最低气温对兰州市呼吸系统疾病就诊人次的影响研究

通过2013年1月1日~2017年7月31日兰州市呼吸系统疾病逐日就诊资料和气象资料,采用分布滞后非线性模型（DLNM）方法,分析0~30d滞后影响,研究极端低温对不同年龄（<15岁、16~45岁、46~60岁、>60岁）和性别人群呼吸系统疾病就诊人次的影响.结果发现：日均最低温度对相对危险度（RR）的最大RR变化范围为0.89~1.41,变化趋势整体呈现出V字型,在低温附近,温度越低,RR越高;滞后0~4d,极端低温代表的冷效应影响最为显著;滞后5~19d,在日均最低温度0℃附近,出现新的峰值,冷效应对RR的贡献有所下降,热效应对RR的贡献完全消失;滞后20~30d,日均最低温度为-10℃时,出现RR最小值,0℃温度点附近维持RR峰值;滞后22d时,滞后影响完全消失.在极端低温条件下,呼吸系统疾病对低龄人群表现出长期滞后性和高发性,对高龄人群表现出短期滞后性.男性比女性患病的滞后效应更长,患病风险更大.     

垃圾渗滤液与再生水生物毒性的体外检测方法应用

本文总结了近年来在垃圾渗滤液和再生水生物毒性检测方面的主要体外试验模型和检测方法,并整理了这些模型和方法在生物毒性评价中的应用.目前常用的体外试验模型包括人源细胞系、其他哺乳动物细胞以及微生物细胞,相比较而言,人源细胞系在检测结果外推至人体时具有更强的说服力,因而其应用最为广泛.体外检测方法可概括为细胞毒性、遗传毒性和内分泌干扰效应检测三个方面,其中内分泌干扰效应的研究较多集中于雌激素效应.最后,本文提出开发三维体外细胞模型、体外试验与化学分析相结合、全面分析内分泌干扰效应和建立成组体外试验体系是渗滤液和再生水生物毒性检测方法的发展方向.     

时空效应下PM2.5的环境库兹涅茨曲线——基于动态空间面板视角

经济发展与环境保护的平衡一直是人们关心的话题,为分析空气质量水平与经济发展程度之间的关系,即环境库兹涅茨曲线,本研究利用2013—2017年中国省级PM_(2.5)污染数据,运用动态空间面板数据模型,从而控制相邻省份的空气质量水平与本地上期污染水平,实证分析中国PM_(2.5)环境库兹涅茨曲线的存在性及空间溢出性.结果表明:①PM_(2.5)的环境库兹涅茨曲线在省级基础上表现为倒U形,即随着初期经济水平的发展,大气污染会不断增加,在经济发展到一定水平后,大气污染开始降低;②东部、中部省份的空气污染表现与全国一致,而西部省份尚无显著趋势,只有第一产业产量与空气污染水平呈负相关;③中国整体的PM_(2.5)污染具有显著的空间溢出性;④对二氧化硫和氮氧化物进行环境库兹涅茨曲线研究,发现全国平均而言还处在大气污染的上升阶段.研究表明,中国各地区处在大气污染发展路径的不同阶段上,而且空气污染的治理模式应采取区域联合分块治理的策略.     

Living at the Edge: Local versus Positional Factors in the Long‐Term Population Dynamics of an Endangered Orchid

Abstract: Populations at the margin of geographic ranges of distribution have been considered more vulnerable than central ones, but recent reviews have caste doubt on this generalization. We examined the reproductive and demographic performance of a rare Euroasiatic orchid (Cypripedium calceolus) at its southwesterly range limit and compared our findings with those of previous studies of nine central populations at the center of the orchid's range. We sought to test the central‐marginal model and to evaluate factors involved in long‐term performance of forest Eurosiberian species with peripheral populations in southern European mountains. We characterized (structure, temporal fluctuations, herbivory, reproductive success, and recruitment at different habitats) four Pyrenean populations of C. calceolus of different sizes (5–3500 ramets) and monitored three of them for up to 13 years. Two quantitative stochastic models (count data and matrix models) were used to assess population trends and viability and the effect of herbivory. Contrary to expectations, and despite the negative effect of sporadic events of herbivory, the peripheral populations we studied (except the smallest one) performed similarly or better than populations occurring in central part of the species’ range in terms of reproductive success and population growth rates. Landscape changes over the last 50 years suggest that natural reforestation could be involved in the success of this plant at its southern limit. Forest expansion in the mountain regions of southern Europe may provide new opportunities for plants with geographic distributions centered mainly at higher latitudes and give some hope for their recovery in future scenarios dominated by biodiversity loss.     

A spatial approach to combatting wildlife crime

Poaching can have devastating impacts on animal and plant numbers, and in many countries has reached crisis levels, with illegal hunters employing increasingly sophisticated techniques. We used data from an 8‐year study in Savé Valley Conservancy, Zimbabwe, to show how geographic profiling—a mathematical technique originally developed in criminology and recently applied to animal foraging and epidemiology—can be adapted for use in investigations of wildlife crime. The data set contained information on over 10,000 incidents of illegal hunting and the deaths of 6,454 wild animals. We used a subset of data for which the illegal hunters’ identities were known. Our model identified the illegal hunters’ home villages based on the spatial locations of the hunting incidences (e.g., snares). Identification of the villages was improved by manipulating the probability surface inside the conservancy to reflect the fact that although the illegal hunters mostly live outside the conservancy, the majority of hunting occurs inside the conservancy (in criminology terms, commuter crime). These results combined with rigorous simulations showed for the first time how geographic profiling can be combined with GIS data and applied to situations with more complex spatial patterns, for example, where landscape heterogeneity means some parts of the study area are less likely to be used (e.g., aquatic areas for terrestrial animals) or where landscape permeability differs (e.g., forest bats tend not to fly over open areas). More broadly, these results show how geographic profiling can be used to target antipoaching interventions more effectively and more efficiently and to develop management strategies and conservation plans in a range of conservation scenarios.     

