Assessing the effects of pseudo-absences on predictive distribution model performance

Modelling species distributions with presence data from atlases, museum collections and databases is challenging. In this paper, we compare seven procedures to generate pseudo-absence data, which in turn are used to generate GLM-logistic regressed models when reliable absence data are not available. We use pseudo-absences selected randomly or by means of presence-only methods (ENFA and MDE) to model the distribution of a threatened endemic Iberian moth species (Graellsia isabelae). The results show that the pseudo-absence selection method greatly influences the percentage of explained variability, the scores of the accuracy measures and, most importantly, the degree of constraint in the distribution estimated. As we extract pseudo-absences from environmental regions further from the optimum established by presence data, the models generated obtain better accuracy scores, and over-prediction increases. When variables other than environmental ones influence the distribution of the species (i.e., non-equilibrium state) and precise information on absences is non-existent, the random selection of pseudo-absences or their selection from environmental localities similar to those of species presence data generates the most constrained predictive distribution maps, because pseudo-absences can be located within environmentally suitable areas. This study shows that if we do not have reliable absence data, the method of pseudo-absence selection strongly conditions the obtained model, generating different model predictions in the gradient between potential and realized distributions.     

Using revealed preferences to estimate the Value of Travel Time to recreation sites

The opportunity Value of Travel Time (VTT) is one of the most important elements of the total cost of recreation day-trips and arguably the most difficult to estimate. Most studies build upon the theoretical framework proposed by Becker (1965) by using a combination of revealed and stated preference data to estimate a value of time which is uniform in all activities and under all circumstances. This restriction is relaxed by DeSerpa's (1971) model which allows the value of saving time to be activity-specific. We present the first analysis which uses actual driving choices between open access and toll roads to estimate a VTT specific for recreation trips, thereby providing a value which conforms to both Becker's and DeSerpa's theoretical models. Using these findings we conduct a Monte Carlo simulation to identify generalizable results for subsequent valuation studies. Our results indicate that 3/4 of the wage rate provides a reasonable approximation of the average VTT for recreation trips, while the commonly implemented assumption of 1/3 of the wage rate generates downward biased results.     

Developing a new alternative risk assessment framework in the work sites by including a stochastic and a deterministic process: A case study for the Greek Public Electric Power Provider

An individual method cannot achieve the optimum risk-assessment result in the worksites, and future perspectives should focus on the parallel application of a deterministic approach with a stochastic approach. In particular, the risk analysis and assessment techniques of the deterministic (DET) approach are classified into three main categories: (a) the qualitative, (b) the quantitative, and (c) the hybrid techniques (qualitative-quantitative, semi-quantitative). Furthermore, the stochastic (STO) approach includes the classic statistical approach (CSA) and the accident forecasting modeling (AFM). The objective of this paper is triple: (a) the presentation and classification of the main risk analysis and risk assessment methods and techniques of the deterministic approach and the stochastic approach as well, (b) the development and presentation of a new alternative risk assessment framework (called as STODET) including a stochastic and a deterministic process, and (c) the application of STODET in the Greek Public Power Corporation (PPC) by using occupational accidents that have been recorded, during the 17-year period of 1993-2009. In particular, the STODET application proves that required actions (or suppressive measures) are essential and must be taken in a medium-term period (1 working year) for abolishing the hazard sources.     

洞庭湖区农业环境与湖垸农业可持续发展模式

围湖垦殖形成了洞庭湖区湖垸农业形式,湖垸农业的形成加速了湖区人口的增长,人口的压力也促进了围垦。不断的围湖垦殖使耕地面积迅速增加。由于湖泊面积减少,湖垸农业处于外洪内涝的环境中。归纳了洞庭湖区的农业环境问题：洪涝灾害发生频率加大;水面减少,生物多样性降低;环境污染严重,湿地生态功能衰退;土壤退化和潜育化严重;血吸虫病死灰复燃等。结合已有经验,提出了湖垸农业可持续发展模式：认真实施生态保护工程,减少泥沙淤积;调整湖垸农业结构,建立可持续发展生态农业模式：避灾、减灾模式、水生经济植物模式、工业原料作物模式、林农复合经营模式、湖洲草地草食畜禽模式、麻基（油菜）渔塘农业模式等。     

深圳市高层建筑地震安全指数判定系统

以深圳市高层建筑为研究实例,介绍了香港大学的城市灾害损失评估及应急反应管理系统,并采用此系统内置的建筑易损性分析模块,结合泛珠三角地区灾害危险性概率模式,场地特征及高层建筑实地调查分析结果,从建筑安全性角度对深圳市的标志性建筑进行危险性分析,进而对深圳市应对突发性灾难事件的管理建设提出建议。作为应用实例,模拟一定概率条件下的灾难事件发生时的建筑物损坏情况,归纳出高层建筑的损坏模式,总结深圳市高层建筑的安全性隐患,提出目前防灾减灾急需实施的相应步骤:建议在一元化的城市灾害预警和应急指挥体系下,整合各个灾种防灾减灾管理部门的资源,以科学分析软件为灾情模拟和决策预案制定的工具,使危机处理的理念由领导决策型转向科学分析型,加强城市应对面对突发事件的能力,保障经济与社会的持续稳定发展。     

Scaling up of biomass simulation for Eucalyptus plantations based on landsenses ecology

