Exploring the influence of lake water chemistry on chlorophyll a: A multivariate statistical model analysis

A multivariate statistical approach integrating the absolute principal components score (APCS) and multivariate linear regression (APCS-MLR), along with structural equation modeling (SEM), was used to model the influence of water chemistry variables on chlorophyll a (Chl a) in Lake Qilu, a severely polluted lake in southwestern China. Water quality was surveyed monthly from 2000 to 2005. APCS-MLR was used to identify key water chemistry variables, mine data for SEM, and predict Chl a. Seven principal components (PCs) were determined as eigenvalues >1, which explained 68.67% of the original variance. Four PCs were selected to predict Chl a using APCS-MLR. The results showed a good fit between the observed data and modeled values, with R² = 0.80. For SEM, Chl a and eight variables were used: NH₄-N (ammonia-nitrogen), total phosphorus (TP), Secchi disc depth (SD), cyanide (CN), arsenic (As), cadmium (Cd), fluoride (F), and temperature (T). A conceptual model was established to describe the relationships among the water chemistry variables and Chl a. Four latent variables were also introduced: physical factors, nutrients, toxic substances, and phytoplankton. In general, the SEM demonstrated good agreement between the sample covariance matrix of observed variables and the model-implied covariance matrix. Among the water chemistry factors, T and TP had the greatest positive influence on Chl a, whereas SD had the largest negative influence. These results will help researchers and decision-makers to better understand the influence of water chemistry on phytoplankton and to manage eutrophication adaptively in Lake Qilu.     

Testing the impact of social forces on the evolution of Sahelian farming systems: A combined agent-based modeling and anthropological approach

This article presents the results of a methodology based on an extensive sociological fieldwork in three different sites settled along a gradient of aridity in Nigerien Sahel. This fieldwork led to build a set of rules for the behaviour of individuals in non-pastoralist villages. We implemented these rules into an agent-based model simulating three village archetypes. Each archetype includes biophysical, economical, social agricultural and livestock modules. Results from simulations with no social transition processes show that villages specialize themselves into different economic activities according to natural resource specificities: A decreasing intensification gradient is observed from the most favoured site, with more local productions and good ecological indicators, to the less-favoured site, with a growing proportion of the population wealth coming from migration remittances and “off-shore” livestock. Two family transition processes were implemented, following field observations and literature-based hypotheses: family organizations evolve between a patriarchal mode and a non-cooperative mode following tensions due to income redistribution. Family inheritance systems evolve from a “customary” one-heir mode to a “local Muslim” mode in which all males inherits land. This evolution depends on family tensions due to land availability. Once introducing these processes, the population of each site differentiates itself into specialized groups according to size, assets and social status. Meanwhile, the group proportions and specializations strongly vary according to the sites but they are all characterized by the emergence of individualistic family types and the increase of the village populations’ robustness.     

Hydraulic modeling of mussel habitat at a bridge-replacement site, Allegheny River, Pennsylvania, USA

The Allegheny River in Pennsylvania supports a large and diverse freshwater-mussel community, including two federally listed endangered species, Pleurobema clava (Clubshell) and Epioblasma torulosa rangiana (Northern Riffleshell). It is recognized that river hydraulics and morphology play important roles in mussel distribution. To assess the hydraulic influences of bridge replacement on mussel habitat, metrics such as depth, velocity, and their derivatives (shear stress, Froude number) were collected or computed.The objectives of the project were to evaluate mussel and hydraulic data at a reference site and to compare those findings to a bridge-replacement site. The findings were used to support a statistical analysis, which establishes correlations between mussel count and hydraulics, and a numerical model to forecast habitat based on the statistics.ArcGIS was selected to manage the data and generate a grid to compute area statistics for 3319, 4.9-m × 4.9-m cells (cell) for total mussel count, depth, velocity, shear stress, and Froude number. The Wilcoxon Rank Sum test indicated no statistical significance between the total mussel count and the hydraulic variables; however, trellis graphs were used to account for the spatial variability in the data set. For the flow conditions measured, the total mussel count per cell is greatest at sections where (1) velocities range from 0.061 to 0.21 m/s, (2) shear stresses range from 0.48 to 3.8 dyne/cm², and (3) Froude numbers range from 0.006 to 0.04.Based on the statistical targets established, the hydraulic model results suggest that an additional 2428 m² or a 30-percent increase in suitable mussel habitat could be generated at the replacement-bridge site when compared to the baseline condition associated with the existing bridge at that same location. The study did not address the influences of substrate, acid mine drainage, sediment loads from tributaries, and surface-water/ground-water exchange on mussel habitat. Future studies could include methods for quantifying (1) channel-substrate composition and distribution using tools such as hydroacoustic echosounders specifically designed and calibrated to identify bed composition and mussel populations, (2) surface-water and ground-water interactions, and (3) a high-streamflow event.     

