Arsenic occurrence in Brazil and human exposure

Environmental exposure to arsenic (As) in terms of public health is receiving increasing attention worldwide following cases of mass contamination in different parts of the world. However, there is a scarcity of data available on As geochemistry in Brazilian territory, despite the known occurrence of As in some of the more severely polluted areas of Brazil. The purpose of this paper is to discuss existing data on As distribution in Brazil based on recent investigations in three contaminated areas as well as results from the literature. To date, integrated studies on environmental and anthropogenic sources of As contamination have been carried out only in three areas in Brazil: (1) the Southeastern region, known as the Iron Quadrangle, where As was released into the drainage systems, soils and atmosphere as a result of gold mining; (2) the Ribeira Valley, where As occurs in Pb-Zn mine wastes and naturally in As-rich rocks and soils; (3) the Amazon region, including the Santana area, where As is associated with manganese ores mined over the last 50 years. Toxicological studies revealed that the populations were not exposed to elevated levels of As, with the As concentrations in surface water in these areas rarely exceeding 10 microg/L. Deep weathering of bedrocks along with formation of Fe/Al-enriched soils and sediments function as a chemical barrier that prevents the release of As into the water. In addition, the tropical climate results in high rates of precipitation in the northern and southeastern regions and, hence, the As contents of drinking water is diluted. Severe cases of human As exposure related to non-point pollution sources have not been reported in Brazil. However, increasing awareness of the adverse health effects of As will eventually lead to a more complete picture of the distribution of As in Brazil.     

Analysis of periodic patterns of daily precipitation through simultaneous modeling of its serially observed occurrence and amount

Precipitation is of great importance to agriculture, environment and ecosystem as a regular precipitation pattern is usually vital to healthy plants; excessive or insufficient rainfall can be harmful. Periodic patterns of precipitation can be studied based on regularly observed data over time. Since regularly observed precipitation data are generally skewed with many zeros, two common analysis approaches have been proposed recently. One approach investigates precipitation using a two-part model where the occurrence and positive amount of precipitation are analyzed separately (Piantadosi et al. in Environ Model Assess 14:431–438, 2009), whereas the other approach handles occurrence and amount simultaneously using a Tweedie’s compound Poisson model for independent observations (Hasan and Dunn in Int J Climatol 32:1006–1017, 2012). The former approach fails to maintain the regular temporal structure of serially observed precipitation, whereas the latter approach ignores serial dependence. As there is generally substantial serial correlation in the observed sequence of precipitation data over time, we introduce a compound Poisson state-space model with serially correlated random effects for daily precipitation data. This approach characterizes both occurrence and amount of precipitation simultaneously while accounting for the corresponding serial correlation. Our main results depend only on the first- and second-moment assumptions of unobserved random effects. We illustrate our method with the analysis of the daily precipitation data recorded at Mount Washington, NH, USA.     

Plant Diversity Hotspots in the Atlantic Coastal Forests of Brazil

Abstract:  Plant-diversity hotspots on a global scale are well established, but smaller local hotspots within these must be identified for effective conservation of plants at the global and local scales. We used the distributions of endemic and endemic-threatened species of Myrtaceae to indicate areas of plant diversity and conservation importance within the Atlantic coastal forests ( Mata Atlântica ) of Brazil. We applied 3 simple, inexpensive geographic information system (GIS) techniques to a herbarium specimen database: predictive species-distribution modeling (Maxent); complementarity analysis (DIVA-GIS); and mapping of herbarium specimen collection locations. We also considered collecting intensity, which is an inherent limitation of use of natural history records for biodiversity studies. Two separate areas of endemism were evident: the Serra do Mar mountain range from Paraná to Rio de Janeiro and the coastal forests of northern Espírito Santo and southern Bahia. We identified 12 areas of approximately 35 km² each as priority areas for conservation. These areas had the highest species richness and were highly threatened by urban and agricultural expansion. Observed species occurrences, species occurrences predicted from the model, and results of our complementarity analysis were congruent in identifying those areas with the most endemic species. These areas were then prioritized for conservation importance by comparing ecological data for each.     

