Communities And Ecosystems

Soil.

Introduction; Populations; Community structure and composition; Communities and environments; Production; Nutrient circulation; Pollution; Conclusion.

Interactions between competitors, predators and their prey have traditionally been viewed as the foundation of community structure. Parasites – long ignored in community ecology – are now recognized as playing an important part in influencing species interactions and consequently affecting ecosystem function. Parasitism can interact with other ecological drivers, resulting in both detrimental and beneficial effects on biodiversity and ecosystem health. Species interactions involving parasites are also key to understanding many biological invasions and emerging infectious diseases. This book bridges the gap between community ecology and epidemiology to create a wide-ranging examination of how parasites and pathogens affect all aspects of ecological communities, enabling the new generation of ecologists to include parasites as a key consideration in their studies. This comprehensive guide to a newly emerging field is of relevance to academics, practitioners and graduates in biodiversity, conservation and population management, and animal and human health.

A plethora of different theories, models, and concepts make up the field of community ecology. Amid this vast body of work, is it possible to build one general theory of ecological communities? What other scientific areas might serve as a guiding framework? As it turns out, the core focus of community ecology—understanding patterns of diversity and composition of biological variants across space and time—is shared by evolutionary biology and its very coherent conceptual framework, population genetics theory. The Theory of Ecological Communities takes this as a starting point to pull together community ecology's various perspectives into a more unified whole. Mark Vellend builds a theory of ecological communities based on four overarching processes: selection among species, drift, dispersal, and speciation. These are analogues of the four central processes in population genetics theory—selection within species, drift, gene flow, and mutation—and together they subsume almost all of the many dozens of more specific models built to describe the dynamics of communities of interacting species. The result is a theory that allows the effects of many low-level processes, such as competition, facilitation, predation, disturbance, stress, succession, colonization, and local extinction to be understood as the underpinnings of high-level processes with widely applicable consequences for ecological communities. Reframing the numerous existing ideas in community ecology, The Theory of Ecological Communities provides a new way for thinking about biological composition and diversity.

The exponentially increasing amounts of biological data along with comparable advances in computing power are making possible the construction of quantitative, predictive biological systems models. This development could revolutionize those biology-based fields of science. To assist this transformation, the U.S. Department of Energy asked the National Research Council to recommend mathematical research activities to enable more effective use of the large amounts of existing genomic information and the structural and functional genomic information being created. The resulting study is a broad, scientifically based view of the opportunities lying at the mathematical science and biology interface. The book provides a review of past successes, an examination of opportunities at the various levels of biological systemsâ€" from molecules to ecosystemsâ€"an analysis of cross-cutting themes, and a set of recommendations to advance the mathematics-biology connection that are applicable to all agencies funding research in this area.

The Way Forward for Entrepreneurship Around the World We are in the midst of a startup revolution. The growth and proliferation of innovation-driven startup activity is profound, unprecedented, and global in scope. Today, it is understood that communities of support and knowledge-sharing go along with other resources. The importance of collaboration and a long-term commitment has gained wider acceptance. These principles are adopted in many startup communities throughout the world. And yet, much more work is needed. Startup activity is highly concentrated in large cities. Governments and other actors such as large corporations and universities are not collaborating with each other nor with entrepreneurs as well as they could. Too often, these actors try to control activity or impose their view from the top-down, rather than supporting an environment that is led from the bottom-up. We continue to see a disconnect between an entrepreneurial mindset and that of many actors who wish to engage with and support entrepreneurship. There are structural reasons for this, but we can overcome many of these obstacles with appropriate focus and sustained practice. No one tells this story better than Brad Feld and Ian Hathaway. The Startup Community Way: Evolving an Entrepreneurial Ecosystem explores what makes startup communities thrive and how to improve collaboration in these rapidly evolving, complex environments. The Startup Community Way is an explanatory guide for startup communities. Rooted in the theory of complex systems, this book establishes the systemic properties of entrepreneurial ecosystems and explains why their complex nature leads people to make predictable mistakes. As complex systems, value creation occurs in startup communities primarily through the interaction of the "parts" - the people, organizations, resources, and conditions involved - not the parts themselves. This continual process of bottom-up interactions unfolds naturally, producing value in novel and unexpected ways. Through these complex, emergent processes, the whole becomes greater and substantially different than what the parts alone could produce. Because of this, participants must take a fundamentally different approach than is common in much of our civic and professional lives. Participants must take a whole-system view, rather than simply trying to optimize their individual part. They must prioritize experimentation and learning over planning and execution. Complex systems are uncertain and unpredictable. They cannot be controlled, only guided and influenced. Each startup community is unique. Replication is enticing but impossible. The race to become "The Next Silicon Valley" is futile - even Silicon Valley couldn't recreate itself. This book: Offers practical advice for entrepreneurs, community builders, government officials, and other stakeholders who want to harness the power of entrepreneurship in their city Describes the core components of startup communities and entrepreneurial ecosystems, as well as an explanation of the differences between these two related, but distinct concepts Advances a new framework for effective startup community building based on the theory of complex systems and insights from systems thinking Includes contributions from leading entrepreneurial voices Is a must-have resource for entrepreneurs, venture capitalists, executives, business and community leaders, economic development authorities, policymakers, university officials, and anyone wishing to understand how startup communities work anywhere in the world

Until fairly recently, populations were handled as homogenized averages, which made modeling feasible but which ignored the essential fact that in any population there is a great variety of individuals of different ages, sizes, and degrees of fitness. Recently, because of the increased availability of affordable computer power, approaches have been developed which are able to recognize individual differences. Individual-based models are of great use in the areas of aquatic ecology, terrestrial ecology, landscape or physiological ecology, terrestrial ecology, landscape or physiological ecology, and agriculture. This book discusses which biological problems individual-based models can solve, as well as the models' inherent limitations. It explores likely future directions of theoretical development in these models, as well as currently feasible management applications and the best mathematical approaches and computer languages to use. The book also details specific applications to theory and management.