Fr. 95.00

Interpreting Ecological Complexity - A Practical Guide to Avoid Becoming a Mistaken Scientist

English · Paperback / Softback

Will be released 31.01.2021

Description

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This book illustrates how the choice of relevant data collection and data analysis techniques is critical for a sound and objective description of the complexity of space-time patterns and processes in ecology and the aquatic sciences. It reviews the concept of complexity in a general ecological context, before introducing a unifying definition of complexity in marine ecology. The book shows that convincing results can arise from very simple, sound, and well-thought-out approaches. The authors underline the risks related to modern techniques of data assimilation that can lead to data inundation, a loss of focus, and ultimately an erroneous scientific approach.


List of contents










Introduction: Ecological Complexity and the QDCAC vs. DQA Paradigms: Defining marine ecological complexity. The question-driven collection data analysis paradigm. The data-driven question analysis paradigm. Data Collection: Is There More to the Picture Than Meets the Eye? Assessing micro scale heterogeneity: the issue of chronic undersampling. Global, long-term monitoring programs. Data Analysis: There Is More to the Picture Than Meets the Eye! Misleading space-time analyses, or the QCDAC paradigm revisited. The (mis)behaviour of behavioural analyses. From space-time patterns to generating processes. Benthic indicators. Conclusion: How Much Is Too Much?


Summary

This book illustrates how the choice of relevant data collection and data analysis techniques is critical for a sound and objective description of the complexity of space-time patterns and processes in ecology and the aquatic sciences. It reviews the concept of complexity in a general ecological context, before introducing a unifying definition of complexity in marine ecology. The book shows that convincing results can arise from very simple, sound, and well-thought-out approaches. The authors underline the risks related to modern techniques of data assimilation that can lead to data inundation, a loss of focus, and ultimately an erroneous scientific approach.

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