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Machine Learning for Ecology and Sustainable Natural Resource Management

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Cover of 'Machine Learning for Ecology and Sustainable Natural Resource Management'

Table of Contents

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    Book Overview
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    Chapter 1 Machine Learning in Wildlife Biology: Algorithms, Data Issues and Availability, Workflows, Citizen Science, Code Sharing, Metadata and a Brief Historical Perspective
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    Chapter 2 Use of Machine Learning (ML) for Predicting and Analyzing Ecological and ‘Presence Only’ Data: An Overview of Applications and a Good Outlook
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    Chapter 3 Boosting, Bagging and Ensembles in the Real World: An Overview, some Explanations and a Practical Synthesis for Holistic Global Wildlife Conservation Applications Based on Machine Learning with Decision Trees
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    Chapter 4 From Data Mining with Machine Learning to Inference in Diverse and Highly Complex Data: Some Shared Experiences, Intellectual Reasoning and Analysis Steps for the Real World of Science Applications
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    Chapter 5 Ensembles of Ensembles: Combining the Predictions from Multiple Machine Learning Methods
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    Chapter 6 Machine Learning for Macroscale Ecological Niche Modeling - a Multi-Model, Multi-Response Ensemble Technique for Tree Species Management Under Climate Change
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    Chapter 7 Mapping Aboveground Biomass of Trees Using Forest Inventory Data and Public Environmental Variables within the Alaskan Boreal Forest
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    Chapter 8 ‘Batteries’ in Machine Learning: A First Experimental Assessment of Inference for Siberian Crane Breeding Grounds in the Russian High Arctic Based on ‘Shaving’ 74 Predictors
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    Chapter 9 Landscape Applications of Machine Learning: Comparing Random Forests and Logistic Regression in Multi-Scale Optimized Predictive Modeling of American Marten Occurrence in Northern Idaho, USA
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    Chapter 10 Using Interactions among Species, Landscapes, and Climate to Inform Ecological Niche Models: A Case Study of American Marten (Martes americana) Distribution in Alaska
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    Chapter 11 Advanced Data Mining (Cloning) of Predicted Climate-Scapes and Their Variances Assessed with Machine Learning: An Example from Southern Alaska Shows Topographical Biases and Strong Differences
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    Chapter 12 Using TreeNet, a Machine Learning Approach to Better Understand Factors that Influence Elevated Blood Lead Levels in Wintering Golden Eagles in the Western United States
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    Chapter 13 Breaking Away from ‘Traditional’ Uses of Machine Learning: A Case Study Linking Sooty Shearwaters (Ardenna griseus) and Upcoming Changes in the Southern Oscillation Index
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    Chapter 14 Image Recognition in Wildlife Applications
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    Chapter 15 Machine Learning Techniques for Quantifying Geographic Variation in Leach’s Storm-Petrel (Hydrobates leucorhous) Vocalizations
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    Chapter 16 Machine Learning for ‘Strategic Conservation and Planning’: Patterns, Applications, Thoughts and Urgently Needed Global Progress for Sustainability
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    Chapter 17 How the Internet Can Know What You Want Before You Do: Web-Based Machine Learning Applications for Wildlife Management
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    Chapter 18 Machine Learning and ‘The Cloud’ for Natural Resource Applications: Autonomous Online Robots Driving Sustainable Conservation Management Worldwide?
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    Chapter 19 Assessment of Potential Risks from Renewable Energy Development and Other Anthropogenic Factors to Wintering Golden Eagles in the Western United States
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    Chapter 20 A Perspective on the Future of Machine Learning: Moving Away from ‘Business as Usual’ and Towards a Holistic Approach of Global Conservation
Attention for Chapter 10: Using Interactions among Species, Landscapes, and Climate to Inform Ecological Niche Models: A Case Study of American Marten (Martes americana) Distribution in Alaska
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Chapter title
Using Interactions among Species, Landscapes, and Climate to Inform Ecological Niche Models: A Case Study of American Marten (Martes americana) Distribution in Alaska
Chapter number 10
Book title
Machine Learning for Ecology and Sustainable Natural Resource Management
Published by
Springer, Cham, November 2018
DOI 10.1007/978-3-319-96978-7_10
Book ISBNs
978-3-31-996976-3, 978-3-31-996978-7
Authors

Andrew P. Baltensperger, Baltensperger, Andrew P.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Other 2 50%
Researcher 1 25%
Unknown 1 25%
Readers by discipline Count As %
Agricultural and Biological Sciences 2 50%
Environmental Science 1 25%
Unknown 1 25%