a spatially explicit zero-sum metacommunity model where species have an elevation-dependent fitness and otherwise neutral traits. Results show that ecological dynamics over complex terrains lead to the null expectation of a hump-shaped elevational gradient of species richness, a pattern widely observed empirically. Local species richness is found to be related to the landscape elevational connectivity, as quantified by a newly proposed metric that applies tools of complex network theory to measure the closeness of a site to others with similar habitat. Our theoretical results suggest clear geomorphic controls on elevational gradients of species richness and support the use of the landscape elevational connectivity as a null model for the analysis of the distribution of biodiversity.The search for the mechanisms determining the distribution of life on Earth has long been, and still is, a challenge of great importance for ecologists and biogeographers. Indeed, developing conservation strategies demands knowledge of ex ante and ex post biodiversity patterns through their linkage with ecological processes. As a common approach, general patterns in species richness are sought to understand the underlying processes (1–3). One outstanding example is the study of elevational gradients of species richness, the subject of much attention because strong elevational gradients can be observed in any mountainous landscape (4, 5). Among possible drivers, temperature directly controls biological productivity of the community, which, in turn, has been linked to diversity (6). A simplistic association of elevational gradients with temperature gradients in mountainous ecosystems suggests a decline of species richness with increasing elevation (1, 2, 7, 8), echoing the latitudinal decline from the equator to the poles (9). However, such an expectation is clearly inconsistent with empirical observations that often show a hump-shaped rather than a monotonically decreasing pattern (4, 8, 10–12).A possible explanation is that both productivity versus elevation and species richness versus productivity may be described by nonmonotonic relations (10, 13, 14). At low elevations, in particular, human disturbance may play a major role in reducing biodiversity (15). Whereas several factors [such as temperature, habitat capacity, precipitation, anthropogenic pressure and geometric constraints (1, 5, 15)] change (somewhat) predictably with elevation, other relevant factors (such as moisture, clear-sky turbidity and cloudiness, sunshine exposure and aspect, wind strength, season length, and exposed lithology) are not elevation-specific (16). Thus, empirical results may hardly sort out general rules unambiguously. Given the multitude of possible confounding factors, theoretical analyses are key to understand elevational gradients of diversity and how biota respond to geophysical drivers and controls (5, 17, 18) [e.g., the foreseen upward shift in plant species optimum elevation (19)].Here, we identify and analyze three distinctive geomorphic features characteristic of mountainous landscapes that can systematically affect the distribution of species and result in hump-shaped patterns of biodiversity along elevational gradients: (i) finiteness of the landscape elevational range; (ii) frequency distribution of areal extent at different elevation; and (iii) differential elevational connectivity.Geometrically constrained landscapes are subject to the so-called middomain effect, according to which, if the species’ ranges are randomly distributed over a bounded geographic domain free of environmental gradients, ranges would increasingly overlap over the center of the domain (20, 21). Applying the same principle to a finite landscape elevational range would support hump-shaped patterns of local species richness along elevational gradients (e.g., refs. 22–24).The frequency distribution of elevation in real-life landscapes is distinctly hump-shaped, with the majority of land situated at midelevations (Fig. 1 and ref. 25). This pattern is ubiquitous in landscapes shaped by fluvial erosion when a sufficiently large region rather than a single slope or mountain is considered, and the pattern is altered only if large areas outside runoff-producing zones (e.g., large plains) are included in the domain (Supporting Information). This pattern is often overlooked (e.g., refs. 1 and 4) because the mountain-cone analogy suggests a monotonically decreasing distribution of elevation. However, mountains are not cones (26) but complex fractal structures. The area of available habitat within a given elevational band may have a direct effect on the diversity of the regional community it hosts [γ-diversity (8, 13, 22, 27, 28)], as predicted by the species–area relationship (29). The area of available habitat may also have an indirect effect on the local species richness [diversity of equal-area plots (i.e., α-diversity)] because local communities can be assembled from a more diverse regional pool of species that are fit to live at similar elevation (28).Open in a separate windowFig. 