China
Africa
Explore chapters and articles related to this topic
Mixture Extrapolation Approaches
Published in Keith R. Solomon, Theo C.M. Brock, Dick de Zwart, Scott D. Dyer, Leo Posthuma, Sean M. Richards, Hans Sanderson, Paul K. Sibley, Paul J. van den Brink, Extrapolation Practice for Ecotoxicological Effect Characterization of Chemicals, 2008
Theo C. M. Brock, Keith R. Solomon, René van Wijngaarden, Lorraine Maltby
The arrangement and condition of individuals through time in a certain habitat are indicative of the local state of the population. This suggests that the sensitivity of populations may vary throughout the year, or that the habitat is only suitable for the organism at different times of the year. Chemical stresses that affect early developmental stages can potentially have serious consequences on population recruitment. In addition, failure to consider endpoints above the level of the individual often leads to an overestimation of risk, but in some cases may lead to an underestimation of risk (Forbes and Calow 1999). Extrapolating time- and/or development-related effects of chemicals on individuals can be done by building and using quantitative models. An overview of how population-level effects of stressors may be measured or projected from individual effects is provided by Maltby et al. (2001). Choosing an adequate population model for use in a risk assessment will depend on several factors. Primarily the model should address the seasonal variation in demographic structure of the population of interest. An overview of population models that might be used is provided by Bartell et al. (2003) and Pastorok et al. (2003). The models comprise scalar, life history, and individual-based population models. When the effects of toxic chemicals are age or stage dependent, life history models can be considered a realistic extrapolation tool. For some examples of these models, see Caswell (2001) and Spencer and Ferson (1998). Population-based models have been discussed in greater detail in Chapter 4.
Simulation Models
Published in Susan B. Norton, Susan M. Cormier, Glenn W. Suter, Ecological Causal Assessment, 2014
Population models have been developed and used primarily for the management of fisheries and wildlife resources (Quinn and Deriso, 1999; Haddon, 2001; Starfield, 1997). Because these models address the consequences of harvesting, they are most directly applicable to agents that act by killing organisms, such as power plant cooling systems. More recently, population models have been used in the management of rare or declining species. These population viability models simulate the response of species to various management actions such as increasing habitat extent and quality (Beissinger and McCollough, 2002; Morris and Doak, 2002). Population models developed for either of these purposes could be adapted for causal analyses.
EPA and OSHA Guidelines
Published in Jack Daugherty, Assessment of Chemical Exposures, 2020
Two major types of models are single-species population models and multispecies community and ecosystem models. Population models describe the dynamics of a finite group of individuals through time and have been used extensively in ecology and fisheries management and to assess the impacts of power plants and toxicants on specific fish populations. Population models are useful in answering questions related to short- or long-term changes of population size and structure and can be used to estimate the probability that a population will decline below or grow above a specified abundance.
Optimal contraception control for a size-structured population model with extra mortality
Published in Applicable Analysis, 2020
However, populations consist of individuals with many structural differences, such as age, body size, gender, gene and life stage. In the last century, a number of studies appeared on the topic of dynamical population models with individual structure. Especially, age-structured first-order partial differential equations provide a main tool for modeling population systems [2] and are recently employed in economics. Further, a lot of works have been made to study well-posedness, asymptotic behavior and optimal control of models with age structure. To name a few, see [2–7] and references therein. In other words, age-structured population models have played a significant role in the mathematical analysis and control of populations in biology and demography.