The book has evolved and adapted from an earlier book by the same author and provides a detailed introduction to analytical methods commonly used by fishery scientists, ecologists, and advanced students using the open source software R as a programming tool.
The book has evolved and adapted from an earlier book by the same author and provides a detailed introduction to analytical methods commonly used by fishery scientists, ecologists, and advanced students using the open source software R as a programming tool.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Dr. Malcolm Haddon has at least 35 years of experience in fisheries science, having worked in the Department of New Zealand Fisheries, the University of Sydney, the Australian Maritime College, the University of Tasmania, and, most recently, in Australia's Commonwealth Scientific and Industrial Research Organization (CSIRO), from which he recently retired. He has worked with: Crustacea, including crabs, prawns, and rock lobster; Mollusca, including scallops and abalone; and scale-fish, many and various. Dr. Haddon's interests are these days focussed on all aspects of resource assessment and simulation testing of resource management using management strategy evaluation. He considers himself fortunate to have become an adjunct professor in the Institute of Marine and Antarctic Sciences at the University of Tasmania and an Honorary Research Fellow at Oceans and Atmosphere, CSIRO, in Hobart, Tasmania. In both institutions he continues to collaborate with colleagues, most recently beginning to contribute to two research programs at the university on abalone population dynamics and management.
Inhaltsangabe
1. On Modelling 2. A Non-Introduction to R 3. Simple Population Models 4. Model parameter Estimation 5. Static Models 6. On Uncertainty 7. Surplus Production Models
1. On Modelling 2. A Non-Introduction to R 3. Simple Population Models 4. Model parameter Estimation 5. Static Models 6. On Uncertainty 7. Surplus Production Models
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