1.Comparison between a real-life elevation field (a fluvial landscape in the Swiss Alps, 50? × ?50 km²) (A) and an oversimplified, 1D elevation field (B). (C) Frequency distributions of elevation of the two landscapes. Fig. S1 reports other examples.Finally, a feature potentially capable of shaping elevational diversity patterns is the inherent elevational connectivity of fluvial landscapes. When mapping the fitness, assumed to be elevation-dependent, of three hypothetical species with the same niche width but different niche position [the elevation at which fitness is maximum (30)] over a real mountainous landscape, we find that suitable habitat patches for different species feature very different connectivity (Fig. 2). Valleys (low-elevation sites) and mountain tops (high-elevation sites) form fragmented patches nearly isolated from each other, whereas midelevation sites are both more abundant and more interconnected. Habitat size and connectivity are key determinants of extinction and immigration rates, and thus of diversity, as first predicted by the classic theory of island biogeography (31) and later confirmed by many experimental and theoretical studies (e.g., refs. 32–42). It is thus expected that communities at low (high) elevation, being more isolated, exhibit lower species richness than those at midelevation. This effect has already been discussed for mountain tops (4, 31), and yet isolation of valleys has been so far overlooked, and a comprehensive framework to quantify this effect is missing.Open in a separate windowFig. 2.Habitat maps as a function of elevation. (A) A real fluvial landscape (same of Fig. 1A). (B) Fitness of three different species as a function of elevation. (C–E) Fitness maps of the three species shown in B. Darker pixels indicate higher fitness.Another limitation of previous studies (e.g., refs. 4, 10, 11, and 13) is that the environmental matrix and the elevational gradients were considered disconnected, and movement and dispersal of organisms across space ignored. Indeed, deriving species distribution patterns directly from elevational gradients of potential drivers implicitly requires the assumption of a 1D landscape (Fig. 1B), where all sites at the same elevation share the same characteristics. Such a landscape model is in stark contrast to the complexity of a typical real-life mountainous region (Fig. 1). The hypothesis that the very structure of landscapes can lead to nontrivial diversity patterns, even in the absence of species’ preferential elevation or gradients of productivity and habitat capacity, is tested here. To that end, we simulate ecological dynamics in 3D landscapes using a zero-sum metacommunity model (43, 44) (Materials and Methods). The model has been formalized by invoking the minimum set of assumptions principle. Specifically, the following set of rules has been implemented: (i) individuals of each species have a fitness (i.e., a competitive ability in this context) that depends on elevation, with all other vital rates being the same; (ii) different species have different niche positions but the same niche width (Fig. 2B); (iii) niche positions are uniformly distributed along the elevational range of the domain, so that there is no preferential elevation at the metacommunity scale; (iv) dispersal is isotropic (toward the four nearest-neighbor communities in a regular 2D lattice); and (v) the size of local communities is constant over the entire domain (i.e., constant habitat capacity). The above assumptions could be straightforwardly relaxed to mimic more realistic metacommunities. However, this set of assumptions is specifically designed to provide a null model to single out the effect of geomorphic controls of landscape structure on elevational diversity, while deliberately excluding other possible confounding factors.In addition to real-life landscapes, we run the zero-sum model over synthetic elevation fields derived from optimal channel networks (OCNs) (Supporting Information), which are topological structures that minimize a functional describing the total energy dissipated along drainage directions by landscape-forming discharges that hierarchically accumulate toward the outlet of the basin. OCNs are known to systematically reproduce all mutually connected scaling exponents of topological and metric landscape features (25, 45) and are exact steady-state solutions to the landscape evolution equation in the small gradient approximation (46). The use of OCNs has a twofold advantage. First, the use of synthetic elevation fields allows generating consistent replicas of fluvial landscapes in the same domain as the minimization process produces dynamically accessible, yet different, stable states endowed with the same statistical features. Second, OCNs allow producing periodic elevation fields and simulating ecological dynamics over a pseudoinfinite domain, thus avoiding edge effects.     

