Individual-based Methods in Forest Ecology and Management

2. Auflage 2026
ISBN: 978-3-032-32668-3
Verlag: Springer Nature Switzerland AG

Buch, Englisch, 764 Seiten, Format (B × H): 168 mm x 240 mm

Model-driven individual-based forest ecology and management methods are of increasing importance worldwide. This fully revised and expanded second edition integrates three main fields of forest ecology and management, i.e. tree/plant interactions, biometry of plant growth and human behaviour in forests. Adopting a strict bottom-up approach, individual-based forest ecology and management is an interdisciplinary research field with a focus on how the individual behaviour and interactions among plants and between plants and humans contribute to the formation of spatial patterns of communities that evolve over time. This book brings together essential approaches from individual-based forest ecology and management including point process statistics, individual-based modelling, plant growth science and behavioural science with particular emphasis on spatio-temporal patterns and biodiversity. A clear didactic structure, including concise definitions, summaries at the beginning and take-home messages at the end of each chapter enhance the learning experience and accessibility. For ease of access, better understanding and transparency, the methods are accompanied by R code and worked examples, enabling readers to implement and explore the methods directly.

Designed for upper undergraduate and graduate courses in Forest Science, Ecology, Biology, Biodiversity and Conservation, Computer Science and Ecological Statistics, the book also serves as a valuable reference for researchers and professionals working with individual-based approaches.

Zielgruppe

Upper undergraduate

Autoren/Hrsg.

Pommerening, Arne

Grabarnik, Pavel

Fachgebiete

• Naturwissenschaften Agrarwissenschaften Ackerbaukunde, Pflanzenbau Forstwirtschaft, Forstwissenschaft
• Wirtschaftswissenschaften Wirtschaftssektoren & Branchen Energie- & Versorgungswirtschaft Energiewirtschaft: Alternative & Erneuerbare Energien
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Technische Wissenschaften Energietechnik | Elektrotechnik Alternative und erneuerbare Energien
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik

Inhaltsverzeichnis

Chapter 1: Introduction.- Chapter 2: Theories and concepts in individual-based forest ecology.- Chapter 3: Theories and concepts in individual-based forest management.- Chapter 4: Spatial statistics in plant interaction analyses.- Chapter 5: Nearest neighbour summary statistics.- Chapter 6: Sampling nearest-neighbour summary statistics.- Chapter 7: Correlation functions and second-order summary characteristics.- Chapter 8: Spatial edge effects, quantifying variation and hypothesis testing.- Chapter 9: Spatial and individual-based modelling.- Chapter 10: Principles of relative growth analysis.- Chapter 11: Human tree selection and marking behaviour.

Über Autor(innen)

Arne Pommerening is Professor of Mathematical Statistics Applied to Forest Sciences at the Swedish University of Agricultural Sciences (SLU) in Umeå. He is a theoretical forest scientist and biometrician specializing in quantitative forest ecology and management. He is particularly known for his work on woodland structure analysis and modelling, the spatio-temporal dynamics of plant point patterns, individual-based modelling, plant growth analysis, and methods for quantifying and monitoring biodiversity. More recently, his research has expanded to include the analysis of human behaviour in tree selection. His work typically combines field trials with computer-based simulation experiments.

Pavel Grabarnik currently is Director of the Pushchino Scientific Centre of Biological Research of the Russian Academy of Sciences. His scientific research is situated in the Laboratory of Ecosystem Modelling and focuses on the mathematical modelling of ecological systems, particularly on the spatial structure of forest ecosystems. His principal contributions to statistics include parameter estimation and hypothesis testing for spatial point processes. He has also developed computational tools supporting applications in spatial statistics.