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Networks of Invasion: Networks of Invasion: Empirical Evidence and Case Studies, Volume 57 bridges a conceptual gap between ecological network studies and invasion biology s… Read more
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Immediately download your ebook while waiting for your print delivery. No promo code needed.
Networks of Invasion: Networks of Invasion: Empirical Evidence and Case Studies, Volume 57 bridges a conceptual gap between ecological network studies and invasion biology studies. This book contains chapters detailing pressing concerns regarding invasive species in food webs, but also extends the idea of networks of invasion to other systems, such as mutualistic networks or even the human microbiome. Chapters describe the tools, models and empirical methods adapted for tackling invasions in ecological networks, including sections on parasites and biological invasions, invasions in freshwater systems, and those in host-associated microbiome networks.
In addition, the book provides interesting discussions on the importance of microorganisms and their relationship to macroorganisms.
Chapter One: Parasites and Biological Invasions: Predicting Ecological Alterations at Levels From Individual Hosts to Whole Networks
Chapter Two: Novel and Disrupted Trophic Links Following Invasion in Freshwater Ecosystems
Chapter Three: Importance of Microorganisms to Macroorganisms Invasions: Is the Essential Invisible to the Eye? (The Little Prince, A. de Saint-Exupéry, 1943)
Chapter Four: Massively Introduced Managed Species and Their Consequences for Plant–Pollinator Interactions
Chapter Five: Invasions of Host-Associated Microbiome Networks
Advances in Ecological Research Volume 1–57
DB
Dave has most recently begun to work with networks. He developed, with colleagues, a learning methodology to build networks from sample date. This has produced the largest, replicated network in agriculture. One of his particular interests is how behaviours and dynamics at the species level, as studied using the carabid-slug-weed system, build across species and their interactions to the dynamics of networks at the ecosystem level.
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FM