The effects of disturbed ecosystems, from devastating algal blooms to the loss of whale populations, have demonstrated the vulnerability of the oceans'biodiversity. This book provides methods for learning how ocean systems function, how natural and human actions put them in peril, and how we can influence the marine world in order to maintain biodiversity. The difficulties of research in the oceans make computer modeling particularly helpful for marine conservation. The authors demonstrate dynamic modeling through the use of the STELLA modeling program and case studies from marine conservation.

The book presents a collection of large-scale network-modeling studies on coastal systems in Latin America. It includes a novel description of the functioning of coastal complex ecosystems and also predicts how natural and human-made disturbances percolate through the networks. Coastal areas belong to the most populated ecosystems around the globe, and are massively influenced by human impacts such as shipping, mining, fisheries, tourism, pollution and human settlements. Even though many of these activities have facilitated socio-economic development, they have also caused a significant deterioration in natural populations, communities and ecosystems worldwide. Covering coastal marine ecosystems of Latin America such as the NE and SE Pacific, NW Atlantic and Caribbean areas, it discusses the construction of quantitative (Ecopath-Ecosim-Ecospace and Centrality of Node Sets) and semi-quantitative (Loop Analysis) multispecies trophic-network models to describe and assess the impacts of natural and human interventions like pelagic and benthic fishing as well as natural events such as El Niño, and La Niña. The book also features steady state (and/or near moving equilibrium) and dynamical models to support the management of exploited organisms, and applies and quantifies macroscopic indices, based on Ascendency (Ulanowicz) and Local Stability (Levins ́ Loop Analysis). Further, it discusses the determination of the Keystone Species Complex Index, which is a holistic extension of the classical concept of Keystone Species (Paine), offering novel strategies for conservation monitoring and management.

A primer on modeling concepts and applications that is specifically geared toward the environmental field. Sections on modeling terminology, the uses of models, the model-building process, and the interpretation of output provide the foundation for detailed applications. After an introduction to the basics of dynamic modeling, the book leads students through an analysis of several environmental problems, including surface-water pollution, matter-cycling disruptions, and global warming. The scientific and technical context is provided for each problem, and the methods for analyzing and designing appropriate modeling approaches is provided. While the mathematical content does not exceed the level of a first-semester calculus course, the book gives students all of the background, examples, and practice exercises needed both to use and understand environmental modeling. It is suitable for upper-level undergraduate and beginning-graduate level environmental professionals seeking an introduction to modeling in their field.

This book is geared for advanced level research in the general subject area of remote sensing and modeling as they apply to the coastal marine environment. The various chapters focus on the latest scientific and technical advances in the service of better understanding coastal marine environments for their care, conservation and management. Chapters specifically deal with advances in remote sensing coastal classifications, environmental monitoring, digital ocean technological advances, geophysical methods, geoacoustics, X-band radar, risk assessment models, GIS applications, real-time modeling systems, and spatial modeling. Readers will find this book useful because it summarizes applications of new research methods in one of the world’s most dynamic and complicated environments. Chapters in this book will be of interest to specialists in the coastal marine environment who deals with aspects of environmental monitoring and assessment via remote sensing techniques and numerical modeling.

A fundamental objective in the study of dynamic systems is to understand and predict their behavior. The research presented in this thesis addresses this goal using the general framework of empirical dynamic modeling (EDM). In the classical approach, system behavior is described using fixed mathematical equations, and multiple effects are often treated as linearly separable (i.e. in a reductionist framework). In contrast, EDM applies Takens' Theorem and the method of time delay embeddings to reconstruct system dynamics from time series data. This gives EDM the flexibility to model nonlinear, state-dependent interactions that are otherwise challenging for traditionally linear mathematical models. The first part of this thesis applies EDM towards the study of sockeye salmon populations from the Fraser River in British Columbia, Canada in order to understand the factors that affect recruitment and to produce better models for the annual returns. Whereas classical (linear) fisheries models do not improve when incorporating the environment, I show that Fraser River sockeye salmon actually exhibit nonlinear dynamics, and therefore are not amenable to these methods. Instead, EDM models that can account for nonlinearity show improved forecasts, and moreover, benefit greatly from the incorporation of state-dependent environmental effects. In addition, I demonstrate that the abrupt changes in the salmon populations, correlated with North Pacific climate indices can be explained as state-dependent nonlinear behavior. Whereas classical fisheries models or linear correlations would suggest sudden shifts in behavior associated with climate regimes, an appropriate nonlinear lens indicates that environmental effects are state-dependent, and that aggregation of data at the regional level produces the apparent linear patterns. The second part of this thesis involves the development of new methods in the EDM framework to distill data (i.e. time series) into information (i.e. inferences and conclusions). I show that a lagged form of convergent cross mapping (CCM), a method to infer causation in time series, can greatly enhance its capabilities, by quantifying the time delay associated with causation. This new method can be used to distinguish between direct and indirect, transitive, effects as well as produce more reliable estimates of interaction strength. I also develop Multiview Embedding (MVE) to address the issues of noise and short time series length in high-dimensional complex systems. By using a multimodel approach that leverages the "equation-free'" framework of EDM, MVE combines multiple reconstructions of system behavior, producing more accurate and precise forecasts, and demonstrating that complexity can be an asset, because of how information about the system dynamics is duplicated across interacting variables. Finally, these methods are included in a software package for EDM, developed for the R statistical language. A user guide for this software package, including installation instructions and examples, is included as an appendix.

The world consists of many complex systems, ranging from our own bodies to ecosystems to economic systems. Despite their diversity, complex systems have many structural and functional features in common that can be effectively si- lated using powerful, user-friendly software. As a result, virtually anyone can - plore the nature of complex systems and their dynamical behavior under a range of assumptions and conditions. This ability to model dynamic systems is already having a powerful influence on teaching and studying complexity. The books in this series will promote this revolution in “systems thinking” by integrating computational skills of numeracy and techniques of dynamic mod- ing into a variety of disciplines. The unifying theme across the series will be the power and simplicity of the model-building process, and all books are designed to engage the reader in developing their own models for exploration of the dyn- ics of systems that are of interest to them. Modeling Dynamic Systems does not endorse any particular modeling paradigm or software. Rather, the volumes in the series will emphasize simplicity of lea- ing, expressive power, and the speed of execution as priorities that will facilitate deeper system understanding.