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The modern ecologist usually works in both the field and laboratory, uses statistics and computers, and often works with ecological concepts that are model-based, if not model-driven. How do we make the field and laboratory coherent? How do we link models and data? How do we use statistics to help experimentation? How do we integrate modeling and statistics? How do we confront multiple hypotheses with data and assign degrees of belief to different hypotheses? How do we deal with time series (in which data are linked from one measurement to the next) or put multiple sources of data into one inferential framework? These are the kinds of questions asked and answered by The Ecological Detective. Ray Hilborn and Marc Mangel investigate ecological data much as a detective would investigate a crime scene by trying different hypotheses until a coherent picture emerges. The book is not a set of pat statistical procedures but rather an approach. The Ecological Detective makes liberal use of computer programming for the generation of hypotheses, exploration of data, and the comparison of different models. The authors' attitude is one of exploration, both statistical and graphical. The background required is minimal, so that students with an undergraduate course in statistics and ecology can profitably add this work to their tool-kit for solving ecological problems.

Biomathematics. Biometry. Biostatistics --- General ecology and biosociology --- Ecology --- Mathematical models --- Issue --- Mathematical models. --- Ecology - Mathematical models --- MONOGRAPHS --- ECOLOGY --- MATHEMATICAL MODELS

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Graphs, networks and agent-based modeling are the most thriving and attracting sciences used in ecology and environmental sciences. As such, this book is the first comprehensive treatment of the subject in the areas of ecology and environmental sciences. From this integrated and self-contained book, researchers, university teachers and students will be provided with an in-depth and complete insight on knowledge, methodology and recent advances of graphs, networks and agent-based-modeling in ecology and environmental sciences. Java codes and a standalone software package will be presented in th

Ecology --- Multiagent systems. --- Agent-based model (Computer software) --- MASs (Multiagent systems) --- Multi-agent systems --- Systems, Multiagent --- Intelligent agents (Computer software) --- Mathematical models. --- Biometry --- Evolution (Biology) --- Data processing --- Statistical methods --- Biometry - Data processing --- Ecology - Statistical methods - Data processing --- Evolution (Biology) - Statistical methods - Data processing

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The book describes and discusses the numerical methods which are successfully being used for analysing ecological data, using a clear and comprehensive approach. These methods are derived from the fields of mathematical physics, parametric and nonparametric statistics, information theory, numerical taxonomy, archaeology, psychometry, sociometry, econometry and others.An updated, 3rd English edition of the most widely cited book on quantitative analysis of multivariate ecological dataRelates ecological questions to methods of statistical analysis, with a clear descripti

Biomathematics. Biometry. Biostatistics --- General ecology and biosociology --- Ecology --- Mathematics. --- Environmental sciences --- Environmental science --- Science --- Balance of nature --- Biology --- Bionomics --- Ecological processes --- Ecological science --- Ecological sciences --- Environment --- Environmental biology --- Oecology --- Population biology --- Environnement --- Mathematics --- Mathématiques --- Écologie --- Mathématiques --- ecologie --- ecologische data-analyse --- numerieke ecologie --- Écologie - Mathématiques --- Ecology - Mathematics

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Building on the successful Analysing Ecological Data (2007) by Zuur, Ieno and Smith, the authors now provide an expanded introduction to using regression and its extensions in analysing ecological data. As with the earlier book, real data sets from postgraduate ecological studies or research projects are used throughout. The first part of the book is a largely non-mathematical introduction to linear mixed effects modelling, GLM and GAM, zero inflated models, GEE, GLMM and GAMM. The second part provides ten case studies that range from koalas to deep sea research. These chapters provide an invaluable insight into analysing complex ecological datasets, including comparisons of different approaches to the same problem. By matching ecological questions and data structure to a case study, these chapters provide an excellent starting point to analysing your own data. Data and R code from all chapters are available from www.highstat.com. Alain F. Zuur is senior statistician and director of Highland Statistics Ltd., a statistical consultancy company based in the UK. He has taught statistics to more than 5000 ecologists. He is honorary research fellow in the School of Biological Sciences, Oceanlab, at the University of Aberdeen, UK. Elena N. Ieno is senior marine biologist and co-director at Highland Statistics Ltd. She has been involved in guiding PhD students on the design and analysis of ecological data. She is honorary research fellow in the School of Biological Sciences, Oceanlab, at the University of Aberdeen, UK. Neil J. Walker works as biostatistician for the Central Science Laboratory (an executive agency of DEFRA) and is based at the Woodchester Park research unit in Gloucestershire, South-West England. His work involves him in a number of environmental and wildlife biology projects. Anatoly A. Saveliev is a professor at the Geography and Ecology Faculty at Kazan State University, Russian Federation, where he teaches GIS and statistics. He also provides consultancy in statistics, GIS & Remote Sensing, spatial modelling and software development in these areas. Graham M. Smith is a director of AEVRM Ltd, an environmental consultancy in the UK and the course director for the MSc in ecological impact assessment at Bath Spa University in the UK.