Predicted Climate‐Driven Bird Distribution Changes and Forecasted Conservation Conflicts in a Neotropical Savanna

Abstract: Climate‐change scenarios project significant temperature changes for most of South America. We studied the potential impacts of predicted climate‐driven change on the distribution and conservation of 26 broad‐range birds from South America Cerrado biome (a savanna that also encompass tracts of grasslands and forests). We used 12 temperature or precipitation‐related bioclimatic variables, nine niche modeling techniques, three general circulation models, and two climate scenarios (for 2030, 2065, 2099) for each species to model distribution ranges. To reach a consensus scenario, we used an ensemble‐forecasting approach to obtain an average distribution for each species at each time interval. We estimated the range extent and shift of each species. Changes in range size varied across species and according to habitat dependency; future predicted range extent was negatively correlated with current predicted range extent in all scenarios. Evolution of range size under full or null dispersal scenarios varied among species from a 5% increase to an 80% decrease. The mean expected range shifts under null and full‐dispersal scenarios were 175 and 200 km, respectively (range 15–399 km), and the shift was usually toward southeastern Brazil. We predicted larger range contractions and longer range shifts for forest‐ and grassland‐dependent species than for savanna‐dependent birds. A negative correlation between current range extent and predicted range loss revealed that geographically restricted species may face stronger threat and become even rarer. The predicted southeasterly direction of range changes is cause for concern because ranges are predicted to shift to the most developed and populated region of Brazil. Also, southeastern Brazil is the least likely region to contain significant dispersal corridors, to allow expansion of Cerrado vegetation types, or to accommodate creation of new reserves.     

Effects of Climate Change on Population Persistence of Desert-Dwelling Mountain Sheep in California

Abstract:  Metapopulations may be very sensitive to global climate change, particularly if temperature and precipitation change rapidly. We present an analysis of the role of climate and other factors in determining metapopulation structure based on presence and absence data. We compared existing and historical population distributions of desert bighorn sheep ( Ovis canadensis ) to determine whether regional climate patterns were correlated with local extinction. To examine all mountain ranges known to hold or to have held desert bighorn populations in California and score for variables describing climate, metapopulation dynamics, human impacts, and other environmental factors, we used a geographic information system (GIS) and paper maps. We used logistic regression and hierarchical partitioning to assess the relationship among these variables and the current status of each population (extinct or extant). Parameters related to climate—elevation, precipitation, and presence of dependable springs—were strongly correlated with population persistence in the twentieth century. Populations inhabiting lower, drier mountain ranges were more likely to go extinct. The presence of domestic sheep grazing allotments was negatively correlated with population persistence. We used conditional extinction probabilities generated by the logistic-regression model to rank native, naturally recolonized, and reintroduced populations by vulnerability to extinction under several climate-change scenarios. Thus risk of extinction in metapopulations can be evaluated for global-climate-change scenarios even when few demographic data are available.     

A comparison study of aerosol emissions sources in two receptor sites in Salvador (Brazil) city

Total suspended particles (TSP) and metal concentrations were determined in Salvador, Bahia, Brazil at two sites: a residential area and a residential‐commercial‐industrial neighborhood. The determined metals associated with atmospheric aerosols include Al, Ca, Cr, Cd, Fe, Mg, Mn, V, Zn and Na. Factor analysis was employed to identify the major atmospheric deposition sources. At one site the data were best represented by two sources: resuspended soil + marine aerosol and vehicular emissions, which represents 93% of the total system variance. In the second one the data were best represented by three sources: resuspended soil, metallurgy and building construction, which represents 79% of the total system variance.     

Verification of the geographical origin of modeled air-mass trajectories by means of the isotope composition of rainwater during the SALLJEX experiment

The SALLJEX experiment was held during the summer 2002–2003. It consisted of three-dimensional observation of the atmosphere to study the structure of the low level jet along the eastern slopes of the Andes. Daily precipitation water samples were collected at two stations (Resistencia and Salta) in northern Argentina and isotope content was analyzed. The isotope data were used in conjunction with air parcel trajectories obtained from a 3-D kinematic model (3D-MTC) developed by the University of Sao Paulo, Brazil. Values of deuterium excess were related with air masses of continental origin, whilst low values were associated with air masses with longer oceanic trajectories. Furthermore, although data are scarce, results show that oxygen-18 and deuterium excess in rainwater are related with the occurrence of the low level jet.     

