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Environmental State (environmental + state)
Selected AbstractsEnvironmental state of Lake Kariba and Zambezi River Valley: Lessons learned and not learnedLAKES & RESERVOIRS: RESEARCH AND MANAGEMENT, Issue 3 2010C. H. D. Magadza Abstract Lake Kariba, still the largest reservoir in the world by volume, is 60 years old. It has undergone changes in its thermal properties, associated with global warming, which reflect in turn on its limnology. These changes include a shallower eipilimnion, higher heat content and increased tropicality to near equatorial status. The role of Lake Kariba with regard to its energy characteristics is discussed in light of global warming findings. The lake's water residence time has increased from 3.7 years to ,5.7 years, attributable to a reduced inflow from the Zambezi River. The phytoplankton communities have changed towards a cyanophyceae-dominated community, leading to a decline in entomostracan zooplankton, and a near collapse of the planktivorous Limnothrissa miodon fishery. Prolonged use of pesticides to control Glossina has led to measurable ecosystem level impacts on both terrestrial and aquatic biota. The impacts of the forced relocation of the Tonga people were still evident during this study. Siltation from resettlement areas has led to the loss of habitat and biodiversity in the inflowing streams to the lake. Unplanned shoreline development in the early history of the lake poses health problems. It is projected that global warming will cause the lake temperature to rise by ,4 °C by the end of the century. Higher temperatures will be accompanied by windier conditions, thereby enhancing the risks from storms on the lake. The appropriateness of administrative structures intended to manage the Zambezi River Basin environment also is discussed herein. It is concluded that the management protocol is institutionally a non-inclusive process lacking the capacity to involve other stakeholders in managing the lake's resources, and even less so in the integrated management of the basin. [source] THE EVOLUTION OF ENVIRONMENTAL AND GENETIC SEX DETERMINATION IN FLUCTUATING ENVIRONMENTSEVOLUTION, Issue 12 2003Tom J. M. Van Dooren Abstract Twenty years ago, Bulmer and Bull suggested that disruptive selection, produced by environmental fluctuations, can result in an evolutionary transition from environmental sex determination (ESD) to genetic sex determination (GSD). We investigated the feasibility of such a process, using mutation-limited adaptive dynamics and individual-based computer simulations. Our model describes the evolution of a reaction norm for sex determination in a metapopulation setting with partial migration and variation in an environmental variable both within and between local patches. The reaction norm represents the probability of becoming a female as a function of environmental state and was modeled as a sigmoid function with two parameters, one giving the location (i.e., the value of the environmental variable for which an individual has equal chance of becoming either sex) and the other giving the slope of the reaction norm for that environment. The slope can be interpreted as being set by the level of developmental noise in morph determination, with less noise giving a steeper slope and a more switchlike reaction norm. We found convergence stable reaction norms with intermediate to large amounts of developmental noise for conditions characterized by low migration rates, small differential competitive advantages between the sexes over environments, and little variation between individual environments within patches compared to variation between patches. We also considered reaction norms with the slope parameter constrained to a high value, corresponding to little developmental noise. For these we found evolutionary branching in the location parameter and a transition from ESD toward GSD, analogous to the original analysis by Bulmer and Bull. Further evolutionary change, including dominance evolution, produced a polymorphism acting as a GSD system with heterogamety. Our results point to the role of developmental noise in the evolution of sex determination. [source] ICOADS release 2.1 data and productsINTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 7 2005Steven J. Worley Abstract The International Comprehensive Ocean,Atmosphere Data Set (ICOADS), release 2.1 (1784,2002), is the largest available set of in situ marine observations. Observations from ships include instrument measurements and visual estimates, and data from moored and drifting buoys are exclusively instrumental. The ICOADS collection is constructed from many diverse data sources, and made inhomogeneous by the changes in observing systems and recording practices used throughout the period of record, which is over two centuries. Nevertheless, it is a key reference data set that documents the long-term environmental state, provides input to a variety of critical climate and other research applications, and serves as a basis for many associated products and analyses. The observational database is augmented with higher level ICOADS data products. The observed data are synthesized to products by computing statistical summaries, on a monthly basis, for samples within 2° latitude × 2° longitude and 1° × 1° boxes beginning in 1800 and 1960 respectively. For each resolution the summaries are computed using two different data mixtures and quality control criteria. This partially controls and contrasts the effects of changing observing systems and accounts for periods with greater climate variability. The ICOADS observations and products are freely distributed worldwide. The standard ICOADS release is supplemented in several ways; additional summaries are produced using experimental quality control, additional observations are made available in advance of their formal blending into a release, and metadata that define recent ships' physical characteristics and instruments are available. Copyright © 2005 Royal Meteorological Society [source] Life table response experiment analysis of the stochastic growth rateJOURNAL OF ECOLOGY, Issue 2 2010Hal Caswell Summary 1.,Life table response experiment (LTRE) analyses decompose treatment effects on a dependent variable (usually, but not necessarily, population growth rate) into contributions from differences in the parameters that determine that variable. 2.,Fixed, random and regression LTRE designs have been applied to plant populations in many contexts. These designs all make use of the derivative of the dependent variable with respect to the parameters, and describe differences as sums of linear approximations. 3.,Here, I extend LTRE methods to analyse treatment effects on the stochastic growth rate log ,s. The problem is challenging because a stochastic model contains two layers of dynamics: the stochastic dynamics of the environment and the response of the vital rates to the state of the environment. I consider the widely used case where the environment is described by a Markov chain. 4.,As the parameters describing the environmental Markov chain do not appear explicitly in the calculation of log ,s, derivatives cannot be calculated. The solution presented here combines derivatives for the vital rates with an alternative (and older) approach, due to Kitagawa and Keyfitz, that calculates contributions in a way analogous to the calculation of main effects in statistical models. 5.,The resulting LTRE analysis decomposes log ,s into contributions from differences in: (i) the stationary distribution of environmental states, (ii) the autocorrelation pattern of the environment, and (iii) the stage-specific vital rate responses within each environmental state. 6.,As an example, the methods are applied to a stage-classified model of the prairie plant Lomatium bradshawii in a stochastic fire environment. 7.,Synthesis. The stochastic growth rate is an important parameter describing the effects of environmental fluctuations on population viability. Like any growth rate, it responds to differences in environmental factors. Without a decomposition analysis there is no way to attribute differences in the stochastic growth rate to particular parts of the life cycle or particular aspects of the stochastic environment. The methods presented here provide such an analysis, extending the LTRE analyses already available for deterministic environments. [source] | |||||||||||||