生态系统自调节机制理论探究

平衡或稳态是生物的重要特性。自调节机制是维持生态系统平衡,呈现稳态特性的内在运行机制。指生物个体自我适应环境变化,维持自身机体平衡,永保旺盛生命活力的构造、功能及其相互关系。自调节机制的平衡、稳定、激活、自新功能,决定了它具有适应、生存、共生、自生等作用。     

广州市空气可吸入颗粒物污染现状研究

根据近年来广州市区空气质量监测资料,对广州市空气可吸入颗粒物（PM10）的污染水平、时空变化特征进行了分析、评价。结果表明,2007年广州市PM10平均浓度为0．077mg／m^3,符合国家空气质量二级标准。得益于空气污染控制取得的成效,近5年来PM10年均浓度呈下降趋势。受气候因素影响,PM10污染季节变化明显,冬季节较高,夏季节浓度较低;在空间分布上PM10污染呈现区域性发展趋势。     

企业在发展循环经济中的地位和作用

发展循环经济已成为世界性的潮流,不仅在发达国家篷勃兴起,在我国也摆到了党和国家的重要日程,大力发展循环经济已成为我国的当务之急。政府各有关部门正在制定发展循环经济的规划,许多省、市也在积极进行循环经济的试点,而企业在发展循环经济中则起着至关重要的作用。     

基于TROPOMI的扬州市对流层甲醛和二氧化氮时空分布特征分析

以2020年1月—2021年9月对流层观测仪（TROPOMI）卫星观测资料反演获取的对流层甲醛（HCHO）、二氧化氮（NO₂）柱浓度数据为依据,采用统计方法分析了扬州市HCHO和NO₂柱浓度的时空分布特征。结果表明,扬州市对流层HCHO、NO₂平均柱浓度分别为903.01×10¹³, 633.77×10¹³mole/cm²;受太阳紫外辐射影响,HCHO柱浓度变化特征表现为6月最高、1月最低;受气象条件和人为排放强度影响,NO₂则表现为1月最高、8月最低。2021年1—9月扬州市对流层HCHO、NO₂柱浓度月均值同比2020年分别增长4.0%,40.6%。空间分布特征显示,扬州市对流层HCHO和NO₂浓度高值区主要分布在扬州市南部,且浓度高值区域与重点排污企业分布情况较为一致,多为电力供热、工业锅炉、冶金、石化与化工、表面涂层等行业。相关性分析显示,对流层HCHO与气温、臭氧浓度呈显著正相关,而NO₂与气温、臭氧浓度呈显著负相关。     

The influence of environmental spatial structure on the life-history traits and diversity of species in a metacommunity

Several models have been proposed to understand how so many species can coexist in ecosystems. Despite evidence showing that natural habitats are often patchy and fragmented, these models rarely take into account environmental spatial structure. In this study we investigated the influence of spatial structure in habitat and disturbance regime upon species’ traits and species’ coexistence in a metacommunity. We used a population-based model to simulate competing species in spatially explicit landscapes. The species traits we focused on were dispersal ability, competitiveness, reproductive investment and survival rate. Communities were characterized by their species richness and by the four life-history traits averaged over all the surviving species. Our results show that spatial structure and disturbance have a strong influence on the equilibrium life-history traits within a metacommunity. In the absence of disturbance, spatially structured landscapes favour species investing more in reproduction, but less in dispersal and survival. However, this influence is strongly dependent on the disturbance rate, pointing to an important interaction between spatial structure and disturbance. This interaction also plays a role in species coexistence. While spatial structure tends to reduce diversity in the absence of disturbance, the tendency is reversed when disturbance occurs. In conclusion, the spatial structure of communities is an important determinant of their diversity and characteristic traits. These traits are likely to influence important ecological properties such as resistance to invasion or response to climate change, which in turn will determine the fate of ecosystems facing the current global ecological crisis.     