Controls over foliar N:P ratios in tropical rain forests

Correlations between foliar nutrient concentrations and soil nutrient availability have been found in multiple ecosystems. These relationships have led to the use of foliar nutrients as an index of nutrient status and to the prediction of broadscale patterns in ecosystem processes. More recently, a growing interest in ecological stoichiometry has fueled multiple analyses of foliar nitrogen:phosphorus (N:P) ratios within and across ecosystems. These studies have observed that N:P values are generally elevated in tropical forests when compared to higher latitude ecosystems, adding weight to a common belief that tropical forests are generally N rich and P poor. However, while these broad generalizations may have merit, their simplicity masks the enormous environmental heterogeneity that exists within the tropics; such variation includes large ranges in soil fertility and climate, as well as the highest plant species diversity of any biome. Here we present original data on foliar N and P concentrations from 150 mature canopy tree species in Costa Rica and Brazil, and combine those data with a comprehensive new literature synthesis to explore the major sources of variation in foliar N:P values within the tropics. We found no relationship between N:P ratios and either latitude or mean annual precipitation within the tropics alone. There is, however, evidence of seasonal controls; in our Costa Rica sites, foliar N:P values differed by 25% between wet and dry seasons. The N:P ratios do vary with soil P availability and/or soil order, but there is substantial overlap across coarse divisions in soil type, and perhaps the most striking feature of the data set is variation at the species level. Taken as a whole, our results imply that the dominant influence on foliar N:P ratios in the tropics is species variability and that, unlike marine systems and perhaps many other terrestrial biomes, the N:P stoichiometry of tropical forests is not well constrained. Thus any use of N:P ratios in the tropics to infer larger-scale ecosystem processes must comprehensively account for the diversity of any given site and recognize the broad range in nutrient requirements, even at the local scale.     

Effectiveness of Environmental Surrogates for the Selection of Conservation Area Networks

Abstract:  Rapid biodiversity assessment and conservation planning require the use of easily quantified and estimated surrogates for biodiversity. Using data sets from Québec and Queensland, we applied four methods to assess the extent to which environmental surrogates can represent biodiversity components: (1) surrogacy graphs; (2) marginal representation plots; (3) Hamming distance function; and (4) Syrjala statistical test for spatial congruence. For Québec we used 719 faunal and floral species as biodiversity components, and for Queensland we used 2348 plant species. We used four climatic parameter types (annual mean temperature, minimum temperature during the coldest quarter, maximum temperature during the hottest quarter, and annual precipitation), along with slope, elevation, aspect, and soil types, as environmental surrogates. To study the effect of scale, we analyzed the data at seven spatial scales ranging from 0.01° to 0.10° longitude and latitude. At targeted representations of 10% for environmental surrogates and biodiversity components, all four methods indicated that using a full set of environmental surrogates systematically provided better results than selecting areas at random, usually ensuring that ≥90% of the biodiversity components achieved the 10% targets at scales coarser than 0.02°. The performance of surrogates improved with coarser spatial resolutions. Thus, environmental surrogate sets are useful tools for biodiversity conservation planning. A recommended protocol for the use of such surrogates consists of randomly selecting a set of areas for which distributional data are available, identifying an optimal surrogate set based on these areas, and subsequently prioritizing places for conservation based on the optimal surrogate set.     

河北省夏玉米气候适宜度及其变化特征分析

采用逐日气象要素和统计方法,建立了河北省夏玉米(Zea Mays)各生育期气候适宜度评价模型,计算了河北省1981—2010年逐年夏玉米各生育期气温、降水、日照、气候适宜度值,并对夏玉米各生育期气候适宜度特征及变化趋势进行了分析。结果表明:夏玉米生长期间气温适宜度最高,日照次之,降水最低且变异系数最大。气温适宜度以夏玉米灌浆期最低,变异系数最大;日照和降水适宜度均以抽雄期最低且变异系数最大。多年适宜度变化趋势为:全生育期日照适宜度明显下降(下降速率为0.047/10a);气温适宜度基本持平;降水适宜度波动较大,无明显变化规律。幼苗和灌浆期气候适宜度呈下降趋势(下降速率分别为0.015/10a,0.024/10a),其中幼苗期气候适宜度下降由期间气温和日照适宜度下降引起;灌浆期气候适宜度下降由日照和降水适宜度下降引起。     

Selecting a binary Markov model for a precipitation process

This paper uses rth-order categorical Markov chains to model the probability of precipitation. Several stationary and non-stationary high-order Markov models are proposed and compared using BIC. The number of parameters increases exponentially by adding the Markov order. Several classes of high-order Markov models are proposed which their increase of number of parameters are modest. For example models that use the number of precipitation days in a period prior to date, temperature of the previous day and sines/cosines periodic functions (to model the seasonality) are considered. The theory of partial likelihood is used to estimate the parameters. Parsimonious non-stationary first order Markov models with few seasonal terms are found optimal using BIC and temperature does not turn out to be a useful covariate. However BIC seems to underestimate the number of seasonal terms. We have also compared the results with AIC in some cases which tends to pick parsimonious models with more seasonal terms and higher order. We also show that ignoring seasonal terms result in picking higher order Markov chains. Finally we apply the methods to build confidence intervals for the probability of periods with no precipitation or low number of precipitation days in Calgary using historical data from 1980 to 2000.     