ABSTRACT

Sustainable forest management on a regional scale requires accurate biomass estimation. At present, technologically comprehensive forecasting estimates are generated using process-based ecological models. However, isolation of the ecological factors that cause uncertainty in model behavior is difficult. To solve this problem, this study aimed to construct a meliorization model evaluation framework to explain uncertainty in model behavior with respect to both the mechanisms and algorithms involved in ecological forecasting based on the principle of landsenses ecology. We introduce a complicated ecological driving mechanism to the process-based ecological model using analytical software and algorithms. Subsequently, as a case study, we apply the meliorization model evaluation framework to detect Eucalyptus biomass forest patches at a regional scale (196,158 ha) using the 3PG2 (Physiological Principles in Predicting Growth) model. Our results show that this technique improves the accuracy of ecological simulation for ecological forecasting and prevents new uncertainties from being produced by adding a new driving mechanism to the original model structure. This result was supported by our Eucalyptus biomass simulation using the 3PG2 model, in which ecological factors caused 21.83% and 9.05% uncertainty in model behavior temporal and spatial forecasting, respectively. In conclusion, the systematic meliorization model evaluation framework reported here provides a new method that could be applied to research requiring comprehensive ecological forecasting. Sustainable forest management on regional scales contributes to accurate forest biomass simulation through the principle of landsenses ecology, in which mix-marching data and a meliorization model are combined.     

Influence of hydrodynamic conditions on the production and fate of Posidonia oceanica in a semi-enclosed shallow basin (Stagnone di Marsala,Western Sicily)

An integrated approach using hydrodynamic and transport numerical models, lepidochronology and stable isotope analysis was used to investigate how local hydrodynamic conditions influence the primary production and fate of the seagrass Posidonia oceanica in a Mediterranean semi-enclosed marine system (Stagnone di Marsala). The water mass exchange aptitude of different sectors of the basin was analysed, and data collected were used to select two sectors (colonized by Posidonia oceanica showing the lowest and highest water exchange values) for biological analyses. According to the mean dispersal coefficient differences simulated by the hydrodynamic model, growth rate and primary production of P. oceanica differed between sectors, with average values lower in the central sector where water exchange is lower than in the southern sector. Although P. oceanica coverage and primary production were higher in the southern sector, carbon and nitrogen stable isotope analysis suggests that the transfer of seagrass organic matter to higher trophic levels of the food web was higher in the central sector. The possibility of a link between hydrodynamism, production and fate of organic matter is proposed to explain the observed patterns.     

Effect of stage-specific vital rates on population growth rates and effective population sizes in an endangered iteroparous plant

Effective population size (N(e)) determines the strength of genetic drift and can influence the level of genetic diversity a population can maintain. Assessing how changes in demographic rates associated with environmental variables and management actions affect N(e) thus can be crucial to the conservation of endangered species. Calculation of N(e) through demographic models makes it possible to use elasticity analyses to study this issue. The elasticity of N(e) to a given vital rate is the proportional change in N(e) associated with a proportional increase in that vital rate. In addition, demographic models can be used to study N(e) and population growth rate (λ) simultaneously. Simultaneous examination is important because some vital rates differ diametrically in their associations with λ and N(e). For example, in some cases increasing these vital rates increases λ and decreases N(e). We used elasticity analysis to study the effect of stage-specific survival and flowering rates on N(e), annual effective population size (N(a)), and λ in seven populations of the endangered plant Austrian dragonhead (Dracocephalum austriacum). In populations with λ ≥ 1, the elasticities of N(e) and N(a) were similar to those of λ. Survival rates of adults were associated with greater elasticities than survival rates of juveniles, flowering rates, or fecundity. In populations with λ < 1, N(e) and N(a) exhibited greater elasticities to juvenile than to adult vital rates. These patterns are similar to those observed in other species with similar life histories. We did not observe contrasting effects of any vital rate on λ and N(e); thus, management actions that increase the λ of populations of Austrian dragonhead will not increase genetic drift. Our results show that elasticity analyses of N(e) and N(a) can complement elasticity analysis of λ. Moreover, such analyses do not require more data than standard matrix models of population dynamics.     

Model correlation in stochastic forest simulators—A case of multilevel multivariate model for seedling establishment

Forest development can be predicted by the use of forest simulators based on various statistical models describing the forest and its dynamics. One potential approach to study the reliability of the simulators is to utilise Monte Carlo simulation techniques to generate a predictive distribution of a forest characteristic. One problem in examining the effect of model uncertainty in forestry decision making, however, is correlation between the models. If this is not taken into account, predictions of the model systems may become biased, and the effect of errors on decision making may be underestimated. In reality, the models often are interdependent, but the correlations usually are not known because the models have been estimated in separate studies. The aim of this paper is to study the impacts of between-model dependencies on the predictive distribution of forest characteristics by Monte Carlo simulation techniques. We utilise a case of predicting seedling establishment of planted Norway spruce (Picea abies (L.) Karst.) stands as an example with multivariate multilevel model structures. Regardless of low cross-correlations between the models, ignoring them led to significant underestimation of the amount of competing broadleaves to be removed in pre-commercial thinning. Therefore, we recommend that between-model dependencies are clarified and considered in stochastic simulations. In our case, between-model interdependencies can be reliably estimated with a limited dataset. In addition, estimating the models separately and using the model residuals to estimate interdependencies between models were also sufficient to take the between-model dependencies into account when producing stochastic predictions for silvicultural decision making.