Design of a Domain Specific Language for modelling processes in landscapes

The modelling of processes that occur in landscapes is often confronted to issues related to the representation of space and the difficulty of properly handling time and multiple scales. In order to investigate these issues, a flexible modelling environment is required. We propose to develop such a tool based on a Domain Specific Language (DSL) that capitalises on the service-oriented architecture (SOA) paradigm. The modelling framework around the DSL is composed of a model building environment, a code generator and compiler, and a program execution platform. The DSL introduces five language elements (entity, service, relation, scenario and datafacer) that can be combined to offer a wide range of possibilities for modelling in space and time at different scales. When developing a model, model parts are either built using the DSL or taken from libraries of previously built ones, and adapted to the specific model. The practical usage of the DSL is illustrated first with the Lotka–Volterra model, and then with a landscape modelling experiment on the spread of a mosquito-borne disease in the Sahelian region of West Africa. An interesting characteristic of this approach is the possibility of adding new elements into an existing model, and replacing others with more appropriate ones, thus allowing potentially complex models to be built from simpler parts.     

Are savannas patch-dynamic systems? A landscape model

Savannas are ecosystems characterized by the coexistence of woody species (trees and bushes) and grasses. Given that savanna characteristics are mainly formed from competition, herbivory, fire, woodcutting, and patchy soil and precipitation characteristics, we propose a spatially explicit model to examine the effects of the above-mentioned parameters on savanna vegetation dynamics in space and time. Furthermore, we investigate the effects of the above-mentioned parameters on tree–bush–grass ratios, as well as the degrees of aggregation of tree–bush–grass biomass. We parameterized our model for an arid savanna with shallow soil depth as well as a mesic one with generally deeper and more variable soil depths. Our model was able to reproduce savanna vegetation characteristics for periods of time over 2000 years with daily updated time steps. According to our results, tree biomass was higher than bush biomass in the arid savanna but bush biomass exceeded tree and grass biomass in the simulated mesic savanna. Woody biomass increased in our simulations when the soil's porosity values were increased (mesic savanna), in combination with higher precipitation. Savanna vegetation varied from open savanna to woodland and back to open savanna again. Vegetation cycles varied over ∼300-year cycles in the arid and ∼220-year cycles in the mesic-simulated savanna. Autocorrelation values indicated that there are both temporal and spatial vegetation cycles. Our model indicated cycling savanna vegetation at the landscape scale, cycles in cells, and patchiness, i.e. patch dynamics.     

Geographic patterns of non-carpeted floor dust loading in Syracuse, New York (USA) homes

Residential floor dust loading was measured on the smooth floor surface of 488 houses in Syracuse, New York, during the summers of 2003 and 2004. Using U.S. Environmental Protection Agency (EPA) wipe methods, pre-weighed Ghost Wipes, Lead Wipes, or Whatman Filters were employed to collect duplicate samples from (predominantly) kitchens. The collection efficiency of the various media was determined from multiple wipe tests and side-by-side comparisons. The results were normalized and aggregated at the census tract level to determine whether spatial patterns of dust loading could be observed. Loading was found to be log-normally distributed, with a geometric mean value of 0.311 g m⁻² (29 mg of dust per square foot of floor); 95% of the observations fell in the range of 0.042–2.330 g m⁻² (4–216 mg foot⁻²). The sampling for floor dust loading shows some bias for day of the week in which visits to the residential properties were made. After a first-order correction for this effect, results were aggregated by census tract and mapped in a geographic information system (GIS); strong spatial patterns can be identified in an inverse distance weighted mapping. The geographic patterns exhibit a strong correlation with socio-economic/demographic covariates extracted from the 2000 census summaries. Dust mass on the floors is positively correlated with renter-occupied properties and family size; it is negatively correlated with measures of household income.     