近53年黄河流域降水时空分布特征

利用黄河流域76个气象台站近53 a（1960-2012）的逐日降水资料,采用国际上通用的极端降水事件指数,应用一元线性回归法、移动平均法和径向基函数空间插值法,研究了黄河流域极端降水指数时空变化特征。结果表明：（1）时间上,黄河流域年平均降水量在过去53 a下降趋势较为显著,降水倾向率为-7.2 mm/10a;极端降水量变化趋势表现较为稳定,极端降水倾向率为-0.64 mm/10a,呈不断降低趋势;极端降水强度倾向率为-0.078 mm/10a,呈不断下降趋势;极端降水比率总体表现为微弱增长趋势,倾向率为0.49 mm/10a。（2）空间上,降水量空间分布具有明显的差异性,由北至南呈阶梯式逐渐增多趋势,其中降水量最少的地区是以银川为代表的周边区域,最多的地区为黄河流域南部区域;极端降水量从北至南也具有逐渐增多态势,与降水量具有相似的空间分布特点,且极端降水量越多的地区降水总量也相对较多;极端降水强度表现出由流域西部向东部逐渐增多的趋势,西部最低值为不到20 mm/d,逐渐向东过渡到最大值为76 mm/d;极端降水比率的分布呈由北向南逐渐递减的特点,并且出现了以银川为中心的极大值和以西安为中心的极小值分布格局。     

Effects of precipitation and temperature on rain-use efficiency of China's forests北大核心CSCD

Water plays an important role in the growth of plants. It is important to investigate the responses of different ecosystems to precipitation patterns, especially in terms of global changes involving extreme precipitation or drought. However, little is known about the rain-use efficiency (RUE) in China's forests and the correlation of the RUE with the precipitation and other environmental factors. Furthermore, it is unclear whether differences in the forest types or functional groups result in divergent RUE patterns. To investigate the RUE in China's forests, we analyzed the data from 1 266 sampling plots from 17 forest types. The results indicated that: (1) the mean RUE of China's forests is 1.21 g m-2 mm-1. The RUE and precipitation are negatively correlated in all the 17 forest types; (2) the average RUE of broadleaved forests (mainly angiosperms) is higher than that of coniferous forests (mainly gymnosperms), and the angiosperms are more adaptable to changes in the precipitation than gymnosperms; (3) the average RUE of deciduous forests is higher than that of the evergreen forests. The RUE of deciduous forests declines with increasing precipitation to a threshold value. However, the RUE of evergreen forests are unaffected by changes in the precipitation; and (4) the latitude and mean annual temperature are the major factors constraining the RUE under comprehensive environmental factors. We conclude that the mean RUE declines with increasing precipitation to a threshold value, and that the temperature is a crucial factor for RUE. © 2018 Science Press. All rights reserved.     

Biodiversity and Conservation of Plants in Brazil

Abstract:  With more than 56,000 species (excluding fungi), Brazil has one of the richest floras in the world—nearly 19% of the world flora. Our knowledge of the diversity and status of nonvascular plants in Brazil is still fragmentary, although localized studies on algae have revealed loss of species resulting from environmental pollution. Emphasis on local floral surveys, rather than wider taxonomic studies, has obscured estimates of national totals for most taxonomic groups. Knowledge of angiosperms, especially monocotyledons (of which 45% of the species are endemic), is more complete than most. For this group figures are more reliable, with some distribution patterns, endemism levels, and centers of diversity identified. Much, however, still awaits discovery. Coordinated efforts to catalog Brazil's flora are in progress and include projects such as the conservation priority-setting workshops of the Brazilian Ministry of the Environment, which have identified key conservation areas in the major biomes; development of threatened species lists for plants; and the assembly of type data on species of northeastern Brazil through the Darwin Initiative—all of which greatly assist in increasing our knowledge. These initiatives also underline the urgent need to expand the numbers and geographic spread of projects on plant systematics and taxonomy in Brazil, a measure that demands adequate provision of funding and training programs for plant specialists. Finally, Brazil's environmental agency (IBAMA) could play a proactive role in opening protected areas under its jurisdiction, thereby facilitating botanical research by university departments and research institutes.     