Ecology --- R (Computer program language) --- Ecologie --- R (Langage de programmation) --- Statistical methods --- Méthodes statistiques --- EPUB-LIV-FT LIVBIOLO LIVBIOMO LIVMEDEC SPRINGER-B --- R (Computer program language). --- Environmental Sciences and Forestry. Ecology -- Ecology (General). --- Statistical methods. --- Mathematical statistics --- Simulation models --- Mathematical models --- Linear models --- Computer software --- ecology --- case studies --- Data analysis --- Ecology. --- Statistics. --- Environmental sciences. --- Statistics for Life Sciences, Medicine, Health Sciences. --- Math. Appl. in Environmental Science. --- Monitoring/Environmental Analysis. --- Environmental science --- Science --- Statistical analysis --- Statistical data --- Statistical science --- Mathematics --- Econometrics --- Balance of nature --- Biology --- Bionomics --- Ecological processes --- Ecological science --- Ecological sciences --- Environment --- Environmental biology --- Oecology --- Environmental sciences --- Population biology --- 57.087.1 --- 574 --- 681.3*I63 --- 574 General ecology. Biocoenology. Hydrobiology. Biogeography --- General ecology. Biocoenology. Hydrobiology. Biogeography --- 681.3*I63 Applications (Simulation and modeling) --- Applications (Simulation and modeling) --- 57.087.1 Biometry. Statistical study and treatment of biological data --- Biometry. Statistical study and treatment of biological data --- GNU-S (Computer program language) --- Domain-specific programming languages --- Ecology . --- Statistics . --- Environmental monitoring. --- Biomonitoring (Ecology) --- Ecological monitoring --- Environmental quality --- Monitoring, Environmental --- Applied ecology --- Environmental engineering --- Pollution --- Measurement --- Monitoring --- Ecology - Statistical methods --- Écologie --- Sciences de la vie --- Modèles écologiques. --- Life sciences --- Statistics as Topic. --- Méthodes statistiques. --- Méthodes statistiques --- Mathématiques --- methods. --- R --- Simulation models. --- Mathematical models. --- Linear models. --- Computer software. --- ecology. --- case studies. --- Data analysis. --- Life sciences. --- Biometry. --- Biostatistics. --- Mathematical Applications in Environmental Science. --- Environmental Monitoring. --- Mathematics. --- Biological statistics --- Biometrics (Biology) --- Biostatistics --- Biomathematics --- Statistics

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Anesthesia Student Survival Guide: A Case-Based Approach is an indispensable introduction to the specialty. This concise, easy-to-read, affordable handbook is ideal for medical students, nursing students, and others during the anesthesia rotation. Written in a structured prose format and supplemented with many diagrams, tables, and algorithms, this pocket-sized guide contains essential material covered on the USMLE II-III and other licensing exams. The editors, who are academic faculty at Harvard Medical School, summarize the essential content with 32 informative and compelling case studies designed to help students apply new concepts to real situations. Pharmacology, basic skills, common procedures and anesthesia subspecialties are covered, too, with just the right amount of detail for an introductory text. The unique book also offers a section containing career advice and insider tips on how to receive good evaluations from supervising physicians. With its combination of astute clinical instruction, basic science explanation, and practical tips from physicians that have been there before, this handbook is your one-stop guide to a successful anesthesia rotation.

Anesthesia. --- Anesthesiology. --- Anesthesiology -- Case Reports. --- Anesthesiology -- Handbooks. --- Medicine. --- Medicine --- Anesthesiology --- Health Occupations --- Disciplines and Occupations --- Mathematics --- Surgery & Anesthesiology --- Algebra --- Mathematical Theory --- Physical Sciences & Mathematics --- Health & Biological Sciences --- Associative rings --- Graded rings --- Graded modules --- Filtered rings --- Filtered modules --- Anesthesia --- Filtered modules. --- Filtered rings. --- Graded modules. --- Graded rings. --- Medicine & Public Health. --- Ordered algebraic structures --- Biomathematics. Biometry. Biostatistics --- Histology. Cytology --- Oncology. Neoplasms --- Pharmacology. Therapy --- Cancer --- Cancer cells --- Cytology --- Chemotherapy --- Mathematical models. --- Associative rings. --- Rings (Algebra) --- Modules (Algebra) --- Anaesthesiology --- Surgery --- Algèbres associatives --- Cancer - Chemotherapy - Mathematical models. --- Cancer cells - Mathematical models. --- Cytology - Mathematical models. --- Biomathematique --- Modelisation mathematique