Simulating property exchange in estuarine ecosystem models at ecologically appropriate scales

A computational scheme has been developed and tested to simulate property exchange by advection and dispersion in estuaries at time and space scales that are well suited to ecological and management simulations, but are coarse relative to the demands of physical hydrodynamic models. An implementation of the Regional Ocean Model System (ROMS) for the Providence River and Narragansett Bay (RI, USA) was used to determine property exchanges between the spatial elements of an ecological box model. The basis for the method is the statistical tabulation of numerical dye experiments done with the full ROMS physical model. The ROMS model domain was subdivided into fifteen coarse boxes, each with two vertical layers, defining 30 elements that were used for the box model simulations. Dye concentrations were set to arbitrary initial concentrations for all ROMS grids in the large elements, and the ROMS model was run for 24 h. The final distribution of the dye among the elements was used as a tracer for property exchange over that day and was used to develop an exchange matrix. Box model predictions of salinity over 77 days in each element compared favorably with ROMS simulated salinity averaged over the same spatial elements, although the disparity was greater in areas where large river inflows caused strong gradients in ROMS within elements assumed to be homogeneous in the box model. The 77-day simulation included periods of high and low river flow. Despite the large size of the spatial elements, dispersion artifacts were small, much less than the modeled daily exchanges. While others have taken a similar approach, we found a number of theoretical and practical considerations deserved careful attention for this approach to perform satisfactorily. Whereas the full ROMS model takes 9 days on a powerful computing cluster to compute the physics simulation for 77 days, the box model simulates physics and biology for the same interval in 5 s on a personal computer, and a full year in under 1 min. The exchange matrix mixing model is a fast, cost effective, and convenient way to simulate daily variation of complex estuarine physics in ecological modeling at appropriate scales of space and time.     

A model for wildfire prevention planning in game resources

The high incidence of hunting activity and forest fires in different ecosystems (particularly in Mediterranean ecosystems) requires a model, which allows for the comprehensive management of hunting resources and estimating the potential damage caused by this type of disturbance. This paper proposes a model for evaluating the socio-economic effects of forest fires on hunting. Its cornerstone lies in evaluating hunting resource availability for each ecosystem within its territorial mosaic. Each game management unit (GMU) is identified by vegetation structure and habitat type. It presents a novel approach in which the economic value of each game management unit is linked to potential forest fire damages. The effect a forest fire has on an entire ecosystem depends on the intensity of its flames. A sample study was undertaken in the province of Huelva in Andalusia (southern Spain). The socio-economic hunting vulnerability of the province of Huelva was 45,188,000€. The results obtained confirmed the need for an economic appraisal of non-forest products in the forest and other woodland areas. Geographic Information System increases the flexibility and simplicity of our methodology which permits immediate extrapolation to other agroforestry territories.     

基于多模式逆向水质模型的程海水位调控-水质响应预测研究

以云南省程海为例,基于CE-QUAL-W2计算平台开发了水动力-水质模型.由于程海现有的数据不足以支撑开发可靠的流域水文与污染物负荷模型,为弥补由此引起的水质模型边界条件缺失的限制,本研究提出了基于逆向模拟与遗传算法的多模式负荷-参数识别方法来实现对水质模型的校正和鲁棒校验.应用校验后的模型对程海不同水位调控情景的水质...     