Implications of network particle tracking (NPT) for ecological model interpretation

Network particle tracking (NPT), building on the foundation of network environ analysis (NEA), is a new development in the definition of coherence relations within and between connected systems. This paper evaluates three ecosystem models in a comparison of throughflow- and storage-based NEA and NPT. Compartments in models with high indirect effects and Finn cycling showed low correlation of NEA storage and throughflow with particle repeat visits and numbers of particles in compartments at steady state. Conversely, the correlation between NEA and NPT results was high with two models having lower indirect effects and Finn cycling. Analysis of ecological orientors associated with NEA showed NPT to fully support conventional NEA results when the common conditions of donor control and steady state are satisfied. Particle trajectories are recorded in the new concept of a particle “passport”. Ability to track and record particle in-system histories enables views of multiple scales and opens the possibility of making pathway-dependent modeling decisions. NPT may also enable modeling of time, allowing integration of Newtonian, organismal and stochastic modeling perspectives in a single comprehensive analysis.     

Evaluating impacts of forage fish abundance on marine predators

Forage fish—small, low trophic level, pelagic fish such as herrings, sardines, and anchovies—are important prey species in marine ecosystems and also support large commercial fisheries. In many parts of the world, forage fish fisheries are managed using precautionary principles that target catch limits below the maximum sustainable yield. However, there are increasing calls to further limit forage fish catch to safeguard their fish, seabird, and marine mammal predators. The effectiveness of these extra-precautionary regulations, which assume that increasing prey abundance increases predator productivity, are under debate. In this study, we used prey-linked population models to measure the influence of forage fish abundance on the population growth rates of 45 marine predator populations representing 32 fish, seabird, and mammal species from 5 regions around the world. We used simulated data to confirm the ability of the statistical model to accurately detect prey influences under varying levels of influence strength and process variability. Our results indicate that predator productivity was rarely influenced by the abundance of their forage fish prey. Only 6 predator populations (13% of the total) were positively influenced by increasing prey abundance and the model exhibited high power to detect prey influences when they existed. These results suggest that additional limitation of forage fish harvest to levels well below sustainable yields would rarely result in detectable increases in marine predator populations.     

Long-term drivers of persistence and colonization dynamics in spatially structured amphibian populations

Many organisms live in networks of local populations connected by dispersing individuals, called spatially structured populations (SSPs), where the long-term persistence of the entire network is determined by the balance between 2 processes acting at the scale of local populations: extinction and colonization. When multiple threats act on an SSP, a comparison of the different factors determining local extinctions and colonizations is essential to plan sound conservation actions. We assessed the drivers of long-term population dynamics of multiple amphibian species at the regional scale. We used dynamic occupancy models within a Bayesian framework to identify the factors determining persistence and colonization of local populations. Because connectivity among patches is fundamental to SSPs dynamics, we considered 2 measures of connectivity acting on each focal patch: incidence of the focal species and incidence of invasive crayfish. We used meta-analysis to summarize the effect of different drivers at the community level. Persistence and colonization of local populations were jointly determined by factors acting at different scales. Persistence probability was positively related to the area and the permanence of wetlands, whereas it was negatively related to occurrence of fish. Colonization probability was highest in semipermanent wetlands and in sites with a high incidence of the focal species in nearby sites, whereas it showed a negative relationship with the incidence of invasive crayfish in the landscape. By analyzing long-term data on amphibian population dynamics, we found a strong effect of some classic features commonly used in SSP studies, such as patch area and focal species incidence. The presence of an invasive non-native species at the landscape scale emerged as one of the strongest drivers of colonization dynamics, suggesting that studies on SSPs should consider different connectivity measures more frequently, such as the incidence of predators, especially when dealing with biological invasions.     

In the mixed world of predictive biology—Contradiction or support

Statistical prediction is a tool and aim in ecology and wildlife management and conservation. A prediction may either be supported by or contradicted by observations of an unknown set of observations. A contradiction occurs if the prediction is not included within the range of the unknown observations, i.e. the prediction misses the cloud of observations completely. Mixed-effects models, frequently used for statistical assessment of clustered data, carry information needed for calculating the probability of such contradictions. Here we present a new versatile statistic, the probability of contradiction (P (Contra)), that describes how often we would anticipate a new cluster of observations contradicting our predictions. Some benefits of P (Contra) are: (1) easy to calculate and intuitive interpretation, (2) comparability between datasets, (3) inclusion of residual correlation, (4) summary of the multitude of information from mixed models into one statistics, and (5) applicable to local mixed-effect models.     

High-resolution inventory of NO emissions from agricultural soils over the Ile-de-France region

Arable soils are a significant source of nitric oxide (NO), a precursor of tropospheric ozone, and thereby contribute to ozone pollution. However, their actual impact on ozone formation is strongly related to their spatial and temporal emission patterns, which warrant high-resolution estimates.Here, we combined an agro-ecosystem model and geo-referenced databases to map these sources over the 12 000 km² administrative region surrounding Paris, France, with a kilometric level resolution. The six most frequent arable crop species were simulated, with emission rates ranging from 1.4 kg N-NO ha⁻¹ yr⁻¹ to 11.1 kg N-NO ha⁻¹ yr⁻¹. The overall emission factor for fertilizer-derived NO emissions was 1.7%, while background emissions contributed half of the total NO efflux. Emissions were strongly seasonal, being highest in spring due to fertilizer inputs. They were mostly sensitive to soil type, crops' growing season and fertilizer N rates.