The Importance of Analysis Method for Breeding Bird Survey Population Trend Estimates

Population trends from the Breeding Bird Survey are widely used to focus conservation efforts on species thought to be in decline and to test preliminary hypotheses regarding the causes of these declines. A number of statistical methods have been used to estimate population trends, but there is no consensus as to which is the most reliable. We quantified differences in trend estimates or different analysis methods applied to the same subset of Breeding Bird Survey data. We estimated trends for 115 species in British Columbia using three analysis methods: U.S. National Biological Service route regression, Canadian Wildlife Service route regression, and nonparametric rank-trends analysis. Overall, the number of species estimated to be declining was similar among the three methods, but the number of statistically significant declines was not similar (15, 8, and 29 respectively). In addition, many differences existed among methods in the trend estimates assigned to individual species. Comparing the two route regression methods, Canadian Wildlife Service estimates had a greater absolute magnitude on average than those of the U.S. National Biological Service method. U.S. National Biological Service estimates were on average more positive than the Canadian Wildlife Service estimates when the respective agency's data selection criteria were applied separately. These results imply that our ability to detect population declines and to prioritize species of conservation concern depend strongly upon the analysis method used. This highlights the need for further research to determine how best to accurately estimate trends from the data. We suggest a method for evaluating the performance of the analysis methods by using simulated Breeding Bird Survey data.     

Spatiotemporal hierarchical modelling of extreme precipitation in Western Australia using anisotropic Gaussian random fields

We discuss an approach for the statistical modelling of extreme precipitation events in South-West Australia over space and time, using a latent spatiotemporal process where precipitation maxima follow a generalised extreme value distribution. Temporal features are captured by modelling trends on the location and scale parameters. Spatial features are captured using anisotropic Gaussian random fields. Site specific explanatory variables are also incorporated. We fit several models using Bayesian inferential methods to precipitation extremes recorded at 36 weather stations around the Western Australian state capital city of Perth over the period 1907–2009. Model choice is performed using the DIC criterion. The best fitting model shows significant non-stationarity over time, with extreme precipitation events becoming less frequent. Extreme precipitation events are stronger at coastal locations, with the intensity decreasing as we head to the higher and drier areas to the North-East.     

How Many Species Are There in Brazil?

Abstract:  We produced estimates of the total number of species currently known and the total numbers predicted to occur in Brazil. Lists of species recorded in Brazil were obtained from specialists and the literature. For taxa lacking information on total known species, we produced estimates based on bootstrap resampling from a set of 87 taxa with checklists for Brazil and the world. The estimated proportion of Brazilian species was 9.5% of the world total (95% CI, 8.5 to 11.5%). From this we estimated a known Brazilian biota of 170,000 to 210,000 species. We used a similar procedure to estimate Brazil's total biota—known plus undiscovered. Based on 17 relatively well-known taxa, the average Brazilian share in the world's biota was estimated at 13.1% (CI 10.0 to 17.6%). Accordingly we estimated the country's total biota at 1.8 million species (CI 1.4 to 2.4 million). Given that the Neotropics is the least-studied major region of the world, these figures are still likely to be underestimates and hence may be taken as a lower bound of the actual proportion of the world's species that occur in Brazil. Scientists, policy makers, and citizens will find these numbers useful in appreciating the magnitude of the tasks involved in surveying, describing, and conserving the country's biota. The numbers also bring proposals and priorities into a more realistic perspective.     

Correcting climate model simulations in Heihe River using the multivariate bias correction package

The simulations from climate models require bias correction prior to use in impact assessments or when used as predictors in statistical or dynamic downscaling models. Recent works have sought to address each of these limitations and the results are the Multivariate Recursive Nesting Bias Correction (MRNBC) and Multivariate recursive Quantile-matching Nested Bias Correction (MRQNBC) methods. The model was applied to a mountain region of Heihe River. A comparison of the historical and generated statistics shows that the model preserves all the important characteristics of meteorological variables at daily, monthly, seasonally and annual time scales. This study has documented the performance of Multivariate Recursive Nesting Bias Correction to remove the discrepancy between the predictors in the simulated GCM and the reanalysis NCEP data and assess the projected future precipitation accuracy in the headwater region of Heihe River. A relatively high spatial resolution GCM outputs—ACCESS1-3—from the CMIP5 Earth System Models (ESMs) was employed to downscale for the historical 1960–2005 and the future period 2010–2100 under the scenarios of Representative Concentration Pathways RCP4.5 and RCP8.5. The MRNBC method can dramatically increase the performance of the simulated precipitation data. Verified by statistical score metrics applied for evaluation of the results, the developed method appears to be an important statistical tool in the correction of the bias between the GCM output and the reanalysis data, leading to significant improvements in the predictive performance accuracy of the precipitation projections. The projected precipitation under RCP8.5 appeared to exhibit the significant increasing trend relative to the RCP4.5 scenario in the headwater region of Heihe River. Future precipitation will increasing by 8% and 20% for near and long term period under RCP4.5 and increasing 14% and 37% for near and long term period, under RCP8.5, respectively.