Call number:
9783319969787 (e-book)
Description / Table of Contents:
Ecologists and natural resource managers are charged with making complex management decisions in the face of a rapidly changing environment resulting from climate change, energy development, urban sprawl, invasive species and globalization. Advances in Geographic Information System (GIS) technology, digitization, online data availability, historic legacy datasets, remote sensors and the ability to collect data on animal movements via satellite and GPS have given rise to large, highly complex datasets. These datasets could be utilized for making critical management decisions, but are often “messy” and difficult to interpret. Basic artificial intelligence algorithms (i.e., machine learning) are powerful tools that are shaping the world and must be taken advantage of in the life sciences. In ecology, machine learning algorithms are critical to helping resource managers synthesize information to better understand complex ecological systems. Machine Learning has a wide variety of powerful applications, with three general uses that are of particular interest to ecologists: (1) data exploration to gain system knowledge and generate new hypotheses, (2) predicting ecological patterns in space and time, and (3) pattern recognition for ecological sampling. Machine learning can be used to make predictive assessments even when relationships between variables are poorly understood. When traditional techniques fail to capture the relationship between variables, effective use of machine learning can unearth and capture previously unattainable insights into an ecosystem's complexity. Currently, many ecologists do not utilize machine learning as a part of the scientific process. This volume highlights how machine learning techniques can complement the traditional methodologies currently applied in this field
Type of Medium:
12
Pages:
1 Online-Ressource (xxiv, 441 Seiten)
,
Illustrationen, Diagramme, Karten
ISBN:
9783319969787
,
978-3-319-96978-7
URL:
Ebook (access only within the AWI network)
DOI:
10.1007/978-3-319-96978-7
Language:
English
Note:
Contents
Part I Introduction
1 Machine Learning in Wildlife Biology: Algorithms, Data Issues and Availability, Workflows, Citizen Science, Code Sharing, Metadata and a Brief Historical Perspective / Grant R. W. Humphries and Falk Huettmann
2 Use of Machine Learning (ML) for Predicting and Analyzing Ecological and ‘Presence Only’ Data: An Overview of Applications and a Good Outlook / Falk Huettmann, Erica H. Craig, Keiko A. Herrick, Andrew P. Baltensperger, Grant R. W. Humphries, David J. Lieske, Katharine Miller, Timothy C. Mullet, Steffen Oppel, Cynthia Resendiz, Imme Rutzen, Moritz S. Schmid, Madan K. Suwal, and Brian D. Young
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 / Falk Huettmann
Part II Predicting Patterns
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 / Falk Huettmann
5 Ensembles of Ensembles: Combining the Predictions from Multiple Machine Learning Methods / David J. Lieske, Moritz S. Schmid, and Matthew Mahoney
6 Machine Learning for Macroscale Ecological Niche Modeling - a Multi-Model, Multi-Response Ensemble Technique for Tree Species Management Under Climate Change / Anantha M. Prasad
7 Mapping Aboveground Biomass of Trees Using Forest Inventory Data and Public Environmental Variables within the Alaskan Boreal Forest / Brian D. Young, John Yarie, David Verbyla, Falk Huettmann, and F. Stuart Chapin III
Part III Data Exploration and Hypothesis Generation with Machine Learning
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 / Falk Huettmann, Chunrong Mi, and Yumin Guo
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 / Samuel A. Cushman and Tzeidle N. Wasserman
10 Using Interactions among Species, Landscapes, and Climate to Inform Ecological Niche Models: A Case Study of American Marten (Martes americana) Distribution in Alaska / Andrew P. Baltensperger
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 / Falk Huettmann
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 / Erica H. Craig, Tim H. Craig, and Mark R. Fuller
Part IV Novel Applications of Machine Learning Beyond Species Distribution Models
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 / Grant R. W. Humphries
14 Image Recognition in Wildlife Applications / Dawn R. Magness
15 Machine Learning Techniques for Quantifying Geographic Variation in Leach’s Storm-Petrel (Hydrobates leucorhous) Vocalizations / Grant R. W. Humphries, Rachel T. Buxton, and Ian L. Jones
Part V Implementing Machine Learning for Resource Management
16 Machine Learning for ‘Strategic Conservation and Planning’: Patterns, Applications, Thoughts and Urgently Needed Global Progress for Sustainability / Falk Huettmann
17 How the Internet Can Know What You Want Before You Do: Web-Based Machine Learning Applications for Wildlife Management / Grant R. W. Humphries
18 Machine Learning and ‘The Cloud’ for Natural Resource Applications: Autonomous Online Robots Driving Sustainable Conservation Management Worldwide? / Grant R. W. Humphries and Falk Huettmann
19 Assessment of Potential Risks from Renewable Energy Development and Other Anthropogenic Factors to Wintering Golden Eagles in the Western United States / Erica H. Craig, Mark R. Fuller, Tim H. Craig, and Falk Huettmann
Part VI Conclusions
20 A Perspective on the Future of Machine Learning: Moving Away from ‘Business as Usual’ and Towards a Holistic Approach of Global Conservation / Grant R. W. Humphries and Falk Huettmann
Index
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