基于AERMOD及减排政策的昆明市工业区SO2情景模拟

以昆明市工业区为试点,将2009年作为基准年,2015年作为情景年,利用Aermod模型系统以及相关数据对工业区内SO2环境浓度进行预测.模拟情景的建立基于“十二五”工业源减排规划.研究发现工业源减排措施将对区域SO2浓度有显著影响,降幅最高可达13%.2015年不同模拟时段的SO2等浓度下分布面积均较2009年有明显减小,高浓度地区个数变少,这种现象也出现在污染源非集中地区.研究结果表明利用模型预测可有效分析减排政策对工业区内环境质量状况的影响,有助于建立减排响应机制.     

Physiological changes associated with aging result in lower internal doses of toluene and perchloroethylene in simulations using pharmacokinetic modeling

Physiologically based pharmacokinetic (PBPK) models were developed in order to translate external concentrations into internal dose estimates, but most PBPK models were developed for younger adults. A set of physiological parameters for ages 65, 75, and 85 of both genders were developed and were used with previously established human PBPK models of exposure to toluene and perchloroethylene (PERC) in order to investigate internal dose changes with increasing age. The predicted compartmental concentrations of toluene and PERC for aged adults were lower than predicted concentrations for younger adults, and this suggests that body composition changes with aging do not increase internal doses of inhaled toxicants alone after acute exposure. Hence, susceptibility to either toxicant was not expected to increase solely based on the physiological changes associated with aging. Predictions for a metabolite of PERC, however, were similar in magnitude across ages, which may lead to enhanced susceptibility if metabolic capacity changes with aging.     

Modeling flooding extent from Hurricane Floyd in the coastal plains of North Carolina

Abstract

In this article two modeling approaches were developed based on the use of US Geological Survey digital elevation model (DEM) data. These models were utilized to delineate the extent of flooding induced by precipitation from Hurricane Floyd in a portion of Pitt County, North Carolina. The patterns of flood extent derived from the two models were compared to the extent of flooding indicated on a digital aerial photograph taken two days after peak flood levels had been reached. In addition, floodplain boundaries based on Federal Emergency Management Agency Q3 maps were compared to the extent of flooding on the aerial photo. Actual emergency response operations undertaken through the Pitt County Emergency Operations Center during the flood event are described, and are used to provide a context for evaluating the potential utility of these models. The flood extents produced by the modeling methods performed well at representing the actual extent of the flooding.     

An integrated approach for predicting fates of reintroductions with demographic data from multiple populations

We devised a novel approach to model reintroduced populations whereby demographic data collected from multiple sites are integrated into a Bayesian hierarchical model. Integrating data from multiple reintroductions allows more precise population-growth projections to be made, especially for populations for which data are sparse, and allows projections that account for random site-to-site variation to be made before new reintroductions are attempted. We used data from reintroductions of the North Island Robin (Petroica longipes), an endemic New Zealand passerine, to 10 sites where non-native mammalian predators are controlled. A comparison of candidate models that we based on deviance information criterion showed that rat-tracking rate (an index of rat density) was a useful predictor of robin fecundity and adult female survival, that landscape connectivity and a binary measure of whether sites were on a peninsula were useful predictors of apparent juvenile survival (probably due to differential dispersal away from reintroduction sites), and that there was unexplained random variation among sites in all demographic rates. We used the two best supported models to estimate the finite rate of increase (λ) for populations at each of the 10 sites, and for a proposed reintroduction site, under different levels of rat control. Only three of the reintroduction sites had λ distributions completely >1 for either model. At two sites, λ was expected to be >1 if rat-tracking rates were <5%. At the other five reintroduction sites, λ was predicted to be close to 1, and it was unclear whether growth was expected. Predictions of λ for the proposed reintroduction site were less precise than for other sites because distributions incorporated the full range of site-to-site random variation in vital rates. Our methods can be applied to any species for which postrelease data on demographic rates are available and potentially can be extended to model multiple species simultaneously.