Erosion in southern Tibet shut down at ～10 Ma due to enhanced rock uplift within the Himalaya

Exhumation of the southern Tibetan plateau margin reflects interplay between surface and lithospheric dynamics within the Himalaya–Tibet orogen. We report thermochronometric data from a 1.2-km elevation transect within granitoids of the eastern Lhasa terrane, southern Tibet, which indicate rapid exhumation exceeding 1 km/Ma from 17–16 to 12–11 Ma followed by very slow exhumation to the present. We hypothesize that these changes in exhumation occurred in response to changes in the loci and rate of rock uplift and the resulting southward shift of the main topographic and drainage divides from within the Lhasa terrane to their current positions within the Himalaya. At ∼17 Ma, steep erosive drainage networks would have flowed across the Himalaya and greater amounts of moisture would have advected into the Lhasa terrane to drive large-scale erosional exhumation. As convergence thickened and widened the Himalaya, the orographic barrier to precipitation in southern Tibet terrane would have strengthened. Previously documented midcrustal duplexing around 10 Ma generated a zone of high rock uplift within the Himalaya. We use numerical simulations as a conceptual tool to highlight how a zone of high rock uplift could have defeated transverse drainage networks, resulting in substantial drainage reorganization. When combined with a strengthening orographic barrier to precipitation, this drainage reorganization would have driven the sharp reduction in exhumation rate we observe in southern Tibet.The Himalaya–Tibet orogenic system, formed by collision between India and Asia beginning ca. 50 Ma, is the most salient topographic feature on Earth and is considered the archetype for understanding continental collision. Geophysical and geologic research has illuminated the modern structure and dynamics of the orogen (1). Nonetheless, how the relatively low relief and high elevation Tibetan plateau grew spatially and temporally and what underlying mechanism(s) drove the patterns of plateau growth remain outstanding questions.In the internally drained central Tibetan plateau, evidence from carbonate stable isotopes suggest that high elevations persisted since at least 25–35 Ma (2, 3). Sustained high elevations since shortly after collision commenced have also been used to explain low long-term erosion rates in the internally drained plateau interior (4–6). In contrast to the central plateau, the externally drained Tibetan plateau margins serve as the headwaters for many major river systems in Asia. Because externally drained rivers provide an erosive mechanism to destroy uplifted terrane, understanding why these rivers have not incised further and more deeply into the Tibetan plateau is essential to decipher how the plateau grew. Recent research in the eastern (7, 8) and northern (9) Tibetan plateau indicates that erosion rates have increased significantly since ∼10 Ma. These increases suggest that rock uplift rates have also increased and that the plateau has expanded to the east and north during this time [due to lower crustal flow (7) or upper crustal extrusion (8) to the east and structural reorganization to the north (9)], causing rivers to steepen and erode at faster rates.The history of the southern Tibetan plateau margin, on the other hand, is less well understood. The southern Tibetan plateau is presently drained by the Yarlung and Indus Rivers, which each flow parallel to the Himalayan range for more than 1,000 km before descending from the plateau at the Himalayan syntaxes. Evidence from fossils and carbonate stable isotopes suggest that high elevations in the southern Tibetan plateau persisted since at least 15 Ma (10, 11) and potentially even before collision began (12). Additionally, sediments from the Himalayan foreland, Bengal, and Central Myanmar basins require external drainage of the southern Tibetan plateau since at least 14 Ma and potentially as early as 40 Ma (13–15). High elevations and external drainage since at least Middle Miocene time indicate that rock uplift rates in the southern Tibetan plateau may have kept up with the pace of river incision for tens of millions of years. However, cosmogenic nuclide concentrations indicate low erosion rates (typically <10² m/Ma) in both the Indus and Yarlung drainages over the last several hundred thousand years (16, 17). No data yet exist to test whether these slow erosion rates persisted over longer 10⁶- to 10⁷-y timescales. Therefore, it is uncertain how high elevations in the southern plateau have been sustained: are long-term rock uplift and erosion rates both high or have slow erosion rates persisted despite external drainage by some other mechanism?Here, we examine the exhumation history of the eastern part of the Tibetan plateau’s southern margin using thermochronometry, a technique in which thermal histories of rocks are constrained by the evolution of geochemical systems sensitive to temperatures within Earth’s upper crust. We present apatite ⁴He/³He, apatite and zircon (U-Th)/He, and biotite and K-feldspar ⁴⁰Ar/³⁹Ar thermochronometry data