Description

The theme of this volume is to discuss the Ecological Networks in an Agricultural World. The volume covers important topics such Networking Agroecology, Construction and Validation of Food-webs using Logic-based Machine Learning and Text-mining and Eco-evolutionary dynamics in agricultural networks.

 

Key Features

  • Updates and informs the reader on the latest research findings
  • Written by leading experts in the field
  • Highlights areas for future investigation

Readership

Ecologists, environmentalists

Table of Contents

Series Page

Contributors

Preface: Editorial Commentary: The Potential for Network Approaches to Improve Knowledge, Understanding, and Prediction of the Structure and Functioning of Agricultural Systems

Acknowledgements

References

Chapter One. Networking Agroecology: Integrating the Diversity of Agroecosystem Interactions

Abstract

1 Introduction

2 What is a Network?

3 The Agricultural Landscape as a Network of Agricultural, Semi-natural and Natural Habitats

4 Linking Structure, Functioning and Services

5 Evaluating and Predicting Ecosystem Change

6 Conclusion

References

Glossary

Chapter Two. Connecting the Green and Brown Worlds: Allometric and Stoichiometric Predictability of Above- and Below-Ground Networks

Abstract

Abbreviations

1 Introduction

2 Aims and Rationale

3 Can a Stoichiometrically Explicit First Trophic Level Be Parameterised?

4 The Advantages of Stoichiometric Plasticity

5 Constrained Resources, Isotopic Signatures and Networks

6 Antagonism Above, Mutualism Below: Nature or Agriculture?

7 Scaling Stoichiometry Provides a Bridge to Ecosystem Processes

8 Be Explicit: Can We Reach a Consensus?

Acknowledgements

References

Chapter Three. Empirically Characterising Trophic Networks: What Emerging DNA-Based Methods, Stable Isotope and Fatty Acid Analyses Can Offer

Abstract

1 Introduction

2 Molecular Approaches to Analyse Trophic Interactions

3 Stable Isotope Analysis

4 Fatty Acid Analysis

5 Which Approach to Choose, How to Start and How to Interpret the Data?

Acknowledgements

References

Glossary

Chapter Four. Construction and Validation of Food Webs Using Logic-Based Machine Learning and Text Mining

Abstract

1 Introduction

2 Methods

Details

No. of pages:
524
Language:
English
Copyright:
© 2013
Published:
Imprint:
Academic Press
Electronic ISBN:
9780124200074
Print ISBN:
9780124200029

About the serial-volume-editors

Guy Woodward

Guy Woodward is Professor of Ecology in the Department of Life Sciences at Imperial College London and Series Editor for Advances in Ecological Research. He has authored over 100 peer-reviewed publications, including recent papers in Nature, Science and Nature Climate Change, with a strong emphasis on understanding and predicting how aquatic ecosystems and food webs respond to a wide range of biotic and abiotic stressors, including climate change, chemical pollution, habitat degradation and invasive species. Much of this work covers multiple scales in space and time and also a range of organisational levels - from genes to ecosystems. His research group and ongoing collaborations span the natural and social sciences, reflecting the need for multidisciplinary approaches for addressing the environmental challenges of the 21st Century.

David Bohan

Dave Bohan is an agricultural ecologist with an interest in predator-prey regulation interactions. Dave uses a model system of a carabid beetle predator and two agriculturally important prey; slugs and weed seeds. He has shown that carabids find and consume slug prey, within fields, and that this leads to regulation of slug populations and interesting spatial ‘waves’ in slug and carabid density. The carabids also intercept weed seeds shed by weed plants before they enter the soil, and thus carabids can regulate the long-term store of seeds in the seedbank on national scales. What is interesting about this system is that it contains two important regulation ecosystem services delivered by one group of service providers, the carabids. This system therefore integrates, in miniature, many of the problems of interaction between services. 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.