Scott Ferson – författare

Toxic chemicals can exert effects on all levels of the biological hierarchy, from cells to organs to organisms to populations to entire ecosystems. However, most risk assessment models express their results in terms of effects on individual organisms, without corresponding information on how populations, groups of species, or whole ecosystems may respond to chemical stressors. Ecological Modeling in Risk Assessment: Chemical Effects on Populations, Ecosystems, and Landscapes takes a new approach by compiling and evaluating models that can be used in assessing risk at the population, ecosystem, and landscape levels.The authors give an overview of the current process of ecological risk assessment for toxic chemicals and of how modeling of populations, ecosystems, and landscapes could improve the status quo. They present a classification of ecological models and explain the differences between population, ecosystem, landscape, and toxicity-extrapolation models. The authors describe the model evaluation process and define evaluation criteria. Finally, the results of the model evaluations are presented in a concise format with recommendations on modeling approaches to use now and develop further.The authors present and evaluate various models on the basis of their realism and complexity, prediction of relevant assessment endpoints, treatment of uncertainty, regulatory acceptance, resource efficiency, and other criteria. They provide models that will improve the ecological relevance of risk assessments and make data collection more cost-effective. Ecological Modeling in Risk Assessment serves as a reference for selecting and applying the best models when performing a risk assessment.

Quantitative methods are needed in conservation biology more than ever as an increasing number of threatened species find their way onto international and national “red lists. ” Objective evaluation of population decline and extinction probability are required for sound decision making. Yet, as our colleague Selina Heppell points out, population viability analysis and other forms of formal risk assessment are underused in policy formation because of data uncertainty and a lack of standardized methodologies and unambiguous criteria (i. e. , “rules of thumb”). Models used in conservation biology range from those that are purely heuristic to some that are highly predictive. Model selection should be dependent on the questions being asked and the data that are available. We need to develop a toolbox of quantitative methods that can help scientists and managers with a wide range of systems and that are subject to varying levels of data uncertainty and environmental variability. The methods outlined in the following chapters represent many of the tools needed to fill that toolbox. When used in conjunction with adaptive management, they should provide information for improved monitoring, risk assessment, and evaluation of management alternatives. The first two chapters describe the application of methods for detecting trends and extinctions from sighting data. Presence/absence data are used in general linear and additive models in Chapters 3 and 4 to predict the extinction proneness of birds and to build habitat models for plants.

Quantitative methods are needed in conservation biology more than ever as an increasing number of threatened species find their way onto international and national “red lists. ” Objective evaluation of population decline and extinction probability are required for sound decision making. Yet, as our colleague Selina Heppell points out, population viability analysis and other forms of formal risk assessment are underused in policy formation because of data uncertainty and a lack of standardized methodologies and unambiguous criteria (i. e. , “rules of thumb”). Models used in conservation biology range from those that are purely heuristic to some that are highly predictive. Model selection should be dependent on the questions being asked and the data that are available. We need to develop a toolbox of quantitative methods that can help scientists and managers with a wide range of systems and that are subject to varying levels of data uncertainty and environmental variability. The methods outlined in the following chapters represent many of the tools needed to fill that toolbox. When used in conjunction with adaptive management, they should provide information for improved monitoring, risk assessment, and evaluation of management alternatives. The first two chapters describe the application of methods for detecting trends and extinctions from sighting data. Presence/absence data are used in general linear and additive models in Chapters 3 and 4 to predict the extinction proneness of birds and to build habitat models for plants.

Expanding the risk assessment toolbox, this book provides a comprehensive and practical evaluation of specific ecological models for potential use in risk assessment. Ecological Modeling in Risk Assessment: Chemical Effects on Populations, Ecosystems, and Landscapes goes beyond current risk assessment practices for toxic chemicals as applied to individual-organism endpoints to describe ecological effects models useful at the population, ecosystem, and landscape levels. The authors demonstrate the utility of a set of ecological effects models, eventually improving the ecological relevance of risk assessments and making data collection more cost effective.

Expanding the risk assessment toolbox, this book provides a comprehensive and practical evaluation of specific ecological models for potential use in risk assessment. Ecological Modeling in Risk Assessment: Chemical Effects on Populations, Ecosystems, and Landscapes goes beyond current risk assessment practices for toxic chemicals as applied to individual-organism endpoints to describe ecological effects models useful at the population, ecosystem, and landscape levels. The authors demonstrate the utility of a set of ecological effects models, eventually improving the ecological relevance of risk assessments and making data collection more cost effective.

Toxic chemicals can exert effects on all levels of the biological hierarchy, from cells to organs to organisms to populations to entire ecosystems. However, most risk assessment models express their results in terms of effects on individual organisms, without corresponding information on how populations, groups of species, or whole ecosystems may respond to chemical stressors. Ecological Modeling in Risk Assessment: Chemical Effects on Populations, Ecosystems, and Landscapes takes a new approach by compiling and evaluating models that can be used in assessing risk at the population, ecosystem, and landscape levels.The authors give an overview of the current process of ecological risk assessment for toxic chemicals and of how modeling of populations, ecosystems, and landscapes could improve the status quo. They present a classification of ecological models and explain the differences between population, ecosystem, landscape, and toxicity-extrapolation models. The authors describe the model evaluation process and define evaluation criteria. Finally, the results of the model evaluations are presented in a concise format with recommendations on modeling approaches to use now and develop further.The authors present and evaluate various models on the basis of their realism and complexity, prediction of relevant assessment endpoints, treatment of uncertainty, regulatory acceptance, resource efficiency, and other criteria. They provide models that will improve the ecological relevance of risk assessments and make data collection more cost-effective. Ecological Modeling in Risk Assessment serves as a reference for selecting and applying the best models when performing a risk assessment.

#1 New Release in Elections on Amazon#1 New Release in Political Parties on AmazonWhen Donald Trump won the 2024 American Presidential Election, the nation—and world—was shocked. In this critical examination of the events leading up to and following the election, political advisor Scott Ferson goes straight to the source—the American people—to understand how this happened and where we go from here.In November 2024, the Democrats didn't just lose the election—they lost the voters themselves. In the years and months leading up to the election, a lot of average Joes—enough Joes to swing an election and tilt history’s trajectory—were left looking for confidence and answers in uncertain times. These were the voters who've never chosen a party, who work full-time or overtime just to make ends meet, and who simply want to be able to live the American dream that they were promised. A generation ago, these average Joes would have been a Democrat, by tradition or community or through union organizing. Now, these voters are lost to the Democrats, and, for those in disbelief over the 2024 election, the hard lesson is the Democrats never even tried to win them over.How the Democrats Lost America explains not only why Kamala Harris and the Democrats lost the 2024 election but the larger disconnect the party has with voters in general, particularly those who don’t hug both coasts or live in select big cities in between.Through thousands of interviews over the past eight years, Scott Ferson exposes a larger problem facing our democracy: A profound disconnect with the people. By listening to people and understanding their real concerns, Democrats can find the way forward and take the steps to reconnect the people with the American promise of democracy. Democrats have a choice. These everyday voters can be lost and forgotten, or lost and found.