from granitic bedrock samples of the Cretaceous–Cenozoic Gangdese batholith in the eastern Lhasa terrane, southern Tibet. Samples were collected along a 1.2-km elevation transect near the eastern headwaters of the Lhasa River, a major tributary of the Yarlung River (Fig. 1 and SI Appendix, Table S1). This approach is advantageous for several reasons. First, by using a suite of thermochronometric systems sensitive to temperatures ranging from ∼30 °C to 350 °C, we can identify changes in exhumation rate over a longer duration than would be possible with any subset of them. Second, sampling along an elevation transect leverages the fact that rocks at different elevations within a pluton share a similar exhumation history but have different cooling histories. Resolvable differences in the cooling histories between rocks at different elevations can more tightly constrain the overall exhumation history than the cooling history of a single elevation sample. Third, to avoid the effects of local-scale tectonic exhumation, we collected samples in a location that is not in the footwall of one of the north-south trending rift systems in southern Tibet. Therefore, the data primarily record temporal trends in erosional exhumation of the region. With data from this sampling scheme, we use 3D thermokinematic models to constrain the timing of both large-scale unroofing of the Gangdese batholith and local, kilometer-scale relief development due to river incision. From these data and thermokinematic models, we develop a hypothesis for landscape evolution within the southern Tibetan plateau that we illustrate schematically using a simple numerical model.Open in a separate windowFig. 1.(A) Topography and (B) mean annual precipitation (MAP) in southern Tibet and the Himalaya. The yellow star marks the city of Lhasa and blue circles denote the sample locations. The following generalized geologic structures are also shown in A: GCT, Great Counter Thrust; GT, Gangdese Thrust; IYSZ, Indus-Yarlung Suture Zone; MBT, Main Boundary Thrust; MCT, Main Central Thrust; MFT, Main Frontal Thrust; STD, South Tibetan Detachment; WF, Woka fault. In B, major river networks draining the southern Tibetan plateau and Himalaya are shown in black, with the Yarlung River and the Lhasa River highlighted in white and tan, respectively. C shows a detailed view of our sample locations and the surrounding topography. Topography plotted from 90 m SRTM (Shuttle Radar Topography Mission) data; MAP plotted from TRMM (Tropical Rainfall Measurement Mission) 2B31 data collected between 1998 and 2009 (36); geologic structures based on Styron et al. (30), Decelles et al. (31), Yin et al. (33), and Hodges (44).     

景观疗养社会化保障可行性分析

本文从疗养员、疗养院及社会化保障的供方市场三个方面,对军队疗养院景观疗养实行社会化保障的可行性进行全面系统的分析,认为军队疗养院实行景观疗养社会化保障能进一步满足疗养员日益增长的疗养需求,能促进军队疗养院更好更安全的发展,且社会化保障的供方市场也十分成熟,完全能满足军队疗养院景观疗养社会化保障的需求。     

Estimated global mortality attributable to smoke from landscape fires

Background: Forest, grass, and peat fires release approximately 2 petagrams of carbon into the atmosphere each year, influencing weather, climate, and air quality.Objective: We estimated the annual global mortality attributable to landscape fire smoke (LFS).Methods: Daily and annual exposure to particulate matter ≤ 2.5 μm in aerodynamic diameter (PM_2.5) from fire emissions was estimated globally for 1997 through 2006 by combining outputs from a chemical transport model with satellite-based observations of aerosol optical depth. In World Health Organization (WHO) subregions classified as sporadically affected, the daily burden of mortality was estimated using previously published concentration–response coefficients for the association between short-term elevations in PM_2.5 from LFS (contrasted with 0 μg/m³ from LFS) and all-cause mortality. In subregions classified as chronically affected, the annual burden of mortality was estimated using the American Cancer Society study coefficient for the association between long-term PM_2.5 exposure and all-cause mortality. The annual average PM_2.5 estimates were contrasted with theoretical minimum (counterfactual) concentrations in each chronically affected subregion. Sensitivity of mortality estimates to different exposure assessments, counterfactual estimates, and concentration–response functions was evaluated. Strong La Niña and El Niño years were compared to assess the influence of interannual climatic variability.Results: Our principal estimate for the average mortality attributable to LFS exposure was 339,000 deaths annually. In sensitivity analyses the interquartile range of all tested estimates was 260,000–600,000. The regions most affected were sub-Saharan Africa (157,000) and Southeast Asia (110,000). Estimated annual mortality during La Niña was 262,000, compared with 532,000 during El Niño.Conclusions: Fire emissions are an important contributor to global mortality. Adverse health outcomes associated with LFS could be substantially reduced by curtailing burning of tropical rainforests, which rarely burn naturally. The large estimated influence of El Niño suggests a relationship between climate and the burden of mortality attributable to LFS.