The Interplay between Climate Variability and Density Dependence in the Population Viability of Chinook Salmon

Abstract:  The viability of populations is influenced by driving forces such as density dependence and climate variability, but most population viability analyses (PVAs) ignore these factors because of data limitations. Additionally, simplified PVAs produce limited measures of population viability such as annual population growth rate (λ) or extinction risk. Here we developed a "mechanistic" PVA of threatened Chinook salmon ( Oncorhynchus tshawytscha ) in which, based on 40 years of detailed data, we related freshwater recruitment of juveniles to density of spawners, and third-year survival in the ocean to monthly indices of broad-scale ocean and climate conditions. Including climate variability in the model produced important effects: estimated population viability was very sensitive to assumptions of future climate conditions and the autocorrelation contained in the climate signal increased mean population abundance while increasing probability of quasi extinction. Because of the presence of density dependence in the model, however, we could not distinguish among alternative climate scenarios through mean λ values, emphasizing the importance of considering multiple measures to elucidate population viability. Our sensitivity analyses demonstrated that the importance of particular parameters varied across models and depended on which viability measure was the response variable. The density-dependent parameter associated with freshwater recruitment was consistently the most important, regardless of viability measure, suggesting that increasing juvenile carrying capacity is important for recovery.     

Evaluation of the performance of rural wastewater treatment plants using chemical measurements in combination with statistical techniques: a case study

The performance of a wastewater treatment plant was assessed statistically using multivariate cluster and principle component analysis. This was after measuring some physico-chemical properties in the influent, effluent, downstream, and upstream waters over a 4-month period. The cluster analysis grouped the sampling sites into three clusters: relatively non-polluted (upstream), medium polluted (downstream), and polluted (influent and effluent). The polluted water was further subdivided into very highly (influent) and highly (effluent) polluted. The grouping of influent and effluent into one cluster was due to some water quality parameters such as amount of copper, lead, and phosphates that are not efficiently removed by the plant. Using principal component analysis, samples from the same site taken over a period of 4 months were scattered, indicating inconsistencies in the performance of the plant. This was more pronounced during the rainy season, suggesting that increased water volumes from open sewers make the already poorly performing plant worse. The major loading factors found by principle component analysis were phosphate, lead, iron, zinc, copper, pH, and conductivity. Generally, the wastewater treatment system was found to be efficient in removing heavy metals and these were found in the sludge, but not anions. The mean percentage metal removal could be arranged in the following decreasing order: iron (85%)?>?zinc (57%)?>?copper (40%) and lead (38%) following the concentrations (mg?kg^?1) found in the sludge: iron (11,300)?>?zinc (820)?>?copper (180)?>?lead (20)?>?cadmium (3). Phosphate and iron concentrations in the effluent were found to be above the South African Bureau of Standards (SABS) recommendations. The major cause of poor performance is the high volume of the wastewater, exceeding the capacity of the plant 10 times.     

三峡库区高阳平湖水体富营养化主要驱动因子研究 ——基于主成分分析法

研究各种水质因子(水温、溶解氧、电导率、pH、营养盐等)对三峡库区支流水体富营养化的驱动效果,为库区支流水体富营养化的防控与治理提供科学依据.于2018年11月至2019年10月在三峡库区高阳平湖进行了12次样品采集,获取了水温、水体pH、营养盐、水体溶解氧等指标数据,利用主成分分析法研究三峡库区支流水体富营养化状况,...     

Integrating Landscape and Metapopulation Modeling Approaches: Viability of the Sharp-Tailed Grouse in a Dynamic Landscape

Abstract:  The lack of management experience at the landscape scale and the limited feasibility of experiments at this scale have increased the use of scenario modeling to analyze the effects of different management actions on focal species. However, current modeling approaches are poorly suited for the analysis of viability in dynamic landscapes. Demographic (e.g., metapopulation) models of species living in these landscapes do not incorporate the variability in spatial patterns of early successional habitats, and landscape models have not been linked to population viability models. We link a landscape model to a metapopulation model and demonstrate the use of this model by analyzing the effect of forest management options on the viability of the Sharp-tailed Grouse (  Tympanuchus phasianellus ) in the Pine Barrens region of northwestern Wisconsin (U.S.A.). This approach allows viability analysis based on landscape dynamics brought about by processes such as succession, disturbances, and silviculture. The landscape component of the model (LANDIS) predicts forest landscape dynamics in the form of a time series of raster maps. We combined these maps into a time series of patch structures, which formed the dynamic spatial structure of the metapopulation component (RAMAS). Our results showed that the viability of Sharp-tailed Grouse was sensitive to landscape dynamics and demographic variables such as fecundity and mortality. Ignoring the landscape dynamics gave overly optimistic results, and results based only on landscape dynamics (ignoring demography) lead to a different ranking of the management options than the ranking based on the more realistic model incorporating both landscape and demographic dynamics. Thus, models of species in dynamic landscapes must consider habitat and population dynamics simultaneously.