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Machine Learning for Ecology and Sustainable Natural Resource Management

Overview of attention for book
Machine Learning for Ecology and Sustainable Natural Resource Management
Springer International Publishing

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    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 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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Mentioned by

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2 X users

Citations

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Readers on

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Summary X Dimensions citations
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Chapter title
Machine Learning for Macroscale Ecological Niche Modeling - a Multi-Model, Multi-Response Ensemble Technique for Tree Species Management Under Climate Change
Chapter number 6
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_6
Book ISBNs
978-3-31-996976-3, 978-3-31-996978-7
Authors

Anantha M. Prasad, Prasad, Anantha M.

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X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 13%
Lecturer > Senior Lecturer 1 6%
Other 1 6%
Unspecified 1 6%
Student > Ph. D. Student 1 6%
Other 3 19%
Unknown 7 44%
Readers by discipline Count As %
Environmental Science 2 13%
Agricultural and Biological Sciences 2 13%
Business, Management and Accounting 1 6%
Unspecified 1 6%
Earth and Planetary Sciences 1 6%
Other 1 6%
Unknown 8 50%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 August 2019.
All research outputs
#15,549,350
of 23,109,468 outputs
Outputs from this source
#1,085
of 1,555 outputs
Outputs of similar age
#220,824
of 352,386 outputs
Outputs of similar age from this source
#1
of 1 outputs
Altmetric has tracked 23,109,468 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,555 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.2. This one is in the 17th percentile – i.e., 17% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 352,386 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them

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