Effects of the spatial pattern of disturbance on the patch-occupancy dynamics of juniper–pine open woodland

Typically, studies of the disturbance effect on metapopulation dynamics are limited to understanding the effect of habitat loss although, recently, the spatial pattern of the disturbance has been shown to influence dynamics. In this study, we used a stochastic patch-dynamic model to investigate the effects of spatial disturbance patterns on the persistence of an open woodland community of Juniperus spp. and Pinus spp. First, we estimated patch-occupancy dynamics by using the coefficients that best predicted the occupancy observed in 1998 based on occupancy data from 1957. Next, we evaluated the effects of the rate and pattern of the disturbance on the extinction probability. In modeling the disturbance, we considered (1) the degree of disturbance produced by scenarios of complete destruction or degradation (with the potential for recolonization), (2) the overall rate of disturbance, and (3) the spatial autocorrelation of habitat destruction. Twenty 40-year simulations predicted a 25% increase in the number of patches, and when 50% of the habitat was removed, the impact was more pronounced after complete destruction than it was after degradation of the area. Predictions based on scenarios of complete destruction, including random, contiguous, Brownian, and autoregressive noise, demonstrated that the impact of disturbance depends upon the spatial structure of the disturbance regimen. The autocorrelated structure of the disturbance regimen had the greatest impact on patch persistence. Patch-occupancy was higher after 20 40-year simulations when habitat loss was randomly distributed than when it followed an autocorrelated patch destruction, which was simulated using autoregressive noise to produce 50% habitat destruction. In addition, while habitat loss was negatively linearly correlated with patch persistence when habitat destruction was randomly distributed, a dramatic transition shift occurred when habitat destruction was simulated following an autoregressive spatial distribution after a certain threshold of habitat destruction (40% of the actual open woodland habitat). Our study suggests that the spatial patterns of the disturbance should be considered when predicting the consequences of fragmentation and improving management strategies.     

GIS-based modeling of the geographic distribution of Quercus emoryi Torr. (Fagaceae) in México and identification of significant environmental factors influencing the species’ distribution

The greatest concentration of oak species in the world is believed to be found in Mexico. These species are potentially useful for reforestation because of their capacity to adapt to diverse environments. Knowledge of their geographic distribution and of species–environment relations is essential for decision-making in the management and conservation of natural resources. The objectives of this study were to develop a model of the distribution of Quercus emoryi Torr. in Mexico, using geographic information systems and data layers of climatic and other variables, and to determine the variables that significantly influence the distribution of the species. The study consisted of the following steps: (A) selection of the target species from a botanical scientific collection, (B) characterization of the collecting sites using images with values or categories of the variables, (C) model building with the overlay of images that meet the habitat conditions determined from the characterization of sites, (D) model validation with independent data in order to determine the precision of the model, (E) model calibration through adjustment of the intervals of some variables, and (F) sensitivity analysis using precision and concordance non-parametric statistics applied to pairs of images. Results show that the intervals of the variables that best describe the species’ habitat are the following: altitude from 1650 to 2750 amsl, slope from 0 to 66°; average minimum temperature of January from −12 to −3 °C; mean temperature of June from 11 to 25 °C; mean annual precipitation from 218 to 1225 mm; soil units: lithosol, eutric cambisol, haplic phaeozem, chromic luvisol, rendzina, luvic xerosol, mollic planosol, pellic vertisol, eutric regosol; type of vegetation: oak forest, oak–pine forest, pine forest, pine–oak forest, juniperus forest, low open forest, natural grassland and chaparral. The resulting model of the geographic distribution of Quercus emoryi in Mexico had the following values for non-parametric statistics of precision and agreement: Kappa index of 0.613 and 0.788, overall accuracy of 0.806 and 0.894, sensitivity of 0.650 and 0.825, specificity of 0.963, positive predictive value of 0.945 and 0.957 and negative predictive value of 0.733 and 0.846. Results indicate that the variable average minimum temperature of January, with a maximum value of −3 °C, is an important factor in limiting the species’ distribution.